FN Thomson Reuters Web of Scienceā„¢ VR 1.0 PT J AU Liao, H Zeng, A Zhang, YC AF Liao, Hao Zeng, An Zhang, Yi-Cheng TI Predicting missing links via correlation between nodes SO PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS LA English DT Article DE Link prediction; Correlation coefficient; Node similarity ID NETWORKS; COMMUNITY; COMPLEX AB As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information. Since this problem is related to many applications ranging from uncovering missing data to predicting the evolution of networks, link prediction has been intensively investigated recently and many methods have been proposed so far. The essential challenge of link prediction is to estimate the similarity between nodes. Most of the existing methods are based on the common neighbor index and its variants. In this paper, we propose to calculate the similarity between nodes by the Pearson correlation coefficient. This method is found to be very effective when applied to calculate similarity based on high order paths. We finally fuse the correlation-based method with the resource allocation method, and find that the combined method can substantially outperform the existing methods, especially in sparse networks. (C) 2015 Elsevier B.V. All rights reserved. C1 [Liao, Hao; Zeng, An] Hangzhou Normal Univ, Alibaba Business Coll, Alibaba Res Ctr Complex Sci, Hangzhou 311121, Zhejiang, Peoples R China. [Liao, Hao] Shenzhen Univ, Coll Comp Sci & Software Engn, Guangdong Prov Key Lab Popular High Performance C, Shenzhen 518060, Peoples R China. [Zhang, Yi-Cheng] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland. [Zeng, An] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China. [Zhang, Yi-Cheng] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu 611731, Peoples R China. RP Zeng, A (reprint author), Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China. EM anzeng@bnu.edu.cn FU EU [611272]; Swiss National Science Foundation [200020-143272]; Opening Foundation of Alibaba Research Center for Complex Sciences, Hangzhou Normal University [PD12001003002008, PD12001003002006]; National Science Foundation of China [U1301252, 61170076]; Guangdong Natural Science Foundation [2014A030313553]; Fundamental Research Funds for the Central Universities [2014ZM0079] FX This work was partially supported by the EU FP7 Grant 611272 (project GROWTHCOM) and by the Swiss National Science Foundation (grant no. 200020-143272). The authors would like to acknowledge the support from the Opening Foundation of Alibaba Research Center for Complex Sciences, Hangzhou Normal University under Grant No. PD 12001003002008 and PD12001003002006, H.L acknowledges support from National Science Foundation of China under Grant Nos. U1301252 and 61170076, Guangdong Natural Science Foundation under Grant No. 2014A030313553, and Fundamental Research Funds for the Central Universities under Grant No. 2014ZM0079. CR Adamic LA, 2003, SOC NETWORKS, V25, P211, DOI 10.1016/S0378-8733(03)00009-1 Brockmann D, 2013, SCIENCE, V342, P1337, DOI 10.1126/science.1245200 Clauset A, 2008, NATURE, V453, P98, DOI 10.1038/nature06830 Gavin AC, 2002, NATURE, V415, P141, DOI 10.1038/415141a Gleiser PM, 2003, ADV COMPLEX SYST, V6, P565, DOI 10.1142/S0219525903001067 Guimera R, 2003, PHYS REV E, V68, DOI 10.1103/PhysRevE.68.065103 HANLEY JA, 1982, RADIOLOGY, V143, P29 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P142 Jeh G., 2002, P ACM SIGKDD INT C K Katz L., 1953, PSYCHOMETRIKA, V18, P1 Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226 Liao H, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0097146 Liben-Nowell D., 2007, J ASS INF SCI TECHNO, V58, P7 Lichtenwalter R.N., 2010, P ACM SIGKDD INT C K LIN LI, 1989, BIOMETRICS, V45, P255, DOI 10.2307/2532051 Lorrain F., 1971, J MATH SOCIOL, V1, P49, DOI [DOI 10.1080/0022250X.1971.9989788), 10.1080/0022250X.1971.9989788] Lu L., 2009, EPL-EUROPHYS LETT, V89, P18001 Lu LY, 2009, PHYS REV E, V80, DOI 10.1103/PhysRevE.80.046122 Lu LY, 2011, PHYSICA A, V390, P1150, DOI 10.1016/j.physa.2010.11.027 Lu L, 2009, EUR PHYS J B, V71, P4 Lu Z., 2010, P IEEE INT C DAT MIN Lusseau D, 2003, BEHAV ECOL SOCIOBIOL, V54, P396, DOI 10.1007/s00265-003-0651-y Karrer B, 2011, PHYS REV E, V83, DOI 10.1103/PhysRevE.83.016107 Newman MEJ, 2006, PHYS REV E, V74, DOI 10.1103/PhysRevE.74.036104 Redner S, 2008, NATURE, V453, P47, DOI 10.1038/453047a SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shin D., 2012, P ACM INT C INF KNOW Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159 Wang D., 2013, SCIENCE, V342, P6154 Wang D., 2011, P 17 ACM SIGKDD INT Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Whang J. J., 2013, P IEEE INT C DAT MIN Zeng A, 2012, PHYS REV E, V85, DOI 10.1103/PhysRevE.85.036101 Zeng W, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0079354 Zhang J., 2013, P IEEE INT C DAT MIN Zhang Z. K., 2010, EPL-EUROPHYS LETT, V92, P2 Zhou T, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/12/123008 Zhu YX, 2012, PHYSICA A, V391, P5769, DOI 10.1016/j.physa.2012.06.003 NR 38 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0378-4371 EI 1873-2119 J9 PHYSICA A JI Physica A PD OCT 15 PY 2015 VL 436 BP 216 EP 223 DI 10.1016/j.physa.2015.05.009 PG 8 WC Physics, Multidisciplinary SC Physics GA CM5CN UT WOS:000357704500019 ER PT J AU Carchiolo, V Longheu, A Malgeri, M Mangioni, G AF Carchiolo, Vincenza Longheu, Alessandro Malgeri, Michele Mangioni, Giuseppe TI Searching for experts in a context-aware recommendation network SO COMPUTERS IN HUMAN BEHAVIOR LA English DT Article DE Expertise; Recommendation network; Trust; Context-aware ID SYSTEMS AB The huge amount of data in the web made and is still making harder the issue of,finding the right information. To help users in their choices, recommender systems are used as a valuable tool when dealing with innumerable choices of data, products and services. In this work, expertise is used to improve the quality of recommendations by selecting those provided by users that are considered expert in the same context their recommendations are about, since we believe they are more relevant with respect to recommendation coming from non-expert users. We present an approach of searching for a "guru" user (expert in a specific context) using context-dependent expertise information within the Epinions.com recommendation network, also considering how this can be exploited within technology enhanced learning context. Results show that context-based search can be used to significantly reduce the number of nodes (users) to query with a limited loss of expert nodes. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Carchiolo, Vincenza; Longheu, Alessandro; Malgeri, Michele; Mangioni, Giuseppe] Univ Catania, DIEEI, I-95123 Catania, Italy. RP Longheu, A (reprint author), Univ Catania, DIEEI, Viale Andrea Doria 6, I-95123 Catania, Italy. EM vincenza.carchiolo@dieei.unict.it; alessandro.longheu@dieei.unict.it; michele.malgeri@dieei.unict.it; giuseppe.mangioni@dieei.unict.it CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Banerjee S., 2002, COMPUTATIONAL LINGUI, V2276, P117 Bhasker B., 2010, RECOMMENDER SYSTEMS Drachsler H., 2007, RECOMMENDATIONS LEAR Eppstein D., 2011, CORR Fang H, 2007, LECT NOTES COMPUT SC, V4425, P418 Freeman J, 2009, LECT NOTES ARTIF INT, V5638, P148, DOI 10.1007/978-3-642-02812-0_18 Grandison T, 2000, IEEE COMMUNICATIONS, V3, P2 Hu J, 2008, INT C COMP ELEC ENG, P593, DOI 10.1109/ICCEE.2008.144 Hughes B. D., 1995, RANDOM WALKS RANDOM John A., 2006, P WWW 2006 Koning-bastiaan, 2005, INT CORDRA WORKSH Martin F. J., 2009, P 3 ACM C REC SYST Maybury MT, 2002, J UNIVERS COMPUT SCI, V8, P491 Newman M., 2003, SIAM REV, V45 Pearson K, 1905, NATURE, V72, P294, DOI 10.1038/072294b0 Resnik P, 1995, INT JOINT CONF ARTIF, P448 Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shardanand U., 1995, P ACM CHI 95 C HUM F, V1, P210 Sie RLL, 2011, J UNIVERS COMPUT SCI, V17, P1659 Sielis GA, 2011, J UNIVERS COMPUT SCI, V17, P1743 Smyth B., 2005, IUI 05, P167 Su X., 2009, ADV ARTIFICIAL INTEL, V2009, P1, DOI DOI 10.1155/2009/421425 Verbert K, 2010, J UNIVERS COMPUT SCI, V16, P2175 Watts DJ, 2002, SCIENCE, V296, P1302, DOI 10.1126/science.1070120 Zhang J., 2008, LNCS Zhang J, 2007, WWW 07, P221 NR 30 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0747-5632 EI 1873-7692 J9 COMPUT HUM BEHAV JI Comput. Hum. Behav. PD OCT PY 2015 VL 51 SI SI BP 1086 EP 1091 DI 10.1016/j.chb.2015.03.028 PN B PG 6 WC Psychology, Multidisciplinary; Psychology, Experimental SC Psychology GA CP4VG UT WOS:000359880100061 ER PT J AU Seva, J Schatten, M Grd, P AF Seva, Jurica Schatten, Markus Grd, Petra TI Open Directory Project based universal taxonomy for Personalization of Online (Re)sources SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Recommendation systems; Content personalization; Automatic content classification; Automatic content labeling; Information extraction; Information retrieval; Open Directory Project; Vector Space Modeling; TF-IDF ID RECOMMENDER SYSTEMS; MODEL AB Content personalization, reflects the ability of content classification into (predefined) thematic units or information domains. Content nodes in a single thematic unit are related to a greater or lesser extent. An existing connection between two available content nodes assumes that the user will be interested in both resources (but not necessarily to the same extent). Such a connection (and its value) can be established through the process of automatic content classification and labeling. One approach for the classification of content nodes is the use of a predefined classification taxonomy. With the help of such classification taxonomy it is possible to automatically classify and label existing content nodes as well as create additional descriptors for future use in content personalization and recommendation systems. For these purposes existing web directories can be used in creating a universal, purely content based, classification taxonomy. This work analyzes Open Directory Project (ODP) web directory and proposes a novel use of its structure and content as the basis for such a classification taxonomy. The goal of a unified classification taxonomy is to allow for content personalization from heterogeneous sources. In this work we focus on the overall quality of ODP as the basis for such a classification taxonomy and the use of its hierarchical structure for automatic labeling. Due to the structure of data in ODP different grouping schemes are devised and tested to find the optimal content and structure combination for a proposed classification taxonomy as well as automatic labeling processes. The results provide an in-depth analysis of ODP and ODP based content classification and automatic labeling models. Although the use of ODP is well documented, this question has not been answered to date. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Seva, Jurica; Schatten, Markus; Grd, Petra] Univ Zagreb, Fac Org & Informat, Varazhdin 42000, Croatia. RP Seva, J (reprint author), Univ Zagreb, Fac Org & Informat, Pavlinska 2, Varazhdin 42000, Croatia. EM jseva@foi.hr; markus.schatten@foi.hr; petra.grd@foi.hr CR Amini B, 2015, EXPERT SYST APPL, V42, P913, DOI 10.1016/j.eswa.2014.08.031 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bird S., 2009, NATURAL LANGUAGE PRO Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borges HL, 2010, STUD COMPUT INTELL, V260, P129, DOI 10.1007/978-3-642-04584-4_6 Cowie J, 1996, COMMUN ACM, V39, P80, DOI 10.1145/234173.234209 Duong TH, 2013, LECT NOTES COMPUT SC, V7975, P607 Fathy N, 2014, J UNIVERS COMPUT SCI, V20, P1232 Greenwood M., 2001, IMPLEMENTING VECTOR Ha J., 2014, 2014 INT C DAT SCI A, P607 He L., 2013, INT J MACHINE LEARNI, V5, P491 Hu C., 2003, SYSTEMICS CYBERNETIC, V1, P53 Kalinov P., 2010, P 21 AUSTR C DAT TEC, V104, P113 Lee JH, 2013, ACM T WEB, V7, DOI 10.1145/2529995.2529997 Marath ST, 2014, P ANN HICSS, P1813, DOI 10.1109/HICSS.2014.229 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Pedregosa F, 2011, J MACH LEARN RES, V12, P2825 Perkins J., 2010, PYTHON TEXT PROCESSI Perugini S, 2008, INFORM PROCESS MANAG, V44, P910, DOI 10.1016/j.ipm.2007.06.005 Porter MF, 2006, PROGRAM-ELECTRON LIB, V40, P211, DOI 10.1108/00330330610681286 Rajalakshmi R., 2013, 2013 5 INT C ADV COM, V263, P15 Rehurek R., 2004, P LREC 2010 WORKSH N, P45 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vargiu E., 2013, ACM T WEB, V7, P1 Yun JL, 2012, EXPERT SYST APPL, V39, P2035, DOI 10.1016/j.eswa.2011.08.027 Zhu D., 2010, P 6 INT C SEM KNOWL, P227 Zubiaga A., 2013, WWW 13 COMP P 22 INT, P225 NR 33 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT PY 2015 VL 42 IS 17-18 BP 6306 EP 6314 DI 10.1016/j.eswa.2015.04.033 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA CK3NE UT WOS:000356122000004 ER PT J AU Mahmoud, A Niu, N AF Mahmoud, Anas Niu, Nan TI On the role of semantics in automated requirements tracing SO REQUIREMENTS ENGINEERING LA English DT Article DE Information retrieval; Traceability; Semantics ID IN-SOURCE CODE; INFORMATION-RETRIEVAL; TRACEABILITY LINKS; RELATEDNESS; SYSTEMS; MODELS; TOPICS; TREC-6 AB In this paper, we investigate the potential benefits of utilizing natural language semantics in automated traceability link retrieval. In particular, we evaluate the performance of a wide spectrum of semantically enabled information retrieval methods in capturing and presenting requirements traceability links in software systems. Our objectives are to gain more operational insights into these methods and to provide practical guidelines for the design and development of effective requirements tracing and management tools. To achieve our research objectives, we conduct an experimental analysis using three datasets from various application domains. Results show that considering more semantic relations in traceability link retrieval does not necessarily lead to higher quality results. Instead, a more focused semantic support, that targets specific semantic relations, is expected to have a greater impact on the overall performance of tracing tools. In addition, our analysis shows that explicit semantic methods, that exploit local or domain-specific sources of knowledge, often achieve a more satisfactory performance than latent methods, or methods that derive semantics from external or general-purpose knowledge sources. C1 [Mahmoud, Anas; Niu, Nan] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA. RP Mahmoud, A (reprint author), Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA. EM amm560@msstate.edu FU US NSF (National Science Foundation) [CCF1238336] FX This work is supported in part by the US NSF (National Science Foundation) Grant CCF1238336. CR Abebe S, 2010, ICPC 10, P156, DOI DOI 10.1109/ICPC.2010.29 Ahn D, 2004, INT POST DEM SESS, P73 Andrzejewski D, 2007, LECT NOTES ARTIF INT, V4701, P6 Anquetil N, 1999, WORK C REV ENG, P235 Anquetil N, 1998, CASCON 98, P4 Antoniol G, 2000, ANN SOFTW ENG, V9, P35, DOI 10.1023/A:1018916522804 Aslam J, 2005, ANN INT ACM SIGIR C, P573 Asuncion HU, 2010, P 32 ACM IEEE INT C, V1, P95, DOI [10.1145/1806799.1806817, DOI 10.1145/1806799.1806817] Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Ben Charrada E, 2012, INT REQ ENG C, P61 Biggers L, 2012, EMPIR SOFTW ENG, P1 BIGGERSTAFF TJ, 1994, COMMUN ACM, V37, P72, DOI 10.1145/175290.175300 Binkley D, 2010, COMPUTER TECHNOLOGIE Binkley D, 2011, ENCY SOFTWARE ENG, P454 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Capobianco G, 2013, J SOFTW-EVOL PROC, V25, P743, DOI 10.1002/smr.1564 Chen PI, 2010, EXPERT SYST APPL, V37, P1928, DOI 10.1016/j.eswa.2009.07.016 Chowdhury A, 1998, TECHNICAL REPORT Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Cleland-Huang J, 2012, INT C REQ ENG FDN SO, P179 Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 De Lucia A, 2007, ACM T SOFTW ENG METH, V16, DOI 10.1145/1276933.1276934 Dean A, 1999, DESIGN ANAL EXPT DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEISSENBOECK F, 2005, IWPC 05 WASH, P97 Dekhtyar A, 2007, INT S GRAND CHALL TR De Lucia A, 2006, INT C PROGRAM COMPRE, P317, DOI 10.1109/ICPC.2006.28 DEMMEL J, 1990, SIAM J SCI STAT COMP, V11, P873, DOI 10.1137/0911052 Demsar J, 2006, J MACH LEARN RES, V7, P1 Dumais S, 1993, 1 TEXT RETRIEVAL C T, P137 Etzkorn L, 1995, IEEE WORKSH PROGR CO, P14 Evans D, 1996, ACL 96, P17 Falleri J, 2010, INT C PROGR COMPR, P4 Fang J, 2011, INT WORKSH INT SYST, P1 Feilkas M, 2009, INT C PROGRAM COMPRE, P188 Fellbaum C, 1998, WORDNET ELECT LEXICA Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 Furnas G, 1988, ACM SIGIR 88, P465 Gabrilovich E, 2007, IJCAI 07, P1606 Gibiec M, 2010, INT C AUT SOFTW ENG, P245 Gligorov R, 2007, INT C WORLD WID WEB, P767 Gotel O, 1995, INT C REQ ENG, P100 Gotel O, 1994, INT C REQ ENG, P94 Gotel OCZ, 2011, INT REQUIR ENG CONF, P121, DOI 10.1109/RE.2011.6051655 Grant S, 2010, INT WORK C SOURC COD, P65 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Grzywaczewski A, 2012, J COMPUT SYST SCI, V78, P1204, DOI 10.1016/j.jcss.2011.10.009 Guo Y, 2009, INT S KNOWL ACQ MOD, P160 Han EH, 2000, LECT NOTES COMPUT, V1910, P424 Hata M, 2009, NEUROSCI LETT, V501, P72 Hazen T, 2010, SPOK LANG TECHN WORK, P366 Hindle A, 2012, 2012 28TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM), P243, DOI 10.1109/ICSM.2012.6405278 Huffman-Hayes J, 2006, IEEE T SOFTWARE ENG, V32, P4 Huffman-Hayes J, 2003, INT C REQ ENG, P138 Jurafsky D, 2000, SPEECH LANGUAGE PROC Kit L, 2006, ACM C OBJ OR PROGR L, P383 Kuhn A, 2007, INFORM SOFTWARE TECH, V49, P230, DOI 10.1016/j.infsof.2006.10.017 Lawrie D, 2007, IEEE INT WORK C SOUR, P213 Lehman M, 1984, J SYST SOFTW, V1, P213, DOI DOI 10.1016/0164-1212(79)90022-0 Linstead E, 2007, INT C AUT SOFTW ENG, P461 Lioma C, 2009, LECT NOTES COMPUT SC, V5478, P412, DOI 10.1007/978-3-642-00958-7_37 Liu YY, 2009, ICVE&VM 2009: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON VALUE ENGINEERING AND VALUE MANAGEMENT, P233 Lormans M, 2006, EUR C SOFTW MAINT RE, P47 Luisa M., 2004, Requirements Engineering, V9, DOI 10.1007/s00766-003-0179-8 Mahmoud A, 2010, ANN COMP SOFTW APPL, P246 Mahmoud A, 2012, INT C PROGR COMPR IC, P183 Mahmoud A, 2011, INT WORKSH TRAC EM F, P3 Maletic JI, 2000, PROC INT C TOOLS ART, P46, DOI 10.1109/TAI.2000.889845 Marcus A, 2001, 16TH ANNUAL INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2001), PROCEEDINGS, P107 Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 Maskeri G, 2008, ISEC 08, P113 Meneely A, 2012, ITRUST ELECT HLTH CA Milne D, 2008, AAAI 2008 WORKSH WIK, P25 Nallapati R, 2007, ICDMW 07, P349 Niu N, 2008, INT REQUIR ENG CONF, P155, DOI 10.1109/RE.2008.49 Niu N, 2012, IEEE INT REQ ENG C, P81 Nuseibeh B, 2000, C FUT SOFTW ENG, P35 Oliveto R, 2010, ICPC, P68 Panichella A, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P522 Patwardhan S, 2005, INT POST DEM SESS, P73 Pedersen JO, 2000, INFORM PROCESS MANAG, V36, P187, DOI 10.1016/S0306-4573(99)00051-5 Porteous I, 2008, ACM SIGKDD, P569 Porter F, 1997, ALGORITHM SUFFIX STR, P313 Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Pucher M, 2007, ANN M ACL INT POST D, P129 Ramesh B, 2001, IEEE T SOFTWARE ENG, V27, P58, DOI 10.1109/32.895989 Rao A, 2000, INFORM PROCESS MANAG, V36, P179, DOI 10.1016/S0306-4573(99)00050-3 Rosario B, 2000, LATENT SEMANTIC INDE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarukkai R, 2002, SPRINGER INT SERIES, P106 Schmidt H., 1994, P INT C NEW METH LAN, V12, P44 Settimi R, 2004, IEEE INT WORKSH PRIN, P49 Shepherd D, 2007, INT C ASP OR SOFTW D, P212 Sorg P, 2012, DATA KNOWL ENG, V74, P26, DOI 10.1016/j.datak.2012.02.003 Spanoudakis G, 2004, HDB SOFTW ENG KNOWL, V3, P395 Strube M., 2006, P AAAI, V6, P1419 Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6 Teh YW, 2006, J AM STAT ASSOC, V101, P1566, DOI 10.1198/016214506000000302 Teufel S, 2007, TEXT SPEECH LANG TEC, V37, P163 Thomas S, 2010, IEEE INT WORKSH SOUR, P55 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 von Knethen A, 2002, INT WORKSH TRAC EM F Wei X, 2006, ACM SIGIR 2006, P178 Wong S, 2012, ACM T DATABASE SYST, P299 Zhai K, 2012, INT C WORLD WID WEB, P879 Zhang Y, 2006, INT WORKSH MET SCHEM, P36 NR 108 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0947-3602 EI 1432-010X J9 REQUIR ENG JI Requir. Eng. PD SEP PY 2015 VL 20 IS 3 SI SI BP 281 EP 300 DI 10.1007/s00766-013-0199-y PG 20 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA CP3WJ UT WOS:000359813200005 ER PT J AU Green, CD Feinerer, I AF Green, Christopher D. Feinerer, Ingo TI The Evolution of The American Journal of Psychology 1, 1887-1903: A Network Investigation SO AMERICAN JOURNAL OF PSYCHOLOGY LA English DT Article AB The American Journal of Psychology (AJP) was the first academic journal in the United States dedicated to the "new" scientific form of the discipline. But where did the journal's founding owner/editor, G. Stanley Hall, find the "psychologists" he needed to fill the pages of such a venture 1887, when he was still virtually the only professor of psychology in the country? To investigate this question we used the substantive vocabularies of every full article published in AJP's first 14 volumes to generate networks of verbally similar articles. These networks reveal the variety of research communities that Hall drew on to launch and support the journal. Three separate networks, corresponding to 3 successive time blocks, show how Hall's constellation of participating research communities changed over AJP's first 17 years. Many of these communities started with rather nebulous boundaries but soon began to differentiate into groups of more distinct specialties. Some topics declined over time, but new ones regularly appeared to replace them. We sketch a quasievolutionary model to describe the intellectual ecology of AJP's early years. C1 [Green, Christopher D.] York Univ, N York, ON M3J 1P3, Canada. [Feinerer, Ingo] Vienna Univ Technol, Vienna, Austria. RP Green, CD (reprint author), Univ Haifa, Dept Psychol, IL-31905 Haifa, Israel. EM rakover@psy.haifa.ac.il FU Social Sciences & Humanities Research Council of Canada FX We gratefully acknowledge financial support for this research from the Social Sciences & Humanities Research Council of Canada. CR Bastian M., 2009, P INT AAAI C WEBL SO, V8, P361 EVANS RB, 1987, AM J PSYCHOL, V100, P321 Feinerer I, 2008, J STAT SOFTW, V25, P1 Green C. D., 2015, HIST PSYCHOL, V8, P15 Green C. D., HIST PSYCHO IN PRESS Green CD, 2009, HIST HUM SCI, V22, P37, DOI 10.1177/0952695108099134 Green CD, 2009, AM PSYCHOL, V64, P75, DOI 10.1037/a0013338 Hornik K, 2012, J STAT SOFTW, V50, P1 Manning C.D., 2008, INTRO INFORM RETRIEV Moretti Franco, 2013, DISTANT READING Moretti Franco, 2005, GRAPHS MAPS TREES AB Neary F., 2001, PSYCHOL BRITAIN HIST, P54 Osier D. V., 1984, CENTURY SERIAL PUBLI R Core Team, 2013, R LANG ENV STAT COMP Ross D., 1972, GS HALL PSYCHOL PROP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Young JL, 2013, HIST PSYCHOL, V16, P249, DOI 10.1037/a0033118 NR 17 TC 0 Z9 0 PU UNIV ILLINOIS PRESS PI CHAMPAIGN PA 1325 S OAK ST, CHAMPAIGN, IL 61820-6903 USA SN 0002-9556 EI 1939-8298 J9 AM J PSYCHOL JI Am. J. Psychol. PD FAL PY 2015 VL 128 IS 3 BP 387 EP 401 PG 15 WC Psychology, Multidisciplinary SC Psychology GA CO2VG UT WOS:000359014200009 ER PT J AU Lu, K Mao, J AF Lu, Kun Mao, Jin TI An Automatic Approach to Weighted Subject Indexing-An Empirical Study in the Biomedical Domain SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE subject indexing; text mining; weighting ID INFORMATION-RETRIEVAL; QUERY EXPANSION; ORGANIZATION; ASSIGNMENT; VOCABULARY; ABOUTNESS; RELEVANCE; FIELD; MESH AB Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associated with the documents. On the other hand, the idea of probabilistic or weighted indexing was proposed a long time ago and has seen success in capturing uncertainties in the automatic indexing process. One hurdle to overcome in implementing weighted indexing in manual subject indexing systems is the practical burden that could be added to the already intensive indexing process. This study proposes a method to infer automatically the associations between subject terms and documents through text mining. By uncovering the connections between MeSH descriptors and document text, we are able to derive the weights of MeSH descriptors manually assigned to documents. Our initial results suggest that the inference method is feasible and promising. The study has practical implications for improving subject indexing practice and providing better support for information retrieval. C1 [Lu, Kun] Univ Oklahoma, Sch Lib & Informat Studies, Norman, OK 73019 USA. [Mao, Jin] Wuhan Univ, Ctr Studies Informat Resources, Wuhan 430072, Hunan, Peoples R China. RP Lu, K (reprint author), Univ Oklahoma, Sch Lib & Informat Studies, 401 West Brooks, Norman, OK 73019 USA. EM kunlu@ou.edu; danveno@163.com CR An XY, 2011, SCIENTOMETRICS, V88, P133, DOI 10.1007/s11192-011-0374-1 Blair D. C., 1990, LANGUAGE REPRESENTAT Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826 Chung E, 2010, J AM SOC INF SCI TEC, V61, P688, DOI 10.1002/asi.21272 Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 Foskett A. C., 1996, SUBJECT APPROACH INF FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Hjorland B, 2008, KNOWL ORGAN, V35, P86 HjOrland B., 2005, B AM SOC INFORM SCI, V30, P17, DOI 10.1002/bult.312 HJORLAND B, 1992, J DOC, V48, P172, DOI 10.1108/eb026895 Hjorland B, 2001, J AM SOC INF SCI TEC, V52, P774, DOI 10.1002/asi.1131 Hjorland B, 2011, J AM SOC INF SCI TEC, V62, P72, DOI 10.1002/asi.21451 HUTCHINS WJ, 1978, ASLIB PROC, V30, P172, DOI 10.1108/eb050629 Jalali V, 2011, KNOWL INF SYST, V29, P237, DOI 10.1007/s10115-010-0327-7 Kent A., 1978, ENCY LIB INFORM SCI, P213 Kleineberg M, 2013, KNOWL ORGAN, V40, P340 KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P79, DOI 10.1016/0020-0271(73)90020-X Lavrenko V., 2009, GENERATION THEORY RE Lu K., 2013, P 76 ANN C ASS INF S Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8 Mai JE, 2001, J DOC, V57, P591, DOI 10.1108/EUM0000000007095 Manning C.D., 2008, INTRO INFORM RETRIEV MARON ME, 1979, J AM SOC INFORM SCI, V30, P224, DOI 10.1002/asi.4630300409 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Medelyan Olena, 2008, Journal of the American Society for Information Science and Technology, V59, DOI 10.1002/asi.20790 Meij E, 2010, INFORM PROCESS MANAG, V46, P448, DOI 10.1016/j.ipm.2009.09.005 Mu XM, 2010, J INF SCI, V36, P194, DOI 10.1177/0165551509357856 Plaunt C, 1998, J AM SOC INFORM SCI, V49, P888 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Ruch P, 2006, BIOINFORMATICS, V22, P658, DOI 10.1093/bioinformatics/bti783 Salton G., 1975, A THEORY OF INDEXING SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shin K, 2004, LECT NOTES COMPUT SC, V3136, P388 Stokes N, 2009, INFORM RETRIEVAL, V12, P17, DOI 10.1007/s10791-008-9073-9 Taylor A. G., 2008, ORG INFORM, V3rd TRAVIS IL, 1982, ANNU REV INFORM SCI, V17, P123 Trieschnigg D., 2008, P 31 ANN INT ACM SIG, P823, DOI 10.1145/1390334.1390523 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 Willis C, 2013, J AM SOC INF SCI TEC, V64, P1330, DOI 10.1002/asi.22853 Wilson P., 1968, 2 KINDS POWER ESSAY Wolfram D, 2008, J AM SOC INF SCI TEC, V59, P3, DOI 10.1002/asi.20688 Zhang H, 2011, INFORM TECHNOL LIBR, V30, P75 NR 46 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD SEP PY 2015 VL 66 IS 9 BP 1776 EP 1784 DI 10.1002/asi.23290 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CO2XU UT WOS:000359021200003 ER PT J AU Pitsilis, G Wang, W AF Pitsilis, Georgios Wang, Wei TI Harnessing the power of social bookmarking for improving tag-based recommendations SO COMPUTERS IN HUMAN BEHAVIOR LA English DT Article DE Recommender systems; Collaborative tagging; Clustering; Affinity propagation; citeUlike; Taxonomy ID SYSTEMS; FOLKSONOMIES AB Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users' Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately. (C) 2015 Elsevier Ltd. All rights reserved. C1 [Pitsilis, Georgios] Comp Sci Res, Athens, Greece. [Wang, Wei] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China. RP Pitsilis, G (reprint author), Comp Sci Res, Athens, Greece. EM georgios.pitsilis@gmail.com FU Fundamental Research funds for the central Universities of China [K15JB00190]; Ph.D. Programs Foundation of Ministry of Education of China [20120009120010]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [K14C300020] FX The work reported in this paper is partially supported by the Fundamental Research funds for the central Universities of China (No. K15JB00190), the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120009120010), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No. K14C300020). CR DuBois T., 2009, ACM RECSYS 09 WORKSH Fellbaum C., 2005, ENCY LANGUAGE LINGUI, P665 Frey BJ, 2007, SCIENCE, V315, P972, DOI 10.1126/science.1136800 Gemmell J., 2008, P 6 WORKSH INT TECHN Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P241 Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 Kevin E., 2007, ARIADNE APR Li J, 2008, IEEE T PATTERN ANAL, V30, P985, DOI 10.1109/TPAMI.2007.70847 Li Y., 2008, P 2008 IEEE WIC ACM, P59 Lipczak M, 2010, P 4 ACM C REC SYST R, P167, DOI 10.1145/1864708.1864741 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 McNee SM, 2006, CHI 06, P1097, DOI DOI 10.1145/1125451.1125659 Mehta B., 2007, P 22 NAT C ART INT A, V2, P1402 Moxley E., 2008, P 1 ACM INT C MULT I, P24 Nakamoto R., 2007, INT J COMPUTER SCI I, V34 Parra D., 2009, RECSYS 2009, P237 Pedersen T., 2004, HLT NAACL 2004, P38 Peng J., 2009, P 19 ANN WORKSH INF Peng J, 2009, ISI: 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, P269 Peng J., 2010, P 19 ACM INT C INF K, P809, DOI DOI 10.1145/1871437.1871541 Peng J., 2010, P 2010 INT C EL BUS Pitsilis G., 2011, IFIP ADV INFORM COMM, V358, P82 Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sen S., 2009, 18 INT WORLD WID WEB, P671 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Sood S., 2007, P INT C WEBL SOC MED Su X.-F., 2005, SPEC INT TRACKS 14 W, P960, DOI 10.1145/1062745.1062818 Symeonidis P, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P43 Tso-sutter K. H. L., 1995, P 2 ACM S APPL COMP Venetis P., 2011, 4 ACM INT C WEB SEAR Wang W., 2010, P 6 INT C INF SYST S Wei C., 2011, P 34 INT ACM SIGIR C, P1023 Xu GD, 2011, LECT NOTES COMPUT SC, V6997, P158 Zeng D, 2008, LECT NOTES COMPUT SC, V5075, P320, DOI 10.1007/978-3-540-69304-8_32 Zhang XL, 2013, J COMPUT SCI TECH-CH, V28, P616, DOI 10.1007/s11390-013-1362-0 NR 39 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0747-5632 EI 1873-7692 J9 COMPUT HUM BEHAV JI Comput. Hum. Behav. PD SEP PY 2015 VL 50 BP 239 EP 251 DI 10.1016/j.chb.2015.03.045 PG 13 WC Psychology, Multidisciplinary; Psychology, Experimental SC Psychology GA CK9GU UT WOS:000356549400025 ER PT J AU Lee, LS Glass, J Lee, HY Chan, CA AF Lee, Lin-shan Glass, James Lee, Hung-yi Chan, Chun-an TI Spoken Content Retrieval-Beyond Cascading Speech Recognition with Text Retrieval SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING LA English DT Article DE Spoken content retrieval; spoken term detection; query by example; semantic retrieval; joint optimization; pseudo-relevance feedback; graph-based random walk; unsupervised acoustic pattern discovery; query expansion; interactive retrieval; summarization; key term extraction ID NONNEGATIVE MATRIX FACTORIZATION; DOCUMENT-RETRIEVAL; TERM DETECTION; INFORMATION; SYSTEMS; MODEL; OPTIMIZATION; ORGANIZATION; QUERIES; SEARCH AB Spoken content retrieval refers to directly indexing and retrieving spoken content based on the audio rather than text descriptions. This potentially eliminates the requirement of producing text descriptions for multimedia content for indexing and retrieval purposes, and is able to precisely locate the exact time the desired information appears in the multimedia. Spoken content retrieval has been very successfully achieved with the basic approach of cascading automatic speech recognition (ASR) with text information retrieval: after the spoken content is transcribed into text or lattice format, a text retrieval engine searches over the ASR output to find desired information. This framework works well when the ASR accuracy is relatively high, but becomes less adequate when more challenging real-world scenarios are considered, since retrieval performance depends heavily on ASR accuracy. This challenge leads to the emergence of another approach to spoken content retrieval: to go beyond the basic framework of cascading ASR with text retrieval in order to have retrieval performances that are less dependent on ASR accuracy. This overview article is intended to provide a thorough overview of the concepts, principles, approaches, and achievements of major technical contributions along this line of investigation. This includes five major directions: 1) Modified ASR for Retrieval Purposes: cascading ASR with text retrieval, but the ASR is modified or optimized for spoken content retrieval purposes; 2) Exploiting the Information not present in ASR outputs: to try to utilize the information in speech signals inevitably lost when transcribed into phonemes and words; 3) Directly Matching at the Acoustic Level without ASR: for spoken queries, the signals can be directly matched at the acoustic level, rather than at the phoneme or word levels, bypassing all ASR issues; 4) Semantic Retrieval of Spoken Content: trying to retrieve spoken content that is semantically related to the query, but not necessarily including the query terms themselves; 5) Interactive Retrieval and Efficient Presentation of the Retrieved Objects: with efficient presentation of the retrieved objects, an interactive retrieval process incorporating user actions may produce better retrieval results and user experiences. C1 [Lee, Lin-shan; Lee, Hung-yi] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan. [Glass, James] MIT, Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA. [Chan, Chun-an] Google, London SW1W 9TQ, England. RP Lee, LS (reprint author), Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan. EM lslee@gate.sinica.edu.tw; glass@mit.edu; hungyilee@ntu.edu.tw; chunanchan@gmail.com CR AKBACAK M, 2008, P ICASSP 08 LOS ANG, P5240 AKBACAK M, 2013, P ICASSP, P8267 Akiba T., 2011, P INTERSPEECH Akiba T., 2011, P NTCIR 9 WORKSH ALBERTI C, 2009, P ICASSP, P4873 Allauzen C., 2004, P WORKSH INT APPR SP Anguera X., 2013, P MEDIAEVAL WORKSH Anguera X., 2013, P ICME, P1 Anguera X., 2013, P INTERSPEECH Anguera X., 2014, P MEDIAEVAL WORKSH [Anonymous], 2012, BBN IARPA BAB PI M S Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223 Billerbeck B., 2005, P 10 AUSTR DOC COMP Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Buzo A., 2014, P MEDIAEVAL Cai XY, 2013, IEEE T AUDIO SPEECH, V21, P1424, DOI 10.1109/TASL.2013.2253098 Calvo M., 2014, P MEDIAEVAL Can D, 2011, IEEE T AUDIO SPEECH, V19, P2338, DOI 10.1109/TASL.2011.2134087 CAN D, 2009, P ICASSP 09 TAIP TAI, P3957 Can D., 2009, P HUM LANG TECHN ANN Cao G., 2008, P 31 ANN INT ACM SIG Carlin M. A., 2011, P INTERSPEECH Carpineto C., 2009, ACM COMPUT SURV, V41 Cernocky J., 2007, P PROC APPL PUBL SEC C-H Lee, 1988, P ICASSP, V1, P501 Chan C.-A., 2011, P INTERSPEECH CHAN CA, 2013, P ICASSP, P8550 Chan C.-A., 2010, P INTERSPEECH Chan C.-A., 2012, THESIS NATL TAIWAN U Chan CA, 2013, IEEE T AUDIO SPEECH, V21, P1330, DOI 10.1109/TASL.2013.2248714 CHANG HL, 2008, P SLT, P285 Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 Chaudhari U. V., 2007, P ASRU, P665 Chaudhari UV, 2012, IEEE T AUDIO SPEECH, V20, P1633, DOI 10.1109/TASL.2012.2186805 Chelba C., 2005, P 43 ANN M ASS COMP, P443, DOI http://dx.doi.org/10.3115/1219840.1219895 Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992 Chen B, 2012, IEEE T AUDIO SPEECH, V20, P2602, DOI 10.1109/TASL.2012.2208628 Chen B., 2011, P IEEE WORKSH AUT SP, P389 Chen BL, 2002, IEEE T SPEECH AUDI P, V10, P303, DOI 10.1109/TSA.2002.802541 CHEN GG, 2013, P ASRU, P416 Chen I.-F., P INTERSPEECH, V13 Chen I.-F., 2013, P INTERSPEECH Chen I.-F., 2014, P ISCSLP Chen I.-F., 2014, P INTERSPEECH Chen I.-F., 2012, P INTERSPEECH CHEN NF, 2013, P ICASSP, P8357 Chen P.-N., 2011, P INTERSPEECH Chen Y.-H., 2010, P ICASSP, P5278 CHEN YN, 2011, P ICASSP, P5644 Chen Y.-N., 2010, P IEEE SPOK LANG TEC, P265 CHEN YN, 2012, P ICASSP, P5041 Chia T. K., 2008, P SIGIR Chia T. K., 2010, ACM T INFORM SYST, V28 Chia T. K., 2007, P JOINT C EMP METH N Chien JT, 2009, IEEE T AUDIO SPEECH, V17, P267, DOI 10.1109/TASL.2008.2008366 CHUNG CT, 2013, P ICASSP, P8081 Chung C.-T., 2014, P ICASSP, P7814 Colbath S., 1999, ACM COMPUT SURVEYS CUI J, 2013, P ICASSP, P6753 Cui J., 2014, P ICASSP, P7839 Dahl GE, 2012, IEEE T AUDIO SPEECH, V20, P30, DOI 10.1109/TASL.2011.2134090 D'Avanzo E., 2004, P DOC UND C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Deng L, 2013, IEEE T AUDIO SPEECH, V21, P1060, DOI 10.1109/TASL.2013.2244083 Driesen J., 2012, Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), DOI 10.1109/ICASSP.2012.6289077 DUBOIS C, 2008, P ICASSP, P4961 Fu Q., 2007, P ASRU, P278 Fu Q, 2012, IEEE T AUDIO SPEECH, V20, P780, DOI 10.1109/TASL.2011.2165279 Fujii Y., 2008, P INTERSPEECH Furui S, 2004, IEEE T SPEECH AUDI P, V12, P401, DOI 10.1109/TSA.2004.828699 Gajjar M. R., 2008, P INTERSPEECH Gales M, 2012, IEEE SIGNAL PROC MAG, V29, P70, DOI 10.1109/MSP.2012.2207140 Gandhe A., 2014, P ICASSP, P4888 GARCIA A, 2006, P ICASSP, P949 Garg N., 2009, P INTERSPEECH Garofolo J. S., 1999, P 7 TEXT RETR C TREC Garofolo J. S., 2000, TREC SPOKEN DOCUMENT GILLICK D, 2009, P IEEE INT C AC SPEE, P4769 Glass J., 2007, P INTERSPEECH Glass J., 2012, POW SYST TECHN POWER, P1 Goldwater S., 2009, SPEECH COMMUN, V52, P181 Harwath D., 2012, Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), DOI 10.1109/ICASSP.2012.6289061 HARWATH DF, 2013, P ICASSP, P8555 Hauptmann A. G., 2005, P 4 INT C IM VID RET Hazen T. J., 2007, P ASRU, P659 Hazen T. J., 2011, P INTERSPEECH Hazen Timothy J, 2009, Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU 2009), DOI 10.1109/ASRU.2009.5372889 He XD, 2008, IEEE SIGNAL PROC MAG, V25, P14, DOI 10.1109/MSP.2008.926652 He XD, 2013, P IEEE, V101, P1116, DOI 10.1109/JPROC.2012.2236631 HE XD, 2012, P ICASSP, P5241 Heigold G, 2012, IEEE SIGNAL PROC MAG, V29, P58, DOI 10.1109/MSP.2012.2197232 Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597 Hofmann T., 2000, Intelligent Data Analysis, V4 Hofmann T., 1999, P UNC ART INT UAI 99 HORI T, 2007, P ICASSP, P73 HSIAO R, 2013, P ASRU, P440 Hsu W. H., 2007, P ACM INT C MULT, P971, DOI DOI 10.1145/1291233.1291446.ISBN Hu X., 2010, P INTERSPEECH Huang SF, 2010, IEEE T AUDIO SPEECH, V18, P1941, DOI 10.1109/TASL.2010.2040782 HUIJBREGTS M, 2011, P ICASSP, P4436 Inoue A., 2004, P SPEECH PROS Itoh Y., 2007, P INTERSPEECH IWAMI K, 2011, P ICASSP, P5664 Jansen A., 2011, P INTERSPEECH Jansen A., 2013, IEEE T SIGNAL PROCES, V6, P1698 Jansen A, 2009, IEEE T AUDIO SPEECH, V17, P1457, DOI 10.1109/TASL.2009.2021307 JANSEN A, 2013, P IEEE INT C AC SPEE, P8091 Jansen A., 2010, P INTERSPEECH Jansen A., 2012, P INTERSPEECH Jansen A., 2011, P ASRU, P401 Jansen B. J., 1998, SIGIR Forum, V32 Jiang X., 2009, P 32 INT ACM SIGIR C JOHNSON SE, 1999, P IEEE INT C AC SPEE, P49 Juang BH, 1997, IEEE T SPEECH AUDI P, V5, P257 KANDA N, 2012, P SLT, P170 Karakos D, 2013, P ASRU, P210 Karakos D., 2011, P ASRU, P407 Karanasou P., 2012, P INTERSPEECH Keogh E., 2011, P 28 INT C VER LARG, P406 Keshet J, 2009, SPEECH COMMUN, V51, P317, DOI 10.1016/j.specom.2008.10.002 Keshet J., 2007, P NOLISP Kesiraju S., 2014, P MEDIAEVAL KINGSBURY B, 2013, P ICASSP, P8277 Kintzley K, 2014, P ICASSP, P7859 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 KONG SY, 2006, P IEEE INT C AC SPEE, P941 KONG SY, 2009, P ICASSP, P4709 Kong SY, 2011, IEEE T AUDIO SPEECH, V19, P1875, DOI 10.1109/TASL.2010.2102592 KONG SY, 2008, P IEEE ACL WORKSH SP, P165 Koumpis K, 2005, IEEE SIGNAL PROC MAG, V22, P61, DOI 10.1109/MSP.2005.1511824 Kubo Y, 2012, IEEE T AUDIO SPEECH, V20, P2240, DOI 10.1109/TASL.2012.2199112 Kurland O., 2005, P 28 ANN INT ACM SIG LANGVILLE AN, 2005, P SIAM, V47, P135 Larson Martha, 2011, Foundations and Trends in Information Retrieval, V5, DOI 10.1561/1500000020 Larson M., 2009, ADV INFORM RETRIEVAL Lavrenko V., 2001, P 24 ANN INT ACM SIG Lee C.-Y., 2012, P ACL Lee DD, 1999, NATURE, V401, P788 LEE H, 2012, P GLOB S MILL WAV GS, P182 Lee HY, 2014, IEEE-ACM T AUDIO SPE, V22, P80, DOI 10.1109/TASLP.2013.2285469 Lee HY, 2012, IEEE T AUDIO SPEECH, V20, P2095, DOI 10.1109/TASL.2012.2196514 Lee HY, 2013, IEEE T AUDIO SPEECH, V21, P1272, DOI 10.1109/TASL.2013.2248721 Lee H.-Y., 2009, P ASRU Lee H.-Y., 2010, P INTERSPEECH Lee H.-Y., 2010, P ICASSP Lee H.-Y., 2014, IEEE-ACM T AUDIO SPE, V22, P881 Lee H.-Y., 2014, SPEC INF EXTACT RETR, P1045 LEE HY, 2013, P ICASSP, P8347 LEE HY, 2012, P ICASSP, P5065 Lee H.-Y., 2012, P SLT Lee H.-Y., 2012, THESIS NATL TAIWAN U LEE HY, 2011, P ICASSP, P5648 Lee H.-Y., 2013, P ICASSP, P8297 Lee H.-Y., 2012, P INTERSPEECH Lee H.-Y., 2014, P INTERSPEECH Lee H.-Y., 2011, P APSIPA Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42 Lee L.-s., 2009, P ASRU Lee SangBok, 2005, Proceedings of the 4th World Congress on Allelopathy, "Establishing the Scientific Base", Wagga Wagga, New South Wales, Australia, 21-26 August 2005, P505 Lemon O, 2011, COMPUT SPEECH LANG, V25, P210, DOI 10.1016/j.csl.2010.04.005 Levin E, 2000, IEEE T SPEECH AUDI P, V8, P11, DOI 10.1109/89.817450 Li H., 2012, P INTERSPEECH Li T.-H., 2005, P INTERSPEECH Li Y, 2013, PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS), P198 Li Y. chi, 2003, ISCA WORKSH MULT SPO, P85 LIGHT M, 2002, P COMM ACM, V45, P54 LIN SH, 2010, P IEEE INT C AC SPEE, P5314 Lin S.-H., 2009, ACM T ASIAN LANG INF, V8 Lin W.-H., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) Liu C., 2014, P INTERSPEECH Liu F, 2011, IEEE T AUDIO SPEECH, V19, P538, DOI 10.1109/TASL.2010.2052119 Liu F., 2009, P HUM LANG TECHN ANN Liu F, 2013, IEEE T AUDIO SPEECH, V21, P1469, DOI 10.1109/TASL.2013.2255279 Liu FH, 2008, LECT NOTES COMPUT SC, V5299, P181 LIU JJ, 2013, P ICASSP, P8386 Logan B., 2000, P ICSLP Logan B, 2005, IEEE T MULTIMEDIA, V7, P899, DOI 10.1109/TMM.2005.854429 Lv Y., 2009, CIKM 09 Lv Y., 2010, P 33 ACM SIGIR C RES Ma C., 2007, P INTERSPEECH Mamou J., 2008, P INTERSPEECH Mamou J., 2009, SIGIR, P51 MAMOU J, 2013, P ICASSP, P8272 Mamou J., 2007, P SIGIR Mandal A., 2013, INT J SPEECH TECHNOL, P1 Mandal A., 2013, P INTERSPEECH Manning C. D., 2008, INTRO INFORM RETRIEV Manning C.D., 2008, INTRO INFORM RETRIEV Mantena G. V., 2013, P MEDIAEVAL Marston J., 2004, P IEEE INT C MULT EX Maskey S., 2005, P INTERSPEECH Maskey S. R., 2008, THESIS COLUMBIA U NE Masumura R., 2011, P INTERSPEECH MENG CH, 2009, P ICASSP, P4893 Meng S., 2010, P INTERSPEECH Mertens T., 2009, P INTERSPEECH MERTENS T, 2009, P ICASSP, P4885 Metze F., 2012, Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), DOI 10.1109/ICASSP.2012.6289083 Metze F., 2012, P MEDIAEVAL WORKSH Metze F, 2014, COMPUT SPEECH LANG, V28, P1066, DOI 10.1016/j.csl.2013.12.004 Metzler D., 2007, P 30 ANN INT ACM SIG Miller D. R. H., 2007, P INTERSPEECH Misu T, 2010, SPEECH COMMUN, V52, P61, DOI 10.1016/j.specom.2009.08.007 Mohamed AR, 2012, IEEE T AUDIO SPEECH, V20, P14, DOI 10.1109/TASL.2011.2109382 Mueller M., 2007, INFORM RETRIEVAL MUS, P69 NANJO H, 2005, P IEEE ICASSP, P1053 Natsev A. P., 2005, P ACM MM 2005, P598, DOI 10.1145/1101149.1101288 Ng K., 2000, THESIS MIT CAMBRIDGE Nishizaki H., 2011, P APSIPA NOROUZIAN A, 2013, P ICASSP, P8292 Norouzian A., 2012, P INTERSPEECH Norouzian A, 2010, Proceedings 2010 IEEE Spoken Language Technology Workshop (SLT 2010), DOI 10.1109/SLT.2010.5700850 Norouzian A., 2013, P INTERSPEECH Oard D. W., 2013, P FIRE Ogata J., 2009, P INTERSPEECH OHNO T, 2012, P SLT, P204 Olsson J. S., 2007, P SSCS Olsson J. S., 2008, P INTERSPEECH Otterbacher J, 2009, INFORM PROCESS MANAG, V45, P42, DOI 10.1016/j.ipm.2008.06.004 Pan YC, 2012, IEEE T AUDIO SPEECH, V20, P632, DOI 10.1109/TASL.2011.2163512 Pan Y.-C., 2007, P INTERSPEECH Pan Y.-C., 2007, P IEEE AUT SPEECH RE, P677 Pan Y.-C., 2006, P INTERSPEECH Pan Y.-C., 2006, P SLT, P38 Pan YC, 2010, IEEE T AUDIO SPEECH, V18, P1562, DOI 10.1109/TASL.2009.2037404 Parada Carolina, 2010, Proceedings 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010, DOI 10.1109/ICASSP.2010.5494966 Parada C., 2010, P HUM LANG TECHN ANN Parada Carolina, 2009, Proceedings of the 2009 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU 2009), DOI 10.1109/ASRU.2009.5373341 Park AS, 2008, IEEE T AUDIO SPEECH, V16, P186, DOI 10.1109/TASL.2007.909282 PARLAK S, 2008, P ICASSP LAS VEG NV, P5244 Parlak S, 2012, IEEE T AUDIO SPEECH, V20, P731, DOI 10.1109/TASL.2011.2164531 Pham VT, 2014, P ICASSP, P7078 Ponte J. M., 1998, P SIGIR POVEY D, 2002, P ICASSP, P105 Povey D., 2003, THESIS CAMBRIDGE U C Povey D., 2010, P ICASSP PRABHAVALKAR R, 2013, P ICASSP, P8287 Prabhavalkar R., 2012, P MLSLP Proena J., 2014, P MEDIAEVAL QIAO Y, 2008, P IEEE INT C AC SPEE, P3989 Rastrow A., 2009, P INTERSPEECH RIEDHAMMER K, 2012, P SLT, P392 Rocchio J., 1971, SMART RETRIEVAL SYST Rodrguez-Fuentes L. J., 2013, P MEDIAEVAL Rodriguez-Fuentes L. J., 2014, P MEDIAEVAL Rudinac S., 2009, P 10 WORKSH IM AN MU Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Ruthven I., 2008, INTERACTIVE INFORM R, V42 Sainath T. N., 2010, P INTERSPEECH Sainath TN, 2012, IEEE SIGNAL PROC MAG, V29, P98, DOI 10.1109/MSP.2012.2208663 SAKOE H, 1978, IEEE T ACOUST SPEECH, V26, P43, DOI 10.1109/TASSP.1978.1163055 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saon G, 2012, IEEE SIGNAL PROC MAG, V29, P18, DOI 10.1109/MSP.2012.2197156 Saraclar M, 2004, P HLT NAACL, P129 SARACLAR M, 2013, P IEEE WORKSH AUT SP, P464 Schatzmann J., 2007, P HUM LANG TECHN C N Schneider D., 2010, P INTERSPEECH SEIDE F, 2007, P ASRU 07, P629 SEIGEL MS, 2013, P ICASSP, P8565 Shao J., 2008, P INTERSPEECH Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Shiang S.-R., 2013, P INTERSPEECH Silva J., 2006, P SLT, P46 SILVA J, 2006, P ICASSP, P945 Singhal A., 1999, P 22 ANN INT ACM SIG Stern RM, 2012, IEEE SIGNAL PROC MAG, V29, P34, DOI 10.1109/MSP.2012.2207989 Stouten V, 2008, IEEE SIGNAL PROC LET, V15, P131, DOI 10.1109/LSP.2007.911723 Sukkar RA, 1996, IEEE T SPEECH AUDI P, V4, P420, DOI 10.1109/89.544527 Szepesvari C., 2005, P ICML Szoke I., 2008, P 31 ANN INT ACM SIG SZOKE I, 2008, P IEEE WORKSH SPOK L, P273 Szoke I., 2013, P MEDIAEVAL Szoke Igor, 2014, P ICASSP, P7849 Tao T., 2006, P 29 ANN INT ACM SIG Tejedor J., 2011, P CBMI, P151 Tejedor J., 2012, ACM T INF SYST Tejedor J., 2010, P INTERSPEECH Tejedor J., 2010, P FALA Thambiratnam K, 2007, IEEE T AUDIO SPEECH, V15, P346, DOI 10.1109/TASL.2006.872615 THAMBIRATNAM K, 2005, P ICASSP, P465 Thorsten J., 2002, P KDD Tian X., 2008, P ACM INT C MULT, P131, DOI DOI 10.1145/1459359.1459378.ISBN Tomoyosi Akiba Y. Y., 2008, P INTERSPEECH Tsakalidis S., 2012, P INTERSPEECH Tsuge S., 2011, P 9 NTCIR WORKSH M TU TW, 2012, P ICASSP, P5085 Tu T.-W., 2011, P ASRU, P383 Tur G., 2011, SPOKEN LANGUAGE UNDE, P257 Tur G., 2011, SPOKEN LANGUAGE UNDE Tur G., 2011, SPOKEN LANGUAGE UNDE, P417 Turunen V. T., 2007, P 30 ANN INT ACM SIG Turunen V. T., 2008, P INTERSPEECH van der Werff L., 2007, P SSCS Vanhainen N., 2012, P INTERSPEECH Vergyri D., 2007, P INTERSPEECH Vesely K., 2013, P INTERSPEECH Voorhees E. M., 2000, OV 9 TEXT RETR C TRE, P1 Wallace Roy, 2010, Proceedings 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010, DOI 10.1109/ICASSP.2010.5494969 Wallace R., 2007, P INTERSPEECH Wallace R, 2011, IEEE T AUDIO SPEECH, V19, P1677, DOI 10.1109/TASL.2010.2096215 Wallace R., 2009, P SSCS Wang D., 2009, P INTERSPEECH WANG D, 2008, P ICASSP MARCH APR, P4969 WANG D, 2010, P ICASSP, P5294 Wang D., 2010, P INTERSPEECH WANG H, 2012, P ICASSP, P5157 Wang H., 2014, P MEDIAEVAL Wang H., 2012, P MEDIAEVAL WANG HP, 2013, P ICASSP, P8545 Wang L., 2011, P APSIPA Wang Q., 2011, P 34 INT ACM SIGIR C Wang Y.-Y., 2008, IEEE SIGNAL PROCESSI, V25, P28 WEGMANN S, 2013, P ASRU, P192 Wei X., 2006, P 29 ANN INT ACM SIG Wen T.-H., 2012, P INTERSPEECH WEN TH, 2013, P ICASSP, P8510 Weng C., 2012, P INTERSPEECH WENG C, 2013, P ICASSP 13, P6960 Williams JD, 2007, COMPUT SPEECH LANG, V21, P393, DOI 10.1016/j.csl.2006.06.008 WOLLMER M, 2009, P ICASSP TAIP TAIW, P3949 Xie S., 2009, P INTERSPEECH Xie S., 2010, P HUM LANG TECHN ANN XIE S, 2008, P ICASSP LAS VEG NV, P4985 Xie SS, 2010, COMPUT SPEECH LANG, V24, P495, DOI 10.1016/j.csl.2009.04.007 XIE SS, 2009, P ASRU, P387 YAN R, 2003, P ACM INT C IM VID R, V2728, P238 Yan R., 2003, P 11 ACM INT C MULT Yang P., 2014, P MEDIAEVAL Yang P., 2014, P INTERSPEECH Yi X., 2009, P 31 EUR C IR RES AD Young S., 2002, P ICSLP Young S., 2010, P INTERSPEECH Yu D, 2013, IEEE T AUDIO SPEECH, V21, P388, DOI 10.1109/TASL.2012.2227738 Yu P., 2005, P C HUM LANG TECHN E Zechner K., 2000, P 1 N AM CHAP ASS CO Zhai C., 2001, P 24 ANN INT ACM SIG Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2001, P CIKM Zhang B., 2012, P INTERSPEECH Zhang J., 2007, P INTERSPEECH Zhang JJA, 2010, IEEE T AUDIO SPEECH, V18, P1147, DOI 10.1109/TASL.2009.2030951 Zhang X., 2014, P ICASSP, P215 Zhang Y., 2014, P ICASSP, P185 ZHANG Y, 2010, P ICASSP DALL TEX US, P4366 Zhang Y., 2011, P INTERSPEECH Zhang Y., 2010, P ICASSP, P398 ZHANG Y, 2012, P ICASSP, P5161 ZHANG YD, 2012, P ICASSP, P5173 ZHANG YD, 2011, P ICASSP, P5660 Zhou Z.-Y., 2006, P HLT, P415, DOI 10.3115/1220835.1220888 Zhu X., 2009, P JOINT C 47 ANN M A NR 353 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2329-9290 J9 IEEE-ACM T AUDIO SPE JI IEEE-ACM Trans. Audio Speech Lang. PD SEP PY 2015 VL 23 IS 9 BP 1389 EP 1420 DI 10.1109/TASLP.2015.2438543 PG 32 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA CK1YW UT WOS:000356006900001 ER PT J AU Kim, D AF Kim, Dohan TI Group-theoretical vector space model SO INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS LA English DT Article ID REPRESENTATION; INFORMATION; RETRIEVAL; MATRICES AB This paper presents a group-theoretical vector space model (VSM) that extends the VSM with a group action on a vector space of the VSM. We use group and its representation theory to represent a dynamic transformation of information objects, in which each information object is represented by a vector in a vector space of the VSM. Several groups and their matrix representations are employed for representing different kinds of dynamic transformations of information objects used in the VSM. We provide concrete examples of how a dynamic transformation of information objects is performed and discuss algebraic properties involving certain dynamic transformations of information objects used in the VSM. C1 AI Res Co, Songnam 461811, Kyunggi Do, South Korea. RP Kim, D (reprint author), AI Res Co, 2537-1 Kyungwon Plaza 201, Songnam 461811, Kyunggi Do, South Korea. EM dkim@airesearch.kr CR Aisbett J, 2001, ARTIF INTELL, V133, P189, DOI 10.1016/S0004-3702(01)00144-8 Alperin J. L., 1995, GROUPS REPRESENTATIO Anton H., 1993, ELEMENTARY LINEAR AL Beiser A., 1995, CONCEPTS MODERN PHYS Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 BISCHOF C, 1987, SIAM J SCI STAT COMP, V8, pS2, DOI 10.1137/0908009 Bretscher O., 1996, LINEAR ALGEBRA APPL Datta K. B., 1991, MATRIX LINEAR ALGEBR Duda R.O., 2001, PATTERN CLASSIFICATI DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Dummit D. S., 2004, ABSTRACT ALGEBRA, Vthird Feynman R., 1965, LECT PHYS, V3 Fortuna B., 2005, VISUALIZATION TEXT D, P497 Fraleigh J. B., 1998, 1 COURSE ABSTRACT AL Grossman D. A., 2004, INFORM RETRIEVAL ALG Hall B. C., 2003, GROUPS ALGEBRAS REPR Harris J., 1991, REPRESENTATION THEOR Hoenkamp E, 2003, J AM SOC INF SCI TEC, V54, P314, DOI 10.1002/asi.10211 HOUSEHOLDER AS, 1958, J ACM, V5, P339, DOI 10.1145/320941.320947 Hungerford T. W., 1980, ALGEBRA Johnsonbaugh R., 1981, FDN MATH ANAL Lane S.M., 1998, CATEGORIES WORKING M, V2nd Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Lee J. M., 2000, INDRO TOPOLOGICAL MA Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Manning C.D., 2008, INTRO INFORM RETRIEV Melucci M, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361687 Nunes S, 2011, J AM SOC INF SCI TEC, V62, P2471, DOI 10.1002/asi.21597 Roach E., 1978, COGNITION CATEGORIZA Rotman J. J., 1994, INTRO THEORY GROUPS Sagan Bruce E., 2001, SYMMETRIC GROUP REPR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Turney PD, 2012, J ARTIF INTELL RES, V44, P533 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Widdows Dominic, 2004, GEOMETRY AND MEANING Zezula P., 2006, SIMILARITY SEARCH ME NR 38 TC 0 Z9 0 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 0020-7160 EI 1029-0265 J9 INT J COMPUT MATH JI Int. J. Comput. Math. PD AUG 3 PY 2015 VL 92 IS 8 BP 1536 EP 1550 DI 10.1080/00207160.2014.958079 PG 15 WC Mathematics, Applied SC Mathematics GA CH0MQ UT WOS:000353714900002 ER PT J AU Feng, YN Jiao, MY Duan, HL Deng, N AF Feng Ya-Ning Jiao Meng-Ying Duan Hui-Long Deng Ning TI A MeSH-based Biomedical Literature Mining Method for Exploring Associations Between Genes and Clinical Terms SO PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS LA Chinese DT Article DE literature mining; quantitative analysis; association mining; colorectal cancer; PubMed; medical subject headings ID CANCER; TEXT AB The causes and progressions of cancers have close associations with the mutations of genes in our body, which lead to abnormal symptoms and detection indicators. Therefore, providing clinical decision support for early diagnosis and precise treatment of cancers is very urgent and necessary, which can be achieved by mining the associations between genes and clinical behaviors from conclusive biomedical literature data. A MeSH-based (Medical Subject Headings, MeSH) method was proposed for biomedical objects association mining in this paper. By using MeSH (which is provided in PubMed) to represent each object as a vector in the Vector Space Model and taking the citations between articles into consideration, we translated the associations mining into mathematical operating successfully. We finally obtained 203 genes and 462 associations related to colorectal cancer (CRC) after applying our method in the associations mining between genes and clinical behaviors of CRC. In order to analyze and verify the mining results, some bioinformatics tools, such as g:Profiler and KEGG were used for functions and pathway analysis of genes. The results show that this MeSH-based method works robust in the association mining. Besides removing the restriction of co-occurrence for the indirect associations mining, our proposed method also avoid complex grammatical analysis which lead to massive calculation. C1 [Feng Ya-Ning; Jiao Meng-Ying; Duan Hui-Long; Deng Ning] Zhejiang Univ, Minist Educ China, Key Lab Biomed Engn, Coll Biomed Engn & Instrument Sci, Hangzhou 310027, Zhejiang, Peoples R China. RP Deng, N (reprint author), Zhejiang Univ, Minist Educ China, Key Lab Biomed Engn, Coll Biomed Engn & Instrument Sci, Hangzhou 310027, Zhejiang, Peoples R China. EM zju.dengning@gmail.com FU National Natural Science Foundation of China [31100592]; Hi-Tech Research and Development Program of China [2012AA02A601]; National Science and Technology Major Project of China [2013ZX03005012] FX This work was supported by grants from The National Natural Science Foundation of China(31100592), Hi-Tech Research and Development Program of China (2012AA02A601) and National Science and Technology Major Project of China(2013ZX03005012). CR Deng N, 2013, COMPUTATIONAL MATH M, V2013 Fan LS, 2012, ONCOL LETT, V4, P1225, DOI 10.3892/ol.2012.934 Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Hu S Q, 2007, WORLD CHINESE J DIGE, V15, P88 Jass JR, 2000, ANN NY ACAD SCI, V910, P62 Kanehisa M, 2000, NUCLEIC ACIDS RES, V28, P27, DOI 10.1093/nar/28.1.27 Kushi LH, 2012, CA-CANCER J CLIN, V62, P30, DOI 10.3322/caac.20140 Lark AL, 2003, CLIN CANCER RES, V9, P215 Liu F, 2014, BIOMED RES INT, DOI 10.1155/2014/170289 Narayanasamy V, 2004, J BIOMED SCI, V11, P864, DOI 10.1159/000081834 Ozgur A, 2008, BIOINFORMATICS, V24, pi277, DOI DOI 10.1093/BIOINFORMATICS/BTN182 P Page L, 1999, TECHNICAL REPORT, P1 Phillips Robin KS, 2013, COMMUNICATION, P1 Povey S, 2001, HUM GENET, V109, P678, DOI 10.1007/s00439-001-0615-0 Reimand J, 2007, NUCLEIC ACIDS RES, V35, pW193, DOI 10.1093/nar/gkm226 Safran M, 2010, DATABASE-OXFORD, V2010, DOI DOI 10.1093/DATABASE/BAQ020 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sayers E, 2009, ENTREZ PROGRAMMING U Siegel RL, 2015, CA-CANCER J CLIN, V65, P5, DOI 10.3322/caac.21254 Weinberg R, 2013, BIOL CANC, P32 Wu X, 2011, ROLE MUTS HOMOLOGUES, P1 Zheng S, 2008, COLORECTAL CANC, P32 NR 23 TC 0 Z9 0 PU CHINESE ACAD SCIENCES, INST BIOPHYSICS PI BEIJING PA 15 DATUN RD, CHAOYAND DISTRICT, BEIJING, 100101, PEOPLES R CHINA SN 1000-3282 J9 PROG BIOCHEM BIOPHYS JI Prog. Biochem. Biophys. PD AUG PY 2015 VL 42 IS 8 BP 770 EP 779 PG 10 WC Biochemistry & Molecular Biology; Biophysics SC Biochemistry & Molecular Biology; Biophysics GA CP6AQ UT WOS:000359967100009 ER PT J AU Zhao, R Mao, KZ AF Zhao, Rui Mao, Kezhi TI Semi-Random Projection for Dimensionality Reduction and Extreme Learning Machine in High-Dimensional Space SO IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE LA English DT Article AB Random Projection (RP) is a popular technique for dimensionality reduction because of its high computational efficiency. However, RP may not yield highly discriminative low-dimensional space to produce best pattern classification performance since the random transformation matrix of RP is independent of data. In this paper, we propose a Semi-Random Projection (SRP) framework, which takes the merit of random feature sampling of RP, but employs learning mechanism in the determination of the transformation matrix. One advantage of SRP is that it achieves a good balance between computational complexity and classification accuracy. Another advantage of SRP is that multiple SRP modules can be stacked to form a deep learning architecture for compact and robust feature learning. In addition, based on the insight on the relationship between RP and Extreme Learning Machine (ELM), the SRP is applied to ELM to derive Partially Connected ELM (PC-ELM). The hidden nodes of PC-ELM are more discriminative and hence a smaller number of nodes are needed. Experiments on two real-world text corpus, i.e., 20 News-groups and Farms Ads., verify the effectiveness and efficiency of the proposed SRP. Experimental results also show that PC-ELM outperforms ELM for high-dimensional data. C1 [Zhao, Rui; Mao, Kezhi] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore. RP Zhao, R (reprint author), Nanyang Technol Univ, Sch Elect & Elect Engn, Nanyang Ave, Singapore 639798, Singapore. CR Bache K., 2013, UCI MACHINE LEARNING Bengio Yoshua, 2009, Foundations and Trends in Machine Learning, V2, DOI 10.1561/2200000006 Beyer K, 1999, LECT NOTES COMPUT SC, V1540, P217 Bingham Ella, 2001, P 7 ACM SIGKDD INT C, P245, DOI 10.1145/502512.502546 Cambria E., 2015, P AAAI, P508 Cambria E, 2013, IEEE INTELL SYST, V28, P30 Chen M., 2012, ARXIV12064683 Chen X., 2011, P SIAM INT C DAT MIN, P474 Fukunaga K, 1990, INTRO STAT PATTERN R, V2nd Han J., 2006, DATA MINING SE ASIA Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527 Hinton GE, 2007, TRENDS COGN SCI, V11, P428, DOI 10.1016/j.tics.2007.09.004 Huang GB, 2004, IEEE IJCNN, P985 Huang GB, 2006, NEUROCOMPUTING, V70, P489, DOI 10.1016/j.neucom.2005.12.126 Jolliffe I., 2005, PRINCIPAL COMPONENT Li P., 2006, P 12 ACM SIGKDD INT, P287, DOI 10.1145/1150402.1150436 Martinez AM, 2001, IEEE T PATTERN ANAL, V23, P228, DOI 10.1109/34.908974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sorensen D. C., 1997, IMPLICITLY RESTARTED Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267 Vincent P, 2010, J MACH LEARN RES, V11, P3371 Yan SC, 2007, IEEE T PATTERN ANAL, V29, P40, DOI 10.1109/TPAMI.2007.250598 Zou H, 2006, J COMPUT GRAPH STAT, V15, P265, DOI 10.1198/106186006X113430 NR 23 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1556-603X EI 1556-6048 J9 IEEE COMPUT INTELL M JI IEEE Comput. Intell. Mag. PD AUG PY 2015 VL 10 IS 3 BP 30 EP 41 DI 10.1109/MCI.2015.2437316 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA CN5SO UT WOS:000358490600005 ER PT J AU Glavas, G Snajder, J AF Glavas, Goran Snajder, Jan TI Construction and evaluation of event graphs SO NATURAL LANGUAGE ENGINEERING LA English DT Article ID NATURAL-LANGUAGE; SEMANTIC ROLES; TRACKING; CORPUS; TEXT AB Events play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we propose event graphs as a novel way of structuring event-based information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing research, which has mainly focused on individual event extraction tasks, we describe a complete end-to-end system for event graph extraction from text. Our system is a three-stage pipeline that performs anchor extraction, argument extraction, and relation extraction (temporal relation extraction and event coreference resolution), each at a performance level comparable with the state of the art. We present EVEXTRA, a large newspaper corpus annotated with event mentions and event graphs, on which we train and evaluate our models. To measure the overall quality of the constructed event graphs, we propose two metrics based on the tensor product between automatically and manually constructed graphs. Finally, we evaluate the overall quality of event graphs with the proposed evaluation metrics and perform a headroom analysis of the system. C1 [Glavas, Goran; Snajder, Jan] Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Zagreb 10000, Croatia. RP Glavas, G (reprint author), Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Unska 3, Zagreb 10000, Croatia. EM goran.glavas@fer.hr; jan.smajder@fer.hr FU Ministry of Science, Education and Sports, Republic of Croatia [036-1300646-1986] FX This work has been supported by the Ministry of Science, Education and Sports, Republic of Croatia under the Grant 036-1300646-1986. We thank the reviewers for their insightful comments and suggestions. CR ACE, 2005, EV DET REC ACE ENT V Agirre E., 2009, P 12 C EUR CHAPT ASS, P33, DOI 10.3115/1609067.1609070 Ahn D., 2006, P 5 INT WORKSH INT C, P127 Ahn David, 2006, P WORKSH ANN REAS TI, P1, DOI 10.3115/1629235.1629236 Allan J, 2002, TOPIC DETECTION TRAC, V12 Allen J.F., 1983, ACM COMMUN, V26, P832, DOI DOI 10.1145/182.358434 Bagga Amit, 1999, P WORKSH COR ITS APP, P1, DOI 10.3115/1608810.1608812 Baker C., 1998, P 36 ANN M ASS COMP, V1, P86, DOI DOI 10.3115/980845.980860 Bejan C. A., 2007, P 4 INT WORKSH SEM E, P460, DOI 10.3115/1621474.1621576 Bejan Cosmin, 2010, P 48 ANN M ASS COMP, P1412 Bejan Cosmin Adrian, 2008, P 6 INT C LANG RES E Bethard S., 2013, 2 JOINT C LEX COMP S, V2, P10 Bethard S., 2008, THESIS U COLORADO BO Boguraev B., 2005, ANNOTATING EXTRACTIN Borgwardt K. M., 2007, THESIS LUDWIG MAXIMI Bramsen P., 2006, P 2006 C EMP METH NA, P189, DOI 10.3115/1610075.1610105 Chambers N., 2009, P JOINT C 47 ANN M A, V2, P602, DOI 10.3115/1690219.1690231 Chambers Nathanael, 2008, P 46 ANN M ASS COMP, P789 Chang A. X., 2012, SUTIME LIB RECOGNIZI Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chen Bin, 2011, P 5 INT JOINT C NAT Cordella LP, 2004, IEEE T PATTERN ANAL, V26, P1367, DOI 10.1109/TPAMI.2004.75 Davidson D., 1967, ESSAYS ACTIONS EVENT, V5, P105 Derczynski L., 2013, P ANN M ASS COMP LIN, V78 Fan RE, 2008, J MACH LEARN RES, V9, P1871 FILLMORE CJ, 1976, ANN NY ACAD SCI, V280, P20, DOI 10.1111/j.1749-6632.1976.tb25467.x Finkel J. R., 2005, P 43 ANN M ASS COMP, P363 Gartner T, 2003, LECT NOTES ARTIF INT, V2777, P129, DOI 10.1007/978-3-540-45167-9_11 Gildea D, 2002, COMPUT LINGUIST, V28, P245, DOI 10.1162/089120102760275983 Grishman R., 1996, P INT C COMP LING CO, V96, P466, DOI DOI 10.3115/992628.992709 Grover C., 2010, P 5 INT WORKSH SEM E, P333 Haghighi A., 2005, P CONLL ANN ARB MI, P173, DOI 10.3115/1706543.1706574 Hammack R., 2011, HDB PRODUCT GRAPHS D Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 Humphreys K., 1998, P 7 MESS UND C MUC 7 Humphreys Kevin, 1997, P WORKSH OP FACT PRA, P75, DOI 10.3115/1598819.1598830 Jans B., 2012, P 13 C EUR CHAPT ASS, P336 Kingsbury P., 2002, P 3 INT C LANG RES E, P1989 Kolomiyets O., 2012, P 50 ANN M ASS COMP, P88 Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044 Lavie Alon, 2009, MACHINE TRANSLATION, V23, P105 Lee H., 2012, P 2012 JOINT C EMP M, P489 Lee Heeyoung, 2011, P 15 C COMP NAT LANG, P28 Llorens H, 2013, INFORM PROCESS MANAG, V49, P179, DOI 10.1016/j.ipm.2012.05.005 Llorens H, 2010, P 5 INT WORKSH SEM E, P284 MacCartney B., 2006, P LREC 06, V319, P449 Makkonen J., 2003, P 2003 C N AM CHAPT, P43 Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86 McCallum A, 2003, P 7 C NAT LANG LEARN, V4, P188, DOI DOI 10.3115/1119176.1119206 Melli G., 2006, P DOC UND C NEW YORK MENCHETTI S, 2005, P 22 INT C MACH LEAR, V119, P585 Min Y., 2012, P INT C COMP LING CO, P2109 Moreda P, 2011, INFORM PROCESS MANAG, V47, P870, DOI 10.1016/j.ipm.2010.03.008 Nadeau D., 2007, LINGVISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD Nallapati R., 2004, P 13 ACM INT C INF K, P446, DOI 10.1145/1031171.1031258 Navigli R, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1683 Palmer M, 2005, COMPUT LINGUIST, V31, P71, DOI 10.1162/0891201053630264 Polanyi L, 2006, INFORM RETRIEVAL SER, V20, P1 Pradhan S., 2011, P 15 C COMP NAT LANG, P1 Pustejovsky J., 2003, CORPUS LINGUISTICS, V2003, P40 PUSTEJOVSKY J, 1991, COGNITION, V41, P47, DOI 10.1016/0010-0277(91)90032-Y Pustejovsky J., 2003, NEW DIRECTIONS QUEST, V2003, P28 Quine W., 1985, ACTIONS EVENTS PERSP, P162 Ramos-Santacruz M., 2000, P 6 APPL NAT LANG PR, P76, DOI 10.3115/974147.974158 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Rosen S. T., 1999, GLOT INT, V4, P3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saquete E, 2009, J ARTIF INTELL RES, V35, P775 Saric F., 2012, P 6 INT WORKSH SEM E Sauri R, 2012, COMPUT LINGUIST, V38, P261, DOI 10.1162/COLI_a_00096 Setzer A, 2003, P 4 INT WORKSH INF C, P25 Smith CS, 1999, LINGUIST PHILOS, V22, P479, DOI 10.1023/A:1005439826271 Snajder J., 2013, P 51 ANN M ASS COMP Soon WM, 2001, COMPUT LINGUIST, V27, P521, DOI 10.1162/089120101753342653 Sun WY, 2013, J AM MED INFORM ASSN, V20, P806, DOI 10.1136/amiajnl-2013-001628 Surdeanu M, 2003, P 41 ANN M ASS COMP, V1, P8, DOI DOI 10.3115/1075096.1075098 Tannier X, 2008, SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, P150 TENNY C, 2000, EVENTS GRAMMATICAL O, V32, P3 Tong HH, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P737 Turmo J., 2005, P CONLL 2005, P221, DOI 10.3115/1706543.1706586 UzZaman N., 2010, P 5 INT WORKSH SEM E, P276 UzZaman N, 2011, P 49 ANN M ACL HUM L, P351 UzZaman Naushad, 2013, 2 JOINT C LEX COMP M, V2, P1 VENDLER Z, 1957, PHILOS REV, V66, P143, DOI 10.2307/2182371 Verhagen M, 2007, LECT NOTES COMPUT SC, V4795, P7 Verhagen M., 2007, P 4 INT WORKSH SEM E, P75, DOI 10.3115/1621474.1621488 Verhagen M., 2010, P 5 INT WORKSH SEM E, P57 Wei CP, 2007, IEEE T SYST MAN CY A, V37, P273, DOI 10.1109/TSMCA.2006.886377 Wolf F, 2005, COMPUT LINGUIST, V31, P249, DOI 10.1162/0891201054223977 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yang CC, 2009, IEEE T SYST MAN CY A, V39, P850, DOI 10.1109/TSMCA.2009.2015885 Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 Yangarber R, 2000, P 18 C COMP LING, V2, P940, DOI 10.3115/992730.992782 Zaenen A., 2005, ANNOTATING EXTRACTIN NR 94 TC 0 Z9 0 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 EI 1469-8110 J9 NAT LANG ENG JI Nat. Lang. Eng. PD AUG PY 2015 VL 21 IS 4 BP 607 EP 652 DI 10.1017/S1351324914000060 PG 46 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA CN6EK UT WOS:000358526600005 ER PT J AU Jameel, S Lam, W Bing, LD AF Jameel, Shoaib Lam, Wai Bing, Lidong TI Supervised topic models with word order structure for document classification and retrieval learning SO INFORMATION RETRIEVAL LA English DT Article DE Topic modeling; Maximum-margin; Document classification; Learning-to-rank; Structured topic model ID LATENT DIRICHLET ALLOCATION; INFORMATION-RETRIEVAL; POSTERIOR REGULARIZATION; MACHINE; THEOREM; SEARCH; RANK AB One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models. C1 [Jameel, Shoaib; Lam, Wai] Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. [Bing, Lidong] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA. RP Jameel, S (reprint author), Chinese Univ Hong Kong, Dept Syst Engn & Engn Management, Shatin, Hong Kong, Peoples R China. EM msjameel@se.cuhk.edu.hk; wlam@se.cuhk.edu.hk; lbing@cs.cmu.edu FU Research Grant Council of the Hong Kong Special Administrative Region, China [413510, 14203414]; Direct Grant of the Faculty of Engineering, CUHK [4055034] FX The work described in this paper is substantially supported by Grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Codes: 413510 and 14203414) and the Direct Grant of the Faculty of Engineering, CUHK (Project Code: 4055034). This work is also affiliated with the CUHK MoE-Microsoft Key Laboratory of Human-centric Computing and Interface Technologies. The authors would like to thank anonymous reviewers for their comments and suggestions. CR Acharya Ayan, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference (ECML PKDD 2013). Proceedings: LNCS 8189, DOI 10.1007/978-3-642-40991-2_24 Allan J., 2005, HARD TRACK OVERVIEW Andrzejewski D., 2011, KDD, P600 Asadi N., 2013, SPEC INT GROUP INF R, P997 Bai B, 2010, INFORM RETRIEVAL, V13, P291, DOI 10.1007/s10791-009-9117-9 Bartlett N., 2010, INT C MACH LEARN, P63 Bicego M., 2010, ACM SYMPOSIUM ON APP, P1516 Blei D. M., 2008, NIPS, V20, P121 Blei D. M., 2001, NIPS, P601 Blei D. M., 2009, TEXT MINING CLASSIFI, V10, P71 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Broder A., 2002, ACM SIGIR FORUM, V36, P3, DOI 10.1145/792550.792552 Burges C.J.C., 2005, ICML, V119, P89 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Busa-Fekete R, 2013, MACH LEARN, V93, P261, DOI 10.1007/s10994-013-5360-9 Cai P., 2011, SPEC INT GROUP INF R, P115 Cao J, 2007, ICSC 2007: International Conference on Semantic Computing, Proceedings, P155, DOI 10.1109/ICSC.2007.26 Cao Z, 2007, LECT NOTES COMPUT SC, V4464, P129 Chang J., 2009, INT C ART INT STAT, V5, P81 Chen B., 2009, ACM T ASIAN LANGUAGE, V8, P2 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Dang V., 2013, EUR C INF RETR, V7814, P423 Duan DS, 2012, IEEE DATA MINING, P211, DOI 10.1109/ICDM.2012.12 Egozi O., 2011, T INFORM SYSTEMS, V29 Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451 Ganchev K, 2010, J MACH LEARN RES, V11, P2001 Gao J., 2011, SIGIR 11, P675 Gao W., 2014, WEB SEARCH DATA MINI, P63 Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Hang Li, 2011, IEICE Transactions on Information and Systems, VE94-D, DOI 10.1587/transinf.E94.D.1854 Hasler E., 2014, EUR CHAPT ASS COMP L, P328 Hazen T, 2010, SPOK LANG TECHN WORK, P366 HEATH D, 1976, AM STAT, V30, P188, DOI 10.2307/2683760 Jagarlamudi J., 2013, SPEC INT GROUP INF R, P483 Jameel S., 2015, EUR C INF RETR, V9022, P648 Jameel S., 2013, SPEC INT GROUP INF R, P203 Jameel S., 2013, EUR C INF RETR, P292 Jameel S., 2013, AS INF RETR SOC C, P74 Jarvelin K., 2002, TOIS, V20, P422, DOI DOI 10.1145/582415.582418 Jiang Q., 2012, NEURAL INFORM PROCES, P1601 Joachims T., 1998, EUR C MACH LEARN, V1398, P137 Joachims Thorsten, 2002, KDD 02, P133, DOI DOI 10.1145/775047.775067 Kawamae N., 2014, WEB SEARCH DATA MINI, P473 Kim Sungchul, 2012, HDB NATURAL COMPUTIN, P479 Lacoste-Julien S., 2008, NIPS 2008 P 22 ANN C, V21, P897 Lai HJ, 2013, IEEE T COMPUT, V62, P1221, DOI 10.1109/TC.2012.62 Lakshminarayanan B., 2011, MACHINE LEARNING SIG, P1 Li H, 2013, FOUND TRENDS INF RET, V7, P345, DOI 10.1561/1500000035 Li P., 2007, NEURAL INFORM PROCES, V7, P845 Li W., 2006, ICML 06, P577 Li XM, 2015, NEUROCOMPUTING, V149, P811, DOI 10.1016/j.neucom.2014.07.053 Liao R., 2014, WEB SEARCH DATA MINI, P493 Lindsey R. V., 2012, EMPIRICAL METHODS NA, P214 Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Liu Y., 2009, ICML, P665 Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9 Maas A. L., 2011, ASS COMPUTATIONAL LI, V1, P142 Macdonald C, 2013, INFORM RETRIEVAL, V16, P584, DOI 10.1007/s10791-012-9209-9 Metzler D, 2007, INFORM RETRIEVAL, V10, P257, DOI 10.1007/s10791-006-9019-z Minka T., 2008, SIGIR WORKSH LEARN R, P48 Nallapati R., 2004, SIGIR, P64 Niu S., 2014, SPEC INT GROUP INF R, P1191 Noji H., 2013, EMPIRICAL METHODS NA, P1180 Park LAF, 2009, LECT NOTES ARTIF INT, V5782, P176 Perotte A, 2011, NIPS, P2609 Pinoli P., 2014, COMPUTATIONAL INTELL, P1 Pitman J, 1997, ANN PROBAB, V25, P855 Porteous I., 2008, KDD 08, P569 Qin T, 2010, INFORM RETRIEVAL, V13, P346, DOI 10.1007/s10791-009-9123-y Quoc C., 2007, NEURAL INFORM PROCES, V19, P193 Ramage D., 2009, EMNLP, V1, P248 Rubin TN, 2012, MACH LEARN, V88, P157, DOI 10.1007/s10994-011-5272-5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafiei MM, 2006, IEEE DATA MINING, P542 Shao Q.-M., 2000, MONTE CARLO METHODS Sordoni A., 2013, C INF KNOWL MAN, P1197 Steyvers Mark, 2007, HDB LATENT SEMANTIC, V427, P424 Storkey A. J., 2014, T PATTERN ANAL MACHI, V37, P243 Sun Y., 2012, MINING TEXT DATA, P259 Tan M., 2013, KNOWLEDGE DISCOVERY, P856 Tang J., 2011, C INF KNOWL MAN, P605 Vapnik V. N., 2000, NATURE STAT LEARNING Wallach H. M., 2008, THESIS Wallach H. M., 2009, NEURAL INFORM PROCES, V22 Wallach H. M., 2006, ICML 06, P977 WANG C., 2009, CVPR, P1903 Wang L., 2014, WORLD WID WEB C, P209 Wang Q., 2011, SIGIR, V11, P685 Wang Q., 2013, T INFORM SYSTEMS, V31, P5 Wang S., 2013, SUPERVISED TOPIC MOD Wang X., 2006, KDD 06, P424, DOI DOI 10.1145/1150402.1150450 Wang X., 2005, TECHNICAL REPORT Wang XR, 2007, IEEE DATA MINING, P697 Wei X., 2006, SIGIR 06, P178 Wu QA, 2010, INFORM RETRIEVAL, V13, P254, DOI 10.1007/s10791-009-9112-1 Wu W., 2013, WORLD WID WEB C COMP, P137 Xie B., 2012, NATURAL LANGUAGE PRO, P197 Xu J., 2007, SIGIR 07, P391 Yan X., 2013, WORLD WID WEB C, P1445 Yao LM, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P937 Yi X., 2008, CIKM, P1431 Yi X, 2009, LECT NOTES COMPUT SC, V5478, P29 Yu Z., 2014, SPEC INT GROUP INF R, P999 Yuan N. J., 2013, ONLINE SOCIAL NETWOR, P3 Yue Y., 2007, SIGIR 07, P271 ZELLNER A, 1988, AM STAT, V42, P278, DOI 10.2307/2685143 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang C., 2013, ICCV WORKSH INF PROB, P254 Zhang J., 2003, P WORKSH LEARN IMB D Zhu J., 2013, KNOWLEDGE DISCOVERY, P964 Zhu J., 2009, ICML, P1257 Zhu J., 2012, CORR Zhu J., 2013, ASS COMPUTATIONAL LI, P187 Zhu J, 2012, J MACH LEARN RES, V13, P2237 Zhu J., 2011, NEURAL INFORM PROCES, V24, P1620 Zhu J., 2013, ICML J MACH LEARN RE, V28, P124 Zhu J, 2014, J MACH LEARN RES, V15, P1799 Zong WW, 2014, NEURAL PROCESS LETT, V39, P155, DOI 10.1007/s11063-013-9295-8 NR 121 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD AUG PY 2015 VL 18 IS 4 BP 283 EP 330 DI 10.1007/s10791-015-9254-2 PG 48 WC Computer Science, Information Systems SC Computer Science GA CL2LM UT WOS:000356775500001 ER PT J AU Wei, FW Grubesic, TH Bishop, BW AF Wei, Fangwu Grubesic, Tony H. Bishop, Bradley Wade TI Exploring the GIS Knowledge Domain Using CiteSpace SO PROFESSIONAL GEOGRAPHER LA English DT Article DE geographic information systems; GIS; knowledge domain; scientometric analysis; GIS; sistemas de informacion geografica; SIG; dominio del conocimiento; analisis cienciometrico ID GEOGRAPHIC INFORMATION-SYSTEMS; COCITATION ANALYSIS; BUILT ENVIRONMENT; SCIENTIFIC LITERATURE; AUTHOR COCITATION; PHYSICAL-ACTIVITY; SCIENCE; CITATION; REPRESENTATION; COMMUNITIES AB Since the 1980s, geographic information systems (GIS) have developed rapidly and have been applied widely in the social, natural, and space sciences. In line with this explosive development and use, thousands of articles have been published on GIS, detailing both technical development and application-oriented research. Given this flood of material over the past thirty years, the purpose of this article is to apply a suite of scientometric and bibliometric approaches to help discover and benchmark the most important and highly cited papers in this burgeoning field. Not only do these techniques facilitate the connection of research themes and authors in the GIS community, but the approaches help highlight how research interests and foci evolve over time, greatly contributing to our understanding of the GIS knowledge domain. Although the scale and scope of this analysis are somewhat limited, the outlined method is highly generalizable and its results show that several key publications have helped forge the field and facilitate new trends in the literature. C1 [Wei, Fangwu; Grubesic, Tony H.] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA. [Grubesic, Tony H.] Drexel Univ, Ctr Spatial Comp & Analyt, Philadelphia, PA 19104 USA. [Bishop, Bradley Wade] Univ Tennessee, Sch Informat Sci, Knoxville, TN 37996 USA. RP Wei, FW (reprint author), Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA. EM fw48@drexel.edu; grubesic@drexel.edu; bbish013@utk.edu FU Institute for Museum and Library Sciences (IMLS) [RE-05-12-0052-12] FX This material is based upon work supported by the Institute for Museum and Library Sciences (IMLS) under Grant No. RE-05-12-0052-12. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IMLS. CR ANSELIN L, 1995, GEOGR ANAL, V27, P93 Bailey T. C., 1995, INTERACTIVE SPATIAL Barnes TJ, 2001, ANN ASSOC AM GEOGR, V91, P546, DOI 10.1111/0004-5608.00258 Bishop BW, 2010, LIBR HI TECH, V28, P536, DOI 10.1108/07378831011096213 Borner K, 2009, J INFORMETR, V3, P161, DOI 10.1016/j.joi.2009.03.008 Boyack KW, 2013, J AM SOC INF SCI TEC, V64, P1759, DOI 10.1002/asi.22896 Boyack KW, 2010, J AM SOC INF SCI TEC, V61, P2389, DOI 10.1002/asi.21419 Brauer M, 2003, EPIDEMIOLOGY, V14, P228, DOI 10.1097/00001648-200303000-00019 Brownson RC, 2009, AM J PREV MED, V36, pS99, DOI 10.1016/j.amepre.2009.01.005 Burrough P A, 1998, PRINCIPLES GEOGRAPHI Chang NB, 2008, J ENVIRON MANAGE, V87, P139, DOI 10.1016/j.jenvman.2007.01.011 Chen C., 2012, TURNING POINTS, P139 Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309 Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100 Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317 Church R, 1974, PAPERS REGIONAL SCI, V32, P101, DOI DOI 10.1007/BF01942293 Cobo MJ, 2011, J AM SOC INF SCI TEC, V62, P1382, DOI 10.1002/asi.21525 CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016/0034-4257(91)90048-B Dunning T., 1993, Computational Linguistics, V19 Elwood S, 2006, T GIS, V10, P693, DOI DOI 10.1111/J.1467-9671.2006.01023.X Eto M., 2012, SCIENTOMETRICS, V94, P651 Gipp B, 2009, PRO INT CONF SCI INF, V2, P571 Girvan M., 2002, P NATL ACAD SCI USA, V99, P8271 Goodchild M. F., 2005, GEOGRAPHIC INFORM SY, V2nd GRIFFITH BC, 1974, SCI STUD, V4, P339, DOI 10.1177/030631277400400402 Grubesic TH, 2008, INT REGIONAL SCI REV, V31, P88, DOI 10.1177/0160017607308679 Guisan A, 2000, ECOL MODEL, V135, P147, DOI 10.1016/S0304-3800(00)00354-9 Jenny H., 1941, FACTORS SOIL FORMATI JENSON SK, 1988, PHOTOGRAMM ENG REM S, V54, P1593 Kick E. L., 1977, AM SOCIOL REV, V42, P522 Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI DOI 10.1023/A:1024940629314 Larson NI, 2009, AM J PREV MED, V36, P74, DOI 10.1016/j.amepre.2008.09.025 Leslie E, 2007, HEALTH PLACE, V13, P111, DOI 10.1016/j.healthplace.2005.11.001 Liu SB, 2012, SCIENTOMETRICS, V91, P495, DOI 10.1007/s11192-011-0575-7 Longley P., 2011, GEOGRAPHIC INFORM SY Lu D, 2007, INT J REMOTE SENS, V28, P823, DOI 10.1080/01431160600746456 Malczewski J, 1999, GIS MULTICRITERIA DE MCCAIN KW, 1991, J AM SOC INFORM SCI, V42, P290, DOI 10.1002/(SICI)1097-4571(199105)42:4<290::AID-ASI5>3.0.CO;2-9 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q McGinn AP, 2007, J URBAN HEALTH, V84, P162, DOI 10.1007/s11524-006-9136-4 Miller HJ, 1996, INT J GEOGR INF SYST, V10, P791, DOI 10.1080/02693799608902110 Miller HJ, 2003, ANN ASSOC AM GEOGR, V93, P574, DOI 10.1111/1467-8306.9303004 Morland K, 2006, AM J PREV MED, V30, P333, DOI 10.1016/j.amepre.2005.11.003 Murray A. T., 2002, J GEOGRAPHICAL SYSTE, V4, P171, DOI 10.1007/s101090200084 Murray AT, 2005, ANN ASSOC AM GEOGR, V95, P761, DOI 10.1111/j.1467-8306.2005.00485.x Najman JM, 2003, J SOCIOL, V39, P62, DOI 10.1177/0004869003039001320 Nash JE, 1970, J HYDROL, V10, P282, DOI DOI 10.1016/0022-1694(70)90255-6 Neuhaus C, 2006, PORTAL-LIBR ACAD, V6, P127, DOI 10.1353/pla.2006.0026 Ni CQ, 2013, SCIENTOMETRICS, V94, P1161, DOI 10.1007/s11192-012-0849-8 Papas MA, 2007, EPIDEMIOL REV, V29, P129, DOI 10.1093/epirev/mxm009 PRICE DJD, 1965, SCIENCE, V149, P510 Saaty T.L., 1980, ANAL HIERARCHY PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuurman N, 2000, PROG HUM GEOG, V24, P569, DOI 10.1191/030913200100189111 Sener B, 2006, ENVIRON GEOL, V49, P376, DOI 10.1007/s00254-005-0075-2 Sieber R, 2006, ANN ASSOC AM GEOGR, V96, P491, DOI 10.1111/j.1467-8306.2006.00702.x Skupin A, 2009, J INFORMETR, V3, P233, DOI 10.1016/j.joi.2009.03.002 Skupin A, 2004, P NATL ACAD SCI USA, V101, P5274, DOI 10.1073/pnas.0307654100 SMALL H, 1980, J DOC, V36, P183, DOI 10.1108/eb026695 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SWANSON DR, 1990, B MED LIBR ASSOC, V78, P29 SWANSON DR, 1993, LIBR TRENDS, V41, P606 Tarboton DG, 1997, WATER RESOUR RES, V33, P309, DOI 10.1029/96WR03137 Tomlinson R. F., 1968, S LAND EV COMM SCI I TOREGAS C, 1971, OPER RES, V19, P1363, DOI 10.1287/opre.19.6.1363 Tran N., 2009, P ANN AMIA S, P650 Web of Science, 2013, WEB SCI White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302 NR 69 TC 1 Z9 1 PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND SN 0033-0124 EI 1467-9272 J9 PROF GEOGR JI Prof. Geogr. PD JUL 3 PY 2015 VL 67 IS 3 BP 374 EP 384 DI 10.1080/00330124.2014.983588 PG 11 WC Geography SC Geography GA CK9WS UT WOS:000356590800007 ER PT J AU Zhang, T Cho, JHD Zhai, CX AF Zhang, Thomas Cho, Jason H. D. Zhai, Chengxiang TI Understanding User Intents in Online Health Forums SO IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS LA English DT Article DE Forum intents; online health forums; pattern based features; support vector machines; user intent classification ID GENERIC CLINICAL QUESTIONS; WEB QUERIES; CLASSIFICATION; AGREEMENT; INTERNET; TAXONOMY; MODEL AB Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics. C1 [Zhang, Thomas; Cho, Jason H. D.; Zhai, Chengxiang] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Zhang, T (reprint author), Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. EM zhang156@illinois.edu; hcho33@illinois.edu; czhai@illinois.edu FU Jump Trading; Siebel Foundation FX The authors would like to thank A. Szmelter and N. Chitturu for providing their excellent medical expertise in labeling health forum posts, S. Nguyen and J. Friedman for labeling our dataset, and H. Lin for helping us with revision. In addition, we thank the anonymous reviewers for their insightful comments. Finally, we would also like to thank Jump Trading and the Siebel Foundation for their generous scholarships that supported this research. CR Aronson A R, 2001, Proc AMIA Symp, P17 Ayers SL, 2007, HEALTH, V11, P327, DOI 10.1177/1363459307077547 Baeza-Yates R, 2006, LECT NOTES COMPUT SC, V4209, P98 Bhavnani S. K., 2003, P C HUM FACT COMP SY, P816 Boot CRL, 2010, HEALTH COMMUN, V25, P175, DOI 10.1080/10410230903544969 Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401 Broder A., 2002, SIGIR Forum, V36 Cartright M.-A., 2011, P ACM SIGIR, P65 Chang C.C., 2013, LIBSVM LIB SUPPORT V Chee B., 2009, P AMIA ANN S, V2009, P92 Chen L., 2012, P 21 INT C COMP WORL, P823 Chen L., 2013, P 36 INT ACM SIGIR C, P973 Cho J. H., 2013, P INT C BIOINF COMP COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104 De Choudhury M., 2014, P 32 ANN ACM C HUM F, P1365 Dogan R. I., 2009, DATABASE, V2009, P1 Ely JW, 2002, BRIT MED J, V324, P710, DOI 10.1136/bmj.324.7339.710 Ely JW, 2000, BRIT MED J, V321, P429, DOI 10.1136/bmj.321.7258.429 Eysenbach G, 2003, CA-CANCER J CLIN, V53, P356 FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619 Hartzler A, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.1728 Jansen BJ, 2008, INFORM PROCESS MANAG, V44, P1251, DOI 10.1016/j.ipm.2007.07.015 Jiang Yunliang, 2012, AMIA Annu Symp Proc, V2012, P417 Jiang Y., 2011, WEB SCI MED DOMAIN, P3 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Kobayashi T., 2006, P AMIA ANN S, V2006, P987 LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310 Lee U., 2005, P 14 INT C WORLD WID, P391, DOI DOI 10.1145/1060745.1060804 Li B., 2008, P C EMP METH NAT LAN, P937, DOI 10.3115/1613715.1613836 Li B., 2008, P 31 ANN INT ACM SIG, P735, DOI 10.1145/1390334.1390477 Liu Y., 2008, P 22 INT C COMP LING, P497 McNemar Q, 1947, PSYCHOMETRIKA, V12, P153, DOI 10.1007/BF02295996 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slaughter LA, 2006, INT J MED INFORM, V75, P513, DOI 10.1016/j.ijmedinf.2005.07.025 Spink Amanda, 2004, Health Info Libr J, V21, P44, DOI 10.1111/j.1471-1842.2004.00481.x Van De Belt TH, 2010, J MED INTERNET RES, V12, DOI 10.2196/jmir.1350 Vydiswaran V. V., 2011, P WORKSH DAT MIN MED, P42 Yu H., 2005, P AAAI WORKSH QUEST, P27 Yu H., 2008, P AMIA ANN S, P96 Zhang T., 2014, P 5 ACM C BIOINF COM, P220 Zhang YY, 2009, IEEE SYS MAN CYBERN, P1153, DOI 10.1109/ICSMC.2009.5345957 NR 42 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2168-2194 J9 IEEE J BIOMED HEALTH JI IEEE J. Biomed. Health Inform. PD JUL PY 2015 VL 19 IS 4 BP 1392 EP 1398 DI 10.1109/JBHI.2015.2416252 PG 7 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics SC Computer Science; Mathematical & Computational Biology; Medical Informatics GA CN7NE UT WOS:000358620500025 PM 25823052 ER PT J AU Wu, L Huang, XD Zhang, CY Shepherd, J Wang, Y AF Wu, Lin Huang, Xiaodi Zhang, Chengyuan Shepherd, John Wang, Yang TI An efficient framework of Bregman divergence optimization for co-ranking images and tags in a heterogeneous network SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Co-ranking; Random walks; Heterogeneous network; Bregman divergence ID ANNOTATION AB Graph-based ranking is an effective way of ranking images by making use of the graph structure. However, its applications are usually limited to individual image graphs, which are derived from self-contained features of images. Nowadays, many images in social web sites are often associated with semantic information (i.e., tags). Ranking of these orderless tags is helpful in understanding and retrieving images, thus, improving the overall ranking performance if their mutual reinforcement is considered. Unlike previous work only focusing on individual image or tag graphs, in this paper, we investigate the problem of co-ranking images and tags in a heterogeneous network. Considering that ranking on images and tags can be conducted simultaneously, we present a novel co-ranking method with random walks that is able to significantly improve the ranking effectiveness on both images and tags. We further improve the performance of our algorithm in computational complexity and the out-of-sample problem. This is achieved by casting the co-ranking as a Bregman divergence optimization, under which we transform the original random walks into an equivalent optimal kernel matrix learning problem. Extensive experiments conducted on three benchmarks show that our approach outperforms the state-of-the-art local ranking approaches and scales on large-scaled databases. C1 [Wu, Lin; Zhang, Chengyuan; Shepherd, John; Wang, Yang] Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia. [Huang, Xiaodi] Charles Sturt Univ, Sch Comp & Math, Albury, NSW 2640, Australia. RP Zhang, CY (reprint author), Univ New S Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia. EM linw@cse.unsw.edu.au; xhuang@csu.edu.au; zhangc@cse.unsw.edu.au; jas@cse.unsw.edu.au; wangy@cse.unsw.edu.au FU Charles Sturt University, Australia [A541-2003-xxx-25242] FX This research was supported in part by round 4 compact funding A541-2003-xxx-25242, Charles Sturt University, Australia. CR Agarwal S, 2005, CVPR Ames M, 2007, SIG CHI Bay H., 2006, ECCV Bay H, 2007, INT C IM VID RETR Bengio Y, 2004, ADV NEUR IN, V16, P177 Berkhin P, 2005, INTERNET MATH, V2, P73, DOI 10.1080/15427951.2005.10129098 Bregman L. M., 1967, USSR COMP MATH MATH, V7, P200, DOI 10.1016/0041-5553(67)90040-7 Canny J, 1986, MACH INTELL, V8, P679, DOI DOI 10.1109/TPAMI.1986.4767851 Chen H, 2008, ACM MULTIMEDIA Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Everingham M., 2008, PASCAL VISUAL OBJECT Farhadi A., 2009, CVPR Gao W, 2010, ACM SIGIR Gao Y, 2011, ACM MULTIMEDIA Guan Z, 2009, SIGIR He JR, 2006, IEEE T IMAGE PROCESS, V15, P3170, DOI 10.1109/TIP.2006.877491 Hoi SCH, 2010, ACM T MULTIMED COMPU, V6 Huang Y, 2010, CVPR Huang YC, 2011, IEEE T PATTERN ANAL, V33, P1266, DOI 10.1109/TPAMI.2011.25 Jeon J, 2003, ACM SIGIR Jing Y, 2008, WWW Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kulis B, 2006, ICML LADES M, 1993, IEEE T COMPUT, V42, P300, DOI 10.1109/12.210173 Langville A.N., 2005, INTERNET MATH, V1, P335 Li J, 2008, IEEE T PATTERN ANAL, V30, P985, DOI 10.1109/TPAMI.2007.70847 Liu D, 2009, WWW, P351 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Manning C.D., 2008, INTRO INFORM RETRIEV Nikolopoulos S, 2013, SIGNAL PROCESS, V93, P2212, DOI 10.1016/j.sigpro.2012.08.004 Papadopoulos S, 2011, IEEE MULTIMEDIA, V18, P52, DOI 10.1109/MMUL.2010.68 Richter F, 2012, MULTIMED TOOLS APPL, V56, P35, DOI 10.1007/s11042-010-0554-7 Russell BC, 2008, INT J COMPUT VISION, V77, P157, DOI 10.1007/s11263-007-0090-8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Y, 2010, ACM MULTIMEDIA Shlens J, 2005, TECHNICAL REPORT Sigurbjornsson B, 2008, WWW van Gemert J. C., 2008, ECCV Wan X, 2007, IJCAI Wang Y, 2013, PAKDD, V2, P449 Wang Y, 2013, CIKM, P805 Wu L, 2013, ACM MULTIMEDIA Wu L, 2009, WWW, P361 Wu L, 2013, MULTIMEDIA MODELING Xu B, 2011, SIGIR, P525 Yuan X, 2006, ACM MULTIMEDIA Zhang H, 2012, ACM MULTIMEDIA Zhou D., 2003, NIPS Zhou D, 2006, NIPS Zhou D, 2007, IEEE DATA MINING, P739 NR 50 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 EI 1573-7721 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JUL PY 2015 VL 74 IS 15 BP 5635 EP 5660 DI 10.1007/s11042-014-1873-x PG 26 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA CN1XM UT WOS:000358214100013 ER PT J AU Tang, LY Ni, ZW Xiong, H Zhu, HS AF Tang, Liyang Ni, Zhiwei Xiong, Hui Zhu, Hengshu TI Locating targets through mention in Twitter SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE Recommender system; Ranking SVM; Mention mechanism; Twitter; Social media marketing AB With the explosive development of social networks, there are excessive amount of user-generated contents available on social media platforms. Indeed, in social networks, it is now a big challenge to promote the right information to the right audiences at the right time. To this end, in this paper, we propose an integrated study of the mention mechanism in social media platforms, such as Twitter, towards locating target audiences for specific information. The study goal is to identify effective targets with high relevance and achieve high response rate as well. Along this line, we formulate the problem of locating targets when posting promotion-oriented messages as a ranking based recommendation task, and present a context-aware recommendation framework as a solution. Specifically, we first extract four categories of features, namely content, social, location and time based features, to measure the relevance among publishers, targets and promotion messages. Then, we employ Ranking Support Vector Machine (SVM) model as the solution to our ranking based recommendation problem. By introducing two bias adjustment parameters, i.e., confidence contributions of publishers and the responsiveness of targets, our framework can effectively recommend top K proper users to mention. Finally, to validate the proposed approach, we conduct extensive experiments on a real world dataset collected from Twitter. The experimental results clearly show that our approach outperforms other baselines with a significant margin. C1 [Tang, Liyang; Ni, Zhiwei] Hefei Univ Technol, Hefei, Peoples R China. [Xiong, Hui] Rutgers State Univ, Newark, DE USA. [Zhu, Hengshu] Univ Sci & Technol China, Hefei 230026, Peoples R China. RP Tang, LY (reprint author), Hefei Univ Technol, Hefei, Peoples R China. EM tangliyang921@gmail.com; nzwgd@hfut.edu.cn; hxiong@rutgers.edu; zhs@mail.ustc.edu.cn CR Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15 Airola A, 2011, LECT NOTES COMPUT SC, V6791, P134, DOI 10.1007/978-3-642-21735-7_17 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Berger C., 1975, HUMAN COMMUNICATION, V1, P99, DOI DOI 10.1111/J.1468-2958.1975.TB00258.X Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 boyd danah, 2010, TWEET TWEET RETWEET Burton S, 2011, J CONSUM MARK, V28, P491, DOI 10.1108/07363761111181473 Cao Y., 2006, ADAPTING RANKING SYM Castillo C., 2011, INFORM CREDIBILITY T Cha M., 2010, ICWSM, V10, P10, DOI DOI 10.1093/JSCR/2010.10.10 Chen J., 2010, SHORT TWEET EXPT REC Cui A., 2012, DISCOVER BREAKING EV Davidov D., 2010, ENHANCED SENTIMENT L Duan Y., 2010, EXPIRICAL STUDY LEAR Easley D, 2010, NETWORKS CROWDS MARK Emerson B., 2012, SOCIAL MEDIA MARKETI Griffiths TL, 2005, ADV NEURAL INFORM PR, V17, P537 Guy I., 2009, PERSONALIZED RECOMME Hannon J., 2010, RECOMMENDING TWITTER Herbrich R., 1999, ADV NEURAL INFORM PR, V88, P115 Hill W., 1996, USING FREQUENCY OF M Hoffman DL, 2010, MIT SLOAN MANAGE REV, V52, P41 Honey C., 2009, MICROBLOGGING CONVER Hong L., 2010, EMPIRICAL STUDY TOPI Joachims T., 2002, OPTIMIZING SEARCH EN KATZ L, 1953, PSYCHOMETRIKA, V18, P39 Kim Y.S., 2011, SELECTING TARGET USE Kotler P, 2009, MARKETING MANAGEMENT Kullback S., 1997, INFORM THEORY STAT Kwak H, 2010, WHAT IS TWITTER SOCI Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591 Lv Y., 2011, LEARNING MODEL RELAT Melville P., 2002, CONTENT BOOSTED COLL Mooney R.J., 2000, CONTENT BASED BOOK R Naaman M., 2010, IS IT REALLY ABOUT M Pazzani M.J., 1996, SYSKILL WEBERT IDENT Rendle S., 2009, LEARNING OPTIMAL RAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stelzner M., 2011, SOCIAL MEDIA MARKETI Suh B., 2010, WANT RETWEETED LARGE Takeuchi Y., 2006, CITYVOYAGER OUTDOOR Veness C., 2007, VINCENTY FORMULA DIS Wang B., 2013, INT WORLD WID WEB C Weng J., 2010, TWITTERANK FINDING T Wu L., 2009, LEARNING TO TAG Wu W., 2010, AUTOMATIC GENERATION Xing W., 2004, WEIGHTED PAGERANK AL Xiong H., 2004, FRAMEWORK DISCOVERIN, P89 Yang L., 2012, WE KNOW WHAT YOU TAG Ye M., 2010, LOCATION RECOMMENDAT Zarrella D., 2010, SOCIAL MEDIA MARKETI Zhao W.X., 2011, COMP TWITTER TRADITI Zheng V.W., 2010, COLLABORATIVE LOCATI Zheng Y., 2009, MINING INTERESTING L NR 54 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD JUL PY 2015 VL 18 IS 4 BP 1019 EP 1049 DI 10.1007/s11280-014-0299-8 PG 31 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA CL4PY UT WOS:000356936800011 ER PT J AU Moskovitch, R Shahar, Y AF Moskovitch, Robert Shahar, Yuval TI Classification-driven temporal discretization of multivariate time series SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article DE Temporal knowledge discovery; Temporal data mining; Temporal abstraction; Time intervals mining; Frequent pattern mining; Classification; Discretization ID KNOWLEDGE; ABSTRACTION; INTERVALS; PATTERNS; EVENTS; RULES AB Biomedical data, in particular electronic medical records data, include a large number of variables sampled in irregular fashion, often including both time point and time intervals, thus providing several challenges for analysis and data mining. Classification of multivariate time series data is a challenging task, but is often necessary for medical care or research. Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series. In this paper, we introduce a novel supervised discretization method, geared towards enhancement of classification accuracy, which determines the cutoffs that will best discriminate among classes through the distribution of their states. We present a framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal-abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation patterns (TIRPs), using versions of Allen's 13 temporal relations; (3) using the patterns as features to induce a classifier. We evaluated the framework, focusing on the comparison of three versions of the new, supervised, temporal discretization for classification (TD4C) method, each relying on a different symbolic-state distribution-distance measure among outcome classes, to several commonly used unsupervised methods, on real datasets in the domains of diabetes, intensive care, and infectious hepatitis. Using only three abstract temporal relations resulted in a better classification performance than using Allen's seven relations, especially when using three symbolic states per variable. Similarly when using the horizontal support and mean duration as the TIRPs feature representation, rather than a binary (existence) representation. The classification performance when using the three versions of TD4C was superior to the performance when using the unsupervised (EWD, SAX, and KB) discretization methods. C1 [Moskovitch, Robert; Shahar, Yuval] Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. [Moskovitch, Robert] Columbia Univ, Dept Biomed Informat Syst Biol & Med, New York, NY USA. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. EM robertmo@bgu.ac.il; yshahar@bgu.ac.il FU Deutsche Telekom Laboratories, HP labs Innovation Research Program FX The authors wish to thank Marion Verduijn for sharing the ICU12h dataset, and Prof. Avi Porath, M.D., for his assistance regarding the knowledge-based abstraction of the diabetes data set. For insightful discussions of time intervals mining and classification using TIRPs, we wish to express our thanks to Christos Faloutsos, Christian Freksa, Fabian Moerchen, Dhaval Patel, and Iyad Batel. This work was supported in part by Grants from Deutsche Telekom Laboratories, HP labs Innovation Research Program. CR ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434 Azulay R, 2007, WORKSH INT DAT AN BI Batal I, 2012, P KNOWL DISC DAT MIN Batal I, 2013, ACM T INTEL SYST TEC, V4, DOI 10.1145/2508037.2508044 Bellazzi R, 2011, METHOD INFORM MED, V50, P536, DOI 10.3414/ME11-06-0002 FREKSA C, 1992, ARTIF INTELL, V54, P199, DOI 10.1016/0004-3702(92)90090-K Hauskrecht M, 2013, P ANN S AM MED INF A Hoppner F, 2001, P WLTSD Hoppner F, 2014, WIRES DATA MIN KNOWL, V4, P196, DOI 10.1002/widm.1122 Hoppner F, 2002, WORKSH KNOWL DISC DA Hu B, 2013, P 13 SIAM C DAT MIN, P578 Jakkula VR, 2011, ARTIF INTELL SMARTER, V11, P1 Kam PS, 2000, P DAWAK 00 Kohavi R, 1996, P KDD KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lin J, 2003, 8 ACM SIGMOD WORKSH, P2 Morchen F, 2006, P KDD Morchen F, 2005, P KDD05 Moskovitch R, 2014, ACM KDD WORKSH CONN Moskovitch R, 2015, KNOWL INF SYST, V42, P21, DOI 10.1007/s10115-013-0707-x Moskovitch R, 2011, THESIS B GURION U Moskovitch R, 2008, COMPUT STAT DATA AN, V52, P4544, DOI 10.1016/j.csda.2008.01.028 Moskovitch R, 2004, MEDINFO 1, V11, P140 Moskovitch R, 2014, KNOWL INF SYST, DOI [10.1007/s10115-014-0784-5, DOI 10.1007/S10115-014-0784-5] Moskovitch R, 2007, ANAL ICU PATIENTS US Moskovitch R, 2007, CISDA 2007, P169 Moskovitch R, 2009, AMIA 2009 SAN FRANC Moskovitch R, 2009, IDAMAP 2009 VER IT Papapetrou P, 2009, KNOWL INF SYST, V21, P133 Patel D, 2008, P 2008 ACM SIGMOD IN, P393, DOI 10.1145/1376616.1376658 Peter S, 2013, P SGAI Pei J, 2001, PROC INT CONF DATA, P215 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Ratanamahatana C, 2005, P SIAM DAT MIN Roddick J, 2002, IEEE T KNOWL DATA EN, V4, P750 Sacchi L, 2007, DATA MIN KNOWL DISC, V15, P217, DOI 10.1007/s10618-007-0077-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahar Y, 1997, ARTIF INTELL, V90, P79, DOI 10.1016/S0004-3702(96)00025-2 Shahar Y, 1999, J AM MED INFORM ASSN, V6, P494 Shahar Y, 1999, J EXP THEOR ARTIF IN, V11, P123, DOI 10.1080/095281399146643 Shknevsky A, 2014, ACM KDD WORKSH CONN Stopel D, 2006, P 3 INT C NEUR NETW Stopel D, 2006, IEEE IJCNN, P2362 Verduijn M, 2007, ARTIF INTELL MED, V41, P112 Villafane R, 2000, J INTELL INF SYST, V15, P71, DOI 10.1023/A:1008781812242 Winarko E, 2007, DATA KNOWL ENG, V63, P76, DOI 10.1016/j.datak.2006.10.009 Wu SY, 2007, IEEE T KNOWL DATA EN, V19, P742, DOI 10.1109/TKDE.2007.1032 Yi-Cheng C, 2010, P CIKM Yi-Cheng C, 2011, IEEE 11 INT C DAT MI NR 49 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1384-5810 EI 1573-756X J9 DATA MIN KNOWL DISC JI Data Min. Knowl. Discov. PD JUL PY 2015 VL 29 IS 4 SI SI BP 871 EP 913 DI 10.1007/s10618-014-0380-z PG 43 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA CJ8LX UT WOS:000355753900002 ER PT J AU Mateos, C Rodriguez, JM Zunino, A AF Mateos, Cristian Manuel Rodriguez, Juan Zunino, Alejandro TI A tool to improve code-first Web services discoverability through text mining techniques SO SOFTWARE-PRACTICE & EXPERIENCE LA English DT Article DE Web services; code first; WSDL anti-patterns; automatic detection; service discovery ID FRAMEWORK; MODEL AB Service-oriented development is challenging mainly because Web service developers tend to disregard the importance of the exposed service APIs, which are specified using Web Service Description Language (WSDL) documents. Methodologically, WSDL documents can be either manually generated or inferred from service implementations using WSDL generation tools. The latter option, called code first, is the most used approach in the industry. However, it is known that there are some bad practices in service implementations or defects in WSDL generation tools that may cause WSDL documents to present WSDL anti-patterns, which in turn compromise the chances of documents of being discovered and understood. In this paper, we present a software tool that assists developers in obtaining WSDL documents with as few WSDL anti-patterns as possible. The tool combines text mining and meta-programming techniques to process service implementations and is developed as an Eclipse plug-in. An evaluation of the tool by using a data-set of real service implementations in terms of anti-pattern avoidance accuracy and discovery performance by using classical Information Retrieval metricsPrecision-at-n, Recall and Normalized Discounted Cumulative Gainis also reported.Copyright (c) 2014 John Wiley & Sons, Ltd. C1 [Mateos, Cristian; Manuel Rodriguez, Juan; Zunino, Alejandro] CONICET UNICEN, ISISTAN, Buenos Aires, DF, Argentina. RP Mateos, C (reprint author), CONICET UNICEN, ISISTAN, Tandil B7001BBO, Buenos Aires, DF, Argentina. EM cmateos@conicet.gov.ar FU ANPCyT [PICT-2012-0045] FX We acknowledge the financial support provided by ANPCyT through grant PICT-2012-0045. CR Agichtein E., 2006, 29 ANN INT ACM SIGIR, P3 Al-Masri E., 2007, 16 INT C COMP COMM N, P529 Al-Masri E, 2010, SOFTWARE PRACT EXPER, V40, P917, DOI 10.1002/spe.989 Baina K, 2004, LECT NOTES COMPUTER, V3084 Basili VR, 1996, IEEE T SOFTWARE ENG, V22, P751, DOI 10.1109/32.544352 BEATON J, 2008, IEEE S VIS LANG HUM, P193 Benatallah B, 2004, IEEE INTERNET COMPUT, V8, P46, DOI 10.1109/MIC.2004.1260703 Bhagat J, 2010, NUCLEIC ACIDS RES, V38, pW689, DOI 10.1093/nar/gkq394 Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112 Blake MB, 2011, IEEE T KNOWL DATA EN, V23, P889, DOI 10.1109/TKDE.2010.168 Allen EE, 2006, SCI COMPUT PROGRAM, V59, P26, DOI 10.1016/j.scico.2005.07.003 Chae HS, 2000, SOFTWARE PRACT EXPER, V30, P1405, DOI 10.1002/1097-024X(200010)30:12<1405::AID-SPE330>3.0.CO;2-3 Crasso M, 2011, INFORM SYST FRONT, V13, P407, DOI 10.1007/s10796-009-9221-9 Crasso M., 2011, J DATABASE MANAGE, V22, P103 Crasso M, 2010, IEEE INTERNET COMPUT, V14, P48, DOI 10.1109/MIC.2010.81 Dan Klein C. D. M., 2003, P 41 M ASS COMP LING, P423 Dong X., 2004, P 30 INT C VER LARG, V30, P372 Erl T., 2007, SOA PRINCIPLES SERVI Feldman R, 2006, TEXT MINING HDB ADV Garofalakis J, 2006, J WEB ENG, V5, P265 Grechanik M, 2010, 32 ACM IEEE INT C SO Gronmo R, 2004, 2004 IEEE INTERNATIONAL CONFERNECE ON E-TECHNOLOGY, E-COMMERE AND E-SERVICE, PROCEEDINGS, P42 Guruge A, 2004, WEB SERVICES, P95, DOI 10.1016/B978-155558282-1/50005-1 Hao YN, 2010, FUTURE GENER COMP SY, V26, P1053, DOI 10.1016/j.future.2010.04.012 He A, 2004, LECT NOTES COMPUTER, P320 Henning M, 2009, COMMUN ACM, V52, P46, DOI 10.1145/1506409.1506424 Jiang W, 2011, J COMPUT SCI TECH-CH, V26, P837, DOI 10.1007/s11390-011-0183-2 KASTNER C, 2009, ICSE, P611 Kim JB, 2005, DECIS SUPPORT SYST, V40, P71, DOI 10.1016/j.dss.2004.04.005 Mateos C, 2011, INT J WEB GRID SERV, V7, P357, DOI 10.1504/IJWGS.2011.044710 Milne D, 2013, ARTIF INTELL, V194, P222, DOI 10.1016/j.artint.2012.06.007 Papazoglou MP, 2008, INT J COOP INF SYST, V17, P223, DOI 10.1142/S0218843008001816 Pasley J, 2006, IEEE INTERNET COMPUT, V10, P72, DOI 10.1109/MIC.2006.45 Rodriguez J, 2010, IFIP ADV INFORM COMM, V341 Rodriguez JM, 2013, J WEB ENG, V12, P131 Rodriguez JM, 2010, SCI COMPUT PROGRAM, V75, P1001, DOI 10.1016/j.scico.2010.01.002 Rodriguez JM, 2013, SOA FRONTIER EXPERIE Rodriguez JM, 2013, SOFTWARE PRACT EXPER, V43, P613, DOI 10.1002/spe.2123 Rodriguez JM, 2013, IEEE INTERNET COMPUT, V17, P44, DOI 10.1109/MIC.2011.162 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos RLT, 2008, J SYST SOFTWARE, V81, P809, DOI 10.1016/j.jss.2007.07.029 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 Vara JM, 2005, International Conference on Next Generation Web Services Practices, P319, DOI 10.1109/NWESP.2005.87 Wei MJ, 2012, COMPUT SCI INF SYST, V9, P813, DOI 10.2298/CSIS101222001W Wu C, 2012, SCI COMPUT PROGRAM, V77, P355, DOI 10.1016/j.scico.2011.08.001 Zhang MW, 2010, J COMPUT SCI TECH-CH, V25, P1143, DOI 10.1007/s11390-010-1091-6 Zhu DL, 2009, INFORM SCIENCES, V179, P2606, DOI 10.1016/j.ins.2009.01.038 NR 47 TC 1 Z9 1 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0038-0644 EI 1097-024X J9 SOFTWARE PRACT EXPER JI Softw.-Pract. Exp. PD JUL PY 2015 VL 45 IS 7 BP 925 EP 948 DI 10.1002/spe.2268 PG 24 WC Computer Science, Software Engineering SC Computer Science GA CJ8EW UT WOS:000355733900004 ER PT J AU Mateos, C Crasso, M Zunino, A Coscia, JLO AF Mateos, Cristian Crasso, Marco Zunino, Alejandro Ordiales Coscia, Jose Luis TI A Stitch in Time Saves Nine: Early Improving Code-First Web Services Discoverability SO INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS LA English DT Article DE Service-Oriented Computing; Web Services; code-first; Web Services Discovery; object-oriented metrics; WSDL anti-Patterns ID OF-THE-ART; FAULT PREDICTION; INFORMATION-SYSTEMS; METRICS; SOFTWARE; ARCHITECTURE AB Web Services represent a number of standard technologies and methodologies that allow developers to build applications under the Service-Oriented Computing paradigm. Within these, the WSDL language is used for representing Web Service interfaces, while code-first remains the de facto standard for building such interfaces. Previous studies with contract-first Web Services have shown that avoiding a specific catalog of bad WSDL specification practices, or anti-patterns, can reward Web Service publishers as service understandability and discoverability are considerably improved. In this paper, we study a number of simple and well-known code service refactorings that early reduce anti-pattern occurrences in WSDL documents. This relationship relies upon a statistical correlation between common OO metrics taken on a service's code and the anti-pattern occurrences in the generated WSDL document. We quantify the effects of the refactorings - which directly modify OO metric values and indirectly alter anti-pattern occurrences - on service discovery. All in all, we show that by applying the studied refactorings, anti-patterns are reduced and Web Service discovery is significantly improved. For the experiments, a dataset of real-world Web Services and an academic service registry have been employed. C1 [Mateos, Cristian; Zunino, Alejandro] Univ Nacl Ctr Prov Buenos Aires, ISISTAN CONICET, Buenos Aires, DF, Argentina. [Crasso, Marco] IBM Res, Buenos Aires, DF, Argentina. [Ordiales Coscia, Jose Luis] Klarna, Stockholm, Sweden. RP Mateos, C (reprint author), Univ Nacl Ctr Prov Buenos Aires, ISISTAN CONICET, Campus Univ,Paraje Arroyo Seco B7001BBO, Buenos Aires, DF, Argentina. EM cristian.mateos@isistan.unicen.edu.ar FU ANPCyT [PICT-2012-0045] FX We thank the anonymous reviewers for their constructive comments to improve the paper. We acknowledge the financial support provided by ANPCyT through grant PICT-2012-0045. CR Agichtein E., 2006, 29 ANN INT ACM SIGIR, P3 Al Dallal J, 2011, IEEE T SOFTWARE ENG, V37, P788, DOI 10.1109/TSE.2010.97 Becker K, 2011, INT J WEB SERV RES, V8, P21, DOI 10.4018/jwsr.2011010102 Berón M., 2015, S LANG APPL TECHN SL Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112 Campbell-Kelly M, 2009, COMMUN ACM, V52, P28, DOI [10.1145/150B409.1506419, 10.1145/1506409.1506419] CHIDAMBER SR, 1994, IEEE T SOFTWARE ENG, V20, P476, DOI 10.1109/32.295895 Cong Z., 2013, GOVERNANCE TECHNOLOG, V8, P137 Crasso M, 2011, INFORM SYST FRONT, V13, P407, DOI 10.1007/s10796-009-9221-9 CRASSO M, 2010, IEEE INTERNET COMPUT, V14, P30 Crasso M., 2011, J DATABASE MANAGE, V22, P103 Dong X., 2004, 30 INT C VER LARG DA, V30, P372 Elish MO, 2011, ADV ENG SOFTW, V42, P852, DOI 10.1016/j.advengsoft.2011.06.001 Erickson J, 2008, J DATABASE MANAGE, V19, P42, DOI 10.4018/jdm.2008070103 Erl T., 2007, SOA PRINCIPLES SERVI Fan JC, 2005, SIGMOD RECORD, V34, P24 Flores A, 2012, SOFTW TEST VERIF REL, V22, P529, DOI 10.1002/stvr.438 Gallivan K. A., 2002, 2 IEEE ACM INT S CLU, P128, DOI 10.1109/CCGRID.2002.1017120 Garriga M, 2013, INT J WEB GRID SERV, V9, P369, DOI 10.1504/IJWGS.2013.057469 Gyimothy T, 2005, IEEE T SOFTWARE ENG, V31, P897, DOI 10.1109/TSE.2005.112 Harrison R, 1998, IEEE T SOFTWARE ENG, V24, P491, DOI 10.1109/32.689404 Heβ A., LECT NOTES COMPUTER, V3298, P320 Juric MB, 2009, J SYST SOFTWARE, V82, P1326, DOI 10.1016/j.jss.2009.03.001 Karam O., 2006, ESSENTIALS SOFTWARE Laxmaiah B., 2013, INT J COMPUT TRENDS, V4, P13 Li SH, 2007, J DATABASE MANAGE, V18, P1, DOI 10.4018/jdm.2007100101 Li Y., 2013, INF TECHNOL J, V12, P857 Mateos C, 2015, SOFTWARE PRACT EXPER, V45, P925, DOI 10.1002/spe.2268 Mateos C, 2011, INT J WEB GRID SERV, V7, P357, DOI 10.1504/IJWGS.2011.044710 Mateos C, 2015, ENTERP INF SYST-UK, V9, P58, DOI 10.1080/17517575.2012.717234 McMillan C, 2012, IEEE T SOFTWARE ENG, V38, P1069, DOI 10.1109/TSE.2011.84 Meirelles P., 2010, BRAZ S SOFTW ENG SBE, P11 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Motahari Nezhad H. R., 2010, 19 INT C WORLD WID W, P731 Murad S, 2013, IEEE POTENTIALS, V32, P36, DOI 10.1109/MPOT.2012.2187806 Oh SC, 2009, INT J WEB SERV RES, V6, P1, DOI 10.4018/jwsr.2009092301 Coscia JLO, 2013, INT J WEB GRID SERV, V9, P107 Papazoglou MP, 2007, COMPUTER, V40, P38, DOI 10.1109/MC.2007.400 Papazoglou MP, 2007, VLDB J, V16, P389, DOI 10.1007/s00778-007-0044-3 Papazoglou M. P., 2006, International Journal of Web Engineering and Technology, V2, DOI 10.1504/IJWET.2006.010423 Pasley J, 2006, IEEE INTERNET COMPUT, V10, P72, DOI 10.1109/MIC.2006.45 Perepletchikov Mikhail, 2008, Journal of Software, V3, DOI 10.4304/jsw.3.2.1-14 Pfleeger S. L., 1998, SOFTWARE METRICS RIG Radjenovic D, 2013, INFORM SOFTWARE TECH, V55, P1397, DOI 10.1016/j.infsof.2013.02.009 Rodriguez J. M., 2015, J WEB ENG, V14, P251 Rodriguez J. M., 2015, 9 INT C COMPL INT SO Rodriguez JM, 2013, J WEB ENG, V12, P131 Rodriguez JM, 2010, SCI COMPUT PROGRAM, V75, P1001, DOI 10.1016/j.scico.2010.01.002 Roman D, 2005, APPL ONTOL, V1, P77 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarjoughian H., 2013, SIMULATION FDN METHO, P205 Sheng QZ, 2014, INFORM SCIENCES, V280, P218, DOI 10.1016/j.ins.2014.04.054 Sivashanmugam K, 2003, ICWS'03: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON WEB SERVICES, P395 Spinellis D, 2005, IEEE SOFTWARE, V22, P9, DOI 10.1109/MS.2005.111 Sripairojthikoon P., 2014, 16 INT C ADV COMM TE, P378 Sripairojthikoon P, 2013, INT CONF ADV COMMUN, P853 Stigler S., 2008, CHANCE, V21, P12, DOI DOI 10.1007/S00144-008-0033-3 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 The Apache Software Foundation, 2010, COMM MATH AP COMM MA NR 59 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-8430 EI 1793-6365 J9 INT J COOP INF SYST JI Int. J. Coop. Inf. Syst. PD JUN PY 2015 VL 24 IS 2 AR 1550004 DI 10.1142/S0218843015500045 PG 38 WC Computer Science, Information Systems SC Computer Science GA CN9RW UT WOS:000358787700003 ER PT J AU Pittke, F Leopold, H Mendling, J AF Pittke, Fabian Leopold, Henrik Mendling, Jan TI Automatic Detection and Resolution of Lexical Ambiguity in Process Models SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING LA English DT Article DE Identification of lexical ambiguity; resolution of lexical ambiguity; business process models ID BUSINESS PROCESS MODELS; INFORMATION-TECHNOLOGY; TRANSFORMATION; GUIDELINES; QUALITY AB System-related engineering tasks are often conducted using process models. In this context, it is essential that these models do not contain structural or terminological inconsistencies. To this end, several automatic analysis techniques have been proposed to support quality assurance. While formal properties of control flow can be checked in an automated fashion, there is a lack of techniques addressing textual quality. More specifically, there is currently no technique available for handling the issue of lexical ambiguity caused by homonyms and synonyms. In this paper, we address this research gap and propose a technique that detects and resolves lexical ambiguities in process models. We evaluate the technique using three process model collections from practice varying in size, domain, and degree of standardization. The evaluation demonstrates that the technique significantly reduces the level of lexical ambiguity and that meaningful candidates are proposed for resolving ambiguity. C1 [Pittke, Fabian; Mendling, Jan] WU Vienna, Inst Informat Business, Vienna, Austria. [Leopold, Henrik] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands. RP Pittke, F (reprint author), WU Vienna, Inst Informat Business, Vienna, Austria. EM fabian.pittke@wu.ac.at; h.leopold@vu.nl; jan.mendling@wu.ac.at CR Abran A., 2004, GUIDE SOFTWARE ENG B, V2004 Agirre E., 2000, P COLING 2000 WORKSH, P11 Anda B., 2002, P 14 INT C SOFTW ENG, P127 Attaran M, 2004, INFORM MANAGE-AMSTER, V41, P585, DOI 10.1016/S0378-7206(03)00098-3 Azevedo LG, 2009, LECT NOTES BUS INF, V29, P99 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barjis J, 2008, SCI COMPUT PROGRAM, V71, P73, DOI 10.1016/j.scico.2008.01.002 Becker J., 2009, P EUR C INF SYST JUN, P2636 Becker J, 2000, LECT NOTES COMPUT SC, V1806, P30 Becker J., 2012, P AM C INF SYST, P1 Ben-Ari D., 1988, P 2 INT C THEOR METH, P175 Berger J, 1985, STAT DECISION THEORY Berry D. M., 2003, CONTRACT DRAFTING SO Boehm B.W., 1988, J PARAMETR, V8, P32 Bolloju N, 2006, COMMUN ACM, V49, P108, DOI 10.1145/1139922.1139926 Bouzeghoub M., 1985, P 11 INT C VER LARG, P82 Brown LD, 2001, STAT SCI, V16, P101 Cardoso Evellin Cristine Souza, 2009, Proceedings of the 2009 13th Enterprise Distributed Object Computing Conference Workshops, EDOCW. IEEE EDOC 2009 Workshops and Short Papers, DOI 10.1109/EDOCW.2009.5331974 Ceccato M., 2004, DIT0411 U TRENT DEP Chantree F., 2006, P IEEE INT REQ ENG C, P59 Cockburn A., 2001, WRITING EFFECTIVE US, V1 Cox K., 2000, Empirical Software Engineering, V5, DOI 10.1023/A:1026542700033 DAVENPORT TH, 1990, SLOAN MANAGE REV, V31, P11 Davies I, 2006, DATA KNOWL ENG, V58, P358, DOI 10.1016/j.datak.2005.07.007 Deissenboeck F, 2006, SOFTWARE QUAL J, V14, P261, DOI 10.1007/s11219-006-9219-1 Delfmann P., 2009, ENTERPRISE MODEL INF, V4, P3 Denger C., 2003, Proceedings IEEE International Conference on Software - Science, Technology and Engineering (SwSTE'03), DOI 10.1109/SWSTE.2003.1245428 Dijkman R, 2011, INFORM SYST, V36, P498, DOI 10.1016/j.is.2010.09.006 Dijkman R, 2012, COMPUT IND, V63, P91, DOI 10.1016/j.compind.2011.12.003 Dijkman R, 2009, LECT NOTES COMPUT SC, V5701, P48 Dumas M, 2005, PROCESS-AWARE INFORMATION SYSTEMS: BRIDGING PEOPLE AND SOFTWARE THROUGH PROCESS TECHNOLOGY, P1, DOI 10.1002/0471741442 Ehrig M, 2007, P 4 AS PAC C CONC MO, V67, P71 Fabbrini F., 2000, P SOFTW INT QUAL WEE, P1 Fantechi A., 2003, Requirements Engineering, V8, DOI 10.1007/s00766-003-0174-0 FRAKES WB, 1994, IEEE T SOFTWARE ENG, V20, P617, DOI 10.1109/32.310671 Ambriola V., 2006, Automated Software Engineering, V13, DOI 10.1007/s10515-006-5468-2 Gleich B, 2010, LECT NOTES COMPUT SC, V6182, P218, DOI 10.1007/978-3-642-14192-8_20 GORDIJN J, 2000, P INT WORKSH CONC MO, V1921, P40 HAYNE S, 1990, PROCEEDINGS : 6TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, P402 Heitmeyer C. L., 1996, ACM Transactions on Software Engineering and Methodology, V5, DOI 10.1145/234426.234431 Hornby A. S., 1974, OXFORD ADV LEARNED D, V1428 Jayal A., 2009, INNOVATION TEACHING, V8, P3 Jayal A., 2009, J ED RESOURCES COMPU, V8, P12 Kamsties E, 2005, ENGINEERING AND MANAGING SOFTWARE REQUIREMENTS, P245, DOI 10.1007/3-540-28244-0_11 Kamsties E., 2001, P 1 WORKSH INSP SOFT, P68 Kamsties E., 2000, P 13 INT C SOFTW SYS, P89 Keller G., 1998, SAP R R 3 PROCESS OR Kilgarriff A, 1998, COMPUT SPEECH LANG, V12, P453, DOI 10.1006/csla.1998.0108 Knackstedt R., 2008, P EUR C INF SYST, P1965 Lami G., 2005, CMUSEI2005TR014 Lee K, 2002, LECT NOTES COMPUT SC, V2319, P62 Lehmann T., 2007, P WORKSH SEM WEB EN, P1 Leopold H., 2012, P 10 INT C BUS PROC, V7481, P319 Leopold H., 2013, THESIS HUMBOLDT U BE Leopold H, 2012, INFORM SYST, V37, P443, DOI 10.1016/j.is.2012.01.004 Leopold H., 2012, BUSINESS INFORM SYST, V117, P84 Leopold H., 2014, DECIS SUPPORT SYST, V56, P310 Lin D., 1998, P 15 INT C MACH LEAR, V1, P296 Lin D., 1998, P 36 ANN M ASS COMP, V2, P768, DOI [DOI 10.3115/980691.980696, DOI 10.3115/980432.980696] Lin X, 1999, LIBR INFORM SCI RES, V21, P153, DOI 10.1016/S0740-8188(99)00007-9 LINDLAND OI, 1994, IEEE SOFTWARE, V11, P42, DOI 10.1109/52.268955 Marcus A, 2005, INT J SOFTW ENG KNOW, V15, P811, DOI 10.1142/S0218194005002543 McCarthy D., 2004, P 42 M ASS COMP LING, P279, DOI 10.3115/1218955.1218991 McCarthy D, 2007, COMPUT LINGUIST, V33, P553, DOI 10.1162/coli.2007.33.4.553 Mendling J, 2010, INFORM SYST, V35, P467, DOI 10.1016/j.is.2009.03.009 MENDLING J, 2009, T PETRI NETS OTHER M, V5460, P208, DOI 10.1007/978-3-642-00899-3_12 Mendling J, 2010, INFORM SOFTWARE TECH, V52, P127, DOI 10.1016/j.infsof.2009.08.004 Mens T, 2007, SOFTW SYST MODEL, V6, P269, DOI 10.1007/s10270-006-0044-6 Mens T, 2006, LECT NOTES COMPUT SC, V4199, P200 Mili A, 1998, ANN SOFTW ENG, V5, P349, DOI 10.1023/A:1018964121953 Mili H, 2010, ACM COMPUT SURV, V43, DOI 10.1145/1824795.1824799 Miller G., 1998, WORDNET ELECT LEXICA MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moha N, 2010, IEEE T SOFTWARE ENG, V36, P20, DOI 10.1109/TSE.2009.50 Navigli R., 2012, P ACL 2012 SYST DEM, P67 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Navigli R., 2012, P 2012 JOINT C EMP M, P1399 Pantel Patrick, 2002, P ACM SIGKDD C KNOWL, P613 Pelleg D., 2000, P 17 INT C MACH LEAR, V1, P727 Piegorsch WW, 2004, COMPUT STAT DATA AN, V46, P309, DOI 10.1016/j.csda.2003.10.002 Pittke F, 2013, LECT NOTES BUS INF P, V147, P292 Plachouras V., 2003, P 12 TEXT RETR C TRE, P636 Ponzetto S. P., 2010, P 48 ANN M ASS COMP, P1522 Procter P., 1981, LONGMAN DICT CONT EN Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN, V1 Resnik P., 2000, NAT LANG ENG, V5, P113 Rolland C, 1998, DATA KNOWL ENG, V25, P125, DOI 10.1016/S0169-023X(97)86223-4 Rolland C, 1998, IEEE T SOFTWARE ENG, V24, P1055, DOI 10.1109/32.738339 Rosemann M., 2006, BUSINESS PROCESS MAN, V12, P249, DOI 10.1108/14637150610657567 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 LEITE JCSD, 1991, IEEE T SOFTWARE ENG, V17, P1253, DOI 10.1109/32.106986 Schuette R., 1998, P 17 INT C CONC MOD, V1507, P240 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Schutze H., 1992, Proceedings. Supercomputing '92. (Cat. No.92CH3216-9), DOI 10.1109/SUPERC.1992.236684 Shull F., 1999, CSTR4070 U MAR, P1 Stevenson A., 2010, OXFORD DICT ENGLISH Torner F., 2006, P ACM IEEE INT S EMP, P115, DOI 10.1145/1159733.1159753 van Rijsbergen C. J., 1979, INFORM RETRIEVAL VANDERAALST WMP, 2000, BPM, V1806, P161 Veronis J., 1995, COMPUTATIONAL LEXICA, P251, DOI 10.1017/CBO9780511527227.016 Wang Y., 2013, NATURAL LANGUAGE PRO, P25 Weber I, 2010, DISTRIB PARALLEL DAT, V27, P271, DOI 10.1007/s10619-010-7060-9 Wimmer M., 2011, P INT C MOD SOFTW EN, P150 Yang H, 2011, REQUIR ENG, V16, P163, DOI 10.1007/s00766-011-0119-y Yang H., 2010, P IEEE ACM INT C AUT, P53, DOI 10.1145/1858996.1859007 Zhong Z., 2012, P 50 ANN M ASS COMP, V1, P273 NR 106 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 0098-5589 EI 1939-3520 J9 IEEE T SOFTWARE ENG JI IEEE Trans. Softw. Eng. PD JUN PY 2015 VL 41 IS 6 BP 526 EP 544 DI 10.1109/TSE.2015.2396895 PG 19 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA CK3ZL UT WOS:000356155700001 ER PT J AU Madzin, H Zainuddin, R Sharef, NM AF Madzin, Hizmawati Zainuddin, Roziati Sharef, Nurfadhlina Mohd TI IFM3IRS: Information fusion retrieval system with knowledge-assisted text and visual features based on medical conceptual model SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Information fusion; Late fusion technique; Text-based retrieval; Visual-based image retrieval; Query expansion; Boolean model; Supervised classification ID IMAGE RETRIEVAL; CLASSIFICATION AB The technology of medical data production has been rapidly changed over the past few years. Modern computer technology has created the possibility of creating multi-modal medical images. Medical data often contain multi-modal information such as visual information (image) as well as textual information. Both types of information are important for medical retrieval system (MRS). Due to the information limitation at different levels of sources, the application of information fusion becomes a real need in medical application. In this research, an information fusion framework was built to develop the multi-modality medical image retrieval system (IFM3IRS). The framework utilizes two sources of information involving text and visual-based retrieval process. The application is based on sequential order where the result from text-based process will automatically be the input in visual-based process. The main contributions of this paper are the development of a new ranking model called MedHieCon ranking model which applies semantic concepts of modality, anatomy and pathology in text-based process and also the learning approach of medical images using medical concept model in visual-based process. ImageCLEFmed 2010 data collection was used to evaluate IFM3IRS and it shows that our information fusion framework is in top list among other researchers. Although text-based retrieval system has proven to be a better performance in MRS; it is significant to determine the overall performance improvements which include the fusion of text and image. C1 [Madzin, Hizmawati] Univ Putra Malaysia, Multimedia Dept, Fac Comp Sci & Informat Technol, Serdang 43400, Malaysia. [Zainuddin, Roziati] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence, Kuala Lumpur, Malaysia. [Sharef, Nurfadhlina Mohd] Univ Putra Malaysia, Dept Comp Sci, Fac Comp Sci & Informat Technol, Serdang 43400, Malaysia. RP Madzin, H (reprint author), Univ Putra Malaysia, Multimedia Dept, Fac Comp Sci & Informat Technol, Serdang 43400, Malaysia. EM iezma.madzin@gmail.com; roziati@um.edu.my; nurfadhlina@upm.edu.my RI Mohd Sharef, Nurfadhlina/ OI Mohd Sharef, Nurfadhlina/0000-0003-4335-0513 CR Adiego J, 2004, IEEE DATA COMPR CONF, P522 Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI 10.1093/nar/gkh131 Bedrick S, 2010, CLEF NOTEBOOK PAPERS Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061 Bostrom H, 2007, DEFINITION INFORM FU Cavnar W. B., 1994, ANN ARBOR MI, V48113, P161 Chbeir R, 2000, MIMS PROTOTYPE MED I Chevallet JP, 2005, WORK NOT 2005 CLEF W Depeursinge A, 2010, INFORM RETRIEVAL SER, V32, P95, DOI 10.1007/978-3-642-15181-1_6 Diaz-Galiano M, 2009, SINAI IMAGECLEF 2010 Diaz-Galiano MC, 2009, COMPUT BIOL MED, V39, P396, DOI 10.1016/j.compbiomed.2009.01.012 Durao F, 2012, MULTIMED TOOLS APPL Hall DL, 1997, P IEEE, V85, P6, DOI 10.1109/5.554205 HARALICK RM, 1973, IEEE T SYST MAN CYB, VSMC3, P610, DOI 10.1109/TSMC.1973.4309314 Harris C., 1988, ALV VIS C, V15, P50 Hearst MA, 2007, BIOINFORMATICS, V23, P2196, DOI 10.1093/bioinformatics/btm301 Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hsu William, 2009, Int J Med Inform, V78 Suppl 1, pS13, DOI 10.1016/j.ijmedinf.2008.09.006 HU M, 1962, IRE T INFORM THEOR, V8, P179 Ibrahim R, 2010, MED IMAGE RETRIVAL I Lehmann TM, 2003, P SOC PHOTO-OPT INS, V5033, P109, DOI 10.1117/12.481942 Muller H, 2010, CLEF NOTEBOOK PAPERS Muller H, 2011, MULTILINGUAL INFORM, P72 Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024 Ozer S, 2010, MED PHYS, V37, P1873, DOI 10.1118/1.3359459 Parikshit Sondhi JS, 2010, CLEF NOTEBOOK PAPERS Peters C, 2001, WORKSH CROSS LANG EV Prasad M, 2009, PATTERN ANAL APPL, V12, P9, DOI 10.1007/s10044-007-0093-7 Rahman Md Mahmudur, 2012, Medical Content-Based Retrieval for Clinical Decision Support. Second MICCAI International Workshop, MCBR-CDS 2011. Revised Selected Papers, DOI 10.1007/978-3-642-28460-1_3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stougiannis A, 2010, CLEF NOTEBOOK PAPERS Vapnik VN, 1999, IEEE T NEURAL NETWOR, V10, P988, DOI 10.1109/72.788640 Wang W, 2006, T ASABE, V49, P1607 Zhou X., 2010, P 20 INT C REC PATT, P1590 NR 34 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 EI 1573-7721 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JUN PY 2015 VL 74 IS 11 BP 3651 EP 3674 DI 10.1007/s11042-013-1792-2 PG 24 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA CI6UQ UT WOS:000354898800003 ER PT J AU Pohl, D Bouchachia, A Hellwagner, H AF Pohl, Daniela Bouchachia, Abdelhamid Hellwagner, Hermann TI Social media for crisis management: clustering approaches for sub-event detection SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Social media; Sub-event detection; Clustering; Information search and retrieval; Crisis management ID MODEL AB Social media is getting increasingly important for crisis management, as it enables the public to provide information in different forms: text, image and video which can be valuable for crisis management. Such information is usually spatial and time-oriented, useful for understanding the emergency needs, performing decision making and supporting learning/training after the emergency. Due to the huge amount of data gathered during a crisis, automatic processing of the data is needed to support crisis management. One way of automating the process is to uncover sub-events (i.e., special hotspots) in the data collected from social media to enable better understanding of the crisis. We propose in the present paper clustering approaches for sub-event detection that operate on Flickr and YouTube data since multimedia data is of particular importance to understand the situation. Different clustering algorithms are assessed using the textual annotations (i.e., title, tags and description) and additional metadata information, like time and location. The empirical study shows in particular that social multimedia combined with clustering in the context of crisis management is worth using for detecting sub-events. It serves to integrate social media into crisis management without cumbersome manual monitoring. C1 [Pohl, Daniela; Hellwagner, Hermann] Alpen Adria Univ Klagenfurt, Inst Informat Technol ITEC, Multimedia Commun MMC, A-9020 Klagenfurt Am Worthersee, Austria. [Bouchachia, Abdelhamid] Bournemouth Univ, Sch Design Engn & Comp, Smart Technol Res Ctr, Fern Barrow Poole BH12 5BB, England. RP Pohl, D (reprint author), Alpen Adria Univ Klagenfurt, Inst Informat Technol ITEC, Multimedia Commun MMC, Univ Str 65-67, A-9020 Klagenfurt Am Worthersee, Austria. EM daniela@itec.uni-klu.ac.at; abouchachia@bournemouth.ac.uk; hermann.hellwagner@aau.at CR [Anonymous], 2012, BBC NEWS EUROPE [Anonymous], 2012, PUBLIC HEALTH EMERGE Becker H., 2010, P 3 ACM INT C WEB SE, P291, DOI 10.1145/1718487.1718524 Bergstrand F, 2009, PROCEEDINGS OF THE 6 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bouchachia A, 2006, FUZZY SET SYST, V157, P1733, DOI 10.1016/j.fss.2006.02.015 Choudhary A, 2012, COMMUN ACM, V55, P74, DOI 10.1145/2160718.2160736 Cover T, 2006, ELEMENTS OF INFORMAT DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Duda P, 2001, PATTERN CLASSIFICATI Fellbaum C, 1998, WORDNET AN ELECTRONI Fontugne R, 2011, P SPEC WORKSH INT DI, P5 Han DF, 2008, MULTIMED TOOLS APPL, V39, P169, DOI 10.1007/s11042-008-0203-6 Ireson N, 2009, 3 IEEE INT C DIG EC, P49 Jaffe A, 2006, P 8 ACM INT WORKSH M, P89, DOI 10.1145/1178677.1178692 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Larose D. T., 2005, DISCOVERING KNOWLEDG Liu S, 2008, PROCEEDINGS OF THE I Manning CD, 2008, INTRODUCTION TO INFO Marcus Adam, 2011, P 2011 ANN C HUM FAC, P227, DOI DOI 10.1145/1978942.1978975 Mathioudakis M., 2010, P 2010 INT C MAN DAT, P1155, DOI DOI 10.1145/1807167.180730 PETKOS G, 2012, P 2 ACM INT C MULT R, V23, P1 Petrovic S, 2010, THE 2010 ANNUAL CONF, P181 Pohl D, 2012, INTERNATIONAL CONFER Pohl D, 2012, FIRST INTERNATIONAL Rattenbury T., 2007, P 30 ANN INT ACM SIG, P103, DOI 10.1145/1277741.1277762 Rogstadius J, 2011, PROCEEDINGS CHI WORK ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 Sagun A, 2010, EFFICIENT DEV ICT TO, P95 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slaney M, 2011, IEEE MULTIMEDIA, V18, P12, DOI 10.1109/MMUL.2011.34 Strehl A., 2002, J MACHINE LEARNING R, V3, P583, DOI DOI 10.1162/153244303321897735 Terpstra T, 2012, PROCEEDINGS OF THE 9 Theodoridis S, 2006, PATTERN RECOGNITION, 3RD EDITION, P1 Tucker S, 2012, PROCEEDINGS OF THE 9 Vesanto J, 2000, TOOLMET 2000 SYMPOSI Vieweg S, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1079 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 Yates D, 2011, INT J INFORM MANAGE, V31, P6, DOI 10.1016/j.ijinfomgt.2010.10.001 Yin J, 2012, IEEE T IND IN PRESS, V99, P1 Zhou C, 2007, ACM T INFORM SYST, V25, P65 NR 42 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 EI 1573-7721 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JUN PY 2015 VL 74 IS 11 BP 3901 EP 3932 DI 10.1007/s11042-013-1804-2 PG 32 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA CI6UQ UT WOS:000354898800014 ER PT J AU Shoaib, M Daud, A Khiyal, MSH AF Shoaib, Muhammad Daud, Ali Khiyal, Malik Sikandar Hayat TI Improving Similarity Measures for Publications with Special Focus on Author Name Disambiguation SO ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING LA English DT Article DE Entity resolution; Bibliographic databases; Syntactic similarity; Vector space model ID CLASSIFICATION; ONTOLOGIES; EXTRACTION; MODEL AB In many real-life text mining applications such as clustering academic documents, citation matching and author name disambiguation (AND), similar publications are grouped together by exploiting similarity among them. Most of AND approaches, especially unsupervised ones, focus on either proposing new/alternate algorithms and/or using new/alternate sources of information. Researchers from digital library community pay least attention to similarity measures. They try ready-made alternate measures to estimate optimum similarity among publications. These ready-made measures may not provide real picture of similarity among publications. In this work, we propose four similarity measures specially designed for author names, co-authors and short and long text segments. Our proposed measures provide more realistic picture of similarity among publications than previous measures. Our proposed measures can be applied in many real-life scenarios where either name entities (not necessarily human names) or text documents or both are compared in pairwise fashion. We compare our measures with Jaccard coefficient and state-of-the-art cosine measure based on vector space model. Experiments on synthetic and real data show that our proposed measures are more logical and realistic than baseline methods. C1 [Shoaib, Muhammad; Daud, Ali] Int Islamic Univ, Fac Sci Appl, Dept Comp Sci & Software Engn, Islamabad, Pakistan. [Shoaib, Muhammad] COMSATS Inst Informat Technol, Fac Informat Technol, Dept Comp Sci, Sahiwal, Pakistan. [Khiyal, Malik Sikandar Hayat] Preston Univ, Fac Comp Sci, Islamabad, Pakistan. RP Shoaib, M (reprint author), Int Islamic Univ, Fac Sci Appl, Dept Comp Sci & Software Engn, Islamabad, Pakistan. EM shoaibishaaqiiu@yahoo.com CR Al-Rajebah NI, 2014, ARAB J SCI ENG, V39, P2749, DOI 10.1007/s13369-013-0791-y Al-Safadi L, 2013, ARAB J SCI ENG, V38, P2985, DOI 10.1007/s13369-012-0473-1 Aslam J. A., 2003, 26 INT ACM SIGIR C R Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI 10.1145/1217299.1217304 Bhattacharya I., 2006, SIAM C DAT MIN Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Branting L., 2003, INT C ART INT LAW Butt AS, 2014, ARAB J SCI ENG, V39, P1805, DOI 10.1007/s13369-013-0753-4 Cohen W., 2003, IJCAI 03 WORKSH INF Cota R. G., 2007, BRAZ S DAT BAS Culotta A., 2007, 6 INT WORKSH INF INT Daud A, 2010, FRONT COMPUT SCI CHI, V4, P280, DOI 10.1007/s11704-009-0062-y Fan X., 2011, ACM J DATA INF QUAL, V2, P1 Ferreira A. A., 2010, 10 ACM IEEE JOINT C Ferreira A. A., 2012, 12 ACM IEEE CS JOINT FERREIRA AA, 2012, ACM SIGMOD REC, V41, P15 Galvez C, 2007, J AM SOC INF SCI TEC, V58, P1960, DOI 10.1002/asi.20671 Ghareb AS, 2014, ARAB J SCI ENG, V39, P7807, DOI 10.1007/s13369-014-1304-3 Giles L., 2004, ACM IEEE JOINT C DIG Han H., 2005, ACM IEEE JOINT C DIG Han H., 2005, ACM S APPL COMP Hassell J., 2006, 5 INT SEM WEB C Hofmann T., 1999, 15 ANN C UNC ART INT Huang J., 2006, EUR C PRINC DAT MIN Kanani P., IJCAI Kang IS, 2009, INFORM PROCESS MANAG, V45, P84, DOI 10.1016/j.ipm.2008.06.006 Khan Hikmat Ullah, 2013, International Journal of Future Computer and Communication, V2, DOI 10.7763/IJFCC.2013.V2.229 Khan K, 2014, ARAB J SCI ENG, V39, P3681, DOI 10.1007/s13369-014-0990-1 Khan S, 2013, ARAB J SCI ENG, V38, P859, DOI 10.1007/s13369-012-0373-4 Kleb J., 2009, 4 INT C DIG INF MAN La L, 2014, ARAB J SCI ENG, V39, P3733, DOI 10.1007/s13369-014-1015-9 Lee D., 2005, 2 WORKSH INF QUAL IN Li C., 2013, ARAB J SCI ENG, V39, P181 Malin B., 2005, WORKSH LINK AN COUNT Mansouri D, 2014, ARAB J SCI ENG, V39, P1849, DOI 10.1007/s13369-013-0761-4 Oliveira J. W. A., 2005, S BRAS BANC DAD On B., 2005, ACM IEEE JOINT C DIG Pandit S., 2011, IEEE T NEURAL NETWOR, V2, P29 Park Y., 2008, 2 INT C UB INF MAN C Pereira D. A., 2009, ACM IEEE JOINT C DIG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahparast H, 2014, ARAB J SCI ENG, V39, P799, DOI 10.1007/s13369-013-0697-8 Shoaib M., 2009, INT C EM TECHN IEEE Shoaib M., INT ARAB J IN PRESS Shoaib M., 2014, J BASIC APPL SCI RES, V4, P215 Shu L., 2009, IEEE 25 INT C DAT EN Smalheiser N. R., 2009, ANNU REV INFORM SCI, V43, P1, DOI DOI 10.1002/ARIS.2009.1440430113 Soler JM, 2007, SCIENTOMETRICS, V72, P281, DOI 10.1007/s11192-007-1730-z Song D, 2013, J DATA INF QUALITY, V4, P7 Song M, 2010, KNOWL INF SYST, V25, P371, DOI 10.1007/s10115-009-0254-7 Song Y., 2007, ACM IEEE JOINT C DIG Tan Y. F., 2006, 6 AMC IEEE JOINT C D Tang J, 2012, IEEE T KNOWL DATA EN, V24, P975, DOI 10.1109/TKDE.2011.13 Torvik V. I., 2009, ACM T KNOWL DISCOV D, V3 Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] Treeratpituk P., 2009, 9 ACM IEEE JOINT C D Varelas G., 2005, 7 ACM INT WORKSH WEB Wan XJ, 2007, INFORM SCIENCES, V177, P3718, DOI 10.1016/j.ins.2007.02.045 Wang F, 2010, FRONT COMPUT SCI CHI, V4, P100, DOI 10.1007/s11704-009-0064-9 Wang X., 2012, IEEE INT C DAT MIN Yang K. H., 2008, EUR C RES ADV TECHN Yin X., 2007, INT C DAT ENG Yu XF, 2012, KNOWL INF SYST, V32, P415, DOI 10.1007/s10115-011-0455-8 Zahedi M, 2013, ARAB J SCI ENG, V38, P2095, DOI 10.1007/s13369-013-0569-2 Zhang D., 2007, ACM C INF KNOWL MAN NR 65 TC 0 Z9 0 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1319-8025 EI 2191-4281 J9 ARAB J SCI ENG JI Arab. J. Sci. Eng. PD JUN PY 2015 VL 40 IS 6 BP 1591 EP 1605 DI 10.1007/s13369-015-1636-7 PG 15 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA CH9ZR UT WOS:000354395800005 ER PT J AU Wang, FF Wolfram, D AF Wang, Feifei Wolfram, Dietmar TI Assessment of Journal Similarity Based on Citing Discipline Analysis SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE citation analysis; co-citation analysis ID PEARSONS CORRELATION-COEFFICIENT; CO-CLASSIFICATION ANALYSIS; AUTHOR COCITATION ANALYSIS; INFORMATION-SCIENCE; CITATION; COMMUNICATION; INDICATORS; RETRIEVAL; LIBRARY; WORDS AB This study compares the range of disciplines of citing journal articles to determine how closely related journals assigned to the same Web of Science research area are. The frequency distribution of disciplines by citing articles provides a signature for a cited journal that permits it to be compared with other journals using similarity comparison techniques. As an initial exploration, citing discipline data for 40 high-impact-factor journals assigned to the information science and library science category of the Web of Science were compared across 5 time periods. Similarity relationships were determined using multidimensional scaling and hierarchical cluster analysis to compare the outcomes produced by the proposed citing discipline and established cocitation methods. The maps and clustering outcomes reveal that a number of journals in allied areas of the information science and library science category may not be very closely related to each other or may not be appropriately situated in the category studied. The citing discipline similarity data resulted in similar outcomes with the cocitation data but with some notable differences. Because the citing discipline method relies on a citing perspective different from cocitations, it may provide a complementary way to compare journal similarity that is less labor intensive than cocitation analysis. C1 [Wang, Feifei] Beijing Univ Technol, Sch Econ & Management, Beijing 100124, Peoples R China. [Wolfram, Dietmar] Univ Wisconsin, Sch Informat Studies, Milwaukee, WI 53201 USA. RP Wang, FF (reprint author), Beijing Univ Technol, Sch Econ & Management, Pingleyuan 100, Beijing 100124, Peoples R China. EM feifeiwang@bjut.edu.cn; dwolfram@uwm.edu CR Ahlgren P, 2004, J AM SOC INF SCI TEC, V55, P843, DOI 10.1002/asi.20030 Ahlgren P, 2003, J AM SOC INF SCI TEC, V54, P550, DOI 10.1002/asi.10242 Ajiferuke I, 2010, J AM SOC INF SCI TEC, V61, P2086, DOI 10.1002/asi.21383 Bonnevie-Nebelong E, 2006, SCIENTOMETRICS, V66, P411, DOI 10.1007/s11192-006-0029-9 Bonnevie-Nebelong Ellen, 2006, J DOC, V62, P30 Boyack KW, 2010, J AM SOC INF SCI TEC, V61, P2389, DOI 10.1002/asi.21419 CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280 Ding Y, 2000, SCIENTOMETRICS, V47, P55, DOI 10.1023/A:1005665709109 Egghe L, 2009, J AM SOC INF SCI TEC, V60, P1027, DOI 10.1002/asi.21009 Glanzel W, 2003, SCIENTOMETRICS, V56, P357, DOI 10.1023/A:1022378804087 HINZE S, 1994, SCIENTOMETRICS, V29, P353, DOI 10.1007/BF02033445 Leydesdorff L, 2006, J AM SOC INF SCI TEC, V57, P1616, DOI 10.1002/asi.20335 Leydesdorff L., 2007, J AM SOC INFORM SCI, V59, P77 Leydesdorff L, 2008, J AM SOC INF SCI TEC, V59, P1810, DOI 10.1002/asi.20891 Leydesdorff L, 1997, J AM SOC INFORM SCI, V48, P418 Leydesdorff L, 2006, J AM SOC INF SCI TEC, V57, P601, DOI 10.1002/asi.20322 Leydesdorff L., 1993, SCIENTOMETRICS, V26, P133 Milojevic S, 2011, J AM SOC INF SCI TEC, V62, P1933, DOI 10.1002/asi.21602 Ni CQ, 2013, SCIENTOMETRICS, V94, P1161, DOI 10.1007/s11192-012-0849-8 Nisonger TE, 2005, COLL RES LIBR, V66, P341 Pudovkin AI, 2002, J AM SOC INF SCI TEC, V53, P1113, DOI 10.1002/asi.10153 Rafols I, 2009, J AM SOC INF SCI TEC, V60, P1823, DOI 10.1002/asi.21086 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seglen PO, 1997, ALLERGY, V52, P1050, DOI 10.1111/j.1398-9995.1997.tb00175.x SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 Spasser MA, 1997, SCIENTOMETRICS, V39, P77, DOI 10.1007/BF02457431 TIJSSEN RJW, 1992, RES POLICY, V21, P27, DOI 10.1016/0048-7333(92)90025-Y TODOROV R, 1990, SCIENTOMETRICS, V19, P35, DOI 10.1007/BF02130464 White HD, 2001, J AM SOC INF SCI TEC, V52, P87, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1542>3.3.CO;2-K White HD, 2000, ASIST MON SER, P475 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 Yan E, 2012, J INFORMETR, V6, P140, DOI 10.1016/j.joi.2011.10.001 NR 32 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD JUN PY 2015 VL 66 IS 6 BP 1189 EP 1198 DI 10.1002/asi.23241 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CI1BY UT WOS:000354476600008 ER PT J AU Sharef, NM Martin, T Kasmiran, KA Mustapha, A Sulaiman, MN Azmi-Murad, MA AF Sharef, Nurfadhlina Mohd Martin, Trevor Kasmiran, Khairul Azhar Mustapha, Aida Sulaiman, Md Nasir Azmi-Murad, Masrah Azrifah TI A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization SO SOFT COMPUTING LA English DT Article DE Text categorization; Text expression; Evolving fuzzy grammar; Machine learning; Incidents; Medical ID INFORMATION EXTRACTION; WIKIPEDIA KNOWLEDGE; SEMANTIC ANALYSIS; CLASSIFICATION; SYSTEM; FEATURES; MODEL; RULE AB Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Na < ve Bayes, support vector machine, -nearest neighbor, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while ImageCLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both categorization tasks, where a standard evaluation technique (i.e., recall, precision, and -measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task. C1 [Sharef, Nurfadhlina Mohd; Martin, Trevor; Kasmiran, Khairul Azhar; Mustapha, Aida; Sulaiman, Md Nasir; Azmi-Murad, Masrah Azrifah] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Serdang 43400, Selangor, Malaysia. RP Sharef, NM (reprint author), Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Serdang 43400, Selangor, Malaysia. EM fadhlinams81@gmail.com; trevor.martin@bris.ac.uk; kazhar@upm.edu.my; aida@upm.edu.my; nasir@upm.edu.my; masrah@upm.edu.my FU University of Putra Malaysia Research University Grant Scheme FX This project is part of the progress of a research grant, funded under the University of Putra Malaysia Research University Grant Scheme. CR Abulaish M, 2007, DATA KNOWL ENG, V61, P228, DOI 10.1016/j.datak.2006.06.007 Achananuparp P, 2008, LECT NOTES COMPUT SC, V5182, P305, DOI 10.1007/978-3-540-85836-2_29 Almeida V, 2011, P 3 INT ACM C WEB SC, P1 Al Zamil MGH, 2011, KNOWL-BASED SYST, V24, P58, DOI 10.1016/j.knosys.2010.07.005 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Apte C, 1998, P C AUT LEARN DISC W Baoli L, 2003, P INT C COMP PROC OR Bharati A, 2005, P 9 C COMP NAT LANG, P165, DOI 10.3115/1706543.1706572 Biebow B., 1999, LECT NOTES ARTIF INT, V1621, P49 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 Chew C, 2009, PLOS ONE, V5 Chiang DA, 2008, EXPERT SYST APPL, V35, P102, DOI 10.1016/j.eswa.2007.06.019 Chieu H L, 2002, P 19 INT C COMP LING, V1, P1, DOI 10.3115/1072228.1072253 Frasconi P, 2001, P 1 ACM IEEE CS JOIN, P11, DOI 10.1145/379437.379440 Fuller CM, 2011, EXPERT SYST APPL, V38, P8392, DOI 10.1016/j.eswa.2011.01.032 Gooch P, 2012, J BIOMED INFORM, V45, P901, DOI 10.1016/j.jbi.2012.02.012 Guo GD, 2006, SOFT COMPUT, V10, P423, DOI 10.1007/s00500-005-0503-y Guo Y, 2010, INFORM SCIENCES, V180, P613, DOI 10.1016/j.ins.2009.11.012 Han E.-H (S.), 2001, LECT NOTES COMPUTER, V2035, P53 Hu YH, 2006, INFORM PROCESS MANAG, V42, P1276, DOI 10.1016/j.ipm.2005.12.001 Hung SH, 2010, EXPERT SYST APPL, V37, P341, DOI 10.1016/j.eswa.2009.05.060 IJntema W, 2012, J WEB SEMANT, V15, P37, DOI 10.1016/j.websem.2012.01.002 Jiang CT, 2010, KNOWL-BASED SYST, V23, P302, DOI 10.1016/j.knosys.2009.11.010 Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Joachims T, 1998, MACH LEARN, P2 Johnson DE, 2002, IBM SYST J, V41, P428 Khoo CSG, 2008, LECT NOTES COMPUT SC, V4993, P676, DOI 10.1007/978-3-540-68636-1_82 Kiyavitskaya N, 2009, DATA KNOWL ENG, V68, P1470, DOI 10.1016/j.datak.2009.07.012 Kotsiantis S B, 2007, Informatica, V31 Lampos V, 2010, INFORM SYST Leite D, 2012, STUD FUZZ SOFT COMP, V271, P209 Li ZX, 2011, PATTERN RECOGN LETT, V32, P441, DOI 10.1016/j.patrec.2010.11.001 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Luo QM, 2011, EXPERT SYST APPL, V38, P12708, DOI 10.1016/j.eswa.2011.04.058 Martin T, 2008, P IEEE INT C FUZZ SY, P2224 Martin T, 2008, IEEE T FUZZY SYST, V16, P1425, DOI 10.1109/TFUZZ.2008.925920 Martinez-Gil J, 2014, ARTIF INTELL REV, V42, P935, DOI 10.1007/s10462-012-9349-8 Mikheev A, 1999, P 9 C EUR CHAPT ASS Nadeau D., 2007, LINGVISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD Pedrycz W, 2012, LEARNING NONSTATIONA, P57, DOI 10.1007/978-1-4419-8020-5 Pedrycz Witold, 2010, Evolving Systems, V1, DOI 10.1007/s12530-010-9002-1 Pestian J., 2010, BIOMEDICAL INFORM IN, V3, P19 Petasis G, 2004, LECT NOTES ARTIF INT, P3264 Preot D, 2012, P INT AAAI C WEBL SO, P4 Qiu Q, 2009, LECT NOTES COMPUT SC, V5678, P277 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 Sharef NM, 2010, TEXT FRAGMENT IDENTI Sharef NM, 2010, P WORLD C COMP INT B, P18 Sharef NM, 2011, P 3 SEM TECHN KNOWL, P75 Sharef NM, 2009, 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, P1221, DOI 10.1109/ISDA.2009.169 Soderland S, 1999, MACH LEARN, V34, P1 Stavrianou A, 2007, SIGMOD RECORD, V36, P23, DOI 10.1145/1324185.1324190 Sun AX, 2003, LECT NOTES COMPUT SC, V2665, P1 Todorovic BT, 2008, P 2008 9 S NEUR NETW, V1, P43 Torii M, 2011, INT J MED INFORM, V80, P56, DOI 10.1016/j.ijmedinf.2010.10.015 Uematsu S, 2009, P 11 INT C PARS TECH, P85, DOI 10.3115/1697236.1697254 Unold O, 2010, APPL SOFT COMPUT, V10, P44, DOI 10.1016/j.asoc.2009.06.006 Unold O, 2007, P 7 INT C HYBR INT S, P348 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wenyin L., 2010, INFORM SCI, V180, P4031 Xu Y, 2010, P 2 INT WORKSH INT S, V1, P1, DOI [10.1109/IWISA.2010.5473381, DOI 10.1109/IWISA.2010.5473381] Xue XB, 2009, IEEE T KNOWL DATA EN, V21, P428, DOI 10.1109/TKDE.2008.166 Yu B, 2008, KNOWL-BASED SYST, V21, P900, DOI 10.1016/j.knosys.2008.03.045 NR 69 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1432-7643 EI 1433-7479 J9 SOFT COMPUT JI Soft Comput. PD JUN PY 2015 VL 19 IS 6 SI SI BP 1701 EP 1714 DI 10.1007/s00500-014-1358-x PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA CI1JM UT WOS:000354500300019 ER PT J AU Xu, GD Wu, ZD Li, GL Chen, EH AF Xu, Guandong Wu, Zongda Li, Guiling Chen, Enhong TI Improving contextual advertising matching by using Wikipedia thesaurus knowledge SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Wikipedia; Contextual advertising; Similarity measure AB As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial ads within the content of a Web page, to provide a better user experience and as a result increase the user's ad-click rate. However, due to the intrinsic problems of homonymy and polysemy, the low intersection of keywords, and a lack of sufficient semantics, traditional keyword matching techniques are not able to effectively handle contextual matching and retrieve relevant ads for the user, resulting in an unsatisfactory performance in ad selection. In this paper, we introduce a new contextual advertising approach to overcome these problems, which uses Wikipedia thesaurus knowledge to enrich the semantic expression of a target page (or an ad). First, we map each page into a keyword vector, upon which two additional feature vectors, the Wikipedia concept and category vector derived from the Wikipedia thesaurus structure, are then constructed. Second, to determine the relevant ads for a given page, we propose a linear similarity fusion mechanism, which combines the above three feature vectors in a unified manner. Last, we validate our approach using a set of real ads, real pages along with the external Wikipedia thesaurus. The experimental results show that our approach outperforms the conventional contextual advertising matching approaches and can substantially improve the performance of ad selection. C1 [Xu, Guandong] Univ Technol Sydney, Fac Engn & IT, Sydney, NSW 2007, Australia. [Wu, Zongda] Wenzhou Univ, Oujiang Coll, Wenzhou, Zhejiang, Peoples R China. [Li, Guiling] China Univ Geosci, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China. [Chen, Enhong] Univ Sci & Technol China, Sch Comp Sci, Hefei 230026, Peoples R China. RP Wu, ZD (reprint author), Wenzhou Univ, Oujiang Coll, Wenzhou, Zhejiang, Peoples R China. EM zongda1983@163.com FU National Natural Science Foundation of China [61202171, 61272018]; China Postdoctoral Science Foundation [2013T60623, 2012M521251]; Zhejiang Provincial Natural Science Foundation of China [LY12F01016, LQ13F020009]; Provincial Natural Science Foundation of Hubei [2013CFB415]; Fundamental Research Funds for the Central Universities [CUGL120281] FX We thank anonymous reviewers for their very useful comments and suggestions. This work is supported by grants from the National Natural Science Foundation of China (Nos. 61202171 and 61272018), the China Postdoctoral Science Foundation funded projects (Nos. 2013T60623 and 2012M521251), the Zhejiang Provincial Natural Science Foundation of China (Nos. LY12F01016 and LQ13F020009), the Provincial Natural Science Foundation of Hubei (No. 2013CFB415) and the Fundamental Research Funds for the Central Universities (No. CUGL120281). CR Anagnostopoulous A., 2011, ACM T INTEL SYST TEC, V3, P14 Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 [Anonymous], ARTIF INTELL Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906 Ciaramita M, 2008, J ELECTRON COMMER RE, V9, P1 Evgeniy G, 2007, P 20 AAAI INT C ART Evgeniy G, 2009, J ARTIF INTELL RES, V34, P443 Fan TK, 2011, EXPERT SYST APPL, V38, P1777, DOI 10.1016/j.eswa.2010.07.105 Fan TK, 2010, KNOWL INF SYST, V23, P321, DOI 10.1007/s10115-009-0222-2 Gupta S, 2005, WORLD WIDE WEB, V8, P179, DOI 10.1007/s11280-004-4873-3 Gupta S, 2009, ESWA, V36, P10 Hu J, 2009, P 18 INT C WORLD WID, P471, DOI 10.1145/1526709.1526773 Hu Jian, 2008, P 31 ANN INT ACM SIG, P179, DOI 10.1145/1390334.1390367 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Lacerda A., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148265 Mei T, 2011, P IEEE, V98, P1416 Mei T, 2008, P 16 ACM INT C MULT, P439, DOI 10.1145/1459359.1459418 Milne D., 2006, P 2006 IEEE WIC ACM, P442 Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 Pak AN, 2011, P 4 INT C HUM LANG T, P533 Pak AN, 2010, WORLD WIDE WEB, V13, P251, DOI 10.1007/s11280-010-0084-2 Papadopoulos S, 2009, LECT NOTES COMPUT SC, V5478, P216, DOI 10.1007/978-3-642-00958-7_21 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang C, 2002, P 8 AM C INF SYST, P1143 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686 Wu ZD, 2011, P 20 ACM INT C INF K, P2105 Wu ZD, 2012, COMPUT J, V55, P277, DOI 10.1093/comjnl/bxr056 Yih Wen-tau, 2006, P 15 INT C WORLD WID, P213, DOI 10.1145/1135777.1135813 Zhang Y, 2004, P 10 INT C THEOR MET, P4 NR 32 TC 0 Z9 0 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 EI 0219-3116 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD JUN PY 2015 VL 43 IS 3 BP 599 EP 631 DI 10.1007/s10115-014-0745-z PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA CH7ED UT WOS:000354197500005 ER PT J AU Luo, ZC Osborne, M Wang, T AF Luo, Zhunchen Osborne, Miles Wang, Ting TI An effective approach to tweets opinion retrieval SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE Opinion retrieval; Twitter; Learning to rank; Social media AB Opinion retrieval deals with finding relevant documents that express either a negative or positive opinion about some topic. Social Networks such as Twitter, where people routinely post opinions about almost any topic, are rich environments for opinions. However, spam and wildly varying documents makes opinion retrieval within Twitter challenging. Here we demonstrate how we can exploit social and structural textual information of Tweets and improve Twitter-based opinion retrieval. In particular, within a learning-to-rank technique, we explore the question of whether aspects of an author (such as the number of friends they have), information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that social features can improve retrieval performance. Retrieval using a novel unsupervised opinionatedness feature achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval. Finally, we further verify the effectiveness of our approach for opinion retrieval in re-tagged TREC Tweets2011 corpus. C1 [Luo, Zhunchen; Wang, Ting] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. [Osborne, Miles] Univ Edinburgh, Sch Informat, Edinburgh EH8 9AB, Midlothian, Scotland. RP Luo, ZC (reprint author), Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. EM zhunchenluo@nudt.edu.cn; miles@inf.ed.ac.uk; tingwang@nudt.edu.cn FU National Natural Science Foundation of China [61170156, 61202337, 60933005]; CSC scholarship FX We would like to thank Victor Lavrenko for his comments and Bo Dai, Ming Lei, Zhengshuai Lin, Xianling Mao, Hongguang Ren, He Wang, Chenkun Wu and Xianglilan Zhang for tagging the data. The research is supported by the National Natural Science Foundation of China (Grant No. 61170156, 61202337 and 60933005) and a CSC scholarship. CR Akritidis L., 2013, WORLD WIDE WEB, P1 Amati G, 2008, LECT NOTES COMPUT SC, V4956, P89 Barbosa, 2010, P 23 INT C COMP LING, P36 Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007 Duan Y., 2010, P 23 INT C COMP LING, P295 Efron M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P787 Eguchi K, 2006, P 2006 C EMP METH NA, P345, DOI 10.3115/1610075.1610124 GERANI S, 2009, P 31 EUR C IR RES AD, V5478, P313, DOI DOI 10.1007/978-3-642-00958-7 Gerani S, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P403 Gerani S, 2011, LECT NOTES COMPUT SC, V6611, P747, DOI 10.1007/978-3-642-20161-5_85 Gerani S, 2012, ACM T INFORM SYST, V30, DOI 10.1145/2382438.2382445 Go A., 2009, PROCESSING, P1 Gouws S., 2011, P WORKSH LANG SOC ME, P20 He B., 2008, P 17 ACM C INF KNOWL, P1063, DOI 10.1145/1458082.1458223 Huang X., 2009, P 18 ACM C INF KNOWL, P947, DOI 10.1145/1645953.1646075 Jansen BJ, 2009, J AM SOC INF SCI TEC, V60, P2169, DOI 10.1002/asi.21149 Jiang L., 2011, P 49 ANN M ASS COMP, V1, P151 Jijkoun V., 2010, P 48 ANN M ASS COMP, P585 Korenek P., 2013, WORLD WIDE WEB, P1 Li B., 2010, P 48 ANN M ASS COMP, P1367 Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Luo Z., 2012, AAAI 12 Luo Z., 2012, 6 INT AAAI C WEBL SO Macdonald C., 2007, TREC Manning C.D., 2008, INTRO INFORM RETRIEV Massoudi K, 2011, LECT NOTES COMPUT SC, V6611, P362, DOI 10.1007/978-3-642-20161-5_36 Metzler D., 2011, TREC Na SH, 2009, LECT NOTES COMPUT SC, V5478, P734 Naveed N., 2011, P 20 ACM INT C INF K, P183 O'Connor B., 2010, ICWSM Orimaye S. O., 2012, WORLD WIDE WEB, P1 Ounis I., 2011, TREC Ounis I., 2008, TREC Ounis I., 2006, TREC Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seki K., 2009, P ACM INT C WEB SEAR, P25, DOI [10.1145/1498759.1498805, DOI 10.1145/1498759.1498805] Tsur Oren, 2010, P 23 INT C COMP LING, P241 Zhang M., 2008, P 31 ANN INT ACM SIG, P411, DOI 10.1145/1390334.1390405 Zhang WS, 2007, PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON AGRICULTURE ENGINEERING, P831, DOI 10.1145/1321440.1321555 NR 40 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD MAY PY 2015 VL 18 IS 3 BP 545 EP 566 DI 10.1007/s11280-013-0268-7 PG 22 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA CH9WG UT WOS:000354386500003 ER PT J AU Cao, QM Guo, Q Wang, YL Wu, XH AF Cao Qimin Guo Qiao Wang Yongliang Wu Xianghua TI Text clustering using VSM with feature clusters SO NEURAL COMPUTING & APPLICATIONS LA English DT Article DE Text clustering; Feature clusters; Distributed representation; FC-VSM; Non-contiguous phrases ID MODEL AB Representation of documents is the basis of clustering systems. In addition, non-contiguous phrases appear more and more frequent in the text in the Web 2.0 age, and these phrases can affect the result of text clustering. In order to improve the quality of text clustering, this paper proposed a feature cluster-based vector space model (FC-VSM) which used the text feature clusters co-occurrence matrix to represent document and proposed to identify non-contiguous phrases in the text preprocessing stage. Our method can reduce dimension of features compared with the traditional VSM-based model. It identified non-contiguous phrases, used distributed representation of features, and implements feature clusters. Despite their simplicity, our methods are surprisingly effective and can improve the accuracy of clustering significantly which is shown in experimental results. C1 [Cao Qimin; Guo Qiao; Wang Yongliang; Wu Xianghua] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China. RP Cao, QM (reprint author), Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China. EM caoqiminisbest@163.com CR Bengio Y, 2003, J MACH LEARN RES, V3, P1137, DOI 10.1162/153244303322533223 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Collobert R, 2011, J MACH LEARN RES, V12, P2493 Collobert R., 2008, ICML, V307, P160, DOI DOI 10.1145/1390156.1390177 Doucet A., 2004, P 42 ANN M ASS COMP, P88, DOI 10.3115/1613186.1613198 Grabmeier J, 2002, DATA MIN KNOWL DISC, V6, P303, DOI 10.1023/A:1016308404627 Huang E.H., 2012, ANN M ASS COMP LING, V1, P873 Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9 Meyer CD, 2012, SIAM J MATRIX ANAL A, V33, P1214, DOI 10.1137/100804395 Mikolov T, 2012, THESIS BRNO U TECHNO Mikolov T., 2013, EFFICIENT ESTIMATION Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045 Mikolov T., 2013, ADV NEURAL INF PROCE, V26, P3111 Mnih A, 2008, ADV NEURAL INF PROCE, V21, P1081 Morin F, 2005, P INT WORKSH ART INT, P246 RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi Z, 2002, KNOWLEDGE DISCOVERY Simard Michel, 2005, P HUM LANG TECHN C C, P755, DOI DOI 10.3115/1220575.1220670 Socher R., 2011, P 28 INT C MACH LEAR, P129 Turian Joseph, 2010, P 48 ANN M ASS COMP, P384 NR 23 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0941-0643 EI 1433-3058 J9 NEURAL COMPUT APPL JI Neural Comput. Appl. PD MAY PY 2015 VL 26 IS 4 BP 995 EP 1003 DI 10.1007/s00521-014-1792-9 PG 9 WC Computer Science, Artificial Intelligence SC Computer Science GA CG5SX UT WOS:000353356000021 ER PT J AU Ashton, T Evangelopoulos, N Prybutok, VR AF Ashton, Triss Evangelopoulos, Nicholas Prybutok, Victor R. TI Quantitative quality control from qualitative data: control charts with latent semantic analysis SO QUALITY & QUANTITY LA English DT Article DE Analysis of text data; Big data; Control charts; Customer satisfaction; Latent semantic analysis (LSA); Statistical process control (SPC); Text mining ID MACHINE AB Large quantities of data, often referred to as big data, are now held by companies. This big data includes statements of customer opinion regarding product or service quality in an unstructured textual form. While many tools exist to extract meaningful information from big data, automation tools do not exist to monitor the ongoing conceptual content of that data. We use latent semantic analysis to extract concept factors related to service quality categories. Customer comments found in the data that express dissatisfaction are then considered as representing a non-conforming observation in a process. Once factors are extracted, proportions of nonconformities for service quality failure categories are plotted on a control chart. The results are easily interpreted and the approach allows for the quantitative evaluation of customer acceptance of system process improvement initiatives. C1 [Ashton, Triss] Univ Texas Pan Amer, Edinburg, TX 78539 USA. [Evangelopoulos, Nicholas; Prybutok, Victor R.] Univ N Texas, Denton, TX 76203 USA. RP Ashton, T (reprint author), Univ Texas Pan Amer, 1201 West Univ, Edinburg, TX 78539 USA. EM ashtonta@utpa.edu CR Arora S, 2012, ANN IEEE SYMP FOUND, P1, DOI 10.1109/FOCS.2012.49 Ashton Triss, 2013, International Journal of Services and Standards, V8 Ashton T, 2014, QUAL QUANT, V48, P2277, DOI 10.1007/s11135-013-9891-8 Bollegala D, 2013, IEEE T KNOWL DATA EN, V25, P1719, DOI 10.1109/TKDE.2012.103 Bradford R., 2008, CIKM 08 P 17 ACM C I, P153 Cocco M, 2013, QUAL QUANT, V47, P3135, DOI 10.1007/s11135-012-9708-1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Duncan A., 1986, QUALITY CONTROL IND, V5th, P436 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Evangelopoulos NE, 2013, WIRES COGN SCI, V4, P683, DOI 10.1002/wcs.1254 Evans J., 2008, MANAGING QUALITY PER, V7th, P462 Feldman R, 2013, COMMUN ACM, V56, P82, DOI 10.1145/2436256.2436274 Fielding J, 2013, QUAL QUANT, V47, P3261, DOI 10.1007/s11135-012-9716-1 Heider Fritz, 1958, PSYCHOL INTERPERSONA Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Hu X., 2007, HDB LATENT SEMANTIC, P401 Landauer T., 2007, HDB LATENT SEMANTIC, P1 Liu B., 2012, SENTIMENT ANAL OPINI Lo S, 2008, EXPERT SYST APPL, V34, P603, DOI 10.1016/j.eswa.2006.09.026 LUCAS JM, 1990, TECHNOMETRICS, V32, P1, DOI 10.2307/1269835 Manning C., 2008, INTRO INFORM RETRIEV, P403 Montgomery D., 1996, INTRO STAT QUALITY C, P252 Russom P., 2011, BIG DATA ANAL SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scharkow M, 2013, QUAL QUANT, V47, P761, DOI 10.1007/s11135-011-9545-7 Schott J., 2005, MATRIX ANAL STAT, P221 Shehata S., 2013, IEEE T KNOWL DATA EN, V22, P1360 Sidorova A, 2008, MIS QUART, V32, P467 Stock J., 2001, STRATEGIC LOGISTICS, V4th, P9 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 31 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0033-5177 EI 1573-7845 J9 QUAL QUANT JI Qual. Quant. PD MAY PY 2015 VL 49 IS 3 BP 1081 EP 1099 DI 10.1007/s11135-014-0036-5 PG 19 WC Social Sciences, Interdisciplinary; Statistics & Probability SC Social Sciences - Other Topics; Mathematics GA CG3VX UT WOS:000353208800017 ER PT J AU Pang, GS Jin, HD Jiang, SY AF Pang, Guansong Jin, Huidong Jiang, Shengyi TI CenKNN: a scalable and effective text classifier SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article DE Text classification; KNN; Centroid; Dimension reduction; Imbalanced classification ID K-NEAREST-NEIGHBOR; SUPPORT VECTOR MACHINES; WEIGHTING METHODS; CATEGORIZATION; SEARCH; MODEL; KNN; ALGORITHMS; TREE AB A big challenge in text classification is to perform classification on a large-scale and high-dimensional text corpus in the presence of imbalanced class distributions and a large number of irrelevant or noisy term features. A number of techniques have been proposed to handle this challenge with varying degrees of success. In this paper, by combining the strengths of two widely used text classification techniques, K-Nearest-Neighbor (KNN) and centroid based (Centroid) classifiers, we propose a scalable and effective flat classifier, called CenKNN, to cope with this challenge. CenKNN projects high-dimensional (often hundreds of thousands) documents into a low-dimensional (normally a few dozen) space spanned by class centroids, and then uses the -d tree structure to find nearest neighbors efficiently. Due to the strong representation power of class centroids, CenKNN overcomes two issues related to existing KNN text classifiers, i.e., sensitivity to imbalanced class distributions and irrelevant or noisy term features. By working on projected low-dimensional data, CenKNN substantially reduces the expensive computation time in KNN. CenKNN also works better than Centroid since it uses all the class centroids to define similarity and works well on complex data, i.e., non-linearly separable data and data with local patterns within each class. A series of experiments on both English and Chinese, benchmark and synthetic corpora demonstrates that although CenKNN works on a significantly lower-dimensional space, it performs substantially better than KNN and its five variants, and existing scalable classifiers, including Centroid and Rocchio. CenKNN is also empirically preferable to another well-known classifier, support vector machines, on highly imbalanced corpora with a small number of classes. C1 [Pang, Guansong] Monash Univ, Clayton Sch Informat Technol, Melbourne, Vic 3800, Australia. [Jin, Huidong] CSIRO Computat Informat, Canberra, ACT 2601, Australia. [Jiang, Shengyi] Guangdong Univ Foreign Studies, Sch Informat, Guangzhou 510006, Guangdong, Peoples R China. RP Pang, GS (reprint author), Monash Univ, Clayton Sch Informat Technol, Melbourne, Vic 3800, Australia. EM Guansong.Pang@monash.edu; Warren.Jin@csiro.au; jiangshengyi@163.com RI jin, Huidong/B-3187-2009 OI jin, Huidong/0000-0002-3925-0256 FU National Natural Science Foundation of China [61070061, 61202271]; National Social Science Foundation of China [13CGL130] FX We thank the anonymous reviewers whose constructive comments helped improve the paper substantially. We also wish to thank Dr. Alexander B. Zwart for his helpful comments on refining this paper. This paper's revision was partly conducted when Guansong Pang was a visiting student in the Web Sciences Center at the University of Electronic Science and Technology of China. He would like to thank his supervisor Prof. Mingsheng Shang in the Web Sciences Center for the support on this work. This work was supported in part by the National Natural Science Foundation of China under Grant No. 61070061 and No. 61202271, and by the National Social Science Foundation of China under Grant No. 13CGL130. CR Achlioptas D, 2003, J COMPUT SYST SCI, V66, P671, DOI 10.1016/S0022-0000(03)00025-4 Aggarwal CC, 2012, SURVEY TEXT CLASSIFI Batista G.E., 2004, SIGKDD EXPLORATIONS, V1, P20, DOI DOI 10.1145/1007730.1007735 BENTLEY JL, 1975, COMMUN ACM, V18, P509, DOI 10.1145/361002.361007 Bingham Ella, 2001, P 7 ACM SIGKDD INT C, P245, DOI 10.1145/502512.502546 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chen YS, 2007, PATTERN RECOGN, V40, P360, DOI 10.1016/j.patcog.2005.08.016 Cunningham P, 2007, UCDCSI20074 Du L, 2010, MACH LEARN, V81, P5, DOI 10.1007/s10994-010-5197-4 Du L, 2012, KNOWL INF SYST, V31, P475, DOI 10.1007/s10115-011-0425-1 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Guan H., 2009, P 18 INT C WORLD WID, P201, DOI 10.1145/1526709.1526737 Guo GD, 2006, SOFT COMPUT, V10, P423, DOI 10.1007/s00500-005-0503-y Han EH, 2000, PRINCIPLES DATA MINI, P116 Han E.-H (S.), 2001, LECT NOTES COMPUTER, V2035, P53 Han X, 2012, 3 PASCAL CHALLENGE L He HB, 2009, IEEE T KNOWL DATA EN, V21, P1263, DOI 10.1109/TKDE.2008.239 Jagadish HV, 2005, ACM T DATABASE SYST, V30, P364, DOI 10.1145/1071610.1071612 Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Joachims T, 1996, P 14 INT C MACH LEAR, P143 Joachims T., 1998, P ECML 98 10 EUR C M, V1398, P137 Joachims Thorsten, 2001, P 24 ANN INT ACM SIG, P128, DOI 10.1145/383952.383974 Katayama N, 1997, ACM SIGMOD RECORD, V26, P369, DOI 10.1145/253262.253347 Kim H, 2005, J MACH LEARN RES, V6, P37 Kosmopoulos A, 2010, ACM SIGIR FORUM, V44, P23 Lam W, 2003, IEEE T PATTERN ANAL, V25, P628 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Lin J, 2003, P SDM 2003 WORKSH TE Liu T, 2004, P 4 IEEE INT C DAT M, P162 Mani I, 2003, P ICML 2003 WORKSH L Manning C.D., 2008, INTRO INFORM RETRIEV Miao Y., 2009, 1 PASCAL CHALLENGE L Moore AW, 1990, THESIS U CAMBRIDGE Pang G, 2013, ADV DATA MINING APPL Pang GS, 2013, INFORM PROCESS MANAG, V49, P576, DOI 10.1016/j.ipm.2012.10.003 Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sun JT, 2004, P ICDM 2004, P535 Sun YM, 2009, INT J PATTERN RECOGN, V23, P687 Tan SB, 2005, EXPERT SYST APPL, V28, P667, DOI 10.1016/j.eswa.2004.12.023 Tan SB, 2007, LECT NOTES ARTIF INT, V4702, P581 Tan SB, 2006, EXPERT SYST APPL, V30, P290, DOI 10.1016/j.eswa.2005.07.019 Tang L, 2005, P 5 IEEE INT C DAT M, P781 Vilalta R, 2003, P 3 IEEE INT C DAT M Wan CH, 2012, EXPERT SYST APPL, V39, P11880, DOI 10.1016/j.eswa.2012.02.068 Wang X, 2011, 2 PASCAL CHALLENGE L Wang XL, 2014, IEEE T KNOWL DATA EN, V26, P500, DOI 10.1109/TKDE.2013.30 Wettschereck D, 1997, ARTIF INTELL REV, V11, P273, DOI 10.1023/A:1006593614256 Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2 Yang HQ, 2009, LECT NOTES COMPUT SC, V5507, P53 Yang Y., 1997, ICML, V97, P412 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y, 2000, P 23 ANN INT ACM SIG, P65, DOI 10.1145/345508.345550 YANG Y., 1994, SIGIR, V94, P13 Zhang ML, 2007, PATTERN RECOGN, V40, P2038, DOI 10.1016/j.patcog.2006.12.019 NR 56 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1384-5810 EI 1573-756X J9 DATA MIN KNOWL DISC JI Data Min. Knowl. Discov. PD MAY PY 2015 VL 29 IS 3 BP 593 EP 625 DI 10.1007/s10618-014-0358-x PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA CF6BY UT WOS:000352642800002 ER PT J AU Bharti, KK Singh, PK AF Bharti, Kusum Kumari Singh, Pramod Kumar TI Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Text clustering; Feature selection; Feature extraction; Term variance; Document frequency; Principal component analysis ID SUPPORT VECTOR MACHINES; GENETIC ALGORITHM; COMPONENT ANALYSIS; CATEGORIZATION; OPTIMIZATION; INFORMATION; SPACE AB High dimensionality of the feature space is one of the major concerns owing to computational complexity and accuracy consideration in the text clustering. Therefore, various dimension reduction methods have been introduced in the literature to select an informative subset (or sublist) of features. As each dimension reduction method uses a different strategy (aspect) to select a subset of features, it results in different feature sublists for the same dataset. Hence, a hybrid approach, which encompasses different aspects of feature relevance altogether for feature subset selection, receives considerable attention. Traditionally, union or intersection is used to merge feature sublists selected with different methods. The union approach selects all features and the intersection approach selects only common features from considered features sublists, which leads to increase the total number of features and loses some important features, respectively. Therefore, to take the advantage of one method and lessen the drawbacks of other, a novel integration approach namely modified union is proposed. This approach applies union on selected top ranked features and applies intersection on remaining features sublists. Hence, it ensures selection of top ranked as well as common features without increasing dimensions in the feature space much. In this study, feature selection methods term variance (TV) and document frequency (DF) are used for features' relevance score computation. Next, a feature extraction method principal component analysis (PCA) is applied to further reduce dimensions in the feature space without losing much information. The effectiveness of the proposed method is tested on three benchmark datasets namely Reuters-21,578, Classic4, and WebKB. The obtained results are compared with TV, DF, and variants of the proposed hybrid dimension reduction method. The experimental studies clearly demonstrate that our proposed method improves clustering accuracy compared to the competitive methods. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Bharti, Kusum Kumari; Singh, Pramod Kumar] ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & DataMin Res Lab, Gwalior, Madhya Pradesh, India. RP Bharti, KK (reprint author), ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & DataMin Res Lab, Morena Link Rd, Gwalior, Madhya Pradesh, India. EM kkusum.bharti@gmail.com; pksingh@iiitm.ac.in CR Akadi A., 2011, KNOWL INF SYST, V26, P487 Arauzo-Azofra A, 2011, EXPERT SYST APPL, V38, P8170, DOI 10.1016/j.eswa.2010.12.160 Bharti KK, 2014, J COMPUT SCI-NETH, V5, P156, DOI 10.1016/j.jocs.2013.11.007 Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 Bradley P.S., 1998, P 15 INT C MACH LEAR, V98, P82 Buckley C., 1995, P 4 TEXT RETR C TREC, P25 Burges CJC, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P59, DOI 10.1007/0-387-25465-X_4 COMON P, 1994, SIGNAL PROCESS, V36, P287, DOI 10.1016/0165-1684(94)90029-9 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DEERWESTER S, 1988, P ASIS ANNU MEET, V25, P36 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Ferreira AJ, 2012, PATTERN RECOGN LETT, V33, P1794, DOI 10.1016/j.patrec.2012.05.019 Figueiredo F, 2011, INFORM SYST, V36, P843, DOI 10.1016/j.is.2011.02.002 Hsu HH, 2011, EXPERT SYST APPL, V38, P8144, DOI 10.1016/j.eswa.2010.12.156 Huang S., 2011, 2011 7 INT C NAT LAN, P280 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Kabir MM, 2012, EXPERT SYST APPL, V39, P3747, DOI 10.1016/j.eswa.2011.09.073 Kira K., 1992, P 10 NAT C ART INT, P129 Koller D., 1997, P 14 INT C MACH LEAR, P170 Kononerko I., 1994, P EUR C MACH LEARN, P171 Kriegel HP, 2011, WIRES DATA MIN KNOWL, V1, P231, DOI 10.1002/widm.30 Kruskal J.B., 1978, SAGE U PAPER SERIES, V07-011 Li CH, 2012, EXPERT SYST APPL, V39, P765, DOI 10.1016/j.eswa.2011.07.070 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 Liu LY, 2005, Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'05), P597 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Maldonado S, 2009, INFORM SCIENCES, V179, P2208, DOI 10.1016/j.ins.2009.02.014 Meena MJ, 2012, EXPERT SYST APPL, V39, P5861, DOI 10.1016/j.eswa.2011.11.081 Meng JN, 2011, COMPUT MATH APPL, V62, P2793, DOI 10.1016/j.camwa.2011.07.045 Mengle SSR, 2009, J AM SOC INF SCI TEC, V60, P1037, DOI 10.1002/asi.21023 Miranda J, 2005, LECT NOTES COMPUT SC, V3776, P188 Pantel P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Pearson K, 1901, PHILOS MAG, V2, P559 Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Reed J. W., 2006, 5 INT C MACH LEARN A, P258 Robertson S. E., 1999, TREC8, V8, P151 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 Sahu B, 2012, PROCEDIA ENGINEER, V38, P27, DOI 10.1016/j.proeng.2012.06.005 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 Smith L. I., 2002, TUTORIAL PRINCIPAL C, V51, P52 Song W, 2009, COMPUT MATH APPL, V57, P1901, DOI 10.1016/j.camwa.2008.10.010 Tenenhaus M, 2005, COMPUT STAT DATA AN, V48, P159, DOI 10.1016/j.csda.2004.03.005 Tsai CF, 2010, DECIS SUPPORT SYST, V50, P258, DOI 10.1016/j.dss.2010.08.028 Uguz H, 2011, KNOWL-BASED SYST, V24, P1024, DOI 10.1016/j.knosys.2011.04.014 Uguz H., 2012, KNOWL-BASED SYST, V24, P1024 Unler A, 2011, INFORM SCIENCES, V181, P4625, DOI 10.1016/j.ins.2010.05.037 Wang XC, 2003, PATTERN RECOGN, V36, P2429, DOI 10.1016/S0031-3203(03)00044-X Weston J., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753751 Wu YL, 2011, EXPERT SYST APPL, V38, P2727, DOI 10.1016/j.eswa.2010.08.062 Yan J, 2011, IEEE T KNOWL DATA EN, V23, P1103, DOI 10.1109/TKDE.2010.34 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Zamir O., 1997, P 3 INT C KNOWL DISC, V97, P287 Zhang Y., 2008, IEEE 7 INT C BIOINF, P127 NR 56 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD APR 15 PY 2015 VL 42 IS 6 BP 3105 EP 3114 DI 10.1016/j.eswa.2014.11.038 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA CA9UY UT WOS:000349271500023 ER PT J AU Yang, Z Gao, KM Fan, KF Lai, YX AF Yang, Zhen Gao, Kaiming Fan, Kefeng Lai, Yingxu TI Sensational Headline Identification By Normalized Cross Entropy-Based Metric SO COMPUTER JOURNAL LA English DT Article DE multimedia social networks; sensational headline; normalized cross entropy ID STATISTICAL KEYWORD DETECTION; EXTRACTION AB Nowadays multimedia social networks are fueled by sensational coverage of sex, violence and crime. In this paper, we provide a normalized cross entropy metric to determine whether a headline is a sensational headline or not by the literal consistency between the headline and its corresponding document. Experiments on a Chinese data set show that the traditional relevancy measurements-vector cosine, relative entropy, likelihood and cross entropy-suffer from strong dependence on text length and are unable to effectively identify sensational headline. The experimental results on both Chinese data sets and English data sets show that our metric can cover the positive effects of high-frequency words and overcome the negative effects of the lengths of the title and the document. C1 [Yang, Zhen; Gao, Kaiming; Lai, Yingxu] Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China. [Fan, Kefeng] China Elect Standardizat Inst, Beijing 100007, Peoples R China. RP Yang, Z (reprint author), Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China. EM yangzhen@bjut.edu.cn FU National Natural Science Foundation of China [61001178]; Beijing Natural Science Foundation [4102012]; National Soft Science Research Program [2010GXQ5D317]; National Information Security 242 Project of China [2014A104]; Beijing Excellent Talent Development Foundation; Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions [CITTCD201404052]; Scientific Research Common Program of Beijing Municipal Commission of Education [KM201210005024]; Beijing Key Laboratory of Trusted Computing FX This work was partially supported by the National Natural Science Foundation of China (No. 61001178), the Beijing Natural Science Foundation (No. 4102012), the National Soft Science Research Program (No. 2010GXQ5D317), the National Information Security 242 Project of China (No. 2014A104), the Beijing Excellent Talent Development Foundation, the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No. CIT&TCD201404052), the Scientific Research Common Program of Beijing Municipal Commission of Education (No. KM201210005024), and the Open Research Fund of Beijing Key Laboratory of Trusted Computing. CR Bun K.K., 2002, P 3 INT C WEB INF SY, P73 Carretero-Campos C, 2013, PHYSICA A, V392, P1481, DOI 10.1016/j.physa.2012.11.052 Chen KY, 2007, IEEE T KNOWL DATA EN, V19, P1016, DOI 10.1109/TKDE.2007.1040 Fang H, 2011, ACM T INFORM SYST, V29, DOI 10.1145/1961209.1961210 Fang H., 2004, P 27 ANN INT ACM SIG, P49, DOI 10.1145/1008992.1009004 Herrera JP, 2008, EUR PHYS J B, V63, P135, DOI 10.1140/epjb/e2008-00206-x Jiaul H., 2013, P 36 ANN INT ACM SIG, P343 Jin L., 2013, J MONTHLY, V2, P65 Laerty J., 2001, P 24 INT ACM SIGIR C, P111 LUHN HP, 1958, IBM J RES DEV, V2, P314 Mizuguchi H., 2008, P 7 NTCIR WORKSH M E, P272 Ortuno M, 2002, EUROPHYS LETT, V57, P759, DOI 10.1209/epl/i2002-00528-3 Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Yang Z, 2013, PHYSICA A, V392, P4523, DOI 10.1016/j.physa.2013.05.052 NR 18 TC 0 Z9 0 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 EI 1460-2067 J9 COMPUT J JI Comput. J. PD APR PY 2015 VL 58 IS 4 BP 644 EP 655 DI 10.1093/comjnl/bxu107 PG 12 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA CO4NL UT WOS:000359137500009 ER PT J AU Oukid, L Benblidia, N Bentayeb, F Asfari, O Boussaid, O AF Oukid, Lamia Benblidia, Nadjia Bentayeb, Fadila Asfari, Ounas Boussaid, Omar TI Contextualized Text OLAP Based on Information Retrieval SO International Journal of Data Warehousing and Mining LA English DT Article DE Aggregation Operator; Context; Information Retrieval; Query Expansion; Text Cube; Text OLAP ID DOCUMENTS; MODEL AB Current data warehousing and On-Line Analytical Processing (OLAP) systems are not yet particularly appropriate for textual data analysis. It is therefore crucial to develop a new data model and an OLAP system to provide the necessary analyses for textual data. To achieve this objective, this paper proposes a new approach based on information retrieval (IR) techniques. Moreover, several contextual factors may significantly affect the information relevant to a decision-maker. Thus, the paper proposes to consider contextual factors in an OLAP system to provide relevant results. It provides a generalized approach for Text OLAP analysis which consists of two parts: The first one is a context-based text cube model, denoted CXT-Cube. It is characterized by several contextual dimensions. Hence, during the OLAP analysis process, CXT-Cube exploits the contextual information in order to better consider the semantics of textual data. Besides, the work associates to CXT-Cube a new text analysis measure based on an OLAP-adapted vector space model and a relevance propagation technique. The second part is an OLAP aggregation operator called ORank (OLAP-Rank) which allows to aggregate textual data in an OLAP environment while considering relevant contextual factors. To consider the user context, this paper proposes a query expansion method based on a decision-maker profile. Based on IR metrics, it evaluates the proposed aggregation operator in different cases using several data analysis queries. The evaluation shows that the precision of the system is significantly better than that of a Text OLAP system based on classical IR. This is due to the consideration of the contextual factors. C1 [Oukid, Lamia; Benblidia, Nadjia] Univ Blida 1, LRDSI Lab, Blida, Algeria. [Bentayeb, Fadila; Asfari, Ounas; Boussaid, Omar] Univ Lyon 2, ERIC Lab, Lyon, France. RP Oukid, L (reprint author), Univ Blida 1, LRDSI Lab, Blida, Algeria. CR Aknouche R., 2012, P INT CROSS DOM C WO, P244, DOI [10.1007/978-3-642-32498-7_19, DOI 10.1007/978-3-642-32498-7_19] Asfari O., 2008, P 5 FRENCH INF RETR, P377 Asfari O., 2011, INT J ADV INTELLIGEN, V4, P128 Bringay S., 2011, ACT 3 JOURN FRANC EN, P87 Cassens J., 2006, P 19 INT FLOR ART IN, P619 Chaudhuri S., 2005, P 2 BIENN C INN DAT, P1 Dey A. K., 2011, J PERSONAL UBIQUITOU, V5, P4, DOI [10.1007/s007790170019, DOI 10.1007/S007790170019] Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Kraft R., 2006, P 15 INT C WORLD WID, P367 Lin CX, 2008, IEEE DATA MINING, P905, DOI 10.1109/ICDM.2008.135 Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 Mothe J, 2003, J AM SOC INF SCI TEC, V54, P650, DOI 10.1002/asi.10257 Perez-Martinez JM, 2008, DECIS SUPPORT SYST, V45, P77, DOI 10.1016/j.dss.2006.12.005 Ravat F, 2007, LECT NOTES COMPUT SC, V4801, P550 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sauvagnat K, 2004, LECT NOTES COMPUT SC, V3246, P242 Zhang D., 2011, P 2011 C INT DAT UND, P204 Zhang D., 2009, STAT ANAL DATA MININ, V2, P378, DOI [10.1002/sam.10059, DOI 10.1002/SAM.10059] NR 19 TC 0 Z9 0 PU IGI PUBL PI HERSHEY PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA SN 1548-3924 EI 1548-3932 J9 INT J DATA WAREHOUS JI Int. J. Data Warehous. Min. PD APR-JUN PY 2015 VL 11 IS 2 BP 1 EP 21 DI 10.4018/ijdwm.2015040101 PG 21 WC Computer Science, Software Engineering SC Computer Science GA CG4YT UT WOS:000353296200001 ER PT J AU McCoy, TH Castro, VM Rosenfield, HR Cagan, A Kohane, IS Perlis, RH AF McCoy, Thomas H. Castro, Victor M. Rosenfield, Hannah R. Cagan, Andrew Kohane, Isaac S. Perlis, Roy H. TI A Clinical Perspective on the Relevance of Research Domain Criteria in Electronic Health Records SO AMERICAN JOURNAL OF PSYCHIATRY LA English DT Article ID RDOC; FRAMEWORK; MODEL AB Objective: The limitations-of the DSM nosology for capturing dimensionality and overlap in psychiatric syndromes, and its poor correspondence to underlying neurobiology, have been well established. The Research Domain Criteria (RDoC), a proposed dimensional model of psychopathology, may offer new insights into psychiatric illness. For psychiatric clinicians, however, because tools for capturing these domains in clinical practice have not yet been established, the relevance and means of transition from the categorical system of DSM-5 to the dimensional models of RDoC remains unclear. The authors explored a method of extracting these dimensions from existing electronic health record (EHR) notes. Method: The authors used information retrieval and natural language processing methods to extract estimates of the RDoC dimensions in the EHRs of a large health system. They parsed and scored EHR documentation for 2,484 admissions covering 2,010 patients admitted to a psychiatric inpatient unit between 2011 and 2013. These domain scores were compared with DSM-IV-based ICD-9 codes to assess face validity. As a measure of predictive validity, these scores were examined for association with two outcomes: length of hospital stay and time to all-cause hospital readmission. Together, these analyses were intended to address the extent to which RDoC symptom domains might capture clinical features already available in narrative notes but not reflected in DSM diagnoses. Results: In mixed-effects models, loadings for the RDoC cognitive and arousal domains were associated with length of hospital stay, while the negative valence and social domains were associated with hazard of all-cause hospital readmission. Conclusions: These findings show that a computationally derived tool based on RDoC workgroup reports identifies symptom distributions in clinician notes beyond those captured by ICD-9 codes, and these domains have significant predictive validity. More generally, they point to the possibility that clinicians already document RDoC-relevant symptoms, albeit not in a quantified form. C1 [Perlis, Roy H.] Massachusetts Gen Hosp, Dept Psychiat, Ctr Expt Drugs & Diagnost, Boston, MA 02114 USA. Massachusetts Gen Hosp, Ctr Human Genet Res, Boston, MA 02114 USA. Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA. RP Perlis, RH (reprint author), Massachusetts Gen Hosp, Dept Psychiat, Ctr Expt Drugs & Diagnost, Boston, MA 02114 USA. EM rperlis@mgh.harvard.edu FU NIMH [MH086026, 5R25MH094612]; Stanley Center for Psychiatric Research at the Broad Institute FX Dr Perlis is supported by NIMH grant MH086026 and the Stanley Center for Psychiatric Research at the Broad Institute. Dr. McCoy is a participant in NIMH grant 5R25MH094612. CR Barch DM, 2010, SCHIZOPHRENIA BULL, V36, P43, DOI 10.1093/schbul/sbp139 Bird S., 2009, NATURAL LANGUAGE PRO Bradford R.B., 2008, P 17 ACM C INF KNOWL, P153, DOI 10.1145/1458082.1458105 Casey BJ, 2013, NAT REV NEUROSCI, V14, P810, DOI 10.1038/nrn3621 Castro VM, 2013, BMJ-BRIT MED J, V346, DOI 10.1136/bmj.f288 Cuthbert BN, 2014, WORLD PSYCHIATRY, V13, P28, DOI 10.1002/wps.20087 Cuthbert BN, 2013, BMC MED, V11, DOI 10.1186/1741-7015-11-126 Deerwester S., 1988, P 51 ANN M AM SOC IN Insel T, 2010, AM J PSYCHIAT, V167, P748, DOI 10.1176/appi.ajp.2010.09091379 Insel T, 2012, INSEL T DIRECTORS BL Kiss T, 2006, COMPUT LINGUIST, V32, P485, DOI 10.1162/coli.2006.32.4.485 Lee SH, 2013, NAT GENET, V45, P984, DOI 10.1038/ng.2711 Marcus M.P., 1993, COMPUTATIONAL LINGUI, V19, P313 Mattmann C.A., 2011, TIKA IN ACTION Miller G., 1990, INT J LEXICOGR, V3, P235 Murphy S. N., 2007, P AM MED INF ASS ANN, V2007, P548 Perlis RH, 2012, PSYCHOL MED, V42, P41, DOI 10.1017/S0033291711000997 Rehurek R, 2011, LECT NOTES COMPUT SC, V6611, P289, DOI 10.1007/978-3-642-20161-5_29 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Zelikovitz S, 2006, P 19 INT FLOR AI RES NR 21 TC 1 Z9 1 PU AMER PSYCHIATRIC PUBLISHING, INC PI ARLINGTON PA 1000 WILSON BOULEVARD, STE 1825, ARLINGTON, VA 22209-3901 USA SN 0002-953X EI 1535-7228 J9 AM J PSYCHIAT JI Am. J. Psychiat. PD APR PY 2015 VL 172 IS 4 BP 316 EP 320 DI 10.1176/appi.ajp.2014.14091177 PG 5 WC Psychiatry SC Psychiatry GA CE8TS UT WOS:000352117600006 PM 25827030 ER PT J AU Spreitzenbarth, M Schreck, T Echtler, F Arp, D Hoffmann, J AF Spreitzenbarth, Michael Schreck, Thomas Echtler, Florian Arp, Daniel Hoffmann, Johannes TI Mobile-Sandbox: combining static and dynamic analysis with machine-learning techniques SO INTERNATIONAL JOURNAL OF INFORMATION SECURITY LA English DT Article DE Android; Malware; Automated analysis; Machine learning AB Smartphones in general and Android in particular are increasingly shifting into the focus of cyber criminals. For understanding the threat to security and privacy, it is important for security researchers to analyze malicious software written for these systems. The exploding number of Android malware calls for automation in the analysis. In this paper, we present Mobile-Sandbox, a system designed to automatically analyze Android applications in novel ways: First, it combines static and dynamic analysis, i.e., results of static analysis are used to guide dynamic analysis and extend coverage of executed code. Additionally, it uses specific techniques to log calls to native (i.e., "non-Java") APIs, and last but not least it combines these results with machine-learning techniques to cluster the analyzed samples into benign and malicious ones. We evaluated the system on more than 69,000 applications from Asian third-party mobile markets and found that about 21 % of them actually use native calls in their code. C1 [Spreitzenbarth, Michael; Schreck, Thomas] Univ Erlangen Nurnberg, D-91054 Erlangen, Germany. [Echtler, Florian] Univ Regensburg, D-93053 Regensburg, Germany. [Arp, Daniel] Univ Gottingen, D-37073 Gottingen, Germany. [Hoffmann, Johannes] Ruhr Univ Bochum, Bochum, Germany. RP Spreitzenbarth, M (reprint author), Univ Erlangen Nurnberg, D-91054 Erlangen, Germany. EM michael.spreitzenbarth@cs.fau.de; thomas.schreck@cs.fau.de; florian.echtler@ur.de; daniel.arp@informatik.uni-goettingen.de; johannes.hoffmann@rub.de FU Federal Ministry of Education and Research [01BY1021] FX This work has been supported by the Federal Ministry of Education and Research (Grant 01BY1021 - MobWorm). We would also like to thank Felix Freiling, Konrad Rieck and Thorsten Holz for their valuable input and comments. Last but not least, we would like to thank the anonymous reviewers for proofreading our paper and for giving us helpful comments to improve this work. CR Aafer Y., 2013, P INT C SEC PRIV COM [Anonymous], 2014, ANDR PLATF VERS [Anonymous], 2012, US ANDR EM Arp D., 2014, P NETW DISTR SYST SE Blasing T., 2010, P 5 INT C MAL UNW SO Burguera I., 2011, P 1 ACM WORKSH SEC P Cristianini N., 2000, INTRO SUPPORT VECTOR Department of Computer Science Friedrich-Alexander-University Erlangen-Nuremberg, 2012, MOB SANDB Desnos A., 2011, ANDROGUARD Desnos A., 2011, P BLACK HAT AB DHAB Echtler F., LTRACE FOR ANDROID Enck W., 2010, P USENIX S OP SYST D Felt A. P., 2011, P 1 ACM WORKSH SEC P, P3 Felt A. P., 2011, P 18 ACM C COMP COMM Freke J., 2009, SMALI DISASSEMBLER A Google Inc, 2009, ANDR SDK Groves R. M., 1989, SURVEY ERRORS SURVEY Groves R.M., 1987, PUBLIC OPIN QUART, V51, P157 Lantz P, 2011, DROIDBOX ANDROID APP Moser A., 2007, P 23 ANN COMP SEC AP Peng H., 2012, USING PROBABILISTIC, P241 Raffetseder T, 2007, LECT NOTES COMPUT SC, V4779, P1 Rieck K, 2012, DERRICK SIMPLE NETWO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarma B. P., 2012, P 17 ACM S ACC CONTR, P13 Schmidt A.-D., 2009, P ICC COMM INF SYST Six J., 2011, APPL SECURITY ANDROI Spreitzenbarth M., 2013, CURRENT ANDROID MALW Spreitzenbarth M., 2012, CS201204 U ERL NUR Vienna University of Technology, 2012, ANDR AN ANDR APKS Willems C, 2007, IEEE SECUR PRIV, V5, P32, DOI 10.1109/MSP.2007.45 Xie L., 2010, P 3 ACM C WIR NETW S Zhou Y., 2012, P 19 ANN S NETW DIST Zhou Y, 2012, P 33 IEEE S SEC PRIV NR 34 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1615-5262 EI 1615-5270 J9 INT J INF SECUR JI Int. J. Inf. Secur. PD APR PY 2015 VL 14 IS 2 SI SI BP 141 EP 153 DI 10.1007/s10207-014-0250-0 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA CF1ER UT WOS:000352287000004 ER PT J AU Shokripour, R Anvik, J Kasirun, ZM Zamani, S AF Shokripour, Ramin Anvik, John Kasirun, Zarinah M. Zamani, Sima TI A time-based approach to automatic bug report assignment SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE Term weighting technique time metadata; Tf-idf Technique ID INFORMATION-RETRIEVAL; FEATURE LOCATION; ACCURATE; EXECUTION; SOFTWARE AB Bug assignment is one of the important activities in bug triaging that aims to assign bugs to the appropriate developers for fixing. Many recommended automatic bug assignment approaches are based on text analysis methods such as machine learning and information retrieval methods. Most of these approaches use term-weighting techniques, such as term frequency-inverse document frequency (tf-idf), to determine the value of terms. However, the existing term-weighting techniques only deal with frequency of terms without considering the metadata associated with the terms that exist in software repositories. This paper aims to improve automatic bug assignment by using time-metadata in tf-idf (Time-tf-idf). In the Time-tf-idf technique, the recency of using the term by the developer is considered in determining the values of the developer expertise. An evaluation of the recommended automatic bug assignment approach that uses Time-tf-idf, called ABA-Time-tf-idf, was conducted on three open-source projects. The evaluation shows accuracy and mean reciprocal rank (MRR) improvements of up to 11.8% and 8.94%, respectively, in comparison to the use of tf-idf. Moreover, the ABA-Time-tf-idf approach outperforms the accuracy and MRR of commonly used approaches in automatic bug assignment by up to 45.52% and 55.54%, respectively. Consequently, consideration of time-metadata in term weighting reasonably leads to improvements in automatic bug assignment. (C) 2014 Elsevier Inc. All rights reserved. C1 [Shokripour, Ramin; Kasirun, Zarinah M.; Zamani, Sima] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. [Anvik, John] Cent Washington Univ, Dept Comp Sci, Ellensburg, WA 98926 USA. RP Shokripour, R (reprint author), Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. EM rshokripour@gmail.com CR Abebe S, 2010, ICPC 10, P156, DOI DOI 10.1109/ICPC.2010.29 Ahsan SN, 2009, 2009 FOURTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING ADVANCES (ICSEA 2009), P216, DOI 10.1109/ICSEA.2009.92 Anvik, 2011, ACM T SOFTW ENG METH, V20, p[10, 1, 35] Aswani Kumar C., 2012, CYBERNETICS INFORM T, V12, P34 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bai J., 2004, AS INF RETR S AIRS B Banitaan S, 2013, INT CONF COMM INF T, P215, DOI 10.1109/ICCITechnology.2013.6579552 Baysal O, 2009, INT C PROGRAM COMPRE, P297 Bhattacharya P, 2012, J SYST SOFTWARE, V85, P2275, DOI 10.1016/j.jss.2012.04.053 Butler S., 2011, LECT NOTES COMPUTER, V6813, P130 Capobianco G, 2013, J SOFTW-EVOL PROC, V25, P743, DOI 10.1002/smr.1564 Cavalcanti YC, 2014, J SOFTW-EVOL PROC, V26, P620, DOI 10.1002/smr.1639 Chen L, 2011, J SOFTWARE, V6, P421 Cubranic D., 2004, P 16 INT C SOFTW ENG, P92 Dit B, 2013, EMPIR SOFTW ENG, V18, P277, DOI 10.1007/s10664-011-9194-4 Dit B., 2012, P INT C PROGR COMPR, P203, DOI 10.1109/1CPC.2012.6240489 Feldman R, 2006, TEXT MINING HDB ADV GOMEZ VU, 2009, P JOINT INT ANN ERCI, P79, DOI DOI 10.1145/1595808.1595825 Gunn E.R., 1998, 14 ISIS Hassan AE, 2005, PROC IEEE INT CONF S, P263 Jeong G, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P111, DOI 10.1145/1595696.1595715 Kagdi H, 2012, J SOFTW-EVOL PROC, V24, P3, DOI 10.1002/smr.530 Linares-Vasquez M, 2012, 2012 28TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM), P451, DOI 10.1109/ICSM.2012.6405306 Liu D., 2007, P 22 IEEE ACM INT C, P234, DOI DOI 10.1145/1321631.1321667 Lucca G. D., 2002, P INT C SOFTW MAINT, P93 Manning C.D., 2008, INTRO INFORM RETRIEV Matter D, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P131, DOI 10.1109/MSR.2009.5069491 Moin A. H., 2012, 7 INT C SOFTW ENG AD, P22 Nagwani N., 2012, P 9 INT C ICT KNOWL, P113, DOI [10.1109/ICTKE.2012.6152388, DOI 10.1109/ICTKE.2012.6152388] Nasim S., 2011, Proceedings of the 2011 Frontiers of Information Technology (FIT 2011), DOI 10.1109/FIT.2011.62 Nguyen A. T., 2012, P ACM SIGSOFT 20 INT, P63, DOI [10.1145/2393596.2393671, DOI 10.1145/2393596.2393671] Panichella A, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P522 Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Rao S., 2011, P 8 WORK C MIN SOFTW, P43, DOI DOI 10.1145/1985441.1985451 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Servant F, 2012, PROC INT CONF SOFTW, P36, DOI 10.1109/ICSE.2012.6227208 Shokripour R., 2013, P 10 INT WORKSH MIN, P2 Sun X., 2014, P 2014 3 INT WORKSH, P32, DOI [10.1145/26275082627514, DOI 10.1145/26275082627514] Tamrawi A., 2011, P 19 ACM SIGSOFT S F, P365, DOI [10.1145/2025113.2025163, DOI 10.1145/2025113.2025163] Witten I. H., 2000, DATA MINING PRACTICA, V5, P22 WITTEN IH, 1991, IEEE T INFORM THEORY, V37, P1085, DOI 10.1109/18.87000 Wohlin C., 2012, EXPT SOFTWARE ENG Wu W., 2011, P 2011 18 AS PAC SOF, P389 Xia X, 2013, WORK CONF REVERSE EN, P72 Zamani S, 2014, INFORM SOFTWARE TECH, V56, P991, DOI 10.1016/j.infsof.2014.03.007 Zhang T., 2013, P 28 ANN ACM S APPL, P1088, DOI DOI 10.1145/2480362.2480568 Zhou J, 2012, PROC INT CONF SOFTW, P14 NR 47 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 EI 1873-1228 J9 J SYST SOFTWARE JI J. Syst. Softw. PD APR PY 2015 VL 102 BP 109 EP 122 DI 10.1016/j.jss.2014.12.049 PG 14 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA CD2RS UT WOS:000350927300007 ER PT J AU Takano, W Hamano, S Nakamura, Y AF Takano, Wataru Hamano, Seiya Nakamura, Yoshihiko TI Correlated space formation for human whole-body motion primitives and descriptive word labels SO ROBOTICS AND AUTONOMOUS SYSTEMS LA English DT Article DE Motion primitive; Word label; Canonical correlation analysis ID LATENT SEMANTIC ANALYSIS; ORGANIZATION; RECOGNITION; FEATURES; MODEL AB The motion capture technology has been improved, and widely used for motion analysis and synthesis in various fields, such as robotics, animation, rehabilitation, and sports engineering. A massive amount of captured human data has already been collected. These prerecorded motion data should be reused in order to make the motion analysis and synthesis more efficient. The retrieval of a specified motion data is a fundamental technique for the reuse. Imitation learning frameworks have been developed in robotics, where motion primitive data is encoded into parameters in stochastic models or dynamical systems. We have also been making research on encoding motion primitive data into Hidden Markov Models, which are referred to as "motion symbol", and aiming at integrating the motion symbols with language. The relations between motions and words in natural language will be versatile and powerful to provide a useful interface for reusing motion data. In this paper, we construct a space of motion symbols for human whole body movements and a space of word labels assigned to those movements. Through canonical correlation analysis, these spaces are reconstructed such that a strong correlation is formed between movements and word labels. These spaces lead to a method for searching for movement data from a query of word labels. We tested our proposed approach on captured human whole body motion data, and its validity was demonstrated. Our approach serves as a fundamental technique for extracting the necessary movements from a database and reusing them. (C) 2015 The Authors. Published by Elsevier B.V. C1 [Takano, Wataru; Hamano, Seiya; Nakamura, Yoshihiko] Univ Tokyo, Mechanoinformat, Bunkyo Ku, Tokyo 1138656, Japan. RP Takano, W (reprint author), Univ Tokyo, Mechanoinformat, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan. EM takano@ynl.t.u-tokyo.ac.jp; hamano@ynl.t.u-tokyo.ac.jp; nakamura@ynl.t.u-tokyo.ac.jp FU PREST "Information Environment and Humans"; Japan Science and Technology Agency; Japan Society for the Promotion of Science [26700021] FX This research was supported by PREST "Information Environment and Humans", Japan Science and Technology Agency, and Grant-in-Aid for Young Scientists (A) (26700021), Japan Society for the Promotion of Science. CR Argall BD, 2009, ROBOT AUTON SYST, V57, P469, DOI 10.1016/j.robot.2008.10.024 Billard AG, 2006, ROBOT AUTON SYST, V54, P370, DOI 10.1016/j.robot.2006.01.007 Breazeal C, 2002, TRENDS COGN SCI, V6, P481, DOI 10.1016/S1364-6613(02)02016-8 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Haruno M, 2001, NEURAL COMPUT, V13, P2201, DOI 10.1162/089976601750541778 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hotelling H., 1936, RELATION 2 SETS VARI Ijspeert A. J., 2003, NEURAL INFORM PROCES, V15, P1547 Inamura T, 2004, INT J ROBOT RES, V23, P363, DOI 10.1177/0278364904042199 Inamura T., 2003, P IEEE INT C HUM ROB, plb Kadone H., 2005, P 2005 IEEE RSJ INT, P2900 Okada M., 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), DOI 10.1109/ROBOT.2002.1014741 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saussure F., 1966, COURSE GEN LINGUISTI TAKANE Y, 1977, PSYCHOMETRIKA, V42, P7, DOI 10.1007/BF02293745 Takano W, 2007, IEEE INT CONF ROBOT, P3092, DOI 10.1109/ROBOT.2007.363942 Takano W, 2010, P IEEE RAS-EMBS INT, P52 Tani J, 2003, IEEE T SYST MAN CY A, V33, P481, DOI 10.1109/TSMCA.2003.809171 NR 19 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0921-8890 EI 1872-793X J9 ROBOT AUTON SYST JI Robot. Auton. Syst. PD APR PY 2015 VL 66 BP 35 EP 43 DI 10.1016/j.robot.2014.11.020 PG 9 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Robotics SC Automation & Control Systems; Computer Science; Robotics GA CD2TI UT WOS:000350931500004 ER PT J AU Liu, YC Liu, M Wang, X AF Liu, Yuanchao Liu, Ming Wang, Xin TI Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension SO PLOS ONE LA English DT Article ID SELF-ORGANIZING MAPS; WEBSOM AB The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach. C1 [Liu, Yuanchao; Liu, Ming; Wang, Xin] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, Peoples R China. RP Liu, YC (reprint author), Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, Peoples R China. EM lyc@insun.hit.edu.cn CR Azcarraga AP, 2004, IEEE T KNOWL DATA EN, V16, P380 Bagga A, 1998, P 17 INT C COMP LING, V1, P79 Chim H, 2008, IEEE T KNOWL DATA EN, V20, P1217, DOI 10.1109/TKDE.2008.50 Cui X., 2005, IEEE SWARM INTELLIGE, V10, P185 Dhillon S, 2003, J MACHINE LEARNING R, V3, P1265 Dong Q., 2000, HOWNET Elahi A, 2012, J ADV COMP SCI TECHN, V2012, P2 Ercan G, 2007, INFORM PROCESS MANAG, V43, P1705, DOI 10.1016/j.ipm.2007.01.015 Gabrilovich E, 2007, P 20 INT JOINT C ART, P6 Granados A, 2012, EXPERT SYST APPL, V39, P8537, DOI 10.1016/j.eswa.2012.01.215 Griffiths T., 2002, STANDFORD U, V518, P1 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guerrero R, 2002, INFORM PROCESSING MA, V38, P79 Hammouda M, 2004, IEEE T KNOWL DATA EN, V16, P1279 Hartigan A, 1979, J R STAT SOC C, V28, P100 Heinrich G, 2013, TECHNICAL REPORT Hofmann T, 1999, P 15 ANN C UNC ART I, P34 Jiaju M, 1983, SHANGHAI DICT PUBLIC, V1983 Jing J, 2007, JISUANJI XUEBAO CHIN, V30, P1354 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T., 2001, SELF ORGANIZING MAPS Kwak M, 2013, J BIOMED INFORM, V46, P929, DOI 10.1016/j.jbi.2013.07.006 Letsche A, 1997, INFORM SCI, V100, P105 Liu Y, 2006, WEB AG INF MAN WORKS, P16 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Metzler D, 2007, P 29 EUR C IR RES EC, P12 Nasir JA, 2013, KNOWL-BASED SYST, V54, P216, DOI 10.1016/j.knosys.2013.09.012 Pinto D, 2007, LECT NOTES COMPUT SC, V4394, P611 Rennie J, 2013, ORIGINAL 20 NEWSGROU SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shady S, 2007, IEEE INT C DATA MINI, P1043 Shan C, 2005, INT C COMP INT MOD C, V2005, P7 Song SX, 2005, LECT NOTES ARTIF INT, V3801, P713 Shen Huang, 2006, IEEE Transactions on Knowledge and Data Engineering, V18, DOI 10.1109/TKDE.2006.1599384 Turney D, 2012, J ARTIF INTELL RES J, V44, P533 Xiang Ji, 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Yang Y., 1997, ICML, V97, P412 Yubao L, 2007, LECT NOTES COMPUTER, V2007, P389 NR 38 TC 0 Z9 0 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD MAR 20 PY 2015 VL 10 IS 3 AR e0117390 DI 10.1371/journal.pone.0117390 PG 18 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA CE8IA UT WOS:000352084200013 PM 25794172 ER PT J AU Wang, MY Fang, SC Chang, YH AF Wang, Ming-Yeu Fang, Shih-Chieh Chang, Yu-Hsuan TI Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE LA English DT Article DE Science and technology; Technological opportunity; Text mining; Microalgae; Biofuel; High-dimensional data ID SYSTEMATIC-APPROACH; INNOVATION; COMMUNITIES; DISCOVERY; KNOWLEDGE; PATENTS; TELL; US AB The interaction between scientific and technological knowledge facilitates exploration of new technological opportunities; however, gaps between them typically impede exploration of these opportunities. Scientific papers and technological patents record modern and advanced knowledge in scientific discovery and technological development; therefore, comparing their statuses can identify the gaps and explore potential technological opportunities. Because microalgal biofuels are a promising alternative energy resource devoid of territorial land use problems, this study applies text mining and an algorithm that can cluster objects of high-dimensional data to microalgal biofuel papers and patents, and explores their potential technological opportunities. The results demonstrate that a text-based clustering approach is appropriate for identifying scientific and technological applications for microalgal biofuels. The results indicate that microalgal photosynthesis and light utilization have abundant scientific outcomes for technological engineers to potentially apply. Technological opportunities exist in synthesis, harvesting, extraction, and lipid conversion. Scientific knowledge underlying biofuels accompanying high-value co-products of production require sustained exploration and reporting through research. These needs represent potential technological opportunities. (C) 2014 Elsevier Inc. All rights reserved. C1 [Wang, Ming-Yeu] Natl Chiayi Univ, Dept BioBusiness Management, Chiayi, Taiwan. [Fang, Shih-Chieh] Natl Cheng Kung Univ, Dept Business Adm, Tainan 70101, Taiwan. [Fang, Shih-Chieh] Natl Cheng Kung Univ, Inst Int Business, Tainan 70101, Taiwan. [Fang, Shih-Chieh] Natl Cheng Kung Univ, Ctr Energy Technol & Strategy, Tainan 70101, Taiwan. [Chang, Yu-Hsuan] Gainia Intellectual Asset Serv Inc, Hsinchu, Taiwan. RP Wang, MY (reprint author), Natl Chiayi Univ, Dept BioBusiness Management, Chiayi, Taiwan. EM mingyeu.wang@gmail.com; fangsc@mail.ncku.edu.tw; shikodoofmy@gmail.com FU Ministry of Science and Technology of the Republic of China (Taiwan) [NSC 102-2410-H-415-011] FX The authors thank two anonymous referees for their helpful comments on this article and thank the Biotechnology Business Section, Solvent and Chemical Business Division, CPC Corporation, Republic of China (Taiwan) for providing technology consultancy in biofuels. This research was supported by the Ministry of Science and Technology of the Republic of China (Taiwan). Grant number is NSC 102-2410-H-415-011. CR Aggarwal CC, 2000, SIGMOD RECORD, V29, P70 [Anonymous], 2013, SUBJ MATT EL AN PROC Beyer K, 1999, LECT NOTES COMPUT SC, V1540, P217 COHEN WM, 1989, ECON J, V99, P569, DOI 10.2307/2233763 COHEN WM, 1987, J IND ECON, V35, P543, DOI 10.2307/2098587 Darzins A., 2010, IEA BIOENERGY TASK 3, pV Davis R, 2011, APPL ENERG, V88, P3524, DOI 10.1016/j.apenergy.2011.04.018 Feldman R, 2007, TEXT MINING HDB ADV Frenz M, 2012, IND INNOV, V19, P285, DOI 10.1080/13662716.2012.694676 Glanzel W, 2003, SCIENTOMETRICS, V58, P415, DOI 10.1023/A:1026248929668 Gunnemann S., 2011, P ACM C INF KNOWL MA, P1363 Hellmann T, 2007, J ECON BEHAV ORGAN, V63, P624, DOI 10.1016/j.jebo.2006.05.013 IPCC, 1992, CLIM CHNAG IPCC 1992 KLEVORICK AK, 1995, RES POLICY, V24, P185, DOI 10.1016/0048-7333(93)00762-I Konur O, 2011, APPL ENERG, V88, P3532, DOI 10.1016/j.apenergy.2010.12.059 Kostoff RN, 2006, TECHNOL FORECAST SOC, V73, P923, DOI 10.1016/j.techfore.2005.09.004 Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P165, DOI 10.1016/j.techfore.2007.11.004 Kriegel HP, 2009, ACM T KNOWL DISCOV D, V3, DOI 10.1145/1497577.1497578 Lee S, 2009, TECHNOL FORECAST SOC, V76, P769, DOI 10.1016/j.techfore.2009.01.003 Lefstin J. A., 2008, FORMAL STRUCTURE PAT Martino JP, 2003, TECHNOL FORECAST SOC, V70, P719, DOI 10.1016/S0040-1625(02)00375-X Meyer M, 2000, SCIENTOMETRICS, V48, P151, DOI 10.1023/A:1005692621105 Meyer M., 2002, ECON SYST RES, V14, P323, DOI 10.1080/0953531022000024824 Milner E., 2009, P VLDB ENDOWMENT, V2, P1270 Moise G, 2009, KNOWL INF SYST, V21, P299, DOI 10.1007/s10115-009-0226-y Narin F, 1997, RES POLICY, V26, P317, DOI 10.1016/S0048-7333(97)00013-9 NELSON RR, 1982, Q J ECON, V97, P453, DOI 10.2307/1885872 Nieto M, 2005, TECHNOVATION, V25, P1141, DOI 10.1016/j.technovation.2004.05.001 Olsson O, 2005, J ECON GROWTH, V10, P35 Parsons L., 2004, ACM SIGKDD EXPLORATI, V6, P90, DOI DOI 10.1145/1007730.1007731 Petrescu AS, 2009, REV POLICY RES, V26, P839, DOI 10.1111/j.1541-1338.2009.00420.x PRICE DJD, 1965, TECHNOL CULT, V6, P553 Robinson DKR, 2013, TECHNOL FORECAST SOC, V80, P267, DOI 10.1016/j.techfore.2011.06.004 Rosenberg N., 1982, INSIDE BLACK BOX TEC, P141 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shane S, 2001, MANAGE SCI, V47, P205, DOI 10.1287/mnsc.47.2.205.9837 Shibata N, 2011, J AM SOC INF SCI TEC, V62, P1360, DOI 10.1002/asi.21477 Shibata Naoki, 2011, Foresight, V13, DOI 10.1108/14636681111170211 Shibata N, 2010, TECHNOL FORECAST SOC, V77, P1147, DOI 10.1016/j.techfore.2010.03.008 Singh J, 2010, RENEW SUST ENERG REV, V14, P2596, DOI 10.1016/j.rser.2010.06.014 Smith VH, 2010, TRENDS ECOL EVOL, V25, P301, DOI 10.1016/j.tree.2009.11.007 Szepannek G., 2013, PACKAGE ORCULS R PRO U.S. DOE, 2010, NAT ALG BIOF TECHN R Wang MY, 2010, R&D MANAGE, V40, P491, DOI 10.1111/j.1467-9310.2010.00612.x Weiss S. M., 2005, TEXT MINING PREDICTI Wu FS, 2011, TECHNOL FORECAST SOC, V78, P386, DOI 10.1016/j.techfore.2010.08.006 Yoon B, 2005, TECHNOL FORECAST SOC, V72, P145, DOI 10.1016/j.techfore.2004.08.011 Yoon J, 2012, SCIENTOMETRICS, V90, P445, DOI 10.1007/s11192-011-0543-2 Ziman J., 1988, INTRO SCI STUDIES NR 49 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0040-1625 EI 1873-5509 J9 TECHNOL FORECAST SOC JI Technol. Forecast. Soc. Chang. PD MAR PY 2015 VL 92 BP 182 EP 195 DI 10.1016/j.techfore.2014.07.008 PG 14 WC Business; Planning & Development SC Business & Economics; Public Administration GA CE7SL UT WOS:000352042000014 ER PT J AU Zhu, LJ Shi, C Guo, JF AF Zhu, Lijun Shi, Chen Guo, Jianfeng TI Mapping discovery modeling and its empirical research for the scientific and technological knowledge concept in unified concept space SO CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS LA English DT Article DE Knowledge organization system; Concept space; Mapping discovery; Mapping transfer ID MEDIATORS; SYSTEMS AB For heterogeneous scientific and technical knowledge organization systems (HSTKOSs), computer-aided concept mapping discovery becomes very difficult among HSTKOSs. First, this paper puts forward and establishes a common data model (CDM) oriented to the HSTKOS used for the standardized description of scientific and technological knowledge concepts. Then, in the mapping discovery algorithm, the algorithms to discover the relations of inheritance, "is a characteristic of", "is a part of", relevance and other partial ordering relations between heterogeneous concepts are put forward and designed through mapping transfer. Finally, the empirical results show that in public concept space, the mapping discovery algorithm, put forward and designed by this paper, is feasible and can have certain practical significance. C1 [Zhu, Lijun] Inst Sci & Tech Informat China, Beijing, Peoples R China. [Shi, Chen] Inst Sci & Tech Informat China, Informat Technol Supporting Ctr, Beijing, Peoples R China. [Guo, Jianfeng] Chinese Acad Sci, Inst Policy & Management, Ctr Energy & Environm Policy Res, Beijing, Peoples R China. RP Guo, JF (reprint author), Chinese Acad Sci, Inst Policy & Management, Ctr Energy & Environm Policy Res, Beijing, Peoples R China. EM zhulj@istic.ac.cn; shichen2012@istic.ac.cn; Guojf@casipm.ac.cn FU National Natural Science Foundation of China [71271200]; National Twelfth "Five-Year Plan" for Science and Technology Support Program [2011BAH10B04, 2011BAH10B02] FX Support from the National Natural Science Foundation of China under Grant No 71271200 and National Twelfth "Five-Year Plan" for Science and Technology Support Program under Grant Nos 2011BAH10B04, 2011BAH10B02 are greatly acknowledged. CR Adali S., 1995, IEEE P 11 INT C DAT, P513 ADALI S, 1994, INT J INTELL COOP I, V3, P349, DOI 10.1142/S0218215794000193 Brahami Menaouer, 2013, Journal of Information Processing Systems, V9, P1, DOI 10.3745/JIPS.2013.9.1.001 Bringer J, 2012, HUMAN CENTRIC COMPUT, V2, P1, DOI DOI 10.1186/2192-1962-2-2 Castano S, 2001, IEEE T KNOWL DATA EN, V13, P277, DOI 10.1109/69.917566 CHAPLAN MA, 1995, LIBR QUART, V65, P39 Cilia M., 2004, DEALING HETEROGENEOU, P26 Czarnecki K, 2005, LECT NOTES COMPUT SC, V3676, P422 Doerr M., 2006, J DIGIT INF, V1 Farquhar A, 1997, INT J HUM-COMPUT ST, V46, P707, DOI 10.1006/ijhc.1996.0121 Gruber T, 2008, J WEB SEMANT, V6, P4, DOI 10.1016/j.websem.2007.11.011 Gruber T. R., 1993, TRANSLATION APPROACH, P513 Hsueh H. Y., 2013, HUM CTR COMPUT INF S, V3, P1 Hudon M., 1997, Information Services & Use, V17 Kim B, 2012, INT J INF PROCESS SY, V8, P555 Kosovac B., 2000, CONSTRUCTION INFORM, V2, P556 Leizeng M., 2004, J AM SOC INF SCI TEC, V55, P377, DOI 10.1002/asi.10387 Liang A. C., 2007, CATALOGING CLASSIFIC, V43, P161, DOI 10.1300/J104v43n03_10 Liping Z., 2007, J SYSTEMS ENG ELECT, V18, P641, DOI 10.1016/S1004-4132(07)60141-8 Mayr P., 2008, ARXIV08063765 Nagpal G, 2012, INT J INF PROCESS SY, V8, P621 Omid M., 2013, J CONVERG, V4, P47 Pan R., 2012, J CONVERG, V3 REDDY MP, 1994, IEEE T KNOWL DATA EN, V6, P920, DOI 10.1109/69.334882 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Teraoka T, 2012, HUMAN CENTRIC COMPUT, V2, P1, DOI [10.1186/2192-1962-2-1, DOI 10.1186/2192-1962-2-1] Valencio C, 2012, HUM CENT COMPUT INF, V2, P1 Wang SH, 2008, LECT NOTES COMPUT SC, V5318, P339 Whitehead C., 1990, BOOK EXTENDING MARC, P81 WIEDERHOLD G, 1992, COMPUTER, V25, P38, DOI 10.1109/2.121508 Yen N. Y., 2012, J CONVERGENCE, V3, P37 NR 31 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-7857 EI 1573-7543 J9 CLUSTER COMPUT JI Cluster Comput. PD MAR PY 2015 VL 18 IS 1 BP 103 EP 112 DI 10.1007/s10586-013-0339-7 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA CC5JS UT WOS:000350395500008 ER PT J AU Banchs, RE D'Haro, LF Li, HZ AF Banchs, Rafael E. D'Haro, Luis F. Li, Haizhou TI Adequacy-Fluency Metrics: Evaluating MT in the Continuous Space Model Framework SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING LA English DT Article DE Machine translation; natural language processing; system evaluation ID NETWORKS AB This work extends and evaluates a two-dimensional automatic evaluation metric for machine translation, which is designed to operate at the sentence level. The metric is based on the concepts of adequacy and fluency, aiming at decoupling both semantic and syntactic components of the translation process to provide a more balanced view on translation quality. These two elements are independently evaluated by using continuous space and -gram language modeling frameworks, respectively. Two different implementations are evaluated: a monolingual version that fully operates on the target language side, and a cross-language version that has the main advantage of not requiring reference translations. Both implementations are evaluated by comparing their performance with state-of-the-art automatic metrics over a dataset involving five different European languages. C1 [Banchs, Rafael E.; D'Haro, Luis F.; Li, Haizhou] Human Language Technol Dept, I2R, Singapore 138632, Singapore. [Li, Haizhou] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore. RP Banchs, RE (reprint author), Human Language Technol Dept, I2R, Singapore 138632, Singapore. EM rembanchs@i2r.a-star.edu.sg; luisdhe@i2r.a-star.edu.sg; hli@i2r.a-star.edu.sg FU Human Language Technology Department of the Institute for Infocomm Research (I2R) FX This work was supported by the Human Language Technology Department of the Institute for Infocomm Research (I2R). The guest editor coordinating the review of this manuscript and approving it for publication was Dr. Bowen Zhou. CR Albrecht J. S., 2007, P ASS COMP LING ACL, P296 Banchs R. E., 2013, APPL ARTIF INTELL, V27, P1 Banchs R. E., 2011, P ACL 11 JUN, P153 Birch Alexandra, 2013, P 8 WORKSH STAT MACH, P52 Blatz J., 2003, WS2003 CLSP SUMM WOR Callison-Burch Chris, 2007, P 2 WORKSH STAT MACH, P136, DOI 10.3115/1626355.1626373 Doddington G., 2002, P HUM LANG TECH C SA Dumais S., 1997, P SIGIR WORKSH CROSS, P16 Gamon M., 2005, P 10 EUR ASS MACH TR, P103 Gao J., 2014, P 52 ANN M ASS COMP, V1, P699 Gers FA, 2001, IEEE T NEURAL NETWOR, V12, P1333, DOI 10.1109/72.963769 Golub G., 1965, J SOC IND APPL MAT B, V2, P205, DOI DOI 10.1137/0702016 Han A. L. F., 2012, P 24 INT C COMP LING Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647 Hovy E., 2002, Machine Translation, V17, DOI 10.1023/A:1025510524115 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lavie Alon, 2009, MACHINE TRANSLATION, V23, P105 Lo C. K., 2014, P 52 ANN M ASS COMP, P765 Lo C. K., 2014, P 14 INT C LANG RES, P602 Lo C. K., 2013, P 8 WORKSH STAT MACH, P422 Lo C. K., 2011, P 49 ANN M ASS COMP, P220 Lopez A, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1380584.1380586 Machacek Matous, 2013, P 8 WORKSH STAT MACH, P45 Manning C. D., 1999, FDN STAT NATURAL LAN Mikolov T, 2010, 11TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2010 (INTERSPEECH 2010), VOLS 1-2, P1045 Mikolov T., 2013, P ADV NEUR INF PROC, V26, P3111 Papineni Kishore, 2002, P 40 ANN M ASS COMP, P311, DOI DOI 10.3115/1073083.1073135 Quirk C. B., 2004, P 4 INT C LANG RES E, P825 Rapp R., 2009, P ACL IJCNLP 2009 C, P133, DOI 10.3115/1667583.1667625 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Snover M., 2009, P 4 WORKSH STAT MACH Snover Matthew, 2006, P 7 BIENN C ASS MACH Socher R., 2011, P C EMP METH NAT LAN, P151 Somers H., 2005, P WORKSH AUSTR LANG, P127 Specia L., 2009, P MT SUMM 12 OTT ON Stolcke A., 2002, P 7 INT C SPOK LANG Tillmann Christoph, 1997, P 5 EUR C SPEECH COM, P2667 Vilar David, 2007, P 2 WORKSH STAT MACH, P96, DOI 10.3115/1626355.1626368 White J., 1994, P 1 C ASS MACH TRANS, P193 Zhang J., 2014, P 52 ANN M ASS COMP, V1, P111 NR 40 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2329-9290 J9 IEEE-ACM T AUDIO SPE JI IEEE-ACM Trans. Audio Speech Lang. PD MAR PY 2015 VL 23 IS 3 BP 472 EP 482 DI 10.1109/TASLP.2015.2405751 PG 11 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA CD2AO UT WOS:000350876100006 ER PT J AU Tang, GY Xia, YQ Cambria, E Jin, P Zheng, TF AF Tang, Guoyu Xia, Yunqing Cambria, Erik Jin, Peng Zheng, Thomas Fang TI Document Representation with Statistical Word Senses in Cross-Lingual Document Clustering SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE LA English DT Article DE Word sense; cross-lingual document representation; cross-lingual document clustering ID MODEL; CLASSIFICATION AB Cross-lingual document clustering is the task of automatically organizing a large collection of multi-lingual documents into a few clusters, depending on their content or topic. It is well known that language barrier and translation ambiguity are two challenging issues for cross-lingual document representation. To this end, we propose to represent cross-lingual documents through statistical word senses, which are automatically discovered from a parallel corpus through a novel cross-lingual word sense induction model and a sense clustering method. In particular, the former consists in a sense-based vector space model and the latter leverages on a sense-based latent Dirichlet allocation. Evaluation on the benchmarking datasets shows that the proposed models outperform two state-of-the-art methods for cross-lingual document clustering. C1 [Tang, Guoyu; Xia, Yunqing; Zheng, Thomas Fang] Tsinghua Univ, TNList, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. [Cambria, Erik] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. [Jin, Peng] Leshan Normal Univ, Sch Comp Sci, Leshan 614000, Peoples R China. RP Xia, YQ (reprint author), Tsinghua Univ, TNList, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. EM sweetyuer@gmail.com; yqxia@tsinghua.edu.cn; cambria@ntu.edu.sg; jandp@pku.edu.cn; fzheng@tsinghua.edu.cn FU National Science Foundation of China (NSFC) [61272233] FX This research work is supported by National Science Foundation of China (NSFC: 61272233). We thank the anonymous reviewers for their valuable comments. CR Agirre Eneko, 2007, P 4 INT WORKSH SEM E, P7, DOI 10.3115/1621474.1621476 Apidianaki M, 2009, P 12 C EUR CHAPT ASS, P77, DOI 10.3115/1609067.1609075 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boyd-Graber J., 2009, P 25 C UNC ART INT, P75 Brody Samuel, 2009, P 12 C EUR CHAPT ASS, P103, DOI 10.3115/1609067.1609078 Cambria E, 2009, LECT NOTES COMPUT SC, V5707, P252, DOI 10.1007/978-3-642-04391-8_33 Cambria E, 2015, NEUROCOMPUTING, V149, P443, DOI 10.1016/j.neucom.2014.01.064 Cambria E, 2014, IEEE COMPUT INTELL M, V9, P48, DOI 10.1109/MCI.2014.2307227 Denkowski M., 2009, LANGUAGE STAT 2 LIT, P1 Dhillon I., 2001, P 7 ACM SIGKDD INT C, P269, DOI DOI 10.1145/502512.502550 Duan JY, 2007, INT J ARTIF INTELL T, V16, P465, DOI 10.1142/S0218213007003369 Evans D. K., 2003, TECHNICAL REPORT Farahat AK, 2011, KNOWL INF SYST, V28, P365, DOI 10.1007/s10115-010-0367-z Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Huang HH, 2010, IEEE T FUZZY SYST, V18, P1098, DOI 10.1109/TFUZZ.2010.2065811 Kishida K, 2011, J INF SCI, V37, P304, DOI 10.1177/0165551511404867 Kong J., 2005, TDT4 MULTILINGUAL BR Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lau RYK, 2014, IEEE COMPUT INTELL M, V9, P31, DOI 10.1109/MCI.2013.2291689 Leek T., 1999, 1999 TDT EVALUATION, P214 Li Y, 2007, INFORM PROCESS MANAG, V43, P1183, DOI 10.1016/j.ipm.2006.11.005 Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9 Mathieu B, 2004, P 7 TRIENN C RECH IN, P116 Mihalcea R. F., 2001, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V10, DOI 10.1142/S0218213001000398 Mimno D., 2009, P 2009 C EMP METH NA, P880, DOI 10.3115/1699571.1699627 Navigli R., 2010, P 2010 C EMP METH NA, P116 Ni X., 2009, P 18 INT C WORLD WID, P1155, DOI 10.1145/1526709.1526904 Pessiot JF, 2010, INFORM PROCESS MANAG, V46, P180, DOI 10.1016/j.ipm.2009.09.007 Pouliquen B., 2004, P 20 INT C COMP LING, P959, DOI 10.3115/1220355.1220493 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt H., 1994, P INT C NEW METH LAN, V12, P44 Schtze H., 1995, P 4 ANN S DOC AN INF, P161 Shi Lei, 2010, P 2010 C EMP METH NA, P1057 Steinbach M, 2000, P KDD WORKSH TEXT MI, V400, P525 Steinberger R., 2005, P 27 INT C INF TECHN Stokoe C, 2003, P 26 ANN INT ACM SIG, P159, DOI DOI 10.1145/860435.860466 Tang G., 2011, P IJCNLP 2010, P580 Teh YW, 2006, J AM STAT ASSOC, V101, P1566, DOI 10.1198/016214506000000302 Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Xia R, 2013, IEEE INTELL SYST, V28, P10 Yao X., 2011, P TEXTGRAPHS 6 GRAPH, P10 Yogatama D., 2009, P 2009 C EMP METH NA, V2, P871, DOI 10.3115/1699571.1699626 Zhai LD, 2011, INT J PATTERN RECOGN, V25, P909, DOI 10.1142/S0218001411008890 NR 46 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-0014 EI 1793-6381 J9 INT J PATTERN RECOGN JI Int. J. Pattern Recognit. Artif. Intell. PD MAR PY 2015 VL 29 IS 2 AR 1559003 DI 10.1142/S021800141559003X PG 26 WC Computer Science, Artificial Intelligence SC Computer Science GA CD0IW UT WOS:000350755900011 ER PT J AU Farjo, J Abou Assi, R Masri, W AF Farjo, Joan Abou Assi, Rawad Masri, Wes TI Reducing execution profiles: techniques and benefits SO SOFTWARE TESTING VERIFICATION & RELIABILITY LA English DT Article DE execution profiles; data mining; redundancy reduction; genetic algorithm; cluster analysis; software testing; test suite minimization; profile-based intrusion and failure detection ID FEATURE-SELECTION; INTRUSION AB The interest in leveraging data mining and statistical techniques to enable dynamic program analysis has increased tremendously in recent years. Researchers have presented numerous techniques that mine and analyze execution profiles to assist software testing and other reliability enhancing approaches. Previous empirical studies have shown that the effectiveness of such techniques is likely to be impacted by the type of profiled program elements. This work further studies the impact of the characteristics of execution profiles by focusing on their size; noting that a typical profile comprises a large number of program elements, in the order of thousands or higher. Specifically, the authors devised six reduction techniques and comparatively evaluated them by measuring the following: (1) reduction rate; (2) information loss; (3) impact on two applications of dynamic program analysis, namely, cluster-based test suite minimization (App-I), and profile-based online failure and intrusion detection (App-II). The results were promising as the following: (a) the average reduction rate ranged from 92% to 98%; (b) three techniques were lossless and three were slightly lossy; (c) reducing execution profiles exhibited a major positive impact on the effectiveness and efficiency of App-I; and (d) reduction exhibited a positive impact on the efficiency of App-II, but a minor negative impact on its effectiveness. Copyright (c) 2014 John Wiley & Sons, Ltd. C1 [Farjo, Joan; Abou Assi, Rawad; Masri, Wes] Amer Univ Beirut, Elect & Comp Engn Dept, Beirut 11072020, Lebanon. RP Masri, W (reprint author), Amer Univ Beirut, Elect & Comp Engn Dept, Beirut 11072020, Lebanon. EM wm13@aub.edu.lb FU Lebanese National Council for Scientific Research FX This work was supported in part by the Lebanese National Council for Scientific Research. CR Abou-Assi R, 2014, REGR ICST 2014 CLEV Abou-Assi R, 2011, 1 INT WORKSH TEST DE Ball Thomas, 1996, MICRO 29, P46, DOI DOI 10.1109/MICRO.1996.566449 Chen Y, 2013, ACM SIGPLAN NOTICES, V48, P197 Cover T. M., 1991, WILEY SERIES TELECOM Dickinson W, 2001, PROC INT CONF SOFTW, P339, DOI 10.1109/ICSE.2001.919107 Dickinson W, 2001, P JOINT 8 EUR SOFTW, P246 Duda R.O., 2001, PATTERN CLASSIFICATI Farjo J, 2014, REGR ICST 2014 CLEV Farjo J, 2013, IEEE ICST WORKSHOP, P400, DOI 10.1109/ICSTW.2013.52 Fodor I. K., 2002, SURVEY DIMENSION RED Francis P., 2004, ISSRE 04, P451 Gu Q, 2011, GEN FISHER SCORE FEA Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 He X, 2005, LAPLACIAN SCORE FEAT John GH, 1994, MACHINE LEARNING Jones JA, 2002, ICSE 2002: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P467 Kononenko I., 1994, EUR C MACH LEARN, V784, P171 Liblit B, 2005, ACM SIGPLAN NOTICES, V40, P15, DOI 10.1145/1064978.1065014 Liu H, 1995, P 7 IEEE INT C TOOLS Masri W, 2008, EMPIR SOFTW ENG, V13, P369, DOI 10.1007/s10664-008-9071-y Masri W, 2012, REGR ICST MONTR CAN Masri W, 2009, EMPIRICAL STUDY FACT Masri W, 2007, IEEE T SOFTWARE ENG, V33, P454, DOI 10.1109/TSE.2007.1020 Masri W, 2010, SOFTW TEST VERIF REL, V20, P121, DOI 10.1002/stvr.409 Masri W, 2010, CLEANSING TEST SUITE Masri W, 2014, REGR ICST Masri W, 2008, COMPUT SECUR, V27, P176, DOI 10.1016/j.cose.2008.06.002 Masri W, 2011, J SYST SOFTWARE, V84, P1171, DOI 10.1016/j.jss.2011.02.007 Masri W, 2014, INFORM SOFTWARE TECH, V56, P238, DOI 10.1016/j.infsof.2013.09.004 Masri W, ACM T SOFTWARE ENG M Masri W, 2009, ACM T SOFTW ENG METH, V19, P33 Mateo-Sanz JM, 2005, DATA MIN KNOWL DISC, V11, P181, DOI 10.1007/s10618-005-0011-9 NARENDRA P, 1977, IEEE T COMPUT, V26, P917 Podgurski A, 2003, PROC INT CONF SOFTW, P465, DOI 10.1109/ICSE.2003.1201224 Press W., 1988, NUMERICAL RECIPES C PUDIL P, 1994, PATTERN RECOGN LETT, V15, P1119, DOI 10.1016/0167-8655(94)90127-9 Raileanu LE, 2004, ANN MATH ARTIF INTEL, V41, P77, DOI 10.1023/B:AMAI.0000018580.96245.c6 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shlens J., 2009, ARXIV14041100 Sikonja M., 2003, MACH LEARN, V53, P23 Song L, 2012, J MACH LEARN RES, V13, P1393 Stearns SD., 1976, SELECTING FEATURES P, P71 Zhao Z, 2013, IEEE T KNOWL DATA EN, V25, P619, DOI 10.1109/TKDE.2011.222 Zhao Z., 2007, ICML, P1151 NR 46 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0960-0833 EI 1099-1689 J9 SOFTW TEST VERIF REL JI Softw. Test. Verif. Reliab. PD MAR PY 2015 VL 25 IS 2 BP 115 EP 137 DI 10.1002/stvr.1563 PG 23 WC Computer Science, Software Engineering SC Computer Science GA CC4FO UT WOS:000350307100003 ER PT J AU Gao, JB Zhang, BW Chen, XH AF Gao, Jian-Bo Zhang, Bao-Wen Chen, Xiao-Hua TI A Word Net-based semantic similarity measurement combining edge-counting and information content theory SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE LA English DT Article DE Semantic similarity; Word Net; Edge-counting; Information content ID MANAGEMENT; RETRIEVAL; FEATURES AB Semantic similarity measuring between words can be applied to many applications, such as Artificial Intelligence, Information Processing, Medical Care and Linguistics. In this paper, we present a new approach for semantic similarity measuring which is based on edge-counting and information content theory. Specifically, the proposed measure nonlinearly transforms the weighted shortest path length between the compared concepts to achieve the semantic similarity results, and the relation between parameters and the correlation value is discussed in detail. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has better distribution characteristics of the correlation coefficient compared with several related works in the literature. In addition, the proposed method is computationally efficient due to the simplified ways of weighting the shortest path length between the concept pairs. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Gao, Jian-Bo; Zhang, Bao-Wen] Shanghai Jiao Tong Univ, Informat Secur Dept, Shanghai 200240, Peoples R China. [Chen, Xiao-Hua] China Informat Secur Certificat Ctr, Beijing 100020, Peoples R China. RP Zhang, BW (reprint author), Shanghai Jiao Tong Univ, Informat Secur Dept, Shanghai 200240, Peoples R China. EM gaojb@sjtu.edu.cn; zhangbw@sjtu.edu.cn; chenxh@isccc.gov.cn FU National Key Basic Research and Development (973) Program of China [2010CB731403]; Information Network Security Key Laboratory Open Project of The Ministry of Public Security [C09603]; Shanghai Key Scientific and Technological Project [11511504302] FX This research is supported in part by the following projects: The National Key Basic Research and Development (973) Program of China (No. 2010CB731403); Information Network Security Key Laboratory Open Project of The Ministry of Public Security (No. C09603); Shanghai Key Scientific and Technological Project (No. 11511504302). CR Bajo J, 2008, ENG APPL ARTIF INTEL, V21, P769, DOI 10.1016/j.engappai.2007.07.006 Batet M, 2013, INFORM SCIENCES, V242, P49, DOI 10.1016/j.ins.2013.04.020 Budanitsky A., 1999, TECHNICAL REPORT Budnitsky A., 2006, COMPUT LINGUIST, V32, P13 Cao H, 2008, J BIOMED INFORM, V41, P882, DOI 10.1016/j.jbi.2008.03.006 Chu HC, 2009, EXPERT SYST APPL, V36, P2360, DOI 10.1016/j.eswa.2007.12.067 Francis W. N., 1979, BROWN CORPUS MANUAL Jabrouni H, 2013, COMPUT IND, V64, P1035, DOI 10.1016/j.compind.2013.07.004 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 Jing LP, 2010, KNOWL INF SYST, V25, P35, DOI 10.1007/s10115-009-0256-5 Li H, 2009, EXPERT SYST APPL, V36, P4363, DOI 10.1016/j.eswa.2008.05.019 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Lin D., 1998, P 15 INT C MACH LEAR, V1, P296 Liu M, 2009, EXPERT SYST APPL, V36, P12480, DOI 10.1016/j.eswa.2009.04.034 Manning C. D., 1999, FDN STAT NATURAL LAN Mares J, 2013, KNOWL-BASED SYST, V49, P134, DOI 10.1016/j.knosys.2013.05.002 McHale M, 1998, P COLING ACL WORKSH, P115 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Nasir J.A., 2013, KNOWL-BASED SYST, V54, P1 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Patwardhan S., 2006, P EACL 2006 WORKSH M, V1501, P1 Petrakis E. G. M., 2006, Journal of Digital Information Management, V4 Pirro G, 2009, DATA KNOWL ENG, V68, P1289, DOI 10.1016/j.datak.2009.06.008 Qi J, 2011, EXPERT SYST APPL, V38, P894, DOI 10.1016/j.eswa.2010.07.067 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 Richardson R., 1994, CA1294 DUBL CIT U SC Robles K, 2012, INFORM SOFTWARE TECH, V54, P72, DOI 10.1016/j.infsof.2011.07.003 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 Ruiz PAP, 2013, KNOWL-BASED SYST, V50, P171, DOI 10.1016/j.knosys.2013.06.005 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez D, 2012, EXPERT SYST APPL, V39, P7718, DOI 10.1016/j.eswa.2012.01.082 Sanchez D, 2013, INFORM SCIENCES, V249, P110, DOI 10.1016/j.ins.2013.06.042 Sanchez D, 2011, APPL INTELL, V34, P311, DOI 10.1007/s10489-009-0197-4 Sanchez D, 2011, KNOWL-BASED SYST, V24, P297, DOI 10.1016/j.knosys.2010.10.001 Seco N, 2004, FR ART INT, V110, P1089 Sole-Ribalta A, 2014, KNOWL-BASED SYST, V55, P101, DOI 10.1016/j.knosys.2013.10.015 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 Uddin MN, 2013, EXPERT SYST APPL, V40, P1645, DOI 10.1016/j.eswa.2012.09.006 Wang TH, 2014, ENG APPL ARTIF INTEL, V27, P167, DOI 10.1016/j.engappai.2013.08.007 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zhao Y, 2012, KNOWL-BASED SYST, V32, P47, DOI 10.1016/j.knosys.2011.08.020 Zheng HT, 2012, INFORM SCIENCES, V216, P138, DOI 10.1016/j.ins.2012.06.012 NR 45 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0952-1976 EI 1873-6769 J9 ENG APPL ARTIF INTEL JI Eng. Appl. Artif. Intell. PD MAR PY 2015 VL 39 BP 80 EP 88 DI 10.1016/j.engappai.2014.11.009 PG 9 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA CB8KL UT WOS:000349878400007 ER PT J AU Granados, A Koroutchev, K Rodriguez, FD AF Granados, Ana Koroutchev, Kostadin de Borja Rodriguez, Francisco TI Discovering Data Set Nature through Algorithmic Clustering Based on String Compression SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Normalized compression distance; data compression; word removal; compression-based text clustering; PPMD order; dendrogram silhouette coefficient; multidimensional projections ID DISTANCE AB Text data sets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the data set nature, there can be advantages using a model that preserves text structure over one that does not, and vice versa. The key is to determine the best way of representing a particular data set, based on the data set itself. In this work, we proposde B"orjae to investigate this problem by combining text distortion and algorithmic clustering based on string compression. Specifically, a distortion technique previously developed by the authors is applied to destroy text structure progressively. Following this, a clustering algorithm based on string compression is used to analyze the effects of the distortion on the information contained in the texts. Several experiments are carried out on text data sets and artificially-generated data sets. The results show that in strongly structural data sets the clustering results worsen as text structure is progressively destroyed. Besides, they show that using a compressor which enables the choice of the size of the left-context symbols helps to determine the nature of the data sets. Finally, the results are contrasted with a method based on multidimensional projections and analogous conclusions are obtained. C1 [Granados, Ana] Univ Complutense Madrid, CES Felipe 2, Aranjuez, Spain. [Granados, Ana; Koroutchev, Kostadin; de Borja Rodriguez, Francisco] Univ Autonoma Madrid, Escuela Politecn Super, Madrid, Spain. RP Granados, A (reprint author), Univ Complutense Madrid, CES Felipe 2, Aranjuez, Spain. EM ana.granados@ajz.ucm.es; k.koroutchev@uam.es; f.rodriguez@uam.es RI Rodriguez, Francisco de Borja/F-7812-2013 OI Rodriguez, Francisco de Borja/0000-0003-4053-099X FU [TIN2010-19607]; [TIN2010-21575-C01] FX The authors would like to thank the anonymous reviewers for their constructive comments on the manuscript. This work was supported by the Spanish projects TIN2010-19607 and TIN2010-21575-C01. CR [Anonymous], MEDLINEPLUS HLTH INF Bloom C., DEOBFUSCATING LZMA BOOTH TL, 1973, IEEE T COMPUT, VC 22, P442, DOI 10.1109/T-C.1973.223746 Burrows M., 1994, 124 DIG SRC RES Cao G, 2005, P 28 ANN INT ACM SIG, P298 Cerra D, 2013, ENTROPY-SWITZ, V15, P407, DOI 10.3390/e15010407 Chau R., 2009, P 32 AUSTR C COMP SC, V91, P141 Chen T.-C., 2010, ACM T INTERNET TECHN, V10 Cilibrasi R, 2004, COMPUT MUSIC J, V28, P49, DOI 10.1162/0148926042728449 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 Cilibrasi R., COMPLEARN TOOLKIT Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 CLEARY JG, 1984, IEEE T COMMUN, V32, P396, DOI 10.1109/TCOM.1984.1096090 Cleary J. G., 1995, Proceedings. DCC '95 Data Compression Conference (Cat. No.95TH8037), DOI 10.1109/DCC.1995.515495 Cohen AR, 2009, IEEE T PATTERN ANAL, V31, P1386, DOI 10.1109/TPAMI.2008.162 Granados A, 2011, IEEE T KNOWL DATA EN, V23, P1090, DOI 10.1109/TKDE.2010.173 Granados A, 2012, EXPERT SYST APPL, V39, P8537, DOI 10.1016/j.eswa.2012.01.215 Granados A, 2012, AI COMMUN, V25, P381, DOI 10.3233/AIC-2012-0529 Granados A, 2014, KNOWL INF SYST, V41, P223, DOI 10.1007/s10115-013-0664-4 Hinton G., 2002, P ADV NEUR INF PROC, V15, P833 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 IMDB, INT MOV DAT Kolmogorov A.N., 1965, Problems of Information Transmission, V1 Kullback S., 1951, ANN MATH STAT, V22, P49 Lavesson N, 2012, KNOWL INF SYST, V32, P167, DOI 10.1007/s10115-011-0438-9 Li M., 1997, INTRO KOLMOGOROV COM Li M, 2004, IEEE T INFORM THEORY, V50, P3250, DOI 10.1109/TIT.2004.838101 Manning C. D., 1999, FDN STAT NATURAL LAN MOFFAT A, 1990, IEEE T COMMUN, V38, P1917, DOI 10.1109/26.61469 Mooney R. J., 2005, ACM SIGKDD EXPLORATI, V7, P3, DOI 10.1145/1089815.1089817 Paulovich FV, 2007, SIBGRAPI, P27, DOI 10.1109/SIBGRAPI.2007.21 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 Salomon D, 2010, HANDBOOK OF DATA COMPRESSION, FIFTH EDITION, P1, DOI 10.1007/978-1-84882-903-9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seward J., BZIP2 Shkarin D., PPMD SIPSER M., 2006, INTRO THEORY COMPUTA, V2nd Stolcke A., 1994, P 2 INT C GRAMM INF, P106 Telles GP, 2007, COMPUT GRAPH-UK, V31, P327, DOI 10.1016/j.cag.2007.01.024 Turing AM, 1937, P LOND MATH SOC, V42, P230 van der Maaten L, 2008, J MACH LEARN RES, V9, P2579 Wallach H. M., 2006, P 23 INT C MACH LEAR, P977, DOI DOI 10.1145/1143844.1143967 Wang XR, 2007, IEEE DATA MINING, P697 ZIV J, 1977, IEEE T INFORM THEORY, V23, P337, DOI 10.1109/TIT.1977.1055714 ZIV J, 1978, IEEE T INFORM THEORY, V24, P530, DOI 10.1109/TIT.1978.1055934 NR 45 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 EI 1558-2191 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAR PY 2015 VL 27 IS 3 BP 699 EP 711 DI 10.1109/TKDE.2014.2345396 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA CB4TT UT WOS:000349621800009 ER PT J AU Sharef, NM Martin, T AF Sharef, Nurfadhlina Mohd Martin, Trevor TI Evolving fuzzy grammar for crime texts categorization SO APPLIED SOFT COMPUTING LA English DT Article DE Evolving fuzzy grammar; Machine learning; Text categorization; Crime; Soft computing; Incremental learning ID SEMANTIC ANALYSIS; CLASSIFICATION; SYSTEM; NETWORK; MODEL; RULE AB Text mining refers to the activity of identifying useful information from natural language text. This is one of the criteria practiced in automated text categorization. Machine learning (ML) based methods are the popular solution for this problem. However, the developed models typically provide low expressivity and lacking in human-understandable representation. In spite of being highly efficient, the ML based methods are established in train-test setting, and when the existing model is found insufficient, the whole processes need to be reinvented which implies train-test-retrain and is typically time consuming. Furthermore, retraining the model is not usually practical and feasible option whenever there is continuous change. This paper introduces the evolving fuzzy grammar (EFG) method for crime texts categorization. In this method, the learning model is built based on a set of selected text fragments which are then transformed into their underlying structure called fuzzy grammars. The fuzzy notion is used because the grammar matching, parsing and derivation involve uncertainty. Fuzzy union operator is also used to combine and transform individual text fragment grammars into more general representations of the learned text fragments. The set of learned fuzzy grammars is influenced by the evolution in the seen pattern; the learned model is slightly changed (incrementally) as adaptation, which does not require the conventional redevelopment. The performance of EFG in crime texts categorization is evaluated against expert-tagged real incidents summaries and compared against C4.5, support vector machines, naive Bayes, boosting, and k-nearest neighbour methods. Results show that the EFG algorithm produces results that are close in performance with the other ML methods while being highly interpretable, easily integrated into a more comprehensive grammar system and with lower model retraining adaptability time. (C) 2014 Elsevier B.V. All rights reserved. C1 [Sharef, Nurfadhlina Mohd] Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Intelligent Comp Grp, Upm Serdang 43400, Selangor, Malaysia. [Martin, Trevor] Univ Bristol, Intelligent Syst Lab, Bristol BS8 1UB, Avon, England. RP Sharef, NM (reprint author), Univ Putra Malaysia, Fac Comp Sci & Informat Technol, Intelligent Comp Grp, Upm Serdang 43400, Selangor, Malaysia. EM nurfadhlina@upm.edu.my; trevor.martin@bristol.ac.uk FU Ministry of Higher Learning Malaysia; Universiti Putra Malaysia, Malaysia FX I wish to extend my greatest appreciation to Professor Dr. Trevor Martin of Intelligent System Lab, University of Bristol, United Kingdom for being my PhD supervisor. It was under his guidance that the EFG method was developed and became the contribution of my PhD work. I address my thankful note to Ministry of Higher Learning Malaysia and Universiti Putra Malaysia, Malaysia as the scholarship provider during the study. CR Achananuparp P, 2008, LECT NOTES COMPUT SC, V5182, P305, DOI 10.1007/978-3-540-85836-2_29 Ali N.M., 2011, P 2 INT C VIS INF, P284 Almeida V, 2011, P 3 INT ACM C WEB SC, P1 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Apte C, 1998, P C AUT LEARN DISC W Baoli L., 2003, INT C COMP PROC OR L Biebow B., 1999, LECT NOTES COMPUTER, P49 Carvalho J.P., 2012, 2012 IEEE INT C FUZZ, P1 Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 Chengdu I.T., 2004, IEEE INT C SYST MAN, P3371 Chiang DA, 2008, EXPERT SYST APPL, V35, P102, DOI 10.1016/j.eswa.2007.06.019 Denoyer L, 2004, INFORM PROCESS MANAG, V40, P807, DOI 10.1016/j.ipm.2004.04.009 Erhardt RAA, 2006, DRUG DISCOV TODAY, V11, P315, DOI 10.1016/j.drudis.2006.02.011 Frasconi P, 2001, P 1 ACM IEEE CS JOIN, P11, DOI 10.1145/379437.379440 Fuller CM, 2011, EXPERT SYST APPL, V38, P8392, DOI 10.1016/j.eswa.2011.01.032 Garciaadeva J., 2007, ENG APPL ARTIF INTEL, V20, P555, DOI 10.1016/j.engappai.2006.09.001 Han E.-H (S.), 2001, LECT NOTES COMPUTER, V2035, P53 Harrag F., 2009, EVALUATION, P110 Hullermeier E, 2009, IEEE T FUZZY SYST, V17, P1233, DOI 10.1109/TFUZZ.2009.2026640 Jiang CT, 2010, KNOWL-BASED SYST, V23, P302, DOI 10.1016/j.knosys.2009.11.010 Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Johnson DE, 2002, IBM SYST J, V41, P428 Lampos V, 2010, 2010 2nd International Workshop on Cognitive Information Processing (CIP 2010), DOI 10.1109/CIP.2010.5604088 Li ZX, 2011, PATTERN RECOGN LETT, V32, P441, DOI 10.1016/j.patrec.2010.11.001 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Liu Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Luo QM, 2011, EXPERT SYST APPL, V38, P12708, DOI 10.1016/j.eswa.2011.04.058 Martin T, 2008, IEEE T FUZZY SYST, V16, P1425, DOI 10.1109/TFUZZ.2008.925920 Martin T, 2009, PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, P986 Martin T., 2008, IEEE INT C FUZZ SYST, P2224 Martin T.P., 2011, 2011 IEEE WORKSH EV, P39 Nasser S., 2007, P IEEE S COMP INT BI, P304 Peng Y, 2011, DECIS SUPPORT SYST, V51, P316, DOI 10.1016/j.dss.2010.11.025 Qiu Q., 2009, P 5 INT C ADV DAT MI, P277 Runciman WB, 2006, QUAL SAF HEALTH CARE, V15, P82 Salton, 1975, COMMUN ACM, V18, P613 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 Segura-Bedmar I, 2008, DRUG DISCOV TODAY, V13, P816, DOI 10.1016/j.drudis.2008.06.001 Sharef NM, 2010, TEXT FRAGMENT IDENTI Sharef N.M., 2009, 9 INT C INT SYST DES, P1221 Sharef N.M., 2010, WORLD C COMP INT, P18 Sharef N.M., 2009, MINIMAL COMBINATION, P909 Sharef NM, 2011, P 3 SEM TECHN KNOWL, P75 Shen Y., 2009, 2009 INT C COMP INT, P74, DOI 10.1109/CIS.2009.10 Sidhu A. S., 2005, J KUALA LUMPUR ROYAL, V4, P1 Stavrianou A, 2007, SIGMOD RECORD, V36, P23, DOI 10.1145/1324185.1324190 Torii M, 2011, INT J MED INFORM, V80, P56, DOI 10.1016/j.ijmedinf.2010.10.015 Uematsu S, 2009, P 11 INT C PARS TECH, P85, DOI 10.3115/1697236.1697254 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Ying-wei L., 2008, 2 INT C FUT GEN COMM, V2, P149 Yu B, 2008, KNOWL-BASED SYST, V21, P900, DOI 10.1016/j.knosys.2008.03.045 Zamil M.G.H.A., 2010, KNOWL-BASED SYST, V24, P58 NR 56 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1568-4946 EI 1872-9681 J9 APPL SOFT COMPUT JI Appl. Soft. Comput. PD MAR PY 2015 VL 28 BP 175 EP 187 DI 10.1016/j.asoc.2014.11.038 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA AZ8GF UT WOS:000348452500019 ER PT J AU Kazemian, HB Ahmed, S AF Kazemian, H. B. Ahmed, S. TI Comparisons of machine learning techniques for detecting malicious webpages SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE K-Nearest Neighbor; Support Vector Machine; Naive Bayes; Affinity Propagation; K-Means; Supervised and unsupervised learning ID VISUAL SIMILARITY ASSESSMENT; TEXT CATEGORIZATION; RETRIEVAL AB This paper compares machine learning techniques for detecting malicious webpages. The conventional method of detecting malicious webpages is going through the black list and checking whether the webpages are listed. Black list is a list of webpages which are classified as malicious from a user's point of view. These black lists are created by trusted organizations and volunteers. They are then used by modern web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues and the crawlers' inability to visit intranet webpages that require computer operators to log in as authenticated users. In this paper therefore alternative and novel approaches are used by applying machine learning algorithms to detect malicious webpages. In this paper three supervised machine learning techniques such as K-Nearest Neighbor, Support Vector Machine and Naive Bayes Classifier, and two unsupervised machine learning techniques such as K-Means and Affinity Propagation are employed. Please note that K-Means and Affinity Propagation have not been applied to detection of malicious webpages by other researchers. All these machine learning techniques have been used to build predictive models to analyze large number of malicious and safe webpages. These webpages were downloaded by a concurrent crawler taking advantage of gevent. The webpages were parsed and various features such as content, URL and screenshot of webpages were extracted to feed into the machine learning models. Computer simulation results have produced an accuracy of up to 98% for the supervised techniques and silhouette coefficient of close to 0.96 for the unsupervised techniques. These predictive models have been applied in a practical context whereby Google Chrome can harness the predictive capabilities of the classifiers that have the advantages of both the lightweight and the heavyweight classifiers. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Kazemian, H. B.; Ahmed, S.] London Metropolitan Univ, Intelligent Syst Res Ctr, Sch Comp, London, England. RP Kazemian, HB (reprint author), London Metropolitan Univ, Intelligent Syst Res Ctr, Sch Comp, London, England. FU Technology Strategy Board KTP, UK [ktp006367] FX The authors would like to thank Technology Strategy Board KTP, UK for their generous support for part of the research [Grant No. ktp006367]. CR Abbasi A, 2012, ACM T INFORM SYST, V30, DOI 10.1145/2382438.2382441 Abbasi A, 2010, MIS QUART, V34, P435 ALTMAN NS, 1992, AM STAT, V46, P175, DOI 10.2307/2685209 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Bannur S. N., 2011, P 4 ACM WORKSH SEC A, P1 Bayes M., 1763, PHILOS T, V53, P370, DOI DOI 10.1098/RSTL.1763.0053 Braganza R., 2006, Network Security, V2006, DOI 10.1016/S1353-4858(06)70425-1 Chang C. C., 2012, LIBSVM LIB SUPPORT V Cohen W., 1996, P 13 NAT C ART INT A, P709 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dueck D, 2009, AFFINITY PROPAGATION Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Facebook, 2010, NETWORK SECURITY, V2010, P20 Frakes W. B., 1992, INFORM RETRIEVAL DAT Fu AY, 2006, IEEE T DEPEND SECURE, V3, P301, DOI 10.1109/TDSC.2006.50 Garera S, 2007, WORM'07: PROCEEDINGS OF THE 2007 ACM WORKSHOP ON RECURRING MALCODE, P1 Guan D., 2009, P JOINT WORKSH INF S Hansen R., 2008, CLICKJACKING Harley D., 2008, 3 INT C MAL UNW SOFT, P98 Jordan A., 2002, ADV NEURAL INFORM PR, V14, P841 Kan M.Y., 2005, P 14 ACM INT C INF K, P325, DOI 10.1145/1099554.1099649 Le A., 2010, CORR Liu WY, 2006, IEEE INTERNET COMPUT, V10, P58 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lucca G. A. D., 2004, P 6 IEEE INT WORKSH, P71 Ma J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1245 Ma J., 2009, P 26 ANN INT C MACH, P681, DOI DOI 10.1145/1553374.1553462 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Malware, 2011, COMPUTER FRAUD SECUR, V2011, P20 Mcgrath D. K., 2008, P USENIX WORKSH LARG Okanovic V., 2011, MIPRO 2011 P 34 INT, P1315 Porter M. F., 1997, READINGS INFORM RETR, P313 Provos N., 2008, P USENIX SEC S, P1 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami Mehran, 1998, LEARN TEXT CAT 1998, V62 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sood Aditya K, 2011, Network Security, V2011, DOI [10.1016/S1353-4858(11)70050-2, 10.1016/S1353-4858(11)70105-2] Spertus E., 1997, P INN APPL ART INT I, P1058 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 43 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD FEB 15 PY 2015 VL 42 IS 3 BP 1166 EP 1177 DI 10.1016/j.eswa.2014.08.046 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AU6TN UT WOS:000345734700017 ER PT J AU Vo, DT Ock, CY AF Vo, Duc-Thuan Ock, Cheol-Young TI Learning to classify short text from scientific documents using topic models with various types of knowledge SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Data sparseness; Information retrieval; Latent Dirichlet Allocation; Short text classification; Topic model ID CATEGORIZATION; RETRIEVAL; MACHINE; WORD AB Classification of short text is challenging due to data sparseness, which is a typical characteristic of short text. In this paper, we propose methods for enhancing features using topic models, which make short text seem less sparse and more topic-oriented for classification. We exploited topic model analysis based on Latent Dirichlet Allocation for enriched datasets, and then we presented new methods for enhancing features by combining external texts from topic models that make documents more effective for classification. In experiments, we utilized the title contents of scientific articles as short text documents, and then enriched these documents using topic models from various types of universal datasets for classification in order to show that our approach performs efficiently. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Vo, Duc-Thuan; Ock, Cheol-Young] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea. RP Ock, CY (reprint author), Univ Ulsan, Sch Elect Engn, 93 Daehak Ro, Ulsan 680749, South Korea. EM thuanvd@gmail.com; okcy@ulsan.ac.kr FU Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2012R1A1A2006906] FX This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012R1A1A2006906). CR Banerjee S., 2007, P ACM SIGIR 2007 Liu B, 2011, DATA CENTRIC SYST AP, P1, DOI 10.1007/978-3-642-19460-3 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bollegala B., 2007, P 16 INT C WORLD WID Cai L., 2003, P ACM SIGIR 2003 Cancedda N, 2003, J MACH LEARN RES, V3, P1059, DOI 10.1162/153244303322533197 Chen M., 2011, P 22 INT JOINT C ART, V3, P1776 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Efron M., 2012, P SIGIR 12 2012 Gabrilovich E., 2007, P 20 INT JOINT C ART Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Heinrich G., 2008, PARAMETER ESTIMATION Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hunnisett D. S., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009129 Joachims T., 2002, THESIS KLUWER Le D. T., 2011, P REC ADV NAT LANG P Letsche TA, 1997, INFORM SCIENCES, V100, P105, DOI 10.1016/S0020-0255(97)00044-3 Lewis D.D., 1998, P 10 EUR C MACH LEAR, V1398, P4 Liu T., 2004, P IEEE INT C DAT MIN Long G., 2012, P ACM CIKM 12 Ma L., 2003, P 14 AUSTR DAT C DAT, V17, P59 McCallum A., 1998, P AAAI 98 WORKSH LEA, P137 Metzler D., 2007, P 29 EUR C IR RES EC Nigam K., 1999, WORKSH MACH LEARN IN, P61 Phan XH, 2011, IEEE T KNOWL DATA EN, V23, P961, DOI 10.1109/TKDE.2010.27 Quan XJ, 2010, KNOWL INF SYST, V25, P473, DOI 10.1007/s10115-009-0250-y Ritter A., 2010, P 48 ANN M ASS COMP, P424 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Speer R., 2013, PEOPLES WEB MEETS NL Stamatatos E, 2000, COMPUT LINGUIST, V26, P471, DOI 10.1162/089120100750105920 Sun A., 2012, P ACM SIGIR 12 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 Yih W., 2007, P 22 NAT C ART INT A Zhang B., 2005, P 14 ACM INT C INF K, P477, DOI 10.1145/1099554.1099688 Zhang W, 2008, KNOWL-BASED SYST, V21, P879, DOI 10.1016/j.knosys.2008.03.044 NR 38 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD FEB 15 PY 2015 VL 42 IS 3 BP 1684 EP 1698 DI 10.1016/j.eswa.2014.09.031 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AU6TN UT WOS:000345734700055 ER PT J AU Zhang, J Wang, XY Hao, D Xie, B Zhang, L Mei, H AF Zhang Jie Wang XiaoYin Hao Dan Xie Bing Zhang Lu Mei Hong TI A survey on bug-report analysis SO SCIENCE CHINA-INFORMATION SCIENCES LA English DT Review DE survey; bug report; bug-report analysis; bug-report triage; bug localization; bug fixing ID INFORMATION-RETRIEVAL; TRACKING-SYSTEMS; DEFECT REPORTS; DUPLICATE; TRIAGE; PRIORITIZATION; SEVERITY; LOCATION; PRIORITY; PROGRAM AB Bug reports are essential software artifacts that describe software bugs, especially in open-source software. Lately, due to the availability of a large number of bug reports, a considerable amount of research has been carried out on bug-report analysis, such as automatically checking duplication of bug reports and localizing bugs based on bug reports. To review the work on bug-report analysis, this paper presents an exhaustive survey on the existing work on bug-report analysis. In particular, this paper first presents some background for bug reports and gives a small empirical study on the bug reports on Bugzilla to motivate the necessity for work on bug-report analysis. Then this paper summaries the existing work on bug-report analysis and points out some possible problems in working with bug-report analysis. C1 [Zhang Jie; Hao Dan; Xie Bing; Zhang Lu; Mei Hong] Peking Univ, Minist Educ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China. [Wang XiaoYin] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX USA. RP Hao, D (reprint author), Peking Univ, Minist Educ, Key Lab High Confidence Software Technol, Beijing 100871, Peoples R China. EM haodan@pku.edu.cn; zhanglucs@pku.edu.cn FU National Basic Research Program of China (973 Program) [2015CB3522-01]; National High-tech R&D Program of China (863 Program) [2013AA01A605]; National Natural Science Foundation of China [61228203, 61272157]; Fund for the Advisors of Beijing Excellent Doctoral Dissertations [20131000111] FX This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2015CB3522-01), National High-tech R&D Program of China (863 Program) (Grant No. 2013AA01A605), National Natural Science Foundation of China (Grant Nos. 61228203, 61272157), and Fund for the Advisors of Beijing Excellent Doctoral Dissertations (Grant No. 20131000111). CR Alenezi Mamdouh, 2013, Journal of Software, V8, DOI 10.4304/jsw.8.9.2185-2190 Alipour A, 2013, P WORK C MIN SOFTW R, P183 Aljarah I., 2011, P 7 INT C PRED MOD S, P12 Anbalagan P, 2009, PROC IEEE INT CONF S, P523, DOI 10.1109/ICSM.2009.5306337 Antoniol G, 2008, P 2008 C CTR ADV STU, P23 Anvik J., 2005, P OOPSLA WORKSH ECL, P35, DOI DOI 10.1145/1117696.1117704 Anvik J., 2006, P 28 INT C SOFTW ENG, P937, DOI 10.1145/1134285.1134457 Anvik J, 2006, P 28 INT C SOFTW ENG, V2006, P361 Ashok B, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P373 Bachmann A., 2010, P 18 ACM SIGSOFT INT, P97 Banerjee S, 2012, P INT IEEE S HIGH AS, P74 BAYSAL O, 2009, P IEEE INT C PROGR C, P297 Bettenburg N., 2008, P 16 ACM SIGSOFT INT, P308, DOI 10.1145/1453101.1453146 Bettenburg N, 2008, PROC IEEE INT CONF S, P337, DOI 10.1109/ICSM.2008.4658082 Bettenburg N., 2007, P 2007 OOPSLA WORKSH, P21, DOI DOI 10.1145/1328279.1328284 Bhattacharya P, 2010, P IEEE INT C SOFTW M, P1 Bhattacharya P, 2013, EUR CON SFTWR MTNCE, P133, DOI 10.1109/CSMR.2013.23 Bhattacharya P., 2011, P 8 WORK C MIN SOFTW, P207 Bird C, 2010, P 18 ACM SIGSOFT INT, P369 Bird C, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P121, DOI 10.1145/1595696.1595716 Bissyande TF, 2013, EUR CON SFTWR MTNCE, P89, DOI 10.1109/CSMR.2013.19 Breu S, 2010, 2010 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, P301 Canfora G, 2006, P ACM S APPL COMP SA, V2, P1767 Cavalcanti Yguarata Cerqueira, 2010, Proceedings of the 14th European Conference on Software Maintenance and Reengineering (CSMR 2010), DOI 10.1109/CSMR.2010.52 Cavalcanti YC, 2013, SOFTWARE QUAL J, V21, P39, DOI 10.1007/s11219-011-9164-5 Chawla I, 2013, P INT C CONT COMP NO, P375 Cubranic D, 2005, IEEE T SOFTWARE ENG, V31, P446, DOI 10.1109/TSE.2005.71 Cubranic D., 2004, P 16 INT C SOFTW ENG, P92 Dang YN, 2012, PROC INT CONF SOFTW, P1084 Davidson JL, 2011, S VIS LANG HUM CEN C, P101, DOI 10.1109/VLHCC.2011.6070386 Davies S, 2012, P 2012 19 WORK C REV, P125 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Falessi D, 2013, IEEE T SOFTWARE ENG, V39, P18, DOI 10.1109/TSE.2011.122 Feng L, 2013, P AS PAC WEB C WEB T, P709 Fischer M, 2003, PROC IEEE INT CONF S, P23, DOI 10.1109/ICSM.2003.1235403 Fischer Michael, 2003, P 10 WORK C REV ENG, P90 Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315 Giger E., 2010, P 2 INT WORKSH REC S, P52, DOI 10.1145/1808920.1808933 Guo PJ, 2010, P 32 INT C SOFTW ENG, P495 Guo P.J., 2011, P ACM 2011 C COMP SU, P395 Herraiz I, 2008, P 2008 INT WORK C MI, P145, DOI 10.1145/1370750.1370786 Herzig K, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P392 Hiew L., 2006, THESIS U BRIT COLUMB Hooimeijer P., 2007, P 22 IEEE ACM INT C, P34, DOI 10.1145/1321631.1321639 Hu H, 2014, P IEEE INT S SOFTW R, P122 Jalbert N, 2008, I C DEPEND SYS NETWO, P52, DOI 10.1109/DSN.2008.4630070 Jeong G, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P111, DOI 10.1145/1595696.1595715 Kanwal J, 2012, J COMPUT SCI TECH-CH, V27, P397, DOI 10.1007/s11390-012-1230-3 Kevic K, 2013, 2013 6TH INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING (CHASE), P17 Kim D, 2013, IEEE T SOFTWARE ENG, V39, P1597, DOI 10.1109/TSE.2013.24 Kim S, 2011, I C DEPEND SYS NETWO, P486 Ko A J, 2011, P ICONFERENCE, P106, DOI 10.1145/1940761.1940776 Ko A J, 2006, P 2006 IEEE S VIS LA, P127, DOI DOI 10.1109/VLHCC.2006.3 Lamkanfi Ahmed, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463284 Lamkanfi A, 2013, EUR CON SFTWR MTNCE, P327, DOI 10.1109/CSMR.2013.42 Lamkanfi A, 2011, EUR CON SFTWR MTNCE, P249, DOI 10.1109/CSMR.2011.31 Lerch J, 2013, EUR CON SFTWR MTNCE, P69, DOI 10.1109/CSMR.2013.17 Li W, 2012, SCI CHINA INFORM SCI, V55, P133, DOI 10.1007/s11432-011-4530-2 Lin Z, 2009, P 2009 3 INT S EMP S, P451 Liu D., 2007, P 22 IEEE ACM INT C, P234, DOI DOI 10.1145/1321631.1321667 Liu K, 2012, P ACM SIGSOFT 20 INT, P28 Lukins SK, 2008, WORK CONF REVERSE EN, P155, DOI 10.1109/WCRE.2008.33 Lukins SK, 2010, INFORM SOFTWARE TECH, V52, P972, DOI 10.1016/j.infsof.2010.04.002 Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10 Marcus A, 2005, PROG COMPREHEN, P33, DOI 10.1109/WPC.2005.33 Matter D, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P131, DOI 10.1109/MSR.2009.5069491 Menzies T, 2008, PROC IEEE INT CONF S, P346, DOI 10.1109/ICSM.2008.4658083 Nguyen A T, 2011, P 26 IEEE ACM INT C, P263 Nguyen A. T., 2012, P ACM SIGSOFT 20 INT, P63 Nguyen AT, 2012, IEEE INT CONF AUTOM, P70 Nguyen T H D, 2010, Proceedings 17th Working Conference on Reverse Engineering (WCRE 2010), DOI 10.1109/WCRE.2010.37 Panjer L.D., 2007, P 4 INT WORKSH MIN S, P29 Park J., 2011, P 25 C ART INT AAAI, P139 Pingclasai N, 2013, P AS PAC SOFTW ENG C, P13 Podgurski A, 2003, PROC INT CONF SOFTW, P465, DOI 10.1109/ICSE.2003.1201224 Poshyvanyk D, 2007, INT C PROGRAM COMPRE, P37 Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Rao S., 2011, P 8 WORK C MIN SOFTW, P43, DOI DOI 10.1145/1985441.1985451 Rastkar S., 2010, P 32 ACM IEEE INT C, P505 Raymond E., 1999, KNOWLEDGE TECHNOLOGY, V12, P23, DOI DOI 10.1007/S12130-999-1026-0 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 Runeson P, 2007, PROC INT CONF SOFTW, P499 Saha R K, 2014, P SOFTW EV WEEK IEEE, P144 Saha RK, 2013, IEEE INT CONF AUTOM, P345, DOI 10.1109/ASE.2013.6693093 Sahoo SK, 2010, P 32 ACM IEEE INT C, V1, P485, DOI 10.1145/1806799.1806870 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schroter A., 2006, P 5 INT S EMP SOFTW, V2, P18 Servant F, 2012, PROC INT CONF SOFTW, P36, DOI 10.1109/ICSE.2012.6227208 Shokripour R., 2013, P 10 INT WORKSH MIN, P2 Si XS, 2010, SCI CHINA INFORM SCI, V53, P2032, DOI 10.1007/s11432-010-4073-y Sliwerski J, 2005, ACM SIGSOFT SOFTWARE, V30, P1, DOI DOI 10.1145/1083142.1083147 Song Y, 2010, PROCEEDINGS OF THE 2010 CONFERENCE ON COMPUTER SCIENCE IN SPORTS, P315 Sun C, 2010, P 32 ACM IEEE INT C, V1, P45, DOI DOI 10.1145/1806799.1806811 Sun C., 2011, P 26 IEEE ACM INT C, P253 Sureka Ashish, 2010, Proceedings 2010 17th Asia Pacific Software Engineering Conference (APSEC 2010). Software for Improving Quality of Life, DOI 10.1109/APSEC.2010.49 Tamrawi A., 2011, P 33 INT C SOFTW ENG, P884 Tian Y, 2012, EUR CON SFTWR MTNCE, P385, DOI 10.1109/CSMR.2012.48 Tian Y, 2012, APPL MECH MATER, V166-169, P215, DOI 10.4028/www.scientific.net/AMM.166-169.215 Tian Y, 2013, PROC IEEE INT CONF S, P200, DOI 10.1109/ICSM.2013.31 Toutanova K., 2003, P 2003 C N AM CHAPT, V1, P173, DOI DOI 10.3115/1073445.1073478 Wang J, 2012, INT SYMP EMP SOFTWAR, P191 Wang XH, 2009, PROCEEDINGS OF THE 2009 PACIFIC-ASIA CONFERENCE ON CIRCUITS, COMMUNICATIONS AND SYSTEM, P197, DOI 10.1109/PACCS.2009.153 WANG XY, 2008, ICS P 30 INT C, P461 Weiss C., 2007, P 4 INT WORKSH MIN S, P1, DOI DOI 10.1109/MSR.2007.13 William C, 1995, P 12 INT C MACH LEAR, P115 Wong C P, 2014, P IEEE INT C SOFTW M, P181 Wu Leon, 2011, P 25 INT C SOFTW ENG, P95 Wu R., 2011, P 19 ACM SIGSOFT S 1, P15 Xia X, 2014, P SOFTW EV WEEK IEEE, P174 Xia X, 2013, WORK CONF REVERSE EN, P72 Xie J, 2013, P ACM IEEE INT S EMP, P247 Xie J, 2014, P 36 INT C SOFTW ENG, P556 Xie T, 2014, J COMPUT SCI TECH-CH, V29, P713, DOI 10.1007/s11390-014-1461-6 Xuan J, 2010, P 22 INT C SOFTW ENG, P209 Xuan JF, 2012, PROC INT CONF SOFTW, P25 Ye X, 2014, P ACM SIGSOFT INT S, P66 Yu L, 2010, LECT NOTES ARTIF INT, V6441, P356 Zanetti MS, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P1032 Zhang HY, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P1042 Zhou J, 2012, P 21 ACM INT C INF K, P852 Zhou J, 2012, PROC INT CONF SOFTW, P14 Zimmermann T., 2007, P 3 INT WORKSH PRED, P9, DOI 10.1109/PROMISE.2007.10 Zou WQ, 2011, P INT COMP SOFTW APP, P576, DOI 10.1109/COMPSAC.2011.80 NR 123 TC 0 Z9 0 PU SCIENCE PRESS PI BEIJING PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA SN 1674-733X EI 1869-1919 J9 SCI CHINA INFORM SCI JI Sci. China-Inf. Sci. PD FEB PY 2015 VL 58 IS 2 AR 021101 DI 10.1007/s11432-014-5241-2 PG 24 WC Computer Science, Information Systems SC Computer Science GA CC7FV UT WOS:000350534000008 ER PT J AU Green, CD Feinerer, I Burman, JT AF Green, Christopher D. Feinerer, Ingo Burman, Jeremy T. TI SEARCHING FOR THE STRUCTURE OF EARLY AMERICAN PSYCHOLOGY: Networking Psychological Review, 1894-1908 SO HISTORY OF PSYCHOLOGY LA English DT Article DE psychology; history; journal; vocabulary; network; digital ID ONE MENTAL FUNCTION; SECONDARY SENSORY ELEMENTS; FUNCTIONAL PSYCHOLOGY; NERVOUS CORRELATE; VISUAL-FIELD; IMPROVEMENT; EFFICIENCY; UNPLEASANTNESS; CONSCIOUSNESS; PLEASANTNESS AB This study investigated the intellectual structure of early American psychology by generating 3 networks that collectively included every substantive article published in Psychological Review during the 15-year period from the journal's start in 1894 until 1908. The networks were laid out so that articles with strongly correlated vocabularies were positioned close to each other spatially. Then, we identified distinct research communities by locating and interpreting article clusters within the networks. We found that, from the first 5-year time block to the second, psychological specialties rapidly differentiated themselves from each other. Between the second and third 5-year time blocks, however, the number of specialties shrunk. We discuss the degree to which this shift may have been attributable either to a change in the journal's editorship in 1904, or to a broader crisis of confidence, beginning that same year, in the use of "consciousness" as the discipline's defining concept. C1 [Green, Christopher D.; Burman, Jeremy T.] York Univ, Dept Psychol, Toronto, ON M3J 1P3, Canada. [Feinerer, Ingo] Vienna Univ Technol, Dept Psychol, Vienna, Austria. RP Green, CD (reprint author), York Univ, Dept Psychol, Toronto, ON M3J 1P3, Canada. EM christo@yorku.ca FU Social Sciences & Humanities Research Council of Canada FX This work was financially supported by a grant from the Social Sciences & Humanities Research Council of Canada. CR Abbott A. H., 1900, U TORONTO MONTHLY, V1, P106 Abbott A. H., 1900, U TORONTO MONTHLY, V1, P85 Alexander HB, 1904, PSYCHOL REV, V11, P319, DOI 10.1037/h0069793 Angell J. R., 1896, PSYCHOL REV, V3, P245, DOI 10.1037/h0069918 Angell JR, 1907, PSYCHOL REV, V14, P61, DOI 10.1037/h0070817 [Anonymous], 2009, P INT AAAI C WEBL SO Baldwin J. M., 1894, PSYCHOL REV, V1, P363, DOI 10.1037/h0066737 Baldwin JM, 1905, PSYCHOL REV, V12, P144 Baldwin JM, 1904, PSYCHOL REV, V11, P216, DOI 10.1037/h0069765 Baldwin JM, 1906, PSYCHOL REV, V13, P388, DOI 10.1037/h0072399 Baldwin JM, 1904, PSYCHOL REV, V11, P30, DOI 10.1037/h0074043 Bawden H. H., 1910, PRINCIPLES OF PRAGMA Bawden HH, 1908, PSYCHOL REV, V15, P217, DOI 10.1037/h0073010 Buchner EF, 1900, PSYCHOL REV, V7, P494, DOI 10.1037/h0067763 Caldwell W., 1899, PSYCHOL REV, V6, P187, DOI 10.1037/h0075735 Calkins M. W., 1896, PSYCHOL REV, V3, P426, DOI 10.1037/h0064618 Calkins MW, 1906, PSYCHOL REV, V13, P61, DOI 10.1037/h0071994 Carr H, 1908, PSYCHOL REV, V15, P139, DOI 10.1037/h0070815 Carr H, 1907, PSYCHOL REV, V14, P357, DOI 10.1037/h0075612 Cattell J. M., 1896, PSYCHOL REV, V3, P134, DOI DOI 10.1037/H0071520 Cattell J. M., 1896, PSYCHOL REV, V3, P618, DOI DOI 10.1037/H0070786 Cattell J. Mc K., 1898, PSYCHOL REV, V5, P655, DOI 10.1037/h0065060 Colvin S. S., 1908, PSYCHOL REV, V15, P158, DOI 10.1037/h0068608 Davies H., 1898, PSYCHOL REV, V5, P183, DOI 10.1037/h0065900 Dearborn WF, 1904, PSYCHOL REV, V11, P297, DOI 10.1037/h0070748 Dewey J, 1900, Science, V11, P321, DOI 10.1126/science.11.270.321 Dewey J., 1896, PSYCHOL REV, V3, P357, DOI [10.1037/h0070405, DOI 10.1037/H0070405] Dodge R, 1904, PSYCHOL REV, V11, P1, DOI 10.1037/h0071641 Feinerer I., 2012, TM TEXT MINING R PAC Feinerer I, 2008, J STAT SOFTW, V25, P1 Fullerton G. S., 1897, PSYCHOL REV, V4, P1, DOI 10.1037/h0073810 GIERYN TF, 1983, AM SOCIOL REV, V48, P781, DOI 10.2307/2095325 Green CD, 2013, J HIST BEHAV SCI, V49, P167, DOI 10.1002/jhbs.21592 Haines TH, 1905, PSYCHOL REV, V12, P18, DOI 10.1037/h0076048 Herrick CL, 1907, PSYCHOL REV, V14, P205, DOI 10.1037/h0075503 Herrick CL, 1904, PSYCHOL REV, V11, P395, DOI 10.1037/h0069521 Hornik K, 2012, J STAT SOFTW, V50, P1 Howison G. H., 1896, PSYCHOL REV, V3, P652, DOI 10.1037/h0066187 Hylan JP, 1903, PSYCHOL REV, V10, P373, DOI 10.1037/h0070820 JAMES W., 1904, J PHILOS PSYCHOL SCI, V1, P533, DOI 10.2307/2011912 JAMES W., 1904, J PHILOS PSYCHOL SCI, V1, P561, DOI 10.2307/2011356 James W., 1904, J PHILOS PSYCHOL SCI, V1, P477, DOI DOI 10.2307/2011942 Jastrow J., 1894, PSYCHOL REV, V1, P152, DOI 10.1037/h0069606 Jastrow J, 1901, PSYCHOL REV, V8, P1, DOI 10.1037/h0075124 Jastrow J., 1896, PSYCHOL REV, V3, P430, DOI 10.1037/h0069042 Ladd G. T., 1899, PSYCHOL REV, V6, P121, DOI 10.1037/h0074123 Ladd G. T., 1897, PSYCHOL REV, V4, P180, DOI 10.1037/h0065419 Manning C. D., 2008, AN INTRODUCTION TO I Meyer M, 1908, PSYCHOL REV, V15, P292, DOI 10.1037/h0070891 Meyer M, 1908, PSYCHOL REV, V15, P201, DOI 10.1037/h0070569 Moretti Franco, 2005, GRAPHS MAPS TREES AB Munsterberg H., 1899, PSYCHOL REV, V6, P1, DOI 10.1037/h0071306 Nevers C. C., 1895, PSYCHOL REV, V2, P363, DOI 10.1037/h0074975 Perry RB, 1904, PSYCHOL REV, V11, P282, DOI 10.1037/h0075223 Pillsbury WB, 1908, PSYCHOL REV, V15, P150, DOI 10.1037/h0075505 Pratt JB, 1906, PSYCHOL REV, V13, P50, DOI 10.1037/h0067044 R Core Team, 2013, R LANG ENV STAT COMP Royce J, 1902, PSYCHOL REV, V9, P105, DOI 10.1037/h0075605 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanford EC, 1903, PSYCHOL REV, V10, P105, DOI 10.1037/h0071541 Sellars RW, 1907, PSYCHOL REV, V14, P315, DOI 10.1037/h0073208 Sidis B, 1908, PSYCHOL REV, V15, P106, DOI 10.1037/h0075219 Sidis B, 1908, PSYCHOL REV, V15, P44, DOI 10.1037/h0074092 Sokal MM, 1997, HIST HUM SCI, V10, P57, DOI 10.1177/095269519701000104 Stratton G. M., 1896, PSYCHOL REV, V3, P611, DOI 10.1037/h0072918 Stratton GM, 1906, PSYCHOL REV, V13, P82, DOI 10.1037/h0072441 Strong C. A., 1895, PSYCHOL REV, V2, P329, DOI 10.1037/h0075245 Strong C. A., 1896, PSYCHOL REV, V3, P64, DOI 10.1037/h0067290 Tanner A., 1896, PSYCHOL REV, V3, P548, DOI 10.1037/h0068394 Thorndike EL, 1901, PSYCHOL REV, V8, P247 Urban WM, 1907, PSYCHOL REV, V14, P1, DOI 10.1037/h0075818 Washburn MF, 1900, PSYCHOL REV, V7, P39, DOI 10.1037/h0068553 Woodworth R. S., 1931, CONTEMPORARY SCHOOLS NR 73 TC 0 Z9 0 PU EDUCATIONAL PUBLISHING FOUNDATION-AMERICAN PSYCHOLOGICAL ASSOC PI WASHINGTON PA 750 FIRST ST, NE, WASHINGTON, DC 20002-4242 USA SN 1093-4510 EI 1939-0610 J9 HIST PSYCHOL JI Hist. Psychol. PD FEB PY 2015 VL 18 IS 1 BP 15 EP 31 DI 10.1037/a0038406 PG 17 WC History Of Social Sciences; Psychology, Multidisciplinary SC Social Sciences - Other Topics; Psychology GA CB4QW UT WOS:000349613900002 PM 25664883 ER PT J AU Vandevenne, D Verhaegen, PA Dewulf, S Duflou, JR AF Vandevenne, Dennis Verhaegen, Paul-Armand Dewulf, Simon Duflou, Joost R. TI A scalable approach for ideation in biologically inspired design SO AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING LA English DT Article DE Bioinspired Design; Biomimicry; Creativity and Ideation ID BIOMIMETIC DESIGN; GENERATION; RETRIEVAL AB This paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents. C1 [Vandevenne, Dennis; Verhaegen, Paul-Armand; Duflou, Joost R.] Katholieke Univ Leuven, Ctr Ind Management, Dept Mech Engn, B-3001 Leuven, Belgium. RP Vandevenne, D (reprint author), Katholieke Univ Leuven, Ctr Ind Management, Dept Mech Engn, Celestijnenlaan 300A, B-3001 Leuven, Belgium. EM dennis.vandevenne@kuleuven.be CR Altshuller G S, 1984, CREATIVITY EXACT SCI Attenborough D., 1979, LIFE EARTH Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE Bar-Cohen Y., 2011, BIOMIMETICS NATURE B Benyus Janine M., 1997, BIOMIMICRY INNOVATIO Gerner M, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-85 Brants T., 2000, P 6 C APPL NAT LANG, P224, DOI 10.3115/974147.974178 Chakrabarti A, 2005, AI EDAM, V19, P113, DOI 10.1017/S0890060405050109 Charniak E, 1997, AI MAG, V18, P33 Cheong H., 2009, P ASME 2009 INT DES Cheong H., 2011, J MECH DESIGN, V133 Cheong H., 2012, P ASME 2012 INT DES Chiu I, 2007, AI EDAM, V21, P45, DOI 10.1017/S0890060407070138 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 D'hondt J, 2011, INFORM SCIENCES, V181, P3783, DOI 10.1016/j.ins.2011.04.044 Fox C, 1989, ACM SIGIR FORUM, V24, P19 Gentner D, 1997, AM PSYCHOL, V52, P45, DOI 10.1037/0003-066X.52.1.45 Goel AK, 2012, COMPUT AIDED DESIGN, V44, P879, DOI 10.1016/j.cad.2011.03.010 Haddock SHD, 2010, ANNU REV MAR SCI, V2, P443, DOI 10.1146/annurev-marine-120308-081028 International Organization of Standardization, 2006, ISO14040 Lenau T, 2010, 11TH INTERNATIONAL DESIGN CONFERENCE (DESIGN 2010), VOL 1-3, P331 Linnaeus C, 1767, SYSTEMA NATURAE Nagel JKS, 2012, AI EDAM, V26, P161, DOI 10.1017/S0890060412000054 Nagel JKS, 2010, AI EDAM, V24, P521, DOI 10.1017/S0890060410000375 Neinhuis C, 1997, ANN BOT-LONDON, V79, P667, DOI 10.1006/anbo.1997.0400 Purves W. K., 2001, LIFE SCI BIOL, V6th SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sartori J, 2010, AI EDAM, V24, P483, DOI 10.1017/S0890060410000351 Shu LH, 2010, AI EDAM, V24, P507, DOI 10.1017/S0890060410000363 Srinivasan V., 2012, P CIRP DES 2012, P17 Stark M., 1998, WORDNET ELECT LEXICA Vandevenne D, 2011, INT CONF ENG DES, V6, P210 Vandevenne D., 2012, P CIRP DES 2012, P83 Vandevenne D., 2012, P IDETC2012, P383 Vattam S., 2010, P INT C DES CREAT, P115 Vattam S., 2011, P 6 INT C KNOWL CAPT, P165 Verhaegen PA, 2011, COMPUT IND, V62, P446, DOI 10.1016/j.compind.2010.12.007 Vincent JFV, 2006, J R SOC INTERFACE, V3, P471, DOI 10.1098/rsif.2006.0127 NR 39 TC 0 Z9 0 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA SN 0890-0604 EI 1469-1760 J9 AI EDAM JI AI EDAM-Artif. Intell. Eng. Des. Anal. Manuf. PD FEB PY 2015 VL 29 IS 1 BP 19 EP 31 DI 10.1017/S0890060414000122 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary; Engineering, Manufacturing SC Computer Science; Engineering GA AZ8YB UT WOS:000348496500002 ER PT J AU Wu, H Pei, YJ Li, B Kang, ZZ Liu, XX Li, H AF Wu, Hao Pei, Yijian Li, Bo Kang, Zongzhan Liu, Xiaoxin Li, Hao TI Item recommendation in collaborative tagging systems via heuristic data fusion SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Collaborative tagging systems; Recommender systems; Data fusion; Item recommendation; Performance comparison ID OF-THE-ART; INFORMATION-RETRIEVAL; DIVERSITY; MODELS AB Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we examine if data fusion can be helpful for improving effectiveness of item recommendation in these systems. For this, we first summarize the state-of-the-art recommendation methods which are classified into several categories according to their algorithmic principles. Then, we experiment with about 40 recommending components against the datasets from three social tagging systems-Delicious, Lastfm and CiteULike. Based on these, several heuristic data fusion models including rank-based and score-based are used to combine selected components. We also put forward a hybrid linear combination (HLC) model for fusing item recommendation. We use four kinds of evaluation metrics, which respectively consider accuracy, inner-diversity, inter-diversity and novelty, to systematically assess quality of recommendations obtained by various components or fusion models. Depending on experimental results, combining evidence from separate components can lead to performance improvement in the accuracy of recommendations, with a little or without loss of recommendation diversity and novelty, if separate components can suggest similar sets of relevant items but recommend different sets of non-relevant items. Particularly, fusing recommendation sets formed from different combinations of profile representations and similarity functions in user-based and item-based collaborative filtering can significantly improve recommendation accuracy. In addition, some other useful findings are also drawn: (i) Using the tag to represent users profiles or items profiles maybe not as good as profiling users with the item or profiling items with the user, however, exploiting tags in the topic models and random walks can notably improve the accuracy, diversity and novelty of recommendations; (ii) Generally, user-based collaborative filtering, item-based collaborative filtering and random walks methods are robust for the task of item recommendation in social tagging systems, thus can be chosen as the basic components of data fusion process; and (iii) The proposed method (HLC) is more flexible and robust than traditional data fusion models. (C) 2014 Elsevier B.V. All rights reserved. C1 [Wu, Hao; Pei, Yijian; Li, Bo; Kang, Zongzhan; Liu, Xiaoxin; Li, Hao] Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Peoples R China. RP Li, B (reprint author), Yunnan Univ, Sch Informat Sci & Engn, 2 North Green Lake Rd, Kunming 650091, Peoples R China. EM haowu@ynu.edu.cn; pei3p@ynu.edu.cn; libo@ynu.edu.cn; kangzongzhan@gmail.com; lxxs369@163.com; lihao0730@sina.com FU Applied Basic Research Project of Yunnan Province [2013FB009, 2014FA023]; Special Funds for "Middle-aged and Young Core Instructor Training Program" of Yunnan University [61472345]; National Natural Science Foundation of China [61472345] FX This work is supported by the Applied Basic Research Project of Yunnan Province (2013FB009 and 2014FA023) and the Special Funds for "Middle-aged and Young Core Instructor Training Program" of Yunnan University, the National Natural Science Foundation of China (61472345). We are grateful to anonymous reviewers for their constructive comments and suggestions which contribute to substantially improving this paper. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Adomavicius G, 2012, IEEE T KNOWL DATA EN, V24, P896, DOI 10.1109/TKDE.2011.15 Ah-Pine J., 2011, AGENT SYST AN INT J, V9, P43 Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Ben-Shimon D, 2007, ADV SOFT COMP, V43, P47, DOI 10.1007/978-3-540-72575-6_8 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bogers T, 2011, INT J ELECTRON COMM, V15, P31, DOI 10.2753/JEC1086-4415150303 Bradley K., 2001, P 12 NAT C ART INT C, P75 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Burke R., 2007, LNCS, V4321, P377, DOI DOI 10.1109/WAINA.2009.127 Cai Yi, 2010, P 19 ACM INT C INF K, P969, DOI 10.1145/1871437.1871561 Cantador I., 2010, P 4 ACM C REC SYST R, P237, DOI DOI 10.1145/1864708.1864756 Cantador I., 2011, P 5 ACM C REC SYST, P387 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Gemmell J, 2012, J COMPUT SYST SCI, V78, P1160, DOI 10.1016/j.jcss.2011.10.006 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Gupta M., 2010, ACM SIGKDD EXPLORATI, V12, P58, DOI 10.1145/1882471.1882480 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Hsu DF, 2005, INFORM RETRIEVAL, V8, P449, DOI 10.1007/s10791-005-6994-4 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jaschke R, 2008, AI COMMUN, V21, P231, DOI 10.3233/AIC-2008-0438 Jones K. S., 2000, Information Processing & Management, V36 Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 LEVANDOW.M, 1971, NATURE, V234, P34, DOI 10.1038/234034a0 Li H., 2014, P 19 INT C DAT SYST, P192 Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9 Macdonald C, 2009, COMPUT J, V52, P729, DOI 10.1093/comjnl/bxm112 Macgregor G., 2006, Library Review, V55, DOI 10.1108/00242530610667558 Milicevic A.K., 2009, ARTIF INTELL REV, V33, P187 Montague M., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.026118 Rosen-Zvi M, 2010, ACM T INFORM SYST, V28, DOI 10.1145/1658377.1658381 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shambour Q, 2012, DECIS SUPPORT SYST, V54, P768, DOI 10.1016/j.dss.2012.09.005 Shaw J.A., 1994, P 2 TEXT RETR C TREC, P243 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Song F., 1999, P 8 INT C INF KNOWL, P315 Su X., 2009, ADV ARTIFICIAL INTEL, V2009, P1, DOI DOI 10.1155/2009/421425 Vander-Wal T., 2007, FOLKSONOMY Vargas S, 2011, P 5 ACM C REC SYST R, P109 Wang J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148257 Wu H, 2013, 2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), P888 Wu H, 2014, PERS UBIQUIT COMPUT, V18, P1855, DOI 10.1007/s00779-014-0785-0 WU S, 2012, ADAPT LEARN OPTIM, V13, P1 Wu SL, 2006, INFORM PROCESS MANAG, V42, P899, DOI 10.1016/j.ipm.2005.08.004 Wu SL, 2012, DATA KNOWL ENG, V71, P114, DOI 10.1016/j.datak.2011.08.003 Xu G., 2011, P 25 AAAI C ART INT, P1267 Zhang ZK, 2010, PHYSICA A, V389, P179, DOI 10.1016/j.physa.2009.08.036 Zhang ZK, 2011, J COMPUT SCI TECH-CH, V26, P767, DOI 10.1007/s11390-011-0176-1 Zhou T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107 NR 56 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD FEB PY 2015 VL 75 BP 124 EP 140 DI 10.1016/j.knosys.2014.11.026 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA CA8TI UT WOS:000349193400010 ER PT J AU Athanasopoulos, M Kontogiannis, K AF Athanasopoulos, Michael Kontogiannis, Kostas TI Extracting REST resource models from procedure-oriented service interfaces SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE REST; Software reengineering; Service oriented architectures ID WEB AB During the past decade a number of procedure-oriented protocols and standards have emerged for making service-offering systems available on the Web. The WS-* stack of protocols is the most prevalent example. However, this procedure and message-oriented approach has not aligned with the true potential of the Web's own architectural principles, such as the uniform identification and manipulation of resources, caching, hypermedia, and layering. In this respect, Resource Oriented Architectures based on the REST architectural style, have been proposed as a possible alternative to the operation-based view of service offerings. To date, compiling a REST API for back-end procedure-oriented services is considered as a manual process that requires as input specialized models, such as, service requirements and behavioral models. In this paper, we propose a resource extraction method in which service descriptions are analyzed, using natural language processing techniques and graph transformations, in order to yield a collection of hierarchically organized elements forming REST resources that semantically correspond to the functionality offered by the service. The proposed approach has been applied as a proof of concept with positive results, for the extraction of resource models from a sizable number of procedure-oriented Web Service interfaces that have been obtained from an open service directory. (C) 2014 Elsevier Inc. All rights reserved. C1 [Athanasopoulos, Michael; Kontogiannis, Kostas] NTUA, Sch Elect & Comp Engn, Athens, Greece. RP Athanasopoulos, M (reprint author), NTUA, Sch Elect & Comp Engn, Athens, Greece. EM athanm@softlab.ntua.gr FU IBM Canada CAS Research [754] FX This work is supported by IBM Canada CAS Research under the Research Fellowship Project no. 754. CR Adamczyk P, 2011, REST: FROM RESEARCH TO PRACTICE, P35, DOI 10.1007/978-1-4419-8303-9_2 Athanasopoulos M., 2011, P 2 INT WORKSH REFTF, P47 Athanasopoulos M., 2010, 2010 17 WORK C REV E, P215, DOI 10.1109/WCRE.2010.31 Battle R, 2008, J WEB SEMANT, V6, P61, DOI 10.1016/j.websem.2007.11.002 Bean J., 2009, SOA WEB SERVICE INTE Berners-Lee T., 2009, SHORT HIST RESOURCE Bies A., 1995, BRACKETING GUIDELINE Brown P.C., 2012, ARCHITECTING COMPOSI Castillo P.A., 2011, SOAP VS REST COMPARI Corazza A, 2012, 2012 28TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM), P233, DOI 10.1109/ICSM.2012.6405277 David J, 2008, LECT NOTES COMPUT SC, V5318, P245, DOI 10.1007/978-3-540-88564-1_16 deOliveira Sanchez R.R., 2013, 2013 IEEE 7 INT S MA, P40 Erl T., 2008, SOA DESIGN PATTERNS Feng XY, 2009, 2009 FIRST INTERNATIONAL CONFERENCE ON FUTURE INFORMATION NETWORKS, P7 Fielding R., 2000, THESIS U CALIFORNIA Fowler M., 2009, RICHARSON MATURITY M Guinard D, 2012, MOBILE UBIQUITOUS SY, P326 Hadley M. J., 2006, TECHNICAL REPORT Jiang W., 2012, 2012 IEEE 19 INT C W, P218 Kennedy S, 2011, ELECTRON COMMER RES, V11, P245, DOI 10.1007/s10660-011-9075-3 Kopecky J., 2008, IEEE WIC ACM INT C W, V01, P619 Kuhn H. W., 1955, NAV RES LOG, V2, P83, DOI DOI 10.1002/NAV.3800020109 Laitkorpi M, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, VOLS 1 AND 2, P173, DOI 10.1109/ICWS.2009.63 Laitkorpi M, 2006, WORK CONF REVERSE EN, P134 Leotta M, 2012, PROC INT CONF SOFTW, P1441, DOI 10.1109/ICSE.2012.6227074 Li Z., 2012, INT J SPACE BASED SI, V2, P31 Liu Y., 2008, 32 ANN IEEE INT COMP, P785 Madani N., 2010, 2010 14 EUR C SOFTW, P68, DOI 10.1109/CSMR.2010.31 Maleshkova M., 2010, 2010 IEEE 8 EUR C WE, P107, DOI 10.1109/ECOWS.2010.9 Manning CD, 1999, FDN STAT NATURAL LAN, V999 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 OASIS, 2010, OAS WS I Pautasso C., 2009, P 18 INT C WORLD WID, P911, DOI DOI 10.1145/1526709.1526832 Pautasso C., 2008, P 17 INT C WORLD WID, P805, DOI DOI 10.1145/1367497.1367606 Perrone M.P., 1992, NETWORKS DISAGREE EN Philip Markey C.G., 2013, 12 INF TECHN TEL IT Rensink A., 2004, Applications of Graph Transformations with Industrial Relevance. Second International Workshop, AGTIVE 2003. Revised Selected and Invited Papers (Lecture Notes in Comput. Sci. Vol.3062) Renzel D., 2012, WEB INFORM SYSTEMS E, P354 Rodriguez J.M., 2011, CLEI ELECT J, V14, P2 Rokach L., 2010, PATTERN CLASSIFICATI, V75 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schreier S., 2011, P 2 INT WORKSH RESTF, P15, DOI DOI 10.1145/1967428.1967434 Selonen P, 2011, REST: FROM RESEARCH TO PRACTICE, P259, DOI 10.1007/978-1-4419-8303-9_11 Sheth AP, 2007, IEEE INTERNET COMPUT, V11, P91, DOI 10.1109/MIC.2007.133 Strauch J., 2012, P 3 INT WORKSH RESTF, P11 Tarjan R., 1972, SIAM Journal on Computing, V1, DOI 10.1137/0201010 U.N. National Cancer Institute, 2009, SERV CAP NAM STAND D Upadhyaya B., 2011, 2011 13th IEEE International Symposium on Web Systems Evolution, DOI 10.1109/WSE.2011.6081828 Vinoski S, 2008, IEEE INTERNET COMPUT, V12, P84, DOI 10.1109/MIC.2008.20 Vinoski S, 2002, IEEE INTERNET COMPUT, V6, P90, DOI 10.1109/MIC.2002.1020331 Voutilainen A., 2003, OXFORD HDB COMPUTATI, P219 NR 51 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 EI 1873-1228 J9 J SYST SOFTWARE JI J. Syst. Softw. PD FEB PY 2015 VL 100 BP 149 EP 166 DI 10.1016/j.jss.2014.10.038 PG 18 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA AY7KM UT WOS:000347739200013 ER PT J AU Ma, SH Tian, L AF Ma, Songhua Tian, Ling TI Ontology-based semantic retrieval for mechanical design knowledge SO INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING LA English DT Article DE extended semantic key; semantic similarity; ontology-based search; semantic key; domain ontology ID WEB AB The semantic retrieval of mechanical domain knowledge is critical in product design process. To address the problems with existing keyword-based and semantic-enabled methods, an ontology-based search system (OBSS) for knowledge search and retrieval from knowledge base is proposed. In our scheme, the mechanical design knowledge ontology is first constructed, and query semantic extension and retrieval are then adopted for knowledge retrieval. For the query semantic extension, the semantic similarity analysis is adopted to discover the semantic distance between the semantic key in query and its extended semantic keys. Then, the query boosts of extended semantic keys are modified by the semantic similarity. In the precision and recall experiments, the results demonstrate that our method outperforms the keyword-based search technique. Further, two query cases ensure that the semantic retrieval algorithm based on domain ontology could understand the latent search intents. This research contributes to the query intent capture and the application of engineering ontology for design knowledge retrieval. C1 [Ma, Songhua; Tian, Ling] Tsinghua Univ, Dept Mech Engn, Beijing 100084, Peoples R China. RP Ma, SH (reprint author), Tsinghua Univ, Dept Mech Engn, Beijing 100084, Peoples R China. EM msh_1216@yahoo.cn FU National High Technology Research and Development Program of China (863 Program) [2006AA04Z112]; National Natural Science Foundation of China [51175287] FX This work was supported in part by National High Technology Research and Development Program of China (863 Program) [grant number 2006AA04Z112] and in part by National Natural Science Foundation of China [grant number 51175287]. CR Ahmed S, 2004, J ENG DESIGN, V15, P475, DOI 10.1080/095448208410001708430 Alejandra Segura N., 2011, KNOWL-BASED SYST, V24, P119, DOI DOI 10.1016/J.KNOSYS.2010.07.012 Ameri F, 2014, INT J COMPUT INTEG M, V27, P570, DOI 10.1080/0951192X.2013.834467 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 Calegari S, 2013, INFORM PROCESS MANAG, V49, P640, DOI 10.1016/j.ipm.2012.07.010 Chakrabarty S, 2009, INT J ADV MANUF TECH, V44, P1237, DOI 10.1007/s00170-009-1925-y Chi YL, 2010, EXPERT SYST APPL, V37, P1400, DOI 10.1016/j.eswa.2009.06.097 Gonzalo J., 1998, P COLING ACL 98 WORK GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Hogenboom F., 2013, SCI COMPUT PROGRAM, DOI [10.1016/j.eswa.2013.02.011, DOI 10.1016/J.ESWA.2013.02.011] Jarmasz M., 2003, THESIS U OTTAWA OTTA Jarvelin K, 2001, INFORM RETRIEVAL, V4, P231, DOI 10.1023/A:1011998222190 Jiang J. J., 1997, P 1997 INT C RES COM Jiang X, 2009, INFORM SCIENCES, V179, P2794, DOI 10.1016/j.ins.2009.04.005 Kara S, 2012, INFORM SYST, V37, P294, DOI 10.1016/j.is.2011.09.004 Liao M., 1999, P 11 INT C SOFTW ENG, P11 Mihalcea R., 2000, P ACL 2000 WORKSH RE, P35 Muller HM, 2004, PLOS BIOL, V2, P1984, DOI 10.1371/journal.pbio.0020309 Pierra G, 2004, INT FED INFO PROC, V156, P13 Rijsbergen C. J, 1986, P ACM C RES DEV IR SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Tsinaraki C, 2007, IEEE T KNOWL DATA EN, V19, P219, DOI 10.1109/TKDE.2007.33 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 Zadrozny S, 2012, INFORM PROCESS MANAG, V48, P390, DOI 10.1016/j.ipm.2011.05.001 Zhang W Y, 2009, International Journal of Computer Integrated Manufacturing, V22, DOI 10.1080/09511920903030379 NR 29 TC 0 Z9 0 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 0951-192X EI 1362-3052 J9 INT J COMPUT INTEG M JI Int. J. Comput. Integr. Manuf. PD FEB 1 PY 2015 VL 28 IS 2 BP 226 EP 238 DI 10.1080/0951192X.2013.874593 PG 13 WC Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AS7ZP UT WOS:000344470700007 ER PT J AU Kundu, A Jain, V Kumar, S Chandra, C AF Kundu, Anirban Jain, Vipul Kumar, Sameer Chandra, Charu TI A journey from normative to behavioral operations in supply chain management: A review using Latent Semantic Analysis SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Review DE Behavioral operations; Literature review; Latent Semantic Analysis; Supply chain management; Taxonomy of power influences in supply chain; Data and text mining ID INFORMATION; COORDINATION; PERFORMANCE; KNOWLEDGE; SELECTION; NETWORKS; SYSTEMS; MODEL AB This study aims to systematically review the cross disciplinary literature covering the time period from 1934 to January 2013 on behavioral operations in supply chain in order to identify and define the taxonomy of the research on power influences in supply chain. A list of noted journals and search results from Science Direct and Web of Knowledge, IEEE Xplore, and INFORMS (approximately 11,000 journal articles) is used to prepare content collection. Latent Semantic Analysis (LSA) is applied as the review and knowledge extraction methodology. Using the text analysis and mining method we can combine statistical methods and expert human judgment to extract knowledge in the form of key latent factors. The LSA based analysis gives the study a scientific grounding which helps to overcome the subjectivity of collective opinion about the trends. This approach allows proposing taxonomy of the research on power influences in supply chain. The adopted systems approach is used to find research gaps in each class of taxonomy. An emerging trend is noticed in the research of behavioral operations in supply chain. Understanding such a scholarly structure and future trends will assist researchers to assimilate the divergent developments of this multidisciplinary research in one place. This review will be beneficial for practitioners as they consider behavioral aspects in decision making. We have also studied articles related to supply chain published in Expert Systems with Applications (ESWA) journal. We have speculated what an ESWA-related community would like to see in future publications. This will encourage researchers to explore the recommended areas and publish to these outlets. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Kundu, Anirban] Indian Inst Technol, Dept Mech Engn, New Delhi 110016, India. [Jain, Vipul] Univ Sharjah, Dept Ind Engn & Management, Sharjah, U Arab Emirates. [Kumar, Sameer] Univ St Thomas, Opus Coll Business, Dept Operat & Supply Chain Management, Minneapolis, MN 55403 USA. [Chandra, Charu] Univ Michigan, Coll Business Adm, Dept Management Studies, Dearborn, MI 48126 USA. RP Kumar, S (reprint author), Univ St Thomas, Opus Coll Business, Dept Operat & Supply Chain Management, 1000 LaSalle Ave, Minneapolis, MN 55403 USA. EM anirban.kundu.iitd@gmail.com; Vjain@sharjah.ac.ae; sameerkumar724@gmail.com; charu@umich.edu CR Albino V, 2012, J CLEAN PROD, V37, P304, DOI 10.1016/j.jclepro.2012.07.033 Atanasova Y, 2011, IND MARKET MANAG, V40, P278, DOI 10.1016/j.indmarman.2010.08.011 Bendoly E, 2010, PROD OPER MANAG, V19, P434, DOI 10.1111/j.1937-5956.2009.01108.x Bradford R. B., 2008, EMPIRICAL STUDY REQU Caro F, 2012, OPER RES, V60, P1404, DOI 10.2139/ssrn.1731402 Cios K. J., 2007, DATA MINING KNOWLEDG Claro DP, 2010, IND MARKET MANAG, V39, P221, DOI 10.1016/j.indmarman.2009.03.009 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Delp P., 1977, SYSTEM TOOLS PROJECT Dumais S., 2007, HDB LATENT SEMANTIC, P293 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Faraj S, 2011, ORGAN SCI, V22, P1224, DOI 10.1287/orsc.1100.0614 Forrester JW, 1961, IND DYNAMICS Han J., 2006, DATA MINING CONCEPTS Kim HN, 2011, DECIS SUPPORT SYST, V51, P772, DOI 10.1016/j.dss.2011.01.012 Kim SH, 2013, MANAGE SCI, V59, P189, DOI 10.1287/mnsc.1120.1573 Klassen RD, 2012, INT J PROD ECON, V140, P103, DOI 10.1016/j.ijpe.2012.01.021 Kravari K, 2012, EXPERT SYST APPL, V39, P9571, DOI 10.1016/j.eswa.2012.02.160 Landauer T., 2008, SCHOLARPEDIA, V3, P4356, DOI DOI 10.4249/SCHOLARPEDIA.4356 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Lavie D, 2012, ORGAN SCI, V23, P704, DOI 10.1287/orsc.1110.0656 Liu Q, 2013, MANAGE SCI, V59, P84, DOI 10.1287/mnsc.1120.1564 Lozano S., 2013, EUROPEAN J OPERATION Manning C. D., 2009, INTRO INFORM RETRIEV Martin D. I., 2007, MATH FDN LATENT SEMA Monroy CR, 2010, INT J PROD ECON, V126, P314, DOI 10.1016/j.ijpe.2010.04.008 Nyaga GN, 2010, J OPER MANAG, V28, P101, DOI 10.1016/j.jom.2009.07.005 Peng D. X., 2012, PRODUCTION OPERATION Pietrobelli C, 2011, WORLD DEV, V39, P1261, DOI 10.1016/j.worlddev.2010.05.013 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sidorova A, 2008, MIS QUART, V32, P467 Smirnova M, 2011, IND MARKET MANAG, V40, P54, DOI 10.1016/j.indmarman.2010.09.010 Tang CS, 2010, INT J PROD ECON, V125, P22, DOI 10.1016/j.ijpe.2010.01.014 Topolsek D, 2010, PROMET-ZAGREB, V22, P353 Valle-Lisboa JC, 2007, INFORM SCIENCES, V177, P4122, DOI 10.1016/j.ins.2007.04.007 Verma D, 2011, J OPER MANAG, V29, P462, DOI 10.1016/j.jom.2010.11.010 Wei C., 2008, J MANAGE INFORM SYST, V24, P269 Wu YZ, 2011, J OPER MANAG, V29, P650, DOI 10.1016/j.jom.2011.03.004 Xia YS, 2013, DECISION SCI, V44, P39, DOI 10.1111/j.1540-5915.2012.00381.x Yoon SW, 2010, DECIS SUPPORT SYST, V49, P442 Yu M, 2013, EUR J OPER RES, V224, P273, DOI 10.1016/j.ejor.2012.07.033 Zhang J., 2013, INT J PRODUCTION EC Zhang J, 2013, OMEGA-INT J MANAGE S, V41, P345, DOI 10.1016/j.omega.2012.03.009 Zhu M, 2006, COMPUT STAT DATA AN, V51, P918, DOI 10.1016/j.csda.2005.09.010 NR 48 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD FEB 1 PY 2015 VL 42 IS 2 BP 796 EP 809 DI 10.1016/j.eswa.2014.08.035 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AR8XO UT WOS:000343854900009 ER PT J AU Shen, W Wang, JY Han, JW AF Shen, Wei Wang, Jianyong Han, Jiawei TI Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Entity linking; entity disambiguation; knowledge base ID WIKIPEDIA; WEB AB The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions. C1 [Shen, Wei] Nankai Univ, Coll Comp & Control Engn, Tianjin 300071, Peoples R China. [Wang, Jianyong] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. [Han, Jiawei] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Shen, W (reprint author), Nankai Univ, Coll Comp & Control Engn, Tianjin 300071, Peoples R China. EM shenwei@nankai.edu.cn; jianyong@tsinghua.edu.cn; hanj@cs.uiuc.edu FU National Key Basic Research Program of China (973 Program) [2014CB340505]; National Natural Science Foundation of China [61272088]; Samsung; Tsinghua University Initiative Scientific Research Program; US Army Research Laboratory [W911NF-09-2-0053]; US Army Research Office [W911NF-13-1-0193]; U.S. National Science Foundation [CNS-0931975, IIS-1017362, IIS-1320617, IIS-1354329]; DTRA; NASA [NRA-NNH10ZDA001N]; MIAS, a DHS-IDS Center for Multimodal Information Access and Synthesis at UIUC FX This work was supported in part by National Key Basic Research Program of China (973 Program) under Grant No. 2014CB340505, National Natural Science Foundation of China under Grant No. 61272088, a Samsung Research Grant, Tsinghua University Initiative Scientific Research Program, the US Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA), the US Army Research Office under Cooperative Agreement No. W911NF-13-1-0193, U.S. National Science Foundation grants CNS-0931975, IIS-1017362, IIS-1320617, IIS-1354329, DTRA, NASA NRA-NNH10ZDA001N, and MIAS, a DHS-IDS Center for Multimodal Information Access and Synthesis at UIUC. CR Adeva G. J. J., 2005, CLEI ELECT J, V9 Agichtein E., 2000, P 5 ACM INT C DIG LI, P85, DOI 10.1145/336597.336644 Agirrey E., 2009, P TEXT AN C WORKSH Artiles Javier, 2007, P 4 INT WORKSH SEM E, P64, DOI 10.3115/1621474.1621486 Auer S., 2007, P 6 INT SEM WEB C, P11 Bagga A., 1998, P 36 ANN M ASS COMP, P79 Balog K., 2010, P 19 TEXT RETRIEVAL Bentivogli L., 2010, P COL WORKSH PEOPL W, P19 Berners-Lee T, 2001, SCI AM, V284, P34 Bilenko M, 2003, IEEE INTELL SYST, V18, P16, DOI 10.1109/MIS.2003.1234765 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bollacker K, 2008, P 2008 ACM SIGMOD IN, P1247, DOI DOI 10.1145/1376616.1376746 Bordino I., 2013, P 22 ACM INT C INF K, P109 Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401 Brinker K., 2003, P 20 INT C MACH LEAR, V3, P59 Bunescu R., 2006, P EACL, V6, P9 Cafarella M.J., 2008, P VLDB ENDOW, V1, P538 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 Carlson A., 2010, P 3 ACM INT C WEB SE, P101, DOI 10.1145/1718487.1718501 Cassidy T., 2011, P TEXT AN C WORKSH Ceccarelli D., 2013, P ACM INT C INF KNOW, P139 Chakrabarti K., 2012, P INT C KNOWL DISC D, P1384 Chen Z., 2012, P JOINT C EMP METH N, P105 Chen Z., 2010, P TEXT AN C WORKSH Cheng T, 2012, IEEE T KNOWL DATA EN, V24, P1862, DOI 10.1109/TKDE.2011.168 Cheng Tao, 2007, P 33 INT C VER LARG, P387 Christen P, 2012, IEEE T KNOWL DATA EN, V24, P1537, DOI 10.1109/TKDE.2011.127 Christen P., 2006, P 6 IEEE INT C DAT M, P290 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Cornolti M., 2013, P 22 INT C WORLD WID, P249 Cucerzan S., 2011, P TEXT AN C WORKSH Cucerzan S., 2007, P 2007 JOINT C EMP M, P708 Dai H.-J., 2011, P 5 INT JOINT C NAT, P846 Dalvi N., 2012, P 5 ACM INT C WEB SE, P43 Daroczy B., 2009, P EV SYST MULT MULT Daume H, 2007, P 45 ANN M ASS COMP, V45, P256 Demartini G., 2012, P 21 INT C WORLD WID, P469, DOI DOI 10.1145/2187836.2187900 Demartini G., 2009, P FOC RETR EV 8 INT, P254 Deorowicz S., 2005, International Journal of Applied Mathematics and Computer Science, V15 Dong X., 2005, P ACM SIGMOD INT C M, P85, DOI 10.1145/1066157.1066168 Dredze M., 2010, P 23 INT C COMP LING, P277 Dreyer M., 2008, P C EMP METH NAT LAN, P1080, DOI 10.3115/1613715.1613856 Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 Elmeleegy H., 2009, P VLDB ENDOWMENT, V2, P1078 Etzioni O., 2004, P 13 INT C WORLD WID, P100, DOI 10.1145/988672.988687 Fellbaum C, 1998, WORDNET ELECT LEXICA Ferragina Paolo, 2010, P 19 ACM INT C INF K, P1625, DOI 10.1145/1871437.1871689 Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 Fumarola F, 2011, P 24 INT C IND ENG O, P285 Gattani A., 2013, P VLDB ENDOW, V6, P1126 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 Gottipati S., 2011, P 2011 C EMP METH NA, P804 Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989 Guo S., 2013, P C N AM CHAPT ASS C, P1020 Guo Y., 2013, P C EMP METH NAT LAN, P863 Hachey B, 2013, ARTIF INTELL, V194, P130, DOI 10.1016/j.artint.2012.04.005 Hadjieleftheriou M., 2011, FDN TRENDS DATAB, V2, P267 Han X, 2011, P 34 INT ACM SIGIR C, P765 Han X., 2012, EMNLP Han X., 2009, P 18 ACM C INF KNOWL, P215, DOI DOI 10.1145/1645953.1645983 Han X., 2011, P 49 ANN M ASS COMP, V1, P945 Han X., 2009, P TEXT AN C WORKSH Hasegawa T., 2004, P 42 ANN M ASS COMP, DOI 10.3115/1218955.1219008 Hassanzadeh O., 2007, P 5 INT WORKSH QUAL Haveliwala T. H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513 He Z., 2013, P ANN M ASS COMP LIN, P30 Herbrich R., 2000, ADV LARGE MARGIN CLA, V88, P115 Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527 Hoffart J., 2011, P C EMP METH NAT LAN, P782 Hoffart J, 2013, ARTIF INTELL, V194, P28, DOI 10.1016/j.artint.2012.06.001 Hoffart J., 2012, P 21 ACM INT C INF K, P545 Isele R., 2012, PVLDB, V5, P1638 Jain A., 2007, P IEEE INT C INF REU, P209 Ji H., 2011, P 49 ANN M ASS COMP, V1, P1148 Ji H., 2011, P TEXT AN C WORKSH Ji H., 2010, P TEXT AN C WORKSH Jiang LL, 2009, IEEE DATA MINING, P199, DOI 10.1109/ICCSIT.2009.5234744 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Klein D, 2003, P 7 C NAT LANG LEARN, V4, P180, DOI 10.3115/1119176.1119204 Koller D., 2009, PROBABILISTIC GRAPHI Kopcke H, 2010, DATA KNOWL ENG, V69, P197, DOI 10.1016/j.datak.2009.10.003 Kulkarni S, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P457 Lehmann J., 2007, P AUT CONT EXT C Lehmann J., 2010, P TEXT AN C WORKSH Li F., 2009, P TEXT AN C WORKSH Li Y., 2013, P 19 ACM SIGKDD INT, P1070 Limaye G., 2010, PVLDB, V3, P1338 Lin T., 2012, P JOINT WORKSH AUT K, P84 Lin T., 2012, P JOINT C EMP METH N, P893 Ling X., 2012, P 26 C ART INT, P94 Liu J, 2010, IUI 2010, P31 Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Liu X., 2013, 51 ANN M ASS COMP LI Liu X., 2011, P 49 ANN M ASS COMP, V1, P359 Mann G. S., 2003, P 7 C NAT LANG LEARN, P33 McNamee P., 2009, P TEXT AN C WORKSH McNamee P., 2010, P TEXT AN C WORKSH Meij E., 2012, P 5 ACM INT C WEB SE, P563 Michelson M, 2010, P 4 WORKSH AN NOIS U, P73, DOI 10.1145/1871840.1871852 Mikheev A., 1999, P 9 C EUR CHAPT ASS, P1, DOI 10.3115/977035.977037 Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150 Milne D, 2013, ARTIF INTELL, V194, P222, DOI 10.1016/j.artint.2012.06.007 Monahan S., 2011, P TEXT AN C WORKSH Nadeau D., 2007, LINGVISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD Nakashole N., 2013, P ANN M ASS COMP LIN, P1488 Nakashole N., 2012, JOINT C EMP METH NAT, P1135 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Nemeskey D., 2010, P TEXT AN C WORKSH Opitz D., 1999, J ARTIFICIAL INTELLI, V11, P169, DOI DOI 10.1613/JAIR.614 Pantel P., 2011, 49 ANN M ASS COMP LI, P83 Phelan O., 2009, P 3 ACM C REC SYST R, P385, DOI 10.1145/1639714.1639794 Pilz A., 2011, P ACM INT C INF KNOW, P857 Pu K. Q., 2010, P 19 ACM INT C INF K, P29, DOI 10.1145/1871437.1871446 Ratinov L., 2011, COMPUTATIONAL LINGUI, V1, P1375 Ritter Alan, 2011, P C EMP METH NAT LAN, P1524 Rokach L, 2010, ARTIF INTELL REV, V33, P1, DOI 10.1007/s10462-009-9124-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen D., 2004, P 42 ANN M ASS COMP, DOI 10.3115/1218955.1219030 Shen LB, 2005, MACH LEARN, V60, P73, DOI 10.1007/s10994-005-0918-9 Shen W., 2011, P 20 INT C COMP WORL, P121 Shen W., 2013, P 19 ACM SIGKDD INT, P68 Shen W., 2012, P 18 ACM SIGKDD INT, P1424, DOI DOI 10.1145/2339530.2339753 Shen W., 2012, P 21 ACM INT C INF K, P345 Shen W., 2014, SIGMOD, P1199 Shen W., 2012, P 21 INT C WORLD WID, P449 Sil A., 2013, P 22 ACM INT C INF K, P2369 Singh S., 2012, UMCS2012015 Sozio M., 2010, P 16 ACM SIGKDD INT, P939, DOI DOI 10.1145/1835804 STAJNER T, 2009, P 4 AS C SEM WEB, V5926, P91 Suchanek F. M., 2007, P 16 INT C WORLD WID, P697, DOI DOI 10.1145/1242572.1242667 Taneva B., 2013, P 22 INT C WORLD WID, P1261 Varga Daniel, 2007, Acta Cybernetica, V18 Varma V., 2009, P TEXT AN C WORKSH Varma V., 2010, P TEXT AN C WORKSH Volz J, 2009, LECT NOTES COMPUT SC, V5823, P650, DOI 10.1007/978-3-642-04930-9_41 Wang J., 2012, PVLDB, V5, P1483 Welty C., 2012, LNCS, V7650, P243 Weng J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Wu W, 2012, P 2012 ACM SIGMOD IN, P481, DOI DOI 10.1145/2213836.2213891 Yin X., 2007, P IEEE 23 INT C DAT, P1242 Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang T., 2013, P 23 INT JOINT C ART, P2218 Zhang W., 2010, 23 INT C COMP LING, P1290 Zhang W., 2011, P TEXT AN C WORKSH Zhang W., 2011, 22 INT JOINT C ART I, P1909 Zhang W., 2010, P TEXT AN C WORKSH Zhang Z., 2012, P 18 ACM SIGKDD INT, P1560 Zheng Z., 2010, P 11 ANN C N AM CHAP, P483 NR 150 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 EI 1558-2191 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD FEB PY 2015 VL 27 IS 2 BP 443 EP 460 DI 10.1109/TKDE.2014.2327028 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AX5QU UT WOS:000346982900010 ER PT J AU Liu, ZY Wang, WP Wang, Y Lu, WY Ji, ZZ AF Liu, Z. Y. Wang, W. P. Wang, Y. Lu, W. Y. Ji, Z. Z. BE Chan, K Yeh, J TI Bayesian Parameter Estimation in LDA SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL APPLICATIONS (CISIA 2015) SE ACSR-Advances in Comptuer Science Research LA English DT Proceedings Paper CT International Conference on Computer Information Systems and Industrial Applications (CISIA) CY JUN 28-29, 2015 CL Bangkok, THAILAND DE bayesian; parameter estimation; LDA; topic model AB Latent Dirichlet Allocation (LDA) probabilistic topic model is widely used in text mining, natural language processing and so on. But LDA's mathematical theory is particularly complex, thus it is very difficult to understand LDA for a novice. In order to more quickly and easily learn LDA, and further promote its application, this paper will deeply analyze LDA from the perspective of Bayesian parameter estimation. At first we explain the advantage of Bayesian parameter estimation by an instance, and then introduce a simple Bayesian Unigram model. Next based on the simple Bayesian Unigram model and PLSA model, a full Bayesian probabilistic topic model-LDA is presented. C1 [Liu, Z. Y.; Wang, Y.; Lu, W. Y.] Beijing Inst Technol, Sch Software, Beijing, Peoples R China. [Liu, Z. Y.; Wang, W. P.] Chinese Acad Sci, Inst Informat Engn, Beijing 100864, Peoples R China. [Ji, Z. Z.] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China. RP Liu, ZY (reprint author), Beijing Inst Technol, Sch Software, Beijing, Peoples R China. CR Blei D. M., 2003, MACHINE LEARNING RES, V3, P993 Chen H., 1994, BAYESIAN STAT INFERE Heinrich G., PARMETER ESTIMATION Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Lv X., 2011, MATH STAT Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steyvers M., 2006, LATENT SEMANTIC ANAL Zhang W., 2013, BAYESIAN ANAL NR 9 TC 0 Z9 0 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE SN 2352-538X BN 978-94-62520-72-1 J9 ACSR ADV COMPUT PY 2015 VL 18 BP 837 EP 840 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BD3LO UT WOS:000359866200225 ER PT S AU Silva, LL Valente, MT Maia, MD AF Silva, Luciana Lourdes Valente, Marco Tulio Maia, Marcelo de A. BE Chiba, S Tanter, E Ernst, E Hirschfeld, R TI Co-change Clusters: Extraction and Application on Assessing Software Modularity SO TRANSACTIONS ON ASPECT-ORIENTED SOFTWARE DEVELOPMENT XII SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 13th International Conference on Modularity (Modularity) CY APR 22-25, 2014 CL Lugano, SWITZERLAND DE Modularity; Software changes; Version control systems; Co-change graphs; Co-change clusters; Agglomerative hierarchical clustering algorithm ID METRICS; DESIGN AB The traditional modular structure defined by the package hierarchy suffers from the dominant decomposition problem and it is widely accepted that alternative forms of modularization are necessary to increase developer's productivity. In this paper, we propose an alternative form to understand and assess package modularity based on co-change clusters, which are highly inter-related classes considering co-change relations. We evaluate how co-change clusters relate to the package decomposition of four real-world systems. The results show that the projection of co-change clusters to packages follows different patterns in each system. Therefore, we claim that modular views based on co-change clusters can improve developers' understanding on how well-modularized are their systems, considering that modularity is the ability to confine changes and evolve components in parallel. C1 [Silva, Luciana Lourdes; Valente, Marco Tulio] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil. [Maia, Marcelo de A.] Univ Fed Uberlandia, Fac Comp, BR-38400 Uberlandia, MG, Brazil. [Silva, Luciana Lourdes] Fed Inst Triangulo Mineiro, Uberaba, Brazil. RP Silva, LL (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil. EM luciana.lourdes@dcc.ufmg.br; mtov@dcc.ufmg.br; marcmaia@facom.ufu.br CR Adams B, 2010, 32 INT C SOFTW ENG I, V1, P305 Agrawal R., 1994, 20 INT C VER LARG DA, V1215, P487 Beyer D, 2005, PROG COMPREHEN, P259, DOI 10.1109/WPC.2005.12 Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008 Breu S, 2006, IEEE INT CONF AUTOM, P221 Brin S., 1997, INT C MAN DAT SIGMOD, V26, P255, DOI 10.1145/253262.253325 CHIDAMBER SR, 1991, SIGPLAN NOTICES, V26, P197 Clark K.B., 2003, DESIGN RULES POWER M Couto C, 2014, J SYST SOFTWARE, V93, P24, DOI 10.1016/j.jss.2014.01.033 Couto C, 2012, EUR CON SFTWR MTNCE, P223, DOI 10.1109/CSMR.2012.31 DAmbros M., 2010, 7 IEEE WORK C MIN SO, P31 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diehl S., 2010, 17 WORK C REV ENG WC, P99 Ducasse S., 2006, 22 IEEE INT C SOFTW, P203, DOI 10.1109/ICSM.2006.22 Gethers M., 2011, 2011 26th IEEE/ACM International Conference on Automated Software Engineering, DOI 10.1109/ASE.2011.6100120 Griswold WG, 2001, PROC INT CONF SOFTW, P265, DOI 10.1109/ICSE.2001.919100 Herzing K., 2013, 10 WORK C MIN SOFTW, P121 Janzen D, 2003, 2 INT C ASP OR SOFTW, V10, P178, DOI 10.1145/643603.643622 Kagdi H, 2013, EMPIR SOFTW ENG, V18, P933, DOI 10.1007/s10664-012-9233-9 Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KASTNER C, 2008, 30 INT C SOFTW ENG I, P311 Kersten M., 2006, 14 ACM SIGSOFT INT S, P1 Kiczales G., 1997, LNCS, V1241 Kouroshfar E, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P1450 Lent B, 1997, PROC INT CONF DATA, P220, DOI 10.1109/ICDE.1997.581756 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Manning C.D., 2008, INTRO INFORM RETRIEV Meyer B, 2000, OBJECT ORIENTED SOFT, V2nd Negara S., 2012, LNCS, V7313, P79 Oliva G. A., 2011, 12 INT WORKSH PRINC, P31 Omiecinski ER, 2003, IEEE T KNOWL DATA EN, V15, P57, DOI 10.1109/TKDE.2003.1161582 Palomba F, 2013, IEEE INT CONF AUTOM, P268, DOI 10.1109/ASE.2013.6693086 PARNAS DL, 1972, COMMUN ACM, V15, P1053, DOI 10.1145/361598.361623 Piatetsky-Shapiro G., 1991, Knowledge discovery in databases Poshyvanyk D., 2008, 1 INT ICPC WORKSH SE, P265 Poshyvanyk D., 2007, 22 IEEE ACM INT C AU, P563 Robillard M., 2007, ACM T SOFTW ENG METH, V16, P1 Robillard MP, 2010, J SOFTW MAINT EVOL-R, V22, P143, DOI 10.1002/smr.413 Robillard M.P., 2005, OOPSLA WORKSH ECL TE Robillard MP, 2002, ICSE 2002: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P406 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos G, 2014, 2014 SOFTWARE EVOLUTION WEEK - IEEE CONFERENCE ON SOFTWARE MAINTENANCE, REENGINEERING, AND REVERSE ENGINEERING (CSMR-WCRE), P224, DOI 10.1109/CSMR-WCRE.2014.6747174 Silva L., 2014, 13 INT C MOD, P49 Sliwerski J., 2005, 2 WORK C MIN SOFTW R, V30, P1 STEVENS WP, 1974, IBM SYST J, V13, P115 Tempero E., 2010, APSEC, P336 Terra R., 2013, SOFTWARE ENG NOTES, V38, P1 Valente MT, 2012, IEEE T SOFTWARE ENG, V38, P737, DOI 10.1109/TSE.2011.57 VANYA A, 2008, 16 IEEE INT C PROGR, P192, DOI DOI 10.1109/ICPC.2008.34 Walker R.J., 2012, 20 INT S FDN SOFTW E, P1 Zimmermann T, 2005, IEEE T SOFTWARE ENG, V31, P429, DOI 10.1109/TSE.2005.72 Zimmermann T., 2007, 3 INT WORKSH PRED MO, P9, DOI DOI 10.1109/PROMISE.2007.10 NR 52 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-662-46734-3; 978-3-662-46733-6 J9 LECT NOTES COMPUT SC PY 2015 VL 8989 BP 96 EP 131 DI 10.1007/978-3-662-46734-3_3 PG 36 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BD3PH UT WOS:000359999900003 ER PT J AU Zhang, YP Wang, L Jiang, K Qian, DM Dong, JC AF Zhang, Yuanpeng Wang, Li Jiang, Kui Qian, Danmin Dong, Jiancheng BE Cheng, A DeSouza, A TI A Hybrid Method for Extracting Deep Web Information SO PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING SE Advances in Intelligent Systems Research LA English DT Proceedings Paper CT International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE) CY APR 24-26, 2015 CL Jinan, PEOPLES R CHINA SP INst Nat Sci & Adv Technol DE information extraction; clinic expert information; domain model; block importance model; SVM AB Some previous works show that more than 60% of the information available on the Web is located in Deep Web database. Such information cannot be directly indexed by search engines. In this paper, a hybrid method, which is composed of a domain model and a block importance model is proposed to extract information in Deep Web. The domain model is used for classifying and identifying whether a form is a WQI. The block importance model is used for filtering noisy information in response pages. These two models are both compared with a rule-based method. The experiment results indicate that the domain model yields a precision6.44% higher than that of the rulebased method, whereas the block importance model yields an F1 measure 10.5% higher thanthat of the XPath method. C1 [Zhang, Yuanpeng; Wang, Li; Jiang, Kui; Qian, Danmin; Dong, Jiancheng] Nantong Univ, Sch Med, Dept Med Informat, Nantong 226001, Jiangsu, Peoples R China. RP Zhang, YP (reprint author), Nantong Univ, Sch Med, Dept Med Informat, 19 Qixiu Rd, Nantong 226001, Jiangsu, Peoples R China. EM maxbirdzhang@ntu.edu.cn; wangli@ntu.edu.cn; kuij@ntu.edu.cn; sandy8793@ntu.edu.cn; dongjc@ntu.edu.cn CR Bergman M.K., 2001, J ELECT PUBLISHING, V7, P3 Burges C. J. C., 1998, DATA MIN KNOWL DISC, V2, P955 Cope J, 2003, P 14 AUSTR DAT C ADC, P181 Fayzrakhmanov R, 2012, CURRENT TRENDS WEB E, V7059, P342 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yan F, 2007, P 3 INT C SEM KNOWL, P450 NR 6 TC 0 Z9 0 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE SN 1951-6851 BN 978-94-62520-64-6 J9 ADV INTEL SYS RES PY 2015 VL 124 BP 777 EP 782 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA BD3BM UT WOS:000359443400137 ER PT J AU Martin-Moncunill, D Garcia-Barriocanal, E Sicilia, MA Sanchez-Alonso, S AF Martin-Moncunill, David Garcia-Barriocanal, Elena Sicilia, Miguel-Angel Sanchez-Alonso, Salvador TI Evaluating the Practical Applicability of Thesaurus-Based Keyphrase Extraction in the Agricultural Domain: Insights from the VOA3R Project SO KNOWLEDGE ORGANIZATION LA English DT Article DE keyphrases; keyphrase extraction; documents; KEA; AGROVOC ID KNOWLEDGE ORGANIZATION SYSTEMS; INFORMATION-RETRIEVAL; ANNOTATION; WIKIPEDIA; DOCUMENTS; SEMANTICS AB The use of Knowledge Organization Systems (KOSs) in aggregated metadata collections facilitates the implementation of search mechanisms operating on the same term or kyphrase space, thus preparing the ground for improved browsing, more accurate retrieval and better user profiling. Automatic thesaurus-based keyphrase extraction appears to be an inexpensive tool to obtain this information, but the studies on its effectiveness are scattered and do not consider the practical applicability of these techniques compared to the quality obtained by involving human experts. This paper presents an evaluation of keyphrase extraction using the KEA software and the AGROVOC vocabulary on a sample of a large collection of metadata in the field of agriculture from the AGRIS database. This effort includes a double evaluation, the classical automatic evaluation based on precision and recall measures, plus a blind evaluation aimed to contrast the quality of the keyphrases extracted against expert-provided samples and against the keyphrases originally recorded in the metadata. Results show not only that KEA outperforms humans in matching the, original keyphrases, but also that the quality of the keyphrases extracted was similar to those provided by humans. C1 [Martin-Moncunill, David; Garcia-Barriocanal, Elena; Sicilia, Miguel-Angel; Sanchez-Alonso, Salvador] Univ Alcala de Henares, Dept Comp Sci, Informat Engn Res Unit, Alcala De Henares 28871, Madrid, Spain. RP Martin-Moncunill, D (reprint author), Univ Alcala de Henares, Dept Comp Sci, Informat Engn Res Unit, Ctra Barcelona Km 33-6, Alcala De Henares 28871, Madrid, Spain. EM d.martin@uah.es; elena.garciab@uah.es; msicilia@uah.es; salvador.sanchez@uah.es FU ICT Policy Support Programme (ICT PSP) [250525] FX Acknowledgments: The research within the project VOA3R leading to these results has received funding from the ICT Policy Support Programme (ICT PSP), Theme 4-"Open access to scientific information," grant agreement no 250525. We would also want to thank the FAO-AGRIS team, especially to Imma Subirats, Stefano Anibaldi and Fabrizio Celli for their implication in the VOA3R project and their quick and precise reply, every time we raised an issue to them. CR Al-Hashemi Rafeeq, 2010, INT ARAB J E TECHNOL, V1, P164 Anibaldi Stefano, 2013, P SEM WEB EFITA C 23 Chen SJ, 2012, ELECTRON LIBR, V30, P278, DOI 10.1108/02640471211221386 Dahlberg I, 2006, KNOWL ORGAN, V33, P11 El-Beltagy Samhaa R., 2006, P INN INF TECHN NOV, P1 El-Beltagy SR, 2009, INFORM SYST, V34, P132, DOI 10.1016/j.is.2008.05.002 El-Haj Mahmoud, 2013, P 5 COMP SCI EL ENG, P13 Erbs Nicolai, 2013, D LIB MAGAZINE, V19 FLEISS JL, 1973, EDUC PSYCHOL MEAS, V33, P613, DOI 10.1177/001316447303300309 Frank E, 1999, P 16 INT JOINT C ART, V2, P668 Frantzi K., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900023 Goovaerts Marc, 2011, P EUR ASS AQ SCI LIB, P58 Harzing A. W. K., 2008, ETHICS SCI ENV POLIT, V8, P61, DOI DOI 10.3354/esep00076 Hjorland B, 2003, KNOWL ORGAN, V30, P87 Hjorland B, 2001, J AM SOC INF SCI TEC, V52, P774, DOI 10.1002/asi.1131 Hjorland B, 2011, J AM SOC INF SCI TEC, V62, P72, DOI 10.1002/asi.21451 Hjorland B, 1998, KNOWL ORGAN, V25, P16 Jones S, 2002, J AM SOC INF SCI TEC, V53, P653, DOI 10.1002/asi.10068 Jones Steve, 2001, P 1 ACM IEEE CS JOIN, P148, DOI 10.1145/379437.379473 Joorabchi A, 2013, J INF SCI, V39, P410, DOI 10.1177/0165551512472138 KUMAR N, 2008, P 2008 ACM S DOC ENG, P199, DOI 10.1145/1410140.1410180 Lim Vicky M. H., 2013, P IEEE S COMP INF LA, P196 Lord Philip, 2004, P UK E SCI ALL HANDS, P371 Medelyan O., 2009, P C EMP METH NAT LAN, V3, P1318, DOI 10.3115/1699648.1699678 Medelyan O, 2009, INT J HUM-COMPUT ST, V67, P716, DOI 10.1016/j.ijhcs.2009.05.004 Medelyan Olena, 2008, P AAAI WORKSH WIK AR, P19 Medelyan Olena, 2005, P 6 AGR ONT SERV AOS, P1122 Milne D., 2006, P 2006 IEEE WIC ACM, P442 Neveol Aurelie, 2007, PAC S BIOCOMPUT, V12, P292 NGUYEN TD, 2007, ASIAN DIGITAL LIB LO, V4822, P317, DOI 10.1007/978-3-540-77094-7_41 Norman G, 2010, ADV HEALTH SCI EDUC, V15, P625, DOI 10.1007/s10459-010-9222-y Park CA, 2013, J BIOMED SEMANT, V4, DOI 10.1186/2041-1480-4-13 Pouliquen Bruno, 2006, P EUROLAN WORKSH ONT Ruiz Miguel E., 2007, USER CTR EVALUATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar K., 2010, INT J COMPUTER SCI I, V7, P16 Simek P, 2013, PLANT SOIL ENVIRON, V59, P378 Sini Margherita, 2008, Library Review, V57, DOI 10.1108/00242530810865745 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 Turney Peter D., 1999, ERB1057 NAT RES COUN van Rijsbergen C. J., 1979, INFORM RETRIEVAL Witten I. H., 1999, P 4 ACM C DIG LIB, P254, DOI 10.1145/313238.313437 Zeng ML, 2004, J AM SOC INF SCI TEC, V55, P377, DOI 10.1002/asi.10387 NR 43 TC 0 Z9 0 PU ERGON-VERLAG PI WURZBURG PA GROMBUHLSTR 7, 97080 WURZBURG, GERMANY SN 0943-7444 J9 KNOWL ORGAN JI Knowl. Organ. PY 2015 VL 42 IS 2 BP 76 EP 89 PG 14 WC Information Science & Library Science SC Information Science & Library Science GA CM5YH UT WOS:000357764400002 ER PT S AU Periakaruppan, R Nadarajan, R AF Periakaruppan, Ramanathan Nadarajan, Rethinaswamy BE Chiu, DKW Wang, M Popescu, E Li, Q Lau, R Shih, TK Yang, CS Sampson, DG TI Automatic Clustering of Research Articles Using Domain Ontology and Fuzzy Logic SO ADVANCES IN WEB-BASED LEARNING SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th International Conference on Advances in Web-Based Learning (ICWL) CY OCT 06-09, 2013 CL TAIWAN SP Hong Kong Web Soc, Tajen Univ, City Univ Hong Kong DE Information retrieval; Domain knowledge; Fuzzy logic; XML ID SIMILARITY AB Today, one of the key tasks for Web based learning is to retrieve research articles of specific domains. To accomplish this task, data mining techniques and semantic web technologies can be used to retrieve user relevant documents. In this work we extract author supplied keywords from a collection of computer science articles, which has a strong influence on the topic of the article when compared with other words. For these keywords term weight is computed using Fuzzy Logic which uses three criteria namely mapping with concept in domain ontology, keyword frequency in the title and keyword frequency in abstract. Using domain ontology, keywords of each document with their term weights are represented hierarchically as XML documents and they are clustered. The experimental results show that the proposed technique yields better precision and recall rates when compared with some of the existing approaches. C1 [Periakaruppan, Ramanathan; Nadarajan, Rethinaswamy] PSG Coll Technol, Coimbatore, Tamil Nadu, India. RP Periakaruppan, R (reprint author), PSG Coll Technol, Coimbatore, Tamil Nadu, India. EM rm_pk@yahoo.com CR Ceravolo P, 2004, LECT NOTES ARTIF INT, V3215, P328 Dalamagas T, 2006, INFORM SYST, V31, P187, DOI 10.1016/j.is.2004.11.009 Damiani E., 2004, P EUROFUSE 2004 8 M, P133 Dogac A., 2002, IEEE DATA ENG B, V25, P10 Fekete J. D., 2004, HIST INFOVIS Ghosh P. M., 2008, P INT C PATT REC, P1 Gil-Garcia R., 2006, PATT REC 2006 ICPR 2, V2, P569 Guerrini G., 2007, WEB DATA MANAGEMENT, P56 Jeong B, 2008, EXPERT SYST APPL, V34, P1651, DOI 10.1016/j.eswa.2007.01.025 Leung H. P., 2003, P 7 PAC AS C KNOWL D, P569 Nagypal G, 2005, LECT NOTES COMPUT SC, V3762, P780 Nierman A., 2002, P 5 INT WORKSH WEB D, P61 Pan H., 2011, INT J INF ENG ELECT, V3, P16 Paralic J., 2003, 14 INT C INF INT SYS, P23 Paukkeri M.-S., 2011, APPL SOFT COMPUT, P1138 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Solvberg I., 1992, Future Generation Computer Systems, V7, DOI 10.1016/0167-739X(92)90053-E Tekli J, 2007, LECT NOTES COMPUT SC, V4362, P783 Tekli J., 2009, COMPUTER SCI REV, V3, P151, DOI DOI 10.1016/J.COSREV.2009.03.001 Tran T., 2008, P 7 AUSTR DAT MIN C, V87, P219 NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-662-46315-4; 978-3-662-46314-7 J9 LECT NOTES COMPUT SC PY 2015 VL 8390 BP 42 EP 51 DI 10.1007/978-3-662-46315-4_5 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BD0QM UT WOS:000357582100005 ER PT S AU Tseng, SP Fu, WH Yang, CS AF Tseng, Shih-Pang Fu, Wen-Hsiang Yang, Chu-Sing BE Chiu, DKW Wang, M Popescu, E Li, Q Lau, R Shih, TK Yang, CS Sampson, DG TI An Weighted-Content Strategy for Integrating Heterogeneous Itembanks SO ADVANCES IN WEB-BASED LEARNING SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th International Conference on Advances in Web-Based Learning (ICWL) CY OCT 06-09, 2013 CL TAIWAN SP Hong Kong Web Soc, Tajen Univ, City Univ Hong Kong DE E-learning; E-assessment; Itembank integration AB The assessment is the most effectively tool for the teachers to realized the learning status of the learners. The effectiveness of e-assessment is exceedingly related to the content richness of the itembank. This paper proposed an improved method to integrated various itembanks into an unified itembank. These integrated itembanks can be with heterogeneous schemas. The experimental result shows that the proposed method is more effective than the previous work. C1 [Tseng, Shih-Pang] Tajen Univ, Dept Comp Sci & Informat Engn, Pingtung, Taiwan. [Fu, Wen-Hsiang; Yang, Chu-Sing] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 70101, Taiwan. RP Tseng, SP (reprint author), Tajen Univ, Dept Comp Sci & Informat Engn, Pingtung, Taiwan. EM tsp@tajen.edu.tw; winters421@gmail.com; csyang@ee.ncku.edu.tw CR Bork A., 2001, TUTORIAL DISTANCE LE, P47 Chua YP, 2012, COMPUT HUM BEHAV, V28, P1580, DOI 10.1016/j.chb.2012.03.020 JISC, 2007, EFF PRACT E ASS OV T Llamas-Nistal M, 2013, COMPUT EDUC, V62, P72, DOI 10.1016/j.compedu.2012.10.021 Pellegrino J. W., 2001, KNOWING WHAT STUDENT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tseng SP, 2010, INT J FUZZY SYST, V12, P116 Tseng SP, 2010, INT J INNOV COMPUT I, V6, P4319 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-662-46315-4; 978-3-662-46314-7 J9 LECT NOTES COMPUT SC PY 2015 VL 8390 BP 192 EP 200 DI 10.1007/978-3-662-46315-4_20 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BD0QM UT WOS:000357582100020 ER PT S AU Judith, JE Jayakumari, J AF Judith, J. E. Jayakumari, J. BE Suresh, LP Dash, SS Panigrahi, BK TI Optimized Distributed Text Document Clustering Algorithm SO ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY ALGORITHMS IN ENGINEERING SYSTEMS, VOL 2 SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES) CY APR 22-23, 2014 CL Kumaracoil, INDIA HO Noorul Islam Univ, Noorul Islam Ctr Higher Educ DE Distributed document clustering; Distributed particle swarm optimization (DPSO); Distributed K-means (DKMeans); Similarity measure; Jaccard coefficient; Information retrieval (IR) AB Due to scientific progression, a variety of challenges exist in the field of information retrieval (IR). These challenges are due to the increased usage of large volumes of data. These enormous amounts of data are available from large-scale distributed networks. Centralization of these data to perform analysis is difficult. There exists a need for distributed text document clustering algorithms that overcomes challenges in clustering. The two main challenges are clustering accuracy and clustering quality. In this paper, an optimized distributed text document clustering algorithm is proposed that uses a distributed particle swarm optimization (DPSO) algorithm for the purpose of optimizing and generating initial centroids for the distributed K-means (DKMeans) clustering algorithm. This improves the quality of clustering. Similarity is determined using Jaccard coefficient that generates coherent clusters, thus improving the accuracy of the proposed algorithm. Extensive evaluations based on simulation are carried out with the given data sets to demonstrate the effectiveness of the algorithm. Data sets such as Reuters-21578 and 20 Newsgroups are used for evaluation. C1 [Judith, J. E.] Noorul Islam Ctr Higher Educ, Dept Comp Sci & Engn, Kanyakumari, Tamil Nadu, India. [Jayakumari, J.] Noorul Islam Ctr Higher Educ, Dept Elect & Commun Engn, Kumaracoil, Kanyakumari, India. RP Judith, JE (reprint author), Noorul Islam Ctr Higher Educ, Dept Comp Sci & Engn, Kanyakumari, Tamil Nadu, India. EM judithjegan@gmail.com; jkumaribharat@yahoo.com CR Cui X., 2005, J COMPUTER SCI, P27 Datta S., 2009, IEEE T KNOWL DATA EN, V2, P1372 Datta S., 2006, IEEE INT COMPUT, P1 Hammouda KM, 2009, IEEE T KNOWL DATA EN, V21, P681, DOI 10.1109/TKDE.2008.189 Han J., 2006, DATA MINING CONCEPTS Huang A., 2008, P 6 NZ COMP SCI RES, P49 Januzaj E., 2003, WORKSH CLUST LARG DA Judith J.E., 2013, INT REV COMPUT SOFTW, V8 JUDITH JE, 2012, EUR J SCI RES, V86, P283 Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/ICNN.1995.488968 Narayanan N, 2013, 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT 2013), P545 Papapetrou O, 2012, IEEE T KNOWL DATA EN, V24, P1848, DOI 10.1109/TKDE.2011.120 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-81-322-2135-7; 978-81-322-2134-0 J9 ADV INTELL SYST PY 2015 VL 325 BP 565 EP 574 DI 10.1007/978-81-322-2135-7_60 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Robotics SC Computer Science; Engineering; Robotics GA BD0MF UT WOS:000357378500060 ER PT J AU Chen, LC Yu, TJ Chang, CL AF Chen, Liang-Chu Yu, Ting-Jung Chang, Chi-Li TI TMTpedia: a case of extended Wikipedia for the military-based application in Taiwan SO ELECTRONIC LIBRARY LA English DT Article DE Wikipedia; Military resources recommendation; Military terminology; TMTpedia ID E-GOVERNMENT; RECOMMENDATION SYSTEM; MANAGEMENT; MUNICIPALITIES; PORTALS; WIKIS; MODEL AB Purpose - This paper aims to collect the terminologies from the Ministry of National Defense military dictionary and to design a military-based wiki system, TMTpedia, to serve as a collaborative and sharing platform for military personnel. Design/methodology/approach - The development of the system is based on a prototype design and case illustration. The framework of the Taiwan Military Terminology Wikipedia system (TMTpedia) consists of three major subsystems, namely, Military Terminology Dictionary Processing, Military Article Contents Extension and Military Article and Resource Recommendation. This paper applies the engines of MediaWiki to design the proposed TMTpedia, and embedded different functions into a variety of system modules are developed by using such tools as C#, Java and SQL Server. Findings - In this demonstration, the focus is on the topics of "Communications, Electronics and Information Operations" that are illustrative of cases that reveal the results of the TMTpedia system. Originality/value - The main contributions of this paper are to transform military terminologies from a traditional dictionary into Wiki-based platform that can provide a reference framework for knowledge collaboration, to extend the content on the TMTpedia system from an external knowledge encyclopedia to an extensible mechanism that can renew military concepts for the accuracy of knowledge sharing and transformation and to implement a recommendation model into the TMTpedia system that dynamically provides relevant military information from external resources to enhance the effectiveness of knowledge acquisition. C1 [Chen, Liang-Chu; Yu, Ting-Jung; Chang, Chi-Li] Natl Def Univ, Dept Informat Management, Taipei, Taiwan. RP Chen, LC (reprint author), Natl Def Univ, Dept Informat Management, Taipei, Taiwan. EM nctuhorse@gmail.com FU National Science Council (NSC), Taiwan [100-2410-H-606-001, NSC 101-2410-H-606-003] FX This research was partially sponsored by the National Science Council (NSC), Taiwan (grant no: 100-2410-H-606-001 & NSC 101-2410-H-606-003). CR Behzadi H, 2012, ELECTRON LIBR, V30, P89, DOI 10.1108/02640471211204088 Bonson E, 2012, GOV INFORM Q, V29, P123, DOI 10.1016/j.giq.2011.10.001 Brannstrom M., 2006, P C CIV MIL COOP CIM Cantador I, 2011, J WEB SEMANT, V9, P1, DOI 10.1016/j.websem.2010.10.001 Chen HL, 2010, ELECTRON LIBR, V28, P361, DOI 10.1108/02640471011051954 Ding L., 2010, AAAI SPRING S LINK D Elgohary A., 2010, INT SYST DES APPL IS, P1465 Fernandez-Luna JM, 2013, INFORM PROCESS MANAG, V49, P637, DOI 10.1016/j.ipm.2012.11.001 Garcia ACB, 2005, LECT NOTES COMPUT SC, V3591, P198 Hsieh JJPA, 2007, EUR J INFORM SYST, V16, P216, DOI 10.1057/palgrave.ejis.3000663 Janev V, 2011, INFORM PROCESS MANAG, V47, P507, DOI 10.1016/j.ipm.2010.11.002 Joorabchi A., 2013, J INF SCI, V40, P211 Kuo MH, 2009, EXPERT SYST APPL, V36, P3543, DOI 10.1016/j.eswa.2008.02.014 Lai YG, 2013, ELECTRON LIBR, V31, P264, DOI 10.1108/EL-08-2011-0126 Laughton P, 2011, ELECTRON LIBR, V29, P225, DOI 10.1108/02640471111125186 Lausin A., 2003, KNOWLEDGE PROCESS MA, V10, P218, DOI 10.1002/kpm.165 McCluney N., 2010, DOD WIKI INCREASES T Mnjama N, 2007, ELECTRON LIBR, V25, P274, DOI 10.1108/02640470710754797 Nielsen J, 2006, PARTICIPATION INEQUA, V9, P2006 Noveck BS, 2009, WIKI GOVERNMENT: HOW TECHNOLOGY CAN MAKE GOVERNMENT BETTER, DEMOCRACY STRONGER, AND CITIZENS MORE POWERFUL, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandoval-Almazan R, 2012, GOV INFORM Q, V29, pS72, DOI 10.1016/j.giq.2011.09.004 Standing C, 2011, TECHNOVATION, V31, P287, DOI 10.1016/j.technovation.2011.02.005 Tai DWS, 2008, ELECTRON LIBR, V26, P329, DOI 10.1108/02640470810879482 Wang R. Q., 2009, J ZHEJIANG U ENG SCI, V48, P315 Wong Pak-kwong, 1996, P 16 C COMP LING, V1, P200, DOI 10.3115/992628.992665 Wulf K. D., 2006, INFORM MANAGE, V43, P434 NR 27 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0264-0473 EI 1758-616X J9 ELECTRON LIBR JI Electron. Libr. PY 2015 VL 33 IS 3 BP 450 EP 467 DI 10.1108/EL-08-2013-0151 PG 18 WC Information Science & Library Science SC Information Science & Library Science GA CK7TV UT WOS:000356437300007 ER PT J AU Guo, JL Wang, HC Lai, MW AF Guo, Jiunn-Liang Wang, Hei-Chia Lai, Ming-Way TI A feature selection approach for automatic e-book classification based on discourse segmentation SO PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS LA English DT Article DE Discourse segmentation; Feature selection; Text classification; Word sense disambiguation ID INFORMATION; MODEL; TEXT; FRAMEWORK AB Purpose - The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents - e-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus. Design/methodology/approach - The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique. Findings - The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books. Research limitations/implications - Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold. Practical implications - The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documents - e-books as against to conventional techniques. Originality/value - A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems. C1 [Guo, Jiunn-Liang] ROC Taiwan Air Force Acad, Kaohsiung, Taiwan. [Wang, Hei-Chia; Lai, Ming-Way] Natl Cheng Kung Univ, Inst Informat Management, Tainan 70101, Taiwan. RP Guo, JL (reprint author), ROC Taiwan Air Force Acad, Kaohsiung, Taiwan. EM jiunnguo@yahoo.com FU National Science Council, Taiwan [NSC 101-2410-H-006-011-MY2] FX The research is based on work supported (in part) by the NSC 101-2410-H-006-011-MY2 project from National Science Council, Taiwan. Besides, the authors sincerely appreciate the anonymous reviewers and editors who offered valuable comments to help improve the quality of this paper significantly. CR Arauzo-Azofra A, 2008, J INTELL INF SYST, V30, P273, DOI 10.1007/s10844-007-0037-0 Boguraev B. K., 2000, PROCEEDINGS OF THE 3, P3004 Brants T., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Budd JM, 1996, INFORM PROCESS MANAG, V32, P217, DOI 10.1016/S0306-4573(96)85007-2 Carberry S., 1990, P 13 INT C COMP LING, V2, P276, DOI 10.3115/997939.997987 Chan SWK, 2004, INT J HUM-COMPUT ST, V61, P138, DOI 10.1016/j.ijhcs.2003.12.002 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chen K.-H., 1995, P WORKSH REC ADV NAT, P152 Cordon O, 2003, INT J APPROX REASON, V34, P241, DOI 10.1016/j.ijar.2003.07.010 Dalal M. K., 2011, INTERNATIONAL JOURNA, V28, P975 Fong A. C. M., 2006, INT J INF TECHNOL, V12, P59 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Gillian B., 1983, DISCOURSE ANALYSIS GROSZ BJ, 1995, COMPUT LINGUIST, V21, P203 Harding JA, 2006, J MANUF SCI E-T ASME, V128, P969, DOI 10.1115/1.2194554 Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Hernault H, 2010, LECT NOTES COMPUT SC, V6008, P315 HOVY EH, 1993, ARTIF INTELL, V63, P341, DOI 10.1016/0004-3702(93)90021-3 Huang KC, 2009, ARTIF INTELL MED, V46, P97, DOI 10.1016/j.artmed.2008.11.008 Ide N, 1998, COMPUT LINGUIST, V24, P1 Kauchak D., 2005, P 43 ANN M ASS COMP, P32, DOI 10.3115/1610230.1610237 Khabbaz M, 2012, IEEE T SYST MAN CY C, V42, P1566, DOI 10.1109/TSMCC.2012.2208102 Konchady M., 2006, PROGRAMMING SERIES KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 Lautamatti L., 1978, 1978 TEXT LINGUISTIC, P71 Lewis D., 1994, 3 ANN S DOC AN INF R, V33, P81 Li H, 2003, INFORM PROCESS MANAG, V39, P521, DOI 10.1016/S0306-4573(02)00035-3 Li S., 2009, P JOINT C 47 ANN M A, V2, P692, DOI 10.3115/1690219.1690243 Mahfouz T., 2011, PROCEEDINGS OF THE C Mann W, 1988, TEXT, V8, P243 Miller G. A., 1993, TECHNICAL REPORT Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Molina A, 2011, LECT NOTES ARTIF INT, V7094, P316, DOI 10.1007/978-3-642-25324-9_27 Neaga EI, 2005, INT J PROD RES, V43, P1089, DOI 10.1080/00207540412331322939 PAICE CD, 1990, INFORM PROCESS MANAG, V26, P171, DOI 10.1016/0306-4573(90)90014-S Paradis F, 2007, INFORM PROCESS MANAG, V43, P344, DOI 10.1016/j.ipm.2006.07.006 Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226 Pham DT, 2005, P I MECH ENG B-J ENG, V219, P395, DOI 10.1243/095440505X32274 Popplewell K., 2006, P 4 IEEE INT C IND I, P519 Ridel M., 2012, PROCEEDINGS OF ACL 2, P37 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 Salton G., 1983, INTRODUCTION TO MODE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soricut R., 2003, P 2003 C N AM CHAPT, V1, P149, DOI 10.3115/1073445.1073475 STARK HA, 1988, DISCOURSE PROCESS, V11, P275 Tagarelli A., 2012, KNOWLEDGE AND INFORM, V30, P1 Tannen D., 2002, THE HANDBOOK OF DISC Viii L., 1997, LS 8 REPORT NO 23 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 Xu Y, 2008, LECT NOTES COMPUT SC, V4993, P71 Yu L., 2003, P 9 ACM SIGKDD INT C, P685 Zhang Y, 2010, J INTELL INF SYST, V35, P333, DOI 10.1007/s10844-009-0102-y NR 54 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0033-0337 EI 1758-7301 J9 PROGRAM-ELECTRON LIB JI Program-Electron. Libr. Inf. Syst. PY 2015 VL 49 IS 1 BP 2 EP 22 DI 10.1108/PROG-12-2012-0071 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CC8AK UT WOS:000350590000001 ER PT J AU Mei, A Morabito, G Santi, P Stefa, J AF Mei, Alessandro Morabito, Giacomo Santi, Paolo Stefa, Julinda TI Social-Aware Stateless Routing in Pocket Switched Networks SO IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS LA English DT Article DE Delay-tolerant networks; opportunistic networks; mobile social networks; community structure; routing ID MOBILE NETWORKS AB Existing social-aware routing protocols for pocket switched networks make use of the information about the social structure of the network deduced by state information of nodes (e.g., history of past encounters) to optimize routing. Although these approaches are shown to have superior performance to social-oblivious, stateless routing protocols (BinarySW, Epidemic), the improvement comes at the cost of considerable storage overhead required on the nodes. In this paper we present SANE, the first routing mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation-that we validate on a real-world trace-that individuals with similar interests tend to meet more often. In SANE, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple routing rule based on interest profile similarity, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through thorough experiments, we show the superiority of SANE over existing approaches, both stateful, social-aware and stateless, social-oblivious. We discuss the statelessness of our approach in the supplementary file, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2307857, of this manuscript. Our interest-based approach easily enables innovative networking services, such as interest-casting. An interest-casting protocol is also introduced in this paper, and evaluated through experiments based on both real-world and synthetic mobility traces. C1 [Mei, Alessandro; Stefa, Julinda] Univ Roma La Sapienza, Dept Comp Sci, I-00198 Rome, Italy. [Morabito, Giacomo] Univ Catania, Dept Comp Sci & Telecommun Engn, I-95125 Catania, Italy. [Santi, Paolo] Ist Informat & Telemat, Dept Computat Math, I-56124 Pisa, Italy. RP Mei, A (reprint author), Univ Roma La Sapienza, Dept Comp Sci, Via Salaria 113, I-00198 Rome, Italy. EM mei@di.uniroma1.it; gmorabi@diit.unict.it; paolo.santi@iit.cnr.it; stefa@di.uniroma1.it CR Boldrini C., 2008, P 11 INT S MOD AN SI Boldrini Chiara, 2008, Pervasive and Mobile Computing, V4, DOI 10.1016/j.pmcj.2008.04.003 Claypool M, 2001, IEEE INTERNET COMPUT, V5, P32, DOI 10.1109/4236.968829 Costa P, 2008, IEEE J SEL AREA COMM, V26, P748, DOI 10.1109/JSAC.2008.080602 Costantino G., 2012, P IEEE WIR COMM NETW Costantino G., 2012, P MOB Daly EM, 2007, P ACM MOBIHOC Diaz J., 2011, P 19 EUR C ALG ESA 1 Eagle N, 2006, PERS UBIQUIT COMPUT, V10, P255, DOI 10.1007/s00779-005-0046-3 Eppstein D., 2011, P INT C COMP ASP SOC Gao W., 2009, P ACM MOBIHOC Gao W., 2011, P IEEE INFOCOM Golbeck J, 2009, ACM T WEB, V3, DOI 10.1145/1594173.1594174 Hossmann T, 2012, COMPUT COMMUN, V35, P1613, DOI 10.1016/j.comcom.2012.05.003 Hsu WJ, 2012, AD HOC NETW, V10, P1586, DOI 10.1016/j.adhoc.2011.06.004 Hsu W.-J., 2008, ACM SIGMOBILE MOBILE, V12, P52, DOI 10.1145/1374512.1374529 Hui P., 2005, P ACM SIGCOMM WORKSH Hui P., 2008, P ACM MOBIHOC Hui P., 2007, P ACM MOB EV INT ARC Hui P., 2007, P 5 IEEE INT C PERV Ioannidis S., 2009, P IEEE INFOCOM Jaho E., 2011, P IEEE 3 INT WORKSH Kosta S., 2010, P 7 ANN IEEE COMM SO, P10 Kosta S, 2014, IEEE T MOBILE COMPUT, V13, P116, DOI 10.1109/TMC.2012.229 Leguay J., 2006, P IEEE INFOCOM Leguay J, 2007, WIREL COMMUN MOB COM, V7, P1171, DOI [10.1002/wcm.520, 10.1002/wem.520] Li F., 2009, P 6 ANN IEEE COMM SO Lindgren A., 2003, ACM SIGMOBILE MOBILE, V7, P19, DOI DOI 10.1145/961268.961272 Malandrino F., 2012, P IEEE INFOCOM McPherson M, 2001, ANNU REV SOCIOL, V27, P415, DOI 10.1146/annurev.soc.27.1.415 Mei A., 2011, P IEEE INFOCOM Mei A., 2009, P IEEE INFOCOM Nguyen H.A., 2007, P IEEE INT S WORLD W Noulas A., 2009, P WORKSH AN NETW LEA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scellato S., 2011, P 17 ACM SIGKDD INT Spyropoulos T, 2008, IEEE ACM T NETWORK, V16, P77, DOI 10.1109/TNET.2007.897964 Vandat A., 2000, TECHNICAL REPORT, DOI 10.1.1.34.6151 Wu J, 2014, IEEE T COMPUT, V63, P383, DOI 10.1109/TC.2012.209 Wu J., 2012, P IEEE INFOCOM NR 40 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1045-9219 EI 1558-2183 J9 IEEE T PARALL DISTR JI IEEE Trans. Parallel Distrib. Syst. PD JAN PY 2015 VL 26 IS 1 BP 252 EP 261 DI 10.1109/TPDS.2014.2307857 PG 10 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AZ4QF UT WOS:000348206700024 ER PT J AU Rotella, F Leuzzi, F Ferilli, S AF Rotella, Fulvio Leuzzi, Fabio Ferilli, Stefano TI Learning and exploiting concept networks with ConNeKTion SO APPLIED INTELLIGENCE LA English DT Article DE Concept network; Text mining; Reasoning; Generalization ID TEXT; CLASSIFICATION; ONTOLOGIES; SIMILARITY; RELEVANCE; FEATURES AB Studying, understanding and exploiting the content of a document collection require automatic techniques that can effectively support the users in extracting useful information from it and reason with this information. Concept networks (e.g., taxonomies) may play a relevant role in this perspective, but are seldom available, and cannot be manually built and maintained cheaply and reliably. On the other hand, automated learning of these resources from text needs to be robust with respect to missing or partial knowledge, because often only sparse fragments of the target network can be extracted. This work presents ConNeKTion, a tool that is able to learn concept networks from plain text and to structure and enrich them by finding concept generalizations. The proposed methodologies are general and applicable to any language. It also provides functionalities for the exploitation of the learned knowledge, and a control panel that allows the user to comfortably carry out these activities. Several experiments and applications are reported, showing the usefulness and flexibility of ConNeKTion. C1 [Rotella, Fulvio; Leuzzi, Fabio; Ferilli, Stefano] Univ Bari, Dept Comp Sci, Bari, Italy. RP Ferilli, S (reprint author), Univ Bari, Dept Comp Sci, Via E Orabona 4, Bari, Italy. EM fulvio.rotella@uniba.it; fabio.leuzzi@uniba.it; stefano.ferilli@uniba.it FU Italian PON project [PON02_00563_3489339] FX This work was partially funded by the Italian PON 2007-2013 project PON02_00563_3489339 "Puglia@Service". CR Angioni M, 2008, COMMUNICATIONS SIWN, V5, P62 Argamon S, 2007, J AM SOC INF SCI TEC, V58, P802, DOI 10.1002/asi.20553 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 Crestani F., 1997, NAT REV GENET, V11, P453, DOI DOI 10.1023/A:1006569829653> Deerwester S., 1988, P 51 ASIS ANN M ASIS, V25 DEFAYS D, 1977, COMPUT J, V20, P364, DOI 10.1093/comjnl/20.4.364 De Raedt L, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2468 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Fellbaum C., 1998, ELECT LEXICAL DATABA Ferilli S., 2011, P WORKSH MIN COMPL P Ferilli S, 2009, POSTPR 5 IT RES C DI, P22 Ferilli S, 2011, ADV PATTERN RECOGNIT, P1, DOI 10.1007/978-0-85729-198-1 Ferilli S, 2011, LECT NOTES ARTIF INT, V6934, P33, DOI 10.1007/978-3-642-23954-0_6 Ferilli S, 2009, LECT NOTES ARTIF INT, V5883, P131, DOI 10.1007/978-3-642-10291-2_14 Gale W. A., 1992, DARPA SPEECH NAT LAN Gupta Vishal, 2009, Journal of Emerging Technologies in Web Intelligence, V1, DOI 10.4304/jetwi.1.1.60-76 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 HAMMING RW, 1950, AT&T TECH J, V29, P147 Hasegawa R, 2009, P 6 APCCM APCCM 09 A, V96, P87 Hensman S, 2004, P STUD RES WORKSH HL, P49, DOI 10.3115/1614038.1614047 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Karypis G, 2000, TECHNICAL REPORT Karypis G, 2000, TR000016 U MINN Kimmig A, 2008, LECT NOTES COMPUT SC, V5366, P175, DOI 10.1007/978-3-540-89982-2_22 Kipper K., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000) Koo SO, 2003, LECT NOTES ARTIF INT, V2871, P93 Leuzzi F, 2013, NEW FRONTIERS MINING, V7765, P232 Leuzzi F, 2013, WORKSH NEW FRONT MIN Leuzzi F, 2013, BRIDG CULT HER I P 9, V385 MacCartney B., 2006, P LREC 06, V319, P449 Maedche A, 2000, ICCS 2000 Maedche A, 2000, LECT NOTES ARTIF INT, V1937, P189 Magnini B, 2000, INTEGRATING SUBJECT, P1413 Manning Christopher D., 2003, ADV NEURAL INFORM PR, V15 Matsuo Y, 2004, INT J ARTIF INTELL T, V13, P2003 Mccarthy P.M., 2006, P FLOR ART INT RES S, P764 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 O'Madadhain J., 2003, TECHNICAL REPORT Ogata N, 2001, LECT NOTES ARTIF INT, V2198, P514 Qiu L., 2004, P 4 INT C LANG RES E, P291 Raghavan S., 2010, P ACL 2010 C SHORT P, P38 Robertson S. E., 1996, OKAPI TREC 3, P109 Rotella F, 2013, P LECT NOTES COMPUTE, V7906, P341 Rotella F, 2013, DIGITAL LIB ARCH, V354, P129 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1984, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1980, INFORM PROCESS MANAG, V16, P1, DOI 10.1016/0306-4573(80)90002-3 Sato T., 1995, P 12 INT C LOG PROGR, P715 Semeraro G, 1997, LECT NOTES COMPUTER, V1463, P300, DOI 10.1007/3-540-49674-2_16 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 van Halteren H., 2004, P 42 ANN M ASS COMP Velardi P., 2006, ONTOLOGY LEARNING TE Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zesch Torsten, 2008, P 6 INT C LANG RES E Zheng R, 2006, J AM SOC INF SCI TEC, V57, P378, DOI 10.1002/asi.20316 NR 58 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-669X EI 1573-7497 J9 APPL INTELL JI Appl. Intell. PD JAN PY 2015 VL 42 IS 1 SI SI BP 87 EP 111 DI 10.1007/s10489-014-0543-z PG 25 WC Computer Science, Artificial Intelligence SC Computer Science GA AY2FA UT WOS:000347402700008 ER PT J AU Dong, H Hussain, FK AF Dong, Hai Hussain, Farookh Khadeer TI Service-requester-centered service selection and ranking model for digital transportation ecosystems SO COMPUTING LA English DT Article DE Digital ecosystems; Generic services; Service ranking; Service selection; Transport services; Trust ID CERTIFICATION AB Transport services are a fundamental utility that drives human society. A Digital Transportation Ecosystem is a sub-system of the Digital Ecosystem, which uses ICT resources to facilitate transport service transactions. This research focuses on the selection and ranking of online transport service information. The previous research in this area has been unable to achieve satisfactory performance or give sufficient freedom to service requesters to rank services based on their preferences. 'User-centered design' is a broad term to describe how end-users influence system design. In this research, we propose a Service-Requester-Centered Service Selection and Ranking Model, guided by the philosophy of user-centered design. Three major sub-models are involved in this model: a model for assisting service requesters to search appropriate transport service ontology concepts to denote their service requests, a model for enhancing the accuracy of automatic transport service concept recommendation by observing service requesters' click behaviours, and a model for enabling service-requester-preference-based service ranking. Implementations and empirical experiments are conducted to evaluate the three sub-models and the drawn conclusions along with directions for future work are outlined. C1 [Dong, Hai] Curtin Univ Technol, Sch Informat Syst, Curtin Business Sch, Bentley, WA 6102, Australia. [Hussain, Farookh Khadeer] Univ Technol Sydney, Sch Software, Fac Engn & Informat Technol, Decis Support & E Serv Intelligence Lab, Ultimo, NSW 2007, Australia. RP Hussain, FK (reprint author), Univ Technol Sydney, Sch Software, Fac Engn & Informat Technol, Decis Support & E Serv Intelligence Lab, Ultimo, NSW 2007, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@uts.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Abras C, 2004, BERKSHIRE ENCY HUMAN, V2, P763 Agarwal S, 2005, LECT NOTES COMPUT SC, V3590, P348 Arcieri F., 2003, Proceedings 2003 Symposium on Applications and the Internet, DOI 10.1109/SAINT.2003.1183057 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Chang E., 2006, WSEAS Transactions on Environment and Development, V2 Chinnici R, 2007, SERVICES DESCRIPTION Deerwester SC, 1988, Patent, U. S., Patent No. 4839853 Dini P, 2005, DIGITAL ECOSYSTEMS R Dini P, 2007, SCI FDN DIGITAL ECOS Dong H, 2008, IEEE/SOLI'2008: PROCEEDINGS OF 2008 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS, VOLS 1 AND 2, P820 Dong H, 2011, CONCURR COMP-PRACT E, V23, P505, DOI 10.1002/cpe.1652 Dong H, 2011, IEEE T IND ELECTRON, V58, P2183, DOI 10.1109/TIE.2009.2031186 Dong H, 2010, WORLD WIDE WEB, V13, P75, DOI 10.1007/s11280-009-0081-5 Dong H., 2012, LNCS, V7636, P591 Dong H, 2011, KNOWL-BASED SYST, V24, P761, DOI 10.1016/j.knosys.2011.02.005 Dong H, 2013, WEB INFORM SYSTEMS E, V8180, P267 Dong H, 2013, CONCURR COMP-PRACT E, V25, P961, DOI 10.1002/cpe.2886 Draper SW, 1986, NEW PERSPECTIVES HUM Endsley M.R., 2004, DESIGNING SITUATION, V2nd Garofalakis J, 2006, J WEB ENG, V5, P265 Hussain FK, 2004, COMPUT SYST SCI ENG, V19, P173 Kopecky J, 2007, IEEE INTERNET COMPUT, V11, P60, DOI 10.1109/MIC.2007.134 Leem CS, 2004, TECHNOVATION, V24, P63, DOI 10.1016/S0166-4972(03)00145-7 Ludwig SA, 2005, J PARALLEL DISTR COM, V65, P1533, DOI 10.1016/j.jpdc.2005.05.022 Martin David, 2004, OWL S SEMANTIC MARKU Mathew TV, 2001, INTRO TRANSPORTATION ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roman D, 2005, APPL ONTOL, V1, P77 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Satyanarayanan M, 2001, IEEE PERS COMMUN, V8, P10, DOI 10.1109/98.943998 Serhani M. A., 2005, Proceedings. 2005 IEEE International Conference on Web Service Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 NR 32 TC 0 Z9 0 PU SPRINGER WIEN PI WIEN PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA SN 0010-485X EI 1436-5057 J9 COMPUTING JI Computing PD JAN PY 2015 VL 97 IS 1 SI SI BP 79 EP 102 DI 10.1007/s00607-013-0347-8 PG 24 WC Computer Science, Theory & Methods SC Computer Science GA AY2FS UT WOS:000347404700005 ER PT J AU Song, M Heo, GE Lee, D AF Song, Min Heo, Go Eun Lee, Dahee TI Identifying the landscape of Alzheimer's disease research with network and content analysis SO SCIENTOMETRICS LA English DT Article DE Alzheimer's disease (AD); Bibliometrics; Document representation; Concept graph; Topic modeling ID BIBLIOMETRIC ANALYSIS; DEMENTIA; RISK; TEXT; PRODUCTIVITY; EXTRACTION; DECLINE; IMPACT; WOMEN; MEN AB Alzheimer's disease (AD) is one of degenerative brain diseases, whose cause is hard to be diagnosed accurately. As the number of AD patients has increased, researchers have strived to understand the disease and develop its treatment, such as medical experiments and literature analysis. In the area of literature analysis, several traditional studies analyzed the literature at the macro level like author, journal, and institution. However, analysis of the literature both at the macro level and micro level will allow for better recognizing the AD research field. Therefore, in this study we adopt a more comprehensive approach to analyze the AD literature, which consists of productivity analysis (year, journal/proceeding, author, and Medical Subject Heading terms), network analysis (co-occurrence frequency, centrality, and community) and content analysis. To this end, we collect metadata of 96,081 articles retrieved from PubMed. We specifically perform the concept graph-based network analysis applying the five centrality measures after mapping the semantic relationship between the UMLS concepts from the AD literature. We also analyze the time-series topical trend using the Dirichlet multinomial regression topic modeling technique. The results indicate that the year 2013 is the most productive year and Journal of Alzheimer's Disease the most productive journal. In discovery of the core biological entities and their relationships resided in the AD related PubMed literature, the relationship with glycogen storage disease is founded most frequently mentioned. In addition, we analyze 16 main topics of the AD literature and find a noticeable increasing trend in the topic of transgenic mouse. C1 [Song, Min; Heo, Go Eun; Lee, Dahee] Yonsei Univ, Dept Lib & Informat Sci, Seoul 120749, South Korea. RP Song, M (reprint author), Yonsei Univ, Dept Lib & Informat Sci, 50 Yonsei Ro, Seoul 120749, South Korea. EM min.song@yonsei.ac.kr FU Bio-Synergy Research Project of the Ministry of Science, ICT and Future Planning through the National Research Foundation [2013M3A9C4078138] FX This work was supported by the Bio-Synergy Research Project (2013M3A9C4078138) of the Ministry of Science, ICT and Future Planning through the National Research Foundation. CR Al-Mubaid H., 2005, AM J BIOCH BIOTECHNO, V1, P145 Andreasen T, 2009, LECT NOTES ARTIF INT, V5822, P323, DOI 10.1007/978-3-642-04957-6_28 Ansari M. A., 2006, TRENDS INFORM MANAGE, V2, P130 BACHMAN DL, 1993, NEUROLOGY, V43, P515 Barnes LL, 2003, NEUROLOGY, V60, P1777 Bastian M, 2009, ICWSM, V8, P361 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bleik S., 2009, IEEE INT C BIOINF BI, P38 Blondel VD, 2008, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2008/10/P10008 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brookmeyer R, 2007, ALZHEIMERS DEMENT, V3, P186, DOI 10.1016/j.jalz.2007.04.381 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 Chen HQ, 2014, SCIENTOMETRICS, V98, P1865, DOI 10.1007/s11192-013-1132-3 Chen Y.-M., 2005, P 2005 IEEE INT C MA, V3, P1937 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Ercan G, 2007, INFORM PROCESS MANAG, V43, P1705, DOI 10.1016/j.ipm.2007.01.015 Erhardt RAA, 2006, DRUG DISCOV TODAY, V11, P315, DOI 10.1016/j.drudis.2006.02.011 Evans DA, 2003, ARCH NEUROL-CHICAGO, V60, P185, DOI 10.1001/archneur.60.2.185 Ravetti MG, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010153 Gong Y., 2001, P 24 ANN INT ACM SIG, P19, DOI DOI 10.1145/383952.383955 Hebert LE, 2001, AM J EPIDEMIOL, V153, P132, DOI 10.1093/aje/153.2.132 Huang C, 2006, IEEE DATA MINING, P275 Krauthammer M, 2004, P NATL ACAD SCI USA, V101, P15148, DOI 10.1073/pnas.0404315101 Kukull WA, 2002, ARCH NEUROL-CHICAGO, V59, P1737, DOI 10.1001/archneur.59.11.1737 Lambiotte R., 2009, ARXIV08121770 Li J, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000450 LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281 Miech RA, 2002, NEUROLOGY, V58, P209 Mimno D., 2008, P 24 C UNC ART INT, P411 Oresic M, 2010, GENOME MED, V2, DOI 10.1186/gm204 Rocca WA, 1998, AM J EPIDEMIOL, V148, P51 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seshadri S, 1997, NEUROLOGY, V49, P1498 Shehata S, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P629 Smalheiser NR, 1996, NEUROLOGY, V47, P809 Smalheiser NR, 1998, COMPUT METH PROG BIO, V57, P149, DOI 10.1016/S0169-2607(98)00033-9 Song M, 2014, J ASSOC INF SCI TECH, V65, P352, DOI 10.1002/asi.22970 Sorensen AA, 2010, PROCD SOC BEHV, V2, P6582, DOI 10.1016/j.sbspro.2010.04.068 Sorensen AA, 2009, J ALZHEIMERS DIS, V16, P451, DOI 10.3233/JAD-2009-1046 Thota Hanuman, 2007, Bioinformation, V2, P91 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wan X., 2007, ANN M ASS COMP LING, V45, P552 Wasserman S., 1994, SOCIAL NETWORK ANAL NR 43 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 EI 1588-2861 J9 SCIENTOMETRICS JI Scientometrics PD JAN PY 2015 VL 102 IS 1 BP 905 EP 927 DI 10.1007/s11192-014-1372-x PG 23 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AY0OZ UT WOS:000347297400044 ER PT J AU Tang, GY Xia, YQ Sun, J Zhang, M Zheng, TF AF Tang, Guoyu Xia, Yunqing Sun, Jun Zhang, Min Zheng, Thomas Fang TI Statistical word sense aware topic models SO SOFT COMPUTING LA English DT Article DE Topic modeling; Word sense induction; Document representation; Document clustering AB LDA has been proved effective in modeling the semantic relation between surface words. This semantic information in the document collection is useful to measure the topic distribution for a document. In general, a surface word may significantly contribute to several topics in a document collection. LDA measures the contribution of a surface word to each topic and considers a surface word to be identical across all documents. However, a surface word may present different signatures in different contexts, i.e., polysemous words can be used with different senses in different contexts. Intuitively, disambiguating word senses for topic models can enhance their discriminative capabilities. In this work, we propose a joint model to automatically induce document topics and word senses simultaneously. Instead of using some pre-defined word sense resources, we capture the word sense information via a latent variable and directly induce them in a fully unsupervised manner from the corpora. Experimental results show that the proposed joint model outperforms the baselines significantly in document clustering and improves the word sense induction as well against a stand-alone non-parametric model. C1 [Tang, Guoyu; Xia, Yunqing; Zheng, Thomas Fang] Tsinghua Univ, Dept Comp Sci & Technol, TNList, Beijing 100084, Peoples R China. [Sun, Jun] ASTAR, Inst Infocomm Res, Singapore, Singapore. [Zhang, Min] Soochow Univ, Suzhou, Peoples R China. RP Xia, YQ (reprint author), Tsinghua Univ, Dept Comp Sci & Technol, TNList, Beijing 100084, Peoples R China. EM yqxia@tsinghua.edu.cn FU Natural Science Foundation of China [NSFC: 61272233] FX We thank the reviewers for the insightful comments. This work is supported by Natural Science Foundation of China (NSFC: 61272233). CR Agirre Eneko, 2007, P 4 INT WORKSH SEM E, P7, DOI 10.3115/1621474.1621476 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boyd-Graber J, 2007, EMNLP CONLL, P1024 Boyd-Graber J, 2007, P 4 INT WORKSH SEM E, P277, DOI 10.3115/1621474.1621534 Brody Samuel, 2009, P 12 C EUR CHAPT ASS, P103, DOI 10.3115/1609067.1609078 Cambria E, 2014, IEEE COMPUT INTELL M, V9, P48, DOI 10.1109/MCI.2014.2307227 Chemudugunta C, 2008, P 17 ACM C INF KNOWL, P1469, DOI DOI 10.1145/1458082.1458337 Denkowski M, 2009, LANGUAGE STAT 2 LIT Dietz L., 2007, P 24 INT C MACH LEAR, P233, DOI 10.1145/1273496.1273526 Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guo W, 2011, P C EMP METH NAT LAN, P552 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Hovy E, 2006, COMPANION VOLUME SHO, P57 Huang HH, 2010, IEEE T FUZZY SYST, V18, P1098, DOI 10.1109/TFUZZ.2010.2065811 Klapaftis IP, 2013, LANG RESOUR EVAL, V47, P579, DOI 10.1007/s10579-012-9205-0 Kong J., 2005, TDT4 MULTILINGUAL BR Lau RYK, 2014, IEEE COMPUT INTELL M, V9, P31, DOI 10.1109/MCI.2013.2291689 Lewis D., 1997, REUTERS 21578 TEXT C Li L., 2010, P 48 ANN M ASS COMP, P1138 McCarthy D., 2004, P 42 M ASS COMP LING, P279, DOI 10.3115/1218955.1218991 Navigli R., 2010, P 2010 C EMP METH NA, P116 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt H., 1994, P INT C NEW METH LAN, V12, P44 Schtze H, 1995, P 4 ANN S DOC AN INF Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Steinbach M., 2000, KDD WORKSH TEXT MIN Stokoe C, 2003, P 26 ANN INT ACM SIG, P159, DOI DOI 10.1145/860435.860466 Teh Y. W., 2004, J AM STAT ASSOC, V101 TUFIS D, 2007, APPL FUZZY SETS THEO, V4578, P447, DOI 10.1007/978-3-540-73400-0_56 Wang XR, 2007, IEEE DATA MINING, P697 Yao X., 2011, P TEXTGRAPHS 6 GRAPH, P10 NR 33 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1432-7643 EI 1433-7479 J9 SOFT COMPUT JI Soft Comput. PD JAN PY 2015 VL 19 IS 1 SI SI BP 13 EP 27 DI 10.1007/s00500-014-1372-z PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA AY2GS UT WOS:000347407500003 ER PT J AU Fu, RJ Qin, B Liu, T AF Fu, Ruiji Qin, Bing Liu, Ting TI Open-categorical text classification based on multi-LDA models SO SOFT COMPUTING LA English DT Article DE Topic model; Text classification; Categorization system construction ID DOCUMENT CLASSIFICATION; CATEGORIZATION; NETWORK; SYSTEM; RULE AB We present a new and realistic problem, open-categorical text classification, which requires us to classify documents without the categorization system known beforehand. To solve this problem, we propose a novel approach to construct the categorization system and classify documents based on multi-latent Dirichlet allocation (LDA) models. We cluster topics and extract topical keywords to help category annotation. Subsequently, the LDA models are applied to predict the categories of documents comprehensively. Our result, amacro-averaged F1 measure of 84.02%, outperforms the state-of-the-art supervised and semi-supervised text classification methods. C1 [Fu, Ruiji; Qin, Bing; Liu, Ting] Harbin Inst Technol, Harbin 150001, Peoples R China. RP Liu, T (reprint author), Harbin Inst Technol, 6th Floor,29 Jiaohua St, Harbin 150001, Peoples R China. EM rjfu@ir.hit.edu.cn; bqin@ir.hit.edu.cn; tliu@ir.hit.edu.cn FU National Natural Science Foundation of China (NSFC) [61133012, 61273321]; National 863 Leading Technology Research Project [2012AA011102] FX This work is supported by National Natural Science Foundation of China (NSFC) via Grant 61133012, 61273321 and the National 863 Leading Technology Research Project via grant 2012AA011102. Special thanks to Jianfei Guo and Xiaocheng Feng for their help in the experiments.. CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blei DM, 2003, NIPS, V16 Blei D.M., 2007, NIPS, V7, P121 Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Brown P. F., 1992, Computational Linguistics, V18 Carletta J, 1996, COMPUT LINGUIST, V22, P249 Carlson A., 2010, P 3 ACM INT C WEB SE, P101, DOI 10.1145/1718487.1718501 Cheng SJ, 2013, INTELLIGENT SCI INTE, P82 Collins M, 1999, P JOINT SIGDAT C EMP, P100 Danesh A., 2007, 10 INT C INF FUS, P1 Donghui C, 2010, 2 INT C COMP ENG TEC, V7, P383 Fu JH, 2012, EXPERT SYST APPL, V39, P3127, DOI 10.1016/j.eswa.2011.08.176 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Joachims T., 1998, LNCS, V1398, P137 Johnson DE, 2002, IBM SYST J, V41, P428 Kim S. B, 2002, P LECT NOTES ARTIFIC, V2417, P414, DOI 10.1007/3-540-45683-X_45 Li CH, 2009, EXPERT SYST APPL, V36, P3208, DOI 10.1016/j.eswa.2008.01.014 Lin Y, 2002, DATA MIN KNOWL DISC, V6, P259, DOI 10.1023/A:1015469627679 Mao X. - L., 2012, P EMNLP CONLL, P800 McClosky D, 2006, P N AM CHAPT ASS COM, V2006, P152 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Petinot Y, 2011, P 49 ANN M ASS COMP, V2, P670 Pham DT, 2005, P I MECH ENG C-J MEC, V219, P103, DOI 10.1243/095440605X8298 Qin Y-P, 2009, 6 INT C FUZZ SYST KN, P300 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Trappey AJC, 2006, EXPERT SYST APPL, V31, P755, DOI 10.1016/j.eswa.2006.01.013 Turian Joseph, 2010, P 48 ANN M ASS COMP, P384 Ueffing N, 2006, P NIPS WORKSH MACH L Vateekul P, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009), P320, DOI 10.1109/ICDMW.2009.94 Wanxiang Che, 2010, COL 2010 DEM, P13 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Zhang Y., 2004, P 4 INT C LANG RES E, P2051 NR 33 TC 1 Z9 1 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1432-7643 EI 1433-7479 J9 SOFT COMPUT JI Soft Comput. PD JAN PY 2015 VL 19 IS 1 SI SI BP 29 EP 38 DI 10.1007/s00500-014-1374-x PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA AY2GS UT WOS:000347407500004 ER PT S AU Nakanishi, T AF Nakanishi, Takafumi BE Lee, R TI A Discovery Method of Anteroposterior Correlation for Big Data Era SO SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING SE Studies in Computational Intelligence LA English DT Proceedings Paper CT 15th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) CY JUN 30-JUL 02, 2014 CL Las Vegas, NV SP IEEE, ACIS ID INFORMATION EXTRACTION SYSTEM; SIMILARITY; RETRIEVAL; MODEL AB In this paper, we present a new knowledge extraction method on Big data era. We introduce new concepts, anteroposterior correlation, and propose an extraction method of anteroposterior correlation. The anteroposterior correlation means the correlation based on the time anteroposterior relation. We consider that Heterogeneity, continuity, and visualization are the most critical features of Big data analytics, which provides a scale and connection merits based on them. No current data analysis methods are based on opened assumptions. Big data analytics provides a new data analysis method based on opening assumptions. In this paper, we especially focus on an aspect of heterogeneity. We discover a correlation in consideration of the continuity of time. By our method, we effectively discover relationships between heterogeneous things, events and phenomena. The anteroposterior correlations are represented in relative comparison with each conditional probability distribution. The one of the features of our method is a measurement correlation by using conditional probability. That is, we calculate the correlation relative by representing all in conditional probability, no absolutely. Our method is determined higher correlation by comparison to each heterogeneous thing, event and phenomenon. This is the most important points on the Big data era. When you apply current association rule extraction techniques, you obtain too big rule base to organize them. By our method, we realize the one of the methods for decision mining. C1 Int Univ Japan, Ctr Global Commun GLOCOM, Tokyo, Japan. RP Nakanishi, T (reprint author), Int Univ Japan, Ctr Global Commun GLOCOM, Tokyo, Japan. EM takafumi@glocom.ac.jp CR Adar E., 2007, WWW Adrian B, 2009, LECT NOTES ARTIF INT, V5803, P249 Berman JJ, 2013, PRINCIPLES OF BIG DATA: PREPARING, SHARING, AND ANALYZING COMPLEX INFORMATION, P1 Buitelaar P., 2006, P INT C LANG RES EV, P2321 Cimiano P., 2004, P 13 INT C WORLD WID, P462, DOI 10.1145/988672.988735 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Embley D., 2004, P 15 AUSTR DAT C ADC, V27, P3 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 Gilbert E., 2010, AAAI Greenberg P., 2013, 10 REASONS 2014 WILL Hwang C., 1999, P 6 INT WORKSH ONT B KIM YW, 1990, J DOC, V46, P113, DOI 10.1108/eb026857 Kitagawa T., 1993, RIDE IMS 93, P130 Kiyoki Y., 1994, ACM SIGMOD RECORD, V23, P34, DOI 10.1145/190627.190639 LEE JH, 1993, J DOC, V49, P188, DOI 10.1108/eb026913 Li Y., 2007, P 16 INT C WORLD WID, P777, DOI DOI 10.1145/1242572.1242677 Maedche A., 2000, SOFTW DEM 8 INT C CO Maedche A, 2003, STUD FUZZ SOFT COMP, V111, P345 McDowell LK, 2006, LECT NOTES COMPUT SC, V4273, P428 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G., 1990, INT J LEXICOGR, V3, P235 Minelli M., 2013, BIG DATA BIG ANAL EM POPOV B, 2004, NAT LANG ENG, V10, P375, DOI 10.1017/S135132490400347X Resnik P, 1995, INT JOINT CONF ARTIF, P448 Sadilek A., 2012, AAAI Saggion H, 2007, LECT NOTES COMPUT SC, V4825, P843 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Takano K., 2007, ACST 07, P510 Todirascu A, 2002, LECT NOTES COMPUT SC, V2553, P64 Vargas-Vera M., 2001, P WORKSH KNOWL MARK Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 Wu Fei, 2008, P 14 ACM SIGKDD INT, P731, DOI 10.1145/1401890.1401978 Yildiz B, 2007, LECT NOTES COMPUT SC, V4707, P660 NR 33 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-319-10389-1; 978-3-319-10388-4 J9 STUD COMPUT INTELL PY 2015 VL 569 BP 161 EP 177 DI 10.1007/978-3-319-10389-1_12 PG 17 WC Computer Science, Artificial Intelligence; Robotics SC Computer Science; Robotics GA BB8RR UT WOS:000347383300012 ER PT J AU Yang, XS Zhang, TZ Xu, CS AF Yang, Xiaoshan Zhang, Tianzhu Xu, Changsheng TI Cross-Domain Feature Learning in Multimedia SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE Cross-domain; deep learning; feature learning; multi-modal ID CLASSIFICATION AB In the Web 2.0 era, a huge number of media data, such as text, image/video, and social interaction information, have been generated on the social media sites (e.g., Facebook, Google, Flickr, and YouTube). These media data can be effectively adopted for many applications (e.g., image/video annotation, image/video retrieval, and event classification) in multimedia. However, it is difficult to design an effective feature representation to describe these data because they have multi-modal property (e.g., text, image, video, and audio) and multi-domain property (e.g., Flickr, Google, and YouTube). To deal with these issues, we propose a novel cross-domain feature learning (CDFL) algorithm based on stacked denoising auto-encoders. By introducing the modal correlation constraint and the cross-domain constraint in conventional auto-encoder, our CDFL can maximize the correlations among different modalities and extract domain invariant semantic features simultaneously. To evaluate our CDFL algorithm, we apply it to three important applications: sentiment classification, spam filtering, and event classification. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach. C1 [Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. [Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng] China Singapore Inst Digital Media, Singapore 119613, Singapore. RP Yang, XS (reprint author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. EM xiaoshang.yang@nlpr.ia.ac.cn; tzzhang@nlpr.ia.ac.cn; csxu@nlpr.ia.ac.cn FU National Program on Key Basic Research Project under the 973 Program [2012CB316304]; National Natural Science Foundation of China [61225009, 61303173, 61373122, 61432019]; Beijing Natural Science Foundation [4131004] FX This work was supported in part by the National Program on Key Basic Research Project under the 973 Program, Project No. 2012CB316304, the National Natural Science Foundation of China under Grants 61225009, 61303173, 61373122, and 61432019, and the Beijing Natural Science Foundation under Grant 4131004. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. K. Selcuk Candan. CR Baktashmotlagh M., 2013, P IEEE INT C COMP VI, P769 Bao Bing-Kun, 2013, P 3 ACM C INT C MULT, P135 Bengio Y., 2006, P NIPS, P153 Bengio Yoshua, 2009, Foundations and Trends in Machine Learning, V2, DOI 10.1561/2200000006 Bengio Y., 2013, P SLSP, P1 Blitzer J., 2007, P 45 ANN M ASS COMP, V7, P440 Blitzer J., 2006, P C EMP METH NAT LAN, P120, DOI 10.3115/1610075.1610094 Borgwardt K. M., 2006, P 14 INT C INT SYST, V22, P49 Boumal N, 2014, J MACH LEARN RES, V15, P1455 Brenner M., 2012, P 2 ACM INT C MULT R, P21 Bruzzone L, 2010, IEEE T PATTERN ANAL, V32, P770, DOI 10.1109/TPAMI.2009.57 Chen M., 2012, P 29 INT C MACH LEAR, P767 Glorot X., 2011, P 28 INT C MACH LEAR, P513 Gong B., 2013, P INT C MACH LEARN, P222 Gong B., 2013, P NIPS, P1286 Gong BQ, 2012, PROC CVPR IEEE, P2066 Gretton A., 2009, DATASET SHIFT MACHIN, V3, P5 Guillaumin M, 2010, PROC CVPR IEEE, P902, DOI 10.1109/CVPR.2010.5540120 Habrard Amaury, 2013, P ECML PKDD, P433 Hardoon DR, 2004, NEURAL COMPUT, V16, P2639, DOI 10.1162/0899766042321814 Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527 Hoffman J., 2012, P ECCV, V7573, P702 Huang J., 2007, P ADV NEUR INF SYST, V19, P601 Jin X., 2010, P ACM MULT, P1235, DOI 10.1145/1873951.1874196 Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68 Liu LQ, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2486 Liu XC, 2013, PROCEEDINGS OF THE 1ST INTERNATIONAL JOINT SYMPOSIUM ON JOINING AND WELDING, P151 Long M., 2013, P IEEE INT C COMP VI, P2200 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lu WT, 2013, IEEE T MULTIMEDIA, V15, P1920, DOI 10.1109/TMM.2013.2280895 Ma Ge, 2013, P IEEE INT C MULT EX, P1 Ma Z., 2012, P 20 ACM INT C MULT, P469 Naaman M, 2012, MULTIMED TOOLS APPL, V56, P9, DOI 10.1007/s11042-010-0538-7 Ngiam J., 2011, P 28 INT C MACH LEAR, P689 Orlando S., 2013, P 22 INT C WORLD WID, P1285 Over P., 2013, P TRECVID, P1 Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191 Pan SJ, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1187 PETKOS G, 2012, P 2 ACM INT C MULT R, V23, P1 Qi GJ, 2011, P 20 INT C WORLD WID, P297 Ranzato M., 2006, P NIPS, V19, P1137 Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 Rasiwasia N., 2010, P INT C MULT, P251, DOI 10.1145/1873951.1873987 Reuter T., 2012, P 2 ACM INT C MULT R, p[22, 1, 8] Roy S. D., 2012, P ACM MULT, P649 Saenko K, 2010, LECT NOTES COMPUT SC, V6314, P213, DOI 10.1007/978-3-642-15561-1_16 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sang J., 2011, TOMCCAP, V7, P30 Sang Jitao, 2012, P 20 ACM INT C MULT, P19 Schapire R., 1996, MACH LEARN P 13 INT, V13, P148 Smolensky P., 1986, PARALLEL DISTRIBUTED, V1, P194 Steinwart I., 2001, J MACHINE LEARNING R, V2, P67, DOI 10.1162/153244302760185252 Tan C, 2011, P 17 ACM SIGKDD INT, P1397, DOI DOI 10.1145/2020408.2020614 THEIL H, 1988, STAT PROBABIL LETT, V6, P137, DOI 10.1016/0167-7152(88)90107-1 Vincent P., 2008, P 25 INT C MACH LEAR, P1096, DOI 10.1145/1390156.1390294 Wang Y., 2012, P 20 ACM MM 2012 ACM, P865 Yang JC, 2009, PROC CVPR IEEE, P1794 Zaharieva M., 2013, P 3 ACM C INT C MULT, P167 NR 59 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 EI 1941-0077 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD JAN PY 2015 VL 17 IS 1 BP 64 EP 78 DI 10.1109/TMM.2014.2375793 PG 15 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA AX6QW UT WOS:000347047400007 ER PT J AU Vulic, I De Smet, W Tang, J Moens, MF AF Vulic, Ivan De Smet, Wim Tang, Jie Moens, Marie-Francine TI Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Multilingual probabilistic topic models; Cross-lingual text mining; Cross-lingual knowledge transfer; Cross-lingual information retrieval; Language-independent data representation; Non-parallel data ID INFORMATION-RETRIEVAL; COMPARABLE CORPORA AB Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Vulic, Ivan; De Smet, Wim; Moens, Marie-Francine] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium. [Tang, Jie] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. RP Vulic, I (reprint author), Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A, B-3001 Heverlee, Belgium. EM ivan.vulic@cs.kuleuven.be; wdesmet@gmail.com; jie.tang@tsinghua.cn.edu; marie-francine.moens@cs.kuleuven.be RI Moens, Marie-Francine/B-8378-2014 FU project: CLASS - EU Sixth Framework Programme ICT [EU FP6-027978]; project: AMASS++ - Instituut voor de Aanmoediging van Innovatie door Wetenschap en Technologie in Vlaanderen (IWT) [SBO-060051]; WebInsight [BIL/08/08]; project: TermWise - Flemish government [IOF-KP/09/001] FX The research presented in this article has been carried out in the framework of several research projects. It has been partially supported by the following projects: CLASS (EU FP6-027978) financed by the EU Sixth Framework Programme ICT, AMASS++ (SBO-060051) financed by Instituut voor de Aanmoediging van Innovatie door Wetenschap en Technologie in Vlaanderen (IWT), WebInsight (BIL/08/08), and TermWise (IOF-KP/09/001) financed by the Flemish government. CR Aletras N., 2013, P 10 INT C COMP SEM, P13 Arenas-Garca J., 2007, P IEEE WORKSH MACH L, P419 Asuncion A., 2009, P 25 C UNC ART INT, P27 Azzopardi L., 2003, P 26 ANN INT ACM SIG, P369, DOI DOI 10.1145/860435.860505 Bagga A, 1998, INT C LANG RES EV W, V1, P1 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Bhattacharyya A., 1943, B CALCUTTA MATH SOC, V35, P199 Blei D., 2003, P 16 ANN C ADV NEUR Blei D M, 2007, P NIPS, P121 Blei DM, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-250 Blei D. M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boyd-Graber J., 2009, P 25 C UNC ART INT, P75 Boyd-Graber J, 2007, P 2007 JOINT C EMP M, P1024 Boyd-Graber J., 2010, P 2010 C EMP METH NA, P45 Boyd-Graber J. L., 2008, P 22 ANN C NEUR INF, P185 Bruni E., 2012, P ACM MULT NAR JAP, P1219 Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020 Carbonell JG, 1997, INT JOINT CONF ARTIF, P708 CHANG J., 2009, ADV NEURAL INFORM PR, V22, P288 Chen Hsin-Hsi, 2000, COLING 00 P 18 C COM, V1, P159 Chew PA, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P143 Chu C., 2013, P 15 INT C INT TEXT, P296 Cimiano P, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1513 Dagan I., 1994, P 32 ANN M ASS COMP, P272, DOI 10.3115/981732.981770 Dagan I., 1997, P 35 ANN M ASS COMP, P56 Daume III H., 2011, P 49 ANN M ASS COMP, P407 De Smet W, 2011, LECT NOTES ARTIF INT, V6634, P549, DOI 10.1007/978-3-642-20841-6_45 De Smet W, 2013, DATA MIN KNOWL DISC, V26, P533, DOI 10.1007/s10618-012-0270-1 DeSmet W., 2009, P CIKM 2009 WORKSH S, P57, DOI 10.1145/1651437.1651447 Deveaud R., 2013, P 51 ANN M ASS COMP, P148 Ding C, 2008, COMPUT STAT DATA AN, V52, P3913, DOI 10.1016/j.csda.2008.01.011 Dinu Georgiana, 2010, P 23 INT C COMP LING, P250 Dumais ST, 1996, P ACM SIGIR WORKSH C, P16 Evans D. K., 2004, P HUM LANG TECHN C N, P1 Feng Y., 2010, P ANN C N AM ASS COM, P831 Fukumasu K., 2012, ADV NEURAL INF PROCE, V25, P1295 Fung P., 1998, P 17 INT C COMP LING, V1, P414 Ganguly D., 2012, P 24 INT C COMP LING, P927 Gaussier E, 2005, P 28 ANN INT ACM SIG, P601, DOI DOI 10.1145/1076034.1076148 Gaussier Eric, 2004, P 42 M ASS COMP LING, P526, DOI 10.3115/1218955.1219022 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 Girolami M., 2003, P 26 ANN INT ACM SIG, P433 Gliozzo A. M., 2007, P NAACL HLT 07, P131 Gormley M. R., 2012, P 2012 C N AM CHAPT, P783 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Griffiths T. L., 2004, P ADV NEUR INF PROC, P537 Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Haghighi Aria, 2008, P 46 ANN M ASS COMP, P771 Hall D., 2008, P C EMP METH NAT LAN, P363, DOI 10.3115/1613715.1613763 Harris ZS, 1954, WORD, V10, P146 Heinrich G., 2008, TECHNICAL REPORT Hofmann T, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P289 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hu D., 2009, P 10 INT SOC MUS INF, P441 Jagarlamudi J, 2010, LECT NOTES COMPUT SC, V5993, P444, DOI 10.1007/978-3-642-12275-0_39 Jagarlamudi J., 2012, P 13 C EUR CHAPT ASS, P204 Joachims T., 1999, ADV KERNEL METHODS S, V1999, P169 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Kabadjov M, 2010, LECT NOTES ARTIF INT, V6323, P591, DOI 10.1007/978-3-642-15939-8_40 Kazama J., 2010, P 48 ANN M ASS COMP, P247 Koehn Philipp, 2005, P 10 MACH TRANSL SUM, P79 Koehn Philipp, 2002, P ACL 02 WORKSH UNS, P9 Lacoste-Julien S., 2008, ADV NEURAL INFORM PR, V21, P897 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee D. D., 1999, P NIPS, P556 Li Fei-Fei, 2005, P 2005 IEEE COMP SOC, V2, P524 Li W., 2007, P 23 C UNC ART INT U, P243 LIN JH, 1991, IEEE T INFORM THEORY, V37, P145, DOI 10.1109/18.61115 Littman ML, 1998, KLUW S INF, V2, P51 Liu X., 2013, P 17 C COMP NAT LANG, P212 Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9 Manning C. D., 1999, FDN STAT NATURAL LAN Manning C.D., 2008, INTRO INFORM RETRIEV McCallum A, 2007, J ARTIF INTELL RES, V30, P249 Mei Q, 2008, P 17 INT C WORLD WID, P101, DOI DOI 10.1145/1367497.1367 Mei Q., 2007, P 16 INT C WORLD WID, P171, DOI DOI 10.1145/1242572.1242596 Mimmo D, 2007, P 24 INT C MACH LEAR, P633, DOI DOI 10.1145/1273496.1273576 Mimno D., 2009, P 2009 C EMP METH NA, P880 Mimno D., 2011, P C EMP METH NAT LAN, P262 Mimno D., 2008, P 24 C UNC ART INT, P411 Minka T, 2002, P 18 C UNC ART INT, P352 Montalvo S., 2006, P 44 ANN M ASS COMP Montalvo S, 2007, PATTERN RECOGN LETT, V28, P2305, DOI 10.1016/j.patrec.2007.07.011 Munteanu DS, 2005, COMPUT LINGUIST, V31, P477, DOI 10.1162/089120105775299168 Munteanu DS, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P81 Newman D., 2010, P ANN C N AM CHAPT A, P100 Ni X., 2009, P 18 INT C WORLD WID, P1155, DOI 10.1145/1526709.1526904 Ni Xiaochuan, 2011, P 4 ACM INT C WEB SE, P375, DOI 10.1145/1935826.1935887 Peirsman Y., 2011, ACM T SPEECH LANGUAG, V8 Platt J. C., 2010, P C EMP METH NAT LAN, P251 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Pouliquen B, 2004, P 20 INT C COMP LING Prochasson E., 2011, P 49 ANN M ASS COMP, P1327 Purver M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P17 Resnik P, 2003, COMPUT LINGUIST, V29, P349, DOI 10.1162/089120103322711578 Richardson J., 2013, P 6 INT JOINT C NAT, P261 Ritter A., 2010, P 48 ANN M ASS COMP, P424 Roller S., 2013, P 2013 C EMP METH NA, P1146 Roth B, 2010, LECT NOTES COMPUT SC, V6107, P47, DOI 10.1007/978-3-642-13084-7_5 Russell B., 2006, CVPR, V2, P1605 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmid H., 1994, P INT C NEW METH LAN SPARCK-JONES K, 1973, INFORM STORAGE RET, V9, P619 Stevens K., 2012, P 2012 JOINT C EMP M, P952 Steyvers Mark, 2007, HDB LATENT SEMANTIC, V427, P424 Takasu A., 2010, P 1 INT WORKSH INF H, P52, DOI 10.1145/1869446.1869454 Tamura A, 2012, P 2012 JOINT C EMP M, P24 Tan L, 2013, P 7 INT WORKSH SEM E, P167 Tang G., 2011, P IJCNLP 2010, P580 Tao T., 2005, P 11 ACM SIGKDD INT, P691, DOI 10.1145/1081870.1081958 Tipping ME, 1999, NEURAL COMPUT, V11, P443, DOI 10.1162/089976699300016728 Titov Ivan, 2008, P 46 ANN M ASS COMP, P308 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Utiyama Masao, 2003, P 41 ANN M ASS COMP, P72 Van Gael J, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1744 Voorhees E, 1999, P 8 TEXT RETR C TREC VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X Vu T., 2009, P 12 C EUR CHAPT ASS, P843, DOI 10.3115/1609067.1609161 VULIC I, 2011, P 7 AS INF RETR SOC, V7097, P37 Vulic I., 2013, P 35 ANN EUR C ADV I, P98 Vulic I., 2012, P 13 C EUR CHAPT ASS, P449 Vulic I., 2014, P 37 INT ACM SIGIR C, P1195 Vulic I., 2011, P 49 ANN M ASS COMP, P479 Vulic I, 2013, INFORM RETRIEVAL, V16, P331, DOI 10.1007/s10791-012-9200-5 Wallach H. M., 2006, P 23 INT C MACH LEAR, P977, DOI DOI 10.1145/1143844.1143967 Wallach HM, 2009, P 26 INT C MACH LEAR, P1105 Wang X., 2007, P 20 ANN C ADV NEUR Wang X., 2006, P 12 ACM SIGKDD INT, P424, DOI 10.1145/1150402.1150450 Wang XG, 2009, IEEE T PATTERN ANAL, V31, P539, DOI 10.1109/TPAMI.2008.87 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Wu K, 2007, LECT NOTES COMPUT SC, V4426, P956 Xue G., 2008, P 31 ANN INT ACM SIG, P627, DOI 10.1145/1390334.1390441 Yi X, 2009, LECT NOTES COMPUT SC, V5478, P29 Zavitsanos E, 2011, J MACH LEARN RES, V12, P2749 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang D., 2010, P 48 ANN M ASS COMP, P1128 Zhang T., 2013, P 23 INT JOINT C ART, P2218 Zhao B., 2006, P COLING ACL POST SE, P969, DOI 10.3115/1273073.1273197 Zhao B., 2007, P ADV NEUR INF PROC, P1689 Zhu Z., 2013, P 51 ANN M ASS COMP, P278 Zoghbi S., 2013, P 2013 INT CIKM WORK, P45 NR 142 TC 0 Z9 0 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2015 VL 51 IS 1 BP 111 EP 147 DI 10.1016/j.ipm.2014.08.003 PG 37 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AU3DC UT WOS:000345491900007 ER PT J AU Liu, YH Chen, YL Ho, WL AF Liu, Yi-Hung Chen, Yen-Liang Ho, Wu-Liang TI Predicting associated statutes for legal problems SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Text mining; Statute; Criminal judgment; Normalized Google Distance (NGD); Support vector machines (SVM); Apriori algorithm ID VECTOR-SPACE MODEL; FEATURE-SELECTION; RETRIEVAL; CLASSIFICATION AB Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although a few previous studies focused on assisting professionals in the retrieval of related legal documents, to our knowledge, no previous studies could provide relevant statutes to the general public using problem statements. In this work, we design a text mining based method, the three-phase prediction (TPP) algorithm, which allows the general public to use everyday vocabulary to describe their problems and find pertinent statutes for their cases. The experimental results indicate that our approach can help the general public, who are not familiar with professional legal terms, to acquire relevant statutes more accurately and effectively. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Liu, Yi-Hung; Chen, Yen-Liang] Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. [Ho, Wu-Liang] Straits Exchange Fdn, Dept Legal Serv, Taipei 105, Taiwan. RP Liu, YH (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. EM lyh0315@gmail.com CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bergholz Andre, 2010, Journal of Computer Security, V18, DOI 10.3233/JCS-2010-0371 Buckley C., 1985, TECHNICAL REPORT TR8 Calvo R. A., 2001, PROC OF THE 6TH AUST Chang Chih-Chung, 2001, LIBSVM A LIBRARY FOR Chen L., 2013, PROCEEDINGS OF THE 3 Chen YL, 2013, J AM SOC INF SCI TEC, V64, P280, DOI 10.1002/asi.22767 Chen YL, 2011, INFORM PROCESS MANAG, V47, P309, DOI 10.1016/j.ipm.2010.06.001 Chi Jeffery Y. P., 2010, PACIS 2010 P, P706 Chou SC, 2010, LECT NOTES COMPUT SC, V6122, P113 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 CKIP, 2013, On-line Chinese words segmented service, Patent No. 7257530 Clare A., 2001, P 5 EUR C PRINC DAT, P42 Conrad J. G., 2007, ICAIL 2007, P231 Diekema A., 2004, P ACL WORKSH QUEST A, P2 Evangelista A., 2006, FRONTIERS IN UNDERGR Feldman R., 2007, THE TEXT MINING HAND Ferrucci D, 2010, AI MAG, V31, P59 Goldstein J., 2000, P ANLP NAACL WORKSH, P40 Gomez-Perez A., 2007, PROCEEDINGS OF THE 1, P1007 Hotho A., 2005, LDV FORUM, V20, P19 Hsu H. H., 2012, THE 26TH INTERNATION, P875 Judicial Yuan, 2013, LAW AND REGULATIONS Katz B., 1997, P 5 RIAO C COMP ASS, P136 Kaur J., 2010, THE 2ND INTERNATIONA, P732 Kawai H, 2011, IEICE T INF SYST, VE94D, P552, DOI 10.1587/transinf.E94.D.552 L'opez-Moreno P., 2007, THE PROBLEMS IN A QU Lee M., 2006, P AMIA 2006 S AMIA, P469 LexisNexis, 2013, WEB BAS LEG DAT SYST Li N, 2010, DECIS SUPPORT SYST, V48, P354, DOI 10.1016/j.dss.2009.09.003 Li X, 2013, IEEE T KNOWL DATA EN, V25, P1162, DOI 10.1109/TKDE.2012.42 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moens M. F., 2001, ARTIF INTELL, P29 Moens M. F., 2005, ICAIL 05 P 10 INT C, P141 Nie L., 2011, PROCEEDINGS OF THE 3 Reyes A, 2012, DATA KNOWL ENG, V74, P1, DOI 10.1016/j.datak.2012.02.005 Ribeiro M. N., 2008, 15 ICONIP, P45 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRODUCTION TO MODE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Schumaker RP, 2012, DECIS SUPPORT SYST, V53, P458, DOI 10.1016/j.dss.2012.03.001 Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 Tikk D., 2007, EMERGING TECHNOLOGIE, P244 Trappey AJC, 2008, IND MANAGE DATA SYST, V108, P245, DOI 10.1108/02635570810847608 Tsoumakas G, 2010, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION, P667, DOI 10.1007/978-0-387-09823-4_34 Thomaidou S., 2011, Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), DOI 10.1109/ASONAM.2011.70 Wang Dengting, 2009, Proceedings of the 5th International Conference on Asian and Pacific Coasts. APAC 2009, DOI 10.3115/1667583.1667675 Wang J., 2011, MED CHEM NUCL ACIDS, P1 Wang K., 2010, PROCEEDINGS OF THE 3 Wei CP, 2008, DECIS SUPPORT SYST, V45, P491, DOI 10.1016/j.dss.2007.06.010 Westlaw, 2013, WEB BAS LEG INF DAT Witten IH, 2011, MOR KAUF D, P1 Yang Y., 2002, CIKM 02, P659 Yin H., 2007, UNION DAILY NEWS, Patent No. 7257530 Zheng R, 2006, J AM SOC INF SCI TEC, V57, P378, DOI 10.1002/asi.20316 Zheng WH, 2002, AMIA 2002 SYMPOSIUM, PROCEEDINGS, P949 NR 62 TC 0 Z9 0 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2015 VL 51 IS 1 BP 194 EP 211 DI 10.1016/j.ipm.2014.07.003 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AU3DC UT WOS:000345491900011 ER PT J AU Shokripour, R Anvik, J Kasirun, ZM Zamani, S AF Shokripour, Ramin Anvik, John Kasirun, Zarinah M. Zamani, Sima TI Improving automatic bug assignment using time-metadata in term-weighting SO IET SOFTWARE LA English DT Article DE program debugging; statistical analysis; meta data; information retrieval; learning (artificial intelligence); software fault tolerance; automatic bug assignment improvement; time-metadata technique; term-weighting technique; manual bug triage process; machine learning technique; information-retrieval technique; statistical computation technique; term frequency AB Assigning newly reported bugs to project developers is a time-consuming and tedious task for triagers using the traditional manual bug triage process. Previous efforts for creating automatic bug assignment systems use machine learning and information-retrieval techniques. These approaches commonly use tf-idf, a statistical computation technique for weighting terms based on term frequency. However, tf-idf does not consider the metadata, such as the time frame at which a term was used, when calculating the weight of the terms. This study proposes an alternate term-weighting technique to improve the accuracy of automatic bug assignment approaches that use a term-weighting technique. This technique includes the use of metadata in addition to the statistical computation to calculate the term weights. Moreover, it restricts the set of terms used to only nouns. It was found that when using only nouns and the proposed term-weighting technique, the accuracy of an automatic bug assignment approach improves from 12 to 49% over tf-idf for three open-source projects. C1 [Shokripour, Ramin; Kasirun, Zarinah M.; Zamani, Sima] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. [Anvik, John] Cent Washington Univ, Dept Comp Sci, Ellensburg, WA USA. RP Shokripour, R (reprint author), Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. EM rshokripour@gmail.com CR Anvik J., 2011, ACM T SOFTW ENG METH, V20, DOI DOI 10.1145/2000791.2000794 Bhattacharya P, 2012, J SYST SOFTWARE, V85, P2275, DOI 10.1016/j.jss.2012.04.053 Feldman R, 2006, TEXT MINING HDB ADV Kagdi H, 2012, J SOFTW-EVOL PROC, V24, P3, DOI 10.1002/smr.530 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zimmermann T, 2010, IEEE T SOFTWARE ENG, V36, P618, DOI 10.1109/TSE.2010.63 NR 6 TC 0 Z9 0 PU INST ENGINEERING TECHNOLOGY-IET PI HERTFORD PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND SN 1751-8806 EI 1751-8814 J9 IET SOFTW JI IET Softw. PD DEC PY 2014 VL 8 IS 6 BP 269 EP 278 DI 10.1049/iet-sen.2013.0150 PG 10 WC Computer Science, Software Engineering SC Computer Science GA AX1HX UT WOS:000346699400003 ER PT J AU Gupta, Y Saini, A Saxena, AK AF Gupta, Yogesh Saini, Ashish Saxena, A. K. TI Fuzzy logic-based approach to develop hybrid similarity measure for efficient information retrieval SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE Fuzzy inference system; information retrieval; precision; recall; similarity measure; vector space model ID RANKING; MODEL; SETS AB A similarity measure is used in information retrieval systems to retrieve and rank the relevant documents. In this paper, a new fuzzy-based approach to develop hybrid similarity measure is proposed and implemented. The proposed approach overcomes the limitations of extensively used similarity measures such as Cosine, Jaccard, Euclidean and Okapi-BM25 along with Genetic Algorithm-based hybrid similarity measures proposed by researchers. This approach uses fuzzy rules to infer the weights of different similarity measures. In this paper, the experiments are performed on CACM and CISI benchmark data collections. The performance of the proposed approach is evaluated in terms of precision, recall and average precision and average recall of retrieved relevant documents. The results are compared with different similarity measures available in literature. The results show the marked improvement in performance of information retrieval systems using the proposed fuzzy logic-based hybrid similarity measure. C1 [Gupta, Yogesh; Saini, Ashish; Saxena, A. K.] Dayalbagh Educ Inst, Agra, Uttar Pradesh, India. RP Saini, A (reprint author), Dayalbagh Educ Inst, Dept Elect Engn, Fac Engn, Agra, Uttar Pradesh, India. EM ashish7119@gmail.com CR Bhatnagar P, 2012, INT J EMERGING TECHN, V2, P249 Chen SJ, 2011, INTELL AUTOM SOFT CO, V17, P465 Christopher DM, 2009, INTRO INFORM RETRIEV COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X Dhillion IS, 2001, DATA MINING SCI ENG, V2, P357 Garcia JA, 2011, J AM SOC INF SCI TEC, V62, P2013, DOI 10.1002/asi.21589 Gledson A, 2008, 22 INT C COMP LING, P281 Jahromi MZ, 2006, IRAN J SCI TECHNOL B, V30, P171 Jiyin He, 2011, Journal of the American Society for Information Science and Technology, V62, DOI 10.1002/asi.21468 Kamber M., 2001, DATA MINING CONCEPTS Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 Kou H, 2002, P 13 INT WORKSH DAT, P256 KRAFT DH, 1983, INT J MAN MACH STUD, V19, P45, DOI 10.1016/S0020-7373(83)80041-8 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 MAMDANI EH, 1975, INT J MAN MACH STUD, V7, P1, DOI 10.1016/S0020-7373(75)80002-2 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mooer CN, 1950, P INT C MATH, P572 Pathak P., 2000, P 33 HAW INT C SCI H Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 Salton G., 1998, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savino P., 1998, 2 EUR DIG LIB C TUT Tuomo K., 2007, INFORM SCI, V177, P4893 Usharani J., 2013, INT J ENG RES TECHNO, V2 WALLER WG, 1979, INFORM PROCESS MANAG, V15, P235, DOI 10.1016/0306-4573(79)90030-X Weng SS, 2003, EXPERT SYST APPL, V25, P355, DOI 10.1016/S0957-4174(03)00076-9 Witten I. H., 1999, MANAGING GIGABYTES C Xia F., 2008, P 25 INT C MACH LEAR, P1192, DOI 10.1145/1390156.1390306 Yates R. B., 1999, MODERN INFORM RETRIE Yeh JY, 2007, 30 ANN INT ACM SIGIR ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 33 TC 0 Z9 0 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 EI 1741-6485 J9 J INF SCI JI J. Inf. Sci. PD DEC PY 2014 VL 40 IS 6 BP 846 EP 857 DI 10.1177/0165551514548989 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AW3JK UT WOS:000346182400011 ER PT J AU Parizi, RM Lee, SP Dabbagh, M AF Parizi, Reza Meimandi Lee, Sai Peck Dabbagh, Mohammad TI Achievements and Challenges in State-of-the-Art Software Traceability Between Test and Code Artifacts SO IEEE TRANSACTIONS ON RELIABILITY LA English DT Article DE Software testing; software traceability; test-to-code traceability; traceability recovery ID REQUIREMENTS TRACEABILITY; RESOURCE-ALLOCATION; RELIABILITY; MODEL; DOCUMENTATION; FRAMEWORK; LINKS AB Testing is a key activity of software development and maintenance that determines the level of reliability. Traceability is the ability to describe and follow the life of software artifacts, and has been promoted as a means for supporting various activities, most importantly testing. Traceability information facilitates the testing and debugging of complex software by modeling the dependencies between code and tests. Actively supplementing traceability to testing enables rectifying defects more reliably and efficiently. Despite its importance, the development of test-to-code traceability has not been sufficiently addressed in the literature, and even worse there is currently no organized review of traceability studies in this field. In this work, we have investigated the main conferences, workshops, and journals of the requirements engineering, testing, and reliability, and identified those contributions that refer to traceability topics. From that starting point, we characterized and analyzed the chosen contributions against three research questions by utilizing a comparative framework including nine criteria. As a result, our study arrives to some interesting points, and outlines a number of potential research directions. This, in turn, can pave the way for facilitating and empowering traceability research in this domain to assist software engineers and testers in test management. C1 [Parizi, Reza Meimandi] Taylors Univ, Sch Comp & IT, Subang Jaya 47500, Selangor, Malaysia. [Lee, Sai Peck; Dabbagh, Mohammad] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Software Engn, Kuala Lumpur 50603, Malaysia. RP Parizi, RM (reprint author), Taylors Univ, Sch Comp & IT, Subang Jaya 47500, Selangor, Malaysia. EM rezameimandi.parizi@taylors.edu.my; saipeck@um.edu.my; dabbagh@siswa.um.edu.my FU Ministry of Education, Malaysia [UM.C/625/1/HIR/MOHE/FCSIT/13]; Taylor's University [TRGS/ERFS/1/2014/SOCIT/006] FX This work was supported by a High Impact Research Grant with reference UM.C/625/1/HIR/MOHE/FCSIT/13, funded by the Ministry of Education, Malaysia, and a Taylor's University grant with reference TRGS/ERFS/1/2014/SOCIT/006. CR Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30 Aizenbud-Reshef N, 2006, IBM SYST J, V45, P515 Almeida JP, 2006, IEEE EDOC CONF, P355 Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Asuncion HU, 2010, P 32 ACM IEEE INT C, P95, DOI DOI 10.1145/1806799.1806817 Bacchelli A., 2010, P 32 ACM IEEE INT C, V1, P375, DOI 10.1145/1806799.1806855 Batory D, 2004, IEEE T SOFTWARE ENG, V30, P355, DOI 10.1109/TSE.2004.23 Bavota A. Q. G., 2013, J SOFTWARE EVOLUTION, DOI [10.1002/smr.1573, DOI 10.1002/SMR.1573] Beck K., 1998, JAVA REPORT, V3, P51 Borges M., 2012, P 2012 IEEE 5 INT C, P111 Bouillon P, 2007, LECT NOTES COMPUT SC, V4536, P101 Buckley J, 2005, J SOFTW MAINT EVOL-R, V17, P309, DOI 10.1002/smr.319 Capobianco G, 2009, WORK CONF REVERSE EN, P195, DOI 10.1109/WCRE.2009.14 Chen X., 2011, P 26 IEEE ACM ASE 11, P223 Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 Corley C.S., 2011, P 6 INT WORKSH TRAC, P31, DOI DOI 10.1145/1987856.1987863 Cotroneo D, 2013, IEEE T RELIAB, V62, P408, DOI 10.1109/TR.2013.2257051 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Deursen A. V., 2001, P 2 INT C EXTR PROGR, P92 Domges R, 1998, COMMUN ACM, V41, P54, DOI 10.1145/290133.290149 Egyed A, 2003, IEEE T SOFTWARE ENG, V29, P116, DOI 10.1109/TSE.2003.1178051 Espinoza Angelina, 2011, Innovations and Systems and Software Engineering, V7, DOI 10.1007/s11334-011-0144-5 Fewster M., 1999, SOFTWARE TEST AUTOMA Galvao I, 2007, 11TH IEEE INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE, PROCEEDINGS, P313, DOI 10.1109/EDOC.2007.42 Ghazarian A., 2010, IEEE T REL IEEE RELI, V59, P449 Globerson A, 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753689 Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), DOI 10.1109/ICRE.1994.292398 Her JS, 2010, INFORM SOFTWARE TECH, V52, P845, DOI 10.1016/j.infsof.2010.03.012 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Huang CY, 2005, IEEE T RELIAB, V54, P592, DOI 10.1109/TR.2005.858099 Huang CY, 2002, IEEE T RELIAB, V51, P261, DOI 10.1109/TR.2002.801847 Ivarsson M, 2011, EMPIR SOFTW ENG, V16, P365, DOI 10.1007/s10664-010-9146-4 Jirapanthong W., 2009, SOFTW SYST MODEL, V8, P1619 Kannenberg A., 2009, CROSSTALK J DEFENSE, V22, P14 Kaushik N., 2011, P 18 WORK C REV ENG, P411 Kiczales G., 1996, ACM COMPUT SURVEYS, V28 Knethen A. V., 2002, P INT C SOFTW MAINT, P482 KOREL B, 1990, J SYST SOFTWARE, V13, P187, DOI 10.1016/0164-1212(90)90094-3 KUBAT P, 1983, IEEE T RELIAB, V32, P299 Kuo SY, 2001, IEEE T RELIAB, V50, P310 Lago P, 2009, J SYST SOFTWARE, V82, P168, DOI 10.1016/j.jss.2008.08.026 Lormans M., 2006, P 10 EUR C SOFTW MAI, P47 Lucia A. D., 2009, EMPIR SOFTW ENG, V14, P57, DOI DOI 10.1007/S10664-008-9090-8 Lucia A. D., 2007, ACM T SOFTW ENG METH, V16 MAGHSOODLOO S, 1992, IEEE T RELIAB, V41, P547, DOI 10.1109/24.249582 Malaiya YK, 2002, IEEE T RELIAB, V51, P420, DOI 10.1109/TR.2002.804489 Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 Matias R, 2010, IEEE T RELIAB, V59, P102, DOI 10.1109/TR.2009.2034292 Meszaros G., 2007, XUNIT TEST PATTERNS OHTERA H, 1990, IEEE T RELIAB, V39, P171, DOI 10.1109/24.55878 Paech B., 2002, 09501E FRAUNH I EXP Pinheiro F. A. C., 2003, PERSPECTIVES SOFTWAR, P93 Qusef A., 2011, P 18 WORK C REV ENG, P417 Qusef A, 2014, J SYST SOFTWARE, V88, P147, DOI 10.1016/j.jss.2013.10.019 Qusef A, 2011, P 27 IEEE INT C SOFT, P63 Qusef A., 2010, P 26 IEEE INT C SOFT, P1 Rochimah S., 2007, P 2007 INT C SOFTW E, P19 Rompaey B. V., 2009, THESIS U ANTWERPEN A Rompaey B.V., 2009, P EUR C SOFTW MAINT, P209 RYDER BG, 1979, IEEE T SOFTWARE ENG, V5, P216, DOI 10.1109/TSE.1979.234183 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sneed H. M., 2004, P 8 WORK C SOFTW MAI, P69 Spanoudakis G, 2004, J SYST SOFTWARE, V72, P105, DOI 10.1016/S0164-1212(03)00242-5 Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6 Torkar R, 2012, INT J SOFTW ENG KNOW, V22, P385, DOI 10.1142/S021819401250009X Wang X, 2009, ELECT NOTES THEOR CO, V243, P121 Wang Z, 2010, IEEE T RELIAB, V59, P563, DOI 10.1109/TR.2010.2057310 WATKINS R, 1994, IEEE SOFTWARE, V11, P104, DOI 10.1109/52.300100 Wiederseiner Christian, 2011, Proceedings of the 2011 International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11 (TrustCom 2011), DOI 10.1109/TrustCom.2011.151 Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0 Witte R., 2008, IET SOFTW, V2, P1 Zachariah B., 2007, IEEE T RELIAB, V65, P246 Zachariah B, 2012, IEEE T RELIAB, V61, P569, DOI 10.1109/TR.2012.2194195 Ziftci C., 2012, P 19 WORK C REV ENG, P155 NR 75 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 0018-9529 EI 1558-1721 J9 IEEE T RELIAB JI IEEE Trans. Reliab. PD DEC PY 2014 VL 63 IS 4 BP 913 EP 926 DI 10.1109/TR.2014.2338254 PG 14 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AU9IA UT WOS:000345904000009 ER PT J AU Mazloom, M Gavves, E Snoek, CGM AF Mazloom, Masoud Gavves, Efstratios Snoek, Cees G. M. TI Conceptlets: Selective Semantics for Classifying Video Events SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE Concept detection; cross-entropy optimization; event recognition ID RECOGNITION; INFORMATION; ATTRIBUTES; MULTIMEDIA; DETECTORS; RETRIEVAL; ONTOLOGY; MODEL; GAP AB An emerging trend in video event classification is to learn an event from a bank of concept detector scores. Different from existing work, which simply relies on a bank containing all available detectors, we propose in this paper an algorithm that learns from examples what concepts in a bank are most informative per event, which we call the conceptlet. We model finding the conceptlet out of a large set of concept detectors as an importance sampling problem. Our proposed approximate algorithm finds the optimal conceptlet using a cross-entropy optimization. We study the behavior of video event classification based on conceptlets by performing four experiments on challenging internet video from the 2010 and 2012 TRECVID multimedia event detection tasks and Columbia's consumer video dataset. Starting from a concept bank of more than thousand precomputed detectors, our experiments establish there are (sets of) individual concept detectors that are more discriminative and appear to be more descriptive for a particular event than others, event classification using an automatically obtained conceptlet is more robust than using all available concepts, and conceptlets obtained with our cross-entropy algorithm are better than conceptlets from state-of-the-art feature selection algorithms. What is more, the conceptlets make sense for the events of interest, without being programmed to do so. C1 [Mazloom, Masoud; Gavves, Efstratios; Snoek, Cees G. M.] Univ Amsterdam, Inst Informat, Intelligent Syst Lab, NL-1098 XH Amsterdam, Netherlands. RP Mazloom, M (reprint author), Univ Amsterdam, Inst Informat, Intelligent Syst Lab, NL-1098 XH Amsterdam, Netherlands. EM m.mazloom@uva.nl; efstratios.gavves@gmail.com; cgmsnoek@uva.nl FU STW STORY project; Dutch national program COMMIT; Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center [D11PC20067] FX This work was supported in part by the STW STORY project, the Dutch national program COMMIT, and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center under Contract D11PC20067. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Chong- Wah Ngo. CR Aha D. W., 1995, P 5 INT WORKSH ART I, P199 Ballan L, 2011, MULTIMED TOOLS APPL, V51, P279, DOI 10.1007/s11042-010-0643-7 Ballan L, 2010, MULTIMED TOOLS APPL, V48, P69, DOI 10.1007/s11042-009-0351-3 Berg A., IMAGENET LARGE SCALE Bucklew J. A., 2004, INTRO RARE EVENT SIM Deng J., 2009, P CVPR Duan LX, 2012, IEEE T PATTERN ANAL, V34, P1667, DOI 10.1109/TPAMI.2011.265 Ebadollahi S., 2006, P ICME Gkalelis N., 2011, P CBMI Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Habibian A., 2014, CVIU, V124 Haering N, 2000, IEEE T CIRC SYST VID, V10, P857, DOI 10.1109/76.867923 Hauptmann A, 2007, IEEE T MULTIMEDIA, V9, P958, DOI 10.1109/TMM.2007.900150 Huurnink B., 2008, P MIR Inoue N., 2011, P NIST TRECVID WORKS Inza I, 2000, ARTIF INTELL, V123, P157, DOI 10.1016/S0004-3702(00)00052-7 Ivanov YA, 2000, IEEE T PATTERN ANAL, V22, P852, DOI 10.1109/34.868686 Jiang Y.-G., 2011, P ICMR Jiang Y.-G., 2010, P NIST TRECVID WORKS Jiang YG, 2010, IEEE T MULTIMEDIA, V12, P42, DOI 10.1109/TMM.2009.2036235 Jiang Y.-G., 2012, P ICMR Jiang Y.-G., 2013, INT J MULTIMEDIA INF, V2, P73, DOI DOI 10.1007/S13735-012-0024-2 Lavee G, 2009, IEEE T SYST MAN CY C, V39, P489, DOI 10.1109/TSMCC.2009.2023380 Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68 Li L.-J., 2010, P ADV NEUR INF PROC, P1378 Li X., 2007, P CIVR Li X., 2011, P SUST CHEM C ANTW B, P233 Lin DH, 2006, LECT NOTES COMPUT SC, V3951, P68 Liu JE, 2013, IEEE WORK APP COMP, P339 Lloyd M. H., 1999, P 12 INT FLOR ART IN, P235 Ma Z., 2013, THESIS U TRENTO TREN Ma ZG, 2013, PROC CVPR IEEE, P2627, DOI 10.1109/CVPR.2013.339 Mannor S., 2005, P ICML Mazloom M., 2013, P ICMR Mazloom M., 2013, MM Merler M, 2012, IEEE T MULTIMEDIA, V14, P88, DOI 10.1109/TMM.2011.2168948 Meyer PE, 2008, IEEE J-STSP, V2, P261, DOI 10.1109/JSTSP.2008.923858 Liu H, 2008, CH CRC DATA MIN KNOW, P3 Myers G. K., 2014, MACH VIS APPL, V25 Naphade M, 2006, IEEE MULTIMEDIA, V13, P86, DOI 10.1109/MMUL.2006.63 Natarajan P, 2012, PROC CVPR IEEE, P1298, DOI 10.1109/CVPR.2012.6247814 Natsev A. P., 2007, P ACM INT C MULT, P991, DOI 10.1145/1291233.1291448 Neo S.-Y., 2006, P CIVR Ng A. Y., 2004, P ICML Oh S., 2014, MACH VIS APPL, V25 Oneata D., 2012, P NIST TRECVID WORKS Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226 Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 Rubinstein R. Y., 2004, CROSS ENTROPY METHOD Rudinac S., 2012, P INT J MULT INF RET, P263 Sadanand S, 2012, PROC CVPR IEEE, P1234, DOI 10.1109/CVPR.2012.6247806 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZ J, 2013, P IJCV, V105, P222 Shalev-Shwartz S., 2011, MATH PROGRAM, V127 Siedlecki W., 1988, P INT J PATT REC Smeaton A. F., 2006, P MIR Snoek C., 2009, FOUND TRENDS INF RET, V2, P215 Snoek CGM, 2007, IEEE T MULTIMEDIA, V9, P975, DOI 10.1109/TMM.2007.900156 Tamrakar A, 2012, PROC CVPR IEEE, P3681, DOI 10.1109/CVPR.2012.6248114 Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267 Torresani L, 2010, LECT NOTES COMPUT SC, V6311, P776, DOI 10.1007/978-3-642-15549-9_56 van de Sande KEA, 2010, IEEE T PATTERN ANAL, V32, P1582, DOI 10.1109/TPAMI.2009.154 Vedaldi A, 2012, IEEE T PATTERN ANAL, V34, P480, DOI 10.1109/TPAMI.2011.153 Wei XY, 2008, IEEE T MULTIMEDIA, V10, P1085, DOI 10.1109/TMM.2008.2001382 Xie LX, 2008, P IEEE, V96, P623, DOI 10.1109/JPROC.2008.916362 Xu D, 2008, IEEE T PATTERN ANAL, V30, P1985, DOI 10.1109/TPAMI.2008.129 Younessian E., 2012, P ICMR NR 68 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 EI 1941-0077 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD DEC PY 2014 VL 16 IS 8 BP 2214 EP 2228 DI 10.1109/TMM.2014.2359771 PG 15 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA AU4HL UT WOS:000345571300012 ER PT J AU Borg, M Runeson, P Ardo, A AF Borg, Markus Runeson, Per Ardo, Anders TI Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability SO EMPIRICAL SOFTWARE ENGINEERING LA English DT Article DE Traceability; Information retrieval; Software artifacts; Systematic mapping study ID LATENT SEMANTIC ANALYSIS; OBJECT-ORIENTED DESIGN; SOURCE-CODE; REQUIREMENTS TRACEABILITY; CENTRIC TRACEABILITY; IR; LINKS; DOCUMENTATION; RELEVANCE; MODELS AB Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery. C1 [Borg, Markus; Runeson, Per] Lund Univ, Dept Comp Sci, S-22100 Lund, Sweden. [Ardo, Anders] Lund Univ, Dept Elect & Informat Technol, Lund, Sweden. RP Borg, M (reprint author), Lund Univ, Dept Comp Sci, Box 118, S-22100 Lund, Sweden. EM markus.borg@cs.lth.se; per.runeson@cs.lth.se; anders.ardo@eit.lth.se RI Runeson, Per/ OI Runeson, Per/0000-0003-2795-4851 FU Industrial Excellence Center EASE - Embedded Applications Software Engineering FX This work was funded by the Industrial Excellence Center EASE - Embedded Applications Software Engineering.6 Thanks go to our librarian Mats Berglund for working on the search strings, and Lorand Dali for excellent comments on IR details. CR Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30 Aitchison J., 2000, THESAURUS CONSTRUCTI Ali N, 2011, CONF PROC INT SYMP C, P111, DOI 10.1109/ICPC.2011.42 Ali N, 2011, P 18 WORK C REV ENG, P45 Ali N, 2012, SOFTWARE SYSTEMS TRA Antonelli G., 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), DOI 10.1109/IROS.1999.812994 Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Antoniol G, 1999, PROG COMPREHEN, P151, DOI 10.1109/WPC.1999.777754 Antoniol G, 2000, PROC IEEE INT CONF S, P40, DOI 10.1109/ICSM.2000.883003 Antoniol G, 2000, PROG COMPREHEN, P79, DOI 10.1109/WPC.2000.852482 Assawamekin N, 2010, KNOWL INF SYST, V25, P493, DOI 10.1007/s10115-009-0259-2 Asuncion HU, 2010, P 32 ACM IEEE INT C, P95, DOI DOI 10.1145/1806799.1806817 Ayari K., 2007, P 2007 C CTR ADV STU, P215, DOI 10.1145/1321211.1321234 Bacchelli A, 2010, P ICSE 2010 32 INT C, P375 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE Banko Michele, 2001, P 39 ANN M ASS COMP, P26, DOI 10.3115/1073012.1073017 Ben Charrada E, 2011, P 12 INT WORKSH PRIN, P21 Bianchi A, 2000, PROG COMPREHEN, P149, DOI 10.1109/WPC.2000.852489 Binkley D, 2010, ENCY SOFTWARE ENG Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borg M, 2011, P INT WORKSH MACH LE, P27 Borg M, 2012, P 16 INT C EV ASS SO Borillo M, 1992, P 10 EUR C ART INT, P593 BRAS M, 1993, APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN ENGINEERING VIII, VOL 1, P275 Brereton P, 2007, J SYST SOFTWARE, V80, P571, DOI 10.1016/j.jss.2006.07.009 Canfora G, 2006, P INT WORKSH MIN SOF, P105, DOI DOI 10.1145/1137983.1138009 Capobianco G, 2009, WORK CONF REVERSE EN, P195, DOI 10.1109/WCRE.2009.14 Capobianco G, 2009, INT C PROGRAM COMPRE, P148 Carnegie Mellon Software Engineering Institute, 2010, CMMI DEV VERS 1 3 Castell N, 1994, P 7 INT C IND ENG AP, P37 Chang J, 2010, ANN APPL STAT, V4, P124, DOI 10.1214/09-AOAS309 Charikar M., 1997, P 29 ANN ACM S THEOR, P626, DOI 10.1145/258533.258657 Chen X, 2011, P 33 INT C SOFTW ENG, P912 Chen X, 2010, P INT C AUT SOFTW EN, P505, DOI 10.1145/1858996.1859098 Chen X., 2011, P 26 IEEE ACM ASE 11, P223 Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 Cleland-Huang J, 2008, IEEE T SOFTWARE ENG, V34, P685, DOI 10.1109/TSE.2008.45 CLELANDHUANG J, 2010, P ICSE, P155 Cleland-Huang J, 2003, IEEE T SOFTWARE ENG, V29, P796, DOI 10.1109/TSE.2003.1232285 Cleland-Huang J., 2012, SOFTWARE SYSTEMS TRA Cleland-Huang J, 2006, COETGCT060109 Cleland-Huang J, 2011, P 6 INT WORKSH TRAC Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Croft B, 1991, P SIGIR 91, P32 Cuddeback D, 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), DOI 10.1109/RE.2010.35 Czauderna A, 2011, P 6 INT WORKSH TRAC, P75 Dag JNO, 2006, EMPIR SOFTW ENG, V11, P303, DOI 10.1007/s10664-006-6405-5 De Lucia A, 2012, SOFTWARE SYSTEMS TRA De Lucia A, 2007, T SOFTW ENG METHODOL, V16 De Lucia A, 2008, P 23 INT C AUT SOFTW, P39, DOI 10.1109/ASE.2008.14 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dekhtyar A, 2007, INT REQUIR ENG CONF, P141, DOI 10.1109/RE.2007.17 Dekhtyar A, 2006, P 22 INT C SOFTW MAI Dekhtyar A, 2011, INT REQUIR ENG CONF, P111, DOI 10.1109/RE.2011.6051649 Dekhtyar A, 2007, P 3 INT WORKSH PRED Dekhtyar A, 2007, P INT S GRAND CHALL De Lucia A, 2006, INT C PROGRAM COMPRE, P317, DOI 10.1109/ICPC.2006.28 De Lucia A, 2008, 2008 FRONTIERS OF SOFTWARE MAINTENANCE, P21, DOI 10.1109/FOSM.2008.4659245 De Lucia A, 2006, PROC IEEE INT CONF S, P274 De Lucia A, 2009, EMPIR SOFTW ENG, V14, P57, DOI 10.1007/s10664-008-9090-8 De Lucia A, 2011, CONF PROC INT SYMP C, P21, DOI 10.1109/ICPC.2011.34 De Lucia A, 2005, NINTH EUROPEAN CONFERENCE ON SOFTWARE MAINTENANCE AND REENGINEERING, PROCEEDINGS, P32, DOI 10.1109/CSMR.2005.7 De Lucia A, 2006, PROC IEEE INT CONF S, P299 De Lucia A, 2006, INT C PROGRAM COMPRE, P307, DOI 10.1109/ICPC.2006.15 De Lucia A, 2004, PROC IEEE INT CONF S, P306, DOI 10.1109/ICSM.2004.1357816 De Lucia A, 2009, PROC IEEE INT CONF S, P371, DOI 10.1109/ICSM.2009.5306317 Di F, 2009, P INT C COMP INT SOF, P1 Di Penta M, 2002, PROG COMPREHEN, P207, DOI 10.1109/WPC.2002.1021342 Dit B, 2011, J SOFTWARE MAINTEN E, V25, P53 Domges R, 1998, COMMUN ACM, V41, P54, DOI 10.1145/290133.290149 Duan C., 2007, P 22 IEEE ACM INT C, P244, DOI [10.1145/1321631.1321668, DOI 10.1145/1321631.1321668] Egyed A, 2002, ASE 2002: 17TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, P163 Eisenbarth T, 2003, IEEE T SOFTWARE ENG, V29, P210, DOI 10.1109/TSE.2003.1183929 Falessi D, 2010, P 4 INT S EMP SOFTW Felizardo K. R., 2011, Proceedings of the 2011 5th International Symposium on Empirical Software Engineering and Measurement (ESEM 2011), DOI 10.1109/ESEM.2011.16 Fiutem R, 1998, PROC IEEE INT CONF S, P94, DOI 10.1109/ICSM.1998.738497 Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315 Gethers M, 2011, P IEEE ACM INT C AUT, P540 Gibiec M, 2010, P ASE, P245, DOI 10.1145/1858996.1859046 Gotel O, 2012, SOFTWARE SYSTEMS TRA Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), DOI 10.1109/ICRE.1994.292398 Hayes JH, 2008, WORK CONF REVERSE EN, P165, DOI 10.1109/WCRE.2008.36 HAYES JH, 2005, P 3 INT WORKSH TRAC, P20, DOI 10.1145/1107656.1107661 Hayes JH, 2005, INT J SOFTW ENG KNOW, V15, P751, DOI 10.1142/S021819400500252X Hayes J. H., 2003, Proceedings 11th IEEE International Requirements Engineering Conference Hayes JH, 2006, IEEE T SOFTWARE ENG, V32, P4, DOI 10.1109/TSE.2006.3 Heindl M., 2005, P 10 EUR SOFTW ENG C, P60, DOI 10.1145/1081706.1081717 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Huffman Hayes J, 2005, P 2005 INT WORKSH MI, P1, DOI 10.1145/1083142.1083153 Huffman Hayes J, 2011, P 6 INT WORKSH TRAC, P50 Hayes J. H., 2004, Proceedings. 12th IEEE International Requirements Engineering Conference Hayes Jane Huffman, 2007, Innovations in Systems and Software Engineering, V3, DOI 10.1007/s11334-007-0024-1 International Electrotechnical Commission, 2003, 615111 IEC International Organization for Standardization, 2011, 2626212011 ISO Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jarvelin K, 2005, TURN INTEGRATION INF Jedlitschka A, 2008, GUIDE ADV EMPIRICAL, P201, DOI DOI 10.1007/978-1-84800-044-5_8 Jiang H, 2008, P 23 IEEE ACM INT C, P59 Jones K. S., 2000, Information Processing & Management, V36 Katta V, 2011, P COMPL SYST DES MAN Kaushik N., 2011, P 18 WORK C REV ENG, P411 Kekalainen J, 2002, COLIS4: EMERGING FRAMEWORKS AND METHODS, P253 Kitchenham B., 2007, GUIDELINES PERFORMIN Kitchenham BA, 2011, INFORM SOFTWARE TECH, V53, P638, DOI 10.1016/j.infsof.2010.12.011 Kitchenham BA, 2002, IEEE T SOFTWARE ENG, V28, P721, DOI 10.1109/TSE.2002.1027796 Klock S, 2011, P 6 INT WORKSH TRAC, P24 Kong L, 2009, P 21 INT C SOFTW ENG Kong W, 2011, P 1 INT WORKSH EMP R, P45 Kong W, 2011, P CHASE, P32 Kruchten P., 2004, RATIONAL UNIFIED PRO Leuser J, 2010, LECT NOTES COMPUT SC, V6182, P203, DOI 10.1007/978-3-642-14192-8_19 Leuser J, 2009, P 2009 ICSE WORKSH T, P31 Lewis D.D., 1998, MACHINE LEARNING ECM, V1398, P4, DOI 10.1007/BFb0026666 Li Y, 2008, LECT NOTES COMPUT SC, V5007, P100 Liddy E, 2001, NATURAL LANGUAGE PRO Lin J, 2006, P 14 IEEE RE 06, P363 Lindvall M, 2009, P S APPL COMP, P496, DOI 10.1145/1529282.1529387 Lormans M, 2006, P 10 EUR C SOFTW MAI, P45 Lormans M, 2008, EMPIR SOFTW ENG, V13, P727, DOI 10.1007/s10664-008-9078-4 Lormans M, 2006, WORK CONF REVERSE EN, P275 Mahmoud A, 2011, P 6 INT WORKSH TRAC, P3 Mahmoud A, 2010, P INT COMP SOFTW APP, P246 Manning C.D., 2008, INTRO INFORM RETRIEV Marcus A, 2005, INT J SOFTW ENG KNOW, V15, P811, DOI 10.1142/S0218194005002543 Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10 Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McMillan C, 2009, 2009 ICSE WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE), P41, DOI 10.1109/TEFSE.2009.5069582 Natt och Dag J, 2004, P 12 INT REQ ENG C, P283, DOI DOI 10.1109/RE.2004.47 och Dag J. N., 2002, REQUIR ENG, V7, P20 Oliveto R, 2008, THESIS U SALERNO Oliveto Rocco, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.20 Olsson T, 2002, THESIS LUND U Park S, 2000, INFORM SOFTWARE TECH, V42, P429, DOI 10.1016/S0950-5849(99)00102-0 Parvathy Anju G, 2008, Proceedings Tenth International Conference on Enterprise Information Systems. Information Systems Analysis and Specification Petersen K, 2008, P 12 INT C EV ASS SO, P71 Petersen K., 2009, P 3 INT S EMP SOFTW, P401 Pohl K., 2005, SOFTWARE PRODUCT LIN Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Port D, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P701, DOI 10.1145/1985793.1985891 Randolph JJ, 2005, JOENS LEARN INSTR S Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S., 1999, MASTERING REQUIREMEN ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Runeson P, 2007, PROC INT CONF SOFTW, P499 Runeson P, 2012, CASE STUDY RES SOFTW Sabaliauskaite G, 2010, LECT NOTES COMPUT SC, V6182, P128, DOI 10.1007/978-3-642-14192-8_14 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scacchi W., 2002, IEE Proceedings-Software, V149, DOI 10.1049/ip-sen:20020202 Settimi R., 2004, Proceedings. 7th International Workshop on Principles of Software Evolution, DOI 10.1109/IWPSE.2004.1334768 Shull FJ, 2008, EMPIR SOFTW ENG, V13, P211, DOI 10.1007/s10664-008-9060-1 Singhal A, 2001, DATA ENG B, V24, P1 Smeaton AF, 1997, J INFORM SCI, V23, P169, DOI 10.1177/016555159702300208 Spanoudakis G, 2004, J SYST SOFTWARE, V72, P105, DOI 10.1016/S0164-1212(03)00242-5 Spanoudakis G, 2003, P 15 INT C SOFTW ENG Stone A, 2006, P 1 INT C SOFTW DAT, P139 Sultanov H, 2010, Proceedings of the 2010 IEEE 18th International Conference on Requirements Engineering (RE2010), DOI 10.1109/RE.2010.33 Sundaram S, 2005, P WORKSH PRED MOD SO, P1, DOI 10.1145/1083165.1083169 Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6 Torchiano M., 2010, P 4 INT S EMP SOFTW, P47 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Van Rompaey B, 2009, EUR CON SFTWR MTNCE, P209, DOI 10.1109/CSMR.2009.39 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang X, 2009, ELECT NOTES THEOR CO, V243, P121 Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0 Winkler S, 2009, 2009 ICSE WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE), P49, DOI 10.1109/TEFSE.2009.5069583 Wohlin C, 2012, EXPT SOFTWARE ENG PR Yadla S, 2005, INNOVATIONS SYSTEMS, V1, P116, DOI DOI 10.1007/S11334-005-0011-3 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2007, TECHNICAL REPORT Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhao W, 2003, ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE, PROCEEDINGS, P68 Zhou X, 2007, LECT NOTES COMPUT SC, V4422, P412 Zou X, 2008, P INT C SOFTW ENG RE, P157 Zou XC, 2006, P INT COMP SOFTW APP, P265 Zou XC, 2010, EMPIR SOFTW ENG, V15, P119, DOI 10.1007/s10664-009-9114-z NR 181 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1382-3256 EI 1573-7616 J9 EMPIR SOFTW ENG JI Empir. Softw. Eng. PD DEC PY 2014 VL 19 IS 6 BP 1565 EP 1616 DI 10.1007/s10664-013-9255-y PG 52 WC Computer Science, Software Engineering SC Computer Science GA AR9QU UT WOS:000343910700002 ER PT J AU Amini, B Ibrahim, R Othman, MS Selamat, A AF Amini, Bahram Ibrahim, Roliana Othman, Mohd Shahizan Selamat, Ali TI Capturing scholar's knowledge from heterogeneous resources for profiling in recommender systems SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Knowledge acquisition; Scholar profiling; Recommender system; Mediated profile ID DISAMBIGUATION; EXTRACTION; WIKIPEDIA; SEARCH; MODEL AB In scholars' recommender systems, acquisition knowledge for construction profiles is crucial because profiles provide fundamental information for accurate recommendation. Despite the availability of various knowledge resources, identification and collecting extensive knowledge in an unobtrusive manner is not straightforward. In order to capture scholars' knowledge, some questions must be answered: what knowledge resource is appropriate for profiling, how knowledge items can be unobtrusively captured, and how heterogeneity among different knowledge resources should be resolved. To address these issues, we first model the scholars' academic behavior and extract different knowledge items, diffused over the Web including mediated profiles in digital libraries, and then integrate those heterogeneous knowledge items by Wikipedia. Additionally, we analyze the correlation between knowledge items and partition the scholars' research areas for multi-disciplinary profiling. Compared to the state-of-the-art, the result of empirical evaluation shows the efficiency of our approach in terms of completeness and accuracy. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Amini, Bahram; Ibrahim, Roliana; Othman, Mohd Shahizan; Selamat, Ali] UTM, Fac Comp, Johor Baharu 81310, Johor, Malaysia. RP Amini, B (reprint author), UTM, Fac Comp, Johor Baharu 81310, Johor, Malaysia. EM avbahram2@live.utm.my; roliana@utm.my; shahizan@utm.my; aselamat@utm.my FU Research Management Centre at the Universiti Teknologi Malaysia [Q.J130000.2528.05H84] FX The authors would thank Dr. Stuart Middleton from IT Innovation Centre, University of Southampton for his assistance. This work is supported by the Research Management Centre at the Universiti Teknologi Malaysia under the Research University Grant Scheme (Vote No. Q.J130000.2528.05H84). CR Amini B, 2013, LECT NOTES COMPUT SC, V7803, P158, DOI 10.1007/978-3-642-36543-0_17 Amini B., 2011, 5 MAL C SOFTW ENG MY, P516 Batyrshin I, 2014, EXPERT SYST APPL, V41, P779, DOI 10.1016/j.eswa.2013.08.007 Bilchev G, 2003, BT TECHNOL J, V21, P84, DOI 10.1023/A:1022460428681 Bitonto Di P., 2010, RECOMMENDATION METHO, V6, P31 Bollacker K. D., 1999, P 4 ACM C DIG LIB, V2, P105 Borges HL, 2010, STUD COMPUT INTELL, V260, P129, DOI 10.1007/978-3-642-04584-4_6 British Colombia, 2005, ESS US CAS MOD STAND, P1 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 Craven M., 1998, NAT C ART INT AAAI, P509 Cure O, 2014, J WEB SEMANT, V24, P27, DOI 10.1016/j.websem.2013.12.001 Das S., 2011, P ACM IEEE JOINT C D, P123 Devedgie V., 2006, SEMANTIC WEB ED, P175, DOI [10.1007/978-0-387-35417-0_6, DOI 10.1007/978-0-387-35417-0_6] Eric B., 1992, APPL NATURAL LANGUAG, P152 Eyharabide V, 2012, APPL INTELL, V36, P857, DOI 10.1007/s10489-011-0301-4 Frantzi K., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900023 Gagne R. M., 1968, CLASSIC WRITINGS INS, V2, P63 Gauch S., 2007, LNCS, V4321, P54 Haghighi PD, 2008, LECT NOTES COMPUT SC, V5279, P112 Huang A., 2008, NZ COMP SCI RES STUD, P49 Huang J, 2006, LECT NOTES ARTIF INT, V4213, P536 Kodakateri A., 2009, RECSYS 09, P241 Kozakov L, 2004, IBM SYST J, V43, P546 Lafferty J., 1999, 18 INT C MACH LEARN, P282 Li CL, 2011, LECT NOTES COMPUT SC, V6611, P653 Liang TP, 2008, DECIS SUPPORT SYST, V45, P401, DOI 10.1016/j.dss.2007.05.004 Liao IE, 2010, ELECTRON LIBR, V28, P386, DOI 10.1108/02640471011051972 Liao SC, 2009, ELECTRON LIBR, V27, P496, DOI 10.1108/02640470910966925 Lopes G. R., 2007, WEBMEDIA08 P 14 BRAZ, P59 Manning C. D., 2009, INTRO INFORM RETRIEV Manning C. D., 1999, FDN STAT NATURAL LAN, P117 Marcus Mitchell, 1994, ARPA HUM LANG TECHN, P114 Matsuo Y., 2003, J INT TOOLS ARTIFICI, V13, P157 Medelyan O., 2009, INT J HUM-COMPUT ST, V67, P716 Medelyan Olena, 2008, P AAAI WORKSH WIK AR, P19 Middleton S. E., 2009, HDB ONTOLOGIES INT H, P779, DOI DOI 10.1007/978-3-540-92673-3 Middleton S. E., 2001, INT C KNOWLEDGE CAPT, P100 Mihalcea R., 2007, CIKM07 LISB PORT ACM, P1 Mihalcea Rada, 2007, P NAACL HLT 2007, P196 Miller L., 2010, FOAF VOCABULARY SPEC Nakayama K, 2008, LECT NOTES COMPUT SC, V4947, P690, DOI 10.1007/978-3-540-78568-2_64 Piao S., 2010, P DIG EC ALL HANDS M, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiaffino S. N., 2000, OP DISC TRACK P INT, P12 Sclano F., 2007, C TIA 2007, P8 Shams M., 2013, COMPUTER SCI CURRICU, P50 Sidorov G, 2014, EXPERT SYST APPL, V41, P853, DOI 10.1016/j.eswa.2013.08.015 Steinbach M., 2000, IEEE KDD WORKSH TEXT, V400, P1 Strobbe M., 2011, KNOWL INF SYST, V31, P581, DOI [10.1007/s10115-011-0411-7, DOI 10.1007/S10115-011-0411-7] Sugiyama K., 2011, JCDL11 ONT CAN ACM, P307 Sugiyama K., 2010, JCDL10, P29 Tang J, 2007, IEEE DATA MINING, P292 Tang J., 2008, KDD 08, P990, DOI DOI 10.1145/1401890.1402008 Uchyigit G, 2009, STUD COMPUT INTELL, V172, P25 Vellino A, 2007, PROCEEDING OF THE 2007 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS, P111 Wartena C, 2008, INT WORKSHOP DATABAS, P54 Weiss SM, 2010, TEXTS COMPUT SCI, P75, DOI 10.1007/978-1-84996-226-1_4 Yao L., 2007, IEEE WIC ACM INT C W, P359, DOI 10.1109/WI.2007.60 Zhang Z., 2008, 6 INT C LANG RES EV Zhiqiang L., 2009, INT C WEB INF SYST M, P251, DOI DOI 10.1109/WISM.2009.59 Zhou XJ, 2012, ARTIF INTELL REV, V37, P119, DOI 10.1007/s10462-011-9222-1 NR 61 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD DEC 1 PY 2014 VL 41 IS 17 BP 7945 EP 7957 DI 10.1016/j.eswa.2014.06.039 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AO6LC UT WOS:000341462600024 ER PT J AU Weng, C Thomson, DL Haffner, P Juang, BH AF Weng, Chao Thomson, David L. Haffner, Patrick Juang, Biing-Hwang (Fred) TI Latent Semantic Rational Kernels for Topic Spotting on Conversational Speech SO IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING LA English DT Article DE LDA; LSA; PLSA; rational kernels; tf-idf; topic spotting; WFSTs ID SIMILARITY; RETRIEVAL AB In this work, we propose latent semantic rational kernels (LSRK) for topic spotting on conversational speech. Rather than mapping the input weighted finite-state transducers (WFSTs) onto a high dimensional n-gram feature space as in n-gram rational kernels, the proposed LSRK maps the WFSTs onto a latent semantic space. With the proposed LSRK, all available external knowledge and techniques can be flexibly integrated into a unified WFST based framework to boost the topic spotting performance. We present how to generalize the LSRK using tf-idf weighting, latent semantic analysis, WordNet and probabilistic topic models. To validate the proposed LSRK framework, we conduct the topic spotting experiments on two datasets, Switchboard and AT&T HMIHY0300 initial collection. The experimental results show that with the proposed LSRK we can achieve significant and consistent topic spotting performance gains over the n-gram rational kernels. C1 [Weng, Chao; Juang, Biing-Hwang (Fred)] Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30332 USA. [Thomson, David L.; Haffner, Patrick] AT&T Labs Res, Florham Pk, NJ 07932 USA. RP Weng, C (reprint author), Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30332 USA. EM chao.weng@ece.gatech.edu; thomson@research.att.com; juang@ece.gatech.edu CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Cortes C, 2004, J MACH LEARN RES, V5, P1035 Cortes C., 2003, P ICASSP 03, P628 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Foote J, 1999, MULTIMEDIA SYST, V7, P2, DOI 10.1007/s005300050106 Gaussier E, 2005, P 28 ANN INT ACM SIG, P601, DOI DOI 10.1145/1076034.1076148 Gorin A. L., 1997, P EUROSPEECH97 Gorin AL, 1997, SPEECH COMMUN, V23, P113, DOI 10.1016/S0167-6393(97)00040-X Griffiths T, 2002, GIBBS SAMPLING GENER Hazen T. J., 2007, P ASRU, P659 Hofmann T, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P289 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hofmann T., 1999, NIPS, P914 Jaakkola T, 1998, ADV NEURAL INFORM PR, V11, P487 Jiang J., 1997, CORR Leacock C., 1972, WORDNET ELECT LEXICA Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 Lin D., 1998, P 15 INT C MACH LEAR, V1, P296 Miller G., 1990, INT J LEXICOGR, V3, P235 Mohri M, 2002, COMPUT SPEECH LANG, V16, P69, DOI 10.1006/csla.2001.0184 Myers K., 2000, P ICML00, P662 Pedersen T., 2004, HLT NAACL 2004, P38 Povey D., 2011, P ASRU 11 Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Weng C., 2013, P ICASSP 13 WRIGHT JH, 1995, INT CONF ACOUST SPEE, P313, DOI 10.1109/ICASSP.1995.479536 Wu Z., 1994, ACL, P133 NR 31 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2329-9290 J9 IEEE-ACM T AUDIO SPE JI IEEE-ACM Trans. Audio Speech Lang. PD DEC PY 2014 VL 22 IS 12 BP 1738 EP 1749 DI 10.1109/TASLP.2014.2347133 PG 12 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA AO8TJ UT WOS:000341627500005 ER PT J AU Sun, K Buchan, N Larminie, C Przulj, N AF Sun, Kai Buchan, Natalie Larminie, Chris Przulj, Natasa TI The integrated disease network SO INTEGRATIVE BIOLOGY LA English DT Article ID GENOME-WIDE ASSOCIATION; INFLAMMATORY-BOWEL-DISEASE; PROTEIN INTERACTION NETWORK; CROHNS-DISEASE; MASS-SPECTROMETRY; MICROARRAY DATA; RNA-SEQ; EXPRESSION PROFILES; BIOLOGICAL PATHWAYS; GENETIC-DISORDERS AB The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes' will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases. C1 [Sun, Kai; Przulj, Natasa] Univ London Imperial Coll Sci Technol & Med, Dept Comp, London SW7 2AZ, England. [Buchan, Natalie; Larminie, Chris] GlaxoSmithKline, Computat Biol, Stevenage SG1 2NY, Herts, England. RP Przulj, N (reprint author), Univ London Imperial Coll Sci Technol & Med, Dept Comp, 180 Queens Gate, London SW7 2AZ, England. EM christopher.g.larminie@gsk.com; natasha@imperial.ac.uk FU GSK Research & Development Ltd; European Research Council (ERC) Starting Independent Researcher Grant [278212]; National Science Foundation (NSF) Cyber-Enabled Discovery and Innovation (CDI) [OIA-1028394]; Serbian Ministry of Education and Science Project [III44006]; ARRS Project [J1-5454] FX We thank members of GlaxoSmithKline (GSK) Computational Biology group, especially Peter Woollard, Hannah Tipney, Bhushan Bonde, Joanna Betts, Enrico Ferrero and Pankaj Agarwal, for their helpful comments and suggestions. We thank John Stone, Martin Sims, Jennifer Wright and Soumitra Ghosh for useful discussions on Crohn's disease. We also thank Matt Nelson for his help with GWAS catalog data. This work was supported by GSK Research & Development Ltd, the European Research Council (ERC) Starting Independent Researcher Grant 278212, the National Science Foundation (NSF) Cyber-Enabled Discovery and Innovation (CDI) OIA-1028394, the Serbian Ministry of Education and Science Project III44006 and the ARRS Project J1-5454. CR Aerts S, 2006, NAT BIOTECHNOL, V24, P537, DOI 10.1038/nbt1203 Ahn J, 2011, BIOINFORMATICS, V27, P1846, DOI 10.1093/bioinformatics/btr283 Alcaraz N, 2012, INTEGR BIOL-UK, V4, P756, DOI 10.1039/c2ib00133k Alcaraz N, 2011, INTERNET MATH, V7, P299 Burton PR, 2007, NATURE, V447, P661, DOI 10.1038/nature05911 Braun P, 2011, SCIENCE, V333, P601, DOI 10.1126/science.1203877 Ashburner M, 2000, NAT GENET, V25, P25 Aza-Blanc P, 2003, MOL CELL, V12, P627, DOI 10.1016/S1097-2765(03)00348-4 Baumgart DC, 2012, LANCET, V380, P1590, DOI 10.1016/S0140-6736(12)60026-9 Brost S, 2010, PATHOL RES PRACT, V206, P43, DOI 10.1016/j.prp.2009.09.005 Butte A, 2002, NAT REV DRUG DISCOV, V1, P951, DOI 10.1038/nrd.961 Campillos M, 2008, SCIENCE, V321, P263, DOI 10.1126/science.1158140 Cheng FX, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002503 Cho JH, 2011, GASTROENTEROLOGY, V140, P1704, DOI 10.1053/j.gastro.2011.02.046 Craig J., 2008, NAT ED, V1, P184 Croft D, 2011, NUCLEIC ACIDS RES, V39, pD691, DOI 10.1093/nar/gkq1018 Dahlquist KD, 2002, NAT GENET, V31, P19, DOI 10.1038/ng0502-19 Daminelli S, 2012, INTEGR BIOL-UK, V4, P778, DOI 10.1039/c2ib00154c Davis AP, 2011, NUCLEIC ACIDS RES, V39, pD1067, DOI 10.1093/nar/gkq813 Dudley JT, 2011, BRIEF BIOINFORM, V12, P303, DOI 10.1093/bib/bbr013 Duerr RH, 2006, SCIENCE, V314, P1461, DOI 10.1126/science.1135245 Ellinghaus D, 2012, AM J HUM GENET, V90, P636, DOI 10.1016/j.ajhg.2012.02.020 Felipez LM, 2012, J PEDIATR GASTR NUTR, V54, P28, DOI 10.1097/MPG.0b013e318228349e Franke L, 2006, AM J HUM GENET, V78, P1011, DOI 10.1086/504300 Gavin AC, 2006, NATURE, V440, P631, DOI 10.1038/nature04532 Gavin AC, 2002, NATURE, V415, P141, DOI 10.1038/415141a Gentschew L, 2012, MOL NUTR FOOD RES, V56, P524, DOI 10.1002/mnfr.201100630 Giogha C, 2014, CYTOKINE GROWTH F R, V25, P235, DOI 10.1016/j.cytogfr.2013.12.012 Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104 Gottlieb A., 2011, MOL SYST BIOL, V7, P1 Grant AV, 2012, J INFECT DIS, V206, P1763, DOI 10.1093/infdis/jis588 Gunther C, 2013, GUT, V62, P1062, DOI 10.1136/gutjnl-2011-301364 Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514 Haugen AC, 2004, GENOME BIOL, V5, DOI 10.1186/gb-2004-5-12-r95 Haupt VJ, 2011, BRIEF BIOINFORM, V12, P312, DOI 10.1093/bib/bbr011 Hirschhorn JN, 2005, NAT REV GENET, V6, P95, DOI 10.1038/nrg1521 Hu GH, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006536 Huang WH, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S1-S68 Huttenhower C, 2009, GENOME RES, V19, P1093, DOI 10.1101/gr.082214.108 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jostins L, 2012, NATURE, V491, P119, DOI 10.1038/nature11582 Jung J, 2013, BIOINFORMATICS, V29, P2017, DOI 10.1093/bioinformatics/btt327 Kanehisa M, 2012, NUCLEIC ACIDS RES, V40, pD109, DOI 10.1093/nar/gkr988 Kapushesky M, 2012, NUCLEIC ACIDS RES, V40, pD1077, DOI 10.1093/nar/gkr913 Keiser MJ, 2009, NATURE, V462, P175, DOI 10.1038/nature08506 Kelder T, 2012, NUCLEIC ACIDS RES, V40, pD1301, DOI 10.1093/nar/gkr1074 Kelley R, 2005, NAT BIOTECHNOL, V23, P561, DOI 10.1038/nbt1096 Khanna R, 2013, IMMUNOTHERAPY-UK, V5, P803, DOI [10.2217/imt.13.81, 10.2217/IMT.13.81] Knox C, 2011, NUCLEIC ACIDS RES, V39, pD1035, DOI 10.1093/nar/gkq1126 Krogan NJ, 2006, NATURE, V440, P637, DOI 10.1038/nature04670 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 Lamb J, 2006, SCIENCE, V313, P1929, DOI 10.1126/science.1132939 Lazzerini M, 2013, JAMA-J AM MED ASSOC, V310, P2164, DOI 10.1001/jama.2013.280777 Lee DS, 2008, P NATL ACAD SCI USA, V105, P9880, DOI 10.1073/pnas.0802208105 Lewis SN, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0027175 Li J, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-266 Li S, 2013, NATURE, V501, P242, DOI 10.1038/nature12436 Li WQ, 2013, ANN RHEUM DIS, V72, P1200, DOI 10.1136/annrheumdis-2012-202143 Li Y, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0004346 Li YJ, 2010, BIOINFORMATICS, V26, P1219, DOI 10.1093/bioinformatics/btq108 Linghu B, 2009, GENOME BIOL, V10, DOI 10.1186/gb-2009-10-9-r91 Loscalzo J., 2007, MOL SYSTEMS BIOL, V3, P1 Maglott D, 2005, NUCLEIC ACIDS RES, V33, pD54, DOI 10.1093/nar/gki031 Marioni JC, 2008, GENOME RES, V18, P1509, DOI 10.1101/gr.079558.108 Mathur S, 2012, J BIOMED INFORM, V45, P363, DOI 10.1016/j.jbi.2011.11.017 McGonagle D, 2006, PLOS MED, V3, P1242, DOI 10.1371/journal.pmed.0030297 Mortazavi A, 2008, NAT METHODS, V5, P621, DOI 10.1038/nmeth.1226 Osborne JD, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-S1-S6 Park SY, 2009, BMB REP, V42, P511 Petryszak R, 2014, NUCLEIC ACIDS RES, V42, pD926, DOI 10.1093/nar/gkt1270 Prieto-Perez R, 2013, PHARMACOGENOMICS J, V13, P297, DOI 10.1038/tpj.2012.53 Quackenbush J, 2001, NAT REV GENET, V2, P418, DOI 10.1038/35076576 Radivojac P, 2008, PROTEINS, V72, P1030, DOI 10.1002/prot.21989 Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017 Roessner U, 2000, PLANT J, V23, P131, DOI 10.1046/j.1365-313x.2000.00774.x Rual JF, 2005, NATURE, V437, P1173, DOI 10.1038/nature04209 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanseau P, 2012, NAT BIOTECHNOL, V30, P317, DOI 10.1038/nbt.2151 Sartor RB, 2006, NAT CLIN PRACT GASTR, V3, P390, DOI 10.1038/ncpgasthep0528 Settesoldi A, 2014, EXPERT REV GASTROENT, V8, P5, DOI 10.1586/17474124.2014.850414 Silva LCR, 2010, IMMUNOTHERAPY-UK, V2, P817, DOI [10.2217/imt.10.67, 10.2217/IMT.10.67] Simonis N, 2008, NAT METHODS, V6, P47, DOI DOI 10.1038/NMETH.1279 Sladek R, 2007, NATURE, V445, P881, DOI 10.1038/nature05616 Smith CA, 2006, ANAL CHEM, V78, P779, DOI 10.1021/ac051437y Smoot ME, 2011, BIOINFORMATICS, V27, P431, DOI 10.1093/bioinformatics/btq675 Stark C, 2006, NUCLEIC ACIDS RES, V34, pD535, DOI 10.1093/nar/gkj109 Stelzl U, 2005, CELL, V122, P957, DOI 10.1016/j.cell.2005.08.029 Suthram S, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000662 Tanay A, 2004, P NATL ACAD SCI USA, V101, P2981, DOI 10.1073/pnas.0308661100 van Driel MA, 2006, EUR J HUM GENET, V14, P535, DOI 10.1038/sj.ejhg.5201585 von Eichborn J, 2011, NUCLEIC ACIDS RES, V39, pD1060, DOI 10.1093/nar/gkq1037 Wang WX, 2003, ANAL CHEM, V75, P4818, DOI 10.1021/ac026468x Wang Z, 2009, NAT REV GENET, V10, P57, DOI 10.1038/nrg2484 Welter D, 2014, NUCLEIC ACIDS RES, V42, pD1001, DOI 10.1093/nar/gkt1229 Xu R, 2013, BIOINFORMATICS, V29, P2186, DOI 10.1093/bioinformatics/btt359 Yang L, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0028025 Ye H, 2014, PLOS ONE, V9, DOI 10.1371/journal.pone.0087864 Zeissig Y., 2014, GUT, DOI [10.1136/gutjnl-2013-306520, DOI 10.1136/GUTJN1-2013-306520] Zitnik M., 2013, SCI REP, V3 NR 100 TC 1 Z9 1 PU ROYAL SOC CHEMISTRY PI CAMBRIDGE PA THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS, ENGLAND SN 1757-9694 EI 1757-9708 J9 INTEGR BIOL-UK JI Integr. Biol. PD NOV PY 2014 VL 6 IS 11 BP 1069 EP 1079 DI 10.1039/c4ib00122b PG 11 WC Cell Biology SC Cell Biology GA AS3UH UT WOS:000344203000007 PM 25133803 ER PT J AU Schedl, M Hauger, D Urbano, J AF Schedl, Markus Hauger, David Urbano, Julian TI Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework SO MULTIMEDIA SYSTEMS LA English DT Article DE Social media mining; Music information retrieval; Microblog analysis; Similarity measurement; Trend prediction ID RETRIEVAL AB Microtexts are a valuable, albeit noisy, source to infer collaborative information. As music plays an important role in many human lives, microblogs on music-related activities are available in abundance. This paper investigates different strategies to estimate music similarity from these data sources. In particular, we first present a framework to extract co-occurrence scores between music artists from microblogs and then investigate 12 similarity estimation functions to subsequently derive resemblance scores. We evaluate the approaches on a collection of microblogs crawled from Twitter over a period of 10 months and compare them to standard tf-idf approaches. As evaluation criteria we use precision and recall in an artist retrieval task as well as rank proximity. We show that collaborative chatter on music can be effectively used to develop music artist similarity measures, which are a core part of every music retrieval and recommendation system. Furthermore, we analyze the effects of the "long tail" on retrieval results and investigate whether results are consistent over time, using a second dataset. C1 [Schedl, Markus; Hauger, David] Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. [Urbano, Julian] Univ Carlos III Madrid, Dept Comp Sci, Madrid, Spain. RP Schedl, M (reprint author), Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. EM Markus.Schedl@jku.at; david.hauger@jku.at; jurbano@inf.uc3m.es FU Austrian Science Funds (FWF) [P22856, P25655]; FP7-ICT-2011-9 project [601166] FX This work was supported by the Austrian Science Funds (FWF): P22856 and P25655, and by the FP7-ICT-2011-9 project: 601166. CR Armentano M.G., 2011, P IJCAI 2011 INT WOR Aucouturier JJ, 2003, J NEW MUSIC RES, V32, P83, DOI 10.1076/jnmr.32.1.83.16801 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baltrunas L., 2011, P INT C EL COMM WEB Baumann S., 2003, P 3 INT C WEB DEL MU Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Casey MA, 2008, P IEEE, V96, P668, DOI 10.1109/JPROC.2008.916370 Celma O, 2010, MUSIC RECOMMENDATION AND DISCOVERY, P1, DOI 10.1007/978-3-642-13287-2 Celma O., 2006, P 7 INT C MUS INF RE Cimiano P., 2004, P 13 INT C WORLD WID, P462, DOI 10.1145/988672.988735 Cimiano P., 2004, ACM SIGKDD EXPLORATI, V6, P24, DOI 10.1145/1046456.1046460 Cohen WW, 2000, COMPUT NETW, V33, P685, DOI 10.1016/S1389-1286(00)00057-8 Geleijnse G., 2006, P 7 INT C MUS INF RE Govaerts S., 2009, P IEEE INT S MULT IS Hu X., 2005, P 6 INT C MUS INF RE Jones M.C., 2007, INT C MUS INF RETR, P539 Knees P., 2004, P 5 INT S MUS INF RE, P517 Knees P., 2007, P 30 ANN INT ACM SIG Knees P., 2006, P 14 ACM INT C MULT McFee B., 2011, P 12 INT SOC MUS INF Metzler D., 2007, P 29 EUR C INF RETR Naveed N., 2011, P 20 ACM INT C INF K, P183 Pampalk E., 2006, P 7 INT C MUS INF RE PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 Pohle T, 2007, IEEE T MULTIMEDIA, V9, P567, DOI 10.1109/TMM.2006.887991 Ramzan N., 2012, SOCIAL MEDIA RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schedl M., 2011, P 9 WORKSH AD MULT R Schedl M, 2012, INFORM RETRIEVAL, V15, P183, DOI 10.1007/s10791-012-9187-y Schedl M., 2005, P 4 INT WORKSH CONT Schedl M, 2011, P IJCAI 2011 INT WOR Schedl M., 2012, P 21 INT WORLD WID W Schedl M., 2011, P 12 INT SOC MUS INF Schedl M, 2012, CH CRC DATA MIN KNOW, P219 Schedl M., 2010, P 11 INT SOC MUS INF Schedl M, 2013, P 35 EUR C INF RETR Urbano J., 2013, INT J MULTIMEDIA INF, V2, P59 Vapnik V., 1995, NATURE STAT LEARNING Weng J. S., 2010, P 3 ACM INT C WEB SE Whitman Brian, 2002, P 2002 INT COMP MUS, P591 Zadel M., 2004, P 5 INT S MUS INF RE NR 41 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0942-4962 EI 1432-1882 J9 MULTIMEDIA SYST JI Multimedia Syst. PD NOV PY 2014 VL 20 IS 6 SI SI BP 693 EP 705 DI 10.1007/s00530-013-0321-5 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA AS1TW UT WOS:000344065400006 ER PT J AU Xiao, F Noro, T Tokuda, T AF Xiao, Feng Noro, Tomoya Tokuda, Takehiro TI FINDING NEWS-TOPIC ORIENTED INFLUENTIAL TWITTER USERS BASED ON TOPIC RELATED HASHTAG COMMUNITY DETECTION SO JOURNAL OF WEB ENGINEERING LA English DT Article DE Social Network Analysis; Twitter; hashtag; PageRank; characteristic co-occurrence word AB Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user's retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations. C1 [Xiao, Feng; Noro, Tomoya; Tokuda, Takehiro] Tokyo Inst Technol, Dept Comp Sci, Meguro Ku, Tokyo 1528552, Japan. RP Xiao, F (reprint author), Tokyo Inst Technol, Dept Comp Sci, Meguro Ku, Tokyo 1528552, Japan. EM xiao@tt.cs.titech.ac.jp; noro@tt.cs.titech.ac.jp; tokuda@tt.cs.titech.ac.jp RI Noro, Tomoya/B-9307-2015 OI Noro, Tomoya/0000-0002-5645-8497 CR Anger I., 2011, P 11 INT C KNOWL MAN, P31 Bigonha C., 2010, P BRAZ S MULT WEB BE, P107 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Cano A. E., 2011, SEMANTIC WEB J, P1 Cha M., 2010, P 4 INT AAAI C WEBL EFRON M, 2010, P 33 INT ACM SIGIR C, P787, DOI DOI 10.1145/1835449.1835616 Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 Hajian B., 2011, IEEE 3 INT C SOC COM, P497 Huang J, 2010, P 21 ACM C HYP HYP, P173, DOI DOI 10.1145/1810617.181064 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kwak H., 2010, P 19 INT C WORLD WID, V112, P591, DOI DOI 10.1145/1772690.1772751 KYWE SM, 2012, P 4 INT C SOC INF, V7710, P337 Leavitt A., 2009, INFLUENTIALS NEW APP Lehmann J., 2012, P 21 INT C WORLD WID, P251 Manning C. D., 1999, FDN STAT NATURAL LAN Mazzia A., 2011, SUGGESTING HASHTAGS Noro T., 2012, 22 EUR JAP C INF MOD, P31 Pak A., 2010, P 7 C INT LANG RES E, P1320 ROMERO DM, 2011, ECML PKDD 11, V6913, P18 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt H., 1994, P INT C NEW METH LAN, V12, P44 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Tokuda T., 2011, 21 EUR JAP C INF MOD, V237, P187 Weng J., 2010, P 2010 IEEE INT C DA, P1121 Weng J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Xiao F., 2012, P 12 INT C WEB ENG, P16 Yang L, 2012, P 21 INT C WORLD WID, P261, DOI DOI 10.1145/2187836.2187872 Ye SZ, 2010, LECT NOTES COMPUT SC, V6430, P216 Zangerle E., 2011, P WORKSH SEM AD SOC, P67 NR 31 TC 0 Z9 0 PU RINTON PRESS, INC PI PARAMUS PA 565 EDMUND TERRACE, PARAMUS, NJ 07652 USA SN 1540-9589 EI 1544-5976 J9 J WEB ENG JI J. Web Eng. PD NOV PY 2014 VL 13 IS 5-6 BP 405 EP 429 PG 25 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA AP5JZ UT WOS:000342116800003 ER PT J AU Si, JF Li, Q Qian, TY Deng, XT AF Si, Jianfeng Li, Qing Qian, Tieyun Deng, Xiaotie TI Users' interest grouping from online reviews based on topic frequency and order SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE Review analysis; Structural topic modeling; Interest grouping AB Large volume of online review data can reveal consumers' major interests on domain product, which attracts great research interests from the academic community. Most of the existing works focus on the problems of review summarization, aspect identification or opinion mining from an item's point of view such as the quality or popularity of products. Considering the fact that users who generate those review texts draw different attentions to product aspects with respect to their own interests, in this article, we aim to learn K users' interest groups indicated by their review writings. Such K interest groups' identification can facilitate better understanding of major and potential consumers' concerns which are crucial for applications like product improvement on customer-oriented design or diverse marketing strategies. Instead of using a traditional text clustering approach, we treat the groupId/clusterId as a hidden variable and use a permutation-based structural topic model called KMM. Through this model, we infer K interest groups' distribution by discovering not only the frequency of product aspects (Topic Frequency), but also the occurrence priority of respective aspects (Topic Order). They jointly present an informative summarization on the raw review corpus. Our experiment on several real-world review datasets demonstrates a competitive solution. C1 [Si, Jianfeng; Li, Qing] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. [Qian, Tieyun] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China. [Deng, Xiaotie] Shanghai Jiao Tong Univ, Dept Comp Sci, AIMS Lab, Shanghai, Peoples R China. RP Si, JF (reprint author), City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. EM thankjeff@gmail.com; itqli@cityu.edu.hk; qty@whu.edu.cn; deng-xt@cs.sjtu.edu.cn FU City University of Hong Kong [7002770]; NSFC [61272275, 61070011]; 111 Project [B07037] FX The work described in this article has been supported by a strategic research grant from City University of Hong Kong (project no. 7002770), the NSFC Project (61272275, 61070011), and the 111 Project (B07037). CR Abdul-Mageed Muhammad, 2011, P 49 ANN M ASS COMP, V2, P587 Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487 Azzopardi L., 2003, P 26 ANN INT ACM SIG, P369, DOI DOI 10.1145/860435.860505 Beineke P., 2003, P AAAI, P12 Bernardo J., 2000, WILEY SERIES PROBABI Bishop C. M., 2006, PATTERN RECOGNITION Blei D.M., 2010, J ACM, V57 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chen H, 2009, J ARTIF INTELL RES, V36, P129 FLIGNER MA, 1986, J ROY STAT SOC B MET, V48, P359 Gamon M, 2005, LECT NOTES COMPUT SC, V3646, P121 Ganesan Kavita, 2011, INFORM RETRIEVAL Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Gruber A., 2007, ARTIFICIAL INTELLIGE Heinrich G., 2004, PARAMETER ESTIMATION Jindal N., 2010, P 19 ACM INT C INF K, P1549, DOI 10.1145/1871437.1871669 Jindal N., 2008, P INT C WEB SEARCH W, P219, DOI DOI 10.1145/1341531.1341560 Jordan M., 1999, LEARNING GRAPHICAL M Kawamae N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P887 Leung CWK, 2011, WORLD WIDE WEB, V14, P187, DOI 10.1007/s11280-011-0117-5 Li W., 2006, ICML Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797 Mei Q., 2006, P 15 INT C WORLD WID, P533, DOI 10.1145/1135777.1135857 Mukherjee A., 2012, P 21 INT C WORLD WID, P191, DOI DOI 10.1145/2187836.2187863 Mukherjee Arjun, 2011, P 20 INT C COMP WORL, P93 Phan Xuan-Hieu, 2007, GIBBSLDA C C IMPLEME Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 Purver M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P17 Rosen-Zvi M., 2004, P 20 C UNC ART INT, P487 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sensoy M, 2012, WORLD WIDE WEB, V15, P285, DOI 10.1007/s11280-011-0134-4 Si J., 2012, P MACH LEARN DAT MIN, P321 Titov I., 2008, P 17 INT C WORLD WID, P111, DOI 10.1145/1367497.1367513 WANG H, 2011, PROT PRIV CHIN RES, P1 Zhao Y., 2002, CRITERION FUNCTIONS Zhou X., 2007, P 20 INT JOINT C ART, P2928 NR 36 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD NOV PY 2014 VL 17 IS 6 BP 1321 EP 1342 DI 10.1007/s11280-013-0239-z PG 22 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AP6GE UT WOS:000342174800004 ER PT J AU Glavas, G Snajder, J AF Glavas, Goran Snajder, Jan TI Event graphs for information retrieval and multi-document summarization SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Event extraction; Information extraction; Information retrieval; Multi-document summarization; Natural language processing ID VECTOR-SPACE MODEL; SEMANTICS; ONTOLOGY; TRACKING AB With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Glavas, Goran; Snajder, Jan] Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Zagreb 10000, Croatia. RP Snajder, J (reprint author), Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Unska 3, Zagreb 10000, Croatia. EM goran.glavas@fer.hr; jan.snajder@fer.hr CR ACE, 2005, EV DET REC ACE ENT V Ahn David, 2006, P WORKSH ANN REAS TI, P1, DOI 10.3115/1629235.1629236 Allan J, 2002, TOPIC DETECTION TRAC, V12 ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434 Amati Giambattista, 2003, THESIS U GLASGOW Atkinson J, 2013, EXPERT SYST APPL, V40, P4346, DOI 10.1016/j.eswa.2013.01.017 Atkinson M., 2009, P 18 INT WORLD WID W, P1153, DOI 10.1145/1526709.1526903 Baralis E, 2013, EXPERT SYST APPL, V40, P6976, DOI 10.1016/j.eswa.2013.06.047 Barzilay R., 1999, P 37 ANN M ASS COMP, P550, DOI 10.3115/1034678.1034760 Bejan Cosmin Adrian, 2008, P 6 INT C LANG RES E Bethard S., 2013, 2 JOINT C LEX COMP S, V2 Borgwardt K. M., 2007, THESIS LUDWIG MAXIMI Bramsen P., 2006, P 2006 C EMP METH NA, P189, DOI 10.3115/1610075.1610105 Buttcher S., 2007, P SIGIR 2007, P63, DOI 10.1145/1277741.1277755 Canhasi E, 2014, EXPERT SYST APPL, V41, P535, DOI 10.1016/j.eswa.2013.07.079 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chang A.X., 2012, P 8 INT C LANG RES E Christensen Janara, 2013, P NAACL HLT, P1163 Conroy J. M., 2004, P DOC UND C DUC 2004 Dang H. T., 2008, P TEXT AN C, P1 Daniel N., 2003, P HLT NAACL 03 WORKS, V5, P9, DOI 10.3115/1119467.1119469 Du P., 2010, P ACM INT C INF KNOW, P1757, DOI 10.1145/1871437.1871722 Filatova E., 2004, P ACL WORKSH SUMM, V111). Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 Gartner T, 2003, LECT NOTES ARTIF INT, V2777, P129, DOI 10.1007/978-3-540-45167-9_11 Ge J., 2003, P 2003 DOC UND WORKS Gennari SP, 2002, COGNITION, V83, P49, DOI 10.1016/S0010-0277(01)00166-4 Glavas G., 2013, P CICLING 2013, P408 Glavas G., 2013, P 51 ANN M ASS COMP, P797 Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 Grishman R., 1996, P INT C COMP LING CO, V96, P466, DOI DOI 10.3115/992628.992709 Grover C., 2010, P 5 INT WORKSH SEM E, P333 Hammack R., 2011, HDB PRODUCT GRAPHS D Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 He RF, 2012, EXPERT SYST APPL, V39, P2375, DOI 10.1016/j.eswa.2011.08.084 Humphreys K., 1998, P 7 MESS UND C MUC 7 Kawahara D., 2013, P INT JOINT C NAT LA, P37 Kingsbury P., 2002, P 3 INT C LANG RES E, P1989 Kolomiyets O., 2012, P 50 ANN M ASS COMP Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044 Li WJ, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P369 Lin C.-H., 2007, IADIS INT C WWW INT Lin C-Y., 2000, P 18 C COMP LING ASS, V1, P495, DOI 10.3115/990820.990892 Lin C.-Y., 2004, P WORKSH TEXT SUMM B, P74 Llorens H, 2010, P 5 INT WORKSH SEM E, P284 MacCartney B., 2006, P LREC 06, V319, P449 Makkonen J., 2003, P 2003 C N AM CHAPT, P43 Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86 Mayo B., 1950, ANALYSIS, V10, P109, DOI 10.2307/3326687 Menchetti S., 2005, P 22 INT C MACH LEAR, P585, DOI 10.1145/1102351.1102425 Michel JB, 2011, SCIENCE, V331, P176, DOI 10.1126/science.1199644 Nallapati R., 2004, P 13 ACM INT C INF K, P446, DOI 10.1145/1031171.1031258 Nenkova A., 2005, MSRTR2005101 Noh TG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P492, DOI 10.1145/1571941.1572026 Ouyang Y, 2011, INFORM PROCESS MANAG, V47, P227, DOI 10.1016/j.ipm.2010.03.005 Over P., 2004, P WORKSH AUT SUMM DU Over P., 2002, P WORKSH AUT SUMM DU Page L, 1998, PAGERANK CITATION RA Pai MY, 2013, EXPERT SYST APPL, V40, P2447, DOI 10.1016/j.eswa.2012.10.056 Pan Z., 1993, POLIT COMMUN, V10, P55, DOI DOI 10.1080/10584609.1993.9962963 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Pustejovsky J., 2003, CORPUS LINGUISTICS, V2003, P40 PUSTEJOVSKY J, 1991, COGNITION, V41, P47, DOI 10.1016/0010-0277(91)90032-Y Pustejovsky J., 2003, NEW DIRECTIONS QUEST, V2003, P28 Ramos-Santacruz M., 2000, P 6 APPL NAT LANG PR, P76, DOI 10.3115/974147.974158 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Saggion H., 2004, P DOC UND C, P6 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saric F., 2012, P 6 INT WORKSH SEM E Sarkar IN, 2012, J AM MED INFORM ASSN, V19, P249, DOI 10.1136/amiajnl-2011-000480 Sbattella L, 2013, J SYST SOFTWARE, V86, P1426, DOI 10.1016/j.jss.2013.01.029 Suzuki J., 2005, THESIS NARA I SCI TE Suzuki J., 2004, P 42 ANN M ASS COMP, P119, DOI 10.3115/1218955.1218971 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 UzZaman N., 2010, P 5 INT WORKSH SEM E, P276 UzZaman N., 2013, P 7 INT WORKSH SEM E Van Dijk T. A., 1985, DISCOURSE COMMUN, V10, P69 Verhagen M., 2007, P 4 INT WORKSH SEM E, P75, DOI 10.3115/1621474.1621488 Verhagen M., 2010, P 5 INT WORKSH SEM E, P57 Wei CP, 2007, IEEE T SYST MAN CY A, V37, P273, DOI 10.1109/TSMCA.2006.886377 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yan R., 2011, P C EMP METH NAT LAN, P433 Yang CC, 2009, IEEE T SYST MAN CY A, V39, P850, DOI 10.1109/TSMCA.2009.2015885 Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 NR 84 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD NOV 1 PY 2014 VL 41 IS 15 BP 6904 EP 6916 DI 10.1016/j.eswa.2014.04.004 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AM2PX UT WOS:000339694400032 ER PT J AU Diallo, G AF Diallo, Gayo TI An effective method of large scale ontology matching SO JOURNAL OF BIOMEDICAL SEMANTICS LA English DT Article DE Ontology matching; Life sciences ontologies; Entity similarity; Information retrieval; Machine learning; Semantic interoperability ID CHALLENGES; RESOURCES; EVOLUTION; ANATOMY; DOMAIN; MODEL; WEB AB Background: We are currently facing a proliferation of heterogeneous biomedical data sources accessible through various knowledge-based applications. These data are annotated by increasingly extensive and widely disseminated knowledge organisation systems ranging from simple terminologies and structured vocabularies to formal ontologies. In order to solve the interoperability issue, which arises due to the heterogeneity of these ontologies, an alignment task is usually performed. However, while significant effort has been made to provide tools that automatically align small ontologies containing hundreds or thousands of entities, little attention has been paid to the matching of large sized ontologies in the life sciences domain. Results: We have designed and implemented ServOMap, an effective method for large scale ontology matching. It is a fast and efficient high precision system able to perform matching of input ontologies containing hundreds of thousands of entities. The system, which was included in the 2012 and 2013 editions of the Ontology Alignment Evaluation Initiative campaign, performed very well. It was ranked among the top systems for the large ontologies matching. Conclusions: We proposed an approach for large scale ontology matching relying on Information Retrieval (IR) techniques and the combination of lexical and machine learning contextual similarity computing for the generation of candidate mappings. It is particularly adapted to the life sciences domain as many of the ontologies in this domain benefit from synonym terms taken from the Unified Medical Language System and that can be used by our IR strategy. The ServOMap system we implemented is able to deal with hundreds of thousands entities with an efficient computation time. C1 Univ Bordeaux, ISPED, Ctr INSERM U897, F-33000 Bordeaux, France. RP Diallo, G (reprint author), Univ Bordeaux, ISPED, Ctr INSERM U897, F-33000 Bordeaux, France. EM gayo.diallo@u-bordeaux.fr FU French Agence Nationale de la Recherche (ANR); DGA [ANR-11-TECS-012] FX We acknowledge the support from the French Agence Nationale de la Recherche (ANR) and the DGA under the TecSan grant number ANR-11-TECS-012. CR Aguirre J-L, 2012, P 8 ISWC WORKSH ONT Algergawy A, 2011, LECT NOTES COMPUT SC, V6909, P415, DOI 10.1007/978-3-642-23737-9_30 Aumueller D., 2005, P 2005 ACM SIGMOD IN, P906, DOI 10.1145/1066157.1066283 Ba M, 2013, IRBM, V34, P56, DOI 10.1016/j.irbm.2012.12.011 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, P267 Cheatham M, 2013, LECT NOTES COMPUT SC, V8219, P294, DOI 10.1007/978-3-642-41338-4_19 Diallo G, 2012, CEUR WORKSH P SWAT4L, V952 Diallo G, 2008, WEB INT IAT WORKSH S, V3, P50 Diallo G, 2013, LEARNING BASED STRAT, V1114 Diallo G, 2011, IEEE P SITIS 2011, P159 Diallo G, 2008, COMP MED SY, P308 Diallo G, 2011, ACM P SWAT4LS 11 4 I, P8 Doan A.H., 2004, HDB ONTOLOGIES, P385 Drame K, 2014, J BIOMED INFORM, V48, P171, DOI 10.1016/j.jbi.2013.12.013 Esteban-Gutierrez M, 2010, IWEST 2010, V666 Euzenat J., 2011, J DATA SEMANTICS, P158, DOI DOI 10.1007/978-3-642-22630-4_6 Fung KW, 2007, ST HEAL T, V129, P605 Golbeck J, 2003, J WEB SEMANT, V1, P2003 Grau B.C., 2013, P 8 ISWC WORKSH ONT, P61 Halevy A.Y., 2006, P 32 INT C VER LARG, P9 Hamdi F, 2010, P 5 INT WORKSH ONT M, P212 Hao Yanan, 2007, P 13 AUSTR C COMP SC, V62, P107 He YQ, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-29 Hoehndorf R, 2014, J BIOMED SEMANT, V5, DOI 10.1186/2041-1480-5-15 Hu W, 2006, LECT NOTES COMPUT SC, V4185, P72 Ichise R, 2008, 7TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE/ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS, P340, DOI 10.1109/ICIS.2008.51 Jaccard P., 1912, NEW PHYTOL, V11, P37, DOI DOI 10.1111/J.1469-8137.1912.TB05611.X Jimenez-Ruiz E, 2013, P 26 INT WORKSH DESC, P246 Kalfoglou Y, 2003, KNOWL ENG REV, V18, P1, DOI 10.1017/S0269888903000651 Kirsten T, 2011, J BIOMED SEMANT, V2, DOI 10.1186/2041-1480-2-6 Lambrix Patrick, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882 Lambrix P, 2006, J WEB SEMANT, V4, P206 Lambrix P, 2009, LECT NOTES COMPUT SC, V5554, P188, DOI 10.1007/978-3-642-02121-3_17 Lassila O., 1998, RESOURCE DESCRIPTION Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 McCandless M., 2010, LUCENE ACTION, V2nd McGuinness DL, 2004, W3C RECOMM, V10, P10 Meilicke C, 2009, LECT NOTES COMPUT SC, V5554, P878, DOI 10.1007/978-3-642-02121-3_74 Meilicke C, 2011, THESIS U MANNHEIM GE Miles A, 2005, P INT C DUBL COR MET, P3 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Monge A.E., 1996, P 2 INT C KNOWL DISC, P267 Nezhadi Azadeh Haratian, 2011, International Journal of Computer Science & Information Technology, V3, DOI 10.5121/ijcsit.2011.3210 Ngo D., 2012, P 18 INT C EKAW 2012, V7603, P421 Ngo D, 2013, CEUR WORKSH P OM 201, V1111, P211 Noy NF, 2009, NUCLEIC ACIDS RES, V37, pW170, DOI 10.1093/nar/gkp440 Oliveira JL, 2013, PHARMACOEPIDEM DR S, V22, P459, DOI 10.1002/pds.3375 Pesquita C, 2013, P 8 ISWC ONT MATCH W, P13 Pham MH, 2007, DATAMINING ONTOLOGIE, P65 Pirro G, 2008, J INF SCI, V34, P845, DOI 10.1177/0165551508091014 Qu Y., 2006, P 15 INT C WORLD WID, P23, DOI DOI 10.1145/1135777.1135786 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Rahm E, 2011, DATA CENTRIC SYST AP, P3, DOI 10.1007/978-3-642-16518-4_1 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 Rector AL, 2006, P 2 INT WORKSH FORM Rosse C, 2003, J BIOMED INFORM, V36, P478, DOI 10.1016/j.jbi.2003.11.007 Ruiz EJ, 2012, P 20 EUR C ART INT E, P444 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schulz S, 2011, APPL ONTOL, V6, P1, DOI 10.3233/AO-2011-0084 Shvaiko P, 2008, LECT NOTES COMPUT SC, V5332, P1164 Shvaiko P, 2013, IEEE T KNOWL DATA EN, V25, P158, DOI 10.1109/TKDE.2011.253 Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346 Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624 Taboada M, 2009, IEEE T BIO-MED ENG, V56, P1598, DOI 10.1109/TBME.2009.2015651 UKKONEN E, 1992, THEOR COMPUT SCI, V92, P191, DOI 10.1016/0304-3975(92)90143-4 Wang P, 2011, P 22 INT JOINT C ART, V3, P2343 Zhang SM, 2007, ARTIF INTELL MED, V39, P227, DOI 10.1016/j.artmed.2006.12.002 Zhou L, 2012, J BIOMED INFORM, V45, P626, DOI 10.1016/j.jbi.2011.11.006 Zhou L, 2011, AMIA ANN S P AMIA S, P1639 NR 71 TC 0 Z9 0 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 2041-1480 J9 J BIOMED SEMANT JI J. Biomed. Semant. PD OCT 28 PY 2014 VL 5 AR 44 DI 10.1186/2041-1480-5-44 PG 19 WC Mathematical & Computational Biology SC Mathematical & Computational Biology GA CC9UQ UT WOS:000350717100001 PM 25411633 ER PT J AU Kumar, H Lee, S Kim, HG AF Kumar, Harshit Lee, Sungin Kim, Hong-Gee TI Exploiting social bookmarking services to build clustered user interest profile for personalized search SO INFORMATION SCIENCES LA English DT Article DE User profiling; Clustering; Personalized search; Social bookmarking service ID WEB SEARCH; ENGINE AB Search engine users tend to write short queries, generally comprising of two or three query words. As these queries are often ambiguous or incomplete, search engines tend to return results whose rankings reflect a community of intent. Moreover, search engines are designed to satisfy the needs of the general populace, not those of a specific searcher. To address these issues, we propose two methods that use Singular Value Decomposition (SVD) to build a Clustered User Interest Profile (CUIP), for each user, from the tags annotated by a community of users to web resources of interest. A CUIP consists of clusters of semantically or syntactically related tags, each cluster identifying a topic of the user's interest. The matching cluster, to the given user's query, aids in disambiguation of user search needs and assists the search engine to generate a set of personalized search results. A series of experiments was executed against two data sets to judge the clustering tendency of the cluster structure CUIP, and to evaluate the quality of personalized search. The experiment results indicate that the CUIP based personalized search outperforms the baseline search and is better than the other approaches that use social bookmarking services for building a user profile and use it for personalized search. (C) 2014 Elsevier Inc. All rights reserved. C1 [Kumar, Harshit; Lee, Sungin; Kim, Hong-Gee] Seoul Natl Univ, BiKE Lab, Seoul 151, South Korea. RP Kumar, H (reprint author), Seoul Natl Univ, BiKE Lab, Yeongun Campus, Seoul 151, South Korea. EM kumar@bike.snu.ac.kr FU MSIP (the Ministry of Science, ICT and Future Planning), Korea, under the IT-CRSP (IT Convergence Research Support Program) [NIPA-2013-H0401-13-1001] FX This research was supported by MSIP (the Ministry of Science, ICT and Future Planning), Korea, under the IT-CRSP (IT Convergence Research Support Program) (NIPA-2013-H0401-13-1001) supervised by the NIPA (National IT Industry Promotion Agency). CR Abel F, 2010, LECT NOTES COMPUT SC, V6075, P16, DOI 10.1007/978-3-642-13470-8_4 ABEL F, 2011, P 19 INT C US MOD AD, V6787, P1 Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Agichtein Eugene, 2006, SIGIR 06, P3 Arapakis I, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P371 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Budanitsky A, 2001, WORKSH WORDN OTH LEX Cai Yi, 2010, P 19 ACM INT C INF K, P969, DOI 10.1145/1871437.1871561 Carroll J. M., 1987, INTERFACING THOUGHT, P80 Chirita P, 2006, P 15 ACM INT C INF K, P287, DOI 10.1145/1183614.1183658 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Das A. S., 2007, P 16 INT C WORLD WID, P271, DOI 10.1145/1242572.1242610 De Vries Christopher M., 2012, DOCUMENT CLUSTERING DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dom B., 2002, P 18 C UNC ART INT, P137 Dou Z, 2007, P 16 INT C WORLD WID, P581, DOI DOI 10.1145/1242572.1242651 Ferragina Paolo, 2005, 14 INT C WORLD WID W, P801 Garcia-Plaza AP, 2012, EXPERT SYST APPL, V39, P9483, DOI 10.1016/j.eswa.2012.02.108 Gauch S., 2003, Web Intelligence and Agent Systems, V1 GOWER JC, 1969, ROY STAT SOC C-APP, V18, P54 Kaufman L, 1990, FINDING GROUPS DATA Koshman S, 2006, J AM SOC INF SCI TEC, V57, P1875, DOI 10.1002/asi.20408 Kumar H, 2011, LECT NOTES COMPUT SC, V6787, P438, DOI 10.1007/978-3-642-22362-4_46 Kumar Harshit, 2012, SEMANTICALLY ENRICHE Kumar Harshit, 2012, ICADL, P333 Liu Bing, 2006, WEB DATA MINING EXPL Ma Zhongming, 2007, ACM T INF SYST, V25 Malik Hassan H., 2006, ICWE 06 P 6 INT C WE, P48 Noll MG, 2007, LECT NOTES COMPUT SC, V4825, P367 Pitkow J, 2002, COMMUN ACM, V45, P50 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Speretta M., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Tan P. N., 2005, INTRO DATA MINING Kelly D., 2003, SIGIR Forum, V37 Teevan J., 2007, P 30 ANN INT ACM SIG, P757, DOI 10.1145/1277741.1277894 Teevan J., 2005, P 28 ANN INT ACM SIG Vallet David, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348396 Vallet D, 2010, LECT NOTES COMPUT SC, V5993, P420, DOI 10.1007/978-3-642-12275-0_37 Voorhees E. M., 1999, P TREC 8, P77 Wang Q., 2010, P 19 ACM INT C INF K, P999, DOI DOI 10.1145/1871437.1871564 Xu Shengliang, 2008, P 31 ANN INT ACM SIG NR 45 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 EI 1872-6291 J9 INFORM SCIENCES JI Inf. Sci. PD OCT 10 PY 2014 VL 281 BP 399 EP 417 DI 10.1016/j.ins.2014.05.008 PG 19 WC Computer Science, Information Systems SC Computer Science GA AN1AR UT WOS:000340315600028 ER PT J AU Blikstein, P Worsley, M Piech, C Sahami, M Cooper, S Koller, D AF Blikstein, Paulo Worsley, Marcelo Piech, Chris Sahami, Mehran Cooper, Steven Koller, Daphne TI Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming SO JOURNAL OF THE LEARNING SCIENCES LA English DT Article ID INSTRUCTION; DISCOVERY; THINKING; SYSTEM; MODEL AB New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students' change in programming patterns is only weakly predictive of course performance. We subsequently hone in on 1 single assignment, trying to map students' learning process and trajectories and automatically identify productive and unproductive (sink) states within these trajectories. Results show that our process-based metric has better predictive power for final exams than the midterm grades. We conclude with recommendations about the use of such methods for assessment, real-time feedback, and course improvement. C1 [Blikstein, Paulo; Worsley, Marcelo] Stanford Univ, Sch Educ, Stanford, CA 94305 USA. [Blikstein, Paulo; Piech, Chris; Sahami, Mehran; Cooper, Steven; Koller, Daphne] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA. RP Blikstein, P (reprint author), Stanford Univ, Sch Educ, 520 Galvez Mall CERAS 232, Stanford, CA 94305 USA. EM paulob@stanford.edu CR Aleven V, 2010, EDUC PSYCHOL-US, V45, P224, DOI 10.1080/00461520.2010.517740 [Anonymous], 2001, J ED RESOURCES COMPU, V1 Baker RSJD, 2008, USER MODEL USER-ADAP, V18, P287, DOI 10.1007/s11257-007-9045-6 Barron B., 2010, NATURE LEARNING USIN, P199 Basawapatna A, 2011, SIGCSE 11: PROCEEDINGS OF THE 42ND ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, P245 Beckwith L., 2006, SIGCHI C HUM FACT CO Berland M., 2008, THESIS NW U EVANSTON Berland M., 2011, ANN M AM ED RES ASS Blikstein P., 2011, P 1 INT C LEARN AN K, P110, DOI DOI 10.1145/2090116.2090132 Blikstein P., 2011, ANN M AM ED RES ASS Blikstein P., 2009, THESIS NW U EVANSTON Blikstein P., 2013, 3 INT C LEARN AN KNO Booth S. A., 1992, GOTEBORG STUDIES ED, V89 Bruce C., 2004, J INF TECHNOL, V3, P143 Burnett MM, 2011, INTERACT COMPUT, V23, P450, DOI 10.1016/j.intcom.2011.06.004 College Board AP, COMP SCI COURS DESCR Cooper S., 2005, P 36 SIGCSE TECHN S, P260, DOI 10.1145/1047344.1047437 Cope C., 2000, P AUSTR C COMP ED, P48, DOI 10.1145/359369.359377 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dehnadi S., 2009, THESIS MIDDLESEX U L DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dewey J., 1902, SCH SOC diSessa A. A., 2000, CHANGING MINDS COMPU Dutson A., 1997, J ENG EDUC, V76, P17 Dym C. L., 1999, J ENG EDUC, V88, P145 Dym CL, 2005, J ENG EDUC, V94, P103 Fern X, 2010, ACM T COMPUT-HUM INT, V17, DOI 10.1145/1721831.1721834 Freire P, 1970, PEDAGOGIA OPRIMIDO Fuller Ursula, 2007, SIGCSE Bulletin, V39, DOI 10.1145/1345375.1345438 Gall Harald C, 2009, IEEE Software, V26, DOI 10.1109/MS.2009.6 Ioannidou A., 2011, ANN M AM ED RES ASS Jadud M. C., 2006, P 2006 INT WORKSH CO, P73, DOI 10.1145/1151588.1151600 Kafai Y. B., 2006, GAMES CULTURE, V1, P36, DOI DOI 10.1177/1555412005281767 Kirschner PA, 2006, EDUC PSYCHOL, V41, P75, DOI 10.1207/s15326985ep4102_1 Klahr D, 2004, PSYCHOL SCI, V15, P661, DOI 10.1111/j.0956-7976.2004.00737.x Levy Frank, 2004, NEW DIVISION LABOR C Lister R., 2004, SIGCSE Bulletin, V36, DOI 10.1145/1041624.1041673 Maloney J, 2008, SIGCSE'08: PROCEEDINGS OF THE 39TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, P367 Marion B, 2007, SIGCSE 2007: PROCEEDINGS OF THE THIRTY-EIGHTH SIGCSE TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, P131 McCracken M., 2001, SIGCSE B, V33, P1 Montessori M., 1965, SPONTANEOUS ACTIVITY Montessori M., 1964, ADV MONTESSORI METHO National Research Council, 2012, FRAM K 12 SCI ED PRA NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 Nemirovsky R, 2011, J LEARN SCI, V20, P308, DOI 10.1080/10508406.2011.528316 Papert S., 1980, MINDSTORMS CHILDREN Papert S., 1987, EDUC RES, V16, P22 Pattis Richard E., 1981, KAREL ROBOT GENTLE I, V1st Pea R., 1987, MIRRORS MIND Perkins D. N., 1986, Journal of Educational Computing Research, V2 Piech C., 2012, P 43 ACM TECHN S COM, P153 Rabiner Lawrence R., 1986, ASSP MAGAZINE IEEE, V3, P4 Roll I, 2010, LECT NOTES COMPUT SC, V6094, P115 Roll I, 2011, LECT NOTES ARTIF INT, V6738, P288, DOI 10.1007/978-3-642-21869-9_38 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schoenfeld A. H., 1991, ADV INSTRUCTIONAL PS, P55 Shawe-Taylor J., 2000, INTRO SUPPORT VECTOR SIEGLER RS, 1991, AM PSYCHOL, V46, P606, DOI 10.1037/0003-066X.46.6.606 Simon B., 2006, P 2 INT WORKSH COMP, P29, DOI 10.1145/1151588.1151594 Simon B., 2008, COMPUTER SCI ED, V18, P117, DOI 10.1080/08993400802114698 Smyth P, 1997, ADV NEUR IN, V9, P648 SOLOWAY E, 1984, IEEE T SOFTWARE ENG, V10, P595 Soloway E., 1988, STUDYING NOVICE PROG Turkle Sherry, 1992, J MATH BEHAV, V11, P33 VanDeGrift T., 2010, P 10 KOL CALL INT C, P76, DOI 10.1145/1930464.1930479 Wing JM, 2006, COMMUN ACM, V49, P33, DOI 10.1145/1118178.1118215 Worsley M., 2013, 3 INT C LEARN AN KNO Yacef K, 2009, J ED DATA MINING, V1, P3 NR 68 TC 1 Z9 1 PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND SN 1050-8406 EI 1532-7809 J9 J LEARN SCI JI J. Learn. Sci. PD OCT 2 PY 2014 VL 23 IS 4 BP 561 EP 599 DI 10.1080/10508406.2014.954750 PG 39 WC Education & Educational Research; Psychology, Educational SC Education & Educational Research; Psychology GA AS1SH UT WOS:000344060800004 ER PT J AU Choi, DY AF Choi, Dae-Young TI Toward a Trend-Based Web Page Rank by Using Big Data on Smartphones SO ADVANCED SCIENCE LETTERS LA English DT Proceedings Paper CT 2nd International Conference on Internet Services Technology and Information Engineering (ISTIE) CY MAY 31-JUN 01, 2014 CL Bali, INDONESIA DE Private Personalization; SNS Data; Collaboration Personalization; Trend-Based Page Ranking ID SEARCH; MODEL AB We propose two phase Web personalization method for smartphones. The proposed approach is effective for reflecting both private personalization and collaboration personalization. At present, Web personalization is generally achieved by using private personalization such as Web server log, Google's Web history, etc. In this paper, collaboration personalization is derived from the weighting order of keywords based on SNS data such as Facebook, Twitter, etc. We also propose Web page ranking algorithm applying collaboration personalization to the collection of Web pages based on private personalization. Thus, the proposed Web search engine can provide users with trend-based page ranking. It is particularly useful to find current focal issues such as trend, what's new, etc., on the Web. C1 Yuhan Univ, Dept MIS, Puchon, South Korea. RP Choi, DY (reprint author), Yuhan Univ, Dept MIS, Puchon, South Korea. CR Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 Carriere J., 1997, P 6 INT C WORLD WID Choi D. Y., 2010, RCIS, V641 Choi DY, 2014, ADV SCI LETT, V20, P222, DOI 10.1166/asl.2014.5254 Choi DY, 2007, DECIS SUPPORT SYST, V43, P31, DOI 10.1016/j.dss.2005.05.005 Fensel D., 2001, IEEE INTELLIGENT SYS, V24 Kao B, 2000, IEEE T SYST MAN CY C, V30, P364, DOI 10.1109/5326.885118 Manyika J., 2011, BIG DATA NEXT FRONTI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smyth B., 2007, IEEE COMPUTER, V42 NR 10 TC 0 Z9 0 PU AMER SCIENTIFIC PUBLISHERS PI VALENCIA PA 26650 THE OLD RD, STE 208, VALENCIA, CA 91381-0751 USA SN 1936-6612 EI 1936-7317 J9 ADV SCI LETT JI Adv. Sci. Lett. PD OCT-DEC PY 2014 VL 20 IS 10-12 BP 2134 EP 2137 DI 10.1166/asl.2014.5601 PG 4 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA CB2ZF UT WOS:000349496300086 ER PT J AU Chi, NW Lin, KY Hsieh, SH AF Chi, Nai-Wen Lin, Ken-Yu Hsieh, Shang-Hsien TI Using ontology-based text classification to assist Job Hazard Analysis SO ADVANCED ENGINEERING INFORMATICS LA English DT Article DE Construction safety; Information retrieval; Job Hazard Analysis; Knowledge management; Ontology; Text classification ID CONSTRUCTION; INFORMATION AB The dangers of the construction industry due to the risk of fatal hazards, such as falling from extreme heights, being struck by heavy equipment or materials, and the possibility of electrocution, are well known. The concept of Job Hazard Analysis is commonly used to mitigate and control these occupational hazards. This technique analyzes the major tasks in a construction activity, identifies all potential task-related hazards, and suggests safe approaches to reduce or avoid each of these hazards. In this paper, the authors explore the possibility of leveraging existing construction safety resources to assist JHA, aiming to reduce the level of human effort required. Specifically, the authors apply ontology-based text classification (TC) to match safe approaches identified in existing resources with unsafe scenarios. These safe approaches can serve as initial references and enrich the solution space when performing JHA. Various document modification strategies are applied to existing resources in order to achieve superior TC effectiveness. The end result of this research is a construction safety domain ontology and its underlying knowledge base. A user scenario is also discussed to demonstrate how the ontology supports JHA in practice. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Chi, Nai-Wen; Hsieh, Shang-Hsien] Natl Taiwan Univ, Dept Civil Engn, Taipei 10617, Taiwan. [Lin, Ken-Yu] Univ Washington, Dept Construct Management, Seattle, WA 98195 USA. RP Hsieh, SH (reprint author), Natl Taiwan Univ, Dept Civil Engn, 1 Roosevelt Rd,Sec 4, Taipei 10617, Taiwan. EM naiwenchi@ntu.edu.tw; kenyulin@uw.edu; shhsieh@ntu.edu.tw FU Ministry of Science and Technology of Taiwan [NSC102-2917-I-564-040]; Ministry of Education of Taiwan FX The authors would like to thank both the Ministry of Science and Technology of Taiwan (under Project No. NSC102-2917-I-564-040) and the Ministry of Education of Taiwan for kindly providing financial support for this research. CR Abdi H, 2010, COMPUT STAT, V2, P433, DOI DOI 10.1002/WICS.101 Berk R., 2008, SUPPORT VECTOR MACHI Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bobick TG, 2004, J CONSTR ENG M ASCE, V130, P895, DOI 10.1061/(ASCE)0733-9364(2004)130:6(895) Brank J., 2002, MSRTR200263 Buckley C., 1994, P 3 TEXT RETR C TREC, P69 Caldas CH, 2002, J COMPUT CIVIL ENG, V16, P234, DOI 10.1061/(ASCE)0887-3801(2002)16:4(234) Caldas CH, 2003, AUTOMAT CONSTR, V12, P395, DOI 10.1016/S0926-5805(03)00004-9 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chao E. L., 2002, JOB HAZARD ANAL Geigle S., 2013, JOB HAZARD ANAL TEAC Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Han E.H., 2001, ADV KNOWLEDGE DISCOV, V2035, P53, DOI 10.1007/3-540-45357-1_9 Herman I., 2000, P S GRAPH DRAW 2000, P52 Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Joachims T., 1998, Machine Learning: ECML-98. 10th European Conference on Machine Learning. Proceedings Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Manning C.D., 2008, INTRO INFORM RETRIEV NIOSH, 2012, CDC NIOSH FAT ASS CO Noy N., 2001, SMI20010880 STANF U Qiu Y.Y., 2010, P PERV COMP SIGN PRO, P243 Rennie J.D.M., 2003, P 20 INT C MACH LEAR, P616 Rozenfeld O, 2010, SAFETY SCI, V48, P491, DOI 10.1016/j.ssci.2009.12.017 Salama D., 2013, J COMPUT CIVIL ENG SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOROCK GS, 1993, J OCCUP ENVIRON MED, V35, P916, DOI 10.1097/00043764-199309000-00015 Srivastava Ashok, 2009, TEXT MINING CLASSIFI Wang H., 2010, P INT COMP INT SYST, P828 Wang HH, 2011, J COMPUT CIVIL ENG, V25, P442, DOI 10.1061/(ASCE)CP.1943-5487.0000125 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 NR 31 TC 0 Z9 0 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1474-0346 EI 1873-5320 J9 ADV ENG INFORM JI Adv. Eng. Inform. PD OCT PY 2014 VL 28 IS 4 BP 381 EP 394 DI 10.1016/j.aei.2014.05.001 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Multidisciplinary SC Computer Science; Engineering GA AW4AR UT WOS:000346224500011 ER PT J AU Kulkarni, SS Apte, UM Evangelopoulos, NE AF Kulkarni, Shailesh S. Apte, Uday M. Evangelopoulos, Nicholas E. TI The Use of Latent Semantic Analysis in Operations Management Research SO DECISION SCIENCES LA English DT Article DE Big Data Analytics; Latent Semantic Analysis; Operations Management Research; and Unstructured Text ID AUTHOR COCITATION ANALYSIS; INFORMATION-SYSTEMS; INTELLECTUAL STRUCTURE; CITATION/CO-CITATION; CONSUMER RESEARCH; DISCIPLINE; EVOLUTION; SCIENCE; RETRIEVAL; KNOWLEDGE AB In this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative method for content analysis that combines rigorous statistical techniques and scholarly judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise explanation and illustration for implementing LSA. To demonstrate LSA's ability to uncover the intellectual structure of a discipline, we present a study of the field of Operations Management. We also discuss a number of potential applications of LSA to show how it can be used in empirical Operations Management research, specifically in areas that can benefit from analyzing large volumes of unstructured textual data. C1 [Kulkarni, Shailesh S.; Evangelopoulos, Nicholas E.] Univ N Texas, Dept Informat Technol & Decis Sci, Denton, TX 76201 USA. [Apte, Uday M.] Naval Postgrad Sch, Grad Sch Business & Publ Policy, Monterey, CA 93943 USA. RP Kulkarni, SS (reprint author), Univ N Texas, Dept Informat Technol & Decis Sci, 1307 W Highland, Denton, TX 76201 USA. EM Shailesh.Kulkarni@unt.edu; umapte@nps.edu; Nick.Evangelopoulos@unt.edu CR Acharya A, 2013, INT J MED INFORM, V82, P1171, DOI 10.1016/j.ijmedinf.2013.06.007 Amoako-Gyampah K., 1989, J OPERATIONS MANAGEM, V8, P250, DOI 10.1016/0272-6963(89)90027-2 Applebaum W, 1947, J MARKETING, V11, P355, DOI 10.2307/1246274 Banker RD, 2004, MANAGE SCI, V50, P281, DOI 10.1287/mnsc.1040.0206 Behara R., 2013, 2013 INT IEEE SYST C Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 BOULDING KE, 1958, ADMIN SCI QUART, V3, P1, DOI 10.2307/2390602 Bradford R., 2008, CIKM 08 P 17 ACM C I, P153 Brutus S, 2013, J MANAGE, V39, P48, DOI 10.1177/0149206312455245 Chopra S, 2004, MANAGE SCI, V50, P8, DOI 10.1287/mnsc.1030.0189 Cios K. J., 2007, DATA MINING KNOWLEDG Craighead CW, 2008, INT J OPER PROD MAN, V28, P710, DOI 10.1108/01443570810888625 CULNAN MJ, 1986, MANAGE SCI, V32, P156, DOI 10.1287/mnsc.32.2.156 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dumais S., 2007, HDB LATENT SEMANTIC, P293 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Filippini R, 1997, INT J OPER PROD MAN, V17, P655, DOI 10.1108/01443579710175583 Government Accountability Office, 2012, GAO12384 Han J., 2006, DATA MINING CONCEPTS HOFFMAN DL, 1993, J CONSUM RES, V19, P505, DOI 10.1086/209319 KASSARJIAN HH, 1977, J CONSUM RES, V4, P8, DOI 10.1086/208674 Landauer T., 2007, HDB LATENT SEMANTIC, P3 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Manning C.D., 2008, INTRO INFORM RETRIEV Martin D. I., 2007, HDB LATENT SEMANTIC, P33 Meredith J.R., 1989, J OPERATIONS MANAGEM, V8, P297, DOI 10.1016/0272-6963(89)90033-8 Nerur SP, 2008, STRATEG MANAGE J, V29, P319, DOI 10.1002/smj.659 Pannirselvam GP, 1999, J OPER MANAG, V18, P95, DOI 10.1016/S0272-6963(99)00009-1 Pelayo S, 2013, INT J MED INFORM, V82, pE321, DOI 10.1016/j.ijmedinf.2012.09.001 Pilkington A, 2006, INT J OPER PROD MAN, V26, P1255, DOI 10.1108/01443570610705854 Pilkington A, 1999, INT J OPER PROD MAN, V19, P7, DOI 10.1108/01443579910244188 Pilkington A, 2009, J OPER MANAG, V27, P185, DOI 10.1016/j.jom.2008.08.001 Porter MF, 2006, PROGRAM-ELECTRON LIB, V40, P211, DOI 10.1108/00330330610681286 Rendon R., 2012, DEFENSE ACQUISITION, V19, P3 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Rungtusanatham MJ, 2003, J OPER MANAG, V21, P475, DOI 10.1016/S0272-6963(03)00020-2 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scudder G. D., 1998, Journal of Operations Management, V16, DOI 10.1016/S0272-6963(97)00008-9 Sidorova A, 2008, MIS QUART, V32, P467 Tsai WP, 2010, ACAD MANAGE J, V53, P441 Valle-Lisboa JC, 2007, INFORM SCIENCES, V177, P4122, DOI 10.1016/j.ins.2007.04.007 Vessey I, 2002, J MANAGE INFORM SYST, V19, P129 Vosbergen S, 2013, INT J MED INFORM, V82, P1183, DOI 10.1016/j.ijmedinf.2013.09.001 Wei C., 2008, J MANAGE INFORM SYST, V24, P269 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 Zhu M, 2006, COMPUT STAT DATA AN, V51, P918, DOI 10.1016/j.csda.2005.09.010 NR 49 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0011-7315 EI 1540-5915 J9 DECISION SCI JI Decis. Sci. PD OCT PY 2014 VL 45 IS 5 BP 971 EP 994 DI 10.1111/deci.12095 PG 24 WC Management SC Business & Economics GA AS8BA UT WOS:000344474200006 ER PT J AU Debortoli, S Muller, O vom Brocke, J AF Debortoli, Stefan Mueller, Oliver vom Brocke, Jan TI Comparing Business Intelligence and Big Data Skills A Text Mining Study Using Job Advertisements SO BUSINESS & INFORMATION SYSTEMS ENGINEERING LA English DT Article DE Big data; Business intelligence; Competencies; Latent semantic analysis; Text mining ID INFORMATION-SYSTEMS; DATA SCIENCE; ANALYTICS; MANAGEMENT; MODEL AB While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies. C1 [Debortoli, Stefan; Mueller, Oliver; vom Brocke, Jan] Univ Liechtenstein, Inst Informat Syst, FL-9490 Vaduz, Liechtenstein. RP Debortoli, S (reprint author), Univ Liechtenstein, Inst Informat Syst, Furst Franz Josef Str, FL-9490 Vaduz, Liechtenstein. EM stefan.debortoli@uni.li; oliver.mueller@uni.li; jan.vom.brocke@uni.li CR BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108 Buhl HU, 2013, BUS INFORM SYST ENG+, V5, P101, DOI 10.1007/s12599-013-0253-9 Buhl HU, 2013, BUS INFORM SYST ENG+, V5, P65, DOI 10.1007/s12599-013-0249-5 Burton B, 2006, ORG STRUCTURE BUSINE, P1 Casey T, 2013, WHO SHOULD OWN BIG D Cates J. E., 2005, International Journal of Business Information Systems, V1, DOI 10.1504/IJBIS.2005.007408 Chen HC, 2012, MIS QUART, V36, P1165 Coussement K, 2008, DECIS SUPPORT SYST, V44, P870, DOI 10.1016/j.dss.2007.10.010 Cox M., 1997, Proceedings. Visualization '97 (Cat. No.97CB36155), DOI 10.1109/VISUAL.1997.663888 Davenport T. H., 2012, MIT SLOAN MANAGE REV, V54, P22 Davenport TH, 2012, HARVARD BUS REV, V90, P70 Davenport TH, 2006, HARVARD BUS REV, V84, P98 de Lange C, 2013, NAT JOBS BLOG Dhar V, 2013, COMMUN ACM, V56, P64, DOI 10.1145/2500499 Dinter B, 2012, P AM C INF SYST SEAT, P1 Eckerson W, 2004, GAUGE YOUR DATA WARE Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Evangelopoulos N, 2012, COMMUN ACM, V55, P55, DOI 10.1145/2076450.2076467 Forbes, 2013, FORBES Gallivan M. J., 2004, Data Base for Advances in Information Systems, V35, DOI 10.1145/1017114.1017121 Golden B, 2013, CIO Herbst A, 2014, P 22 EUR C INF SYST Hostmann B, 2010, IT SCORE OVERVIEW BU IBM and Said Business School, 2012, AN REAL WORLD US BIG Lahrmann G., 2011, P HAW INT C SYST SCI, P1 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Laney D, 2001, META GR Larsen KR, 2008, DECIS SUPPORT SYST, V45, P884, DOI 10.1016/j.dss.2008.02.009 Lavalle S, 2011, MIT SLOAN MANAGE REV, V52, P21 Litecky C, 2010, IEEE SOFTWARE, V27, P78, DOI 10.1109/MS.2009.150 Loukides M, 2012, WHAT IS DATA SCI Manning C.D., 2008, INTRO INFORM RETRIEV Manyika J., 2011, BIG DATA NEXT FRONTI, P1 Marchand DA, 2013, HARVARD BUS REV, V91, P104 Melville N, 2004, MIS QUART, V28, P283 Polock R, 2013, OPEN KNOWL FDN BLOG Power D.J., 2007, BRIEF HIST DECISION Provost F, 2013, BIG DATA, V1, P51, DOI DOI 10.1089/BIG.2013.1508 Reuter N, 2014, P 22 EUR C INF SYST Russell S, 2010, P AM C INF SYST Russom P, 2011, TDWI RES SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAP, 2014, SAP HANA INT PRED AN Schlegel K., 2013, MAGIC QUADRANT BUSIN Sidorova A, 2008, MIS QUART, V32, P467 TODD PA, 1995, MIS QUART, V19, P1, DOI 10.2307/249709 Van Roekel H, 2009, BI FRAMEWORK TURN IN Varon E, 2012, RETHINK YOUR ORG CHA vom Brocke J, 2014, COMMUN ASS INF SYST, V34, P151 Waller MA, 2013, J BUS LOGIST, V34, P77, DOI 10.1111/jbl.12010 Wixom B, 2011, COMM ASS INFORM SYST, V29, P299 NR 51 TC 0 Z9 0 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1867-0202 J9 BUS INFORM SYST ENG+ JI Bus. Inf. Syst. Eng. PD OCT PY 2014 VL 6 IS 5 BP 289 EP 300 DI 10.1007/s12599-014-0344-2 PG 12 WC Computer Science, Information Systems SC Computer Science GA AP6SD UT WOS:000342207000005 ER PT J AU Luo, XF Zhang, J Li, Q Wei, X Lu, L AF Luo, Xiangfeng Zhang, Jun Li, Qing Wei, Xiao Lu, Lei TI Measuring Algebraic Complexity of Text Understanding Based on Human Concept Learning SO IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS LA English DT Article DE Cognitive informatics; text understanding; web search ID COGNITIVE INFORMATICS; READABILITY FORMULA; WEB SEARCH; MODEL; CYBERNETICS AB This paper advocates for a novel approach to recommend texts at various levels of difficulties based on a proposed method, the algebraic complexity of texts (ACT). Different from traditional complexity measures that mainly focus on surface features like the numbers of syllables per word, characters per word, or words per sentence, ACT draws from the perspective of human concept learning, which can reflect the complex semantic relations inside texts. To cope with the high cost of measuring ACT, the Degree-2 Hypothesis of ACT is proposed to reduce the measurement from unrestricted dimensions to three dimensions. Based on the principle of "mental anchor," an extension of ACT and its general edition [denoted as extension of text algebraic complexity (EACT) and general extension of text algebraic complexity (GEACT)] are developed, which take keywords' and association rules' complexities into account. Finally, using the scores given by humans as a benchmark, we compare our proposed methods with linguistic models. The experimental results show the order GEACT>EACT>ACT> Linguistic models, which means GEACT performs the best, while linguistic models perform the worst. Additionally, GEACT with lower convex functions has the best ability in measuring the algebraic complexities of text understanding. It may also indicate that the human complexity curve tends to be a curve like lower convex function rather than linear functions. C1 [Luo, Xiangfeng; Zhang, Jun; Wei, Xiao; Lu, Lei] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. [Zhang, Jun] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China. [Li, Qing] City Univ Hong Kong, Multimedia Software Engn Res Ctr, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. RP Luo, XF (reprint author), Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. EM luoxf@shu.edu.cn; zhangjun_shu@shu.edu.cn; itqli@cityu.edu.hk; xwei@shu.edu.cn; luleijs@shu.edu.cn CR Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487 Alrfai M., 2012, ACM T WEB, V6 [Anonymous], 2012, READABILITY CALCULAT COLEMAN M, 1975, J APPL PSYCHOL, V60, P283, DOI 10.1037/h0076540 Douglas L., 1987, COGNITIVE SCI, V11, P299 Eleanor R., 1975, COGNITIVE PSYCHOL, V7, P573 Fang N, 2008, 2008 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING, VOLS 1 AND 2, PROCEEDINGS, P1110 Fang N, 2008, PROCEEDINGS OF THE SEVENTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, P195 Fang N., 2010, J SOFTW SCI COMPUT I, V1, P61 Feldman J, 2003, J MATH PSYCHOL, V47, P75, DOI 10.1016/S0022-2496(02)00025-1 Feldman J, 2006, J MATH PSYCHOL, V50, P339, DOI 10.1016/j.jmp.2006.03.002 Feldman J, 2000, NATURE, V407, P630, DOI 10.1038/35036586 Flesch R, 1948, J APPL PSYCHOL, V32, P221, DOI 10.1037/h0057532 Gaeta M, 2011, IEEE T SYST MAN CY A, V41, P798, DOI 10.1109/TSMCA.2011.2132713 Galotti K., 2004, COGNITIVE PSYCHOL OU Gao YJ, 2011, ACM T DATABASE SYST, V36, DOI 10.1145/1966385.1966387 Givone D., 1970, INTRO SWITCHING CIRC Graesser AC, 2004, BEHAV RES METH INS C, V36, P193, DOI 10.3758/BF03195564 Gu XJ, 2011, ADV INTEL SOFT COMPU, V104, P99 Gunning R., 1952, TECHNIQUE CLEAR WRIT Kincaid J., 1975, 875 NAT TECHN INF SE Knauf R, 2007, IEEE T SYST MAN CY A, V37, P120, DOI 10.1109/TSMCA.2006.886365 Lamberti F, 2009, IEEE T KNOWL DATA EN, V21, P123, DOI 10.1109/TKDE.2008.113 Li Q, 2009, IEEE INTERNET COMPUT, V13, P11 Luo XF, 2014, IEEE T SYST MAN CY-S, V44, P86, DOI 10.1109/TSMCC.2012.2231674 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 MCLAUGHLIN GH, 1969, J READING, V12, P639 Papagelis A, 2012, IEEE T SYST MAN CY A, V42, P1271, DOI 10.1109/TSMCA.2012.2187887 Pitler Emily, 2008, P C EMP METH NAT LAN, P186, DOI 10.3115/1613715.1613742 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer M, 2008, ACM T WEB, V2, DOI 10.1145/1346237.1346242 Scott A., 2008, TESOL Q, V42, P475 Shao JQ, 2003, CAN J ELECT COMPUT E, V28, P69 Si L., 2001, P 10 INT C INF KNOWL, P574 Susan R., 1986, J MEM LANG, V25, P401 To D, 2001, PRESENCE-TELEOP VIRT, V10, P62, DOI 10.1162/105474601750182324 Wang J., 2010, P 48 ANN M ASS COMP, P257 Wang J., 2010, P 24 C ART INT ATL G, P1449 Wang YX, 2009, IEEE T SYST MAN CY B, V39, P818, DOI 10.1109/TSMCB.2009.2017294 Wang YX, 2009, IEEE T SYST MAN CY B, V39, P823, DOI 10.1109/TSMCB.2009.2013721 Wegener I., 1987, COMPLEXITY BOOLEAN F Zhang D., 2011, ADV SPATIAL TEMPORAL, V6849, P348 Zhang L., 2011, P IEEE INT C COGN IN, P18 NR 43 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2168-2291 EI 2168-2305 J9 IEEE T HUM-MACH SYST JI IEEE T. Hum.-Mach. Syst. PD OCT PY 2014 VL 44 IS 5 BP 638 EP 649 DI 10.1109/THMS.2014.2329874 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA AP7AJ UT WOS:000342229500007 ER PT J AU Saari, P Eerola, T AF Saari, Pasi Eerola, Tuomas TI Semantic Computing of Moods Based on Tags in Social Media of Music SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Semantic analysis; social tags; music; music information retrieval; moods; genres; prediction ID INFORMATION-RETRIEVAL; EMOTIONS; MODELS; EXPRESSION; SYSTEMS; TEXT AB Social tags inherent in online music services such as Last. fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling. We validate the technique by predicting listener ratings of moods in music tracks, and compare the results to prediction with the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). The results show that ACT consistently outperforms the baseline techniques, and its performance is robust against a low number of track-level mood tags. The results give validity and analytical insights for harnessing millions of music tracks and associated mood data available through social tags in application development. C1 [Saari, Pasi; Eerola, Tuomas] Univ Jyvaskyla, Dept Mus, Jyvaskyla 40014, Finland. RP Saari, P (reprint author), Univ Jyvaskyla, Dept Mus, Jyvaskyla 40014, Finland. EM pasi.saari@jyu.fi; tuomas.eerola@jyu.fi RI Eerola, Tuomas/I-6190-2013 OI Eerola, Tuomas/0000-0002-2896-929X FU Academy of Finland (Finnish Centre of Excellence in Interdisciplinary Music Research) FX The authors wish to thank the reviewers for their invaluable feedback and suggestions. This work was supported by the Academy of Finland (Finnish Centre of Excellence in Interdisciplinary Music Research). CR Abbasi A, 2008, IEEE T KNOWL DATA EN, V20, P1168, DOI 10.1109/TKDE.2008.51 Bao SH, 2012, IEEE T KNOWL DATA EN, V24, P1658, DOI 10.1109/TKDE.2011.188 Becavin C, 2011, BIOINFORMATICS, V27, P1413, DOI 10.1093/bioinformatics/btr143 Bischoff K., 2008, P 17 ACM C INF KNOWL, P193, DOI 10.1145/1458082.1458112 Bradley M. M., 1999, C1 U FLOR CTR RES PS Calvo RA, 2013, COMPUT INTELL-US, V29, P527, DOI 10.1111/j.1467-8640.2012.00456.x DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eerola T., 2012, MUSIC PERCEPT, V30, P307, DOI DOI 10.1525/MP.2012.30.3.307 Eerola T, 2011, PSYCHOL MUSIC, V39, P18, DOI 10.1177/0305735610362821 Eerola T, 2011, J NEW MUSIC RES, V40, P349, DOI 10.1080/09298215.2011.602195 Garcia-Silva A, 2012, KNOWL ENG REV, V27, P57, DOI 10.1017/S026988891100018X Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 GOWER J. C., 2004, PROCRUSTES PROBLEMS, V3 Hevner K, 1936, AM J PSYCHOL, V48, P246, DOI 10.2307/1415746 Heymann P., 2008, P 31 ANN INT ACM SIG, P531, DOI DOI 10.1145/1390334.1390425 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hopkins B., 1954, ANN BOT, V18, P231 Hu X., 2008, P INT C MUS INF RETR, P462 Hu X., 2007, P 8 ISMIR VIENN AUST Juslin P. N., 2009, HDB MUSIC EMOTION TH Juslin PN, 2004, J NEW MUSIC RES, V33, P217, DOI 10.1080/0929821042000317813 Juslin PN, 2011, MUSIC SCI, V15, P174, DOI 10.1177/1029864911401169 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 Lamere P, 2008, J NEW MUSIC RES, V37, P101, DOI 10.1080/09298210802479284 Laurier C., 2009, P INT C MUS INF RETR, P381 Law E, 2010, LECT NOTES ARTIF INT, V6322, P211, DOI 10.1007/978-3-642-15883-4_14 LAWSON RG, 1990, J CHEM INF COMP SCI, V30, P36, DOI 10.1021/ci00065a010 Levy M., 2007, P 8 ISMIR VIENN AUST Levy M, 2008, J NEW MUSIC RES, V37, P137, DOI 10.1080/09298210802479292 Mandel MI, 2011, ACM T MULTIM COMPUT, V7, DOI 10.1145/2037676.2037689 Miller G., 1998, WORDNET ELECT LEXICA Nguyen T, 2014, KNOWL INF SYST, V39, P667, DOI 10.1007/s10115-013-0628-8 Nunnally J. C., 1978, PSYCHOMETRIC THEORY EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068 Peng J., 2008, ACM T MANAG INFORM S, V2 Peter Rakesh, 2009, International Journal of Recent Trends in Engineering, V1 RUSSELL JA, 1980, J PERS SOC PSYCHOL, V39, P1161, DOI 10.1037/h0077714 Saari P, 2011, IEEE T AUDIO SPEECH, V19, P1802, DOI 10.1109/TASL.2010.2101596 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scherer K. R., 1984, REV PERSONALITY SOCI, V5, P37 Schmidt S, 2009, J AM SOC INF SCI TEC, V60, P863, DOI 10.1002/asi.21043 Seung D., 2001, ADV NEURAL INFORM PR, V13, P556 SHAVER P, 1987, J PERS SOC PSYCHOL, V52, P1061, DOI 10.1037/0022-3514.52.6.1061 Song Y, 2011, ACM T WEB, V5, DOI 10.1145/1921591.1921595 Thayer R. E., 1989, BIOPSYCHOLOGY MOOD A Wetzker R., 2009, P WSDM 09 WORKSH EXP, P25, DOI 10.1145/1506250.1506255 Yang YH, 2008, IEEE T AUDIO SPEECH, V16, P448, DOI 10.1109/TASL.2007.911513 Zentner M, 2008, EMOTION, V8, P494, DOI 10.1037/1528-3542.8.4.494 Zentner M. R., 2010, HDB MUSIC EMOTION TH, P187 Zhou N, 2011, IEEE T PATTERN ANAL, V33, P1281, DOI 10.1109/TPAMI.2010.204 NR 50 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 EI 1558-2191 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD OCT PY 2014 VL 26 IS 10 BP 2548 EP 2560 DI 10.1109/TKDE.2013.128 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AP4VH UT WOS:000342076600016 ER PT J AU Boella, G Di Caro, L Ruggeri, A Robaldo, L AF Boella, Guido Di Caro, Luigi Ruggeri, Alice Robaldo, Livio TI Learning from syntax generalizations for automatic semantic annotation SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Ontology learning; Automatic annotation; Information extraction ID CONSTRUCTION AB Nowadays, there is a huge amount of textual data coming from on-line social communities like Twitter or encyclopedic data provided by Wikipedia and similar platforms. This Big Data Era created novel challenges to be faced in order to make sense of large data storages as well as to efficiently find specific information within them. In a more domain-specific scenario like the management of legal documents, the extraction of semantic knowledge can support domain engineers to find relevant information in more rapid ways, and to provide assistance within the process of constructing application-based legal ontologies. In this work, we face the problem of automatically extracting structured knowledge to improve semantic search and ontology creation on textual databases. To achieve this goal, we propose an approach that first relies on well-known Natural Language Processing techniques like Part-Of-Speech tagging and Syntactic Parsing. Then, we transform these information into generalized features that aim at capturing the surrounding linguistic variability of the target semantic units. These new featured data are finally fed into a Support Vector Machine classifier that computes a model to automate the semantic annotation. We first tested our technique on the problem of automatically extracting semantic entities and involved objects within legal texts. Then, we focus on the identification of hypernym relations and definitional sentences, demonstrating the validity of the approach on different tasks and domains. C1 [Boella, Guido; Di Caro, Luigi; Ruggeri, Alice; Robaldo, Livio] Univ Turin, Turin, Italy. RP Di Caro, L (reprint author), Univ Turin, Turin, Italy. EM dicaro@di.unito.it FU project ITxLaw; Compagnia di San Paolo FX The work has been funded by the project ITxLaw with Compagnia di San Paolo. CR Berland M., 1999, ANN M ASS COMP LING, V37, P57, DOI DOI 10.3115/1034678.1034697 Biagioli C., 2005, P 10 INT C ART INT L, P133, DOI 10.1145/1165485.1165506 Biemann C., 2005, LDV FORUM, V20, P75 Boella G., 2012, P INF SYST CROSSR OR Boella Guido, 2013, Theory, Practice, and Applications of Rules on the Web. 7th International Symposium, RuleML 2013. Proceedings. LNCS 8035, DOI 10.1007/978-3-642-39617-5_20 Boella Guido, 2013, Machine Learning and Knowledge Discovery in Databases. European Conference (ECML PKDD 2013). Proceedings: LNCS 8189, DOI 10.1007/978-3-642-40991-2_5 Boella G., 2012, P 8 INT C LANG RES E Borg C., 2009, P 1 WORKSH DEF EXTR, P26 Candan K.S., 2008, P 2008 ACM WORKSH SE, P75, DOI DOI 10.1145/1458583.1458597 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cui H, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229182 Del Gaudio R, 2007, LECT NOTES ARTIF INT, V4874, P659 de Maat E, 2010, FRONT ARTIF INTEL AP, V223, P87, DOI 10.3233/978-1-60750-682-9-87 Fahmi I., 2006, P EACL 2006 WORKSH L, P64 Ferraresi A., 2008, P 4 WEB CORP WORKSH, P47 Fortuna B, 2006, LECT NOTES COMPUT SC, V4289, P121 Gibson J. J., 1977, PERCEIVING ACTING KN, P67 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Harris ZS, 1954, WORD, V10, P146 Hearst M. A., 1992, P 14 C COMP LING, V2, P539, DOI 10.3115/992133.992154 Hoffart J., 2012, ARTIFICIAL INTELLIGE Hovy E., 2003, P 2003 ANN NAT C DIG, P1 Klavans JL, 2001, J AM MED INFORM ASSN, P324 Lesmo L., 2009, P EVALITA, P9 Lesmo L., 2013, ARTIF INTELL, P1 Magnini B, 2005, ONTOLOGY LEARNING TE, V123, P3 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moschitti A., 2004, CONLL 2004 Navigli R., 2010, P 48 ANN M ASS COMP, P1318 Navigli R, 2010, P 7 INT C LANG RES E Navigli Roberto, 2010, P 48 ANN M ASS COMP, P216 Norman D. A., 1999, Interactions, V6, DOI 10.1145/301153.301168 Ponzetto S., 2007, P NAT C ART INT, V22, P1440 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Storrer A., 2006, P LREC, V2006 Velardi P., 2012, ONTOLEARN RELOADED Westerhout E., 2009, P 1 WORKSH DEF EXTR, P61 Yamada I., 2009, P 2009 C EXP METH NA, V2, P929, DOI 10.3115/1699571.1699634 Yang H., 2008, P 2008 INT C DIG GOV, P254 Zhang CX, 2009, 2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 2, P364, DOI 10.1109/ICCSIT.2009.5234687 NR 40 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 EI 1573-7675 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD OCT PY 2014 VL 43 IS 2 BP 231 EP 246 DI 10.1007/s10844-014-0320-9 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA AP5WY UT WOS:000342150600002 ER PT J AU Murphy, J Fu, K Otto, K Yang, M Jensen, D Wood, K AF Murphy, Jeremy Fu, Katherine Otto, Kevin Yang, Maria Jensen, Dan Wood, Kristin TI Function Based Design-by-Analogy: A Functional Vector Approach to Analogical Search SO JOURNAL OF MECHANICAL DESIGN LA English DT Article ID INFORMATION-RETRIEVAL; GENERATION; SIMILARITY; ALGORITHM; MODEL AB Design-by-analogy is a powerful approach to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. While the concept of design-by-analogy has been known for some time, few actual methods and tools exist to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. A new method for extracting functional analogies from data sources has been developed to provide this capability, here based on a functional basis rather than form or conflict descriptions. Building on past research, we utilize a functional vector space model (VSM) to quantify analogous similarity of an idea's functionality. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We also develop document parsing algorithms to reduce text descriptions of the data sources down to the key functions, for use in the functional similarity analysis and functional vector space modeling. To do this, we apply Zipf's law on word count order reduction to reduce the words within the documents down to the applicable functionally critical terms, thus providing a mapping process for function based search. The reduction of a document into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. As a verification of the approach, two original design problem case studies illustrate the distance range of analogical solutions that can be extracted. This range extends from very near-field, literal solutions to far-field cross-domain analogies. C1 [Murphy, Jeremy] Schlumberger Ltd, Sugar Land, TX 77478 USA. [Fu, Katherine] Singapore Univ Technol & Design, MIT, Cambridge, MA 02139 USA. [Otto, Kevin; Wood, Kristin] Singapore Univ Technol & Design, Singapore 138682, Singapore. [Yang, Maria] MIT, Cambridge, MA 02139 USA. [Jensen, Dan] US Air Force Acad, Colorado Springs, CO 80840 USA. RP Fu, K (reprint author), Singapore Univ Technol & Design, MIT, Cambridge, MA 02139 USA. FU National Science Foundation [CMMI-0855326, CMMI-0855510, CMMI-0855293]; SUTD-MIT international Design Centre (IDC) FX The authors would like to thank Dr. Matthew Campbell, Dr. Richard Crawford, and Dr. Joseph Beaman for their discussions and insights on this work. This work was supported by the National Science Foundation, Grant Nos. CMMI-0855326, CMMI-0855510, and CMMI-0855293, and the SUTD-MIT international Design Centre (IDC) (http://http://idc.sutd.edu.sg/). CR Ahmed S., 2005, DETC200699705 ASME Altshuller G. S., 1956, PSYCHOL ISSUES, V6, P37 BHATTA S, 1994, ARTIFICIAL INTELLIGENCE IN DESIGN '94, P57 Bhatta SR, 1996, AI EDAM, V10, P131 Bohm M., 2004, DETC200457693 ASME Bohm M., 2004, IMECE200461746 ASME Bohm M. R., 2005, DETC200585105 ASME Bryant C., 2005, 15 INT C ENG DES ENG, P1702 Bryant C., 2005, DETC200585323 ASME Cascini G., 2007, International Journal of Product Development, V4, DOI 10.1504/IJPD.2007.011533 Chakrabarti A, 2009, PRODUCT RESEARCH: THE ART AND SCIENCE BEHIND SUCCESSFUL PRODUCT LAUNCHES, P17, DOI 10.1007/978-90-481-2860-0_2 Chakrabarti A, 2005, AI EDAM, V19, P113, DOI 10.1017/S0890060405050109 Chakrabarti A., 2001, DESIGN STUD, V22, P493 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Chan J, 2011, J MECH DESIGN, V133, DOI 10.1115/1.4004396 Charlton C. T., 2000, Design Studies, V21, DOI 10.1016/S0142-694X(99)00040-X Cheong H, 2011, J MECH DESIGN, V133, DOI 10.1115/1.4003249 Chiu I, 2007, AI EDAM, V21, P103, DOI 10.1017/S0890060407070175 Chiu I., 2005, DETC200584908 ASME Chiu M, 2003, DESIGN STUDIES, V24, P1, DOI 10.1016/S0142-694X(02)00007-8 CREAX, 2012, CREAX CREAT INN Dumais S. T., 1995, P TREC Duran-Novoa R, 2011, COMPUT IND, V62, P437, DOI 10.1016/j.compind.2010.12.006 FALKENHAINER B, 1989, ARTIF INTELL, V41, P1, DOI 10.1016/0004-3702(89)90077-5 Fellbaum C, 1998, WORDNET ELECT LEXICA Fu K, 2013, J MECH DESIGN, V135, DOI 10.1115/1.4023484 Fu K., 2013, KOR JAP DES ENG WORK Fu K., THESIS CARNEGIE MELL Fu K, 2013, J MECH DESIGN, V135, DOI 10.1115/1.4023158 Gentner D, 1997, AM PSYCHOL, V52, P45, DOI 10.1037/0003-066X.52.1.45 GENTNER D, 1983, COGNITIVE SCI, V7, P155, DOI 10.1207/s15516709cog0702_3 Gentner D., 1981, COGNITION BRAIN THEO, V4, P161 Goel A, 1997, ISSUES APPL CASE BAS, P87 Goel A., 1989, P 11 INT JOINT C ART, P1388 Goel AK, 2004, ADV ENG INFORM, V18, P85, DOI 10.1016/j.aei.2004.09.003 Goldfire IM, 2012, INVENTION MACHINE GO Goldschmidt G, 1998, DES ISSUES, V14, P85, DOI 10.2307/1511899 Hacco E., 2002, DETC2002DFM34177 ASM Hernandez N. V., 2012, DETC201270294 ASME, DOI [10.1115/DETC2012-70294, DOI 10.1115/DETC2012-70294] Hernandez N. V., 2012, ASEE ANN C SAN ANT T Hirtz J, 2002, RES ENG DES, V13, P65, DOI 10.1007/S00163-001-0008k-3 Houssin R, 2011, COMPUT IND, V62, P398, DOI 10.1016/j.compind.2010.12.009 Kang IS, 2007, INFORM PROCESS MANAG, V43, P1173, DOI 10.1016/j.ipm.2006.11.006 Koch S., 2009, IEEE S VIS AN SCI TE, P203 Krasnoslobodtsev V, 2005, 1 TRIZ S JAP Kryssanov VV, 2001, ARTIF INTELL ENG, V15, P329, DOI 10.1016/S0954-1810(01)00023-1 Kurfman M., 2001, DETC200121709 ASME Kurfman MA, 2003, J MECH DESIGN, V125, P682, DOI 10.1115/1.1625400 Liang YH, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON MANAGEMENT OF INNOVATION AND TECHNOLOGY, VOLS 1-3, P1147 Linsey J., 2006, DETC200699383 ASME Linsey J., 2008, ASEE ANN C Linsey J., 2008, DETC200849317 ASME Linsey J., 2010, J MECH DESIGN, V132 Linsey JS, 2011, J MECH DESIGN, V133, DOI 10.1115/1.4003498 Linsey JS, 2012, J MECH DESIGN, V134, DOI 10.1115/1.4006145 Liu Y.-C., 2003, Design Studies, V24, DOI 10.1016/S0142-694X(03)00003-6 Liu YC, 2000, ARTIFICIAL INTELLIGENCE IN DESIGN '00, P499 Mann D, 2003, MATRIX 2003 UPDATING Manning C. D., 2009, INTRO INFORM RETRIEV Markman A. B., 2009, TOOLS INNOVATION SCI McAdams DA, 2002, J MECH DESIGN, V124, P173, DOI 10.1115/1.1475317 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moldovan A., 2005, International Journal of Applied Mathematics and Computer Science, V15 Mukherjea S, 2005, IEEE T KNOWL DATA EN, V17, P1099, DOI 10.1109/TKDE.2005.130 Murphy JT, 2011, THESIS U TEXAS AUSTI Nagel J. K. S., 2010, DETC201028233 ASME Nakagawa T, 2012, B CULTURAL NATURAL S, V64 Navinchandra D., 1991, 7 IEEE C ART INT APP, V1, P217 Nix A. A., 2011, DETC201147973 ASME Osborn A. F., 1957, APPL IMAGINATION Otto Kevin, 2001, PRODUCT DESIGN TECHN Pahl G., 1996, ENG DESIGN SYSTEMATI PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Potter S, 2003, RES ENG DES, V14, P131, DOI 10.1007/S00163-003-0034-4 QIAN L, 1992, ARTIFICIAL INTELLIGENCE IN DESIGN 92, P795 Rindflesch T. C., 1996, ANNU REV APPL LINGUI, V16, P70 Salonen Mikko, 2008, International Journal of Product Development, V5, DOI 10.1504/IJPD.2008.016373 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 Salton G., 1986, INTRO MODERN INFORM Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMID H, 1994, INT C NEW METH LANG, V12, P44 Segers N., 2003, 10 INT C COMP AID AR Segers NM, 2005, DESIGN STUD, V26, P625, DOI 10.1016/j.destud.2005.05.002 Shu LH, 2011, CIRP ANN-MANUF TECHN, V60, P673, DOI 10.1016/j.cirp.2011.06.001 Souili A., 2012, CIRP DESIGN, P28 Souili A., 2011, TRIZ FUTURE 2011 Stone R. B., 2000, Design Studies, V21, DOI 10.1016/S0142-694X(99)00003-4 Stone RB, 2000, J MECH DESIGN, V122, P359, DOI 10.1115/1.1289637 Szykman S., 1999, ADV SOFT COMPUTING E Szykman S, 2000, IEEE INTELL SYST APP, V15, P48, DOI 10.1109/5254.846285 Terpenny J., 2004, DETC200457492 ASME Trippe A. J., 2003, World Patent Information, V25, DOI 10.1016/S0172-2190(03)00079-6 Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 Van Gundy A.B, 1988, TECHNIQUES STRUCTURE van Rijsbergen C. J., 1979, INFORM RETRIEVAL Verhaegen PA, 2011, COMPUT IND, V62, P446, DOI 10.1016/j.compind.2010.12.007 Vincent JFV, 2006, J R SOC INTERFACE, V3, P471, DOI 10.1098/rsif.2006.0127 Wood W. H., 1998, ASME DES ENG TECHN C WYLLYS RE, 1981, LIBR TRENDS, V30, P53 Yang M. C., 1997, ICED 1997 INT C ENG, P191 Yang MC, 2005, ENG COMPUT-GERMANY, V21, P177, DOI 10.1007/s00366-005-0003-9 Zhang R., 2007, P 2007 IEEE IEEM, P1807 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 104 TC 1 Z9 1 PU ASME PI NEW YORK PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA SN 1050-0472 J9 J MECH DESIGN JI J. Mech. Des. PD OCT PY 2014 VL 136 IS 10 AR 101102 DI 10.1115/1.4028093 PG 16 WC Engineering, Mechanical SC Engineering GA AO4HV UT WOS:000341298800002 ER PT J AU Cholakov, K AF Cholakov, Kostadin TI Lexical acquisition and semantic space models: Learning the semantics of unknown words SO NATURAL LANGUAGE ENGINEERING LA English DT Article AB In recent studies it has been shown that syntax-based semantic space models outperform models in which the context is represented as a bag-of-words in several semantic analysis tasks. This has been generally attributed to the fact that syntax-based models employ corpora that are syntactically annotated by a parser and a computational grammar. However, if the corpora processed contain words which are unknown to the parser and the grammar, a syntax-based model may lose its advantage since the syntactic properties of such words are unavailable. On the other hand, bag-of-words models do not face this issue since they operate on raw, non-annotated corpora and are thus more robust. In this paper, we compare the performance of syntax-based and bag-of-words models when applied to the task of learning the semantics of unknown words. In our experiments, unknown words are considered the words which are not known to the Alpino parser and grammar of Dutch. In our study, the semantics of an unknown word is defined by finding its most similar word in CORNETTO, a Dutch lexico-semantic hierarchy. We show that for unknown words the syntax-based model performs worse than the bag-of-words approach. Furthermore, we show that if we first learn the syntactic properties of unknown words by an appropriate lexical acquisition method, then in fact the syntax-based model does outperform the bag-of-words approach. The conclusion we draw is that, for words unknown to a given grammar, a bag-of-words model is more robust than a syntax-based model. However, the combination of lexical acquisition and syntax-based semantic models is best suited for learning the semantics of unknown words. C1 Univ Groningen, NL-9712 EK Groningen, Netherlands. RP Cholakov, K (reprint author), Univ Groningen, Oude Kijk Jatstr 26, NL-9712 EK Groningen, Netherlands. EM k.cholakov@rug.nl CR Almuhareb A., 2004, P EMNLP, P158 Baldwin T., 2005, P 6 M PAC ASS COMP L, P23 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Cholakov K., 2009, PROCEEDINGS OF THE 7, P60 Cholakov K., 2010, PROCEEDINGS OF THE 2, P153 Crysmann B., 2003, PROCEEDINGS OF RANLP Curran James R., 2002, P WORKSH UNS LEX ACQ, P59, DOI DOI 10.3115/1118627.1118635 Erbach G., 1990, IWBS TECHNICAL REPOR Erk Katrin, 2007, P 45 ANN M ASS COMP, P216 Fellbaum C, 1998, WORDNET AN ELECTRONI Flickinger D., 2000, PROCEEDINGS OF THE 2 Fouvry F., 2003, COMP 10 EACL BUD HUN, P87 Golub G., 1996, MATRIX COMPUTATIONS, V3 Grefenstette G., 1994, EXPLORATIONS IN AUTO Church K. W., 1990, Computational Linguistics, V16 Horak A, 2007, GWC 2008: FOURTH GLOBAL WORDNET CONFERENCE, PROCEEDINGS, P200 Lin D., 1998, P 17 INT C COMP LING, P768 Lin D., 1998, P 15 INT C MACH LEAR, V1, P296 Lowe W., 2001, P 23 ANN C COGN SCI, P576 Malouf R., 2002, P 6 C NAT LANG LEARN, P49 McCarthy D., 2004, P 42 M ASS COMP LING, P279, DOI 10.3115/1218955.1218991 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Ordelman R., 2002, TECHNICAL REPORT Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Rapp R., 2004, P 4 LANG RES EV C, P395 Rothenhausler K., 2009, P EACL GEMS WORKSH A, P17, DOI 10.3115/1705415.1705418 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Van de Cruys T., 2008, P ESSLLI WORKSH DIST, P47 van der Plas L., 2006, P COLING ACL 2006 MA, P866, DOI 10.3115/1273073.1273184 van Noord G., 2011, PROCEEDINGS OF THE 8, P355 van Noord G., 2006, P 31 ANN M EUR THYR, P20 Vossen P., 1998, EUROWORDNET A MULTIL Walther M., 1998, PROCEEDINGS OF THE 3, P91 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zhang Y, 2006, P LREC, P275 Zhang Y., 2008, PROCEEDINGS OF COLIN, P57, DOI 10.3115/1611546.1611554 NR 38 TC 0 Z9 0 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 EI 1469-8110 J9 NAT LANG ENG JI Nat. Lang. Eng. PD OCT PY 2014 VL 20 IS 4 BP 537 EP 555 DI 10.1017/S1351324913000053 PG 19 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA AN7II UT WOS:000340772500004 ER PT J AU Nissim, N Moskovitch, R Rokach, L Elovici, Y AF Nissim, Nir Moskovitch, Robert Rokach, Lior Elovici, Yuval TI Novel active learning methods for enhanced PC malware detection in windows OS SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Malware; Malicious code; Machine Learning; Active learning; SVM ID SUPPORT VECTOR MACHINES; MALICIOUS EXECUTABLES; CLASSIFICATION; WORMS; CODE AB The formation of new malwares every day poses a significant challenge to anti-virus vendors since antivirus tools, using manually crafted signatures, are only capable of identifying known malware instances and their relatively similar variants. To identify new and unknown malwares for updating their anti-virus signature repository, anti-virus vendors must daily collect new, suspicious files that need to be analyzed manually by information security experts who then label them as malware or benign. Analyzing suspected files is a time-consuming task and it is impossible to manually analyze all of them. Consequently, anti-virus vendors use machine learning algorithms and heuristics in order to reduce the number of suspect files that must be inspected manually. These techniques, however, lack an essential element - they cannot be daily updated. In this work we introduce a solution for this updatability gap. We present an active learning (AL) framework and introduce two new AL methods that will assist anti-virus vendors to focus their analytical efforts by acquiring those files that are most probably malicious. Those new AL methods are designed and oriented towards new malware acquisition. To test the capability of our methods for acquiring new malwares from a stream of unknown files, we conducted a series of experiments over a ten-day period. A comparison of our methods to existing high performance AL methods and to random selection, which is the naive method, indicates that the AL methods outperformed random selection for all performance measures. Our AL methods outperformed existing AL method in two respects, both related to the number of new malwares acquired daily, the core measure in this study. First, our best performing AL method, termed "Exploitation", acquired on the 9th day of the experiment about 2.6 times more malwares than the existing AL method and 7.8 more times than the random selection. Secondly, while the existing AL method showed a decrease in the number of new malwares acquired over 10 days, our AL methods showed an increase and a daily improvement in the number of new malwares acquired. Both results point towards increased efficiency that can possibly assist anti-virus vendors. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Nissim, Nir; Moskovitch, Robert; Rokach, Lior; Elovici, Yuval] Ben Gurion Univ Negev, Dept Informat Syst Engn, Telekom Innovat Labs, IL-84105 Beer Sheva, Israel. RP Nissim, N (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, Telekom Innovat Labs, POB 653, IL-84105 Beer Sheva, Israel. EM nirni@bgu.ac.il; robertmo@bgu.ac.il; liorrk@bgu.ac.il; elovici@bgu.ac.il RI Rokach, Lior/F-8247-2010 FU National Cyber Bureau of the Israeli Ministry of Science, Technology and space FX This research was partly supported by the National Cyber Bureau of the Israeli Ministry of Science, Technology and space. We would like to thank Clint Feher, who assisted in the data-set creation and Yuval Fledel for meaningful discussions and comments in the efficient implementation aspects. CR Abou-Assaleh T, 2004, P INT COMP SOFTW APP, P41 Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Baram Y, 2004, J MACH LEARN RES, V5, P255 Bayer U., 2009, NDSS, V9, P8 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 CERT, 1999, TROJ HORS VERS TCP W Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Chen ZQ, 2012, J SYST SOFTWARE, V85, P1650, DOI 10.1016/j.jss.2012.02.015 Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 HENCHURI O, 2006, 6 INT C DAT MIN, P891, DOI 10.1109/ICDM.2006.4 Herbich R, 2001, J MACH LEARN RES, V1, P245, DOI 10.1162/153244301753683717 Bi J., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753643 Jacob G, 2009, LECT NOTES COMPUT SC, V5758, P81, DOI 10.1007/978-3-642-04342-0_5 Jang J., 2011, P 18 ACM C COMP COMM, P309 Jnanamurthy HK, 2013, THREAT ANAL MALICIOU Joachims T., 1999, MAKING LARGE SCALE S Kiem H., 2004, JOINT WORKSH VIET SO, P61 Kolbitsch Clemens, 2009, USENIX SEC S, P351 KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 Lanzi A., 2009, NDSS Lewis D. D., 1994, INFORM RETRIEVAL, V17, P3 Masud MM, 2007, LECT NOTES COMPUT SC, V4426, P205 Menahem E, 2009, COMPUT STAT DATA AN, V53, P1483, DOI 10.1016/j.csda.2008.10.015 Mitchell TM, 1997, MACH LEARN, P45 Mokube Iyatiti, 2007, Proceedings of the 45th ACM Southeast Conference. ACMSE 07, DOI 10.1145/1233341.1233399 Moore D., 2003, IEEE SECURITY PRIVAC Moore D., 2002, Proceedings of the Second Internet Measurement Workshop. IMW 2002 Moser A, 2007, TWENTY-THIRD ANNUAL COMPUTER SECURITY APPLICATIONS CONFERENCE, PROCEEDINGS, P421, DOI 10.1109/ACSAC.2007.21 Moser A, 2007, P IEEE S SECUR PRIV, P231, DOI 10.1109/SP.2007.17 Moskovitch R, 2008, LECT NOTES COMPUT SC, V5376, P204, DOI 10.1007/978-3-540-89900-6_21 Moskovitch R, 2008, ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, P156 Moskovitch R., 2008, 11 INT C INF FUS COL Moskovitch R, 2008, COMPUT STAT DATA AN, V52, P4544, DOI 10.1016/j.csda.2008.01.028 Moskovitch R., 2008, J COMPUTER VIROLOGY, V5, P295, DOI [10.1007/s11416-009-0122-8, DOI 10.1007/S11416-009-0122-8] Moskovitch R, 2007, CISDA 2007, P169 Moskovitch R, 2009, LECT NOTES COMPUT SC, V5456, P74, DOI 10.1007/978-3-642-01718-6_6 Nachenberg C, 1997, COMMUN ACM, V40, P46, DOI 10.1145/242857.242869 Nataraj L., 2011, P 4 ACM WORKSH SEC A, P21 Nataraj L., 2011, P 8 INT S VIS CYB SE, P4 Newsome J, 2005, P IEEE S SECUR PRIV, P226, DOI 10.1109/SP.2005.15 Newsome J., 2005, DYNAMIC TAINT ANAL A Nissim N, 2012, PATTERN ANAL APPL, V15, P459, DOI 10.1007/s10044-012-0296-4 Perdisci R, 2008, ANN COMPUT SECURITY, P301, DOI 10.1109/ACSAC.2008.22 Provos N., 2008, VIRTUAL HONEYPOTS BO, P231 Rieck Konrad, 2011, Journal of Computer Security, V19, DOI 10.3233/JCS-2010-0410 Rieck K, 2008, LECT NOTES COMPUT SC, V5137, P108, DOI 10.1007/978-3-540-70542-0_6 Rossow C, 2012, P IEEE S SECUR PRIV, P65, DOI 10.1109/SP.2012.14 Royal P, 2006, ANN COMPUT SECURITY, P289, DOI 10.1109/ACSAC.2006.38 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz MG, 2001, P IEEE S SECUR PRIV, P38, DOI 10.1109/SECPRI.2001.924286 Shabtai Asaf, 2009, Information Security Technical Report, V14, DOI 10.1016/j.istr.2009.03.003 Sharif M, 2009, P IEEE S SECUR PRIV, P94, DOI 10.1109/SP.2009.27 Song D, 2008, LECT NOTES COMPUT SC, V5352, P1, DOI 10.1007/978-3-540-89862-7_1 Stopel D, 2006, P 3 INT C NEUR NETW Tahan G, 2012, J MACH LEARN RES, V13, P949 TONG S, 2001, J MACHINE LEARNING R, V2, P45, DOI DOI 10.1162/153244302760185243 Vapnik V., 1998, STAT LEARNING THEORY Vapnik V. N., 1982, ESTIMATION DEPENDENC, V41 Wang X, 2007, 2007 THIRD INTERNATIONAL CONFERENCE ON SECURITY AND PRIVACY IN COMMUNICATION NETWORKS AND WORKSHOPS, P412 Willems C, 2007, IEEE SECUR PRIV, V5, P32, DOI 10.1109/MSP.2007.45 Yu Z. H. U., 2008, MECH ELECT ENG MAGAZ, V8, P2 Zhao W., 2012, MODELING DECISIONS A, P222 NR 63 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT 1 PY 2014 VL 41 IS 13 BP 5843 EP 5857 DI 10.1016/j.eswa.2014.02.053 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AI4YJ UT WOS:000336872300016 ER PT J AU Uysal, AK Gunal, S AF Uysal, Alper Kursat Gunal, Serkan TI Text classification using genetic algorithm oriented latent semantic features SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Feature selection; Genetic algorithm; Latent semantic indexing; Text classification ID FEATURE-SELECTION METHOD; PRINCIPAL COMPONENT ANALYSIS; NEURAL-NETWORK; CATEGORIZATION; RECOGNITION; LSI AB In this paper, genetic algorithm oriented latent semantic features (GALSF) are proposed to obtain better representation of documents in text classification. The proposed approach consists of feature selection and feature transformation stages. The first stage is carried out using the state-of-the-art filter-based methods. The second stage employs latent semantic indexing (LSI) empowered by genetic algorithm such that a better projection is attained using appropriate singular vectors, which are not limited to the ones corresponding to the largest singular values, unlike standard LSI approach. In this way, the singular vectors with small singular values may also be used for projection whereas the vectors with large singular values may be eliminated as well to obtain better discrimination. Experimental results demonstrate that GALSF outperforms both LSI and filter-based feature selection methods on benchmark datasets for various feature dimensions. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Uysal, Alper Kursat; Gunal, Serkan] Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. RP Uysal, AK (reprint author), Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. EM akuysal@anadolu.edu.tr; serkangunal@anadolu.edu.tr CR ALHABASHNEH O, 2011, 9 INT C CONC STRUCT, V6828, P346 Asuncion A., 2007, UCI MACHINE LEARNING Barros AS, 1998, CHEMOMETR INTELL LAB, V40, P65, DOI 10.1016/S0169-7439(98)00002-1 Ding J., 2014, COMPUTERS ELECT ENG Goldberg D. E., 1989, GENETIC ALGORITHMS S Gud A, 2009, EXPERIENCE OF DESIGNING AND APPLICATION OF CAD SYSTEMS IN MICROELECTRONICS, P536 Gulmezoglu MB, 2001, IEEE T SPEECH AUDI P, V9, P655, DOI 10.1109/89.943343 Gunal S, 2009, EXPERT SYST APPL, V36, P10266, DOI 10.1016/j.eswa.2009.01.051 Gunal S, 2012, TURK J ELECTR ENG CO, V20, P1296, DOI 10.3906/elk-1101-1064 Gunal S, 2008, INFORM SCIENCES, V178, P3716, DOI 10.1016/j.ins.2008.06.001 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Joachims T., 1997, 14 INT C MACH LEARN, P143 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Theodoridis S, 2009, PATTERN RECOGNITION, 4RTH EDITION, P1 Liu T, 2004, FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P162 Manning C.D., 2008, INTRO INFORM RETRIEV Meng JN, 2011, COMPUT MATH APPL, V62, P2793, DOI 10.1016/j.camwa.2011.07.045 Metsis V., 2006, 3 C EM ANT, V17, P28 Montanes E, 2003, LECT NOTES COMPUT SC, V2686, P230 Ogura H, 2009, EXPERT SYST APPL, V36, P6826, DOI 10.1016/j.eswa.2008.08.006 OSU-SVM, 2003, OSU SVM CLASS TOOLB Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Thorleuchter D, 2013, EXPERT SYST APPL, V40, P1786, DOI [10.1016/j.eswa.2012.09.023, 10.1016/j.eswa.2012,09.023] Uguz H, 2011, KNOWL-BASED SYST, V24, P1024, DOI 10.1016/j.knosys.2011.04.014 Uysal AK, 2012, KNOWL-BASED SYST, V36, P226, DOI 10.1016/j.knosys.2012.06.005 Wang Q., 2013, ACM T INFORM SYSTEMS, V31, P1 Wang W, 2009, NEURAL COMPUT APPL, V18, P875, DOI 10.1007/s00521-008-0193-3 Yan P, 2009, J SYST ENG ELECTRON, V20, P651 Yang HQ, 2009, LECT NOTES COMPUT SC, V5507, P53 Yang XQ, 2009, INT J INNOV COMPUT I, V5, P4491 Yang Y., 1997, 14 INT C MACH LEARN, P412 Yu B, 2008, KNOWL-BASED SYST, V21, P900, DOI 10.1016/j.knosys.2008.03.045 Zhang W, 2011, EXPERT SYST APPL, V38, P2758, DOI 10.1016/j.eswa.2010.08.066 Zhang W, 2008, KNOWL-BASED SYST, V21, P879, DOI 10.1016/j.knosys.2008.03.044 Zheng WS, 2005, IEEE T SYST MAN CY B, V35, P1065, DOI 10.1109/TSMCB.2005.850175 NR 37 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT 1 PY 2014 VL 41 IS 13 BP 5938 EP 5947 DI 10.1016/j.eswa.2014.03.041 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA AI4YJ UT WOS:000336872300023 ER PT J AU Jiang, L Edwards, SM Thomsen, B Workman, CT Guldbrandtsen, B Sorensen, P AF Jiang, Li Edwards, Stefan M. Thomsen, Bo Workman, Christopher T. Guldbrandtsen, Bernt Sorensen, Peter TI A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records SO BMC BIOINFORMATICS LA English DT Article ID AMYOTROPHIC-LATERAL-SCLEROSIS; INTERACTION NETWORKS; MUTANT SOD1; FUNCTIONAL INTERACTION; COPPER CHAPERONE; ALPHA-SYNUCLEIN; HUMAN PHENOME; MOUSE MODEL; MUTATIONS; INTEGRATION AB Background: Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. Results: We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. Conclusion: We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes. C1 [Jiang, Li; Edwards, Stefan M.; Thomsen, Bo; Guldbrandtsen, Bernt; Sorensen, Peter] Aarhus Univ, Dept Mol Biol & Genet, DK-8830 Tjele, Denmark. [Workman, Christopher T.] Tech Univ Denmark, Ctr Biol Sequence Anal, Dept Syst Biol, DK-2800 Lyngby, Denmark. RP Jiang, L (reprint author), Aarhus Univ, Dept Mol Biol & Genet, DK-8830 Tjele, Denmark. EM jiang@cbs.dtu.dk; pso@mbg.au.dk RI Workman, Christopher/K-1066-2015 OI Workman, Christopher/0000-0002-2210-3743 FU European Commission [FOOD-CT-2006-016250]; Cutting Edge Genomics for Sustainable Animal Breeding (SABRE) research project; Quantomics, a collaborative project under the Seventh Framework Program FX We thank Rui Jiang and Xuebing Wu, from the MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua University, for providing the data for comparison. We also acknowledge Olof E. L. Karlberg and Pall I. Olason, from the Center for Biological Sequence Analysis, Technical University of Denmark, for their assistance with the data processing. This work was in part supported by a Cutting Edge Genomics for Sustainable Animal Breeding (SABRE) research project that was co-financed by the European Commission within the Sixth Framework Program, contract no. FOOD-CT-2006-016250. Additional financial support was kindly granted by Quantomics, a collaborative project under the Seventh Framework Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Ackermann M, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-47 Atkin JD, 2006, J BIOL CHEM, V281, P30152, DOI 10.1074/jbc.M603393200 Bader GD, 2003, NUCLEIC ACIDS RES, V31, P248, DOI 10.1093/nar/gkg056 Belin AC, 2008, FEBS J, V275, P1377, DOI 10.1111/j.1742-4658.2008.06301.x Biskup S, 2008, J NEUROL, V255, P8, DOI 10.1007/s00415-008-5005-2 Bornigen D, 2012, BIOINFORMATICS, V28, P3081, DOI 10.1093/bioinformatics/bts581 Brunner HG, 2004, NAT REV GENET, V5, P545, DOI 10.1038/nrg1383 Chen J, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-73 Chen YZ, 2004, AM J HUM GENET, V74, P1128, DOI 10.1086/421054 Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Ferraiuolo L, 2011, NAT REV NEUROL, V7, P616, DOI 10.1038/nrneurol.2011.152 Fiesel FC, 2011, FEBS J, V278, P3550, DOI 10.1111/j.1742-4658.2011.08258.x Forsberg K, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0011552 Franceschini A, 2013, NUCLEIC ACIDS RES, V41, pD808, DOI 10.1093/nar/gks1094 Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104 von Mering C, 2003, NUCLEIC ACIDS RES, V31, P258, DOI 10.1093/nar/gkg034 Ishigaki S, 2004, J BIOL CHEM, V279, P51376, DOI 10.1074/jbc.M406683200 Iwata A, 2003, HUM MOL GENET, V12, P2625, DOI 10.1093/hmg/ddg283 Jensen LJ, 2009, NUCLEIC ACIDS RES, V37, pD412, DOI 10.1093/nar/gkn760 Jiang L, 2012, PHYSIOL GENOMICS, V44, P305, DOI 10.1152/physiolgenomics.00047.2011 Johnson JO, 2010, NEURON, V68, P857, DOI 10.1016/j.neuron.2010.11.036 Kurian MA, 2009, J CLIN INVEST, V119, P1595, DOI 10.1172/JCI39060 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 Licata L, 2012, NUCLEIC ACIDS RES, V40, pD857, DOI 10.1093/nar/gkr930 Lindberg C, 1990, J Am Med Rec Assoc, V61, P40 Lindersson E, 2005, J BIOL CHEM, V280, P5703, DOI 10.1074/jbc.M410409200 Marciniak SJ, 2006, PHYSIOL REV, V86, P1133, DOI 10.1152/physrev.00015.2006 Newton MA, 2007, ANN APPL STAT, V1, P85, DOI 10.1214/07-AOAS104 Nishitoh H, 2008, GENE DEV, V22, P1451, DOI 10.1101/gad.1640108 Niwa J, 2002, J BIOL CHEM, V277, P36793, DOI 10.1074/jbc.M206559200 Osborne John D., 2007, V408, P153 Peri S, 2004, NUCLEIC ACIDS RES, V32, pD497, DOI 10.1093/nar/gkh070 Polymenidou M, 2012, BRAIN RES, V1462, P3, DOI 10.1016/j.brainres.2012.02.059 Proescher JB, 2008, HUM MOL GENET, V17, P1728, DOI 10.1093/hmg/ddn063 Puffenberger EG, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0028936 ROGERS FB, 1963, B MED LIBR ASSOC, V51, P114 Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Skourti-Stathaki K, 2011, MOL CELL, V42, P794, DOI 10.1016/j.molcel.2011.04.026 Snel B, 2000, NUCLEIC ACIDS RES, V28, P3442, DOI 10.1093/nar/28.18.3442 Son M, 2007, P NATL ACAD SCI USA, V104, P6072, DOI 10.1073/pnas.0610923104 Sone J, 2010, J NEUROSCI RES, V88, P123, DOI 10.1002/jnr.22175 Sreedharan J, 2008, SCIENCE, V319, P1668, DOI 10.1126/science.1154584 Subramaniam JR, 2002, NAT NEUROSCI, V5, P301, DOI 10.1038/nn823 Szklarczyk D, 2011, NUCLEIC ACIDS RES, V39, pD561, DOI 10.1093/nar/gkq973 Tirian L, 2003, P NATL ACAD SCI USA, V100, P13976, DOI 10.1073/pnas.2436331100 van Driel MA, 2006, EUR J HUM GENET, V14, P535, DOI 10.1038/sj.ejhg.5201585 Vanunu O, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000641 von Mering C, 2005, NUCLEIC ACIDS RES, V33, pD433, DOI 10.1093/nar/gki005 von Mering C, 2007, NUCLEIC ACIDS RES, V35, pD358, DOI 10.1093/nar/gkl825 Wong PC, 2000, P NATL ACAD SCI USA, V97, P2886, DOI 10.1073/pnas.040461197 Wu XB, 2008, MOL SYST BIOL, V4, DOI 10.1038/msb.2008.27 Yu S, 2008, BIOINFORMATICS, V24, pI119, DOI 10.1093/bioinformatics/btn291 Yu S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-28 NR 55 TC 0 Z9 0 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD SEP 24 PY 2014 VL 15 AR 315 DI 10.1186/1471-2105-15-315 PG 13 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA AQ3NL UT WOS:000342700700002 PM 25253562 ER PT J AU Hua, Y Liu, X Jiang, H AF Hua, Yu Liu, Xue Jiang, Hong TI ANTELOPE: A Semantic-Aware Data Cube Scheme for Cloud Data Center Networks SO IEEE TRANSACTIONS ON COMPUTERS LA English DT Article DE Cloud computing; data center networks; semantic awareness; data cube ID SINGULAR-VALUE DECOMPOSITION; INFORMATION-RETRIEVAL AB Today's cloud data centers contain more than millions of servers and offer high bandwidth. A fundamental problem is how to significantly improve the large-scale system's scalability to interconnect a large number of servers and meanwhile support various online services in cloud computing. One way is to deal with the challenge of potential mismatching between the network architecture and the data placement. To address this challenge, we present ANTELOPE, a scalable distributed data-centric scheme in cloud data centers, in which we systematically take into account both the property of network architecture and the optimization of data placement. The basic idea behind ANTELOPE is to leverage precomputation based data cube to support online cloud services. Since the construction of data cube suffers from the high costs of full materialization, we use a semantic-aware partial materialization solution to significantly reduce the operation and space overheads. Extensive experiments on real system implementations demonstrate the efficacy and efficiency of our proposed scheme. C1 [Hua, Yu] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China. [Liu, Xue] McGill Univ, Sch Comp Sci, Montreal, PQ H3A 0E9, Canada. [Jiang, Hong] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA. RP Hua, Y (reprint author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China. EM csyhua@hust.edu.cn; xueliu@cs.mcgill.ca; jiang@cse.unl.edu FU National Natural Science Foundation of China (NSFC) [61173043]; National Basic Research 973 Program of China [2011CB302301]; NSFC [61025008, 61232004]; Fundamental Research Funds for the central universities, HUST [2012QN098]; NSERC [341823]; US NSF [NSF-IIS-0916859, NSF-CCF-0937993, NSF-CNS-1016609, NSF-CNS-1116606] FX This work was supported in part by National Natural Science Foundation of China (NSFC) under Grant 61173043; National Basic Research 973 Program of China under Grant 2011CB302301; NSFC under Grant 61025008, 61232004; Fundamental Research Funds for the central universities, HUST, under grant 2012QN098; the NSERC Discovery Grant 341823; US NSF under Grants NSF-IIS-0916859, NSF-CCF-0937993, NSF-CNS-1016609 and NSF-CNS-1116606. The authors greatly appreciate anonymous reviewers for constructive comments. CR Al-Fares M, 2008, ACM SIGCOMM COMP COM, V38, P63, DOI 10.1145/1402946.1402967 Andoni A, 2008, COMMUN ACM, V51, P117, DOI 10.1145/1327452.1327494 [Anonymous], 2011, SCIENCE, V331, P692 Babcock B, 2003, P 2003 ACM INT C MAN, P539 Barbara D, 2001, J INTELL INF SYST, V16, P255, DOI 10.1023/A:1011224019249 Bell G, 2009, SCIENCE, V323, P1297, DOI 10.1126/science.1170411 BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bi B, 2011, PROC INT CONF DATA, P27 Buckley C., 1985, 85686 TR CORN U DEP Casado M., 2010, UCBEECS201093 Chang F., 2006, Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI'06) Chen K, 2010, ACM SIGCOMM COMP COM, V40, P39, DOI 10.1145/1851275.1851190 Dai J.Q., 2011, P USENIX ANN TECH C, P87 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 De Lathauwer L, 2000, SIAM J MATRIX ANAL A, V21, P1253, DOI 10.1137/S0895479896305696 Ding BL, 2010, PROC INT CONF DATA, P381, DOI 10.1109/ICDE.2010.5447838 Feng Y, 2004, PROC INT CONF DATA, P658 Ghemawat S., 2003, ACM SIGOPS OPERATING, V37, P43 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd GOLUB GH, 1970, NUMER MATH, V14, P403, DOI 10.1007/BF02163027 Gray J, 1997, DATA MIN KNOWL DISC, V1, P29, DOI 10.1023/A:1009726021843 Greenberg A, 2009, ACM SIGCOMM COMP COM, V39, P51 Guo C., 2008, P ACM SIGCOMM AUG, P75, DOI 10.1145/1402958.1402968 Guo CX, 2009, ACM SIGCOMM COMP COM, V39, P63 Guttman A, 1984, P ACM SIGMOD INT C M, P47 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Hellerstein J. L., 2010, GOOGLE CLUSTER DATA Hua Y, 2012, IEEE T PARALL DISTR, V23, P337, DOI 10.1109/TPDS.2011.169 Hua Y., 2008, P 37 INT C PAR PROC, P644 I.D.C. (IDC), 2013, 2010 DIG UN STUD DIG Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 Isard M., 2007, P 2 ACM SIGOPS EUROS, P59 Jin X., 2010, P CIKM, P849, DOI 10.1145/1871437.1871546 Katz RH, 2009, IEEE SPECTRUM, V46, P40, DOI 10.1109/MSPEC.2009.4768855 Kavalanekar S., 2008, P IEEE INT S WORKL C, P119 Lee J., 2010, P DEM INT C DAT ENG Lee S., 2000, P ICDE, P599 Li D, 2009, IEEE INFOCOM SER, P2276, DOI 10.1109/INFCOM.2009.5062153 Li D., 2011, P IEEE ICNP OCT, P266 Li D., 2011, P IEEE 19 INT C NETW, P256 Liu M, 2010, PROC INT CONF DATA, P1097, DOI 10.1109/ICDE.2010.5447886 Los Alamos National Lab (LANL), FIL SYST DAT Lu C., 2002, P USENIX C FIL STOR, P219 Lu W., 2002, P INT C DAT ENG ICDE, P155 Missaoui R., 2007, P ACM DAT WAR ON LIN, P33, DOI 10.1145/1317331.1317338 Mudigonda J., 2011, P USENIX ANN TECH C Mudigonda J., 2010, P NSDI SAN JOS CA, P265 Mysore N., 2009, P ACM SPEC INT GROUP, P39 Olston C., 2008, P 2008 ACM SIGMOD IN, P1099, DOI 10.1145/1376616.1376726 Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 Pu C., 1991, ACM SIGMOD RECORD, V20, P377, DOI 10.1145/119995.115856 Qureshi A, 2009, ACM SIGCOMM COMP COM, V39, P123 Riedel E., 2002, Proceedings of the FAST'02 Conference on File and Storage Technologies Riedewald M., 2000, Proceedings. 12th International Conference on Scientific and Statistica Database Management, DOI 10.1109/SSDM.2000.869781 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shanmugasundaram J., 1999, P 5 ACM SIGKDD INT C, P223, DOI 10.1145/312129.312231 Shieh A., 2011, P USENIX S NETW SYST, P309 Tang C., 2004, P 27 ANN INT ACM SIG, P112, DOI 10.1145/1008992.1009014 Tang CQ, 2003, ACM SIGCOMM COMP COM, V33, P175 Thusoo A, 2010, PROC INT CONF DATA, P996, DOI 10.1109/ICDE.2010.5447738 Thusoo A., 2010, P 2010 INT C MAN DAT, P1013, DOI 10.1145/1807167.1807278 Viswanathan A., 2011, P USENIX S NETW SYST, P127 Weber R., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Weil S. A., 2006, Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI'06) Wu H., 2009, P 5 INT C EM NETW EX, P25, DOI DOI 10.1145/1658939.1658943 Wu T., 2008, P 2008 ACM SIGMOD IN, P79, DOI 10.1145/1376616.1376627 Xin D., 2006, P INT C VER LARG DAT, P463 Xu Z, 2003, P 23 INT C DISTR COM, P500 Yu Y., 2009, P INT C VER LARG DAT, P1622 Zhao P., 2011, P INT C MAN DAT, P853 NR 72 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 0018-9340 EI 1557-9956 J9 IEEE T COMPUT JI IEEE Trans. Comput. PD SEP PY 2014 VL 63 IS 9 BP 2146 EP 2159 DI 10.1109/TC.2013.110 PG 14 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AR9IE UT WOS:000343886200004 ER PT J AU Mocanu, S Din, R Saru, D Popa, C AF Mocanu, Stefan Din, Ramona Saru, Daniela Popa, Cosmin TI Using Graphics Processing Units for Accelerated Information Retrieval SO STUDIES IN INFORMATICS AND CONTROL LA English DT Article DE content based information retrieval; graphic processing unit; GPU; GPGPU; inverted file; parallel computing ID GPU; PARALLEL AB The Parallel computing platforms enable dramatic increases in computing performance by harnessing the power of Graphics Processing Units (GPUs). Their design, based on a high level of hardware parallelization achieved through a big number of processing cores, made GPUs serious competitors for CPU based processing architectures. This fact is most obvious when it comes to processing huge amount of data. Many recent studies aimed at the development of GPU based implementations for various fields such as: astronomy, medicine, image processing, data compression and others. However, very few of them aimed at achieving information retrieval improvement based on GPU. Considering this, along with the latest stage of content based information retrieval algorithms and their practical efficiency for a wide variety of applications, this paper focuses on emphasizing parallel GPGPU algorithms performances against their CPU equivalents. C1 [Mocanu, Stefan; Din, Ramona; Saru, Daniela; Popa, Cosmin] Univ Politehn Bucuresti, Fac Automat Control & Comp Sci, Bucharest 060042, Romania. RP Mocanu, S (reprint author), Univ Politehn Bucuresti, Fac Automat Control & Comp Sci, Bucharest 060042, Romania. EM stefan.mocanu@upb.ro FU Romanian Ministry of Labor, Family and Social Protection [POSDRU/89/1.5/S/62557] FX This work was partially supported by the Romanian Ministry of Labor, Family and Social Protection, in contract POSDRU/89/1.5/S/62557. CR Akenine-Moller T, 2008, P IEEE, V96, P779, DOI 10.1109/JPROC.2008.917719 AMDHAHL G. M., 1967, AFIPS C P BROWN K., 2012, UCSD HOSP TESTS GPU BUETTCHER S., 2010, INFORM RETRIEVAL IMP Cohen J, 2009, COMPUT SCI ENG, V11, P58, DOI 10.1109/MCSE.2009.144 DIN R., 2012, THESIS UPB Ding S., 2009, WWW 09, P421 DOBRESCU R., 2011, REV ROMANA INFORM SI, V21, P81 Feinbube F, 2011, IEEE SOFTWARE, V28, P51, DOI 10.1109/MS.2010.134 HUANG C. H., 2010, NEUR NETW IJCNN 2012, P1 Kirk D. B., 2010, PROGRAMMING MASSIVEL Manning C. D., 2009, INTRO INFORM RETRIEV MITEA A.-C., 2010, STUDIES INFORM CONTR, V19, P185 MOCANU S., 2010, REV ROMANA INFORM SI, V20, P97 NVIDIA, 2012, IMPR QUAL HEALTHC MA Owens JD, 2008, P IEEE, V96, P879, DOI 10.1109/JPROC.2008.917757 QUAN L. H., P 19 INT C COMP LING, P315 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siromascenko AA, 2013, STUD INFORM CONTROL, V22, P279 Spoerk J, 2012, Z MED PHYS, V22, P13, DOI 10.1016/j.zemedi.2011.06.002 Strohman T., 2009, SEARCH ENGINES INFOR SULLIVAN T., 2007, TECHNICAL REPORT TEODORO G., 2009, CLUSTER, P1 WENBIN F., 2008, HKUSTCS0807 Zaki MJ, 1999, IEEE CONCURR, V7, P14, DOI 10.1109/4434.806975 NR 25 TC 2 Z9 2 PU NATL INST R&D INFORMATICS-ICI PI BUCHAREST PA PUBL DEPT, 8-10 AVERESCU BLVD, SECTOR 1, BUCHAREST, 011455, ROMANIA SN 1220-1766 J9 STUD INFORM CONTROL JI Stud. Inform. Control PD SEP PY 2014 VL 23 IS 3 BP 249 EP 256 PG 8 WC Automation & Control Systems; Operations Research & Management Science SC Automation & Control Systems; Operations Research & Management Science GA AQ3SD UT WOS:000342712900003 ER PT J AU Leake, D Maguitman, A Reichherzer, T AF Leake, David Maguitman, Ana Reichherzer, Thomas TI Experience-based support for human-centered knowledge modeling SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Case-based reasoning; Concept mapping; Intelligent user interfaces; Knowledge discovery; Knowledge modeling ID SIMILARITY; ONTOLOGY AB The construction, capture and sharing of human knowledge is one of the fundamental problems of human-centered computing. Electronic concept maps have proven to be a useful vehicle for building knowledge models. However, the user has to deal with the difficult task of deciding what information to include in these models. This article reports the culmination of a multi-year research project aimed at developing intelligent suggesters designed to aid users of concept mapping tools as they build their knowledge models. It describes DISCERNER and EXTENDER, two proactive suggesters that can be incorporated into the CmapTools concepts mapping system. DISCERNER applies case-based reasoning techniques to suggest potentially useful propositions mined from other users' knowledge models, while EXTENDER mines search engines to suggest new related areas to model. The article presents experimental results addressing two previously open questions for the project: DISCERNERS retrieval accuracy and EXTENDER'S ability to generate artificial topics with content similar to topics determined by domain experts. Both experiments show satisfactory results. (C) 2014 Elsevier B.V. All rights reserved. C1 [Leake, David] Indiana Univ, Sch Informat & Comp Bloomington, Bloomington, IN 47408 USA. [Maguitman, Ana] Univ Nacl Sur, Dept Ciencias & Ingn Computac, RA-8000 Bahia Blanca, Buenos Aires, Argentina. [Reichherzer, Thomas] Univ W Florida, Dept Comp Sci, Pensacola, FL 32514 USA. RP Leake, D (reprint author), Indiana Univ, Sch Informat & Comp Bloomington, 901 E 10th St, Bloomington, IN 47408 USA. EM leake@cs.indiana.edu; agm@cs.uns.edu.ar; treichherzer@uwf.edu FU NASA [NCC 2-1216] FX We gratefully acknowledge the contributions of our collaborators Alberto Canas and the IHMC CmapTools team. This research is supported in part by NASA under award No NCC 2-1216. We thank Google for the use of their Web APIs service. Portions of this article are based on [30,33,29]. CR Aha DW, 1997, LECT NOTES ARTIF INT, V1266, P267 Bache K, 2013, P 19 ACM SIGKDD KDD, P23 Bannon Liam, 2011, Interactions, V18, DOI 10.1145/1978822.1978833 Bergmann R., 2012, P ICCBR 12 WORKSH PR Blythe J., 2001, P 2001 INT C INT US, P13, DOI 10.1145/359784.359819 Briggs G., 2004, CONCEPT MAPS THEORY, P125 Buckley C., 1995, TREC Canas A., 2004, CONCEPT MAPS THEORY Canas A., 1998, P 11 FLOR AI RES S F, P79 Canas A.J., 2012, P 5 INT C CONC MAPP Clark P., 2001, Proceedings of the First International Conference on Knowledge Capture Coffey J., 2012, CONCEPT MAPS THEORY Coffey J.W., 1999, I MEMORY PRESE UNPUB Cunningham P, 2009, IEEE T KNOWL DATA EN, V21, P1532, DOI 10.1109/TKDE.2008.227 Dai H., 1997, P 4 AS PAC SOFTW ENG Davies J., 2003, P 2 INT C KNOWL CAPT, P20 Denny M., 2004, ONTOLOGY TOOLS SURVE Eskridge TC, 2012, IEEE INTELL SYST, V27, P58, DOI 10.1109/MIS.2012.101 Gil RJ, 2012, KNOWL-BASED SYST, V36, P340, DOI 10.1016/j.knosys.2012.07.007 Hoffman RR, 2003, IEEE INTELL SYST, V18, P73, DOI 10.1109/MIS.2003.1234774 Hoffman R.R., 2001, P 45 ANN M HUM FACT Hsieh SH, 2011, ADV ENG INFORM, V25, P288, DOI 10.1016/j.aei.2010.08.004 Kendall-Morwick J, 2011, P ICCBR 11 WORKSH PR Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Leake D., 2003, P 2 INT C KNOWL CAPT, P44 Leake D, 2005, LECT NOTES ARTIF INT, V3554, P254 Leake D., 2004, P 26 ANN C COGN SCI, P785 Leake D., 2003, P FLAIRS 2002, P325 Leake D., 2004, CONCEPT MAPS THEORY Leake D.B., 2002, P 15 INT FLOR ART IN, P168 Lorenzetti CM, 2009, INFORM SCIENCES, V179, P1881, DOI 10.1016/j.ins.2009.01.029 Maguitman A., 2005, P 2005 INT C INT US, P207, DOI 10.1145/1040830.1040876 Maguitman A., 2004, P 13 C INF KNOWL MAN, P463, DOI 10.1145/1031171.1031260 Mantaras R., 2005, KNOWL ENG REV, V20 Milicic V., 2013, P 22 INT C WORLD WID, P285 Moon B. M., 2011, APPL CONCEPT MAPPING Mostek T., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000) Novak J., 1984, LEARNING LEARN Noy N., 2000, P EKAW Pan R, 2007, ARTIF INTELL, V171, P1039, DOI 10.1016/j.artint.2007.04.018 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santoso HA, 2011, KNOWL-BASED SYST, V24, P457, DOI 10.1016/j.knosys.2010.11.003 Shafto MG, 2002, IEEE INTELL SYST, V17, P10, DOI 10.1109/MIS.2002.1039827 Smyth B, 1998, ARTIF INTELL, V102, P249, DOI 10.1016/S0004-3702(98)00059-9 Smyth B, 2001, LECT NOTES ARTIF INT, V2080, P347 Wang LD, 2008, KNOWL-BASED SYST, V21, P842, DOI 10.1016/j.knosys.2008.03.042 Weber RO, 2005, KNOWL ENG REV, V20, P255, DOI 10.1017/S0269888906000713 NR 47 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD SEP PY 2014 VL 68 SI SI BP 77 EP 87 DI 10.1016/j.knosys.2014.01.013 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA AP7IR UT WOS:000342251500006 ER PT J AU Zhu, L Jin, H Zheng, R Feng, XW AF Zhu, Lei Jin, Hai Zheng, Ran Feng, Xiaowen TI Weighting scheme for image retrieval based on bag-of-visual-words SO IET IMAGE PROCESSING LA English DT Article ID RELEVANCE FEEDBACK; OBJECT CATEGORIES; CLASSIFICATION; FEATURES AB Inspired by the success of bag-of-words in text retrieval, bag-of-visual-words and its variants are widely used in content-based image retrieval to describe visual content. Various weighting schemes have also been proposed to integrate different yet complementary visual-words. However, most of these weighting schemes tend to use fixed weight for every visual-word extracted from the query image, which may lose the discriminative information. This study presents a novel combining method which captures query-specific weights for visual-words in query image. The method mainly contains two stages. Firstly, in offline weight learning, the authors introduce a linear classifier to build a query-category mapping table, and max-margin learning to build category-weight mapping table. Query-category mapping table is used to map the query image to the most likely image class, and category-weight mapping table is used to map image class to the weights of visual-words. Secondly, in online weight mapping, the weights of visual-words are determined efficiently by looking into the prelearned mapping tables. Experimental results on WANG database and Caltech 101 demonstrate that the proposed weighting scheme can effectively weight visual-words of query image according to their discriminative information. In addition, comparative experiments demonstrate the proposed weighting scheme can obtain higher retrieval performance than other weighting schemes. C1 [Zhu, Lei; Jin, Hai; Zheng, Ran; Feng, Xiaowen] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Serv Comp Technol & Syst Lab, Wuhan 430074, Peoples R China. RP Zhu, L (reprint author), Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Cluster & Grid Comp Lab, Serv Comp Technol & Syst Lab, Wuhan 430074, Peoples R China. EM hjin@hust.edu.cn FU National Natural Science Foundation of China [61133008]; National High Technology Research and Development Program of China [2012AA01A306] FX The authors would like to thank Liang Xie and Xiaobai Liu for their work on the revised manuscript. The authors thank the anonymous reviewers for their helpful and valuable suggestions. This work is supported by the National Natural Science Foundation of China (Grant no. 61133008) and the National High Technology Research and Development Program of China, under Grant 2012AA01A306. CR Ai Liefu, 2012, P INT C ADV MULT INF, P801 Bordes A, 2009, J MACH LEARN RES, V10, P1737 Bottou L., 2010, P 19 INT C COMP STAT, V19, P177, DOI 10.1007/978-3-7908-2604-3_16 Bouachir W, 2009, Proceedings of the 2009 Fifth International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2009), DOI 10.1109/SITIS.2009.43 Cao Y, 2010, PROC CVPR IEEE, P3352, DOI 10.1109/CVPR.2010.5540021 Chen X., 2009, P INT C ADV KNOWL DI, P27 Das G, 2006, LECT NOTES COMPUT SC, V4071, P193 Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248 Deng J., 2011, P IEEE C COMP VIS PA, P785 Deselaers T, 2008, INFORM RETRIEVAL, V11, P77, DOI 10.1007/s10791-007-9039-3 Elsayad I., 2010, P INT C CONT BAS MUL, P1 Fan RE, 2008, J MACH LEARN RES, V9, P1871 Kong W., 2012, P 35 INT ACM SIGIR C, P45 Lee RS, 2008, MULTIMED TOOLS APPL, V40, P385, DOI 10.1007/s11042-008-0212-5 Li FF, 2007, COMPUT VIS IMAGE UND, V106, P59, DOI 10.1016/j.cviu.2005.09.012 Li J, 2003, IEEE T PATTERN ANAL, V25, P1075 Liu LQ, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2486 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Nister D, 2006, IEEE COMP SOC C COMP, V2, P2161, DOI DOI 10.1109/CVPR.2006.264 Philbin J., 2007, P IEEE C COMP VIS PA, P1 Rahman MM, 2011, IEEE T INF TECHNOL B, V15, P640, DOI 10.1109/TITB.2011.2151258 Rui Y, 1999, J VIS COMMUN IMAGE R, V10, P39, DOI 10.1006/jvci.1999.0413 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shai S.S., 2011, MATH PROGRAM, V127, P3 Shi MJ, 2013, IEEE T IMAGE PROCESS, V22, P1209, DOI 10.1109/TIP.2012.2228494 Singha M, 2012, IET IMAGE PROCESS, V6, P1221, DOI 10.1049/iet-ipr.2011.0453 Sivic J., 2003, P 9 IEEE C COMP VIS, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Su JH, 2011, IEEE T KNOWL DATA EN, V23, P360, DOI 10.1109/TKDE.2010.124 Tirilly P, 2010, P INT C MULT INF RET, P323, DOI 10.1145/1743384.1743438 Vedaldi A, 2012, IEEE T PATTERN ANAL, V34, P480, DOI 10.1109/TPAMI.2011.153 Wang X.Y., 2013, ENG APPL ARTIF INTEL, V26, P640 Zhang J, 2007, INT J COMPUT VISION, V73, P213, DOI 10.1007/s11263-006-9794-4 NR 33 TC 0 Z9 0 PU INST ENGINEERING TECHNOLOGY-IET PI HERTFORD PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND SN 1751-9659 EI 1751-9667 J9 IET IMAGE PROCESS JI IET Image Process. PD SEP PY 2014 VL 8 IS 9 BP 509 EP 518 DI 10.1049/iet-ipr.2013.0375 PG 10 WC Engineering, Electrical & Electronic SC Engineering GA AO6ZJ UT WOS:000341501200002 ER PT J AU Zhu, HS Chen, EH Xiong, H Cao, HH Tian, JL AF Zhu, Hengshu Chen, Enhong Xiong, Hui Cao, Huanhuan Tian, Jilei TI Ranking user authority with relevant knowledge categories for expert finding SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE Authority ranking; Expert finding; Category relevancy; Link analysis; Question answering AB The problem of expert finding targets on identifying experts with special skills or knowledge for some particular knowledge categories, i.e. knowledge domains, by ranking user authority. In recent years, this problem has become increasingly important with the popularity of knowledge sharing social networks. While many previous studies have examined authority ranking for expert finding, they have a focus on leveraging only the information in the target category for expert finding. It is not clear how to exploit the information in the relevant categories of a target category for improving the quality of authority ranking. To that end, in this paper, we propose an expert finding framework based on the authority information in the target category as well as the relevant categories. Along this line, we develop a scalable method for measuring the relevancies between categories through topic models, which takes consideration of both content and user interaction based category similarities. Also, we provide a topical link analysis approach, which is multiple-category-sensitive, for ranking user authority by considering the information in both the target category and the relevant categories. Finally, in terms of validation, we evaluate the proposed expert finding framework in two large-scale real-world data sets collected from two major commercial Question Answering (Q&A) web sites. The results show that the proposed method outperforms the baseline methods with a significant margin. C1 [Zhu, Hengshu; Chen, Enhong] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Anhui, Peoples R China. [Cao, Huanhuan; Tian, Jilei] Nokia, Beijing 100010, Peoples R China. [Xiong, Hui] Rutgers State Univ, Rutgers Business Sch, Management Sci & Informat Syst Dept, Newark, NJ 07102 USA. RP Chen, EH (reprint author), Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Anhui, Peoples R China. EM zhs@mail.ustc.edu.cn; cheneh@ustc.edu.cn; hxiong@rutgers.edu; happia.cao@nokia.com; jilei.tian@nokia.com FU Natural Science Foundation of China (NSFC) [61073110, 71028002]; Research Fund for the Doctoral Program of Higher Education of China [20113402110024]; Key Program of National Natural Science Foundation of China [60933013]; Nokia; National Science Foundation (NSF) [CCF-1018151, IIS-1256016] FX This work was supported in part by grants from Natural Science Foundation of China (NSFC, Grant Numbers 61073110, 71028002), Research Fund for the Doctoral Program of Higher Education of China (20113402110024), the Key Program of National Natural Science Foundation of China (Grant No. 60933013), and Nokia. It was also partially supported by grants from National Science Foundation (NSF) via grant numbers CCF-1018151, IIS-1256016. CR Azzopardi L., 2003, P 26 ANN INT ACM SIG, P369, DOI DOI 10.1145/860435.860505 Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003 Balog K., 2006, RES DEV INFORM RETRI, P43 Bao T., 2010, IEEE INT C DAT MIN I, P38 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bouguessa M., 2008, P 14 ACM SIGKDD INT, P866, DOI 10.1145/1401890.1401994 Campbell C. S., 2003, P 12 INT C INF KNOWL, P528, DOI DOI 10.1145/956863.956965 Dom B., 2003, P 8 ACM SIGMOD WORKS, P42, DOI DOI 10.1145/882082.882093 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Heinrich G., 2009, TECHNICAL REPORT Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Huang A., 2008, P 6 NZ COMP SCI RES, P49 Jiang J, 1997, ROCLING 1997, P19 Jurczyk P., 2007, P 16 ACM C INF KNOWL, P919, DOI 10.1145/1321440.1321575 Kao W.-C., 2010, P 2010 ACM S APPL CO, P867, DOI 10.1145/1774088.1774266 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kullback S., 1951, INFORM SUFFICIENCY, P79 Lappas T, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P467 Liu L., 2010, P 19 ACM INT C INF K, P199, DOI DOI 10.1016/J.POWTEC.2010.01.007 Liu X., 2005, P 14 ACM INT C INF K, P315, DOI DOI 10.1145/1099554.1099644 Liu Y., 2008, P 31 ANN INT ACM SIG, P483, DOI 10.1145/1390334.1390417 Lu Y, 2009, 2009 FIFTH INTERNATIONAL CONFERENCE ON SEMANTICS, KNOWLEDGE AND GRID (SKG 2009), P54, DOI 10.1109/SKG.2009.54 McCallum A, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P786 Nie L., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148189 Nie L., 2007, P 22 NAT C ART INT A, V2, P1421 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Page L., 1998, STANFORD DIGITAL LIB SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P807 tau Yih W., 2013, P 15 C COMP NAT LANG Wasserman S., 2002, SOCIAL NETWORK ANAL Weng J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Zhang J., 2007, P 16 INT C WORLD WID, P221, DOI DOI 10.1145/1242572.1242603 Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 Zhu H., 2011, P 20 ACM C INF KNOWL Zhu HS, 2011, LECT NOTES ARTIF INT, V7091, P183 Zhu JH, 2010, KNOWL INF SYST, V23, P29, DOI 10.1007/s10115-009-0202-6 NR 37 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD SEP PY 2014 VL 17 IS 5 BP 1081 EP 1107 DI 10.1007/s11280-013-0217-5 PG 27 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AN6DP UT WOS:000340684500011 ER PT J AU Zhang, WZ Zhang, Y Kim, TH AF Zhang, Weizhe Zhang, You Kim, Tai-Hoon TI Detecting bad information in mobile wireless networks based on the wireless application protocol SO COMPUTING LA English DT Article DE Mobile wireless network; Wireless application protocol; Information crawling; Text classification; Image recognition ID SUPPORT VECTOR MACHINES; PERFORMANCE AB The scale of mobile wireless networks is increasing sharply. However, many websites in the WAP networks contain obscenity information, viruses, and Trojans. How to crawl, judge and locate these bad websites is a challenging problem. In this article, a WAP bad information detection system is proposed, which contains crawling, judgment and location subsystems to identify bad WAP websites. The distributed crawling is adapted to break the IP limitation of WAP networks. Text and image automatic classification are introduced in the judgment subsystem. The location subsystem can locate the bad sites and collect the evidence. The experiment results verify that our WAP bad information detection system has high efficiency and accuracy. C1 [Zhang, Weizhe; Zhang, You] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, HL, Peoples R China. [Kim, Tai-Hoon] Univ Tasmania, Sch Comp & Informat Sci, Sandy Bay, Tas, Australia. RP Zhang, WZ (reprint author), Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150006, HL, Peoples R China. EM wzzhang@hit.edu.cn; taihoonn@daum.net FU National Basic Research Program of China [G2011CB302605]; National Natural Science Foundation of China (NSFC) [61173145]; National High Technology Research and Development Program of China [2011AA010705] FX This work was supported by the National Basic Research Program of China under Grant No. G2011CB302605, the National Natural Science Foundation of China (NSFC) under grant No. 61173145, and the National High Technology Research and Development Program of China under Grant No. 2011AA010705. CR Agustina JV, 2003, IEEE ISCC03, P137 Baohuai S, 2008, J COMPLEXITY, V24, P241, DOI 10.1016/j.jco.2007.04.001 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Joachims T., 1998, LNCS, V1398, P137 Lin TH, 2005, COMPUT COMMUN, V28, P1028, DOI 10.1016/j.comcom.2004.12.001 Mason D, 2002, NDDL, P29 Mitra K, 2009, INT J GRID DISTRIBUT, V2, P1 Musznicki B, 2012, INT J GRID DISTRIBUT, V5, P23 Roussev V, 2009, IEEE SECUR PRIV, V7, P49, DOI 10.1109/MSP.2009.40 Rutagemwa H, 2003, IEEE T MOBILE COMPUT, V2, P221, DOI 10.1109/TMC.2003.1233528 Salton G., 1984, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sharma K, 2011, INT J SECURITY APPL, V5, P35 Xu J., 2013, INT J SECURITY ITS A, V7, P1 Yu S., 2003, WWW, P11 Zhu Y, 2012, IEEE COMPUT GRAPH, V32, P6, DOI 10.1109/MCG.2012.114 NR 19 TC 0 Z9 0 PU SPRINGER WIEN PI WIEN PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA SN 0010-485X EI 1436-5057 J9 COMPUTING JI Computing PD SEP PY 2014 VL 96 IS 9 SI SI BP 855 EP 874 DI 10.1007/s00607-013-0325-1 PG 20 WC Computer Science, Theory & Methods SC Computer Science GA AN3MS UT WOS:000340492900007 ER PT J AU Chiu, DY Pan, YC AF Chiu, Deng-Yiv Pan, Ya-Chen TI Topic knowledge map and knowledge structure constructions with genetic algorithm, information retrieval, and multi-dimension scaling method SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Knowledge structure; Topic knowledge map; Information retrieval; Genetic algorithm; Independent chi-square; Multi-dimension scaling ID FUZZY COGNITIVE MAPS; SELF-ORGANIZING MAP; NETWORKS; NEWS AB This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic. First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps. (C) 2014 Elsevier B.V. All rights reserved. C1 [Chiu, Deng-Yiv; Pan, Ya-Chen] ChungHua Univ, Dept Informat Management, Hsinchu 300, Taiwan. RP Chiu, DY (reprint author), ChungHua Univ, Dept Informat Management, 707,Sec 2,Wu Fu Rd, Hsinchu 300, Taiwan. EM chiuden@chu.edu.tw; d09403019@cc.chu.edu.tw FU National Science Council of the Republic of China, Taiwan (NSC) [98-2221-E-216-038] FX The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 98-2221-E-216-038. CR Carroll J.D., 1997, J MARKETING RES, V34, P385 Chen TT, 2010, ELECTRON LIBR, V28, P477, DOI 10.1108/02640471011033602 Chen T.T., 2005, P INF VIS 9 INT C IV, P135 Chiu DY, 2009, EXPERT SYST APPL, V36, P9438, DOI 10.1016/j.eswa.2008.12.032 Chou HC, 2007, EXPERT SYST APPL, V33, P499, DOI 10.1016/j.eswa.2006.05.020 Ding Y, 2012, PROCEDIA ENGINEER, V29, P537, DOI 10.1016/j.proeng.2011.12.757 Dittenbach M, 2002, NEUROCOMPUTING, V48, P199, DOI 10.1016/S0925-2312(01)00655-5 Dwivedi YK, 2011, INFORM SYST MANAGE, V28, P43, DOI 10.1080/10580530.2011.536112 Fujita S, 2009, INFORM PROCESS MANAG, V45, P664, DOI 10.1016/j.ipm.2009.04.008 Goode S, 2011, DECIS SUPPORT SYST, V50, P702, DOI 10.1016/j.dss.2010.08.018 Greenberg Y, 2009, SPEECH COMMUN, V51, P585, DOI 10.1016/j.specom.2007.10.006 Hong TH, 2002, EXPERT SYST APPL, V23, P1, DOI 10.1016/S0957-4174(02)00022-2 Kohonen T., 2001, SELF ORGANIZING MAPS Kuo RJ, 2010, DECIS SUPPORT SYST, V49, P451, DOI 10.1016/j.dss.2010.05.006 Lee JH, 2012, COMPUT EDUC, V59, P353, DOI 10.1016/j.compedu.2012.01.017 Lin F.R., 2006, INFORM PROCESS MANAG, V42, P551 Lin FR, 2009, DECIS SUPPORT SYST, V46, P774, DOI 10.1016/j.dss.2008.11.020 Nie K, 2009, SYST RES BEHAV SCI, V26, P629, DOI 10.1002/sres.926 Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008 PARK KS, 1995, INT J HUM-COMPUT ST, V42, P157, DOI 10.1006/ijhc.1995.1007 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rahman N., 2012, EXPERT SYST APPL, V39, P4729 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schvaneveldt R. W., 1985, MCCS859 NEW MEX STAT Shamsinejadbabki P, 2012, J INTELL INF SYST, V38, P669, DOI 10.1007/s10844-011-0172-5 Shih JY, 2008, EXPERT SYST APPL, V34, P850, DOI 10.1016/j.eswa.2006.10.031 TABER R, 1991, EXPERT SYST APPL, V2, P83, DOI 10.1016/0957-4174(91)90136-3 Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 Wan M, 2012, J INTELL INF SYST, V38, P321, DOI 10.1007/s10844-011-0158-3 Webster J., 2002, MIS Q, V26, P8 ZHANG WR, 1992, IEEE T SYST MAN CYB, V22, P103, DOI 10.1109/21.141315 NR 31 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD SEP PY 2014 VL 67 BP 412 EP 428 DI 10.1016/j.knosys.2014.03.008 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA AM9SP UT WOS:000340221600032 ER PT J AU Laorden, C Ugarte-Pedrero, X Santos, I Sanz, B Nieves, J Bringas, PG AF Laorden, Carlos Ugarte-Pedrero, Xabier Santos, Igor Sanz, Borja Nieves, Javier Bringas, Pablo G. TI Study on the effectiveness of anomaly detection for spam filtering SO INFORMATION SCIENCES LA English DT Article DE Spam filtering; Anomaly detection; Secure e-commerce; Computer security ID IDENTIFICATION; MODELS AB Spam has become an important problem for computer security because it is a channel for spreading threats, including computer viruses, worms and phishing. Currently, more than 85% of received emails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or using email signatures, are no longer completely reliable on their own. Many solutions utilise machine-learning approaches trained with statistical representations of the terms that usually appear in the emails. Nevertheless, these methods require a time-consuming training step with labelled data. Dealing with the limited availability of labelled training instances slows down the progress of filtering systems and offers advantages to spammers. In this paper, we present a study of the effectiveness of anomaly detection applied to spam filtering, which reduces the necessity of labelling spam messages and only employs the representation of one class of emails (i.e., legitimate or spam). This study includes a presentation of the first anomaly based spam filtering system, an enhancement of this system that applies a data reduction algorithm to the labelled dataset to reduce processing time while maintaining detection rates and an analysis of the suitability of choosing legitimate emails or spam as a representation of normality. (C) 2014 Elsevier Inc. All rights reserved. C1 [Laorden, Carlos; Ugarte-Pedrero, Xabier; Santos, Igor; Sanz, Borja; Nieves, Javier; Bringas, Pablo G.] Univ Deusto, Lab Smartness Semant & Secur S3Lab, Bilbao 48007, Spain. RP Laorden, C (reprint author), Univ Deusto, Lab Smartness Semant & Secur S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM claorden@deusto.es; xabier.ugarte@deusto.es; isantos@deusto.es; borja.sanz@deusto.es; jnieves@deusto.es; pablo.garcia.bringas@deusto.es CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Awad A, 2008, IEEE EDOC CONF, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bates M., 1993, CHALLENGES NATURAL L Becker J, 2003, P 6 INT C BUS INF SY, P7 Bratko A, 2006, J MACH LEARN RES, V7, P2673 Burton B., 2003, P SPAM C Carnap Rudolf, 1955, PHILOS STUD, VVI, P33, DOI DOI 10.1007/BF02330951 Chiu Y., 2007, 3 INT C NAT COMP 200, P203, DOI 10.1109/ICNC.2007.173 Cohen D., 1974, EXPLAINING LINGUISTI Cormack G., 2007, 16 TEXT RETRIEVAL C Cruse D., 1975, ARCH LINGUIST, V6, P26 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Elkan Charles, 2001, P 17 INT JOINT C ART, P973 Heyer LJ, 1999, GENOME RES, V9, P1106, DOI 10.1101/gr.9.11.1106 hirita P., 2005, P 14 ACM INT C INF K, P373 Ide N., 1998, COMPUTATIONAL LINGUI, V24, P2 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Karlberger C., 2007, P 1 USENIX WORKSH OF, P1 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence Kumar V., 2000, INTRO CLUSTER ANAL D Kuropka D., 2004, ADV INFORM SYST MANA, V10 Laorden C., 2012, ELECT COMMERCE RES A Leung C., 2009, THESIS HKU Maron O, 1998, ADV NEUR IN, V10, P570 Mason J., 2002, HEANET ANN C McGill M. J., 1983, INTRO MODERN INFORM Ming-Tzu KW, 2004, APPL LINGUIST, V25, P291, DOI 10.1093/applin/25.3.291 Mostafa Raad N., 2010, AFRI J BUS MANAGE, V4, P2362 Polyvyanyy A., 2007, THESIS Radden Gunter, 1999, METONYMY LANGUAGE TH, P17 Raymond E., 2005, BOGOFILTER FAST OPEN Robinson G., 2003, LINUX J, V2003, P3 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos I, 2012, EXPERT SYST APPL, V39, P437, DOI 10.1016/j.eswa.2011.07.034 Schryen G., 2006, P 39 ANN HAW INT C S, P129 Wittel G., 2004, P 1 C EM ANT CEAS Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 Zhou Y, 2007, PROC INT C TOOLS ART, P298, DOI 10.1109/ICTAI.2007.120 NR 43 TC 1 Z9 1 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 EI 1872-6291 J9 INFORM SCIENCES JI Inf. Sci. PD SEP 1 PY 2014 VL 277 BP 421 EP 444 DI 10.1016/j.ins.2014.02.114 PG 24 WC Computer Science, Information Systems SC Computer Science GA AK4JE UT WOS:000338390200028 ER PT J AU Coscia, JLO Mateos, C Crasso, M Zunino, A AF Ordiales Coscia, Jose Luis Mateos, Cristian Crasso, Marco Zunino, Alejandro TI Refactoring code-first Web Services for early avoiding WSDL anti-patterns: Approach and comprehensive assessment SO SCIENCE OF COMPUTER PROGRAMMING LA English DT Article DE Web services; Code-first; WSDL anti-patterns; Service understandability; Service retrievability ID METRICS SUITE; DOCUMENTS; DESIGN; ART AB Previous research of our own [34] has shown that by avoiding certain bad specification practices, or WSDL anti-patterns, contract-first Web Service descriptions expressed in WSDL can be greatly improved in terms of understandability and retrievability. The former means the capability of a human discoverer to effectively reason about a Web Service functionality just by inspecting its associated WSDL description. The latter means correctly retrieving a relevant Web Service by a syntactic service registry upon a meaningful user's query. However, code-first service construction dominates in the industry due to its simplicity. This paper proposes an approach to avoid WSDL anti-patterns in code-first Web Services. We also evaluate the approach in terms of services understandability and retrievability, deeply discuss the experimental results, and delineate some guidelines to help code-first Web Service developers in dealing with the trade-offs that arise between these two dimensions. Certainly, our approach allows services to be more understandable, due to anti-pattern remotion, and retrievable as measured by classical Information Retrieval metrics. (C) 2014 Elsevier B.V. All rights reserved. C1 [Ordiales Coscia, Jose Luis; Mateos, Cristian; Crasso, Marco; Zunino, Alejandro] UNICEN Univ, ISISTAN Res Inst, Tandil, Argentina. RP Mateos, C (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,B7001BBO, Tandil, Argentina. EM jlordiales@gmail.com; cmateos@conicet.gov.ar; mcrasso@gmail.com; azunino@conicet.gov.ar RI Zunino, Alejandro/ OI Zunino, Alejandro/0000-0002-9537-3541 FU ANPCyT [PAE-PICT 2007-02311, PICT-2012-0045] FX We thank the anonymous reviewers for their comments and suggestions to improve the paper. We also acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311 and PICT-2012-0045. CR Agichtein E., 2006, 29 ANN INT ACM SIGIR, P3 Al Dallal J, 2011, IEEE T SOFTWARE ENG, V37, P788, DOI 10.1109/TSE.2010.97 Al-Masri E., 2007, P 16 INT C WORLD WID, P1257, DOI DOI 10.1145/1242572.1242795 Baski D, 2011, IET SOFTW, V5, P320, DOI 10.1049/iet-sen.2010.0089 Beaton J, 2008, 2008 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING, PROCEEDINGS, P193, DOI 10.1109/VLHCC.2008.4639084 Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112 Bloch J, 2006, 21 ACM SIGPLAN S OBJ, P506 CHIDAMBER SR, 1994, IEEE T SOFTWARE ENG, V20, P476, DOI 10.1109/32.295895 Crasso M, 2011, INFORM SYST FRONT, V13, P407, DOI 10.1007/s10796-009-9221-9 Crasso M., 2011, J DATABASE MANAGE, V22, P103 Crasso M, 2008, SCI COMPUT PROGRAM, V71, P144, DOI 10.1016/j.scico.2008.02.002 Crasso M, 2010, IEEE INTERNET COMPUT, V14, P48, DOI 10.1109/MIC.2010.81 Erl T., 2007, SOA PRINCIPLES SERVI Feldman J.S.R., 2006, TEXT MINING HDB ADV Fowler M., 1999, REFACTORING IMPROVIN Huston B, 2001, J SYST SOFTWARE, V58, P261, DOI 10.1016/S0164-1212(01)00043-7 Karam O., 2006, ESSENTIALS SOFTWARE Korfhage R., 1997, INFORM STORAGE RETRI Mateos C., 2012, SADIO ELECT J INFORM, V11, P31 Mateos C, 2013, IEEE INTERNET COMPUT, V17, P46, DOI 10.1109/MIC.2013.4 McCandless M., 2010, LUCENE ACTION 2 EDIT Milne D, 2013, ARTIF INTELL, V194, P222, DOI 10.1016/j.artint.2012.06.007 Ordiales J.L. Coscia, 2012, CLEI ELECT J, V16 Coscia JLO, 2013, INT J WEB GRID SERV, V9, P107 OW2 Consortium, 2010, EASYWSDL TOOLB Papazoglou MP, 2008, INT J COOP INF SYST, V17, P223, DOI 10.1142/S0218843008001816 Pasley J, 2006, IEEE INTERNET COMPUT, V10, P72, DOI 10.1109/MIC.2006.45 Ramey J., 2006, CHI 2006 EXT ABSTR H, P45, DOI 10.1145/1125451.1125464 Rodriguez JM, 2013, J WEB ENG, V12, P131 Rodriguez JM, 2010, SCI COMPUT PROGRAM, V75, P1001, DOI 10.1016/j.scico.2010.01.002 Rodriguez J. M., 2013, MIGRATING LEGACY APP, P126 Rodriguez JM, 2010, IFIP ADV INF COMM TE, V341, P139 Rodriguez JM, 2013, IEEE INTERNET COMPUT, V17, P44, DOI 10.1109/MIC.2011.162 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sneed H.M., 2010, 12 IEEE INT S WEB SY, P111 Spinellis D, 2005, IEEE SOFTWARE, V22, P9, DOI 10.1109/MS.2005.111 The Apache Software Foundation, 2005, JAVA2WSDL BUILD WSDL NR 37 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6423 EI 1872-7964 J9 SCI COMPUT PROGRAM JI Sci. Comput. Program. PD SEP 1 PY 2014 VL 89 BP 374 EP 407 DI 10.1016/j.scico.2014.03.015 PN C PG 34 WC Computer Science, Software Engineering SC Computer Science GA AJ8CB UT WOS:000337929200008 ER PT J AU Yager, RR Petry, FE AF Yager, Ronald R. Petry, Frederick E. TI Hypermatching: Similarity Matching With Extreme Values SO IEEE TRANSACTIONS ON FUZZY SYSTEMS LA English DT Article DE Attribute importance; extreme values; hypermatching; Jaccard index; similarity; Yager-Petry index ID TRIANGLE INEQUALITY; INDEX; MODEL AB An approach is developed for object similarity to support a focus on the role of extreme values in object matching, which is termed hypermatching. Importance weights are first introduced to the matching and variations formulated for objects that do not share all the same attributes. Extreme attribute values are considered by introducing amplification of attribute importance and several significant cases examined. The objects can both possess the same or different extreme valued attributes. Another case is where one object may have an extreme attribute, which the other object does not have. Then, the analysis of the effect of multiple extreme attributes is presented. Finally for attributes with 0-1 values (features), similarity matching is considered and a closed form solution for the similarity is developed. This provides the basis for the Yager-Petry index, which is then compared with the Jaccard similarity index. C1 [Yager, Ronald R.] Iona Coll, Inst Machine Intelligence, New Rochelle, NY 10801 USA. [Petry, Frederick E.] Naval Res Lab, Stennis Space Ctr, MS 39529 USA. [Yager, Ronald R.] Natl Sci Fdn, Informat Sci Program, Arlington, VA 22230 USA. [Yager, Ronald R.] Univ Calif Berkeley, Berkeley, CA 94720 USA. [Yager, Ronald R.] New York Acad Sci, New York, NY USA. [Petry, Frederick E.] Univ Alabama, Huntsville, AL 35899 USA. [Petry, Frederick E.] Ohio State Univ, Columbus, OH 43210 USA. [Petry, Frederick E.] Tulane Univ, New Orleans, LA 70118 USA. RP Yager, RR (reprint author), Iona Coll, Inst Machine Intelligence, New Rochelle, NY 10801 USA. EM yager@panix.com; fpetry@nrlssc.navy.mil FU Naval Research Laboratory's Base Program [0602435N]; Office of Naval Research [N000141010121] FX This work was supported in part by the Naval Research Laboratory's Base Program under Program 0602435N and in part by the Office of Naval Research under Grant N000141010121. CR Anderson DT, 2012, INT J INTELL SYST, V27, P733, DOI 10.1002/int.21544 Balcan M-F., 2006, P 23 INT C MACH LEAR, P73, DOI 10.1145/1143844.1143854 Bernecker T, 2011, PROC INT CONF DATA, P339, DOI 10.1109/ICDE.2011.5767908 Bremmer I., 2010, FAT TAIL POWER POLIT Castillo E., 1998, EXTREME VALUE THEORY Chen S., 2010, INT J MULTIMEDIA DAT, V1, P4 Coles S., 2001, INTRO STAT MODELING Cristianina N., 2000, INTRO SUPPORT VECTOR Elmasri R., 2010, FUNDAMENTALS DATABAS Grossman D. A., 2004, INFORM RETRIEVAL ALG HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 Haveliwala T., 2002, P WORLD WID WEB C, P701 He B, 2006, ACM T DATABASE SYST, V31, P346, DOI 10.1145/1132863.1132872 HERBRICH R., 2002, LEARNING KERNEL CLAS Izsak C, 2001, MAR ECOL PROG SER, V215, P69, DOI 10.3354/meps215069 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Klement E. P., 2000, TRIANGULAR NORMS Le Capitaine H, 2012, IEEE T FUZZY SYST, V20, P57, DOI 10.1109/TFUZZ.2011.2166079 LINDGREN G, 1987, SCAND J STAT, V14, P241 Lipkus AH, 1999, J MATH CHEM, V26, P263, DOI 10.1023/A:1019154432472 Novak S., 2011, EXTREME VALUES METHO Nwosu K., 2011, MULTIMEDIA DATABASE Real R., SYST BIOL, V45, P380 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 1999, IEEE T PATTERN ANAL, V21, P871, DOI 10.1109/34.790428 TAKAGI T, 1985, IEEE T SYST MAN CYB, V15, P116 Taleb N. N., 2010, BLACK SWAN IMPACT HI Tan P., 2006, INTRO DATA MINING Tung A., 2006, P 32 INT C VER LARG, P631 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 TVERSKY A, 1982, PSYCHOL REV, V89, P123, DOI 10.1037/0033-295X.89.2.123 Wilbik A, 2013, IEEE T FUZZY SYST, V21, P183, DOI 10.1109/TFUZZ.2012.2214225 Yager RR, 2013, SOFT COMPUT, V17, P391, DOI 10.1007/s00500-012-0914-5 Yager RR, 2013, PROCEEDINGS OF THE 2013 JOINT IFSA WORLD CONGRESS AND NAFIPS ANNUAL MEETING (IFSA/NAFIPS), P386 Zezula P., 2006, SIMILARITY SEARCH ME NR 35 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1063-6706 EI 1941-0034 J9 IEEE T FUZZY SYST JI IEEE Trans. Fuzzy Syst. PD AUG PY 2014 VL 22 IS 4 BP 949 EP 957 DI 10.1109/TFUZZ.2013.2278988 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AT0ZB UT WOS:000344661600019 ER PT J AU Symonds, M Bruza, P Zuccon, G Koopman, B Sitbon, L Turner, I AF Symonds, Michael Bruza, Peter Zuccon, Guido Koopman, Bevan Sitbon, Laurianne Turner, Ian TI Automatic Query Expansion: A Structural Linguistic Perspective SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; SPACE; MODEL; COOCCURRENCE AB A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine. C1 [Symonds, Michael; Bruza, Peter; Koopman, Bevan] Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia. [Zuccon, Guido] CSIRO, Australian E Hlth Res Ctr, Brisbane, Qld 4001, Australia. [Sitbon, Laurianne] Queensland Univ Technol, Dept Comp Sci, Brisbane, Qld 4001, Australia. [Turner, Ian] Queensland Univ Technol, Dept Math, Brisbane, Qld 4001, Australia. RP Symonds, M (reprint author), Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia. EM michael.symonds@qut.edu.au; p.bruza@qut.edu.au; guido.zuccon@csiro.au; bevan.koopman@csiro.au; laurianne.sitbon@qut.edu.au; i.turner@qut.edu.au CR Allan J., 2012, SIGIR FORUM, V46, P2 Bai J, 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725 Balasubramanian N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P571 Bendersky Michael, 2011, P 34 INT ACM SIGIR C, P605, DOI [DOI 10.1145/2009916.2009998, 10.1145/2009916.2009998] Bendersky Michael, 2009, P 2009 WORKSH WEB SE, P8, DOI 10.1145/1507509.1507511 Billerbeck B., 2004, P AUSTR DAT C DUN NZ, V27, P69 Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Bruza P., 2013, 21 TEXT RETRIEVAL C Bruza P., 2011, P 16 AUSTR DOC COMP, P87 Buckley Chris, 1995, P 3 TEXT RETR C TREC, P69 Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Chapelle O., 2009, P 18 ACM C INF KNOWL, P621, DOI DOI 10.1145/1645953.1646033 Chung YM, 2001, J AM SOC INF SCI TEC, V52, P283, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1073>3.3.CO;2-X Collins-Thompson K., 2009, P 18 ACM C INF KNOWL, P837, DOI 10.1145/1645953.1646059 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Fisher D., 2000, P 19 TEXT RETRIEVAL Greenberg J, 2001, J AM SOC INF SCI TEC, V52, P487, DOI 10.1002/asi.1093 Grefenstette G., 1992, P 15 ANN INT ACM SIG, P89, DOI 10.1145/133160.133181 Harris ZS, 1954, WORD, V10, P146 Hoenkamp E, 2009, LECT NOTES COMPUT SC, V5766, P116, DOI 10.1007/978-3-642-04417-5_11 Holland N. N., 1992, CRITICAL 1 Huston S, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P291 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kanerva P., 2008, P 30 ANN M COGN SCI, P23 Keen M., 1966, FACTORS DETERMINING, V2 Keen M., 1966, FACTORS DETERMINING, V1 Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X Koopman B., 2012, P 10 AUSTR LANGU TEC, P15 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lavrenko V., 2004, THESIS U MASSACHUSET Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Lyons John, 1968, INTRO THEORETICAL LI Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Metzler D, 2009, LECT NOTES COMPUT SC, V5766, P42, DOI 10.1007/978-3-642-04417-5_5 Miller G., 1990, INT J LEXICOGR, V3, P235 Moffat A., 2012, P 17 AUSTR DOC COMP, P47 Ogilvie P, 2009, INFORM RETRIEVAL, V12, P666, DOI 10.1007/s10791-009-9104-1 Osgood C. E., 1957, MEASUREMENT MEANING Pavel T. C., 2001, SPELL LANGUAGE POSTS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Bruza P. D., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Symonds M., 2012, P 17 AUSTR DOC COMP, P123 Symonds M., 2011, P 25 PAC AS C LANG I, P313 Symonds M., 2012, P 21 ACM INT C INF K, P2267 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xue X., 2013, ACM T INFORM SYSTEMS, V31, P1 Yilmaz E., 2008, P 31 ANN INT ACM SIG, P603, DOI DOI 10.1145/1390334.1390437 Zhai C., 2006, P 29 ANN INT ACM SIG, P115, DOI DOI 10.1145/1148170.1148193 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zuccon G., 2011, P 16 AUSTR DOC COMP NR 60 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD AUG PY 2014 VL 65 IS 8 BP 1577 EP 1596 DI 10.1002/asi.23065 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AL3XY UT WOS:000339066500005 ER PT J AU Xu, Z Liu, YH Mei, L Hu, CP Chen, L AF Xu, Zheng Liu, Yunhuai Mei, Lin Hu, Chuanping Chen, Lan TI Generating temporal semantic context of concepts using web search engines SO JOURNAL OF NETWORK AND COMPUTER APPLICATIONS LA English DT Article DE Temporal semantic context; Semantic annotation; Content analysis; Web mining ID SPATIOTEMPORAL CONTEXT; REPRESENTATION; INFORMATION AB In this paper, the problem of generating temporal semantic context for concepts is studied. The goal of the proposed problem is to annotate a concept with temporal, concise, and structured information, which can reflect the explicit and faceted meanings of the concept. The temporal semantic context can help users learn and understand unfamiliar or newly emerged concepts. The proposed temporal semantic context structure integrates the features from dictionary, Wikipedia, and Linkedln web sites. A general method to generate temporal semantic context of a concept by constructing its associated words, associated concepts, context sentences, context graph, and context communities is proposed. Empirical experiments on three different datasets including Q-A dataset, Linkedln dataset, and Wikipedia dataset show that the proposed algorithm is effective and accurate. Different from manually generated context repositories such as Linkedln and Wikipedia, the proposed method can automatically generate the context and does not need any prior knowledge such as ontology or a hierarchical knowledge base. The proposed method is used on some applications such as trend analysis, faceted exploration, and query suggestion. These applications prove the effectiveness of the proposed temporal semantic context problem in many web mining tasks. (C) 2014 Elsevier Ltd. All rights reserved. C1 [Xu, Zheng; Liu, Yunhuai; Mei, Lin; Hu, Chuanping; Chen, Lan] Minist Publ Secur, Res Inst 3, Shanghai 201142, Peoples R China. [Xu, Zheng] Tsinghua Univ, Beijing 100084, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, 339 Bisheng Rd, Shanghai 201142, Peoples R China. EM xuzheng@shu.edu.cn FU National Science and Technology Major Project [2013ZX01033002-003]; National High Technology Research and Development Program of China (863 Program) [2012AA011504, 2013AA014601, 2013AA014603]; National Key Technology Support Program [2012BAH07B01]; National Natural Science Foundation of China [61300202]; Science Foundation of Shanghai [13ZR1452900] FX This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grants 2012AA011504, 2013AA014601, 2013AA014603, in part by National Key Technology Support Program under Grant 2012BAH07B01, in part by the National Natural Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900. CR Agrawal R., 1994, P 20 INT C VER LARG, P487 Al-Khalifa H., 2006, P 2 INT IEEE C INN I, P1 Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 Berners-Lee T, 2001, SCI AM, V284, P34 Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Boughareb D, 2011, COMM COM INF SC, V166, P33 Choi D, 2014, J NETW COMP IN PRESS Dumont E, 2012, P 2012 IEEE INT C MU, P973 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Glaser T, 2011, P 2011 IEEE INT C CO, P1562 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Han JW, 2007, DATA MIN KNOWL DISC, V15, P55, DOI 10.1007/s10618-006-0059-1 Howard MW, 2011, TOP COGN SCI, V3, P48, DOI 10.1111/j.1756-8765.2010.01112.x Hwang M, 2011, IEEE T KNOWL DATA EN, V23, P845, DOI 10.1109/TKDE.2010.163 Jannach D, 2007, J NETW COMPUT APPL, V30, P958, DOI 10.1016/j.jnca.2005.12.007 Jansen M, 1998, P ACM SIGIR FOR, V32, P5 Leung K, 2003, IEEE T KNOWL DATA EN, V20, P1505 Leung KWT, 2008, IEEE T KNOWL DATA EN, V20, P1505, DOI 10.1109/TKDE.2008.84 Li YN, 2010, IEEE T MULTIMEDIA, V12, P814, DOI 10.1109/TMM.2010.2066960 Liu XB, 2011, IEEE T CIRC SYST VID, V21, P393, DOI 10.1109/TCSVT.2010.2087570 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Manica E, 2012, SIGMOD REC, V41, P15 Milea V, 2008, P 8 INT C WEB ENG IC, P65 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Nguyen HT, 2007, IEEE T PATTERN ANAL, V29, P52, DOI 10.1109/TPAMI.2007.250599 Pandit S, 2007, P 16 INT C WORLD WID, P319 Radinsky K., 2011, P 20 INT C WORLD WID, P337, DOI DOI 10.1145/1963405.1963455 Ramage D., 2009, P 2 ACM INT C WEB SE, P54, DOI 10.1145/1498759.1498809 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenkel R., 2008, P 31 ANN INT ACM SIG, P523, DOI 10.1145/1390334.1390424 Sparrow B, 2011, SCIENCE, V333, P776, DOI 10.1126/science.1207745 Suchanek F. M., 2008, P 17 ACM C INF KNOWL, P223, DOI DOI 10.1145/1458082.1458114 van Zwol R, 2011, P 19 INT WORLD WID W, P1 Weng J, 2009, P IEEE 8 INT C DEV L, P1 White R. W., 2007, P 16 INT C WORLD WID, P21, DOI 10.1145/1242572.1242576 Xu S., 2007, P 15 ACM INT C INF K, P1003, DOI 10.1145/1321440.1321596 Xu Z, 2011, CONCURR COMP-PRACT E, V23, P1101, DOI 10.1002/cpe.1689 Xu Z, 2011, CONCURR COMP-PRACT E, V23, P2496, DOI 10.1002/cpe.1816 Zhou D, 2008, P 17 INT C WORLD WID, P715, DOI DOI 10.1145/1367497.1367594 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 NR 41 TC 1 Z9 1 PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD PI LONDON PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND SN 1084-8045 J9 J NETW COMPUT APPL JI J. Netw. Comput. Appl. PD AUG PY 2014 VL 43 BP 42 EP 55 DI 10.1016/j.jnca.2014.04.002 PG 14 WC Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering SC Computer Science GA AK4KP UT WOS:000338393900004 ER PT J AU Bai, X Guerraoui, R Kermarrec, AM AF Bai, Xiao Guerraoui, Rachid Kermarrec, Anne-Marie TI Personalizing Top-k Processing Online in a Peer-to-Peer Social Tagging Network SO ACM TRANSACTIONS ON INTERNET TECHNOLOGY LA English DT Article DE Design; Algorithms; Performance; Peer-to-peer systems; social tagging networks; top-k processing; personalization; gossip; online ID INFORMATION AB The rapidly increasing amount of user-generated content in social tagging systems provides a huge source of information. Yet, performing effective search in these systems is very challenging, especially when we seek the most appropriate items that match a potentially ambiguous query. Collaborative filtering-based personalization is appealing in this context, as it limits the search within a small network of participants with similar preferences. Offline personalization, which consists in maintaining, for every user, a network of similar participants based on their tagging behaviors, is effective for queries that are close to the querying user's tagging profile but performs poorly when the queries, reflecting emerging interests, have little correlation with the querying user's profile. We present (PTK2)-T-2, the first protocol to personalize query processing in social tagging systems online. (PTK2)-T-2 is completely decentralized, and this design choice stems from the observation that the evolving social tagging systems naturally resemble P2P systems where users are both producers and consumers. This design exploits the power of the crowd and prevents any central authority from controlling personal information. (PTK2)-T-2 is gossip-based and probabilistic. It dynamically associates each user with social acquaintances sharing similar tagging behaviors. Appropriate users for answering a query are discovered at query time with the help of social acquaintances. This is achieved according to the hybrid interest of the querying user, taking into account both her tagging behavior and her query. Results are iteratively refined and returned to the querying user. We evaluate (PTK2)-T-2 on CiteULike and Delicious traces involving up to 50,000 users. We highlight the advantages of online personalization compared to offline personalization, as well as its efficiency, scalability, and inherent ability to cope with user departure and interest evolution in P2P systems. C1 [Bai, Xiao] Yahoo Labs Barcelona, Barcelona, Spain. [Guerraoui, Rachid] Ecole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lausanne, Switzerland. [Kermarrec, Anne-Marie] INRIA Rennes Bretagne Atlantique, Rennes, France. RP Bai, X (reprint author), Yahoo Labs Barcelona, Barcelona, Spain. EM xbai@yahoo-inc.com FU ERC SG GOSSPLE [204742] FX This work has been supported by the ERC SG GOSSPLE 204742. CR Amer-Yahia Sihem, 2008, P VLDB ENDOW, V1, P710 Bai Xiao, 2012, P 35 INT ACM SIGIR C, P641 Bai Xiao, 2010, P 13 INT C EXT DAT T, P87, DOI 10.1145/1739041.1739055 Bai X, 2011, ACM T DATABASE SYST, V36, DOI 10.1145/2043652.2043659 Bell Robert M., 2007, P 1 KDDCUP 07 Bender Matthias, 2007, IEEE DATA ENG B, V30, P51 Cao P, 1997, PROCEEDINGS OF THE USENIX SYMPOSIUM ON INTERNET TECHNOLOGIES AND SYSTEMS, P193 Cover Thomas M., 1991, ELEMENTS INFORM THEO Dou Z, 2007, P 16 INT C WORLD WID, P581, DOI DOI 10.1145/1242572.1242651 Eugster PT, 2004, COMPUTER, V37, P60, DOI 10.1109/MC.2004.1297243 Fagin R, 2002, SIGMOD RECORD, V31, P109 Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558 Jelasity M, 2007, ACM T COMPUT SYST, V25, DOI 10.1145/1275517.1275520 Jelasity M., 2004, Proceedings. 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Loupasakis Andreas, 2011, P 5 BIENN C INN DAT, P85 Luo X, 2012, PATTERN RECOGN LETT, V33, P263, DOI 10.1016/j.patrec.2011.10.016 Mislove Alan, 2006, P 5 ACM WORKSH HOT T, P79 Montresor A., 2009, P 9 INT C PEER TO PE, P99 Noll MG, 2007, LECT NOTES COMPUT SC, V4825, P367 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenkel R., 2008, P 31 ANN INT ACM SIG, P523, DOI 10.1145/1390334.1390424 Speretta M., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence Stoyanovich Julia, 2008, P AAAI SOC INF SPRIN, P104 Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764 Sun J. T., 2005, P 14 INT C WORLD WID, P382, DOI 10.1145/1060745.1060803 Teevan J., 2009, P 2 ACM INT C WEB SE, P15, DOI DOI 10.1145/1498759.1498786 Tsegay Yohannes, 2007, P 16 ACM C C INF KNO, P987, DOI 10.1145/1321440.1321592 Voulgaris S, 2005, LECT NOTES COMPUT SC, V3648, P1143 Young Neal E., 1998, P 9 ACM SIAM S DISCR, P82 NR 30 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1533-5399 EI 1557-6051 J9 ACM T INTERNET TECHN JI ACM Trans. Internet. Technol. PD JUL PY 2014 VL 13 IS 4 AR 11 DI 10.1145/2602572 PG 36 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AO0BV UT WOS:000340975000001 ER PT J AU Foremski, P Callegari, C Pagano, M AF Foremski, Pawel Callegari, Christian Pagano, Michele TI DNS-Class: immediate classification of IP flows using DNS SO INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT LA English DT Article ID TRAFFIC CLASSIFICATION AB Nowadays we see a tremendous growth of the Internet, especially in terms of the amont of data being transmitted and new network protocols being introduced. This poses a challenge for network administrators, who need adequate tools for network management. Recent findings show that DNS can contribute valuable information on IP flows and improve traffic visibility in a computer network. In this paper, we apply these findings on DNS to propose a novel traffic classification algorithm with interesting features. We experimentally show that the information carried in domain names and port numbers is sufficient for immediate classification of a highly significant portion of the traffic. We present DNS-Class: an innovative, fast and reliable flow-based traffic classification algorithm, which on average yields 99.8% of true positives and <0.1% of false positives on real traffic traces. The algorithm can work as a major element of a modular system in a cascade architecture. Additionally, we provide an analysis on how various network protocols depend on DNS in terms of flows, packets and bytes. We release the complete source code implementing the presented system as open source. Copyright (C) 2014 John Wiley & Sons, Ltd C1 [Foremski, Pawel] Polish Acad Sci, Inst Theoret & Appl Informat, Gliwice, Poland. [Callegari, Christian; Pagano, Michele] Univ Pisa, Dept Informat Engn, I-56100 Pisa, Italy. RP Foremski, P (reprint author), IITiS PAN, Ul Baltycka 5, PL-44100 Gliwice, Poland. EM pjf@iitis.pl RI Foremski, Pawel/ OI Foremski, Pawel/0000-0003-1187-4793 FU Polish National Science Centre [2011/01/N/ST6/07202] FX This work was funded by the Polish National Science Centre, under research grant no. 2011/01/N/ST6/07202: project 'Multilevel Traffic Classification', http://mutrics.iitis.pl/. The traffic traces were collected thanks to ASN Sp. z o.o., http://www.asn.pl/. We would like to thank Michal Gorawski and Michal Romaszewski of IITiS PAN for proofreading the manuscript of this paper. CR Adami D, 2012, INT J COMMUN SYST, V25, P386, DOI 10.1002/dac.1247 Alcock S, 2014, TECHNICAL REPORT Bermudez I, 2012, P 12 ACM SIGCOMM C I, V1101, P413, DOI DOI 10.1145/2398776.2398819 Callado A, 2009, IEEE COMMUN SURV TUT, V11, P37, DOI 10.1109/SURV.2009.090304 Crammer K, 2002, J MACH LEARN RES, V2, P265, DOI 10.1162/15324430260185628 Dainotti A, 2011, LECT NOTES COMPUT SC, V6613, P122, DOI 10.1007/978-3-642-20305-3_11 Duda R. O., 2012, PATTERN CLASSIFICATI Fan RE, 2008, J MACH LEARN RES, V9, P1871 Fiadino P., 2013, INT C COMP COMM INFO Foremski P., 2013, THEORETICAL APPL INF, V25, P119 Foremski P., 2012, THEORETICAL APPL INF, V24, P23 Foremski P, COMMUNICATI IN PRESS Gomes J. V., 2013, ACM COMPUT SURV, V45, P1 Karagiannis T, 2004, GLOB TELECOMM CONF, P1532 Karagiannis T, 2005, ACM SIGCOMM COMP COM, V35, P229, DOI 10.1145/1090191.1080119 Keerthi S, 2008, P 14 ACM SIGKDD INT, P408, DOI 10.1145/1401890.1401942 Keys K, 2001, WORKSH PASS ACT MEAS Kuncheva L., 2004, COMBINING PATTERN CL Maier G, 2009, IMC'09: PROCEEDINGS OF THE 2009 ACM SIGCOMM INTERNET MEASUREMENT CONFERENCE, P90 Mockapetris P, 2014, 1035 RFC INT ENG TAS Nguyen TTT, 2008, IEEE COMMUN SURV TUT, V10, P56, DOI 10.1109/SURV.2008.080406 Norvig P, 2014, BEAUTIFUL DATA STORI, P221 Park B, 2013, INT J NETW MANAG, V23, P350, DOI 10.1002/nem.1837 Plonka D, 2011, P WORKSH SEC TRUST I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Szabo Geza, 2007, IEEE INT S WORLD WIR, P1 Yu HF, 2013, TECHNICAL REPORT NR 27 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1055-7148 EI 1099-1190 J9 INT J NETW MANAG JI Int. J. Netw. Manag. PD JUL-AUG PY 2014 VL 24 IS 4 BP 272 EP 288 DI 10.1002/nem.1864 PG 17 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA AL9RT UT WOS:000339479100005 ER PT J AU Zhu, HS Chen, EH Xiong, H Cao, HH Tian, JL AF Zhu, Hengshu Chen, Enhong Xiong, Hui Cao, Huanhuan Tian, Jilei TI Mobile App Classification with Enriched Contextual Information SO IEEE TRANSACTIONS ON MOBILE COMPUTING LA English DT Article DE Mobile App classification; web knowledge; real-world contexts; enriched contextual information ID AWARE RECOMMENDATIONS AB The study of the use of mobile Apps plays an important role in understanding the user preferences, and thus provides the opportunities for intelligent personalized context-based services. A key step for the mobile App usage analysis is to classify Apps into some predefined categories. However, it is a nontrivial task to effectively classify mobile Apps due to the limited contextual information available for the analysis. For instance, there is limited contextual information about mobile Apps in their names. However, this contextual information is usually incomplete and ambiguous. To this end, in this paper, we propose an approach for first enriching the contextual information of mobile Apps by exploiting the additional Web knowledge from the Web search engine. Then, inspired by the observation that different types of mobile Apps may be relevant to different real-world contexts, we also extract some contextual features for mobile Apps from the context-rich device logs of mobile users. Finally, we combine all the enriched contextual information into the Maximum Entropy model for training a mobile App classifier. To validate the proposed method, we conduct extensive experiments on 443 mobile users' device logs to show both the effectiveness and efficiency of the proposed approach. The experimental results clearly show that our approach outperforms two state-of-the-art benchmark methods with a significant margin. C1 [Zhu, Hengshu; Chen, Enhong] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Anhui, Peoples R China. [Cao, Huanhuan; Tian, Jilei] Nokia Res Ctr, Beijing 100010, Peoples R China. [Xiong, Hui] Rutgers State Univ, Management Sci & Informat Syst Dept, Rutgers Business Sch, Newark, NJ 07102 USA. RP Zhu, HS (reprint author), Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230026, Anhui, Peoples R China. EM zhs@mail.ustc.edu.cn; cheneh@ustc.edu.cn; hxiong@rutgers.edu; happia.cao@gmail.com; jilei.tian@nokia.com FU National Science Foundation for Distinguished Young Scholars of China [61325010]; Natural Science Foundation of China (NSFC) [71329201]; National High Technology Research and Development Program of China [SS2014AA012303]; Science and Technology Development of Anhui Province [1301022064]; International Science and Technology Cooperation Plan of Anhui Province [1303063008]; US National Science Foundation (NSF) [CCF-1018151, IIS-1256016] FX This work was supported in part by grants from National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), Natural Science Foundation of China (NSFC, Grant No. 71329201), National High Technology Research and Development Program of China (Grant No. SS2014AA012303), Science and Technology Development of Anhui Province (Grants No. 1301022064), the International Science and Technology Cooperation Plan of Anhui Province (Grant No. 1303063008). This work was also partially supported by grants from US National Science Foundation (NSF) via grant numbers CCF-1018151 and IIS-1256016. This is a substantially extended and revised version of [32], which appears in Proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM 2012). CR Bao T., 2010, P 10 IEEE INT C DAT, P38 Berger AL, 1996, COMPUT LINGUIST, V22, P39 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Broder A.Z., 2007, P 30 ANN INT ACM SIG, P231, DOI 10.1145/1277741.1277783 Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI 10.1145/1401890.1401995 Cao HH, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P3, DOI 10.1145/1571941.1571945 DellaPietra S, 1997, IEEE T PATTERN ANAL, V19, P380, DOI 10.1109/34.588021 Ge Y, 2011, P 17 ACM SIGKDD INT, P983, DOI DOI 10.1145/2020408.2020568 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Heinrich G., 2008, PARAMTER STIMAION TE Huanhuan Cao, 2010, P CIKM, P1677, DOI 10.1145/1871437.1871702 Kahng Minsuk, 2011, P 20 INT C COMP WORL, P65 Li X., 2012, P MDM BENG IND, P238 Liu Qi, 2011, P IEEE 11 INT C DAT, P407 Ma H., 2012, P 21 INT C WORLD WID, P231 Malouf R., 2002, P 6 C NAT LANG LEARN, P1 Nigam K., 1999, P IJCAI 99 WORKSH MA, P61 Phan X.-H., 2010, IEEE T KNOWL DATA EN, V23, P961 Phan Xuan-Hieu, 2008, P 17 INT C WORLD WID, P91, DOI DOI 10.1145/1367497.1367510 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen D., 2006, P 29 ANN INT ACM SIG, P131, DOI 10.1145/1148170.1148196 van Setten M, 2004, LECT NOTES COMPUT SC, V3137, P235 Woerndl W, 2007, I C DATA ENGIN WORKS, P871, DOI 10.1109/ICDEW.2007.4401078 Yih W.T., 2007, P 22 NAT C ART INT, V2, P1489 Yu Kuifei, 2012, P 1 PAC AS KNOWL DIS, P431 Zhu H., 2011, P 20 ACM INT C INF K, P2221 Zhu H., 2012, P 21 ACM INT C INF K, P1617 Zhu HS, 2012, IEEE DATA MINING, P1212, DOI 10.1109/ICDM.2012.31 NR 29 TC 3 Z9 3 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1536-1233 EI 1558-0660 J9 IEEE T MOBILE COMPUT JI IEEE. Trans. Mob. Comput. PD JUL PY 2014 VL 13 IS 7 BP 1550 EP 1563 DI 10.1109/TMC.2013.113 PG 14 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA AL5JF UT WOS:000339169100013 ER PT J AU Xu, Z Luo, XF Zhang, SX Wei, X Mei, L Hu, CP AF Xu, Zheng Luo, Xiangfeng Zhang, Shunxiang Wei, Xiao Mei, Lin Hu, Chuanping TI Mining temporal explicit and implicit semantic relations between entities using web search engines SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE LA English DT Article DE Temporal semantic relation; Semantic annotation; Content analysis; Web mining ID EXTRACTION; NETWORKS AB In this paper, we study the problem of mining temporal semantic relations between entities. The goal of the studied problem is to mine and annotate a semantic relation with temporal, concise, and structured information, which can release the explicit, implicit, and diversity semantic relations between entities. The temporal semantic annotations can help users to learn and understand the unfamiliar or new emerged semantic relations between entities. The proposed temporal semantic annotation structure integrates the features from IEEE and Renlifang. We propose a general method to generate temporal semantic annotation of a semantic relation between entities by constructing its connection entities, lexical syntactic patterns, context sentences, context graph, and context communities. Empirical experiments on two different datasets including a LinkedIn dataset and movie star dataset show that the proposed method is effective and accurate. Different from the manually generated annotation repository such as Wikipedia and LinkedIn, the proposed method can automatically mine the semantic relation between entities and does not need any prior knowledge such as ontology or the hierarchical knowledge base. The proposed method can be used on some applications, which proves the effectiveness of the proposed temporal semantic relations on many web mining tasks. (C) 2013 Elsevier B.V. All rights reserved. C1 [Xu, Zheng; Mei, Lin; Hu, Chuanping] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. [Luo, Xiangfeng; Wei, Xiao] Shanghai Univ, Shanghai, Peoples R China. [Zhang, Shunxiang] Anhui Univ Sci & Technol, Hefei, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. EM xuzheng@shu.edu.cn; luoxf@shu.edu.cn; sxzhang@shu.edu.cn; xwei@shu.edu.cn; l_mei72@hotmail.com; cphu@vip.sina.com FU National Science and Technology Major Project [2013ZX01033002-003]; National High Technology Research and Development Program of China (863 Program) [2013AA014601, 2013AA014603]; National Key Technology Support Program [2012BAH07B01]; National Science Foundation of China [61300202]; Science Foundation of Shanghai [13ZR1452900] FX This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014601, 2013AA014603, in part by National Key Technology Support Program under Grant 2012BAH07B01, in part by the National Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900. CR Agichtein E., 2000, INT C DIG LIBR Banko M., 2009, P INT JOINT C ART IN, P2670 Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Bollegala DT, 2010, P 19 WWW, P151, DOI 10.1145/1772690.1772707 Brin S., 1998, INT WORKSH WEB DAT Conforti D, 1999, FUTURE GENER COMP SY, V15, P287, DOI 10.1016/S0167-739X(98)00073-9 Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Giuliano C., 2006, EACL Han JW, 2002, COMPUTER, V35, P64 Harabagiu A., 2005, P 19 INT JOINT C ART, P1061 Ji YQ, 2013, IEEE T KNOWL DATA EN, V25, P721, DOI 10.1109/TKDE.2012.28 Luo G., 2007, P 16 INT C WORLD WID, P561, DOI 10.1145/1242572.1242648 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Moschopoulos T, 2013, IEEE T KNOWL DATA EN, V25, P2404, DOI 10.1109/TKDE.2012.159 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shinyama Y., 2006, P MAIN C HUM LANG TE, P304, DOI 10.3115/1220835.1220874 Solvberg I., 1992, Future Generation Computer Systems, V7, DOI 10.1016/0167-739X(92)90053-E Steinbach M., 2000, KDD WORKSH TEXT MIN TREC, 2005, TREC 2005 P Yen NY, 2013, ACM T INTEL SYST TEC, V4, DOI [10.1145/2438653.2438665, 10.1145/2438653.2438668] Yen NY, 2010, IEEE T LEARN TECHNOL, V3, P250, DOI 10.1109/TLT.2010.15 Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205 Zhou G., 2007, P 2007 JOINT C EMP M, P728 Zhu J., 2009, P 18 INT C WORLD WID, P101, DOI 10.1145/1526709.1526724 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 NR 28 TC 6 Z9 6 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-739X EI 1872-7115 J9 FUTURE GENER COMP SY JI Futur. Gener. Comp. Syst. PD JUL PY 2014 VL 37 BP 468 EP 477 DI 10.1016/j.future.2013.09.027 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA AJ8CV UT WOS:000337931200045 ER PT J AU Gu, YH Yang, ZL Xu, GD Nakano, M Toyoda, M Kitsuregawa, M AF Gu, Yanhui Yang, Zhenglu Xu, Guandong Nakano, Miyuki Toyoda, Masashi Kitsuregawa, Masaru TI Exploration on efficient similar sentences extraction SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE semantic similarity; query aggregation; top-k ID LATENT SEMANTIC ANALYSIS; SPACE; TEXT; WORDS; NETS AB Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them. C1 [Gu, Yanhui; Yang, Zhenglu; Nakano, Miyuki; Toyoda, Masashi; Kitsuregawa, Masaru] Univ Tokyo, Inst Ind Sci, Tokyo, Japan. [Xu, Guandong] Univ Technol Sydney, Adv Analyt Inst, Sydney, NSW 2007, Australia. RP Yang, ZL (reprint author), Univ Tokyo, Inst Ind Sci, Tokyo, Japan. EM guyanhui@tkl.iis.u-tokyo.ac.jp; yangzl@tkl.iis.u-tokyo.ac.jp; guandong.xu@uts.edu.au; miyuki@tkl.iis.u-tokyo.ac.jp; toyoda@tkl.iis.u-tokyo.ac.jp; kitsure@tkl.iis.u-tokyo.ac.jp CR Atallah M.J., 1998, ALGORITHMS AND THEOR Blake M.B., 2012, SEMANTIC WEB SERVICE Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Ceccarelli Diego, 2011, P 14 INT C EXT DAT T, P93 Chowdhury G.G., 2010, INTRODUCTION TO MODE Cohen W. W., 1998, P ACM SIGMOD INT C M, P201, DOI 10.1145/276304.276323 Cui Hang, 2005, P 28 ANN INT ACM SIG, P400, DOI 10.1145/1076034.1076103 DAMERAU FJ, 1964, COMMUN ACM, V7, P171, DOI 10.1145/363958.363994 Dolan B., 2004, P 20 INT C COMP LING, P350, DOI 10.3115/1220355.1220406 Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Goyal A, 2011, P JOINT C EMP METH N, P250 Goyal A., 2009, PROCEEDINGS OF HUMAN, P512, DOI 10.3115/1620754.1620829 HAMMING RW, 1950, AT&T TECH J, V29, P147 Han W.-S., 2007, P 33 INT C VER LARG, P423 Hatzivassiloglou V, 1999, P JOINT SIGDAT C EMP, P203 Hellerstein JM, 1999, COMPUTER, V32, P51, DOI 10.1109/2.781635 HIRSCHBERG DS, 1975, COMMUN ACM, V18, P341, DOI 10.1145/360825.360861 Hua M, 2009, VLDB J, V18, P809, DOI 10.1007/s00778-008-0128-8 Hua M., 2007, P 33 INT C VER LARG, P890 Islam A., 2006, P INT C LANG RES EV, P1033 Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 JARO MA, 1989, J AM STAT ASSOC, V84, P414, DOI 10.2307/2289924 Jiang J., 1997, CORR Jones K.S., J DOC Kim J.W., 2010, PROCEEDINGS OF THE I, P1813, DOI 10.1145/1871437.1871736 Koren J., 2008, P 17 INT C WORLD WID, P477, DOI 10.1145/1367497.1367562 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Leacock C., 1998, WORDNET ELECT LEXICA, P305 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Maguitman A., 2005, P 14 INT C WORLD WID, P107, DOI 10.1145/1060745.1060765 MAYNARD D, 2012, PROCEEDINGS OF THE I, P3487 Meadow C.T., 1992, TEXT INFORMATION RET Metzler D, 2007, LECT NOTES COMPUT SC, V4425, P16 Mihalcea R., 2006, P 21 NAT C ART INT A, P775 Mihalcea Rada, 2004, P EMNLP, P404 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Navarro G, 2001, ACM COMPUT SURV, V33, P31, DOI 10.1145/375360.375365 Navarro G., 1998, CLEI ELECTR J, V1 Pantel P., 2009, P 2009 C EMP METH NA, P938 Radinsky K., 2011, P 20 INT C WORLD WID, P337, DOI DOI 10.1145/1963405.1963455 Radlinski F., 2008, P 31 ANN INT ACM SIG, P403, DOI DOI 10.1145/1390334.1390404 Re C., 2007, P 23 INT C DAT ENG I, P886 Resnik P, 1995, INT JOINT CONF ARTIF, P448 RYENG NH, 2011, PROCEEDINGS OF THE I, V6588, P280 Sahami M., 2006, PROCEEDINGS OF THE I SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1988, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarawagi S., 2004, P ACM SIGMOD INT C M, P743, DOI 10.1145/1007568.1007652 SHANNON CE, 1948, AT&T TECH J, V27, P623 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 Turney Peter D., 2001, P 12 EUR C MACH LEAR, P491 UKKONEN E, 1992, THEOR COMPUT SCI, V92, P191, DOI 10.1016/0304-3975(92)90143-4 Vernicaand R., 2009, P 1 INT WORKSH KEYW, P9, DOI 10.1145/1557670.1557677 Vlachou A, 2008, P ACM INT C MAN DAT, P753, DOI DOI 10.1145/1376616.1376692 WANG K, 2010, P ACM SIGIR C RES DE, P387, DOI 10.1145/1835449.1835515 Wei F., 2008, P 31 ANN INT ACM SIG, P283, DOI 10.1145/1390334.1390384 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yang Z., 2010, P AAAI C ART INT AAA, P1467 Yang Z., 2011, PROCEEDINGS OF THE I, P2373 Zhang X, 2009, DISTRIB PARALLEL DAT, V26, P67, DOI 10.1007/s10619-009-7050-y Zhuge H., 2008, THE WEB RESOURCE SPA Zhuge H., 2008, ACM TRANS INTERNET T, V8 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H., 2012, THE KNOWLEDGE GRID T Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 Zhuge H., 2010, ARTIF INTELL, V174, P190 Zhuge H, 2012, IEEE T SERV COMPUT, V5, P404, DOI 10.1109/TSC.2011.12 Zhuge H, 2010, FUTURE GENER COMP SY, V26, P359, DOI 10.1016/j.future.2009.10.010 NR 75 TC 1 Z9 1 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD JUL PY 2014 VL 17 IS 4 BP 595 EP 626 DI 10.1007/s11280-012-0195-z PG 32 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AI8KT UT WOS:000337163500008 ER PT J AU Lin, SY Lai, CH Wu, CH Lo, CC AF Lin, Szu-Yin Lai, Chin-Hui Wu, Chih-Heng Lo, Chi-Chun TI A trustworthy QoS-based collaborative filtering approach for web service discovery SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE Service selection; Service discovery; Quality of Service (QoS); Collaborative filtering ID FRAMEWORK; SELECTION; QUALITY AB Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services' Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality. (C) 2014 Elsevier Inc. All rights reserved. C1 [Lin, Szu-Yin; Lai, Chin-Hui] Chung Yuan Christian Univ, Dept Informat Management, Chung Li City, Taoyuan County, Taiwan. [Wu, Chih-Heng; Lo, Chi-Chun] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan. RP Lai, CH (reprint author), Chung Yuan Christian Univ, Dept Informat Management, Chung Li City, Taoyuan County, Taiwan. EM chlai@cycu.edu.tw FU National Science Council of the Taiwan [NSC 99-2410-H-009-036-MY3, NSC 100-2410-H-033-037] FX This work was supported in part by the National Science Council of the Taiwan [grant numbers NSC 99-2410-H-009-036-MY3 and NSC 100-2410-H-033-037]. CR Ali AS, 2006, CONCURR COMP-PRACT E, V18, P817, DOI 10.1002/cpe.977 Al-Masri Eyhab, 2007, Proceedings 16th International Conference on Computer Communications and Networks Birukou A, 2007, IEEE SOFTWARE, V24, P47, DOI 10.1109/MS.2007.169 Canfora G, 2008, J SYST SOFTWARE, V81, P1754, DOI 10.1016/j.jss.2007.12.792 Comuzzi M, 2009, ACM T WEB, V3, DOI 10.1145/1541822.1541825 Fernandez A., 2008, P 2 INT WORKSH SERV, P115 Gonzalez-Valenzuela S., 2011, COMPUT J, V55, P218 Gouscos D., 2003, P 4 INT C WEB INF SY, P121 Haas H., 2004, WEB SERVICES GLOSSAR Harshavardhanan P., 2012, INT C COMP COMM INF, P1 Herlocker JL, 2000, P 2000 ACM C COMP SU, P241, DOI DOI 10.1145/358916.358995 Huang AFM, 2009, INFORM SCIENCES, V179, P3309, DOI 10.1016/j.ins.2009.05.018 Keller A., 2003, Journal of Network and Systems Management, V11, DOI 10.1023/A:1022445108617 Kerrigan M., 2006, P 2006 ACM S APPL CO, P1664, DOI 10.1145/1141277.1141671 Khapre S., 2011, INT J WEB SEMANTIC T, V2, P78 Ko JM, 2008, J SYST SOFTWARE, V81, P2079, DOI 10.1016/j.jss.2008.04.044 Koloniari G, 2004, COMPUT J, V47, P461, DOI 10.1093/comjnl/47.4.461 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Li L.Y., 2012, IEEE 11 INT C COGN I, P103 Liangzhao Z., 2004, SOFTWARE ENG IEEE T, V30, P311 Manikrao U.S., 2005, P INT C NEXT GEN WEB, P117 Martin-Diaz O, 2004, FOURTH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS ENGINEERING WORKSHOPS, PROCEEDINGS, P156 Maximilien EM, 2004, IEEE INTERNET COMPUT, V8, P84, DOI 10.1109/MIC.2004.27 Maximilien E. M., 2005, Proceedings. Second International Conference on Autonomic Computing Menasce DA, 2002, IEEE INTERNET COMPUT, V6, P72, DOI 10.1109/MIC.2002.1067740 Mokhtar S.B., 2008, J SYST SOFTWARE, V81, P785 Mukhopadhyay D., 2012, ADV COMPUTER SCI ENG, V166, P1001 Nair M.K., 2010, INT J COMPUTER APPL, V7, P22, DOI 10.5120/1159-1508 Papazoglou M. P., 2003, P 4 INT C WEB INF SY, P3, DOI 10.1109/WISE.2003.1254461 Raj R.J.R., 2010, TRENDZ INFORM SCI CO, P156, DOI 10.1109/TISC.2010.5714629 Ran Shuping, 2003, J SIGECOM EXCHANGES, V4, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2001, P 10 INT C WORLD WID, VI, P285 ShaikhAli A., 2003, P 2003 S APPL INT WO, P85 Torres R., 2011, IEEE INT C SERV COMP, P64 Wang J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148257 Wang J, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361689 Wishart R., 2005, P 28 AUSTR C COMP SC, P49 XU ZQ, 2007, 2007 IEEE INT C WEB, P249 Yan J, 2009, LECT NOTES COMPUT SC, V5472, P200 Yang Y, 2012, J SYST SOFTWARE, V85, P1818, DOI 10.1016/j.jss.2012.03.005 ZHENG ZB, 2009, 2009 IEEE INT C WEB, P437 NR 42 TC 1 Z9 1 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 EI 1873-1228 J9 J SYST SOFTWARE JI J. Syst. Softw. PD JUL PY 2014 VL 93 BP 217 EP 228 DI 10.1016/j.jss.2014.01.036 PG 12 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA AI5AJ UT WOS:000336877500013 ER PT J AU Nio, L Sakti, S Neubig, G Toda, T Nakamura, S AF Nio, Lasguido Sakti, Sakriani Neubig, Graham Toda, Tomoki Nakamura, Satoshi TI Utilizing Human-to-Human Conversation Examples for a Multi Domain Chat-Oriented Dialog System SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS LA English DT Article DE dialog corpora; response generation; example-based dialog modeling; semantic similarity; cosine similarity; machine translation AB This paper describes the design and evaluation of a method for developing a chat-oriented dialog system by utilizing real human-to-human conversation examples from movie scripts and Twitter conversations. The aim of the proposed method is to build a conversational agent that can interact with users in as natural a fashion as possible, while reducing the time requirement for database design and collection. A number of the challenging design issues we faced are described, including (1) constructing an appropriate dialog corpora from raw movie scripts and Twitter data, and (2) developing an multi domain chat-oriented dialog management system which can retrieve a proper system response based on the current user query. To build a dialog corpus, we propose a unit of conversation called a tri-turn (a trigram conversation turn), as well as extraction and semantic similarity analysis techniques to help ensure that the content extracted from raw movie/drama script files forms appropriate dialog-pair (query-response) examples. The constructed dialog corpora are then utilized in a data-driven dialog management system. Here, various approaches are investigated including example-based (EBDM) and response generation using phrase-based statistical machine translation (SMT). In particular, we use two EBDM: syntactic-semantic similarity retrieval and TF-IDF based cosine similarity retrieval. Experiments are conducted to compare and contrast EBDM and SMT approaches in building a chat-oriented dialog system, and we investigate a combined method that addresses the advantages and disadvantages of both approaches. System performance was evaluated based on objective metrics (semantic similarity and cosine similarity) and human subjective evaluation from a small user study. Experimental results show that the proposed filtering approach effectively improve the performance. Furthermore, the results also show that by combing both EBDM and SMT approaches, we could overcome the shortcomings of each. C1 [Nio, Lasguido; Sakti, Sakriani; Neubig, Graham; Toda, Tomoki; Nakamura, Satoshi] Nara Inst Sci & Technol, Grad Sch Informat Sci, Ikoma 6300192, Japan. RP Nio, L (reprint author), Nara Inst Sci & Technol, Grad Sch Informat Sci, Ikoma 6300192, Japan. EM lasguido.kp9@is.naist.jp; ssakti@is.naist.jp; neubig@is.naist.jp; tomoki@is.naist.jp; s-nakamura@is.naist.jp FU I2R FX Part of this work was supported by the collaborative research with I2Rdagger. CR Banchs R.E., 2012, ACL 2012, P37 Bessho F., 2012, P P 13 ANN M SPEC IN, P227 Chambers Nathanael, 2004, P 5 SIGDIAL WORKSH D, P9 Echihabi A., 2003, P 41 ANN M ASS COMP, P16 Holmes J.N., 2001, SPEECH SYNTHESIS REC Jung S., 2006, P DIAL DIAL MULT EV Kim K., 2010, P SLT BERK CAL US, P324 Lee C, 2009, 2009 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION & UNDERSTANDING (ASRU 2009), P474, DOI 10.1109/ASRU.2009.5373242 Lee C, 2009, SPEECH COMMUN, V51, P466, DOI 10.1016/j.specom.2009.01.008 Liu D., 2012, J COMPUTATIONAL INFO, V8, P1027 Murao H., 2003, P SIGDIAL WORKSH DIS, P140 Ritter A., 2011, P C EMP METH NAT LAN, P583 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seneff E., 1991, P 4 DARPA SPEECH NAT, P354 Sproat R., 2001, Computer Speech and Language, V15, DOI 10.1006/csla.2001.0169 Walker M., 2000, P EUR C SPEECH COMM, P1371 Wallace R., 2003, YOUR OWN BOTMASTER WEIZENBA.J, 1966, COMMUN ACM, V9, P36, DOI 10.1145/365153.365168 Wong Yuk Wah, 2006, P HUM LANG TECHN C N, P439, DOI 10.3115/1220835.1220891 Wong Yuk Wah, 2007, P HUM LANG TECHN C N, P172 NR 20 TC 0 Z9 0 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN SN 1745-1361 J9 IEICE T INF SYST JI IEICE Trans. Inf. Syst. PD JUN PY 2014 VL E97D IS 6 BP 1497 EP 1505 DI 10.1587/transinf.E97.D.1497 PG 9 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AQ4QX UT WOS:000342784300012 ER PT J AU Zhang, TZ Xu, CS AF Zhang, Tianzhu Xu, Changsheng TI Cross-Domain Multi-Event Tracking via CO-PMHT SO ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS LA English DT Article DE Algorithms; Experimentation; Performance; Cross-domain; multi-modality; multi-event tracking; PMHT; CO-PMHT ID VIDEO ANNOTATION; NEWS; TEXT AB With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. After tracking, we can visualize the whole evolutionary process of the event over time and mine the semantic topics of each event for deep understanding and event prediction. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for cross-domain multi-event tracking. C1 [Xu, Changsheng] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. China Singapore Inst Digital Media, Singapore 119613, Singapore. RP Xu, CS (reprint author), Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China. EM csxu@nlpr.ia.ac.cn FU National Program on Key Basic Research Project (973 Program) [2012CB316304]; National Natural Science Foundation of China [61225009, 61303173]; Singapore National Research Foundation under International Research Centre at Singapore Funding Initiative FX This work is supported in part by the National Program on Key Basic Research Project (973 Program, Project No. 2012CB316304) and National Natural Science Foundation of China (61225009, 61303173), and also by the Singapore National Research Foundation under International Research Centre at Singapore Funding Initiative and administered by the IDM Programme Office. CR [Anonymous], 2002, P 27 ANN ACM SIGIR I Bar-Shalom Y., 2009, P 27 ANN ACM SIGIR I, V29, P82 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chieu H. L., 2004, P 27 ANN ACM SIGIR I Cox I., 1979, IEEE T PATTERN ANAL, V24, P843 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Duygulu P., 2005, P ACM MULT OCT, P820 Kender JR, 2005, PROC CVPR IEEE, P1174 Knan Z., 2005, IEEE T PATTERN ANAL, V27, P82 KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Kumar R., 2004, P 10 ACM SIGKDD INT Lin FR, 2008, DECIS SUPPORT SYST, V45, P473, DOI 10.1016/j.dss.2007.06.009 Liu LQ, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2486 Liu S, 2012, IEEE T MULTIMEDIA, V14, P361, DOI 10.1109/TMM.2011.2174780 Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shitrit H., 2011, P IEEE INT C COMP VI, P1 Streit R. L., 1995, 10 NAV UND WARF CTR, P428 Wu X, 2008, IEEE T MULTIMEDIA, V10, P188, DOI 10.1109/TMM.2007.911778 Xie L, 2004, IEEE IMAGE PROC, P2383 Xing J., 2009, P INT C WIR COMM SEP, P1, DOI 10.1145/1654059.1654086 Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 Yu Q., 2009, IEEE T PATTERN ANAL, V31, P1 Zhai Y., 2005, P 13 ACM INT C MULT, P2, DOI 10.1145/1101149.1101152 Zhang T., 2012, P 12 EUR C COMP VIS Zhang Tianzhu, 2013, P IEEE INT C COMP VI Zhang TZ, 2012, IEEE T MULTIMEDIA, V14, P1206, DOI 10.1109/TMM.2012.2191944 Zhang TZ, 2012, PROC CVPR IEEE, P2042 Zhang TZ, 2013, IEEE T IND INFORM, V9, P149, DOI 10.1109/TII.2012.2218251 Zhang TZ, 2013, INT J COMPUT VISION, V101, P367, DOI 10.1007/s11263-012-0582-z NR 31 TC 1 Z9 1 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1551-6857 EI 1551-6865 J9 ACM T MULTIM COMPUT JI ACM Trans. Multimed. Comput. Commun. Appl. PD JUN PY 2014 VL 10 IS 4 AR 31 DI 10.1145/2602633 PG 19 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA AO4FY UT WOS:000341292900001 ER PT J AU Green, CD Feinerer, I Burman, JT AF Green, Christopher D. Feinerer, Ingo Burman, Jeremy T. TI BEYOND THE SCHOOLS OF PSYCHOLOGY 2: A DIGITAL ANALYSIS OF PSYCHOLOGICAL REVIEW, 1904-1923 SO JOURNAL OF THE HISTORY OF THE BEHAVIORAL SCIENCES LA English DT Article ID RETENTION; ASSOCIATION AB In order to better understand the broader trends and points of contention in early American psychology, it is conventional to organize the relevant material in terms of "schools" of psychology-structuralism, functionalism, etc. Although not without value, this scheme marginalizes many otherwise significant figures, and tends to exclude a large number of secondary, but interesting, individuals. In an effort to address these problems, we grouped all the articles that appeared in the second and third decades of Psychological Review into five-year blocks, and then cluster analyzed each block by the articles' verbal similarity to each other. This resulted in a number of significant intellectual "genres" of psychology that are ignored by the usual "schools" taxonomy. It also made "visible" a number of figures who are typically downplayed or ignored in conventional histories of the discipline, and it provide us with an intellectual context in which to understand their contributions. (C) 2014 Wiley Periodicals, Inc. C1 [Green, Christopher D.] York Univ, Dept Psychol, Hist & Theory Psychol Program, N York, ON M3J 1P3, Canada. [Feinerer, Ingo] Vienna Univ Technol, Fac Informat, Vienna, Austria. [Burman, Jeremy T.] York Univ, Hist & Theory Psychol Program, N York, ON M3J 1P3, Canada. RP Green, CD (reprint author), York Univ, Dept Psychol, 4700 Keele St, N York, ON M3J 1P3, Canada. EM christo@yorku.ca CR [Anonymous], 1928, J AM MED ASS, V90, P1402 [Anonymous], 1912, LAW DIVIDES FAMILY G, P5 [Anonymous], 1943, B NEW YORK ACAD MED, V19, P522 [Anonymous], 1889, AM ENG, V17, P148 Baker D. B., 2003, SEANCE SCI HIST PROF Baldwin J. M., 1913, HIST PSYCHOL SKETCH Baldwin J. M., 1906, THINGS STUDY DEV MEA Bartelmez G. W., 1973, BIOGRAPHICAL MEMOIRS, V43, P75 Bartlett S. L., HIST DEPT BOT Bazar J., 2010, PSYCHOL FEMINIST VOI Boring E. G., 1929, HIST EXPT PSYCHOL Buckley K. W., 1989, MECH MAN JB WATSON B CAHAN ED, 1984, DEV PSYCHOL, V20, P128, DOI 10.1037/0012-1649.20.1.128 Cordeschi R, 2006, Imagination and Rigor, P39, DOI 10.1007/88-470-0472-1_4 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dimmick F. L., 1943, AM J PSYCHOL, V56, P137 DORCUS RM, 1950, AM J PSYCHOL, V63, P114 Feinerer I, 2012, TM TEXT MINING PACKA Feinerer I, 2008, J STAT SOFTW, V25, P1 Fernberger SW, 1943, PSYCHOL REV, V50, P33, DOI 10.1037/h0054180 Furumoto L., 1987, UNTOLD LIVES 1 GEN A Garvey CR, 1929, PSYCHOL BULL, V26, P652, DOI 10.1037/h0075811 Green CD, 2013, J HIST BEHAV SCI, V49, P167, DOI 10.1002/jhbs.21592 Haberman J. V, 1917, HEREDITY RELATION PS Haberman J. V., 1914, J ABNORM PSYCHOL, V9, P265, DOI 10.1037/h0070291 Haberman J. V., 1911, BAS CLIN LECT HELD D Haberman J. V., 1917, PROBING MIND NORMAL Haberman JV, 1915, J AMER MED ASSOC, V64, P1141 Harris JA, 1919, BIOMETRIC STUDY BASA Harris JA, 1918, P NATL ACAD SCI USA, V4, P370, DOI 10.1073/pnas.4.12.370 Hebb D. O., 1949, ORG BEHAV HENLE M, 1984, J HIST BEHAV SCI, V20, P9, DOI 10.1002/1520-6696(198401)20:1<9::AID-JHBS2300200103>3.0.CO;2-U Hoffding H, 1891, OUTLINES PSYCHOL Hoffding H, 1900, HIST MODERN PHILOSOP Hornik K., 2012, SKMEANS SPHERICAL KM Hornik K, 2012, J STAT SOFTW, V50, P1 JAMES W., 1904, J PHILOS PSYCHOL SCI, V1, P533, DOI 10.2307/2011912 James W., 1904, J PHILOS PSYCHOL SCI, V1, P477, DOI DOI 10.2307/2011942 Jeffrey L. R., 2000, ENCY PSYCHOL, V8, P247 Jolly J. L., 2007, GIFTED CHILD TODAY, V30, P55 KINTSCH W, 1994, PSYCHOL REV, V101, P195, DOI 10.1037//0033-295X.101.2.195 Lahham D, 2013, CAN PSYCHOL, V54, P124, DOI 10.1037/a0026286 Lloyd Morgan C., 1891, ANIMAL LIFE INTELLIG Macfarlane J. W., 1958, MEMORIAM Manning C.D., 2008, INTRO INFORM RETRIEV Marquis A. N., 1912, BOOK ST LOUISANS BIO Marshall E., 1911, NY TIMES, pSM8 Marshall E., 1912, NY TIMES, pSM1 Meyer M., 1911, LECT FDN MENTAL SOCI Meyer M, 1913, AM J PSYCHOL, V24, P555, DOI 10.2307/1413452 Moore T. V., 1910, PROCESS ABSTRACTION Morgan C. L, 1923, EMERGENT EVOLUTION Morgan C. L., 1900, ANIMAL BEHAV Netsell R., 1989, J ACOUST SOC AM, V85, P976 Ogden RM, 1911, PSYCHOL BULL, V8, P183, DOI 10.1037/h0072078 Pearsall, 1972, THOUGHT CHARACTER W Peterson J, 1928, ANN AM ACAD POLIT SS, V140, P178, DOI 10.1177/000271622814000123 Peterson J, 1934, J NEGRO EDUC, V3, P403, DOI 10.2307/2292380 Peterson J., 1923, COMP PSYCHOL MONOGR, V1, P141 REED HB, 1946, J EXP PSYCHOL, V36, P71, DOI 10.1037/h0059961 Reed H. B., 1946, J EXPT PSYCHOL, P36 Reed H. B., 1969, HIST PSYCHOL ASS KAN REED HB, 1950, J EXP PSYCHOL, V40, P504, DOI 10.1037/h0059446 REED HB, 1946, J EXP PSYCHOL, V36, P166, DOI 10.1037/h0055244 RICHARD WC, 1968, PEABODY J EDUC, V46, P3 Rodkey E., 2010, PSYCHOL FEMINIST VOI Russell S. B., 1898, T ASCE, V39, P237 Russell S. B., 1900, T AM SOC CIVIL ENG, V43, P1 Russell S. B., 1913, J ANIMAL BEHAV, V3, P15, DOI 10.1037/h0070584 Russell S. B., 1891, T AM SOC CIVIL ENG, V25, P495 Russell SB, 1915, PSYCHOL REV, V22, P163, DOI 10.1037/h0075899 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMELSON F, 1981, J HIST BEHAV SCI, V17, P399, DOI 10.1002/1520-6696(198107)17:3<399::AID-JHBS2300170310>3.0.CO;2-2 Sellars R. W., 1922, EVOLUTIONARY NATURAL Sidis B., 1902, PSYCHOPATHOLOGICAL R Sidis B., 1922, NERVOUS ILLS THEIR C Siewert T. A., 2000, PENDULUM IMPACT TEST Slater J. G., 2005, MINEVARS AVIARY PHIL Smith K. D., 2010, WATER WORKS 150 YEAR Sokal MM, 1997, HIST HUM SCI, V10, P57, DOI 10.1177/095269519701000104 STAFFORD JW, 1970, AM J PSYCHOL, V83, P286 Stetson R. H., 1928, MOTOR PHONETICS STUD Watson J, 1928, WAYS BEHAV Watson J. B., 1919, PSYCHOL STANDPOINT B Watson J. B., 1928, PSYCHOL CARE INFANT Watson J. B., 1914, BEHAV TEXTBOOK COMPA Wells F. L., 1917, MENTAL ADJUSTMENTS Wheeler R. H., 1932, LAWS HUMAN NATURE Wheeler R. H., 1929, SCI PSYCHOL WHEELER RH, 1932, PRINCIPLES MENTAL DE Whipple G. M., 1919, CLASSES GIFTED CHILD Wozniak R. H., 1997, AP WEISS THEORETICAL Wozniak R. H., 2009, RASSEGNA PSICOLOGIA, V26, P111 Wozniak RH, 2013, HIST PSYCHOL, V16, P227, DOI 10.1037/a0033575 Young J. L., 2010, PSYCHOL FEMINIST VOI Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 Zusne L., 1984, BIOGRAPHICAL DICT PS, P460 NR 97 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0022-5061 EI 1520-6696 J9 J HIST BEHAV SCI JI J. Hist. Behav. Sci. PD SUM PY 2014 VL 50 IS 3 BP 249 EP 279 DI 10.1002/jhbs.21665 PG 31 WC History Of Social Sciences SC Social Sciences - Other Topics GA AL4IJ UT WOS:000339096100002 PM 24911464 ER PT J AU Zhou, XZ Menche, J Barabasi, AL Sharma, A AF Zhou, XueZhong Menche, Joerg Barabasi, Albert-Laszlo Sharma, Amitabh TI Human symptoms-disease network SO NATURE COMMUNICATIONS LA English DT Article ID ELECTRONIC HEALTH RECORDS; INFLAMMATORY-BOWEL-DISEASE; PARKINSONS-DISEASE; PHENOTYPE ONTOLOGY; ALZHEIMERS-DISEASE; GENES; INFORMATION; MODEL; INTERACTOME; DISCOVERY AB In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design. C1 [Zhou, XueZhong] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China. [Zhou, XueZhong] Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China. [Zhou, XueZhong; Menche, Joerg; Barabasi, Albert-Laszlo; Sharma, Amitabh] Northeastern Univ, Dept Phys, Ctr Complex Network Res, Boston, MA 02115 USA. [Zhou, XueZhong; Menche, Joerg; Barabasi, Albert-Laszlo; Sharma, Amitabh] Dana Farber Canc Inst, Ctr Canc Syst Biol, Boston, MA 02215 USA. [Menche, Joerg; Barabasi, Albert-Laszlo] Budapest Univ Technol & Econ, Dept Theoret Phys, H-1111 Budapest, Hungary. [Barabasi, Albert-Laszlo] Cent European Univ, Ctr Network Sci, H-1051 Budapest, Hungary. [Barabasi, Albert-Laszlo; Sharma, Amitabh] Harvard Univ, Sch Med, Brigham & Womens Hosp, Channing Div Network Med, Boston, MA 02115 USA. RP Zhou, XZ (reprint author), Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China. EM xzzhou@bjtu.edu.cn; amitabh.sharma@channing.harvard.edu FU National Science Foundation of China [61105055, 81230086]; National Basic Research Program of China [2014CB542903]; National Key Technology RD Program [2013BAI02B01, 2013BAI13B04]; National S&T Major Special Project [2012ZX09503-001-003]; Beijing Municipal S&T Program of China [Z131110002813118]; MapGen grant [1U01HL108630-01]; EC-FP7 Program, Synergy-COPD [270086]; U.S. National Institutes of Health [HL066289, HL105339] FX We thank Baoyan Liu, Chaoming Song, Dashun Wang and Andrew Michaelson for useful discussions and suggestions, and especially Lili Xu, Guangli Song, Haixun Qi, Minghui Lv, Yiwei Wang, Xiaofeng Zhou and Hongwei Chu for the manual validation of the selected PubMed records. X.Z. was supported by the National Science Foundation of China (61105055, 81230086), National Basic Research Program of China (2014CB542903), National Key Technology R&D Program (2013BAI02B01, 2013BAI13B04), National S&T Major Special Project (2012ZX09503-001-003) and Beijing Municipal S&T Program of China (Z131110002813118). This work was supported by MapGen grant (1U01HL108630-01) and by the EC-FP7 Program, Synergy-COPD, GA no 270086. Additional support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health. CR Barabasi AL, 2011, NAT REV GENET, V12, P56, DOI 10.1038/nrg2918 Botstein D, 2003, NAT GENET, V33, P228, DOI 10.1038/ng1090 Brunner HG, 2004, NAT REV GENET, V5, P545, DOI 10.1038/nrg1383 Campillos M, 2008, SCIENCE, V321, P263, DOI 10.1126/science.1158140 Cao H, 2007, J BIOMED INFORM, V40, P343, DOI 10.1016/j.jbi.2006.11.003 Chapman SJ, 2012, NAT REV GENET, V13, P175, DOI 10.1038/nrg3114 Coletti MH, 2001, J AM MED INFORM ASSN, V8, P317 Cormen T.H., 2001, INTRO ALGORITHMS COTE RA, 1980, JAMA-J AM MED ASSOC, V243, P756, DOI 10.1001/jama.243.8.756 Doelken SC, 2013, DIS MODEL MECH, V6, P358, DOI 10.1242/dmm.010322 Dudley AM, 2005, MOL SYST BIOL, V1, DOI 10.1038/msb4100004 Duty S, 2011, BRIT J PHARMACOL, V164, P1357, DOI 10.1111/j.1476-5381.2011.01426.x FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543 Freudenberg J, 2002, BIOINFORMATICS, V18, pS110 Gandhi TKB, 2006, NAT GENET, V38, P285, DOI 10.1038/ng1747 Gavin AC, 2006, NATURE, V440, P631, DOI 10.1038/nature04532 Giot L, 2003, SCIENCE, V302, P1727, DOI 10.1126/science.1090289 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Goh KI, 2007, P NATL ACAD SCI USA, V104, P8685, DOI 10.1073/pnas.0701361104 Grivennikov SI, 2010, CELL, V140, P883, DOI 10.1016/j.cell.2010.01.025 Gulbahce N, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002531 Hidalgo CA, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000353 Hripcsak G, 2013, J AM MED INFORM ASSN, V20, P117, DOI 10.1136/amiajnl-2012-001145 Hwang W, 2008, P 14 ACM SIGKDD INT, P336, DOI 10.1145/1401890.1401934 Imfeld P, 2013, EPILEPSIA, V54, P700, DOI 10.1111/epi.12045 Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768 Jensen PB, 2012, NAT REV GENET, V13, P395, DOI 10.1038/nrg3208 Jostins L, 2012, NATURE, V491, P119, DOI 10.1038/nature11582 Khor B, 2011, NATURE, V474, P307, DOI 10.1038/nature10209 KNOTTNERUS JA, 1987, MED DECIS MAKING, V7, P139, DOI 10.1177/0272989X8700700304 Kohane IS, 2011, NAT REV GENET, V12, P417, DOI 10.1038/nrg2999 Kuhn M, 2010, MOL SYST BIOL, V6, DOI 10.1038/msb.2009.98 Lee DS, 2008, P NATL ACAD SCI USA, V105, P9880, DOI 10.1073/pnas.0802208105 Li SM, 2004, SCIENCE, V303, P540, DOI 10.1126/science.1091403 LITTLE RA, 1986, ARCH EMERG MED, V3, P20 Liu L, 2010, LECT NOTES COMPUT SC, V5981, P384 Loscalzo J, 2007, MOL SYST BIOL, V3, DOI 10.1038/msb4100163 Loscalzo J., 2011, SYST BIOL MED, V3, P619 LOWE HJ, 1994, JAMA-J AM MED ASSOC, V271, P1103, DOI 10.1001/jama.271.14.1103 MCKUSICK VA, 1969, PERSPECT BIOL MED, V12, P298 MCKUSICK VA, 1975, AM J HUM GENET, V27, P261 Molodecky NA, 2012, GASTROENTEROLOGY, V142, P46, DOI 10.1053/j.gastro.2011.10.001 Moreau Y, 2012, NAT REV GENET, V13, P523, DOI 10.1038/nrg3253 Newman M., 2010, NETWORKS INTRO Oti M, 2006, J MED GENET, V43, DOI 10.1136/jmg.2006.041376 Park J, 2009, MOL SYST BIOL, V5, DOI 10.1038/msb.2009.16 Pathak J, 2013, J AM MED INFORM ASSN, V20, pE206, DOI 10.1136/amiajnl-2013-002428 Robinson PN, 2014, GENOME RES, V24, P340, DOI 10.1101/gr.160325.113 Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017 Rzhetsky A, 2007, P NATL ACAD SCI USA, V104, P11694, DOI 10.1073/pnas.0704820104 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez PE, 2012, P NATL ACAD SCI USA, V109, pE2895, DOI 10.1073/pnas.1121081109 Sanchez-Betancourt J, 2012, NEUROTOXICOLOGY, V33, P1346, DOI 10.1016/j.neuro.2012.08.012 SCHWEDER T, 1982, BIOMETRIKA, V69, P493 Serrano MA, 2009, P NATL ACAD SCI USA, V106, P6483, DOI 10.1073/pnas.0808904106 Smith CL, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2004-6-1-r7 Muzny DM, 2012, NATURE, V487, P330, DOI 10.1038/nature11252 van Driel MA, 2006, EUR J HUM GENET, V14, P535, DOI 10.1038/sj.ejhg.5201585 VanDuyn N, 2010, TOXICOL SCI, V118, P613, DOI 10.1093/toxsci/kfq285 Vanunu O, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000641 Vidal M, 2011, CELL, V144, P986, DOI 10.1016/j.cell.2011.02.016 Wang QH, 2012, EUR J HUM GENET, V20, P1162, DOI 10.1038/ejhg.2012.74 Wang XJ, 2011, BRIEF FUNCT GENOMICS, V10, P280, DOI 10.1093/bfgp/elr024 Warner TT, 2003, ANN NEUROL, V53, pS16, DOI 10.1002/ana.10487 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Wheeler DL, 2007, NUCLEIC ACIDS RES, V35, pD5, DOI 10.1093/nar/gkl1031 Wu XB, 2008, MOL SYST BIOL, V4, DOI 10.1038/msb.2008.27 Yates F., 1943, STAT TABLES BIOL AGR Yildirim MA, 2007, NAT BIOTECHNOL, V25, P1119, DOI 10.1038/nbtl338 NR 69 TC 12 Z9 13 PU NATURE PUBLISHING GROUP PI LONDON PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND SN 2041-1723 J9 NAT COMMUN JI Nat. Commun. PD JUN PY 2014 VL 5 AR 4212 DI 10.1038/ncomms5212 PG 10 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AL0TI UT WOS:000338839300001 PM 24967666 ER PT J AU Wang, D Irani, D Pu, C AF Wang, De Irani, Danesh Pu, Calton TI A Perspective of Evolution After Five Years: A Large-Scale Study of Web Spam Evolution SO INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS LA English DT Article DE Web spam; evolution; spam corpus AB Identifying and detecting web spam is an ongoing battle between spam-researchers and spammers which has been going on since search engines allowed searching of web pages to the modern sharing of web links via social networks. A common challenge faced by spam-researchers is the fact that new techniques depend on requiring a corpus of legitimate and spam web pages. Although large corpora of legitimate web pages are available to researchers, the same cannot be said about web spam or spam web pages. In this paper, we introduce the Webb Spam Corpus 2011 - a corpus of approximately 330,000 spam web pages - which we make available to researchers in the fight against spam. By having a standard corpus available, researchers can collaborate better on developing and reporting results of spam filtering techniques. The corpus contains web pages crawled from links found in over 6.3 million spam emails. We analyze multiple aspects of this corpus including redirection, HTTP headers, web page content, and classification evaluation. We also provide insights into changes in web spam since the last Webb Spam Corpus was released in 2006. These insights include: (1) spammers manipulate social media in spreading spam; (2) HTTP headers and content also change over time; (3) spammers have evolved and adopted new techniques to avoid the detection based on HTTP header information. C1 [Wang, De; Irani, Danesh; Pu, Calton] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA. RP Wang, D (reprint author), Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA. EM wang6@gatech.edu FU National Science Foundation [1250260, 1402266, 1127904, 1138666, 0905493]; Singapore Government, Fujitsu Labs, and Georgia Tech Foundation through the John P. Imlay, Jr. Chair endowment [DARPA/I2O] FX This research has been partially funded by National Science Foundation by CNS/SAVI (1250260, 1402266), IUCRC/FRP (1127904), CISE/CNS (1138666), NetSE (0905493) programs, and gifts, grants, or contracts from DARPA/I2O, Singapore Government, Fujitsu Labs, and Georgia Tech Foundation through the John P. Imlay, Jr. Chair endowment. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding agencies and companies mentioned above. CR Amitay E., 2003, P 14 ACM C HYP HYP H, P38 Antoniades D., 2011, P 20 INT C WORLD WID, P715 Benczur AA, 2005, P 1 INT WORKSH ADV I Chandrinos KV, 2000, LECT NOTES COMPUT SC, V1923, P403 CHINCHOR N, 1992, FOURTH MESSAGE UNDERSTANDING CONFERENCE (MUC-4), P22 Chung Y., 2011, THESIS U TOKYO TOKYO Cutts M., 2013, GOOGLE BLOG USING DA Davison B. D., 2000, ARTIF INTELL, P23 Drost I., 2005, P 16 EUR C MACH LEAR, P233 Fetterly D., 2003, P 12 INT WORLD WID W, P669 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Gyongyi Z., 2005, P 1 INT WORKSH ADV I Gyongyi Z., 2004, P 30 INT C VER LARG Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Internet Engineering Task Force (IETF), 2013, 5988 IETF RFC Irani D., 2008, ECRIME RES SUMM, V2008, P1 KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lewis D.D., 1998, MACHINE LEARNING ECM, V1398, P4, DOI 10.1007/BFb0026666 OWASP, 2013, CROSS SIT SCRIPT XSS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stanford University, 2013, STANF WEBB PROJ Twitter Blog, 2013, TWITT BLOG SHUTT DOW Wang D, 2011, P 8 ANN COLL EL MESS, P46 Wang D., 2014, P PH D S 30 IEEE INT Webb S., 2006, P 3 C EM ANT CEAS 20 Webb S., 2008, P 17 C INF KNOWL MAN Webb S., 2007, P 4 C EM ANT CEAS 20, P84 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 28 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-8430 EI 1793-6365 J9 INT J COOP INF SYST JI Int. J. Coop. Inf. Syst. PD JUN PY 2014 VL 23 IS 2 SI SI DI 10.1142/S0218843014410019 PG 29 WC Computer Science, Information Systems SC Computer Science GA AJ9IP UT WOS:000338022700002 ER PT J AU Visinescu, LL Evangelopoulos, N AF Visinescu, Lucian L. Evangelopoulos, Nicholas TI Orthogonal rotations in latent semantic analysis: An empirical study SO DECISION SUPPORT SYSTEMS LA English DT Article DE Latent semantic analysis; Factor rotations; Varimax; Quartimax; Equamax; Big data; COALS ID EXPLORATORY FACTOR-ANALYSIS; QUARTIMAX METHOD; INFORMATION AB The Latent Semantic Analysis (LSA) literature has recently started to address the issue of interpretability of the extracted dimensions. On the software implementation front, recent versions of SAS Text Miner 0 started incorporating Varimax rotations. Considering open source software such as R, when it comes to rotation procedures the user has many more options. However, there is a little work in providing guidance for selecting an appropriate rotation procedure. In this paper we further previous research on LSA rotations by introducing two well known orthogonal rotations, namely Quartimax and Equamax, and comparing them to Varimax. We present a study that empirically tests the influence of the chosen orthogonal rotations on the extraction and interpretation of LSA factors. Our results indicate that, in most cases, Varimax and Equamax produce factors with similar interpretation, while Quartimax tends to produce a single factor. We conclude with recommendations on how these rotation procedures should be used and suggestions for future research. We note that orthogonal rotations can be used to improve the interpretability of other SVD-based models, such as COALS. (C) 2014 Elsevier B.V. All rights reserved. C1 [Visinescu, Lucian L.] Texas State Univ, McCoy Coll Business, Dept Comp Informat Syst & Quantitat Methods, San Marcos, TX 78666 USA. [Evangelopoulos, Nicholas] Univ N Texas, Coll Business, Dept Informat Syst & Decis Sci, Denton, TX 76203 USA. RP Visinescu, LL (reprint author), Texas State Univ, McCoy Coll Business, Dept Comp Informat Syst & Quantitat Methods, 601 Univ Dr, San Marcos, TX 78666 USA. EM llv19@txstate.edu; evangeln@unt.edu CR Abdi H., 2003, FACTOR ROTATIONS FAC Albright R., 2004, TAMING TEXT SVD Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826 Bollegala D, 2012, INFORM SCIENCES, V217, P78, DOI 10.1016/j.ins.2012.06.015 Browne MW, 2001, MULTIVAR BEHAV RES, V36, P111, DOI 10.1207/S15327906MBR3601_05 Cao Q, 2011, DECIS SUPPORT SYST, V50, P511, DOI 10.1016/j.dss.2010.11.009 CARROLL JB, 1953, PSYCHOMETRIKA, V18, P23 Coussement K, 2008, DECIS SUPPORT SYST, V44, P870, DOI 10.1016/j.dss.2007.10.010 CRAWFORD CB, 1970, PSYCHOMETRIKA, V35, P321, DOI 10.1007/BF02310792 Cree G.S., 2012, CAMBRIDGE HDB PSYCHO, P259 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Evangelopoulos N, 2012, COMMUN ACM, V55, P55, DOI 10.1145/2076450.2076467 Feldman R., 2007, TEXT MINING HDB Ferguson G.A., 1954, PSYCHOMETRIKA, V19, P347 Furlan B, 2013, DECIS SUPPORT SYST, V55, P710, DOI 10.1016/j.dss.2013.02.002 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hu X., 2007, HDB LATENT SEMANTIC, P401 Jamieson D.G., 2012, J BIOL DATABASES CUR, P1 JORESKOG KG, 1969, PSYCHOMETRIKA, V34, P183, DOI 10.1007/BF02289343 Kaiser H.F., 1979, CURR CONTENTS, V24, P33 Lackey N. R., 2003, MAKING SENSE FACTOR Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lee JH, 2009, INFORM PROCESS MANAG, V45, P20, DOI 10.1016/j.ipm.2008.06.002 Lee S, 2010, J COMPUT INFORM SYST, V51, P1 MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037//0033-295X.101.2.343 Mirman D., 2008, J EXPT PSYCHOL LEARN, V32, P398 MULAIK SA, 1987, MULTIVAR BEHAV RES, V22, P267, DOI 10.1207/s15327906mbr2203_3 Neuhaus JO, 1954, BRIT J STATIST PSYCH, V7, P81 Podsakoff PM, 2003, J APPL PSYCHOL, V88, P879, DOI 10.1037/0021-9101.88.5.879 Rohde D.L.T., 2005, IMPROVED MODEL UNPUB Russom F., 2012, BIG DATA ANAL TDWI B SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAUNDERS DR, 1962, PSYCHOL REP, V10, P241 Saunders D.R., 1953, AM PSYCHOL, V8, P428 Sharp M., 2001, TEXT MINING Sidorova A, 2008, MIS QUART, V32, P467 Spearman C, 1904, AM J PSYCHOL, V15, P201, DOI 10.2307/1412107 Swanson D.R., 1988, J AM SOC INFORM SCI, V39, P183 Thurstone L. L., 1947, MULTIPLE FACTOR ANAL Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Wild F., 2005, OPEN SOURCE LSA PACK WRIGLEY C, 1958, PSYCHOMETRIKA, V23, P151, DOI 10.1007/BF02289011 Yu LC, 2013, DECIS SUPPORT SYST, V55, P146, DOI 10.1016/j.dss.2012.12.038 NR 45 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 EI 1873-5797 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD JUN PY 2014 VL 62 BP 131 EP 143 DI 10.1016/j.dss.2014.03.010 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA AI4HI UT WOS:000336825900012 ER PT J AU Golfarelli, M Turricchia, E AF Golfarelli, Matteo Turricchia, Elisa TI A characterization of hierarchical computable distance functions for data warehouse systems SO DECISION SUPPORT SYSTEMS LA English DT Article DE Testing; Similarity measures; Hierarchical data; Categorical data ID SEMANTIC SIMILARITY; SPACES; DOMAIN AB A data warehouse is a huge multidimensional repository used for statistical analysis of historical data. In a data warehouse events are modeled as multidimensional cubes where cells store numerical indicators while dimensions describe the events from different points of view. Dimensions are typically described at different levels of details through hierarchies of concepts. Computing the distance/similarity between two cells has several applications in this domain. In this context distance is typically based on the least common ancestor between attribute values, but the effectiveness of such distance functions varies according to the structure and to the number of the involved hierarchies. In this paper we propose a characterization of hierarchy types based on their structure and expressiveness, we provide a characterization of the different types of distance functions and we verify their effectiveness on different types of hierarchies in terms of their intrinsic discriminant capacity. (C) 2014 Elsevier B.V. All rights reserved. C1 [Golfarelli, Matteo; Turricchia, Elisa] Univ Bologna, DISI, I-40126 Bologna, Italy. RP Golfarelli, M (reprint author), Univ Bologna, DISI, I-40126 Bologna, Italy. EM matteo.golfarelli@unibo.it; elisa.turricchia2@unibo.it CR Aggarwal CC, 2001, LECT NOTES COMPUT SC, V1973, P420 Aggarwal CC, 2008, PROC INT CONF DATA, P1460, DOI 10.1109/ICDE.2008.4497589 Baikousi E, 2011, PROC INT CONF DATA, P171, DOI 10.1109/ICDE.2011.5767869 Beyer K, 1999, LECT NOTES COMPUT SC, V1540, P217 BORIAH S., 2008, P 8 SIAM INT C DAT M, P243 Buneman P., 1974, J COMBINATORIAL TH B, V17, P48, DOI 10.1016/0095-8956(74)90047-1 Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808 Eskin E., 2002, GEOMETRIC FRAMEWORK Faver T., 2012, ARXIV E PRINTS Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 Golfarelli M., 2009, DATA WAREHOUSE DESIG GOODALL DW, 1966, BIOMETRICS, V22, P882, DOI 10.2307/2528080 Hinneburg A., 2000, P 26 INT C VER LARG, P506 Houle ME, 2010, LECT NOTES COMPUT SC, V6187, P482, DOI 10.1007/978-3-642-13818-8_34 Houle ME, 2005, PROC INT CONF DATA, P619 Hsu CC, 2008, EXPERT SYST APPL, V35, P1177, DOI 10.1016/j.eswa.2007.08.049 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kriegel H.-P., 2009, TKDD, V3 Kriegel H.P., 2008, P KDD, P444 Kriegel H.P., 2008, P SSDBM Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Lin S, 2006, DECIS SUPPORT SYST, V41, P604, DOI 10.1016/j.dss.2004.06.005 Minnesota Population Center, 2008, ITN PUBL US MICR SER Mo DY, 2012, IEEE T KNOWL DATA EN, V24, P59, DOI 10.1109/TKDE.2010.225 Moise G, 2009, KNOWL INF SYST, V21, P299, DOI 10.1007/s10115-009-0226-y Muller E., 2009, PVLDB, V2, P1270 Muller E., 2008, ICDE WORKSH, P600 Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 Pestov V, 2008, NEURAL NETWORKS, V21, P204, DOI 10.1016/j.neunet.2007.12.030 Qian G, 2006, ACM T DATABASE SYST, V31, P439, DOI 10.1145/1138394.1138395 Roy R., 1989, IEEE T SYST MAN CYB, V19, P17 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Skopal T., 2011, ACM COMP SURV, V43, P1, DOI DOI 10.1145/1978802.1978813 Tan P. N., 2005, INTRO DATA MINING Zezula P., 2002, SIMILARITY SEARCH ME NR 35 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 EI 1873-5797 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD JUN PY 2014 VL 62 BP 144 EP 157 DI 10.1016/j.dss.2014.03.011 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA AI4HI UT WOS:000336825900013 ER PT J AU Prado, TC de Macedo, DDJ Dantas, MAR von Wangenheim, A AF Prado, Thiago C. de Macedo, Douglas D. J. Dantas, M. A. R. von Wangenheim, Aldo TI Optimization of PACS Data Persistency Using Indexed Hierarchical Data SO JOURNAL OF DIGITAL IMAGING LA English DT Article DE DICOM; Hierarchical data format; PACS; Data Indexing AB We present a new approach for the development of a data persistency layer for a Digital Imaging and Communications in Medicine (DICOM)-compliant Picture Archiving and Communications Systems employing a hierarchical database. Our approach makes use of the HDF5 hierarchical data storage standard for scientific data and overcomes limitations of hierarchical databases employing inverted indexing for secondary key management and for efficient and flexible access to data through secondary keys. This inverted indexing is achieved through a general purpose document indexing tool called Lucene. This approach was implemented and tested using real-world data against a traditional solution employing a relational database, in various store, search, and retrieval experiments performed repeatedly with different sizes of DICOM datasets. Results show that our approach outperforms the traditional solution on most of the situations, being more than 600 % faster in some cases. C1 [Prado, Thiago C.; Dantas, M. A. R.; von Wangenheim, Aldo] INE, Dept Informat & Stat, Florianopolis, SC, Brazil. [de Macedo, Douglas D. J.] Univ Fed Santa Catarina, Postgrad Program Knowledge Engn & Management PPGE, Florianopolis, SC, Brazil. [de Macedo, Douglas D. J.] Univ Fed Santa Catarina, Dept Informat & Estat Sala 320, BR-88040900 Florianopolis, SC, Brazil. RP de Macedo, DDJ (reprint author), Univ Fed Santa Catarina, Postgrad Program Knowledge Engn & Management PPGE, Florianopolis, SC, Brazil. EM coelho@telemedicina.ufsc.br; macedo@inf.ufsc.br; mario@inf.ufsc.br; awangenh@inf.ufsc.br RI Macedo, Douglas/E-9819-2012 CR Abduljwad F, 2010, SMX R EFFICIENT WAY Acr, 2008, ACR GUID TECHN STAND, P1 Amaral E, 8 INT INF TEL TECHN BMJ, 1987, VER SCHUTZ SCHAD DUR CLucene, 2011, CLUCENE LIGHTN FAST Cohen S, 2006, SIGMOD RECORD, V35, P10 Costa C, 2009, INT J COMPUT ASS RAD, V4, P71, DOI 10.1007/s11548-008-0269-7 Douglas K., 2005, POSTGRE SQL Eichelberg M, 2004, 10 YEARS MED IMAGING Erik H, 2005, LUCENE ACTION Folino G, 2009, COMP BAS MED SYST 20 Gosink L, 2006, SCI STAT DAT MAN 200 HDFGROUP, 2010, HIER DAT FORM VERS 5 HDFGROUP, 2011, WHO US HDF HDFGROUP, 2011, HDF GROUP INF SUPP S HDFGROUP, 2011, IS HDF5 DIFF HDF4 ISO/IEC, 2008, INF TECHN DAT LANG 1 LuceneA, 2011, LUC FAQ Macedo D. D., 2009, INT J HIGH PERFORM C, V2, P99 Macedo DDJ, 2011, IMPROVEMENT DIFFEREN Magnus M, 2012, J PHYS C SERIES Maia RS, 2006, STATEWIDE TELEMEDICI Nam B, 2003, IMPROVING ACCESS MUL NEMA, 2011, DIG IM COMM MED D 10 NEMA, 2011, DIG IM COMM MED DI 5 NEMA, 2011, DIG IM COMM MED DI 1 NEMA, 2011, DIG IM COMM MED DI 4 NEMA, 2011, DIG IM COMM MED DI 8 NEMA(a), 2011, DIG IM COMM MED DI 3 ORACLE, 2011, OR DAT 11G DICOM MED PostgreSQL, 2009, SQL CONF Pourma E, 2010, BALANCING PERFORMANC PyTables, 2011, PYTABLES GETT MOST O RCR, 2008, RET STOR IM RAD PAT, P3 Rew R., 1990, COMPUTER GRAPHICS AP, V10, P76 Sahoo S, 2005, LANGUAGES COMPILERS, P922 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wallauer Jader, 2008, IT Professional, V10, DOI 10.1109/MITP.2008.23 Wangenheim A, 2010, LAT AM J TELEHEALTH, V1, P364 NR 39 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0897-1889 EI 1618-727X J9 J DIGIT IMAGING JI J. Digit. Imaging PD JUN PY 2014 VL 27 IS 3 BP 297 EP 308 DI 10.1007/s10278-013-9665-9 PG 12 WC Radiology, Nuclear Medicine & Medical Imaging SC Radiology, Nuclear Medicine & Medical Imaging GA AH8PN UT WOS:000336400300003 PM 24402455 ER PT J AU Ferreira, AA Veloso, A Goncalves, MA Laender, AHF AF Ferreira, Anderson A. Veloso, Adriano Goncalves, Marcos Andre Laender, Alberto H. F. TI Self-Training Author Name Disambiguation for Information Scarce Scenarios SO JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID LAZY ASSOCIATIVE CLASSIFICATION; MODEL AB We present a novel 3-step self-training method for author name disambiguation-SAND (self-training associative name disambiguator)-which requires no manual labeling, no parameterization (in real-world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real-world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state-of-the-art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self-citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information. C1 [Ferreira, Anderson A.] Univ Fed Ouro Preto, Dept Computacao, BR-35400000 Ouro Preto, MG, Brazil. [Veloso, Adriano; Goncalves, Marcos Andre; Laender, Alberto H. F.] Univ Fed Minas Gerais, Dept Ciencia Computacao, BR-31270010 Belo Horizonte, MG, Brazil. RP Ferreira, AA (reprint author), Univ Fed Ouro Preto, Dept Computacao, Campus Morro do Cruzeiro, BR-35400000 Ouro Preto, MG, Brazil. EM ferreira@dcc.ufmg.br; adrianov@dcc.ufmg.br; mgoncalv@dcc.ufmg.br; laender@dcc.ufmg.br FU InWeb; Brazilian National Institute of Science and Technology for the Web (MCT/CNPq/FAPEMIG) [573871/2008-6]; CAPES; CNPq; FAPEMIG FX This research is partially funded by InWeb, The Brazilian National Institute of Science and Technology for the Web (MCT/CNPq/FAPEMIG grant 573871/2008-6), and by the authors' individual grants from CAPES, CNPq, and FAPEMIG. CR Agrawal R., 1993, P ACM SIGMOD C MAN D, V22, P207, DOI DOI 10.1145/170035.170072 Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI 10.1145/1217299.1217304 Bhattacharya I., 2006, P 6 SIAM INT C DAT M Bordes A, 2005, J MACH LEARN RES, V6, P1579 Carvalho A., 2011, J INFORM DATA MANAGE, V2, P289 Chang C.-C., 2001, LIBSVM LIB SUPPORT V CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cota RG, 2010, J AM SOC INF SCI TEC, V61, P1853, DOI 10.1002/asi.21363 Culotta A., 2007, P INT WORKSH INF INT Ester M., 1996, P 2 INT C KNOWL DISC, V1996, P226 Fan X., 2011, ACM J DATA INFORM QU, V2, p[1, 23] Ferreira A., 2012, P 2012 ACM IEEE JOIN, P175 Ferreira A. A., 2010, P 2010 ACM IEEE JOIN, P39, DOI 10.1145/1816123.1816130 Ferreira AA, 2012, SIGMOD REC, V41, P15, DOI 10.1145/2350036.2350040 Ferreira AA, 2012, INFORM SCIENCES, V206, P42, DOI 10.1016/j.ins.2012.04.022 Geisser S, 1993, PREDICTIVE INFERENCE Han H, 2005, PROCEEDINGS OF THE 5TH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, PROCEEDINGS, P334, DOI 10.1145/1065385.1065462 Han H., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996419 Han H., 2005, P 2005 ACM S APPL CO, P1065, DOI 10.1145/1066677.1066920 Huang J, 2006, LECT NOTES ARTIF INT, V4213, P536 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Laender A. H. F., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996357 Laender A.H.F., 2011, P INT ACM IEEE JOINT, P427 Laender AHF, 2008, DOCENG'08: PROCEEDINGS OF THE EIGHTH ACM SYMPOSIUM ON DOCUMENT ENGINEERING, P257 Lapidot I., 2002, TECHNICAL REPORT Lee D., 2005, P 2 INT WORKSH INF Q, P69, DOI 10.1145/1077501.1077514 Levin F. H., 2010, J INFORM DATA MANAGE, V1, P183 Levin M, 2012, J AM SOC INF SCI TEC, V63, P1030, DOI 10.1002/asi.22621 Ley M., 2009, PVLDB, V2, P1493 Mitchell TM, 1997, MACHINE LEARNING Oliveira J. W. A, 2005, THESIS U FEDERAL MIN On BW, 2006, IEEE DATA MINING, P1008 On BW, 2007, PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, P575 On BW, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P344, DOI 10.1145/1065385.1065463 Pereira DA, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P49 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soler JM, 2007, SCIENTOMETRICS, V72, P281, DOI 10.1007/s11192-007-1730-z Tang J, 2012, IEEE T KNOWL DATA EN, V24, P975, DOI 10.1109/TKDE.2011.13 Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] Torvik V.I., 2009, ACM T KNOWL DISCOV D, V3, P1 Treeratpituk P, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P39 Veloso A, 2012, INFORM PROCESS MANAG, V48, P680, DOI 10.1016/j.ipm.2011.08.005 Veloso A, 2006, IEEE DATA MINING, P645 Veloso A, 2006, P 15TH ACM INT C INF, P218, DOI 10.1145/1183614.1183649 Veloso A, 2007, LECT NOTES ARTIF INT, V4702, P605 Yang KH, 2008, LECT NOTES COMPUT SC, V5173, P185 Zha H., 2001, NEURAL INFORM PROCES, V14, P1057 NR 48 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2330-1635 EI 2330-1643 J9 J ASSOC INF SCI TECH PD JUN PY 2014 VL 65 IS 6 BP 1257 EP 1278 DI 10.1002/asi.22992 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AG7FO UT WOS:000335583900013 ER PT J AU Simpson, MS Demner-Fushman, D Antani, SK Thoma, GR AF Simpson, Matthew S. Demner-Fushman, Dina Antani, Sameer K. Thoma, George R. TI Multimodal biomedical image indexing and retrieval using descriptive text and global feature mapping SO INFORMATION RETRIEVAL LA English DT Article DE Multimodal image retrieval; Image indexing; Clustering ID BAYES POINT MACHINES; ANNOTATION; SEARCH; QUANTIZATION; DATABASES; PICTURES; FUSION; SCALE; QUERY AB The images found within biomedical articles are sources of essential information useful for a variety of tasks. Due to the rapid growth of biomedical knowledge, image retrieval systems are increasingly becoming necessary tools for quickly accessing the most relevant images from the literature for a given information need. Unfortunately, article text can be a poor substitute for image content, limiting the effectiveness of existing text-based retrieval methods. Additionally, the use of visual similarity by content-based retrieval methods as the sole indicator of image relevance is problematic since the importance of an image can depend on its context rather than its appearance. For biomedical image retrieval, multimodal approaches are often desirable. We describe in this work a practical multimodal solution for indexing and retrieving the images contained in biomedical articles. Recognizing the importance of text in determining image relevance, our method combines a predominately text-based image representation with a limited amount of visual information, in the form of quantized content-based visual features, through a process called global feature mapping. The resulting multimodal image surrogates are easily indexed and searched using existing text-based retrieval systems. Our experimental results demonstrate that our multimodal strategy significantly improves upon the retrieval accuracy of existing approaches. In addition, unlike many retrieval methods that utilize content-based visual features, the response time of our approach is negligible, making it suitable for use with large collections. C1 [Simpson, Matthew S.; Demner-Fushman, Dina; Antani, Sameer K.; Thoma, George R.] NIH, Lister Hill Natl Ctr Biomed Commun, US Natl Lib Med, Bethesda, MD 20892 USA. RP Simpson, MS (reprint author), NIH, Lister Hill Natl Ctr Biomed Commun, US Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA. EM simpsonmatt@mail.nih.gov; ddemner@mail.nih.gov; santani@mail.nih.gov; gthoma@mail.nih.gov RI Antani, Sameer/ OI Antani, Sameer/0000-0002-0040-1387 FU U.S. National Library of Medicine, National Institutes of Health FX The authors would like to thank Dr. Md. Mahmudur Rahman and Srinivas Phadnis for extracting and preparing the content-based and text-based features of the images used in this work. This work is supported by the intramural research program of the U.S. National Library of Medicine, National Institutes of Health, and by an appointment to the NLM Research Participation Program administered by the Oak Ridge Institute for Science and Education. CR Alpkocak A., 2012, CLEF 2011 WORKSH [Anonymous], 2010, ENTR PROGR UT HELP Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027 Atrey PK, 2010, MULTIMEDIA SYST, V16, P345, DOI 10.1007/s00530-010-0182-0 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Beatty M, 1997, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL II, P835 Bezdek J. C., 1999, FUZZY MODELS ALGORIT Blei D. M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460 Brin S., 1995, P 21 INT C VER LARG, P574 Caicedo J. C., 2010, P INT C MULT INF RET, P359, DOI 10.1145/1743384.1743442 Callan J. P., 1992, DEXA 92. Database and Expert Systems Applications. Proceedings of the International Conference Chang E, 2003, IEEE T CIRC SYST VID, V13, P26, DOI 10.1109/TCSVT.2002.808079 Chang SF, 2001, IEEE T CIRC SYST VID, V11, P688 Chatzichristofis SA, 2008, LECT NOTES COMPUT SC, V5008, P312 Chatzichristofis S. A., 2008, P 9 INT WORKSH IM AN, P191, DOI DOI 10.1109/WIAMIS.2008.24 Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426 Clinchant S., 2010, CLEF 2010 WORKSH Datta R, 2007, IEEE MULTIMEDIA, V14, P24, DOI 10.1109/MMUL.2007.67 Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248 de Vries A. P., 1999, THESIS U TWENTE Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63 Demner-Fushman D., 2008, P LANG RES CONT BAS, P18 Demner-Fushman D., 2009, INT J MED INFORM, V78, P59, DOI DOI 10.1016/J.IJMEDINF.2009.05.003 Demner-Fushman D., 2012, J COMPUTING IN PRESS de Vries AP, 2004, IEEE IMAGE PROC, P2387 Duygulu P., 2006, LECT NOTES COMPUTER, V2353, P349 Ferhatosmanoglu H, 2001, PROC INT CONF DATA, P503, DOI 10.1109/ICDE.2001.914864 Gkoufas Yiannis, 2011, Open Med Inform J, V5, P50, DOI 10.2174/1874431101105010050 Guttman A, 1984, P ACM SIGMOD INT C M, P47 Hamer OW, 2006, RADIOGRAPHICS, V26, P1637, DOI 10.1148/rg.266065004 Harris C., 1988, P 4 ALV VIS C, p147 Helbich TH, 1999, RADIOLOGY, V213, P537 Herbich R, 2001, J MACH LEARN RES, V1, P245, DOI 10.1162/153244301753683717 Hersh W, 2009, J DIGIT IMAGING, V22, P648, DOI 10.1007/s10278-008-9154-8 Ide NC, 2007, J AM MED INFORM ASSN, V14, P253, DOI 10.1197/jamia.M2233 Indyk P., 2004, HDB DISCRETE COMPUTA, P877 Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 Jegou H, 2011, IEEE T PATTERN ANAL, V33, P117, DOI 10.1109/TPAMI.2010.57 Kadir T, 2001, INT J COMPUT VISION, V45, P83, DOI 10.1023/A:1012460413855 Kalpathy-Cramer J, 2010, P INT C MULT INF RET, P165, DOI 10.1145/1743384.1743415 Kohonen T., 2001, SELF ORG MAPS INFORM, V30 Lacoste C, 2007, IEEE T CIRC SYST VID, V17, P889, DOI 10.1109/TCSVT.2007.897114 Lancaster F. W., 1973, INFORM RETRIEVAL ON Langlotz CP, 2006, RADIOGRAPHICS, V26, P1595, DOI 10.1148/rg.266065168 Lavrenko V., 2003, P 17 ANN C NEUR INF, V16, P553 Li J, 2008, IEEE T PATTERN ANAL, V30, P985, DOI 10.1109/TPAMI.2007.70847 Lim JH, 2005, LECT NOTES COMPUT SC, V3689, P84 LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281 LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/TIT.1982.1056489 Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lux M, 2008, P 16 ACM INT C MULT, P1085, DOI DOI 10.1145/1459359.1459577 Maenpaa T, 2003, THESIS U OULU MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Muler H., 2003, NEW NAVIGATORS PROFE, V95, P480 Muller H., 2010, CLEF 2010 Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024 Muller H, 2010, INFORM RETRIEVAL SER, V32, P1, DOI 10.1007/978-3-642-15181-1 Muller H., 2012, CLEF 2012 WORKSH Ng R, 1996, P SOC PHOTO-OPT INS, V2670, P50, DOI 10.1117/12.234809 Nowak E, 2006, LECT NOTES COMPUT SC, V3954, P490 Pham T-T, 2007, P 16 ACM C INF KNOWL, P439, DOI 10.1145/1321440.1321503 Rahman M. M., 2009, P 22 IEEE INT S COMP Rahman MM, 2010, LECT NOTES COMPUT SC, V5853, P110 Rasiwasia N., 2010, P INT C MULT, P251, DOI 10.1145/1873951.1873987 Richardson W S, 1995, ACP J Club, V123, pA12 Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054 Sakai T., 2007, P 30 ANN INT ACM SIG, P71, DOI DOI 10.1145/1277741.1277756 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simpson M., 2009, CLEF 2009 WORKSH Simpson M., 2010, CLEF 2010 WORKSH Simpson M., 2011, CLEF 2011 WORKSH Simpson M. S., 2012, P ANN S AM IN PRESS Simpson M. S., 2012, CLEF 2012 WORKSH Sivic J., 2003, P 9 IEEE C COMP VIS, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 Squire DM, 2000, PATTERN RECOGN LETT, V21, P1193, DOI 10.1016/S0167-8655(00)00081-7 Srinivasa G N, 2008, P WORLD ACAD SCI ENG, V36, P1264 TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999 UHLMANN JK, 1991, INFORM PROCESS LETT, V40, P175, DOI 10.1016/0020-0190(91)90074-R Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang CH, 2008, MULTIMEDIA SYST, V14, P205, DOI 10.1007/s00530-008-0128-y Yang J., 2007, P INT WORKSH MULT IN, P197, DOI DOI 10.1145/1290082.1290111 YIANILOS PN, 1993, PROCEEDINGS OF THE FOURTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P311 Zezula P., 2006, SIMILARITY SEARCH ME, V32 Zhou X, 2010, LECT NOTES COMPUT SC, V6388, P129 NR 86 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2014 VL 17 IS 3 BP 229 EP 264 DI 10.1007/s10791-013-9235-2 PG 36 WC Computer Science, Information Systems SC Computer Science GA AF1SQ UT WOS:000334494200002 ER PT J AU Huang, J Nie, FP Huang, H Ding, C AF Huang, Jin Nie, Feiping Huang, Heng Ding, Chris TI Robust Manifold Nonnegative Matrix Factorization SO ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA LA English DT Article DE Clustering; nonnegative matrix factorization (NMF); manifold; robust NMF ID DIMENSIONALITY REDUCTION; MICROARRAY DATA; REPRESENTATION; MODEL AB Nonnegative Matrix Factorization (NMF) has been one of the most widely used clustering techniques for exploratory data analysis. However, since each data point enters the objective function with squared residue error, a few outliers with large errors easily dominate the objective function. In this article, we propose a Robust Manifold Nonnegative Matrix Factorization (RMNMF) method using l(2,1)-norm and integrating NMF and spectral clustering under the same clustering framework. We also point out the solution uniqueness issue for the existing NMF methods and propose an additional orthonormal constraint to address this problem. With the new constraint, the conventional auxiliary function approach no longer works. We tackle this difficult optimization problem via a novel Augmented Lagrangian Method (ALM)-based algorithm and convert the original constrained optimization problem on one variable into a multivariate constrained problem. The new objective function then can be decomposed into several subproblems that each has a closed-form solution. More importantly, we reveal the connection of our method with robust K-means and spectral clustering, and we demonstrate its theoretical significance. Extensive experiments have been conducted on nine benchmark datasets, and all empirical results show the effectiveness of our method. C1 [Huang, Jin; Nie, Feiping; Huang, Heng; Ding, Chris] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA. RP Huang, J (reprint author), Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 76019 USA. EM heng@uta.edu RI Nie, Feiping/B-3039-2012 FU NSF [DMS-0915228, IIS-1117965, IIS-1302675, IIS-1344152] FX This research was partially supported by NSF DMS-0915228, IIS-1117965, IIS-1302675, IIS-1344152. CR Belkin M, 2003, NEURAL COMPUT, V15, P1373, DOI 10.1162/089976603321780317 Berry M., 2006, COMPUTATIONAL STAT D, P155 Berry M. W., 1995, SIAM REV, V37, P575 Bertsekas D. P., 1996, CONSTRAINED OPTIMIZA Bertsekas D. P., 1982, COMPUTER SCI APPL MA, V1 Bertsekas D. P., 2003, NONLINEAR PROGRAMMIN, V2nd Cai D, 2011, IEEE T PATTERN ANAL, V33, P1548, DOI 10.1109/TPAMI.2010.231 Cai X., 2013, INT JOINT C ART INT, P1240 Chapelle O., 2006, SEMISUPERVISED LEARN Cooper M., 2000, P IEEE WORKSH MULT S, P25 Dhillon L. S., 2008, EXPT MATH, V17, P9 Ding C, 2010, IEEE T PATTERN ANAL, V32, P45, DOI 10.1109/TPAMI.2008.277 Fogel P, 2007, BIOINFORMATICS, V23, P44, DOI 10.1093/bioinformatics/btl550 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Frank A., TECHNICAL REPORT Gabay D., 1976, COMPUT MATH APPL, V2, P1416 Gu QQ, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P359 Hamza A. B., 2006, IEEE Transactions on Signal Processing, V54, DOI 10.1109/TSP.2006.879282 Hestenes M. R., 1969, Journal of Optimization Theory and Applications, V4, DOI 10.1007/BF00927673 Hoyer PO, 2004, J MACH LEARN RES, V5, P1457 Huang J, 2013, 27 AAAI C ART INT AA, P424 Huang J., 2013, INT JOINT C ART INT, P2647 Ke Q., 2005, P IEEE C COMP VIS PA, P592 Kim H, 2007, BIOINFORMATICS, V23, P1495, DOI 10.1093/bioinformatics/btm134 Kong D., 2011, P 20 ACM INT C INF K, P673 Lee DD, 2001, ADV NEUR IN, V13, P556 Lin Z., 2009, TECHNICAL REPORT Luxburg U.V., 2007, STAT COMPUT, V17, P395, DOI DOI 10.1007/S11222-007-9033-Z Lyons M., 1998, 3 IEEE INT C AUT FAC Martinez AM, 2001, IEEE T PATTERN ANAL, V23, P228, DOI 10.1109/34.908974 Martinez AM, 1998, 24 CVC Ng A.Y, 2001, ADV NEURAL INFORM PR, V14, P849 Nie F., 2012, 26 AAAI C ART INT AA, P655 Nie FP, 2012, IEEE DATA MINING, P566, DOI 10.1109/ICDM.2012.160 Paatero P., 1994, ENVIRONMETRICS, V5, P111 Powell M. J. D., 1969, OPTIMIZATION, P283 Rennie J. D., 2005, ICML 2005, P713 Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samaria F., 1994, 2 IEEE WORKSH APPL C Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Sim T, 2003, IEEE T PATTERN ANAL, V25, P1615, DOI 10.1109/TPAMI.2003.1251154 Wang H., 2013, INT C RES COMP MOL B, P302 Wang H., 2011, IEEE INT C DAT MIN I, P774 Wang H., 2011, ACM INT C INF KNOWL, P279 Wright J., 2009, P ADV NEUR INF PROC, V22, P2080 Wright J, 2009, IEEE T PATTERN ANAL, V31, P210, DOI 10.1109/TPAMI.2008.79 Xu Y, 2011, MATRIX, V1, P1 Yu X, 2003, P INT C COMP VIS, P313 Yuan M, 2006, J ROY STAT SOC B, V68, P49, DOI 10.1111/j.1467-9868.2005.00532.x NR 50 TC 2 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1556-4681 EI 1556-472X J9 ACM T KNOWL DISCOV D JI ACM Trans. Knowl. Discov. Data PD MAY PY 2014 VL 8 IS 3 AR 11 DI 10.1145/2601434 PG 21 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AL4OC UT WOS:000339111300001 ER PT J AU Ngo-Ye, TL Sinha, AP AF Ngo-Ye, Thomas L. Sinha, Atish P. TI The influence of reviewer engagement characteristics on online review helpfulness: A text regression model SO DECISION SUPPORT SYSTEMS LA English DT Article DE Online review; Text regression; Vector space model; Reviewer engagement characteristics; RFM analysis ID PRODUCT REVIEWS; COMMUNITIES; ALGORITHM; QUALITY; SYSTEMS AB The era of Web 2.0 is witnessing the proliferation of online social media platforms, which develop new business models by leveraging user-generated content. One rapidly growing source of user-generated data is online reviews, which play a very important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. In this paper, we develop and compare several text regression models for predicting the helpfulness of online reviews. In addition to using review words as predictors, we examine the influence of reviewer engagement characteristics such as reputation, commitment, and current activity. We employ a reviewer's RPM (Recency, Frequency, Monetary Value) dimensions to characterize his/her overall engagement and investigate if the inclusion of those dimensions helps improve the prediction of online review helpfulness. Empirical findings from text mining experiments conducted using reviews from Yelp and Amazon offer strong support to our thesis. We find that both review text and reviewer engagement characteristics help predict review helpfulness. The hybrid approach of combining the textual features of bag-of-words model and RPM dimensions produces the best prediction results. Furthermore, our approach facilitates the estimation of the helpfulness of new reviews instantly, making it possible for social media platforms to dynamically adjust the presentation of those reviews on their websites. (C) 2014 Elsevier B.V. All rights reserved. C1 [Ngo-Ye, Thomas L.] Dalton State Coll, Sch Business, Dalton, GA 30720 USA. [Sinha, Atish P.] Univ Wisconsin, Sheldon B Labor Sch Business, Milwaukee, WI 53201 USA. RP Sinha, AP (reprint author), Univ Wisconsin, Sheldon B Labor Sch Business, Milwaukee, WI 53201 USA. EM tngoye@daltonstate.edu; sinha@uwm.edu CR ANDERSONLEHMAN R, 2004, MIS Q EXEC, V3, P163 Cao Q, 2011, DECIS SUPPORT SYST, V50, P511, DOI 10.1016/j.dss.2010.11.009 Chen CC, 2011, DECIS SUPPORT SYST, V50, P755, DOI 10.1016/j.dss.2010.08.023 CHEN HC, 2010, IEEE INTELL SYST, V25, P74 Chen YB, 2008, MANAGE SCI, V54, P477, DOI 10.1287/mnsc.1070.0810 Cheung CMK, 2012, DECIS SUPPORT SYST, V53, P218, DOI 10.1016/j.dss.2012.01.015 Fader PS, 2005, J MARKETING RES, V42, P415, DOI 10.1509/jmkr.2005.42.4.415 Frank E, 2005, DATA MINING PRACTICA Gama J., 1995, LECT NOTES COMPUTER, V990, P189 Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188 Ghose A., 2008, WORKING PAPER Ghoshal A., 2007, P IEEE INT C COMP VI, P1 Gilbert E, 2010, 2010 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, P225 Hall M, 2003, IEEE T KNOWL DATA EN, V15, P1 Hall M. A., 2000, P 17 INT C MACH LEAR, P359 Hoang L, 2008, LECT NOTES COMPUT SC, V4993, P496 Hu N, 2012, DECIS SUPPORT SYST, V52, P674, DOI 10.1016/j.dss.2011.11.002 Hu N, 2008, INFORM TECHNOL MANAG, V9, P201, DOI 10.1007/s10799-008-0041-2 Kahan R., 1998, J CONSUMER MARKETING, V15, P491, DOI 10.1108/07363769810235965 Kim S., 2006, P 2006 C EMP METH NA, P423, DOI 10.3115/1610075.1610135 Kroenke D., 2012, EXPERIENCING MIS Ku YC, 2012, DECIS SUPPORT SYST, V53, P534, DOI 10.1016/j.dss.2012.03.003 Li XX, 2008, INFORM SYST RES, V19, P456, DOI 10.1287/isre.1070.0154 Liu B., 2010, HDB NATURAL LANGUAGE, P1 Liu Huiqing, 2002, Genome Inform, V13, P51 Liu J., 2007, P JOINT C EMP METH N, P334 Liu Y, 2008, IEEE DATA MINING, P443, DOI 10.1109/ICDM.2008.94 Liu Y., 2008, 2008 IEEE WIC ACM IN, P793, DOI 10.1109/WIIAT.2008.299 Marcus C., 1998, J CONSUMER MARKETING, V15, P494, DOI DOI 10.1108/07363769810235974 Mierswa I, 2006, P 12 ACM SIGKDD INT, P935 Mudambi SM, 2010, MIS QUART, V34, P185 Ngo-Ye T.L., 2012, ACM T MANAGEMENT INF O'Mahony M.P., 2009, RECSYS09, P305 Olson DL, 2012, DECIS SUPPORT SYST, V54, P443, DOI 10.1016/j.dss.2012.06.005 O'Mahony MP, 2010, RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI, P49, DOI 10.1007/978-1-84882-983-1_4 Otterbacher J, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P955 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Piramuthu S, 2012, DECIS SUPPORT SYST, V53, P418, DOI 10.1016/j.dss.2012.02.006 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Reinartz WJ, 2000, J MARKETING, V64, P17, DOI 10.1509/jmkg.64.4.17.18077 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shim B, 2012, EXPERT SYST APPL, V39, P7736, DOI 10.1016/j.eswa.2012.01.080 Shmueli G, 2007, DATA MINING BUSINESS Shmueli G, 2011, MIS QUART, V35, P553 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wang GA, 2013, DECIS SUPPORT SYST, V54, P1442, DOI 10.1016/j.dss.2012.12.020 Wang R. Y., 1996, Journal of Management Information Systems, V12 Ware M, 2001, INT J HUM-COMPUT ST, V55, P281, DOI 10.1006/ijhc.2001.0499 Woodcock N., 2011, SOCIAL CRM BUSINES S, V18, P50 Y Lu, 2010, EXPLOITING SOCIAL CO, P691 Yu XH, 2012, IEEE T KNOWL DATA EN, V24, P720, DOI 10.1109/TKDE.2010.269 Zhang Z, 2008, IEEE INTELL SYST, V23, P42 Zhang Z., 2006, P 15 ACM INT C INF K, P51, DOI 10.1145/1183614.1183626 NR 55 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 EI 1873-5797 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD MAY PY 2014 VL 61 BP 47 EP 58 DI 10.1016/j.dss.2014.01.011 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA AG0OB UT WOS:000335113700005 ER PT J AU Han, XG Wei, W Miao, CY Mei, JP Song, HJ AF Han, Xiaogang Wei, Wei Miao, Chunyan Mei, Jian-Ping Song, Hengjie TI Context-Aware Personal Information Retrieval From Multiple Social Networks SO IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE LA English DT Article ID FUZZY; TEXT AB People use a variety of social networking services to collect and organize web information for future reuse. When such contents are actually needed as reference to reply a post in an online conversation, however, the user may not be able to retrieve them with proper cues or may even forget their existence at all. In this paper, we study this problem in the online conversation context and investigate how to automatically retrieve the most context-relevant previously-seen web information without user intervention. We propose a Context-aware Personal Information Retrieval (CPIR) algorithm, which considers both the participatory and implicit-topical properties of the context to improve the retrieval performance. Since both the context and the user's web information are usually short and ambiguous, the participatory context is utilized to formulate and expand the query. Moreover, the implicit-topical context is exploited to implicitly determine the importance of each web information of the targeting user in the given context. The experimental results using real-world dataset demonstrate that CPIR can achieve significant improvements over several baselines. C1 [Han, Xiaogang; Wei, Wei; Miao, Chunyan; Mei, Jian-Ping] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. [Song, Hengjie] Kyoto Univ, Kyoto, Japan. RP Han, XG (reprint author), Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. FU Interactive and Digital Media Programme Office (IDMPO), National Research Foundation (NRF) hosted at Media Development Authority (MDA) of Singapore [MDA/IDM/2012/8/8-2 VOL 01] FX This research is supported by Interactive and Digital Media Programme Office (IDMPO), National Research Foundation (NRF) hosted at Media Development Authority (MDA) of Singapore under Grant No.: MDA/IDM/2012/8/8-2 VOL 01. Wei Wei (weiwei8329@gmail.com) serves as the corresponding author of this article. The authors would also like to thank anonymous reviewers for their comments on improving the quality of this paper. CR Abel F., 2011, P 11 INT C WEB ENG, P28 Araujo S., 2010, P WEBSCI10 EXT FRONT Bigi B, 2003, LECT NOTES COMPUT SC, V2633, P305 Bruce H, 2004, P ASIST ANNU, V41, P129, DOI 10.1002/meet.1450410115 Celli F, 2010, LECT NOTES COMPUT SC, V6007, P346, DOI 10.1007/978-3-642-12079-4_43 Cutrell E., 2006, P SIGCHI C HUM FACT, P261, DOI 10.1145/1124772.1124812 Dong A., 2010, P 3 ACM INT C WEB SE, P11, DOI 10.1145/1718487.1718490 Dumais S., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009137 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Fano R. M., 1961, AM J PHYS, V29, P793, DOI 10.1119/1.1937609 Garg S, 2009, IMC'09: PROCEEDINGS OF THE 2009 ACM SIGCOMM INTERNET MEASUREMENT CONFERENCE, P315 Gimpel K., 2011, P 49 ANN M ASS COMP, V2, P42 Gubernatis JE, 2008, J COMPUT PHYS, V227, P8508, DOI 10.1016/j.jcp.2008.06.001 Gupta T., 2009, P INT AAAI C WEBL SO Iofciu T., 2011, P 5 INT AAAI C WEBL, P522 Java A., 2007, P JOINT 9 WEBKDD 1 S, P56, DOI DOI 10.1145/1348549.1348556 Jeon J., 2005, P 14 ACM INT C INF K, P84, DOI DOI 10.1145/1099554.1099572.ISBN:1-59593-140-6 Kazemzadeh A, 2013, IEEE COMPUT INTELL M, V8, P34, DOI 10.1109/MCI.2013.2247824 Kullback S., 1968, INFORM THEORY STAT Kuncheva LI, 2003, IEEE T FUZZY SYST, V11, P729, DOI 10.1109/TFUZZ.2003.819842 Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 Manning C.D., 2008, INTRO INFORM RETRIEV Marlow C., 2006, P 17 C HYP HYP, P31, DOI DOI 10.1145/1149941.1149949 Massoudi K, 2011, LECT NOTES COMPUT SC, V6611, P362, DOI 10.1007/978-3-642-20161-5_36 Metzler D, 2007, LECT NOTES COMPUT SC, V4425, P16 Michelson M, 2010, P 4 WORKSH AN NOIS U, P73, DOI 10.1145/1871840.1871852 Owoputi Olutobi, 2013, P N AM CHAPT ASS COM, P380 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rhee FCH, 2007, IEEE COMPUT INTELL M, V2, P44 Riezler S, 2007, P 45 ANN M ASS COMP, V45, P464 Robu V, 2009, ACM T WEB, V3, DOI 10.1145/1594173.1594176 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schall D, 2011, LECT NOTES COMPUT SC, V6909, P285, DOI 10.1007/978-3-642-23737-9_21 Sellen A, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P81 Shen J, 2008, Proceedings of the 27th Chinese Control Conference, Vol 5, P207, DOI 10.1145/1378773.1378801 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Stegmayer G, 2012, IEEE COMPUT INTELL M, V7, P22, DOI 10.1109/MCI.2012.2215122 Szomszor M, 2008, LECT NOTES COMPUT SC, V5318, P632, DOI 10.1007/978-3-540-88564-1_40 Teevan J., 2007, P 30 ANN INT ACM SIG, P151, DOI 10.1145/1277741.1277770 Tungare M., 2006, P 2 INV WORKSH PERS Wetzker Robert, 2008, P ECAI 2008 MIN SOC, P26 Xue X., 2009, P 18 ACM C INF KNOWL, P2037, DOI DOI 10.1145/1645953.1646295 Zhou X., 2007, P 20 INT JOINT C ART, P2928 NR 44 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1556-603X EI 1556-6048 J9 IEEE COMPUT INTELL M JI IEEE Comput. Intell. Mag. PD MAY PY 2014 VL 9 IS 2 BP 18 EP 28 DI 10.1109/MCI.2014.2307222 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA AF7VH UT WOS:000334922500004 ER PT J AU Shermis, MD AF Shermis, Mark D. TI State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration SO Assessing Writing LA English DT Article DE Automated essay scoring; High-stakes assessment; Writing; Race-to-the-Top; Performance assessment; Human raters AB This article summarizes the highlights of two studies: a national demonstration that contrasted commercial vendors' performance on automated essay scoring (AES) with that of human raters: and an international competition to match or exceed commercial vendor performance benchmarks. In these studies, the automated essay scoring engines performed well on five of seven measures and approximated human rater performance on the other two. With additional validity studies, it appears that automated essay scoring holds the potential to play a viable role in high-stakes writing assessments. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Shermis, Mark D.] Univ Akron, Dept Educ Fdn & Leadership, Akron, OH 44325 USA. [Shermis, Mark D.] Univ Akron, Dept Psychol, Akron, OH 44325 USA. RP Shermis, MD (reprint author), Univ Akron, Dept Educ Fdn & Leadership, 213 Crouse Hall, Akron, OH 44325 USA. EM mshermis@uakron.edu FU William and Flora Hewlett Foundation FX An earlier version of this work was presented as a paper presented at National Council on Measurement in Education in Vancouver, Canada, 2012. This work was supported through funding from the William and Flora Hewlett Foundation. The opinions expressed in this paper are those of the author and do not necessarily represent the policy or views of the William and Flora Hewlett Foundation or their Board of Directors. The author would like to thank Jaison Morgan (The Common Pool), Tom Vander Ark, Lynn Van Deventer (OpenEd Solutions), Ben Hamner (Kaggle), Tony Albert (SBAC), and Jeff Nellhouse (PARCC) for their tireless efforts in executing this study. The author is also indebted to two anonymous reviewers, and to David Williamson and Norbert Elliot for their encouragement and insightful comments that helped shape the final version of this manuscript. CR Ajay H. B., 1973, ANAL ESSAYS COMPUTER, P231 Applebee A. N., 2009, ENGL J, V98, P18 Attewell P, 2006, J HIGH EDUC, V77, P886, DOI 10.1353/jhe.2006.0037 Bennett R. E., 2011, AUTOMATED SCORING CO Bowen WG, 2009, CROSSING THE FINISH LINE: COMPLETING COLLEGE AT AMERICAS PUBLIC UNIVERSITIES, P1 Bridgeman B, 2012, APPL MEAS EDUC, V25, P27, DOI 10.1080/08957347.2012.635502 Burstein J., 1998, P P NCME S AUT SCOR Condon W, 2013, ASSESS WRIT, V18, P100, DOI 10.1016/j.asw.2012.11.001 Elliot S, 2003, AUTOMATED ESSAY SCORING: A CROSS-DISCIPLINARY PERSPECTIVE, P71 Finkel J.R., 2005, 43 ANN M ASS COMP LI Kane MT, 2006, ED MEASUREMENT, P17 Klobucar A., 2012, INT ADV WRITING RES, P103 Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mayfield E., 2010, DEM SESS INT C N AM Mayfield E., 2013, HDB AUTOMATED ESSAY, P124 Mazzeo J., 2006, ED MEASUREMENT, P469 McCurry D., 2010, ASSESS WRIT, V15, P118 Mislevy R. J., 2007, ED RES, V36, P463, DOI DOI 10.3102/0013189X07311660 Page EB, 1966, PHI DELTA KAPPAN, V48, P238 Porter A, 2011, EDUC RESEARCHER, V40, P103, DOI 10.3102/0013189X11405038 Ramineni C, 2013, ASSESS WRIT, V18, P25, DOI 10.1016/j.asw.2012.10.004 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shermis M. D., 2012, NAT COUNC MEAS ED Shermis M. D., 2013, HDB AUTOMATED ESSAY, P313 Shermis M. D., 2003, AUTOMATED ESSAY SCOR Shermis MD, 2013, HDB AUTOMATED ESSAY Stemler SE., 2004, PRACT ASSESS RES EVA, V9, P4 Striven M., 1987, J PERSONNEL EVALUATI, V1, P9 Tucker B, 2009, EDUC LEADERSHIP, V67, P48 Weigle SC, 2013, ASSESS WRIT, V18, P85, DOI 10.1016/j.asw.2012.10.006 Weir C. J., 2005, LANG TEST, V22, P261 Williamson DM, 2012, ED MEASUREMENT ISSUE, V31, P2 NR 33 TC 3 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1075-2935 J9 ASSESS WRIT JI Assess. Writ. PD APR PY 2014 VL 20 BP 53 EP 76 DI 10.1016/j.asw.2013.04.001 PG 24 WC Education & Educational Research; Linguistics SC Education & Educational Research; Linguistics GA CA5KJ UT WOS:000348947000005 ER PT J AU Yang, HJ Meinel, C AF Yang, Haojin Meinel, Christoph TI Content Based Lecture Video Retrieval Using Speech and Video Text Information SO IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES LA English DT Article DE Lecture videos; automatic video indexing; content-based video search; lecture video archives AB In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript as well as detected slide text line types are adopted for keyword extraction, by which both video-and segment-level keywords are extracted for content-based video browsing and search. The performance and the effectiveness of proposed indexing functionalities is proven by evaluation. C1 [Yang, Haojin; Meinel, Christoph] Hasso Plattner Inst Software Syst Engn GmbH HPI, D-14440 Potsdam, Germany. RP Yang, HJ (reprint author), Hasso Plattner Inst Software Syst Engn GmbH HPI, POB 900460, D-14440 Potsdam, Germany. EM haojin.yang@hpi.uni-potsdam.de; meinel@hpi.uni-potsdam.de CR Adcock J., 2010, P INT C MULT MM 10, P241, DOI 10.1145/1873951.1873986 [Anonymous], 2013, GROUND TRUTH DATA Chang F, 2004, COMPUT VIS IMAGE UND, V93, P206, DOI 10.1016/j.cviu.2003.09.002 Dala B. T. N., 2005, P IEEE C CVPR, V1, P886 Eisenstein J., 2007, P 22 NAT C ART INT, V1, P877 Epshtein B, 2010, PROC CVPR IEEE, P2963, DOI 10.1109/CVPR.2010.5540041 Glass J., 2004, P HLT NAACL 2004 WOR, P9, DOI 10.3115/1626307.1626309 Grcar M, 2009, LECT NOTES ARTIF INT, V5782, P730 Haubold A., 2005, P 13 ANN ACM INT C M, P51, DOI 10.1145/1101149.1101158 Hurst W., 2002, P IADIS INT C WWW IN, P135 Jeong H.J., 2012, P 10 INT C ADV MOB C, P285 Karatzas D, 2011, PROC INT CONF DOC, P1485, DOI 10.1109/ICDAR.2011.295 Lee D., 2008, P ACM SPEC INT GROUP Leeuwis E., 2003, P ICASSP, P232 Meinel C., 2011, P 2 INT C E ED E BUS Munteanu C., 2006, P 8 INT C MULT INT Nandzik J., 2012, MULTIMED TOOLS APPL, P1 Pong T.-C., 2008, J PATTERN RECOG, V41, P3257 Repp S, 2008, IEEE T LEARN TECHNOL, V1, P145, DOI 10.1109/TLT.2008.22 Sack H., 2006, P 1 SEM AUTH ANN WOR SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Toutanova K., 2003, P HLT NAACL, P252 Tuna T., 2012, P 43 ACM TECH S COMP, P129 Yang H., 2012, MULTIMED TOOLS APPL, P1 NR 25 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1939-1382 J9 IEEE T LEARN TECHNOL JI IEEE Trans. Learn. Technol. PD APR-JUN PY 2014 VL 7 IS 2 BP 142 EP 154 DI 10.1109/TLT.2014.2307305 PG 13 WC Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA AM9IR UT WOS:000340195400005 ER PT J AU Ittipanuvat, V Fujita, K Sakata, I Kajikawa, Y AF Ittipanuvat, Vitavin Fujita, Katsuhide Sakata, Ichiro Kajikawa, Yuya TI Finding linkage between technology and social issue: A Literature Based Discovery approach SO JOURNAL OF ENGINEERING AND TECHNOLOGY MANAGEMENT LA English DT Article DE Literature Based Discovery; Text mining; Link mining; Citation analysis; Innovation ID LAPAROSCOPIC RADICAL PROSTATECTOMY; TOTAL HIP-REPLACEMENT; CITATION ANALYSIS; FISH-OIL; MEDICAL LITERATURES; COCITATION ANALYSIS; KNEE-REPLACEMENT; PATENT ANALYSIS; ROBOT; SCIENCE AB This paper investigates Literature Based Discovery (LBD) approach to reveal linkages between technology and social issue to elucidate plausible contribution of science and technology for solving social issues. Robotics and gerontology were selected as an example in our analysis. The result shows various technological options of robotics contributing to healthcare and well-being of elderly people, mainly in surgery, rehabilitation, and companionship. In addition, we comparatively evaluated effectiveness of semantic similarity measures to extract these linkages from bibliographic database. Our methodology can be utilized as a decision support tool for managers and policy makers to extract and design promising research targets. (C) 2013 Elsevier B.V. All rights reserved. C1 [Ittipanuvat, Vitavin] Univ Tokyo, Grad Sch Engn, Dept Technol Management Innovat, Bunkyo Ku, Tokyo 1138656, Japan. [Ittipanuvat, Vitavin; Kajikawa, Yuya] Univ Tokyo, Innovat Policy Res Ctr, Bunkyo Ku, Tokyo 1138656, Japan. [Fujita, Katsuhide] Tokyo Univ Agr & Technol, Fac Engn, Koganei, Tokyo 1848588, Japan. [Sakata, Ichiro] Univ Tokyo, Todai Policy Alternat Res Inst, Bunkyo Ku, Tokyo 1130033, Japan. [Kajikawa, Yuya] Tokyo Inst Technol, Grad Sch Innovat Management, Minato Ku, Tokyo 1080023, Japan. RP Kajikawa, Y (reprint author), Tokyo Inst Technol, Grad Sch Innovat Management, Minato Ku, 3-3-6 Shibaura, Tokyo 1080023, Japan. EM kajikawa@mot.titech.ac.jp RI Fujita, Katsuhide/B-9974-2013; Kajikawa, Yuya/C-1996-2015 OI Fujita, Katsuhide/0000-0001-7867-4281; Kajikawa, Yuya/0000-0003-3577-5167 CR Adai AT, 2004, J MOL BIOL, V340, P179, DOI 10.1016/j.jmb.2004.04.047 Alcacer J, 2006, REV ECON STAT, V88, P774, DOI 10.1162/rest.88.4.774 Almeida P, 1996, STRATEGIC MANAGE J, V17, P155 [Anonymous], 1846, DOES TECHNOLOGY DRIV Aoyagi D, 2007, IEEE T NEUR SYS REH, V15, P387, DOI 10.1109/TNSRE.2007.903922 Arnold E, 2004, RES EVALUAT, V13, P3, DOI 10.3152/147154404781776509 Banks MR, 2008, J AM MED DIR ASSOC, V9, P173, DOI 10.1016/j.jamda.2007.11.007 Barnason S, 2006, HEART LUNG, V35, P225, DOI 10.1016/j.hrtlng.2005.10.003 Bellotto N, 2010, AUTON ROBOT, V28, P425, DOI 10.1007/s10514-009-9167-2 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Borner M, 1997, UNFALLCHIRURG, V100, P640, DOI 10.1007/s001130050169 Boyack KW, 2002, J AM SOC INF SCI TEC, V53, P764, DOI 10.1002/asi.10066 Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6 Charlifue S, 2004, NEUROREHABILITATION, V19, P91 Chen CM, 2002, J AM SOC INF SCI TEC, V53, P678, DOI 10.1002/asi.10075 Cory KA, 1997, COMPUT HUMANITIES, V31, P1, DOI 10.1023/A:1000422220677 Daim TU, 2006, TECHNOL FORECAST SOC, V73, P981, DOI 10.1016/j.techfore.2006.04.004 D'Attellis N, 2002, J CARDIOTHOR VASC AN, V16, P397, DOI 10.1053/jcan.2002.125154 Davidson GS, 1998, J INTELL INF SYST, V11, P259, DOI 10.1023/A:1008690008856 Eriksson J., 2005, P IEEE 9 INT C REH R Estey EP, 2009, CUAJ-CAN UROL ASSOC, V3, P488 Federspil PA, 2004, BIOMED TECH, V49, P78, DOI 10.1515/BMT.2004.016 Figl M, 2010, COMPUT MED IMAG GRAP, V34, P61, DOI 10.1016/j.compmedimag.2009.08.002 Francois D, 2009, INTERACT STUD, V10, P324, DOI 10.1075/is.10.3.04fra Frantzi K., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900023 FREEMAN C, 1979, FUTURES, V11, P206, DOI 10.1016/0016-3287(79)90110-1 Ganiz M. C., 2005, TECHNICAL REPORT Gordon M. D., 2002, ACM T INTERNET TECHN, V2, P261, DOI 10.1145/604596.604597 Gordon MD, 1996, J AM SOC INFORM SCI, V47, P116, DOI 10.1002/(SICI)1097-4571(199602)47:2<116::AID-ASI3>3.0.CO;2-1 Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674 Harwin WS, 2006, P IEEE, V94, P1717, DOI 10.1109/JPROC.2006.880671 Hatzinger M, 2006, J ENDOUROL, V20, P848, DOI 10.1089/end.2006.20.848 Johnson KL, 2010, PHYS MED REH CLIN N, V21, P267, DOI 10.1016/j.pmr.2009.12.004 Jorm AF, 1997, MED J AUSTRALIA, V166, P376 Joseph JV, 2005, BJU INT, V96, P39, DOI 10.1111/j.1464-410X.2005.05563.x Kajikawa Y, 2007, SUSTAIN SCI, V2, P221, DOI 10.1007/s11625-007-0027-8 Kidd C. D., 1999, P 2 INT WORKSH COOP Kirkim G, 2007, MEDITERR J OTOL, V3, P126 Klavans R, 2006, J AM SOC INF SCI TEC, V57, P251, DOI 10.1002/asi.20274 Klavans R, 2009, J AM SOC INF SCI TEC, V60, P455, DOI 10.1002/asi.20991 Kluev V. V., 2001, ADV AUTOMATION MULTI Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P165, DOI 10.1016/j.techfore.2007.11.004 Lee YS, 2007, YONSEI MED J, V48, P341, DOI 10.3349/ymj.2007.48.2.341 Llorente MD, 2005, AM J GERIAT PSYCHIAT, V13, P195, DOI 10.1176/appi.ajgp.13.3.195 Lo SC, 2007, SCIENTOMETRICS, V70, P183, DOI 10.1007/s11192-007-0111-y Lupsakko T, 2002, INT J GERIATR PSYCH, V17, P808, DOI 10.1002/gps.689 Martelli M, 2000, ANN BIOMED ENG, V28, P1146, DOI 10.1114/1.1313774 McMurray A, 2002, J ADV NURS, V40, P663, DOI 10.1046/j.1365-2648.2002.02426.x Melson GF, 2009, J SOC ISSUES, V65, P545 Mitchell JM, 2006, REHABIL COUNS BULL, V49, P157, DOI 10.1177/00343552060490030301 Mynatt E., 2001, P ACM C HUM FACT COM NARIN F, 1994, SCIENTOMETRICS, V30, P147, DOI 10.1007/BF02017219 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133 Osareh F, 1996, LIBRI, V46, P217, DOI 10.1515/libr.1996.46.4.217 Osareh F, 1996, LIBRI, V46, P149, DOI 10.1515/libr.1996.46.3.149 Pirraglia PA, 1999, INT J GERIATR PSYCH, V14, P668, DOI 10.1002/(SICI)1099-1166(199908)14:8<668::AID-GPS988>3.0.CO;2-9 Plinkert PK, 2001, HNO, V49, P514, DOI 10.1007/s001060170075 Porter AL, 2009, SCIENTOMETRICS, V81, P719, DOI 10.1007/s11192-008-2197-2 Qin J, 1997, J AM SOC INFORM SCI, V48, P893, DOI 10.1002/(SICI)1097-4571(199710)48:10<893::AID-ASI5>3.0.CO;2-X Robinson L, 2007, INT J GERIATR PSYCH, V22, P9, DOI 10.1002/gps.1643 Sakata I, 2013, TECHNOL FORECAST SOC, V80, P1085, DOI 10.1016/j.techfore.2012.03.009 Sakata I., 2010, P PORTL INT CTR MAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sausville J, 2010, INT J CLIN PRACT, V64, P1740, DOI 10.1111/j.1742-1241.2010.02534.x Schaffernicht E, 2005, LECT NOTES ARTIF INT, V3698, P320 Shibata N, 2011, J AM SOC INF SCI TEC, V62, P1360, DOI 10.1002/asi.21477 Shibata N, 2008, TECHNOVATION, V28, P758, DOI 10.1016/j.technovation.2008.03.009 Shibata Naoki, 2011, Foresight, V13, DOI 10.1108/14636681111170211 Shibata N, 2010, TECHNOL FORECAST SOC, V77, P1147, DOI 10.1016/j.techfore.2010.03.008 Shibata N, 2009, J AM SOC INF SCI TEC, V60, P571, DOI 10.1002/asi.20994 Shoval N, 2010, J TRANSP GEOGR, V18, P603, DOI 10.1016/j.jtrangeo.2010.03.012 Smalheiser NR, 1996, NEUROLOGY, V47, P809 Smalheiser NR, 1998, COMPUT METH PROG BIO, V57, P149, DOI 10.1016/S0169-2607(98)00033-9 Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.3.CO;2-7 Small H, 2006, SCIENTOMETRICS, V68, P595, DOI 10.1007/s11192-006-0132-y Smith A, 2010, RES POLICY, V39, P435, DOI 10.1016/j.respol.2010.01.023 SWANSON DR, 1986, PERSPECT BIOL MED, V30, P7 SWANSON DR, 1990, PERSPECT BIOL MED, V33, P157 SWANSON DR, 1987, J AM SOC INFORM SCI, V38, P228, DOI 10.1002/(SICI)1097-4571(198707)38:4<228::AID-ASI2>3.0.CO;2-G Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8 SWANSON DR, 1988, PERSPECT BIOL MED, V31, P526 SWANSON DR, 1989, J AM SOC INFORM SCI, V40, P432, DOI 10.1002/(SICI)1097-4571(198911)40:6<432::AID-ASI5>3.0.CO;2-# Thelwall M, 2008, J INF SCI, V34, P605, DOI 10.1177/0165551507087238 Truesdale MD, 2010, J ENDOUROL, V24, P1055, DOI 10.1089/end.2010.0128 VALDESPEREZ RE, 1994, ARTIF INTELL, V65, P247, DOI 10.1016/0004-3702(94)90018-3 Wada K, 2004, P IEEE, V92, P1780, DOI 10.1109/JPROC.2004.835378 Wang J, 2011, SCIENTOMETRICS, V87, P563, DOI 10.1007/s11192-011-0362-5 Weber BA, 2000, GERIATR NURS, V21, P250, DOI 10.1067/mgn.2000.110836 Webster KE, 2006, J AGING PHYS ACTIV, V14, P181 Weeber M, 2005, BRIEF BIOINFORM, V6, P277, DOI 10.1093/bib/6.3.277 Weeber M, 2001, J AM SOC INF SCI TEC, V52, P548, DOI 10.1002/asi.1104.abs Williams R, 1996, RES POLICY, V25, P865, DOI 10.1016/0048-7333(96)00885-2 Wilson TD, 1997, INFORM PROCESS MANAG, V33, P551, DOI 10.1016/S0306-4573(97)00028-9 NR 93 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0923-4748 EI 1879-1719 J9 J ENG TECHNOL MANAGE JI J. Eng. Technol. Manage. PD APR-JUN PY 2014 VL 32 SI SI BP 160 EP 184 DI 10.1016/j.jengtecman.2013.05.006 PG 25 WC Business; Engineering, Industrial; Management SC Business & Economics; Engineering GA AK7MZ UT WOS:000338613700011 ER PT J AU Wagner, H Dlotko, P AF Wagner, Hubert Dlotko, Pawel TI Towards topological analysis of high-dimensional feature spaces SO COMPUTER VISION AND IMAGE UNDERSTANDING LA English DT Article DE Computational topology; Persistent homology; Rips complex; Text mining; Feature space AB In this paper we present ideas from computational topology, applicable in analysis of point cloud data. In particular, the point cloud can represent a feature space of a collection of objects such as images or text documents. Computing persistent homology reveals the global structure of similarities between the data. Furthermore, we argue that it is essential to incorporate higher-degree relationships between objects. Finally, we show that new computational topology algorithms expose much better practical performance compared to standard techniques. (C) 2014 Elsevier Inc. All rights reserved. C1 [Wagner, Hubert; Dlotko, Pawel] Jagiellonian Univ, Inst Comp Sci, Krakow, Poland. [Dlotko, Pawel] Univ Penn, Dept Math, Philadelphia, PA 19104 USA. RP Wagner, H (reprint author), Jagiellonian Univ, Inst Comp Sci, Lojasiewicza 6, Krakow, Poland. EM wagner@ii.uj.edu.pl; dlotko@sas.upenn.edu FU Foundation for Polish Science IPP Programme "Geometry and Topology in Physical Models"; federal Contracts [FA9550-12-1-0416, FA9550-09-1-0643] FX Both authors are supported by Google Research Awards programme. We thank Prof. Marian Mrozek and Witold Jarnicki for their supervision, and Herbert Edelsbrunner, Leonidas Guibas, Ulrich Bauer and Jan Reininghaus for helpful comments and discussions. H.W. is also supported by a Foundation for Polish Science IPP Programme "Geometry and Topology in Physical Models". P.D. is supported by federal Contracts FA9550-12-1-0416 and FA9550-09-1-0643. CR BAUER U, 2013, CLEAR COMPRESS COMPU Biasotti S, 2013, COMPUT GRAPH FORUM, V32, P13, DOI 10.1111/cgf.12168 Carlsson G, 2009, B AM MATH SOC, V46, P255 CERRI A, 2013, MATH METHOD APPL SCI, V36, P1099 CHAZAL F, 2012, ARXIV12073885V1 CHEN C, 2011, 27 EUR WORKSH COMP G Cohen-Steiner D, 2007, DISCRETE COMPUT GEOM, V37, P103, DOI 10.1007/s00454-006-1276-5 de Silva V, 2011, INVERSE PROBL, V27, DOI 10.1088/0266-5611/27/12/124003 Edelsbrunner H., 2010, COMPUTATIONAL TOPOLO EPPSTEIN D, 2011, SEA 2011 FENG AX, EMPIRICAL INVESTIGAT Forman R., 2002, SEM LOTHAR COMBIN C, V48, P1 Jaccard P., 1912, NEW PHYTOL, V11, P37, DOI DOI 10.1111/J.1469-8137.1912.TB05611.X LEWINER T, 2005, THESIS Lewiner T, 2003, EXP MATH, V12, P271 POLANCO X, 2006, P INSCIT, P586 Robins V, 2011, IEEE T PATTERN ANAL, V33, P1646, DOI 10.1109/TPAMI.2011.95 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHOLKOPF B, 1999, IEEE T NEURAL NETWOR, V10 Wagner H, 2012, LECT NOTES COMPUT SC, V7309, P68, DOI 10.1007/978-3-642-30238-1_8 Zomorodian A, 2010, COMPUT GRAPH-UK, V34, P263, DOI 10.1016/j.cag.2010.03.007 NR 21 TC 0 Z9 0 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1077-3142 EI 1090-235X J9 COMPUT VIS IMAGE UND JI Comput. Vis. Image Underst. PD APR PY 2014 VL 121 BP 21 EP 26 DI 10.1016/j.cviu.2014.01.005 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA AF0HS UT WOS:000334395500004 ER PT J AU Liang, H AF Liang, Hai TI Coevolution of Political Discussion and Common Ground in Web Discussion Forum SO SOCIAL SCIENCE COMPUTER REVIEW LA English DT Article DE common ground; political deliberation; social network analysis; text mining ID KNOWLEDGE; INTERNET; PARTICIPATION; NETWORKS; TALK AB Common ground is vital for developing deliberative democracy. The current study employs text mining techniques to measure common ground in online political discussions and examines how the structure of political discussions coevolves with common ground over time. The present study collected 175,960 messages over a period of 13 months, from a popular discussion forum on 2012 U.S. presidential election. Common ground is measured by a semantic similarity network and an interpretive framework network. The former emphasizes shared political knowledge, while the latter emphasizes shared interpretations. In addition, this study explores the coevolutionary process of political discussion and common ground. Results were obtained by employing longitudinal network analysis. They suggest that political discussions could facilitate the achievement of common ground that might further serve as a facilitator of political discussion among the participants. C1 [Liang, Hai] City Univ Hong Kong, Dept Media & Commun, Kowloon, Hong Kong, Peoples R China. RP Liang, H (reprint author), City Univ Hong Kong, Dept Media & Commun, Run Run Shaw Creat Media Ctr, M5009,5-F,18 Tat Ave, Kowloon, Hong Kong, Peoples R China. EM hai.liang@my.cityu.edu.hk RI Liang, Hai/F-3797-2015 OI Liang, Hai/0000-0003-1779-9552 CR Ahdesmaki M, 2010, ANN APPL STAT, V4, P503, DOI 10.1214/09-AOAS277 American National Election Studies, 2008, ANES 2012 TIM SERIES Budge Ian, 1996, NEW CHALLENGE DIRECT Cohen Joshua, 1989, GOOD POLITY, P17 Coleman S., 2001, BOWLING TOGETHER ONL COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 Corrado A., 1996, ELECTIONS CYBERSPACE Dahl R., 1989, DEMOKRATION DESS ANT DiMaggio P, 2001, ANNU REV SOCIOL, V27, P307, DOI 10.1146/annurev.soc.27.1.307 Ellwardt L, 2012, SOC NETWORKS, V34, P623, DOI 10.1016/j.socnet.2012.07.002 Eveland WP, 2004, COMMUN RES, V31, P82, DOI 10.1177/0093650203260203 Eveland WP, 2009, J COMMUN, V59, P205, DOI 10.1111/j.1460-2466.2009.01412.x Fiorina Morris P., 2005, CULTURE WAR MYTH POL, VThird Fishbein M, 1980, Nebr Symp Motiv, V27, P65 FISKE ST, 1983, J EXP SOC PSYCHOL, V19, P381, DOI 10.1016/0022-1031(83)90029-X Gastil John, 2008, POLITICAL COMMUNICAT Goodin Robert E., 2003, REFLECTIVE DEMOCRACY Graber D., 2001, PROCESSING POLITICS Habermas J., 1962, STRUCTURAL TRANSFORM Harding C. T., 1996, HDB MOTIVATION COGNI, V3, P28 Hauben R., 1997, NETIZENS Himelboim I, 2011, COMMUN RES, V38, P634, DOI 10.1177/0093650210384853 Janssen D, 2005, ACTA POLIT, V40, P317, DOI 10.1057/palgrave.ap.5500115 Jost J. T., 2008, SOCIAL PERSONALITY P, V2, P171, DOI [10.1111/j.1751-9004.2007.00056.x, DOI 10.1111/J.1751-9004.2007.00056.X] Kavanaugh AL, 2005, Communities and Technologies 2005, P19, DOI 10.1007/1-4020-3591-8_2 Kim J, 2008, COMMUN THEOR, V18, P51, DOI 10.1111/j.1468-2885.2007.00313.x KUKLINSKI JH, 1991, AM POLIT SCI REV, V85, P1341, DOI 10.2307/1963949 Levine P., 2005, J PUBLIC DELIBERATIO, V1, P1 Mansbridge Jane, 2006, J PUBLIC DELIBERATIO, V2 MCGRAW KM, 1990, SOC COGNITION, V8, P9, DOI 10.1521/soco.1990.8.1.9 Mossberger K., 2010, OXFORD HDB AM ELECTI, P200 NEUMAN WR, 1981, AM J SOCIOL, V86, P1236, DOI 10.1086/227384 Neuman WR, 1992, COMMON KNOWLEDGE NEW Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103 Papacharissi Z, 2002, NEW MEDIA SOC, V4, P9, DOI 10.1177/14614440222226244 Polat RK, 2005, EUR J COMMUN, V20, P435, DOI 10.1177/0267323105058251 Rheingold H., 1993, VIRTUAL COMMUNITY HO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Snijders T. A. B., 2012, SOC NETWORKS, V35, P265 Snijders TAB, 2010, SOC NETWORKS, V32, P44, DOI 10.1016/j.socnet.2009.02.004 Sunstein C. R., 2001, REPUBLIC COM Wojcieszak M, 2010, NEW MEDIA SOC, V12, P637, DOI 10.1177/1461444809342775 NR 42 TC 0 Z9 0 PU SAGE PUBLICATIONS INC PI THOUSAND OAKS PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA SN 0894-4393 EI 1552-8286 J9 SOC SCI COMPUT REV JI Soc. Sci. Comput. Rev. PD APR PY 2014 VL 32 IS 2 BP 155 EP 169 DI 10.1177/0894439313506844 PG 15 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science; Social Sciences, Interdisciplinary SC Computer Science; Information Science & Library Science; Social Sciences - Other Topics GA AE0HW UT WOS:000333645600004 ER PT J AU Aly, R Demeester, T Robertson, S AF Aly, Robin Demeester, Thomas Robertson, Stephen TI Probabilistic models in IR and their relationships SO INFORMATION RETRIEVAL LA English DT Article DE Probabilistic models; Probability of relevance; Probability ranking principle; Language models ID INFORMATION-RETRIEVAL; RELEVANCE; SPACE AB A solid research path towards new information retrieval models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit probability ranking principle (PRP)-based models, probability of relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP. C1 [Aly, Robin] Univ Twente, NL-7500 AE Enschede, Netherlands. [Demeester, Thomas] Univ Ghent, iMinds, B-9000 Ghent, Belgium. [Robertson, Stephen] UCL, London, England. RP Aly, R (reprint author), Univ Twente, POB 217, NL-7500 AE Enschede, Netherlands. EM r.aly@ewi.utwente.nl FU EU Project AXES [FP7-269980] FX The work reported in this paper was funded partly by the EU Project AXES (FP7-269980) and carried out at the University of Twente, in the Netherlands, and partly by Ghent University-iMinds in Flanders. We would like to thank Djoerd Hiemstra, Arjen de Vries and the anonymous reviewers for their comments that greatly helped to improve this work and especially forced us to reflect critically on our motivations and results. CR Aly R, 2011, LECT NOTES COMPUT SC, V6931, P164, DOI 10.1007/978-3-642-23318-0_16 Bishop C. M., 2006, PATTERN RECOGNITION Chen H., 2006, SIGIR 2006, P429 Cooper W. S., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval COX RT, 1946, AM J PHYS, V14, P1, DOI 10.1119/1.1990764 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 Fang H., 2005, SIGIR 05, P480 Feller W., 1968, INTRO PROBABILITY TH, V1 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Hiemstra D., 2001, THESIS U TWENTE KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lafferty J., 2003, PROBABILISTIC RELEVA, V13, P1 Lavrenko Victor, 2003, LANGUAGE MODELING IN, P11 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Luk RWP, 2008, INFORM RETRIEVAL, V11, P539, DOI 10.1007/s10791-008-9062-z Lv Y., 2012, THESIS U ILLINOIS UR Manning C. D., 1999, FDN STAT NATURAL LAN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Robertson S, 2005, INFORM RETRIEVAL, V8, P319, DOI 10.1007/s10791-005-5665-9 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roelleke T., 2006, SIGIR 2006, P107, DOI [10.1145/1148170.1148192, DOI 10.1145/11.48170.1148192] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck-Jones K., 2003, LANGUAGE MODELING IN, P57 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 32 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD APR PY 2014 VL 17 IS 2 BP 177 EP 201 DI 10.1007/s10791-013-9226-3 PG 25 WC Computer Science, Information Systems SC Computer Science GA AD0ZP UT WOS:000332963700004 ER PT J AU Li, G Liu, F AF Li, Gang Liu, Fei TI Sentiment analysis based on clustering: a framework in improving accuracy and recognizing neutral opinions SO APPLIED INTELLIGENCE LA English DT Article DE Sentiment analysis; Opinion mining; Clustering; Semantic web ID K-MEANS; TEXT; ALGORITHM AB Clustering-based sentiment analysis is a novel approach for analyzing opinions expressed in reviews, comments or blogs. In contrast to the two traditional mainstream approaches (supervised learning and symbolic techniques), the clustering-based approach is able to produce basically accurate analysis results without any human participation, linguist knowledge or training time. This paper introduces new techniques designed to extend the capability of the clustering-based sentiment analysis approach in two aspects: firstly by applying opposite opinion contents processing and non-opinion contents processing techniques to further enhance accuracy; and secondly by using a modified voting mechanism and distance measurement method to conduct fine-grained (three classes) sentiment analysis. According to the experiment results, the clustering-based approach is proven to be useful in performing high quality sentiment analysis result, and suitable for recognizing neutral opinions. C1 [Li, Gang; Liu, Fei] La Trobe Univ, Dept Comp Sci & Comp Engn, Melbourne, Vic, Australia. RP Li, G (reprint author), La Trobe Univ, Dept Comp Sci & Comp Engn, Melbourne, Vic, Australia. EM g9li@students.latrobe.edu.au CR Al-Harbi SH, 2006, APPL INTELL, V24, P219, DOI 10.1007/s10489-006-8513-8 Andrews NO, 2007, COMPUTER SCI Bai X, 2011, DECIS SUPPORT SYST, V50, P732, DOI 10.1016/j.dss.2010.08.024 Bello-Orgaz G, 2012, INT J NEURAL SYST, V22 Boiy E, 2007, INT C EL PUBL PAG VI, P349 Cesarano C, 2004, AAAI SPRING S COMP A Chaovalit P., 2005, P 38 HAW INT C SYST Chiu CM, 2004, J INF SCI, V30, P60, DOI 10.1177/0165551504041679 Goldberg AB, 2006, ASS COMPUTATIONAL LI, P45 Group ctK, 2007, ONL CONS GEN REV HAV Hatzivassiloglou V, 2001, CITESEER Hitlin PLR, 2004, PEW INTERNET AM LIFE, DOI [10.1016/j.dss.2005.05.019, DOI 10.1016/J.DSS.2005.05.019] Hong T-P, 2012, APPL INTELL, P1 Huang ZX, 1998, DATA MIN KNOWL DISC, V2, P283, DOI 10.1023/A:1009769707641 Kamps J, 2004, INT C LANG RES EV KLEINBERG JM, 1999, IEEE S FDN COMP SCI, P14 Koppel M, 2006, COMPUT INTELL, V22, P100, DOI 10.1111/j.1467-8640.2006.00276.x Larsen B., 1999, KDD 99, P16 Laszlo M, 2007, PATTERN RECOGN LETT, V28, P2359, DOI 10.1016/j.patrec.2007.08.006 Li G, 2012, J INF SCI, V38, P127, DOI 10.1177/0165551511432670 Manthey B, 2009, PROCEEDINGS OF THE TWENTIETH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P461 Menendez H., 2012, INT DAT ENG AUT LEAR, V7435, P216 Pang B., 2005, P 43 ANN M ASS COMP, V43, P115, DOI DOI 10.3115/1219840.1219855 Pang B., 2002, C EMP METH NAT LANG, V10, P79, DOI DOI 10.3115/1118693.1118704 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Pang B., 2004, P 42 ANN M ASS COMP, V42, P271, DOI DOI 10.3115/1218955.1218990 Poomagal S, 2011, P INT C WEB INT MIN, P65 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi K, 2012, APPL INTELL, P1 Tan P., 2006, INTRO DATA MINING Tan SB, 2008, EXPERT SYST APPL, V35, P279, DOI 10.1016/j.eswa.2007.06.028 Tang HF, 2009, EXPERT SYST APPL, V36, P10760, DOI 10.1016/j.eswa.2009.02.063 Turney PD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P417 von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z Xia R, 2011, INFORM SCIENCES, V181, P1138, DOI 10.1016/j.ins.2010.11.023 Yang M-S, 2012, PATTERN RECOGNIT Yokoyama S, 2009, COMPUTATION STAT, V24, P165, DOI 10.1007/s00180-008-0114-z Yu H, 2003, C EMP METH NAT LANG, P129, DOI [10.3115/1119355.1119372, DOI 10.3115/1119355.1119372] NR 39 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-669X EI 1573-7497 J9 APPL INTELL JI Appl. Intell. PD APR PY 2014 VL 40 IS 3 BP 441 EP 452 DI 10.1007/s10489-013-0463-3 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA AC8MM UT WOS:000332787200005 ER PT J AU Wiesner, M Pfeifer, D AF Wiesner, Martin Pfeifer, Daniel TI Health Recommender Systems: Concepts, Requirements, Technical Basics and Challenges SO INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH LA English DT Article DE health recommender systems; information needs; personal health record; personalized medicine; health information seeking; Internet; relevance computation ID OF-THE-ART; INFORMATION-SEEKING; CARE PROFESSIONALS; INTERNET; WEB; OPPORTUNITIES; EMPOWERMENT; ALGORITHM; KNOWLEDGE; MEDICINE AB During the last decades huge amounts of data have been collected in clinical databases representing patients' health states (e.g., as laboratory results, treatment plans, medical reports). Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS) are meant to centralize an individual's health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS). In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed. C1 [Wiesner, Martin; Pfeifer, Daniel] Heilbronn Univ, Dept Med Informat, D-74081 Heilbronn, Germany. RP Wiesner, M (reprint author), Heilbronn Univ, Dept Med Informat, Max Planck Str 39, D-74081 Heilbronn, Germany. EM martin.wiesner@hs-heilbronn.de; daniel.pfeifer@hs-heilbronn.de CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Agarwal D, 2013, COMMUN ACM, V56, P92, DOI 10.1145/2461256.2461277 Ahmed F., 2009, RES J COMPUTER SCI C, V40, P39 Anderson RM, 2005, PATIENT EDUC COUNS, V57, P153, DOI 10.1016/j.pec.2004.05.008 [Anonymous], MED INF 6500 HB NIJM [Anonymous], 2005, 205142005 ISOTR Baird A, 2011, P 2011 ICONFERENCE, P281, DOI 10.1145/1940761.1940800 Baldi P., 2003, MODELLING INTERNET W, V1 Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002 Chase D, WHY GOOGLE HLTH REAL Chomutare T, 2011, STUD HEALTH TECHNOL, V169, P48, DOI 10.3233/978-1-60750-806-9-48 Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671 Eysenbach G, 2002, BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573 Eysenbach G, 2005, CONSUM HLTH INFORM, DOI [10.1007/0-387-27652-1_4, DOI 10.1007/0-387-27652-1] Eysenbach G, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e19 Farrell R.G., 2012, P 1 INT WORKSH REC T, P24 Felfernig A., 2013, MULTIMEDIA SERVICES, P81 Fernandez-Luque L, 2012, J MED INTERNET RES, V14, DOI 10.2196/jmir.1985 Fernandez-Luque L, 2009, STUD HEALTH TECHNOL, V150, P903, DOI 10.3233/978-1-60750-044-5-903 Gavgani V.Z., 2010, P 1 ACM INT HLTH INF, P575, DOI 10.1145/1882992.1883086 Gerber BS, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e15 Ghorai Koel, 2013, Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS 2013), DOI 10.1109/HICSS.2013.89 Grossman D., 2004, INFORM RETRIEVAL SER Hamp Birgit, 1997, P ACL WORKSH AUT INF, P9 Hardey M, 1999, SOCIOL HEALTH ILL, V21, P820, DOI 10.1111/1467-9566.00185 Hayes GR, 2011, P 2011 ICONFERENCE, P363, DOI DOI 10.1145/1940761.1940811 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 HON - Health on the Net Foundation, HONS 4 SURV US INT M Hripcsak G, 2005, J AM MED INFORM ASSN, V12, P296, DOI 10.1197/jamia.M1733 Huang ZX, 2012, ARTIF INTELL MED, V55, P13, DOI 10.1016/j.artmed.2011.10.002 Huba N, 2012, J MED SYST, V36, P3893, DOI 10.1007/s10916-012-9861-z Joachims T., 1998, TEXT CATEGORIZATION Johnson J. D., 2012, HLTH INFORM SEEKING Jones DA, 2010, J MED LIBR ASSOC, V98, P243, DOI 10.3163/1536-5050.98.3.013 Kandula S., 2008, P AMIA ANN S P WASH, V2008, P353 Kantor P. B., 2011, RECOMMENDER SYSTEMS Kivits J, 2006, J HEALTH PSYCHOL, V11, P269, DOI 10.1177/1359105306061186 LOVINS JB, 1968, MECH TRANSL, V11, P22 Markle Foundation, PERS HLTH WORK GROUP McCann E., PHR PROGR STILL HANG McMullan M, 2006, PATIENT EDUC COUNS, V63, P24, DOI 10.1016/j.pec.2005.10.006 Morrell T.G., 2013, P 2013 IEEE 29 INT C, P55 Muller H, 2012, METHOD INFORM MED, V51, P516 Munir K., 2008, P 2008 INT S DB ENG, P81, DOI 10.1145/1451940.1451953 Niederdeppe J, 2007, HEALTH COMMUN, V22, P153 Pandolfini C., 2000, PEDIATRICS, V105 Pew Internet & American Life project, HLTH ONL 2013 81 US PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Powell J, 2011, J MED INTERNET RES, V13, DOI 10.2196/jmir.1600 Princeton University Cognitive Science Laboratory, WORDN 3 0 LEX DAT EN Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Raisinghani Mahesh S, 2008, Int J Electron Healthc, V4, P67, DOI 10.1504/IJEH.2008.018921 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Rivero-Rodriguez A., 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems (CBMS), DOI 10.1109/CBMS.2013.6627798 ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J Roitman H, 2010, P 1 ACM INT HLTH INF, P430, DOI 10.1145/1882992.1883057 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2010, TEST COLLECTION BASE Silberg WM, 1997, JAMA-J AM MED ASSOC, V277, P1244, DOI 10.1001/jama.277.15.1244 Sommerhalder K, 2009, PATIENT EDUC COUNS, V77, P266, DOI 10.1016/j.pec.2009.03.028 Sonnenberg FA, 1997, ARCH INTERN MED, V157, P151, DOI 10.1001/archinte.157.2.151 Sowa J.F., 1991, PRINCIPLES SEMANTIC Swan M, 2009, INT J ENV RES PUB HE, V6, P492, DOI 10.3390/ijerph6020492 Tan P. N., 2005, INTRO DATA MINING Tang Paul C, 2006, J Am Med Inform Assoc, V13, P121, DOI 10.1197/jamia.M2025 Ueckert F, 2003, INT J MED INFORM, V70, P99, DOI 10.1016/S1386-5056(03)00052-2 U.S. National Library of Medicine National Institutes of Health, UN MED LANG SYST Wendel S, 2013, BMC HEALTH SERV RES, V13, DOI 10.1186/1472-6963-13-126 Wiesner M, 2010, P 1 ACM INT HLTH INF, P410, DOI DOI 10.1145/1882992.1883053 Wiesner M., 2012, P 25 INT S IEEE COMP, P1 Wilson TD, 2001, HLTH INFORM J, V7, P112, DOI DOI 10.1177/146045820100700210 Zhang Y., 2010, P 1 ACM INT HLTH INF, P210 NR 72 TC 1 Z9 1 PU MDPI AG PI BASEL PA POSTFACH, CH-4005 BASEL, SWITZERLAND SN 1660-4601 J9 INT J ENV RES PUB HE JI Int. J. Environ. Res. Public Health PD MAR PY 2014 VL 11 IS 3 BP 2580 EP 2607 DI 10.3390/ijerph110302580 PG 28 WC Environmental Sciences SC Environmental Sciences & Ecology GA AF0XM UT WOS:000334438200015 PM 24595212 ER PT J AU Noel, GE Peterson, GL AF Noel, George E. Peterson, Gilbert L. TI Applicability of Latent Dirichlet Allocation to multi-disk search SO DIGITAL INVESTIGATION LA English DT Article DE Latent Dirichlet Allocation; Topic models; Query by document; Data mining; Text mining; Document search ID TEXT; SEGMENTATION; CORPUS; MODEL AB Digital forensics practitioners face a continual increase in the volume of data they must analyze, which exacerbates the problem of finding relevant information in a noisy domain. Current technologies make use of keyword based search to isolate relevant documents and minimize false positives with respect to investigative goals. Unfortunately, selecting appropriate keywords is a complex and challenging task. Latent Dirichlet Allocation (LDA) offers a possible way to relax keyword selection by returning topically similar documents. This research compares regular expression search techniques and LDA using the Real Data Corpus (RDC). The RDC, a set of over 2400 disks from real users, is first analyzed to craft effective tests. Three tests are executed with the results indicating that, while LDA search should not be used as a replacement to regular expression search, it does offer benefits. First, it is able to locate documents when few, if any, of the keywords exist within them. Second, it improves data browsing and deals with keyword ambiguity by segmenting the documents into topics. C1 [Noel, George E.; Peterson, Gilbert L.] Air Force Inst Technol, Dept Elect & Comp Engn, Wright Patterson AFB, OH 45433 USA. RP Noel, GE (reprint author), Air Force Inst Technol, Dept Elect & Comp Engn, 2950 Hobson Way, Wright Patterson AFB, OH 45433 USA. EM george.noel@us.af.mil; gilbert.peterson@afit.edu CR Andrews NO, 2007, TR0735 VIRG TECH [Anonymous], 2011, OPENCV COMPUTER VISI Beebe N, 2007, INT FED INFO PROC, V242, P179 Beebe NL, 2011, DECIS SUPPORT SYST, V51, P732, DOI 10.1016/j.dss.2011.01.009 Blei D., 2003, SIGIR 03, P127 Blei D, 2007, C NEUR INF PROC SYST, P1 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blei DM, 2004, ADV NEUR IN, V16, P17 Chaney AJ-B, 2012, 6 INT AAAI C WEBL SO, P419 Chen YH, 2009, IEEE T VIS COMPUT GR, V15, P1161, DOI 10.1109/TVCG.2009.140 Craiger JP, 2010, HDB TECHNOLOGY MANAG, P921 Denoyer L, 2007, LECT NOTES COMPUT SC, V4518, P12 Du L, 2008, ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, P70 Fagin R, 2000, P COMP ASS INF RETR, P682 Farrahi K, 2009, P 2009 INT C MULT IN, P277, DOI 10.1145/1647314.1647373 Feldman Susan, 2003, HIGH COST NOT FINDIN Felzenszwalb PF, 2004, INT J COMPUT VISION, V59, P167, DOI 10.1023/B:VISI.0000022288.19776.77 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Garfinkel S, 2009, DIGIT INVEST, V6, pS2, DOI 10.1016/j.diin.2009.06.016 Garfinkel SL, 2006, DIGIT INVEST, V3, P71, DOI 10.1016/j.diin.2006.06.007 Garrie DB, 2012, PACE LAW REV, V32, P399 Gormley M. R., 2012, P 2012 C N AM CHAPT, P783 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Hauptmann A. G., 2004, P ACM MULT NEW YORK, P668, DOI 10.1145/1027527.1027681 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Larson T., 2002, HDB COMPUTER CRIME I, P17 Mueller L., 2013, CRAFTING GOOD KEYWOR Nance Kara, 2009, P 42 HAW INT C SYST, P1 Napolitano J., 2011, MASS I TECHN Noel GE, 2013, 26 INT FLOR ART INT, P462 Noel GE, 2012, INFORM SCI UNPUB DEC Perotte A, 2011, 25 ANN C NEUR INF PR, P12 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quercia D, 2012, PROCEEDINGS OF THE 3RD ANNUAL ACM WEB SCIENCE CONFERENCE, 2012, P247 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafiei MM, 2006, IEEE DATA MINING, P542 Shrout P E, 1998, Stat Methods Med Res, V7, P301, DOI 10.1191/096228098672090967 SHROUT PE, 1979, PSYCHOL BULL, V86, P420, DOI 10.1037//0033-2909.86.2.420 Velivelli A, 2003, LECT NOTES COMPUT SC, V2728, P227 Viola Paul, 2001, P IEEE C COMP VIS PA, V1, P511 Wang G, 2009, PROC CVPR IEEE, P1367 Zhai Y, 2005, LECT NOTES COMPUT SC, V3568, P92 NR 43 TC 1 Z9 1 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1742-2876 EI 1873-202X J9 DIGIT INVEST JI Digit. Investig. PD MAR PY 2014 VL 11 IS 1 BP 43 EP 56 DI 10.1016/j.diin.2014.02.001 PG 14 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA AF5PE UT WOS:000334764600005 ER PT J AU Merkley, KJ AF Merkley, Kenneth J. TI Narrative Disclosure and Earnings Performance: Evidence from R&D Disclosures SO ACCOUNTING REVIEW LA English DT Article DE narrative disclosure; research and development; earnings; content analysis ID DISCRETIONARY DISCLOSURE; INFORMATION-CONTENT; SHAREHOLDER LITIGATION; CORPORATE DISCLOSURE; ANALYST COVERAGE; MARKET VALUE; COMPETITION; INCENTIVES; VALUATION; BEHAVIOR AB This paper examines how earnings performance relates to firms' narrative R&D disclosure decisions. The unique nature of R&D investments and financial statements' limited ability to communicate the value of such investments highlight the role of narrative disclosure as a supplement to the financial statements. I predict and find that current earnings performance (adjusted for R&D expense) is negatively related to the quantity of narrative R&D disclosure. Conducting a content analysis of the detail, tone, and readability of narrative R&D disclosures, I find that managers adjust R&D disclosures based on earnings performance to provide relevant information rather than to obfuscate performance. Finally, I provide evidence that market participants find narrative R&D disclosure informative because it significantly affects sell-side analyst behavior, disclosure information content, and information asymmetry. C1 Cornell Univ, Ithaca, NY 14853 USA. RP Merkley, KJ (reprint author), Cornell Univ, Ithaca, NY 14853 USA. CR Aboody D, 2000, J FINANC, V55, P2747, DOI 10.1111/0022-1082.00305 Bagnoli M, 2007, J ACCOUNT RES, V45, P885, DOI 10.1111/J.1475-679X.2007.00258.x Bamber LS, 1998, J ACCOUNTING RES, V36, P167, DOI 10.2307/2491473 BARNEY J, 1991, J MANAGE, V17, P99, DOI 10.1177/014920639101700108 Barth ME, 2001, J ACCOUNTING RES, V39, P1, DOI 10.1111/1475-679X.00001 Boone J.P., 2001, J ACCOUNTING PUBLIC, V20, P97, DOI 10.1016/S0278-4254(01)00023-0 Botosan CA, 2005, ACCOUNT REV, V80, P751, DOI 10.2308/accr.2005.80.3.751 Brown S, 2009, J ACCOUNT ECON, V47, P208, DOI 10.1016/j.jacceco.2008.12.002 Bushee BJ, 1998, ACCOUNT REV, V73, P305 Bushee BJ, 2010, J ACCOUNT RES, V48, P1, DOI 10.1111/j.1475-679X.2009.00357.x CACIOPPO JT, 1989, BASIC APPL SOC PSYCH, V10, P3, DOI 10.1207/s15324834basp1001_2 Chan LKC, 2001, J FINANC, V56, P2431, DOI 10.1111/0022-1082.00411 Chen SP, 2011, J ACCOUNT ECON, V51, P134, DOI 10.1016/j.jacceco.2010.10.006 Chiesa V., 2009, EVALUATION PERFORMAN, P166 Cready WM, 2002, ACCOUNT REV, V77, P891, DOI 10.2308/accr.2002.77.4.891 Dontoh A., 1989, J ACCOUNTING AUDITIN, V4, P480 Drake MS, 2012, J ACCOUNT RES, V50, P1001, DOI 10.1111/j.1475-679X.2012.00443.x Entwistle G., 1999, ACCOUNT HORIZ, V13, P323, DOI 10.2308/acch.1999.13.4.323 Ertimur Y., 2004, WORKING PAPER FASB, 2012, DISCL FRAM Frankel R, 2010, REV ACCOUNT STUD, V15, P220, DOI 10.1007/s11142-009-9089-4 Franzen LA, 2007, J FINANC, V62, P2931, DOI 10.1111/j.1540-6261.2007.01297.x Frattini F., 2009, EVALUATION PERFORMAN, P143 Glassman C. A., 2003, COMMUNICATION Graham JR, 2005, J ACCOUNT ECON, V40, P3, DOI 10.1016/j.jacceco.2005.01.002 GRILICHES Z, 1981, ECON LETT, V7, P183, DOI 10.1016/0165-1765(87)90114-5 Griliches Z., 1998, R D PRODUCTIVITY Guo RJ, 2004, J ACCOUNTING RES, V42, P319, DOI 10.1111/j.1475-679X.2004.00140.x Hall B., 1993, AM ECON REV, V82, P259 Hand J., 2003, INTANGIBLES VALUES M Harris MS, 1998, J ACCOUNTING RES, V36, P111, DOI 10.2307/2491323 HAYN C, 1995, J ACCOUNT ECON, V20, P125, DOI 10.1016/0165-4101(95)00397-2 Healy PM, 2001, J ACCOUNT ECON, V31, P405, DOI 10.1016/S0165-4101(01)00018-0 Henry E., 2008, J BUS COMMUN, V45, P363, DOI DOI 10.1177/0021943608319388 Henry E., 2010, WORKING PAPER Houston JF, 2010, CONTEMP ACCOUNT RES, V27, P143, DOI 10.1111/j.1911-3846.2010.01005.x JAFFE AB, 1986, AM ECON REV, V76, P984 James S., 2009, WORKING PAPER Jones DA, 2007, CONTEMP ACCOUNT RES, V24, P489, DOI 10.1506/G3M3-2532-514H-1517 Joos P, 2005, ACCOUNT REV, V80, P847, DOI 10.2308/accr.2005.80.3.847 Karuna C, 2007, J ACCOUNT ECON, V43, P275, DOI 10.1016/j.jacceco.2007.02.004 Kimbrough MD, 2007, ACCOUNT REV, V82, P1195, DOI 10.2308/accr.2007.82.5.1195 Koch AS, 2004, J FINANC, V59, P2093, DOI 10.1111/j.1540-6261.2004.00693.x Kothari S. P., 2002, REV ACCOUNT STUD, V7, P355, DOI 10.1023/A:1020764227390 LANG M, 1993, J ACCOUNTING RES, V31, P246, DOI 10.2307/2491273 Lang MH, 1996, ACCOUNT REV, V71, P467 Lansford B., 2006, WORKING PAPER Leder M., 2003, FINANCIAL FINE PRINT Lehavy R, 2011, ACCOUNT REV, V86, P1087, DOI 10.2308/accr.00000043 Leuz C., 2004, EC POLITICS ACCOUNTI, P164 Lev B., 2001, INTANGIBLES MANAGEME Lev B, 1996, J ACCOUNT ECON, V21, P107, DOI 10.1016/0165-4101(95)00410-6 Lev B., 1999, J APPL CORPORATE FIN, V4, P21, DOI 10.1111/j.1745-6622.1999.tb00511.x Lev B, 1999, J ACCOUNTING RES, V37, P353, DOI 10.2307/2491413 Li F, 2008, J ACCOUNT ECON, V45, P221, DOI 10.1016/j.jacceco.2008.02.003 Li F, 2010, J ACCOUNT RES, V48, P1049, DOI 10.1111/j.1475-679X.2010.00382.x Li X, 2010, REV ACCOUNT STUD, V15, P663, DOI 10.1007/s11142-010-9129-0 LIPPMAN SA, 1982, BELL J ECON, V13, P418, DOI 10.2307/3003464 Matsumoto D, 2011, ACCOUNT REV, V86, P1383, DOI 10.2308/accr-10034 Miller B., 2010, ACCOUNT REV, V85, P1487 Miller GS, 2002, J ACCOUNTING RES, V40, P173, DOI 10.1111/1475-679X.00043 Nagar V, 2003, J ACCOUNT ECON, V34, P283, DOI 10.1016/S0165-4101(02)00075-7 Nichols D. C., 2009, WORKING PAPER Penumatsa P, 2006, INT J ARTIF INTELL T, V15, P767, DOI 10.1142/S021821300600293X Previts G. J., 1994, ACCOUNT HORIZ, V8, P55 Rogers JL, 2011, ACCOUNT REV, V86, P2155, DOI 10.2308/accr-10137 Rogers JL, 2009, J ACCOUNT ECON, V48, P90, DOI 10.1016/j.jacceco.2009.07.001 Rogers JL, 2009, J ACCOUNT ECON, V47, P136, DOI 10.1016/j.jacceco.2008.04.003 Rogers JL, 2008, J ACCOUNT RES, V46, P1265, DOI 10.1111/j.1475-679X.2008.00308.x Rogers R. K., 1997, J FINANCIAL STATEMEN, V3, P17 Sadka G., 2004, WORKING PAPER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schipper K., 1991, ACCOUNT HORIZ, V5, P105 SEGERSTROM PS, 1991, J POLIT ECON, V99, P807, DOI 10.1086/261779 SOUGIANNIS T, 1994, ACCOUNT REV, V69, P44 Tucker J., 2010, J ACCOUNT RES, V49, P309 VERRECCHIA RE, 1983, J ACCOUNT ECON, V5, P179, DOI 10.1016/0165-4101(83)90011-3 Wolfe R.M., 2010, 10322 NSF NR 78 TC 2 Z9 2 PU AMER ACCOUNTING ASSOC PI SARASOTA PA 5717 BESSIE DR, SARASOTA, FL 34233 USA SN 0001-4826 EI 1558-7967 J9 ACCOUNT REV JI Account. Rev. PD MAR PY 2014 VL 89 IS 2 BP 725 EP 757 DI 10.2308/accr-50649 PG 33 WC Business, Finance SC Business & Economics GA AE1JX UT WOS:000333726700011 ER PT J AU Suarez-Tangil, G Tapiador, JE Pens-Lopez, P Blasco, J AF Suarez-Tangil, Guillermo Tapiador, Juan E. Pens-Lopez, Pedro Blasco, Jorge TI DENDROID: A text mining approach to analyzing and classifying code structures in Android malware families SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Malware analysis; Software similarity and classification; Text mining; Information retrieval; Smartphones; Android OS AB The rapid proliferation of smartphones over the last few years has come hand in hand with and impressive growth in the number and sophistication of malicious apps targetting smartphone users. The availability of reuse-oriented development methodologies and automated malware production tools makes exceedingly easy to produce new specimens. As a result, market operators and malware analysts are increasingly overwhelmed by the amount of newly discovered samples that must be analyzed. This situation has stimulated research in intelligent instruments to automate parts of the malware analysis process. In this paper, we introduce DENDROID, a system based on text mining and information retrieval techniques for this task. Our approach is motivated by a statistical analysis of the code structures found in a dataset of ANDROID OS malware families, which reveals some parallelisms with classical problems in those domains. We then adapt the standard Vector Space Model and reformulate the modelling process followed in text mining applications. This enables us to measure similarity between malware samples, which is then used to automatically classify them into families. We also investigate the application of hierarchical clustering over the feature vectors obtained for each malware family. The resulting dendo-grams resemble the so-called phylogenetic trees for biological species, allowing us to conjecture about evolutionary relationships among families. Our experimental results suggest that the approach is remarkably accurate and deals efficiently with large databases of malware instances. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Suarez-Tangil, Guillermo; Tapiador, Juan E.; Pens-Lopez, Pedro; Blasco, Jorge] Univ Carlos III Madrid, Dept Comp Sci, Comp Secur COSEC Lab, Madrid 28911, Spain. RP Suarez-Tangil, G (reprint author), Univ Carlos III Madrid, Dept Comp Sci, Comp Secur COSEC Lab, Madrid 28911, Spain. EM guillermo.suarez.tangil@uc3m.es; jesteve-z@inf.uc3m.es; pperis@inf.uc3m.es; jbalis@in-f.uc3m.es RI Tapiador, Juan/; Blasco Alis, Jorge/ OI Tapiador, Juan/0000-0002-4573-3967; Blasco Alis, Jorge/0000-0003-4392-9023 CR Cesares S, 2010, P 8 AUSTR S PAR DIST, V107, P61 Chibelushi C., 2004, NLUCS CIT, P51 Chin E., 2012, S US PRIV SEC Crussell J., 2012, COMPUTER SECURITY ES, V2, P37 Dediu H., 2012, WILL TABLETS OUTSELL Delany SJ, 2012, EXPERT SYST APPL, V39, P9899, DOI 10.1016/j.eswa.2012.02.053 Desnos A., 2013, ANDROGUARD REVERSE E Desnos A., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), DOI 10.1109/HICSS.2012.114 Dunham K., 2008, MOBILE MALWARE ATTAC Egele M., 2012, ACM COMPUTING SURVEY, V44 Egele M, 2011, P NETW DISTR SYST SE Elish K. O., 2013, STATIC ASSURANCE ANA Enrique C.-M., 2012, EXPERT SYSTEMS APPL, V39, p9367 Felt A. P., 2011, SPSM 11, P3, DOI DOI 10.1145/2046614.2046618 Fenske J., 2012, BIOMETRIC TECHNOLOGY, V2012, P9 F-Secure, 2012, MOB THREAT REP Q1 20 Gadia V, 2008, IEEE INT C BIO BIO W, P107, DOI 10.1109/BIBMW.2008.4686216 Goasduff L., 2012, GARTNER SAYS WORLDWI Grace M., 2012, P 19 ANN S NETW DIST Grace M., 2012, P 10 INT C MOB SYST, P281 Hanna S., 2012, P 9 C DET INTR MALW, P62 Hou YT, 2010, EXPERT SYST APPL, V37, P55, DOI 10.1016/j.eswa.2009.05.023 Juniper Networks, 2012, 2011 MOB THREATS REP Liao SH, 2012, EXPERT SYST APPL, V39, P11303, DOI 10.1016/j.eswa.2012.02.063 Lu L., 2012, P 2012 ACM C COMP CO, P229 McAfee, 2013, THREATS REP 4 Q 2012 Nielsen, 2012, STAT APPN YEAR CHANG Nielson F., 1999, PRINCIPLES PROGRAM A Oberreuter G, 2013, EXPERT SYST APPL, V40, P3756, DOI 10.1016/j.eswa.2012.12.082 Peng H., 2012, P ACM C COMP COMM SE, P241 Rastogi V., 2013, ASIA CCS 13, P329 Rodriguez-Gonzalez AY, 2013, EXPERT SYST APPL, V40, P6823, DOI 10.1016/j.eswa.2013.06.041 Rosen S., 2013, P 3 ACM C DAT APPL S, P221 Ruzgar E, 2012, EXPERT SYST APPL, V39, P89, DOI 10.1016/j.eswa.2011.06.049 Sahin S, 2012, EXPERT SYST APPL, V39, P4609, DOI 10.1016/j.eswa.2011.08.130 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schipka Maksym, 2009, Network Security, V2009, DOI 10.1016/S1353-4858(09)70006-6 Seo S.-H., 2013, J NETWORK COMPUTER A Shih DH, 2008, IND MANAGE DATA SYST, V108, P478, DOI 10.1108/02635570810869344 Suarez-Tangil G., 2013, TECHNICAL REPORT Tan SB, 2005, EXPERT SYST APPL, V28, P667, DOI 10.1016/j.eswa.2004.12.023 Thiruvadi Sheela, 2011, Information Technology Journal, V10, DOI 10.3923/itj.2011.710.716 Thorleuchter D., 2012, EXPERT SYSTEMS APPL van der Meulen R., 2013, GARTNER SAYS WORLDWI Zhou W., 2012, P 2 ACM C DAT APPL S, P317, DOI DOI 10.1145/2133601.2133640 Zhou YJ, 2012, P IEEE S SECUR PRIV, P95, DOI 10.1109/SP.2012.16 NR 46 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAR PY 2014 VL 41 IS 4 BP 1104 EP 1117 DI 10.1016/j.eswa.2013.07.106 PN 1 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 296AY UT WOS:000330158700018 ER PT J AU Lee, SK Kim, B Huh, M Park, J Kang, S Cho, S Lee, D Lee, D AF Lee, Seung-Kyung Kim, Bongseok Huh, Minhoe Park, Jooseoung Kang, Seokho Cho, Sungzoon Lee, Dongha Lee, Daehyung TI Knowledge discovery in inspection reports of marine structures SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Knowledge Discovery in Textual Databases; Text mining; Shipbuilding and marine engineering industry; Inspection process ID SELF-ORGANIZING MAPS; TEXT CLASSIFICATION; WEBSOM AB Inspection reports, commonly called "punches" in the marine structuring domain, are written documents about defects or supplementations on marine structures. Analyzing the inspection reports improves the construction process for the structure and prevents additional "punches." This consequently reduces construction delays and supplementary costs. The free-form texts of the reports, however, hinder management from understanding the nature of defects. Therefore, we applied Knowledge Discovery in the Textual Databases (KDT) process to answer the questions, "what kinds of defects are reported while inspecting a marine structure, and which of them are closely related?" In particular, we propose a concept extraction and linkage approach as an "add-on" module for the Self-Organizing Map (SOM), a clustering algorithm for document organization. A purely data-driven graph is derived for defect-types, which gives it in an easy-to-understand form for domain experts and reduces the gap between data analysis and its practical use. Interpretation with domain experts showed that our KDT process is useful in understanding the nature of defects in the domain and systematically responding to some other related defects. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Lee, Seung-Kyung; Kim, Bongseok; Huh, Minhoe; Park, Jooseoung; Kang, Seokho; Cho, Sungzoon] Seoul Natl Univ, Dept Ind Engn, Seoul 151744, South Korea. [Lee, Dongha; Lee, Daehyung] Daewoo Shipbldg & Marine Engn Co Ltd, Cent R&D Inst, Seoul, South Korea. RP Cho, S (reprint author), Seoul Natl Univ, Dept Ind Engn, 1 Gwanakro, Seoul 151744, South Korea. EM sklee83@snu.ac.kr; kiolol2@gmail.com; dninb.kr@gmail.com; uni208@snu.ac.kr; prokids@snu.ac.kr; zoon@snu.ac.kr; dongha@dsme.co.kr; dhlee8@dsme.co.kr FU Brain Korea 21 Project; Daewoo Shipbuilding & Marine Engineering (DSME); National Research Foundation of Korea (NRF); Korea government (MSIP) [2011-0030814]; Seoul National University Brain Fusion Program Research Grant; Engineering Research Institute of SNU FX This work was supported by the second stage of the Brain Korea 21 Project in 2011, Daewoo Shipbuilding & Marine Engineering (DSME), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030814) and 2013 Seoul National University Brain Fusion Program Research Grant. This work was also supported by the Engineering Research Institute of SNU. CR Andrews N., 2007, TR0735 VIRG TECH COM Azcarraga AP, 2004, IEEE T KNOWL DATA EN, V16, P380 Azcarraga AP, 2005, IEEE T SYST MAN CY C, V35, P595, DOI 10.1109/TSMCC.2004.843177 Bolasco S, 2005, STUD FUZZ SOFT COMP, V185, P31 Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007 Carpineto C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541884 CARRILLO P, 2011, CONSTRUCTION MANAGEM, V29, P713 Chan S. W. K., 2009, P 9 INT C EL BUS, P142 Choudhary AK, 2009, J INTELL MANUF, V20, P501, DOI 10.1007/s10845-008-0145-x Choudhary AK, 2009, COMPUT IND, V60, P728, DOI 10.1016/j.compind.2009.05.006 Cox T.F., 1994, MULTIDIMENSIONAL SCA Cutting D, 1992, P 3 C APPL NAT LANG, P133, DOI 10.3115/974499.974523 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Denicia-Carral C, 2006, LECT NOTES ARTIF INT, V4139, P76 Dransfield E, 2004, FOOD QUAL PREFER, V15, P477, DOI 10.1016/j.foodqual.2003.08.004 Fan W., 2006, COMMUN ACM, V49, P76, DOI DOI 10.1145/1151030.1151032 Fayyad UM, 1996, ADV KNOWLEDGE DISCOV Feldman R., 1995, P 1 INT C KNOWL DISC, P112 Gamon M, 2005, LECT NOTES COMPUT SC, V3646, P121 Gegick Michael, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463340 Godbole S, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING, PROCEEDINGS, VOL 2, P441, DOI 10.1109/SCC.2008.99 Gupta Vishal, 2009, Journal of Emerging Technologies in Web Intelligence, V1, DOI 10.4304/jetwi.1.1.60-76 Hearst M. A, 1999, P 37 ANN M ASS COMP, P3, DOI DOI 10.3115/1034678.1034679 Holton C, 2009, DECIS SUPPORT SYST, V46, P853, DOI 10.1016/j.dss.2008.11.013 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Iiritano S., 2001, P 12 INT WORKSH DAT, P454 Kang P., 2001, EXPERT SYSTEMS APPL, V38, P2508 Kazakov D., 2004, P 3 WORKSH ROB METH, P104, DOI 10.3115/1621445.1621458 Kiviluoto K, 1996, IEEE IJCNN, P294, DOI 10.1109/ICNN.1996.548907 Kohonen T., 1998, P ICANN98 8 INT C AR, V1, P65 Kohonen T., 1995, SELF ORGANIZING MAPS Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Lagus K, 1999, ARTIF INTELL REV, V13, P345, DOI 10.1023/A:1006586221250 Lagus K, 2004, INFORM SCIENCES, V163, P135, DOI 10.1016/j.ins.2003.03.017 Lee S, 2009, TECHNOVATION, V29, P481, DOI 10.1016/j.technovation.2008.10.006 Lee SK, 2013, EXPERT SYST APPL, V40, P83, DOI 10.1016/j.eswa.2012.07.033 Luo CN, 2009, DATA KNOWL ENG, V68, P1271, DOI 10.1016/j.datak.2009.06.007 Malin JT, 2010, IEEE INTELL SYST, V25, P20, DOI 10.1109/MIS.2010.110 Menon R, 2005, RELIAB ENG SYST SAFE, V88, P171, DOI 10.1016/j.ress.2004.07.007 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Nasukawa T, 2001, IBM SYST J, V40, P967 Ong H.-L., 2001, P 10 INT C INF KNOWL, P523 Rumelhart D. E., 1986, FEATURE DISCOVERY CO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SWANSON DR, 1987, J AM SOC INFORM SCI, V38, P228, DOI 10.1002/(SICI)1097-4571(198707)38:4<228::AID-ASI2>3.0.CO;2-G Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 Ultsch A., 1990, P INT NEUR NETW C IN, P305 Ur-Rahman N, 2012, EXPERT SYST APPL, V39, P4729, DOI 10.1016/j.eswa.2011.09.124 Wu X., 2003, KNOWL INF SYST, V5, P248, DOI 10.1007/s10115-003-0101-1 Yen GG, 2005, IEEE IJCNN, P1587 Yu L., 2005, INT J KNOWLEDGE SYST, V2, P33 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 NR 53 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAR PY 2014 VL 41 IS 4 BP 1153 EP 1167 DI 10.1016/j.eswa.2013.07.109 PN 1 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 296AY UT WOS:000330158700021 ER PT J AU Ma, N Liu, YJ AF Ma, Ning Liu, Yijun TI SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE SuperedgeRank algorithm; Supernetwork; Opinion leader ID SOCIAL NETWORK; IDENTIFICATION; DISTRUST; TRUST AB Opinion leaders on the internet are very important figures in online communities, which play an important role in promoting the formation of public opinions. Many theories have been introduced to identify opinion leaders by social network analysis, text mining and PageRank-based algorithm in different fields, but few has addressed the issue of opinion leader identification by combining the methods above, and there is no research using supernetwork analysis to identify opinion leaders. This paper proposed an SuperedgeRank algorithm for opinion leader identification based on supernetwork theory, which combined the network topology analysis and text mining. First, the study established a supernetwork model with multidimensional subnetworks, which are social, psychological, environmental and viewpoint subnetworks. Then, the study proposed four supernetwork indexes: node superdegree, superedge degree, superedge-superedge distance and superedge overlap. The later two indexes are developed by us to help evaluate the identified opinion leaders. Based on them, our study applied SuperedgeRank algorithm to rank superedges, and used the ranking result to identify opinion leaders in opinion supernetwork model. Finally, the feasibility and innovativeness of this method were verified by a case study. (C) 2013 Published by Elsevier Ltd. C1 [Ma, Ning; Liu, Yijun] Chinese Acad Sci, Inst Policy & Management, Beijing 100190, Peoples R China. [Liu, Yijun] Chinese Acad Sci, Ctr Interdisciplinary Studies Nat & Social Sci, Beijing 100190, Peoples R China. [Ma, Ning] Univ Chinese Acad Sci, Beijing 100049, Peoples R China. RP Liu, YJ (reprint author), 15 ZhongGuanCun BeiYiTiao Alley, Beijing 100190, Peoples R China. EM maningzoe@gmail.com; yijunliu@casipm.ac.cn FU National Natural Science Foundation of China (NSFC) [91024010]; Innovative Research Team Program of Chinese Academy of Sciences [KACX1-YW-1011, GH13041]; Major Research Program of Institute of Policy; Management, Chinese Academy of Sciences [Y201201Z06] FX The author gratefully acknowledges the support of the National Natural Science Foundation of China (NSFC) (91024010), Innovative Research Team Program of Chinese Academy of Sciences (KACX1-YW-1011,GH13041), and Major Research Program of Institute of Policy and Management, Chinese Academy of Sciences (Y201201Z06). CR Balog K, 2012, FOUND TRENDS INF RET, V6, P127, DOI 10.1561/1500000024 Biran O., 2012, P 2 WORKSH LANG SOC, P37 Bodendorf F., 2009, P 2 ACM WORKSH SOC W, P65, DOI DOI 10.1145/1651437.1651448 Carley K. M., 2005, DYNAMIC NETWORK ANAL Carley K. M., 2003, DYNAMIC NETWORK ANAL Cho Y, 2012, TECHNOL FORECAST SOC, V79, P97, DOI 10.1016/j.techfore.2011.06.003 Choi SM, 2013, EXPERT SYST APPL, V40, P1274, DOI 10.1016/j.eswa.2012.08.063 Cruz FL, 2012, INFORM PROCESS MANAG, V48, P271, DOI 10.1016/j.ipm.2011.08.003 Ghoshal G., 2009, PHYS REV E, V79, P066 Goldenberg J, 2009, J MARKETING, V73, P1 Goldsmith R. E., 2006, J INTERACTIVE ADVERT, V6, P3 Goyal A, 2008, P 17 ACM C INF KNOWL, P499, DOI DOI 10.1145/1458082.145814 Han YS, 2012, COMPUT INFORM, V31, P447 Ho JYC, 2010, J BUS RES, V63, P1000, DOI 10.1016/j.jbusres.2008.08.010 Huillier G. L., 2010, P ACM SIGKDD WORKSH Iyengar R, 2011, MARKET SCI, V30, P195, DOI 10.1287/mksc.1100.0566 Kelly E, 2008, COMPUT HUM BEHAV, V24, P2372, DOI 10.1016/j.chb.2008.02.013 Kim YS, 2013, EXPERT SYST APPL, V40, P3500, DOI 10.1016/j.eswa.2012.12.058 Kiss C, 2008, DECIS SUPPORT SYST, V46, P233, DOI 10.1016/j.dss.2008.06.007 Kratzer J, 2009, J CONSUM RES, V36, P646, DOI 10.1086/599324 Kwak H, 2010, P 19 INT C WORLD WID, P591, DOI DOI 10.1145/1772690.1772751 Lazarsfeld P., 1944, PEOPLES CHOICE VOTER Li F, 2011, DECIS SUPPORT SYST, V51, P190, DOI 10.1016/j.dss.2010.12.007 Li YY, 2013, KNOWL-BASED SYST, V43, P43, DOI 10.1016/j.knosys.2013.01.005 Liu Y.J., 2012, B CHINESE ACAD SCI, V28 Nagurney A., 2002, SUPERNETWORKS DECISI Nagurney A., 2005, INT J KNOWLEDGE CULT, V4, P1 O'Cass A., 2003, J RETAILING CONSUMER, V10, P81, DOI DOI 10.1016/S0969-6989(02)00004-8 Ortega FJ, 2012, COMPUT NETW, V56, P2884, DOI 10.1016/j.comnet.2012.05.002 Page L., 1999, TECHNICAL REPORT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sheffi Y., 1985, URBAN TRANSPORTATION Song K., 2012, WEB AGE INFORM MANAG, V7418, P95 Song K., 2012, WEB AGE INFORM MANAG, V7142, P197 Stelzner M. A., 2010, 2010 SOCIAL MEDIA MA Tejavibulya P., 2011, SYSTEMS ENG PROCEDIA, V2, P12 Trusov M, 2010, J MARKETING RES, V47, P643 Tsang ASL, 2005, J BUS RES, V58, P1186, DOI 10.1016/j.jbusres.2004.05.002 Wang JW, 2010, EUR PHYS J B, V77, P493, DOI 10.1140/epjb/e2010-00297-8 Yu XA, 2010, LECT NOTES COMPUT SC, V6318, P360 Zhu T, 2012, EXPERT SYST APPL, V39, P4222, DOI 10.1016/j.eswa.2011.09.112 NR 41 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAR PY 2014 VL 41 IS 4 BP 1357 EP 1368 DI 10.1016/j.eswa.2013.08.033 PN 1 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 296AY UT WOS:000330158700038 ER PT J AU Song, GY Cheon, Y Lee, K Park, KM Rim, HC AF Song, Gil-Young Cheon, Youngjoon Lee, Kihwang Park, Kyung Min Rim, Hae-Chang TI Inter-category Map: Building Cognition Network of General Customers through Big Data Mining SO KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS LA English DT Article DE social media; big data mining; brand choice; inter-category map ID PRODUCT CATEGORY; SOCIAL-STRUCTURE; MARKET; INDUSTRY; MODEL; PRICE; ENTREPRENEURSHIP; INFORMATION; INNOVATION; EVOLUTION AB Social media is considered a valuable platform for gathering and analyzing the collective and subconscious opinions of people in Internet and mobile environments, where they express, explicitly and implicitly, their daily preferences for brands and products. Extracting and tracking the various attitudes and concerns that people express through social media could enable us to categorize brands and decipher individuals' cognitive decision-making structure in their choice of brands. We investigate the cognitive network structure of consumers by building an inter-category map through the mining of big data. In so doing, we create an improved online recommendation model. Building on economic sociology theory, we suggest a framework for revealing collective preference by analyzing the patterns of brand names that users frequently mention in the online public sphere. We expect that our study will be useful for those conducting theoretical research on digital marketing strategies and doing practical work on branding strategies. C1 [Song, Gil-Young; Rim, Hae-Chang] Korea Univ, Dept Comp & Radio Commun, Seoul 136701, South Korea. [Cheon, Youngjoon] Yonsei Univ, Res Ctr Technol Management, Seoul 120749, South Korea. [Lee, Kihwang] Daumsoft Inc, Min Co, Seoul 140887, South Korea. [Park, Kyung Min] Yonsei Univ, Sch Business, Seoul 120749, South Korea. RP Park, KM (reprint author), Yonsei Univ, Sch Business, Seoul 120749, South Korea. EM cielblau@korea.ac.kr; taisaman@naver.com; leekh@daumsoft.com; kminpark@yonsei.ac.kr; rim@nlp.korea.ac.kr FU National Research Foundation of Korea; Korean Government [NRF-2011-330-B00076]; Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF); Ministry of Science, ICT & Future Planning [2012M3C4A7033344] FX This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2011-330-B00076).This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (2012M3C4A7033344). CR Abolafia M.Y., 1984, SOCIAL DYNAMICS FINA, P129 Aral S, 2011, MANAGE SCI, V57, P1623, DOI 10.1287/mnsc.1110.1421 Barnett WP, 2013, IND CORP CHANGE, V22, P585, DOI 10.1093/icc/dts030 Bhaskarabhatia A., 2014, IND CORPORATE CHANGE, V23 BRADACH JL, 1989, ANNU REV SOCIOL, V15, P97 Burt R. S., 1992, STRUCTURAL HOLES SOC BURT RS, 1993, SOC NETWORKS, V15, P133, DOI 10.1016/0378-8733(93)90002-3 Burt RS, 2000, RES ORGAN BEHAV, V22, P345, DOI 10.1016/S0191-3085(00)22009-1 Carroll GR, 2010, RES SOCIOL ORGAN-RES, V31, P203, DOI 10.1108/S0733-558X(2010)0000031009 COLEMAN JS, 1984, AM ECON REV, V74, P84 Dellarocas C, 2003, MANAGE SCI, V49, P1407, DOI 10.1287/mnsc.49.10.1407.17308 DelVecchio D, 2005, J ACAD MARKET SCI, V33, P184, DOI 10.1177/0092070304269753 DiMaggio P., 1991, NEW I ORG ANAL, P267 Dobrev SD, 2003, ORGAN SCI, V14, P264, DOI 10.1287/orsc.14.3.264.15163 Duh K., 2012, ICWSM Faulkner Robert R., 1983, MUSIC DEMAND COMPOSE Feldman R., 2006, TEXT MINING HDB Fligstein N, 2001, SOCIOL THEOR, V19, P105, DOI 10.1111/0735-2751.00132 George G, 2011, ENTREP THEORY PRACT, V35, P83, DOI 10.1111/j.1540-6520.2010.00424.x Glynn MA, 2005, J MANAGE STUD, V42, P1031, DOI 10.1111/j.1467-6486.2005.00531.x Godart FC, 2010, POETICS, V38, P567, DOI 10.1016/j.poetic.2010.09.003 Goldenberg J., 2009, J MARKETING, V73, P1 GRANOVETTER M, 1985, AM J SOCIOL, V91, P481, DOI 10.1086/228311 Han Joon, 2004, [Korean Journal of Sociology, ķ•œźµ­ģ‚¬ķšŒķ•™], V38, P187 Hannan MT, 2007, LOGICS OF ORGANIZATION THEORY: AUDIENCES, CODES, AND ECOLOGIES, P1 HIRSCH PM, 1972, AM J SOCIOL, V77, P639, DOI 10.1086/225192 Hsu G, 2005, ORGAN SCI, V16, P474, DOI 10.1287/orsc.1050.0151 Hsu G, 2006, IND CORP CHANGE, V15, P467, DOI 10.1093/icc/dt1009 Huang E, 2006, CONVERGENCE, V12, P83, DOI 10.1177/1354856506061557 Jan Kietzmann H, 2011, BUS HORIZONS, V54, P241 Kao A, 2006, NATURAL LANGUAGE PRO Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003 Kim BK, 2011, ADMIN SCI QUART, V56, P238, DOI 10.1177/0001839211427535 Kim TY, 2006, ACAD MANAGE REV, V31, P704, DOI 10.5465/AMR.2006.21318926 Kocak O., 2009, ORG EC WORKSH VER Lounsbury M, 2001, ADMIN SCI QUART, V46, P29, DOI 10.2307/2667124 Lounsbury M, 2001, STRATEGIC MANAGE J, V22, P545, DOI 10.1002/smj.188 Negro G, 2011, ORGAN SCI, V22, P1449, DOI 10.1287/orsc.1100.0619 Negro G, 2010, RES SOCIOL ORGAN-RES, V31, P3, DOI 10.1108/S0733-558X(2010)0000031003 Obstfeld D, 2005, ADMIN SCI QUART, V50, P100 Pallis D, 2011, WEB DATA MANAGEMENT Park H, 2013, SCIENCE, V342 Phillips DJ, 2001, AM J SOCIOL, V107, P379, DOI 10.1086/324072 PODOLNY JM, 1993, AM J SOCIOL, V98, P829, DOI 10.1086/230091 Polos L, 2002, SOCIOL METHODOL, V32, P133, DOI 10.1111/1467-9531.00114 Pontikes EG, 2012, ADMIN SCI QUART, V57, P81, DOI 10.1177/0001839212446689 Rao H, 2005, AM SOCIOL REV, V70, P968 ROTH MS, 1992, J INT BUS STUD, V23, P477, DOI 10.1057/palgrave.jibs.8490276 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simmel G., 1950, SOCIOLOGY G SIMMEL, P45 Soh C, 2006, MIS QUART, V30, P705 Tan P. N., 2005, INTRO DATA MINING Tilly C., 1998, DURABLE INEQUALITY Van Alstyne M, 2005, MANAGE SCI, V51, P851, DOI 10.1287/mnsc.1050.0363 VONHIPPEL E, 1994, MANAGE SCI, V40, P429, DOI 10.1287/mnsc.40.4.429 VONHIPPEL E, 1986, MANAGE SCI, V32, P791, DOI 10.1287/mnsc.32.7.791 von Krogh G, 2006, MANAGE SCI, V52, P975, DOI 10.1287/mnsc.1060.0560 Weber M, 1947, THEORY SOCIAL EC ORG, P158 Weick K., 1995, SENSEMAKING ORG Wellman B., 1988, SOCIAL STRUCTURES NE, P19 White H. C., 2007, THEOR CULT SOC, V24, P181 White H.C, 1993, EXPLORATIONS EC SOCI, P161 NR 62 TC 1 Z9 1 PU KSII-KOR SOC INTERNET INFORMATION PI GANGNAM-GU PA KOR SCI & TECHNOL CTR, 409 ON 4TH FLR, MAIN BLDG, 635-4 YEOKSAM 1-DONG, GANGNAM-GU, SEOUL 00000, SOUTH KOREA SN 1976-7277 J9 KSII T INTERNET INF JI KSII Trans. Internet Inf. Syst. PD FEB 27 PY 2014 VL 8 IS 2 BP 583 EP 600 DI 10.3837/tiis.2014.02.015 PG 18 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA AE0ZM UT WOS:000333694900015 ER PT J AU Caldas, J Vinga, S AF Caldas, Jose Vinga, Susana TI Global Meta-Analysis of Transcriptomics Studies SO PLOS ONE LA English DT Article ID GENE-EXPRESSION; REPLICATED EXPERIMENTS; MICROARRAY EXPERIMENTS; STEM-CELLS; CANCER; KINASE; GENERATION; PROFILES; DATABASE; MAP AB Transcriptomics meta-analysis aims at re-using existing data to derive novel biological hypotheses, and is motivated by the public availability of a large number of independent studies. Current methods are based on breaking down studies into multiple comparisons between phenotypes (e.g. disease vs. healthy), based on the studies' experimental designs, followed by computing the overlap between the resulting differential expression signatures. While useful, in this methodology each study yields multiple independent phenotype comparisons, and connections are established not between studies, but rather between subsets of the studies corresponding to phenotype comparisons. We propose a rank-based statistical meta-analysis framework that establishes global connections between transcriptomics studies without breaking down studies into sets of phenotype comparisons. By using a rank product method, our framework extracts global features from each study, corresponding to genes that are consistently among the most expressed or differentially expressed genes in that study. Those features are then statistically modelled via a term-frequency inverse-document frequency (TF-IDF) model, which is then used for connecting studies. Our framework is fast and parameter-free; when applied to large collections of Homo sapiens and Streptococcus pneumoniae transcriptomics studies, it performs better than similarity-based approaches in retrieving related studies, using a Medical Subject Headings gold standard. Finally, we highlight via case studies how the framework can be used to derive novel biological hypotheses regarding related studies and the genes that drive those connections. Our proposed statistical framework shows that it is possible to perform a meta-analysis of transcriptomics studies with arbitrary experimental designs by deriving global expression features rather than decomposing studies into multiple phenotype comparisons. C1 [Caldas, Jose] INESC ID, Lisbon, Portugal. [Vinga, Susana] Univ Lisbon, Inst Super Tecn, LAETA, IDMEC, P-1699 Lisbon, Portugal. RP Caldas, J (reprint author), INESC ID, Lisbon, Portugal. EM jose@kdbio.inesc-id.pt RI Vinga, Susana/B-8450-2008 OI Vinga, Susana/0000-0002-1954-5487 FU national funds through Fundacao para a Cieancia e a Tecnologia (FCT, Portugal) [PestOE/EEI/LA0021/2011]; national funds through IDMEC under LAETA [PestOE/EME/LA0022]; project PneumoSyS [PTDC/SAUMII/100964/2008]; project InteleGen [PTDC/DTPFTO/1747/2012]; project BacHBerry [FP7KBBE20137, 613793]; FCT [IF/00653/2012]; European Social Fund (ESF) through the Operational Program Human Potential(POPH) FX This work was supported by national funds through Fundacao para a Cieancia e a Tecnologia (FCT, Portugal) under contracts PestOE/EEI/LA0021/2011; through IDMEC, under LAETA (PestOE/EME/LA0022); and projects PneumoSyS (PTDC/SAUMII/100964/2008), InteleGen (PTDC/DTPFTO/1747/2012), and BacHBerry (FP7KBBE20137 singlestage, Grant Agreement no 613793). SV acknowledges support by Program Investigador FCT (IF/00653/2012) from FCT, co-funded by the European Social Fund (ESF) through the Operational Program Human Potential(POPH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Baum AE, 2008, MOL PSYCHIATR, V13, P197, DOI 10.1038/sj.mp.4002012 Breitling R, 2004, FEBS LETT, V573, P83, DOI 10.1016/j.febslet.2004.07.055 Caldas J, 2012, BIOINFORMATICS, V28, P246, DOI 10.1093/bioinformatics/btr634 Chan JY, 2008, CIRC RES, V102, P571, DOI 10.1161/CIRCRESAHA.107.161687 Duncan EM, 2008, CELL, V135, P284, DOI 10.1016/j.cell.2008.09.055 Eisinga R, 2013, FEBS LETT, V587, P677, DOI 10.1016/j.febslet.2013.01.037 Fujibuchi W, 2007, BIOINFORMATICS, V23, P3103, DOI 10.1093/bioinformatics/btm462 Heo JS, 2006, AM J PHYSIOL-CELL PH, V290, pC123, DOI 10.1152/ajpcell.00142.2005 Hollander M, 2000, NONPARAMETRIC STAT M Hong FX, 2008, BIOINFORMATICS, V24, P374, DOI 10.1093/bioinformatics/btm620 Huang HY, 2011, INT J ONCOL, V38, P1557, DOI 10.3892/ijo.2011.994 Huang HY, 2010, P NATL ACAD SCI USA, V107, P6823, DOI 10.1073/pnas.0912043107 Jacobsen FE, 2011, METALLOMICS, V3, P38, DOI 10.1039/c0mt00050g Jiang K, 2013, APOPTOSIS, V18, P556 Koziol JA, 2010, FEBS LETT, V584, P941, DOI 10.1016/j.febslet.2010.01.031 Kupershmidt I, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0013066 Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265 Lukk M, 2010, NAT BIOTECHNOL, V28, P322, DOI 10.1038/nbt0410-322 Maglich JM, 2009, J LIPID RES, V50, P439, DOI 10.1194/jlr.M800226-JLR200 Malone J, 2010, BIOINFORMATICS, V26, P1112, DOI 10.1093/bioinformatics/btq099 Manning C.D., 2008, INTRO INFORM RETRIEV Mardis ER, 2008, ANNU REV GENOM HUM G, V9, P387, DOI 10.1146/annurev.genom.9.081307.164359 Network CGAR, 2008, NATURE, V455, P1061 Nordmann AJ, 2012, SWISS MED WKLY, V142, DOI 10.4414/smw.2012.13518 Parkinson H, 2009, NUCLEIC ACIDS RES, V37, pD868, DOI 10.1093/nar/gkn889 Potapova IA, 2010, STEM CELL RES THER, V1, DOI 10.1186/scrt35 Pruitt KD, 2007, NUCLEIC ACIDS RES, V35, pD61, DOI 10.1093/nar/gkl842 Reyes-Caballero H, 2010, J MOL BIOL, V403, P197, DOI 10.1016/j.jmb.2010.08.030 Rhodes DR, 2007, NEOPLASIA, V9, P166, DOI 10.1593/neo.07112 Rung J, 2013, NAT REV GENET, V14, P89, DOI 10.1038/nrg3394 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Segal E, 2004, NAT GENET, V36, P1090, DOI 10.1038/ng1434 Shafeeq S, 2011, METALLOMICS, V3, P609, DOI 10.1039/c1mt00030f Shi WJ, 2006, J BIOL CHEM, V281, P23319, DOI 10.1074/jbc.M601811200 Sirota M, 2011, SCI TRANSL MED, V3, P77, DOI DOI 10.1126/SCITRANSLMED.3001318 Squire JA, 2009, NAT GENET, V41, P509, DOI 10.1038/ng0509-509 Storey JD, 2003, P NATL ACAD SCI USA, V100, P9440, DOI 10.1073/pnas.1530509100 Subramanian A, 2005, P NATL ACAD SCI USA, V102, P15545, DOI 10.1073/pnas.0506580102 Tseng GC, 2012, NUCLEIC ACIDS RES, V40, P3785, DOI 10.1093/nar/gkr1265 NR 40 TC 1 Z9 1 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD FEB 26 PY 2014 VL 9 IS 2 AR e89318 DI 10.1371/journal.pone.0089318 PG 11 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AC3BC UT WOS:000332389000039 PM 24586684 ER PT J AU Crasso, M Mateos, C Zunino, A Campo, M AF Crasso, Marco Mateos, Cristian Zunino, Alejandro Campo, Marcelo TI EasySOC: Making web service outsourcing easier SO INFORMATION SCIENCES LA English DT Article DE Service-oriented computing; Service outsourcing; Text mining; Machine learning; Dependency injection ID SEMANTIC WEB; DISCOVERY; ARCHITECTURE; SIMILARITY; METRICS; DESIGN AB Service-oriented computing has been widely recognized as a revolutionary paradigm for software development. Despite the important benefits this paradigm provides, current approaches for service-enabling applications still lead to high costs for outsourcing services with regard to two phases of the software life cycle. During the implementation phase, developers have to invest much effort into manually discovering services and then providing code to invoke them. Mostly, the outcome of the second task is software containing service-aware code, therefore it is more difficult to modify and to test during the maintenance phase. This paper describes EasySOC, an approach that aims to decrease the costs of creating and maintaining service-oriented applications. EasySOC combines text mining, machine learning, and best practices from component-based software development to allow developers to quickly discover and non-invasively invoke services. We evaluated the performance of the EasySOC discovery mechanism using 391 services. In addition, through a case study, we conducted a comparative analysis of the software technical quality achieved by employing EasySOC versus not using it. (C) 2010 Elsevier Inc. All rights reserved. C1 UNICEN Univ, ISISTAN Res Inst, Buenos Aires, DF, Argentina. Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Mateos, C (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,Tandil B7001BB0, Buenos Aires, DF, Argentina. EM cmateos@exa.unicen.edu.ar RI Zunino, Alejandro/ OI Zunino, Alejandro/0000-0002-9537-3541 FU ANPCyT [PAE-PICT 2007-02311, PAE-PICT 2007-02312] FX We deeply thank the anonymous reviewers for their helpful comments and suggestions to improve the quality of the paper. We acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311 and PAE-PICT 2007-02312. CR Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112 Blake MB, 2007, DISTRIB PARALLEL DAT, V21, P39, DOI 10.1007/s10619-006-7001-9 Buckley C., 1994, 17 ANN INT ACM SIGIR Burstein M, 2005, IEEE INTERNET COMPUT, V9, P72, DOI 10.1109/MIC.2005.96 Cavallaro L., 2008, 2008 INT WORKSH SOFT CHIDAMBER SR, 1994, IEEE T SOFTWARE ENG, V20, P476, DOI 10.1109/32.295895 Cibran MA, 2007, WORLD WIDE WEB, V10, P211, DOI 10.1007/s11280-006-0017-2 COLEMAN D, 1994, COMPUTER, V27, P44, DOI 10.1109/2.303623 Crasso M., 2008, INTELIGENCIA ARTIFIC, V12, P25 Crasso M., 2008, 2008 ACM S APPL COMP Curbera F, 2003, COMMUN ACM, V46, P29, DOI 10.1145/944217.944234 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong X., 2004, 30 INT C VER LARG DA Erl T., 2005, SERVICE ORIENTED ARC Fensel D, 2006, ENABLING SEMANTIC WE Gamma E, 1995, DESIGN PATTERNS ELEM Garofalakis J, 2006, J WEB ENG, V5, P265 Gomez-Perez A., 2003, ONTOLOGICAL ENG Hess A., 2004, LECT NOTES COMPUTER, V3298 Huhns MN, 2005, IEEE INTERNET COMPUT, V9, P75, DOI 10.1109/MIC.2005.21 International Organization for Standardization, 9126 ISO Joachims T., 1997, 14 INT C MACH LEARN Johnson R, 2005, COMPUTER, V38, P107, DOI 10.1109/MC.2005.22 Jones T. C., 1998, ESTIMATING SOFTWARE Kiczales G, 2001, COMMUN ACM, V44, P59, DOI 10.1145/383845.383858 Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 Kittredge R., 1982, American Journal of Computational Linguistics, V8 Korfhage R., 1997, INFORM STORAGE RETRI Kozlenkov A, 2007, INT J WEB SERV RES, V4, P82, DOI 10.4018/jwsr.2007040104 Lakshmi Narasimhan V., 2007, INFORM SCI, V17, P844 Lertnattee V, 2004, INFORM SCIENCES, V158, P89, DOI 10.1016/j.ins.2003.07.007 LOSEE RM, 1995, J AM SOC INFORM SCI, V46, P519, DOI 10.1002/(SICI)1097-4571(199508)46:7<519::AID-ASI5>3.0.CO;2-R Martin R.C., 1995, REPORT OBJECT ANAL D, V2 Mateos C., 2006, LECT NOTES ARTIFICIA, V4140 McCool R, 2005, IEEE INTERNET COMPUT, V9, P88, DOI 10.1109/MIC.2005.133 McIlraith SA, 2003, IEEE INTELL SYST, V18, P90, DOI 10.1109/MIS.2003.1179199 Nezhad H.R.M., 2007, 16 INT C WORLD WID W OASIS Consortium, 2004, UDDI VERS 3 IN PRESS Paolucci M, 2003, IEEE INTERNET COMPUT, V7, P34, DOI 10.1109/MIC.2003.1232516 Papazoglou MP, 2007, VLDB J, V16, P389, DOI 10.1007/s00778-007-0044-3 Porter M. F., 1997, READINGS INFORM RETR, P313 Ran Shuping, 2003, J SIGECOM EXCHANGES, V4, P1 Razina E., 2007, 11 IASTED INT C SOFT Reed J. W., 2006, 5 INT C MACH LEARN A Resendiz MP, 2005, 2005 2nd International Conference on Electrical & Electronics Engineering (ICEEE), P48 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt C, 2004, WORLD WIDE WEB, V7, P211, DOI 10.1023/B:WWWJ.0000017210.55153.3d Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] Sivashanmugam K., 2003, 2003 INT C WEB SERV Spinellis D, 2008, IEEE SOFTWARE, V25, P89, DOI 10.1109/MS.2008.101 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 Suvee D., 2003, 2 INT C ASP OR SOFT Vaughan-Nichols SJ, 2002, COMPUTER, V35, P18, DOI 10.1109/2.982908 Vinoski S, 2005, IEEE INTERNET COMPUT, V9, P86, DOI 10.1109/MIC.2005.3 Vitharana P, 2004, IEEE T SYST MAN CY C, V34, P460, DOI 10.1109/TSMCC.2004.829258 W3C Consortium, 2007, WSDL VERS 2 0 1 Wang H, 2004, J WEB SEMANT, V1, P309 NR 59 TC 3 Z9 3 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 EI 1872-6291 J9 INFORM SCIENCES JI Inf. Sci. PD FEB 20 PY 2014 VL 259 BP 452 EP 473 DI 10.1016/j.ins.2010.01.013 PG 22 WC Computer Science, Information Systems SC Computer Science GA 288FK UT WOS:000329597200034 ER PT J AU Chary, M Park, EH McKenzie, A Sun, J Manini, AF Genes, N AF Chary, Michael Park, Emily H. McKenzie, Andrew Sun, Julia Manini, Alex F. Genes, Nicholas TI Signs & Symptoms of Dextromethorphan Exposure from YouTube SO PLOS ONE LA English DT Article ID ABUSE; DEXTRORPHAN; RECEPTOR AB Detailed data on the recreational use of drugs are difficult to obtain through traditional means, especially for substances like Dextromethorphan (DXM) which are available over-the-counter for medicinal purposes. In this study, we show that information provided by commenters on YouTube is useful for uncovering the toxicologic effects of DXM. Using methods of computational linguistics, we were able to recreate many of the clinically described signs and symptoms of DXM ingestion at various doses, using information extracted from YouTube comments. Our study shows how social networks can enhance our understanding of recreational drug effects. C1 [Chary, Michael; McKenzie, Andrew; Sun, Julia] Icahn Sch Med Mt Sinai, New York, NY USA. [Park, Emily H.] Rutgers New Jersey Med Sch, Newark, NJ USA. [Manini, Alex F.] Icahn Sch Med Mt Sinai, Div Med Toxicol, New York, NY USA. [Genes, Nicholas] Icahn Sch Med Mt Sinai, Dept Emergency Med, New York, NY 10029 USA. RP Genes, N (reprint author), Icahn Sch Med Mt Sinai, Dept Emergency Med, New York, NY 10029 USA. EM nicholas.genes@mountsinai.org RI Chary, Michael/ OI Chary, Michael/0000-0002-9000-1765 CR Anderson I, 2013, DEXTROMETHORPHAN ABU [Anonymous], 2011, J ADDICT DIS, V30, P243 Ball J. K., 2006, NEW DAWN REP, V32, P1 Banerji S, 2001, AM J HEALTH-SYST PH, V58, P1811 Bobo WV, 2004, AM J EMERG MED, V22, P624, DOI 10.1016/j.ajem.2004.09.011 Boyer EW, 2007, CYBERPSYCHOL BEHAV, V10, P1, DOI 10.1089/cpb.2006.9999 Boyer EW, 2004, PEDIATR EMERG CARE, V20, P858, DOI 10.1097/01.pec.0000148039.14588.d0 Bronstein AC, 2012, CLIN TOXICOL, V50, P911, DOI 10.3109/15563650.2012.746424 Bryner JK, 2006, ARCH PEDIAT ADOL MED, V160, P1217, DOI 10.1001/archpedi.160.12.1217 Casselman I, 2011, J ETHNOPHARMACOL, V138, P662, DOI 10.1016/j.jep.2011.07.065 Chou YC, 1999, BRAIN RES, V821, P516, DOI 10.1016/S0006-8993(99)01125-7 Daniulaityte R, 2012, DRUG ALCOHOL DEPENDE GINSKI MJ, 1994, PSYCHOPHARMACOLOGY, V114, P573, DOI 10.1007/BF02244987 Lange JE, 2010, DRUG ALCOHOL DEPEN, V108, P138, DOI 10.1016/j.drugalcdep.2009.11.010 Manning P, 2012, DRUGS ED PREVENTION, P1 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Mitkov, 2002, OXFORD HDB COMPUTATI Murff HJ, 2011, JAMA-J AM MED ASSOC, V306, P848, DOI 10.1001/jama.2011.1204 Murphy PN, 2011, HUM PSYCHOPHARM CLIN, V26, P460, DOI 10.1002/hup.1228 Pham T, 2010, TECHNICAL REPORT Price LH, 2000, AM J PSYCHIAT, V157, P304, DOI 10.1176/appi.ajp.157.2.304 RAMACHANDER G, 1977, J PHARM SCI, V66, P1047, DOI 10.1002/jps.2600660740 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Substance Abuse and Mental Health Services Administration (SAMHSA), 2007, NSDUH REP MIS OV THE Trugenberger CA, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-51 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wilson MD, 2011, CLIN TOXICOL, V49, P409, DOI 10.3109/15563650.2011.585429 Zack M, 2004, NEUROPSYCHOPHARMACOL, V29, P195, DOI 10.1038/sj.npp.1300333 NR 28 TC 2 Z9 2 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD FEB 12 PY 2014 VL 9 IS 2 AR e82452 DI 10.1371/journal.pone.0082452 PG 10 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AA7FJ UT WOS:000331262600002 PM 24533044 ER PT J AU Henriksson, A Moen, H Skeppstedt, M Daudaravicius, V Duneld, M AF Henriksson, Aron Moen, Hans Skeppstedt, Maria Daudaravicius, Vidas Duneld, Martin TI Synonym extraction and abbreviation expansion with ensembles of semantic spaces SO JOURNAL OF BIOMEDICAL SEMANTICS LA English DT Article ID MEDLINE; TEXT; COOCCURRENCE; DICTIONARY; SIMILARITY AB Background: Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results: A combination of two distributional models - Random Indexing and Random Permutation - employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora - a corpus of clinical text and a corpus of medical journal articles - further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions: This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models - with different model parameters - and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks. C1 [Henriksson, Aron; Skeppstedt, Maria; Duneld, Martin] Stockholm Univ, Dept Comp & Syst Sci DSV, SE-16440 Kista, Sweden. [Moen, Hans] Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, NO-7491 Trondheim, Norway. [Daudaravicius, Vidas] Vytautas Magnus Univ, Fac Informat, LT-44404 Kaunas, Lithuania. RP Henriksson, A (reprint author), Stockholm Univ, Dept Comp & Syst Sci DSV, Forum 100, SE-16440 Kista, Sweden. EM aronhen@dsv.su.se FU Swedish Foundation for Strategic Research through the project High-Performance Data Mining for Drug Effect Detection at Stockholm University, Sweden [IIS11-0053]; Research Council of Norway through the project EviCare - Evidence-based care processes: Integrating knowledge in clinical information systems (NFR project) [193022] FX This work was partly (AH) supported by the Swedish Foundation for Strategic Research through the project High-Performance Data Mining for Drug Effect Detection (ref. no. IIS11-0053) at Stockholm University, Sweden. It was also partly (HM) supported by the Research Council of Norway through the project EviCare - Evidence-based care processes: Integrating knowledge in clinical information systems (NFR project no. 193022). We would like to thank the members of our former research network HEXAnord, within which this study was initiated. We would especially like to thank Ann-Marie Eklund for her contributions to the initial stages of this work. We are also grateful to Staffan Cederblom and Studentlitteratur for giving us access to their database of medical abbreviations. Finally, we would like to thank the three reviewers for their insightful comments. CR Ao H, 2005, J AM MED INFORM ASSN, V12, P576, DOI 10.1197/jamia.M1757 Blondel VD, 2004, SIAM REV, V46, P647, DOI 10.1137/S0036144502415960 Cederblom S, 2005, MED FORKORTNINGAROCH Chang JT, 2002, J AM MED INFORM ASSN, V9, P612, DOI 10.1197/jamia.M1139 Cohen AM, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-103 Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57 Cohen T, 2009, J BIOMED INFORM, V42, P390, DOI 10.1016/j.jbi.2009.02.002 Conway M, 2012, P AMIA ANN S, P1604 Curran JR, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P222 Dalianis Hercules, 2009, P ISHIMR 2009 EV IMP, P243 Dannells D, 2006, P 11 C EUR CHAPT ASS, P167, DOI 10.3115/1608974.1608999 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200 Dietterich T.G., 2000, P 1 INT WORKSH MULT, P1 Eriksson R, 2013, J AM MED INFORM ASSN, V20, P947, DOI 10.1136/amiajnl-2013-001708 Gaudan S, 2005, BIOINFORMATICS, V21, P3658, DOI 10.1093/bioinformatics/bti586 Harris ZS, 1954, WORD, V10, P146 Hassel M, 2004, JAVASDM PACKAGE Hearst M. A., 1992, P 14 INT C COMP LING, P539 Henriksson A, 2013, P BIONLP Henriksson A, 2013, P LOUH WORKSH HLTH D Henriksson A, 2013, P AMIA ANN S, P600 Isenius N, 2012, P CLEF 2012 WORKSH C Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kanerva P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P1036 Karolinska Institutet, 2012, HUR MAN ANV SVENSK M Keselman A, 2007, P AMIA ANN S, P399 Knutsson O, 2003, P NOD 2003 Kokkinakis D, 2012, 3 WORKSH BUILD EV RE Koopman B., 2012, P 21 ACM INT C INF K, P2439 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Leroy G, P AMIA ANN S Leroy G, 2001, IEEE T INF TECHNOL B, V5, P261, DOI 10.1109/4233.966101 Liu Hongfang, 2002, Proc AMIA Symp, P464 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 McCrae J, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-159 Meystre SM, 2008, YB MED INFORM S1, V47, P128 Moffat A, 2008, ACM T INFORM SYST, V27, P2 Movshovitz-Attias D., 2012, P 2012 WORKSH BIOM N, P47 Nakayama K, 2007, LECT NOTES COMPUT SC, V4831, P322 Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 Peirsman Y., 2009, P 12 C EUR CHAPT ASS, P648, DOI [10.3115/1609067.1609139, DOI 10.3115/1609067.1609139] Saeed J. I., 1997, SEMANTICS Sahlgren M., 2006, THESIS STOCKHOLM U Sahlgren M, P 30 ANN M COGN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Schwartz Ariel S, 2003, Pac Symp Biocomput, P451 Symonds M, 2011, P 25 PAC AS C LANG I Symonds M, 2012, AUSTR LANG TECHN ASS, P15 Thorne B., 2003, STAT BUSINESS EC US National Library of Medicine, MESH MED SUBJ HEAD Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203 van der Plas L., 2006, P COLING ACL 2006 MA, P866, DOI 10.3115/1273073.1273184 Wu H, 2003, P 2 INT WORKSH PAR, V16, P72, DOI 10.3115/1118984.1118994 Yu H, 2003, BIOINFORMATICS, V1, P340 Yu H, 2002, J AM MED INFORM ASSN, V9, P262, DOI 10.1197/jamia.M0913 Zeng QT, 2012, P AMIA ANN S, P1050 NR 58 TC 3 Z9 3 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 2041-1480 J9 J BIOMED SEMANT JI J. Biomed. Semant. PD FEB 5 PY 2014 VL 5 AR 6 DI 10.1186/2041-1480-5-6 PG 25 WC Mathematical & Computational Biology SC Mathematical & Computational Biology GA AR6QL UT WOS:000343707900002 PM 24499679 ER PT J AU Carneiro, D Novais, P Andrade, F Zeleznikow, J Neves, J AF Carneiro, Davide Novais, Paulo Andrade, Francisco Zeleznikow, John Neves, Jose TI Online dispute resolution: an artificial intelligence perspective SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article DE Alternative dispute resolution; Online dispute resolution; Artificial intelligence ID MULTIAGENT SYSTEMS; SUPPORT; NEGOTIATION AB Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI. C1 [Carneiro, Davide; Novais, Paulo; Neves, Jose] Univ Minho, Dept Informat, P-4710057 Braga, Portugal. [Andrade, Francisco] Univ Minho, Sch Law, P-4710057 Braga, Portugal. [Zeleznikow, John] Victoria Univ, Sch Management & Informat Syst, Melbourne, Vic 8001, Australia. RP Novais, P (reprint author), Univ Minho, Dept Informat, Campus Gualtar, P-4710057 Braga, Portugal. EM dcarneiro@di.uminho.pt; pjon@di.uminho.pt; fandrade@direito.uminho.pt; john.zeleznikow@vu.edu.au; jneves@di.uminho.pt RI Novais, Paulo/M-4053-2013; NEVES, JOSE/M-4203-2013; Andrade, Francisco/; Carneiro, Davide/A-3490-2014 OI Novais, Paulo/0000-0002-3549-0754; NEVES, JOSE/0000-0002-8863-0351; Andrade, Francisco/0000-0001-5764-7815; Carneiro, Davide/0000-0002-6650-0388 FU FCT (Science & Technology Foundation), Portugal [PTDC/JUR/71354/2006]; FCT [SFRH/BD/64890/2009] FX The work described in this paper is included in TIARAC-Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009). CR AAMODT A, 1994, AI COMMUN, V7, P39 Aleven V., 1997, THESIS U PITTSBURGH Ashley K. D., 1991, MODELING LEGAL ARGUM Ashley K. D., 1991, P 3 INT C ART INT LA, P42, DOI 10.1145/112646.112651 Ashley KD, 2004, INT C COMP CULT LEG Beer M, 1999, KNOWL ENG REV, V14, P285, DOI 10.1017/S0269888999003021 Bellucci E, 2001, J DECISION SUPPORT, V10, P449, DOI 10.3166/jds.10.449-479 Benjamins RV, 2005, LAW SEMANTIC WEB LEG Bennett SC, 2002, ARBITRATION ESSENTIA BONCZEK R H, 1981, FDN DECISION SUPPORT Brachman R.J., 2004, KNOWLEDGE REPRESENTA Brown H., 1999, ADR PRINCIPLES PRACT Bruninghaus S, 2003, P 9 INT C ART INT LA, P234 BUCHANAN BG, 1970, STANFORD LAW REV, V23, P40, DOI 10.2307/1227753 Caceres E, 2008, FRONT ARTIF INTEL AP, V189, P78, DOI 10.3233/978-1-58603-952-3-78 Corcho O, 2005, LECT NOTES COMPUT SC, V3369, P142, DOI 10.1007/978-3-540-32253-5_9 De Vries B, 2005, P 2 INT ODR WORKSH, P59 Forsyth R, 1986, ARTIF INTELL, P186 Greinke A, 1994, MURDOCH U ELECT J LA, V1 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Guasco MP, 2007, PRINCIPLES NEGOTIATI Harmon P, 1985, EXPERT SYSTEMS ARTIF Hayes-Roth F., 1983, BUILDING EXPERT SYST Jackson P., 1990, INTRO EXPERT SYSTEMS Katsh E. E., 2001, ONLINE DISPUTE RESOL Katsh Ethan, 2000, OHIO ST J DISP RESOL, V15, P705 Kolodner J., 1993, CASE BASED REASONING KOLODNER JL, 1992, ARTIF INTELL REV, V6, P3, DOI 10.1007/BF00155578 LANDES WM, 1976, J LAW ECON, V19, P249, DOI 10.1086/466868 Lodder A., 2003, WORKSH ONL DISP RES Lodder A. R., 2010, ENHANCED DISPUTE RES Lodder A. R., 2006, Information & Communications Technology Law, V15, DOI 10.1080/13600830600676438 Matthijssen L, 1995, P 5 INT C ART INT LA Matthijssen L, 1999, INTERFACING LAWYERS Notini J., 2005, EFFECTIVE ALTERNATIV Olson G. M., 2001, COORDINATION THEORY Oskamp A., 1995, Artificial Intelligence and Law, V3, DOI 10.1007/BF00872531 Parunak HV, 1997, ANN OPER RES, V75, P69 Peruginelli G., 2002, P WORKSH LAW EL AG L Popple J, 1996, APPL LEGAL PHILOS SE POPPLE J, 1991, AUST COMPUT J, V23, P11 Raiffa H, 2002, ART SCI NEGOTIATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEARLE JR, 1980, BEHAV BRAIN SCI, V3, P417 Rahwan I, 2009, ARGUMENTATION IN ARTIFICIAL INTELLIGENCE, P1, DOI 10.1007/978-0-387-98197-0 Sowa J., 2000, KNOWLEDGE REPRESENTA Span G, 1993, P 4 INT C ART INT LA, P76, DOI 10.1145/158976.158986 Steinbach M, 2005, INTRO DATA MINING Susskind R, 1987, EXPERT SYSTEMS LAW J SYCARA KP, 1993, DECIS SUPPORT SYST, V10, P121, DOI 10.1016/0167-9236(93)90034-Z Taylor A.D., 1996, FAIR DIVISION CAKE C Thiessen EM, 1993, THESIS CORNELL U ITH Thiessen EM, 2003, P UNECE FOR ODR Turban E., 1993, DECISION SUPPORT EXP Turing A. M., 1950, MIND, P433, DOI DOI 10.1093/MIND/LIX.236.433 Tyler C, 2005, P 3 ANN FOR ONL DISP Velasqueze J.D., 1997, P AAAI 97, P10 Visser PRS, 1998, ARTIF INTELL LAW, V6, P1998 Walton PRE, 1991, BEHAV THEORY LABOR N Waterman DA, 1980, P 1 NAT C ART INT Watson I., 1997, APPL CASE BASED REAS WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 Wooldridge MJ, 2002, INTRO MULTIAGENT SYS Zeleznikow J, 1994, KLUWER COMPUTER LAW, P230 Zeleznikow J, 1995, P 5 INT C ART INT LA, P185, DOI 10.1145/222092.222235 Zeleznikow J, 2003, FR ART INT, V106, P21 Zeleznikow J, 2004, P IFIP INT C DEC SUP Zweigert K., 1998, INTRO COMP LAW, Vthird NR 68 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 EI 1573-7462 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD FEB PY 2014 VL 41 IS 2 BP 211 EP 240 DI 10.1007/s10462-011-9305-z PG 30 WC Computer Science, Artificial Intelligence SC Computer Science GA 304ED UT WOS:000330728800003 ER PT J AU Xu, Z Luo, XF Mei, L Hu, CP AF Xu, Zheng Luo, Xiangfeng Mei, Lin Hu, Chuanping TI Measuring the semantic discrimination capability of association relations SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE LA English DT Article DE association relation; semantic computing; knowledge discovery ID KNOWLEDGE DISCOVERY; WEB; SIMILARITY AB Association relations between concepts are a class of simple but powerful regularities in binary data, which play important roles in enterprises and organizations with huge amounts of data. However, although there can be easily large number of association relation mined from databases, since existing objective and subjective methods scarcely take semantics into consideration, it has been recognized early in the knowledge discovery literature that most of them are of no interest to the user. In this paper, the semantic discrimination capability (SDC) of association relation is measured based on discrimination value model first. The formula of SDC integrating both statistical and graph features is proposed from five different strategies. The high correlation coefficient of the proposed method against discrimination value shows that the proposed SDC measure is accuracy. Moreover, an application using SDC on document clustering is carried out, which shows that SDC has broad prospects on data-related task such as document clustering. Copyright 2013 John Wiley (c) Sons, Ltd. C1 [Xu, Zheng; Mei, Lin; Hu, Chuanping] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. [Xu, Zheng; Luo, Xiangfeng] Shanghai Univ, Shanghai, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, Res Ctr Internet Things, Shanghai, Peoples R China. EM xuzheng@shu.edu.cn FU National Science Foundation of China [91024012, 61071110]; National Key Technology Support Program [2012BAH07B00] FX This work was supported in part by the National Science Foundation of China under Grant 91024012, Grant 61071110, and in part by National Key Technology Support Program under Grant 2012BAH07B00. CR Agrawal R., 1993, P ACM SIGMOD C MAN D, V22, P207, DOI DOI 10.1145/170035.170072 Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487 Bayardo RJ, 1999, KDD 1999, P145 Berners-Lee T, 2001, SCI AM, V284, P34 Blake C., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989501 Cai D, 2010, IEEE T KNOWL DATA EN, V22, P1262, DOI 10.1109/TKDE.2009.134 Chim H, 2008, IEEE T KNOWL DATA EN, V20, P1217, DOI 10.1109/TKDE.2008.50 Chim H., 2007, P 16 INT C WORLD WID, P121, DOI 10.1145/1242572.1242590 DHAR V, 1993, IEEE T KNOWL DATA EN, V5, P926, DOI 10.1109/69.250075 Frawley W J, 1991, KNOWLEDGE DISCOVERY Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 HAN JW, 1992, PROC INT CONF VERY L, P547 Han JW, 2007, DATA MIN KNOWL DISC, V15, P55, DOI 10.1007/s10618-006-0059-1 Hoschka P, 1991, SUPPORT SYSTEM INTER Klemettinen M, 1994, P 3 INT C INF KNOWL Liu B., 1999, KNOWLEDGE DISCOVERY, P125, DOI DOI 10.1145/312129.312216 Liu YH, 2010, IEEE T PARALL DISTR, V21, P405, DOI 10.1109/TPDS.2009.57 Liu YH, 2011, IEEE T PARALL DISTR, V22, P2100, DOI 10.1109/TPDS.2011.113 Luo XF, 2010, IEEE T LEARN TECHNOL, V3, P344, DOI 10.1109/TLT.2010.26 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Matheus CJ, 1994, P AAAI 94 WORKSH KNO Padmanabhan B, 1999, DECIS SUPPORT SYST, V27, P303, DOI 10.1016/S0167-9236(99)00053-6 Pasquier N, 1999, LECT NOTES COMPUT SC, V1540, P398 Piatetsky-Shapiro G., 1994, P AAAI 94 WORKSH KNO, V1, P25 Roberto J., 1998, P 1998 ACM SIGMOD IN, P85 Salton G., 1988, P 11 ANN INT ACM SIG, p147 , DOI 10.1145/62437.62447 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEPARD RN, 1987, SCIENCE, V237, P1317, DOI 10.1126/science.3629243 Silberschatz A, 1996, IEEE T KNOWL DATA EN, V8, P970, DOI 10.1109/69.553165 Silberschatz A, 1995, KDD 95 Steinbach M., 2000, KDD WORKSH TEXT MIN Xin D, 2005, P 31 INT C VER LARG, P709 Xu Z, 2011, CONCURR COMP-PRACT E, V23, P1101, DOI 10.1002/cpe.1689 Xu Z, 2011, CONCURR COMP-PRACT E, V23, P2496, DOI 10.1002/cpe.1816 Yan X, 2005, P 11 ACM SIGKDD INT, P314, DOI 10.1145/1081870.1081907 Zamir O, 1998, SIGIR 98 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 Zhuge H., 2010, ARTIF INTELL, V174, P190 Zhuge H., 2012, KNOWLEDGE GRID CYBER NR 40 TC 2 Z9 2 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-0626 EI 1532-0634 J9 CONCURR COMP-PRACT E JI Concurr. Comput.-Pract. Exp. PD FEB PY 2014 VL 26 IS 2 BP 380 EP 395 DI 10.1002/cpe.2999 PG 16 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 290ND UT WOS:000329764200004 ER PT J AU Liu, WJ Du, YJ AF Liu, WenJun Du, YaJun TI A novel focused crawler based on cell-like membrane computing optimization algorithm SO NEUROCOMPUTING LA English DT Article DE Focused crawler; Membrane computing; Optimization algorithm; VSM ID WEB RESOURCE DISCOVERY; P-SYSTEMS; SIMILARITY AB In many research works, topical priorities of unvisited hyperlinks are computed based on linearly integrating topic-relevant similarities of various texts and corresponding weighted factors. However, these weighted factors are determined based on the personal experience, so that these values may make topical priorities of unvisited hyperlinks serious deviations directly. To solve this problem, this paper proposes a novel focused crawler applying the cell-like membrane computing optimization algorithm (CMCFC). The CMCFC regards all weighted factors corresponding to contribution degrees of similarities of various texts as one object, and utilizes evolution regulars and communication regulars in membranes to achieve the optimal object corresponding to the optimal weighted factors, which make the root measure square error (RMS) of priorities of hyperlinks achieve the minimum. Then, it linearly integrates optimal weighted factors and corresponding topical similarities of various texts, which are computed by using a Vector Space Model (VSM), to compute priorities of unvisited hyperlinks. The CMCFC obtains more accurate unvisited URLs' priorities to guide crawlers to collect higher quality web pages. The experimental results indicate that the proposed method improves the performance of focused crawlers by intelligently determining weighted factors. In conclusion, the mentioned approach is effective and significant for focused crawlers. (c) 2013 Elsevier B.V. All rights reserved. C1 [Liu, WenJun; Du, YaJun] Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. RP Du, YJ (reprint author), Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. EM dyjdoc2003@aliyun.com FU National Natural Science Foundation of China [60872089, 61271413]; Innovation Fund of Postgraduate, Xihua University FX This research work is supported by the National Natural Science Foundation of China (Grant nos. 60872089 and 61271413) and the Innovation Fund of Postgraduate, Xihua University. CR Aggarwal C.C., 2001, P 10 INT WORLD WID W, P96, DOI DOI 10.1145/371920.371955 Ahmadi-Abkenari F, 2012, INFORM SCIENCES, V184, P266, DOI 10.1016/j.ins.2011.08.022 Batsakis S, 2009, DATA KNOWL ENG, V68, P1001, DOI 10.1016/j.datak.2009.04.002 Bernardini F, 2005, SOFT COMPUT, V9, P640, DOI 10.1007/s00500-004-0393-4 Bra P.D., 1994, P 4 RIAO C NEW YORK, P481 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Diligenti M., 2000, P 26 INT C VER LARG, P527 Dong H, 2011, IEEE T IND ELECTRON, V58, P2106, DOI 10.1109/TIE.2010.2050754 Du Y.J., 2009, J COMPUTATIONAL INFO, V5, P1097 Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 Hliaoutakis A, 2006, INT J SEMANT WEB INF, V2, P55, DOI 10.4018/jswis.2006070104 Huang L., 2007, RES MEMBRANE COMPUTI Huang L, 2011, INFORM SCIENCES, V181, P2370, DOI 10.1016/j.ins.2010.12.015 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Krishna SN, 2007, THEOR COMPUT SCI, V371, P83, DOI 10.1016/j.tcs.2006.10.016 Liao S., 2007, J COMPUTATIONAL INFO, V3, P879 Liu HY, 2006, DATA KNOWL ENG, V59, P270, DOI 10.1016/j.datak.2006.01.012 Liu Z., 2011, J INFORM COMPUTATION, V8, P1909 Martin Vide C., 2003, THEORETICAL COMPUTER, V296, P295 Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI 10.1145/1031114.1031117 Michele P.G., 2012, THEORETICAL COMPUTER, V431, P13 Mukherjea S., 2000, P 11 ACM C HYP HYP S, P133, DOI 10.1145/336296.336346 Pant G., 2000, WWW 02 WORKSH WEB DY Patel A, 2011, COMPUT STAND INTER, V33, P325, DOI 10.1016/j.csi.2010.08.002 Paun G, 2000, J COMPUT SYST SCI, V61, P108, DOI 10.1006/jcss.1999.1693 Paun G, 2010, J LOGIC ALGEBR PROGR, V79, P291, DOI 10.1016/j.jlap.2010.04.002 Paun G., 2004, 1 BRAINST WORKSH UNC, P17 Peng QS, 2009, J COMPUT SCI TECHNOL, V24, P1, DOI 10.1007/s11390-009-9214-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Susmit B., 2012, APPL SOFT COMPUT, V12, P3023 Uemura Y, 2012, STUD COMPUT INTELL, V376, P61 Varelas G., 2005, 7 ACM INT WORKSH WEB Wang Y.B., 2010, CONTROL AUTOM, V26, P32 Yang YK, 2008, LECT NOTES COMPUT SC, V5227, P840 Zhang HX, 2010, APPL SOFT COMPUT, V10, P490, DOI 10.1016/j.asoc.2009.08.017 Zhang HX, 2005, LECT NOTES COMPUT SC, V3521, P301 NR 37 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 EI 1872-8286 J9 NEUROCOMPUTING JI Neurocomputing PD JAN 10 PY 2014 VL 123 SI SI BP 266 EP 280 DI 10.1016/j.neucom.2013.06.039 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA 251EC UT WOS:000326909600027 ER PT J AU Alouane-Ksouri, S Hidri, MS Barkaoui, K AF Alouane-Ksouri, Sonia Hidri, Minyar Sassi Barkaoui, Kamel GP IEEE TI A Tripartite Graph-based Fuzzy Co-similarities for Document Retrieval SO 2014 11TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) LA English DT Proceedings Paper CT 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) CY AUG 19-21, 2014 CL Xiamen, PEOPLES R CHINA DE Fuzzy conceptual scaling; Document retrieval; Triadic co-similarities; Fuzzy nested Lattice ID CONCEPT LATTICES AB This work exploits the use of a fuzzy many-valued concept analysis for document retrieval to efficiently answer users' queries. For the purpose of visualization, we propose to deal with fuzzy many-valued formal contexts in a tripartite graph with respect to fuzzy triadic document co-similarities. The proposed model aims at describing the documents according to three hierarchical levels. It is based on a prototyping of feature vectors applied to documents, sentences and words. The recourse to fuzzy conceptual scaling has enabled us to visualize many-valued concepts through the construction of fuzzy nested lattices. Once this built, we proceed to document retrieval using building process and lattice incremental path such a simple information retrieval. C1 [Alouane-Ksouri, Sonia; Hidri, Minyar Sassi] Univ Tunis El Manar, Ecole Natl Ingn Tunis, Tunis, Tunisia. [Barkaoui, Kamel] CEDRIC CNAM, F-75003 Paris, France. RP Alouane-Ksouri, S (reprint author), Univ Tunis El Manar, Ecole Natl Ingn Tunis, BP 37,Blvd 1002, Tunis, Tunisia. EM sonia.alounae@enit.rnu.tn; minyar.sassi@enit.rnu.tn; kamel.barkaoui@cnam.fr CR Alouane-Ksouri S., 2013, P 5 C WEB INF TECHN, P265 Alouane-Ksouri S., 2013, P 23 INT C DAT EXP S, P48 BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 Godin R., 1995, REV INTELLIGENCE ART, V9, P105 Lehmann F, 1995, LECT NOTES ARTIF INT, V954, P32 Messai N, 2008, FR ART INT, V178, P127, DOI 10.3233/978-1-58603-891-5-127 Quan T., 2004, CLA 2004, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Szathmary L., 2004, 11 RENC SOC FRANC CL, V1, P153 Valtchev P, 2002, DISCRETE MATH, V256, P801, DOI 10.1016/S0012-365X(02)00349-7 Valtchev P, 2002, J EXP THEOR ARTIF IN, V14, P115, DOI 10.1080/09528130210164198 Wille R., 1982, NATO ASI, V83, P445 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-5148-2 PY 2014 BP 575 EP 580 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BD3KM UT WOS:000359803500102 ER PT S AU Koppel, M AF Koppel, Moshe BE Dershowitz, N Nissan, E TI The Responsa Project: Some Promising Future Directions SO LANGUAGE, CULTURE, COMPUTATION: COMPUTING OF THE HUMANITIES, LAW, AND NARRATIVES: ESSAYS DEDICATED TO YAACOV CHOUEKA ON THE OCCASION OF HIS 75TH BIRTHDAY, PT II SE Lecture Notes in Computer Science LA English DT Article; Book Chapter ID RETRIEVAL SYSTEMS; DISAMBIGUATION; ABBREVIATIONS; DOCUMENTS C1 Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. RP Koppel, M (reprint author), Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. CR Adler M., 2006, ACL 2006 ATTAR R, 1978, J ACM, V25, P52, DOI 10.1145/322047.322052 ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 Baharad E., 2008, CONDORCET OPTI UNPUB Bar-Haim Roy, 2008, Natural Language Engineering, V14, DOI 10.1017/S135132490700455X Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X CHOUEKA Y, 1985, COMPUT HUMANITIES, V19, P147, DOI 10.1007/BF02259530 Choueka Y., 1981, P 19 ALL C COMM CONT, P762 Choueka Y., 1964, J ACAD HEBREW LANGUA, V27, P354 Choueka Y., 1988, P INT C US OR CONT B, P609 Choueka Y., 1995, NAKDAN TEXT Choueka Y., 1983, ALLC J, V4, P34 Choueka Y., 1988, SIGIR 1988, P597 Choueka Y., 1990, COMPUTATIONAL LEXICO, P181 Dagan I., 2000, HDB NATURAL LANGUAGE GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471 HaCohen-Kerner Y, 2004, LECT NOTES ARTIF INT, V3230, P58 Hacohen-Kerner Y, 2008, CYBERNET SYST, V39, P213, DOI 10.1080/01969720801944299 Hacohen-Kerner Y., 2008, P ANN M ASS COMP LIN, P61, DOI 10.3115/1557690.1557707 Hanani S., 1987, THESIS BAR ILAN U Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Koppel M., 2006, COMPUTATIONAL APPL L, V57, P5 Kucera H., 1967, COMPUTATIONAL ANAL P Lin D., 1998, COLING ACL 98, V2, P768 Mughaz D., 2003, THESIS BAR ILAN U RA Rabinowitz R., 1986, THESIS BAR ILAN U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wintner S, 2004, ARTIF INTELL REV, V21, P113, DOI 10.1023/B:AIRE.0000020865.73561.bc Yu H, 2007, J BIOMED INFORM, V40, P150, DOI 10.1016/j.jbi.2006.06.001 NR 29 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-45324-3; 978-3-642-45323-6 J9 LECT NOTES COMPUT SC PY 2014 VL 8002 BP 1 EP 8 D2 10.1007/978-3-642-45324-3 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BC7SL UT WOS:000355190700002 ER PT J AU Broda, B Ogrodniczuk, M Niton, B Gruszczynski, W AF Broda, Bartosz Ogrodniczuk, Maciej Niton, Bartlomiej Gruszczynski, Wlodzimierz BE Calzolari, N Choukri, K Declerck, T Loftsson, H Maegaard, B Mariani, J Moreno, A Odijk, J Piperidis, S TI Measuring Readability of Polish Texts: Baseline Experiments SO LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION LA English DT Proceedings Paper CT 9th International Conference on Language Resources and Evaluation (LREC) CY MAY 26-31, 2014 CL Reykjavik, ICELAND SP Holmes Semant Solut, European Media Lab GmBH, EML, VoiceBox Technologies, KDICTIONARIES DE readability; distributional similarity; text understandability ID FORMULA AB Measuring readability of a text is the first sensible step to its simplification. In this paper we present an overview of the most common approaches to automatic measuring of readability. Of the described ones, we implemented and evaluated: Gunning FOG index, Flesch-based Pisarek method. We also present two other approaches. The first one is based on measuring distributional lexical similarity of a target text and comparing it to reference texts. In the second one, we propose a novel method for automation of Taylor test - which, in its base form, requires performing a large amount of surveys. The automation of Taylor test is performed using a technique called statistical language modelling. We have developed a free on-line web-based system and constructed plugins for the most common text editors, namely Microsoft Word and OpenOffice. org. Inner workings of the system are described in detail. Finally, extensive evaluations are performed for Polish - a Slavic, highly inflected language. We show that Pisarek's method is highly correlated to Gunning FOG Index, even if different in form, and that both the similarity-based approach and automated Taylor test achieve high accuracy. Merits of using either of them are discussed. C1 [Broda, Bartosz; Ogrodniczuk, Maciej; Niton, Bartlomiej] Polish Acad Sci, Inst Comp Sci, Warsaw, Poland. [Gruszczynski, Wlodzimierz] Warsaw Sch Social Sci & Humanities, Warsaw, Poland. RP Broda, B (reprint author), Polish Acad Sci, Inst Comp Sci, Jana Kazimierza 5, Warsaw, Poland. CR Bormuth J. R., 1966, READING RES Q, P79 Broda B., 2012, P LREC 2012 Choldin M. T., 1979, ENCY LIB INFORM SCI, V26, P178 Cygal-Krupa Z., 1986, 514 U JAG I BAD POL, V514 Dale E., 1948, ED RES B, V27, P1 Dale E, 1948, EDUC RES BULL, V27, P37 Flesch R., 1960, WRITE SPEAK THINK MO Flesch R. F., 1943, CONTRIBUTIONS ED, V897 FRY E, 1968, J READING, V11, P513 Graesser A. C., 1999, NARRATIVE COMPREHENS Gunning R., 1971, TECHNIQUE CLEAR WRIT Imiolczyk J., 1987, PARDOPODOBIENSTWO SU Jurafsky D., 2008, SPEECH LANGUAGE PROC Kincaid J. P., 1975, TECHNICAL REPORT Manning C. D., 1999, FDN STAT NATURAL LAN Markowski A., 1990, LEKSYKA WSPOLNA ROZN Miles T. H., 1990, CRITICAL THINKING WR Mistrik J, 1968, SLOVENSKA REC, V33, P171 Pisarek W., 1969, ZESZYTY PRASOZNAWCZE, V4, P35 Pisarek W, 2002, NOWA RETORYKA DZIENN Pisarek W., 2007, O MEDIACH I JEZYKU, P245 Pomikalek J., 2011, THESIS U BRNO CZECH Przepiorkowski A., 2012, NARODOWY KORPUS JEZY Radziszewski A., 2013, INTELLIGENT TOOLS BU SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159 Stolcke A., 2002, P INT C SPOK LANG PR, V2, P901 Tanaka-Ishii K, 2010, COMPUT LINGUIST, V36, P203, DOI 10.1162/coli.09-036-R2-08-050 Taylor M. C., 1986, INT J BASIC ED, V10, P155 Taylor WL, 1953, JOURNALISM QUART, V30, P415 NR 30 TC 0 Z9 0 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE BN 978-2-9517408-8-4 PY 2014 BP 573 EP 580 PG 8 WC Linguistics; Language & Linguistics SC Linguistics GA BC8FH UT WOS:000355611002028 ER PT J AU Di Buccio, E Di Nunzio, GM Silvello, G AF Di Buccio, Emanuele Di Nunzio, Giorgio Maria Silvello, Gianmaria BE Calzolari, N Choukri, K Declerck, T Loftsson, H Maegaard, B Mariani, J Moreno, A Odijk, J Piperidis, S TI A Vector Space Model for Syntactic Distances Between Dialects SO LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION LA English DT Proceedings Paper CT 9th International Conference on Language Resources and Evaluation (LREC) CY MAY 26-31, 2014 CL Reykjavik, ICELAND SP Holmes Semant Solut, European Media Lab GmBH, EML, VoiceBox Technologies, KDICTIONARIES DE Digital Geolinguistics; Language Distance; Vector Space Model AB Syntactic comparison across languages is essential in the research field of linguistics, e.g. when investigating the relationship among closely related languages. In IR and NLP, the syntactic information is used to understand the meaning of word occurrences according to the context in which their appear. In this paper, we discuss a mathematical framework to compute the distance between languages based on the data available in current state-of-the-art linguistic databases. This framework is inspired by approaches presented in IR and NLP. C1 [Di Buccio, Emanuele; Di Nunzio, Giorgio Maria; Silvello, Gianmaria] Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. RP Di Buccio, E (reprint author), Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. EM dibuccio@dei.unipd.it; dinunzio@dei.unipd.it; silvello@dei.unipd.it CR Agosti M, 2010, COMM COM INF SC, V91, P89 Colonna V, 2010, HUM HERED, V70, P245, DOI 10.1159/000317374 Dryer M. S., 2013, WALS ONLINE Erk K., 2008, P C EMP METH NAT LAN, P897, DOI 10.3115/1613715.1613831 HAMMING RW, 1950, AT&T TECH J, V29, P147 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Melucci M, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361687 Nerbonne J., 2006, P WORKSH LING DIST L, P82, DOI 10.3115/1641976.1641987 Nguyen Q., 2012, LECT NOTES COMPUTER, V7244, P109 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spruit M. R., 2008, LOT DISSERTATION SER, V174 Spruit M. R., 2006, Literary & Linguistic Computing, V21, DOI 10.1093/llc/fql043 Spruit MR, 2009, LINGUA, V119, P1624, DOI 10.1016/j.lingua.2009.02.001 NR 13 TC 1 Z9 1 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE BN 978-2-9517408-8-4 PY 2014 BP 2486 EP 2489 PG 4 WC Linguistics; Language & Linguistics SC Linguistics GA BC8FH UT WOS:000355611004016 ER PT J AU Qu, Q Liu, SY Yang, B Jensen, CS AF Qu, Qiang Liu, Siyuan Yang, Bin Jensen, Christian S. GP IEEE TI Efficient Top-k Spatial Locality Search for Co-located Spatial Web Objects SO 2014 IEEE 15TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM), VOL 1 SE IEEE International Conference on Mobile Data Management LA English DT Proceedings Paper CT 15th IEEE International Conference on Mobile Data Management (IEEE MDM) CY JUL 14-18, 2014 CL Brisbane, AUSTRALIA SP IEEE, CSIRO, Univ Queensland, Univ Pittsburgh, IEEE Tech Comm Data Engn, IBM Res Labs, Australia, IBM Res Labs, Austin, IEEE Comp Soc AB In step with the web being used widely by mobile users, user location is becoming an essential signal in services, including local intent search. Given a large set of spatial web objects consisting of a geographical location and a textual description (e.g., online business directory entries of restaurants, bars, and shops), how can we find sets of objects that are both spatially and textually relevant to a query? Most of existing studies solve the problem by requiring that all query keywords are covered by the returned objects and then rank the sets by spatial proximity. The needs for identifying sets with more textually relevant objects render these studies inapplicable. We propose localitySearch, a query that returns top-k sets of spatial web objects and integrates spatial distance and textual relevance in one ranking function. We show that computing the query is NP-hard, and we present two efficient exact algorithms and one generic approximate algorithm based on greedy strategies for computing the query. We report on findings from an empirical study with three real-life datasets. The study offers insight into the efficiency and effectiveness of the proposed algorithms. C1 [Qu, Qiang; Yang, Bin; Jensen, Christian S.] Aarhus Univ, Dept Comp Sci, DK-8000 Aarhus C, Denmark. [Liu, Siyuan] Carnegie Mellon Univ, Heinz Coll, Pittsburgh, PA 15213 USA. RP Qu, Q (reprint author), Aarhus Univ, Dept Comp Sci, DK-8000 Aarhus C, Denmark. EM qu@cs.au.dk; siyuan@cmu.edu; byang@cs.au.dk; csj@cs.aau.dk CR Bender MA, 2008, ALGORITHMICA, V50, P279, DOI 10.1007/s00453-007-9037-2 Borodin A., 2012, P 31 S PRINC DAT SYS, P155 Cao X., 2011, SIGMOD, P373 Cao X., 2012, LNCS, V7532, P16 Chandra B, 2001, J ALGORITHM, V38, P438, DOI 10.1006/jagm.2000.1145 Cong G., 2009, PVLDB, V2, P337 Derczynski L. R. A., 2013, EDBT, P137 Drosou M, 2010, SIGMOD RECORD, V39, P41, DOI 10.1145/1860702.1860709 Felipe I. D., 2008, ICDE, P656 Long C., 2013, SIGMOD, P689 Manning C.D., 2008, INTRO INFORM RETRIEV Qu Q, 2008, FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS, P541, DOI 10.1109/FSKD.2008.7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wu D., 2013, ACM T DATABASE SYST, V38 Wu DM, 2011, PROC INT CONF DATA, P541 Yao B, 2010, PROC INT CONF DATA, P545, DOI 10.1109/ICDE.2010.5447836 Zhang DX, 2010, PROC INT CONF DATA, P521, DOI 10.1109/ICDE.2010.5447897 Zhang DX, 2009, PROC INT CONF DATA, P688 NR 18 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1551-6245 BN 978-1-4799-5705-7 J9 IEEE INT CONF MOB DA PY 2014 BP 269 EP 278 DI 10.1109/MDM.2014.39 PG 10 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BD1WH UT WOS:000358404800034 ER PT J AU Yang, XQ Yang, D Yuan, M Lv, XH AF Yang, Xi-Quan Yang, Dian Yuan, Ming Lv, Xing-Hua BE Watada, J Ito, A Pan, JS Chao, HC Chen, CM TI Scientific literature retrieval model based on weighted term frequency SO 2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014) LA English DT Proceedings Paper CT 10th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) CY AUG 27-29, 2014 CL Kitakyushu, JAPAN SP IEEE Comp Soc, IEEE, Natl Kaohsiung Univ Appl Sci, IEEE Tainan Sect, IEEE Signal Proc Soc, Tainan Chapter, Int Soc Management Engineers, JSPS NSFC NRF A3 Foresight Program Ultra Realist Acoust Interact Commun Next Generat Internet HO Waseda Univ DE information retrieval; simulated annealing AB Science and technology literature retrieval system is commonly used by researchers as document retrieval tools. The classic information retrieval models such as Boolean Model [1], Vector Space Model and Probabilistic model neglect the position of query words in every paper. We propose a weighted term frequency model (WTFM) based on term frequency, and through a simulated annealing algorithm to learn the weighted factor. The results of experiments show that our weighted factors model gets better performance than ordinary models. C1 [Yang, Xi-Quan; Lv, Xing-Hua] NE Normal Univ, Sch Comp Sci & Informat Technol, Changchun, Peoples R China. [Yang, Xi-Quan; Lv, Xing-Hua] NE Normal Univ, Coll Humanities & Sci, Changchun, Peoples R China. [Yang, Dian] Shanghai Jiao Tong Univ, Sch Microelect, Shanghai 200030, Peoples R China. [Yuan, Ming] Jilin Teachers Inst Engn & Technol, Changchun, Peoples R China. RP Yang, XQ (reprint author), NE Normal Univ, Sch Comp Sci & Informat Technol, Changchun, Peoples R China. EM Yangxq375@nenu.edu.cn; lucyyd@tju.edu.cn CR BROOKS SP, 1995, STATISTICIAN, V44, P241, DOI 10.2307/2348448 Cutler Michal, 1997, USENIX S INT TECHN S Lancaster Frederick Wilfrid, 1973, INFORM RETRIEVAL Nie Zaiqing, 2005, P 14 INT C WORLD WID Perea-Ortega JM, 2008, LECT NOTES COMPUT SC, V5039, P142 Pereira RA Marques, 2005, SOFT COMPUT, V9.7, P481 Robertson Stephen E., 1996, NIST SPECIAL PUBLICA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1968, J ACM, V15.1, P8 Song M, 2007, DATA KNOWL ENG, V63, P63, DOI 10.1016/j.datak.2006.10.010 Whissell JS, 2011, INFORM RETRIEVAL, V14, P466, DOI 10.1007/s10791-011-9163-y NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-5390-5 PY 2014 BP 427 EP 430 DI 10.1109/IIH-MSP.2014.113 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BD2HI UT WOS:000358743800107 ER PT J AU Nakanishi, T AF Nakanishi, Takafumi GP IEEE TI Toward a Realization of Knowledge Creation Grid for Big Data Era SO 2014 IIAI 3RD INTERNATIONAL CONFERENCE ON ADVANCED APPLIED INFORMATICS (IIAI-AAI 2014) LA English DT Proceedings Paper CT 3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI) CY AUG 31-SEP 04, 2014 CL Kitakyushu, JAPAN SP Int Inst Appl Informat DE Knowledge Creation Grid; Big data; diversity; consensus building; correlation ID INFORMATION-RETRIEVAL; SIMILARITY; MODEL AB We represent a new framework - Knowledge creation grid for Big data era. Currently, there are various types of data in various fields. The essences of ICT are "scale merit," "scope merit," and "connection merit." The Big data itself represents "scale merit" and "scope merit," because there are massive of data and these data are utilized in various fields. However, the aspect of the " connection merit" in big data has not represented. To lead to value creation from such data, it is important to interconnect data set among heterogeneous fields. We propose Knowledge creation grid as the one method of interconnection for data set. Almost correlation between data sets are discovered by synchronization or co-occurring on spatiotemporal. We, persons discover correlation from the meaning of data. In this framework, a meaningful data set is abstracted and represented as knowledge. This abstraction as knowledge is possible to discover various effective correlations by diversity and consensus building. In this paper, we represent a whole design of Knowledge creation grid and its primitive functions to realize diversity and consensus building. C1 Int Univ Japan, Ctr Global Commun GLOCOM, Tokyo, Japan. RP Nakanishi, T (reprint author), Int Univ Japan, Ctr Global Commun GLOCOM, Tokyo, Japan. EM takafumi@glocom.ac.jp CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 KIM YW, 1990, J DOC, V46, P113, DOI 10.1108/eb026857 Kitagawa T., 1993, RIDE IMS 93, P130 Kiyoki Y., 1994, SIGMOD Record, V23 LEE JH, 1993, J DOC, V49, P188, DOI 10.1108/eb026913 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G., 1990, INT J LEXICOGR, V3, P235 Nakanishi T., 2014, P 8 IEEE IN IN PRESS Nakanishi T., 2014, P 24 INT C IN PRESS Nakanishi T, 2011, CONCURR COMP-PRACT E, V23, P940, DOI 10.1002/cpe.1622 Page S. E., 2008, DIFFERENCE POWER DIV Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Takano K., 2007, ACST 07, P510 Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-4173-5 PY 2014 BP 167 EP 172 DI 10.1109/IIAI-AAI.2014.43 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BD1PY UT WOS:000358256400031 ER PT J AU Okada, R Nakanishi, T Kitagawa, T AF Okada, Ryotaro Nakanishi, Takafumi Kitagawa, Takashi GP IEEE TI A Method of Knowledge Creation and Knowledge Utilization by Generalized Inverse Operator SO 2014 IIAI 3RD INTERNATIONAL CONFERENCE ON ADVANCED APPLIED INFORMATICS (IIAI-AAI 2014) LA English DT Proceedings Paper CT 3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI) CY AUG 31-SEP 04, 2014 CL Kitakyushu, JAPAN SP Int Inst Appl Informat DE Generalized inverse operator; Knowledge representation; Knowledge creation; Knowledge utilization ID INFORMATION-RETRIEVAL; SIMILARITY; MODEL AB We represent a new model of knowledge creation and knowledge utilization. Recently, the one of the important knowledge management issues is how to create knowledge and how to utilize their knowledge. Actually, we cannot maximize the accumulated know-how on various fields, although there are various and a massive of data on the World Wide Web. It is important to consider how to organize knowledge and how to use their knowledge. The one of the solutions is to measurement correlation between data sets on heterogeneous fields. Furthermore, we have to consider how to use the organized data in the interconnection. We propose a representation method of knowledge as a matrix. Generally, a matrix represents relationships between each element of rows and columns. This is the one of the knowledge creation, because the matrix creation process is as same as the relationships between each element of data as knowledge. Moreover, we represent that the generalized inverse operation of the matrix is as same as knowledge utilization. Knowledge creation and knowledge utilization can be regarded as reverse operation. Therefore, we can define that knowledge utilization operator as generalized inverse operator, when we abstract knowledge as a matrix from various data. The features of this method are simplification of a knowledge representation, an inverse operation of knowledge creation and knowledge utilization, and the knowledge management by correlation. By our proposed method, we get new values by interconnection among data sets that are in heterogeneous fields. C1 [Okada, Ryotaro] Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 3058573, Japan. [Nakanishi, Takafumi] Int Univ Japan, Ctr Global Commun, Tokyo 1060032, Japan. [Kitagawa, Takashi] Univ Tsukuba, Div Informat Engn, Tsukuba, Ibaraki 3058573, Japan. RP Okada, R (reprint author), Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 3058573, Japan. EM rytrkd@gmail.com; takafumi@glocom.ac.jp; takashi@cs.tsukuba.ac.jp CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 Hevner K, 1935, PSYCHOL REV, V42, P186, DOI 10.1037/h0054832 KIM YW, 1990, J DOC, V46, P113, DOI 10.1108/eb026857 Kitagawa T., 1993, RIDE IMS 93, P130 Kiyoki Y., 1994, SIGMOD Record, V23 Kobayashi S., 1984, COLOR IMAGE SCALE LEE JH, 1993, J DOC, V49, P188, DOI 10.1108/eb026913 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G., 1990, INT J LEXICOGR, V3, P235 Nakanishi T., 2014, KIEL COMPUTER SCI SE, P364 Nakanishi T., 2014, P 8 IEEE I IN PRESS Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Takano K., 2007, ACST 07, P510 Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 Zhang J, 2011, IEEE COMPUT INTELL M, V6, P68, DOI 10.1109/MCI.2011.942584 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-4173-5 PY 2014 BP 253 EP 258 DI 10.1109/IIAI-AAI.2014.60 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BD1PY UT WOS:000358256400048 ER PT J AU Ortmann, J De Felice, G Wang, D Daniel, D AF Ortmann, Jens De Felice, Giorgio Wang, Dong Daniel, Desiree TI An egocentric semantic reference system for affordances SO SEMANTIC WEB LA English DT Article DE Affordance; egocentric reference system; human observation; semantic reference system ID ENVIRONMENT; PERCEPTION; MODEL AB This article suggests a theory of egocentric semantic reference systems for human observations of affordances that can be used to semantically account for subjective human observations on the web. Based on the perceptual theory of affordances, which suggests that humans perceive the potential actions the environment affords, an egocentric semantic reference frame is established, which is anchored in the observer's specific capabilities for perception and action. The theory is completed with transformations that allow to project values of observed affordances from one user's ordinal reference frame into another user's ordinal reference frame. The potency of the theory to capture the semantics of human observations is demonstrated through the implementation of a full fledged egocentric semantic reference system for the prototypical affordance of hikability that a mountain path affords to a hiker. The prototype uses real user-ratings from a community driven web portal. C1 [Ortmann, Jens] Softplant GmbH, Munich, Germany. [De Felice, Giorgio] Univ Bremen, Dept Math & Informat, D-28359 Bremen, Germany. [Wang, Dong; Daniel, Desiree] Univ Munster, Inst Geoinformat IfGI, Munster, Germany. RP Ortmann, J (reprint author), Softplant GmbH, Munich, Germany. EM jens.ortmann@softplant.de; defelice@informatik.uni-bremen.de; wang.dong@uni-muenster.de; desiree.daniel@uni-muenster.de FU China Scholarship Council; International Research Training Group on Semantic Integration of Geospatial Information by the DFG (German Research Foundation) [GRK 1498] FX This work has been partly supported through the International Research Training Group on Semantic Integration of Geospatial Information by the DFG (German Research Foundation), GRK 1498 and by the China Scholarship Council. CR Berners-Lee T., 2009, LINKED DATA DESIGN I Bunge M., 1973, METHODOLOGICAL UNITY, P105 CARELLO C, 1989, Ecological Psychology, V1, P27, DOI 10.1207/s15326969eco0101_3 Carral D., 2013, LECT NOTES COMPUTER, V7882, P76 Chrisman N. R., 2002, EXPLORING GEOGRAPHIC, P15 Degbelo A., 2013, P GI FOR 20 IN PRESS Freksa C., 1982, P 2 WORLD C MATH SER, P288 Freksa C., 1980, 22 INT C PSYCH LEIPZ Freksa C., 1982, APPROXIMATE REASONIN, P297 Gangemi A., 2009, INT HDB INFORM SYSTE, P221 Gangemi A, 2005, LECT NOTES COMPUT SC, V3729, P262 Gangemi A., 2010, SEMANTIC WEB, V1, P61 Ghilani C., 2010, ADJUSTMENT COMPUTATI Gibson J J, 1979, ECOLOGICAL APPROACH Gibson J. J., 1977, PERCEIVING ACTING KN, P67 HARNAD S, 1990, PHYSICA D, V42, P335, DOI 10.1016/0167-2789(90)90087-6 HEFT H, 1989, J THEOR SOC BEHAV, V19, P1, DOI 10.1111/j.1468-5914.1989.tb00133.x Iliffe J., 2000, DATUMS MAP PROJECTIO Janowicz K., 2010, ENCY GEOGRAPHY Jones KS, 2003, ECOL PSYCHOL, V15, P107, DOI 10.1207/S15326969ECO1502_1 Klatzky R. L., 1998, LECT NOTES ARTIF INT, V1404, P1 Kuhn W, 2003, INT J GEOGR INF SCI, V17, P405, DOI 10.1080/1365881031000114116 Kuhn W., 2003, P 6 AGILE C GEOGR IN, P63 MARK L S, 1990, Ecological Psychology, V2, P325, DOI 10.1207/s15326969eco0204_2 Masolo C., 2005, P WORKSH FDN ASP ONT, P2 Masolo C., 2003, WONDERWEB DELIVERABL Ortmann J, 2011, LECT NOTES COMPUT SC, V7031, P537, DOI 10.1007/978-3-642-25073-6_34 Ortmann J, 2010, FRONT ARTIF INTEL AP, V209, P117, DOI 10.3233/978-1-60750-535-8-117 Oudejans RRD, 1996, ECOL PSYCHOL, V8, P259, DOI 10.1207/s15326969eco0803_4 Popper, 1962, CONJECTURES REFUTAT, P33 Probst F., 2008, APPL ONTOL, V3, P63 Probst F., 2007, THESIS U MUNSTER GER Raubal M, 2008, LECT NOTES ARTIF INT, V4760, P91, DOI 10.1007/978-3-540-77915-5_7 Saffiotti A., 2008, LECT NOTES COMPUTER, V4760, P106 Saffiotti A., 2008, IEEE RSJ INT C INT R, P2329 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schade S., 2010, GISDISS, V1 Scheider S, 2010, FRONT ARTIF INTEL AP, V209, P131, DOI 10.3233/978-1-60750-535-8-131 Scheider S, 2012, FRONT ARTIF INTEL AP, V244, P1, DOI 10.3233/978-1-61499-107-6-i Scheider S, 2009, LECT NOTES COMPUT SC, V5756, P69 Shaw R., 1982, COGNITION SYMBOLIC P, V2, P159 STEVENS SS, 1946, SCIENCE, V103, P677, DOI 10.1126/science.103.2684.677 Stoffregen TA, 2003, ECOL PSYCHOL, V15, P115, DOI 10.1207/S15326969ECO1502_2 Turvey M. T., 1992, Ecological Psychology, V4, P173, DOI 10.1207/s15326969eco0403_3 Warren W., 1984, J EXP PSYCHOL, V10, P683 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Zadeh LA, 2004, DATA KNOWL ENG, V50, P291, DOI 10.1016/j.datak.2004.04.001 Zadeh LA, 2001, AI MAG, V22, P73 Zadeh L. A., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90029-5 Zadeh LA, 2002, J STAT PLAN INFER, V105, P233, DOI 10.1016/S0378-3758(01)00212-9 NR 50 TC 0 Z9 0 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 1570-0844 EI 2210-4968 J9 SEMANT WEB JI Semant. Web PY 2014 VL 5 IS 6 BP 449 EP 472 DI 10.3233/SW-130119 PG 24 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA CM7KY UT WOS:000357872700003 ER PT J AU Jiang, F Hong, XG Peng, ZH Li, QZ AF Jiang, Fei Hong, Xiaoguang Peng, Zhaohui Li, Qingzhong GP IEEE BE Babu, SP Wenzheng, L Tsui, E TI Finding Dimensions for Text Based on Heterogeneous Information Network SO 2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) LA English DT Proceedings Paper CT 5th IEEE International Conference on Software Engineering and Service Science (ICSESS) CY JUN 27-29, 2014 CL Beijing, PEOPLES R CHINA SP IEEE DE heterogeneous information network; information network analysis method; network mining; text dimension AB We propose an approach applicable in the problem of multi dimensions text mining that finds out several sets of phrases which were referred to as the text dimension. Based on the dimensions of text found by the proposed approach, a network could be built by similarities between documents. A method is proposed to transform the network from a coarse-grained one to a fine-grained one. By repeatedly mining phrases sets from the networks of different granularities, we could get a refined text dimensions set. We provide experimental results on text mining showing the computational feasibility and effectiveness for finding text dimensions which combines text mining with network mining and can be used for learning interesting knowledge. C1 [Jiang, Fei; Hong, Xiaoguang; Peng, Zhaohui; Li, Qingzhong] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Shandong, Peoples R China. RP Hong, XG (reprint author), Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Shandong, Peoples R China. EM jf29762@gmail.com; hxg@sdu.edu.cn; pzh@sdu.edu.cn; lqz@sdu.edu.cn CR Duan Xiaodong, 2009, J COMPLEX SYSTEM COM, V6, P34 Han J., 2010, P 2010 ACM SIGMOD IN, P1251, DOI 10.1145/1807167.1807333 LIN CX, 2008, DAT MIN 2008 ICDM 08, P905 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shankar S., 2000, KDD 2000 WORKSH TEXT Simitsis A, 2008, PVLDB, V1, P660 Sun Y, 2012, P VLDB ENDOWMENT, V5, P2022 Sun Y., 2009, P 12 INT C EXT DAT T, P565, DOI DOI 10.1145/1516360.1516426 Sun YZ, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P797 Yu Y, 2009, P VERY LARGE DATA BA, V2, P1622 Zhang D, 2009, SDM, V9, P1124 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3279-5 PY 2014 BP 819 EP 823 PG 5 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC8OA UT WOS:000355926400191 ER PT S AU Cevahir, A AF Cevahir, Ali BE Peng, WC Wang, H Bailey, J Tseng, VS Ho, TB Zhou, ZH Chen, ALP TI Scalable Textual Similarity Search on Large Document Collections Through Random Indexing and K-means Clustering SO TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) CY MAY 13-16, 2014 CL Tainan, TAIWAN DE Textual similarity search; Vector-space model; Inverted index; Random indexing; K-means ID SYSTEMS AB This work presents an index partitioning technique for large-scale text-based search engines. Large e-commerce sites contain millions of products visited by millions of users. Textual similarity search has many uses in e-commerce sites, for instance in building recommendation engines. However, the size of the corpus makes it prohibitive to use naive approaches for real-time search. In order to reduce response times, the search is executed within a small subset of most related documents. To achieve this goal, documents are clustered using k-means. However, vectors used for k-means clustering are very high dimensional. Random indexing is applied to reduce dimensionality. We boosted these steps with GPUs to reduce preprocessing overheads. Once clusters are built, text queries are executed within the closest clusters. Our experiments on a large document collection for a recommendation scenario reveal that only 1.7 % loss in recommendation precision is possible by realizing only 28 % of search operations in the inverted index. C1 Rakuten Inc, Rakuten Inst Technol, Tokyo, Japan. RP Cevahir, A (reprint author), Rakuten Inc, Rakuten Inst Technol, Tokyo, Japan. EM ali.cevahir@mail.rakuten.com CR Achlioptas D, 2003, J COMPUT SYST SCI, V66, P671, DOI 10.1016/S0022-0000(03)00025-4 [Anonymous], 2007, P 13 ACM SIGKDD INT Cambazoglu BB, 2006, LECT NOTES COMPUT SC, V4263, P717 Cevahir A., 2013, INT J MULTIMEDIA DAT, V4, P24 Goldberg K, 2001, INFORM RETRIEVAL, V4, P133, DOI 10.1023/A:1011419012209 Johnson W. B., 1984, CONT MATH, V26, P189 Kanerva P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P1036 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Li P., 2006, P 12 ACM SIGKDD INT, P287, DOI 10.1145/1150402.1150436 Rennie J. D. M., 2005, P 22 INT C MACH LEAR, P713, DOI 10.1145/1102351.1102441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-13186-3; 978-3-319-13185-6 J9 LECT NOTES ARTIF INT PY 2014 VL 8643 BP 231 EP 238 DI 10.1007/978-3-319-13186-3_22 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC7AH UT WOS:000354705300022 ER PT S AU Banchs, RE AF Banchs, Rafael E. BE Jaafar, A Ali, NM Noah, SAM Smeaton, AF Bruza, P AbuBakar, Z Jamil, N Sembok, TMT TI A Comparative Evaluation of 2D And 3D Visual Exploration of Document Search Results SO INFORMATION RETRIEVAL TECHNOLOGY, AIRS 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Asia-Information-Retrieval-Societies Conference (AIRS) CY DEC 03-05, 2014 CL Kuching, MALAYSIA SP Sarawak Convent Bur, Perbadanan Nas Berhad, Majlis Profesor Negara, Inst Visual Informat, FTSM Knowledge Technol Res Grp, Univ Kebangsaan Malaysia, Malaysian Soc Informat Retrieval & Knowledge Management, Univ Teknologi MARA, Univ Putra Malaysia, Univ Pertahanan Nas Malaysia, Kolej Univ Islam Antarabangsa Selangor, Univ Malaysia Sarawak, Int Islam Univ Malaysia, Asia Informat Retrieval Soc DE Document Search; Ranking; Visual Exploration; Interface ID COLLECTION AB This work presents and experimental comparison between 2D and 3D search and visualization platforms. The main objective of the study is two explore the following two research questions: what method is most robust in terms of the success rate? And, what method is faster in terms of average search time? The obtained results show that, although successful rates and subject preferences are higher for 3D search and visualization, search times are still lower for 2D search and visualization. C1 Inst Infocomm Res, Human Language Technol, Singapore, Singapore. RP Banchs, RE (reprint author), Inst Infocomm Res, Human Language Technol, Singapore, Singapore. EM rembanchs@i2r.a-star.edu.sg CR Benford S., 1995, EUROGRAPHICS, P349 Bush V., 1945, ATLANTIC MONTHLY, P101 Correa R.F., 2007, P 6 INT WORKSH SELF Crow V., 1994, TECHNICAL REPORT Deerwester S., 1988, P 51 ANN M AM SOC IN Feldman R., 1997, P 3 ANN C KNOWL DISC Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Jones K., 1971, AUTOMATIC KEYWORD CL Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kandogan E., 2001, P 7 ACM SIGKDD INT C, P107, DOI DOI 10.1145/502512.502530 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kruskal J. B., 1978, SAGE U PAPER SERIES, P7 Lagus K, 2004, INFORM SCIENCES, V163, P135, DOI 10.1016/j.ins.2003.03.017 Morse EL, 1997, IEEE SYS MAN CYBERN, P1680 Newman D., 2010, WEB SEMANTICS SCI SE, V8 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wise J.A., 1995, P IEEE S INF VIS INF NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-12844-3; 978-3-319-12843-6 J9 LECT NOTES COMPUT SC PY 2014 VL 8870 BP 100 EP 111 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC7DB UT WOS:000354760900009 ER PT S AU Butt, AS Haller, A Xie, LX AF Butt, Anila Sahar Haller, Armin Xie, Lexing BE Janowicz, K Schlobach, S Lambrix, P Hyvonen, E TI Relationship-Based Top-K Concept Retrieval for Ontology Search SO KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT, EKAW 2014 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW) CY NOV 24-28, 2014 CL Linkoping, SWEDEN ID SEMANTIC WEB; LINKED DATA AB With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. We apply this algorithm on the task of searching and ranking concepts in ontologies and compare it with state-of-the-art ontology ranking models and traditional information retrieval algorithms such as PageRank and tf-idf. Our evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. C1 [Butt, Anila Sahar; Haller, Armin] CSIRO, Canberra, ACT, Australia. [Butt, Anila Sahar; Haller, Armin; Xie, Lexing] Australian Natl Univ, Canberra, ACT 0200, Australia. RP Butt, AS (reprint author), CSIRO, Canberra, ACT, Australia. EM anila.butt@anu.edu.au; armin.haller@anu.edu.au; lexing.xie@anu.edu.au CR Alani H., 2006, P INT SEM WEB C ISWC, P5 Butt A.S., 2014, P INT SEM WEB C RIV, P130 Chen G, 2008, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCE AND INFORMATION IN ENGINEERING CONFERENCE, VOL 1, PTS A-C, P1101, DOI 10.1145/1367497.1367676 d'Aquin M, 2011, SEMANT WEB, V2, P55, DOI 10.3233/SW-2011-0031 d'Aquin M, 2009, LECT NOTES COMPUT SC, V5554, P913, DOI 10.1007/978-3-642-02121-3_81 Ding L., 2004, P 13 ACM INT C INF K, P652, DOI 10.1145/1031171.1031289 Ding L, 2005, LECT NOTES COMPUT SC, V3729, P156 Fernandez M., 2008, P 2008 IEEE INT C SE, P253, DOI DOI 10.1109/ICSC.2008.52 Fogaras D, 2003, LECT NOTES COMPUT SC, V2877, P65 Harth A, 2007, LECT NOTES COMPUT SC, V4825, P211 Hogan A, 2011, J WEB SEMANT, V9, P365, DOI 10.1016/j.websem.2011.06.004 Hogan A., 2006, P 2 WORKSH SCAL SEM Noy N.F., 2012, WEB SEMANTICS SCI SE, V11 Noy N.F., 2009, NUCL ACIDS RES Page L., 1998, P 7 INT WORLD WID WE, P161 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Thomas E., 2007, CEUR WORKSHOP P, V258 Tummarello G, 2007, LECT NOTES COMPUT SC, V4825, P552 Vandenbussche P.-Y., 2014, ERCIM NEWS, V96, P21 Wu G, 2008, LECT NOTES COMPUT SC, V5318, P33 NR 22 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-13704-9; 978-3-319-13703-2 J9 LECT NOTES ARTIF INT PY 2014 VL 8876 BP 485 EP 502 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC7HI UT WOS:000354879800037 ER PT S AU Yan, CW Zhang, F Huang, LN AF Yan, Chunwei Zhang, Fan Huang, Lian'en BE Pham, DN Park, SB TI DRWS: A Model for Learning Distributed Representations for Words and Sentences SO PRICAI 2014: TRENDS IN ARTIFICIAL INTELLIGENCE SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI) CY DEC 01-05, 2014 CL Gold Coast, AUSTRALIA SP Griffith Univ, NICTA Ltd, AF Off Sci Res, Asian Off Aerosp Res & Dev, Commonwealth Sci & Ind Corp, Gold Coast Convent Bur, MIMOS Berhad, Queensland Univ Technol, Univ Queensland DE Neural network; Distributed representation; Global context; Word vector; Sentence vector AB Vector-space distributed representations of words can capture syntactic and semantic regularities in language and help learning algorithms to achieve better performance in natural language processing tasks by grouping similar words. With progress of machine learning techniques in recent years, much attention has been paid on this field. However, many NLP tasks such as text summary and sentence matching treat sentences as atomic units. In this paper, we introduce a new model called DRWS which can learn distributed representations for words and variable-length sentences. Feature vectors for words and sentences are learned based on their probability of co-occurrence between words and sentences using a neural network. To evaluate feature vectors learned by our model, we applied our model on the tasks of detecting word similarity and text summarization. Extensive experiments demonstrate the effectiveness of our proposed model in learning vector representations for words and sentences. C1 [Yan, Chunwei; Zhang, Fan; Huang, Lian'en] Peking Univ, Shenzhen Grad Sch, Shenzhen Key Lab Cloud Comp Technol & Applicat, Shenzhen, Guangdong, Peoples R China. RP Huang, LN (reprint author), Peking Univ, Shenzhen Grad Sch, Shenzhen Key Lab Cloud Comp Technol & Applicat, Shenzhen, Guangdong, Peoples R China. EM yanchunwei@outlook.com; fan.zhgf@gmail.com; hle@net.pku.edu.cn CR Agirre E., 2009, P HUM LANG TECHN 200, P19, DOI 10.3115/1620754.1620758 Bengio Y, 2006, STUD FUZZ SOFT COMP, V194, P137 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Collobert R., 2008, ICML, V307, P160, DOI DOI 10.1145/1390156.1390177 Huang E.H., 2012, ANN M ASS COMP LING, V1, P873 Klein D., 2003, P 41 ANN M ASS COMP, V1, P423, DOI DOI 10.3115/1075096.1075150 Mikolov T., 2013, ADV NEURAL INF PROCE, V26, P3111 Mikolov T., 2013, ARXIV13013781 Mikolov Tomas, 2013, P NAACL HLT, P746 Mnih A., 2012, ARXIV12066426 Morin F, 2005, P AISTATS, V5, P246 Quasthoff U., 2006, P 5 INT C LANG RES E, P1799 Rumelhart D. E., 1988, LEARNING REPRESENTAT SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Socher R., 2011, P C EMP METH NAT LAN, P151 Socher R., 2010, P NIPS 2010 DEEP LEA, P1 Socher Richard, 2011, ADV NEURAL INFORM PR, V24, P801 Zhang Z., 2013, LNCS, V7923, P338 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-13560-1; 978-3-319-13559-5 J9 LECT NOTES ARTIF INT PY 2014 VL 8862 BP 196 EP 207 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BC7DM UT WOS:000354778700016 ER PT J AU Akthar, N Ahamad, MV Khan, AUS AF Akthar, Nadeem Ahamad, Mohd Vasim Khan, Azeem Ush Shan GP IEEE TI A Real Time Clustering Method Using Document Index Graph SO 2014 INTERNATIONAL CONFERENCE ON DATA MINING AND INTELLIGENT COMPUTING (ICDMIC) LA English DT Proceedings Paper CT International Conference on Data Mining and Intelligent Computing (ICDMIC) CY SEP 05-06, 2014 CL New Delhi, INDIA SP Indira Gandhi Delhi Tech Univ Women, IEEE Delhi Sect DE Clustering; Document Index Graph; Suffix Tree Clustering; Phrase Cluster; Incremental Algorithm; Web-Snippets AB From a previous survey, 45% of users did not get what they are actually looking for in the web using any search engine. Suppose, you have a million of text file in your server or in your computer, then there is a need to categorize them on the basis of their content in a very efficient way. As a result, IR (Information Retrieval) tool has been developed, it provides a more effective ways for users to categorize relevant data. Most of the clustering algorithm like Vector Space Model considers only single words but it is not incremental so it can't be applied online and another algorithm, STC, involves ` trie' concept to identify shared phrases suitable to apply on-line, but the main problem is, it doesn't work for large number of data set. In this paper, we have introduced DIGE clustering algorithm which generates the clusters based on the common phrases and also on the single terms. DIGE clustering algorithm based on the DIG model for the representation of documents. The construction of DIG model is incremental, so DIGE is also capable to produce cluster using online document and also it doesn't occupy much memory, so also applicable for offline. C1 [Akthar, Nadeem; Ahamad, Mohd Vasim; Khan, Azeem Ush Shan] Aligarh Muslim Univ, ZHCET, Dept Comp Engn, Aligarh 202002, Uttar Pradesh, India. RP Akthar, N (reprint author), Aligarh Muslim Univ, ZHCET, Dept Comp Engn, Aligarh 202002, Uttar Pradesh, India. EM nadeemalakhtar@gmail.com; vasim.iu@gmail.com; azeem5257@gmail.com CR Aas K., 1999, 941 NORW COMP CTR Agrawal C C, 2003, P 29 INT C VER LARG, P81 Allen P. O. R., 1993, P ACM C ORG COMP SYS, P166 Croft W. B., 1978, THESIS U CAMBRIDGE De Vries Chris, 2010, THESIS MASTER IT RES Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kotsiantis S. B., 2004, WSEAS T INFORM SCI A, V1, P73 Momin B. F., 2006, WEB DOCUMENT CLUSTER O'Callaghan L, P ICDE 2002 SAN JOS, p685{704 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, MCGRAW HILL COMPUTER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, P KDD 2000 WORKSH TE, P3 Weiner P., 1973, P 14 IEEE S SWITCH A, P1, DOI DOI 10.1109/SWAT.1973.13 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zamir O., 1998, P SIGIR 98 U WASH SE Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-4674-7 PY 2014 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC5XO UT WOS:000353622500002 ER PT S AU Pliakos, K Kotropoulos, C AF Pliakos, Konstantinos Kotropoulos, Constantine BE Likas, A Blekas, K Kalles, D TI Simultaneous Image Clustering, Classification and Annotation for Tourism Recommendation SO ARTIFICIAL INTELLIGENCE: METHODS AND APPLICATIONS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 8th Hellenic Conference on Artificial Intelligence (SETN) CY MAY 15-17, 2014 CL Ioannina, GREECE SP Univ Ioannina, Dept Comp Sci & Engn, Hellen Artificial Intelligence Soc, Hellen Open Univ, Klidarithmos Publicat DE Probabilistic Latent Semantic Analysis (PLSA); Clustering; Image Classification; Image Annotation; Recommendation systems; Hypergraph ID RECOGNITION AB The exponential increase in the amount of data uploaded to the web has led to a surge of interest in multimedia recommendation and annotation. Due to the vast volume of data, efficient algorithms for recommendation and annotation are needed. Here, a novel two-step approach is proposed, which annotates an image received as input and recommends several tourist destinations strongly related to the image. It is based on probabilistic latent semantic analysis and hypergraph ranking enhanced with the visual attributes of the images. The proposed method is tested on a dataset of 30000 images bearing text information (e.g., title, tags) collected from Flickr. The experimental results are very promising, as they achieve a top rank precision of 80% for tourism recommendation. C1 [Pliakos, Konstantinos; Kotropoulos, Constantine] Aristotle Univ Thessaloniki, Dept Informat, Thessaloniki 54124, Greece. RP Pliakos, K (reprint author), Aristotle Univ Thessaloniki, Dept Informat, Box 451, Thessaloniki 54124, Greece. EM kpliakos@aiia.csd.auth.gr; costas@aiia.csd.auth.gr CR Agarwal S., 2006, P 23 INT C MACH LEAR, P17, DOI 10.1145/1143844.1143847 Bassiou N, 2011, COMPUT SPEECH LANG, V25, P741, DOI 10.1016/j.csl.2010.12.002 Bassiou N., 2014, IEEE T NEURAL NETWOR Bu J., 2010, P ACM INT C MULT, P391, DOI 10.1145/1873951.1874005 Cao L., 2010, P IEEE INT C AC SPEE, P2274 Hofmann T, 1999, UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P289 Oliva A, 2006, PROG BRAIN RES, V155, P23, DOI 10.1016/S0079-6123(06)55002-2 Papadopoulos S., 2011, MULTIMEDIA, P52 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang C., 2009, P IEEE C COMP VIS PA, P1903 Zheng YT, 2009, PROC CVPR IEEE, P1085 NR 11 TC 0 Z9 0 PU SPRINGER INT PUBLISHING AG PI CHAM PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND SN 0302-9743 BN 978-3-319-07063-6; 978-3-319-07064-3 J9 LECT NOTES ARTIF INT PY 2014 VL 8445 BP 630 EP 640 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BC4LO UT WOS:000352632400054 ER PT J AU Yang, KH Ye, D AF Yang, KeHua Ye, Dan BE Datta, A TI A Novel Improved TFIDF Algorithm SO PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND SERVICE SYSTEM (CSSS) SE Advances in Intelligent Systems Research LA English DT Proceedings Paper CT 3rd International Conference on Computer Science and Service System (CSSS) CY JUN 13-15, 2014 CL Bangkok, THAILAND DE text categorization; TFIDF; semantics; information entropy; information gain AB Feature weighting algorithm has a great effect on the accuracy of text categorization. The classical Term Frequency and Inverse Documentation Frequency algorithm (TFIDF) ignores the semantic relationships between terms in the document set, thus to influence the accuracy of term weight calculation. To calculate the weight of words more correctly, the paper introduces the semantic association between words and proposed a new improved algorithm (S-TFIDFIGE) combined with semantic, information entropy and information gain. Experimental results show that the proposed method has better classification results than the traditional TFIDF and other improved algorithms. C1 [Yang, KeHua; Ye, Dan] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China. RP Yang, KH (reprint author), Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China. EM khyang@hnu.edu.cn; dancy@hnu.edu.cn CR Aas K., 1999, TEXT CATEGORISATION, V941 Basili R., 1999, TEXT CLASSIFIER BASE Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 How BC, 2004, IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS, P599 Li Xue-ming, 2012, Computer Engineering, V38, DOI 10.3969/j.issn.1000-3428.2012.08.013 Liu Q., 2002, COMPUTATIONAL LINGUI, V7, P59 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Tan S, 2005, P 14 ACM INT C INF K, P469, DOI DOI 10.1145/1099554.1099687 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Zhang Y., 2008, COMPUTER ENG APPL, V43, P159 NR 12 TC 0 Z9 0 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE SN 1951-6851 BN 978-94-6252-012-7 J9 ADV INTEL SYS RES PY 2014 VL 109 BP 162 EP 166 PG 5 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Automation & Control Systems; Computer Science GA BC3ZP UT WOS:000352132500040 ER PT J AU Arora, R Srinivasa, S AF Arora, Rajeev Srinivasa, Srinath GP IEEE TI A Faceted Characterization of the Opinion Mining Landscape SO 2014 SIXTH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORKS (COMSNETS) SE International Conference on Communication Systems and Networks LA English DT Proceedings Paper CT 6th International Conference on Communication Systems and Networks (COMSNETS) CY JAN 07-10, 2014 CL Bangalore, INDIA DE Opinion Mining; Sentiment Analysis; Survey AB Increasing amounts of user generated content (UGC) on the Internet, is creating research interest in opinion mining. This involves automatic detection of opinions about products, services, political parties, celebrities and events from user generated content. Research efforts in opinion mining are thus far, fragmented and have followed several approaches. However, most of them require understanding of domain-specific opinion words and their polarity, and language-specific opinion rules. In addition, semantic constructs like sarcasm pose open challenges. In this paper, we try to characterize the opinion-mining landscape by proposing a faceted taxonomy of the different aspects of opinion mining. We then survey literature and place these in appropriate places in the proposed model. We also propose a general purpose workflow required from any opinion mining engine. Finally, we speculate on specific challenges in the opinion mining landscape. C1 [Arora, Rajeev; Srinivasa, Srinath] IIIT Bangalore, Bangalore 560100, Karnataka, India. RP Arora, R (reprint author), IIIT Bangalore, Bangalore 560100, Karnataka, India. EM rajeev.arora@iiitb.org; sri@iiitb.ac.in CR Anthony Aue and Michael Gamon, CUSTOMIZING SENTIMEN Bespalov D., 2011, P 20 ACM INT C INF K, P375 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blitzer J, 2007, ACL, P187 Boyd-Graber Jordan, 2007, TOPIC MODEL WORD SEN DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gamon Michael, 2005, COLING 2005, P841 Gonzalez-Ibanez R., 2011, P 49 ANN M ASS COMP, V2, P581 Hatzivassiloglou Vasileios, 1997, P 8 C EUR CHAPT ASS, P174 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Hu MQ, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P755 Liu B, 2010, CH CRC MACH LEARN PA, P627 Liu B, 2010, IEEE INTELL SYST, V25, P76 Mei Q., 2007, P 16 INT C WORLD WID, P171, DOI DOI 10.1145/1242572.1242596 Pan SJ, 2010, P 19 INT C WORLD WID, P751, DOI 10.1145/1772690.1772767 Pang B., 2002, P ACL 02 C EMP METH, V10, P79, DOI DOI 10.3115/1118693.1118704 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Pennebaker J. W., DEV PSYCHOMETRIC PRO Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 Qiu GA, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1199 Qu L., 2010, P 23 INT C COMP LING, P913 Rachakonda A R, 2012, P 38 INT C VER LARG Reyes Antonio, 2011, P 2 WORKSH COMP APPR, P118 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoyanov V., 2006, P 2006 C EMP METH NA, P336, DOI 10.3115/1610075.1610123 Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 Valitutti R, 2004, P 4 INT C LANG RES E, P1083 Xia Rui, 2011, IJCNLP, P614 Yu H, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P129 NR 30 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2155-2487 J9 INT CONF COMMUN SYST PY 2014 PG 6 WC Computer Science, Hardware & Architecture; Telecommunications SC Computer Science; Telecommunications GA BC2AE UT WOS:000350659500072 ER PT J AU He, M Wang, ZZ Du, YP AF He, Ming Wang, Zhen-zhen Du, Yong-ping BE Liu, DL Zhu, XB Xu, KL Fang, DM TI Document Similarity Measure Based on Topic Model SO APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY SE Applied Mechanics and Materials LA English DT Proceedings Paper CT International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI) CY JAN 11-12, 2014 CL Xian, PEOPLES R CHINA DE latent Dirichlet allocation; document similarity computation; topic model AB Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing ( NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic modelbased method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study using real data set demonstrates the efficiency of our method. C1 [He, Ming; Wang, Zhen-zhen; Du, Yong-ping] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China. RP He, M (reprint author), Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China. EM heming@bjut.edu.cn; wangzhen820355@126.com; ypdu@bjut.edu.cn CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Cios K. J., 1998, DATA MINING METHODS Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Heinrich G., 2008, TECHNICAL REPORT Hoffman M, 2010, NEURAL INFORM PROCES Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steyvers M., 2006, LATENT SEMANTIC ANAL NR 8 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1660-9336 BN 978-3-03835-012-5 J9 APPL MECH MATER PY 2014 VL 513-517 BP 1280 EP 1284 DI 10.4028/www.scientific.net/AMM.513-517.1280 PG 5 WC Construction & Building Technology; Engineering, Civil; Mechanics SC Construction & Building Technology; Engineering; Mechanics GA BC0UW UT WOS:000349668901129 ER PT J AU Yik-Wai, NG Qu, HM AF Yik-Wai, N. G. Qu, Huamin GP IEEE TI TrendFocus: Visualization of Trends in Financial News with Indicator Sets SO 2014 INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) SE International Conference on Big Data and Smart Computing LA English DT Proceedings Paper CT International Conference on Big Data and Smart Computing (BIGCOMP) CY JAN 15-17, 2014 CL Bangkok, THAILAND DE Financial News Visualization; Trend Focus; Domain Knowledge Visualization AB Although many current visualization approaches to time-series data can show a good general trend of a topic or theme over time, most cannot help people gain a deeper understanding of key issues. They may illustrate how some key issues come up and disappear from time to time, but they typically provide limited information about relationships of interest. Financial and economic analysts are often interested in how financial or economic performance indicators interplay or change over time. With a set of well-defined and commonly used equations or indicators in the domain of Economics and Finance, we can refine our visualization by keeping track of these variables. In this paper, we make use of a line plot layout to track some of these indicators and leverage word clouds to reveal how these indicators are affected by the changes in social and economic environments. C1 [Yik-Wai, N. G.; Qu, Huamin] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. RP Yik-Wai, NG (reprint author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. EM ywng@ust.hk; huamin@cse.ust.hk CR Bak P., 2009, IEEE T VISUALIZATION, V15 Cui Weiwei, 2012, ACM T INTELL SYST TE, V3, P17 Cui Weiwei, COMPUTER GRAPHICS AP, V30 Dragicevic P., 2002, P SIGCHI C HUM FACT Forbes A G, 2012, INTERACTIVE FOLKSONO Havre S., 2000, INF VIS INFOVIS 2000 Kapler T., 2005, INFORM VISUALIZATION, V4 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHANNON CE, 1948, AT&T TECH J, V27, P623 Viegas FB, 2008, INTERACTIONS, V15, P49, DOI DOI 10.1145/1374489.1374501 Viegas Fernanda B., 2009, IEEE T VISUALIZATION, V15 Wang Dingding, 2008, P 31 ANN INT ACM SIG Weber M., 2001, Proceedings IEEE Symposium on Information Visualization 2001. INFOVIS 2001 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2375-933X BN 978-1-4799-3919-0 J9 INT CONF BIG DATA PY 2014 BP 7 EP 12 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC1AI UT WOS:000349894500002 ER PT J AU Harish, BS Kumar, SVA Manjunath, S AF Harish, B. S. Kumar, S. V. Aruna Manjunath, S. GP IEEE TI Classifying Text Documents using Unconventional Representation SO 2014 INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) SE International Conference on Big Data and Smart Computing LA English DT Proceedings Paper CT International Conference on Big Data and Smart Computing (BIGCOMP) CY JAN 15-17, 2014 CL Bangkok, THAILAND DE Classification; Text Documents; Representation; Clustering Algorithms AB Classification of text documents is one of the most common themes in the field of machine learning. Although a text document expresses a wide range of information, but it lacks the imposed structure of tradition database. Thus, unstructured data, particularly free running text data has to be transferred into a structured data. Hence, in this paper we represent the text document unconventionally by making use of symbolic data analysis concepts. We propose a new method of representing documents based on clustering of term frequency vectors. Term frequency vectors of each cluster are used to form a symbolic representation by the use of Mean and Standard Deviation. Further, term frequency vectors are used in the form a interval valued features. To cluster the term frequency vectors, we make use of Single Linkage, Complete Linkage, Average Linkage, K-Means and Fuzzy C-Means clustering algorithms. To corroborate the efficacy of the proposed model we conducted extensive experimentations on standard datasets like 20 Newsgroup Large, 20 Mini Newsgroup, Vehicles Wikipedia datasets and our own created datasets like Google Newsgroup and Research Article Abstracts. Experimental results reveal that the proposed model gives better results when compared to the state of the art techniques. In addition, as the method is based on a simple matching scheme, it requires a negligible time. C1 [Harish, B. S.; Kumar, S. V. Aruna] SJ Coll Engn, Dept Informat Sci & Engn, Mysore, Karnataka, India. RP Harish, BS (reprint author), SJ Coll Engn, Dept Informat Sci & Engn, Mysore, Karnataka, India. EM bsharish80@gmail.com; arunkumarsv55@gmail.com; manju_uom@yahoo.co.in CR [Anonymous], IEEE T KNOWLEDGE DAT Bezdek J. C., 1981, PATTERN RECOGNITION Bock H.H, ANAL SYMBOLIC DATA Cai D., 2007, P 16 ACM C INF KNOWL, P741, DOI 10.1145/1321440.1321544 Cavnar William B., 1994, 3 TEXT RETR C TREC 3, P269 Choudhary B, 2002, P 11 INT WORLD WID W Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon Inderjit S., 2002, P 8 ACM SIGKDD INT C, P191 Esteban M, 2006, P 4 LAT AM WEB C CHO Guru D. S., 2006, PATTERN RECOGN, V28, P144 Guru D.S., 2010, P 3 ANN ACM COMP BAN He X, 2004, P 27 ANN INT ACM SIG, P96, DOI 10.1145/1008992.1009012 Hotho A, 2005, J LANGUAGE TECHNOLOG, V20, P19 Jiang S, 2012, J EXPERT SYSTEMS APP, V39, P1503 Kyriakopoulou A, 2006, P ECML PKDD DISC CHA Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] Raskutti B, 2002, P 19 INT C MACH LEAR, P514 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seabastiani F, 2002, ACM COMPUT SURV, V34, P1 Slonim N, 2001, P EUR C IR RES ECIR Takamura H, 2002, P CONLL 2002, P29 Wan C H, 2012, J EXPERT SYSTEMS APP, V39, P11880 Zeng H.J., 2003, P 3 IEEE INT C DAT M, P443 NR 25 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2375-933X BN 978-1-4799-3919-0 J9 INT CONF BIG DATA PY 2014 BP 210 EP 216 PG 7 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC1AI UT WOS:000349894500043 ER PT J AU Choukri, D AF Choukri, Djellali BE Shakshuki, EM TI A semantic-based variables selection for ontology learning Taking Jaccard alignment as case SO 5TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS / THE 4TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE / AFFILIATED WORKSHOPS SE Procedia Computer Science LA English DT Proceedings Paper CT 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) CY SEP 22-25, 2014 CL Halifax, CANADA DE Machine Learning; variables selection; clustering; Semantic Web; Ontology; alignment AB In the past decade, research on numerical schemes on ontology learning has been quite intensive. Several learning approaches have been proposed to help developers during the maintenance process. Most of the proposed approaches do not process the curse of dimensionality and the semantic contained in the information structure. A novel semantic-based method for ontology learning, which can provide improvement in both alignment and learning, is described. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach. (C) 2014 The Authors. Published by Elsevier B.V. C1 [Choukri, Djellali] LANCI UQAM, Montreal, PQ H2X 3Y7, Canada. RP Choukri, D (reprint author), LANCI UQAM, Local W 5353 Case Postale 8888,Succ Ctr Ville, Montreal, PQ H2X 3Y7, Canada. EM djellali.choukri@courrier.uqam.ca CR Baena-Garcia M, 2011, INT SYST DES APPL IS, P1044 Bechhofer S, 2003, DIG DESCRIPTION LOGI Djellali C, 2013, IEEE ACM INT C ADV S, P1078 DJELLALI C, 2013, 3 INT C COMM INF TEC, P61 Gomez-Perez A, 2003, SURVEY ONTOLOGY LEAR, P1 Haarslev V, 2011, SEMANTIC WEB, P1 Hazman Maryam, 2011, INT J COMPUTER APPL, V22, P0975 ISAWA H, 2008, NEUR NETW 2008 IJCNN, P3595 Issac B, 2009, TENCON 2009 2009 IEE, P1, DOI 10.1109/TENCON.2009.5396056 Liu Ning, 2010, INFORM TECHNOLOGY AP, P159 Lv Yanhui, 2011, FUZZ SYST KNOWL DISC, P1262 Maedche A, 2003, INTELLIGENT SYSTEMS, V16, P72 Napoli Amedeo, 1997, INTRO DESCRIPTION LO Nasoni S, 2010, INFORM PROCESS KNOWL, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Yanfen, 2007, THESIS UQTR Shibata N, 2010, TECHNOLOGY MANAGEMEN, P1 Starr R, 2010, ENT DISTR OBJ COMP C, P199 TSENG MC, 2007, MACHINE LEARNING CYB, P2280 Wei JJ, 2001, IEEE T INF TECHNOL B, V5, P290 Zaman A N K, 2011, DIG INF MAN ICDIM 20, P133 NR 21 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1877-0509 J9 PROCEDIA COMPUT SCI PY 2014 VL 37 BP 56 EP 63 DI 10.1016/j.procs.2014.08.012 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC1CR UT WOS:000349985800006 ER PT J AU Panasyuk, A Yu, ESL Mehrotra, KG AF Panasyuk, Aleksey Yu, Edmund Szu-Li Mehrotra, Kishan G. BE Dagli, CH TI Controversial Topic Discovery on Members of Congress with Twitter SO COMPLEX ADAPTIVE SYSTEMS SE Procedia Computer Science LA English DT Proceedings Paper CT Conference on Conquering Complexity - Challenges and Opportunities CY NOV 03-05, 2014 CL Philadelphia, PA SP Missouri Univ Sci & Technol DE Twitter; Latent Dirichlet Allocation; Topic Modeling; Polarizing Topics; Semantic Extraction; Social Media Mining AB This paper addresses how Twitter can be used for identifying conflict between communities of users. We aggregate documents by topic and by community and perform sentiment analysis, which allows us to analyze the overall opinion of each community about each topic. We rank the topics with opposing views (negative for one community and positive for the other). For illustration of the proposed methodology we chose a problem whose results can be evaluated using news articles. We look at tweets for republican and democrat congress members for the 112th House of Representatives from September to December 2013 and demonstrate that our approach is successful by comparing against articles in the news media. C1 [Panasyuk, Aleksey; Yu, Edmund Szu-Li; Mehrotra, Kishan G.] Syracuse Univ, Dept Elect Engn & Comp Sci, New York, NY 10065 USA. RP Mehrotra, KG (reprint author), Syracuse Univ, Dept Elect Engn & Comp Sci, New York, NY 10065 USA. EM apanasyu@syr.edu; esyu@syr.edu; mehrotra@syr.edu CR Baccianella S, 2010, LREC, V10 BLEI DM, 2003, JMLR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Feng Chi, 2010, REV NETWORK EC, V10 Furnas G. W, 1984, HUMAN FACTORS COMPUT Golbeck J, 2010, J AM SOC INF SCI TEC, V61, P1612, DOI 10.1002/asi.21344 Hofmann T., 1999, P 22 ANN INT SIGIR C Lassen DS, 2011, SOC SCI COMPUT REV, V29, P419, DOI 10.1177/0894439310382749 Manning C. D., 2008, INTRO INFORM RETRIEV, V1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wallach Hanna M., 2009, NIPS, V22 Weng J. S., 2010, P 3 ACM INT C WEB SE Williams Christine B, 2010, COMMUNICATING CONSTI ZHAO WNX, 2011, ADV INFORM RETRIEVAL, V6611, P338 NR 14 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1877-0509 J9 PROCEDIA COMPUT SCI PY 2014 VL 36 BP 160 EP 167 DI 10.1016/j.procs.2014.09.073 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BC1CI UT WOS:000349978000020 ER PT J AU Zheng, C Shen, WM Ghenniwa, H AF Zheng, Cheng Shen, Weiming Ghenniwa, Hamada TI An Adaptive Intent Resolving Scheme for Service Discovery and Integration SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE LA English DT Article DE Service Discovery and Integration; Web Services; Intents; Intent Resolving ID WEB SERVICES; INFORMATION; MODEL AB Service discovery and integration is an important research area with efforts invested to explore the potential advantages of collaborative computing in general and service-oriented computing in particular. However, current technologies still limit their application within the reach of enterprise systems or privately available services. Intents is an emerging and innovative technique aimed to discover and integrate publically available services. In Intents, intent message resolving is a critical step which is deemed to decide the quality of the whole system. However, existing schemes applied in intent resolving adopt the exact-matching strategy which may rule out services desired by the user. This paper brings in information retrieval techniques and applies them to intent resolving. We take an empirical approach through extensive experiments and analyses on a real dataset to obtain guiding principles. Based on the resulting principles, an adaptive intent resolving scheme is designed. Afterwards, we integrate the scheme into the Intents user agent developed in a previous project. C1 [Zheng, Cheng; Shen, Weiming; Ghenniwa, Hamada] Western Univ, London, ON, Canada. RP Zheng, C (reprint author), Western Univ, London, ON, Canada. EM czheng23@uwo.ca; wshen@uwo.ca; hghenniwa@uwo.ca CR Efron Miles, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348405 Espada JP, 2012, J UNIVERS COMPUT SCI, V18, P1410 Fang H., 2007, THESIS U ILLINOIS UR Fellbaum C, 1998, WORDNET ELECT LEXICA FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Imran Hazra, 2009, International Journal of Computer Science & Information Technology, V1 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Manning C. D., 2009, INTRO INFORM RETRIEV McCandless M., 2010, LUCENE ACTION, V2nd Mian AN, 2009, IEEE PERVAS COMPUT, V8, P66, DOI 10.1109/MPRV.2009.2 Neyem A, 2008, J UNIVERS COMPUT SCI, V14, P88 Pan Y, 2011, J UNIVERS COMPUT SCI, V17, P2029 Pedrinaci C, 2010, J UNIVERS COMPUT SCI, V16, P1694 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen WM, 2007, ROBOT CIM-INT MANUF, V23, P315, DOI 10.1016/j.rcim.2006.02.009 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Walker D., 2001, QUERY EXPANSION USIN Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zheng C, 2013, INT C COMP SUPP COOP, P207 Zheng C, 2013, INT C COMP SUPP COOP, P275 Zhu F, 2005, IEEE PERVAS COMPUT, V4, P81, DOI 10.1109/MPRV.2005.87 NR 28 TC 0 Z9 0 PU GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM PI GRAZ PA INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA SN 0948-695X J9 J UNIVERS COMPUT SCI JI J. Univers. Comput. Sci. PY 2014 VL 20 IS 13 BP 1791 EP 1812 PG 22 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA CC2YS UT WOS:000350211700005 ER PT J AU Ramana, AV Naidu, MM AF Ramana, Venkata A. Naidu, M. M. BE Batra, U Sujata Arpita TI OPTIMIZING FEATURES BY CORRELATING FOR CONCEPT LABELING IN TEXT CLASSIFICATION SO SOUVENIR OF THE 2014 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) SE IEEE International Advance Computing Conference LA English DT Proceedings Paper CT 4th IEEE International Advance Computing Conference (IACC) CY FEB 21-22, 2014 CL Gurgaon, INDIA SP IEEE, ITM Univ, Dept Comp Sci Engn & Informat Technol, IEEE Comp Soc, IEEE Student Branch HO ITM Univ DE text mining; classification; machine learning; concept labeling; feature optimization ID LINEAR DISCRIMINANT-ANALYSIS; FEATURE-SELECTION; PATTERN-RECOGNITION; SEMANTIC ANALYSIS; CATEGORIZATION; INFORMATION AB Unstructured form of text documents has seen a huge growth. Feature selection methods are important for the preprocessing of such text documents for dynamic text classification. Appropriate and useful features are focused during feature selection. This can decrease the cost involved while huge amount of data is dispensed out and will also amplify the next textual classifying work. This paper devised a novel geometric optimization method labeling for textual classification. An experimental study on the said geometric feature optimization method is conducted using divergent sizes of text data sets. Experimentally it is shown that how effective this method and how it is better than the tradition methods. C1 [Ramana, Venkata A.] SV Univ, Tirupati, Andhra Pradesh, India. RP Ramana, AV (reprint author), SV Univ, Tirupati, Andhra Pradesh, India. EM avr_rdg@yahoo.co.in CR Bellman R. E., 1961, ADAPTIVE CONTROL PRO Chen YT, 2011, EXPERT SYST APPL, V38, P3085, DOI 10.1016/j.eswa.2010.08.100 Cooley R, 1999, IJCAI 99 WORKSH TEXT Das S., 2001, P ICML, V1, P74 Debole F., 2003, P SAC 03 18 ACM S AP, P784 Dumais S. T., 1998, INDUCTIVE LEAR UNPUB Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Gunal S, 2008, INFORM SCIENCES, V178, P3716, DOI 10.1016/j.ins.2008.06.001 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Joachims T., 1998, P 10 EUR C MACH LEAR Joachims T., 1997, J MACHINE LEARNING R, V14, P143 Kohavi G, 1997, ARTIF INTELL, V97, P273 Lee CH, 2009, EXPERT SYST APPL, V36, P2400, DOI 10.1016/j.eswa.2007.12.052 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Li ZX, 2011, PATTERN RECOGN LETT, V32, P441, DOI 10.1016/j.patrec.2010.11.001 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Liu HW, 2009, PATTERN RECOGN, V42, P1330, DOI 10.1016/j.patcog.2008.10.028 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Manning C.D., 2008, INTRO INFORM RETRIEV Mladenic D, 2003, DECIS SUPPORT SYST, V35, P45, DOI 10.1016/S0167-9236(02)00097-0 NARENDRA P, 1977, IEEE T COMPUT, V26, P917 Ozgur A., 2005, LECT NOTES COMPUTER, V3733, P607 Ozgur A., 2006, LECT NOTES ARTIF INT, V4314, P91 Park CH, 2008, PATTERN RECOGN LETT, V29, P878, DOI 10.1016/j.patrec.2008.01.003 Pehlivan I, 2011, TURK J ELECTR ENG CO, V19, P59, DOI 10.3906/elk-1002-10 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Selamat A, 2004, INFORM SCIENCES, V158, P69, DOI 10.1016/j.ins.2003.03.003 Tang N., 2001, TEXT CATEGORIZATION Tasci S., 2008, COMP INF SCI ISCIS 0, P1 Tasci S, 2006, COMPUTER ENG Tin J., 1998, INT C NEUR INF PROC Torkkola K, 2003, PATTERN ANAL APPL, V6, P301, DOI 10.1007/s10044-003-0196-8 Vapnik V., 1998, STAT LEARNING THEORY Wang JY, 2004, PROC INT CONF DATA, P79, DOI 10.1109/ICDE.2004.1319986 Yang Y., 1997, ICML, V97, P412 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Yang Y., 1997, P 14 INT C MACH LEAR, V97, P412 Ye JP, 2005, IEEE T PATTERN ANAL, V27, P929 Yizhang G., 2010, NEW ADV MACHINE LEAR, P49 Yu B, 2008, KNOWL-BASED SYST, V21, P900, DOI 10.1016/j.knosys.2008.03.045 Zhang Y, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S2-S27 NR 47 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2164-8263 BN 978-1-4799-2571-1 J9 IEEE INT ADV COMPUT PY 2014 BP 561 EP 567 PG 7 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC0ZG UT WOS:000349866100102 ER PT B AU Godert, W Hubrich, J Nagelschmidt, M AF Godert, W Hubrich, J Nagelschmidt, M TI Semantic Knowledge Representation for Information Retrieval SO SEMANTIC KNOWLEDGE REPRESENTATION FOR INFORMATION RETRIEVAL LA English DT Book ID VECTOR-SPACE MODEL; SEARCH; CLASSIFICATION; SERVICES; TERMS; WEB CR Almeida MB, 2013, J AM SOC INF SCI TEC, V64, P1682, DOI 10.1002/asi.22861 American Library Association / Subcommittee on Subject Relationships/ Reference Structures, 1997, FIN REP ALCTS CCS SU [Anonymous], 2006, 132502 ISO [Anonymous], 1974, 2788 ISO [Anonymous], 2005, 87232 BS [Anonymous], 2011, 259641 ISO [Anonymous], 1985, 5963 ISO [Anonymous], 2013, 259642 ISO [Anonymous], 2007, VER 3 4 [Anonymous], 1999, 13250 ISO [Anonymous], 1985, 5964 ISO ANSI/NISO, 2005, GUID CONSTR FORM MAN Antoniou Grigoris, 2008, SEMANTIC WEB PRIMER ASIS& T, 2005, TECHN LIBR Assem M. v., 2006, SEMANTIC WEB RES APP, P95 Austin D., 1976, CLASSIFICATION 1970S, P158 AUSTIN D, 1969, J LIBR, V1, P149, DOI 10.1177/096100066900100302 Austin D., 1987, ENCY LIB INFORMTI S7, V42, P375 Austin D., 2009, ENCY LIB INFORM SCI, P4251 Austin D., 1984, PRECIS MANUAL CONCEP Austin D., 1976, VARIETY LIBRARIANSHI, P19 Austin D., 1969, CATALOGUE INDEX, V14, P11 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Bayer O., 2005, INFORM SERVICES USE, V25, P181 Bean C. A., 2001, RELATIONSHIPS ORG KN Beckett D., 2004, RDF XML SYNTAX SPECI Begthol C., 1986, LIBR RESOUR TECH SER, V30, P109 Berners-Lee T., 2011, TURTLE TERSE RDF TRI Berners-Lee T., 2001, SCI AM, V284, P35, DOI DOI 10.1038/SCIENTIFICAMERICAN0501-34 Berners-Lee T, 2011, NOTATION3 N3 READABL Bibel W., 1993, WISSENSREPRASENTATIO Bizer C., 2009, INT J SEMANT WEB INF, V5, P1 Boteram F., 2008, SEMANTISCHE RELATION Boteram F, 2009, THEMATISCHE RECHERCH Boteram F., 2011, CONCEPTS IN CONTEXT Boteram F, 2010, KNOWL ORGAN, V37, P216 Bray T., 2009, NAMESPACES XML 1 0 W Bray T., 2008, EXTENSIBLE MARKUP LA Broughton V., 2001, New Review of Hypermedia and Multimedia, V7, DOI 10.1080/13614560108914727 Buchanan B., 1979, THEORY LIB CLASSIFIC BUCKLAND M, 1994, J AM SOC INFORM SCI, V45, P12, DOI 10.1002/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chan L. M., 2004, J AM SOC INFORM SCI, V55, P377 Chan L. M., 1985, THEORY SUBJECT ANAL, P154 CHAPLAN MA, 1995, LIBR QUART, V65, P39 Classification Research Group, 1969, CLASS INF CONTR PAP Clavel-Merrin G., 2006, REPORT CROSS LANGUAG Coates E. J., 1969, CLASSIFICATION INFOR, P19 Connolly D. W., 2006, WEB NAMING ADDRESSIN Cool C., 2011, INTERACTIVE INFORM S, P1 Cruse DA, 2002, INFO SCI KNOW MANAGE, V3, P3 de Bruijn J., 2003, DERI20031029 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Denton W., MAKE FACETED CLASSIF Dextre Clarke S. G., 2011, ENCY LIB INFORM SCI, P3164 Dextre-Clarke S., 2011, CONCEPTS IN CONTEXT, P91 Dunsire G, 2010, KNOWL ORGAN, V37, P280 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Ellis D., 1996, PROGR PROBLEMS INFOR Euzenat J., 2010, ONTOLOGY MATCHING Fellbaum C, 1998, WORDNET ELECT LEXICA Foskett D. J., 1970, CLASSIFICATION GEN I Fuhr N., 2003, P 26 ANN INT ACM SIG, P290 Garshol L. M., 2002, WHAT ARE TOPIC MAPS Garshol L. M., TOLOG TOPIC MAP QUER Garshol LM, 2004, J INFORM SCI, V30, P378, DOI 10.1177/0165551504045856 Geroimenko V., 2003, DICT XML TECHNOLOGIE Godert W., 2011, INFORMATIONSERSCHLIE Godert W., 2008, NEW PERSPECTIVES SUB, P233 Godert W., 2010, INFORM WISSENSCHAFT, V61, P19 GODERT W, 1987, NACHR DOK, V38, P91 Godert W., 2010, INFORM WISSENSCHAFT, V61, P5 Godert W., 2007, WAHRE INFORM, P233 Green R., 2006, KNOWLEDGE ORG INFORM, P111 Green R., 2008, CULT ID KNOWL ORG P, P8 Green R., 2011, CLASSIFICATION ONTOL, P161 Grossman D. A., 2004, INFORM RETRIEVAL ALG GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Harken S. E., 2006, SUBJECT SEMANTIC INT Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 Hodge G., 2000, SYSTEMS KNOWLEDGE OR Hoppenbrouwers J., 2001, MACS MULTILINGUAL AC Horridge M., 2012, 11 W3C Hubrich J., 2011, CONCEPTS IN CONTEXT, P69 Hubrich J., 2010, NEXT GENERATION ANGE, P234 Hubrich J., 2013, WISSEN WISSENSCHAFT, P161 Hunting S., 2003, XML TOPIC MAPS CREAT, P81 IFLA Study Group on the Functional Requirements for Bibliographic Records, 2009, FUNCT REQ BIBL REC F IFLA Study Group on the Functional Requirements for Bibliographic Records, 1998, FUNCT REQ BIBL REC F INFODATA Thesaurus, 2000, INFODATA THES VERS O Ingwersen P., 1992, INFORM RETRIEVAL INT Isaac A., 2009, SKOS SIMPLE KNOWLEDG Jacobs J. H., 2011, CONCEPTS IN CONTEXT, P37 Jacobs JH, 2010, ADV KNOW OR, V12, P236 Jahns Y., 2011, CONCEPTS IN CONTEXT, P51 Jouis C, 2002, INFO SCI KNOW MANAGE, V3, P127 Kekalainen J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290978 Kohler J, 2006, KNOWL-BASED SYST, V19, P744, DOI 10.1016/j.knosys.2006.04.015 Kowalski G, 2011, INFORMATION RETRIEVAL ARCHITECTURE AND ALGORITHMS, P1, DOI 10.1007/978-1-4419-7716-8 Landry P., 2000, P WORKSH ADV TECHN D, P34 Lenat D., 1990, BUILDING LARGE KNOWL Lenat D. B., 1995, Communications of the ACM, V38 Lepsky K., 2006, ABI-Technik, V26, DOI 10.1515/ABITECH.2006.26.1.18 McCulloch E, 2008, J INF SCI, V34, P70, DOI 10.1177/0165551507079130 MADS/RDF, 2012, MADS RDF PRIM Mandala R., 1998, COLING ACL 98 WORKSH, P31 Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979 Matuszek C., 2006, FOM COMP BACKGR KNOW, P44 Meyer A., 2013, INFORM WISSENSCHAFT, V64, P115 Meyer CD, 2006, GOOGLES PAGERANK SCI Michel D., 1997, TAXONOMY SUBJECT REL Miles A., 2004, SKOS MAPPING VOCABUL Miles A., 2009, SKOS SIMPLE KNOWLEDG Mills J., 1992, BLISS BIBLIO CLASSIF Mitchell J. S., 2011, CLASSIFICATION ONTOL, P241 Mitchell JS, 2001, INFO SCI KNOW MANAGE, V2, P211 Moens M.-F., 2002, AUTOMATIC INDEXING A Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 Motik B., 2012, OWL 2 WEB ONTOLOGY L, Vsecond Murphy M. L., 2008, SEMANTIC RELATIONS L Nicholson D., 2001, New Review of Information Networking, V7 Nicholson D., 2003, International Cataloguing and Bibliographic Control, V32 Nicholson D. M., 2006, Cataloging & Classification Quarterly, V42, DOI 10.1300/J104v42n03_08 Odgen C. K., 1923, MEANING MEANING STUD Panzer M., 2011, CONCEPTS IN CONTEXT, P157 Paralic J., 2003, P 14 INT C INF INT S, P23 Park J., 2003, XML TOPIC MAPS CREAT Pepper S., 2002, TAO TOPIC MAPS FINDI Pepper S., 2006, SURVEY RDF TOPIC MAP Pepper S., 2001, XML TOPIC MAPS XTM 1 Pepper S., 2006, GUIDELINES RDF TOPIC Pepper S., 2010, ENCY LIB INFORM SCI, P5247 PERREAULT J, 1994, KNOWL ORGAN, V21, P189 Peterson D., 2012, XML SCHEMA DEFINIT 2 Petras V., 2009, INT CATALOGUING BIBL, V38, P43 Pribbenow S, 2002, INFO SCI KNOW MANAGE, V3, P35 Probets S., 2010, ASLIB P, V62, P70 Putkey T., 2011, LIB PHILOS PRACTICE Ranganathan S. R., 1987, COLON CLASSIFICATION, V1 Rasmussen E., 2011, INTERACTIVE INFORM S, P95 Rath H., 2003, XML TOPIC MAPS CREAT, P357 Reiner U., 2008, NEW PESPECTIVES SUBJ, P121 Reiner U, 2008, ST CLASS DAT ANAL, P697, DOI 10.1007/978-3-540-78246-9_82 Riesthuis G. J. A., 2009, IFLA PROFESSIONAL RE, V115 Riesthuis G. J. A., 2008, NEW PERSPECTIVES SUB, P257 Rijsbergen C. J. v., 1979, INFORM RETRIEVAL Robertson S. E., 1997, READINGS INFORM RETR, P281 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shiri AA, 2002, J INFORM SCI, V28, P111, DOI 10.1177/0165551024234011 Shudi G., 2012, XML SCHEMA DEFINIT 1 Smiraglia R., 2011, CLASSIFICATION ONTOL, P25 Stock W. G., 2013, HDB INFORM SCI Stock WG, 2010, J AM SOC INF SCI TEC, V61, P1951, DOI 10.1002/asi.21382 Stojanovic N., 2005, THESIS U KARLSRUHE Stuckenschmidt H, 2004, IEEE INTELL SYST, V19, P34, DOI 10.1109/MIS.2004.9 Summers E., 2008, METADATA SEMANTIC SO, P25 Svenonius E., 2000, INTELLECTUAL FDN INF Tudhope D., 2001, J DIGITAL INFORM, V1 Ullrich M., 2003, ONTOPRISE WHITEPAPER Vatant B., 2003, XML TOPIC MAPS CREAT, P67 Vizine-Goetz D., 2004, J DIGITAL INFORM, V4 Weller K., 2008, RECONSIDERING RELATI White R. W., 2009, EXPLORATORY SEARCH Q World Wide Web Consortium, 2007, SEM LAYER CAK World Wide Web Consortium OWL Working Group, 2009, OML 2 ONT LANG OV W3 Zapilko B., 2009, TECHNICAL REPORTS GE, V2009/7 Zeng M., 2009, SEMANTIC INTEROPERAB, P3 Zeng M. L., 2011, FUNCTINAL REQUIREMEN Zeng M. L., 2011, P ISKO UK BIENN C 4 Zumer M., 2011, CONCEPTS IN CONTEXT, P149 Zumer Maja, 2011, FUNCTIONAL REQUIREME NR 172 TC 0 Z9 0 PU WALTER DE GRUYTER GMBH PI BERLIN PA GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY BN 978-3-11-032970-4; 978-3-11-030477-0 PY 2014 BP 1 EP 294 DI 10.1515/9783110329704 PG 294 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BB9MR UT WOS:000348499800013 ER PT S AU Souza, C Magalhaes, J Costa, E Fechine, J Reis, R AF Souza, Cleyton Magalhaes, Jonathas Costa, Evandro Fechine, Joseana Reis, Ruan BE Murgante, B Misra, S Rocha, AMAC Torre, C Rocha, JG Falcao, MI Taniar, D Apduhan, BO Gervasi, O TI Enhancing the Status Message Question Asking Process on Facebook SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2014, PT IV SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th International Conference on Computational Science and Its Applications (ICCSA) CY JUN 30-JUL 03, 2014 CL Guimaraes, PORTUGAL SP Univ Minho, Univ Perugia, Univ Basilicata, Monash Univ, Kyushu Sangyo Univ, Assoc Portuguesa Investigacao Operac DE Social Search; Social Query; Social Network; Status Message Question Asking ID SEARCH; MODEL AB People have been using Social Networks to search for help by broadcasting messages that reflect their information needs. However, several factors, usually not considered by the user, influence the outcome of receiving or not an answer. In this work, we aim to increase the users' chances of finding someone who could help them. For this purpose, we propose a mobile app called Social Query, which guides the users through some steps before they share the problem with their friends. As far as we know, this is the first work to merge these three aspects of the social search: Question Rephrasing, Expert Search Filtering and Expertise Finding. To evaluate our proposal, we ran a questionnaire in which users considered Useful most functions of the app. C1 [Souza, Cleyton; Magalhaes, Jonathas; Fechine, Joseana; Reis, Ruan] Fed Univ Campina Grande UFCG, Lab Artificial Intelligence LIA, Campina Grande, PB, Brazil. RP Souza, C (reprint author), Fed Univ Campina Grande UFCG, Lab Artificial Intelligence LIA, Campina Grande, PB, Brazil. CR Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Comarela G, 2012, P 23 ACM C HYP SOC M, P123 Davitz J, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P931 de Souza CC, 2012, LECT NOTES COMPUT SC, V7335, P714 Furlan B., 2012, 2012 6 IEEE INT C IN, P014 Ghosh Saptarshi, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348361 Gray R., 2013, P 2013 C COMP SUPP C, P1213, DOI DOI 10.1145/2441776.2441913 Horowitz D., 2010, P 19 INT C WORLD WID, P431, DOI DOI 10.1145/1772690.1772735 Karch M., 2010, GOOGLE LABS AARDVARK Lin CY, 2009, PROC INT CONF DATA, P1483 Macdonald C, 2009, COMPUT J, V52, P729, DOI 10.1093/comjnl/bxm112 Magid L., 2014, FACEBOOK TWEAKS NEWS Manoranjitham G., 2013, INT J ADV RES COMPUT, V2, P3620 Morris MR, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1739 Nichols J., 2012, P ACM C COMP SUPP CO, P999 Oeldorf-Hirsch A., 2014, P 17 ACM C COMP SUPP, P16 Ross M., 2014, FACEBOOK TURNS 10 WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Souza C., 2013, P 8 INT C INT WEB AP, P147 Souza C, 2013, 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P109, DOI 10.1109/WI-IAT.2013.16 Teevan J., 2011, ICWSM Tomiyasu H., 2006, P 7 INT C MOB DAT MA, P105 Wayne T., 2009, SOCIAL NETWORKS ECLI NR 23 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-09147-1; 978-3-319-09146-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8582 BP 682 EP 695 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BC0TK UT WOS:000349533900049 ER PT S AU Baazaoui-Zghal, H Besbes, G AF Baazaoui-Zghal, Hajer Besbes, Ghada BE Kontchakov, R Mugnier, ML TI Ontology-Based Answer Extraction Method SO Web Reasoning and Rule Systems, RR 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Conference on Web Reasoning and Rule Systems (RR) CY SEP 15-17, 2014 CL Athens, GREECE SP NSF, Google, Artificial Intelligence Journal, Oracle, Optique, ICCSNTUA, EETN, Inria, Siemens DE Answer extraction; ontology; lexico-syntactic patterns AB Question Answering (QA) systems aim at providing answers to Natural Language questions in an open domain context and can provide a solution to the problem of response accuracy. This paper describes an answer extraction method based on ontologies. Our goal consists on performing an efficient question answering by extracting answers based on the question graph, lexico-syntactic patterns and score computation. C1 [Baazaoui-Zghal, Hajer; Besbes, Ghada] ENSI Campus Univ Manouba, Riadi, Tunis, Tunisia. RP Baazaoui-Zghal, H (reprint author), ENSI Campus Univ Manouba, Riadi, Tunis, Tunisia. EM hajer.baazaouizghal@riadi.rnu.tn; ghada.besbes@gmail.com CR Besbes G., 2013, LNCS, V8132, P100 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 2 TC 0 Z9 0 PU SPRINGER INT PUBLISHING AG PI CHAM PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND SN 0302-9743 BN 978-3-319-11113-1; 978-3-319-11112-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8741 BP 239 EP 240 PG 2 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC0DK UT WOS:000348927200021 ER PT J AU Dave, K Varma, V AF Dave, Kushal Varma, Vasudeva TI Computational Advertising: Techniques for Targeting Relevant Ads SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article AB Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean - content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context. CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party webpages, but the ads in this form are rich multimedia ads - image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising. Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user's probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser's side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above. Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end. C1 [Dave, Kushal; Varma, Vasudeva] Int Inst Informat Technol, LTRC, Hyderabad, Andhra Pradesh, India. RP Dave, K (reprint author), Int Inst Informat Technol, LTRC, Hyderabad, Andhra Pradesh, India. EM kushal.dave@research.iiit.ac.in; vv@iiit.ac.in CR *ACM, 2009, ARKDD 09 P 3 WORKSH Agarwal D., 2010, P 16 ACM SIGKDD INT, P213, DOI 10.1145/1835804.1835834 Agarwals D, 2014, P 7 ACM INT C WEB SE, P173 Agrawal R., 2013, P 22 INT C WORLD WID, P13 Ahmed Amr, 2011, P 17 ACM SIGKDD INT, P114 Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 ANASTASAKOS T, 2009, P 18 ACM C INF KNOWL, P1927, DOI 10.1145/1645953.1646267 ANICK P, 2003, P 26 ANN INT ACM SIG, P88 ARAL ASS, 2009, DISTINGUISHING INFLU ASHKAN A, 2009, WI IAT 09 P 2009 IEE, P222 ASHKAN A, 2012, KNOWL INF SYST, P1 AZIZYAN M, 2009, P 15 ANN INT C MOB C, P261, DOI 10.1145/1614320.1614350 BaezaYates R, 2003, LECT NOTES COMPUT SC, V2857, P56 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Banerjee Somnath, 2007, P 30 ANN INT ACM SIG, P787, DOI 10.1145/1277741.1277909 BARFORD P, 2014, P 23 INT C WORLD WID, P597 Becker H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P656, DOI 10.1145/1571941.1572063 BEN, 2011, SSP DSP COOKIE SYNCH BENDERSKY M, 2010, P 19 INT C WORLD WID, P101, DOI 10.1145/1772690.1772702 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BHARGAVA HK, 2002, P 11 INT C WORLD WID, P117 BHATT R, 2010, CIKM 10 BILENKO M, 2009, SIGIR FORUM, V43, P29 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BOUMA G, 2009, P GSCL C Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Broder A., 2009, P 18 INT C WORLD WID, P511, DOI 10.1145/1526709.1526778 Broder A, 2008, PROCEEDINGS OF THE NINETEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P992 BRODER A, 2010, P 33 INT ACM SIGIR C, P908, DOI 10.1145/1835449.1835680 Broder A., 2008, P 17 ACM C INF KNOWL Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Broder Andrei Z., 2008, P 17 ACM C INF KNOWL, P1013, DOI DOI 10.1145/1458082.1458217 Brown P. F., 1993, Computational Linguistics, V19 Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 Burges C. J., 2006, NIPS, V6, P193 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 CARRASCO JJ, 2003, CLUSTERING BIPARTITE CHAKRABARTI D, 2008, WWW 08 P 17 INT C WO, P417 CHAPELLE O, 2011, NIPS, P2249 Chapelle Olivier, 2009, WWW 2009, P1 CHEN BW, 2014, P 20 ACM SIGKDD C KN Chen W., 2010, P 16 ACM SIGKDD INT, P1029, DOI DOI 10.1145/1835804.1835934 Chen W, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P199, DOI 10.1145/1557019.1557047 Chen Y., 2011, P 17 ACM SIGKDD INT, P1307 Chen-Guang He, 2010, Proceedings 2010 IEEE Youth Conference on Information, Computing and Telecommunications (YC-ICT 2010), DOI 10.1109/YCICT.2010.5713117 Cheng Haibin, 2012, P 18 ACM SIGKDD INT, P777 CHOI YJ, 2010, WWW 10 ACM, P251 Ciaramita M., 2008, P 17 INT C WORLD WID, P227, DOI 10.1145/1367497.1367529 COSSOCK D, 2006, P 19 ANN C LEARN THE, V4005, P605 Craswell Nick, 2008, WSDM 2008, P87 Dave K.S., 2010, P 19 ACM INT C INF K, P1885, DOI 10.1145/1871437.1871754 DAVE KS, 2011, ICWSM DAVE KS, 2012, ICWSM Dave V, 2012, ACM SIGCOMM COMP COM, V42, P175, DOI 10.1145/2377677.2377715 DEBMBSCZYNSKI K, 2008, WORKSH TARG RANK ONL DEMBCZYNSKI K, 2008, P 9 INT C ART INT SO, V5097, P533 DENG HB, 2009, SIGIR 09, P339 Domingos P, 2012, COMMUN ACM, V55, P78, DOI 10.1145/2347736.2347755 Domingos P., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining DUONG Q, 2011, LECT NOTES COMPUTER, V7090, P134 Dupret G. E., 2008, P 31 ANN INT ACM SIG, P331, DOI 10.1145/1390334.1390392 EDELMAN B, 2010, TECHNICAL REPORT EDELMAN B, 2005, AM ECON REV, P97 FAIN DC, 2006, 2 WORKSH SPONS SEARC FAN TK, 2009, P 31 EUR C FIR RES A, V5478, P202 FERRAGINA P, 2010, CIKM 2010, P1625 Frank E, 1999, IJCAI 99, V2, P668 FREDRIKSON M, REPRIV REENVISIONING Freund Y., 2003, J MACHINE LEARNING R, V6, P933, DOI 10.1162/jmlr.2003.4.6.933 Freund Y., 1995, DECISION THEORETIC G FULD H, 2014, MOST AMAZING VIRAL C GABRILOVICH E, 2008, P 46 ANN M ASS COMP, P1, DOI 10.3115/1564169.1564170 Ghosh A., 2009, P 18 INT C WORLD WID, P251, DOI 10.1145/1526709.1526744 Ghosh A., 2011, P 12 ACM C EL COMM E, P199 Goodman J., 2006, WWW 06, P213 *GOOGL ADW, 2014, ENH YOUR AD EXT *GOOGL DEV BLOG, 2014, COOK MATCH Goyal Amit, 2010, WSDM, P241 GRAEPEL T, 2010, ICML, P13 Granka L. A., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009079 GUHA S, 2009, P 8 WORKSH HOT TOP N Haddadi H., 2010, P 5 ACM INT WORKSH M, P33, DOI 10.1145/1859983.1859993 HARDT M, 2012, P 2012 ACM C COMP CO, P662 Hartline J., 2008, WWW, P189 Hastie T., 2001, SPRINGER SERIES STAT Herbrich R., 2000, ADV LARGE MARGIN CLA, V88, P115 Hillard Dustin, 2010, P 3 ACM INT C WEB SE, P361, DOI 10.1145/1718487.1718532 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Hulth A, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P216 *IAB, 2014, IAB INTN ADV REV REP IEONG S, 2014, P 23 INT C WORLD WID, P29 *INT ADV BUR, 2014, INT ADV REV HIT ALL Jansen Bernard J, 2008, International Journal of Electronic Business, V6, DOI 10.1504/IJEB.2008.018068 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN JAWORSKA J, 2008, P 9 INT C WEB INF SY, V5175, P62 Joachims T., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 Karimi M, 2011, PROCEEDINGS OF THE 2011 3RD INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGY AND ENGINEERING (ICSTE 2011), P377 KAUFMANN E, 2012, P 23 INT C ALG LEARN, P199 Kempe D., 2003, KDD 03, P137, DOI DOI 10.1145/956750.956769 KIRMANI A, 1991, ADV CONSUM RES, V18, P414 Kleinberg J.M., 1998, SODA 98, P668 Kolcz A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Koller D., 2009, PROBABILISTIC GRAPHI Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944 Korolova A, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010), DOI 10.1109/ICDMW.2010.137 KUSHAL S, 2010, P 33 INT ACM SIGIR C, P897 KUSHAL S, 2012, P WORKSH MACH LEARN LACERDA A, 2006, SIGIR 06, P549 Lafferty J.D., 2001, P INT C MACH LEARN, V18, P282 Leskovec J., 2006, P 7 ACM C EL COMM EC, P228, DOI 10.1145/1134707.1134732 LI P, 2007, ADV NEURAL INFORM PR Li X, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P572, DOI 10.1145/1571941.1572039 LI Y, 2007, SIGKDD EXPLOR NEWSL, V9, P96 Li YM, 2009, ELECTRON COMMER R A, V8, P16, DOI 10.1016/j.elerap.2008.06.001 LIU KM, 2008, P WORKSH TARG RANK O Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] LU H, 2009, P 7 INT C MOB SYST A, P165, DOI 10.1145/1555816.1555834 McMahan H. B., 2013, P 19 ACM SIGKDD INT, P1222 Menon Aditya Krishna, 2011, P 17 ACM SIGKDD INT, P141 Mihalcea Rada, 2007, P 16 ACM C INF KNOWL, P233, DOI DOI 10.1145/1321440.1321475 Milne D., 2008, P AAAI 2008 Milne D., 2008, CIKM 08, P509, DOI DOI 10.1145/1458082.1458150 Miluzzo E., 2010, P 8 INT C MOB SYST A, P5, DOI 10.1145/1814433.1814437 Montague M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Nath A., 2013, P 22 INT C WORLD WID, P943 NETO BR, 2005, SIGIR 05 ACM, P496 OENTARYO RJ, 2014, P 7 ACM INT C WEB SE, P123 *ONEUPWEB, 2005, RELEVANCE FEEDBACK I Page L, 1998, PAGERANK CITATION RA PANDEY V, 2013, DO AD EXCHANGES REAL Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 PAPADIMITRIOU P, 2011, KDD, P1019 Haghirian P., 2007, International Journal of Mobile Communications, V5, DOI 10.1504/IJMC.2007.011489 PATIL A, 2013, P 356 EUR C ADV INF, P780 Phan Xuan-Hieu, 2008, P 17 INT C WORLD WID, P91, DOI DOI 10.1145/1367497.1367510 PIN F, 2011, ACM C EL COMM ACM, P61 Quan XJ, 2010, KNOWL INF SYST, V25, P473, DOI 10.1007/s10115-009-0250-y Radlinski F., 2008, P 31 ANN INT ACM SIG, P403, DOI DOI 10.1145/1390334.1390404 RAGHAVAN H, 2010, P 19 ACM INT C INF K, P1019, DOI 10.1145/1871437.1871567 RAGHAVAN H, 2009, SIGIR 09, P762 Reeves C. R., 1993, MODERN HEURISTIC TEC REGELSON M, 2006, EL COMM EC ACM RICHARDSON M, 2007, WWW 2007, P521 Robertson S. E., 1996, OKAPI TREC 3, P109 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M, 2012, P IEEE, V100, P1444, DOI 10.1109/JPROC.2012.2189916 Sculley D., 2011, P 17 ACM SIGKDD INT, P274 SCULLEY D, 2009, P 15 ACM SIGKDD INT, P1325, DOI 10.1145/1557019.1557161 SHAPARENKO B, 2009, ADKDD 09, P46 Shen D, 2004, P 27 ANN INT C RES D, P242, DOI 10.1145/1008992.1009035 Shen D, 2005, SIGKDD EXPLOR NEWSL, V7, P100 SHEN D, 2008, SIGKDD EXPLOR NEWSL, V10, P47 Shen FY, 2002, J ADVERTISING, V31, P59 Shen LB, 2005, MACH LEARN, V60, P73, DOI 10.1007/s10994-005-0918-9 SINCLAIR L, 2011, CLICK FRAUD RAMPANT Singla P., 2008, WWW 08, P655 Smola A., 2010, P VLDB ENDOWMENT, V3, P703 SPARCKJONES K, 1972, J DOC, V28, P11 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Spitkovsky V. I., 2012, P 8 INT C LANG RES E TAGAMI Y, 2013, P 17 INT WORKSH DAT Tang J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P807 Thompson WR, 1933, BIOMETRIKA, V25, P285, DOI 10.2307/2332286 Toubiana V., 2010, NDSS Tsang MM, 2004, INT J ELECTRON COMM, V8, P65 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 TUZHILIN A, 2008, CLICK FRAUD RAMPANT VAIDYA V, 2010, COOKIE SYNCHING Wang C., 2002, 8 AM C INF SYST, P1143 WANG HF, 2009, P 32 INT ACM SIGIR C, P51 WANG J, 2013, CIKM 2013 WANG J, 2014, P 20 ACM SIGKDD INT Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 WEISS S, 2003, LIGHTWEIGHT RULE IND Widdows D, 2010, Proceedings 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), DOI 10.1109/ICSC.2010.94 WILLIAM W, 1999, P 16 NAT C ART INT, P335 Wu X., 2008, P 17 INT C WORLD WID, P1195, DOI 10.1145/1367497.1367723 Xu J., 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809 XU WH, 2010, P 33 INT ACM SIGIR C, P106, DOI 10.1145/1835449.1835470 YAN J, 2009, WWW 09, P261 Yandong Liu, 2012, P 5 ACM INT C WEB SE, P473 YI Y, 1990, COGNITIVE AFFECTIVE YIN DW, 2014, P 7 ACM INT C WEB SE, P113 Yu K, 2006, P 23 INT C MACH LEAR, V148, P1081 YU XH, 2009, P 1 INT WORKSH KEYW, P21, DOI 10.1145/1557670.1557680 YUAN S, 2013, P 7 INT WORKSH DAT M Zhang Wei Vivian, 2007, P 30 SIGIR, P853, DOI 10.1145/1277741.1277942 2012, ADKDD 12 P 6 INT WOR 2013, ADKDD 13 P 7 INT WOR 1965, COMPUTER J, V7, P308 2010, ADKDD 10 P 4 INT WOR 2011, ADKDD 11 P 5 INT WOR NR 197 TC 0 Z9 0 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2014 VL 8 IS 4-5 BP 265 EP + DI 10.1561/1500000045 PG 156 WC Computer Science, Information Systems SC Computer Science GA AZ4QO UT WOS:000348207600001 ER PT S AU Butt, AS Haller, A Xie, LX AF Butt, Anila Sahar Haller, Armin Xie, Lexing BE Mika, P Tudorache, T Bernstein, A Welty, C Knoblock, C Vrandecic, D Groth, P Noy, N Janowicz, K Goble, C TI Ontology Search: An Empirical Evaluation SO SEMANTIC WEB - ISWC 2014, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 13th International Semantic Web Conference (ISWC) CY OCT 19-23, 2014 CL Riva del Garda, ITALY SP Elsevier, Yahoo Labs, Telefonica, Parlance Project, Google, IBM Res, Microsoft Res, LinkedUp Project, Open PHACTS Fdn, Systap, Semant Valley, OpenLink Software, Poolparty, Fluid Operat, News Reader Project, Synerscope, Okkam, SICRaS, Bpeng, Centro Ric GPI, Expert Syst, Informatica Trentina s p a, CNR, Inst Cognit Sci & Technologies, iMinds, Videolectures, Fondazione Bruno Kessler, Semant Web Sci Assoc, Artificial Intelligence Journal ID LINKED DATA; WEB AB Much of the recent work in Semantic Search is concerned with addressing the challenge of finding entities in the growing Web of Data. However, alongside this growth, there is a significant increase in the availability of ontologies that can be used to describe these entities. Whereas several methods have been proposed in Semantic Search to rank entities based on a keyword query, little work has been published on search and ranking of resources in ontologies. To the best of our knowledge, this work is the first to propose a benchmark suite for ontology search. The benchmark suite, named CBRBench(1), includes a collection of ontologies that was retrieved by crawling a seed set of ontology URIs derived from prefix. cc and a set of queries derived from a real query log from the Linked Open Vocabularies search engine. Further, it includes the results for the ideal ranking of the concepts in the ontology collection for the identified set of query terms which was established based on the opinions of ten ontology engineering experts. We compared this ideal ranking with the top-k results retrieved by eight state-of-the-art ranking algorithms that we have implemented and calculated the precision at k, the mean average precision and the discounted cumulative gain to determine the best performing ranking model. Our study shows that content-based ranking models outperform graph-based ranking models for most queries on the task of ranking concepts in ontologies. However, as the performance of the ranking models on ontologies is still far inferior to the performance of state-of-the-art algorithms on the ranking of documents based on a keyword query, we put forward four recommendations that we believe can significantly improve the accuracy of these ranking models when searching for resources in ontologies. C1 [Butt, Anila Sahar; Haller, Armin] CSIRO CCI, Canberra, ACT, Australia. RP Butt, AS (reprint author), CSIRO CCI, Canberra, ACT, Australia. EM anila.butt@csiro.au; armin.haller@csiro.au; lexing.xie@anu.edu.au CR Alani H, 2006, LECT NOTES COMPUT SC, V4273, P1 d'Aquin M, 2011, SEMANT WEB, V2, P55, DOI 10.3233/SW-2011-0031 d'Aquin M, 2009, LECT NOTES COMPUT SC, V5554, P913, DOI 10.1007/978-3-642-02121-3_81 Ding L., 2004, P 13 ACM INT C INF K, P652, DOI 10.1145/1031171.1031289 Fernandez M., 2008, P 2008 IEEE INT C SE, P253, DOI DOI 10.1109/ICSC.2008.52 Gangemi A., 2005, P 2 IT SEM WEB WORKS, V166 Guarino N, 2002, COMMUN ACM, V45, P61 Harth A, 2007, LECT NOTES COMPUT SC, V4825, P211 Hogan A, 2011, J WEB SEMANT, V9, P365, DOI 10.1016/j.websem.2011.06.004 Hogan A., 2006, P 2 WORKSH SCAL SEM Lozano-Tello A, 2004, J DATABASE MANAGE, V15, P1, DOI 10.4018/jdm.2004040101 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Noy N.F., 2012, WEB SEMANTICS SCI SE, V11 Noy N.F., 2009, NUCL ACIDS RES Page L., 1998, P 7 INT WORLD WID WE, P161 Pound J., 2010, P 19 INT C WORLD WID, P771, DOI DOI 10.1145/1772690.1772769 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Thomas E., 2007, CEUR WORKSHOP P, V258 Tummarello G, 2007, LECT NOTES COMPUT SC, V4825, P552 Vandenbussche P.-Y., 2014, ERCIM NEWS, V96, P21 NR 22 TC 0 Z9 0 PU SPRINGER INT PUBLISHING AG PI CHAM PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND SN 0302-9743 BN 978-3-319-11915-1; 978-3-319-11914-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8797 BP 130 EP 147 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB8WG UT WOS:000347622500009 ER PT J AU Nakanishi, T AF Nakanishi, Takafumi GP IEEE TI Semantic Context-dependent Weighting for Vector Space Model SO 2014 IEEE INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) SE IEEE International Conference on Semantic Computing LA English DT Proceedings Paper CT 8th IEEE International Conference on Semantic Computing CY JUN 16-18, 2014 CL Newport Beach, CA DE Semantic computing; context-dependent weighting; vector space model; relative semantics; correlation ID INFORMATION EXTRACTION SYSTEM; RETRIEVAL; SIMILARITY; TEXT AB In this paper, we represent a dynamic context-dependent weighting method for vector space model. A meaning is relatively decided by a context dynamically. A vector space model, including latent semantic indexing (LSI), etc. relatively measures correlations of each target thing that represents in each vector. However, the vectors of each target thing in almost method of the vector space models are static. It is important to weight each element of each vector by a context. Recently, it is necessary to understand a certain thing by not reading one data but summarizing massive data. Therefore, the vectors in the vector space model create from data set corresponding to represent a certain thing. That is, we should create vectors for the vector space model dynamically corresponding to a context and data distribution. The features of our method are a dynamic calculation of each element of vectors in a vector space model corresponding to a context. Our method reduces a vector dimension corresponding to context by context-depending weighting. Therefore, We can measure correlation with low calculation cost corresponding to context because of dimension deduction. C1 Int Univ Japan, Ctr Global Commun GLOCOM, Minamiuonuma, Niigata, Japan. RP Nakanishi, T (reprint author), Int Univ Japan, Ctr Global Commun GLOCOM, Minamiuonuma, Niigata, Japan. EM takafumi@glocom.ac.jp CR Adrian B, 2009, LECT NOTES ARTIF INT, V5803, P249 Buitelaar P., 2006, P INT C LANG RES EV, P2321 Cimiano P., 2004, P 13 INT C WORLD WID, P462, DOI 10.1145/988672.988735 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Embley D., 2004, P 15 AUSTR DAT C ADC, V27, P3 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 Harman D., 1992, INFORMATION RETRIEVA, P363 Hwang C., 1999, P 6 INT WORKSH ONT B KIM YW, 1990, J DOC, V46, P113, DOI 10.1108/eb026857 Kitagawa T., 1993, RIDE IMS 93, P130 Kiyoki Y., 1994, SIGMOD Record, V23 Kolda T. G., 1998, J ACM T INFORM SYSTE, V16, P322 LEE JH, 1993, J DOC, V49, P188, DOI 10.1108/eb026913 Li Y., 2007, P 16 INT C WORLD WID, P777, DOI DOI 10.1145/1242572.1242677 Maedche A., 2000, SOFTW DEM 8 INT C CO Maedche A, 2003, STUD FUZZ SOFT COMP, V111, P345 McDowell LK, 2006, LECT NOTES COMPUT SC, V4273, P428 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G. A., 1990, J LEXICOGRAPHY, V3, P235 POPOV B, 2004, NAT LANG ENG, V10, P375, DOI 10.1017/S135132490400347X Resnik P, 1995, INT JOINT CONF ARTIF, P448 Saggion H, 2007, LECT NOTES COMPUT SC, V4825, P843 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Takano K., 2007, ACST 07, P510 Todirascu A, 2002, LECT NOTES COMPUT SC, V2553, P64 Vargas-Vera M., 2001, P WORKSH KNOWL MARK Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 Wu Fei, 2008, P 14 ACM SIGKDD INT, P731, DOI 10.1145/1401890.1401978 Yildiz B, 2007, LECT NOTES COMPUT SC, V4707, P660 NR 30 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2325-6516 BN 978-1-4799-4003-5 J9 IEEE INT C SEMANT CO PY 2014 BP 262 EP 266 DI 10.1109/ICSC.2014.49 PG 5 WC Computer Science, Artificial Intelligence SC Computer Science GA BB8AI UT WOS:000346176100047 ER PT J AU Candea, GS Kifor, CV Candea, C Constantinescu, C AF Candea, G. S. Kifor, C. V. Candea, C. Constantinescu, C. BE Popescu, D TI CASE BASED REASONING WITHIN FAILURE MODE AND EFFECT ANALYSIS CONTEXT SO 2014 INTERNATIONAL CONFERENCE ON PRODUCTION RESEARCH - REGIONAL CONFERENCE AFRICA, EUROPE AND THE MIDDLE EAST AND 3RD INTERNATIONAL CONFERENCE ON QUALITY AND INNOVATION IN ENGINEERING AND MANAGEMENT (ICPR-AEM 2014) LA English DT Proceedings Paper CT International Conference on Production Research - Regional Conference Africa, Europe and the Middle East (ICPR-AEM) / 3rd International Conference on Quality and Innovation in Engineering and Management (QIEM) CY JUL 01-05, 2014 CL Cluj Napoca, ROMANIA SP IFPR, Tech Univ Cluj Napoca DE FMEA; Knowledge reuse; CBR AB Failure Mode and Effect Analysis (FMEA) is among the most widely used safety analysis procedures in the various industries. The procedure is generally perceived as complex and time-consuming, hindering an effective reuse of previous knowledge. In this paper we present a system that will improve the quality by capturing and reusing the knowledge circulated inside product design and development phase. The system proposed is an easy to use and proactive system that will complement and support the team members during the product design and development phase through the whole process in order to meet the quality requirements. There are tested and evaluated four algorithms used in textual case based reasoning and the conclusions are presented. C1 [Candea, G. S.; Kifor, C. V.] Univ Lucian Blaga Sibiu, Sibiu 550024, Romania. RP Candea, GS (reprint author), Univ Lucian Blaga Sibiu, Bd Ul Victoriei 10, Sibiu 550024, Romania. CR AAMODT A, 1994, AI COMMUN, V7, P39 Berry M., 1994, SIAM C PAR PROC SCI, P159 Chakraborti S, 2006, LECT NOTES ARTIF INT, V4106, P400 Deerwester S., 1990, J AM SOC INFORM SCI, V41 Lenz M., 1996, 12 ECAI 1996, P127 McAdam R, 2000, J KNOWLEDGE MANAGEME, V5, P231, DOI DOI 10.1108/13673270110400870 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wong K. Y., 2004, KNOWLEDGE PROCESS MA, V11, P93, DOI DOI 10.1002/KPM.193 NR 8 TC 0 Z9 0 PU TECHNICAL UNIV CLUJ-NAPOCA PI CLUJ-NAPOCA PA 28 MEMORANDUMULUI STREET, CLUJ-NAPOCA, 400114, ROMANIA BN 978-973-662-978-5 PY 2014 BP 59 EP 64 PG 6 WC Engineering, Industrial; Engineering, Manufacturing; Operations Research & Management Science SC Engineering; Operations Research & Management Science GA BB8DD UT WOS:000346410700011 ER PT J AU Sotiropoulos, DN Kounavis, CD Kourouthanassis, P Giaglis, GM AF Sotiropoulos, Dionisios N. Kounavis, Chris D. Kourouthanassis, Panos Giaglis, George M. GP IEEE TI What drives social sentiment? An entropic measure-based clustering approach towards identifying factors that influence social sentiment polarity SO 5TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS AND APPLICATIONS, IISA 2014 LA English DT Proceedings Paper CT 5th International Conference on Information, Intelligence, Systems and Applications (IISA) CY JUL 07-09, 2014 CL Chania, GREECE SP Inst Elect & Elect Engineers, Biol & Artificial Intelligence Fdn, Univ Piraeus DE Sentiment Analysis; Topic Modelling; Entropic Measure-based Clustering; Support Vector Machines ID MEDIA AB Analyzing the public sentiment over social media streams constitutes an extremely demanding task mainly due to the difficulties that are imposed by the wide spectrum of discussion topics that underlie a given collection of posts. This paper addresses the problem of determining the underlying semantic factors that influence the social sentiment polarity in a given corpus of posts through the utilization of an entropic measure-based clustering approach. Extant studies examine the semantic structure of social network data primarily through topic modeling or sentiment analysis methods. The novelty of our approach lies upon the utilization of a semantically-aware clustering procedure that effectively combines topic modeling and sentiment analysis algorithms. Our approach extends the fundamental assumption behind traditional sentiment analysis methods, according to which sentiment can be associated with low level document features such as words, phrases or sentences. We argue that sentiment can be associated with higher level entities such as the semantic axes that span a given volume of posts, thus performing sentiment analysis at the topic level. Our experimentation provides strong evidence that combining topic modeling and sentiment analysis results by a semantically-aware clustering procedure can reveal the distribution of the overall public sentiment on the underlying semantic axes. CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bollen J, 2011, J COMPUT SCI-NETH, V2, P1, DOI 10.1016/j.jocs.2010.12.007 Bollen Johan, 2011, IEEE COMPUT, V44, P91, DOI 10.1109/MC.2011.323 Cambria E, 2012, MULTIMED TOOLS APPL, V59, P557, DOI 10.1007/s11042-011-0815-0 Cheong M, 2011, INFORM SYST FRONT, V13, P45, DOI 10.1007/s10796-010-9273-x Hong S, 2012, GOV INFORM Q, V29, P455, DOI 10.1016/j.giq.2012.06.004 Jansen BJ, 2009, P 27 INT C HUM FACT, P3859, DOI 10.1145/1520340.1520584 Luo XM, 2013, INFORM SYST RES, V24, P146, DOI 10.1287/isre.1120.0462 MISHNE G, 2006, AAAI 2006 SPRING S C, V30, P301 O'Connor B., 2010, P INT AAAI C WEBL SO, P122 Paltoglou G., 2012, ACM T INTEL SYST TEC, V3, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *SUP, SUPERBOWL AN TAK US Thelwall M, 2011, J AM SOC INF SCI TEC, V62, P406, DOI 10.1002/asi.21462 Tumasjan A., 2010, P 4 INT AAAI C WEBL, P178 Vapnik V., 1995, NATURE STAT LEARNING NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA PY 2014 BP 361 EP + PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BB7PA UT WOS:000345861900102 ER PT S AU Le, VT Tran-Nguyen, TM Pham, KN Do, NT AF Le, Van T. Tran-Nguyen, Thu M. Pham, Khang N. Do, Nghi T. BE Dang, TK Wagner, R Neuhold, E Takizawa, M Kung, J Thoai, N TI Forests of Oblique Decision Stumps for Classifying Very Large Number of Tweets SO FUTURE DATA AND SECURITY ENGINEERING, FDSE 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 1st International Conference on Future Data and Security Engineering (FDSE) CY NOV 19-21, 2014 CL Ho Chi Minh, VIETNAM HO HCMC Univ Technol, Fac Comp Sci & Engn DE Sentiment analysis in Twitter; the bag-of-words model; oblique decision stump forests; supervised learning ID LINEAR CLASSIFICATION AB Our investigation aims at constructing oblique decision stump forests to classify very large number of twitter messages (tweets). Twitter sentiment analysis is not a trivial task because tweets are short and getting generated at very fast rate. Supervised learning algorithms can thus be useful to automatically detect positive or negative sentiments. The pre-processing step performs the cleaning tasks and the representation of tweets using the bag-of-words model (BoW). And then we propose oblique decision stump forests based on the linear support vector machines (SVM) that is suitable for classifying large amounts of high dimensional datapoints. The experimental results on twittersentiment.appspot.com corpora (with 1,600,000 tweets) show that our oblique decision stump forests are efficient compared to baseline algorithms. C1 [Le, Van T.] Univ Technol, VNUHCM, Fac Comp Sci & Engn, Ho Chi Minh City, Vietnam. RP Le, VT (reprint author), Univ Technol, VNUHCM, Fac Comp Sci & Engn, 268 Ly Thuong Kiet St,Dist 10, Ho Chi Minh City, Vietnam. EM ltvan@cse.hcmut.edu.vn; tnmthu@cit.ctu.edu.vn; pnkhang@cit.ctu.edu.vn; dtnghi@cit.ctu.edu.vn CR Agarwal A., 2011, P WORKSH LANG SOC ME, P30 Barbosa L., 2010, P INT C COMP LING CO Berger AL, 1996, COMPUT LINGUIST, V22, P39 Bifet A, 2010, LECT NOTES ARTIF INT, V6332, P1, DOI 10.1007/978-3-642-16184-1_1 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bottou L., 2008, ADV NEURAL INFORM PR, V20, P161 Breiman L, 1998, ANN STAT, V26, P801 Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350 Breiman L., 1984, CLASSIFICATION REGRE Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Dietterich T., 1995, TECHNICAL REPORT Do T.-N., 2014, ADV APPROACHES INTEL, V551, P25 Do TN, 2010, STUD COMP INTELL, V292, P39 Do T.-N., 2004, P 6 INT C ENTR INF S, P36 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fan RE, 2008, J MACH LEARN RES, V9, P1871 Fellbaum C, 1998, WORDNET ELECT LEXICA Freund Y., 1999, Journal of Japanese Society for Artificial Intelligence, V14 Go A., TWITTER SENTIMENT Go A., 2009, PROCESSING, P1 Harris ZS, 1954, WORD, V10, P146 Hassan S., 2012, SENTIMENT ANAL MICRO Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Joachims T., 1998, LNCS, V1398, P137 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Liu B., 2012, MINING TEXT DATA, P415 Manning C.D., 2008, INTRO INFORM RETRIEV McCallum A.K., 1996, BOW TOOLKIT STAT LAN MURTHY S, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P322 Pak A., 2010, P 7 C INT LANG RES E, P1320 Pang B., 2002, P ACL 02 C EMP METH, V10, P79, DOI DOI 10.3115/1118693.1118704 Pang B., 2008, OPINION MINING SENTI Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Read J., 2009, P 1 INT CIKM WORKSH, P45, DOI DOI 10.1145/1651461.1651470 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd Saif H., 2012, ISWC 2012 LNCS SPR 1, V7649, P508 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shalev-Shwartz S, 2007, P 24 INT C MACH LEAR, P807, DOI DOI 10.1145/1273496.1273598 Song G., J MULTIMEDIA, V9, P635 Vapnik V., 1995, NATURE STAT LEARNING Wayne I., 1992, P 9 INT C MACH LEARN, P233 Yang Q, 2006, INT J INF TECH DECIS, V5, P597, DOI 10.1142/S0219622006002258 Yuan GX, 2012, P IEEE, V100, P2584, DOI 10.1109/JPROC.2012.2188013 NR 44 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-12778-1; 978-3-319-12777-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8860 BP 16 EP 28 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB8BF UT WOS:000346239200002 ER PT S AU Dai, YL Tada, S Ban, T Nakazato, J Shimamura, J Ozawa, S AF Dai, Yuli Tada, Shunsuke Ban, Tao Nakazato, Junji Shimamura, Jumpei Ozawa, Seiichi BE Loo, CK Yap, KS Wong, KW Teoh, A Huang, K TI Detecting Malicious Spam Mails: An Online Machine Learning Approach SO NEURAL INFORMATION PROCESSING, ICONIP 2014, PT III SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 21st International Conference on Neural Information Processing (ICONIP) CY NOV 03-06, 2014 CL Kuching, MALAYSIA DE malicious spam detection; online learning; tf-idf; vector space model AB Malicious spam is one of the major problems of the Internet nowadays. It brings financial damage to companies and security threat to governments and organizations. Most recent spam emails contain URLs that redirect spam receivers to malicious Web servers. In this paper, we propose an online machine learning based malicious spam email detection system. The term-weighting scheme represents each spam email. These feature vectors are then used as the input of the classifier. The learning is periodically performed to update the classifier so that the system provides increased adaptability to take account of spam emails whose contents change from time to time. A real data set is labeled by the SPIKE system which is developed by NICT. Evaluation experiments show that the detection system is efficient and accurate to identify malicious spam emails. C1 [Dai, Yuli; Ozawa, Seiichi] Kobe Univ, Grad Sch Engn, Nada Ku, Kobe, Hyogo 6578501, Japan. RP Dai, YL (reprint author), Kobe Univ, Grad Sch Engn, Nada Ku, 1-1 Rokko Dai, Kobe, Hyogo 6578501, Japan. EM 131t272t@stu.kobe-u.ac.jp; bantao@nict.go.jp; nakazato@nict.go.jp; shimamura@clwit.co.jp; ozawasei@kobe-u.ac.jp CR Chang Y-W, 2008, J MACHINE LEARNING R, V3, P53 Guzella TS, 2009, EXPERT SYST APPL, V36, P10206, DOI 10.1016/j.eswa.2009.02.037 Inoue D, 2008, WISTDCS 2008: WOMBAT WORKSHOP ON INFORMATION SECURITY THREATS DATA COLLECTION AND SHARING, P58 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Nakao K., 2007, 2 JOINT WORKSH INF S, P267 Prabhakar R., 2010, PHILOS T R SOC A, V247, P529 Rajagopal A, 2011, MOD ASIAN STUD, V45, P1003, DOI 10.1017/S0026749X10000314 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tretyakov K., 2004, TECHNICAL REPORT Zhu J, 2004, ADV NEUR IN, V16, P49 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-12643-2; 978-3-319-12642-5 J9 LECT NOTES COMPUT SC PY 2014 VL 8836 BP 365 EP 372 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB8BI UT WOS:000346245200045 ER PT J AU Zheng, C Shen, WM Ghenniwa, HH AF Zheng, Cheng Shen, Weiming Ghenniwa, Hamada H. BE Hou, JL Trappey, AJC Wu, CW Chang, KH Liao, CS Shen, WM Barthes, JP Luo, JZ TI A Study of Intents Resolving for Service Discovery SO PROCEEDINGS OF THE 2014 IEEE 18TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) SE International Conference on Computer Supported Cooperative Work in Design LA English DT Proceedings Paper CT IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD) CY MAY 21-23, 2014 CL Hsinchu, TAIWAN SP IEEE, Natl Tsing Hua Univ, IEEE Systems, Man, & Cybernet Soc DE service discovery; Intents; Web services AB Intents is an emerging framework which is employed for service discovery and integration. Currently the main strategy applied in Intents for resolving an intent message is exactly matching which may miss some valuable service candidates for the user. In order to address this issue, techniques in Information Retrieval (IR) are potential alternatives to find the missing services. This paper makes an empirical study of some classic IR techniques on a practical Intents dataset and demonstrates the findings of interest from the experiments which can be employed in designing matching schemes based on similarity. C1 [Zheng, Cheng; Shen, Weiming; Ghenniwa, Hamada H.] Univ Western Ontario, Dept Elect & Comp Engn, London, ON N6A 3K7, Canada. RP Zheng, C (reprint author), Univ Western Ontario, Dept Elect & Comp Engn, 1151 Richmond St, London, ON N6A 3K7, Canada. EM czheng23@uwo.ca; wshen@uwo.ca; hghenniw@uwo.ca CR Fang H., 2007, THESIS U ILLINOIS UR FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 McCandless M., 2010, LUCENE ACTION, V2nd Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Zheng C, 2013, INT C COMP SUPP COOP, P207 Znai C., 2001, P 24 ANN INT ACM SIG, P334 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3776-9 J9 INT C COMP SUPP COOP PY 2014 BP 649 EP 654 PG 6 WC Computer Science, Interdisciplinary Applications SC Computer Science GA BB7ZB UT WOS:000346136400115 ER PT S AU Soler, LJG Perez-Suarez, A Chang, L AF Gonzalez Soler, Lazaro J. Perez-Suarez, Airel Chang, Leonardo BE BayroCorrochano, E Hancock, E TI Efficient Overlapping Document Clustering Using GPUs and Multi-core Systems SO PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th Iberoamerican Congress on Pattern Recognition (CIARP) CY NOV 01-05, 2014 CL Puerto Vallarta, MEXICO SP CINVESTAV, Campus Guadalajara, Mexican Assoc Comp Vis, Neural Comp & Robot, Int Assoc Pattern Recognit, Cuban Assoc Pattern Recognit, Chilean Assoc Pattern Recognit, Brazilian Comp Soc, Special Interest Grp, Spanish Assoc Pattern Recognt & Image Anal, Portuguese Assoc Pattern Recognit, INTEL Educ DE Data Mining; Overlapping Clustering; Parallel Algorithms AB Overlapping clustering algorithms have been successfully applied in several contexts. Among the reported overlapping clustering algorithms, OClustR is the one showing the best trade-of between quality of the clusters and efficiency, in the task of document clustering; however, it has a quadratic computational complexity so it could be less useful in applications dealing with a very large number of documents. In this paper, we propose two parallel versions of the OClustR algorithm, specifically tailored for GPUs and multi-core CPUs, which enhance the efficiency of OClustR in problems dealing with a very large number of documents. The experimental evaluation over standard document collections showed the correctness and good performance of our proposals. C1 [Gonzalez Soler, Lazaro J.; Perez-Suarez, Airel; Chang, Leonardo] Adv Technol Applicat Ctr CENATAV, Havana 12200, Cuba. RP Soler, LJG (reprint author), Adv Technol Applicat Ctr CENATAV, 7ma A 21406 E 214 & 216, Havana 12200, Cuba. EM jsoler@cenatav.co.cu; asuarez@cenatav.co.cu; lchang@cenatav.co.cu CR Amigo E, 2009, INFORM RETRIEVAL, V12, P461, DOI 10.1007/s10791-008-9066-8 Berry M.W., 2004, SURVEY TEXT MINING C Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Perez-Suarez A, 2013, NEUROCOMPUTING, V121, P234, DOI 10.1016/j.neucom.2013.04.025 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanders J., 2010, CUDA EXAMPLE INTRO G NR 6 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-12568-8; 978-3-319-12567-1 J9 LECT NOTES COMPUT SC PY 2014 VL 8827 BP 264 EP 271 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematical & Computational Biology SC Computer Science; Mathematical & Computational Biology GA BB8DA UT WOS:000346407400033 ER PT J AU Diaby, M Viennet, E AF Diaby, Mamadou Viennet, Emmanuel BE Bajec, M Collard, M Deneckere, R TI Taxonomy-based Job Recommender Systems On Facebook and LinkedIn Profiles SO 2014 IEEE EIGHTH INTERNATIONAL CONFERENCE ON RESEARCH CHALLENGES IN INFORMATION SCIENCE (RCIS) SE International Conference on Research Challenges in Information Science LA English DT Proceedings Paper CT IEEE 8th International Conference on Research Challenges in Information Science (RCIS) CY MAY 28-30, 2014 CL Marrakesh, MOROCCO SP IEEE, IEEE Morocco Sect, EMSI, Lab Math Informatique & Applicat DE Job recommendation; Taxonomy; O*NET; Dimensionality reduction; SVM; Facebook; LinkedIn AB This paper presents taxonomy-based recommender systems that propose relevant jobs to Facebook and LinkedIn users; they are being developed by Work4, a San Francisco-based software company and the Global Leader in Social and Mobile Recruiting that offers Facebook recruitment solutions; to use its applications, Facebook or LinkedIn users explicitly grant access to some parts of their data, and they are presented with the jobs whose descriptions are matching their profiles the most. In this paper, we use the O*NET-SOC taxonomy, a taxonomy that defines the set of occupations across the world of work, to develop a new taxonomy-based vector model for social network users and job descriptions suited to the task of job recommendation; we propose two similarity functions based on the AND and OR fuzzy logic's operators, suited to the proposed vector model. We compare the performance of our proposed vector model to the TF-IDF model using our proposed similarity functions and the classic heuristic measures; the results show that the taxonomy-based vector model outperforms the TF-IDF model. We then use SVMs (Support Vector Machines) with a mechanism to handle unbalanced datasets, to learn similarity functions from our data; the learnt models yield better results than heuristic similarity measures. The comparison of our methods to two methods of the literature (a matrix factorization method and the Collaborative Topic Regression) shows that our best method yields better results than those two methods in terms of AUC. The proposed taxonomy-based vector model leads to an efficient dimensionality reduction method in the task of job recommendation. C1 [Diaby, Mamadou; Viennet, Emmanuel] Univ Paris 13, Sorbonne Paris Cite, L2TI, F-93430 Villetaneuse, France. RP Diaby, M (reprint author), Univ Paris 13, Sorbonne Paris Cite, L2TI, F-93430 Villetaneuse, France. EM mamadou.diaby@univ-paris13.fr; emmanuel.viennet@univ-paris13.fr CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Anand A, 2010, AMINO ACIDS, V39, P1385, DOI 10.1007/s00726-010-0595-2 Aseervatham S., 2007, THESIS U PARIS 13 VI Biemann C., 2005, LDV FORUM, V20, P75 Bobadilla J, 2013, KNOWL-BASED SYST, V46, P109, DOI 10.1016/j.knosys.2013.03.012 Boyd D. M., 2008, J COMPUT-MEDIAT COMM, V13, P210, DOI DOI 10.1111/J.1083-6101.2007.00393.X CASTRO JL, 1995, IEEE T SYST MAN CYB, V25, P629, DOI 10.1109/21.370193 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 de Campos LM, 2010, INT J APPROX REASON, V51, P785, DOI 10.1016/j.ijar.2010.04.001 Diaby M., 2013, P 2013 IEEE ACM INT, P821 Han J., 1996, SIGMOD REC, V25, P545 Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6 Joachims T, 1998, EUR C MACH LEARN ECM, P137 Kazienko P, 2011, IEEE T SYST MAN CY A, V41, P746, DOI 10.1109/TSMCA.2011.2132707 Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3 Omary Z., 2010, INT J INFONOMICS IJI, V3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang C., 2011, KDD, P448 Xiao B, 2007, MIS QUART, V31, P137 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2151-1349 BN 978-1-4799-2393-9 J9 INT CONF RES CHAL PY 2014 PG 6 WC Computer Science, Interdisciplinary Applications SC Computer Science GA BB7NI UT WOS:000345780700022 ER PT J AU Chen, KY Lee, HS Wang, HM Chen, B Chen, HH AF Chen, Kuan-Yu Lee, Hung-Shin Wang, Hsin-Min Chen, Berlin Chen, Hsin-Hsi GP IEEE TI I-VECTOR BASED LANGUAGE MODELING FOR SPOKEN DOCUMENT RETRIEVAL SO 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) SE International Conference on Acoustics Speech and Signal Processing ICASSP LA English DT Proceedings Paper CT IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) CY MAY 04-09, 2014 CL Florence, ITALY SP IEEE DE Spoken document retrieval; i-vector; language modeling; inductive; transductive ID INFORMATION-RETRIEVAL; RECOGNITION; SPEAKER; MATRIX; WORD AB Since more and more multimedia data associated with spoken documents have been made available to the public, spoken document retrieval (SDR) has become an important research subject in the past two decades. The i-vector based framework has been proposed and introduced to language identification (LID) and speaker recognition (SR) tasks recently. The major contribution of the i-vector framework is to reduce a series of acoustic feature vectors of a speech utterance to a low-dimensional vector representation, and then numbers of well-developed post-processing techniques (such as probabilistic linear discriminative analysis, PLDA) can be readily and effectively used. However, to our best knowledge, there is no research up to date on applying the i-vector framework for SDR or information retrieval (IR). In this paper, we make a step forward to formulate an i-vector based language modeling (IVLM) framework for SDR. Furthermore, we evaluate the proposed IVLM framework with both inductive and transductive learning strategies. We also exploit multi-levels of index features, including word-and subword-level units, in concert with the proposed framework. The results of SDR experiments conducted on the TDT-2 (Topic Detection and Tracking) collection demonstrate the performance merits of our proposed framework when compared to several existing approaches. C1 [Chen, Kuan-Yu; Lee, Hung-Shin; Wang, Hsin-Min] Acad Sinica, Inst Informat Sci, Taipei, Taiwan. RP Chen, KY (reprint author), Acad Sinica, Inst Informat Sci, Taipei, Taiwan. EM kychen@iis.sinica.edu.tw; hslee@iis.sinica.edu.tw; whm@iis.sinica.edu.tw; berlin@ntnu.edu.tw; hhchen@ntu.edu.tw CR Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Blei D., 2009, TEXT MINING THEORY A Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992 Chen KY, 2012, IEICE T INF SYST, VE95D, P1195, DOI 10.1587/transinf.E95.D.1195 Chen KY, 2013, INT CONF ACOUST SPEE, P8530 Chien J. T., 2004, INT J COMPUTATIONAL, V9, P29 Croft W. B., 2003, KLUWER INT SERIES IN, V13 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 D'Haro LF, 2012, 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, P42 Garcia-Romero D., 2011, P INTERSPEECH, P249 Garofolo J., 2000, P 8 TEXT RETR C, P107 Glembek O, 2011, INT CONF ACOUST SPEE, P4516 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guo W., 2012, P ACL, P864 Hoffman T., 2001, MACH LEARN, V42, P177 Huang C. L., 2011, P INTERSPEECH, P717 Jones K. S., 2000, Information Processing & Management, V36 Kanagasundaram A., 2011, P INTERSPEECH, P2341 Kenny P, 2007, IEEE T AUDIO SPEECH, V15, P1448, DOI 10.1109/TASL.2007.894527 Kenny P, 2007, IEEE T AUDIO SPEECH, V15, P1435, DOI 10.1109/TASL.2006.881693 LDC, 2000, PROJ TOP DET TRACK Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42 Maas A. L., 2010, P NIPS WORKSH Manning C.D., 2008, INTRO INFORM RETRIEV Martinez D., 2011, P INT FIR IT, P861 Naptali W., 2009, INT J COMPUTERS, P85 Naptali W, 2008, REC ADV COMPUT ENG, P275 Pang SN, 2004, IEEE IJCNN, P1197 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soufifar M, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P4853 Soufifar M., 2011, P INT 2011, P2913 Wang Q., 2011, P 34 INT ACM SIGIR C, P685 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 NR 35 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1520-6149 BN 978-1-4799-2893-4 J9 INT CONF ACOUST SPEE PY 2014 PG 5 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA BB5BJ UT WOS:000343655307024 ER PT J AU Kim, S Banchs, RE Li, HZ AF Kim, Seokhwan Banchs, Rafael E. Li, Haizhou GP IEEE TI WIKIPEDIA-BASED KERNELS FOR DIALOGUE TOPIC TRACKING SO 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) SE International Conference on Acoustics Speech and Signal Processing ICASSP LA English DT Proceedings Paper CT IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) CY MAY 04-09, 2014 CL Florence, ITALY SP IEEE DE Spoken Dialogue Systems; Dialogue Topic Tracking; Kernel Methods; Wikipedia AB Dialogue topic tracking aims to segment on-going dialogues into topically coherent sub-dialogues and predict the topic category for each next segment. This paper proposes a kernel method for dialogue topic tracking to utilize various types of information obtained from Wikipedia. The experimental results show that our proposed approach can significantly improve the performances of the task in mixed-initiative human-human dialogues. C1 [Kim, Seokhwan; Banchs, Rafael E.; Li, Haizhou] Inst Infocomm Res, Human Language Technol Dept, Singapore 138632, Singapore. RP Kim, S (reprint author), Inst Infocomm Res, Human Language Technol Dept, Singapore 138632, Singapore. EM kims@i2r.a-star.edu.sg; rembanchs@i2r.a-star.edu.sg; hli@i2r.a-star.edu.sg CR Adams P.H., 2008, P 2008 IEEE INT C SE, P581 Bohus D., 2003, P 8 EUR C SPEECH COM, P597 Breuing A., 2011, P 2011 IEEE WIC ACM, P445 Celikyilmaz A., 2011, P 12 ANN C INT SPEEC, P713 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 IKEDA S, 2008, P 9 ANN C INT SPEECH, P487 Lagus K., 2002, P 3 SIGDIAL WORKSH D, P95 Lee C., 2008, P 46 ANN M ASS COMP, P630 Lin B., 1999, P IEEE AUT SPEECH RE Nakata T., 2002, P 19 INT C COMP LING, P1 ROY S, 2006, P 21 INT C COMP LING, P737, DOI 10.3115/1220175.1220268 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang Pu, 2008, P 14 ACM SIGKDD INT, P713, DOI 10.1145/1401890.1401976 Wilcock G., 2012, P WORKSH QUEST ANSW, P5770 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Young S., P INT C AC SPEECH SI NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1520-6149 BN 978-1-4799-2893-4 J9 INT CONF ACOUST SPEE PY 2014 PG 5 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA BB5BJ UT WOS:000343655300027 ER PT S AU Liu, TY Wang, HF AF Liu, Tingyu Wang, Huifen BE Luo, Y TI Matching Workflow Contexts for Collaborative New Product Development Task Knowledge Provisioning SO COOPERATIVE DESIGN, VISUALIZATION, AND ENGINEERING, CDVE 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 11th International Conference on Cooperative Design, Visualization, and Engineering (CDVE) CY SEP 14-17, 2014 CL Seattle, WA DE context-aware workflow; collaborative knowledge management; ontology matching; TF-IDF ID MANAGEMENT; EVOLUTION; SYSTEMS; SPACE; MODEL AB The variety of product types and spesifications make new product development (NPD) tasks tough work in discrete manufacturing enterprises, which makes it a common strategy to refer to similar outcomes (e. g. the product drawings, work instructions, etc.) of former tasks during NPD processes. To improve the efficiency of discovering similar historical outcomes, this paper presents a novel approach to measure the similarity between task execution contexts in process-aware information systems, and exploit it for runtime task knowledge recommendation. In our framework, the measurement of similarity is preceded by 1) modeling the task context with ontology theory, 2) using the ontology matching algorithms to evaluate the similarities between the corresponding context ontology entities of different tasks instances. The TD-IDF is then utilized to compute the context cohesion between the user's current task and historical tasks, and the tasks with the highest similarity will be recommended to the task executors, along with their outcomes. Comparative evaluation is performed using TF-IDF, Levenshtein and Affine Gaps, and results demonstrate that the proposed approach achieves good precision and recall, and is efficient in task knowledge recommendation. C1 [Liu, Tingyu; Wang, Huifen] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Jiangsu, Peoples R China. RP Liu, TY (reprint author), Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Jiangsu, Peoples R China. EM tyliu@njust.edu.cn CR Abecker A, 2000, KNOWL-BASED SYST, V13, P271, DOI 10.1016/S0950-7051(00)00087-3 Abecker A, 2004, FORSCHUNGSZENTRUM IN, VDoctor Alexopoulos K., 2011, CIRP J MANUFACTURING, V4, P290 Bettini C, 2010, PERVASIVE MOB COMPUT, V6, P161, DOI 10.1016/j.pmcj.2009.06.002 Bilenko M., 2003, ACM SIGKDD INT C KNO, P39 Bolchini C, 2007, SIGMOD REC, V36, P19, DOI 10.1145/1361348.1361353 Chandrasegaran SK, 2013, COMPUT AIDED DESIGN, V45, P204, DOI 10.1016/j.cad.2012.08.006 Christophe F, 2010, CIRP ANN-MANUF TECHN, V59, P155, DOI 10.1016/j.cirp.2010.03.105 Christopher DM, 2009, INTRO INFORM RETRIEV Farhoomand AF, 2002, COMMUN ACM, V45, P127, DOI 10.1145/570907.570909 Harmon P, 2010, INT HANDB INFORM SYS, P37, DOI 10.1007/978-3-642-00416-2_3 Harms R, 2010, CIRP ANN-MANUF TECHN, V59, P45, DOI 10.1016/j.cirp.2010.03.045 Li BM, 2011, KNOWL-BASED SYST, V24, P1108, DOI 10.1016/j.knosys.2011.05.005 Liu DR, 2012, J AM SOC INF SCI TEC, V63, P2100, DOI 10.1002/asi.22705 Liu T., 2010, 2010 2 INT C COMP EN Liu TY, 2012, KNOWL-BASED SYST, V35, P320, DOI 10.1016/j.knosys.2012.05.010 Porter M, 1980, PROGRAM, V14, P8 Ristad ES, 1998, IEEE T PATTERN ANAL, V20, P522, DOI 10.1109/34.682181 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strang T, 2004, WORKSH ADV CONT MOD Van Der Aalst W., 2004, WORKFLOW MANAGEMENT Vanderfeesten I, 2011, INFORM SYST, V36, P517, DOI 10.1016/j.is.2010.09.008 Xu LD, 2011, IEEE T IND INFORM, V7, P630, DOI 10.1109/TII.2011.2167156 Zhen L, 2011, INT J PROD RES, V49, P2959, DOI 10.1080/00207541003705864 Zhen L, 2009, DECIS SUPPORT SYST, V48, P237, DOI 10.1016/j.dss.2009.08.002 Zhen L, 2009, EXPERT SYST APPL, V36, P7873, DOI 10.1016/j.eswa.2008.11.047 Zhuge H, 2002, INFORM SOFTWARE TECH, V44, P445, DOI 10.1016/S0950-5849(02)00022-8 Zhuge H, 2002, KNOWL-BASED SYST, V15, P465, DOI 10.1016/S0950-7051(02)00031-X Zhuge H, 2002, EXPERT SYST APPL, V23, P23, DOI 10.1016/S0957-4174(02)00024-6 NR 29 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-10831-5; 978-3-319-10830-8 J9 LECT NOTES COMPUT SC PY 2014 VL 8683 BP 269 EP 276 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB7BF UT WOS:000345314100039 ER PT J AU Khattak, AS Heyer, G AF Khattak, Akmal Saeed Heyer, Gerhard BE Gruca, A Czachorski, T Kozielski, S TI Exploiting Co-Occurrence of Low Frequent Terms in Patents SO MAN-MACHINE INTERACTIONS 3 SE Advances in Intelligent and Soft Computing LA English DT Proceedings Paper CT 3rd International Conference on Man-Machine Interactions (ICMMI) CY OCT 22-25, 2013 CL Brenna, POLAND DE patent classification; co-occurrence; multi-term features ID TEXT CATEGORIZATION AB This paper investigates the role of co-occurrence of low frequent terms in patent classification. A comparison is made between indexing, weighting single term features and multi-term features based on low frequent terms. Three datasets are used for experimentation. An increase of almost 21 percent in classification accuracy is observed through experimentation when multi-term features based on low frequent terms in patents are considered as compared to when all word types are considered. C1 [Khattak, Akmal Saeed; Heyer, Gerhard] Fac Math & Comp Sci, Dept Comp Sci, Nat Language Proc Res Grp, Leipzig, Germany. RP Khattak, AS (reprint author), Fac Math & Comp Sci, Dept Comp Sci, Nat Language Proc Res Grp, Leipzig, Germany. EM akhattak@informatik.uni-leipzig.de; heyer@informatik.uni-leipzig.de CR Agrawal R., 1993, ACM SIGMOD RECORD, V22, P207, DOI DOI 10.1145/170035.170072 Bashir S, 2006, LECT NOTES ARTIF INT, V3918, P504 Ceci M, 2007, J INTELL INF SYST, V28, P37, DOI 10.1007/s10844-006-0003-2 Chakrabarti S., 1998, ACM SIGMOD RECORD, V27, P307, DOI 10.1145/276305.276332 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Fall C. J., 2003, ACM SIGIR FORUM, V37, P10, DOI 10.1145/945546.945547 Han EH, 2000, LECT NOTES COMPUT, V1910, P424 Han JW, 2000, SIGMOD RECORD, V29, P1 Joachims T., 1998, LNCS, V1398, P137 Khattak A.S., 2011, LECT NOTES INFORM P, VP-186, P239 Larkey Leah S., 1999, P DL 99 4 ACM C DIG, P179, DOI 10.1145/313238.313304 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lupu M., 2009, ACM SIGIR FORUM, V43, P63 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SEBASTIANI F, 2005, ADV MANAG INFORM, V2, P109 Tikk D, 2005, INT SER INTELL TECHN, V20, P283 Vapnik V., 1995, NATURE STAT LEARNING Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 NR 23 TC 0 Z9 0 PU SPRINGER INT PUBLISHING AG PI CHAM PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND SN 1867-5662 BN 978-3-319-02309-0 J9 ADV INTEL SOFT COMPU PY 2014 VL 242 BP 459 EP 466 DI 10.1007/978-3-319-02309-0_50 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Mathematics, Applied; Robotics SC Computer Science; Mathematics; Robotics GA BB7CR UT WOS:000345344100050 ER PT S AU Zhang, R Chen, XJ Shi, JQ Xu, F Pu, YG AF Zhang, Rui Chen, Xiaojun Shi, Jinqiao Xu, Fei Pu, Yiguo BE Han, W Huang, Z Hu, C Zhang, H Guo, L TI Detecting Insider Threat Based on Document Access Behavior Analysis SO WEB TECHNOLOGIES AND APPLICATIONS, APWEB 2014, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 16th APWeb Workshops CY SEP 05, 2014 CL Changsha, PEOPLES R CHINA SP Univ Sci & Technol Beijing, Harbin Inst Technol, CAS, Inst Informat Engn DE insider threats; anomaly detection; text classification; individual behavior; community behavior AB In recent years, the major source of information leakage is due to insiders. In order to detect information leakage by some internal insiders, anomaly detection using individual and community behavior models have been developed. The basic assumption of anomaly detection is each user has his/her own profile of activities and anomaly detection algorithm attempts to identify any deviation from the basic profile by each user. Both models neglected the possibility of change of individual user profile, e.g. change of individual interests. We propose here an anomaly detection model of insider threat using file content. The proposed model uses the document segmentation and Naive Bayes algorithm to classify the contents of files in an organization. We then set up the correlation matrices between users and their interests, and also the user community and their interests. We then propose a comprehensive model to detect the insider threat, which takes into consideration of the deviations of individual users' current behaviors, their historical behaviors and their associated community behaviors simultaneously. According to the experimental test results, the proposed model can successfully detect the anomaly access to files in the internal systems. C1 [Zhang, Rui] Beijing Jiaotong Univ, Beijing, Peoples R China. RP Zhang, R (reprint author), Beijing Jiaotong Univ, Beijing, Peoples R China. EM zhangrui@nelmail.iie.ac.cn; chenxiaojun@iie.ac.cn; shijinqiao@iie.ac.cn; xufei@iie.ac.cn; puyiguo@iie.ac.cn CR [Anonymous], 2014, CORPUS TEST CATEGORI Chen Y, 2012, IEEE T DEPEND SECURE, V9, P332, DOI 10.1109/TDSC.2012.11 Harrington P., 2012, MACHINE LEARNING ACT Hongbin Z., 2014, J XIDIAN U Maloof MA, 2007, LECT NOTES COMPUT SC, V4637, P146 Manning C.D., 2008, INTRO INFORM RETRIEV, V100 Ray I, 2005, LECT NOTES COMPUT SC, V3679, P231 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandhu RS, 1996, COMPUTER, V29, P38, DOI 10.1109/2.485845 Wood B., 2000, SRI INT RES MITIGATI, V2, P1 Zheng N., 2011, P 18 ACM C COMP COMM, P139 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-11119-3; 978-3-319-11118-6 J9 LECT NOTES COMPUT SC PY 2014 VL 8710 BP 376 EP 387 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB7FY UT WOS:000345507000035 ER PT S AU Liu, C Chu, WW Sabb, F Parker, DS Bilder, R AF Liu, Chen Chu, Wesley W. Sabb, Fred Parker, D. Stott Bilder, Robert BE Chu, WW TI Path Knowledge Discovery: Multilevel Text Mining as a Methodology for Phenomics SO DATA MINING AND KNOWLEDGE DISCOVERY FOR BIG DATA: METHODOLOGIES, CHALLENGE AND OPPORTUNITIES SE Studies in Big Data LA English DT Article; Book Chapter ID WORKING-MEMORY; PREFRONTAL CORTEX; ASSOCIATION RULES; QUERY EXPANSION; RETRIEVAL; RECEPTORS; ONTOLOGY AB Transdisciplinary research is a rapidly expanding part of science and engineering, demanding new methods for connecting results across fields. In biomedicine for example, modeling complex biological systems requires linking knowledge across multi-level of science, from genes to disease. The move to multilevel research requires new strategies; in this discussion we present path knowledge discovery, a novel methodology for linking published research findings. The development of path knowledge discovery was motivated by problems in neuropsychiatry, where researchers need to discover interrelationships extending across brain biology that link genotype (such as dopamine gene mutations) to phenotype (observable characteristics of organisms such as cognitive performance measures). To advance an understanding of the complex bases of neuropsychiatric diseases, researchers need to search and discover relations among the many manifestations of these diseases across multiple biological and behavioral levels (i.e., genotypes and phenotypes at levels from molecular expression through complex syndromes). Phenomics - the study of phenotypes on a genome-wide scale requires close collaboration among specialists in multiple fields. We developed a computer-aided path knowledge discovery methodology to accomplish this goal. Path knowledge discovery consists of two integral tasks: 1) association path mining among concepts in multipart phenotypes that cross disciplines, and 2) fine-granularity knowledge-based content retrieval along the path(s) to permit deeper analysis. Implementing this methodology with our PhenoMining tools has required development of innovative measures of association strength for pairwise associations, as well as the strength for sequences of associations, in addition to powerful lexicon-based association expansion to increase the scope of matching. In our discussions we describe the validation of the methodology using a published heritability study from cognition research, and we obtain comparable results. We show how PhenoMining tools can greatly reduce a domain expert's time (by several orders of magnitude) when searching and gathering knowledge from the published literature, and can facilitate derivation of interpretable results. We built these PhenoMining tools on an existing knowledge base (PhenoWiki. org), now called PhenoWiki+, which can greatly speed up the knowledge acquisition process. Further, using the Resource Description Framework (RDF) data model in the PhenoWiki knowledge repository allows us to connect with different knowledge sources to enlarge the knowledge scope. The knowledge base also supports annotation, an important capability for collaborative knowledge discovery. C1 [Liu, Chen; Chu, Wesley W.; Parker, D. Stott] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA. [Sabb, Fred; Bilder, Robert] Univ Calif Los Angeles, Semel Inst Neurosci & Human Behav, Los Angeles, CA 90024 USA. RP Liu, C (reprint author), Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA. EM chenliu@cs.ucla.edu; wwc@cs.ucla.edu; fsabb@mednet.ucla.edu; stott@cs.ucla.edu; rbilder@mednet.ucla.edu CR Agrawal R., 1994, P 20 INT C VER LARG, P487 Anokhin AP, 2010, NEUROSCI LETT, V472, P119, DOI 10.1016/j.neulet.2010.01.067 Ashburner M, 2000, NAT GENET, V25, P25 Baker NC, 2010, J BIOMED INFORM, V43, P510, DOI 10.1016/j.jbi.2010.03.008 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Bilder Robert M, 2009, Cogn Neuropsychiatry, V14, P419, DOI 10.1080/13546800902787180 Bilder RM, 2009, NEUROSCIENCE, V164, P30, DOI 10.1016/j.neuroscience.2009.01.027 Creighton C, 2003, BIOINFORMATICS, V19, P79, DOI 10.1093/bioinformatics/19.1.79 Glausier JR, 2009, CEREB CORTEX, V19, P1820, DOI 10.1093/cercor/bhn212 Han JW, 2000, SIGMOD RECORD, V29, P1 Hristovski D, 2006, AMIA ANN S P, V2006, P349 Karlsgodt KH, 2010, J NEUROSCI, V30, P8197, DOI 10.1523/JNEUROSCI.0359-10.2010 Kremen WS, 2008, J GERONTOL B-PSYCHOL, V63, pP84 Lesh T.A., 2010, NEUROPSYCHOPHARMACOL, V36, P316 Liu ZY, 2007, INFORM RETRIEVAL, V10, P173, DOI 10.1007/s10791-006-9020-6 Oyama T, 2002, BIOINFORMATICS, V18, P705, DOI 10.1093/bioinformatics/18.5.705 Parker D.S., 2009, SUMMIT TRANSLAT BIOI, V2009, P90 Poldrack R.A., 2011, FRONTIERS NEUROINFOR, V5 Prud'hommeaux E., 2008, SPARQL QUERY LANGUAG Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Runyan JD, 2005, LEARN MEMORY, V12, P103, DOI 10.1101/lm.89405 Sabb FW, 2008, MOL PSYCHIATR, V13, P350, DOI 10.1038/sj.mp.4002124 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seamans JK, 2001, P NATL ACAD SCI USA, V98, P301, DOI 10.1073/pnas.011518798 Silverstein C, 1998, DATA MIN KNOWL DISC, V2, P39, DOI 10.1023/A:1009713703947 Smalheiser NR, 2009, COMPUT METH PROG BIO, V94, P190, DOI 10.1016/j.cmpb.2008.12.006 Stins JF, 2004, BMC NEUROSCI, V5, DOI 10.1186/1471-2202-5-49 Tan PN, 2000, LECT NOTES COMPUT, V1910, P632 U.S. National Library of Medicine, FACT SHEET MED SUBJ Vinkhuyzen AAE, 2010, BEHAV GENET, V40, P315, DOI 10.1007/s10519-009-9315-7 Von Huben SN, 2006, PSYCHOPHARMACOLOGY, V188, P586, DOI 10.1007/s00213-006-0347-x Voytek J.B., 2012, J NEUROSCIENCE METHO NR 33 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2197-6503 BN 978-3-642-40837-3; 978-3-642-40836-6 J9 STUD BIG DATA PY 2014 VL 1 BP 153 EP 192 DI 10.1007/978-3-642-40837-3_5 D2 10.1007/978-3-642-40837-3 PG 40 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BB6EK UT WOS:000344730000006 ER PT S AU Santos, I Galan-Garcia, P Gomez, CL Nieves, J Sanz, B Bringas, PG Gomez, JM AF Santos, Igor Galan-Garcia, Patxi Laorden Gomez, Carlos Nieves, Javier Sanz, Borja Garcia Bringas, Pablo Maria Gomez, Jose BE DeLaPuerta, JG Ferreira, IG Bringas, PG Klett, F Abraham, A DeCarvalho, ACPLF Herrero, A Baruque, B Quintian, H Corchado, E TI An Empirical Study on Word Sense Disambiguation for Adult Content Filtering SO INTERNATIONAL JOINT CONFERENCE SOCO'14-CISIS'14-ICEUTE'14 SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT International Joint Conference on SOCO, CISIS, ICEUTE CY JUN 25-27, 2014 CL Bilbao, SPAIN SP IEEE Secc Espana, IEEE Syst Man & Cybernet Capitulo Espanol, AEPIA, Gobierno Vasco, Diputac Foral Bizkaia, Ayuntamiento Bilbao, MIR Labs, Int Federat Computat Log DE information filtering; content filtering; machine learning; web categorisation AB It is obvious that Internet can act as a powerful source of information. However, as happens with other media, each type of information is targeted to a different type of public. Specifically, adult content should not be accessible for children. In this context, several approaches for content filtering have been proposed both in the industry and the academia. Some of these approaches use the text content of a webpage to model a classic bag-of-word model to categorise them and filter the inappropriate content. These methods, to the best of our knowledge, have no semantic information at all and, therefore, they may be surpassed using different attacks that exploit the well-known ambiguity of natural language. Given this background, we present the first semantics-aware adult filtering approach that models webpages, applying a previous wordsense-disambiguation step in order to face the ambiguity. We show that this approach can improve the filtering results of the classic statistical models. abstract environment. C1 [Santos, Igor; Galan-Garcia, Patxi; Laorden Gomez, Carlos; Nieves, Javier; Sanz, Borja; Garcia Bringas, Pablo; Maria Gomez, Jose] Univ Deusto, DeustoTech Comp, Bilbao, Spain. RP Santos, I (reprint author), Univ Deusto, DeustoTech Comp, Bilbao, Spain. EM isantos@deusto.es; patxigg@deusto.es; claorden@deusto.es; jnieves@deusto.es; borja.sanz@deusto.es; pablo.garcia.bringas@deusto.es; jmgomez@deusto.es CR Agirre E., 2009, P 12 C EUR CHAPT ASS, P33, DOI 10.3115/1609067.1609070 Becker J, 2003, P 6 INT C BUS INF SY, P7 Carreras X., 2004, P 4 LREC, V4 Carreras X., 2002, P LREC, V2 Choi B, 2009, ICCIT: 2009 FOURTH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND CONVERGENCE INFORMATION TECHNOLOGY, VOLS 1 AND 2, P659 Du R., 2003, 11 IEEE INT C NETW I, P325 Garner S.R., WEKA WAIKATO ENV KNO Hidalgo JMG, 2009, ADV COMPUT, V76, P257, DOI 10.1016/S0065-2458(09)01007-9 Ho WH, 2004, IEEE SYS MAN CYBERN, P4792 Kim Y., 2006, 8 INT C ADV COMM TEC, V1 Laorden C, 2012, ELECTRON COMMER R A, V11, P290, DOI 10.1016/j.elerap.2011.11.004 Mavroeidis D, 2005, LECT NOTES ARTIF INT, V3721, P181 Nelson B., 2009, MACHINE LEARNING CYB, P17, DOI 10.1007/978-0-387-88735-7_2 Padr L., 2012, P LANG RES EV C LREC Page L, 1999, PAGERANK CITATION RA Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Santos I, 2012, EXPERT SYST APPL, V39, P437, DOI 10.1016/j.eswa.2011.07.034 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Xu H, 2010, EXPERT SYST APPL, V37, P18, DOI 10.1016/j.eswa.2009.02.059 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-3-319-07995-0; 978-3-319-07994-3 J9 ADV INTELL SYST PY 2014 VL 299 BP 537 EP 544 DI 10.1007/978-3-319-07995-0_53 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB5ER UT WOS:000343754200053 ER PT S AU Mbarek, R Tmar, M Hattab, H AF Mbarek, Rabeb Tmar, Mohamed Hattab, Hawete BE Gu, Q Hell, P Yang, B TI An Optimal Context for Information Retrieval SO ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, AAIM 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th International Conference on Algorithmic Aspects of Information and Management (AAIM) CY JUL 08-11, 2014 CL Vancouver, CANADA HO Harbour Ctr, Simon Fraser Univ DE optimal context; relevance feedback; vector space model AB In general, document representation and ranking are dependent on context. In this work, we introduce the notion of optimal context, i.e. a context which gives the best ranking. We develop an algorithm to compute this optimal context and we show that it has an effect of query reformulation. Our approach gives substantial improvements in retrieval performance over known models. C1 [Mbarek, Rabeb; Tmar, Mohamed] Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. RP Mbarek, R (reprint author), Univ Sfax, High Inst Comp Sci & Multimedia, Multimedia Informat Syst & Adv Comp Lab, Sfax, Tunisia. EM rabeb.hattab@gmail.com; mohamedtmar@yahoo.fr; hattab.hawete@yahoo.fr CR Campos L.M.D., 2001, DOCUMENT INSTANTIATI Croft W.B., 2006, P JOINT DELOS NSF WO, P49 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ide E., 1971, SMART RETRIEVAL SYST, P337 Mbarek R, 2012, LECT NOTES COMPUT SC, V7608, P342, DOI 10.1007/978-3-642-34109-0_36 Melucci M., 2008, ACM T INFORM SYSTEMS, V26, P1 Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 Robertson S.E., 1992, OKAPI TREC TREC, P21 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruthven I., 2002, 4 INT C CONC LIB INF, P199 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-07956-1; 978-3-319-07955-4 J9 LECT NOTES COMPUT SC PY 2014 VL 8546 BP 323 EP 330 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BB4WZ UT WOS:000343424800029 ER PT S AU Perrie, J Islam, A Milios, E AF Perrie, Jessica Islam, Aminul Milios, Evangelos BE Gelbukh, A TI How Document Properties Affect Document Relatedness Measures SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PART II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) CY APR 06-12, 2014 CL Kathmandu, NEPAL SP Inst Politecnico Nacl Centro Invest Computac Nat Language &Text Proc Lab, Mexican Soc Artificial Intelligence HO Ctr Commun & Dev DE Document Relatedness Models; kNN-classification; Document attributes; Google trigram model AB We address the question of how document properties (word count, term frequency, cohesiveness, genre) affect the quality of unsupervised document relatedness measures (Google trigram model and vector space model). We use three genres of documents: aviation safety reports, medical equipment failure descriptions, and biodiversity heritage library text. Quality of document relatedness is assessed by the accuracy of a classification task using the kNN method. Experiments discover correlations between document property values and document relatedness quality, and we discuss how one approach may perform better depending on property values of the dataset. C1 [Perrie, Jessica; Islam, Aminul; Milios, Evangelos] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 4R2, Canada. RP Perrie, J (reprint author), Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 4R2, Canada. EM jssc.perrie@gmail.com; islam@cs.dal.ca; eem@cs.dal.ca CR Berry M.W., 2007, AUTOMATING DETECTION BHL consortium, 2010, DOWNL ALL FIL TYP DE Bickmore T, 2006, J BIOMED INFORM, V39, P556, DOI 10.1016/j.jbi.2005.12.004 Bliznakov Z., 2014, IFMBE P, V41, P1174 Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Colas F, 2006, INT FED INFO PROC, V217, P169 Erkan G, 2004, J ARTIF INTELL RES, V22, P457 Gomaa W.H., 2013, INT J COMPUTER APPL, V68, P13 Islam A., 2012, LNCS, V7310, P312 Islam A., 2012, INT C COMP LING DEC, P495 Islam MA, 2007, LECT NOTES COMPUT SC, V4394, P175 Kiros R., 2012, LNCS, V7413, P463 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Lewis D.D., 2004, RCV1 V2 LYRL2004 LYR Liu Q., 2011, SUPERVISED LEARNING Liu T., 2003, P 20 INT C MACH LEAR, P488 Oza N., 2010, SIAM 2007 TEXT MININ SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sathya R., 2013, International Journal of Advanced Research in Artificial Intelligence, V2 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Taylor R., 1990, J DIAGNOSTIC MED SON, V1, P35 Thirumuruganathan S., 2013, DETAILED INTRO K NEA Turney P.D., 2001, LNCS LNAI, V2167, P491 Wagstaff K.L., 2007, INTRO 1 2 SUPERVISED NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54902-1; 978-3-642-54903-8 J9 LECT NOTES COMPUT SC PY 2014 VL 8404 BP 392 EP 403 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PT UT WOS:000342990000033 ER PT S AU Yildirim, S AF Yildirim, Savas BE Gelbukh, A TI A Knowledge-Poor Approach to Turkish Text Categorization SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PART II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) CY APR 06-12, 2014 CL Kathmandu, NEPAL SP Inst Politecnico Nacl Centro Invest Computac Nat Language &Text Proc Lab, Mexican Soc Artificial Intelligence HO Ctr Commun & Dev DE Text Categorization; Vector Space Model; Machine Learning ID CLASSIFICATION AB Document categorization is a way of determining a category for a given document. Supervised methods mostly rely on a training data and rich linguistic resources that are either language-specific or generic. This study proposes a knowledge-poor approach to text categorization without using any sets of rules or language specific resources such as part-of-speech tagger or shallow parser. Knowledge-poor here refers to lack of a reasonable amount of background knowledge. The proposed system architecture takes data as-is and simply separates tokens by space. Documents represented in vector space models are used as training data for many machine learning algorithm. We empirically examined and compared a several factors from similarity metrics to learning algorithms in a variety of experimental setups. Although researchers believe that some particular classifiers or metrics are better than others for text categorization, the recent studies disclose that the ranking of the models purely depends on the class, experimental setup and domain as well. The study features extensive evaluation, comparison within a variety of experiments. We evaluate models and similarity metrics for Turkish language as one of the agglutinative language especially within poor-knowledge framework. It is seen that output of the study would be very beneficial for other studies. C1 Istanbul Bilgi Univ, Fac Engn, Dept Comp Engn, Istanbul, Turkey. RP Yildirim, S (reprint author), Istanbul Bilgi Univ, Fac Engn, Dept Comp Engn, Santral Istanbul Campus, Istanbul, Turkey. CR Amasyali MF, 2006, LECT NOTES COMPUT SC, V3999, P221 Chang C.-C., 2011, ACM T INTELL SYST TE, V2 Chen X., 2008, P 14 ACM SIGKDD INT, P124, DOI 10.1145/1401890.1401910 Chen YT, 2011, EXPERT SYST APPL, V38, P3085, DOI 10.1016/j.eswa.2010.08.100 Debole F., 2003, P SAC 03 18 ACM S AP, P784 Forman G., 2004, ICML 04, P297 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Guran A., 2009, INT S INN INT SYST A, P369 Hersh W., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Ogura H, 2011, EXPERT SYST APPL, V38, P4978, DOI 10.1016/j.eswa.2010.09.153 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Lang K., 1995, M P 12 INT C MACH LE Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis DD, 2004, J MACH LEARN RES, V5, P361 Li S., 2009, ACL, P692 Liu T., 2004, ICDM 04, P162 Manning C. D., 1999, FDN STAT NATURAL LAN Manning C.D., 2008, INTRO INFORM RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schtze H., 1995, ACM SIGIR 1995 NEW Y, P229 Scott S., 1998, COLING ACL Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Sriurai W., 2011, ADV COMPUTING INT J, V2, P21 Witten I. H., 2000, DATA MINING PRACTICA Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1997, 14 INT C MACH LEARN, P412 Zhang W, 2011, EXPERT SYST APPL, V38, P2758, DOI 10.1016/j.eswa.2010.08.066 NR 29 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54902-1; 978-3-642-54903-8 J9 LECT NOTES COMPUT SC PY 2014 VL 8404 BP 428 EP 440 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PT UT WOS:000342990000036 ER PT S AU Moreno, N Jimenez, S Baquero, J AF Moreno, Nelly Jimenez, Sergio Baquero, Julia BE Gelbukh, A TI Automatically Assessing Children's Writing Skills Based on Age-Supervised Datasets SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PART II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) CY APR 06-12, 2014 CL Kathmandu, NEPAL SP Inst Politecnico Nacl Centro Invest Computac Nat Language &Text Proc Lab, Mexican Soc Artificial Intelligence HO Ctr Commun & Dev ID NARRATIVES AB In this paper, we propose an approach for predicting the age of the authors of narrative texts written by children between 6 and 13 years old. The features of the proposed model, which are lexical and syntactical (part of speech), were normalized to avoid that the model uses the length of the text as a predictor. In addition, the initial features were extended using n-grams representations and combined using machine learning techniques for regression (i.e. SMOreg). The proposed model was tested with collections of texts retrieved from Internet in Spanish, French and English, obtaining mean-absolute-error rates in the age-prediction task of 1.40, 1.20 and 1.72 years-old, respectively. Finally, we discuss the usefulness of this model to generate rankings of documents by written proficiency for each age. C1 [Moreno, Nelly; Jimenez, Sergio; Baquero, Julia] Univ Nacl Colombia, Bogota, Colombia. RP Moreno, N (reprint author), Univ Nacl Colombia, Bogota, Colombia. EM nemorenoc@unal.edu.co; sgjimenezv@unal.edu.co; jmbaquerov@unal.edu.co CR Aguilar C., 2003, ESTUDIOS LINGUISTICA, V22, P33 Becerra C., 2011, P RECSYS 2011 WORKSH, P27 Carreras X., 2004, P 4 INT C LANG RES E Cheng N, 2011, DIGIT INVEST, V8, P78, DOI 10.1016/j.diin.2011.04.002 Colletta JM, 2010, SPEECH COMMUN, V52, P565, DOI 10.1016/j.specom.2010.02.009 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 De-Arteaga M., 2013, 10 PAN EV LAB UNC PL Dikli S., 2006, J TECHNOLOGY LEARNIN, P5 Dumais S., 2004, ANNU REV INFORM SCI, V38, P188, DOI DOI 10.1002/ARIS.1440380105 Furman R, 2007, J PRAGMATICS, V39, P1742, DOI 10.1016/j.pragma.2007.01.008 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Ilgaz H, 2005, COGNITIVE DEV, V20, P526, DOI 10.1016/j.cogdev.2005.08.004 Kakkonen Tuomo, 2005, P 2 WORKSH BUILD ED, P29, DOI 10.3115/1609829.1609835 Landauer T., 2003, P HLT NAACL 2003 WOR Lopez-Monroy A.P., 2013, 10 PAN EV LAB UNC PL Nguyen D., 2011, P 5 ACL HLT WORKSH L, P115 Pennebaker JW, 2003, J PERS SOC PSYCHOL, V85, P291, DOI 10.1037/0022-3514.85.2.291 Rangel F., 2013, 10 PAN EV LAB UNC PL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmid H., 1994, P INT C NEW METH LAN Shevade SK, 2000, IEEE T NEURAL NETWOR, V11, P1188, DOI 10.1109/72.870050 Smola A. J., 1998, LEARNING KERNELS Stadler M. A., 2005, EARLY CHILDHOOD ED J, V33, P73, DOI [10.1007/s10643-005-0024-4, DOI 10.1007/S10643-005-0024-4] NR 23 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54902-1; 978-3-642-54903-8 J9 LECT NOTES COMPUT SC PY 2014 VL 8404 BP 566 EP 577 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PT UT WOS:000342990000047 ER PT S AU Tang, GY Xia, YQ Sun, J Zhang, M Zheng, TF AF Tang, Guoyu Xia, Yunqing Sun, Jun Zhang, Min Zheng, Thomas Fang BE Gelbukh, A TI Topic Models Incorporating Statistical Word Senses SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2014, PT I SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing) CY APR 06-12, 2014 CL Kathmandu, NEPAL SP Inst Politecnico Nacl Centro Invest Computac Nat Language &Text Proc Lab, Mexican Soc Artificial Intelligence HO Ctr Commun & Dev DE topic modeling; word sense induction; document representation; document clustering AB LDA considers a surface word to be identical across all documents and measures the contribution of a surface word to each topic. However, a surface word may present different signatures in different contexts, i.e. polysemous words can be used with different senses in different contexts. Intuitively, disambiguating word senses for topic models can enhance their discriminative capabilities. In this work, we propose a joint model to automatically induce document topics and word senses simultaneously. Instead of using some pre-defined word sense resources, we capture the word sense information via a latent variable and directly induce them in a fully unsupervised manner from the corpora. Experimental results show that the proposed joint model outperforms the classic LDA and a standalone sense-based LDA model significantly in document clustering. C1 [Tang, Guoyu; Xia, Yunqing; Zheng, Thomas Fang] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Div Tech Innovat & Dev, Ctr Speech & Language Technol, Beijing 100084, Peoples R China. RP Tang, GY (reprint author), Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Div Tech Innovat & Dev, Ctr Speech & Language Technol, Beijing 100084, Peoples R China. EM sweetyuer@gmail.com; yqxia@tsinghua.edu.cn; sunj@i2r.a-star.edu.sg; mzhang@suda.edu.cn; fzheng@tsinghua.edu.cn CR Agirre Eneko, 2007, P 4 INT WORKSH SEM E, P7, DOI 10.3115/1621474.1621476 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boyd-Graber J, 2007, EMNLP CONLL, P1024 Brody Samuel, 2009, P 12 C EUR CHAPT ASS, P103, DOI 10.3115/1609067.1609078 Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guo W, 2011, P C EMP METH NAT LAN, P552 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Kong J., 2005, TDT4 MULTILINGUAL BR Lewis D., 1997, REUTERS 21578 TEXT C Navigli R., 2010, P 2010 C EMP METH NA, P116 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt H., 1994, P INT C NEW METH LAN, V12, P44 Steinbach M., 2000, KDD WORKSH TEXT MIN Teh Y. W., 2004, J AM STAT ASS, V101 Tufis D, 2007, LECT NOTES ARTIF INT, V4578, P447 Yao X., 2011, P TEXTGRAPHS 6 GRAPH, P10 NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54905-2; 978-3-642-54906-9 J9 LECT NOTES COMPUT SC PY 2014 VL 8403 BP 151 EP 162 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PO UT WOS:000342989200013 ER PT S AU Xing, K Jiang, D Ng, W Hao, XT AF Xing, Kai Jiang, Di Ng, Wilfred Hao, Xiaotian BE Bhowmick, SS Dyreson, CE Jensen, CS Lee, ML Muliantara, A Thalheim, B TI ADI: Towards a Framework of App Developer Inspection SO DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2014, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th International Conference on Database Systems for Advanced Applications (DASFAA) CY APR 21-24, 2014 CL Bali, INDONESIA DE App Developer; Profiling; App Searching; App Development Outsourcing AB With the popularity of smart mobile devices, the amount of mobile applications (or simply called apps) has been increasing dramatically in recent years. However, due to low threshold to enter app industry, app developers vary significantly with respect to their expertise and reputation in the production of apps. Currently, there is no well-recognized objective and effective means to profile app developers. As the mobile market grows, it already gives rise to the problem of finding appropriate apps from the user point of view. In this paper, we propose a framework called App Developer Inspector (ADI), which aims to effectively profile app developers in aspects of their expertise and reputation in developing apps. ADI is essentially founded on two underlying models: the App Developer Expertise (ADE) model and the App Developer Reputation (ADR) model. In a nutshell, ADE is a generative model that derives the latent expertise for each developer and ADR is a model that exploits multiple features to evaluate app developers' reputation. Using the app developer profiles generated in ADI, we study two new applications which respectively facilitate app search and app development outsourcing. We conduct extensive experiments on a large real world dataset to evaluate the performance of ADI. The results of experiments demonstrate the effectiveness of ADI in profiling app developers as well as its boosting impact on the new applications. C1 [Xing, Kai; Jiang, Di; Ng, Wilfred; Hao, Xiaotian] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. RP Xing, K (reprint author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. EM kxing@cse.ust.hk; dijiang@cse.ust.hk; wilfred@cse.ust.hk; xhao@cse.ust.hk CR Alazab M., 2012, ICDCSW Balog K., 2006, SIGIR Barbosa L., 2010, ACL Blei D, 2003, J MACHINE LEARNING R Chia P., 2012, WWW, P311 Elena Renda M., 2003, P 2003 ACM S APPL CO Griffiths T.L., 2004, P NATL ACAD SCI US Ha-Thuc V., 2009, SIGIR Jiang D., 2013, SIGIR Jiang D., 2013, EDBT Jiang D., 2011, CIKM Jiang D., 2012, CIKM Joachims T., 2002, SIGKDD Kang D., 2011, WSDM Manning C. D., 2008, INTRO INFORM RETRIEV, V1 Manning C.D., 2008, INTRO INFORM RETRIEV Mazurek J., ACTA ACAD KARVINIENS Mei Q., 2006, WWW Newman D, 2009, J MACH LEARN RES, V10, P1801 Rosen-Zvi M., 2004, P UAI C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vosecky J., 2012, LNCS, V7238, P397 Wallach H. M., 2008, THESIS U CAMBRIDGE Wei X., 2012, ICDEW IEEE, P251 Zhou Y., 2012, NDSS NR 25 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-05813-9; 978-3-319-05812-2 J9 LECT NOTES COMPUT SC PY 2014 VL 8422 BP 266 EP 280 PG 15 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3PZ UT WOS:000342991200018 ER PT S AU Sun, J Wang, B Yang, XC AF Sun, Jing Wang, Bin Yang, Xiaochun BE Li, F Li, G Hwang, SW Yao, B Zhang, Z TI A Correlation-Based Semantic Model for Text Search SO WEB-AGE INFORMATION MANAGEMENT, WAIM 2014 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th International Conference on Web-Age Information Management (WAIM) CY JUN 16-18, 2014 CL Macau, PEOPLES R CHINA DE text search; Wikipedia; semantic correlation AB With the exponential growth of texts on the Internet, text search is considered a crucial problem in many fields. Most of the traditional text search approaches are based on "bag of words" text representation based on frequency statics. However, these approaches ignore the semantic correlation of words in the text. So this may lead to inaccurate ranking of the search results. In this paper, we propose a new Wikipedia-based similar text search approach that the words in the texts and query text could be semantic correlated in Wikipedia. We propose a new text representation model and a new text similarity metric. Finally, the experiments on the real dataset demonstrate the high precision, recall and efficiency of our approach. C1 [Sun, Jing; Wang, Bin; Yang, Xiaochun] Northeastern Univ, Coll Informat Sci & Engn, Liaoning 110819, Peoples R China. RP Sun, J (reprint author), Northeastern Univ, Coll Informat Sci & Engn, Liaoning 110819, Peoples R China. EM jsunneu@gmail.com; binwang@mail.neu.edu.cn; yangxc@mail.neu.edu.cn CR Hotho A., 2003, SIGIR, P143 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Ribeiro B., 1999, MODERN INFORM RETRIE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang P, 2007, IEEE DATA MINING, P332 Zhu H., 2012, LNCS, V7419, P192 NR 6 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-319-08010-9; 978-3-319-08009-3 J9 LECT NOTES COMPUT SC PY 2014 VL 8485 BP 703 EP 706 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3LO UT WOS:000342838500075 ER PT J AU [Anonymous] AF [Anonymous] TI Music Information Retrieval: Recent Developments and Applications SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article ID FUNDAMENTAL-FREQUENCY ESTIMATION; GENRE CLASSIFICATION; MATRIX FACTORIZATION; ONSET DETECTION; AUDIO; SIGNALS; SYSTEMS; PITCH; WEB; TRANSCRIPTION AB We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey. FU Austrian Science Fund (FWF) [P22856, P25655]; European Union through PHENICX project [601166]; A4U postdoctoral grants programme; Junta of Andalusia through the COFLA2 project [P12-TIC-1362] FX This work is supported by the Austrian Science Fund (FWF): P22856 and P25655, as well as by the European Union Seventh Framework Programme FP7 / 2007-2013 through the PHENICX project under grant agreement no. 601166, the A4U postdoctoral grants programme, and the Junta of Andalusia through the COFLA2 project (P12-TIC-1362). The authors would further like to thank Masataka Goto, Mohamed Sordo, and Edith Law for granting permission to include their visual material in this survey, and Juan J. Bosch for his comments on the manuscript. Furthermore, the authors would like to express their gratitude to the anonymous reviewers for their highly valuable suggestions for improving the manuscript. CR [Anonymous], TITLE ERROR [Anonymous], 2014, INT SOC MUS INF RETR [Anonymous], 2000, RHYTHM PERCEPTION PR [Anonymous], 2002, ISMIR [Anonymous], 2005, INT S MUS INF RETR I [Anonymous], 2013, J INTELLIGENT INFORM [Anonymous], 2005, P 6 INT C MUS INF RE [Anonymous], 2009, P 10 INT SOC MUS IFN [Anonymous], IEEE SIGNLA IN PRESS [Anonymous], 2012, P 4 INT WORKSH ADV M [Anonymous], 2011, ACM T INFORM SYSTEMS [Anonymous], 2008, CONTENT PROCESSING M [Anonymous], 2001, P IEEE C COMP VIS PA [Anonymous], 2013, INT J INTELLIGENT [Anonymous], 2001, P IEEE INT C PEER PE [Anonymous], 2011, THESIS [Anonymous], 2008, P 9 INT C MUS INF RE [Anonymous], 2009, P 12 INT C DIG AUD E [Anonymous], 2009, ISMIR [Anonymous], 2012, MULTIMODAL MUSIC PRO [Anonymous], 2012, P 10 INT WORKSH AD M [Anonymous], 2009, MUSIC INFORM RETRIEL [Anonymous], 2000, P INT S MUS INF RETI [Anonymous], 2010, P 4 ACM C REC SYST [Anonymous], 1967, J ACOUST SOC AM Armstrong Timothy G, 2009, P 32 INT ACM SIGIR C, P833 Aucouturier J.-J., 2004, J NEGATIVE RESULTS S, V1 Aucouturier J.-J., 2002, Proceedings 2002 IEEE International Conference on Multimedia and Expo (Cat. No.02TH8604), DOI 10.1109/ICME.2002.1035729 Baccigalupo C., 2008, P 9 INT C MUS INF RE Baeza-Yates Ricardo, 2011, MODERIN INFORM RETRI Bainbridge D., 2003, P 4 INT C MUS INF RE, P221 Baltrunas Linas, 2011, INT C EL COMM WEB TE Battenberg Eric, 2013, P INT SOC MUS INF RE Baumann S., 2003, P 3 INT C WEB DEL MU Bee Suan Ong, 2007, STRUCTURAL ANAL SEGM Bello JP, 2005, IEEE T SPEECH AUDI P, V13, P1035, DOI 10.1109/TSA.2005.851998 Bennett J., 2007, P KDD CUP WORKSH, V2007, P35 Berenzweig A., 2003, P 4 INT C MUS INF RE Berenzweig Adam, 2003, P IEEE INT C MULT EX Bertin-Mahieux T., 2011, P 12 INT SOC MUS INF Bertin-Mahieux Thierry, 2012, P 13 INT SOC MUS INF Bogdanov D, 2011, IEEE T MULTIMEDIA, V13, P687, DOI 10.1109/TMM.2011.2125784 Braschler M, 2004, INFORM RETRIEVAL, V7, P7, DOI 10.1023/B:INRT.0000009438.69013.fa Brill E., 1992, P 3 C APPL NAT LANG, P152, DOI 10.3115/974499.974526 Cano P, 2005, STUD COMP INTELL, V2, P233 Cano P., 2004, P 4 INT C WEB DEL MU Cano Pedro, 2006, ISMIR 2004 AUD DESCR Casey MA, 2008, P IEEE, V96, P668, DOI 10.1109/JPROC.2008.916370 Celma O, 2010, MUSIC RECOMMENDATION AND DISCOVERY, P1, DOI 10.1007/978-3-642-13287-2 Celma O., 2006, P 7 INT C MUS INF RE Chai W., 2006, IEEE SIGNAL PROCESSI, V23 Chai Wei, 2005, THESIS Chew Elaine, 2000, THESIS Cohen WW, 2000, COMPUT NETW, V33, P685, DOI 10.1016/S1389-1286(00)00057-8 Cooper M, 2006, COMPUT MUSIC J, V30, P42, DOI 10.1162/comj.2006.30.2.42 Coviello E, 2011, IEEE T AUDIO SPEECH, V19, P1343, DOI 10.1109/TASL.2010.2090148 Cunningham S. J., 2012, P ISMIR, P259 Cunningham Sally Jo, 2013, P INT SOC MUS I NF R Dannenberg RB, 2007, J AM SOC INF SCI TEC, V58, P687, DOI 10.1002/asi.20532 De Mulder T., 2004, IEEE INT C AC SPEECH, V4, P233 de Cheveigne A, 2005, SPR HDB AUD, V24, P169 de Cheveigne A, 2002, J ACOUST SOC AM, V111, P1917, DOI 10.1121/1.1458024 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dixon Simon, 2005, INT C MUS INF RETIR Dornbush Sandor, 2007, P WORKSH AMB ITN 20 Downie J. S., 2005, P 6 INT C MUS INF RE, P320 Downie JS, 2010, STUD COMPUT INTELL, V274, P93 Downie JS, 2003, ANNU REV INFORM SCI, V37, P295, DOI 10.1002/aris.1440370108 Downie JS, 2004, COMPUT MUSIC J, V28, P12, DOI 10.1162/014892604323112211 Dressler Karin, 2012, EIGH MUSIC INFORM RE Dressler Karin, 2006, P 9 INT C DIG AUD EF, P247 Dror Gideon, 2011, P KDD CUP WORKSH, pAugust 2011 Ehmann Andreas F., 2007, INT C MUS INF RETRI, P323 Elliott Greg T., 2006, CHI 2006 HUM FACT CO, P736 Ellis Daniel PW, 2002, P 3 INT C MUS IN RET, P170 Flexer A, 2006, J NEW MUSIC RES, V35, P113, DOI 10.1080/09298210600834946 Flexer Arthur, 2012, ISMIR, P175 Flexer Arthur, 2014, INT SOC MUS INF RETR Foote J, 1999, ACM MULTIMEDIA 99, PROCEEDINGS, P77 Foote Jonathan, 2002, ISMIR Forsblom A., 2012, P 2012 ACM ITN C INT, P253 Fort K., 2012, INT SOC MUS INF RETR, P25 Geleijnse G., 2007, P 8 INT C MUS INF RE Gillet Olivier, 2006, P 7 INT S MUS INF RE, P156 Gomez E, 2013, COMPUT MUSIC J, V37, P73, DOI 10.1162/COMJ_a_00180 Gomez E., 2006, INFORMS J COMPUTING, V18 Gomez E, 2003, J NEW MUSIC RES, V32, P23, DOI 10.1076/jnmr.32.1.23.16799 Gomez Emilia, 2006, THESIS Goto M., 2003, P 4 INT C MUS INF RE, V3, P229 Goto Masataka, 2005, P 6 INT C MUS INF RE Gouyon F, 2005, COMPUT MUSIC J, V29, P34, DOI 10.1162/comj.2005.29.1.34 Govaerts S., 2009, P 10 INT SOC MUS INF Govert Norbert, 2002, P 1 WORKSH IN EV XML, P1 Grachten Maarten, 2009, P 10 INT SOC MUS INF Grosche Peter, 2012, MEINARD MULLER MASAT, V3, P157 Hamasaki Masahiro, 2013, P 9 INT S OP COLL WI Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Harman D., 2011, SYNTHESIS LECT INFOR, V3, P1 Harte Christopher, 2010, THESIS Hartmann WM, 1996, J ACOUST SOC AM, V100, P3491, DOI 10.1121/1.417248 Hauger David, 2013, P 17 INT SOC MUS IFN Herrera P., 2009, INT SOC MUSIC INFORM Herrera P., 2003, J NEW MUSIC RES, V32 Herrera Perfecto, 2010, 1 WORKSH MUS REC DIS Herrera-Boyer Perfecto, 2006, SIGNAL PROCESSING ME, P163, DOI 10.1007/0-387-32845-9_6 Holzapfel A, 2012, IEEE T AUDIO SPEECH, V20, P2539, DOI 10.1109/TASL.2012.2205244 Holzapfel A, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P89 Homburg H., 2005, P INT C MUS INF RETR, P528 Hu X., 2005, P 6 INT C MUS INF RE Ioannidis Leonidas, 2011, CONTENT BASED MULTIM, P31 Jensen JH, 2009, IEEE T AUDIO SPEECH, V17, P693, DOI 10.1109/TASL.2008.2012314 Lee JH, 2013, J INTELL INF SYST, V41, P499, DOI 10.1007/s10844-013-0259-2 Jones M.C., 2007, INT C MUS INF RETR, P539 Kaminskas M, 2011, LECT NOTES COMPUT SC, V6787, P183, DOI 10.1007/978-3-642-22362-4_16 Kaminskas Marius, 2013, P 6 ACM C REC SYST 2 Kim Y. E., 2008, P INT S MUS INF RETR, P231 Kim YK, 2002, P 3 INT C MUS INF RE, V13, P17 KLAPURI A, 1999, ACOUST SPEECH SIG PR, P3089 Klapuri A., 2006, SIGNAL PROCESSING ME Klapuri Anssi, 2006, INT S MUS INF RETR V, P216 Klapuri AP, 2005, INT INTEG REL WRKSP, P291, DOI 10.1109/ASPAA.2005.1540227 Knees P., 2004, P 5 INT S MUS INF RE, P517 Knees P., 2007, P 30 ANN INT ACM SIG Knees Peter, 2007, P 5 INT WORKSH AD MU Knees Peter, 2013, T MULTIMEDIA COMPUTI Knees Peter, 2011, P 2 WORKSH MUS REC D Koenigstein Noam, 2009, P 10 INT SOC MUS INF Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Kosugi N., 2000, Proceedings ACM Multimedia 2000, DOI 10.1145/354384.354520 Krenmayer Andreas, 2013, THESIS Krumhansl C. L., 1990, COGNITIVE FDN MUSICA, V17 Lamere P, 2007, P 8 INT C MUS INF RE, P173 Laurier C, 2010, MULTIMED TOOLS APPL, V48, P161, DOI 10.1007/s11042-009-0360-2 Law E., 2007, P 8 INT C MUS INF TR Law E, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1197 Lee DD, 1999, NATURE, V401, P788 Lee J. H., 2004, P 5 INT C MUS INF RE, P441 Lee J. H., 2012, P 12 ACM IEECS JOINT, P129 Lee JS, 2007, LECT NOTES COMPUT SC, V4836, P45 Leitich Stefan, 2007, P 8 INT C MUS INF RE, P167 LEMAN M, 1994, J NEW MUSIC RES, V23, P169, DOI 10.1080/09298219408570654 Leman Marc, 2002, REV ACUSTICA, V33 Levy M., 2007, P 8 INT C MUS INF RE Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Liem Cynthia CS, 2012, WORKING NOTES P MEDI Logan B., 2003, P 26 ANN INT ACM SIG Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410 Lubbers D., 2009, P 10 INT SOC MUS INF MAHER RC, 1994, J ACOUST SOC AM, V95, P2254, DOI 10.1121/1.408685 Mandel M, 2008, J NEW MUSIC RES, V37, P151, DOI 10.1080/09298210802479300 Mandel Michael I., 2011, ACM T MULTIMEDIA C S, V7S Mandel Michael I., 2010, P INT S MUS INF RETR, P399 Mardirossian Arpi, 2007, PROCEEDINGS OF THE 8, P189 Martorell A., 2011, 3 INT C MATH COMP MU Mauch M., 2010, THESIS Mayer Rudolf, 2012, P INT C MUS INF RETR, P337 Miotto R., 2012, ACM T INFORM SYSTEMS, V30 Miotto Riccardo, 2010, P 11 INT SOC MUS INF Moelants D., 2004, P 8 INT C MUS PERC C, P558 Moens B., 2010, P 7 SOUND MUS COMP C, P451 Muller H, 2010, INFORM RETRIEVAL SER, V32, P1, DOI 10.1007/978-3-642-15181-1 Muller M, 2010, IEEE T AUDIO SPEECH, V18, P649, DOI 10.1109/TASL.2010.2041394 Muller Meinard, 2012, P 13 INT SOC MUS IFN Niedermayer Bernhard, 2012, THESIS Niedermayer Bernhard, 2011, P 12 INT SOC MUS I F Nurnberger A, 2003, LECT NOTES COMPUT SC, V2714, P883 Orio Nicola, 2006, Foundations and Trends in Information Retrieval, V1, DOI 10.1561/1500000002 Orio Nicola, 2011, P 12 INT SOC MUS INF Oudre Laurent, 2009, P 10 INT SOC MUS INF, P153 Pachet F., 2004, J NEGATIVE RESULTS S, V1, P1 Pachet F., 2001, P 1 INT C WEB DEL MU Page Kevin R., 2013, INT J INTELLIGENT IF Pampalk E., 2006, P 7 INT C MUS INF RE Pampalk E., 2005, P 6 INT C MUS INF RE, P11 Papadopoulos Helene, 2007, CONTENT BASED MULTIM, P53 Pauwels J, 2013, INT CONF ACOUST SPEE, P749, DOI 10.1109/ICASSP.2013.6637748 Peeters G, 2011, J ACOUST SOC AM, V130, P2902, DOI 10.1121/1.3642604 Peeters G., 2009, P INT C DIG AUD EFF, P408 Peeters Geoffroy, 2012, P 13 INT SOC MUSIC I Peeters Geoffroy, 2002, P INT C MUS INF RETR, P94 Peeters Geoffroy, 2004, CUIDADO INTERNAL REP Pohle T, 2007, IEEE T MULTIMEDIA, V9, P567, DOI 10.1109/TMM.2006.887991 Poliner GE, 2007, IEEE T AUDIO SPEECH, V15, P1247, DOI 10.1109/TASL.2006.889797 Rabiner L.R., 2007, FDN TRENDS SIGNAL PR Rauber Andreas, 2012, P 13 INT SOC MUSIC I, P469 Reynolds G, 2007, P AUD MOSTL C INT SO, P84 Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1 Salamon J., 2013, INT J MULTIMEDIA IN, P1 Salamon J, 2012, IEEE T AUDIO SPEECH, V20, P1759, DOI 10.1109/TASL.2012.2188515 Salamon Justin, 2012, INT SOC MUS INF RETR, P289 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 Sapp Craig Stuart, 2005, COMPUTERS ENTERTAINM, V3, P1, DOI DOI 10.1145/1095534.1095544 Sarwar B., 2001, PROC WWW Scaringella N, 2006, IEEE SIGNAL PROC MAG, V23, P133, DOI 10.1109/MSP.2006.1598089 Schedl M., 2006, P 28 EUR C INF RETR Schedl M., 2007, P 5 WORKSH AD MULT R Schedl M, 2012, INFORM RETRIEVAL, V15, P183, DOI 10.1007/s10791-012-9187-y Schedl M, 2012, CH CRC DATA MIN KNOW, P219 Schedl M, 2013, P 35 EUR C INF RETR Schedl Markus, 2013, MULTIMEDIA SYSTEMS Schedl Markus, 2005, P 4 INT WORKSH COTN Schedl Markus, 2011, INFORM PROCESSING MA, V47 Schedl Markus, 2012, P 16 INT C INT US IN Schedl Markus, 2013, P 35 ANN INT ACM SIG Schedl Markus, 2013, INT J J INTELLIGENT Schedl Markus, 2012, MULTIMODAL MUSIC PRO, V3, P135 Schedl Markus, 2008, THESIS Schedl Markus, 2014, P 20 INT C MULT MOD Schedl Markus, 2013, P 4 ACM MULT SYST C Schmuckler M. A., 2004, ECOLOGICAL PSYCHOACO, P271, DOI 10.1016/B978-012515851-0/50012-6 Schnitzer D, 2012, J MACH LEARN RES, V13, P2871 Schnitzer Dominik, 2007, P 6 INT C MOB UB MUL Selfridge-Field Eleanor, 1998, COMPUTING MUSICOLOGY, V11, P3 Serra J, 2010, STUD COMPUT INTELL, V274, P307 Serra X., 2013, ROADMAP MUSIC INFORM Serra Xavier, 2012, P 21 INT WORLD WI DE Seyerlehner Klaus, 2009, MUS INF RETR EV EXCH Seyerlehner Klaus, 2010, THESIS Shavitt Yuval, 2009, P IEEE INT S S MULT Sigurdsson S., 2006, ISMIR, P286 Silla Jr CN, 2008, P 9 INT C MUS INF RE, P451 Skowronek Janto, 2006, P 7 INT C MUS INF RE, P395 Slaney M, 2011, IEEE MULTIMEDIA, V18, P12, DOI 10.1109/MMUL.2011.34 Smeaton A. F., 2006, MIR 06, P321, DOI DOI 10.1145/1178677.1178722 Smith Jordan BL, 2013, P 14 INT SOC MUS INF Smolensky P., 1986, PARALLEL DISTRIBUTED, V1, P194 Soleymani Mohammad, 2013, MEDIA EVAL Sordo M., 2008, P 9 INT C MUS INF RE, P255 Sordo Mohamed, 2009, P 10 I NT SOC MUS IN Stephen Downie J., 2002, ISMIR PANAEL MUSIC, P43 Stephen Downie J, 2004, P 5 INT C MUS INF TR, P9 Stewart Rebecca, 2011, P IEEE INT C MULT EX Stober Sebastian, 2010, P 7 INT S COMP MUS M Sturm B. L., 2012, P ACM WORKSH MUS INF, P69 TAGUESUTCLIFFE J, 1992, INFORM PROCESS MANAG, V28, P467, DOI 10.1016/0306-4573(92)90005-K Temperley D, 1999, MUSIC PERCEPT, V17, P65 Temperley David, 2005, 2005 MIREX CONTEST S Teng Yuan-Ching, 2013, IEEE INT C MULT EXP, P1 Turnbull D., 2007, P 8 INT C MUS INF RE Turnbull Douglas, 2009, P 32 ANN INT ACM SIG Typke R., 2005, Journal of Digital Information Management, V3 Typke Rainer, 2006, IEEE INT C MULT EXP, P1793 Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560 Urbano J., 2013, INT J MULTIMEDIA INF, V2, P59 Urbano Julian, 2013, INT J INTELLIGENT IF Urbano Julian, 2014, IEEE SOC MUS INF RET Urbano Julian, 2010, P ACM SIGIR 2010 WOR, P9 Urbano Julian, 2011, INT SOC MUS INF RETR, P597 Urbano Julian, 2013, THESIS Urbano Julian, 2012, INT SOC MUS INF RETR, P181 Vladimir N., 1998, STAT LEARNING THEORY von Ahn L., 2004, CHI 04, P319, DOI DOI 10.1145/985692.985733 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Voorhees Ellen M., 2002, WHITHER MUSIC IR EVA, P7 Wakefield GH, 1999, P SOC PHOTO-OPT INS, V3807, P637, DOI 10.1117/12.367679 Wang Avery Li-Chun, 2003, P 4 INT C MUS INF RE Wang X., 2012, P ACM MULT, P99 Wartena Christian, 2012, CONTENT BASED MULTIM, P1 Wessel D. L., 1979, Computer Music Journal, V3, DOI 10.2307/3680283 Whitman Brian, 2002, P 2002 INT COMP MUS, P591 Wolff Daniel, 2011, P 12 INT SOC MUS INF Hu Xiang, 2012, Proceedings 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM 2012), DOI 10.1109/EEESym.2012.6258657 Yang Y.-H., 2011, MUSIC EMOTION RECOGN Yang Yi-Hsuan, 2013, T INTELLIGNET SYSTEM, V3 Yeh C, 2010, IEEE T AUDIO SPEECH, V18, P1116, DOI 10.1109/TASL.2009.2030006 Zadel M., 2004, P 5 INT S MUS INF RE Zangerle Eva, 2012, P 21 INT WORLD WID W, P14 Zhang BJ, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P403, DOI 10.1145/1571941.1572011 Zhang Yuan Cao, 2012, P 5 ACM INT C WEB SE Zobel Justin, 2011, 13 INT SOC MUS INF R NR 271 TC 0 Z9 0 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2014 VL 8 IS 2-3 BP 128 EP + DI 10.1561/1500000042 PG 135 WC Computer Science, Information Systems SC Computer Science GA AP4RW UT WOS:000342067000001 ER PT J AU Har-Peled, S Kumar, N AF Har-Peled, Sariel Kumar, Nirman TI DOWN THE RABBIT HOLE: ROBUST PROXIMITY SEARCH AND DENSITY ESTIMATION IN SUBLINEAR SPACE SO SIAM JOURNAL ON COMPUTING LA English DT Article DE proximity search; kth nearest neighbor; approximate Voronoi diagram; geometric approximation algorithms ID APPROXIMATE NEAREST-NEIGHBOR; VORONOI DIAGRAMS; LINEAR-SIZE; DIMENSIONS; ALGORITHM; QUERIES AB For a set of n points in R-d, and parameters k and epsilon, we present a data structure that answers (1 + epsilon, k) approximate nearest neighbor queries in logarithmic time. Surprisingly, the space used by the data structure is (O) over tilde (n/k), where the (O) over tilde(center dot) notation here hides terms that are exponential in d, roughly varying as 1/epsilon(d); as such, the space used is sublinear in the input size if k is sufficiently large. Our approach provides a novel way to summarize geometric data, such that meaningful proximity queries on the data can be carried out using this sketch. Using this, we provide a sublinear space data structure that can estimate the density of a point set under various measures, including (i) sum of distances of k closest points to the query point and (ii) sum of squared distances of k closest points to the query point. Our approach generalizes to other distance-based estimations of densities of similar flavor. We also study the problem of approximating some of these quantities when using sampling. In particular, we show that a sample of size (O) over tilde (n/k) is sufficient, in some restricted cases, to estimate the above quantities. Remarkably, the sample size has only linear dependency on the dimension. C1 [Har-Peled, Sariel; Kumar, Nirman] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Har-Peled, S (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM sariel@illinois.edu; nkumar5@illinois.edu FU NSF AF awards [CCF-0915984, CCF-1217462]; National Science Foundation [NSF CCF-1252272] FX The authors thank Pankaj Agarwal and Kasturi Varadarajan for useful discussions on the problems studied in this paper. The authors also thank the anonymous referees for their detailed and insightful comments. NK thanks the National Science Foundation, grant NSF CCF-1252272, for a travel award to the conference FOCS 2012, where preliminary results of this paper were presented. CR Agarwal P. K., 1993, SIAM J COMPUT, V22, P540 Agarwal P.K., 1999, ADV DISCRETE COMPUTA, P1 Agarwal PK, 2004, J ACM, V51, P606, DOI 10.1145/1008731.1008736 Andoni A, 2008, COMMUN ACM, V51, P117, DOI 10.1145/1327452.1327494 Arya S, 1998, J ACM, V45, P891, DOI 10.1145/293347.293348 Arya S, 2002, SIAM PROC S, P147 Arya S, 2005, PROCEEDINGS OF THE SIXTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P535 Arya S, 2009, J ACM, V57, DOI 10.1145/1613676.1613677 AURENHAMMER F, 1991, COMPUT SURV, V23, P345 Berg M., 2010, COMP GEOM-THEOR APPL, V43, P493 Bernstein M., 2000, TECHNICAL REPORT Bespamyatnikh S. N., 1996, P 8 CAN C COMP GEOM, P252 CALLAHAN PB, 1995, J ASSOC COMPUT MACH, V42, P67, DOI 10.1145/200836.200853 Carmi P, 2005, ALGORITHMICA, V41, P233, DOI 10.1007/s00453-004-1130-1 Chan TM, 2010, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL SYMPOSIUM ON COMPUTATIONAL GEOMETRY (SCG'10), P1 Chan TM, 1998, DISCRETE COMPUT GEOM, V20, P359, DOI 10.1007/PL00009390 Chazelle B, 2008, COMMUN ACM, V51, P115, DOI 10.1145/1327452.1327493 CLARKSON KL, 1988, SIAM J COMPUT, V17, P830, DOI 10.1137/0217052 Clarkson K. L., 2006, NEAREST NEIGHBOR MET, P15 CLARKSON KL, 1989, DISCRETE COMPUT GEOM, V4, P387, DOI 10.1007/BF02187740 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Devroye L., 1982, HDB STAT, V2 Duda R.O., 2001, PATTERN CLASSIFICATI Fix E., 1949, 4 USAF SCH AV MED Gersho A., 1991, VECTOR QUANTIZATION Guibas L, 2011, COMPUTATIONAL GEOMETRY (SCG 11), P57 Har-Peled S., 2012, P 53 ANN IEEE S FDN, P430 Har-Peled S, 2011, DISCRETE COMPUT GEOM, V45, P462, DOI 10.1007/s00454-010-9248-1 Har-Peled S., 2011, MATH SURVEYS MONOGR, V173 Har-Peled S, 2011, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P854 Har-Peled S, 1999, SIAM J COMPUT, V28, P1182, DOI 10.1137/S0097539797325223 Har-Peled S, 2001, ANN IEEE SYMP FOUND, P94 Har-Peled S, 2006, LECT NOTES COMPUT SC, V4337, P33 Har-Peled S., 2012, THEORY COMPUT, V8, P321 Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 Kohonen T., 2001, SPRINGER SERIES INFO, V30 Kushilevitz E, 2000, SIAM J COMPUT, V30, P457, DOI 10.1137/S0097539798347177 Li Y, 2001, J COMPUT SYST SCI, V62, P516, DOI 10.1006/jcss.2000.1741 MARTINETZ T, 1994, NEURAL NETWORKS, V7, P507, DOI 10.1016/0893-6080(94)90109-0 MATOUSEK J, 1992, DISCRETE COMPUT GEOM, V8, P315, DOI 10.1007/BF02293051 Matousek J., 2002, GRAD TEXTS MATH, V212 MEISER S, 1993, INFORM COMPUT, V106, P286, DOI 10.1006/inco.1993.1057 Merigot Q., 2013, P 29 ANN S IN PRESS Minsky M. L., 1969, PERCEPTRONS RUPPERT J, 1995, J ALGORITHM, V18, P548, DOI 10.1006/jagm.1995.1021 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shakhnarovich G., 2006, NEAREST NEIGHBOR MET Silverman B. W., 1986, MONOGR STAT APPL PRO Tenenbaum JB, 1998, ADV NEUR IN, V10, P682 Weinberger KQ, 2006, INT J COMPUT VISION, V70, P77, DOI 10.1007/s11263-005-4939-z NR 50 TC 0 Z9 0 PU SIAM PUBLICATIONS PI PHILADELPHIA PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA SN 0097-5397 EI 1095-7111 J9 SIAM J COMPUT JI SIAM J. Comput. PY 2014 VL 43 IS 4 BP 1486 EP 1511 DI 10.1137/130916448 PG 26 WC Computer Science, Theory & Methods; Mathematics, Applied SC Computer Science; Mathematics GA AO8BC UT WOS:000341576200009 ER PT J AU Bhoir, V Potey, MA AF Bhoir, Varsha Potey, M. A. BE Ganesan, A Fong, S MizeraPietraszko, J TI Question Answering System : A Heuristic Approach SO 2014 FIFTH INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT) LA English DT Proceedings Paper CT 5th International Conference on the Applications of Digital Information and Web Technologies (ICADIWT) CY FEB 17-19, 2014 CL Chennai, INDIA SP IEEE Commun Soc, PRIST Univ DE question answering; natural language; information retrieval; information extraction; crawler; token; seed url AB The exponential growth in digital information led to the need of increasingly sophisticated search tools like the web search engines. Search engines return ranked list of documents and are less effective when users need precise answers to natural language questions. Question Answering systems involve this critical capability required for the next generation web search engines, to reduce the painstaking search through the long list of documents. The proposed solution of Question Answering system works for a specific domain of tourism which is a global and routine activity for leisure. The users have to struggle to navigate through these overloaded sites for a short piece of information of their interest. The crawler developed in the system gathers web page information which is processed using Natural Language Processing and Procedure programming for a specific keyword. The system returns precise short string answers or list to natural language questions related to tourism domain like distance, person, date, list of hotels, list of forts, etc. C1 [Bhoir, Varsha; Potey, M. A.] DY Patil Coll Engn, Dept Comp, Pune, Maharashtra, India. RP Bhoir, V (reprint author), DY Patil Coll Engn, Dept Comp, Pune, Maharashtra, India. EM varshabhoir279@gmail.com; mapotey@gmail.com CR Bilotti M. W., 2004, THESIS MIT Green B.F, 1961, P W COMP C, V19, P219 Grossan B., 1997, SEARCH ENGINES 0221 Harabagiu S., 2001, P 9 TEXT RETRIEVAL C Hermjakob U., 2001, P WORKSH OP DOM QUES, V12, P1 Hirschman L, 2002, NAT LANG ENG, V7, P275, DOI DOI 10.1017/S1351324901002807 Hu H., 2006, THESIS U TOKUSHIMA T Ittycheriah A., 2001, P 9 TEXT RETRIEVAL C Loni B., 2011, SURVEY STATE ART MET MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 2001, P 9 TEXT RETRIEVAL C Srihari R. R., 2000, P 6 APPL NAT LANG PR, P166, DOI 10.3115/974147.974170 Turtle H. R., 1991, THESIS U MASSACHUSET Voorhees E. M., 2001, P 9 TEXT RETR C NIST, P71 Woods W. A., 1973, P AM FED INF PROC SO, P441, DOI 10.1145/1499586.1499695 Zhang D., 2003, P 26 ANN INT ACM SIG, P26 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2258-1 PY 2014 BP 165 EP 170 PG 6 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BB0UC UT WOS:000340720700028 ER PT J AU Misopoulos, F Mitic, M Kapoulas, A Karapiperis, C AF Misopoulos, Fotis Mitic, Miljana Kapoulas, Alexandros Karapiperis, Christos TI Uncovering customer service experiences with Twitter: the case of airline industry SO MANAGEMENT DECISION LA English DT Article DE Social networks; Customer service; Airlines; Service industries ID SOCIAL MEDIA; MODEL AB Purpose - In this paper the authors present a study that uses Twitter to identify critical elements of customer service in the airline industry. The goal of the study was to uncover customer opinions about services by monitoring and analyzing public Twitter commentaries. The purpose of this paper is to identify elements of customer service that provide positive experiences to customers as well as to identify service processed and features that require further improvements. Design/methodology/approach - The authors employed the approach of sentiment analysis as part of the netnography study. The authors processed 67,953 publicly shared tweets to identify customer sentiments about services of four airline companies. Sentiment analysis was conducted using the lexicon approach and vector-space model for assessing the polarity of Twitter posts. Findings - By analyzing Twitter posts for their sentiment polarity the authors were able to identify areas of customer service that caused customer satisfaction, dissatisfaction as well as delight. Positive sentiments were linked mostly to online and mobile check-in services, favorable prices, and flight experiences. Negative sentiments revealed problems with usability of companies' web sites, flight delays and lost luggage. Evidence of delightful experiences was recorded among services provided in airport lounges. Originality/value - Paper demonstrates how sentiment analysis of Twitter feeds can be used in research on customer service experiences, as an alternative to Kano and SERVQUAL models. C1 [Misopoulos, Fotis; Kapoulas, Alexandros; Karapiperis, Christos] Univ Sheffield, Int Fac, CITY Coll, Business Adm & Econ Dept, Thessaloniki, Greece. [Mitic, Miljana] Univ Sheffield, South East European Res Ctr, Sch Management, Thessaloniki, Greece. RP Mitic, M (reprint author), Univ Sheffield, South East European Res Ctr, Sch Management, Thessaloniki, Greece. EM milmitic@seerc.org CR Arazy O, 2007, MIS QUART, V31, P525 Arnold S., 2011, KM WORLD, V20, P1 Boyd D., 2007, J COMPUT-MEDIAT COMM, V13, P56 Burton J., 2010, J MARKETING MANAGEME, V26, P230 Chandra N., 2011, INT J BUSINESS RES, V11, P76 Constantinides E., 2008, J DIRECT DATA DIGITA, V9, P231, DOI DOI 10.1057/PALGRAVE.DDDMP.4350098 Culnan MJ, 2010, MIS Q EXEC, V9, P243 Danisman T., 2008, AISB 2008 CONV COMM, P53 Dave K., 2003, P 12 INT WORLD WID W Deshpande M., 2010, BUSINESS INTELLIGENC, V15, P41 Garcia C., 2006, COSINE SIMILARITY TE Garg R, 2011, J MANAGE INFORM SYST, V28, P11 Gayo-Avello D, 2011, COMMUN ACM, V54, P121, DOI 10.1145/2001269.2001297 Geho P., 2010, ENTREPRENEURIAL EXEC, V15, P73 Godbole N., 2007, INT C WEBL SOC MED I, P26 Hine Christine, 2005, VIRTUAL METHODS ISSU Jacobson L., 2009, PUBLIC RELATIONS TAC, V16, P18 Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003 Kho N. D., 2010, KM WORLD, V19, P10 Koppel M, 2006, COMPUT INTELL, V22, P100, DOI 10.1111/j.1467-8640.2006.00276.x Kozinets RV, 2009, NETNOGRAPHY DOING ET Ku LW, 2009, J AM SOC INF SCI TEC, V60, P1486, DOI 10.1002/asi.21067 Li J.H., 2011, ADV PHYS CHEM, V2011, P1, DOI DOI 10.1371/J0URNAL.P0NE.0021506 Lula P., 2011, EC MANAGEMENT, V16, P1286 MacLeod M., 2010, AUTOMOTIVE LOGISTICS, V13, P6 Nasukawa T., 2003, K CAP 2003, P70 O'leary Daniel E., 2011, International Journal of Intelligent Systems in Accounting, Finance and Management, V18, DOI 10.1002/isaf.327 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savage N., 2011, COMMUN ACM, V53, P27 Seth N., 2005, International Journal of Quality Reliability Management, V22, DOI 10.1108/02656710510625211 Shahin A, 2009, TOTAL QUAL MANAG BUS, V20, P1003, DOI 10.1080/14783360903181867 Thelwall M, 2011, J AM SOC INF SCI TEC, V62, P406, DOI 10.1002/asi.21462 Tikkanen H, 2009, MANAGE DECIS, V47, P1357, DOI 10.1108/00251740910984596 Wei C.P., 2008, INFORM SYSTEMS E BUS, V8, P149 WRIGHT A, 2009, COMMUN ACM, V52, P14 Yesilada F., 2009, INT J BUSINESS RES, V9, P156 NR 36 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0025-1747 EI 1758-6070 J9 MANAGE DECIS JI Manag. Decis. PY 2014 VL 52 IS 4 BP 705 EP 723 DI 10.1108/MD-03-2012-0235 PG 19 WC Business; Management SC Business & Economics GA AN7FS UT WOS:000340765000004 ER PT J AU Nahin, AFMNH Alam, JM Mahmud, H Hasan, K AF Nahin, A. F. M. Nazmul Haque Alam, Jawad Mohammad Mahmud, Hasan Hasan, Kamrul TI Identifying emotion by keystroke dynamics and text pattern analysis SO BEHAVIOUR & INFORMATION TECHNOLOGY LA English DT Article DE human-computer interaction; affective computing; emotion detection; keystroke dynamics; machine learning; text pattern analysis; vector space model AB Emotion is a cognitive process and is one of the important characteristics of human beings that makes them different from machines. Traditionally, interactions between humans and machines like computers do not exhibit any emotional exchanges. If we could build any system that is intelligent enough to interact with humans that involves emotions, that is, it can detect user emotions and change its behaviour accordingly, then using machines could be more effective and friendly. Many approaches have been taken to detect user emotions. Affective computing is the field that detects user emotion in a particular moment. Our approach in this paper is to detect user emotions by analysing the keyboard typing patterns of the user and the type of texts (words, sentences) typed by them. This combined analysis gives us a promising result showing a substantial number of emotional states detected from user input. Several machine learning algorithms were used to analyse keystroke timing attributes and text pattern. We have chosen keystroke because it is the cheapest and most available medium to interact with computers. We have considered seven emotional classes for classifying the emotional states. For text pattern analysis, we have used vector space model with Jaccard similarity method to classify free-text input. Our combined approach showed above 80% accuracies in identifying emotions. C1 [Nahin, A. F. M. Nazmul Haque; Alam, Jawad Mohammad; Mahmud, Hasan; Hasan, Kamrul] Islamic Univ Technol IUT, Syst & Software Lab SSL, Dept Comp Sci & Engn CSE, Gazipur 1704, Bangladesh. RP Mahmud, H (reprint author), Islamic Univ Technol IUT, Syst & Software Lab SSL, Dept Comp Sci & Engn CSE, Gazipur 1704, Bangladesh. EM hasan@iut-dhaka.edu CR Aman S, 2007, LECT NOTES ARTIF INT, V4629, P196 Bergadano F., 2003, Intelligent Data Analysis, V7 Braves S, 2003, HUM FAC ER, P81 Carroll L., 2008, ALICES ADVENTURES WO Chandra A., 1997, HS 97 P 8 10 DEC GRA, P86, DOI [10.1109/IIS.1997.645194, DOI 10.1109/IIS.1997.645194] Danisman T., 2008, AISB 2008 CONV COMM, V2 Epp C., 2011, CHI 11 P 2011 ANN C, P715, DOI 10.1145/1978942.1979046 Frank E, 2005, DATA MINING PRACTICA Hektner J. M., 2007, EXPERIENCE SAMPLING Inkpen D, 2009, KNOWL ENG PRIN TECH, P3 Khanna P, 2010, INT J COMPUTER APPL, V11, P1, DOI 10.5120/1614-2170 Ma CL, 2005, Proceedings of the 2005 International Conference on Active Media Technology (AMT 2005), P546 Mishne G., 2005, P ACM SIGIR 2005 WOR, V19 Monrose F, 2000, FUTURE GENER COMP SY, V16, P351, DOI 10.1016/S0167-739X(99)00059-X EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068 Picard R. W., 2007, AFFECTIVE COMPUTING Russell JA, 2003, PSYCHOL REV, V110, P145, DOI 10.1037/0033-295X.110.1.145 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHERER KR, 1994, J PERS SOC PSYCHOL, V66, P310, DOI 10.1037/0022-3514.66.2.310 NR 19 TC 1 Z9 1 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 0144-929X EI 1362-3001 J9 BEHAV INFORM TECHNOL JI Behav. Inf. Technol. PY 2014 VL 33 IS 9 SI SI BP 987 EP 996 DI 10.1080/0144929X.2014.907343 PG 10 WC Computer Science, Cybernetics; Ergonomics SC Computer Science; Engineering GA AN0UN UT WOS:000340299600011 ER PT S AU Bharti, KK Singh, PK AF Bharti, Kusum Kumari Singh, Pramod Kumar BE Babu, BV Nagar, A Deep, K Pant, M Bansal, JC Ray, K Gupta, U TI A Survey on Filter Techniques for Feature Selection in Text Mining SO PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2012) SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT 2nd International Conference on Soft Computing for Problem Solving (SocProS) CY DEC 28-30, 2012 CL Jaipur, INDIA SP JK Lakshmipat Univ, Inst Engn & Technol HO JK Lakshmipat Univ DE Text mining; Text categorization; Text clustering; Feature extraction; Feature selection; Filter methods ID PRINCIPAL COMPONENT ANALYSIS; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; INFORMATION GAIN; CATEGORIZATION; CLASSIFICATION; SPACE AB A large portion of a document is usually covered by irrelevant features. Instead of identifying actual context of the document, such features increase dimensions in the representation model and computational complexity of underlying algorithm, and hence adversely affect the performance. It necessitates a requirement of relevant feature selection in the given feature space. In this context, feature selection plays a key role in removing irrelevant features from the original feature space. Feature selection methods are broadly categorized into three groups: filter, wrapper, and embedded. Filter methods are widely used in text mining because of their simplicity, computational complexity, and efficiency. In this article, we provide a brief survey of filter feature selection methods along with some of the recent developments in this area. C1 [Bharti, Kusum Kumari; Singh, Pramod Kumar] ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & Data Min Res Lab, Gwalior, Madhya Pradesh, India. RP Bharti, KK (reprint author), ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & Data Min Res Lab, Morena Link Rd, Gwalior, Madhya Pradesh, India. EM kkusum.bharti@gmail.com; pksingh@iiitm.ac.in CR Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 Chen XW, 2003, PATTERN RECOGN LETT, V24, P1925, DOI 10.1016/S0167-8655(03)00020-5 Chuang LY, 2011, EXPERT SYST APPL, V38, P12699, DOI 10.1016/j.eswa.2011.04.057 Chuang LY, 2011, COMPUT BIOL MED, V41, P228, DOI 10.1016/j.compbiomed.2011.02.004 Church K.W., 1990, J COMPUT LINGUIST, V27, P22 DEERWESTER S, 1988, P ASIS ANNU MEET, V25, P36 Ding Chris, 2005, Journal of Bioinformatics and Computational Biology, V3, P185, DOI 10.1142/S0219720005001004 Ferreira AJ, 2012, PATTERN RECOGN LETT, V33, P1794, DOI 10.1016/j.patrec.2012.05.019 Hall Mark A, 1999, THESIS U WAIKATO Hsu HH, 2011, EXPERT SYST APPL, V38, P8144, DOI 10.1016/j.eswa.2010.12.156 Li B, 2009, 2009 INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION, VOL I, P578, DOI 10.1109/ICMTMA.2009.453 Liu L., 2005, P NAT LANG PROC KNOW, P59 Liu Y, 2004, LECT NOTES COMPUT SC, V3314, P425 Meng JN, 2011, COMPUT MATH APPL, V62, P2793, DOI 10.1016/j.camwa.2011.07.045 Mitra P, 2002, IEEE T PATTERN ANAL, V24, P301, DOI 10.1109/34.990133 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Pearson K, 1901, PHILOS MAG, V2, P559 Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226 PUDIL P, 1994, PATTERN RECOGN LETT, V15, P1119, DOI 10.1016/0167-8655(94)90127-9 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 Shevade SK, 2003, BIOINFORMATICS, V19, P2246, DOI 10.1093/bioinformatics/btg308 Song W, 2009, COMPUT MATH APPL, V57, P1901, DOI 10.1016/j.camwa.2008.10.010 Tu C. J., 2006, P INT MULT ENG COMP, P138 Uguz H, 2012, COMPUT METH PROG BIO, V107, P598, DOI 10.1016/j.cmpb.2011.03.013 Uguz H, 2011, KNOWL-BASED SYST, V24, P1024, DOI 10.1016/j.knosys.2011.04.014 Unler A, 2011, INFORM SCIENCES, V181, P4625, DOI 10.1016/j.ins.2010.05.037 Yang C. H., 2009, J MED BIOL ENG, V30, P23 Liu YN, 2011, J BIONIC ENG, V8, P191, DOI 10.1016/S1672-6529(11)60020-6 NR 30 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-81-322-1602-5; 978-81-322-1601-8 J9 ADV INTELL SYST PY 2014 VL 236 BP 1545 EP 1559 DI 10.1007/978-81-322-1602-5_154 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BA7VR UT WOS:000337801800154 ER PT S AU Chen, CC Zheng, XL Zhou, CF Chen, DR AF Chen, Chaochao Zheng, Xiaolin Zhou, Chaofei Chen, Deren BE Huang, Z Liu, C He, J Huang, G TI Making Recommendations on Microblogs through Topic Modeling SO WEB INFORMATION SYSTEMS ENGINEERING - WISE 2013 WORKSHOPS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th Conference on Web Information Systems Engineering (WISE) CY OCT 13-15, 2013 CL Nanjing, PEOPLES R CHINA DE microblog; topic model; recommendation; field time AB The large-data era has made microblogs important platforms for propagating and searching for information. Analyzing microblog content with topic models facilitates the search for users and microblogs of interest from a vast amount of information. However, traditional topic model doesn't work well on microblog because these blogs are short and have irregular writing patterns. Considering that microblog have obviously concentration on the field and time they were posted, we propose a microblog recommender approach called time-field latent Dirichlet allocation (TF-LDA), which effectively makes topics more discriminative and thus improves recommender performance. An experiment shows that user and microblog recommendations based on TF-LDA increases accuracy compared with those based on traditional topic models. C1 [Chen, Chaochao; Zheng, Xiaolin; Zhou, Chaofei; Chen, Deren] Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China. RP Zheng, XL (reprint author), Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China. EM zjuccc@zju.edu.cn; xlzheng@zju.edu.cn; cfzhou@zju.edu.cn; drchen@zju.edu.cn CR [Anonymous], 2012, P 6 ACM C REC SYST Blei D. M., 2010, ARXIV10030783 Blei D. M., 2006, P 23 INT C MACH LEAR Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boyd-Graber J., 2010, ARXIV10024665 Chen C, 2012, PROCEEDINGS OF 2012 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2012), P2107 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Griffiths T.L., 2004, ADV NEURAL INFORM PR Hofmann T., 1999, P 22 ANN INT ACM SIG Hong L., 2010, P 1 WORKSH SOC MED A Java A., 2007, P 9 WEBKDD 1 SNA KDD LIN JH, 1991, IEEE T INFORM THEORY, V37, P145, DOI 10.1109/18.61115 Lu Y., 2008, P 17 INT C WORLD WID Naaman M., 2010, P 2010 ACM C COMP SU Ponte Jay M., 1998, P 21 ANN INT ACM SIG Ramage D, 2009, P 2009 C EMP METH NA, V1 Ramage Daniel, 2010, ICWSM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sousa D., 2010, P 2 INT WORKSH SEARC Wu S., 2012, P 6 ACM C REC SYST Wu X., 2011, P 1 INT S DIG FOOTPR Zhou C, 2012, RES RECOMMENDATION M NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-54370-8; 978-3-642-54369-2 J9 LECT NOTES COMPUT SC PY 2014 VL 8182 BP 252 EP 265 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BA6WR UT WOS:000337296200021 ER PT J AU Lang, S Kienberger, S Tiede, D Hagenlocher, M Pernkopf, L AF Lang, Stefan Kienberger, Stefan Tiede, Dirk Hagenlocher, Michael Pernkopf, Lena TI Geons - domain-specific regionalization of space SO CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE LA English DT Article DE integrated geons; composite geons; expert-oriented regionalization; conditioned information; policy-relevant ID SOCIOECONOMIC VULNERABILITY; GEOGRAPHIC REPRESENTATION; CLASSIFICATION; OBJECTS; GIS; UNCERTAINTY; INFORMATION; DELINEATION; PROJECT; MODEL AB The design of methods and tools to build adequate representations of complex geographical phenomena in a way that spatial patterns are emphasized is one of the core objectives of GIScience. In this paper, we build on the concept of geons as a strategy to represent and analyze latent spatial phenomena across different geographical scales (local, national, regional) incorporating domain-specific expert knowledge. Focusing on two types, we illustrate and exemplify how geons are generated and explored. So-called composite geons represent functional land-use classes, required for regional planning purposes. They are created via class modeling to translate interpretation schemes from mapping keys. Integrated geons, on the other hand, address abstract, yet policy-relevant phenomena such as societal vulnerability to hazards. They are delineated by regionalizing continuous geospatial data sets representing relevant indicators in a multidimensional variable space. Using the geon approach, we create spatially exhaustive sets of units, scalable to the level of policy intervention, homogenous in their domain-specific response, and independent from any predefined boundaries. From a GIScience perspective, we discuss either type of geons in a semantic hierarchy of geographic information constructs. Despite its validity for decision-making and its transferability across scales and application fields, the delineation of geons requires further methodological research to assess their statistical and conceptual robustness. C1 [Lang, Stefan; Kienberger, Stefan; Tiede, Dirk; Hagenlocher, Michael; Pernkopf, Lena] Salzburg Univ, Interfac Dept Geoinformat Z GIS, A-5020 Salzburg, Austria. RP Lang, S (reprint author), Salzburg Univ, Interfac Dept Geoinformat Z GIS, A-5020 Salzburg, Austria. EM stefan.lang@sbg.ac.at RI Tiede, Dirk/ OI Tiede, Dirk/0000-0002-5473-3344 CR Abler R.F., 1971, SPATIAL ORG GEOGRAPH Ahlqvist O, 2008, ENVIRON PLANN B, V35, P169, DOI 10.1068/b3344 Berry B. J., 1967, QUANTITATIVE GEOGR 1, P219 Berry B.J., 1968, PAP REG SCI, V21, P205, DOI 10.1007/BF01952730 Bibby P, 2000, ENVIRON PLANN B, V27, P583, DOI 10.1068/b2647 Birkman J., 2006, MEASURING VULNERABIL, P9 Blaschke T, 2010, ISPRS J PHOTOGRAMM, V65, P2, DOI 10.1016/j.isprsjprs.2009.06.004 Blaschke T., 2001, Z GEOINFORMATIONSSYS, V14, P12 CARVER SJ, 1991, INT J GEOGR INF SYST, V5, P321, DOI 10.1080/02693799108927858 Couclelis H, 2010, INT J GEOGR INF SCI, V24, P1785, DOI 10.1080/13658816.2010.484392 Cova TJ, 2002, INT J GEOGR INF SCI, V16, P509, DOI 10.1080/13658810210137040 Dragut L, 2014, ISPRS J PHOTOGRAMM, V88, P119, DOI 10.1016/j.isprsjprs.2013.11.018 Duque J. C., 2006, WORKING PAPERS RES I, V31 Foody GM, 2002, REMOTE SENS ENVIRON, V80, P185, DOI 10.1016/S0034-4257(01)00295-4 FOTHERINGHAM AS, 1991, ENVIRON PLANN A, V23, P1025, DOI 10.1068/a231025 Gibson J J, 1979, ECOLOGICAL APPROACH Goodchild MF, 1999, INT J GEOGR INF SCI, V13, P731, DOI 10.1080/136588199240996 Goodchild MF, 2007, INT J GEOGR INF SCI, V21, P239, DOI 10.1080/13658810600965271 Grigg D. B., 1967, MODELS GEOGRAPHY, P461 Guo D, 2008, INT J GEOGR INF SCI, V22, P801, DOI 10.1080/13658810701674970 Guo J. Y., 2004, TRANSPORT RES REC, V1898, P138, DOI 10.3141/1898-17 Hancock J. R., 1993, P INT S COMP ASS CAR, P218 Hernando A, 2012, INT J APPL EARTH OBS, V19, P214, DOI [10.1016/j.jag.201105.007, 10.1016/j.jag.2012.05.007] JOHNSTON RJ, 1970, ECON GEOGR, V46, P293, DOI 10.2307/143145 JOHNSTON RJ, 1968, ANN ASSOC AM GEOGR, V58, P575, DOI 10.1111/j.1467-8306.1968.tb01653.x Kienberger S, 2013, GEOSPATIAL HEALTH, V8, P313 Kienberger S, 2012, NAT HAZARDS, V64, P2001, DOI 10.1007/s11069-012-0174-9 Kienberger S., 2014, J SELECTED TOPICS AP Kienberger S, 2009, NAT HAZARD EARTH SYS, V9, P767 Kienberger S, 2013, NAT HAZARDS, V68, P1343, DOI 10.1007/s11069-012-0513-x Lambin EF, 1997, REMOTE SENS ENVIRON, V61, P181, DOI 10.1016/S0034-4257(97)00001-1 Landesanstalt-Fuer-Umweltschutz, 2003, HDB ERST PFLEG ENTW Lang Stefan, 2009, Geocarto International, V24, DOI 10.1080/10106040802121093 Lang S, 2008, GEOSPATIAL CROSSROAD, P180 Lang S, 2008, OBJECT BASED IMAGE A, P3, DOI 10.1007/978-3-540-77058-9_1 Lang S, 2010, J SPAT SCI, V55, P9, DOI 10.1080/14498596.2010.487639 Lang S., 2003, ESRI ANW INNSBR Long J, 2010, ENVIRON MANAGE, V46, P134, DOI 10.1007/s00267-010-9510-6 Longley P. A., 2001, GEOGRAPHIC INFORM SY Maclaurin G. J., 2012, P AUTOCARTO 2012 COL Malczewski J, 1999, GIS MULTICRITERIA DE Mark D. M., 1999, GEOGRAPHICAL INFORM, V2nd, P81 Masolo C, 1999, LECT NOTES COMPUT SC, V1661, P235 Moldan B., 2007, SUSTAINABILITY INDIC, P1 Nardo M., 2005, OECD WORKING PAPERS Ng RT, 2002, IEEE T KNOWL DATA EN, V14, P1003, DOI 10.1109/TKDE.2002.1033770 Openshaw S., 1984, MODIFIABLE AREAL UNI Pernkopf L., 2011, REGIORESOURCES Peuquet D. J., 2002, REPRESENTATIONS SPAC Raper J. F., 1999, GEOGRAPHICAL INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schape A., 2000, QUANTITATIVE GEOGRAP, P12 Schopfer E, 2008, OBJECT BASED IMAGE A, P785, DOI 10.1007/978-3-540-77058-9 Scott D., 2005, GEOJOURNAL, V61, P53, DOI 10.1007/S10708-004-0877-X Shannon C. E., 1949, MATH THEORY COMMUNIC Sharma J, 2000, NATURE, V404, P841, DOI 10.1038/35009043 Smith B., 1994, 11 EUR C ART INT Spence N. A., 1961, REG STUD, VII, P87 Strasser T., 2014, GEOSCIENCE REMOTE SE, V11, P559 Tate E, 2012, NAT HAZARDS, V63, P325, DOI 10.1007/s11069-012-0152-2 Tate E, 2013, ANN ASSOC AM GEOGR, V103, P526, DOI 10.1080/00045608.2012.700616 Tiede D, 2010, PHOTOGRAMM ENG REM S, V76, P193 TOBLER WR, 1970, ECON GEOGR, V46, P234, DOI 10.2307/143141 Tomanev J., 2009, REGION Voudouris V, 2010, INT J GEOGR INF SCI, V24, P1811, DOI 10.1080/13658816.2010.488237 Wise S, 2001, INT J GEOGR INF SCI, V15, P239, DOI 10.1080/13658810010010655 Yuan M., 2001, CARTOGR GEOGR INF SC, V28, P83, DOI 10.1559/152304001782173718 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 68 TC 3 Z9 3 PU TAYLOR & FRANCIS INC PI PHILADELPHIA PA 520 CHESTNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA SN 1523-0406 EI 1545-0465 J9 CARTOGR GEOGR INF SC JI Cartogr. Geogr. Inf. Sci. PY 2014 VL 41 IS 3 SI SI BP 214 EP 226 DI 10.1080/15230406.2014.902755 PG 13 WC Geography SC Geography GA AH2RI UT WOS:000335968300004 ER PT J AU Huang, BC Yu, G Karimi, HR AF Huang, Baocheng Yu, Guang Karimi, Hamid Reza TI The Finding and Dynamic Detection of Opinion Leaders in Social Network SO MATHEMATICAL PROBLEMS IN ENGINEERING LA English DT Article ID SWITCHED LINEAR-SYSTEMS; AVERAGE DWELL TIME; STABILITY AB It is valuable for the real world to find the opinion leaders. Because different data sources usually have different characteristics, there does not exist a standard algorithm to find and detect the opinion leaders in different data sources. Every data source has its own structural characteristics, and also has its own detection algorithm to find the opinion leaders. Experimental results show the opinion leaders and theirs characteristics can be found among the comments from the Weibo social network of China, which is like Facebook or Twitter in USA. C1 [Huang, Baocheng; Yu, Guang] Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China. [Karimi, Hamid Reza] Univ Agder, Fac Sci & Engn, Dept Engn, N-4898 Grimstad, Norway. RP Huang, BC (reprint author), Harbin Inst Technol, Sch Management, Harbin 150001, Peoples R China. EM huanghljhrbin@gmail.com RI Karimi, Hamid Reza/F-9743-2011 OI Karimi, Hamid Reza/0000-0001-7629-3266 FU National Natural Science Foundation of China [71171068]; Polish-Norwegian Research Programme [Pol-Nor/200957/47/2013] FX This work was also supported by the National Natural Science Foundation of China (Project no. 71171068) and the Polish-Norwegian Research Programme (Project no. Pol-Nor/200957/47/2013). The authors highly appreciate the above finanicial supports. CR Aschwanden M. J., 2012, ASTROPHYSICAL J, V756 Bodendorf F, 2010, FOURTH INTERNATIONAL CONFERENCE ON DIGITAL SOCIETY: ICDS 2010, PROCEEDINGS, P124, DOI 10.1109/ICDS.2010.29 Goyal A, 2008, P 17 ACM C INF KNOWL, P499, DOI DOI 10.1145/1458082.145814 Hengmin Z., 2009, P 2009 IEEE INT C IN, P266 Kokkoras F, 2008, LECT NOTES ARTIF INT, V5138, P110, DOI 10.1007/978-3-540-87881-0_11 Ku LW, 2007, J AM SOC INF SCI TEC, V58, P1838, DOI 10.1002/asi.20630 Ku LW, 2009, J AM SOC INF SCI TEC, V60, P1486, DOI 10.1002/asi.21067 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wiebe J, 2005, LECT NOTES COMPUT SC, V3406, P486 Yin S, 2014, IEEE T IND ELECTRON, V61, P2402, DOI 10.1109/TIE.2013.2273477 Yin S, 2012, J PROCESS CONTR, V22, P1567, DOI 10.1016/j.jprocont.2012.06.009 Yin S, 2013, INT J SYST SCI, V44, P1366, DOI 10.1080/00207721.2012.659708 Yin S, 2012, MATH PROBL ENG, DOI 10.1155/2012/832836 Zhao X., 2013, IEEE T IND ELECT Zhao X., 2013, IEEE T FUZZY SYSTEMS Zhao XD, 2012, SYST CONTROL LETT, V61, P1151, DOI 10.1016/j.sysconle.2012.08.010 Zhao XD, 2013, INT J ROBUST NONLIN, V23, P578, DOI 10.1002/rnc.2777 Zhao XD, 2012, IEEE T AUTOMAT CONTR, V57, P1809, DOI 10.1109/TAC.2011.2178629 Zhao XD, 2012, AUTOMATICA, V48, P1132, DOI 10.1016/j.automatica.2012.03.008 Zhao Xudong, 2013, AUTOMATICA NR 20 TC 0 Z9 0 PU HINDAWI PUBLISHING CORPORATION PI NEW YORK PA 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA SN 1024-123X EI 1563-5147 J9 MATH PROBL ENG JI Math. Probl. Eng. PY 2014 AR 328407 DI 10.1155/2014/328407 PG 7 WC Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications SC Engineering; Mathematics GA AC9YH UT WOS:000332890300001 ER PT J AU Xu, Z Luo, XF Liu, YH Mei, L Hu, CP AF Xu, Zheng Luo, Xiangfeng Liu, Yunhuai Mei, Lin Hu, Chuanping TI Measuring Semantic Relatedness between Flickr Images: From a Social Tag Based View SO SCIENTIFIC WORLD JOURNAL LA English DT Article ID NETWORK; CLASSIFICATION; INFORMATION; SEARCH AB Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, we aim at measuring the semantic relatedness of Flickr images. Firstly, four information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. Thirdly, the order information of tags is added to measure the semantic relatedness, which emphasizes the tags with high positions. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robustness of the proposedmethod. Moreover, some applications such as searching and faceted exploration are introduced using the proposed method, which shows that the proposed method has broad prospects on web based tasks. C1 [Xu, Zheng; Liu, Yunhuai; Mei, Lin; Hu, Chuanping] Minist Publ Secur, Res Inst 3, Shanghai 201142, Peoples R China. [Luo, Xiangfeng] Shanghai Univ, Shanghai 200472, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, 339 Bisheng Rd, Shanghai 201142, Peoples R China. EM xuzheng@shu.edu.cn FU National Science and Technology Major Project [2013ZX01033002-003]; National High Technology Research and Development Program of China (863 Program) [2013AA014601, 2013AA014603]; National Key Technology Support Program [2012BAH07B01]; National Science Foundation of China [61300202]; Science Foundation of Shanghai [13ZR1452900] FX This work was supported in part by the National Science and Technology Major Project under Grant no. 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant nos. 2013AA014601 and 2013AA014603, in part by National Key Technology Support Program under Grant no. 2012BAH07B01, in part by the National Science Foundation of China under Grant no. 61300202, and in part by the Science Foundation of Shanghai under Grant no. 13ZR1452900. CR Al-Khalifa H., 2006, P 2 INT IEEE C INN I, P1 Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 Bizer C., 2009, INT J SEMANT WEB INF, V5, P1 van den Broek P, 2010, SCIENCE, V328, P453, DOI 10.1126/science.1182594 Cai D, 2010, IEEE T KNOWL DATA EN, V22, P1262, DOI 10.1109/TKDE.2009.134 Cattuto C, 2007, AI COMMUN, V20, P245 Dalal N, 2005, PROC CVPR IEEE, P886 Evgeniou T, 2003, IEEE T KNOWL DATA EN, V15, P911, DOI 10.1109/TKDE.2003.1209008 Fan JP, 2009, IEEE T CIRC SYST VID, V19, P273, DOI 10.1109/TCSVT.2008.2009258 Fergus R, 2005, IEEE I CONF COMP VIS, P1816 Firth R, 1957, STUDIES LINGUISTIC A Goldberger J, 2006, IEEE T IMAGE PROCESS, V15, P449, DOI 10.1109/TIP.2005.860593 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Gong T., 2010, P 22 INT C TOOLS ART, P197 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Huang D., 2009, P 16 IEEE INT C IM P, P3325 Ji RR, 2010, PROC CVPR IEEE, P918, DOI 10.1109/CVPR.2010.5540118 Jia WJ, 2006, INT C PATT RECOG, P574 Lambiotte R, 2006, LECT NOTES COMPUT SC, V3993, P1114 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Maulik U, 2010, IEEE T SYST MAN CY A, V40, P664, DOI 10.1109/TSMCA.2010.2041225 Ramage D., 2009, P 2 ACM INT C WEB SE, P54, DOI 10.1145/1498759.1498809 Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 Rubner Y., 1998, P IEEE INT C COMP VI, P59 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenkel Ralf, 2008, P 31 ANN INT ACM SIG, P523 Schmid C, 1997, IEEE T PATTERN ANAL, V19, P530, DOI 10.1109/34.589215 Steinbach M, 2000, P KDD WORKSH TEXT MI Suchanek F. M., 2008, P 17 ACM C INF KNOWL, P223, DOI DOI 10.1145/1458082.1458114 Varma M, 2005, INT J COMPUT VISION, V62, P61, DOI 10.1007/s11263-005-4635-4 Vojnovic M, 2009, IEEE T KNOWL DATA EN, V21, P1133, DOI 10.1109/TKDE.2009.34 Wang G, 2012, IEEE T PATTERN ANAL, V34, P2177, DOI 10.1109/TPAMI.2012.29 Wang LW, 2005, IEEE T PATTERN ANAL, V27, P1334 Wu L, 2012, IEEE T PATTERN ANAL, V34, P863, DOI 10.1109/TPAMI.2011.195 Xu S., 2007, P 15 ACM INT C INF K, P1003, DOI 10.1145/1321440.1321596 Xu Z, 2011, CONCURR COMP-PRACT E, V23, P2496, DOI 10.1002/cpe.1816 Zhou D, 2008, P 17 INT C WORLD WID, P715, DOI DOI 10.1145/1367497.1367594 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 NR 41 TC 1 Z9 1 PU HINDAWI PUBLISHING CORPORATION PI NEW YORK PA 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA SN 1537-744X J9 SCI WORLD J JI Sci. World J. PY 2014 AR 758089 DI 10.1155/2014/758089 PG 12 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA AC6PJ UT WOS:000332646000001 ER PT J AU Ha, J Lee, JH Lee, S AF Ha, JongWoo Lee, Jung-Hyun Lee, SangKeun TI EPE: An Embedded Personalization Engine for Mobile Users SO IEEE INTERNET COMPUTING LA English DT Article AB The proposed embedded personalization engine (EPE) utilizes valuable in-device usage data to infer mobile user interests in a privacy-preserving manner. To provide users with personalized services, the proposed approach analyzes both the usage data inside a mobile device and service items - such as news articles and mobile apps - using the Open Directory Project (ODP) as a knowledge base. Embedded classification and ranking methodologies effectively match service items with inferred user interests. The scenario-based evaluation shows that EPE gives users highly personalized services with both reasonable perceived latency and little energy consumption. C1 [Ha, JongWoo; Lee, Jung-Hyun] Korea Univ, Coll Informat & Commun, Seoul, South Korea. [Lee, SangKeun] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea. RP Ha, J (reprint author), Korea Univ, Coll Informat & Commun, Seoul, South Korea. EM okcomputer@korea.ac.kr; jhbslpd@korea.ac.kr; yalphy@korea.ac.kr FU National Research Foundation of Korea (NRF); Ministry of Education, Science, and Technology [2011-0010325, 2012M3C4A7033344] FX This work was supported in part by the Basic Science Research Program and the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, and Technology (numbers 2011-0010325 and 2012M3C4A7033344). CR Ha J., 2010, P INT C INF KNOWL MA, P1935, DOI 10.1145/1871437.1871771 Ha Z., 2007, ACM T INFORM SYSTEMS, V25, P1 Lee J.- H., 2013, ACM T WEB, V7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 4 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1089-7801 EI 1941-0131 J9 IEEE INTERNET COMPUT JI IEEE Internet Comput. PD JAN-FEB PY 2014 VL 18 IS 1 BP 30 EP 37 PG 8 WC Computer Science, Software Engineering SC Computer Science GA AC8EN UT WOS:000332766200005 ER PT J AU Kopliku, A Pinel-Sauvagnat, K Boughanem, M AF Kopliku, Arlind Pinel-Sauvagnat, Karen Boughanem, Mohand TI Aggregated Search: A New Information Retrieval Paradigm SO ACM COMPUTING SURVEYS LA English DT Article DE Theory; Measurement; Algorithms; Information retrieval; aggregated search; focused retrieval; result aggregation; vertical search; information extraction; relational search ID WEB; EXTRACTION; DISCOVERY; DOCUMENTS; RELEVANCE; ONTOLOGY; WORDNET AB Traditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents, and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, etc.) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation, and question answering. Then we focus on more recent trends, namely cross vertical aggregated search and relational aggregated search, which are already present in current Web search. C1 [Kopliku, Arlind; Pinel-Sauvagnat, Karen; Boughanem, Mohand] Univ Toulouse 3, IRIT, F-31062 Toulouse, France. RP Kopliku, A (reprint author), Univ Toulouse 3, IRIT, 118 Rte Narbonne, F-31062 Toulouse, France. EM kopliku@irit.fr; sauvagnat@irit.fr; bougha@irit.fr CR Aditya Pal, 2008, P SIGIR 2008 WORKSH Agichtein E., 2000, P 5 ACM INT C DIG LI, P85, DOI 10.1145/336597.336644 Alfonseca E, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P58 Almuhareb Abdulrahman, 2004, EMNLP, P158 Arguello J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P315, DOI 10.1145/1571941.1571997 Arguello Jaime, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348343 Arguello J, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P691 Arguello J, 2011, LECT NOTES COMPUT SC, V6611, P141, DOI 10.1007/978-3-642-20161-5_15 Arguello Jaime, 2012, P CIKM 2012, P1293 Arguello Jaime, 2012, P WWW 2012 Arguello Jaime, 2011, P CIKM 2011, P201 Aumann Y, 2006, KNOWL INF SYST, V10, P1, DOI 10.1007/s10115-006-0014-x Avrahami TT, 2006, J AM SOC INF SCI TEC, V57, P347, DOI 10.1002/asi.20283 Balog K., 2009, P TREC 2009 Balog K., 2009, TREC 2009 WORKING NO Balog K., 2011, P TREC 2011 Balog K., 2010, P TREC 2010 Banko M, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2670 Baziz M, 2007, STUD FUZZ SOFT COMP, V215, P193 Bellare Kedar, 2007, P MACH LEARN WEB SEA Ben-Yitzhak O., 2008, P INT C WEB SEARCH W, P33, DOI 10.1145/1341531.1341539 Bizer C., 2008, P 17 INT C WORLD WID, P1265, DOI 10.1145/1367497.1367760 Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901 Bouidghaghen O, 2009, LECT NOTES ARTIF INT, V5822, P99, DOI 10.1007/978-3-642-04957-6_9 BOYCE B, 1982, INFORM PROCESS MANAG, V18, P105, DOI 10.1016/0306-4573(82)90033-4 Cafarella Michael J., 2006, TECHNICAL REPORT Cafarella M.J., 2008, P VLDB ENDOW, V1, P538 Cafarella MJ, 2009, P VLDB ENDOWMENT, V2, P1090 Callan Jamie, 2000, ADV INFORM RETRIEVAL, P235 Campinas S., 2011, 1 INT WORKSH ENT OR, P26 Chang C.-C., 2001, TECHNICAL REPORT Chen Hsin-Hsi, 2000, P 18 INT C COMP LING, P166 Church K., 1989, P ACL, P76, DOI DOI 10.3115/981623.981633 Clarke C L, 2008, P 31 ANN INT ACM SIG, P659, DOI 10.1145/1390334.1390446 Crescenzi V., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Dang H. T., 2007, P TREC 2007 Dang Hoa Tang, 2006, P 2006 DOC UND C Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26 Diaz F., 2009, P 2 ACM INT C WEB SE, P182, DOI DOI 10.1145/1498759.1498825 Diaz F, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P323, DOI 10.1145/1571941.1571998 Dong A., 2010, P 3 ACM INT C WEB SE, P11, DOI 10.1145/1718487.1718490 Downey D, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1034 Etzioni O., 2011, P 22 INT JOINT C ART, P3 Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001 Etzioni O, 2008, COMMUN ACM, V51, P68, DOI 10.1145/1409360.1409378 Frank John R., 2012, P TREC 2012 Geva Shlomo, 2009, P INEX 2008 Goecks Jeremy, 2002, P INT US INT C, P87 Gravano Luis, 1997, STARTS STANFORD PROP, V26, P207 Greenshpan O., 2009, P VLDB ENDOWMENT, V2, P538 Grice H. P., 1975, SYNTAX SEMANTICS, V3, P41, DOI DOI 10.1017/S0022226700005296 Grishman R., 1996, P 16 INT C COMP LING, P466 Gulli A., 2005, P 14 INT WORLD WID W, P1004 Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989 HALPIN H., 2010, P INT WORKSH EV SEM Hearst M. A., 1992, P 14 INT C COMP LING, P539 Hearst M. A., 1996, SIGIR Forum Hearst MA, 1998, LANG SPEECH & COMMUN, P131 Hennig S, 2006, LECT NOTES COMPUT SC, V4031, P332 Hirschman L, 2002, NAT LANG ENG, V7, P275, DOI DOI 10.1017/S1351324901002807 JI L., 2009, P CIKM 2009, P1357, DOI 10.1145/1645953.1646125 Jones Christopher B., 2009, ENCY DATABASE SYSTEM, P1227 Kamps Jaap, 2008, SIGIR FORUM, V42, P59, DOI DOI 10.1145/1480506.1480517 Kaptein R, 2010, INFORM RETRIEVAL, V13, P412, DOI 10.1007/s10791-010-9130-z Kato Makoto P., 2009, P CIKM 2009, P27, DOI 10.1145/1645953.1645960 Katz B., 2005, P AAAI 2005 WORKSH I Kelly Diane, 2007, P TEXT RETRIEVAL C 2 Kelly Diane, 2006, P TEXT RETRIEVAL C 2 Kennedy L., 2008, P 17 INT C WORLD WID, P297, DOI 10.1145/1367497.1367539 Kopliku A, 2011, LECT NOTES COMPUT SC, V7024, P117, DOI 10.1007/978-3-642-24583-1_12 Kopliku Arlind, 2011, P JDCL 2011, P397 Kopliku Arlind, 2011, P IEEE WIC ACM INT C, P154 Kopliku Arlind, 2011, P CIKM 2011, P515 Kopliku Arlind, 2009, TECHNICAL REPORT Kopliku Arlind, 2011, P CORIA 2011, P113 Kopliku Arlind, 2011, THESIS U P SABATIER Kopliku Arlind, 2013, P CORIA 2013 Krichen Ines, 2011, P INFORSID 2009, P385 Lalmas M., 2011, ADV TOPICS INFORM RE Lenzerini M., 2002, P 21 ACM SIGACT SIGM, P233 Li Xiao, 2008, P 31 ANN INT ACM SIG, P339, DOI 10.1145/1390334.1390393 Limaye G., 2010, PVLDB, V3, P1338 Lin C.-J., 2008, P SIGIR WORKSH FOC R Lin Thomas, 2010, P EMNLP 2010 Liu Ning, 2009, P INT C WORLD WID WE, P1075, DOI 10.1145/1526709.1526863 Macdonald Craig, 2009, SIGIR Forum, V43 Manning C.D., 2008, INTRO INFORM RETRIEV Manoj M, 2008, J SCI IND RES INDIA, V67, P739 McCurley Kevin S., 2001, P 10 INT C WORLD WID, P221, DOI DOI 10.1145/371920.372056 Meng Xiaofeng, 2003, P COMPSAC Moriceau V, 2010, INFORM RETRIEVAL, V13, P507, DOI 10.1007/s10791-010-9131-y Murdock V., 2008, SIGIR FORUM, V42, P80, DOI DOI 10.1145/1480506.1480520 Naaman M, 2006, COMPUT ENVIRON URBAN, V30, P418, DOI [10.1016/j.compenvurbsys.2006.02.001, 10.1016/j.conpenvurbsys.2006.02.001] Nie Z., 2007, P 16 INT C WORLD WID, P81, DOI 10.1145/1242572.1242584 Nie Zaiqing, 2007, P 3 BIENN C INN DAT, P235 Ou Shiyan, 2008, SIGIR 2008 WORKSH AG Paris C. L., 1988, Computational Linguistics, V14 Paris C., 2005, 05034 CSIRO ICT CTR Paris C, 2001, LECT NOTES ARTIF INT, V2109, P251 Paris C., 2006, TECHNICAL REPORT Paris Cecile, 2010, INFORM RETRIEVAL J, V44, P3 Pasca Marius, 2008, ACL, P19 Ponnuswami Ashok Kumar, 2011, P ACM C WEB SEARCH D, P715, DOI 10.1145/1935826.1935922 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 Pound J, 2012, P CIKM 2012, P305 Ranganathan Anand, 2009, P 18 ACM C INF KNOWL, P711, DOI 10.1145/1645953.1646044 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Rohr C., 2008, P MIR 2008, P371, DOI 10.1145/1460096.1460157 Roy S.B., 2010, P 2010 INT ACM SIGMO, P843 Sahoo N., 2006, P 15 ACM INT C INF K, P357, DOI 10.1145/1183614.1183667 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos Rodrygo L. T., 2011, P 3 INT C THEOR INF Sauper C., 2009, P JOINT C 47 ANN M A, P208 Sauvagnat K, 2006, INFORM SYST, V31, P621, DOI 10.1016/j.is.2005.11.007 Sekine Satoshi, 2002, P LREC 2002 Selberg Erik, 1995, P 4 INT WORLD WID WE, P195 Semantic, 2010, SEM SEARCH CHALL 201 Semantic, 2011, SEM SEARCH CHALL 201 Shokouhi M, 2007, INFORM PROCESS MANAG, V43, P169, DOI 10.1016/j.ipm.2006.04.003 Singhal A., 2012, INTRO KNOWLEDGE GRAP Sparck-Jones K., 2007, SIGIR FORUM, V41, P8, DOI DOI 10.1145/1328964.1328965 Srinivas K., 2011, INT J COMPUTER SCI I, V8, P359 Strotmann Andreas, 2008, P SIGIR 2008 WORKSH Suchanek F. M., 2007, P 16 INT C WORLD WID, P697, DOI DOI 10.1145/1242572.1242667 Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001 Suchanek F.M., 2009, P 18 INT C WORLD WID, P631, DOI 10.1145/1526709.1526794 Sushmita S, 2009, LECT NOTES COMPUT SC, V5721, P322, DOI 10.1007/978-3-642-03784-9_32 Sushmita Shanu, 2010, P ACM C INF KNOWL MA, P519, DOI 10.1145/1871437.1871506 TAC, 2011, P 4 TEXT AN C Taneva Bilyana, 2010, P 3 WSDM, P431, DOI 10.1145/1718487.1718541 Tokunaga K, 2005, LECT NOTES ARTIF INT, V3651, P106 Tran T, 2009, PROC INT CONF DATA, P405 Trotman Andrew, 2010, J INFORM RETRIEVAL Turney Peter D., 2001, P 12 EUR C MACH LEAR, P491 Vaid S, 2005, LECT NOTES COMPUT SC, V3633, P218 Vallet David, 2008, P SIGIR 2008E SING, P857, DOI 10.1145/1390334.1390541 Voorhees Ellen M., 2003, P HLT NAACL 2003, P109 WONG TL, 2004, P 2004 IEEE INT C DA, P257 Wong TL, 2009, DATA KNOWL ENG, V68, P107, DOI 10.1016/j.datak.2008.08.009 Wu Fei, 2008, P 14 ACM SIGKDD INT, P731, DOI 10.1145/1401890.1401978 Wu M., 1997, P 2 AUSTR DOC COMP S, P65 Yoshinaga Naoki, 2007, P WORKSH ONT, P55 Zhou K., 2011, P LSDS IR WORKSH CIK Zhou Ke, 2012, P ACM SIGIR C PORTL, P115 Zhu J., 2009, P 18 INT C WORLD WID, P101, DOI 10.1145/1526709.1526724 NR 146 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 EI 1557-7341 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD JAN PY 2014 VL 46 IS 3 AR 41 DI 10.1145/2523817 PG 31 WC Computer Science, Theory & Methods SC Computer Science GA AB5NS UT WOS:000331836000011 ER PT J AU Bechet, N Chauche, J Prince, V Roche, M AF Bechet, Nicolas Chauche, Jacques Prince, Violaine Roche, Mathieu TI How to Combine Text-Mining Methods to Validate Induced Verb-Object Relations? SO COMPUTER SCIENCE AND INFORMATION SYSTEMS LA English DT Article DE Text-Mining; Web-Mining; Syntactic Analysis AB This paper describes methods using Natural Language Processing approaches to extract and validate induced syntactic relations (here restricted to the Verb-Object relation). These methods use a syntactic parser and a semantic closeness measure to extract such relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vector-based approach, and finally different combinations of both techniques in order to rank induced Verb-Object relations. The Semantic Vector approach is a Roget-based method which computes a syntactic relation as a vector. Web Validation uses a search engine to determine the relevance of a syntactic relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results. C1 [Bechet, Nicolas] Univ Caen Basse Normandie, CNRS, GREYC, UMR 6072, F-14032 Caen, France. [Chauche, Jacques; Prince, Violaine; Roche, Mathieu] Univ Montpellier 2, CNRS, LIRMM, UMR 5506, F-34000 Montpellier, France. [Roche, Mathieu] AgroParisTech, Irstea, TETIS Cirad, F-34093 Montpellier 5, France. RP Bechet, N (reprint author), Univ Caen Basse Normandie, CNRS, GREYC, UMR 6072, F-14032 Caen, France. CR Bechet N., 2008, P INT C DIG INF MAN, P241 Bechet N, 2009, LECT NOTES COMPUT SC, V5478, P786, DOI 10.1007/978-3-642-00958-7_86 Bourigault D., 2002, ACT 9 C ANN TRAIT AU, P75 Bronzi M., 2012, P JOINT WORKSH AUT K, P19 Chauche J., 1984, P COL 1984, P11 Church K.W., 1989, P 27 ANN C ASS COMP, V16, P76 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Daille B., 1996, BALANCING ACT COMBIN, P49 Daille B., 1994, THESIS U PARIS 7 Fabre C., 2006, TALN06, P121 Faure D., 2000, THESIS U PARIS SUD Ferri C., 2002, P 19 INT C MACH LEAR, P139 Furstenau H., 2012, P JOINT C LEX COMP S, P180 Keller F, 2003, COMPUT LINGUIST, V29, P459, DOI 10.1162/089120103322711604 L'Homme Marie-Claude, 1998, CAHIERS LEXICOLOGIE, V73, P61 Larousse T., 1992, THESAURUS LAROUSSE I Lin D, 1998, 1 WORKSH COMP TERM, P57 Pantel P., 2004, HLT NAACL, V4, P321 Prince V., 2008, LREC 08, P438 Roche M, 2006, LECT NOTES COMPUT SC, V4278, P1107 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smadja F, 1996, COMPUT LINGUIST, V22, P1 Thanopoulos A., 2002, P LREC 02, V2, P620 Turney P.D., 2001, LNCS LNAI, V2167, P491 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Vivaldi J., 2001, LECT NOTES COMPUTER, V2167, P515 Weeds Julie, 2004, COLING 04, P1015 Wermter J., 2004, COLING 04, P980 Yan L., 2003, P 20 INT C MACH LEAR, VICML-2003, P848 NR 29 TC 1 Z9 1 PU COMSIS CONSORTIUM PI NOVI SAD PA UNIV NOVI SAD, FAC TECH SCI, TRG DOSITEJA OBRADOVICA 6, NOVI SAD, 21000, SERBIA SN 1820-0214 J9 COMPUT SCI INF SYST JI Comput. Sci. Inf. Syst. PD JAN PY 2014 VL 11 IS 1 BP 133 EP 155 DI 10.2298/CSIS130528021B PG 23 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA AB4SC UT WOS:000331779200011 ER PT J AU Jablonska-Sabuka, M Sitarz, R Kraslawski, A AF Jablonska-Sabuka, Matylda Sitarz, Robert Kraslawski, Andrzej TI Forecasting research trends using population dynamics model with Burgers' type interaction SO JOURNAL OF INFORMETRICS LA English DT Article DE Research trends; Population dynamics; Momentum; Burgers' equation ID RESEARCH-AND-DEVELOPMENT; DECISION-SUPPORT SYSTEM; PORTFOLIO SELECTION; NETWORK ANALYSIS; TECHNOLOGY; PROJECTS; WEB AB The increasing costs of research and the decreasing lifetime of products and processes make the decisions on allocation of R&D funds strategically important. Therefore, ability to predict research trends is crucial in minimizing risks of R&D expenditure planning. The purpose of this paper is to propose a model for efficient prediction of research trends in a chosen branch of science. The approach is based on population dynamics with Burgers' type global interaction and selective neighborhood. The model is estimated based on a training set. Then, an out-of-sample forecast is performed. The research trends of filtration and rectification processes were analyzed in this paper. The simulation results show that the model is able to predict the trends with a considerable accuracy and should, therefore, be tested on a wider range of research fields. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Jablonska-Sabuka, Matylda] Lappeenranta Univ Technol, Dept Math & Phys, FI-53851 Lappeenranta, Finland. [Sitarz, Robert; Kraslawski, Andrzej] Lappeenranta Univ Technol, Sch Ind Engn & Management, FI-53851 Lappeenranta, Finland. [Sitarz, Robert] Rzeszow Univ Technol, Fac Chem, PL-35959 Rzeszow, Poland. [Kraslawski, Andrzej] Lodz Univ Technol, Dept Proc Engn, Lodz, Poland. RP Jablonska-Sabuka, M (reprint author), Lappeenranta Univ Technol, Dept Math & Phys, POB 20, FI-53851 Lappeenranta, Finland. EM matylda.jablonska-sabuka@lut.fi RI Cong , Gao/A-3726-2011 CR Baskurt OK, 2011, SCIENTOMETRICS, V86, P645, DOI 10.1007/s11192-010-0298-1 Callon M., 1991, SCIENTOMETRICS, V22, P153 Chu PYV, 1996, COMPUT IND, V32, P141, DOI 10.1016/S0166-3615(96)00067-X Ding Y., 2001, INFORM PROCESSING MA, V37 Evans R, 2009, NAT REV DRUG DISCOV, V8, P189, DOI 10.1038/nrd2744 Fabry B., 2006, World Patent Information, V28, DOI 10.1016/j.wpi.2005.10.004 Hinich MJ, 2003, J AM SOC INF SCI TEC, V54, P161, DOI 10.1002/asi.10176 Hossain MD, 2012, SCIENTOMETRICS, V90, P57, DOI 10.1007/s11192-011-0507-6 Huang MH, 2012, J INFORMETR, V6, P457, DOI 10.1016/j.joi.2012.03.003 Hull D., 2001, World Patent Information, V23, DOI 10.1016/S0172-2190(01)00024-2 Jablonska M., 2011, THESIS LAPPEENRANTA Jablonska M., 2011, LECT NOTES EC MATH S, V652, P3 Kim H, 2012, J INFORMETR, V6, P97, DOI 10.1016/j.joi.2011.05.006 Kim J, 2009, J AM SOC INF SCI TEC, V60, P679, DOI 10.1002/asi.21035 Klapka J, 2002, EUR J OPER RES, V140, P434, DOI 10.1016/S0377-2217(02)00081-4 Krampen G, 2011, SCIENTOMETRICS, V87, P687, DOI 10.1007/s11192-011-0357-2 Linton JD, 2002, R&D MANAGE, V32, P139, DOI 10.1111/1467-9310.00246 Liu X., 2013, J INFORMETR, V7, P435 Lv PH, 2011, SCIENTOMETRICS, V88, P399, DOI 10.1007/s11192-011-0386-x Ma ZY, 2013, J AM SOC INF SCI TEC, V64, P1399, DOI 10.1002/asi.22844 Mierswa I., 2006, P 12 ACM SIGKDD INT MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Morale D., 2004, ANT COLONIES NATURE Oksendal B., 1995, STOCHASTIC DIFFERENT Rajapakse A, 2005, COMPUT CHEM ENG, V29, P1357, DOI 10.1016/j.compchemeng.2005.02.010 Ringuest JL, 2004, EUR J OPER RES, V154, P157, DOI 10.1016/S0377-2217(02)00708-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shelton RD, 2012, J AM SOC INF SCI TEC, V63, P498, DOI 10.1002/asi.21677 Sitarz R, 2012, COMPUT-AIDED CHEM EN, V30, P437 Tsai HH, 2011, SCIENTOMETRICS, V87, P425, DOI 10.1007/s11192-011-0353-6 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X Xiao CJ, 2013, J AM SOC INF SCI TEC, V64, P1284, DOI 10.1002/asi.22865 Zapata JC, 2008, COMPUT CHEM ENG, V32, P307, DOI 10.1016/j.compchemeng.2007.03.024 Zhang Y, 2009, J AM SOC INF SCI TEC, V60, P557, DOI 10.1002/asi.20993 NR 34 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1751-1577 EI 1875-5879 J9 J INFORMETR JI J. Informetr. PD JAN PY 2014 VL 8 IS 1 BP 111 EP 122 DI 10.1016/j.joi.2013.11.003 PG 12 WC Information Science & Library Science SC Information Science & Library Science GA AB4PH UT WOS:000331771900011 ER PT J AU Shen, B Zhao, YS AF Shen, Bo Zhao, Ying-Si TI An Experimental Study of Incremental SVD on Latent Semantic Analysis SO JOURNAL OF INTERNET TECHNOLOGY LA English DT Article DE Internet; Singular value decomposition; Latent semantic analysis; Experiment; Incremental SVD AB Singular value decomposition is an important method to factorize a matrix, which is useful for many applications, such as principal component analysis in statistics and latent semantic analysis in natural language text processing. In order to diminish the computational complexity, incremental update algorithm is developed, especially for analyzing relationships between text documents. However, it is at the cost of a reduction in precision. In this paper, we study the effect of incremental update on latent semantic analysis; and, by experiments, to investigate the trend downward of computing precision and then try to find the balance point between re-computation and incremental update. The results indicate that when increased documents are less than original documents, incremental update method has an approximate precision to re-computing method, and then its recall ratio will obviously decline. It could be helpful for deciding when the matrix factorization must be recomputed. C1 [Shen, Bo] Being Jiaotong Univ, Sch Elect & Informat Engn, Shanghai, Peoples R China. [Zhao, Ying-Si] Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China. RP Shen, B (reprint author), Being Jiaotong Univ, Sch Elect & Informat Engn, Shanghai, Peoples R China. EM bshen@bjtu.edu.cn; 10113165@bjtu.edu.cn FU National Natural Science Foundation of China [60972012, 61172072, 61271308]; Beijing Natural Science Foundation [4102047, 4112045] FX This work was partly supported by the National Natural Science Foundation of China (No. 60972012, 61172072, 61271308) and Beijing Natural Science Foundation (No. 4102047, 4112045). CR [Anonymous], 2003, NSF RES AWARDS ABSTR Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164 Bradford R.B., 2008, P 17 ACM C INF KNOWL, P153, DOI 10.1145/1458082.1458105 Brand M, 2006, LINEAR ALGEBRA APPL, V415, P20, DOI 10.1016/j.laa.2005.07.021 Bremner D, 2005, DISCRETE COMPUT GEOM, V33, P593, DOI 10.1007/s00454-004-1152-0 Caruana R, 2006, P 23 INT C MACH LEAR, V148, P161 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong H, 2011, CONCURR COMP-PRACT E, V23, P505, DOI 10.1002/cpe.1652 Dumais S., 2004, ANNU REV INFORM SCI, V38, P188, DOI DOI 10.1002/ARIS.1440380105 Everitt Brian S., 2011, MISCELLANEOUS CLUSTE Gregory W., 2009, NONPARAMETRIC STAT N Janowicz K., 2011, J SPATIAL INFORM SCI, V2, P29 Landauer Thomas K, 1998, DISCOURSE PROCESS, V25, P259 Lay DC, 1996, LINEAR ALGEBRA ITS A, V2nd Markovsky I, 2012, COMMUN CONTROL ENG, P1, DOI 10.1007/978-1-4471-2227-2_1 Rosen GL, 2011, BIOINFORMATICS, V27, P127, DOI 10.1093/bioinformatics/btq619 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar Badrul, 2002, INCREMENTAL SINGULAR Wajeed M. A., 2011, Proceedings of the 2011 World Congress on Information and Communication Technologies (WICT), DOI 10.1109/WICT.2011.6141232 Wikipedia, 2013, LAT SEM AN WU CFJ, 1983, ANN STAT, V11, P95, DOI 10.1214/aos/1176346060 Wu H.C., 2008, ACM T INFORM SYSTEMS, V26 Yasuo Matsuyama, 2011, P 2011 INT JOINT C N, P808 Zha HY, 1999, SIAM J SCI COMPUT, V21, P782, DOI 10.1137/S1064827597329266 NR 24 TC 0 Z9 0 PU NATL ILAN UNIV, JIT PI ILAN PA LIB & INFORMATION CTR, ILAN, 26047, TAIWAN SN 1607-9264 EI 2079-4029 J9 J INTERNET TECHNOL JI J. Internet Technol. PD JAN PY 2014 VL 15 IS 1 BP 35 EP 41 DI 10.6138/JIT.2014.15.1.04 PG 7 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA 300XR UT WOS:000330497800004 ER PT J AU Tian, F Gao, PD Li, LZ Zhang, WZ Liang, HJ Qian, YA Zhao, RM AF Tian, Feng Gao, Pengda Li, Longzhuang Zhang, Weizhan Liang, Huijun Qian, Yanan Zhao, Ruomeng TI Recognizing and regulating e-learners' emotions based on interactive Chinese texts in e-learning systems SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Sentiment classification; Interactive Chinese texts; Feature extraction and selection; Emotion regulation; E-learning ID ENVIRONMENT AB Emotional illiteracy exists in current e-learning environment, which will decay learning enthusiasm and productivity, and now gets more attentions in recent researches. Inspired by affective computing and active listening strategy, in this paper, a research and application framework of recognizing emotion based on textual interaction is presented first. Second, an emotion category model for e-learners is defined. Third, many Chinese metaphors are abstracted from the corpus according to the sentence semantics and syntax. Fourth, as the strategy of active learning, topic detection is used to detect the first turn in dialogs and recognize the type of emotion in the turn, which is different from the traditional emotion recognition approaches that try to classify every turn into an emotion category. Fifth, compared with Support Vector Machines (SVM), Naive Bayes, LogitBoost, Bagging, MultiClass Classifier, RBFnetwork, J48 algorithms and their corresponding cost-sensitive approaches, Random Forest and its corresponding cost-sensitive approaches achieve better results in our initial experiment of classifying the e-learners' emotions. Finally, a case-based reasoning for emotion regulation instance recommendation is proposed to guide the listener to regulate the negative emotion of a speaker, in which a weighted sum method of Chinese sentence similarity computation is adopted. The experimental result shows that the ratio of effective cases is 68%. (C) 2013 Elsevier B.V. All rights reserved. C1 [Tian, Feng; Gao, Pengda; Zhang, Weizhan; Liang, Huijun; Qian, Yanan; Zhao, Ruomeng] Xi An Jiao Tong Univ, MOE KLINNS, Xian 710049, Peoples R China. [Li, Longzhuang] Texas A&M Univ, Sch Engn & Comp Sci, Corpus Christi, TX USA. [Tian, Feng] Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Peoples R China. RP Zhang, WZ (reprint author), Xi An Jiao Tong Univ, MOE KLINNS, Xian 710049, Peoples R China. EM zhangwzh@mail.xjtu.edu.cn FU National Science Foundation of China [61103239, 61070072, 61103160]; National Key Technologies R&D Program of China [2013BAK09B01]; Fundamental Research Funds for the Central Universities [xjj20100057] FX This project is partially supported by the National Science Foundation of China under Grant Nos. 61103239, 61070072, 61103160, the National Key Technologies R&D Program of China under Grant No. 2013BAK09B01, and the Fundamental Research Funds for the Central Universities under Grant No. xjj20100057. The authors would like to thank the editor and anonymous reviewers for their valuable comments and suggestions, which were helpful in improving the paper. CR AlZoubi O, 2012, IEEE T AFFECT COMPUT, V3, P298, DOI 10.1109/T-AFFC.2012.4 Ayadi M. E., 2011, PATTERN RECOGN, V44, P572 Bai X, 2011, DECIS SUPPORT SYST, V50, P732, DOI 10.1016/j.dss.2010.08.024 Calvo RA, 2012, IEEE INTELL SYST, V27, P86, DOI 10.1109/MIS.2012.110 Chao C.J., 2012, 2012 IEEE 4 INT C DI, P221 D'Mello S., 2012, ACM T INTERACTIVE IN, V2, P224 D'Mello S, 2007, IEEE INTELL SYST, V22, P53, DOI 10.1109/MIS.2007.79 Durant K.T., 2006, P WORKSH WEB MIN WEB, P1340 Ekman P., 1999, HDB COGNITION EMOTIO Gokulakrishnan B, 2012, INT CONF ADV ICT, P182 GROSS JJ, 1995, CLIN PSYCHOL-SCI PR, V2, P151 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Heraz A, 2007, PROC WRLD ACAD SCI E, V25, P323 Huang C.C., 2004, P IEEE INT C ADV LEA, P326 Huang X.X., 2005, LECT NOTES COMPUTER, V3784 Iovane G., 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), DOI 10.1109/CISIS.2012.207 Izard CE, 2001, EMOTION, V1, P249, DOI 10.1037//1528-3542.1.3.249 Jaques PA, 2007, COMPUT EDUC, V49, P360, DOI 10.1016/j.compedu.2005.09.002 Kapur A, 2005, LECT NOTES COMPUT SC, V3784, P1 Khan K, 2009, P 2009 3 IEEE INT C, P217 Klein J, 2002, INTERACT COMPUT, V14, P119 Li M.C., 2004, J AGING STUD, V18, P261 Litman DJ, 2006, SPEECH COMMUN, V48, P559, DOI 10.1016/j.specom.2005.09.008 Liu B, 2010, CH CRC MACH LEARN PA, P627 Liu CL, 2012, IEEE T SYST MAN CY C, V42, P397, DOI 10.1109/TSMCC.2011.2136334 Liu R., 2010, P 5 INT C COMP SCI E, P24 Lu J., 2004, P 2 INT C INF TECHN, P8 Ma C.L., 2005, LECT NOTES COMPUTER, V3784 McNaughton D., 2008, TOP EARLY CHILD SPEC, V27, P223, DOI DOI 10.1177/0271121407311241 Metallinou A, 2012, IEEE T AFFECT COMPUT, V3, P184, DOI 10.1109/T-AFFC.2011.40 Myers D. G., 2009, PSYCHOLOGY NUGENT WR, 1995, RES SOCIAL WORK PRAC, V5, P152, DOI 10.1177/104973159500500202 Pekrun R, 2002, EDUC PSYCHOL-US, V37, P91, DOI 10.1207/S15326985EP3702_4 Picard R. W., 1997, AFFECTIVE COMPUTING Picard R.W., 1995, 321 MIT MED LAB Plutchik R., 1980, THEORIES EMOTION, V1 Qu L., 2008, P NTCIR 7 WORKSH M T, P16 Raskin J., 1995, PERSON CTR THERAPY Rodrigo MMT, 2012, IEEE T AFFECT COMPUT, V3, P224, DOI 10.1109/T-AFFC.2011.41 Salovey P., 1995, EMOTIONAL INTELLIGEN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sriram S., 2012, SERVICE ORIENTED COM, P1 Tellegen A, 1999, PSYCHOL SCI, V10, P297, DOI 10.1111/1467-9280.00157 Thayer R. E., 1989, BIOPSYCHOLOGY MOOD A Thelwall M, 2010, J AM SOC INF SCI TEC, V61, P2544, DOI 10.1002/asi.21416 Tian F., 2011, P 2011 IEEE 11 INT C, P587 Tian F., 2007, WORKS DESIGN, P973 Tian F., 2006, P IEEE CSCWD2006, P1 VandenBos GR, 2007, APA DICT PSYCHOL Wang W.B., 2012, 2012 INT C PRIV SEC, P587 Wang XW, 2010, PROCEEDINGS OF SHANGHAI CONFERENCE ON MANAGEMENT OF TECHNOLOGY (MOT 2010), P203 Wang XH, 2008, LECT NOTES COMPUT SC, V4993, P592 Wang Y.J., 2009, IEEE T MULTIMEDIA, V11, P10 Wu CH, 2006, ACM T ASIAN LANGUAGE, V5, P165, DOI 10.1145/1165255.1165259 Ye L., 2012, 2012 INT C MACH LEAR, V5, P2019 Zhang WZ, 2012, J NETW COMPUT APPL, V35, P20, DOI 10.1016/j.jnca.2011.02.013 Zhao J., 2008, P C EMP METH NAT LAN, P203 NR 57 TC 1 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD JAN PY 2014 VL 55 BP 148 EP 164 DI 10.1016/j.knosys.2013.10.019 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 287RU UT WOS:000329560700014 ER PT J AU Huang, FL Zhang, SC He, MH Wu, XD AF Huang, Faliang Zhang, Shichao He, Minghua Wu, Xindong TI Clustering web documents using hierarchical representation with multi-granularity SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE web document clustering; hierarchical representation; multi-granularity ID INFORMATION GRANULATION; MODEL AB Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with "false correlation". In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a two-phase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problem resulted from the sparse term-paragraph matrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerance-rough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score. C1 [Huang, Faliang] Fujian Normal Univ, Fac Software, Fuzhou 350007, Peoples R China. [Zhang, Shichao] Guangxi Normal Univ, Coll Comp Sci & IT, Guilin 541004, Peoples R China. [He, Minghua] Aston Univ, Birmingham B4 7ET, Aston Triangle, England. [Wu, Xindong] Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA. [Zhang, Shichao] Univ Technol Sydney, Fac Engn & Informat Technol, Broadway, NSW 2007, Australia. RP Zhang, SC (reprint author), Guangxi Normal Univ, Coll Comp Sci & IT, Guilin 541004, Peoples R China. EM faliang.huang@gmail.com; zhangsc@gxnu.edu.cn; m.he1@aston.ac.uk; xwu@cems.uvm.edu FU Australian Research Council (ARC) [DP0985456]; Nature Science Foundation (NSF) of China [61170131]; China "1000-Plan" National Distinguished Professorship; China 863 Program [SQ2011AAJY2742]; Humanity and Social Science Youth foundation of Ministry of Education of China [12YJCZH074]; Foundation of Fujian Educational Committee [JB11037]; Young Elitist Cultivation Foundation of Fujian Normal University [fjsdjk2012082]; Guangxi Natural Science Foundation [2012GXNSFGA060004]; Guangxi "Bagui" Teams for Innovation and Research; Jiangsu Provincial Key Laboratory of E-business at the Nanjing University of Finance and Economics FX This work is supported in part by the Australian Research Council (ARC) under large grant DP0985456; the Nature Science Foundation (NSF) of China under grant 61170131; the China "1000-Plan" National Distinguished Professorship; the China 863 Program under grant SQ2011AAJY2742; Humanity and Social Science Youth foundation of Ministry of Education of China under grant 12YJCZH074; Foundation of Fujian Educational Committee under grant JB11037; Young Elitist Cultivation Foundation of Fujian Normal University under grant fjsdjk2012082; the Guangxi Natural Science Foundation under grant 2012GXNSFGA060004; the Guangxi "Bagui" Teams for Innovation and Research; and the Jiangsu Provincial Key Laboratory of E-business at the Nanjing University of Finance and Economics. CR Beil F., 2002, P 8 ACM SIGKDD INT C, P436, DOI DOI 10.1145/775047.775110 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bruno E, 2011, WORLD WIDE WEB, V14, P623, DOI 10.1007/s11280-011-0124-6 Chen CL, 2011, KNOWL INF SYST, V28, P687, DOI 10.1007/s10115-010-0364-2 Cui JW, 2011, KNOWL INF SYST, V27, P193, DOI 10.1007/s10115-010-0307-y Derrick C., 2008, P 17 C INF KNOWL MAN, P409 Farahat AK, 2011, KNOWL INF SYST, V28, P365, DOI 10.1007/s10115-010-0367-z Fodeh S, 2011, KNOWL INF SYST, V28, P395, DOI 10.1007/s10115-010-0370-4 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hossain M.S., 2007, P 7 IEEE INT C DAT M, P417 Hotho A., 2002, KUNSTLICHE INTELLIGE, V04, P48 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Huang FL, 2006, LECT NOTES COMPUT SC, V3841, P85 Huang FL, 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering, Vols 1 and 2, P262, DOI 10.1109/ISKE.2008.4730938 Jing L., 2003, P SIAM SDM WORKSH TE Kazakov D., 2004, P 3 WORKSH ROB METH, P104, DOI 10.3115/1621445.1621458 Keller M, 2005, LECT NOTES COMPUT SC, V3697, P667 Khy S, 2008, WORLD WIDE WEB, V11, P1, DOI 10.1007/s11280-007-0018-9 Kryszkiewicz M., 1998, STUDIES FUZZINESS SO, V18, P422 Lang Ngo Chi, 2003, TOLERANCE ROUGH SET Leung CWK, 2011, WORLD WIDE WEB, V14, P187, DOI 10.1007/s11280-011-0117-5 Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 Liu N., 2005, P 5 IEEE INT C DAT M, P725 Ma J, 2012, IEEE T SYST MAN CY A, V42, P784, DOI 10.1109/TSMCA.2011.2172205 Parapar J, 2009, LECT NOTES COMPUT SC, V5478, P645, DOI 10.1007/978-3-642-00958-7_61 Park S, 2011, IEICE T INF SYST, VE94D, P1227, DOI 10.1587/transinf.E94.D.1227 Park S, 2009, LECT NOTES COMPUT SC, V5864, P281 Pawlak Z., 1998, P 1998 IEEE INT C FU, P106 Pawlak Z., 1991, ROUGH SETS THEORETIC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siivola V., 2005, P INTERSPEECH, P1309 Steinbach M., 2000, KDD 2000 WORKSH TEXT Theodosiou T, 2008, BIOINFORMATICS, V24, P1935, DOI 10.1093/bioinformatics/btn318 Tsai FS, 2011, KNOWL INF SYST, V29, P419, DOI 10.1007/s10115-010-0372-2 Varelas G., 2005, P 7 ANN ACM INT WORK, P10, DOI 10.1145/1097047.1097051 Wang F., 2011, SDM, P908 Yao YY, 2004, LECT NOTES COMPUT SC, V3100, P232 Yao YY, 2006, P SPIE C DAT MIN INT, P1 Yao Y.Y., 2003, INFORM RETRIEVAL CLU, P299 Yao YY, 2001, INT J INTELL SYST, V16, P87, DOI 10.1002/1098-111X(200101)16:1<87::AID-INT7>3.0.CO;2-S Zadeh L. A., 1998, Soft Computing, V2, DOI 10.1007/s005000050030 Zadeh LA, 1997, FUZZY SET SYST, V90, P111, DOI 10.1016/S0165-0114(97)00077-8 Zheng W., 1984, ARCHITECTURE PARAGRA NR 43 TC 1 Z9 2 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X EI 1573-1413 J9 WORLD WIDE WEB JI World Wide Web PD JAN PY 2014 VL 17 IS 1 BP 105 EP 126 DI 10.1007/s11280-012-0197-x PG 22 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 289FP UT WOS:000329667400005 ER PT J AU Luo, XF Zhang, J Ye, FY Wang, P Cai, CL AF Luo, Xiangfeng Zhang, Jun Ye, Feiyue Wang, Peng Cai, Chuanliang TI Power Series Representation Model of Text Knowledge Based on Human Concept Learning SO IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS LA English DT Article DE Cognitive informatics; human concept learning; knowledge representation; semantic search; text understanding ID COGNITIVE INFORMATICS; LINEAR SEPARABILITY; CLASSIFICATION; ALLOCATION; BRAIN AB How to build a text knowledge representation model, which carries rich knowledge and has a flexible reasoning ability as well as can be automatically constructed with a low computational complexity, is a fundamental challenge for reasoning-based knowledge services, especially with the rapid growth of web resources. However, current text knowledge representation models either lose much knowledge [e. g., vector space model (VSM)] or have a high complex computation [e. g., latent Dirichlet allocation (LDA)]; even some of them cannot be constructed automatically [e. g., web ontology language, (OWL)]. In this paper, a novel text knowledge representation model, power series representation (PSR) model, which has a low complex computation in text knowledge constructing process, is proposed to leverage the contradiction between carrying rich knowledge and automatic construction. First, concept algebra of human concept learning is developed to represent text knowledge as the form of power series. Then, degree-2 power series hypothesis is introduced to simplify the proposed PSR model, which can be automatically constructed with a lower complex computation and has more knowledge than the VSM and LDA. After that, degree-2 power series hypothesis-based reasoning operations are developed, which provide a more flexible reasoning ability than OWL and LDA. Furthermore, experiments and comparisons with current knowledge representation models show that our model has better characteristics than others when representing text knowledge. Finally, a demo is given to indicate that PSR model has a good prospect over the area of web semantic search. C1 [Luo, Xiangfeng; Zhang, Jun; Ye, Feiyue; Cai, Chuanliang] Shanghai Univ, High Performance Comp Ctr, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. [Wang, Peng] Jiangnan Inst Comp Technol, Wuxi 214083, Peoples R China. RP Luo, XF (reprint author), Shanghai Univ, High Performance Comp Ctr, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. EM luoxf@shu.edu.cn; zhangjun_haha@shu.edu.cn; yefy@shu.edu.cn; wangpeng_shu@shu.edu.cn; caichuanliang@shu.edu.cn FU National Science Foundation of China [91024012, 61071110]; Shanghai Leading Academic Discipline Project [J50103] FX This work was supported by the National Science Foundation of China under Grant 91024012, Grant 61071110, and by the Shanghai Leading Academic Discipline Project under Grant J50103. This paper was recommended by Associate Editor J. A. Keane. CR Agrawal R., 1994, P 20 INT C VER LARG, P487 [Anonymous], 2004, OWL WEB ONTOLOGY LAN Blei David M., 2006, ADV NEURAL INFORM PR, V18 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bollegala D, 2011, IEEE T KNOWL DATA EN, V23, P977, DOI 10.1109/TKDE.2010.172 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fang N., 2010, J SOFTW SCI COMPUT I, V1, P61 Feldman J, 2003, J MATH PSYCHOL, V47, P75, DOI 10.1016/S0022-2496(02)00025-1 Feldman J, 2003, CURR DIR PSYCHOL SCI, V12, P227, DOI 10.1046/j.0963-7214.2003.01267.x Feldman J, 2006, J MATH PSYCHOL, V50, P339, DOI 10.1016/j.jmp.2006.03.002 Feldman J, 2000, NATURE, V407, P630, DOI 10.1038/35036586 Fikes R., 2000, AXIOMATIC SEMANTICS HAYESROTH B, 1977, J VERB LEARN VERB BE, V16, P321, DOI 10.1016/S0022-5371(77)80054-6 Heitmann B, 2012, IEEE T SYST MAN CY C, V42, P51, DOI 10.1109/TSMCC.2011.2145370 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jordan M., 1999, LEARNING GRAPHICAL M Knauf R, 2007, IEEE T SYST MAN CY A, V37, P120, DOI 10.1109/TSMCA.2006.886365 Li Q, 2010, INFORM SCIENCES, V180, P4929, DOI 10.1016/j.ins.2010.08.044 Liu A, 2010, IEEE T SERV COMPUT, V3, P46, DOI 10.1109/TSC.2009.28 Luo X., 2010, P 9 INT C COGN INF, P383 Luo XF, 2010, IEEE T LEARN TECHNOL, V3, P344, DOI 10.1109/TLT.2010.26 Luo XF, 2009, CONCURR COMP-PRACT E, V21, P2018, DOI 10.1002/cpe.1460 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 McCallum A., 2007, J ARTIF INTELL RES, V29, P249 MEDIN DL, 1981, J EXP PSYCHOL-HUM L, V7, P355, DOI 10.1037/0278-7393.7.5.355 Milea V, 2012, IEEE T SYST MAN CY B, V42, P268, DOI 10.1109/TSMCB.2011.2162582 MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037//0033-295X.101.2.343 Minda JP, 2002, J EXP PSYCHOL LEARN, V28, P275, DOI 10.1037//0278-7393.28.2.275 Ni WJ, 1998, J PSYCHOLINGUIST RES, V27, P515, DOI 10.1023/A:1024996828734 Perusich K, 2006, IEEE T SYST MAN CY C, V36, P810, DOI 10.1109/TSMCC.2005.855509 Rayner K, 1998, PSYCHOL BULL, V124, P372, DOI 10.1037/0033-2909.124.3.372 Rosen-Zvi M., 2004, P 20 C UNC ART INT, P487 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sloman SA, 1998, COGNITIVE SCI, V22, P189, DOI 10.1207/s15516709cog2202_2 Smith JD, 1997, J EXP PSYCHOL LEARN, V23, P659, DOI 10.1037/0278-7393.23.3.659 Tao XH, 2011, IEEE T KNOWL DATA EN, V23, P496, DOI 10.1109/TKDE.2010.145 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 W3C Recommendation, 1999, RES DESCR FRAM RDF M Wang Y., 2007, INT J COGNITIVE INFO, V1, P64, DOI DOI 10.4018/JCINI.2007070105 Wang Y, 2006, IEEE T SYST MAN CY C, V36, P124, DOI 10.1109/TSMCC.2006.871126 Wang Y., 2007, INT J COGNITIVE INFO, V1, P1, DOI DOI 10.4018/JCINI.2007010101 Wang YX, 2006, IEEE T SYST MAN CY C, V36, P203, DOI 10.1109/TSMCC.2006.871151 Yao YY, 2009, IEEE T SYST MAN CY B, V39, P855, DOI 10.1109/TSMCB.2009.2013334 Zadeh A. L., 1975, FUZZY SETS THEIR APP NR 44 TC 2 Z9 2 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 2168-2216 J9 IEEE T SYST MAN CY-S JI IEEE Trans. Syst. Man Cybern. -Syst. PD JAN PY 2014 VL 44 IS 1 BP 86 EP 102 DI 10.1109/TSMCC.2012.2231674 PG 17 WC Automation & Control Systems; Computer Science, Cybernetics SC Automation & Control Systems; Computer Science GA 280UF UT WOS:000329055600007 ER PT J AU Uysal, AK Gunal, S AF Uysal, Alper Kursat Gunal, Serkan TI The impact of preprocessing on text classification SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Pattern recognition; Text categorization; Text classification; Text preprocessing ID FEATURE-SELECTION; CATEGORIZATION; INFORMATION; ALGORITHM; MODEL AB Preprocessing is one of the key components in a typical text classification framework. This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy, text domain, text language, and dimension reduction. For this purpose, all possible combinations of widely used preprocessing tasks are comparatively evaluated on two different domains, namely e-mail and news, and in two different languages, namely Turkish and English. In this way, contribution of the preprocessing tasks to classification success at various feature dimensions, possible interactions among these tasks, and also dependency of these tasks to the respective languages and domains are comprehensively assessed. Experimental analysis on benchmark datasets reveals that choosing appropriate combinations of preprocessing tasks, rather than enabling or disabling them all, may provide significant improvement on classification accuracy depending on the domain and language studied on. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Uysal, Alper Kursat; Gunal, Serkan] Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. RP Uysal, AK (reprint author), Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. EM akuysal@anadolu.edu.tr; serkangunal@anadolu.edu.tr CR Asuncion A., 2007, UCI MACHINE LEARNIN Aydin R., 2012, AWERPROCEDIA INFORM, V1, P1007 Can F, 2008, J AM SOC INF SCI TEC, V59, P407, DOI 10.1002/asi.20750 Chen YT, 2011, EXPERT SYST APPL, V38, P3085, DOI 10.1016/j.eswa.2010.08.100 Cheng N, 2011, DIGIT INVEST, V8, P78, DOI 10.1016/j.diin.2011.04.002 Duwairi R, 2009, J AM SOC INF SCI TEC, V60, P2347, DOI 10.1002/asi.21173 Feng GZ, 2012, INFORM PROCESS MANAG, V48, P283, DOI 10.1016/j.ipm.2011.08.002 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Ghiassi M, 2012, EXPERT SYST APPL, V39, P10967, DOI 10.1016/j.eswa.2012.03.027 Goncalves Carlos Adriano, 2010, Pattern Recognition in Information Systems. Proceedings of the 10th International Workshop on Pattern Recognition in Information Systems (PRIS 2010) Gunal S, 2006, LECT NOTES COMPUT SC, V4105, P635 Gunal S, 2008, INFORM SCIENCES, V178, P3716, DOI 10.1016/j.ins.2008.06.001 Joachims T., 1997, 14 INT C MACH LEARN, P143 Theodoridis S, 2009, PATTERN RECOGNITION, 4RTH EDITION, P1 Kumar MA, 2010, PATTERN RECOGN LETT, V31, P1437, DOI 10.1016/j.patrec.2010.02.015 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Liu HW, 2009, PATTERN RECOGN, V42, P1330, DOI 10.1016/j.patcog.2008.10.028 Maks I, 2012, DECIS SUPPORT SYST, V53, P680, DOI 10.1016/j.dss.2012.05.025 Mendez J. R., 2006, P 11 SPAN ASS C CURR Metsis V., 2006, 3 C EM ANT, V17, P28 Ozel SA, 2011, EXPERT SYST APPL, V38, P3407, DOI 10.1016/j.eswa.2010.08.126 Pomikalek J, 2007, PROC WRLD ACAD SCI E, V19, P430 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 Song FX, 2005, PATTERN ANAL APPL, V8, P199, DOI 10.1007/s10044-005-0256-3 Tan SB, 2011, EXPERT SYST APPL, V38, P10264, DOI 10.1016/j.eswa.2011.02.114 Toman M., 2006, P 1 INT C MULT INF S, V2, P354 Toraman C., 2011, INT S INN INT SYST A, P379 Torunoglu D., 2011, INT S INN INT SYST A, P112 Uysal A. K., 2012, P IEEE INT S INN INT Uysal AK, 2012, KNOWL-BASED SYST, V36, P226, DOI 10.1016/j.knosys.2012.06.005 Yang Y., 1997, 14 INT C MACH LEARN, P412 NR 33 TC 4 Z9 4 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2014 VL 50 IS 1 BP 104 EP 112 DI 10.1016/j.ipm.2013.08.006 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 256CO UT WOS:000327287500007 ER PT J AU Moulin, C Largeron, C Ducottet, C Gery, M Barat, C AF Moulin, Christophe Largeron, Christine Ducottet, Christophe Gery, Mathias Barat, Cecile TI Fisher Linear Discriminant Analysis for text-image combination in multimedia information retrieval SO PATTERN RECOGNITION LA English DT Article DE Multimedia information retrieval; Textual and visual information; Bag-of-words; Parameters learning; Fischer LDA ID PATTERN-RECOGNITION; FUSION; VERIFICATION; BIOMETRICS; FEATURES; VIDEO; MODEL AB With multimedia information retrieval, combining different modalities - text, image, audio or video provides additional information and generally improves the overall system performance. For this purpose, the linear combination method is presented as simple, flexible and effective. However, it requires to choose the weight assigned to each modality. This issue is still an open problem and is addressed in this paper. Our approach, based on Fisher Linear Discriminant Analysis, aims to learn these weights for multimedia documents composed of text and images. Text and images are both represented with the classical bag-of-words model. Our method was tested over the ImageCLEF datasets 2008 and 2009. Results demonstrate that our combination approach not only outperforms the use of the single textual modality but provides a nearly optimal learning of the weights with an efficient computation. Moreover, it is pointed out that the method allows to combine more than two modalities without increasing the complexity and thus the computing time. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Moulin, Christophe; Largeron, Christine; Ducottet, Christophe; Gery, Mathias; Barat, Cecile] Univ St Etienne, CNRS, Lab Hubert Curien, Univ Lyon,UMR 5516, F-42000 St Etienne, France. RP Ducottet, C (reprint author), Univ St Etienne, CNRS, Lab Hubert Curien, F-42000 St Etienne, France. EM ducottet@univ-st-etienne.fr CR Aleksic PS, 2002, EURASIP J APPL SIG P, V2002, P1213, DOI 10.1155/S1110865702206162 Alexandre L.A., 2000, INT C PATT REC, V2, P2495 Antani S, 2002, PATTERN RECOGN, V35, P945, DOI 10.1016/S0031-3203(01)00086-3 Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 Atrey PK, 2010, MULTIMEDIA SYST, V16, P345, DOI 10.1007/s00530-010-0182-0 Aucouturier JJ, 2007, J ACOUST SOC AM, V122, P881, DOI 10.1121/1.2750160 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Cheung MC, 2005, INT CONF ACOUST SPEE, P485 Csurka G., 2004, ECCV 04 WORKSH STAT, P59 Datta R., 2008, ACM COMPUTING SURVEY, V40, P40 Depeursinge A, 2010, ARTIF INTELL MED, V50, P13, DOI 10.1016/j.artmed.2010.04.006 Duda R.O., 2001, PATTERN CLASSIFICATI Fisher RA, 1936, ANN EUGENIC, V7, P179 Flickner M., 1995, IEEE COMPUT, V28, P23 Fu Y, 2008, P INT C CONT BAS IM, P127, DOI 10.1145/1386352.1386373 Iskandar D.N.F.A., 2005, INEX 05, P525 Iyengar G., 2003, P 2003 INT C MULT EX, V2, P329 Jolliffe IT, 2002, SPRINGER SERIES STAT Klecka W., 1980, DISCRIMINANT ANAL, V19 Lazebnik S., 2006, CVPR, V2, P2169, DOI DOI 10.1109/CVPR.2006.68 Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045 Lowe D., 1999, ICCV, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Macedonas A., 2007, P 8 INT WORKSH IM AN, P75 Manning C.D., 2008, INTRO INFORM RETRIEV McLachlan G. J., 1992, DISCRIMINANT ANAL ST Montague M., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Moulin C, 2010, LECT NOTES COMPUT SC, V6218, P159, DOI 10.1007/978-3-642-14980-1_15 Moulin C., 2009, CLEF 09 10 WORKSH CR, P164 Nocedal J., 1999, NUMERICAL OPTIMIZATI Nuray R, 2006, INFORM PROCESS MANAG, V42, P595, DOI 10.1016/j.ipm.2005.03.023 Ratsch G., 1999, P IEEE INT WORKSH NE, V9, P41 Rattani A., 2007, BTAS 2007 1 IEEE INT, P1 Robertson S., 1994, TEXT RETRIEVAL C, P21 Ross A, 2003, PATTERN RECOGN LETT, V24, P2115, DOI 10.1016/S0167-8655(03)00079-5 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuldt C, 2004, INT C PATT RECOG, P32, DOI 10.1109/ICPR.2004.1334462 Shaw J.A., 1993, 2 TEXT RETRIEVAL C T, P243 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Snoek C., 2005, ACM MULTIMEDIA, P399 Tollari S., 2007, CIVR 07 ACM INT C IM, P65 Tollari S, 2009, LECT NOTES COMPUT SC, V5478, P701, DOI 10.1007/978-3-642-00958-7_70 Torjmen M., 2008, CLEF, P691 Tsikrika T., 2009, CLEF 09 10 WORKSH CR, P60 Tsikrika T., 2008, CLEF 08 9 WORKSH CRO, P539 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 Wang YH, 2003, LECT NOTES COMPUT SC, V2688, P805 Wu S., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Yan R, 2007, INFORM RETRIEVAL, V10, P445, DOI 10.1007/s10791-007-9031-y Zhai C., 2001, TECHNICAL REPORT NR 53 TC 3 Z9 5 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 EI 1873-5142 J9 PATTERN RECOGN JI Pattern Recognit. PD JAN PY 2014 VL 47 IS 1 SI SI BP 260 EP 269 DI 10.1016/j.patcog.2013.06.003 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 251BT UT WOS:000326903500022 ER PT J AU Perez-Suarez, A Martinez-Trinidad, JF Carrasco-Ochoa, JA Medina-Pagola, JE AF Perez-Suarez, Airel Martinez-Trinidad, Jose F. Carrasco-Ochoa, Jesus A. Medina-Pagola, Jose E. TI OClustR: A new graph-based algorithm for overlapping clustering SO NEUROCOMPUTING LA English DT Article DE Data mining; Overlapping clustering; Graph-based algorithms ID COMMUNITY STRUCTURE; COMPLEX NETWORKS; IDENTIFICATION; SIMILARITY; FUZZY; MODEL AB Clustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster; however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms. (C) 2013 Elsevier B.V. All rights reserved. C1 [Perez-Suarez, Airel; Martinez-Trinidad, Jose F.; Carrasco-Ochoa, Jesus A.] Inst Nacl Astrofis Opt & Electr, Puebla 72840, Mexico. [Perez-Suarez, Airel; Medina-Pagola, Jose E.] Ctr Aplicac Tecnol Avanzada, Havana 12200, Cuba. RP Perez-Suarez, A (reprint author), Inst Nacl Astrofis Opt & Electr, Luis Enrique Erro 1, Puebla 72840, Mexico. EM airel@ccc.inaoep.mx; fmartine@inaoep.mx; ariel@inaoep.mx; jmedina@cenatav.co.cu FU National Council on Science and Technology of Mexico (CONACyT) [CB2008-106443, CB2008-106366] FX We thank the National Council on Science and Technology of Mexico (CONACyT) for its support to this research through the project Grants CB2008-106443 and CB2008-106366. CR Abella-Perez R., 2010, P INT WORKSH HANDL C, P65 Al Hasan M, 2011, KNOWL INF SYST, V28, P665, DOI 10.1007/s10115-010-0360-6 Alzate C, 2011, NEUROCOMPUTING, V74, P1382, DOI 10.1016/j.neucom.2011.01.001 Amigo E, 2009, INFORM RETRIEVAL, V12, P461, DOI 10.1007/s10791-008-9066-8 Aslam J., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288659 Aslam J. A., 2004, J GRAPH ALGORITHMS A, V8, P95 Bagga A, 1998, P 17 INT C COMP LING, V1, P79 Bakus J., 2002, P 9 INT C NEUR INF P, P2212 Banerjee A., 2005, P 11 ACM SIGKDD INT, P532, DOI 10.1145/1081870.1081932 Baumes J, 2005, LECT NOTES COMPUT SC, V3495, P27 Baumes J., 2005, P IADIS INT C APPL C, P97 Berry M.W., 2004, SURVEY TEXT MINING C Civril A, 2007, LECT NOTES COMPUT SC, V4372, P30 Davis GB, 2008, SOC NETWORKS, V30, P201, DOI 10.1016/j.socnet.2008.03.001 Everett M.G., 1998, CONNECTIONS, V21, P49 Gago-Alonso A., 2007, P CIARP2007, P664 Gil-Garcia R, 2010, PATTERN RECOGN LETT, V31, P469, DOI 10.1016/j.patrec.2009.11.011 Gil-Garcia R, 2004, LECT NOTES COMPUT SC, V3287, P402 Gil-Garcia RJ, 2003, LECT NOTES COMPUT SC, V2905, P480 Goldberg Mark, 2010, Proceedings of the 2010 IEEE Second International Conference on Social Computing (SocialCom 2010). the Second IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT 2010), DOI 10.1109/SocialCom.2010.24 Greengrass E., 2001, TRR52008001 Gregory S, 2007, LECT NOTES ARTIF INT, V4702, P91 Gregory S, 2008, LECT NOTES ARTIF INT, V5211, P408, DOI 10.1007/978-3-540-87479-9_45 Gusfield D., 1997, ALGORITHMS STRINGS T Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jo T, 2007, LECT NOTES COMPUT SC, V4492, P871 Knuth D. E., 1973, ART COMPUTER PROGRAM, V3 Larsen B., 1999, KDD 99, P16 Li YY, 2012, NEUROCOMPUTING, V87, P90, DOI 10.1016/j.neucom.2012.02.008 Macropol K, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-283 Magdon-Ismail Malik, 2011, Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and IEEE Third International Conference on Social Computing (PASSAT/SocialCom 2011), DOI 10.1109/PASSAT/SocialCom.2011.237 Meila M, 2003, LECT NOTES ARTIF INT, V2777, P173, DOI 10.1007/978-3-540-45167-9_14 Munir MU, 2012, NEUROCOMPUTING, V85, P62, DOI 10.1016/j.neucom.2012.01.002 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Page L., WORKING PAPER Palla G, 2005, NATURE, V435, P814, DOI 10.1038/nature03607 Perez-Suarez A., 2009, P CIARP2009, P497 Perez-Suarez A., 2007, P MLDM 2007, P248 Perez-Suarez A, 2012, INTELL DATA ANAL, V16, P211, DOI 10.3233/IDA-2012-0520 Pfitzner D, 2009, KNOWL INF SYST, V19, P361, DOI 10.1007/s10115-008-0150-6 Pons-Porrata A., 2002, P CIARP2002, P405 Ramirez EH, 2012, NEUROCOMPUTING, V76, P125, DOI 10.1016/j.neucom.2011.04.032 Rosenberg Andrew, 2007, P 2007 JOINT C EMP M, P410 Roth V, 2002, LECT NOTES COMPUT SC, V2415, P607 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 Steinbach Michael, 2000, KDD WORKSH TEXT MIN, P109 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhang SH, 2007, PHYSICA A, V374, P483, DOI 10.1016/j.physa.2006.07.023 Zhao Y, 2001, 0140 U MINN DEP COMP NR 52 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD DEC 9 PY 2013 VL 121 SI SI BP 234 EP 247 DI 10.1016/j.neucom.2013.04.025 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 229YM UT WOS:000325303800024 ER PT J AU Nasir, JA Varlamis, I Karim, A Tsatsaronis, G AF Nasir, Jamal A. Varlamis, Iraklis Karim, Asim Tsatsaronis, George TI Semantic smoothing for text clustering SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Text clustering; Semantic smoothing kernels; WordNet; Wikipedia; Generalized vector space model kernel ID VECTOR-SPACE MODEL; CLASSIFICATION; RELATEDNESS; KERNELS AB In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to different experimental conditions, such as: (i) the datasets used, (ii) the underlying knowledge sources of the utilized measures, and (iii) the clustering algorithms employed. To the best of our knowledge, the current study is the first to systematically compare, analyze and evaluate the impact of semantic smoothing in text clustering based on 'wisdom of linguists', e.g., WordNets, 'wisdom of crowds', e.g., Wikipedia, and 'wisdom of corpora', e.g., large text corpora represented with the traditional Bag of Words (BoW) model. Three semantic relatedness measures for text are considered; two knowledge-based (Omiotis [1] that uses WordNet, and WLM [2] that uses Wikipedia), and one corpus-based (PMI [3] trained on a semantically tagged SemCor version). For the comparison of different experimental conditions we use the BCubed F-Measure evaluation metric which satisfies all formal constraints of good quality cluster. The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms. (C) 2013 Elsevier B.V. All rights reserved. C1 [Nasir, Jamal A.; Karim, Asim] Lahore Univ Management Sci, KADE Lab, Lahore, Pakistan. [Varlamis, Iraklis] Harokopio Univ Athens, Dept Informat & Tetemat, Athens, Greece. [Tsatsaronis, George] Tech Univ Dresden, BIOTEC, Dresden, Germany. RP Varlamis, I (reprint author), Harokopio Univ Athens, Dept Informat & Tetemat, Athens, Greece. EM varlamis@hua.gr RI Nasir, Jamal/; Tsatsaronis, George/ OI Nasir, Jamal/0000-0002-7866-0020; Tsatsaronis, George/0000-0003-2116-2933 CR Amigo E, 2009, INFORM RETRIEVAL, V12, P461, DOI 10.1007/s10791-008-9066-8 Bagga A., 1998, P 36 ANN M ASS COMP, P79 Bloehdorn S, 2006, IEEE DATA MINING, P808 Bouma G., 2009, FORM MEAN PROC TEXTS, P31 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Cristianini N., 2001, P 18 INT C MACH LEAR, P66 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Demsar J, 2006, J MACH LEARN RES, V7, P1 Domeniconi C, 2011, KNOWL INF SYST, V28, P99, DOI 10.1007/s10115-010-0318-8 Egozi Ofer, 2011, ACM Transactions on Information Systems, V29, DOI 10.1145/1961209.1961211 Fodeh S, 2011, KNOWL INF SYST, V28, P395, DOI 10.1007/s10115-010-0370-4 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Jing LP, 2010, KNOWL INF SYST, V25, P35, DOI 10.1007/s10115-009-0256-5 Karypis G, 2003, CLUTO CLUSTERING TOO Kazakov D., 2004, P 3 WORKSH ROB METH, P104, DOI 10.3115/1621445.1621458 Liu N., 2004, P 13 ACM INT C INF K, P334, DOI 10.1145/1031171.1031240 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Manning C.D., 2008, INTRO INFORM RETRIEV Manning CD, 2000, FDN STAT NATURAL LAN, V2nd Mao WL, 2007, DATA KNOWL ENG, V61, P76, DOI 10.1016/j.datak.2006.02.008 Mavroeidis D, 2005, LECT NOTES ARTIF INT, V3721, P181 Milne D.N., 2008, P 1 AAAI WORKSH WIK, P24 Nasir JA, 2011, LECT NOTES COMPUT SC, V7024, P261, DOI 10.1007/978-3-642-24583-1_25 Navigli R., 2009, ACM COMPUT SURV, V41 Newman D., 2010, P ANN C N AM CHAPT A, P100 Pecina P, 2005, P ACL STUD RES WORKS, P13, DOI 10.3115/1628960.1628964 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SanJuan E, 2007, LECT NOTES COMPUT SC, V4592, P252 Seung H. S., 2000, P NEUR INF PROC SYST, V13, P556 Shahnaz F, 2006, INFORM PROCESS MANAG, V42, P373, DOI 10.1016/j.ipm.2004.11.005 Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Steinbach M, 2000, KDD WORKSH TEXT MIN, V400, P525 Tsatsaronis G., 2009, P 12 EACL SRW SESS, P70, DOI 10.3115/1609179.1609188 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 Turney M., 2001, P 12 ECML, P491 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 Wei Xu X.L., 2005, P SIGIR, P267 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Zhang Q, 2011, LECT NOTES COMPUT SC, V7063, P382 Zhang ZQ, 2013, NAT LANG ENG, V19, P411, DOI 10.1017/S1351324912000125 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Zheng HT, 2009, INFORM SCIENCES, V179, P2249, DOI 10.1016/j.ins.2009.02.019 Zhou X., 2007, IJCAI, P2928 NR 45 TC 6 Z9 6 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 EI 1872-7409 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD DEC PY 2013 VL 54 SI SI BP 216 EP 229 DI 10.1016/j.knosys.2013.09.012 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 261SV UT WOS:000327685800020 ER PT J AU Li, ZH Zhang, BJ Yu, Y Shen, JL Wang, Y AF Li, Zhonghua Zhang, Bingjun Yu, Yi Shen, Jialie Wang, Ye TI Query-Document-Dependent Fusion: A Case Study of Multimodal Music Retrieval SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE Information retrieval; multimodal; query-document-dependent fusion. ID INFORMATION-RETRIEVAL; AUDIO AB In recent years, multimodal fusion has emerged as a promising technology for effective multimedia retrieval. Developing the optimal fusion strategy for different modalities (e. g., content, metadata) has been the subject of intensive research. Given a query, existing methods derive a unified fusion strategy for all documents with the underlying assumption that the relative significance of a modality remains the same across all documents. However, this assumption is often invalid. We thus propose a general multimodal fusion framework, query-document-dependent fusion (QDDF), which derives the optimal fusion strategy for each query-document pair via intelligent content analysis of both queries and documents. By investigating multimodal fusion strategies adaptive to both queries and documents, we demonstrate that existing multimodal fusion approaches are special cases of QDDF and propose two QDDF approaches to derive fusion strategies. The dual-phase QDDF explicitly derives and fuses query-and document-dependent weights, and the regression-based QDDF determines the fusion weight for a query-document pair via a regression model derived from training data. To evaluate the proposed approaches, comprehensive experiments have been conducted using a multimedia data set with around 17 K full songs and over 236 K social queries. Results indicate that the regression-based QDDF is superior in handling single-dimension queries. In comparison, the dual-phase QDDF outperforms existing approaches for most query types. We found that document-dependent weights are instrumental in enhancing multimedia fusion performance. In addition, efficiency analysis demonstrates the scalability of QDDF over large data sets. C1 [Li, Zhonghua; Zhang, Bingjun; Yu, Yi; Wang, Ye] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore. [Shen, Jialie] Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore. RP Li, ZH (reprint author), Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore. EM lizhongh@comp.nus.edu.sg; bingjun@comp.nus.edu.sg; yuy@comp.nus.edu.sg; jlshen@smu.edu.sg; wangye@comp.nus.edu.sg RI SHEN, Jialie/E-8573-2012 FU Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative FX This work was supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office. CR Atrey PK, 2010, MULTIMEDIA SYST, V16, P345, DOI 10.1007/s00530-010-0182-0 Bartell B. T., 1995, P SWED C CONN, P345 Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Benitez AB, 1998, IEEE INTERNET COMPUT, V2, P59, DOI 10.1109/4236.707692 Cai K., 2007, P 2007 INT C MULT UB, P1021 Chua T. S., 2004, P NIST TRECVID WORKS Cui B., 2007, P ACM MM 07, P1055, DOI 10.1145/1291233.1291465 Hu X., 2010, P JOINT C DIG LIB, P159, DOI 10.1145/1816123.1816146 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Kennedy L, 2008, P IEEE, V96, P567, DOI 10.1109/JPROC.2008.916345 Kennedy L.S., 2005, P 13 ANN ACM INT C M, P882, DOI 10.1145/1101149.1101339 Kittler J, 1998, IEEE T PATTERN ANAL, V20, P226, DOI 10.1109/34.667881 Laurier C, 2008, SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, P688, DOI 10.1109/ICMLA.2008.96 Levy M, 2009, IEEE T MULTIMEDIA, V11, P383, DOI 10.1109/TMM.2009.2012913 Li Z, 2011, P ACM MULT, P1105 Manning C.D., 2008, INTRO INFORM RETRIEV Mayer R., 2011, P INT SOC MUS INF RE, P675 Mayer R., 2008, P ACM MULT, P159, DOI 10.1145/1459359.1459382 McFee B., 2009, P 10 C MUS INF RETR, P513 Neumayer R, 2007, LECT NOTES COMPUT SC, V4425, P724 Ngo C. W., 2007, P NIST TRECVID WORKS Olivares X., 2008, P ACM MULT, P189, DOI 10.1145/1459359.1459386 Orio N., 2011, P ISMIR, P603 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rrobertson S. E., 1995, NIST SPECIAL PUBLICA, P73 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B., 2001, LEARNING KERNELS SUP Shaw J.A., 1994, P 2 TEXT RETR C TREC, P243 Shen JL, 2006, IEEE T MULTIMEDIA, V8, P1179, DOI 10.1109/TMM.2006.884618 Shwartz S., 2007, P INT C MACH LEARN I, P807 Shwartz S., 2008, P ICML, P928 Smeaton A., 2003, P IM VID RETR, P451 Snoek C., 2005, P 13 ANN ACM INT C M, P399, DOI 10.1145/1101149.1101236 Tzanetakis G., 2000, ORG SOUND, V4, P169, DOI 10.1017/S1355771800003071 Voorhees Ellen M., 1995, P 18 ANN INT ACM SIG, P172, DOI 10.1145/215206.215357 Wu S., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Wu Y., 2004, P 12 ANN ACM INT C M, P572, DOI 10.1145/1027527.1027665 Wu Y, 2004, IEEE IMAGE PROC, P2391 Xie L., 2007, P IEEE INT C MULT EX, P1499 Yan R, 2006, P 29 ANN INT ACM SIG, P324, DOI 10.1145/1148170.1148228 Yan R, 2004, P 12 ANN ACM INT C M, P548, DOI 10.1145/1027527.1027661 Yom-Tov E., 2005, P ACM SIGIR QUER PRE Yom-Tov Elad, 2005, P 28 ANN INT ACM SIG, P512, DOI 10.1145/1076034.1076121 Zhang B., 2009, P 17 ACM INT C MULT, P213, DOI 10.1145/1631272.1631303 Zhang BJ, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P403, DOI 10.1145/1571941.1572011 NR 45 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 EI 1941-0077 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD DEC PY 2013 VL 15 IS 8 BP 1830 EP 1842 DI 10.1109/TMM.2013.2280437 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA 257OI UT WOS:000327393900009 ER PT J AU Bellogin, A Wang, J Castells, P AF Bellogin, Alejandro Wang, Jun Castells, Pablo TI Bridging memory-based collaborative filtering and text retrieval SO INFORMATION RETRIEVAL LA English DT Article DE Collaborative filtering; Recommender systems; Text retrieval models ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; RECOMMENDER SYSTEMS; LANGUAGE MODELS; RANKING AB When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users' preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived. In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user's ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering. C1 [Bellogin, Alejandro; Castells, Pablo] Univ Autonoma Madrid, Escuela Politecn Super, Madrid, Spain. [Wang, Jun] UCL, Dept Comp Sci, London, England. RP Bellogin, A (reprint author), Univ Autonoma Madrid, Escuela Politecn Super, Madrid, Spain. EM alejandro.bellogin@uam.es; junwang@cs.ucl.ac.uk; pablo.castells@uam.es RI Castells, Pablo/B-5020-2010; Bellogin, Alejandro/G-2025-2010 OI Castells, Pablo/0000-0003-0668-6317; Bellogin, Alejandro/0000-0001-6368-2510 FU Spanish Government [TIN2011-28538-C02-01]; Regional Government of Madrid [S2009TIC-1542] FX This work is supported by the Spanish Government (TIN2011-28538-C02-01) and the Regional Government of Madrid (S2009TIC-1542). We also thank the anonymous reviewers for their valuable comments. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3 Aly R, 2010, LECT NOTES COMPUT SC, V5993, P241, DOI 10.1007/978-3-642-12275-0_23 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bellogin A, 2011, P 5 ACM C REC SYST R, P333 BELLOGIN A, 2011, P 33 EUR C ADV INF R, V6611, P301 Breese J S, 1998, P 14 C UNC ART INT, P43 Brown EricW., 1994, P 20 INT C VER LARG, P192 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000 Clements M, 2009, LECT NOTES COMPUT SC, V5427, P155, DOI 10.1007/978-3-540-95995-3_13 Cohen E, 1999, J ALGORITHM, V30, P211, DOI 10.1006/jagm.1998.0989 Coster R., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Croft W. B., 2009, SEARCH ENGINES INFOR, V1st Demartini G, 2009, LECT NOTES COMPUT SC, V5478, P189, DOI 10.1007/978-3-642-00958-7_19 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Desrosiers C, 2011, RECOMMENDER SYSTEMS HANDBOOK, P107, DOI 10.1007/978-0-387-85820-3_4 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 HERLOCKER JL, 1999, P INT ACM SIGIR C RE, V22, P230 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 2000, Information Processing & Management, V36 Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Losada DE, 2008, INFORM RETRIEVAL, V11, P109, DOI 10.1007/s10791-007-9040-x Manning C.D., 2008, INTRO INFORM RETRIEV McLaughlin M. R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009050 Metzler D, 2009, LECT NOTES COMPUT SC, V5766, P42, DOI 10.1007/978-3-642-04417-5_5 Pavlov D, 2004, IEEE INTELL SYST, V19, P40, DOI 10.1109/MIS.2004.59 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pickens J., 2010, P 19 ACM INT C INF K, P1049, DOI 10.1145/1871437.1871571 Robertson S, 2002, INFORM RETRIEVAL, V5, P239, DOI 10.1023/A:1015702129514 Robertson S. E., 1988, RELEVANCE WEIGHTING, P143 Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 Rolleke T., 2008, P 31 ANN INT ACM SIG, P435 Salakhutdinov R., 2008, ICML, P880 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Yu K, 2004, IEEE T KNOWL DATA EN, V16, P56 Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Sivic J., 2003, COMP VIS IEEE INT C, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663 Soboroff I., 2000, P 23 ANN INT ACM SIG, P351, DOI 10.1145/345508.345646 Takacs Gabor, 2008, P 2008 ACM C REC SYS, P267, DOI 10.1145/1454008.1454049 Tomasic A., 1994, SIGMOD Record, V23 Vargas S, 2011, RECSYS, P109 Voorhees E.M., 1999, P 5 TEXT RETR C TREC Wang J, 2006, LECT NOTES COMPUT SC, V3936, P37 Wang J, 2009, LECT NOTES COMPUT SC, V5839, P218 Wang J, 2008, INFORM RETRIEVAL, V11, P477, DOI 10.1007/s10791-008-9060-1 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Xu S., 2008, P 31 ANN INT ACM SIG, P155 Yilmaz E, 2008, KNOWL INF SYST, V16, P173, DOI 10.1007/s10115-007-0101-7 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 NR 59 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2013 VL 16 IS 6 BP 697 EP 724 DI 10.1007/s10791-012-9214-z PG 28 WC Computer Science, Information Systems SC Computer Science GA 247RQ UT WOS:000326637700003 ER PT J AU Wu, ZD Xu, GD Lu, CL Chen, EH Zhang, YC Zhang, H AF Wu, Zongda Xu, Guandong Lu, Chenglang Chen, Enhong Zhang, Yanchun Zhang, Hong TI Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page SO NEUROCOMPUTING LA English DT Article DE Wikipedia knowledge; Similarity; Contextual advertising ID MATCHING APPROACH AB Web advertising, a form of online advertising, which uses the Internet as a medium to post product or service information and attract customers, has become one of the most important marketing channels. As one prevalent type of web advertising, contextual advertising refers to the placement of the most relevant ads at appropriate positions of a web page, so as to provide a better user experience and increase the user's ad-click rate. However, most existing contextual advertising techniques only take into account how to select as relevant ads for a given page as possible, without considering the positional effect of the ad placement on the page, resulting in an unsatisfactory performance in ad local context relevance. In this paper, we address the novel problem of position-wise contextual advertising, i.e., how to select and place relevant ads properly for a target web page. In our proposed approach, the relevant ads are selected based on not only global context relevance but also local context relevance, so that the embedded ads yield contextual relevance to both the whole target page and the insertion positions where the ads are placed. In addition, to improve the accuracy of global and local context relevance measure, the rich wikipedia knowledge is used to enhance the semantic feature representation of pages and ad candidates. Last, we evaluate our approach using a set of ads and pages downloaded from the Internet, and demonstrate the effectiveness of our approach. (c) 2013 Elsevier B.V. All rights reserved. C1 [Wu, Zongda; Lu, Chenglang] Wenzhou Univ, Wenzhou 325035, Peoples R China. [Wu, Zongda; Chen, Enhong] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China. [Xu, Guandong] Univ Technol Sydney, Sydney, NSW 2007, Australia. [Lu, Chenglang] Northwestern Polytech Univ, Xian 710072, Peoples R China. [Zhang, Yanchun] Victoria Univ, Melbourne, Vic 8001, Australia. [Zhang, Hong] Inst Sci & Technol Informat Zhejiang Prov, Hangzhou 310006, Zhejiang, Peoples R China. RP Lu, CL (reprint author), Wenzhou Univ, Wenzhou 325035, Peoples R China. EM zongda1983@163.com; guandong.xu@vu.edu.au; chenglang.lu@qq.com; cheneh@ustc.edu.cn FU NSFC [61202171, 61272018, 60775037, 71090401/71090400]; China Postdoctoral Science Foundation [2012M521251, 2013T60623]; Doctoral Program of Higher Education of China [20093402110017, 20113402110024]; Zhejiang Provincial Natural Science Foundation of China [Q13F020034, Y1100769, Y1090328] FX The work is supported by Grants from the NSFC projects (Nos.61202171,61272018,60775037,71090401/71090400), the China Postdoctoral Science Foundation funded projects (Nos.2012M521251 and 2013T60623), the Doctoral Program of Higher Education of China (Nos.20093402110017,20113402110024) and the Zhejiang Provincial Natural Science Foundation of China (Nos.Q13F020034,Y1100769, Y1090328). CR Anagnostopoulos A., 2007, CIKM 07, P331 Boyd S., 2004, CONVEX OPTIMIZATION Broder A., 2007, SIGIR 07, P559 Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906 Fan TK, 2010, KNOWL INF SYST, V23, P321, DOI 10.1007/s10115-009-0222-2 Gupta S, 2005, WORLD WIDE WEB, V8, P179, DOI 10.1007/s11280-004-4873-3 Gupta S, 2009, ESWA, V36, P10 Hu J., 2008, SIGIR 08, P179 Hu J., 2009, WWW 09, P471 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Mei T, 2011, P IEEE, V98, P1416 Mei T., 2012, ACM TOMCCAP, V8 Mei Tao, 2008, MM 08, P439 Murdock V., 2007, SIGKDD WORKSH 07, P21 Pak AN, 2011, P 4 INT C HUM LANG T, P533 Pak AN, 2010, WORLD WIDE WEB, V13, P251, DOI 10.1007/s11280-010-0084-2 Papadopoulos S, 2009, LECT NOTES COMPUT SC, V5478, P216, DOI 10.1007/978-3-642-00958-7_21 Ribeiro-Neto B., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686 Wu Z. D., 2011, CIKM 11, P2105 Wu ZD, 2012, COMPUT J, V55, P277, DOI 10.1093/comjnl/bxr056 NR 22 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD NOV 23 PY 2013 VL 120 SI SI BP 524 EP 535 DI 10.1016/j.neucom.2013.04.018 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA 223XZ UT WOS:000324847100057 ER PT J AU Acampora, G Loia, V Vitiello, A AF Acampora, Giovanni Loia, Vincenzo Vitiello, Autilia TI Enhancing ontology alignment through a memetic aggregation of similarity measures SO INFORMATION SCIENCES LA English DT Article DE Ontology alignment; Similarity measure; Similarity aggregation; Memetic algorithm ID DESIGN ISSUES; ALGORITHM; TAXONOMY; MODEL AB Modern infrastructures for information and communication technologies are aimed at providing enhanced services by integrating the knowledge spread on the web through an ontological representation of information. However, ontology usefulness in managing different knowledge sources is limited by the so-called semantic heterogeneity problem arising when several interacting software components use different ontologies for representing the same information. In order to bridge this gap and, consequently, enable a full interoperability across the software components, it is necessary to bring the corresponding ontologies into a mutual agreement by identifying a set of semantic relationships among their entities. This result is achieved by means of a so-called ontology alignment process that, for each pair of entities belonging to the ontologies under alignment, computes their semantic closeness through an optimized aggregation of different similarity measures. Unfortunately, this similarity aggregation is a hard optimization process, above all, when no information is known about ontology features. The aim of this paper is to define an ontology alignment process based on a memetic algorithm able to efficiently aggregate similarity measures without using a priori knowledge about ontologies under alignment. As shown by a statistical multiple comparison procedure, our approach yields high performance in terms of alignment quality with respect to top-performers of well-known Ontology Alignment Evaluation Initiative campaigns. (c) 2013 Elsevier Inc. All rights reserved. C1 [Acampora, Giovanni] Eindhoven Univ Technol, Sch Ind Engn, NL-5600 MB Eindhoven, Netherlands. [Loia, Vincenzo; Vitiello, Autilia] Univ Salerno, Dept Comp Sci, I-84084 Fisciano, Italy. RP Loia, V (reprint author), Univ Salerno, Dept Comp Sci, I-84084 Fisciano, Italy. EM g.acampora@tue.nl; loia@unisa.it; avitiello@unisa.it CR Abdullah S, 2012, INFORM SCIENCES, V191, P146, DOI 10.1016/j.ins.2011.12.018 Acampora G, 2012, INT J INTELL SYST, V27, P189, DOI 10.1002/int.20517 Bernstein P. A., 2001, P 27 INT C VER LARG, P49 Bock J, 2012, INFORM SCIENCES, V192, P152, DOI 10.1016/j.ins.2010.08.013 Cruz I.F., 2010, P 5 INT WORKSH ONT M David J., 2007, THESIS U NANTES David SHESKIN, 2000, HDB PARAMETRIC NONPA Dawkins R, 1976, SELFISH GENE Deb K., 1995, COMPLEX SYSTEMS, V9 Demsar J, 2006, J MACH LEARN RES, V7, P1 Do HH, 2007, INFORM SYST, V32, P857, DOI 10.1016/j.is.2006.09.002 Ehrig M, 2007, SEMAT WEB BEYOND-COM, V4, P1 ESHELMAN LJ, 1993, FDN GENETIC ALGORITH, V2, P187 Euzenat J., 2008, STATE ART ONTOLOGY A Euzenat J., 2010, P 5 INT WORKSH ONT M Euzenat J., 2011, LNCS, V6720, P158 Euzenat J., 2011, P 6 INT WORKSH ONT M Friedman M, 1937, J AM STAT ASSOC, V32, P675, DOI 10.2307/2279372 Garcia S, 2009, J HEURISTICS, V15, P617, DOI 10.1007/s10732-008-9080-4 Gen M, 1997, GENETIC ALGORITHMS E Genesereth M.R., 1987, LOGICAL FDN ARTIFICI Ginsca AL, 2010, 9TH ROEDUNET IEEE INTERNATIONAL CONFERENCE, P118 Gracia J., 2011, P 6 INT WORKSH ONT M GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Hamdi Fayc, 2010, P 5 INT WORKSH ONT M Herrera F, 2003, INT J INTELL SYST, V18, P309, DOI 10.1002/int.10091 HOLM S, 1979, SCAND J STAT, V6, P65 Hu W., 2010, P 5 INT WORKSH ONT M Jean-Mary YR, 2009, J WEB SEMANT, V7, P235, DOI 10.1016/j.websem.2009.04.001 Kirkpatrick S., 1982, 9355 IBM RC Krasnogor N, 2005, IEEE T EVOLUT COMPUT, V9, P474, DOI 10.1109/TEVC.2005.850260 Levenshtein I. V., 1966, CYBERNETICS CONTROL, V10, P707 Martinez-Gil J., 2008, CEUR WORKSHOP P, P419 Moscato P., 1989, 826 CALT CONC COMP P, V826 Nguyen QH, 2007, IEEE C EVOL COMPUTAT, P2390, DOI 10.1109/CEC.2007.4424770 Noessner J., 2010, P 5 INT WORKSH ONT M Noy NF, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P450 Powell M.J.D., 1964, COMPUT J, V7 Quix C., 2010, P 5 INT WORKSH ONT M Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saruladha K., 2011, INT J COMPUTER APPL, V34, P14 Shvaiko P., 2004, P MEAN COORD NEG WOR Shvaiko P., 2007, ONTOLOGY MATCHING Shvaiko P, 2013, IEEE T KNOWL DATA EN, V25, P158, DOI 10.1109/TKDE.2011.253 Stoilos G., 2005, P 4 INT SEM WEB C IS, P623 Tang J, 2007, SOFT COMPUT, V11, P957 Ting CK, 2010, INFORM SCIENCES, V180, P4818, DOI 10.1016/j.ins.2010.08.021 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vazquez Naya J.M., 2010, SOFT COMPUTING METHO, P240 Wang J., 2006, P IEEE AS PAC C SERV Wang Z., 2010, P 5 INT WORKSH ONT M Wei W., 2010, P 5 INT WORKSH ONT M Wen Y, 2011, INFORM SCIENCES, V181, P567, DOI 10.1016/j.ins.2010.10.001 Winkler W., 1999, TECHNICAL REPORT Xu P., 2010, P 5 INT WORKSH ONT M NR 57 TC 2 Z9 3 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD NOV 20 PY 2013 VL 250 BP 1 EP 20 DI 10.1016/j.ins.2013.06.052 PG 20 WC Computer Science, Information Systems SC Computer Science GA 226MV UT WOS:000325041400001 ER PT J AU Jiang, K Yin, HG Wang, P Yu, NH AF Jiang, Kai Yin, Huagang Wang, Peng Yu, Nenghai TI Learning from contextual information of geo-tagged web photos to rank personalized tourism attractions SO NEUROCOMPUTING LA English DT Article DE Geo-tagged photo; Contextual information; Tourism recommendation; RankSVM ID COLLECTIONS AB This paper proposed a method that fully exploits contextual information of geo-tagged web photos to recommend tourism attractions to a user according to his personal interest and current time and location. The proposed method first detects tourism attractions from geo-tags, and estimates their popularity with users' photo quantity. Photos' taken time is used to discover temporal fluctuation of attractions' popularity and distance of consecutive photos is exploited to model the spatial influence to user's travel behavior. Photos' textual and visual information are used to reveal users' personal interests. Collaborative filtering is also adopted in the recommendation process. With all these contextual information, our method predicts a user's preference to a certain attraction from different aspects, and automatically combines the prediction scores to give the final recommendation result with a learning to rank model. Experiments on Panoramio dataset show that our method performs better than the state-of-the-art method, especially for users with little traveling history. (c) 2012 Elsevier B.V. All rights reserved. C1 [Jiang, Kai; Yin, Huagang; Wang, Peng; Yu, Nenghai] Univ Sci & Technol China, MOE Microsoft Key Lab Multimedia Comp & Commun, Hefei, Anhui, Peoples R China. RP Jiang, K (reprint author), Univ Sci & Technol China, MOE Microsoft Key Lab Multimedia Comp & Commun, Hefei, Anhui, Peoples R China. EM kaijiang@mail.ustc.edu.cn; yinhg@mail.ustc.edu.cn; pengwang@mail.ustc.edu.cn; ynh@ustc.edu.cn FU National Natural Science Foundation of China [60933013]; National Science and Technology Major Project [2010ZX03004-003]; Fundamental Research Funds for the Central Universities [WK2100230002] FX This work is supported by National Natural Science Foundation of China (no. 60933013), National Science and Technology Major Project (no. 2010ZX03004-003), and Fundamental Research Funds for the Central Universities (no. WK2100230002). CR Arase Y., 2010, P INT C MULT MM 10, P133, DOI 10.1145/1873951.1873971 Backstrom L., 2010, P 19 INT C WORLD WID, P61, DOI 10.1145/1772690.1772698 Cao L., 2009, P ACM INT C MULT ACM Cao LL, 2010, INT CONF ACOUST SPEE, P2274, DOI 10.1109/ICASSP.2010.5495905 Clements M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P851 Comaniciu D, 2002, IEEE T PATTERN ANAL, V24, P603, DOI 10.1109/34.1000236 Crandall D., 2009, MAPPING WORLDS PHOTO Gao Y., 2010, P ACM C MULT, P123, DOI 10.1145/1873951.1873970 HAYS J., 2008, P IEEE C COMP VIS PA Ivanov I, 2012, MULTIMED TOOLS APPL, V56, P155, DOI 10.1007/s11042-010-0570-7 Ji R., 2009, P 17 ACM INT C MULT, P105, DOI 10.1145/1631272.1631289 Ji R., 2011, ACM T MULTIMED COM S, V7S, P31 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kurashima T., 2010, P 19 ACM INT C INF K, P579, DOI 10.1145/1871437.1871513 Lowe D.G., 2004, INT J COMPUT VISION, V60 Lu X., 2010, P INT C MULT MM 10, P143, DOI 10.1145/1873951.1873972 Luo JB, 2011, MULTIMED TOOLS APPL, V51, P187, DOI 10.1007/s11042-010-0623-y LUO Z., 2009, P 17 ACM INT C MULT, P963, DOI 10.1145/1631272.1631469 Nister D, 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264 Oliva A., 2001, INT J COMPUT VISION, V42 Pei J., 2004, IEEE T KNOWL DATA EN, V16, P2004 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Snavely N, 2006, ACM T GRAPHIC, V25, P835, DOI 10.1145/1141911.1141964 Snavely N, 2008, INT J COMPUT VISION, V80, P189, DOI 10.1007/s11263-007-0107-3 Tang J., 2009, P 17 ACM INT C MULT, P223, DOI DOI 10.1145/1631272.1631305 Tang JH, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1899412.1899418 Tang J., 2010, P ACM C MULT, P1019, DOI 10.1145/1873951.1874139 Tang JH, 2010, IEEE T MULTIMEDIA, V12, P131, DOI 10.1109/TMM.2009.2037373 Vedaldi A., 2008, VLFEAT OPEN PORTABLE Wu L., 2008, P 16 ACM INT C MULT, P31, DOI DOI 10.1145/1459359.1459364 Ye M., 2011, P 34 INT ACM SIGIR C, P325 Yin H., 2010, P INT C MULT ACM, P1579, DOI 10.1145/1873951.1874291 Zheng YT, 2011, MULTIMED TOOLS APPL, V51, P77, DOI 10.1007/s11042-010-0630-z NR 35 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 EI 1872-8286 J9 NEUROCOMPUTING JI Neurocomputing PD NOV 7 PY 2013 VL 119 SI SI BP 17 EP 25 DI 10.1016/j.neucom.2012.02.049 PG 9 WC Computer Science, Artificial Intelligence SC Computer Science GA 210RI UT WOS:000323851800004 ER PT J AU Niessen, ME Van de Cruys, T Cance, C Dubois, D AF Niessen, Maria E. Van de Cruys, Tim Cance, Caroline Dubois, Daniele TI Sound and Noise in Sonic Environmental Studies: Comparing Word Meaning in Discourses of Community Noise and Soundscape Research SO ACTA ACUSTICA UNITED WITH ACUSTICA LA English DT Review ID SPACE MODELS AB Since R. Murray Schafer's musical concept of soundscape started influencing scientific communities of acousticians in the 1970s, part of the Community Noise research has shifted its focus from noise pollution and annoyance to a more holistic perspective on sonic environments, including their positive aspects. To investigate if and how this evolution is represented in the literature of the field, the discourses of two scientific fields in acoustics are compared: the older Community Noise and the more recent Soundscape field. Specifically, the differential use of two key words (Noise and Sound) is analyzed based on the hypothesis that their meanings, which are constructed in discourse, reflect the conceptual grounding of both fields. An algorithm from computational linguistics is applied to calculate the similarity of words according to their use in a local syntactic context. It is shown that Noise and Sound are differently distributed within the discourses of both fields, thereby making explicit the semantic and conceptual differentes between a noise pollution approach and a soundscape approach, which includes a more holistic concern about the quality of sonic environments and acoustic design. C1 [Niessen, Maria E.; Cance, Caroline; Dubois, Daniele] INCAS3, Assen, Netherlands. [Van de Cruys, Tim] Univ Toulouse 3, IRIT, UMR 5505, F-31062 Toulouse, France. [Van de Cruys, Tim] CNRS, Toulouse, France. [Dubois, Daniele] Univ Paris 06, CNRS, Equipe Langage Cognit Prat, LAM, Paris, France. EM marianiessen@gmail.com FU Province of Drenthe; Municipality of Assen; European Fund for Regional Development; Ministry of Economic Affairs, Peaks in the Delta FX This work is co-financed by the Province of Drenthe, the Municipality of Assen, the European Fund for Regional Development, and the Ministry of Economic Affairs, Peaks in the Delta. We are grateful to Heinrich Wortche for his support to this work and his feedback on earlier versions of the paper. CR Axelsson O., 2011, P FOR AC BENVENISTE EMILE, 1966, PROBLEMES LINGUISTIQ Berglund B., 2011, GUIDELINES COMMUNITY Botteldooren D., 2011, P FOR AC Brown A. L., 2011, P C AUSTR AC SOC AC Brown AL, 2011, APPL ACOUST, V72, P387, DOI 10.1016/j.apacoust.2011.01.001 Cance C., 2008, AUTOUR LANGUES LANGA, P161 Cance C., 2009, ACT COLL REPR SENS L, P363 Cheminee P., 2009, SENTIR DIRE CONCEPTS Church K. W., 1990, COMPUTATIONAL LINGUI, V16, P22 de Marneffe M.-C., 2008, MANNING STANFORD TYP de Saussure F., 1962, COURS LINGUISTIQUE G, P1916 Dubois D., 2006, FOOD QUAL PREFER, V17, P669 Dubois D., 2008, P AC 08 PAR Duda R.O., 2001, PATTERN CLASSIFICATI Eco Umberto, 1976, THEORY SEMIOTICS Garfield E., 1979, CITATION INDEXING IT Guastavino C., 2008, P AC 08 PAR Guastavino C, 2007, CAN J EXP PSYCHOL, V61, P54, DOI 10.1037/cjep2007006 Hall D., 2008, P C EMP METH NAT LAN, P363, DOI 10.3115/1613715.1613763 Harris ZS, 1954, WORD, V10, P146 Hiramatsu K, 2006, ACTA ACUST UNITED AC, V92, P857 Karypis G., 2003, 02017 U MINN DEP COM Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Lercher P., 2003, P 8 INT C NOIS PUBL Ludeling A, 2009, HANDB SPRACH KOMMUN, V29, P1, DOI 10.1515/9783110213881.2 Lyons J., 1981, LANGUAGE LINGUISTICS Marcus M.P., 1993, COMPUTATIONAL LINGUI, V19, P313 Niessen M. E., 2010, P INT Nivre J., 2006, P 5 INT C LANG RES E, P2216 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Paul M., 2009, P INT C RANLP 2009, P337 Pratyush A., 2010, P SMC2010 Rastier F, 1991, SEMANTIQUE RECHERCHE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer R. Murray, 1977, TUNING WORLD Schafer R. Murray, 1969, NEW SOUNDSCAPE Schulte-Fortkamp B, 2006, ACTA ACUST UNITED AC, V92, pV Schutze H, 1998, COMPUT LINGUIST, V24, P97 Torigoe K., 1993, P INT S DES AM, P232 Toutanova K., 2003, P HLT NAACL, P252 Truax BD, 1978, HDB ACOUSTIC ECOLOGY Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Van de Cruys T., 2008, P ESSLLI WORKSH DIST, P47 Van de Cruys T., 2010, THESIS U GORNINGEN N NR 46 TC 0 Z9 0 PU S HIRZEL VERLAG PI STUTTGART PA POSTFACH 10 10 61, D-70 009 STUTTGART, GERMANY SN 1610-1928 EI 1861-9959 J9 ACTA ACUST UNITED AC JI Acta Acust. United Acust. PD NOV-DEC PY 2013 VL 99 IS 6 BP 853 EP 862 DI 10.3813/AAA.918664 PG 10 WC Acoustics SC Acoustics GA 251TO UT WOS:000326953800001 ER PT J AU Evangelopoulos, NE AF Evangelopoulos, Nicholas E. TI Latent semantic analysis SO WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE LA English DT Article ID METAPHOR COMPREHENSION; SPACE MODEL; KNOWLEDGE AB This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. (C) 2013 John Wiley & Sons, Ltd. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 Conflict of interest: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. C1 [Evangelopoulos, Nicholas E.] Univ N Texas, Dept Informat Technol & Decis Sci, Denton, TX 76203 USA. [Evangelopoulos, Nicholas E.] Univ N Texas, Coll Business, Denton, TX 76203 USA. RP Evangelopoulos, NE (reprint author), Univ N Texas, Dept Informat Technol & Decis Sci, Denton, TX 76203 USA. EM Nick.Evangelopoulos@unt.edu RI Evangelopoulos, Nicholas/ OI Evangelopoulos, Nicholas/0000-0001-9866-6828 CR Airhead-Research, LAT SEM AN [Anonymous], LAT SEM AN CU BOULD Baroni M, FREGE SPACE PROGRAM Bradford R., 2008, CIKM 08 P 17 ACM C I, P153 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Denhiere G, 2007, HDB LATENT SEMANTIC, P143 Dennis S., 2007, HDB LATENT SEMANTIC, P57 Ding CHQ, 2005, J AM SOC INF SCI TEC, V56, P597, DOI 10.1002/asi.20148 Dumais S, 2003, COGNITIVE SCI, V27, P491, DOI 10.1016/S0364-0213(03)00013-2 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Elvevag B, 2007, SCHIZOPHR RES, V93, P304, DOI 10.1016/j.schres.2007.03.001 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Evangelopoulos N, 2012, COMMUN ACM, V55, P55, DOI 10.1145/2076450.2076467 Evans V, 2004, STRUCTURE TIME LANGU Evans V, 2012, WIRES COGN SCI, V3, P129, DOI 10.1002/wcs.1163 Fauconnier G., 2002, WAY WE THINK CONCEPT Fauconnier Gilles, 1997, MAPPINGS THOUGHT LAN Foltz PW, 1996, BEHAV RES METH INSTR, V28, P197, DOI 10.3758/BF03204765 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Geeraerts Dirk, 1997, DIACHRONIC PROTOTYPE Gong Y., 2001, P 24 ANN INT ACM SIG, P19, DOI DOI 10.1145/383952.383955 Graesser A. C., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT129 Howard MW, 2002, J MEM LANG, V46, P85, DOI 10.1006/jmla.2001.2798 Indulska M, 2012, EUR J INFORM SYST, V21, P49, DOI 10.1057/ejis.2011.37 Jorge-Botana G, 2010, J AM SOC INF SCI TEC, V61, P1706, DOI 10.1002/asi.21355 Kahneman D., 2011, THINKING FAST SLOW Kakkonen T, 2008, EDUC TECHNOL SOC, V11, P275 Franzke M., 2005, Journal of Educational Computing Research, V33, DOI 10.2190/DH8F-QJWM-J457-FQVB Kintsch W, 2011, TOP COGN SCI, V3, P346, DOI 10.1111/j.1756-8765.2010.01107.x Kintsch W, 2000, PSYCHON B REV, V7, P257, DOI 10.3758/BF03212981 Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1 Kintsch W, 2002, METAPHOR SYMBOL, V17, P249, DOI 10.1207/S15327868MS1704_1 Kumar A, 2009, J COMPUT INFORM TECH, V17, P259 Landauer T., 2007, HDB LATENT SEMANTIC, P3 Landauer TK, 2011, SCI STUD READ, V15, P92, DOI 10.1080/10888438.2011.536130 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1998, CURR DIR PSYCHOL SCI, V7, P161, DOI 10.1111/1467-8721.ep10836862 Landauer TK, 2000, IEEE INTELL SYST APP, V15, P27 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Larsen KR, 2004, SOCIOL METHODOL, V34, P349, DOI 10.1111/j.0081-1750.2004.00156.x Manning C., 2008, INTRO INFORM RETRIEV, P403 Manning C.D., 2008, INTRO INFORM RETRIEV, V100 Manning JR, 2012, MEMORY, V20, P511, DOI 10.1080/09658211.2012.683010 Natale F, 2012, SCIENTOMETRICS, V90, P983, DOI 10.1007/s11192-011-0562-z Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38 Penumatsa P, 2006, INT J ARTIF INTELL T, V15, P767, DOI 10.1142/S021821300600293X Sahlgren M., 2008, ITAL J LING, V20, P33 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAS Institute, SAS TEXT MIN Sidorova A, 2008, MIS QUART, V32, P467 Sidorova A, 2008, MIS Q, V32, pA1 Srivastava Ashok, 2009, TEXT MINING CLASSIFI Steinberger J., 2004, P 7 INT C INF SYST I, P93 Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 Steyvers M., 2004, EXPT COGNITIVE PSYCH, DOI 10.1037/10895-018 Talmy L., 2000, COGNITIVE SEMANTICS, V1 Utsumi A, 2011, COGNITIVE SCI, V35, P251, DOI 10.1111/j.1551-6709.2010.01144.x Valle-Lisboa JC, 2007, INFORM SCIENCES, V177, P4122, DOI 10.1016/j.ins.2007.04.007 VanLehn K, 2007, COGNITIVE SCI, V31, P3, DOI 10.1080/03640210709336984 Wenger E., 1998, COMMUNITIES PRACTICE, P51 Wild Fridolin, LSA LATENT SEMANTIC NR 62 TC 4 Z9 4 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1939-5078 EI 1939-5086 J9 WIRES COGN SCI JI Wiley Interdiscip. Rev.-Cogn. Sci. PD NOV PY 2013 VL 4 IS 6 BP 683 EP 692 DI 10.1002/wcs.1254 PG 10 WC Psychology, Experimental SC Psychology GA 234EG UT WOS:000325623200008 PM 26304272 ER PT J AU Chen, CQ Watve, A Pramanik, S Zhu, Q AF Chen, Changqing Watve, Alok Pramanik, Sakti Zhu, Qiang TI The BoND-Tree: An Efficient Indexing Method for Box Queries in Nonordered Discrete Data Spaces SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Box query; nonordered discrete data; categorical data; indexing ID METRIC-SPACES; SIMILARITY SEARCH; KNAPSACK-PROBLEM AB Box queries (or window queries) are a type of query which specifies a set of allowed values in each dimension. Indexing feature vectors in the multidimensional Nonordered Discrete Data Spaces (NDDS) for efficient box queries are becoming increasingly important in many application domains such as genome sequence databases. Most of the existing work in this field targets the similarity queries (range queries and k-NN queries). Box queries, however, are fundamentally different from similarity queries. Hence, the same indexing schemes designed for similarity queries may not be efficient for box queries. In this paper, we present a new indexing structure specifically designed for box queries in the NDDS. Unique characteristics of the NDDS are exploited to develop new node splitting heuristics. For the BoND-tree, we also provide theoretical analysis to show the optimality of the proposed heuristics. Extensive experiments with synthetic data demonstrate that the proposed scheme is significantly more efficient than the existing ones when applied to support box queries in NDDSs. We also show effectiveness of the proposed scheme in a real-world application of primer design for genome sequence databases. C1 [Chen, Changqing; Watve, Alok; Pramanik, Sakti] Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA. [Zhu, Qiang] Univ Michigan, Dept Comp & Informat Sci, Dearborn, MI 48128 USA. RP Chen, CQ (reprint author), Michigan State Univ, Dept Comp Sci & Engn, 3115 Engn Bldg, E Lansing, MI 48824 USA. EM chencha3@cse.msu.edu; watvealo@cse.msu.edu; pramanik@cse.msu.edu; qzhu@umich.edu FU US National Science Foundation (NSF) [IIS-1319909, IIS-1320078]; Michigan State University; University of Michigan; Michigan State University High Performance Computing Center; Institute for Cyber Enabled Research; Office of Science of US Department of Energy (DOE) [DE-FG02-99ER62848] FX Research supported by the US National Science Foundation (NSF) (under Grants # IIS-1319909 and # IIS-1320078), the Michigan State University and the University of Michigan. They wish to acknowledge the support of the Michigan State University High Performance Computing Center and the Institute for Cyber Enabled Research. The authors would like to thank Dr. James Cole and Dr. Benli Chai and Mr. Jordan Fish, who work for Ribosomal Database Project (RDP) under Grant No. DE-FG02-99ER62848 supported by the Office of Science of US Department of Energy (DOE), for their valuable suggestions and help. The authors also acknowledge Dr. Gang Qian for his help. CR Bayer R., 1977, ACM Transactions on Database Systems, V2, DOI 10.1145/320521.320530 BECKMANN N, 1990, SIGMOD REC, V19, P322, DOI 10.1145/93597.98741 Bentley JL, 1997, PROCEEDINGS OF THE EIGHTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P360 Berchtold S, 1996, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P28 Bozkaya T, 1999, ACM T DATABASE SYST, V24, P361, DOI 10.1145/328939.328959 Chakrabarti K, 1999, PROC INT CONF DATA, P440, DOI 10.1109/ICDE.1999.754960 Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808 Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426 Clement J, 2001, ALGORITHMICA, V29, P307, DOI 10.1007/s004530010064 de Vries A.P., 2002, P ACM SIGMOD INT C M, P322 El Baz M.E.D., 2006, P 14 EUR INT C PAR D, P127 Ferragina P, 1999, J ACM, V46, P236, DOI 10.1145/301970.301973 Guttman A, 1984, ACM SIGMOD REC, V14, P47, DOI DOI 10.1145/971697.602266 Henrich A, 1998, PROC INT CONF DATA, P362, DOI 10.1109/ICDE.1998.655799 Hjaltason GR, 2003, ACM T DATABASE SYST, V28, P517, DOI 10.1145/958942.958948 Knuth D. E., 1973, ART COMPUTER PROGRAM, V3 Liu A., 2006, P 6 INT C INT SYST D, P1159 LOOTS W, 1992, INT J PARALLEL PROG, V21, P349, DOI 10.1007/BF01407836 Meyn S. P., 1993, MARKOV CHAINS STOCHA Qian G, 2006, ACM T DATABASE SYST, V31, P439, DOI 10.1145/1138394.1138395 Qian G., 2003, P VLDB, P620, DOI 10.1016/B978-012722442-8/50061-6 Robinson J. J., 1981, Paper, 32nd Annual Meeting of the European Association for Animal Production Rolfe Timothy J, 2007, SIGCSE Bulletin, V39, DOI 10.1145/1345375.1345414 SAHNI S, 1975, J ACM, V22, P115, DOI 10.1145/321864.321873 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weber R., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Weiner P., 1973, P 14 IEEE S SWITCH A, P1, DOI DOI 10.1109/SWAT.1973.13 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 YIANILOS PN, 1993, PROCEEDINGS OF THE FOURTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P311 NR 29 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD NOV PY 2013 VL 25 IS 11 BP 2629 EP 2643 DI 10.1109/TKDE.2012.132 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 225AJ UT WOS:000324934400016 ER PT J AU Rodrigo, A Perez-Iglesias, J Penas, A Garrido, G Araujo, L AF Rodrigo, Alvaro Perez-Iglesias, Joaquin Penas, Anselmo Garrido, Guillermo Araujo, Lourdes TI Answering questions about European legislation SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Question Answering; Answer Validation AB ResPubliQA is a Question Answering (QA) evaluation task over European legislation whose first edition was proposed at the Cross Language Evaluation Forum (CLEF) 2009. The exercise consists of extracting a relevant paragraph of text that satisfies the information need expressed by a natural language question. The definition of the task allows to compare current QA technologies with pure Information Retrieval (IR) approaches and to introduce Answer Validation technologies in QA systems. In this paper we describe a system developed for this task. Our system is composed by an IR phase focused on improving QA results, a validation step for removing not promising paragraphs and a module based on n-grams overlapping for selecting the final answer, as well as a selection module that uses Lexical Entailment. While the IR module has contributed to obtain promising results, the performance of the validation module has to be improved. On the other hand, the n-gram ranking improved the results of the ranking given by the IR module. (C) 2013 Elsevier Ltd. All rights reserved. EM alvarory@lsi.uned.es; joaquin.perez@lsi.uned.es; anselmo@lsi.uned.es; ggarrido@lsi.uned.es; lurdes@lsi.uned.es RI Araujo, Lourdes/G-6272-2015; Penas, Anselmo/H-4308-2015 OI Araujo, Lourdes/0000-0002-7657-4794; Penas, Anselmo/0000-0002-7867-0149 FU Spanish Ministry of Science and Innovation [TIN2010-21128-C02]; Regional Government of Madrid [S2009/TIC1542] FX This work has been partially supported by the Spanish Ministry of Science and Innovation, through the project Holopedia (TIN2010-21128-C02) and the Regional Government of Madrid, through the project MA2VICMR (S2009/TIC1542) CR Carreras X., 2004, P 4 INT C LANG RES E Forner P., 2010, LREC Hovy E., 2001, P 9 TEXT RETR C, P655 Li X., 2002, P 19 INT C COMP LING Magnini B, 2003, LECT NOTES COMPUT SC, V3237, P471 Moldovan D., 2000, P 38 ANN M ASS COMP, P563, DOI 10.3115/1075218.1075289 Penas A., 2011, ACL, P1415 Penas A., 2010, LNCS IN PRESS Prager J., 2000, P 23 ANN INT ACM SIG, P184, DOI 10.1145/345508.345574 Rodrigo A, 2007, LECT NOTES COMPUT SC, V4730, P483 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Voorhees E.M., 1999, TEXT RETR C TREC 8, P83 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval NR 13 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD NOV 1 PY 2013 VL 40 IS 15 BP 5811 EP 5816 DI 10.1016/j.eswa.2013.05.008 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 186NP UT WOS:000322051600004 ER PT J AU Perez-Suarez, A Martinez-Trinidad, JF Carrasco-Ochoa, JA Medina-Pagola, JE AF Perez-Suarez, Airel Fco Martinez-Trinidad, Jose Carrasco-Ochoa, Jesus A. Medina-Pagola, Jose E. TI An algorithm based on density and compactness for dynamic overlapping clustering SO PATTERN RECOGNITION LA English DT Article DE Data mining; Clustering; Overlapping clustering algorithms; Dynamic clustering algorithms ID COMMUNITY STRUCTURE; NETWORKS AB Most clustering algorithms organize a collection of objects into a set of disjoint clusters. Although this approach has been successfully applied in unsupervised learning, there are several applications where objects could belong to more than one cluster. Overlapping clustering is an alternative in those contexts like social network analysis, information retrieval and bioinformatics, among other problems where non-disjoint clusters appear. In addition, there are environments where the collection changes frequently and the clustering must be updated; however, most of the existing overlapping clustering algorithms are not able to efficiently update the clustering. In this paper, we introduce a new overlapping clustering algorithm, called DClustR, which is based on the graph theory approach and it introduces a new strategy for building more accurate overlapping clusters than those built by state-of-the-art algorithms. Moreover, our algorithm introduces a new strategy for efficiently updating the clustering when the collection changes. The experimentation conducted over several standard collections shows the good performance of the proposed algorithm, wit accuracy and efficiency. (C) 2013 Elsevier Ltd. All rights reserved. C1 [Perez-Suarez, Airel; Carrasco-Ochoa, Jesus A.; Medina-Pagola, Jose E.] INAOE, Puebla, Mexico. [Perez-Suarez, Airel] Adv Technol Applicat Ctr CENATAV, Data Min Dept, Havana, Cuba. [Fco Martinez-Trinidad, Jose] Adv Technol Applicat Ctr CENATAV, Havana, Cuba. RP Perez-Suarez, A (reprint author), Adv Technol Applicat Ctr CENATAV, Data Min Dept, Havana, Cuba. EM airel@ccc.inaoep.mx; fmartine@inaoep.mx; ariel@inaoep.mx; jmedina@cenatav.co.cu FU National Council on Science and Technology of Mexico (CONACyT) [CB2008-106443, CB2008-106366] FX We thank to the National Council on Science and Technology of Mexico (CONACyT), for its support to this research through the project Grants CB2008-106443 and CB2008-106366. CR Abella-Perez R., 2010, P INT WORKSH HANDL C, P65 Aggarwal CC, 2003, P 29 INT C VER LARG, V29, P81 Ahmed A., 2008, P 8 SIAM INT C DAT M Amigo E, 2009, INFORM RETRIEVAL, V12, P461, DOI 10.1007/s10791-008-9066-8 Aslam J., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288659 Bagga A, 1998, P 17 INT C COMP LING, V1, P79 Bakus J., 2002, P 9 INT C NEUR INF P, P2212 Berry M.W., 2004, SURVEY TEXT MINING C Chakrabarti D., 2006, P 12 ACM SIGKDD INT, P554, DOI DOI 10.1145/1150402.1150467 Chang L, 2010, LECT NOTES ARTIF INT, V6438, P56 Chi Y, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P153 Correa-Morris J, 2010, PATTERN RECOGN, V43, P2439, DOI 10.1016/j.patcog.2010.01.019 Duan DS, 2012, ARTIF INTELL REV, V38, P129, DOI 10.1007/s10462-011-9250-x Ducournau A, 2012, PATTERN RECOGN, V45, P2788, DOI 10.1016/j.patcog.2012.01.005 Gago-Alonso A., 2007, LNCS, V4756, P664 Gil-Garcia R, 2010, PATTERN RECOGN LETT, V31, P469, DOI 10.1016/j.patrec.2009.11.011 Gil-Garcia RJ, 2003, LECT NOTES COMPUT SC, V2905, P480 Greengrass E., 2001, TRR52008001 Gregory S, 2007, LECT NOTES ARTIF INT, V4702, P91 Gregory S, 2008, LECT NOTES ARTIF INT, V5211, P408, DOI 10.1007/978-3-540-87479-9_45 Guha S, 2000, ANN IEEE SYMP FOUND, P359 Gusfield D., 1997, ALGORITHMS STRINGS T Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Havens T., 2012, IEEE T FUZZY SYST, P1 Hsiu-Hsia Lin, 2012, Pattern Recognition, V45, DOI 10.1016/j.patcog.2011.06.003 Jo T, 2007, LECT NOTES COMPUT SC, V4492, P871 Larsen B., 1999, KDD 99, P16 Maraziotis IA, 2012, PATTERN RECOGN, V45, P637, DOI 10.1016/j.patcog.2011.05.007 Michel V, 2012, PATTERN RECOGN, V45, P2041, DOI 10.1016/j.patcog.2011.04.006 Narasimhamurthy A, 2010, KNOWL INF SYST, V25, P345, DOI 10.1007/s10115-009-0251-x Ning HZ, 2010, PATTERN RECOGN, V43, P113, DOI 10.1016/j.patcog.2009.06.001 O'Callaghan L, 2002, PROC INT CONF DATA, P685, DOI 10.1109/ICDE.2002.994785 Okada S, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P2047, DOI 10.1109/IJCNN.2011.6033478 Palla G, 2005, NATURE, V435, P814, DOI 10.1038/nature03607 Perez-Suarez A., 2009, LNCS, V5856, P497 Perez-Suarez A., 2007, LNCS LNAI, V4571, P248 Perez-Suarez A, 2012, INTELL DATA ANAL, V16, P211, DOI 10.3233/IDA-2012-0520 Pfitzner D, 2009, KNOWL INF SYST, V19, P361, DOI 10.1007/s10115-008-0150-6 Pons-Porrata A., 2002, AVANCES PERSPECTIVAS, P405 Rosenberg Andrew, 2007, P 2007 JOINT C EMP M, P410 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 Steinbach M., 2000, P 6 ACM SIGKDD INT C Tao L., 2008, P 8 IEEE INT C DAT M, P887 Xiang-Ying D., 2010, P INT C MACH LEARN C, P3358 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 Zhao Y, 2001, 0140 U MINN DEP COMP NR 49 TC 3 Z9 5 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD NOV PY 2013 VL 46 IS 11 BP 3040 EP 3055 DI 10.1016/j.patcog.2013.03.022 PG 16 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 175LT UT WOS:000321232900014 ER PT J AU Bin Raies, A Mansour, H Incitti, R Bajic, VB AF Bin Raies, Arwa Mansour, Hicham Incitti, Roberto Bajic, Vladimir B. TI Combining Position Weight Matrices and Document-Term Matrix for Efficient Extraction of Associations of Methylated Genes and Diseases from Free Text SO PLOS ONE LA English DT Article ID DNA METHYLATION; CANCER; DATABASE; EPIGENETICS; MEINFOTEXT; METHDB AB Background: In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually. Methodology: We developed a novel text mining methodology based on a new concept of position weight matrices (PWMs) for text representation and feature generation. We applied PWMs in conjunction with the document-term matrix to extract with high accuracy associations between methylated genes and diseases from free text. The performance results are based on large manually-classified data. Additionally, we developed a web-tool, DEMGD, which automates extraction of these associations from free text. DEMGD presents the extracted associations in summary tables and full reports in addition to evidence tagging of text with respect to genes, diseases and methylation words. The methodology we developed in this study can be applied to similar association extraction problems from free text. Conclusion: The new methodology developed in this study allows for efficient identification of associations between concepts. Our method applied to methylated genes in different diseases is implemented as a Web-tool, DEMGD, which is freely available at http://www.cbrc.kaust.edu.sa/demgd/. The data is available for online browsing and download. C1 [Bin Raies, Arwa; Bajic, Vladimir B.] KAUST, CBRC, Comp Elect & Math Sci & Engn Div CEMSE, Thuwal, Saudi Arabia. [Mansour, Hicham; Incitti, Roberto] KAUST, Biosci Core Labs, Thuwal, Saudi Arabia. RP Bajic, VB (reprint author), KAUST, CBRC, Comp Elect & Math Sci & Engn Div CEMSE, Thuwal, Saudi Arabia. EM vladimir.bajic@kaust.edu.sa RI Bajic, Vladimir/D-2810-2009 OI Bajic, Vladimir/0000-0001-5435-4750 FU KAUST Base Research Funds FX This project was funded by the KAUST Base Research Funds to VBB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Aggarwal CC, 2012, MINING TEXT DATA, P3 ALDOUS D, 1991, ANN PROBAB, V19, P1, DOI 10.1214/aop/1176990534 Amoreira C, 2003, NUCLEIC ACIDS RES, V31, P75, DOI 10.1093/nar/gkg093 Bing Liu, 1998, 4 INT C KNOWL DISC D Bock C, 2007, BIOINFORMATICS, V24, P1, DOI DOI 10.1093/BIOINFORMATICS/BTM546 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Brower V, 2011, NATURE, V471, pS12, DOI 10.1038/471S12a Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199 Chun HW, 2006, P PAC S BIOC PSB 11 CLAVERIE JM, 1994, COMPUT CHEM, V18, P287, DOI 10.1016/0097-8485(94)85024-0 Coenen F, 2005, ADV KNOWLEDGE DISCOV Coulet A, 2010, J BIOMED INFORM, V43, P1009, DOI 10.1016/j.jbi.2010.08.005 Egger G, 2004, NATURE, V429, P457, DOI 10.1038/nature02625 Fang YC, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-22 Fang YC, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-471 Feinberg AP, 2007, NATURE, V447, P433, DOI 10.1038/nature05919 Grunau C, 2001, NUCLEIC ACIDS RES, V29, P270, DOI 10.1093/nar/29.1.270 Haas J, 2013, EMBO MOL MED, V5, P413, DOI 10.1002/emmm.201201553 Hackenberg M, 2011, NUCLEIC ACIDS RES, V39, pD75, DOI 10.1093/nar/gkq942 Hakenberg J, 2012, J BIOMED INFORM, V45, P842, DOI 10.1016/j.jbi.2012.04.006 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Han J, 2012, MOR KAUF D, P1 He XM, 2008, NUCLEIC ACIDS RES, V36, pD836, DOI 10.1093/nar/gkm730 Li W, 2001, 2001 IEEE INT C DAT Luehr S, 2012, NUCLEIC ACIDS RES, V40, pW104, DOI 10.1093/nar/gks602 Lv J, 2012, NUCLEIC ACIDS RES, V40, pD1030, DOI 10.1093/nar/gkr1169 Miner G, 2012, PRACTICAL TEXT MINING AND STATISTICAL ANALYSIS FOR NON-STRUCTURED TEXT DATA APPLICATIONS, P1 Nandi S, 2012, BMC GENOMICS, V13, DOI 10.1186/1471-2164-13-416 Ongenaert M, 2008, NUCLEIC ACIDS RES, V36, pD842, DOI 10.1093/nar/gkm788 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quinlan J, 1993, P EUR C MACH LEARN E Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN, P17 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B, 2002, LEARNING KERNELS, P1 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Stormo GD, 2000, GENOME RES, V10, P394, DOI 10.1101/gr.10.4.394 Summers T, 2013, J CANCER, V4, P210, DOI 10.7150/jca.5839 Suzuki H, 2012, MOL ONCOL, V6, P567, DOI 10.1016/j.molonc.2012.07.007 Wallach HM, 2006, ICML 06 P 23 INT C M Wang S, 2008, DNA METHYLATION MICR, P17 Weinberger KQ, 2009, J MACH LEARN RES, V10, P207 Weiss SM, 2010, TEXTS COMPUT SCI, P1, DOI 10.1007/978-1-84996-226-1 Xin YR, 2012, NUCLEIC ACIDS RES, V40, pD1245, DOI 10.1093/nar/gkr1193 Yang Y, 1997, P 4 INT C MACH LEARN Yin X., 2003, P 3 SIAM INT C DAT M Zheng JH, 2013, CLIN CANCER RES, V19, P1400, DOI 10.1158/1078-0432.CCR-12-2888 NR 47 TC 0 Z9 1 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD OCT 16 PY 2013 VL 8 IS 10 AR e77848 DI 10.1371/journal.pone.0077848 PG 12 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 239JR UT WOS:000326019400140 PM 24147091 ER PT J AU Kim, M Newth, D Christen, P AF Kim, Minkyoung Newth, David Christen, Peter TI Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media SO ENTROPY LA English DT Article DE macro-level diffusion; dynamic influence; meta-populations; social media ID INNOVATION AB Diverse online social networks are becoming increasingly interconnected by sharing information. Accordingly, emergent macro-level phenomena have been observed, such as the synchronous spread of information across different types of social media. Attempting to analyze the emergent global behavior is impossible from the examination of a single social platform, and dynamic influences between different social networks are not negligible. Furthermore, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both the heterogeneity and structural connectivity of social networks. As real-world phenomena, we explore instances of news diffusion across different social media platforms from a dataset that contains over 386 million web documents covering a one-month period in early 2011. We find that influence between different media types is varied by the context of information. News media are the most influential in the arts and economy categories, while social networking sites (SNS) and blog media are in the politics and culture categories, respectively. Furthermore, controversial topics, such as political protests and multiculturalism failure, tend to spread concurrently across social media, while entertainment topics, such as film releases and celebrities, are more likely driven by interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields and that it provides a way of interpreting the dynamics of diffusion in terms of the strength and directionality of influences among populations. C1 [Kim, Minkyoung; Christen, Peter] Australian Natl Univ, Res Sch Comp Sci, Canberra, ACT 0200, Australia. [Newth, David] CSIRO, CSIRO Ctr Complex Syst Sci, CSIRO Marine & Atmospher Res, Canberra, ACT 2600, Australia. RP Kim, M (reprint author), Australian Natl Univ, Res Sch Comp Sci, GPO Box 4, Canberra, ACT 0200, Australia. EM minkyoung.kim@anu.edu.au; david.newth@csiro.au; peter.christen@anu.edu.au RI Newth, David/A-1848-2011 OI Newth, David/0000-0001-6882-7440 CR Adar E., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence Anagnostopoulos A, 2008, P 14 ACM SIGKDD INT, P7, DOI 10.1145/1401890.1401897 [Anonymous], 2011, P INT AAAI C WEBL SO Bailey N., 1975, MATH THEORY INFECT D, V413 Barrat A, 2008, DYNAMICAL PROCESSES BASS FM, 1969, MANAGE SCI, V15, P215, DOI 10.1287/mnsc.15.5.215 Bass F. M., 2004, Management Science, V50, DOI 10.1287/mnsc.1040.0300 Cha M., 2009, P INT AAAI C WEBL SO Christen P., 2012, DATA CENTRIC SYSTEMS Clauset A, 2009, SIAM REV, V51, P661, DOI 10.1137/070710111 Gomez-Rodriguez M, 2012, ACM T KNOWL DISCOV D, V5, DOI 10.1145/2086737.2086741 Gruhl D., 2004, P 13 INT C WORLD WID, P491, DOI DOI 10.1145/988672.988739 Guimera R, 2005, SCIENCE, V308, P697, DOI 10.1126/science.1106340 Kamath KY, 2012, P INT C INF KNOWL MA, P962 KATZ E, 1957, PUBLIC OPIN QUART, V21, P61, DOI 10.1086/266687 Kelley C.T., 1999, ITERATIVE METHODS OP Kim M., 2012, P INT AAAI C WEBL SO Kumar V, 2002, MARKET SCI, V21, P318, DOI 10.1287/mksc.21.3.318.139 Kuperman M, 2001, PHYS REV LETT, V86, P2909, DOI 10.1103/PhysRevLett.86.2909 Kwak H., 2010, P 19 INT C WORLD WID, P591, DOI DOI 10.1145/1772690.1772751 Leskovec J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P497 Leskovec J., 2007, P 7 SIAM INT C DAT M Luu M., 2012, P INT AAAI C WEBL SO Mahajan V., 1995, MARKET SCI, V14, P79 Manning C. D., 2008, INTRO INFORM RETRIEV, V1 Myers S, 2012, P 18 ACM SIGKDD INT, P33, DOI DOI 10.1145/2339530.2339540] Newman M., 2010, NETWORKS INTRO Niu SC, 2002, MATH PROBL ENG, V8, P249, DOI 10.1080/1024123021000036285 Pan W., 2011, P AAAI C ART INT SAN Putsis WP, 1997, MARKET SCI, V16, P354, DOI 10.1287/mksc.16.4.354 Rogers E. M., 1962, DIFFUSION INNOVATION Romero D.M., 2011, P 20 INT C WORLD WID, P695, DOI DOI 10.1145/1963405.1963503 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schilling MA, 2007, MANAGE SCI, V53, P1113, DOI 10.1287/mnsc.1060.0624 Sobkowicz P, 2012, LECT NOTES ARTIF INT, V7103, P288 Young HP, 2009, AM ECON REV, V99, P1899, DOI 10.1257/aer.99.5.1899 NR 36 TC 0 Z9 0 PU MDPI AG PI BASEL PA POSTFACH, CH-4005 BASEL, SWITZERLAND SN 1099-4300 J9 ENTROPY-SWITZ JI Entropy PD OCT PY 2013 VL 15 IS 10 BP 4215 EP 4242 DI 10.3390/e15104215 PG 28 WC Physics, Multidisciplinary SC Physics GA 272UM UT WOS:000328486900013 ER PT J AU Araujo, AFR Rego, RLME AF Araujo, Aluizio F. R. Rego, Renata L. M. E. TI Self-Organizing Maps with a Time-Varying Structure SO ACM COMPUTING SURVEYS LA English DT Article DE Algorithms; Standardization; Neural networks; self-organizing maps; unsupervised learning ID GROWING NEURAL GAS; SURFACE RECONSTRUCTION; CELL STRUCTURES; PATTERN DISCOVERY; NOVELTY DETECTION; SAGE DATA; NETWORK; VISUALIZATION; NAVIGATION; FRAMEWORK AB A number of research studies considering a self-organizing map have been developed since such a map was proposed by Kohonen [1982]. Some of these studies concern SOM-based models that do not use pre-defined structures to produce their mappings. We call these models Self-Organizing Maps with Time-Varying Structure (SOM-TVS). Despite the large number of SOM-TVS models there is not a standard way to describe them. In this article, we propose a framework to describe SOM-TVS models, which we use to describe some of these models and to compare their algorithms, and we present some real-world applications of the models presented. C1 [Araujo, Aluizio F. R.; Rego, Renata L. M. E.] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil. [Rego, Renata L. M. E.] Federale Inst Pernambuco, Recife, PE, Brazil. RP Araujo, AFR (reprint author), Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil. EM aluizioa@cin.ufpe.br CR Albertini MK, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P462 Amenta N., 2001, P 6 ACM S SOL MOD AP, P249, DOI DOI 10.1145/376957.376986] Angelopoulou A, 2006, LECT NOTES COMPUT SC, V3951, P313 ARAUJO A. F. R., 2010, P IEEE INT JOINT C N, P1 Araujo ARF, 2009, IMAGE VISION COMPUT, V27, P1229, DOI 10.1016/j.imavis.2008.11.014 Atsalakis A, 2006, ENG APPL ARTIF INTEL, V19, P769, DOI 10.1016/j.engappai.2006.05.004 Braga APS, 2003, NEURAL COMPUT APPL, V12, P220, DOI 10.1007/s00521-003-0385-9 Brito AD, 2008, IEEE T NEURAL NETWOR, V19, P1130, DOI 10.1109/TNN.2008.2000390 Cai L, 2004, GENOME BIOL, V5, DOI 10.1186/gb-2004-5-7-r51 Campbell C, 2001, ADV NEUR IN, V13, P395 Chang CH, 2005, IEEE T NEURAL NETWOR, V16, P237, DOI 10.1109/TNN.2004.836543 CHENG G., 2006, P INT C COMP INT MOD, P164 Cheng GJ, 2001, NEURAL COMPUT APPL, V10, P89, DOI 10.1007/s005210170021 Chevaillier B, 2011, NEURAL PROCESS LETT, V34, P71, DOI 10.1007/s11063-011-9184-y Corona F, 2009, NEUROCOMPUTING, V73, P71, DOI 10.1016/j.neucom.2008.10.026 Crook PA, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS, P3894 Martins LD, 2009, J SIGNAL PROCESS SYS, V55, P77, DOI 10.1007/s11265-008-0209-3 Dittenbach M, 2002, NEUROCOMPUTING, V48, P199, DOI 10.1016/S0925-2312(01)00655-5 do Rego RLME, 2009, LECT NOTES COMPUT SC, V5768, P515 do Rego RLME, 2010, IEEE T NEURAL NETWOR, V21, P211, DOI 10.1109/TNN.2009.2035312 Frank A., 2010, UCI MACHINE LEARNING Frezza-Buet H, 2008, NEUROCOMPUTING, V71, P1191, DOI 10.1016/j.neucom.2007.12.024 Fritzke B., 1996, P EUR S ART NEUR NET, P61 FRITZKE B, 1995, NEURAL PROCESS LETT, V2, P9, DOI 10.1007/BF02332159 FRITZKE B., 1997, P INT C ART NEUR NET, P613 FRITZKE B., 1996, HDB NEURAL COMPUTATI Fritzke B., 1995, ADV NEURAL INFORMATI, V7, P625 FRITZKE B, 1994, NEURAL NETWORKS, V7, P1441, DOI 10.1016/0893-6080(94)90091-4 Furao Shen, 2006, Neural Netw, V19, P90, DOI 10.1016/j.neunet.2005.04.006 Furao S, 2007, NEURAL NETWORKS, V20, P893, DOI 10.1016/j.neunet.2007.07.008 Garcia-Rodriguez J, 2007, IEEE IJCNN, P366, DOI 10.1109/IJCNN.2007.4370984 Garcia-Rodriguez J, 2011, APPL SOFT COMPUT, V11, P4413, DOI 10.1016/j.asoc.2011.02.007 Hamza H, 2008, PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, P175, DOI 10.1109/DAS.2008.43 Holdstein Y, 2008, VISUAL COMPUT, V24, P295, DOI 10.1007/s00371-007-0202-z Hung CL, 2008, EXPERT SYST APPL, V34, P780, DOI 10.1016/j.eswa.2006.10.012 Ivrissimtzis IP, 2003, SMI 2003: SHAPE MODELING INTERNATIONAL 2003, PROCEEDINGS, P78 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JOCKUSCH J., 1999, P IJCNN 99, P445 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kim MY, 2004, J ROBOTIC SYST, V21, P323, DOI 10.1002/rob.20016 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Gopi M., 2002, Proceedings 15th Brazilian Symposium on Computer Graphics and Image Processing, DOI 10.1109/SIBGRA.2002.1167141 Kurz A, 1996, IEEE T SYST MAN CY B, V26, P233, DOI 10.1109/3477.485835 LACASSIE J., 2010, P INT JOINT C NEUR N, P1 Langer H, 2011, J VOLCANOL GEOTH RES, V200, P1, DOI 10.1016/j.jvolgeores.2010.11.019 Linda O, 2009, IEEE IJCNN, P927 Linda O, 2011, IEEE T IND ELECTRON, V58, P3187, DOI 10.1109/TIE.2009.2037649 Marsland S, 2002, NEURAL NETWORKS, V15, P1041, DOI 10.1016/S0893-6080(02)00078-3 MARTINETZ T, 1994, NEURAL NETWORKS, V7, P507, DOI 10.1016/0893-6080(94)90109-0 Mole VLD, 2010, NEURAL COMPUT, V22, P689, DOI 10.1162/neco.2009.08-08-842 Neto HV, 2007, ROBOT AUTON SYST, V55, P693, DOI 10.1016/j.robot.2007.05.012 Nurnberger A, 2006, APPL SOFT COMPUT, V6, P357, DOI 10.1016/j.asoc.2005.11.008 Oja M., 2003, NEURAL COMPUTING SUR, V3, P1 Penas M, 2008, NEURAL PROCESS LETT, V27, P67, DOI 10.1007/s11063-007-9060-y Qin AK, 2004, NEURAL NETWORKS, V17, P1135, DOI 10.1016/j.neunet.2004.06.013 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shah-Hosseini H, 2002, IEEE T PATTERN ANAL, V24, P1388, DOI 10.1109/TPAMI.2002.1039209 Shen FR, 2011, NEURAL COMPUT APPL, V20, P1061, DOI 10.1007/s00521-010-0428-y Wang HY, 2004, IEEE T INF TECHNOL B, V8, P16, DOI 10.1109/TITB.2004.824727 Wang JH, 1999, NEURAL PROCESS LETT, V10, P139, DOI 10.1023/A:1018789603227 Wong JWH, 2005, J BIOMED INFORM, V38, P322, DOI 10.1016/j.jbi.2005.02.002 Zhang JD, 2009, PROG NAT SCI, V19, P1373, DOI 10.1016/j.pnsc.2009.03.012 Zheng HR, 2008, IEEE T INF TECHNOL B, V12, P459, DOI 10.1109/TITB.2007.901208 NR 64 TC 2 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 EI 1557-7341 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD OCT PY 2013 VL 46 IS 1 AR 7 DI 10.1145/2522968.2522975 PG 38 WC Computer Science, Theory & Methods SC Computer Science GA 258IR UT WOS:000327453300007 ER PT J AU Lee, JH Ha, J Jung, JY Lee, S AF Lee, Jung-Hyun Ha, Jongwoo Jung, Jin-Yong Lee, Sangkeun TI Semantic Contextual Advertising Based on the Open Directory Project SO ACM TRANSACTIONS ON THE WEB LA English DT Article DE Algorithms; Experimentation; Performance; Contextual advertising; classifier; link analysis; open directory project; similarity graph ID CLASSIFICATION AB Contextual advertising seeks to place relevant textual ads within the content of generic webpages. In this article, we explore a novel semantic approach to contextual advertising. This consists of three tasks: (1) building a well-organized hierarchical taxonomy of topics, (2) developing a robust classifier for effectively finding the topics of pages and ads, and (3) ranking ads based on the topical relevance to pages. First, we heuristically build our own taxonomy of topics from the Open Directory Project (ODP). Second, we investigate how to increase classification accuracy by taking the unique characteristics of the ODP into account. Last, we measure the topical relevance of ads by applying a link analysis technique to the similarity graph carefully derived from our taxonomy. Experiments show that our classification method improves the performance of Ma-F-1 by as much as 25.7% over the baseline classifier. In addition, our ranking method enhances the relevance of ads substantially, up to 10% in terms of precision at k, compared to a representative strategy. C1 [Lee, Jung-Hyun; Ha, Jongwoo; Jung, Jin-Yong; Lee, Sangkeun] Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea. RP Lee, S (reprint author), Korea Univ, Dept Comp Sci & Engn, Seoul, South Korea. EM yalphy@korea.ac.kr FU Microsoft Research Asia; Basic Science Research Program through the National Research Foundation of Korea (NRF); Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF); Ministry of Education, Science and Technology [2011-0010325, 2012M3C4A7033344] FX This work was supported in part by a grant from the Microsoft Research Asia and also in part by the Basic Science Research Program and the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0010325 and No. 2012M3C4A7033344). CR Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 Armano Giuliano, 2011, Proceedings International Conference on Knowledge Discovery and Information Retrieval (KDIR 2011) Armano G., 2011, Proceedings of the 2011 22nd International Conference on Database and Expert Systems Applications (DEXA 2011), DOI 10.1109/DEXA.2011.78 Bennett P. N., 2010, P 19 INT C WORLD WID, P111, DOI 10.1145/1772690.1772703 Bennett PN, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P11, DOI 10.1145/1571941.1571946 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Broder A., 2008, P 17 ACM C INF KNOWL, P1003, DOI 10.1145/1458082.1458216 Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Broder A.Z., 2007, P 30 ANN INT ACM SIG, P231, DOI 10.1145/1277741.1277783 Chakrabarti D., 2008, P 17 INT C WORLD WID, P417, DOI 10.1145/1367497.1367554 Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906 Chen Y., 2008, P INT C WEB SEARCH W, P251, DOI 10.1145/1341531.1341564 Chirita P.A., 2005, P 28 ANN INT ACM SIG, P178, DOI 10.1145/1076034.1076067 Craswell N., 2007, P 30 ANN INT ACM SIG, P239, DOI 10.1145/1277741.1277784 Silla CN, 2011, DATA MIN KNOWL DISC, V22, P31, DOI 10.1007/s10618-010-0175-9 Gabrilovich E, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1048 Gao S, 2006, ACM T INFORM SYST, V24, P190, DOI 10.1145/1148020.1148022 HA J., 2010, PROCEEDINGS OF THE 1, P1935 Han EH, 2000, LECT NOTES COMPUT, V1910, P424 Haveliwala T. H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513 Jeh G, 2003, P 12 INT C WORLD WID, P271, DOI DOI 10.1145/775152.775191 Jin X., 2007, P 1 INT WORKSH DAT M, P28, DOI 10.1145/1348599.1348604 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Lacerda A., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148265 Lee Johnny Chung, 2009, P 7 INT WORKSH WEB I, P39, DOI 10.1145/1651587.1651598 LI S., 2011, ACM TRANS INF SYST, V29, DOI http://dx.doi.org/10.1145/1961209.1961213 Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d Liu T.-Y., 2005, SIGKDD EXPLORATIONS, V7, P36 Manning CD, 2008, INTRODUCTION TO INFO McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P359 Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 MURDOCK V., 2008, INT J ELECTRON COMM, V9, P1 PANDEY S., 2009, P 18 INT C WORLD WID, P441, DOI http://dx.doi.org/10.1145/1526709.1526769 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SILLA JR C. N., 2011, DATA MINING AND KNOW, V22, P1 SINKA M, 2002, FRONTIERS ARTIFICIAL, V87, P881 Wang C, 2002, P 8 AM C INF SYST, P1143 Xue G.-R., 2008, P 31 ANN INT ACM SIG, P619, DOI 10.1145/1390334.1390440 Yang Y., 1999, INF RETRIEV, V1, P1 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yih Wen-tau, 2006, P 15 INT C WORLD WID, P213, DOI 10.1145/1135777.1135813 NR 43 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1559-1131 EI 1559-114X J9 ACM T WEB JI ACM Trans. Web PD OCT PY 2013 VL 7 IS 4 AR 24 DI 10.1145/2529995.2529997 PG 22 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 255TM UT WOS:000327263000007 ER PT J AU Habrard, A Peyrache, JP Sebban, M AF Habrard, Amaury Peyrache, Jean-Philippe Sebban, Marc TI ITERATIVE SELF-LABELING DOMAIN ADAPTATION FOR LINEAR STRUCTURED IMAGE CLASSIFICATION SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS LA English DT Article DE Domain adaptation; edit distance; sparse learning ID TREE EDIT DISTANCE; RETRIEVAL; MODELS AB A strong assumption to derive generalization guarantees in the standard PAC framework is that training (or source) data and test (or target) data are drawn according to the same distribution. Because of the presence of possibly outdated data in the training set, or the use of biased collections, this assumption is often violated in real-world applications leading to different source and target distributions. To go around this problem, a new research area known as Domain Adaptation (DA) has recently been introduced giving rise to many adaptation algorithms and theoretical results in the form of generalization bounds. This paper deals with self-labeling DA whose goal is to iteratively incorporate semi-labeled target data in the learning set to progressively adapt the classifier from the source to the target domain. The contribution of this work is three-fold: First, we provide the minimum and necessary theoretical conditions for a self-labeling DA algorithm to perform an actual domain adaptation. Second, following these theoretical recommendations, we design a new iterative DA algorithm, called GESIDA, able to deal with structured data. This algorithm makes use of the new theory of learning with (epsilon, gamma, tau)-good similarity functions introduced by Balcan et al., which does not require the use of a valid kernel to learn well and allows us to induce sparse models. Finally, we apply our algorithm on a structured image classification task and show that self-labeling domain adaptation is a new original way to deal with scaling and rotation problems. C1 [Habrard, Amaury; Peyrache, Jean-Philippe; Sebban, Marc] Univ St Etienne, Lab Hubert Curien, UMR CNRS 5516, F-42000 St Etienne 2, France. RP Habrard, A (reprint author), Univ St Etienne, Lab Hubert Curien, UMR CNRS 5516, F-42000 St Etienne 2, France. EM amaury.habrard@univ-st-etienne.fr; jean.philippe.peyrache@univ-st-etienne.fr; marc.sebban@univ-st-etienne.fr CR Bacchiani M., 2003, P HLT NAACL, P126 Balcan M-F., 2006, P 23 INT C MACH LEAR, P73, DOI 10.1145/1143844.1143854 Balcan M.-F., 2008, P COLT, P287 Ben-David S., 2006, P NIPS 2006, P137 Ben-David S, 2010, MACH LEARN, V79, P151, DOI 10.1007/s10994-009-5152-4 Bernard M, 2008, PATTERN RECOGN, V41, P2611, DOI [10.1016/j.patcog.2008.01.011, 10.1016/j.pateog.2008.01.011] Bickel S., 2007, ICML 2007, P81 Bille P, 2005, THEOR COMPUT SCI, V337, P217, DOI 10.1016/j.tcs.2004.12.030 Blitzer J., 2006, P C EMP METH NAT LAN, P120, DOI 10.3115/1610075.1610094 Blum A., 1998, COLT, P92 Bruzzone L, 2010, IEEE T PATTERN ANAL, V32, P770, DOI 10.1109/TPAMI.2009.57 Cortes C, 2004, J MACH LEARN RES, V5, P1035 Daliri Mohammad Reza, 2008, Pattern Recognition, V41, DOI 10.1016/j.patcog.2007.10.020 Daume H, 2007, ANN M ASS COMP LING, V45, P256 Dudik M., 2005, NIPS Florian R., 2004, P HUM LANG TECHN C N, P1 Freeman H., 1974, ACM COMPUT SURV, V6, P57, DOI 10.1145/356625.356627 Freund Y., 2000, P 12 ANN C COMP LEAR, P102 Freund Y., 1996, P 13 INT C MACH LEAR, P148 Hsieh SM, 2008, INFORM PROCESS MANAG, V44, P1214, DOI 10.1016/j.ipm.2007.09.008 Huang J., 2006, P NIPS, P601 Ji YS, 2011, J COMPUT SCI TECH-CH, V26, P81, DOI 10.1007/s11390-011-1113-z Jiang J., 2008, LIT SURVEY DOMAIN AD Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Mansour Y., 2009, P COLT 2009 Mansour Y, 2009, LECT NOTES ARTIF INT, V5808, P32 Mansour Y., 2012, ISAIM Margolis A., 2011, LIT REV DOMAIN ADAPT, P1 Martinez AM, 2002, IEEE T PATTERN ANAL, V24, P748, DOI 10.1109/TPAMI.2002.1008382 Pan S. J., 2008, AAAI, P677 Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191 Perez O, 2007, LECT NOTES COMPUT SC, V4507, P178 Punitha P, 2008, PATTERN RECOGN, V41, P2068, DOI 10.1016/j.patcog.2007.09.012 Rosenfeld R, 1996, COMPUT SPEECH LANG, V10, P187, DOI 10.1006/csla.1996.0011 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Satpal S, 2007, LECT NOTES ARTIF INT, V4702, P224 SELKOW SM, 1977, INFORM PROCESS LETT, V6, P184, DOI 10.1016/0020-0190(77)90064-3 Sugiyama M., 2008, P NIPS 2007 Tsuboi Y., 2009, J INFORM PROCESSING, V17, P138 VALIANT LG, 1984, COMMUN ACM, V27, P1134, DOI 10.1145/1968.1972 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 NR 41 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-2130 EI 1793-6349 J9 INT J ARTIF INTELL T JI Int. J. Artif. Intell. Tools PD OCT PY 2013 VL 22 IS 5 SI SI AR 1360005 DI 10.1142/S0218213013600051 PG 30 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 243VH UT WOS:000326345900006 ER PT J AU Thomas, SW Nagappan, M Blostein, D Hassan, AE AF Thomas, Stephen W. Nagappan, Meiyappan Blostein, Dorothea Hassan, Ahmed E. TI The Impact of Classifier Configuration and Classifier Combination on Bug Localization SO IEEE TRANSACTIONS ON SOFTWARE ENGINEERING LA English DT Article DE Software maintenance; bug localization; information retrieval; VSM; LSI; LDA; classifier combination ID LATENT DIRICHLET ALLOCATION; FEATURE LOCATION; SYSTEMS; RETRIEVAL AB Bug localization is the task of determining which source code entities are relevant to a bug report. Manual bug localization is labor intensive since developers must consider thousands of source code entities. Current research builds bug localization classifiers, based on information retrieval models, to locate entities that are textually similar to the bug report. Current research, however, does not consider the effect of classifier configuration, i.e., all the parameter values that specify the behavior of a classifier. As such, the effect of each parameter or which parameter values lead to the best performance is unknown. In this paper, we empirically investigate the effectiveness of a large space of classifier configurations, 3,172 in total. Further, we introduce a framework for combining the results of multiple classifier configurations since classifier combination has shown promise in other domains. Through a detailed case study on over 8,000 bug reports from three large-scale projects, we make two main contributions. First, we show that the parameters of a classifier have a significant impact on its performance. Second, we show that combining multiple classifiers-whether those classifiers are hand-picked or randomly chosen relative to intelligently defined subspaces of classifiers-improves the performance of even the best individual classifiers. C1 [Thomas, Stephen W.; Nagappan, Meiyappan; Blostein, Dorothea; Hassan, Ahmed E.] Queens Univ, Sch Comp, Kingston, ON K7K 2N8, Canada. RP Thomas, SW (reprint author), Queens Univ, Sch Comp, Kingston, ON K7K 2N8, Canada. EM sthomas@cs.queensu.ca; ahmed@cs.queensu.ca FU Natural Sciences and Engineering Research Council of Canada (NSERC); Blackberry FX This work was funded in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Blackberry. The authors would like to thank Nguyen et al. [40] for providing them with an early version of their recent work and Nicholas A. Kraft for a helpful discussion about his bug localization experience. They would also like to thank Thanh H. D. Nguyen and Yasutaka Kamei for their help with data collection. Finally, they would like to thank the anonymous reviewers for their very constructive feedback on early drafts of this work. CR [Anonymous], 2011, ECL FDN Arnold R. S., 1993, Proceedings. Conference on Software Maintenance 1993. CSM-93 (Cat. No.93CH3360-5), DOI 10.1109/ICSM.1993.366933 Baeza-Yates R., 1999, MODERN INFORM RETRIE, V463 Bird C, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P121, DOI 10.1145/1595696.1595716 Blei D., 2009, TEXT MINING CLASSIFI, P71 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bohner S., 1996, SOFTWARE CHANGE IMPA Box G.E.P., 1960, TECHNOMETRICS, V2, P455, DOI DOI 10.1080/00401706.1960.10489912 Carpineto C, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2071389.2071390 Chang J., 2009, P 12 INT C ART INT S, P81 Cleary B., 2008, EMPIR SOFTW ENG, V14, P93 Cormack GV, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P758, DOI 10.1145/1571941.1572114 Dallmeier Valentin, 2007, P 22 IEEE ACM INT C, P433, DOI 10.1145/1321631.1321702 D'Ambros M, 2012, EMPIR SOFTW ENG, V17, P531, DOI 10.1007/s10664-011-9173-9 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dit B, 2011, CONF PROC INT SYMP C, P11, DOI 10.1109/ICPC.2011.47 Enslen E, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P71, DOI 10.1109/MSR.2009.5069482 Fischer M., 2003, P 19 INT C SOFTW MAI Glass Robert L., 2003, FACTS FALLACIES SOFT Hillion Erwan, 2012, P 12 INT WORK C SOUR, P1 HO TK, 1994, IEEE T PATTERN ANAL, V16, P66 IBM, 2011, JAZZ Keogh E., 2004, P 10 ACM SIGKDD INT, P206, DOI DOI 10.1145/1014052.1014077 Kittler J, 1998, IEEE T PATTERN ANAL, V20, P226, DOI 10.1109/34.667881 Kocaguneli E, 2012, IEEE T SOFTWARE ENG, V38, P1403, DOI 10.1109/TSE.2011.111 Kuehl RO, 2000, DESIGN EXPT STAT PRI, V2nd Lawrie D, 2011, P 27 IEEE INT C SOFT, P113, DOI [10.1109/ICSM.2011.6080778, DOI 10.1109/ICSM.2011.6080778] Lukins SK, 2008, WORK CONF REVERSE EN, P155, DOI 10.1109/WCRE.2008.33 Lukins SK, 2010, INFORM SOFTWARE TECH, V52, P972, DOI 10.1016/j.infsof.2010.04.002 Madani N, 2010, Proceedings of the 14th European Conference on Software Maintenance and Reengineering (CSMR 2010), DOI 10.1109/CSMR.2010.31 Manning C. D., 2008, INTRO INFORM RETRIEV, V1 Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10 McCallum A. K., 2002, MALLET MACHINE LEARN McMillan C, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P111, DOI 10.1145/1985793.1985809 Misirli AT, 2011, SOFTWARE QUAL J, V19, P515, DOI 10.1007/s11219-010-9128-1 Moser R, 2008, ICSE'08 PROCEEDINGS OF THE THIRTIETH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P181 MYERS RH, 1989, TECHNOMETRICS, V31, P137, DOI 10.2307/1268813 Nguyen A T, 2011, P 26 IEEE ACM INT C, P263 Nguyen T H D, 2010, Proceedings 17th Working Conference on Reverse Engineering (WCRE 2010), DOI 10.1109/WCRE.2010.37 Ostrand T. J., 2004, P 2004 ACM SIGSOFT I, P86, DOI DOI 10.1145/1007512.1007524 Ostrand TJ, 2005, IEEE T SOFTWARE ENG, V31, P340, DOI 10.1109/TSE.2005.49 Page L., 1999, TECHNICAL REPORT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Poshyvanyk D, 2007, INT C PROGRAM COMPRE, P37 Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Rahman F., 2011, P 19 ACM SIGSOFT S 1, P322 Rao S., 2011, P 8 WORK C MIN SOFTW, P43, DOI DOI 10.1145/1985441.1985451 Revelle Meghan, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.10 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Selby RW, 2005, IEEE T SOFTWARE ENG, V31, P495, DOI 10.1109/TSE.2005.69 Sliwerski J., 2005, P 2 WORK C MIN SOFTW Thomas SW, 2012, 2012586 QUEENS U SCH Tukey J, 1991, STAT SCI, V6, P100, DOI DOI 10.1214/SS/1177011945 Tukey J.W., 1994, COLLECTED WORKS JW T, V8 van Erp M., 2000, P 7 INT WORKSH FRONT, P443 Wang HJ, 2012, NEUROCOMPUTING, V92, P124, DOI 10.1016/j.neucom.2011.08.040 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Zimmermann T., 2007, P 3 INT WORKSH PRED NR 59 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 0098-5589 J9 IEEE T SOFTWARE ENG JI IEEE Trans. Softw. Eng. PD OCT PY 2013 VL 39 IS 10 BP 1427 EP 1443 DI 10.1109/TSE.2013.27 PG 17 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 228FO UT WOS:000325171400006 ER PT J AU Jiang, DX Pei, J Li, H AF Jiang, Daxin Pei, Jian Li, Hang TI Mining Search and Browse Logs for Web Search: A Survey SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY LA English DT Article DE Algorithms; Experimentation; Measurement; Search logs; browse log; Web search; survey; log mining; query understanding; document understanding; document ranking; user understanding; monitoring; feedbacks ID SESSION AB Huge amounts of search log data have been accumulated at Web search engines. Currently, a popular Web search engine may receive billions of queries and collect terabytes of records about user search behavior daily. Beside search log data, huge amounts of browse log data have also been collected through client-side browser plugins. Such massive amounts of search and browse log data provide great opportunities for mining the wisdom of crowds and improving Web search. At the same time, designing effective and efficient methods to clean, process, and model log data also presents great challenges. In this survey, we focus on mining search and browse log data for Web search. We start with an introduction to search and browse log data and an overview of frequently-used data summarizations in log mining. We then elaborate how log mining applications enhance the five major components of a search engine, namely, query understanding, document understanding, document ranking, user understanding, and monitoring and feedback. For each aspect, we survey the major tasks, fundamental principles, and state-of-the-art methods. C1 [Jiang, Daxin] Microsoft Corp, Redmond, WA 98052 USA. [Pei, Jian] Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada. RP Pei, J (reprint author), Simon Fraser Univ, Burnaby, BC V5A 1S6, Canada. EM djiang@microsoft.com; jpei@cs.sfu.ca; hangli.h1@huawei.com FU NSERC Discovery Grant project; BCFRST NRAS Endowment Research Team Program project FX J. Pei's research was supported in part by an NSERC Discovery Grant project and a BCFRST NRAS Endowment Research Team Program project. All opinions, findings, conclusions and recommendations in this article are those of the authors and do not necessarily reflect the views of the funding agencies. CR Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 AGICHTEIN E., 2010, P INT C WORLD WID WE Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148175 Backstrom L., 2008, P 17 INT C WORLD WID, P357, DOI DOI 10.1145/1367497.1367546 BAEZA-YATES R., 2004, P 27 AUSTR C COMP SC BAEZA-YATES R. A., 2006, P 13 INT C STRING PR, P98 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbaro M, 2006, NY TIMES Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 BEITZEL S. M., 2007, ACM T INFORM SYST, V25, P2 Beitzel S. M., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009048 Beitzel SM, 2007, J AM SOC INF SCI TEC, V58, P166, DOI 10.1002/asi.20464 Bendersky Michael, 2009, P 2009 WORKSH WEB SE, P8, DOI 10.1145/1507509.1507511 BENNETT P. N., 2012, P 35 INT ACM SIGIR C, P185 Bergsma S., 2007, P JOINT C EMP METH N, P819 Berry M.W., 2003, SURVEY TEXT MINING Boldi P., 2008, P 17 ACM C INF KNOWL, P609, DOI DOI 10.1145/1458082.1458163 Broder A., 2002, SIGIR Forum, V36 Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI 10.1145/1401890.1401995 Cao H., 2009, P 18 INT C WORLD WID, P191, DOI DOI 10.1145/1526709.1526736 Chapelle O., 2009, P 18 INT C WORLD WID, P1, DOI 10.1145/1526709.1526711 CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Craswell N., 2008, P INT C WEB SEARCH W, P87, DOI 10.1145/1341531.1341545 Craswell N., 2007, P 30 ANN INT ACM SIG, P239, DOI 10.1145/1277741.1277784 CROFT W. B., 2010, SIGIR FORUM, V44, P48 Croft W. B., 2009, SEARCH ENGINES INFOR, V1st Cui Hang, 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489 DE RIJKE M., 2011, P 34 INT ACM SIGIR C, P45 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Dean J., 2004, P 6 C S OP SYST DES, V6, P10, DOI DOI 10.HTTP://DL.ACM.0RG/CITATI0N.CFM? DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong A., 2010, P 3 ACM INT C WEB SE, P11, DOI 10.1145/1718487.1718490 Dou Z., 2007, P WWW ACM, V2, P581 Dou Z., 2008, P 17 ACM C INF KNOWL, P73, DOI 10.1145/1458082.1458095 Duan H., 2011, P 20 INT C WORLD WID, P117 Dupret G. E., 2008, P 31 ANN INT ACM SIG, P331, DOI 10.1145/1390334.1390392 Fonseca BM, 2005, P 14 ACM INT C INF K, P696 Fox S, 2005, ACM T INFORM SYST, V23, P147, DOI 10.1145/1059981.1059982 Fuxman A, 2008, P 17 INT C WORLD WID, P61, DOI DOI 10.1145/1367497.1367506 Gao JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/1571941.1572003 Guo F., 2009, P 2 ACM INT C WEB SE, P124, DOI 10.1145/1498759.1498818 Guo F., 2009, P 18 INT C WORLD WID, P11, DOI 10.1145/1526709.1526712 Guo Jiafeng, 2008, P 31 ANN INT ACM SIG, P379, DOI 10.1145/1390334.1390400 Hagen M., 2011, P 20 INT C WORLD WID, P97 Hagen Matthias, 2012, P 21 ACM INT C INF K, P1015 Hassan Ahmed, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348323 Hassan A., 2010, P 3 ACM INT C WEB SE, P221, DOI 10.1145/1718487.1718515 Haveliwala T. H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513 He DQ, 2002, INFORM PROCESS MANAG, V38, P727, DOI 10.1016/S0306-4573(01)00060-7 HOLSCHER C, 2000, P 9 INT WORLD WID WE, V33, P337 Hu B., 2011, PROCEEDINGS OF THE 2, P17 Hu J, 2009, P 18 INT C WORLD WID, P471, DOI 10.1145/1526709.1526773 Hu Y, 2012, P 35 INT ACM SIGIR C, P305, DOI DOI 10.1145/2348283.2348327 Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Immorlica N., 2005, P 14 INT C WORLD WID, P2, DOI 10.1145/1060745.1060752 Jansen BJ, 2006, INFORM PROCESS MANAG, V42, P248, DOI 10.1016/j.ipm.2004.10.007 Jansen BJ, 2001, J AM SOC INF SCI TEC, V52, P235, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1607>3.3.CO;2-6 Jansen BJ, 2007, J AM SOC INF SCI TEC, V58, P862, DOI 10.1002/asi.20564 Ji M., 2011, P 34 INT ACM SIGIR C, P55 Jin X., 2004, P 10 ACM SIGKDD INT, P197, DOI DOI 10.1145/1014052.1014076 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 JONES K. S., 1998, TR446 CAMBR U COMP L Jones R., 2008, P 17 ACM C INF KNOWL, P699, DOI 10.1145/1458082.1458176 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 KANG D., 2011, P 4 ACM INT C WEB SE, P385, DOI 10.1145/1935826.1935888 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kulkarni A., 2011, P 4 ACM INT C WEB SE, P167, DOI DOI 10.1145/1935826.1935862 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lathauwer LD, 2000, SIAM J MATRIX ANAL A, V21, P1253 Lee U., 2005, P 14 INT C WORLD WID, P391, DOI DOI 10.1145/1060745.1060804 Li H., 2011, SYNTHESIS LECT HUMAN, V4, P1, DOI 10.2200/S00348ED1V01Y201104HLT012 LI X., 2010, P 3 ACM INT C WEB SE, P211, DOI 10.1145/1718487.1718514 Li Xiao, 2008, P SIGIR SING JUL, P339 Li Y., 2012, P 35 INT ACM SIGIR C, P611 LI Y., 2011, P ACM SIGIR C RES DE, P285 Liao Z., 2012, P 21 INT C WORLD WID, P489 Liao Z., 2011, ACM T INTEL SYST TEC, V3, P17 LIU C, 2009, P 15 ACM SIGKDD INT, P537, DOI 10.1145/1557019.1557081 Liu F., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Liu Y. T., 2008, P 31 ANN INT ACM SIG, P451, DOI DOI 10.1145/1390334.139041 Lucchese Claudio, 2011, P 4 ACM INT C WEB SE, P277, DOI 10.1145/1935826.1935875 Matthijs N., 2011, P 4 ACM INT C WEB SE, P25, DOI 10.1145/1935826.1935840 MEI Q., 2008, P INT C WEB SEARCH W, P45, DOI 10.1145/1341531.1341540 Mei Q., 2008, P 17 ACM C INF KNOWL, P469, DOI 10.1145/1458082.1458145 Ozertem Umut, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348290 Page L., 1999, 199966 STANF INFOLAB Pasca M, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P596, DOI 10.1145/1571941.1572043 PASCA M., 2007, P 16 WORLD WID WEB C, P101, DOI 10.1145/1242572.1242587 Pitkow J, 2002, COMMUN ACM, V45, P50 Piwowarski B., 2009, P 2 ACM INT C WEB SE, P162, DOI 10.1145/1498759.1498823 Poblete B., 2008, P 17 INT C WORLD WID, P41, DOI 10.1145/1367497.1367504 Pretschner A., 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence, DOI 10.1109/TAI.1999.809829 Qiu F., 2006, P 15 INT C WORLD WID, P727, DOI 10.1145/1135777.1135883 Radlinski F., 2005, P 11 ACM SIGKDD INT, P239, DOI 10.1145/1081870.1081899 RADLINSKI F., 2006, P NATL C AM ASS ART, P1406 RADLINSKI F, 2007, P 13 ACM SIGKDD INT, P570, DOI 10.1145/1281192.1281254 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen C.X, 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747 Shen D., 2006, P 29 ANN INT ACM SIG, P131, DOI 10.1145/1148170.1148196 Shen D, 2006, ACM T INFORM SYST, V24, P320, DOI 10.1145/1165774.1165776 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 Shokouhi Milad, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348364 Silverstein C., 1999, SIGIR Forum, V33 SILVESTRI F., 2010, FDN TRENDS INF RETRI, V4, P1 Speretta M., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence Spink A, 2002, COMPUTER, V35, P107, DOI 10.1109/2.989940 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Sun J. T., 2005, P 14 INT C WORLD WID, P382, DOI 10.1145/1060745.1060803 Szpektor I., 2011, P 20 INT C WORLD WID, P47 Tan B, 2006, P 12 ACM SIGKDD INT, P718, DOI DOI 10.1145/1150402.1150493 Teevan J., 2007, P 30 ANN INT ACM SIG, P151, DOI 10.1145/1277741.1277770 Teevan J, 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 Teevan Jaime, 2008, P 31 ANN INT ACM SIG, P163, DOI DOI 10.1145/1390334.1390364 Tyler S. K., 2010, P 3 ACM INT C WEB SE, P191, DOI DOI 10.1145/1718487.1718512 Vlachos M., 2004, P ACM SIGMOD INT C M, P131, DOI DOI 10.1145/1007568.1007586 Weber I., 2011, P 4 ACM INT C WEB SE, P15, DOI 10.1145/1935826.1935839 Wedig S., 2006, P 12 ACM SIGKDD INT, P742, DOI 10.1145/1150402.1150497 Welch M. J., 2008, P 31 INT ACM SIGIR C, P507, DOI 10.1145/1390334.1390421 Wen J. R., 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974 White RW, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P363, DOI 10.1145/1571941.1572005 Wolfram D, 2001, J AM SOC INF SCI TEC, V52, P1073, DOI 10.1002/asi.1157 Xiang BA, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P451 Xu Jingfang, 2011, P 4 ACM INT C WEB SE, P615, DOI 10.1145/1935826.1935912 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Yi X., 2009, P 18 INT C WORLD WID, P481, DOI [10.1145/1526709.1526774, DOI 10.1145/1526709.1526774] Zhu GY, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1037 NR 128 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 2157-6904 EI 2157-6912 J9 ACM T INTEL SYST TEC JI ACM Trans. Intell. Syst. Technol. PD SEP PY 2013 VL 4 IS 4 AR 57 DI 10.1145/2508037.2508038 PG 37 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 258IB UT WOS:000327451600001 ER PT J AU Bleik, S Mishra, M Huan, J Song, M AF Bleik, Said Mishra, Meenakshi Huan, Jun Song, Min TI Text Categorization of Biomedical Data Sets Using Graph Kernels and a Controlled Vocabulary SO IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS LA English DT Article DE Text categorization; graph representations; graph kernels; biomedical ontologies; mining methods and algorithms; text mining; classifier design and evaluation; modeling structured; textual and multimedia data AB Recently, graph representations of text have been showing improved performance over conventional bag-of-words representations in text categorization applications. In this paper, we present a graph-based representation for biomedical articles and use graph kernels to classify those articles into high-level categories. In our representation, common biomedical concepts and semantic relationships are identified with the help of an existing ontology and are used to build a rich graph structure that provides a consistent feature set and preserves additional semantic information that could improve a classifier's performance. We attempt to classify the graphs using both a set-based graph kernel that is capable of dealing with the disconnected nature of the graphs and a simple linear kernel. Finally, we report the results comparing the classification performance of the kernel classifiers to common text-based classifiers. C1 [Bleik, Said] Univ Hts, New Jersey Inst Technol, Dept Informat Syst, Newark, NJ 07102 USA. [Mishra, Meenakshi; Huan, Jun] Univ Kansas, Dept Elect Engn & Comp Sci, Lawrence, KS 66047 USA. [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Seoul 120749, South Korea. RP Bleik, S (reprint author), Univ Hts, New Jersey Inst Technol, Dept Informat Syst, Newark, NJ 07102 USA. EM sb252@njit.edu; mmishra@ittc.ku.edu; jhuan@ittc.ku.edu; min.song@yonsei.ac.kr RI Huan, Jun/A-1007-2015 OI Huan, Jun/0000-0003-4929-2617 FU Bio and Medical Technology Development Program of the National Research Foundation; Korean Ministry of Science and Technology [2013M3A9C4078138]; US National Science Foundation award (University of Kansas) [IIS 0845951] FX This work was partially supported by the Bio and Medical Technology Development Program of the National Research Foundation funded by the Korean Ministry of Science and Technology (Grant No. 2013M3A9C4078138) and by the US National Science Foundation award IIS 0845951 (University of Kansas). Min Song was the corresponding author. CR Al Hasan M., 2006, P 4 WORKSH LINK AN C Angelova R., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148254 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Arey M., 2005, P 18 INT FLAIRS C Baldi P., 2004, P COMP BIOL WORKSH N Bleik S., 2009, P IEEE INT C BIOINF, P38 Borgwardt K.M., 2005, P IEEE 5 INT C DAT M Chen YM, 2005, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, P1937 Dasarathy B. V., 1991, NEAREST NEIGHBOR NN Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Frohlich H., 2005, P 22 INT C MACH LEAR, P225, DOI 10.1145/1102351.1102380 Gee K.R., 2005, P 18 INT FLAIRS C GOWER JC, 1971, BIOMETRICS, V27, P857, DOI 10.2307/2528823 Horvath T., 2004, P 10 ACM SIGKDD INT, P158, DOI 10.1145/1014052.1014072 Huan J., 2006, P 3 INT C COMP SYST, V4, P227 Jiang CT, 2010, RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XXVI, P21, DOI 10.1007/978-1-84882-983-1_2 Joachims T., 1998, MACH LEARN ECML 98, V1398, P137, DOI DOI 10.1007/BFB0026683 Kashima H., 2003, P INT C MACH LEARN, V20, P321 Leslie C., 2002, P PAC S BIOC, V7, P566 LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Mahe P, 2009, MACH LEARN, V75, P3, DOI 10.1007/s10994-008-5086-2 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 Mishra M., 2012, P 11 INT WORKSH DAT, P26 Salton G., 1988, J INFORM PROCESSING, V24, P513 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A., 2003, P 7 INT C DOC AN REC, V1, P240 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Wan X., 2007, P ANN M ASS COMP LIN, V45, P552 Wang Pu, 2008, P 14 ACM SIGKDD INT, P713, DOI 10.1145/1401890.1401976 NR 30 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1545-5963 EI 1557-9964 J9 IEEE ACM T COMPUT BI JI IEEE-ACM Trans. Comput. Biol. Bioinform. PD SEP-OCT PY 2013 VL 10 IS 5 BP 1211 EP 1217 DI 10.1109/TCBB.2013.16 PG 7 WC Biochemical Research Methods; Computer Science, Interdisciplinary Applications; Mathematics, Interdisciplinary Applications; Statistics & Probability SC Biochemistry & Molecular Biology; Computer Science; Mathematics GA AB0DG UT WOS:000331461400013 PM 24384709 ER PT J AU Kurtz, C Gancarski, P Passat, N Puissant, A AF Kurtz, Camille Gancarski, Pierre Passat, Nicolas Puissant, Anne TI A hierarchical semantic-based distance for nominal histogram comparison SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE Histogram distance; Data representation; Nominal histogram; Semantic-based metric; Unsupervised classification; Information retrieval ID EARTH-MOVERS-DISTANCE; RETRIEVAL AB We propose a new distance called Hierarchical Semantic-Based Distance (HSBD), devoted to the comparison of nominal histograms equipped with a dissimilarity matrix providing the semantic correlations between the bins. The computation of this distance is based on a hierarchical strategy, progressively merging the considered instances (and their bins) according to their semantic proximity. For each level of this hierarchy, a standard bin-to-bin distance is computed between the corresponding pair of histograms. In order to obtain the proposed distance, these bin-to-bin distances are then fused by taking into account the semantic coherency of their associated level. From this modus operandi, the proposed distance can handle histograms which are generally compared thanks to cross-bin distances. It preserves the advantages of such cross-bin distances (namely robustness to histogram translation and histogram bin size issues), while inheriting the low computational cost of bin-to-bin distances. Validations in the context of geographical data classification emphasize the relevance and usefulness of the proposed distance. (C) 2013 Elsevier B.V. All rights reserved. C1 [Kurtz, Camille] Stanford Univ, Stanford, CA 94305 USA. [Kurtz, Camille; Gancarski, Pierre] Univ Strasbourg, ICube, UMR 7357, Strasbourg, France. [Passat, Nicolas] Univ Reims, CReST1C, EA 3804, F-51100 Reims, France. [Puissant, Anne] Univ Strasbourg, LIVE, ERL CNRS 7230, Strasbourg, France. RP Kurtz, C (reprint author), Stanford Univ, Sch Med, Dept Radiol, Lucas Ctr MR Spect & Imaging, Mail Code 5488,Route 8, Stanford, CA 94305 USA. EM ckurtz@stanford.edu; gancarski@unistra.fr; nicolas.passat@univ-reims.fr; anne.puissant@live-cnrs.unistra.fr CR Blaschke T, 2010, ISPRS J PHOTOGRAMM, V65, P2, DOI 10.1016/j.isprsjprs.2009.06.004 Chapelle O, 1999, IEEE T NEURAL NETWOR, V10, P1055, DOI 10.1109/72.788646 CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016/0034-4257(91)90048-B Forestier G, 2010, DATA KNOWL ENG, V69, P211, DOI 10.1016/j.datak.2009.10.004 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 HAFNER J, 1995, IEEE T PATTERN ANAL, V17, P729, DOI 10.1109/34.391417 Kurtz C, 2010, INT J REMOTE SENS, V31, P5941, DOI 10.1080/01431161.2010.512312 Levina E., 2001, P IEEE 8 INT C COMP, V2, P251, DOI 10.1109/ICCV.2001.937632 Ling H, 2007, IEEE T PATTERN ANAL, V29, P840, DOI 10.1109/TPAMI.2007.1058 Ma Y, 2010, COMPUT VIS IMAGE UND, V114, P981, DOI 10.1016/j.cviu.2010.03.006 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 MALLOWS CL, 1972, ANN MATH STAT, V43, P508, DOI 10.1214/aoms/1177692631 Niblack CW, 1993, P SOC PHOTO-OPT INS, V1908, P173, DOI 10.1117/12.143648 Pele O, 2010, LECT NOTES COMPUT SC, V6312, P749, DOI 10.1007/978-3-642-15552-9_54 Petitjean F, 2011, PATTERN RECOGN, V44, P678, DOI 10.1016/j.patcog.2010.09.013 Rath TM, 2003, PROC CVPR IEEE, P521 Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Serratosa F, 2006, PATTERN RECOGN, V39, P921, DOI 10.1016/j.patcog.2005.12.005 SHEN HC, 1983, COMPUT VISION GRAPH, V23, P187, DOI 10.1016/0734-189X(83)90112-3 Stricker M., 1995, Proceedings of the SPIE - The International Society for Optical Engineering, V2420, DOI 10.1117/12.205308 Tousch AM, 2012, PATTERN RECOGN, V45, P333, DOI 10.1016/j.patcog.2011.05.017 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 WERMAN M, 1985, COMPUT VISION GRAPH, V32, P328, DOI 10.1016/0734-189X(85)90055-6 NR 26 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X EI 1872-6933 J9 DATA KNOWL ENG JI Data Knowl. Eng. PD SEP PY 2013 VL 87 BP 206 EP 225 DI 10.1016/j.datak.2013.06.002 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 242PQ UT WOS:000326254900011 ER PT J AU Park, B Won, Y Chung, J Kim, MS Hong, JWK AF Park, Byungchul Won, Youngjoon Chung, JaeYoon Kim, Myung-sup Hong, James Won-Ki TI Fine-grained traffic classification based on functional separation SO INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT LA English DT Article ID INTERNET; IDENTIFICATION; ALGORITHM AB SUMMARY Current efforts to classify Internet traffic highlight accuracy. Previous studies have focused on the detection of major applications such as P2P and streaming applications. However, these applications can generate various types of traffic which are often considered as minor and ignorant traffic portions. As network applications become more complex, the price paid for not concentrating on minor traffic classes is in reduction of accuracy and completeness. In this context, we propose a fine-grained traffic classification scheme and its detailed method, called functional separation. Our proposal can detect, according to functionalities, different types of traffic generated by a single application and should increase completeness by reducing the amount of undetected traffic. We verify our method with real-world traffic. Our performance comparison against existing DPI-based classification frameworks shows that the fine-grained classification scheme achieves consistently higher accuracyand completeness. Copyright (c) 2013 John Wiley & Sons, Ltd. C1 [Park, Byungchul] INRIA, Sophia Antipolis, France. [Won, Youngjoon] Hanyang Univ, Seoul 133791, South Korea. [Chung, JaeYoon; Hong, James Won-Ki] POSTECH, Pohang, South Korea. [Kim, Myung-sup] Korea Univ, Jochiwon, South Korea. RP Won, Y (reprint author), Hanyang Univ, Dept Informat Syst, Seoul 133791, South Korea. EM youngjoon@hanyang.ac.kr FU MSIP, Korea, under the ITRC [NIPA-2013-H0301-13-3002]; World Class University program; MEST through the NRF of Korea [R31-10100]; Hanyang University [HY-2012] FX This research was partially supported by the MSIP, Korea, under the ITRC support program supervised by the NIPA (NIPA-2013-H0301-13-3002), World Class University program funded by the MEST through the NRF of Korea (R31-10100), and the research fund of Hanyang University (HY-2012). CR Auld T, 2007, IEEE T NEURAL NETWOR, V18, P223, DOI 10.1109/TNN.2006.883010 Bartlett G, 2007, 2007 IEEE GLOBAL INTERNET SYMPOSIUM, P55, DOI 10.1109/GI.2007.4301431 Bartlett G, 2007, ISITR645 U SO CAL Bernaille L, 2006, ACM SIGCOMM COMP COM, V36, P23, DOI 10.1145/1129582.1129589 Bernaille Laurent, 2006, P 2006 ACM CONEXT C, P6 Bonfiglio D, 2007, ACM SIGCOMM COMP COM, V37, P37, DOI 10.1145/1282427.1282386 Cho K, 2008, P 2008 ACM CONEXT C, P12 Chung JY, 2010, IEEE IFIP NETW OPER, P286 Claffy K., 1994, THESIS U CALIFORNIA Constantinou F, 2006, 5 IEEE INT S NETW CO, P93 Crotti M, 2007, ACM SIGCOMM COMP COM, V37, P5, DOI 10.1145/1198255.1198257 Dash M, 1997, INTELL DATA ANAL, V1, P131, DOI DOI 10.1016/S1088-467X(97)00008-5 Duffield N, 2003, P 3 ACM SIGCOMM C IN, P179 Erman J., 2007, P MINENET 2007 3 ANN, P35, DOI 10.1145/1269880.1269890 Erman J., 2006, P 2006 SIGCOMM WORKS, P281, DOI 10.1145/1162678.1162679 Erman J., 2007, P 16 INT C WORLD WID, P883, DOI DOI 10.1145/1242572.1242692 Erman J, 2007, PERFORM EVALUATION, V64, P1194, DOI 10.1016/j.peva.2007.06.014 Frank J, 1994, NAT 17 COMP SEC C WA Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Haffner P., 2005, P 2005 ACM SIGCOMM W, P197, DOI 10.1145/1080173.1080183 Hall MA, 2003, IEEE T KNOWL DATA EN, V15, P1437, DOI 10.1109/TKDE.2003.1245283 Iliofotou M, 2008, UCRCS200806080 Iliofotou M, 2007, UCRCS200705001 Iliofotou M, 2009, IEEE INFOCOM 2009 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, P353 Iliofotou M., 2007, P 7 ACM SIGCOMM C IN, P315, DOI 10.1145/1298306.1298349 John W, 2008, INT C INF NETW ICOIN, P1 John W, 2008, LECT NOTES COMPUT SC, V4979, P192, DOI 10.1007/978-3-540-79232-1_20 Karagiannis T, 2004, GLOB TELECOMM CONF, P1532 Karagiannis T., 2004, P 4 ACM SIGCOMM C IN, P121, DOI 10.1145/1028788.1028804 Karagiannis T, 2005, ACM SIGCOMM COMP COM, V35, P229, DOI 10.1145/1090191.1080119 Kim H., 2008, P 6 USENIX C FIL STO, P1, DOI DOI 10.1145/1544012.1544023 Kim H.-A., 2004, Proceedings of the 13th USENIX Security Symposium Kim SS, 2006, IEEE J SEL AREA COMM, V24, P1942, DOI 10.1109/JSAC.2006.877215 Kira K, 1992, P 9 INT C MACH LEARN, P249 Kononerko I., 1994, P EUR C MACH LEARN, P171 Lan KC, 2006, COMPUT NETW, V50, P46, DOI 10.1016/j.comnet.2005.02.008 Lim Y-S, 2010, P 6 INT C CONEXT 10, P9 Ma J., 2006, P 6 ACM SIGCOMM C IN, P313, DOI 10.1145/1177080.1177123 McGregor A, 2004, LECT NOTES COMPUT SC, V3015, P205 Moore A. W., 2005, P 2005 ACM SIGMETRIC, P50, DOI 10.1145/1064212.1064220 Moore AW, 2005, LECT NOTES COMPUT SC, V3431, P41 Moore D., 2001, P 15 USENIX C SYST A, P133 Nguyen TTT, 2008, IEEE COMMUN SURV TUT, V10, P56, DOI 10.1109/SURV.2008.080406 Park B, 2011, IEEE COMMUN MAG, V49, P104 Park BC, 2008, IEEE IFIP NETW OPER, P160 Pietrzyk M, 2009, IMC'09: PROCEEDINGS OF THE 2009 ACM SIGCOMM INTERNET MEASUREMENT CONFERENCE, P122 Plissonneau L, 2005, LECT NOTES COMPUT SC, V3431, P69 Robnik-Sikonja M, 2003, MACH LEARN, V53, P23, DOI 10.1023/A:1025667309714 Roughan M., 2004, P 4 ACM SIGCOMM C IN, P135, DOI 10.1145/1028788.1028805 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton G., 1987, TECHNICAL REPORT Sander J, 1998, DATA MIN KNOWL DISC, V2, P169, DOI 10.1023/A:1009745219419 Sen S., 2004, P 13 INT C WORLD WID, P512, DOI DOI 10.1145/988672.988742 Soule A, 2004, SIGMETRICS PERFORMAN, V32, P49 Suh K, 2006, P 25 IEEE INT C COMP, P1, DOI 10.1109/INFOCOM.2006.313 Szabo Geza, 2007, IEEE INT S WORLD WIR, P1 Thompson K, 1997, IEEE NETWORK, V11, P10, DOI 10.1109/65.642356 Williams N, 2006, ACM SIGCOMM COMP COM, V36, P7 Zander S., 2005, P IEEE C LOC COMP NE, P250, DOI DOI 10.1109/LCN.2005.35] Zuev D, 2005, LECT NOTES COMPUT SC, V3431, P321 NR 60 TC 1 Z9 1 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1055-7148 EI 1099-1190 J9 INT J NETW MANAG JI Int. J. Netw. Manag. PD SEP PY 2013 VL 23 IS 5 BP 350 EP 381 DI 10.1002/nem.1837 PG 32 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA 217BG UT WOS:000324330900003 ER PT J AU Carchiolo, V Longheu, A Di Martino, V Malgeri, M Mangioni, G AF Carchiolo, Vincenza Longheu, Alessandro Di Martino, Vincenzo Malgeri, Michele Mangioni, Giuseppe TI Connecting relatives in virtual worlds: The kinship networks SO CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS LA English DT Article DE Complex networks; social networks; recommendation systems ID COMPLEX NETWORKS AB The effect of the Internet on everyday life is huge, especially for what concerns the number and quality of our social relationships, including friendships and kinship. Fortunately, the complex network theory recently made it feasible to analyze real and large social networks, making it possible to understand their behavior and dynamics, including why and how social connections are established and what actually imply in the real world, the attitudes toward collaboration, and many other issues. In this article, a proposal for building and enhancing personal kinship networks is presented; this Kinship Search System allows each user to craft his or her genealogical tree by exploring and searching existing acquaintances and friendship relations that connect relatives among themselves and with other people. C1 [Carchiolo, Vincenza; Longheu, Alessandro; Di Martino, Vincenzo; Malgeri, Michele; Mangioni, Giuseppe] Univ Catania, Dipartimento Ingn Elettr Elettron & Informat, I-95125 Catania, Italy. RP Longheu, A (reprint author), Univ Catania, Dipartimento Ingn Elettr Elettron & Informat, Viale Andrea Doria 6, I-95125 Catania, Italy. EM alessandro.longheu@dieei.unict.it CR Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47 Bargh AJ, 2004, ANNU REV PSYCHOL, V55, P573 Burke R., 2007, The Adaptive Web. Methods and Strategies of Web Personalization Diestel R, 2010, GRAPH THEORY DINDIA K, 1993, J SOC PERS RELAT, V10, P163, DOI 10.1177/026540759301000201 Ildo G, 2011, SOCIAL RECOMMENDER S Lenzerini M, 2002, P 21 ACM SIGMOD SIGA Martino VD, 2012, EMERGING RELATIONS P Mesch GS, 2006, J FAMILY COMMUNICATI, V6, P119, DOI DOI 10.1207/S15327698JFC0602_2 Mirza J, 2003, J INTELL INF SYST, V20, P131 Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480 Rapoport A, 1961, STUDY LARGE SOCIOGRA Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1 Steiner L, 2011, COMMUN SOC-SER, P191 Rodriguez Marko A, 2010, Bulletin of the American Society for Information Science and Technology, V36 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott J., 2000, SOCIAL NETWORK ANAL, V2nd Shardanand U., 1995, P ACM CHI 95 C HUM F, V1, P210 Valenzuela S, 2009, J COMPUT-MEDIAT COMM, V14, P875, DOI 10.1111/j.1083-6101.2009.01474.x Yu B., 2003, P 2 INT JOINT C AUT NR 20 TC 0 Z9 0 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 1063-293X J9 CONCURRENT ENG-RES A JI Concurrent Eng.-Res. Appl. PD SEP PY 2013 VL 21 IS 3 SI SI BP 217 EP 226 DI 10.1177/1063293X13493451 PG 10 WC Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 227FU UT WOS:000325096800006 ER PT J AU Liu, CC Chang, CJ Tseng, JM AF Liu, Chen-Chung Chang, Chia-Jung Tseng, Jui-Min TI The effect of recommendation systems on Internet-based learning for different learners: A data mining analysis SO BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY LA English DT Article ID HYPERTEXT SYSTEM; WEB; KNOWLEDGE AB A general challenge facing Internet-based learners is howto identify information objects which are helpful in expanding their understanding of important information in a domain. Recommendation systems may assist learners in identifying potentially helpful information objects. However, the recent literature mainly focuses on the technical performance, rather than on how recommendations support searching in educational contexts. This study thus investigates how recommendation systems may influence the searching behaviors and strategies of different learners, when they are used with a mind map. The results suggested two main different searching behavioral patterns, namely focused searchers and broad searchers. It is found that the recommendations may influence the searching behaviors of the two types of searchers in different ways. For the focused searchers, the recommendations may support these searchers to expand their keywords to explore the domain. Regarding the broad searchers, they did not rely on the computer recommendations. However, the mind map interface applied along with the recommendations may support them in monitoring their searching processes. Online Information Searching Strategies Inventory (OISSI) was applied to examine the participants' perceptions of how students applied information searching strategies. The results of OISSI support that both types of searchers perceived a higher level of searching strategies when they search with the recommendation system and mind maps. RP Liu, CC (reprint author), Natl Cent Univ, Inst Network Learning Technol, 300 Jhongda Rd, Jhongli 32001, Taoyuan County, Taiwan. EM ccliu@cl.ncu.edu.tw CR Agrawal R., 1993, ACM SIGMOD RECORD, V22, P207, DOI DOI 10.1145/170035.170072 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 Chen GD, 2001, INNOV EDUC TRAIN INT, V38, P19, DOI 10.1080/147032901300002828 Conklin Jeff, 1987, IEEE COMPUT, V20, P17 Finke R. A, 1995, NATURE INSIGHT, P255 Garfield E, 2003, INFORM TECHNOL LIBR, V22, P183 Good N., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Gunawardana A, 2009, J MACH LEARN RES, V10, P2935 Hill JR, 1997, ETR&D-EDUC TECH RES, V45, P37, DOI 10.1007/BF02299682 Hohl H, 1996, USER MODEL USER-ADAP, V6, P131, DOI 10.1007/BF00143965 Jonassen D. H., 1990, DESIGNING HYPERMEDIA Kanungo T, 2002, IEEE T PATTERN ANAL, V24, P881, DOI 10.1109/TPAMI.2002.1017616 Kim T., 2012, BRIT J ED TECHNOLOGY, DOI 10.1111/j.1467-8535.2012.01306.x Konstan J. A., 2006, ACM C HUM FACT COMP, P1097 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu CC, 2010, ONLINE INFORM REV, V34, P786, DOI 10.1108/14684521011084627 Liu CC, 2005, J INF SCI ENG, V21, P873 Liu CC, 2011, COMPUT EDUC, V56, P873, DOI 10.1016/j.compedu.2010.10.029 Madrid RI, 2009, COMPUT HUM BEHAV, V25, P66, DOI 10.1016/j.chb.2008.06.005 Miller B. N., 2003, P 8 INT C INT US INT, P263, DOI DOI 10.1145/604045.604094 Reiser BJ, 2004, J LEARN SCI, V13, P273, DOI 10.1207/s15327809jls1303_2 Rieman J., 1996, ACM T COMPUT-HUM INT, V3, P189, DOI 10.1145/234526.234527 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shneiderman B., 2000, ACM Transactions on Computer-Human Interaction, V7, DOI 10.1145/344949.345077 Sigurbjornsson B., 2008, P 17 INT C WORLD WID, DOI [10.1145/1367497.1367542, DOI 10.1145/1367497.1367542] Soloway E., 1994, Interactions, V1, DOI 10.1145/174809.174813 Tang T. Y., 2008, THESIS U SASKATCHEWA Torres R., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996402 Tsai MJ, 2009, COMPUT EDUC, V53, P473, DOI 10.1016/j.compedu.2009.03.006 Vygotsky L. S., 1978, MIND SOC DEV HIGHER Walraven A, 2008, COMPUT HUM BEHAV, V24, P623, DOI 10.1016/j.chb.2007.01.030 Woodruff A., 2000, P SIGCHI C HUM FACT, P153, DOI 10.1145/332040.332419 Yang SC, 1997, LIBR INFORM SCI RES, V19, P71, DOI 10.1016/S0740-8188(97)90006-2 NR 34 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0007-1013 J9 BRIT J EDUC TECHNOL JI Br. J. Educ. Technol. PD SEP PY 2013 VL 44 IS 5 BP 758 EP 773 DI 10.1111/j.1467-8535.2012.01376.x PG 16 WC Education & Educational Research SC Education & Educational Research GA 199GY UT WOS:000322983800029 ER PT J AU Al Qady, M Kandil, A AF Al Qady, Mohammed Kandil, Amr TI Document Discourse for Managing Construction Project Documents SO JOURNAL OF COMPUTING IN CIVIL ENGINEERING LA English DT Article DE Documentation; Information management; Contract management AB Several important aspects characterize construction project documents. Large numbers of documents are produced in construction projects. The majority of the documents are unstructured textual documents. The production of documents is a gradual process that extends over the duration of the project. Documents, as information carriers, are an important repository of project knowledge. Project information is recorded, disputed, revised, and reiterated in various successive documents producing links between these documents and creating groups of semantically related documents within the project corpus. Despite the difficulties imposed on document management efforts by these characteristics, an electronic document management (EDM) approach must take into account these features in order to successfully manage project documents. In this study, a prototype electronic document management system (EDMS) that capitalizes on the various aspects that characterize project documents is presented. The system is based on the document discourse concept, which recognizes the semantic relations between groups of documents. The system also employs a text analysis feature which was evaluated for accuracy of classification based on document discourse and produced satisfactory results. The prototype system was evaluated using expert review. The main finding of the review recommends implementation of document discourse management as a module of an integrated project management system to achieve the best and most practical application. (C) 2013 American Society of Civil Engineers. C1 [Al Qady, Mohammed; Kandil, Amr] Purdue Univ, Sch Civil Engn, W Lafayette, IN 47907 USA. RP Al Qady, M (reprint author), Purdue Univ, Sch Civil Engn, W Lafayette, IN 47907 USA. EM malqady@purdue.edu; akandil@purdue.edu CR Al-Sabah SSJA, 2003, ADV ENG SOFTW, V34, P477, DOI 10.1016/S0965-9978(03)00044-9 Bjork B. C., 2003, ELECT J INF TECHNOL, V8, P105 Bjork BC, 2006, ONLINE INFORM REV, V30, P644, DOI 10.1108/14684520610716144 Caldas CH, 2005, J COMPUT CIVIL ENG, V19, P25, DOI 10.1061/(ASCE)0887-3801(2005)19:1(25) Caldas CH, 2002, J COMPUT CIVIL ENG, V16, P234, DOI 10.1061/(ASCE)0887-3801(2002)16:4(234) Caldas CH, 2003, AUTOMAT CONSTR, V12, P395, DOI 10.1016/S0926-5805(03)00004-9 Chassiakos AP, 2008, ADV ENG SOFTW, V39, P865, DOI 10.1016/j.advengsoft.2008.05.006 Dawood N, 2002, AUTOMAT CONSTR, V11, P557, DOI 10.1016/S0926-5805(01)00066-8 Demian P, 2005, J COMPUT CIVIL ENG, V19, P119, DOI 10.1061/(ASCE)0887-3801(2005)19:2(119) Egan J., 1998, RETHINKING CONSTRUCT Finch E. F., 1996, Automation in Construction, V5, DOI 10.1016/S0926-5805(96)00156-2 Forcada N, 2007, AUTOMAT CONSTR, V16, P411, DOI 10.1016/j.autcon.2006.07.011 Hajjar D, 2000, J COMPUT CIVIL ENG, V14, P70, DOI 10.1061/(ASCE)0887-3801(2000)14:1(70) HJELT M, 2006, ELECT J INF TECHNOL, V11, P113 Krishna PR, 2004, DATA KNOWL ENG, V51, P31, DOI 10.1016/j.datak.20014.03.006 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Liu S, 2008, COMPUT IND, V59, P3, DOI 10.1016/j.compind.2007.08.001 Meziane F, 2004, INFORM SCIENCES, V158, P15, DOI 10.1016/j.ins.2003.08.009 Rezgui Y., 1998, ELECT J INFORMATION, V3, P11 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zhu YM, 2003, AUTOMAT CONSTR, V12, P255, DOI 10.1016/S0926-5805(02)00089-4 Zhu YM, 2007, J COMPUT CIVIL ENG, V21, P220, DOI 10.1061/(ASCE)0887-3801(2007)21:3(220) NR 22 TC 1 Z9 1 PU ASCE-AMER SOC CIVIL ENGINEERS PI RESTON PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA SN 0887-3801 J9 J COMPUT CIVIL ENG JI J. Comput. Civil. Eng. PD SEP 1 PY 2013 VL 27 IS 5 BP 466 EP 475 DI 10.1061/(ASCE)CP.1943-5487.0000201 PG 10 WC Computer Science, Interdisciplinary Applications; Engineering, Civil SC Computer Science; Engineering GA 200YP UT WOS:000323108000004 ER PT J AU Kenig, B Gal, A AF Kenig, Batya Gal, Avigdor TI MFIBlocks: An effective blocking algorithm for entity resolution SO INFORMATION SYSTEMS LA English DT Article DE Entity resolution; Blocking ID RECORD LINKAGE AB Entity resolution is the process of discovering groups of tuples that correspond to the same real-world entity. Blocking algorithms separate tuples into blocks that are likely to contain matching pairs. Tuning is a major challenge in the blocking process and in particular, high expertise is needed in contemporary blocking algorithms to construct a blocking key, based on which tuples are assigned to blocks. In this work, we introduce a blocking approach that avoids selecting a blocking key altogether, relieving the user from this difficult task. The approach is based on maximal frequent itemsets selection, allowing early evaluation of block quality based on the overall commonality of its members. A unique feature of the proposed algorithm is the use of prior knowledge of the estimated size of duplicate sets in enhancing the blocking accuracy. We report on a thorough empirical analysis, using common benchmarks of both real-world and synthetic datasets to exhibit the effectiveness and efficiency of our approach. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Kenig, Batya; Gal, Avigdor] Technion Israel Inst Technol, Haifa, Israel. RP Kenig, B (reprint author), Technion Israel Inst Technol, Haifa, Israel. EM batyak@tx.technion.ac.il; avigal@ie.technion.ac.il FU Technion-Microsoft Electronic Commerce research center FX The work was carried out in and partially supported by the Technion-Microsoft Electronic Commerce research center. We thank Peter Christen for useful comments. CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Aizawa A., 2005, WIRI 05, P30 Baxter R., 2003, KDD 2003 WORKSH, P25 Bilenko M, 2006, IEEE DATA MINING, P87 Burdick D, 2001, PROC INT CONF DATA, P443, DOI 10.1109/ICDE.2001.914857 Chaudhuri S, 2005, PROC INT CONF DATA, P865 Christen P., IEEE T KNOWL DATA EN, V99, DOI DOI 10.1109/TKDE.2011.127> Christen P, 2009, LECT NOTES ARTIF INT, V5476, P507, DOI 10.1007/978-3-642-01307-2_47 Dong X., 2007, P INT C VER LARG DAT, P687 Draisbach U., 2011, Proceedings of the 2011 International Conference on Data and Knowledge Engineering (ICDKE 2011), DOI 10.1109/ICDKE.2011.6053920 Elfeky M., 2002, P 8 INT C DAT ENG Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061 Goethals B., 2003, SIGKDD EXPLORATIONS, P2004 Gouda K., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989514 Grahne G, 2005, IEEE T KNOWL DATA EN, V17, P1347, DOI 10.1109/TKDE.2005.166 Han J., 2000, MORGAN KAUFMANN SERI Hernandez MA, 1998, DATA MIN KNOWL DISC, V2, P9, DOI 10.1023/A:1009761603038 Hernandez M. A., 1995, SIGMOD Record, V24 Ignatov DI, 2009, LECT NOTES ARTIF INT, V5662, P185, DOI 10.1007/978-3-642-03079-6_15 Jin L., 2003, DASFAA, P137 Kirsten T, 2010, 8 INT WORKSH QUAL DA Kolb L., 2011, P 3 INT WORKSH CLOUD, P1 Manning C.D., 2008, INTRO INFORM RETRIEV McCallum A., 2000, KNOWLEDGE DISCOVERY, P169 Menestrina D., 2010, P VLDB ENDOWMENT, V3, P208 Michelson M., 2006, P 21 NAT C ART INT J, P440 Naumann F., 2010, INTRO DUPLICATE DETE Parsons L., 2004, WORKSH CLUST HIGH DI, P48 Rastogi V., 2011, PVLDB, V4, P208 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shu LC, 2011, PROC INT CONF DATA, P1067 Weis M., 2005, SIGMOD, P431 Winkler W., 2010, P SECT SURV RES METH Winkler W., 1999, TECHNICAL REPORT Winkler W. E., 1993, P SECT SURV RES METH, P274 Yakout M., 2010, P VLDB ENDOWMENT, V3, P439 Yang G., 2004, P 10 ACM SIGKDD INT, P344, DOI 10.1145/1014052.1014091 NR 38 TC 0 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD SEP PY 2013 VL 38 IS 6 SI SI BP 908 EP 926 DI 10.1016/j.is.2012.11.008 PG 19 WC Computer Science, Information Systems SC Computer Science GA 153XC UT WOS:000319635100007 ER PT J AU Roth, C AF Roth, Camille TI SOCIO-SEMANTIC FRAMEWORKS SO ADVANCES IN COMPLEX SYSTEMS LA English DT Article DE Socio-technical systems; socio-semantic networks; socio-semantic hypergraphs; socio-semantic lattices; coevolution ID NETWORKS; COLLABORATION; MODEL; COORDINATION AB Socio-technical systems involve agents who create and process knowledge, exchange information and create ties between ideas in a distributed and networked manner: webloggers, communities of scientists, software developers and wiki contributors are, among others, examples of such networks. The state-of-the-art in this regard focuses on two main issues which are generally addressed in an independent manner: the description of content dynamics and the study of social network characteristics and evolution. This paper relies on recent endeavors to merge both types of dynamics into co-evolutionary, multi-level modeling frameworks, where social and semantic aspects are being jointly appraised. Case studies featuring socio-semantic graphs, socio-semantic hypergraphs and socio-semantic lattices are notably discussed. C1 [Roth, Camille] CNRS HU MAE, CMB, CNRS, D-10117 Berlin, Germany. [Roth, Camille] CNRS EHESS, CAMS, F-75013 Paris, France. RP Roth, C (reprint author), CNRS HU MAE, CMB, CNRS, Friedrichstr 191, D-10117 Berlin, Germany. EM roth@cmb.hu-berlin.de FU European Commission Future and Emerging Technologies programme [231200] FX I am particularly grateful to Jean-Philippe Cointet for follow-up discussions and comments on the present article. This paper partially relies on cited work done in collaboration with Jean-Philippe Cointet, Carla Taramasco and Paul Bourgine. I also thank two anonymous reviewers for their constructive remarks. This work was additionally partially supported by the European Commission Future and Emerging Technologies programme FP7-COSI-ICT through project QLectives (Grant No.: 231200). CR Adamic L. A., 2010, 1 MONDAY, V15, P1 Aiello L.-M, 2010, Proceedings of the 2010 IEEE Second International Conference on Social Computing (SocialCom 2010). the Second IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT 2010), DOI 10.1109/SocialCom.2010.42 Albert R, 2002, REV MOD PHYS, V74, P47, DOI 10.1103/RevModPhys.74.47 Asur S., 2010, PREDICTING FUTURE SO Balog K., 2006, 11 M EACL Barbut M., 1970, ALGEBRE COMBINATOIRE, V2 Barthelemy M, 2011, PHYS REP, V499, P1, DOI 10.1016/j.physrep.2010.11.002 BEAVER DD, 1986, CZECH J PHYS, V36, P14, DOI 10.1007/BF01599717 Boeck P. D., 1988, PSYCHOMETRIKA, V53, P361, DOI 10.1007/BF02294218 BREIGER RL, 1974, SOC FORCES, V53, P181, DOI 10.2307/2576011 Brynjolfsson E., 1996, P 17 INT C INF SYST, P80 CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280 Callon M., 1986, MAPPING DYNAMICS SCI Cattuto C., 2007, PNAS, V104 Cha M., 2010, P 4 INT AAAI C WEBL, P10 Claidiere N, 2007, J COGNITION CULTURE, V7, P89, DOI DOI 10.1163/156853707X171829 Cointet J.-P., 2009, P IEEE 4 INT C SOC C, P114 Cointet J.-P., 2010, P 3 ICWSM AAAI INT C, P223 Conte R., 2000, DARWINIZING CULTURE, P83 Crandall D, 2008, P 14 ACM SIGKDD INT, P160, DOI 10.1145/1401890.1401914 Cummings JN, 2007, RES POLICY, V36, P1620, DOI 10.1016/j.respol.2007.09.001 Davey B. A., 2002, INTRO LATTICES ORDER Dawkins Richard, 1976, SELFISH GENE, P189 EMIRBAYER M, 1994, AM J SOCIOL, V99, P1411, DOI 10.1086/230450 Fortunato S, 2010, PHYS REP, V486, P75, DOI 10.1016/j.physrep.2009.11.002 FREEMAN LC, 1993, SOCIOL METHODOL, V23, P127, DOI 10.2307/271008 Ganter B., 1999, FORMAL CONCEPT ANAL Ginsberg J, 2009, NATURE, V457, P1012, DOI 10.1038/nature07634 Glance N., 2005, LINKKDD 05, P36 Goetz M., 2009, ICWSM 2009 P 3 INT A Gonzalez MC, 2008, NATURE, V453, P779, DOI 10.1038/nature06958 Goyal S., 2009, CONNECTIONS INTRO EC Gruhl D, 2004, WWW 04, P491 HAAS PM, 1992, INT ORGAN, V46, P1 Hutchins E., 2001, INT ENCY SOCIAL BEHA, P2068 Jaschke R., 2008, WEB SEMANTICS SCI SE, V6, P38, DOI 10.1016/j.websem.2007.11.004 Kitcher P., 1995, SOCIALIZING EPISTEMO, P111 Knorr-Cetina K., 1999, EPISTEMIC CULTURES S Kreuzman H, 2001, SCIENTOMETRICS, V51, P525, DOI 10.1023/A:1019647103469 Lazega E, 1992, MICROPOLITICS KNOWLE Leenders R.T.A.J., 1997, EVOLUTION SOCIAL NET, P165 Lenclud G., 1998, COMMUNICATIONS EHESS, V66, P165 Leskovec J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P497 Leskovec J., 2010, P AAAI ICWSM INT C W, P98 Lloyd L., 2006, AAAI SPRING S COMP A, P117 Manning C., 1999, P 3 ANN WORKSH WEBL McGlohon M., 2007, INT C WEBL SOC MED McPherson M, 2001, ANNU REV SOCIOL, V27, P415, DOI 10.1146/annurev.soc.27.1.415 Melin G, 1996, SCIENTOMETRICS, V36, P363, DOI 10.1007/BF02129600 Mesoudi A, 2004, EVOLUTION, V58, P1 Mika P, 2007, J WEB SEMANT, V5, P5, DOI 10.1016/j.websem.2006.11.002 MULLINS NC, 1972, MINERVA, V10, P51, DOI 10.1007/BF01881390 Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.026118 Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898 Roca CP, 2011, P NATL ACAD SCI USA, V108, P11370, DOI 10.1073/pnas.1101044108 Rosvall M, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0008694 Roth C, 2010, SOC NETWORKS, V32, P16, DOI 10.1016/j.socnet.2009.04.005 Roth C., 2005, MATH POPUL STUD, V12, P107, DOI 10.1080/08898480590931404 Ruef M, 2002, SOC NETWORKS, V24, P135, DOI 10.1016/S0378-8733(01)00054-5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schifanella R., 2010, P 3 ACM INT C WEB SE, P271, DOI 10.1145/1718487.1718521 Shoham Y., 2008, MULTIAGENT SYSTEMS A, P393 Simmons M. P., 2011, P 5 INT AAAI C WEBL, P353 Snijders T, 2007, EUR ASSOC METHODOL S, P41 Sperber D., 1996, EXPLAINING CULTURE N Stokols D, 2008, AM J PREV MED, V35, pS78 Sunstein C., 2008, HARVARD CRIMSON Taramasco C, 2010, SCIENTOMETRICS, V85, P721, DOI 10.1007/s11192-010-0226-4 Wang FY, 2007, IEEE INTELL SYST, V22, P79, DOI 10.1109/MIS.2007.41 Wasserman S., 1994, SOCIAL NETWORK ANAL Woolgar S., 1979, LAB LIFE SOCIAL CONS NR 71 TC 1 Z9 1 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0219-5259 EI 1793-6802 J9 ADV COMPLEX SYST JI Adv. Complex Syst. PD AUG PY 2013 VL 16 IS 4-5 AR 1350013 DI 10.1142/S0219525913500136 PG 26 WC Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences SC Mathematics; Science & Technology - Other Topics GA 248SC UT WOS:000326721000005 ER PT J AU Blanco, R Halpin, H Herzig, DM Mika, P Pound, J Thompson, HS Tran, T AF Blanco, Roi Halpin, Harry Herzig, Daniel M. Mika, Peter Pound, Jeffrey Thompson, Henry S. Thanh Tran TI Repeatable and reliable semantic search evaluation SO JOURNAL OF WEB SEMANTICS LA English DT Article DE Semantic search evaluation; Semantic search; Structured data; Web data; RDF; Web search ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; ASSESSMENTS; TRACK AB An increasing amount of structured data on the Web has attracted industry attention and renewed research interest in what is collectively referred to as semantic search. These solutions exploit the explicit semantics captured in structured data such as RDF for enhancing document representation and retrieval, or for finding answers by directly searching over the data. These data have been used for different tasks and a wide range of corresponding semantic search solutions have been proposed in the past. However, it has been widely recognized that a standardized setting to evaluate and analyze the current state-of-the-art in semantic search is needed to monitor and stimulate further progress in the field. In this paper, we present an evaluation framework for semantic search, analyze the framework with regard to repeatability and reliability, and report on our experiences on applying it in the Semantic Search Challenge 2010 and 2011. (C) 2013 Elsevier B.V. All rights reserved. C1 [Herzig, Daniel M.; Thanh Tran] Karlsruhe Inst Technol, Inst AIFB, D-76021 Karlsruhe, Germany. [Blanco, Roi; Mika, Peter] Yahoo Res, Barcelona, Spain. [Halpin, Harry; Thompson, Henry S.] Univ Edinburgh, Edinburgh, Midlothian, Scotland. [Pound, Jeffrey] Univ Waterloo, David R Cheriton Sch Comp Sci, Waterloo, ON N2L 3G1, Canada. RP Herzig, DM (reprint author), Karlsruhe Inst Technol, Inst AIFB, D-76021 Karlsruhe, Germany. EM roi@yahoo-inc.com; H.Halpin@ed.ac.uk; herzig@kit.edu; pmika@yahoo-inc.com; jpound@cs.uwaterloo.ca; ht@inf.ed.ac.uk; ducthanh.tran@kit.edu FU Microsoft Research grant on 'Beyond Search'; European SEALS project; Amazon; NAACL workshop FX We acknowledge Yahoo! Research for making available under license the 'Yahoo! Search Query Log Tiny Sample, version 1.0' dataset as part of the WebScope program. We also thank Evelyne Viegas and Microsoft Research for allowing a portion of the Microsoft Live Query Log to be used in the 2010 campaign. In particular, we would like to thank Amazon and the NAACL workshop on using the Mechanical Turk for providing the initial funding for the 2010 evaluation, and would like to thank the European PASCAL project for the rest of the support. Of course, none of this work would have been possible without the groups that submitted search engines for evaluation at the Semantic Search Challenge 2010 and 2011. Harry Halpin was partially supported by a Microsoft Research grant on 'Beyond Search'. We acknowledge Yahoo! Labs for hosting the Semantic Search Challenge 2011 website and sponsoring the prizes. The costs of the 2011 evaluation have been sponsored by the European SEALS project, http://www.sealsproject.eu. CR Alonso O, 2010, LECT NOTES COMPUT SC, V5993, P602, DOI 10.1007/978-3-642-12275-0_57 Alonso Omar, 2008, SIGIR Forum, V42, DOI 10.1145/1480506.1480508 [Anonymous], 2010, P 2 INT WORKSH KEYW Bailey P., 2008, SIGIR 08, P667 Balog K., 2010, TREC 2010 Balog K., 2009, NIST SPECIAL PUBLICA Bizer C, 2010, J WEB SEMANT, V8, P341, DOI 10.1016/j.websem.2010.08.005 Blanco R, 2011, LECT NOTES COMPUT SC, V7031, P83, DOI 10.1007/978-3-642-25073-6_6 Blanco R., 2011, P 1 INT WORKSH ENT O Blanco R., 2011, SIGIR, P923 Callison-Burch C., 2009, P 2009 C EMP METH NA, P286 Carpenter B., 2008, MULTILEVEL BAYESIAN Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chu-Carroll J., 2007, CIKM, P505 Chu-Carroll Jennifer, 2006, SIGIR, P445 Cleverdon C. W., 1991, SIGIR Forum Cleverdon C. W., 1966, FACTORS DETERMINING Coffman J., 2010, P 19 ACM INT C INF K, P729, DOI 10.1145/1871437.1871531 Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26 Elbassuoni S., 2009, CIKM, P977, DOI DOI 10.1145/1645953.1646078 Fang H., 2005, SIGIR 05, P480 Guha Ramanathan V., 2003, WWW 2003, P700, DOI DOI 10.1145/775152.775250 Halpin H., 2009, P WWW WORKSH LINK DA Halpin H., 2010, INT WORKSH EV SEM TE Halpin H., 2012, WORKSH HUM COMP AAAI, P85 Harter SP, 1996, J AM SOC INFORM SCI, V47, P37 Herzig D.M., 2012, WWW, P141 Kamps J, 2009, LECT NOTES COMPUT SC, V5631, P1 Luo Y, 2008, PROC INT CONF DATA, P1552, DOI 10.1109/ICDE.2008.4497619 Manning C.D., 2008, INTRO INFORM RETRIEV Mason WA, 2009, P ACM SIGKDD WORKSH, P77, DOI DOI 10.1145/1600150.1600175 McSherry F., 2008, P 30 ECIR Milne David N., 2009, P NZ COMP SCI RES ST, V9 Nowak S., 2010, MIR 10, P557 Oren Eyal, 2008, International Journal of Metadata, Semantics and Ontologies, V3, DOI 10.1504/IJMSO.2008.021204 Ounis I., 2006, P ACM SIGIR 06 WORKS Pound J., 2010, P 19 INT C WORLD WID, P771, DOI DOI 10.1145/1772690.1772769 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Robertson S., P SIGIR C RES DEV IN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soboroff I., 2005, HLT 05, DOI DOI 10.3115/1220575.1220589 Tran T, 2011, J WEB SEMANT, V9, P349, DOI 10.1016/j.websem.2011.08.004 Tran T, 2009, J WEB SEMANT, V7, P189, DOI 10.1016/j.websem.2009.07.001 Tran T., 2007, ICTIR, P55 Unger C., 2011, QALD 1 OPEN CHALLENG von Ahn L, 2008, SCIENCE, V321, P1465, DOI 10.1126/science.1160379 Voorhees E., 2001, P 2 WORKSH CROSS LAN, P355 Voorhees E. M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Voorhees Ellen M., 2005, TREC EXPT EVALUATION Waitelonis Jorg, 2011, Interactive Technology and Smart Education, V8, DOI 10.1108/17415651111189478 Zhai C., 2001, CIKM 2001, P403, DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 52 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-8268 J9 J WEB SEMANT JI J. Web Semant. PD AUG PY 2013 VL 21 SI SI BP 14 EP 29 DI 10.1016/j.websem.2013.05.005 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 216RR UT WOS:000324302200003 ER PT J AU Togia, T McNeill, F AF Togia, Theodosia McNeill, Fiona TI On-the-fly repair of structural and lexical ontology mismatches in multi-agent systems SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article DE Semantic matching; Ontology repair; Sense and reference AB In a Semantic-Web-like multi-agent environment, ontology mismatch is inevitable: we can't realistically expect agents created at different times and places by different people to commit to one unchanging universal ontology. Ontology matching seems to be the only solution to such a problem. However, standard techniques for aligning heterogeneous ontologies are based on time-consuming, off-line and often semi-automated processes and pre-suppose full access to the interacting agents' ontologies. This is far from ideal in situations where agents meet for the first time, interact quickly and have restricted access to other agents' private information. In this paper we present the Ontology Repair System (ORS), which attempts to match fully-fledged first-order ontologies automatically using incomplete information. Particular emphasis is laid on its semantic matching module, the Semantic Matcher, which provides a solution for lexical mismatches, which are the most common and the most challenging to address. ORS and the Semantic Matcher have been implemented and evaluated, with very promising results. C1 [Togia, Theodosia] Univ Cambridge, Comp Lab, Cambridge CB2 3QG, England. [McNeill, Fiona] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland. RP McNeill, F (reprint author), Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland. EM f.j.mcneill@ed.ac.uk CR CARNAP R., 1947, MEANING NECESSITY Frege G, 1892, Z PHILOS PHILOS KRIT, P563 Garcia-Silva A, 2012, KNOWL ENG REV, V27, P57, DOI 10.1017/S026988891100018X Giunchiglia F, 2003, KNOWL ENG REV, V18, P265, DOI 10.1017/S0269888904000074 Halpin H, 2010, P WEBSCI10 EXT FRONT Halpin Harry, 2009, P WEBSCI 09 SOC ON L Kripke S., 1972, NAMING NECESSITY Laurence S, 1999, CONCEPTS CORE READIN, P2 McNeill F, 2007, INT J SEMANT WEB INF, V3, P1, DOI 10.4018/jswis.2007070101 Miller G, 2005, INTRO WORDNET ON LIN Niles I., 2003, P 2003 INT C INF KNO Qu W, 2006, P 15 INT WORLD WID W, P23 Richards I., 1923, MEANING MEANING ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shvaiko P., 2012, IEEE T KNOWL DATA EN Vander-Wal T., 2005, EXPLAINING SHOWING B Wittgenstein Ludwig, 1953, PHILOS INVESTIGATION NR 18 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD AUG PY 2013 VL 40 IS 2 SI SI BP 175 EP 191 DI 10.1007/s10462-012-9352-0 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 189MP UT WOS:000322271600004 ER PT J AU Ye, Y Gong, SR Liu, CP Zeng, J Jia, N Zhang, Y AF Ye, Yun Gong, Shengrong Liu, Chunping Zeng, Jia Jia, Ning Zhang, Yi TI Online belief propagation algorithm for probabilistic latent semantic analysis SO FRONTIERS OF COMPUTER SCIENCE LA English DT Article DE probabilistic latent semantic analysis; topic models; expectation maximization; belief propagation AB Probabilistic latent semantic analysis (PLSA) is a topic model for text documents, which has been widely used in text mining, computer vision, computational biology and so on. For batch PLSA inference algorithms, the required memory size grows linearly with the data size, and handling massive data streams is very difficult. To process big data streams, we propose an online belief propagation (OBP) algorithm based on the improved factor graph representation for PLSA. The factor graph of PLSA facilitates the classic belief propagation (BP) algorithm. Furthermore, OBP splits the data stream into a set of small segments, and uses the estimated parameters of previous segments to calculate the gradient descent of the current segment. Because OBP removes each segment from memory after processing, it is memory-efficient for big data streams. We examine the performance of OBP on four document data sets, and demonstrate that OBP is competitive in both speed and accuracy for online expectation maximization (OEM) in PLSA, and can also give a more accurate topic evolution. Experiments on massive data streams from Baidu further confirm the effectiveness of the OBP algorithm. C1 [Ye, Yun; Gong, Shengrong; Liu, Chunping; Zeng, Jia] Suzhou Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China. [Ye, Yun; Jia, Ning; Zhang, Yi] Baidu Online Network Technol Co LTD, Feng Chao Revenue, Beijing 100000, Peoples R China. RP Gong, SR (reprint author), Suzhou Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China. EM shrgong@suda.edu.cn FU National Natural Science Foundation of China [61272258, 61170124, 61170020, 61070223]; Application Foundation Research Plan of Suzhou City [SYG201116] FX This work was supported by the National Natural Science Foundation of China (Grant Nos. 61272258, 61170124, 61170020, 61070223), and Application Foundation Research Plan of Suzhou City (SYG201116). CR AlSumait L, 2008, IEEE DATA MINING, P3, DOI 10.1109/ICDM.2008.140 Banerjee A, 2007, PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, P431 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bottou L., 1998, ON LINE LEARNING NEU, V17, P9 Canini K.R., 2009, P 2009 INT C ART INT, P65 Eisenstein J., 2010, CMU 2008 POLITICAL B Globerson A, 2007, J MACHINE LEARNING R, V8, P2047 Hoffman M., 2010, ADV NEURAL INFORM PR, V23, P856 Hoffman T, 1999, PROBABILISTIC LATENT Kivinen J, 2004, IEEE T SIGNAL PROCES, V52, P2165, DOI 10.1109/TSP.2004.830991 Mairal J, 2010, J MACH LEARN RES, V11, P19 Nair V, 2004, PROC CVPR IEEE, P317 Pham M. -T., 2007, P IEEE INT C COMP VI, P1 Porteous I, 2008, P 14 ACM SIGKDD INT, P569, DOI DOI 10.1145/1401890.1401960 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shalev-Shwartz S., 2004, P 21 INT C MACH LEAR Singh M, 2012, LECT NOTES ENG COMPU, P2198 Thomas K, 1998, COMMUN ACM, V25, P259 Vijayakumar S, 2005, NEURAL COMPUT, V17, P2602, DOI 10.1162/089976605774320557 Wang Chong, 2011, P 14 INT C ART INT S, P752 XU J, 2009, P 6 IEEE INT C ADV V, P55 Xu J, 2011, LECT NOTES COMPUT SC, V6493, P95 Yao LM, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P937 Zeng J, 2012, J MACH LEARN RES, V13, P2233 Zeng J, 2012, IEEE T PATTERN ANAL, P1 Zhu SF, 2009, BIOINFORMATICS, V25, P1944, DOI 10.1093/bioinformatics/btp338 Zhuang LS, 2009, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS (ICIG 2009), P205, DOI 10.1109/ICIG.2009.153 NR 27 TC 1 Z9 2 PU HIGHER EDUCATION PRESS PI BEIJING PA SHATANHOU ST 55, BEIJING 100009, PEOPLES R CHINA SN 2095-2228 J9 FRONT COMPUT SCI-CHI JI Front.. Comput. Sci. PD AUG PY 2013 VL 7 IS 4 BP 526 EP 535 DI 10.1007/s11704-013-2360-7 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 191CR UT WOS:000322390700007 ER PT J AU Laorden, C Sanz, B Santos, I Galan-Garcia, P Bringas, PG AF Laorden, Carlos Sanz, Borja Santos, Igor Galan-Garcia, Patxi Bringas, Pablo G. TI Collective classification for spam filtering SO LOGIC JOURNAL OF THE IGPL LA English DT Article DE Spam filtering; collective classification; semi-supervised learning AB Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. Many solutions to the spam problem feature machine-learning algorithms that are trained using statistical representations of terms that often appear in spam e-mails. However, these methods require a training step with labelled data. Dealing with situations in which the availability of labelled training instances is limited slows the filtering systems' progress and offers advantages to spammers. Currently, many approaches direct their efforts at Semi-Supervised Learning (SSL). SSL is a halfway method between supervised and unsupervised learning. In addition to using unlabelled data, SSL receives supervision information such as associations between targets and examples. Collective Classification for Text Classification is an interesting method for optimizing the classification of partially labelled data. Here, we propose for the first time the use of Collective Classification algorithms for spam filtering to overcome the amount of unclassified e-mails that are sent every day. C1 [Laorden, Carlos; Sanz, Borja; Santos, Igor; Galan-Garcia, Patxi; Bringas, Pablo G.] Univ Deusto, DeustoTech Comp Lab S3, Bilbao 48007, Spain. RP Laorden, C (reprint author), Univ Deusto, DeustoTech Comp Lab S3, Ave Univ 24, Bilbao 48007, Spain. EM claorden@deusto.es; borja.sanz@deusto.es; isantos@deusto.es; patxigg@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Bishop C. M., 2006, PATTERN RECOGNITION Carreras X, 2001, CS0109015 ARXIV Cormack G., 2007, 16 TEXT RETRIEVAL C Damiani E, 2004, IEEE INT C PEER TO P, P176 Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Gray A, 2004, P 1 C EMAIL ANT Herrero A, 2009, NEUROCOMPUTING, V72, P2775, DOI 10.1016/j.neucom.2008.12.033 Holmes G., 1994, WEKA MACHINE LEARNIN, P357 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Laorden C, 2011, P 8 ANN COLL EL MESS, P13 Laorden C, 2012, ELECTRON COMMER R A, V11, P290, DOI 10.1016/j.elerap.2011.11.004 Namata G. M., 2009, TEXT MINING, P51 Parvathaneni S, 2011, INT J ENG RES APPL, V1, P427 Portnoy L., 2001, P ACM CSS WORKSH DAT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos I., 2011, P 8 ANN COLL EL MESS, P23 Santos I, 2012, EXPERT SYST APPL, V39, P437, DOI 10.1016/j.eswa.2011.07.034 Santos Igor, 2011, Proceedings of the International Conference on Security and Cryptography. SECRYPT 2011 Santos I, 2011, ADV INTEL SOFT COMPU, V91, P415 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 NR 26 TC 1 Z9 1 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 1367-0751 J9 LOG J IGPL JI Log. J. IGPL PD AUG PY 2013 VL 21 IS 4 SI SI BP 540 EP 548 DI 10.1093/jigpal/jzs030 PG 9 WC Mathematics, Applied; Mathematics; Logic SC Mathematics; Science & Technology - Other Topics GA 190MF UT WOS:000322343500002 ER PT J AU Moraes, MC Heuser, CA Moreira, VP Barbosa, D AF Moraes, Mauricio C. Heuser, Carlos A. Moreira, Viviane P. Barbosa, Denilson TI Prequery Discovery of Domain-Specific Query Forms: A Survey SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Deep web; hidden web; domain-specific search; query form discovery ID HIDDEN-WEB DATABASES; DEEP WEB; INFORMATION EXTRACTION; SEARCH INTERFACES; CLASSIFICATION AB The discovery of HTML query forms is one of the main challenges in Deep web crawling. Automatic solutions for this problem perform two main tasks. The first is locating HTML forms on the web, which is done through the use of traditional/focused crawlers. The second is identifying which of these forms are indeed meant for querying, which also typically involves determining a domain for the underlying data source (and thus for the form as well). This problem has attracted a great deal of interest, resulting in a long list of algorithms and techniques. Some methods submit requests through the forms and then analyze the data retrieved in response, typically requiring a great deal of knowledge about the domain as well as semantic processing. Others do not employ form submission, to avoid such difficulties, although some techniques rely to some extent on semantics and domain knowledge. This survey gives an up-to-date review of methods for the discovery of domain-specific query forms that do not involve form submission. We detail these methods and discuss how form discovery has become increasingly more automated over time. We conclude with a forecast of what we believe are the immediate next steps in this trend. C1 [Moraes, Mauricio C.; Heuser, Carlos A.; Moreira, Viviane P.] Univ Fed Rio Grande do Sul, Inst Informat, BR-91501970 Porto Alegre, RS, Brazil. [Barbosa, Denilson] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada. RP Moraes, MC (reprint author), Univ Fed Rio Grande do Sul, Inst Informat, Campus Vale,Av Bento Goncalves 9500 Bloco 4,Predi, BR-91501970 Porto Alegre, RS, Brazil. EM mcmoraes@inf.ufrgs.br; heuser@inf.ufrgs.br; viviane@inf.ufrgs.br; denilson@cs.ualberta.ca RI InWeb, Inct/J-9839-2013; Moreira, Viviane/ OI Moreira, Viviane/0000-0003-4400-054X FU Brazilian National Institute of Science and Technology for the Web (CNPq) [573871/2008-6]; CNPq [480283/2010-9]; CNPq; Capes FX This work was partially supported by the Brazilian National Institute of Science and Technology for the Web (CNPq Grant No. 573871/2008-6) and by CNPq project No. 480283/2010-9. Mauricio C. Moraes received a scholarship from CNPq while working on this paper. He also received a scholarship from Capes during his visit to the University of Alberta, Canada, where he worked on this paper. CR Agichtein E, 2003, PROC INT CONF DATA, P113, DOI 10.1109/ICDE.2003.1260786 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbosa L., 2006, P 15 ACM INT C INF K, P796, DOI 10.1145/1183614.1183735 Barbosa L., 2005, P INT WORKSH WEB DAT, V5, P1 Barbosa L, 2007, P IEEE 23 INT C DAT, P326 Barbosa L., 2010, P 2010 INT C MAN DAT, P1175, DOI DOI 10.1145/1807167.1807311 Barbosa L., 2007, P 16 INT C WORLD WID, P441, DOI DOI 10.1145/1242572.1242632 Bergholz A., 2003, P 4 INT C WEB INF SY, P125, DOI 10.1109/WISE.2003.1254476 Bergman MK, 2001, J ELECT PUBLISHING, V7, DOI [10.3998/3336451.0007.104, DOI 10.3998/3336451.0007.104] Berkhin P, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P25, DOI 10.1007/3-540-28349-8_2 Bezdek JC, 1973, CYBERNET SYST, V3, P58, DOI 10.1080/01969727308546047 Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P469, DOI 10.1016/S0169-7552(98)80047-0 Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chang KCC, 2004, SIGMOD RECORD, V33, P61 Changs KCC, 2005, P CIDR, P44 Cope J, 2003, P 14 AUSTR DAT C ADC, P181 Doorenbos R. B., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267666 Fellbaum C, 1998, WORDNET ELECT LEXICA Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199 Gal A, 2004, PROC INT CONF DATA, P853, DOI 10.1109/ICDE.2004.1320082 Galperin MY, 2005, NUCLEIC ACIDS RES, V33, pD5, DOI 10.1093/nar/gki139 Gravano L, 2003, ACM T INFORM SYST, V21, P1, DOI 10.1145/635484.635485 GRAVANO L., 2004, P 2004 ACM SIGMOD IN, P767, DOI 10.1145/1007568.1007655 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 Han J., 2002, P 2002 IEEE INT C DA, P211 Han JW, 2000, SIGMOD RECORD, V29, P1 He B., 2004, P 13 C INF KNOWL MAN, P22, DOI 10.1145/1031171.1031178 He B., 2005, P WORKSH CURR TRENDS, P494 HE H, 2003, P 29 INT C VER LARG, V29, P357 He H, 2007, WORLD WIDE WEB, V10, P133, DOI 10.1007/s11280-006-0010-9 Hedley Y. L., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009119 Hedley YL, 2006, DATA KNOWL ENG, V59, P213, DOI 10.1016/j.datak.2006.01.009 Hess A, 2003, LECT NOTES COMPUT SC, V2870, P258 Ipeirotis P., 2001, P 3 INT WORKSH WORLD, P245 Ipeirotis PG, 2001, SIGMOD RECORD, V30, P67 Kabra G., 2005, Proceedings. International Workshop on Challenges in Web Information Retrieval and Integration Khare R, 2010, SIGMOD RECORD, V39, P33, DOI 10.1145/1860702.1860708 Kleinberg J.M., 1999, P 5 ANN INT C COMP C, V1627, P1 Kushmerick N, 2003, LECT NOTES COMPUT SC, V2888, P997 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 Le H., 2009, P 5 INT C WEB INF SY Li YJ, 2010, LECT NOTES COMPUT SC, V6184, P453 Lin K., 2002, P INT C INF TECHN CO, P332 Madhavan J., 2008, P VLDB ENDOWMENT, V1, P1241, DOI DOI 10.1145/1454159.1454163 Madhavan J., 2009, P 4 BIENN C INN DAT Mannila H, 1997, DATA MIN KNOWL DISC, V1, P259, DOI 10.1023/A:1009748302351 Menczer F., 2001, P 24 ANN INT ACM SIG, P241, DOI DOI 10.1145/383952.383995 Mitchell T., 1980, READINGS MACHINE LEA Mitchell TM, 1997, MACHINE LEARNING Nguyen H., 2008, P VLDB ENDOWMENT, V1, P684 Noy N., 2000, P 2 INT C KNOWL ENG, P69 Ntoulas A, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P100, DOI 10.1145/1065385.1065407 Pasquier N, 1999, LECT NOTES COMPUT SC, V1540, P398 Peng Q., 2004, P 6 ACM INT WORKSH W, P104 Perkowitz M., 1997, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V8, DOI 10.1023/A:1008672508721 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Raghavan S., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Rasmussen CE, 2004, LECT NOTES ARTIF INT, V3176, P63 Rennie J., 1999, P 16 INT C MACH LEAR, P335 Ru YB, 2005, ONLINE INFORM REV, V29, P249, DOI 10.1108/14684520510607579 Russell S, 2009, ARTIFICIAL INTELLIGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire R. E., 2003, NONLINEAR ESTIMATION Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shen DR, 2008, LECT NOTES COMPUT SC, V5175, P321 Song L, 2008, LECT NOTES COMPUT SC, V4993, P365 VANDERMERWE DW, 2003, P IEEE C EV COMP 200, P215 Walsh A. E., 2002, UDDI SOAP WSDL WEB S Wang J., 2003, P 12 INT C WORLD WID, P187 Wang Y., 2008, P 31 ANN INT ACM SIG, P459, DOI 10.1145/1390334.1390413 Witten IH, 2011, MOR KAUF D, P1 Wright A, 2008, COMMUN ACM, V51, P14, DOI 10.1145/1400181.1400187 Wu P., 2006, P ICDE2006 ATL GA, P47 Xu H., 2007, P 4 INT C FUZZ SYST, V4, P561 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yu C, 2002, IEEE T KNOWL DATA EN, V14, P1347, DOI 10.1109/TKDE.2002.1047772 ZHANG Z., 2004, P 23 ACM SIGMOD INT, P107, DOI 10.1145/1007568.1007583 Zhao PP, 2008, LECT NOTES ARTIF INT, V5139, P683 Zimmermann H. J., 2001, FUZZY SET THEORY ITS NR 82 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 EI 1558-2191 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD AUG PY 2013 VL 25 IS 8 BP 1830 EP 1848 DI 10.1109/TKDE.2012.111 PG 19 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 175VF UT WOS:000321261000012 ER PT J AU Krstajic, M Najm-Araghi, M Mansmann, F Keim, DA AF Krstajic, Milos Najm-Araghi, Mohammad Mansmann, Florian Keim, Daniel A. TI Story Tracker: Incremental visual text analytics of news story development SO INFORMATION VISUALIZATION LA English DT Article DE News stream analysis; topic evolution; dynamic visualization; text analytics ID CARROT(2); EVENTS AB Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system's capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011. C1 [Krstajic, Milos; Najm-Araghi, Mohammad; Mansmann, Florian; Keim, Daniel A.] Univ Konstanz, D-78457 Constance, Germany. RP Krstajic, M (reprint author), Univ Konstanz, Data Anal & Visualizat Grp, D-78457 Constance, Germany. EM milos.krstajic@uni-konstanz.de FU German Research Society (DFG) [GK-1042] FX This work was partially funded by the German Research Society (DFG) under grant GK-1042, "Explorative Analysis and Visualization of Large Information Spaces.'' CR Aigner W, 2011, HUM-COMPUT INT-SPRIN, P1, DOI 10.1007/978-0-85729-079-3 Allan J., 2002, TOPIC DETECTION TRAC Atkinson M, 2009, WWW 09, P1153 Bendix F, 2005, INFOVIS 05: IEEE Symposium on Information Visualization, Proceedings, P133, DOI 10.1109/INFVIS.2005.1532139 Blei David M., 2006, PROCEEDINGS OF THE 2, V138, P113 Byron L, 2008, IEEE T VIS COMPUT GR, V14, P1245, DOI 10.1109/TVCG.2008.166 Dimsdale B., 1990, VIS 90, V1, P361 Dork M, 2010, IEEE T VIS COMPUT GR, V16, P1129, DOI 10.1109/TVCG.2010.129 Fisher D, 2008, IEEE S VIS ANAL, P115 Harrower M, 2011, COLORBREWER ORG ONLI, P261 Havre S., 2000, INF VIS 2000 INFOVIS, P115 Heer J, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1303 Kleinberg J., 2006, DATA STREAM MANAGEME Krstajic M, 2010, 1 INT WORKSH DAT ENG Krstajic M, 2011, IEEE T VIS COMPUT GR, V17, P2432, DOI 10.1109/TVCG.2011.179 Krstajic M, 2010, STREAMKDD 10, P39 Luo DN, 2012, IEEE T VIS COMPUT GR, V18, P93, DOI 10.1109/TVCG.2010.225 Osinski S, 2004, ADV SOFT COMP, P359 Osinski S, 2005, LECT NOTES COMPUT SC, V3528, P439 Rosario GE, 2003, IEEE S INF VIS INFOV Rose SJ, 2009, IEEE S VIS AN SCI TE, P99 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stefanowski J, 2003, LECT NOTES ARTIF INT, V2663, P240 SUGIYAMA K, 1981, IEEE T SYST MAN CYB, V11, P109, DOI 10.1109/TSMC.1981.4308636 Wei F., 2010, P 16 ACM SIGKDD INT, P153 Wong PC, 2003, IEEE S INF VIS INFOV Zamir OE, 1999, TECHNICAL REPORT NR 27 TC 0 Z9 0 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 1473-8716 EI 1473-8724 J9 INFORM VISUAL JI Inf. Vis. PD JUL PY 2013 VL 12 IS 3-4 SI SI BP 308 EP 323 DI 10.1177/1473871613493996 PG 16 WC Computer Science, Software Engineering SC Computer Science GA 287WJ UT WOS:000329572700007 ER PT J AU Capobianco, G De Lucia, A Oliveto, R Panichella, A Panichella, S AF Capobianco, Giovanni De Lucia, Andrea Oliveto, Rocco Panichella, Annibale Panichella, Sebastiano TI Improving IR-based traceability recovery via noun-based indexing of software artifacts SO JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS LA English DT Article DE traceability recovery; vector space model; latent semantic indexing; Jensen-Shannon method; empirical studies ID OBJECT-ORIENTED SYSTEMS; SOURCE-CODE; REQUIREMENTS; LINKS; DOCUMENTATION; RETRIEVAL AB One of the most successful applications of textual analysis in software engineering is the use of information retrieval (IR) methods to reconstruct traceability links between software artifacts. Unfortunately, because of the limitations of both the humans developing artifacts and the IR techniques any IR-based traceability recovery method fails to retrieve some of the correct links, while on the other hand it also retrieves links that are not correct. This limitation has posed challenges for researchers that have proposed several methods to improve the accuracy of IR-based traceability recovery methods by removing the noise' in the textual content of software artifacts (e.g., by removing common words or increasing the importance of critical terms). In this paper, we propose a heuristic to remove the noise' taking into account the linguistic nature of words in the software artifacts. In particular, the language used in software documents can be classified as a technical language, where the words that provide more indication on the semantics of a document are the nouns. The results of a case study conducted on five software artifact repositories indicate that characterizing the context of software artifacts considering only nouns significantly improves the accuracy of IR-based traceability recovery methods. Copyright (c) 2012 John Wiley & Sons, Ltd. C1 [Capobianco, Giovanni; Oliveto, Rocco] Univ Molise, I-86090 Pesche, IS, Italy. [De Lucia, Andrea; Panichella, Annibale] Univ Salerno, I-84084 Fisciano, SA, Italy. [Panichella, Sebastiano] Univ Sannio, I-82100 Benevento, Italy. RP Oliveto, R (reprint author), Univ Molise, I-86090 Pesche, IS, Italy. EM rocco.oliveto@unimol.it CR Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30 Abebe Surafel Lemma, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.29 Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Antoniol G, 2000, PROG COMPREHEN, P79, DOI 10.1109/WPC.2000.852482 Antoniol G., 2000, Proceedings Seventh Working Conference on Reverse Engineering, DOI 10.1109/WCRE.2000.891475 Asuncion HU, 2010, P 32 ACM IEEE INT C, V1, P95, DOI [10.1145/1806799.1806817, DOI 10.1145/1806799.1806817] Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Basili Victor R., 1994, GOAL QUESTION METRIC Binkley D., 2011, P 8 WORK C MIN SOFTW, P203 Canfora G, 2005, P 11 IEEE INT S SOFT, P20 Capobianco G, 2011, TECHNICAL REPORT Capobianco G, 2009, WORK CONF REVERSE EN, P195, DOI 10.1109/WCRE.2009.14 Capobianco G, 2009, INT C PROGRAM COMPRE, P148 Caprile C, 1999, P 6 WORK C REV ENG A, P112 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 CLELANDHUANG J, 2010, P ICSE, P155 Cohen J, 1988, STAT POWER ANAL BEHA, V2nd Conover W. J., 1998, PRACTICAL NONPARAMET, V3rd Cover T. M., 1991, ELEMENTS INFORM THEO Cullum JK, 1998, LANCZOS ALGORITHMS L, V1 De Lucia A, 2007, ACM T SOFTW ENG METH, V16, DOI 10.1145/1276933.1276934 De Lucia A, 2012, INFORM RETRIEVAL MET DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 De Lucia A, 2011, CONF PROC INT SYMP C, P21, DOI 10.1109/ICPC.2011.34 De Lucia A, 2010, SOFTWARE PRACT EXPER, V40, P1007, DOI 10.1002/spe.986 De Lucia A, 2006, PROC IEEE INT CONF S, P299 De Lucia A, 2008, ICSE'08 PROCEEDINGS OF THE THIRTIETH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P839 Di Penta M, 2002, PROG COMPREHEN, P207, DOI 10.1109/WPC.2002.1021342 Enslen E, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P71, DOI 10.1109/MSR.2009.5069482 Etzkorn L, 1999, NATURAL LANGUAGE ENG, V5, P1 Fry ZP, 2008, IET SOFTW, V2, P27, DOI [10.1049/iet-sen:20070112, 10.1049/ict-sen:20070112] Gibiec M, 2010, P ASE, P245, DOI 10.1145/1858996.1859046 Gonzalez R, 2002, DIGITAL IMAGE PROCES Guerrouj L, 2011, J SOFTWARE MAINTENAN, DOI 10.1002/smr.539 Halliday M. A. K., 2004, LANGUAGE SCI Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 Hayes J. H., 2003, Proceedings 11th IEEE International Requirements Engineering Conference Hayes JH, 2006, IEEE T SOFTWARE ENG, V32, P4, DOI 10.1109/TSE.2006.3 Hill E, 2009, PROC INT CONF SOFTW, P232, DOI 10.1109/ICSE.2009.5070524 Jurafsky D, 2000, SPEECH LANGUAGE PROC Kampenes VB, 2007, INFORM SOFTWARE TECH, V49, P1073, DOI 10.1016/j.infsof.2007.02.015 Keenan Edward, 1975, FORMAL SEMANTICS NAT Lawrie D, 2007, J SOFTW MAINT EVOL-R, V19, P205, DOI 10.1002/smr.350 Lawrie D, 2011, P 27 IEEE INT C SOFT, P113, DOI [10.1109/ICSM.2011.6080778, DOI 10.1109/ICSM.2011.6080778] Lawrie Dawn, 2010, Proceedings 17th Working Conference on Reverse Engineering (WCRE 2010), DOI 10.1109/WCRE.2010.10 Lawrie D, 2006, INT C PROGRAM COMPRE, P3, DOI 10.1109/ICPC.2006.51 Lin J, 2006, P 14 IEEE INT REQ EN, P356, DOI DOI 10.1109/RE.2006.48 Lormans M, 2008, EMPIR SOFTW ENG, V13, P727, DOI 10.1007/s10664-008-9078-4 Madani N, 2010, Proceedings of the 14th European Conference on Software Maintenance and Reengineering (CSMR 2010), DOI 10.1109/CSMR.2010.31 Manning C. D., 2000, P 2000 JOINT SIGDAT, V13, P63, DOI DOI 10.3115/1117794.1117802 Marcus A, 2008, IEEE T SOFTWARE ENG, V34, P287, DOI 10.1109/TSE.2007.70768 Marcus A, 2005, P 3 INT WORKSH TRAC, P56, DOI 10.1145/1107656.1107669.1107669 Marcus A, 2005, INT J SOFTW ENG KNOW, V15, P811, DOI 10.1142/S0218194005002543 Marcus A, 2001, 16TH ANNUAL INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2001), PROCEEDINGS, P107 Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 Oliveto R., 2010, P 18 INT C PROGR COM PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Poshyvanyk D, 2006, PROC IEEE INT CONF S, P469 Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Reeves C., 2001, LANGUAGE SCI Revelle Meghan, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.10 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Settimi R., 2004, Proceedings. 7th International Workshop on Principles of Software Evolution, DOI 10.1109/IWPSE.2004.1334768 Takang AA, 1996, J PROGRAM LANG, V4, P143 Toutanova K., 2003, P HLT NAACL, P252 Yadla S, 2005, INNOVATIONS SYSTEMS, V1, P116, DOI DOI 10.1007/S11334-005-0011-3 Zhang Y, 2008, IET SOFTW, V2, P185, DOI 10.1049/iet-sen:20070062 Zou X, 2008, P INT C SOFTW ENG RE, P157 Zou XC, 2006, P INT COMP SOFTW APP, P265 Zou XC, 2010, EMPIR SOFTW ENG, V15, P119, DOI 10.1007/s10664-009-9114-z NR 70 TC 5 Z9 5 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 2047-7473 EI 2047-7481 J9 J SOFTW-EVOL PROC JI J. Softw.-Evol. Proc. PD JUL PY 2013 VL 25 IS 7 BP 743 EP 762 DI 10.1002/smr.1564 PG 20 WC Computer Science, Software Engineering SC Computer Science GA 242KX UT WOS:000326241300004 ER PT J AU Hou, YX Zhao, XZ Song, DW Li, WJ AF Hou, Yuexian Zhao, Xiaozhao Song, Dawei Li, Wenjie TI Mining Pure High-Order Word Associations via Information Geometry for Information Retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Theory; Measurement; Algorithms; Word association; pure high-order dependence; information geometry; text classification; text retrieval ID INDEPENDENCE; MODEL AB The classical bag-of-word models for information retrieval (IR) fail to capture contextual associations between words. In this article, we propose to investigate pure high-order dependence among a number of words forming an unseparable semantic entity, that is, the high-order dependence that cannot be reduced to the random coincidence of lower-order dependencies. We believe that identifying these pure high-order dependence patterns would lead to a better representation of documents and novel retrieval models. Specifically, two formal definitions of pure dependence-unconditional pure dependence (UPD) and conditional pure dependence (CPD)-are defined. The exact decision on UPD and CPD, however, is NP-hard in general. We hence derive and prove the sufficient criteria that entail UPD and CPD, within the well-principled information geometry (IG) framework, leading to a more feasible UPD/CPD identification procedure. We further develop novel methods for extracting word patterns with pure high-order dependence. Our methods are applied to and extensively evaluated on three typical IR tasks: text classification and text retrieval without and with query expansion. C1 [Hou, Yuexian; Zhao, Xiaozhao; Song, Dawei] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. [Li, Wenjie] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. RP Song, DW (reprint author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. EM krete1941@gmail.com; 0.25eye@gmail.com; dawei.song2010@gmail.com; cswjli@comp.polyu.edu.hk RI Li, Wenjie/ OI Li, Wenjie/0000-0002-7360-8864 FU Natural Science Foundation of China [61070044, 61111130190, 61272265]; European Union Framework 7 Marie-Curie International Research Staff Exchange Programme [247590] FX This work is partially supported by the Natural Science Foundation of China (grants no. 61070044, 61111130190 and 61272265), and the European Union Framework 7 Marie-Curie International Research Staff Exchange Programme (grant no. 247590). CR Agrawal R., 1993, ACM SIGMOD RECORD, V22, P207, DOI DOI 10.1145/170035.170072 Amari S, 2001, IEEE T INFORM THEORY, V47, P1701, DOI 10.1109/18.930911 Amari S. I., 2000, METHODS INFORM GEOME, V191 Bakirov NK, 2006, J MULTIVARIATE ANAL, V97, P1742, DOI 10.1016/j.jmva.2005.10.005 Bendersky Michael, 2010, P 3 ACM INT C WEB SE, P31, DOI DOI 10.1145/1718487.1718492 Bendersky Michael, 2008, P 31 ANN INT ACM SIG, P491, DOI DOI 10.1145/1390334.1390419 Caropreso M.F., 2001, LEARNER INDEPENDENT, P78 Chickering DM, 2004, J MACH LEARN RES, V5, P1287 Costa S., 2005, P IEEE INF THEOR WOR, P28 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Hofmann T., 1999, P C ADV NEUR INF PRO, P914 JEFFREYS H, 1946, PROC R SOC LON SER-A, V186, P453, DOI 10.1098/rspa.1946.0056 Kirsch A, 2012, J ACM, V59, DOI 10.1145/2220357.2220359 Korkontzelos I, 2008, LECT NOTES ARTIF INT, V5221, P248 Kumaran G, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P564, DOI 10.1145/1571941.1572038 Lang H., 2010, P 19 ACM INT C INF K, P249, DOI 10.1145/1871437.1871473 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Mihalcea R., 2006, P 21 NAT C ART INT A, P775 Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 Nakahara H, 2002, NEURAL COMPUT, V14, P2269, DOI 10.1162/08997660260293238 Niesler TR, 1996, INT CONF ACOUST SPEE, P164, DOI 10.1109/ICASSP.1996.540316 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rao C.R., 1945, Bulletin of the Calcutta Mathematical Society, V37 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Shi L., 2010, P 19 ACM INT C INF K, P1493, DOI 10.1145/1871437.1871655 Sokal RR, 2012, BIOMETRY PRINCIPLES Song DW, 2008, LECT NOTES COMPUT SC, V4956, P334 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 Taskinen S, 2005, J AM STAT ASSOC, V100, P916, DOI 10.1198/016214505000000097 Tsukiyama S., 1977, SIAM Journal on Computing, V6, DOI 10.1137/0206036 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Xue Xiaobing, 2010, P INT C INF KNOWL MA, P1059, DOI 10.1145/1871437.1871572 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhang S, 2003, PACLIC 17: Language, Information and Computation, Proceedings, P251 Zhong N., 2012, IEEE T KNOWL DATA EN, V24, P1 NR 42 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 EI 1558-2868 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JUL PY 2013 VL 31 IS 3 AR 12 DI 10.1145/2493175.2493177 PG 32 WC Computer Science, Information Systems SC Computer Science GA 197DP UT WOS:000322829900002 ER PT J AU Cao, J Mao, DH Cai, Q Li, HS Du, JP AF Cao, Jian Mao, Dian-hui Cai, Qiang Li, Hai-sheng Du, Jun-ping TI A review of object representation based on local features SO JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS LA English DT Review DE Object presentation; Local feature; Image understanding; Object recognition; Visual words ID FEATURE-SELECTION; IMAGE CLASSIFICATION; FACE RECOGNITION; INFORMATION; ALGORITHMS; DETECTORS; REGIONS; SPACE; MODEL; SCALE AB Object representation based on local features is a topical subject in the domain of image understanding and computer vision. We discuss the defects of global features in present methods and the advantages of local features in object recognition, and briefly explore state-of-the-art recognition methods using local features, especially the main approaches of local feature extraction and object representation. To clearly explain these methods, the problem of local feature extraction is divided into feature region detection, feature region description, and feature space optimization. The main components and merits of these steps are presented. Technologies for object presentation are classified into three types: vector space, sliding window, and structure relationship models. Future development trends are discussed briefly. C1 [Cao, Jian; Mao, Dian-hui; Cai, Qiang; Li, Hai-sheng] Beijing Technol & Business Univ, Coll Comp & Informat Engn, Beijing 100048, Peoples R China. [Du, Jun-ping] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China. RP Cao, J (reprint author), Beijing Technol & Business Univ, Coll Comp & Informat Engn, Beijing 100048, Peoples R China. EM caojian@th.btbu.edu.cn FU National Basic Research Program (973) of China [2012CB821206]; National Natural Science Foundation of China [71201004]; Scientific Research Common Program of Beijing Municipal Commission of Education [KM201310011009]; Funding Project for Innovation on Science, Technology and Graduate Education in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality [PXM2012_014213_000037, PXM2012_014213_000079] FX Project supported by the National Basic Research Program (973) of China (No. 2012CB821206), the National Natural Science Foundation of China (No. 71201004), the Scientific Research Common Program of Beijing Municipal Commission of Education (No. KM201310011009), and the Funding Project for Innovation on Science, Technology and Graduate Education in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Nos. PXM2012_014213_000037 and PXM2012_014213_000079) CR Agarwal S, 2004, IEEE T PATTERN ANAL, V26, P1475, DOI 10.1109/TPAMI.2004.108 Baumberg A, 2000, PROC CVPR IEEE, P774, DOI 10.1109/CVPR.2000.855899 Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014 Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 Berg AC, 2005, PROC CVPR IEEE, P26 Bilen H, 2012, LNCS, V7476, P134, DOI [10.1007/978-3-642-32717-9_14, DOI 10.1007/978-3-642-32717-9_] Cao J, 2011, ADV MATER RES-SWITZ, V225-226, P921, DOI 10.4028/www.scientific.net/AMR.225-226.921 Cao Jian, 2011, Transactions of Beijing Institute of Technology, V31 Cao Jian, 2011, Robot, V33, DOI 10.3724/SP.J.1218.2011.00628 Chen GQG, 2012, ACTA MATH SCI, V32, P1 Chen Q, 2012, PROC CVPR IEEE, P3426 Chia AYS, 2012, IEEE T PATTERN ANAL, V34, P1758, DOI 10.1109/TPAMI.2011.220 Choi JY, 2012, IEEE T IMAGE PROCESS, V21, P1366, DOI 10.1109/TIP.2011.2168413 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Dai DQ, 2003, PATTERN RECOGN, V36, P845, DOI 10.1016/S0031-3203(02)00092-4 Dalal N, 2005, PROC CVPR IEEE, P886 Ding H, 2012, P IEEE INT S INSTR C, P137, DOI [10.1109/ISICT.2012.6291609, DOI 10.1109/ISICT.2012.6291609]] Fergus R, 2005, PROC CVPR IEEE, P380 Fernando B, 2012, PROC CVPR IEEE, P3434, DOI 10.1109/CVPR.2012.6248084 Geng X, 2005, IEEE T SYST MAN CY B, V35, P1098, DOI 10.1109/TSMCB.2005.850151 Guyon I, 2002, MACH LEARN, V46, P389, DOI 10.1023/A:1012487302797 Hancock T, 2012, IEEE T NEUR NET LEAR, V23, P1767, DOI 10.1109/TNNLS.2012.2214057 He R, 2012, PROC CVPR IEEE, P2504 Hyvarinen A, 2000, NEURAL NETWORKS, V13, P411, DOI 10.1016/S0893-6080(00)00026-5 Javed K, 2012, IEEE T KNOWL DATA EN, V24, P465, DOI 10.1109/TKDE.2010.263 Jurie F, 2005, IEEE I CONF COMP VIS, P604 Kadir T, 2004, LECT NOTES COMPUT SC, V3021, P228 Ke Y., 2004, P C COMP VIS PATT RE, P511 Lazebnik S, 2003, PROC CVPR IEEE, P319 Leibe B, 2008, INT J COMPUT VISION, V77, P259, DOI 10.1007/s11263-007-0095-3 Li CS, 2012, PROC CVPR IEEE, P2570 Liu HW, 2009, PATTERN RECOGN, V42, P1330, DOI 10.1016/j.patcog.2008.10.028 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Matas J, 2004, IMAGE VISION COMPUT, V22, P761, DOI 10.1016/j.imavis.2004.02.006 Mikolajczyk K., 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, DOI 10.1109/ICCV.2001.937561 Mikolajczyk K, 2005, IEEE I CONF COMP VIS, P1792 Mikolajczyk K., 2006, P CVPR, P26, DOI DOI 10.1109/CVPR.2006.202] Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2 Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188 Mikolajczyk K, 2005, INT J COMPUT VISION, V65, P43, DOI 10.1007/s11263-005-3848-x Mohan A, 2001, IEEE T PATTERN ANAL, V23, P349, DOI 10.1109/34.917571 Moosmann F, 2008, IEEE T PATTERN ANAL, V30, P1632, DOI 10.1109/TPAMI.2007.70822 Nakayama H, 2010, PROC CVPR IEEE, P2336, DOI 10.1109/CVPR.2010.5539921 Neshatian K, 2009, LECT NOTES COMPUT SC, V5481, P121, DOI 10.1007/978-3-642-01181-8_11 Nowak E, 2006, LECT NOTES COMPUT SC, V3954, P490 Ohba K, 1997, IEEE T PATTERN ANAL, V19, P1043, DOI 10.1109/34.615453 Pan H, 2011, J INFRARED MILLIM W, V30, P85 Papageorgiou C, 2000, INT J COMPUT VISION, V38, P15, DOI 10.1023/A:1008162616689 Perronnin F., 2007, P IEEE C COMP VIS PA, P1, DOI DOI 10.1109/CVPR.2007.383266] Redondo-Cabrera C, 2012, PROC CVPR IEEE, P3458, DOI 10.1109/CVPR.2012.6248087 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saul L., 2003, J MACHINE LEARNING R, V4, P119 Schaffalitzky F, 2002, LECT NOTES COMPUT SC, V2350, P414 Shlens J., 2009, TUTORIAL PRINCIPAL C Tong S, 2002, J MACH LEARN RES, V2, P45 van Gool L, 1996, LECT NOTES COMPUTER, V1064, P642, DOI 10.1007/BFb0015574 Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb Wang Yan-Jie, 2012, Journal of Software, V23, DOI 10.3724/SP.J.1001.2012.04082 Weber M, 2000, LECT NOTES COMPUT SC, V1842, P18 Yang H, 2003, PATTERN RECOGN, V36, P563 Yang J, 2005, PATTERN RECOGN, V38, P1125, DOI 10.1016/j.patcog.2004.11.019 Yang J, 2004, IEEE T PATTERN ANAL, V26, P131 Yu L, 2004, J MACH LEARN RES, V5, P1205 [ę›¾å³¦ Zeng Luan], 2012, [č‡ŖåŠØåŒ–å­¦ęŠ„, Acta Automatica Sinica], V38, P1513 [å¼ ē³ę³¢ Zhang Linbo], 2012, [č‡ŖåŠØåŒ–å­¦ęŠ„, Acta Automatica Sinica], V38, P46 Zhang LJ, 2012, IEEE T IMAGE PROCESS, V21, P2379, DOI 10.1109/TIP.2012.2183879 Zhu Q., 2006, P IEEE C COMP VIS PA, V2, P1491, DOI DOI 10.1109/CVPR.2006.119] NR 67 TC 2 Z9 4 PU ZHEJIANG UNIV PI HANGZHOU PA EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA SN 1869-1951 J9 J ZHEJIANG U-SCI C JI J. Zhejiang Univ.-SCI. C. PD JUL PY 2013 VL 14 IS 7 BP 495 EP 504 DI 10.1631/jzus.CIDE1303 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 185ME UT WOS:000321971400004 ER PT J AU Vila, K Fernandez, A Gomez, JM Ferrandez, A Diaz, J AF Vila, Katia Fernandez, Antonio Gomez, Jose M. Ferrandez, Antonio Diaz, Josval TI Noise-tolerance feasibility for restricted-domain Information Retrieval systems SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE Information Retrieval; Noise-tolerance; Restricted domain; Edit distance ID VECTOR-SPACE MODEL; TEXT; SEARCH; ERRORS; OCR AB Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented. (C) 2013 Elsevier B.V. All rights reserved. C1 [Vila, Katia; Fernandez, Antonio; Diaz, Josval] Univ Matanzas, Dept Informat, Matanzas 40100, Cuba. [Vila, Katia; Gomez, Jose M.; Ferrandez, Antonio] Univ Alicante, Dept Software & Comp Syst, Alicante 03690, Spain. RP Vila, K (reprint author), Univ Alicante, Dept Software & Comp, San Vicente del Raspeig Rd, Alicante 03690, Spain. EM kvila@dlsi.ua.es; antonio.fernandez@umcc.cu; jmgomez@dlsi.ua.es; antonio@dlsi.ua.es; josval.diaz@umcc.cu RI Gomez, Jose/H-7656-2015 OI Gomez, Jose/0000-0003-4314-3661 CR Aunimo L., 2003, ECIR, P193 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bourdaillet J., 2007, IJCAI 2007 WORKSH AN Chen FR, 1998, COMPUT VIS IMAGE UND, V70, P307, DOI 10.1006/cviu.1998.0688 Chen Qing, 2007, P 2007 JOINT C EMP M, P181 Cucerzan S., 2004, EMNLP, P293 DAMERAU FJ, 1964, COMMUN ACM, V7, P171, DOI 10.1145/363958.363994 Esser W.M., 2004, J DIGITAL INFORM, V6, P1368 Esser WM, 2004, LECT NOTES COMPUT SC, V2997, P338 Fernandez A.C., 2009, 4 CONV CIENT INT U M French J.C., 1997, CIKM 97, P9 Gomez J.M., 2007, 5 INT C NAT LANG PRO Gomez J.M., 2007, THESIS U POLITECNICA Hatcher Erik, 2004, LUCENE ACTION Hawking D., 1996, TREC5 ANUACSYS HIRSCHBERG DS, 1977, J ACM, V24, P664, DOI 10.1145/322033.322044 Hodge G., 2000, SYSTEMS KNOWLEDGE OR Imamura K., 2002, 7 INT C SPOK LANG PR, P1713 JARO MA, 1989, J AM STAT ASSOC, V84, P414, DOI 10.2307/2289924 Jing H., 2003, P HLT NAACL 03 TEXT Kantor P. B., 2000, Information Retrieval, V2, DOI 10.1023/A:1009902609570 Kantor P.B., 1996, TREC5 Khadivi S, 2005, LECT NOTES COMPUT SC, V3513, P263 Knoblock C, 2007, INT J DOC ANAL RECOG, V10, P127, DOI 10.1007/s10032-007-0058-9 KUKICH K, 1992, COMPUT SURV, V24, P377 Levenshtein V.I., 1966, BINARY CODES CAPABLE, V8 Li M., 2006, ACL Lopresti D, 2009, INT J DOC ANAL RECOG, V12, P139, DOI 10.1007/s10032-009-0096-6 Manthey B, 2003, LECT NOTES COMPUT SC, V2906, P88 Mihov S, 2004, COMPUT LINGUIST, V30, P451, DOI 10.1162/0891201042544938 Minock M., 2005, P IJCAI WORKSH KNOWL, P4 NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 Ng K.B., 1996, DATA FUSION MACHINE Nie J.-Y., 2001, IEEE S NLP KNOWL ENG, P453 Palmer D.D., 2001, HLT 01 P 1 INT C HUM, P1 Pascual F. L., 2001, THESIS U ALICANTE AL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi L., 2006, CIKM 06, P814 Spasic I., 2005, PACIFIC S BIOCOMPUTI Subramaniam L. V., 2009, AND 2009 P 3 WORKSH, P115 Taghva K, 1996, INFORM PROCESS MANAG, V32, P317, DOI 10.1016/0306-4573(95)00058-5 Taghva K., 2001, International Journal on Document Analysis and Recognition, V3, DOI 10.1007/PL00013558 Tong X., 1996, TREC5 CLARIT Vallin A, 2006, LECT NOTES COMPUT SC, V4022, P307 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vila K, 2010, LECT NOTES COMPUT SC, V6177, P1, DOI 10.1007/978-3-642-13881-2_1 Vilares J, 2011, INFORM PROCESS MANAG, V47, P263, DOI 10.1016/j.ipm.2010.08.004 Vinciarelli A, 2005, IEEE T PATTERN ANAL, V27, P1882, DOI 10.1109/TPAMI.2005.248 Voorhees E. M., 1999, P TREC 8, P77 Winkler W. E., 1999, STATE RECORD LINKAGE NR 50 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X EI 1872-6933 J9 DATA KNOWL ENG JI Data Knowl. Eng. PD JUL PY 2013 VL 86 BP 276 EP 294 DI 10.1016/j.datak.2013.02.002 PG 19 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 163RD UT WOS:000320353700014 ER PT J AU Yoon, S Elhadad, N Bakken, S AF Yoon, Sunmoo Elhadad, Noemie Bakken, Suzanne TI A Practical Approach for Content Mining of Tweets SO AMERICAN JOURNAL OF PREVENTIVE MEDICINE LA English DT Article ID RANDOMIZED CONTROLLED-TRIAL; PHYSICAL-ACTIVITY; SOCIAL DESIRABILITY; HEALTH; INTERVENTION; POPULATIONS; INFORMATION; MEDIA AB Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research. C1 [Yoon, Sunmoo; Bakken, Suzanne] Columbia Univ, Sch Nursing, New York, NY USA. [Yoon, Sunmoo; Elhadad, Noemie; Bakken, Suzanne] Columbia Univ, Dept Biomed Informat, New York, NY USA. RP Yoon, S (reprint author), Sch Nursing, Dept Biomed Informat, 630 West 168th St,Mail Code 6, New York, NY 10032 USA. EM sy2102@columbia.edu FU National Institute of Nursing Research [T32NR007969]; Agency for Healthcare Research and Quality [R01 HS019853] FX The study was supported by grant T32NR007969 from the National Institute of Nursing Research. Article preparation was also supported by grant R01 HS019853 from the Agency for Healthcare Research and Quality. CR Adams SA, 2005, AM J EPIDEMIOL, V161, P389, DOI 10.1093/aje/kwi054 Atienza AA, 2011, AM J PREV MED, V40, pS151, DOI 10.1016/j.amepre.2011.01.008 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Cavallo DN, 2012, AM J PREV MED, V43, P527, DOI 10.1016/j.amepre.2012.07.019 Conway M, 2009, INT J MED INFORM, V78, pE47, DOI 10.1016/j.ijmedinf.2009.03.010 [DHHS Committee PAGA], 2008, PHYS ACT GUID ADV CO Dodds PS, 2010, J HAPPINESS STUD, V11, P441, DOI 10.1007/s10902-009-9150-9 Eysenbach G, 2011, AM J PREV MED, V40, pS154, DOI 10.1016/j.amepre.2011.02.006 Fisher J, 2012, WORLDV EVID-BASED NU, V9, P100, DOI 10.1111/j.1741-6787.2012.00243.x Gershon N, 1997, IEEE COMPUT GRAPH, V17, P66 Go A, 2010, TWITTER SENTIMETN CL Grobelnik M, 2004, P LEARN METH TEXT UN Jackson P., 2007, NATURAL LANGUAGE PRO WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314 Klesges LM, 2004, PREV MED, V38, pS78, DOI 10.1016/j.ypmed.2003.07.003 Liu Bing, 2006, WEB DATA MINING EXPL Mabry PL, 2011, AM J PREV MED, V40, pS159, DOI 10.1016/j.amepre.2011.02.001 Militello LK, 2012, WORLDV EVID-BASED NU, V9, P66, DOI 10.1111/j.1741-6787.2011.00239.x Nathan KC, 2012, AM J PREV MED, V43, P571 O'Connor B., 2010, P INT AAAI C WEBL SO, P122 Oenema A, 2008, ANN BEHAV MED, V35, P125, DOI 10.1007/s12160-008-9023-1 Pak A, 2010, P INT LREC 10 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Paul MJ, P ICWSM 2011 Porter Martin, 2001, SNOWBALL LANGUAGE ST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schwartz JE, 1998, HEALTH PSYCHOL, V17, P6, DOI 10.1037/0278-6133.17.1.6 Tan P., 2006, INTRO DATA MINING Tomlinson S, 2004, LECT NOTES COMPUT SC, V3237, P169 Twitter Team, 2012, TWITTER BLOG Warnecke RB, 1997, ANN EPIDEMIOL, V7, P334, DOI 10.1016/S1047-2797(97)00030-6 NR 32 TC 4 Z9 4 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0749-3797 J9 AM J PREV MED JI Am. J. Prev. Med. PD JUL PY 2013 VL 45 IS 1 BP 122 EP 129 DI 10.1016/j.amepre.2013.02.025 PG 8 WC Public, Environmental & Occupational Health; Medicine, General & Internal SC Public, Environmental & Occupational Health; General & Internal Medicine GA 170CO UT WOS:000320827500016 PM 23790998 ER PT J AU Nt, JAR Tomaz, LFC de Souza, JM Xexeo, G AF Rodrigues Nt, Jose A. Cardoso Tomaz, Luiz Fernando de Souza, Jano Moreira Xexeo, Geraldo TI Bringing knowledge into recommender systems SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE Recommender systems; Knowledge Vector; Knowledge AB Recommender systems are largely used nowadays to support collaborative tasks. However, it is important to consider each user's knowledge of the system for the recommended subject. In this paper we describe the use of user knowledge to improve the recommender system of the Business Process Cooperative Editor (BPCE), a collaborative business process modeling tool. We use the concept of the Knowledge Vector, developed in a previous work on collaborative navigation, to factor user knowledge into recommendations. We present Knowledge Vectors and how they are applied to the editor. A simulation to evaluate the effectiveness of the editor's new recommender system is presented. (C) 2012 Elsevier Inc. All rights reserved. C1 [Rodrigues Nt, Jose A.; Cardoso Tomaz, Luiz Fernando; de Souza, Jano Moreira; Xexeo, Geraldo] COPPE Univ Fed Rio de Janeiro, Programa Engn Sistemas & Comp, BR-21941972 Rio De Janeiro, RJ, Brazil. RP Nt, JAR (reprint author), COPPE Univ Fed Rio de Janeiro, Programa Engn Sistemas & Comp, Caixa Postal 68-511, BR-21941972 Rio De Janeiro, RJ, Brazil. EM rneto@cos.ufrj.br; luiztomaz@cos.ufrj.br; jano@cos.ufrj.br; xexeo@cos.ufrj.br RI Inca, Inct/K-2204-2013 CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Balabanovic M., 1995, ADAPTIVE AGENT AUTOM Bei F., 2008, ADV MANAGEMENT INFOR, P1 Blanco-Fernandez Y, 2008, J SYST SOFTWARE, V81, P2371, DOI 10.1016/j.jss.2008.05.009 Braak P.te., 2009, WEB INTELLIGENCE INT, P147 Burke R., 2000, ENCY LIB INFORM SYST, P2000 Chan WK, 2011, J SYST SOFTWARE, V84, P930, DOI 10.1016/j.jss.2011.01.032 de Rezende J., 2007, COMPUTER SUPPORTED C, VIII, P53 Hoskinson A, 2005, COMPUTER, V38, P97, DOI 10.1109/MC.2005.375 Jamali M., 2009, P 15 ACM SIGKDD INT, P397 Johnson R. B., 2007, ED RES QUANTITATIVE, V3rd Kambouri M., 1995, PSYCHOL TEACHING REV, V4, P124 Kamienski C., 2005, 25 C SOC BRAS COMP KOGUT B, 1992, ORGAN SCI, V3, P383, DOI 10.1287/orsc.3.3.383 Law AM, 1999, SIMULATION MODELING Liu DR, 2005, J SYST SOFTWARE, V77, P181, DOI 10.1016/j.jss.2004.08.031 Martinez-Romo J, 2012, INFORM PROCESS MANAG, V48, P183, DOI 10.1016/j.ipm.2011.03.006 MASSA P, 2007, P 2007 ACM C REC SYS, P17, DOI DOI 10.1145/1297231.1297235 METROPOLIS N, 1949, J AM STAT ASSOC, V44, P335, DOI 10.2307/2280232 Mundinger J., 2006, P 2006 WORKSH INT SY, P3, DOI 10.1145/1190326.1190328 Nonaka I, 1995, KNOWLEDGE CREATING C Nonaka I., 2000, IND CORP CHANGE, V9, P1, DOI DOI 10.1093/ICC/9.1.1 O'Donovan J., 2005, P 10 INT C INT US IN, P167, DOI DOI 10.1145/1040830.1040870 Robillard MP, 2010, IEEE SOFTWARE, V27, P80, DOI 10.1109/MS.2009.161 Rodrigues Nt J. A., 2008, P INT S MAN IND CORP Rodrigues Nt J.A., 2008, HDB RES VIRTUAL WORK, P516 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singh N, 2008, TOURISM MANAGE, V29, P929, DOI 10.1016/j.tourman.2007.11.005 Surowiecki J., 2004, WISDOM CROWDS WHY MA Tintarev N, 2007, I C DATA ENGIN WORKS, P801, DOI 10.1109/ICDEW.2007.4401070 Tomaz L. F., 2009, 5 INT C COLL COMP NE, P1 Vivacqua Adriana S, 2009, International Journal of Web Based Communities, V5, DOI 10.1504/IJWBC.2009.021565 Wang X, 2006, LECT NOTES COMPUT SC, V4181, P48 NR 34 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 J9 J SYST SOFTWARE JI J. Syst. Softw. PD JUL PY 2013 VL 86 IS 7 BP 1751 EP 1758 DI 10.1016/j.jss.2012.10.002 PG 8 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 156TU UT WOS:000319847600004 ER PT J AU Ren, FJ Sohrab, MG AF Ren, Fuji Sohrab, Mohammad Golam TI Class-indexing-based term weighting for automatic text classification SO INFORMATION SCIENCES LA English DT Article DE Text classification; Indexing; Term weighting; Machine learning; Feature selection; Classifier ID FEATURE-SELECTION; NAIVE BAYES; CATEGORIZATION; RETRIEVAL; MACHINE AB Most of the previous studies related on different term weighting emphasize on the document-indexing-based and four fundamental information elements-based approaches to address automatic text classification (ATC). In this study, we introduce class-indexing-based term-weighting approaches and judge their effects in high-dimensional and comparatively low-dimensional vector space over the TF.IDF and five other different term weighting approaches that are considered as the baseline approaches. First, we implement a class-indexing-based TF.IDF.ICF observational term weighting approach in which the inverse class frequency (ICF) is incorporated. In the experiment, we investigate the effects of TF.IDF.ICF over the Reuters-21578, 20 Newsgroups, and RCV1-v2 datasets as benchmark collections, which provide positive discrimination on rare terms in the vector space and biased against frequent terms in the text classification (TC) task. Therefore, we revised the ICF function and implemented a new inverse class space density frequency (ICS delta F), and generated the TF.IDF.ICS delta F method that provides a positive discrimination on infrequent and frequent terms. We present detailed evaluation of each category for the three datasets with term weighting approaches. The experimental results show that the proposed class-indexing-based TF.IDF.ICS delta F term weighting approach is promising over the compared well-known baseline term weighting approaches. (C) 2013 Elsevier Inc. All rights reserved. C1 [Ren, Fuji; Sohrab, Mohammad Golam] Univ Tokushima, Fac Engn, Tokushima 7708506, Japan. RP Ren, FJ (reprint author), Univ Tokushima, Fac Engn, 2-1 Minami Josanjima, Tokushima 7708506, Japan. EM ren@is.tokushima-u.ac.jp; mohammad-sohrab@iss.tokushima-u.ac.jp FU Ministry of Education, Science, Sports and Culture of Japan [22240021] FX This research has been partially supported by the Ministry of Education, Science, Sports and Culture of Japan under Grant-in-Aid for Scientific Research (A) No. 22240021. CR Zhang W, 2011, PROCEDIA ENGINEER, V15, DOI 10.1016/j.proeng.2011.08.404 Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 Covoes TF, 2011, INFORM SCIENCES, V181, P3766, DOI 10.1016/j.ins.2011.04.050 Debole F., 2003, P SAC 03 18 ACM S AP, P784 Figueiredo F, 2011, INFORM SYST, V36, P843, DOI 10.1016/j.is.2011.02.002 Flora S., 2011, EXPERT SYSTEMS APPL, V38, P14094 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Godbole S, 2002, P 8 ACM SIGKDD INT C, P513 Guo Y, 2010, INFORM SCIENCES, V180, P613, DOI 10.1016/j.ins.2009.11.012 Han EH, 2000, LECT NOTES COMPUT, V1910, P424 Ogura H, 2011, EXPERT SYST APPL, V38, P4978, DOI 10.1016/j.eswa.2010.09.153 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims T., 2001, P 24 ACM INT C RES D Jose M.G.H., 2003, TEXT REPRESENTATION Kang BY, 2005, INFORM PROCESS MANAG, V41, P1065, DOI 10.1016/j.ipm.2004.08.005 Kansheng S., 2011, J CHINA U POSTS TELE, V18, P131 Ko Y, 2009, INFORM PROCESS MANAG, V45, P70, DOI 10.1016/j.ipm.2008.07.004 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Lee J.H., 1995, P 18 ANN INT ACM SIG, P180, DOI 10.1145/215206.215358 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 LEWIS DD, 1992, TEXT-BASED INTELLIGENT SYSTEMS : CURRENT RESEARCH AND PRACTICE IN INFORMATION EXTRACTION AND RETRIEVAL, P179 Lewis DD, 2004, J MACH LEARN RES, V5, P361 Li W, 2011, EXPERT SYST APPL, V38, P2030, DOI 10.1016/j.eswa.2010.07.139 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Luo QM, 2011, EXPERT SYST APPL, V38, P12708, DOI 10.1016/j.eswa.2011.04.058 Maldonado S, 2011, INFORM SCIENCES, V181, P115, DOI 10.1016/j.ins.2010.08.047 Peng FC, 2006, INFORM PROCESS MANAG, V42, P963, DOI 10.1016/j.ipm.2005.09.002 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1973, P INT FED INF PROC C Salton G., 1975, THEORY INDEXING Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 SPARCKJONES K, 1972, J DOC, V28, P11 SPARCK-JONES K, 1973, INFORM STORAGE RET, V9, P619 Tagarelli A., 2012, INFORM SCI, V220, P244 Tan SB, 2008, EXPERT SYST APPL, V35, P279, DOI 10.1016/j.eswa.2007.06.028 Theeramnukkong T., 2004, INFORM SCI, V158, P89 Theeramunkong T, 2001, INT S COMM INF TECHN, P33 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 Wan CH, 2012, EXPERT SYST APPL, V39, P11880, DOI 10.1016/j.eswa.2012.02.068 Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2 Xis R., 2011, INFORM SCI, V181, P1138 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 50 TC 7 Z9 7 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JUL 1 PY 2013 VL 236 BP 109 EP 125 DI 10.1016/j.ins.2013.02.029 PG 17 WC Computer Science, Information Systems SC Computer Science GA 141SW UT WOS:000318747800008 ER PT J AU Cordoba, A Astrain, JJ Villadangos, J Echarte, F AF Cordoba, A. Astrain, J. J. Villadangos, J. Echarte, F. TI A self-adapted method for the categorization of social resources SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Resource categorization; Social networks; Folksonomy evolution; Self-adaptation ID FOLKSONOMIES AB Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people. Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system. (c) 2012 Elsevier Ltd. All rights reserved. C1 [Cordoba, A.; Astrain, J. J.; Villadangos, J.; Echarte, F.] Univ Publ Navarra, Dept Ingn Matemat & Informat, Pamplona 31006, Spain. RP Astrain, JJ (reprint author), Univ Publ Navarra, Dept Ingn Matemat & Informat, Campus Arrosadia, Pamplona 31006, Spain. EM alberto.cordoba@unavarra.es; josej.astrain@unavarra.es; jesusv@unavarra.es; patxi@eslomas.com RI chen, zhu/K-5923-2013 FU Spanish Government [TIN2010-17170, TIN2011-28347-CO1-02, TIN2011-28347-CO2-02] FX This work has been supported by the Spanish Government: TIN2010-17170, TIN2011-28347-CO1-02 and TIN2011-28347-CO2-02. CR Abbasi R., 2007, P WORKSH BRIDG GAP S Aliakbary S., 2009, P 2009 INT C COMP SC, V4, P588 Bagga A, 1998, P 17 INT C COMP LING, V1, P79 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Echarte F., 2009, P ACM S APPL COMP, P1226, DOI 10.1145/1529282.1529559 Godoy D, 2010, IFIP ADV INF COMM TE, V341, P151 Godoy D., 2010, P LWA2010 WORKSH WOC Godoy Daniela, 2012, P 3 INT C RES DISC, P88 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Hotho A., 2006, P WORKSH FACHGR INF Korner C., 2010, P 21 ACM C HYP HYP H, P157, DOI 10.1145/1810617.1810645 Lu CM, 2010, J INF SCI, V36, P763, DOI 10.1177/0165551510386173 Markines B., 2009, P 18 INT C WORLD WID, P641, DOI 10.1145/1526709.1526796 Noll M. G., 2008, P IEEE WIC ACM INT C, V1, P640 Noll MG, 2007, DOCENG'07: PROCEEDINGS OF THE 2007 ACM SYMPOSIUM ON DOCUMENT ENGINEERING, P177 Noll MG, 2008, APPLIED COMPUTING 2008, VOLS 1-3, P2315 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, B IEEE COMPUTER SOC, P24 Symeonidis P, 2010, IEEE T KNOWL DATA EN, V22, P179, DOI 10.1109/TKDE.2009.85 Tourne N, 2012, EXPERT SYST APPL, V39, P9723, DOI 10.1016/j.eswa.2012.02.088 van Rijsbergen C., 1974, J DOC, V30 Vatturi P., 2008, P 17 ACM C INF KNOWL, P1395, DOI 10.1145/1458082.1458297 Vojnovic M, 2009, IEEE T KNOWL DATA EN, V21, P1133, DOI 10.1109/TKDE.2009.34 Xia J., 2010, P 2010 13 IEEE INT C, P289 Yin ZJ, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P957 Zubiaga A., 2012, IEEE T KNOWLEDGE DAT, V99 Zubiaga A, 2009, DOCENG'09: PROCEEDINGS OF THE 2009 ACM SYMPOSIUM ON DOCUMENT ENGINEERING, P74 Zubiaga A., 2011, P 22 ACM C HYP HYP H, P93 NR 31 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JUL PY 2013 VL 40 IS 9 BP 3696 EP 3714 DI 10.1016/j.eswa.2012.12.075 PG 19 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 112GS UT WOS:000316581300034 ER PT J AU Burdescu, DD Mihai, CG Stanescu, L Brezovan, M AF Burdescu, Dumitru Dan Mihai, Cristian Gabriel Stanescu, Liana Brezovan, Marius TI Automatic image annotation and semantic based image retrieval for medical domain SO NEUROCOMPUTING LA English DT Article DE Image annotation; Image segmentation; Relevance models; Ontologies; Content based image retrieval ID TRANSLATION AB Automatic image annotation is the process of assigning meaningful words to an image taking into account its content. This process is of great interest as it allows indexing, retrieving, and understanding of large collections of image data. This paper presents a system used in the medical domain for three distinct tasks: image annotation, semantic based image retrieval and content based image retrieval. An original image segmentation algorithm based on a hexagonal structure was used to perform the segmentation of medical images. Image's regions are described using a vocabulary of blobs generated from image features using the K-means clustering algorithm. The annotation and semantic based retrieval task is evaluated for two annotation models: Cross Media Relevance Model and Continuous-space Relevance Model. Semantic based image retrieval is performed using the methods provided by the annotation models. The ontology used by the annotation process was created in an original manner starting from the information content provided by the Medical Subject Headings (MeSH). The experiments were made using a database containing color images retrieved from medical domain using an endoscope and related to digestive diseases. (C) 2012 Elsevier B.V. All rights reserved. C1 [Burdescu, Dumitru Dan; Mihai, Cristian Gabriel; Brezovan, Marius] Univ Craiova, Fac Automat Comp & Elect, Craiova, Romania. [Stanescu, Liana] Univ Craiova, Fac Automat Comp & Elect, Software Engn Dept, Craiova, Romania. RP Stanescu, L (reprint author), Univ Craiova, Fac Automat Comp & Elect, Bvd Decebal 107, Craiova, Romania. EM Burdescu@software.ucv.ro; mihai_gabriel@software.ucv.ro; stanescu@software.ucv.ro; marius.brezovan@software.ucv.ro CR Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Biren S., 2007, SEMANTIC BASED VISUA Blei D. M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460 Bresell Anders, 2006, Appl Bioinformatics, V5, P225, DOI 10.2165/00822942-200605040-00005 Brown P. F., 1993, Computational Linguistics, V19 Burdescu D. D., 2009, NEW METHOD SEGMENTAT Catherine E. C., 1997, MED INFORM, V22, P337 Daniel E., 2003, NSF SYMPOSIUM ON NEX Daniel E., 2003, THESIS Del Bimbo A., 2001, VISUAL INFORMATION R DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Duygulu P, 2002, LECT NOTES COMPUT SC, V2353, P97 Gevers T., 2004, IMAGE SEARCH ENGINES GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Iancu Andreea, 2010, PROCEEDINGS OF THE I, P717 Igor F. A., 2010, PROCEEDINGS OF THE 1 Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 Kherfi M. L., 2004, PROCEEDINGS OF THE P, V2-02, P961 Kherfi Mohammed Lamine, 2006, SEMANTIC BASED VISUA Kononowicz AA, 2008, LECT NOTES COMPUT SC, V5103, P188, DOI 10.1007/978-3-540-69389-5_22 Lavrenko V., 2004, PROCEEDINGS OF THE 1 Li J., 2003, IEEE T PATTERN ANAL, V25 Martin D., 2001, P 8 INT C COMP VIS, V2, P416, DOI 10.1109/ICCV.2001.937655 Mihai Gabriel, 2010, Proceedings 2010 International Multiconference on Computer Science and Information Technology (IMCSIT 2010) Mori Y., 1999, MISRM99 FIRST INTERN Noy Natalya F., ONTOLOGY DEVELOPMENT Rescigno G. U., 1988, NON TRADITIONAL REF SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith J. R., 1997, THESIS Stanescu L., 2006, INTERNATIONAL MULTI Steffen Staab, 2009, HDB ONTOLOGIES INT H Uschold M., 1996, KNOWL ENG REV, V11 NR 32 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD JUN 3 PY 2013 VL 109 SI SI BP 33 EP 48 DI 10.1016/j.neucom.2012.07.030 PG 16 WC Computer Science, Artificial Intelligence SC Computer Science GA 136QX UT WOS:000318379600006 ER PT J AU Chen, Y Groce, A Zhang, CQ Wong, WK Fern, XL Eide, E Regehr, J AF Chen, Yang Groce, Alex Zhang, Chaoqiang Wong, Weng-Keen Fern, Xiaoli Eide, Eric Regehr, John TI Taming Compiler Fuzzers SO ACM SIGPLAN NOTICES LA English DT Article; Proceedings Paper CT 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) CY JUN 16-19, 2013 CL Seattle, WA SP ACM SIGPLAN DE compiler testing; compiler defect; automated testing; fuzz testing; random testing; bug reporting; test-case reduction ID BUG ISOLATION AB Aggressive random testing tools ("fuzzers") are impressively effective at finding compiler bugs. For example, a single test-case generator has resulted in more than 1,700 bugs reported for a single JavaScript engine. However, fuzzers can be frustrating to use: they indiscriminately and repeatedly find bugs that may not be severe enough to fix right away. Currently, users filter out undesirable test cases using ad hoc methods such as disallowing problematic features in tests and grepping test results. This paper formulates and addresses the fuzzer taming problem: given a potentially large number of random test cases that trigger failures, order them such that diverse, interesting test cases are highly ranked. Our evaluation shows our ability to solve the fuzzer taming problem for 3,799 test cases triggering 46 bugs in a C compiler and 2,603 test cases triggering 28 bugs in a JavaScript engine. C1 [Chen, Yang; Eide, Eric; Regehr, John] Univ Utah, Salt Lake City, UT 84112 USA. [Groce, Alex; Zhang, Chaoqiang; Wong, Weng-Keen; Fern, Xiaoli] Oregon State Univ, Corvallis, OR 97331 USA. RP Chen, Y (reprint author), Univ Utah, Salt Lake City, UT 84112 USA. EM chenyang@cs.utah.edu; agroce@gmail.com; zhangch@onid.orst.edu; wong@eecs.oregonstate.edu; xfern@eecs.oregonstate.edu; eeide@cs.utah.edu; regehr@cs.utah.edu CR Andrews James H., 2008, P ASE SEPT, P19 [Anonymous], 2012, FUZZING SECURITY Ceccato M, 2012, PROC INT CONF SOFTW, P452, DOI 10.1109/ICSE.2012.6227170 Cesare Silvio, 2011, Proceedings of the 2011 International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11 (TrustCom 2011), DOI 10.1109/TrustCom.2011.26 Chaki S., 2004, P INT S FDN SOFTW EN, P73, DOI 10.1145/1029894.1029908 Claessen K, 2000, ACM SIGPLAN NOTICES, V35, P268, DOI 10.1145/357766.351266 Cleve H, 2005, PROC INT CONF SOFTW, P342 Fine S, 2007, MACH LEARN, V69, P213, DOI 10.1007/s10994-007-5026-6 Francis P., 2004, P 15 INT S SOFTW REL, P451 Gonzalez Teofilo F., 1985, THEORETICAL COMPUTER, V38, p293 Groce A, 2004, LECT NOTES COMPUT SC, V2988, P108 Groce A, 2007, PROC INT CONF SOFTW, P621 Groce Alex, 2012, P ISSTA JUL, P78 Holler C., 2012, P USENIX SEC AUG, P445 Jones J. A., 2002, Proceedings of the 24th International Conference on Software Engineering. ICSE 2002, DOI 10.1109/ICSE.2002.1007991 Jones James A., 2007, P ISSTA JUL, P16 Jones James A., 2005, P ASE NOV, P273 Jung YB, 2005, LECT NOTES COMPUT SC, V3672, P203 Kremenek T, 2003, LECT NOTES COMPUT SC, V2694, P295 Levenshtein Vladimir I, 1966, SOV PHYS DOKL, V10, P707 Liblit B, 2003, ACM SIGPLAN NOTICES, V38, P141, DOI 10.1145/780822.781148 Liblit B, 2005, ACM SIGPLAN NOTICES, V40, P15, DOI 10.1145/1064978.1065014 Liu C, 2006, P 14 ACM SIGSOFT INT, P46, DOI 10.1145/1181775.1181782 Mckeeman W., 1998, DIGITAL TECH J, V10, P100 Nethercote N, 2007, ACM SIGPLAN NOTICES, V42, P89, DOI 10.1145/1273442.1250746 Pelleg D., 2000, P 17 INT C MACH LEAR, P727 Pelleg Dan, 2004, ADV NEURAL INFORM PR, V18 Podgurski A, 2003, PROC INT CONF SOFTW, P465, DOI 10.1109/ICSE.2003.1201224 Regehr J, 2012, ACM SIGPLAN NOTICES, V47, P335, DOI 10.1145/2345156.2254104 Renieres M., 2003, Proceedings 18th IEEE International Conference on Automated Software Engineering Ruderman Jesse, MOZILLA BUG 349611 Ruderman Jesse, 2010, MY DOM FUZZER IGNORE Ruderman Jesse, INTRO JSFUNFUZZ SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schleimer S., 2003, P 2003 ACM SIGMOD IN, P76 Strehl A., 2002, J MACHINE LEARNING R, V3, P583, DOI DOI 10.1162/153244303321897735 Sun C, 2010, P 32 ACM IEEE INT C, P45 Sun C., 2011, P 26 IEEE ACM INT C, P253 Vangala V, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P293, DOI 10.1145/1595696.1595748 Vatturi P, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P847 Walenstein Andrew, 2006, DUPLICATION REDUNDAN WANG XY, 2008, ICS P 30 INT C, P461 Whalley David B., 1994, TOPLAS, V16, P1648 Yang XJ, 2011, ACM SIGPLAN NOTICES, V46, P283, DOI 10.1145/1993316.1993532 Zeller A, 2002, IEEE T SOFTWARE ENG, V28, P183, DOI 10.1109/32.988498 NR 45 TC 6 Z9 6 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0362-1340 EI 1558-1160 J9 ACM SIGPLAN NOTICES JI ACM Sigplan Not. PD JUN PY 2013 VL 48 IS 6 SI SI BP 197 EP 207 PG 11 WC Computer Science, Software Engineering SC Computer Science GA 184CX UT WOS:000321865400020 ER PT J AU Shi, Y Serdyukov, P Hanjalic, A Larson, M AF Shi, Yue Serdyukov, Pavel Hanjalic, Alan Larson, Martha TI Nontrivial Landmark Recommendation Using Geotagged Photos SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY LA English DT Article DE Algorithms; Experimentation; Performance ID SYSTEMS AB Online photo-sharing sites provide a wealth of information about user behavior and their potential is increasing as it becomes ever-more common for images to be associated with location information in the form of geotags. In this article, we propose a novel approach that exploits geotagged images from an online community for the purpose of personalized landmark recommendation. Under our formulation of the task, recommended landmarks should be relevant to user interests and additionally they should constitute nontrivial recommendations. In other words, recommendations of landmarks that are highly popular and frequently visited and can be easily discovered through other information sources such as travel guides should be avoided in favor of recommendations that relate to users' personal interests. We propose a collaborative filtering approach to the personalized landmark recommendation task within a matrix factorization framework. Our approach, WMF-CR, combines weighted matrix factorization and category-based regularization. The integrated weights emphasize the contribution of nontrivial landmarks in order to focus the recommendation model specifically on the generation of nontrivial recommendations. They support the judicious elimination of trivial landmarks from consideration without also discarding information valuable for recommendation. Category-based regularization addresses the sparse data problem, which is arguably even greater in the case of our landmark recommendation task than in other recommendation scenarios due to the limited amount of travel experience recorded in the online image set of any given user. We use category information extracted from Wikipedia in order to provide the system with a method to generalize the semantics of landmarks and allow the model to relate them not only on the basis of identity, but also on the basis of topical commonality. The proposed approach is computational scalable, that is, its complexity is linear with the number of observed preferences in the user-landmark preference matrix and the number of nonzero similarities in the category-based landmark similarity matrix. We evaluate the approach on a large collection of geotagged photos gathered from Flickr. Our experimental results demonstrate that WMF-CR outperforms several state-of-the-art baseline approaches in recommending nontrivial landmarks. Additionally, they demonstrate that the approach is well suited for addressing data sparseness and provides particular performance improvement in the case of users who have limited travel experience, that is, have visited only few cities or few landmarks. C1 [Shi, Yue; Hanjalic, Alan; Larson, Martha] Delft Univ Technol, Fac Elect Engn Math & Comp Sci, Dept Intelligent Syst, Multimedia Informat Retrieval Lab, NL-2628 CD Delft, Netherlands. [Serdyukov, Pavel] Yandex, Moscow, Russia. RP Shi, Y (reprint author), Delft Univ Technol, Fac Elect Engn Math & Comp Sci, Dept Intelligent Syst, Multimedia Informat Retrieval Lab, Mekelweg 4, NL-2628 CD Delft, Netherlands. EM yshi@tudelft.nl RI Larson, Martha/E-9983-2014 FU European Commission [216444] FX This article is an extended version of the short paper "Personalized Landmark Recommendation Based on Geotags from Photo Sharing Sites" [Shi et al. 2011], which appears in Proceedings of the ICWSM, 2011, The research leading to these results was carried out within the PetMedia Network of Excellence and has received funding from the European Commission 7th Framework Program under grant agreement no. 216444. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Arase Y., 2010, P INT C MULT MM 10, P133, DOI 10.1145/1873951.1873971 CABALLERO M. L., 2010, NEAR2ME DESIGN EVALU Clements M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P851 Cremonesi P., 2011, P 2011 ANN C EXT HUM, P1927 Cremonesi P, 2010, P 4 ACM C REC SYST, P39, DOI 10.1145/1864708.1864721 De Choudhury M., 2010, P 21 ACM C HYP HYP H, P35, DOI 10.1145/1810617.1810626 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 ELBERSE A., 2008, HARVARD BUS REV Gao Y., 2010, P ACM C MULT, P123, DOI 10.1145/1873951.1873970 Ge M., 2010, P 4 ACM C REC SYST, P257, DOI DOI 10.1145/1864708.1864761 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Grossman Lev, 2010, TIME, V175, P20 Herlocker J., 1999, P 22 ANN INT ACM SIG, V54, P230, DOI DOI 10.1145/312624.312682 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Hu YF, 2008, IEEE DATA MINING, P263, DOI 10.1109/ICDM.2008.22 KING I., 2011, ACM T INTELL SYST TE, V2, P29 KOFLER C., 2011, P 3 ACM SIGMM INT WO, P47 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Kurashima T., 2010, P 19 ACM INT C INF K, P579, DOI 10.1145/1871437.1871513 Li Q., 2008, P 16 ACM SIGSPATIAL Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu Nathan N., 2008, P 31 ANN INT ACM SIG, P83, DOI 10.1145/1390334.1390351 Lu X., 2010, P INT C MULT MM 10, P143, DOI 10.1145/1873951.1873972 McNee SM, 2006, HUM FACT COMP SYST C, P1097 NARASIMHAN N., 2006, P INT S APPL INT, P124 O'Mahony M., 2004, ACM T INTERNET TECHN, V4, P344, DOI 10.1145/1031114.1031116 Park YJ, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P11 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Salakhutdinov R., 2008, ADV NEURAL INFORM PR, V20, P1257 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Serdyukov P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P484, DOI 10.1145/1571941.1572025 Shi Y., 2010, P 4 ACM C REC SYST R, P269, DOI 10.1145/1864708.1864764 Shi Yue, 2010, P WORKSH CONT AW MOV, P34, DOI 10.1145/1869652.1869658 Shi Yue, 2011, P 5 INT C WEBL SOC M, P622 Singh Ajit P., 2008, P 14 ACM SIGKDD INT, P650, DOI 10.1145/1401890.1401969 Sinnott R. W., 1984, SKY TELESCOPE, V68, P158, DOI DOI 10.3758/BRM.42.1.141 Sizov S., 2010, P 3 ACM INT C WEB SE, P281, DOI 10.1145/1718487.1718522 Srebro N., 2003, P 20 INT C MACH LEAR, P720 Symeonidis P, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P43 Takeuchi Y, 2006, LECT NOTES COMPUT SC, V4159, P625 Tso-Sutter KHL, 2008, APPLIED COMPUTING 2008, VOLS 1-3, P1995 Voorhees E., 1999, P 8 TEXT RETR C Weimer M., 2007, P 21 ANN C ADV NEUR, P1593 Zhang DQ, 2011, COMPUTER, V44, P21, DOI 10.1109/MC.2011.65 Zheng VW, 2010, P 19 INT C WORLD WID, P1029, DOI DOI 10.1145/1772690.1772795 Zheng Y, 2009, P 18 INT C WORLD WID, P791, DOI DOI 10.1145/1526709.1526816 Zheng Y., 2011, ACM T INTEL SYST TEC, V2, P2 Zheng Y., 2011, ACM T WEB, V5 NR 50 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 2157-6904 J9 ACM T INTEL SYST TEC JI ACM Trans. Intell. Syst. Technol. PD JUN PY 2013 VL 4 IS 3 AR UNSP 47 DI 10.1145/2483669.2483680 PG 27 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 175IH UT WOS:000321223600011 ER PT J AU Fodeh, SJ Brandt, C Luong, TB Haddad, A Schultz, M Murphy, T Krauthammer, M AF Fodeh, Samah Jamal Brandt, Cynthia Thai Binh Luong Haddad, Ali Schultz, Martin Murphy, Terrence Krauthammer, Michael TI Complementary ensemble clustering of biomedical data SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Article DE Ensemble; Clustering; Complementary; Complementary ensemble clustering; Kmeans; Image ID ALGORITHM; MODEL AB The rapidly growing availability of electronic biomedical data has increased the need for innovative data mining methods. Clustering in particular has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on one modality or representation of the data. Complementary ensemble clustering (CEC) is a recently introduced framework in which Kmeans is applied to a weighted, linear combination of the coassociation matrices obtained from separate ensemble clustering of different data modalities. The strength of CEC is its extraction of information from multiple aspects of the data when forming the final clusters. This study assesses the utility of CEC in biomedical data, which often have multiple data modalities, e.g., text and images, by applying CEC to two distinct biomedical datasets (PubMed images and radiology reports) that each have two modalities. Referent to five different clustering approaches based on the Kmeans algorithm, CEC exhibited equal or better performance in the metrics of micro-averaged precision and Normalized Mutual Information across both datasets. The reference methods included clustering of single modalities as well as ensemble clustering of separate and merged data modalities. Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities. (C) 2013 Elsevier Inc. All rights reserved. C1 [Fodeh, Samah Jamal; Brandt, Cynthia; Murphy, Terrence] Yale Univ, Sch Med, New Haven, CT 06520 USA. [Thai Binh Luong] Yale Univ, Program Computat Biol & Bioinforrnat, New Haven, CT 06520 USA. [Haddad, Ali] Yale Univ, Dept Math, New Haven, CT 06520 USA. [Schultz, Martin] Yale Univ, Dept Comp Sci, New Haven, CT 06520 USA. [Krauthammer, Michael] Yale Univ, Dept Pathol, New Haven, CT 06520 USA. [Krauthammer, Michael] Yale Univ, Program Computat Biol & Bioinformat, New Haven, CT 06520 USA. RP Fodeh, SJ (reprint author), Yale Ctr Med Informat, POB 208009, New Haven, CT 06520 USA. EM samah.fodeh@yale.edu; cynthia.brandt@yale.edu; thaibin.luong@yale.edu; ali.haddad@yale.edu; schultz-martin@yale.edu; terrence.murphy@yale.edu; michael.krauthammer@yale.edu FU National Institute of Health NIH/Natural Library of Medicine [NLM 5R01LM009956]; VA, Consortium for Healthcare Informatics [HIR 08-374 HSRD]; National Institute on Aging [1R21AG033130-01A2] FX This study was funded by National Institute of Health NIH/Natural Library of Medicine NLM 5R01LM009956 (MK, SF), VA grant HIR 08-374 HSR&D: Consortium for Healthcare Informatics (CB, SF, MK) and assisted by the Yale Claude D. Pepper Older Americans Independence Center P30AG21342 (TM) and by a grant from the National Institute on Aging (TM) (1R21AG033130-01A2). CR Ahmed MN, 2002, IEEE T MED IMAGING, V21, P193, DOI 10.1109/42.996338 Beli DM, 2003, J MACHINE LEARNING R, V3, P993 Chandra B, 2006, 6 INT JOINT C NEUR N, P3843 Chen Y, 2009, IEEE T KNOWL DATA EN, P1459 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fodeh S.J., 2009, ACM S APPL COMP, P1446 Fred ALN, 2005, IEEE T PATTERN ANAL, V27, P835, DOI 10.1109/TPAMI.2005.113 Fred ALN, 2001, LECT NOTES COMPUTER, P309 Greene D, 2004, IEEE S COMP BAS MED, P576 Hadjitodorov ST, 2006, INFORM FUSION, V7, P264, DOI 10.1016/j.inffus.2005.01.008 HARALICK RM, 1979, P IEEE, V67, P786, DOI 10.1109/PROC.1979.11328 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Kuncheva LI, 2004, IEEE SYS MAN CYBERN, P1214 Mandayam Comar, 2010, WEB INTELL INTELL AG, V1, P476 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rodriguez-Esteban R, 2009, BIOINFORMATICS, V25-16, P2082 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560 Topchy A, 2004, SIAM PROC S, P379 Weingessel A, 2003, 3 INT WORKSH DISTR S Xu SH, 2008, BIOINFORMATICS, V24, P1968, DOI 10.1093/bioinformatics/btn340 NR 22 TC 1 Z9 1 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 EI 1532-0480 J9 J BIOMED INFORM JI J. Biomed. Inform. PD JUN PY 2013 VL 46 IS 3 BP 436 EP 443 DI 10.1016/j.jbi.2013.02.001 PG 8 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 155BD UT WOS:000319719500006 PM 23454721 ER PT J AU Smith, SW Shah, SK AF Smith, Sheryl Winston Shah, Sonali K. TI Do Innovative Users Generate More Useful Insights? An Analysis of Corporate Venture Capital Investments in the Medical Device industry SO STRATEGIC ENTREPRENEURSHIP JOURNAL LA English DT Article DE innovation; user innovation; corporate venture capital; technology entrepreneurship; new product development ID RESEARCH-AND-DEVELOPMENT; OPEN SOURCE SOFTWARE; ABSORPTIVE-CAPACITY; PRODUCT DEVELOPMENT; STICKY INFORMATION; PATENT CITATIONS; UNITED-STATES; KNOWLEDGE; PERFORMANCE; FIRMS AB Users are an important source of innovation. Scholars have suggested that established firms will gain valuable innovative insights by working with user innovators. However, no study compares the extent to which knowledge sourced from innovative users, as compared to other external sources of knowledge, triggers the creation of new technologies and commercial products within established firms. This leaves established firms with little guidance when it comes to choosing where to search for external knowledge that ignites innovation. Based on existing empirical work in the literature on user innovation, we build a theoretical framework that explains why user knowledge will provide established firms with more useful' innovative insights than will other sources of knowledge. We test this claim in the context of corporate venture capital investment in the medical device industry. We find that established firms incorporate more knowledge from user innovators than from other sources of external knowledge into their patents and highly innovative products. Accessing the knowledge contained in user-generated innovations enriches the product development outcomes of established firms. We trace the flow of knowledge from start-ups to established firms using both an established method based on backward patent citation data and a novel algorithmic method that compares the content of regulatory documents. Copyright (c) 2013 John Wiley & Sons, Ltd. C1 [Smith, Sheryl Winston] Temple Univ, Fox Sch Business, Philadelphia, PA 19122 USA. [Shah, Sonali K.] Univ Washington, Foster Sch Business, Seattle, WA 98195 USA. RP Smith, SW (reprint author), Temple Univ, Fox Sch Business, 1801 Liacouras Walk, Philadelphia, PA 19122 USA. EM sheryl.winston.smith@temple.edu; skshah@u.washington.edu CR Agarwal R, 2009, STRATEG MANAGE J, V30, P1349, DOI 10.1002/smj.792 ARORA A, 1990, J IND ECON, V38, P361, DOI 10.2307/2098345 Beckman CM, 2008, ORGAN SCI, V19, P3, DOI 10.1287/orsc.1070.0311 Benson D, 2009, ORGAN SCI, V20, P329, DOI 10.1287/orsc.1080.0386 Boeker W, 1988, ECOLOGICAL MODELS OR Brown JS, 2001, ORGAN SCI, V12, P198, DOI 10.1287/orsc.12.2.198.10116 BROWN SL, 1995, ACAD MANAGE REV, V20, P343, DOI 10.2307/258850 Chandler Alfred, 1994, SCALE SCOPE DYNAMICS Chesbrough H, 2003, WORKING PAPER Chesbrough H. W., 2003, OPEN INNOVATION NEW Clark K. B., 1991, PRODUCT DEV PERFORMA COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553 COOPER RG, 1995, J PROD INNOVAT MANAG, V12, P374, DOI 10.1016/0737-6782(95)00059-3 Delmar F., 2006, Strategic Organization, V4, DOI 10.1177/1476127006066596 Dushnitsky G, 2009, STRATEGIC MANAGE J, V30, P1045, DOI 10.1002/smj.781 Dushnitsky G, 2005, RES POLICY, V34, P615, DOI 10.1016/j.respol.2005.01.017 Dushnitsky G, 2005, STRATEGIC MANAGE J, V26, P947, DOI 10.1002/smj.488 EISENHARDT KM, 1990, ADMIN SCI QUART, V35, P504, DOI 10.2307/2393315 Ernst and Young, 2009, PULS IND MED TECHN R Ernst and Young, 2010, PULS IND MED TECHN R Franke N, 2003, RES POLICY, V32, P157, DOI 10.1016/S0048-7333(02)00006-9 Franz K, 2005, TINKERING: CONSUMERS REINVENT THE EARLY AUTOMOBILE, P1 Gompers P. A., 2000, CONCENTRATED CORPORA Gompers PA, 2002, ECON REV, V87, P1 Grant RM, 1996, STRATEGIC MANAGE J, V17, P109, DOI 10.1002/smj.4250171110 Greene W, 2008, ECONOMETRIC ANAL, V6th Griliches Z., 1998, R D PRODUCTIVITY ECO Guimera R, 2005, SCIENCE, V308, P697, DOI 10.1126/science.1106340 Hall BH, 2005, RAND J ECON, V36, P16 Hanley KW, 2010, REV FINANC STUD, V23, P2821, DOI 10.1093/rfs/hhq024 HAUSMAN J, 1984, ECONOMETRICA, V52, P909, DOI 10.2307/1911191 Helfat CE, 1997, STRATEGIC MANAGE J, V18, P339, DOI 10.1002/(SICI)1097-0266(199705)18:5<339::AID-SMJ883>3.0.CO;2-7 Helfat CE, 2000, STRATEGIC MANAGE J, V21, P961, DOI 10.1002/1097-0266(200010/11)21:10/11<961::AID-SMJ132>3.3.CO;2-5 HENDERSON R, 1993, RAND J ECON, V24, P248, DOI 10.2307/2555761 Hoberg G, 2010, REV FINANC STUD, V23, P3773, DOI 10.1093/rfs/hhq053 Jaffe A. B, 2002, PATENTS CITATIONS IN Jeppesen LB, 2006, ORGAN SCI, V17, P22 Jeppesen LB, 2003, TECHNOL ANAL STRATEG, V15, P363, DOI 10.1080/09537320310001601531 Katila R, 2002, ACAD MANAGE J, V45, P1183, DOI 10.2307/3069433 Katila R, 2008, ADMIN SCI QUART, V53, P295, DOI 10.2189/asqu.53.2.295 Kline R, 1996, TECHNOL CULT, V37, P763, DOI 10.2307/3107097 Kwon OW, 2003, INFORM PROCESS MANAG, V39, P25, DOI 10.1016/S0306-4573(02)00022-5 Lane PJ, 1998, STRATEGIC MANAGE J, V19, P461, DOI 10.1002/(SICI)1097-0266(199805)19:5<461::AID-SMJ953>3.3.CO;2-C Leiponen A, 2010, STRATEG MANAGE J, V31, P224, DOI 10.1002/smj.807 LEVIN RC, 1985, AM ECON REV, V75, P20 Lilien GL, 2002, MANAGE SCI, V46, P1513 Luthje C, 2005, RES POLICY, V34, P951, DOI 10.1016/j.respol.2005.05.005 Manning C.D., 2008, INTRO INFORM RETRIEV March JG, 1991, ORGAN SCI, V2, P71, DOI 10.1287/orsc.2.1.71 Merton R. K., 1979, SOCIOLOGY SCI Mody CCM, 2006, TECHNOL CULT, V47, P56, DOI 10.1353/tech.2006.0085 Mowery DC, 1996, STRATEGIC MANAGE J, V17, P77 Nelson R., 1982, EVOLUTIONARY THEORY Nonaka I, 1995, KNOWLEDGE CREATING C Ogawa S, 1998, RES POLICY, V26, P777, DOI 10.1016/S0048-7333(97)00047-4 Oudshoorn Nelly, 2003, USERS MATTER COCONST Park HD, 2012, STRATEGIC MANAGE J, V33, P1, DOI 10.1002/smj.937 Prandelli E, 2008, COLLABORATING WITH CUSTOMERS TO INNOVATE: CONCEIVING AND MARKETING PRODUCTS IN THE NETWORKING AGE, P1 PricewaterhouseCoopers, 2006, CORP VENT CAP ACT RI Puranam P, 2007, STRATEG MANAGE J, V28, P805, DOI 10.1002/smj.608 RIGGS W, 1994, RES POLICY, V23, P459, DOI 10.1016/0048-7333(94)90008-6 Rosenkopf L, 2003, MANAGE SCI, V49, P751, DOI 10.1287/mnsc.49.6.751.16026 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schildt HA, 2005, ENTREP THEORY PRACT, V29, P493, DOI 10.1111/j.1540-6520.2005.00095.x Schumpeter J. A., 1942, CAPITALISM SOCIALISM Shah S. K., 2012, WHO ARE USER ENTREPR Shah SK, 2007, STRATEG ENTREP J, V1, P123, DOI 10.1002/sej.15 Shah SK, 2006, MANAGE SCI, V52, P1000, DOI 10.1287/mnsc.1060.0553 Shah SK, 2005, OPEN SOURCES SIEGEL R, 1988, J BUS VENTURING, V3, P233, DOI 10.1016/0883-9026(88)90017-1 Sorensen JB, 2000, ADMIN SCI QUART, V45, P81, DOI 10.2307/2666980 Taylor A, 2010, ORGAN SCI, V21, P3 TRAJTENBERG M, 1990, RAND J ECON, V21, P172, DOI 10.2307/2555502 TUSHMAN ML, 1986, ADMIN SCI QUART, V31, P439, DOI 10.2307/2392832 United States Food and Drug Administration, 2012, PREM APPR PMA INF URBAN GL, 1988, MANAGE SCI, V34, P569, DOI 10.1287/mnsc.34.5.569 Tyre MJ, 1997, ORGAN SCI, V8, P71, DOI 10.1287/orsc.8.1.71 von Hippel E, 2010, MANAGE SCI, V58, P1669 VONHIPPEL E, 1994, MANAGE SCI, V40, P429, DOI 10.1287/mnsc.40.4.429 von Hippel E., 2005, DEMOCRATIZING INNOVA Von Hippel E., 1998, SOURCES INNOVATION von Krogh G, 1999, KNOWLEDGE CREATION S von Hippel E, 2002, MANAGE SCI, V48, P821, DOI 10.1287/mnsc.48.7.821.2817 von Hippel E, 2003, ORGAN SCI, V14, P209, DOI 10.1287/orsc.14.2.209.14992 Wadhwa A, 2010, INT STUD ENTREP, V26, P147, DOI 10.1007/978-1-4419-0058-6_8 Wenger E, 1998, COMMUNITIES PRACTICE Williams A. D., 2006, WIKINOMICS Winston Smith S., 2009, CO THEY KEEP INNOVAT NR 89 TC 3 Z9 3 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1932-4391 EI 1932-443X J9 STRATEG ENTREP J JI Strateg. Entrep. J. PD JUN PY 2013 VL 7 IS 2 BP 151 EP 167 DI 10.1002/sej.1152 PG 17 WC Business; Management SC Business & Economics GA 156OC UT WOS:000319830900004 ER PT J AU Goyal, P Behera, L McGinnity, TM AF Goyal, Pawan Behera, Laxmidhar McGinnity, T. M. TI A novel neighborhood based document smoothing model for information retrieval SO INFORMATION RETRIEVAL LA English DT Article DE Information retrieval; Document smoothing; Lexical association ID LATENT SEMANTIC ANALYSIS; LANGUAGE MODELS; QUERY EXPANSION; TRANSFORM AB In this paper, a novel neighborhood based document smoothing model for information retrieval has been proposed. Lexical association between terms is used to provide a context sensitive indexing weight to the document terms, i.e. the term weights are redistributed based on the lexical association with the context words. A generalized retrieval framework has been presented and it has been shown that the vector space model (VSM), divergence from randomness (DFR), Okapi Best Matching 25 (BM25) and the language model (LM) based retrieval frameworks are special cases of this generalized framework. Being proposed in the generalized retrieval framework, the neighborhood based document smoothing model is applicable to all the indexing models that use the term-document frequency scheme. The proposed smoothing model is as efficient as the baseline retrieval frameworks at runtime. Experiments over the TREC datasets show that the neighborhood based document smoothing model consistently improves the retrieval performance of VSM, DFR, BM25 and LM and the improvements are statistically significant. C1 [Goyal, Pawan; Behera, Laxmidhar; McGinnity, T. M.] Univ Ulster, Intelligent Syst Res Ctr, Sch Comp & Intelligent Syst, Londonderry, North Ireland. [Goyal, Pawan] INRIA Rocquencourt, Le Chesnay, France. [Behera, Laxmidhar] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India. RP Goyal, P (reprint author), Univ Ulster, Intelligent Syst Res Ctr, Sch Comp & Intelligent Syst, Magee Campus, Londonderry, North Ireland. EM Goyal-p@email.ulster.ac.uk; l.behera@ulster.ac.uk; tm.mcginnity@ulster.ac.uk FU Centre of Excellence in Intelligent Systems (CoEIS) project; Northern Ireland Integrated Development Fund; InvestNI FX This research is supported under the Centre of Excellence in Intelligent Systems (CoEIS) project, funded by the Northern Ireland Integrated Development Fund and InvestNI. CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Andreopoulos B, 2008, INT J DATA MIN BIOIN, V2, P193, DOI 10.1504/IJDMB.2008.020522 Bai J, 2008, INFORM PROCESS MANAG, V44, P1901, DOI 10.1016/j.ipm.2008.07.006 BENESTY J, 2009, SPRINGER TOP SIGN PR, V2, P1 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blei DM, 2010, J ACM, V57, DOI 10.1145/1667053.1667056 Brookstein A., 1974, J AM SOC INFORM SCI, V25, P312 Collins-Thompson Kevyn, 2005, P 14 ACM INT C INF K, P704, DOI DOI 10.1145/1099554.1099727 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Gao Jianfeng, 2004, SIGIR 04 P 27 ANN IN, P170, DOI DOI 10.1145/1008992.1009024 Hiemstra D., 2002, SIGIR, P35, DOI DOI 10.1145/564376.564385 Hiemstra D., 2004, SIGIR 04, P178, DOI DOI 10.1145/1008992.1009025 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jones K. S., 2000, Information Processing & Management, V36 Kalt T., 1998, NEW PROBABILISTIC MO Karimzadehgan M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P323 Keller M., 2005, ART NEUR NETW BIOL I, P11 Kurland O., 2008, P 31 ANN INT ACM SIG, P547, DOI DOI 10.1145/1390334.1390428 Kurland O, 2009, INFORM RETRIEVAL, V12, P437, DOI 10.1007/s10791-008-9065-9 Kurland O, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1508850.1508851 Lavrenko V., 2001, SIGIR Forum Li H., 2002, NAT LANG ENG, V8, P25, DOI [10.1017/S1351324902002838, DOI 10.1017/S1351324902002838] Liu X., 2004, SIGIR 2004, V186-193 Losada DE, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361690 MacKay David J.C., 1994, NAT LANG ENG, V1, P1 Maisonnasse L., 2007, P ACM SIGIR 2007 AMS, P695, DOI DOI 10.1145/1277741.1277863 Morita K, 2004, INFORM PROCESS MANAG, V40, P957, DOI 10.1016/j.ipm.2003.08.009 Na SH, 2007, INFORM PROCESS MANAG, V43, P121, DOI 10.1016/j.ipm.2006.04.005 Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Park LAF, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1462198.1462200 Park LAF, 2005, IEEE T PATTERN ANAL, V27, P130, DOI 10.1109/TPAMI.2005.2 Park LAF, 2005, ACM T INFORM SYST, V23, P267, DOI 10.1145/1080343.1080345 Park LAF, 2007, LECT NOTES COMPUT SC, V4426, P224 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 Qiu Y., 1993, SIGIR, P160 Robertson S. E., 1981, SIGIR 80, P35 Robertson S. E., 1988, RELEVANCE WEIGHTING, P143 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shakery A, 2008, INFORM RETRIEVAL, V11, P139, DOI 10.1007/s10791-007-9041-9 Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Strzalkowski T., 1992, P 30 ANN M ASS COMP, P104, DOI 10.3115/981967.981981 Tao T, 2006, P MAIN C HUM LANG TE, P407, DOI 10.3115/1220835.1220887 Tao T., 2006, SIGIR 2006, P162 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Vechtomova O, 2007, INFORM PROCESS MANAG, V43, P849, DOI 10.1016/j.ipm.2006.09.004 Voorhees E. M., 1998, P 6 TEXT RETR C TREC, P83 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Wong S. K. Michael, 1985, SIGIR, P18, DOI DOI 10.1145/253495.253506 Xu J., 1996, SIGIR 96, P4 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yoshinari Tomoko, 2008, International Journal of Computer Applications in Technology, V33, DOI 10.1504/IJCAT.2008.021944 Zhai C., 2006, P 29 ANN INT ACM SIG, P115, DOI DOI 10.1145/1148170.1148193 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhao JL, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P291, DOI 10.1145/1571941.1571993 Zhou XH, 2007, IEEE T KNOWL DATA EN, V19, P1276, DOI 10.1109/TKDE.2007.1058. NR 62 TC 0 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2013 VL 16 IS 3 BP 391 EP 425 DI 10.1007/s10791-012-9202-3 PG 35 WC Computer Science, Information Systems SC Computer Science GA 148UP UT WOS:000319273400004 ER PT J AU Park, H Kim, K Choi, S Yoon, J AF Park, Hyunseok Kim, Kwangsoo Choi, Sungchul Yoon, Janghyeok TI A patent intelligence system for strategic technology planning SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Technology intelligence; Technology planning; Subject-Action-Object; SAO; Function; Patent map; Patent network; Patent mining; Intelligence system ID RESEARCH-AND-DEVELOPMENT; MORPHOLOGY ANALYSIS; TRENDS; IDENTIFICATION; OPPORTUNITIES; SIMILARITY; EVOLUTION; MODEL AB Patent intelligence-the transformation of content found in multiple patents into technical, business, and legal insight-is considered a key factor in gaining a competitive advantage in technologically competitive business environments. Although keyword-based patent intelligence tools are widely used due to their simplicity and ease of use, they are limited in that they cannot represent key technological concepts and inventive knowledge by relying only on the frequency of occurrence of defined keywords. As a remedy, this paper proposes a Subject-Action-Object (SAO)-based patent intelligence system. SAO structures that can be extracted from textual patent information are known as the expertise and inventive findings of the relevant patent. On the basis of semantic analysis of patent SAO structures, our proposed intelligence system constructs patent maps and patent networks. Building on the maps and networks, the system provides specific functionalities including identification of technology trends and significant patents, detection of novel technologies, and identification of potential infringement. This paper describes the architecture of our proposed patent intelligence system in detail, and illustrates the system's functionalities using case studies. We anticipate that our proposed system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies. (c) 2012 Elsevier Ltd. All rights reserved. C1 [Park, Hyunseok] Pohang Univ Sci & Technol, Dept Technol & Innovat Management, Pohang 790784, South Korea. [Kim, Kwangsoo] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 790784, South Korea. [Choi, Sungchul] Samsung Adv Inst Technol, Dept Technol Strategy, Suwon 440600, South Korea. [Yoon, Janghyeok] Konkuk Univ, Dept Ind Engn, Seoul 143701, South Korea. RP Yoon, J (reprint author), Konkuk Univ, Dept Ind Engn, 120 Neungdong Ro, Seoul 143701, South Korea. EM howgood@postech.ac.kr; kskim@postech.ac.kr; blissray@gmail.com; janghyoon@konkuk.ac.kr RI Choi, Sungchul/ OI Choi, Sungchul/0000-0002-5836-3838 CR Bergmann I, 2008, R&D MANAGE, V38, P550 BETTIS RA, 1995, STRATEGIC MANAGE J, V16, P7, DOI 10.1002/smj.4250160915 Bollobas B, 2001, RANDOM GRAPHS, V2nd Cantrell R., 1996, COMPETITIVE INTELLIG, V7, P65, DOI 10.1002/cir.3880070113 Cascini G., 2004, DOCUMENT ANAL SYSTEM, VVI, P89 Cascini G, 2008, INT FED INFO PROC, V277, P31 Chen L., 2003, PRESS VESSEL TECHNOL, V20, P1, DOI 10.3115/1119303.1119304 Choi S, 2011, SCIENTOMETRICS, V88, P863, DOI 10.1007/s11192-011-0420-z Choi S, 2012, EXPERT SYST APPL, V39, P11443, DOI 10.1016/j.eswa.2012.04.014 COOPER AC, 1976, BUS HORIZONS, V19, P61, DOI 10.1016/0007-6813(76)90024-0 Franses PH, 1999, J ECONOMETRICS, V89, P293 Fujii A, 2007, INFORM PROCESS MANAG, V43, P1149, DOI 10.1016/j.ipm.2006.11.004 Gerken J. M., 2012, SCIENTOMETRICS, V91, P1 Gogoi P, 2011, COMPUT J, V54, P570, DOI 10.1093/comjnl/bxr026 HOPCROFT J, 1973, COMMUN ACM, V16, P372, DOI 10.1145/362248.362272 Kerr C. I. V., 2006, International Journal of Technology Intelligence and Planning, V2, DOI 10.1504/IJTIP.2006.010511 KIPO, 2010, K2E AUT TRANSL KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 Lee S, 2008, R&D MANAGE, V38, P169, DOI 10.1111/j.1467-9310.2008.00509.x Lee S, 2009, TECHNOVATION, V29, P481, DOI 10.1016/j.technovation.2008.10.006 Lichtenthaler E, 2004, J ENG TECHNOL MANAGE, V21, P331, DOI 10.1016/j.jengtecman.2004.09.003 Lichtenthaler E., 2003, TECHNOLOGY INTELLIGE Moehrle MG, 2005, R&D MANAGE, V35, P513, DOI 10.1111/j.1467-9310.2005.00408.x MOKKEN RJ, 1979, QUAL QUANT, V13, P161, DOI 10.1007/BF00139635 No HJ, 2010, TECHNOL FORECAST SOC, V77, P63, DOI 10.1016/j.techfore.2009.06.006 Park BJ, 2002, J FORECASTING, V21, P381, DOI 10.1002/for.827 Park H, 2013, EXPERT SYST APPL, V40, P736, DOI 10.1016/j.eswa.2012.08.008 Park H, 2012, SCIENTOMETRICS, V90, P515, DOI 10.1007/s11192-011-0522-7 Phaal R, 2004, TECHNOL FORECAST SOC, V71, P5, DOI 10.1016/S0040-1625(03)00072-6 PORTER AL, 1995, TECHNOL FORECAST SOC, V49, P237, DOI 10.1016/0040-1625(95)00022-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sternitzke C, 2009, SCIENTOMETRICS, V78, P113, DOI 10.1007/s11192-007-1961-z STOPWORDS, 2012, ENGL STOPW Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 Utterback J, 1996, MASTERING DYNAMICS I Wanner Leo, 2008, World Patent Information, V30, DOI 10.1016/j.wpi.2007.03.008 Yoon B, 2005, TECHNOL FORECAST SOC, V72, P145, DOI 10.1016/j.techfore.2004.08.011 Yoon B, 2008, EXPERT SYST APPL, V35, P124, DOI 10.1016/j.eswa.2007.06.022 Yoon B, 2008, R&D MANAGE, V38, P51 Yoon B., 2004, J HIGH TECHNOLOGY MA, V15, P37, DOI DOI 10.1016/J.HITECH.2003.09.003 Yoon J, 2012, SCIENTOMETRICS, V90, P445, DOI 10.1007/s11192-011-0543-2 Yoon J, 2011, SCIENTOMETRICS, V88, P213, DOI 10.1007/s11192-011-0383-0 Yoon J, 2011, SCIENTOMETRICS, V86, P687, DOI 10.1007/s11192-010-0303-8 Yoon J, 2012, EXPERT SYST APPL, V39, P2927, DOI 10.1016/j.eswa.2011.08.154 Yoon J., 2012, SCIENTOMETRICS Zha Xianjin, 2010, Journal of Computers, V5, DOI 10.4304/jcp.5.2.274-281 NR 47 TC 6 Z9 6 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JUN 1 PY 2013 VL 40 IS 7 BP 2373 EP 2390 DI 10.1016/j.eswa.2012.10.073 PG 18 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 099JE UT WOS:000315616500003 ER PT J AU Santos, I Brezo, F Ugarte-Pedrero, X Bringas, PG AF Santos, Igor Brezo, Felix Ugarte-Pedrero, Xabier Bringas, Pablo G. TI Opcode sequences as representation of executables for data-mining-based unknown malware detection SO INFORMATION SCIENCES LA English DT Article DE Malware detection; Computer security; Data mining; Machine learning; Supervised learning ID FEATURE-SELECTION; INTRUSION DETECTION; CLASSIFICATION; INFORMATION; CLASSIFIERS; ALGORITHMS; CRITERIA; KERNEL AB Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing faster every year and poses a serious global security threat. Consequently, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. Therefore, the signature-based method consistently fails to detect new malware. In this paper, we propose a new method to detect unknown malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting unknown malware. (C) 2011 Elsevier Inc. All rights reserved. C1 [Santos, Igor; Brezo, Felix; Ugarte-Pedrero, Xabier; Bringas, Pablo G.] Univ Deusto, Lab Smartness Semant & Secur S3Lab, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, Lab Smartness Semant & Secur S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; felix.brezo@deusto.es; xabier.ugarte@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 FU Ministry of Industry of Spain, project Cenit SEGUR@, Security and Trust in the Information Society [BOE 35] FX This research was financially supported by the Ministry of Industry of Spain, project Cenit SEGUR@, Security and Trust in the Information Society, (BOE 35, 09/02/2007, CDTI). We would also like to acknowledge to the anonymous reviewers for their helpful comments and suggestions. CR Amari S, 1999, NEURAL NETWORKS, V12, P783, DOI 10.1016/S0893-6080(99)00032-5 Bayes T., 1764, PHILOS T ROY SOC LON, V53, P370, DOI [10.1093/biomet/45.3-4.296, DOI 10.1098/RSTL.1763.0053] Bergeron J., 1999, Proceedings. IEEE 8th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE'99), DOI 10.1109/ENABL.1999.805197 Bilar Daniel, 2007, International Journal of Electronic Security and Digital Forensics, V1, DOI 10.1504/IJESDF.2007.016865 Bishop C. M., 2006, PATTERN RECOGNITION Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Bruschi D, 2006, LECT NOTES COMPUT SC, V4064, P129 Cai DM, 2005, P SOC PHOTO-OPT INS, V5812, P1, DOI 10.1117/12.603174 Cano JR, 2006, APPL SOFT COMPUT, V6, P323, DOI 10.1016/j.asoc.2005.02.006 Castillo E., 1996, EXPERT SYSTEMS PROBA Chapelle O., 2006, SEMISUPERVISED LEARN Chouchane M., 2006, P 4 ACM WORKSH REC M, P73, DOI DOI 10.1145/1179542.1179558 Christodorescu M, 2003, USENIX ASSOCIATION PROCEEDINGS OF THE 12TH USENIX SECURITY SYMPOSIUM, P169 Christodorescu M, 2004, ACM SIGSOFT SOFTWARE, V29, P34, DOI 10.1145/1013886.1007518 Christodorescu M, 2005, P IEEE S SECUR PRIV, P32, DOI 10.1109/SP.2005.20 Christodorescu M., 2007, THESIS Cooper G. F., 1991, P 1991 C UNC ART INT Czarnowski I., 2006, P SCI SESS ORG 21 FA, P60 Dash M, 2003, ARTIF INTELL, V151, P155, DOI 10.1016/S0004-3702(03)00079-1 Derrac J, 2009, LECT NOTES ARTIF INT, V5572, P557, DOI 10.1007/978-3-642-02319-4_67 Elkan C, 2001, INT JOINT C ART INT, V17, P973 Fisch D, 2010, INFORM SCIENCES, V180, P2421, DOI 10.1016/j.ins.2010.02.023 Fix E., 1952, DISCRIMINATORY ANAL Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199 Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Huang J., 2011, INFORM SCI Idika N., 2007, TECHNICAL REPORT Jacob G., 2008, J COMPUTER VIROLOGY, V4, P251, DOI DOI 10.1007/S11416-008-0086-0 Jin X, 2006, LECT NOTES COMPUT SC, V3916, P106 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Kang MG, 2007, WORM'07: PROCEEDINGS OF THE 2007 ACM WORKSHOP ON RECURRING MALCODE, P46 Karim M. E., 2005, J COMPUTER VIROLOGY, V1, P13, DOI 10.1007/s11416-005-0002-9 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R., 1995, P 14 INT JOINT C ART, V2, P1137 KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kotsiantis S. B., 2004, WSEAS T INFORM SCI A, V1, P73 Kotsiantis SB, 2007, FRONT ARTIF INTEL AP, V160, P3 Kou G, 2009, INFORM SCIENCES, V179, P371, DOI 10.1016/j.ins.2008.10.025 Kuzurin N, 2007, LECT NOTES COMPUT SC, V4779, P281 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Li W., 2005, P 2005 IEEE WORKSH I Liu H., 2001, INSTANCE SELECTION C LO RW, 1995, COMPUT SECUR, V14, P541, DOI 10.1016/0167-4048(95)00012-W Mantaras R.L., 1991, MACH LEARN, V6, P81, DOI 10.1007/BF00153761 Martignoni L., 2007, P 23 ANN COMP SEC AP, P431 McGill M. J., 1983, INTRO MODERN INFORM Morley P., 2001, P 2001 VIR B C VB200, P129 Moskovitch R, 2008, ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, P156 Liu H, 2008, CH CRC DATA MIN KNOW, P3 Ollmann G, 2008, COMPUT FRAUD SECUR, P4 Pearl J., 1982, P AM ASS ART INT NAT, P133 Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226 Perdisci R, 2008, ANN COMPUT SECURITY, P301, DOI 10.1109/ACSAC.2008.22 Perdisci R, 2008, PATTERN RECOGN LETT, V29, P1941, DOI 10.1016/j.patrec.2008.06.016 Perriot F., 2004, P VIR B INT C, P51 Platt J., 1999, ADV KERNEL METHODS S, V208 Potter B, 2009, COMPUT FRAUD SECUR, P12 Pyle D., 1999, DATA PREPARATION DAT Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Royal P, 2006, ANN COMPUT SECURITY, P289, DOI 10.1109/ACSAC.2006.38 Russell S. J., 2003, ARTIFICIAL INTELLIGE, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos I, 2010, LECT NOTES COMPUT SC, V5965, P35, DOI 10.1007/978-3-642-11747-3_3 Santos I, 2009, ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS, P317 Schapire RE, 2003, LECT NOTES STAT, V171, P149 Schultz MG, 2001, P IEEE S SECUR PRIV, P38, DOI 10.1109/SECPRI.2001.924286 Shabtai Asaf, 2009, Information Security Technical Report, V14, DOI 10.1016/j.istr.2009.03.003 Shafiq MZ, 2008, LECT NOTES COMPUT SC, V5137, P88, DOI 10.1007/978-3-540-70542-0_5 Sharif M, 2008, LECT NOTES COMPUT SC, V5283, P481 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Sung A. H., 2004, Proceedings. 20th Annual Computer Security Applications Conference Szor P., 2005, ART COMPUTER VIRUS R Torkkola K., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753742 Tsai CF, 2009, EXPERT SYST APPL, V36, P11994, DOI 10.1016/j.eswa.2009.05.029 Tsang ECC, 2003, IEEE T FUZZY SYST, V11, P202, DOI 10.1109/TFUZZ.2003.809895 Ustun B, 2006, CHEMOMETR INTELL LAB, V81, P29, DOI 10.1016/j.chemolab.2005.09.003 Vapnik V. N., 2000, NATURE STAT LEARNING Vinod P., 2009, SURVEY MALWARE DETEC Xu J.Y., 2004, P 4 INT C HYBR INT S, P378 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang Q., 2007, P 23 ANN COMP SEC AP, P411 Zhou Yan, 2008, P 1 ACM WORKSH AISEC, P53, DOI 10.1145/1456377.1456393 NR 85 TC 14 Z9 16 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 EI 1872-6291 J9 INFORM SCIENCES JI Inf. Sci. PD MAY 10 PY 2013 VL 231 BP 64 EP 82 DI 10.1016/j.ins.2011.08.020 PG 19 WC Computer Science, Information Systems SC Computer Science GA 115UE UT WOS:000316836600006 ER PT J AU Ruotsalo, T Haav, K Stoyanov, A Roche, S Fani, E Deliai, R Makela, E Kauppinen, T Hyvonen, E AF Ruotsalo, Tuukka Haav, Krister Stoyanov, Antony Roche, Sylvain Fani, Elena Deliai, Romina Makela, Eetu Kauppinen, Tomi Hyvonen, Eero TI SMARTMUSEUM: A mobile recommender system for the Web of Data SO JOURNAL OF WEB SEMANTICS LA English DT Article DE Web of Data; Semantic Web; Information retrieval; Recommender systems; Mobile systems; Cultural heritage ID CULTURAL-HERITAGE COLLECTIONS; VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; KNOWLEDGE; ONTOLOGIES; ANNOTATION; SEARCH; GUIDE AB Semantic and context knowledge has been envisioned as an appropriate solution for addressing the content heterogeneity and information overload in mobile Web information access, but few have explored their full potential in mobile scenarios, where information objects refer to their physical counterparts, and retrieval is context-aware and personalized for users. We present SMARTMUSEUM, a mobile ubiquitous recommender system for the Web of Data, and its application to information needs of tourists in context-aware on-site access to cultural heritage. The SMARTMUSEUM system utilizes Semantic Web languages as the form of data representation. Ontologies are used to bridge the semantic gap between heterogeneous content descriptions, sensor inputs, and user profiles. The system makes use of an information retrieval framework wherein context data and search result clustering are used in recommendation of suitable content for mobile users. Results from laboratory experiments demonstrate that ontology-based reasoning, query expansion, search result clustering, and context knowledge lead to significant improvement in recommendation performance. The results from field trials show that the usability of the system meets users' expectations in real-world use. The results indicate that semantic content representation and retrieval can significantly improve the performance of mobile recommender systems in knowledge-rich domains. (C) 2013 Elsevier B.V. All rights reserved. C1 [Ruotsalo, Tuukka; Makela, Eetu; Kauppinen, Tomi; Hyvonen, Eero] Aalto Univ, Dept Media Technol, Helsinki, Finland. [Ruotsalo, Tuukka] Univ Calif Berkeley, Sch Informat, Berkeley, CA 94720 USA. [Ruotsalo, Tuukka] Aalto Univ, HIIT, Helsinki, Finland. [Deliai, Romina] Heritage Malta, Msida, Malta. [Fani, Elena] Museo Galileo, Florence, Italy. [Roche, Sylvain] INRIA Rennes Bretagne Atlantique, Rennes, France. [Stoyanov, Antony] Melon Technol Ltd, Sofia, Bulgaria. RP Ruotsalo, T (reprint author), Aalto Univ, HIIT, Helsinki, Finland. EM tuukka.ruotsalo@aalto.fi; krister@apprise.eu; antony.stoyanov@melon.bg; sroche@inria.fr; elenaf@imss.fi.it; romina.delia@gov.mt; eetu.makela@aalto.fi; tomi.kauppinen@aalto.fi; eero.hyvonen@aalto.fi RI Hyvonen, Eero/G-2252-2013; Kauppinen, Tomi/K-6118-2012; Makela, Eetu/ OI Kauppinen, Tomi/0000-0003-3479-7022; Makela, Eetu/0000-0002-8366-8414 FU Europeans Commission [FP7216923]; FinnONTO project; Finnish Funding Agency for Technology and Innovation (Tekes); Academy of Finland [135536]; Fulbright Technology Industries of Finland; Finnish Cultural Foundation; Finnish Foundation for Technology Promotion FX SMARTMUSEUM (the Cultural Heritage Knowledge Exchange Platform) (www.smartmuseum.eu) is a research and development project sponsored under the Europeans Commission's 7th Framework Programme (FP7216923). This work was also supported by the FinnONTO project, funded by the Finnish Funding Agency for Technology and Innovation (Tekes), the Academy of Finland Grant (135536), a Fulbright Technology Industries of Finland grant, a Finnish Cultural Foundation grant, and a Finnish Foundation for Technology Promotion grant. We also want to thank all of the researchers, developers, project managers, and museum staff who participated in the SMARTMUSEUM project. Especially, we would like to thank Frederic Weis, Nima Dokoohaki, Alar Kuusik, Marco Berni, Mihhail Matskin, Tanel Tammet, Innar Liiv, Brian Restall, Michel Banatre, and Silver Toomla. CR Abowd GD, 1997, WIREL NETW, V3, P421, DOI 10.1023/A:1019194325861 Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714 ALTAKROURI B, 2008, MOMM 08, P460 BECKER C, 2008, P LINK DAT WEB WORKS Bellotti V, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1157 Bobillo F, 2008, EXPERT SYST APPL, V35, P1899, DOI 10.1016/j.eswa.2007.08.090 BOWER J, 2004, P MUS WEB ARCH MUS I Brickley D., 2004, RDF VOCABULARY DESCR Bridge D, 2005, KNOWL ENG REV, V20, P315, DOI 10.1017/S0269888906000567 Brooke J, 1996, USABILITY EVALUATION Burke RD, 1997, IEEE EXPERT, V12, P32, DOI 10.1109/64.608186 Cantador Ivan, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.204 Carletta J, 1996, COMPUT LINGUIST, V22, P249 Carpineto C., 2009, ACM COMPUT SURV, V41 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Conover W. J., 1998, PRACTICAL NONPARAMET, V3rd Corallo A, 2006, LECT NOTES ARTIF INT, V4253, P1092 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Felfernig A, 2006, INT J ELECTRON COMM, V11, P11, DOI 10.2753/JEC1086-4415110201 Fernandez M, 2011, J WEB SEMANT, V9, P434, DOI 10.1016/j.websem.2010.11.003 Gandon F.L., 2004, WEB SEMANTICS SCI SE, V1, P241, DOI 10.1016/j.websem.2003.07.008 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 HECKMANN D, 2005, US MOD 2005 10 INT C Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 HINZE AM, 2010, P 12 INT C HUM COMP, P327, DOI 10.1145/1851600.1851658 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Hyvarinen A, 1997, NEURAL COMPUT, V9, P1483, DOI 10.1162/neco.1997.9.7.1483 Hyvonen E, 2005, J WEB SEMANT, V3, P224, DOI 10.1016/j.websem.2005.05.008 Kamvar M, 2007, COMPUTER, V40, P58, DOI 10.1109/MC.2007.270 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Kruger A., 2007, The Adaptive Web. Methods and Strategies of Web Personalization Liiv Innar, 2009, P 3 INT C ADV SEM PR Maidel V, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P91 MAKELA E, 2007, P 1 IND RES SEM TECH MAKELA E, 2012, SEM WEB INTEROPERAB, V3 Manning C. D., 1999, FDN STAT NATURAL LAN Michalowski M, 2004, IEEE INTELL SYST, V19, P72, DOI 10.1109/MIS.2004.16 MIDDLETON SE, 2001, K CAP 01, P100 MIDDLETON SE, 2003, KCAP 2003, P62 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Noy NF, 2004, SIGMOD RECORD, V33, P65 Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159 Quan D. A., 2004, P 13 INT C WORLD WID, P255, DOI 10.1145/988672.988707 Raptis D., 2005, P 7 INT C HUM COMP I, P153, DOI DOI 10.1145/1085777.1085803 Resnick P., 1994, CSCW 94, P175 Rocchi C., 2004, P 9 INT C INT US INT, P316 ROES I, 2009, P 27 INT C HUM FACT, P3317, DOI 10.1145/1520340.1520479 RUOTSALO T, 2007, P 6 INT SEM WEB C IS Ruotsalo T, 2009, INT J SEMANT WEB INF, V5, P39, DOI 10.4018/jswis.2009100103 Ruotsalo T., 2009, P INT C DIG LIB SEM Ruotsalo T., 2012, LNCS, V7295, P422 Ruotsalo T, 2009, IEEE INTELL SYST, V24, P64, DOI 10.1109/MIS.2009.32 Ruotsalo T., 2007, P INT C DAT EXP SYST Ruotsalo Tuukka, 2010, THESIS AALTO U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2000, EC'00. Proceedings of the 2nd ACM Conference on Electronic Commerce Satyanarayanan M, 2001, IEEE PERS COMMUN, V8, P10, DOI 10.1109/98.943998 Schreiber G, 2008, J WEB SEMANT, V6, P243, DOI 10.1016/j.websem.2008.08.001 Shapiro S. S., 1965, BIOMETRIKA, V3 SPORLEDER C, 2010, LANG LINGUIST COMPAS, V4, P750 Stojanovic N., 2001, Proceedings of the First International Conference on Knowledge Capture van Hage WR, 2010, LECT NOTES COMPUT SC, V6088, P46, DOI 10.1007/978-3-642-13486-9_4 Vozalis M., 2006, Web Intelligence and Agent Systems, V4 Wang YW, 2008, J WEB SEMANT, V6, P283, DOI 10.1016/j.websem.2008.09.002 Weiser M., 1999, SIGMOBILE MOB COMPUT, V3, P3, DOI DOI 10.1145/329124.329126 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zhang LL, 2005, Proceedings of the 2005 International Conference on Management Science and Engineering, P453, DOI 10.1145/1060745.1060812 Ziegler CN, 2008, AI COMMUN, V21, P97, DOI 10.3233/AIC-2008-0430 NR 69 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-8268 J9 J WEB SEMANT JI J. Web Semant. PD MAY PY 2013 VL 20 BP 50 EP 67 DI 10.1016/j.websem.2013.03.001 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 216RP UT WOS:000324302000004 ER PT J AU Ernstberger, J Gruning, M AF Ernstberger, Juergen Gruening, Michael TI How do firm- and country-level governance mechanisms affect firms' disclosure? SO JOURNAL OF ACCOUNTING AND PUBLIC POLICY LA English DT Article ID MANAGEMENT EARNINGS FORECASTS; CORPORATE-GOVERNANCE; EMPIRICAL-ANALYSIS; VOLUNTARY DISCLOSURE; COST; PERFORMANCE; COMPLEMENTARITIES; DETERMINANTS; RATINGS; IMPACT AB This paper examines how a country's regulatory environment interacts with firms' institutional corporate governance arrangements to affect the disclosure that these firms provide in their annual reports. Prior literature indicates that firms with stronger corporate governance arrangements demonstrate higher levels of disclosure. We investigate whether this effect varies with the legal environment. The transparency-increasing effect of strong corporate governance might be reinforced by a strong legal environment, suggesting a complementary relationship between these two factors with respect to transparency. However, strong corporate governance arrangements may serve as bonding mechanisms in weak legal environments, suggesting a substitutive relationship between corporate governance and the regulatory environment. Using a sample of listed firms from 16 European countries, we find evidence suggesting that corporate governance arrangements and the legal environment substitute with respect to their effects on corporate disclosure. (C) 2013 Elsevier Inc. All rights reserved. C1 [Ernstberger, Juergen] Ruhr Univ Bochum, Chair Accounting & Auditing, D-44801 Bochum, Germany. [Gruening, Michael] Ilmenau Univ Technol, Chair Accounting & Management Control, D-98693 Ilmenau, Germany. [Gruening, Michael] Frankfurt Sch Finance & Management, Accounting Dept, D-60314 Frankfurt, Germany. RP Gruning, M (reprint author), Ilmenau Univ Technol, Chair Accounting & Management Control, Helmholtzpl 3, D-98693 Ilmenau, Germany. EM michael.gruening@tu-ilmenau.de CR Aguilera RV, 2012, RES METHOD STRAT MAN, V8, P289, DOI 10.1108/S1479-8387(2012)0000008014 Aguilera RV, 2008, ORGAN SCI, V19, P475, DOI 10.1287/orsc.1070.0322 Ahmed K., 1999, BRIT ACCOUNTING REV, V31, P35, DOI 10.1006/bare.1998.0082 Ajinkya B, 2005, J ACCOUNTING RES, V43, P343, DOI 10.1111/j.1475-679X.2005.00174.x Akhtaruddin M., 2005, INT J ACCOUNTING, V40, P399, DOI DOI 10.1016/J.INTACC.2005.09.007 Aoki M., 2004, CORP GOV, P31 Archambault J. J., 2003, INT J ACCOUNTING, V38, P173, DOI 10.1016/S0020-7063(03)00021-9 Bassett M., 2007, BRIT ACCOUNT REV, V39, P303, DOI 10.1016/j.bar.2007.06.003 Beattie V., 2004, ACCOUNTING FORUM, V28, P205, DOI DOI 10.1016/J.ACCFOR.2004.07.001 Bennedsen M, 2000, J FINANC ECON, V58, P113, DOI 10.1016/S0304-405X(00)00068-4 Botosan CA, 1997, ACCOUNT REV, V72, P323 Botosan CA, 2002, J ACCOUNTING RES, V40, P21, DOI 10.1111/1475-679X.00037 Bruno V, 2010, J FINANC INTERMED, V19, P461, DOI 10.1016/j.jfi.2009.10.001 Bushman RM, 2004, J ACCOUNTING RES, V42, P207, DOI 10.1111/j.1475-679X.2004.00136.x Caylor L.M., 2006, J ACCOUNTING PUBLIC, V25, P409 Cerbioni F, 2007, EUR ACCOUNT REV, V16, P791, DOI 10.1080/09638180701707011 Chau GK., 2002, INT J ACCOUNTING, V37, P247, DOI DOI 10.1016/S0020-7063(02)00153-X Chavent M, 2006, EUR ACCOUNT REV, V15, P181, DOI 10.1080/09638180500253092 Chen C. J. P., 2000, J ACCOUNTING PUBLIC, V19, P285, DOI 10.1016/S0278-4254(00)00015-6 Chen KCW, 2009, J CORP FINANC, V15, P273, DOI 10.1016/j.jcorpfin.2009.01.001 Chen S, 2008, J ACCOUNT RES, V46, P499, DOI 10.1111/j.1475-679X.2008.00288.x Choi JH, 2007, CONTEMP ACCOUNT RES, V24, P13, DOI 10.1506/X478-1075-4PW5-1501 Coffee JC, 2002, COLUMBIA LAW REV, V102, P1757, DOI 10.2307/1123661 Cooke T.E., 2007, CONTINUUM DISCLOSURE Core JE, 2001, J ACCOUNT ECON, V31, P441, DOI 10.1016/S0165-4101(01)00036-2 Courtenay S. M., 2006, INT J ACCOUNTING, V41, P262, DOI 10.1016/j.intacc.2006.07.001 Dahya J, 2008, J FINANC ECON, V87, P73, DOI 10.1016/j.jfineco.2006.10.005 Daines RM, 2010, J FINANC ECON, V98, P439, DOI 10.1016/j.jfineco.2010.06.005 DARROUGH MN, 1990, J ACCOUNT ECON, V12, P219, DOI 10.1016/0165-4101(90)90048-9 Depoers F., 2000, EUROPEAN ACCOUNTING, V9, P245, DOI 10.1080/09638180050129891 DIAMOND DW, 1991, J FINANC, V46, P1325, DOI 10.2307/2328861 Djankov S, 2008, J FINANC ECON, V88, P430, DOI 10.1016/j.jfineco.2007.02.007 Doidge C, 2007, J FINANC ECON, V86, P1, DOI 10.1016/j.jfineco.2006.09.002 Durnev A, 2005, J FINANC, V60, P1461, DOI 10.1111/j.1540-6261.2005.00767.x Eng L. L., 2003, J ACCOUNTING PUBLIC, V22, P325, DOI 10.1016/S0278-4254(03)00037-1 Ennen E, 2010, J MANAGE, V36, P207, DOI 10.1177/0149206309350083 Forker J. J., 1992, ACCOUNTING BUSINESS, V22, P111 Francis JR, 2005, ACCOUNT REV, V80, P1125, DOI 10.2308/accr.2005.80.4.1125 GLOSTEN LR, 1985, J FINANC ECON, V14, P71, DOI 10.1016/0304-405X(85)90044-3 Gruning M, 2011, EUR ACCOUNT REV, V20, P485, DOI 10.1080/09638180.2011.585792 Gul F., 2004, J ACCOUNTING PUBLIC, V23, P351, DOI 10.1016/j.jaccpubpol.2004.07.001 Guthrie J, 2004, J INTELLECTUAL CAPIT, V5, P282, DOI DOI 10.1108/14691930410533704 Haniffa R., 2002, ABACUS, V38, P317, DOI DOI 10.1111/1467-6281.00112 Healy P. M., 1999, CONTEMP ACCOUNT RES, V16, P485 Healy PM, 2001, J ACCOUNT ECON, V31, P405, DOI 10.1016/S0165-4101(01)00018-0 HECKMAN JJ, 1979, ECONOMETRICA, V47, P153, DOI 10.2307/1912352 HECKMAN JJ, 1978, ECONOMETRICA, V46, P931, DOI 10.2307/1909757 Ho S. S., 2001, J INT ACCOUNTING AUD, V10, P139, DOI [10.1016/S1061-9518(01)00041-6, DOI 10.1016/S1061-9518(01)00041-6] Hope O. K., 2003, J INT FIN MANAG ACC, V14, P218, DOI 10.1111/1467-646X.00097 Hossain M., 1994, INT J ACCOUNTING, V29, P334 HUSSAINEY K, 2003, ACCOUNTING BUSINESS, V33, P275 Jaggi B., 2000, INT J ACCOUNTING, V35, P495, DOI 10.1016/S0020-7063(00)00076-5 Karamanou I, 2005, J ACCOUNT RES, V43, P453, DOI 10.1111/j.1475-679X.2005.00177.x Kelton AS, 2008, J ACCOUNT PUBLIC POL, V27, P62, DOI 10.1016/j.jaccpubpol.2007.11.001 Kothari SP, 2009, ACCOUNT REV, V84, P1639, DOI 10.2308/accr.2009.84.5.1639 Krishnan J, 2005, ACCOUNT REV, V80, P649, DOI 10.2308/accr.2005.80.2.649 LaPorta R, 1997, J FINANC, V52, P1131, DOI 10.1111/j.1540-6261.1997.tb02727.x Laksmana I, 2008, CONTEMP ACCOUNT RES, V25, P1147, DOI 10.1506/car.25.4.8 LANG M, 1993, J ACCOUNTING RES, V31, P246, DOI 10.2307/2491273 La Porta R, 1998, J POLIT ECON, V106, P1113 Leung S., 2004, J INT FIN MANAG ACC, V15, P235, DOI 10.1111/j.1467-646X.2004.00109.x Leuz C, 2003, J FINANC ECON, V69, P505, DOI 10.1016/S0304-405X(03)00121-1 Leuz C, 2000, J ACCOUNTING RES, V38, P91, DOI 10.2307/2672910 Leuz C., 2008, WORKING PAPER Li F, 2008, J ACCOUNT ECON, V45, P221, DOI 10.1016/j.jacceco.2008.02.003 Li F, 2010, J ACCOUNT RES, V48, P1049, DOI 10.1111/j.1475-679X.2010.00382.x Marston C., 2004, INT J ACCOUNTING INF, V5, P285, DOI 10.1016/j.accinf.2004.02.009 Marston C., 2003, Corporate Communications, V8, DOI 10.1108/13563280310458894 Marston C.L., 1991, BRIT ACCOUNTING REV, V23, P195, DOI 10.1016/0890-8389(91)90080-L Mastruzzi M., 2008, 4654 WORLD BANK MILGROM P, 1995, J ACCOUNT ECON, V19, P179, DOI 10.1016/0165-4101(94)00382-F Murphy R., 2007, ENGLISH GRAMMAR USE Nagar V, 2003, J ACCOUNT ECON, V34, P283, DOI 10.1016/S0165-4101(02)00075-7 Nowland J, 2008, CORP GOV, V16, P475, DOI 10.1111/j.1467-8683.2008.00707.x Olibe K.O., 2006, J INT ACCOUNTING AUD, V15, P197, DOI 10.1016/j.intaccaudtax.2006.08.006 Patten DM, 2002, ACCOUNT ORG SOC, V27, P763, DOI 10.1016/S0361-3682(02)00028-4 Price R, 2011, J FINANC ECON, V99, P76, DOI 10.1016/j.jfineco.2010.08.005 Ramnath S, 2002, J ACCOUNTING RES, V40, P1351, DOI 10.1111/1475-679X.t01-1-00057 Renders A, 2010, CORP GOV, V18, P87, DOI 10.1111/j.1467-8683.2010.00791.x Ryan P., 2004, J BUSINESS FINANCE A, V31, P49, DOI 10.1111/j.0306-686X.2004.0002.x SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott W. R., 2003, ORG RATIONAL NATURAL Sengupta P, 1998, ACCOUNT REV, V73, P459 Thompson J. D., 1967, ORG ACTION Verrecchia RE, 2001, J ACCOUNT ECON, V32, P97, DOI 10.1016/S0165-4101(01)00025-8 Wang K., 2008, J INT ACCOUNTING AUD, V17, P14, DOI 10.1016/j.intaccaudtax.2008.01.001 Ward AJ, 2009, CORP GOV-OXFORD, V17, P646, DOI 10.1111/j.1467-8683.2009.00766.x Weber R. P., 1990, BASIC CONTENT ANAL Xiao J.Z., 2004, J ACCOUNTING PUBLIC, V23, P191, DOI 10.1016/j.jaccpubpol.2004.04.002 NR 89 TC 0 Z9 0 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0278-4254 J9 J ACCOUNT PUBLIC POL JI J. Account. Public Policy PD MAY-JUN PY 2013 VL 32 IS 3 SI SI BP 50 EP 67 DI 10.1016/j.jaccpubpol.2013.02.003 PG 18 WC Business, Finance; Public Administration SC Business & Economics; Public Administration GA 154AJ UT WOS:000319643600003 ER PT J AU Hulsegge, I Woelders, H Smits, M Schokker, D Jiang, L Sorensen, P AF Hulsegge, Ina Woelders, Henri Smits, Mari Schokker, Dirkjan Jiang, Li Sorensen, Peter TI Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining SO PHYSIOLOGICAL GENOMICS LA English DT Article DE reproduction; gene prioritization; text-mining; gene expression; protein-protein interaction ID DAIRY-COWS; QUANTITATIVE MEASURE; INTERACTION NETWORKS; ESTROUS BEHAVIOR; DISEASE GENES; IDENTIFICATION; PATTERNS; AMYGDALA; BRAIN; BIOINFORMATICS AB Reproduction is of significant economic importance in dairy cattle. Improved understanding of mechanisms that control estrous behavior and other reproduction traits could help in developing strategies to improve and/or monitor these traits. The objective of this study was to predict and rank genes and processes in brain areas and pituitary involved in reproductive traits in cattle using information derived from three different data sources: gene expression, protein-protein interactions, and literature. We identified 59, 89, 53, 23, and 71 genes in bovine amygdala, dorsal hypothalamus, hippocampus, pituitary, and ventral hypothalamus, respectively, potentially involved in processes underlying estrus and estrous behavior. Functional annotation of the candidate genes points to a number of tissue-specific processes of which the "neurotransmitter/ion channel/synapse" process in the amygdala, "steroid hormone receptor activity/ion binding" in the pituitary, "extracellular region" in the ventral hypothalamus, and "positive regulation of transcription/metabolic process" in the dorsal hypothalamus are most prominent. The regulation of the functional processes in the various tissues operate at different biological levels, including transcriptional, posttranscriptional, extracellular, and intercellular signaling levels. C1 [Hulsegge, Ina; Woelders, Henri; Smits, Mari; Schokker, Dirkjan] Wageningen UR Livestock Res, Anim Breeding & Genom Ctr, NL-8200 AB Lelystad, Netherlands. [Jiang, Li; Sorensen, Peter] Ctr Quantitat Genet & Genom, Dept Mol Biol & Genet, Tjele, Denmark. RP Hulsegge, I (reprint author), Wageningen UR Livestock Res, Anim Breeding & Genom Ctr, POB 65, NL-8200 AB Lelystad, Netherlands. RI smits, mari/J-4892-2013; Schokker, Dirkjan/M-6385-2014 OI smits, mari/0000-0002-3493-291X; Schokker, Dirkjan/0000-0001-6082-7227 FU European Commission [FOODCT-2006-016250 (SABRE), FOOD-CT-2004-506416 (EADGENE)]; International Cattle Breeding Cooperative (CRV) FX This work was supported by the European Commission, within the Sixth Framework Programme, contract no. FOODCT-2006-016250 (SABRE) and contract no. FOOD-CT-2004-506416 (EADGENE), and the International Cattle Breeding Cooperative (CRV). The text represents the views of the authors and does not necessarily represent a position of the Commission, which will not be liable for the use made of such information. CR BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289 Bertolin K, 2010, ANIM REPROD, V7, P9 Boer HMT, 2010, ANIMAL, V4, P446, DOI 10.1017/S1751731109991169 Chen J, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-73 Dobson H, 2008, ANIMAL, V2, P1104, DOI 10.1017/S175173110800236X Evans ACO, 2008, REPROD DOMEST ANIM, V43, P359, DOI 10.1111/j.1439-0531.2008.01185.x FOREMAN MM, 1978, HORM BEHAV, V10, P97, DOI 10.1016/0018-506X(78)90028-4 Gallagher M, 1996, CURR OPIN NEUROBIOL, V6, P221, DOI 10.1016/S0959-4388(96)80076-6 Gentleman RC, 2004, GENOME BIOL, V5, DOI 10.1186/gb-2004-5-10-r80 Huang DW, 2007, GENOME BIOL, V8, DOI 10.1186/gb-2007-8-9-r183 Huang DW, 2009, NAT PROTOC, V4, P44, DOI 10.1038/nprot.2008.211 Hulsegge B, 2012, J ANIM SCI, V90, P2061, DOI 10.2527/jas.2011-4251 von Mering C, 2003, NUCLEIC ACIDS RES, V31, P258, DOI 10.1093/nar/gkg034 Jensen LJ, 2009, NUCLEIC ACIDS RES, V37, pD412, DOI 10.1093/nar/gkn760 Jiang L, 2012, PHYSIOL GENOMICS, V44, P305, DOI 10.1152/physiolgenomics.00047.2011 Kommadath A., 2012, Genomic regulation of oestrous behaviour in dairy cows Kommadath A, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-200 Kommadath A, 2010, ANIMAL, V4, P1297, DOI 10.1017/S1751731110000303 KRETTEK JE, 1977, J COMP NEUROL, V172, P687, DOI 10.1002/cne.901720408 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 McKenna NJ, 2009, MOL ENDOCRINOL, V23, P740, DOI 10.1210/me.2009-0135 Micevych P, 2007, HDB NEUROCHEMISTRY M, V3rd, P151, DOI 10.1007/978-0-387-30405-2_4 Mitko K, 2008, REPRODUCTION, V135, P225, DOI 10.1530/REP-07-0415 Morley BJ, 2004, BRAIN RES, V1023, P41, DOI 10.1016/j.brainres.2004.07.006 Newton MA, 2007, ANN APPL STAT, V1, P85, DOI 10.1214/07-AOAS104 Olivier B, 2011, CURR TOP BEHAV NEURO, V8, P15, DOI 10.1007/7854_2010_116 Ortutay C, 2009, NUCLEIC ACIDS RES, V37, P622, DOI 10.1093/nar/gkn982 Osborne John D., 2007, V408, P153 Paterson D, 2000, PROG NEUROBIOL, V61, P75, DOI 10.1016/S0301-0082(99)00045-3 Ritchie ME, 2007, BIOINFORMATICS, V23, P2700, DOI 10.1093/bioinformatics/btm412 Roberts AJ, 2011, J ANIM SCI, V89, P1035, DOI 10.2527/jas.2010-3433 Robinson-Rechavi M, 2003, J CELL SCI, V116, P585, DOI 10.1242/jcs.00247 Rossi S, 2006, NUCLEIC ACIDS RES, V34, pW285, DOI 10.1093/nar/gkl340 Royal M, 2000, VET J, V160, P53, DOI 10.1053/tvjl.1999.0450 Salamon E, 2005, ACTA PHARMACOL SIN, V26, P389, DOI 10.1111/j.1745-7254.2005.00083.x Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smyth G. K., 2004, STAT APPL GENET MOL, V3, DOI DOI 10.2202/1544-6115.1027 Snel B, 2000, NUCLEIC ACIDS RES, V28, P3442, DOI 10.1093/nar/28.18.3442 STUMPF WE, 1970, AM J ANAT, V129, P207, DOI 10.1002/aja.1001290209 Tranchevent LC, 2011, BRIEF BIOINFORM, V12, P22, DOI 10.1093/bib/bbq007 Veerkamp RF, 2007, THERIOGENOLOGY, V68, pS266, DOI 10.1016/j.theriogenology.2007.04.034 von Mering C, 2005, NUCLEIC ACIDS RES, V33, pD433, DOI 10.1093/nar/gki005 von Mering C, 2007, NUCLEIC ACIDS RES, V35, pD358, DOI 10.1093/nar/gkl825 Walf AA, 2006, NEUROPSYCHOPHARMACOL, V31, P1097, DOI 10.1038/sj.npp Walsh SW, 2011, ANIM REPROD SCI, V123, P127, DOI 10.1016/j.anireprosci.2010.12.001 NR 46 TC 2 Z9 3 PU AMER PHYSIOLOGICAL SOC PI BETHESDA PA 9650 ROCKVILLE PIKE, BETHESDA, MD 20814 USA SN 1094-8341 J9 PHYSIOL GENOMICS JI Physiol. Genomics PD MAY PY 2013 VL 45 IS 10 BP 400 EP 406 DI 10.1152/physiolgenomics.00172.2012 PG 7 WC Cell Biology; Genetics & Heredity; Physiology SC Cell Biology; Genetics & Heredity; Physiology GA 146RF UT WOS:000319107500003 PM 23572538 ER PT J AU Zhang, J Wang, Y Vassileva, J AF Zhang, Jie Wang, Yuan Vassileva, Julita TI SocConnect: A personalized social network aggregator and recommender SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Social networking site; Social network aggregator; Personalized recommendation of activities AB Users of Social Networking Sites (SNSs) like Facebook, LinkedIn or Twitter, are facing two problems: (1) it is difficult for them to keep track of their social friendships and friends' social activities scattered across different SNSs; and (2) they are often overwhelmed by the huge amount of social data (friends' updates and other activities). To address these two problems, we propose a user-centric system called "SocConnect" (Social Connect) for aggregating social data from different SNSs and allowing users to create personalized social and semantic contexts for their social data. Users can blend and group friends on different SNSs, and rate the friends and their activities as favourite, neutral or disliked. Soc-Connect then provides personalized recommendation of friends' activities that may be interesting to each user, using machine learning techniques. A prototype is also implemented to demonstrate these functionalities of SocConnect. Evaluation on real users confirms that users generally like the proposed functionalities of our system, and machine learning can be effectively applied to provide personalized recommendation of friends' activities and help users deal with cognitive overload. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Zhang, Jie] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. [Wang, Yuan; Vassileva, Julita] Univ Saskatchewan, Dept Comp Sci, Saskatoon, SK S7N 0W0, Canada. RP Zhang, J (reprint author), Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. EM yuw193@mail.usask.ca; jiv@cs.usask.ca RI Zhang, Jie/A-3737-2011 CR Bojars Uldis, 2008, P WORKSH SOC ASP WEB Carmagnola Francesca, 2009, P 17 INT C US MOD AD Chen JL, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1185 Chisari Michael, 2009, P W3C WORKSH FUT SOC Claypool M., 2000, INTELLIGENT USER INT, P33 Ereteo G., 2009, P W3C WORKSH FUT SOC Heckmann D., 2007, P INT WORKSH CONT ON Lampe C, 2006, P ACM SPEC INT GROUP Lancaster F. W., 1973, INFORM RETRIEVAL ON Mitchell TM, 1997, MACHINE LEARNING Platt J.C., 1999, ADV KERNEL METHODS S Resnick P., 1994, P ACM C COMP SUPP CO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shih C, 2009, FACEBOOK ERA TAPPING van Rijsbergen C. J., 1979, INFORM RETRIEVAL Webster A, 2006, LECT NOTES COMPUT SC, V4018, P223 Yeung C.A., 2009, P W3C WORKSH FUT SOC NR 17 TC 5 Z9 5 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2013 VL 49 IS 3 BP 721 EP 737 DI 10.1016/j.ipm.2012.07.006 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 125HC UT WOS:000317529900011 ER PT J AU Sbattella, L Tedesco, R AF Sbattella, Licia Tedesco, Roberto TI A novel semantic information retrieval system based on a three-level domain model SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE HMM; MaxEnt; Ontology; WordNet; Semantic information retrieval; Word sense disambiguation ID ONTOLOGY; EXTRACTION; KAPPA; WEB AB This paper presents a methodology and a prototype for extracting and indexing knowledge from natural language documents. The underlying domain model relies on a conceptual level (described by means of a domain ontology), which represents the domain knowledge, and a lexical level (based on WordNet), which represents the domain vocabulary. A stochastic model (the ME-2L-HMM2, which mixes - in a novel way - HMM and maximum entropy models) stores the mapping between such levels, taking into account the linguistic context of words. Not only does such a context contain the surrounding words; it also contains morphologic and syntactic information extracted using natural language processing tools. The stochastic model is then used, during the document indexing phase, to disambiguate word meanings. The semantic information retrieval engine we developed supports simple keyword-based queries, as well as natural language-based queries. The engine is also able to extend the domain knowledge, discovering new and relevant concepts to add to the domain model. The validation tests indicate that the system is able to disambiguate and extract concepts with good accuracy. A comparison between our prototype and a classic search engine shows that the proposed approach is effective in providing better accuracy. (c) 2013 Elsevier Inc. All rights reserved. C1 [Sbattella, Licia] Politecn Milan, Dipartimento Elettron & Informaz, I-20133 Milan, Italy. [Tedesco, Roberto] Politecn Milan, MultiChancePoliTeam, I-20133 Milan, Italy. RP Tedesco, R (reprint author), Politecn Milan, MultiChancePoliTeam, Pzza Leonardo da Vinci 32, I-20133 Milan, Italy. EM licia.sbattella@polimi.it; roberto.tedesco@polimi.it CR Alani H, 2003, IEEE INTELL SYST, V18, P14, DOI 10.1109/MIS.2003.1179189 Allison B, 2006, LECT NOTES ARTIF INT, V4188, P327 Anastasi G., 2012, LECT NOTES COMPUTER, V7200 Atserias Jordi, 2006, P 5 INT C LANG RES E, P2281 Aufaure M.-A., 2007, ICDIM, P321 Baader F., 2003, DESCRIPTION LOGIC HD Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Bratus S., 2009, P 3 WORKSH AN NOIS U, P101, DOI 10.1145/1568296.1568313 BRENNAN RL, 1981, EDUC PSYCHOL MEAS, V41, P687, DOI 10.1177/001316448104100307 Charniak E., 2005, P 43 ANN M ASS COMP, P173, DOI DOI 10.3115/1219840.1219862 Che-Yu Yang, 2010, Proceedings of the 2010 Sixth International Conference on Networked Computing and Advanced Information Management (NCM 2010) Croft B., 2009, SEARCH ENGINES INFOR, P301 DARROCH JN, 1972, ANN MATH STAT, V43, P1470, DOI 10.1214/aoms/1177692379 de Marneffe M., 2008, STANFORD TYPED DEPEN DellaPietra S, 1997, IEEE T PATTERN ANAL, V19, P380, DOI 10.1109/34.588021 Di Eugenio B, 2004, COMPUT LINGUIST, V30, P95, DOI 10.1162/089120104773633402 Fellbaum C., 1998, WORDNET ELETTRONIC L Ferrucci D, 2006, RC24122W0611188 IBM Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619 Francis W. N., 1979, BROWN CORPUS MANUAL Goodman J., 2002, P 40 ANN M ASS COMP, P9 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989 He Y, 1988, 9 INT C PATT REC, V2, P718 JAYNES ET, 1968, IEEE T SYST SCI CYB, VSSC4, P227, DOI 10.1109/TSSC.1968.300117 Jelinek F., 1969, IBM Journal of Research and Development, V13 Jurafsky D., 2000, SPEECH LANGUAGE PROC, P254 Kashyap V., 2008, ONTOLOGY AUTHORING M Knublauch H., 2004, P INT WORKSH DESCR L Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Lafferty J.D., 2001, IEEE INT C MACH LEAR, V18, P282 Long Qiu M.-Y.K., 2004, P 4 INT C LANG RES E, V1, P291 Manning C., 1999, FDN STAT NATURAL LAN, P591 Manning C., 1999, FDN STAT NATURAL LAN, P589 Manning Christopher D., 2003, ADV NEURAL INFORM PR, V15 McCallum A., 2000, P 17 INT C MACH LEAR, P591 McCandless M., 2010, LUCENE ACTION, V2nd Montedoro M., 2012, LECT NOTES COMPUTER, V7200, P175 Nadeau D., 2007, LINGVISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Rabiner L. R., 1986, IEEE ASSP Magazine, V3, DOI 10.1109/MASSP.1986.1165342 Randolph J.J., 2005, P JOENS U LEARN INST Ratinov L., 2009, P 13 C COMP NAT LANG, P147, DOI 10.3115/1596374.1596399 Ratnaparkhi A., 1994, P INT C SPOK LANG PR, P803 Ratnaparkhi Adwait, 1997, SIMPLE INTRO MAXIMUM Rinaldi AM, 2009, ACM T INTERNET TECHN, V9, DOI 10.1145/1552291.1552293 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sbattella L., 2012, LECT NOTES COMPUTER, V7200, P193 Schmid H., 1994, P INT C NEW METH LAN Shieber S.M., 1986, CSLI LECT NOTES SERI, V4 Shieber Stuart M., 1984, P 10 INT C COMP LING, P362 Siegel S., 1998, NONPARAMETRIC STAT B Smith M.K., 2004, OWL WEB ONTOLOGY LAN Toutanova K., 2003, P 2003 C N AM CHAPT, V1, P173, DOI DOI 10.3115/1073445.1073478 Wallach H. M., 2004, MSCIS0421 U PENNS Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 NR 57 TC 2 Z9 2 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 J9 J SYST SOFTWARE JI J. Syst. Softw. PD MAY PY 2013 VL 86 IS 5 BP 1426 EP 1452 DI 10.1016/j.jss.2013.01.029 PG 27 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 122MO UT WOS:000317322400019 ER PT J AU Liu, NN Dellandrea, E Chen, LM Zhu, C Zhang, Y Bichot, CE Bres, S Tellez, B AF Liu, Ningning Dellandrea, Emmanuel Chen, Liming Zhu, Chao Zhang, Yu Bichot, Charles-Edmond Bres, Stephane Tellez, Bruno TI Multimodal recognition of visual concepts using histograms of textual concepts and selective weighted late fusion Scheme SO COMPUTER VISION AND IMAGE UNDERSTANDING LA English DT Article DE Image classification; Textual feature; Visual feature; Fusion; ImageCLEF photo annotation ID INFORMATION-RETRIEVAL; CLASSIFICATION; FEATURES; MODEL; CLASSIFIERS; PICTURES; SCALE AB The text associated with images provides valuable semantic meanings about image content that can hardly be described by low-level visual features. In this paper, we propose a novel multimodal approach to automatically predict the visual concepts of images through an effective fusion of textual features along with visual ones. In contrast to the classical Bag-of-Words approach which simply relies on term frequencies, we propose a novel textual descriptor, namely the Histogram of Textual Concepts CHIC), which accounts for the relatedness of semantic concepts in accumulating the contributions of words from the image caption toward a dictionary. In addition to the popular SIFT-like features, we also evaluate a set of mid-level visual features, aiming at characterizing the harmony, dynamism and aesthetic quality of visual content, in relationship with affective concepts. Finally, a novel selective weighted late fusion (SWLF) scheme is proposed to automatically select and weight the scores from the best features according to the concept to be classified. This scheme proves particularly useful for the image annotation task with a multi-label scenario. Extensive experiments were carried out on the MIR FLICKR image collection within the ImageCLEF 2011 photo annotation challenge. Our best model, which is a late fusion of textual and visual features, achieved a MiAP (Mean interpolated Average Precision) of 43.69% and ranked 2nd out of 79 runs. We also provide comprehensive analysis of the experimental results and give some insights for future improvements. (C) 2012 Elsevier Inc. All rights reserved. C1 [Liu, Ningning; Dellandrea, Emmanuel; Chen, Liming; Zhu, Chao; Zhang, Yu; Bichot, Charles-Edmond; Bres, Stephane; Tellez, Bruno] Univ Lyon, CNRS, Lyon, France. [Liu, Ningning; Dellandrea, Emmanuel; Chen, Liming; Zhu, Chao; Zhang, Yu; Bichot, Charles-Edmond] Ecole Cent Lyon, LIRIS, UMR 5205, F-69134 Lyon, France. [Bres, Stephane] Inst Natl Sci Appl, LIRIS, UMR 5205, F-69621 Villeurbanne, France. [Tellez, Bruno] Univ Lyon 1, LIRIS, UMR 5205, F-69622 Villeurbanne, France. RP Liu, NN (reprint author), Univ Lyon, CNRS, Lyon, France. EM ningning.liu@ec-lyon.fr; emmanuel.dellandrea@ec-lyon.fr; liming.chen@ec-lyon.fr; chao.zhu@ec-lyon.fr; yu.zhang@ec-lyon.fr; Charles-Edmond.Bichot@ec-lyon.fr; stephane.bres@insa-lyon.fr; bruno.tellez@univ-lyon1.fr FU French research agency ANR [2009 CORD 026 02] FX This work was supported in part by the French research agency ANR through the VideoSense Project under the Grant 2009 CORD 026 02. CR Ah-Pine J, 2009, MULTIMED TOOLS APPL, V42, P31, DOI 10.1007/s11042-008-0246-8 Anstey N. A., 1966, CAN J EXPLOR GEOPHYS, V2, P55 Ben Amar C, 2010, 3D DATA PROCESSING V Binder A., 2011, CLEF WORKSH Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bradley M. M., 1999, AFFECTIVE NORMS ENGL BRADLEY MM, 1994, J BEHAV THER EXP PSY, V25, P49, DOI 10.1016/0005-7916(94)90063-9 Breiman L, 1996, MACH LEARN, V24, P123, DOI 10.1023/A:1018054314350 Budanitsky A., 2001, WORKSH WORDNET OTH L Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Colombo C, 1999, IEEE MULTIMEDIA, V6, P38, DOI 10.1109/93.790610 Csurka G., 2004, WORKSH STAT LEARN CO, V1, P1 Dalai N., 2005, INT C COMP VIS PATT, V1, P886, DOI DOI 10.1109/CVPR.2005.177 Datta R., 2005, MULTIMEDIA INFORM RE, P253 Dellandrea E, 2010, INT WORKSH CONT BAS, P99 DUDA RO, 1972, COMMUN ACM, V15, P11, DOI 10.1145/361237.361242 Dumais S.T., 2005, LATENT SEMANTIC ANAL Dunker P., 2008, MULTIMEDIA INFORM RE, P97 Escalante HJ, 2011, INFORM RETRIEVAL, V15, P1 Everingham M, 2010, INT J COMPUT VISION, V88, P303, DOI 10.1007/s11263-009-0275-4 Fishbein JM, 2008, LECT NOTES COMPUT SC, V4956, P514 Guillaumin M, 2010, PROC CVPR IEEE, P902, DOI 10.1109/CVPR.2010.5540120 HARALICK RM, 1979, P IEEE, V67, P786, DOI 10.1109/PROC.1979.11328 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Huiskes M J, 2010, MULTIMEDIA INFORM RE, P527, DOI DOI 10.1145/1743384.1743475 Huiskes M. J., 2008, MIR 08, P39 Itten J., 1961, ART COLOR SUBJECTIVE Ke Y, 2006, IEEE COMP SOC C COMP, V1, P419 Kittler J, 1998, IEEE T PATTERN ANAL, V20, P226, DOI 10.1109/34.667881 Lavelli A, 2004, P 13 ACM INT C INF K, P615, DOI 10.1145/1031171.1031284 Lazebnik S., 2006, CVPR, V2, P2169, DOI DOI 10.1109/CVPR.2006.68 Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Li Fei-Fei, 2005, P 2005 IEEE COMP SOC, V2, P524 Li J, 2003, IEEE T PATTERN ANAL, V25, P1075 Liu N., 2011, ACII, P195 Liu N., 2011, INT C COMP VIS THEOR Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Machajdik J., 2010, ACM MULTIMEDIA, P83 Mensink T., 2010, CLEF WORKSH Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mojsilovic A, 2004, INT J COMPUT VISION, V56, P79, DOI 10.1023/B:VISI.0000004833.39906.33 Moschitti A, 2004, LECT NOTES COMPUT SC, V2997, P181 Nowak S., 2011, CLEF WORKSH Nowak S., 2010, CLEF WORKSH Ojala T, 1996, PATTERN RECOGN, V29, P51, DOI 10.1016/0031-3203(95)00067-4 Parshin V, 2006, LECT NOTES COMPUT SC, V3736, P279 PUDIL P, 1994, PATTERN RECOGN LETT, V15, P1119, DOI 10.1016/0167-8655(94)90127-9 Pujol A., 2007, INT C VIS IM IM PROC, P201 Rokach L, 2010, ARTIF INTELL REV, V33, P1, DOI 10.1007/s10462-009-9124-7 RUSSELL JA, 1980, J PERS SOC PSYCHOL, V39, P1161, DOI 10.1037/h0077714 Sahlgren M., 2004, P 20 INT C COMP LING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scherer K. R., 2001, APPRAISAL PROCESSES Schwab D., 2002, P 19 INT C COMP LING, V1, P1, DOI 10.3115/1072228.1072289 Sivic J., 2003, P 9 IEEE C COMP VIS, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663 Smeaton A. F., 2006, MIR 06, P321, DOI DOI 10.1145/1178677.1178722 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Snoek C., 2005, P 13 ANN ACM INT C M, P399, DOI 10.1145/1101149.1101236 Snoek CGM, 2004, P TRECVID WORKSH TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999 Tola E, 2010, IEEE T PATTERN ANAL, V32, P815, DOI 10.1109/TPAMI.2009.77 Tseng B., 2003, ICIP, V2, P535 VALDEZ P, 1994, J EXP PSYCHOL GEN, V123, P394, DOI 10.1037/0096-3445.123.4.394 van de Sande K, COLORDESCRIPTOR SOFT van de Sande KEA, 2010, IEEE T PATTERN ANAL, V32, P1582, DOI 10.1109/TPAMI.2009.154 Vapnik V., 1995, NATURE STAT LEARNING Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb Wang G, 2009, PROC CVPR IEEE, P1367 Westerveld T, 2003, EURASIP J APPL SIG P, V2003, P186, DOI 10.1155/S111086570321101X Worring M., 2006, P 14 ANN ACM INT C M, P785, DOI 10.1145/1180639.1180808 Wu Y., 2004, P 12 ANN ACM INT C M, P572, DOI 10.1145/1027527.1027665 Zhang J, 2007, INT J COMPUT VISION, V73, P213, DOI 10.1007/s11263-006-9794-4 Zhu C., 2010, INT C PATT REC ICPR, P3065 Zhu C, 2011, IEEE T SYST MAN CY A, V41, P569, DOI 10.1109/TSMCA.2010.2093883 Znaidia A., 2011, CLEF WORKSH NR 76 TC 3 Z9 3 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1077-3142 J9 COMPUT VIS IMAGE UND JI Comput. Vis. Image Underst. PD MAY PY 2013 VL 117 IS 5 BP 493 EP 512 DI 10.1016/j.cviu.2012.10.009 PG 20 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 115UD UT WOS:000316836500005 ER PT J AU Memar, S Affendey, LS Mustapha, N Doraisamy, SC Ektefa, M AF Memar, Sara Affendey, Lilly Suriani Mustapha, Norwati Doraisamy, Shyamala C. Ektefa, Mohammadreza TI An integrated semantic-based approach in concept based video retrieval SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Video retrieval; Semantic knowledge; Content-based analysis; Similarity; Search ID IMAGE; SYSTEM; MODEL; WEB AB Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP). C1 [Memar, Sara; Affendey, Lilly Suriani; Mustapha, Norwati; Doraisamy, Shyamala C.; Ektefa, Mohammadreza] Univ Putra Malaysia, Dept Comp Sci, Serdang 43400, Selangor, Malaysia. RP Memar, S (reprint author), Univ Putra Malaysia, Dept Comp Sci, Serdang 43400, Selangor, Malaysia. EM sr.memar@gmail.com CR [Anonymous], 2005, GUIDELINES TRECVID 2 Aytar Y, 2008, IEEE C COMP VIS PATT Bach JR, 1996, P SOC PHOTO-OPT INS, V2670, P76, DOI 10.1117/12.234785 BLAIR DC, 1979, J AM SOC INFORM SCI, V30, P374, DOI 10.1002/asi.4630300621 Campbell M, 2006, TRECVID WORKSH Chang SF, 2006, NIST TRECVID WORKSH Coelho TAS, 2004, IEEE T KNOWL DATA EN, V16, P408, DOI 10.1109/TKDE.2004.1269666 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 Frankel C., 1996, WEBSEER IMAGE SEARCH Frawley William, 1992, LINGUISTIC SEMANTICS Hatzivassiloglou V, 1999, DETECTING TEXT SIMIL Haubold A, 2008, P INT C IM VID RETR, P437, DOI 10.1145/1386352.1386408 Hauptmann A, 2007, IEEE T MULTIMEDIA, V9, P958, DOI 10.1109/TMM.2007.900150 Hauptmann AG, 2008, P IEEE, V96, P602, DOI 10.1109/JPROC.2008.916355 Hopfgartner F, 2008, PVLDB, V1, P1604, DOI 10.1145/1454159.1454233 Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 Jiang Y. G., 2007, P 6 ACM INT C IM VID, P494, DOI DOI 10.1145/1282280.1282352 Jiang Y.-G., 2009, P 17 ACM INT C MULT, P155, DOI DOI 10.1145/1631272.1631296 Jones K. S., 1997, READINGS INFORM RETR Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 Lin C.-Y., 2003, P 2003 C N AM CHAPT, V1, P71, DOI 10.3115/1073445.1073465 Liu T, 2005, LECT NOTES COMPUT SC, V3644, P456 Markkula M., 2000, Information Retrieval, V1, DOI 10.1023/A:1009995816485 Mihalcea R., 2006, AAAI 06, V1, P775 Nowak S, 2010, P CIVR, P303, DOI 10.1145/1816041.1816086 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Park EK, 2005, INFORM PROCESS MANAG, V41, P1207, DOI 10.1016/j.ipm.2004.08.002 Pedersen T, 2004, WORDNET SIMILARITY M, V04, P1024 Radev DR, 2004, J ARTIF INTELL RES, V22, P2004 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rodden K, 2001, P SIGCHI C HUM FACT, P190, DOI DOI 10.1145/365024.365097 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smeaton AF, 2007, INFORM SYST, V32, P545, DOI 10.1016/j.is.2006.09.001 Smith JR, 1997, IEEE MULTIMEDIA, V4, P12, DOI 10.1109/93.621578 Snoek C.G.M., 2007, IEEE T MULTIMEDIA, V9 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Wang D., 2007, P ACM INT C MULT AUG, P285, DOI DOI 10.1145/1291233.1291293 Yanagawa A., 2007, COLUMBIA U BASELINE NR 41 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD MAY PY 2013 VL 64 IS 1 BP 77 EP 95 DI 10.1007/s11042-011-0848-4 PG 19 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 114SU UT WOS:000316763600005 ER PT J AU Yu, WD Hsu, JY AF Yu, Wen-der Hsu, Jia-yang TI Content-based text mining technique for retrieval of CAD documents SO AUTOMATION IN CONSTRUCTION LA English DT Article DE CAD; Text mining; Information retrieval; Characteristic document; Construction engineering ID CONSTRUCTION MANAGEMENT; CLASSIFICATION; INFORMATION; PROJECT AB The computer aided design (CAD) document provides an effective communication medium, a legal contract document, and a reusable design case for a construction project. Due to technological advancements in CAD industry, the volume of CAD documents has been increased dramatically in the database of construction organizations. Traditional retrieval methods relied on textual naming and indexing schemes that require the designers (engineers and architects) to memorize in details the meta-information used to characterize the drawings. Such approaches easily overwhelmed the users' memory capability and thus caused low reusability of CAD documents. In this paper, a content-based text mining technique is adopted to extract the textual content of a CAD document into a characteristic document (CD), which can be retrieved with similarity matching using a Vector Space Model (VSM), so that the automated and expedited retrievals of CAD documents from vast CAD databases become possible. A prototype system, namely Content-based CAD document Retrieval System (CCRS), is developed to implement the proposed method. After preliminary testing with a CAD database with 2094 Chinese annotated CAD drawings collected from two real-world construction projects and a public engineering drawing database, the proposed CCRS is proven to retrieve all relevant CAD documents with relatively high precision when appropriate query is specified. Finally, three search strategies are recommended for the users to narrow down search scope while a target CAD document is desired. It is concluded that the proposed content-based text mining approach provides a promising solution to improve the current difficulty encountered in retrieval and reusability of vast CAD documents for the construction industry. (C) 2012 Elsevier B.V. All rights reserved. C1 [Yu, Wen-der; Hsu, Jia-yang] Chung Hua Univ, Dept Construct Management, Hsinchu 300, Taiwan. RP Yu, WD (reprint author), Chung Hua Univ, Dept Construct Management, Hsinchu 300, Taiwan. EM wenderyu@chu.edu.tw CR Bechtel, 1994, BECHT LIN REF MAN Berchtold S., 1997, P INT C MAN DAT SIGM Brilakis I, 2005, AUTOMAT CONSTR, V14, P537, DOI 10.1016/j.autcon.2004.11.003 Caldas CH, 2002, J COMPUT CIVIL ENG, V16, P234, DOI 10.1061/(ASCE)0887-3801(2002)16:4(234) Caldas CH, 2003, AUTOMAT CONSTR, V12, P395, DOI 10.1016/S0926-5805(03)00004-9 Cao Y, 2002, LECT NOTES COMPUT SC, V2480, P360 Chang S.K., 1999, CONTENT BASED MULTIM Dorre J., 1999, P 5 ACM SIGKDD INT C, V1, P398 Eastman C.M., 2011, BIM HDB GUIDE BUILDI, P3 Feldman R., 1995, P 1 INT C KNOWL DISC, V1, P112 Fonseca MJ, 2003, COMPUT GRAPH-UK, V27, P61, DOI 10.1016/S0097-8493(02)00244-3 Gross M., 1996, P C HUM FACT COMP SY, P5 Hajjar D, 2000, J COMPUT CIVIL ENG, V14, P70, DOI 10.1061/(ASCE)0887-3801(2000)14:1(70) Hendrickson C., 2008, PROJECT MANAGEMENT C, V2.2 Kartam N., 1994, P COMP CIV ENG NEW Y, V2, P1139 Kartam N., 1997, J PERFORM CONSTR FAC, V11, P178, DOI 10.1061/(ASCE)0887-3828(1997)11:4(178) Kupiec J., 1995, P 18 ANN INT ACM SIG, V18, P68 Loss J., 1987, J PERFORMANCE CONSTR, V1, P11 Mao W., 2006, DATA KHOWL ENG, V61, P76 Park JH, 1999, PATTERN RECOGN LETT, V20, P591, DOI 10.1016/S0167-8655(99)00022-7 Pullwitt D, 2002, NEURAL NETWORKS, V15, P1099, DOI 10.1016/S0893-6080(02)00082-5 Rui Y., 1997, J VISUAL COMMUNICATI, V10, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soibelman L, 2008, ADV ENG INFORM, V22, P15, DOI 10.1016/j.aei.2007.08.011 Sullivan D., 2001, DOCUMENT WAREHOUSING Ting L.M., 2012, TECHNICAL DOCUMENT K Wang HJ, 2004, AUTOMAT CONSTR, V13, P575, DOI 10.1016/j.autcon.2004.04.003 Wu J.W., 2007, P 2007 WORLD C ED MU NR 28 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0926-5805 J9 AUTOMAT CONSTR JI Autom. Constr. PD MAY PY 2013 VL 31 BP 65 EP 74 DI 10.1016/j.autcon.2012.11.037 PG 10 WC Construction & Building Technology; Engineering, Civil SC Construction & Building Technology; Engineering GA 103QD UT WOS:000315933100007 ER PT J AU Pantazoglou, M Tsalgatidou, A AF Pantazoglou, Michael Tsalgatidou, Aphrodite TI A Generic Query Model for the Unified Discovery of Heterogeneous Services SO IEEE TRANSACTIONS ON SERVICES COMPUTING LA English DT Article DE Services discovery process and methodology; web services interoperability; metadata of services interfaces ID ARCHITECTURE; SYSTEMS AB In this paper, we propose Proteus, a generic query model for the discovery of operations offered by heterogeneous services. We demonstrate the need for such a model, and show how it unifies the task of service discovery through abstractions, which allow for the technology-independent formulation of service advertisements, queries, and query responses. On top of these generic elements, we build an intuitive, fuzzy-based query evaluation mechanism that supports the service matchmaking process by employing and appropriately combining existing similarity metrics. Thanks to the generality of Proteus, it is possible to seamlessly accommodate the discovery of operations provided by various types of services without the need of changing the existing service infrastructure. Thus, our approach is applicable to a variety of settings ranging from traditional web services to service-oriented grids, peer-to-peer networks, geospatial information systems, and so on. Overall, compared to the existing query models supported by standard service discovery technologies, our approach is marked by openness, flexibility, and improved performance in terms of precision and recall. The feasibility and efficiency of Proteus are verified by a series of experiments. C1 [Pantazoglou, Michael; Tsalgatidou, Aphrodite] Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece. RP Pantazoglou, M (reprint author), Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece. EM michaelp@di.uoa.gr; atsalga@di.uoa.gr CR Al-Ali RJ, 2002, COMPUT INFORM, V21, P363 Athanasopoulos G., 2006, P IEEE INT C SERV CO, P174 Athanasopoulos G., 2010, P 5 INT C SOFTW DAT Botts M, 2008, LECT NOTES COMPUT SC, V4540, P175 Christensen Erik, 2001, WEB SERVICE DESCRIPT Cross V., 2004, P 2004 ANN M N AM FU, V2, P635 Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 Elenius D, 2004, P 1 INT WORKSH PEER Farrell J., 2007, SEMANTIC ANNOTATIONS Fellbaum C, 1998, WORDNET ELECT LEXICA Fielding R., 2000, THESIS U CALIFORNIA Foster I, 2005, LECT NOTES COMPUT SC, V3779, P2 Foster I., 2002, TECHNICAL REPORT GALE D, 1962, AM MATH MON, V69, P9, DOI 10.2307/2312726 Gong L, 2001, IEEE INTERNET COMPUT, V5, P88 Hadley M., 2009, WEB APPL DESCRIPTION Int'l Organization for Standardization, 2004, 1500032004 ISOTS Jin B., 2007, P 6 IEEE C GRID COOP, P203 Klusch M., 2006, P 5 INT JOINT C AUT, P915, DOI DOI 10.1145/1160633.1160796 Kluscha Matthias, 2009, Web Intelligence and Agent Systems, V7, DOI 10.3233/WIA-2009-0153 Kokash N, 2006, FR ART INT, V142, P220 Kona S, 2009, INT J WEB SERV RES, V6, P20, DOI 10.4018/jwsr.2009010102 Kopecky J., 2008, UDDI VERSION 3 02, V1, P619 Kozlenkov A, 2007, INT J WEB SERV RES, V4, P82, DOI 10.4018/jwsr.2007040104 Kuhn H. W., 1955, NAV RES LOG, V2, P83, DOI DOI 10.1002/NAV.3800020109 Lampe Ulrich, 2010, P 5 INT WORKSH ENH W, P10 Laure E., 2006, COMPUTATIONAL METHOD, V12, P33 Limam N, 2007, COMPUT COMMUN, V30, P546, DOI 10.1016/j.comcom.2005.11.017 Ludascher B, 2006, CONCURR COMP-PRACT E, V18, P1039, DOI 10.1002/cpe.994 Madhavan J., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Martin David, 2004, OWL S SEMANTIC MARKU Open Geospatial Consortium Inc, 2010, SENS OBS SERV 1 0 0 Pautasso C., 2004, P 5 VLDB WORKSH TECH, P39 Roelleke T., 2008, P 31 ANN INT ACM SIG, P435, DOI 10.1145/1390334.1390409 Roman D, 2005, APPL ONTOL, V1, P77 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Skoutas D, 2010, IEEE T SERV COMPUT, V3, P163, DOI 10.1109/TSC.2010.14 Srinivasan N, 2005, LECT NOTES COMPUT SC, V3387, P96 Taylor I., 2003, J GRID COMPUT, V1, P199, DOI 10.1023/B:GRID.0000024074.63139.ce Tsalgatidou A., 2008, INFORM SYST, P67 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vinoski S, 2008, IEEE INTERNET COMPUT, V12, P84, DOI 10.1109/MIC.2008.20 von Riegen C., 2004, UDDI VERSION 3 0 2 Yu Q., 2008, ACM T WEB, V2, P1, DOI 10.1145/1326561.1326567 NR 44 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1939-1374 J9 IEEE T SERV COMPUT JI IEEE Trans. Serv. Comput. PD APR-JUN PY 2013 VL 6 IS 2 BP 201 EP 213 DI 10.1109/TSC.2011.47 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 216AM UT WOS:000324253200007 ER PT J AU Zhu, WZ Allen, RB AF Zhu, W. Z. Allen, R. B. TI Document clustering using the LSI subspace signature model SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE text mining; automatic classification; knowledge representation ID PROBABILISTIC MODEL AB We describe the latent semantic indexing subspace signature model (LSISSM) for semantic content representation of unstructured text. Grounded on singular value decomposition, the model represents terms and documents by the distribution signatures of their statistical contribution across the top-ranking latent concept dimensions. LSISSM matches term signatures with document signatures according to their mapping coherence between latent semantic indexing (LSI) term subspace and LSI document subspace. LSISSM does feature reduction and finds a low-rank approximation of scalable and sparse term-document matrices. Experiments demonstrate that this approach significantly improves the performance of major clustering algorithms such as standard K-means and self-organizing maps compared with the vector space model and the traditional LSI model. The unique contribution ranking mechanism in LSISSM also improves the initialization of standard K-means compared with random seeding procedure, which sometimes causes low efficiency and effectiveness of clustering. A two-stage initialization strategy based on LSISSM significantly reduces the running time of standard K-means procedures. C1 [Zhu, W. Z.; Allen, R. B.] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Zhu, WZ (reprint author), Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA. EM weizz2004@yahoo.com; rba@boballen.info FU IMLS [RE-05-05-0085-05] FX This article is based on Zhu (2009). Weizhong Zhu was supported by IMLS Grant RE-05-05-0085-05. Robert B. Allen is now at the School of Information Management at Victoria University of Wellington. CR Ampazis N, 2004, NEURAL PROCESS LETT, V19, P157, DOI 10.1023/B:NEPL.0000023449.95030.8f Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027 Beil F., 2002, P 8 ACM SIGKDD INT C, P436, DOI DOI 10.1145/775047.775110 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding CHQ, 2005, J AM SOC INF SCI TEC, V56, P597, DOI 10.1002/asi.20148 Dubes R.C., 1988, ALGORITHMS CLUSTERIN Fung BCM, 2003, SIAM PROC S, P59 Gelbukh A, 2001, LECT NOTES COMPUT SC, V2004, P332 Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Koll M., 1979, ACM SIGIR FORUM, V13, P32, DOI 10.1145/1095366.1095368 Kontostathis A, 2005, STUD COMP INTELL, V6, P333 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Manning C. D., 2000, P 2000 JOINT SIGDAT, V13, P63, DOI DOI 10.3115/1117794.1117802 Manning D. C., 2008, INTRO INFORM RETRIEV Mason J.E., 2008, TEXT MIN WORKSH 2008 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Rumelhart D. E., 1990, INTRO NEURAL ELECT N, P405 Rumelhart D. E., 1986, PARALLEL DISTRIBUTED, V1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 Silic A, 2008, LECT NOTES ARTIF INT, V5177, P615, DOI 10.1007/978-3-540-85563-7_78 Steinbach M., 2000, KDD WORKSH TEXTM 200 Tougas J.E., 2006, COMPUTATIONAL STAT D, V52, P174 Tougas J.E., 2007, APPL NUMERICAL MATH, V58, P499 Toutanova K., 2003, P HLT NAACL, P252 Wang K, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P483, DOI 10.1145/319950.320054 Werbos P. J., 1994, ROOTS BACKPROPAGATIO WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 Xu W., 2004, P 27 ANN INT ACM SIG Zhu W., ACTIVE LEARNIN UNPUB Zhu W., 2009, THESIS DREXEL U Zhu WZ, 2007, COMPUT GRAPH-UK, V31, P338, DOI 10.1016/j.cag.2007.01.025 NR 38 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD APR PY 2013 VL 64 IS 4 BP 844 EP 860 DI 10.1002/asi.22623 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 204RN UT WOS:000323386300013 ER PT J AU Huang, SL Zheng, XL Kang, HX Chen, DR AF Huang, Shilin Zheng, Xiaolin Kang, Haixiao Chen, Deren TI Word sense disambiguation based on positional weighted context SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE natural language processing; similarity measurement; word sense disambiguation ID ALGORITHM; NETWORKS; ENGLISH AB Word sense disambiguation (WSD) is a key factor in solving natural language processing problems. The purpose of WSD is to make computers automatically determine the specific meaning of a word in a specific context. In this regard, state-of-art studies have focussed on the co-occurrences of words to measure context similarity. However, a problem with these approaches is that they consider all the words within a certain range to have equal influence on the ambiguous word. In this paper, we propose a position-based algorithm for measuring context similarity. By assigning positional weights to context words, we compared the context similarity between a new instance and pre-labelled instances to determine the appropriate sense of the ambiguous word. Experiments on the Senseval-2 English lexical sample task showed that our algorithm can achieve good precision and recall. Even in a minimally supervised state, it performs well with few training instances. C1 [Huang, Shilin; Zheng, Xiaolin; Kang, Haixiao; Chen, Deren] Zhejiang Univ, Hangzhou 310027, Peoples R China. RP Zheng, XL (reprint author), Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China. EM xlzheng@zju.edu.cn FU National Key Technology RD Program [2012BAH16F02]; National Natural Science Foundation of China [61003254, 60903038]; Fundamental Research Funds for the Central Universities FX This work was supported in part by the National Key Technology R&D Program (no. 2012BAH16F02), the National Natural Science Foundation of China (grant nos 61003254 and 60903038) and the Fundamental Research Funds for the Central Universities. CR Azzini A, 2009, LECT NOTES ARTIF INT, V5883, P192, DOI 10.1007/978-3-642-10291-2_20 Azzini A, 2010, ARTIFICIAL LIFE AND EVOLUTIONARY COMPUTATION, P111, DOI 10.1142/9789814287456_0010 Chan Yee Seng, 2007, P 4 INT WORKSH SEM E, P253, DOI 10.3115/1621474.1621528 Fernandez-Amoros D, 2001, P SENSEVAL 2 2 INT W, P75 Fernandez-Amoros D, 2010, LECT NOTES COMPUT SC, V6177, P60, DOI 10.1007/978-3-642-13881-2_6 Fogarolli A, 2009, 2009 IEEE THIRD INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2009), P77, DOI 10.1109/ICSC.2009.7 Gaona MAR, 2009, 2009 EIGHTH MEXICAN INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, P103, DOI 10.1109/MICAI.2009.41 John CM, 1988, 1988 ANN M INT STUD Kishida K, 2009, J INF SCI, V35, P481, DOI 10.1177/0165551509103599 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Li CL, 2011, LECT NOTES COMPUT SC, V6611, P653 Lv YH, 2009, P 32 ANN INT ACM SIG, P299 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Navigli R, 2012, LECT NOTES COMPUT SC, V7147, P115 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Pinto D, 2010, LECT NOTES COMPUT SC, V6256, P352, DOI 10.1007/978-3-642-15992-3_37 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez-de-Madariaga R, 2009, INFORM PROCESS MANAG, V45, P55, DOI 10.1016/j.ipm.2008.07.002 Tanimoto T., 1958, ELEMENTARY MATH THEO Tejada-Carcamo J, 2010, J COMPUT SCI TECH-CH, V25, P1030, DOI 10.1007/s11390-010-1081-8 Turdakov DY, 2010, PROGRAM COMPUT SOFT+, V36, P11, DOI 10.1134/S0361768810010032 Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 Yu JP, 2011, INT J INNOV COMPUT I, V7, P2345 Zavitsanos E, 2010, LECT NOTES ARTIF INT, V6040, P287 Zheng XL, 2012, J INF SCI, V38, P3, DOI 10.1177/0165551511423149 Zhong Z, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1616 NR 27 TC 2 Z9 2 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 EI 1741-6485 J9 J INF SCI JI J. Inf. Sci. PD APR PY 2013 VL 39 IS 2 BP 225 EP 237 DI 10.1177/0165551512459919 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 129SD UT WOS:000317861400006 ER PT J AU Camastra, F Ciaramella, A Staiano, A AF Camastra, Francesco Ciaramella, Angelo Staiano, Antonino TI Machine learning and soft computing for ICT security: an overview of current trends SO JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING LA English DT Article DE Machine Learning; Soft computing; ICT security; Password-based schemes for access control; Intrusion detection system; Spam filtering AB In the last years, people have been seeing the pervasive use of computer, communication technology and Internet, e. g., e-mail, online shopping, banking, gaming, Internet telephony, streaming. Unfortunately, the reliability of the Internet and its services, and in general Information and Communication Technology (ICT) devices, is undermined by insecurity issues. On the other hand, machine learning and soft computing techniques have been widely applied to disparate fields, becoming, in several cases, the leading technology. The aim of the work is to investigate the trends of the machine learning (ML) and soft computing (SC) methodologies for ICT security. In particular, it overviews ML and SC applications for three hot topics in ICT security: password-based schemes for access control, intrusion detection and spam filtering. C1 [Camastra, Francesco; Ciaramella, Angelo; Staiano, Antonino] Univ Naples Parthenope, Dept Appl Sci, Ctr Direz Isola C4, I-80143 Naples, Italy. RP Ciaramella, A (reprint author), Univ Naples Parthenope, Dept Appl Sci, Ctr Direz Isola C4, I-80143 Naples, Italy. EM camastra@ieee.org; angelo.ciaramella@ieee.org; antonino.staiano@uniparthenope.it RI Camastra, Francesco/ OI Camastra, Francesco/0000-0003-4439-7583 CR Abraham A, 2007, J NETW COMPUT APPL, V30, P81, DOI 10.1016/j.jnca.2005.06.001 Abraham A, 2005, STUD COMP INTELL, V4, P191 Al-Subaie M, 2007, IEEE ICC, P1391, DOI 10.1109/ICC.2007.234 Aradhye Hrishikesh B., 2005, P 8 INT C DOC AN REC, V2, P914 Barreno M, 2010, MACH LEARN, V81, P121, DOI 10.1007/s10994-010-5188-5 Bergadano Francesco, 1998, ACM T INFORM SYST, V1, P3, DOI 10.1145/290163.290164 Bezdek J. C., 1981, PATTERN RECOGNITION Bishop C. M., 2006, PATTERN RECOGNITION Bishop C. M., 1995, NEURAL NETWORKS PATT Blundo C, 2002, LECT NOTES COMPUT SC, V2437, P30 Blundo C, 2004, J SYST SOFTWARE, V71, P163, DOI 10.1016/S0164-1212(03)00004-9 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Castiglione A, 2011, COMPUT MATH IN PRESS Chan APF, 2005, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, P3846 Cheng E, 2005, LECT NOTES COMPUT SC, V3619, P471 Cho S, 2000, J ORG COMP ELECT COM, V10, P295, DOI 10.1207/S15327744JOCE1004_07 Ciaramella A, 2006, INT J APPROX REASON, V41, P146, DOI 10.1016/j.ijar.2005.06.016 Ciaramella A, 2006, IEEE T DEPEND SECURE, V3, P327, DOI 10.1109/TDSC.2006.53 Clark J, 2003, P IEEE WIC INT C WEB Cormen T. H., 2009, INTRO ALGORITHMS, V3rd Dainotti A, 2008, COMPUT NETW, V52, P2645, DOI 10.1016/j.comnet.2008.05.004 De Castro LN, 2002, ARTIFICIAL IMMUNE SY de Oliveira M, 2005, P SBC 2005 Debar H, 1999, COMPUT NETW, V31, P805, DOI 10.1016/S1389-1286(98)00017-6 deRu WG, 1997, IEEE EXPERT, V12, P38, DOI 10.1109/64.642960 Duda R., 2000, PATTERN CLASSIFICATI, V2nd Farid DM, 2008, P INT C COMP INF TEC, P652 Fawcett T, 2003, SIGKDD EXPLORATIONS, V5, P140 Freund Y, 1996, P 13 INT C MACH LEAR, P138 Fumera G, 2006, J MACH LEARN RES, V7, P2699 Gabrilovich E, 2007, J MACH LEARN RES, V8, P2297 Gao DB, 2006, LECT NOTES COMPUT SC, V4219, P19 Ghosh A. K., 1999, Proceedings of the Eighth USENIX Security Symposium (Security'99) Ghosh A. K., 1998, Proceedings 14th Annual Computer Security Applications Conference (Cat. No.98EX217), DOI 10.1109/CSAC.1998.738646 Goodman J., 2007, Communications of the ACM, V50, DOI 10.1145/1216016.1216017 Goodman J, 2006, P 3 C EM ANT Gudadhe M, 2010, P INT C COMP COMM TE, P731 Guzella TS, 2009, EXPERT SYST APPL, V36, P10206, DOI 10.1016/j.eswa.2009.02.037 Haider S, 2000, IEEE SYS MAN CYBERN, P1336, DOI 10.1109/ICSMC.2000.886039 Han SJ, 2006, IEEE T SYST MAN CY B, V36, P559, DOI 10.1109/TSMCB.2005.860136 Hastie T, 2001, ELEMENTS STAT LEARNI Haykin S., 1998, NEURAL NETWORKS COMP, V2nd Hoanca B, 2006, IEEE TECHNOL SOC MAG, V25, P22, DOI 10.1109/MTAS.2006.1607720 Hofmann A, 2003, IEEE SYS MAN CYBERN, P1259 Hoglund AJ, 2000, IEEE IJCNN, P411 Hu WM, 2008, IEEE T SYST MAN CY B, V38, P577, DOI 10.1109/TSMCB.2007.914695 HUSSIEN B, 1989, PATTERN RECOGN LETT, V9, P39, DOI 10.1016/0167-8655(89)90026-3 Hyvarinen A., 2001, INDEPENDENT COMPONEN Jamuna K S, 2009, International Journal of Recent Trends in Engineering, V2 Jiang SY, 2006, PATTERN RECOGN LETT, V27, P802, DOI 10.1016/j.patrec.2005.11.007 Kang P, 2007, LECT NOTES COMPUT SC, V4642, P1203 Killourhy KS, 2009, 2009 IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS & NETWORKS (DSN 2009), P125, DOI 10.1109/DSN.2009.5270346 Kruegel C, 2004, INTRUSION DETECTION Kurose JF, 2010, COMPUTER NETWORKING, V5th Li LH, 2001, IEEE T NEURAL NETWOR, V12, P1498, DOI 10.1109/72.963786 Lin C.T., 1996, NEURAL FUZZY SYSTEMS LIN DT, 1997, P INT C NEUR NETW, V1, P174 Liu GS, 2007, NEUROCOMPUTING, V70, P1561, DOI 10.1016/j.neucom.2006.10.146 Markou M, 2003, SIGNAL PROCESS, V83, P2481, DOI [10.1016/j.sigpro.2003.07.018, 10.1016/j.sigpro.2003.018] Mill J, 2004, IEEE INT CONF FUZZY, P407 Mitchell M., 1996, INTRO GENETIC ALGORI Mukkamala S, 2002, IEEE IJCNN, P1702, DOI 10.1109/IJCNN.2002.1007774 Oda T, 2003, LECT NOTES COMPUT SC, V2723, P231 Owezarski P, 2010, LECT NOTES COMPUT SC, V6074, P327 Panda Mrutyunjaya, 2009, International Journal of Recent Trends in Engineering, V1 Panda M, 2009, 2009 INTERNATIONAL CONFERENCE ON ADVANCES IN RECENT TECHNOLOGIES IN COMMUNICATION AND COMPUTING (ARTCOM 2009), P19, DOI 10.1109/ARTCom.2009.121 Patcha A, 2007, COMPUT NETW, V51, P3448, DOI 10.1016/j.comnet.2007.02.001 PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 Peddabachigari S, 2007, J NETW COMPUT APPL, V30, P114, DOI 10.1016/j.jnca.2005.06.003 Platt JC, 1999, ADVANCES IN KERNEL METHODS, P185 Porter MF, 1980, PROGRAM, V14, P77 Qiao Y, 2002, ELECTRON LETT, V38, P663, DOI 10.1049/el:20020467 Rabiner L. R., 1989, READING SPEECH RECOG, V77, P267 Rapaka A, 2003, IEEE IJCNN, P1820 Ren X, 2009, COMPUT INFORM SCI, V2, P100 Revett Kenneth, 2007, International Journal of Electronic Security and Digital Forensics, V1, DOI 10.1504/IJESDF.2007.013592 Revett K, 2005, 2005 IEEE WIC ACM IN, P56 Reyhani SZ, 2007, INT J SMART HOME, V1, P147 Ruffo G, 2005, LECT NOTES COMPUT SC, V3617, P75 Ryan J, 1998, ADV NEUR IN, V10, P943 Salem O, 2008, P 3 INT C INF COMM T, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sang Y, 2005, LNCS, V3320, P666 Sarasamma ST, 2005, IEEE T SYST MAN CY B, V35, P302, DOI 10.1109/TSMCB.2005.843274 SCHAPIRE RE, 1990, MACH LEARN, V5, P197, DOI 10.1023/A:1022648800760 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Shon T, 2007, INFORM SCIENCES, V177, P3799, DOI 10.1016/j.ins.2007.03.025 Sibai FN, 2009, PATTERN RECOGN, P666 Sibai FN, 2008, P INT C INN INF TECH, P628 Singh MK, 2009, INT J COMPUT SCI, V4, P1 Siripanwattana W, 2008, P INT MULT ENG COMP Sperotto A, 2009, LECT NOTES COMPUT SC, V5841, P164, DOI 10.1007/978-3-642-04989-7_13 Srinoy S, 2005, PROC WRLD ACAD SCI E, V9, P140 Stahl B, 2010, P 2010 INT JOINT C N, P1 Stern H, 2008, P 5 C EM ANT Suganya G, 2010, INT J COMPUT APPL, V7, P1, DOI 10.5120/1333-1788 Sung AH, 2003, 2003 SYMPOSIUM ON APPLICATIONS AND THE INTERNET, PROCEEDINGS, P209, DOI 10.1109/SAINT.2003.1183050 Sung K, 2006, LECT NOTES COMPUT SC, V3832, P654 Talbot D, 2008, TECHNOL REV, V111, P28 Tan KM, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P476, DOI 10.1109/ICNN.1995.488223 Toosi AN, 2007, COMPUT COMMUN, V30, P2201, DOI 10.1016/j.comcom.2007.05.002 Tsai CF, 2009, EXPERT SYST APPL, V36, P11994, DOI 10.1016/j.eswa.2009.05.029 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Vijaya MS, 2009, 2009 INT C ADV COMP, P401 Wang SH, 2008, IEEE T SYST MAN CY C, V38, P265, DOI 10.1109/TSMCC.2007.913901 Wang W, 2006, COMPUT SECUR, V25, P539, DOI 10.1016/j.cose.2006.05.005 Weiser M., 1991, COMPUTER 21 CENTURY, V3, P94 Wright CV, 2004, P 2004 ACM WORKSH VI, P9, DOI 10.1145/1029208.1029211 Wu C-T, 2005, P IEEE INT C IM PROC, P509, DOI DOI 10.1109/ICIP.2005.1530440 Wu SX, 2010, APPL SOFT COMPUT, V10, P1, DOI 10.1016/j.asoc.2009.06.019 Yu EZ, 2004, COMPUT SECUR, V23, P428, DOI 10.1016/j.cose.2004.02.004 ZADEH LA, 1994, COMMUN ACM, V37, P77, DOI 10.1145/175247.175255 Zhang CL, 2005, PATTERN RECOGN LETT, V26, P779, DOI 10.1016/j.aptrec.2004.09.045 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 Zhang ZH, 2004, 18TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS, VOL 1 (LONG PAPERS), PROCEEDINGS, P568 Zhao W, 2005, P INT C ACT MED TECH Zhao Y, 2006, WORLD ACAD SCI ENG T, V14 Zorkadis V, 2006, J EXP THEOR ARTIF IN, V18, P523, DOI 10.1080/09528130600975873 NR 119 TC 4 Z9 4 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1868-5137 EI 1868-5145 J9 J AMB INTEL HUM COMP JI J. Ambient Intell. Humaniz. Comput. PD APR PY 2013 VL 4 IS 2 SI SI BP 235 EP 247 DI 10.1007/s12652-011-0073-z PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA V38GN UT WOS:000209332000009 ER PT J AU Green, CD Feinerer, I Burman, JT AF Green, Christopher D. Feinerer, Ingo Burman, Jeremy T. TI BEYOND THE SCHOOLS OF PSYCHOLOGY 1: A DIGITAL ANALYSIS OF PSYCHOLOGICAL REVIEW, 1894-1903 SO JOURNAL OF THE HISTORY OF THE BEHAVIORAL SCIENCES LA English DT Review ID EPISTEMIC COMMUNITIES AB Traditionally, American psychology at the turn of the twentieth century has been framed as a competition among a number of "schools": structuralism, functionalism, behaviorism, etc. But this is only one way in which the "structure" of the discipline can be conceived. Most psychologists did not belong to a particular school, but they still worked within loose intellectual communities, and so their work was part of an implicit psychological "genre," if not a formalized "school." In this study, we began the process of discovering the underlying genres of American psychology at the turn of the twentieth century by taking the complete corpus of articles from the journal Psychological Review during the first decade of its publication and conducting a statistical analysis of the vocabularies they employed to see what clusters of articles naturally emerged. Although the traditional functionalist school was among the clusters we found, we also found distinct research traditions around the topics of color vision, spatial vision, philosophy/metatheory, and emotion. In addition, momentary clusters corresponding to important debates (e. g., the variability hypothesis) appeared during certain years, but not others. (C) 2013 Wiley Periodicals, Inc. C1 [Green, Christopher D.] York Univ, Dept Psychol, Hist & Theory Psychol Program, Toronto, ON M3J 1P3, Canada. [Feinerer, Ingo] Vienna Univ Technol, Fac Informat, Vienna, Austria. [Burman, Jeremy T.] York Univ, Hist & Theory Psychol Program, Toronto, ON M3J 1P3, Canada. RP Green, CD (reprint author), York Univ, Dept Psychol, 4700 Keele St, Toronto, ON M3J 1P3, Canada. EM christo@yorku.ca RI Burman, Jeremy/ OI Burman, Jeremy/0000-0002-4409-8260 CR Boring E. G., 1929, HIST EXPT PSYCHOL Calkins MW, 1906, PSYCHOL REV, V13, P61, DOI 10.1037/h0071994 Crane D., 1972, INVISIBLE COLL DIFFU Danziger K, 2001, PSYCHOL THOUGHT 19 C, P45 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Feinerer I., 2012, TM TEXT MINING R PAC Feinerer I, 2008, J STAT SOFTW, V25, P1 Green C. D, 2005, SCH THEIR OWN PREHIS Green C. D, 2009, HIST HUM SCI, V22, P33 HAAS PM, 1992, INT ORGAN, V46, P1 HAAS PM, 1989, INT ORGAN, V43, P377 Heidbredder E, 1933, 7 PSYCHOL Hornik K, 2012, SKMEANS SPHERICAL K Hornik K, 2012, J STAT SOFTWARE, V50 Jung K, 1999, HIST PHILOS PSYCHOL, V11, P17 Krantz D. L., 1969, SCH PSYCHOL, P1 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Manning C.D., 2008, INTRO INFORM RETRIEV Moretti Franco, 2005, GRAPHS MAPS TREES AB Murphy G, 1929, HIST INTRO PSYCHOL Neary F., 2001, PSYCHOL BRITAIN HIST, P54 Pillsbury W, 1929, HIST PSYCHOL PRICE DJD, 1965, SCIENCE, V149, P510 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sokal M. M, 1992, AM PSYCHOL ASS HIST, P43, DOI 10.1037/10111-002 Wenger E, 1998, COMMUNITIES PRACTICE Woodworth Robert Sessions, 1931, CONT SCH PSYCHOL Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 28 TC 4 Z9 4 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0022-5061 J9 J HIST BEHAV SCI JI J. Hist. Behav. Sci. PD SPR PY 2013 VL 49 IS 2 BP 167 EP 189 DI 10.1002/jhbs.21592 PG 23 WC History Of Social Sciences SC Social Sciences - Other Topics GA 116UO UT WOS:000316908700003 PM 23426740 ER PT J AU Zhu, Y Yu, J Jing, LP AF Zhu, Yan Yu, Jian Jing, Liping TI A novel semi-supervised learning framework with simultaneous text representing SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Semi-supervised learning; Term weighting; Text representation; Classifier ID CATEGORIZATION AB Text representation has received extensive attention in text mining tasks. There are various text representation models. Among them, vector space model is the most commonly used one. For vector space model, the core technique is term weighting. To date, a great deal of different term-weighting methods have been proposed, which can be divided into supervised group and unsupervised group. However, it is not advisable to use these two groups of methods directly in semi-supervised applications. In semi-supervised applications, the majority of the supervised term-weighting methods are not applicable as the label information is insufficient; meanwhile, the unsupervised term-weighting methods cannot make use of the provided category labels. Thus, a semi-supervised learning framework for iteratively revising the text representation by an EM-like strategy is proposed in this paper. Furthermore, a new supervised term-weighting method t f.sd f is proposed. T f.sd f has the ability to emphasize the importance of terms that are unevenly distributed among all the classes and weaken the importance of terms that are uniformly distributed. Experimental results on real text data show that the proposed semi-supervised learning framework with the aid of t f.sd f performs well. Also, t f.sd f is shown to be efficient for supervised learning. C1 [Zhu, Yan; Yu, Jian; Jing, Liping] Beijing Jiaotong Univ, Dept Comp Sci, Beijing, Peoples R China. [Jing, Liping] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China. RP Jing, LP (reprint author), Beijing Jiaotong Univ, Dept Comp Sci, Beijing, Peoples R China. EM lpjing@bjtu.edu.cn FU Technological Innovation Fund for Excellent Doctoral Candidates of Beijing Jiaotong University [141097522]; Fundamental Research Funds for the Central Universities [2009YJS026, 2010RC029, 2011JBM030]; National Natural Science Foundation of China [60905028, 90820013, 60875031, 61033013]; 973 project [2007CB311002]; International Joint Research Project of the Ministry of Science and Technology of China [8-06J]; SRF for ROCS, SEM FX Part of research was supported by the Technological Innovation Fund for Excellent Doctoral Candidates of Beijing Jiaotong University (141097522), the Fundamental Research Funds for the Central Universities (2009YJS026, 2010RC029, 2011JBM030), the National Natural Science Foundation of China (60905028, 90820013, 60875031, 61033013), 973 project (2007CB311002), the International Joint Research Project of the Ministry of Science and Technology of China(8-06J), and the Project sponsored by SRF for ROCS, SEM. CR Aggarwal CC, 2010, KNOWL INF SYST, V24, P171, DOI 10.1007/s10115-009-0241-z Basili R, 1999, P IJCAI Belkin M, 2006, J MACH LEARN RES, V7, P2399 Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Buckley C., 1994, P 3 TEXT RETR C TREC, P69 Chapelle O., 2006, SEMISUPERVISED LEARN CHAPELLE O, 2005, 10 INT WORKSH ART IN Chen K, 2003, P NAT LANG PROC KNOW, P650 Chen Y, 2008, P 17 ACM C INF KNOWL, P1523, DOI 10.1145/1458082.1458367 Chen YH, 2009, LECT NOTES ARTIF INT, V5781, P211 Debole F., 2003, P SAC 03 18 ACM S AP, P784 Deng ZH, 2004, LECT NOTES COMPUT SC, V3007, P588 Fujino A., 2005, P 20 NAT C ART INT, P764 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Hsu C.W., 2003, TECHNICAL REPORT Jing LP, 2010, KNOWL INF SYST, V25, P35, DOI 10.1007/s10115-009-0256-5 Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Li Y.-F., 2009, P 26 INT C MACH LEAR, P633 Miller D, 1997, P NEUR INF PROC SYST, P571 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAHSHAHANI BM, 1994, IEEE T GEOSCI REMOTE, V32, P1087, DOI 10.1109/36.312897 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 SUEN CY, 1979, IEEE T PATTERN ANAL, V1, P164 Wang F, 2008, IEEE T KNOWL DATA EN, V20, P55, DOI 10.1109/TKDE.2007.190672 Wu H., 1981, P 4 ANN INT ACM SIGI, P30, DOI 10.1145/511754.511759 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 Zhou DY, 2004, ADV NEUR IN, V16, P321 Zhou ZH, 2005, IEEE T KNOWL DATA EN, V17, P1529 Zhou ZH, 2010, KNOWL INF SYST, V24, P415, DOI 10.1007/s10115-009-0209-z Zhou Z.-H., 2007, P 22 AAAI C ART INT, P675 Zhu X, 2003, P 20 ICML Zhu X, 2008, COMPUTER SCI TECHNIC NR 40 TC 1 Z9 1 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 EI 0219-3116 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD MAR PY 2013 VL 34 IS 3 BP 547 EP 562 DI 10.1007/s10115-012-0481-1 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 090MG UT WOS:000314982600003 ER PT J AU Na, SH AF Na, Seung-Hoon TI Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Probabilistic co-relevance; Query-sensitive similarity; Inter-document similarity; Cluster hypothesis; Cluster-based retrieval ID AUTOMATIC DOCUMENT CLASSIFICATION; MODEL AB Interdocument similarities are the fundamental information source required in cluster-based retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees' nearest neighbor test (NNT). (C) 2012 Elsevier Ltd. All rights reserved. C1 Elect & Telecommun Res Inst, Taejon, South Korea. RP Na, SH (reprint author), Elect & Telecommun Res Inst, Taejon, South Korea. EM nash@etri.re.kr CR Abdul-Jaleel Nasreen, 2004, P 13 TEXT RETR C TRE Allan James, 2000, P 9 TEXT RETR C TREC Blanco R, 2008, LECT NOTES COMPUT SC, V4956, P394 Buckley C., 1994, P 3 TEXT RETR C TREC Cartright Marc-Allen, 2010, P 19 ACM INT C INF K, P1573, DOI 10.1145/1871437.1871675 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 El-Hamdouchi A, 1986, P ACM C RES DEV INF, P149, DOI 10.1145/253168.253200 Fuhr N., 2011, INFORM RETRIEVAL, V15, P93 GOFFMAN W, 1968, INFORM STORAGE RET, V4, P361, DOI 10.1016/0020-0271(68)90030-2 GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Hiemstra D., 1998, P 2 EUR C RES ADV TE, P569 Hiemstra D., 2001, THESIS U TWENTE Kurland O., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009027 Kurland Oren, 2005, P SIGIR, P306, DOI 10.1145/1076034.1076087 Lafferty J., 2003, KLUWER INT SERIES IN, V13, P258 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V., 2002, P 2 INT C HUM LANG T, P115, DOI 10.3115/1289189.1289268 Lavrenko V., 2010, GENERATIVE THEORY RE Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Losada D. E, 2008, INFORM RETRIEVAL, P11 Lv Yuanhua, 2009, P 18 ACM C INF KNOWL, DOI DOI 10.1145/1645953.1646259 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Na SH, 2007, INFORM PROCESS MANAG, V43, P302, DOI 10.1016/j.ipm.2006.07.003 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rijsbergen C. J. V., 1975, INFORM PROCESSING MA, P171 Rijsbergen C. J. V, 1971, INFORM STORAGE RETRI, V7, P210 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roelleke T., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148192 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Smucker MD, 2009, LECT NOTES COMPUT SC, V5766, P281 Smucker M.D, 2005, INVESTIGATION DIRICH Tao T., 2006, HLT NAACL 2006, P407 Tombros A, 2004, KNOWL INF SYST, V6, P617, DOI 10.1007/s10115-003-0115-8 Tombros A, 2002, INFORM PROCESS MANAG, V38, P559, DOI 10.1016/S0306-4573(01)00048-6 Tombros A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 Wei F., 2008, P 31 ANN INT ACM SIG, P283, DOI 10.1145/1390334.1390384 WILLETT P, 1985, INT FORUM INFORM DOC, V10, P28 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhou ZH, 2006, IEEE DATA MINING, P1211 NR 47 TC 1 Z9 1 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2013 VL 49 IS 2 BP 558 EP 575 DI 10.1016/j.ipm.2012.10.002 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 083FP UT WOS:000314448400011 ER PT J AU Lu, YJ Zhang, PZ Liu, JF Li, J Deng, SS AF Lu, Yingjie Zhang, Pengzhu Liu, Jingfang Li, Jia Deng, Shasha TI Health-Related Hot Topic Detection in Online Communities Using Text Clustering SO PLOS ONE LA English DT Article ID MAXIMUM-LIKELIHOOD; INCOMPLETE DATA; INTERNET; PATIENT; CANCER; WEB AB Recently, health-related social media services, especially online health communities, have rapidly emerged. Patients with various health conditions participate in online health communities to share their experiences and exchange healthcare knowledge. Exploring hot topics in online health communities helps us better understand patients' needs and interest in health-related knowledge. However, the statistical topic analysis employed in previous studies is becoming impractical for processing the rapidly increasing amount of online data. Automatic topic detection based on document clustering is an alternative approach for extracting health-related hot topics in online communities. In addition to the keyword-based features used in traditional text clustering, we integrate medical domain-specific features to represent the messages posted in online health communities. Three disease discussion boards, including boards devoted to lung cancer, breast cancer and diabetes, from an online health community are used to test the effectiveness of topic detection. Experiment results demonstrate that health-related hot topics primarily include symptoms, examinations, drugs, procedures and complications. Further analysis reveals that there also exist some significant differences among the hot topics discussed on different types of disease discussion boards. C1 [Lu, Yingjie; Zhang, Pengzhu; Liu, Jingfang; Li, Jia; Deng, Shasha] Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200030, Peoples R China. [Li, Jia] E China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China. RP Lu, YJ (reprint author), Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Shanghai 200030, Peoples R China. EM luyingjie982@163.com FU National Natural Science Foundation of China (NSFC) [71171131] FX This work was supported by National Natural Science Foundation of China (NSFC) grants 71171131. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Anderson JG, 2003, J MED SYST, V27, P67, DOI 10.1023/A:1021061229743 Bansil P, 2006, PREVENTING CHRONIC D, V3, P1 Basch EM, 2004, CANCER, V100, P2476, DOI 10.1002/cncr.20261 Brody S, 2010, AMIA 2010 ANN S, P202 Buis LR, 2011, CIN-COMPUT INFORM NU, V29, P461, DOI 10.1097/NCN.0b013e318214093b Castleton K, 2011, SUPPORT CARE CANCER, V19, P1183, DOI 10.1007/s00520-010-0935-5 Chen A. T., 2011, PATIENT EDUC COUNS, V87, P250 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Denecke K, 2009, INFORM SCIENCES, V179, P1870, DOI 10.1016/j.ins.2009.01.025 Dickerson SS, 2006, ONCOL NURS FORUM, V33, P8 Fox S., 2009, SOCIAL LIFE HLTH INF Frank E, 2005, DATA MINING PRACTICA Gerber BS, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e15 Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 HARTLEY HO, 1958, BIOMETRICS, V14, P174, DOI 10.2307/2527783 Kandula S, 2011, AMIA ANN S P, P674 Kinnane NA, 2010, SUPPORT CARE CANCER, V18, P1123, DOI 10.1007/s00520-010-0863-4 Klemm P, 1998, Oncol Nurs Forum, V25, P673 Li N, 2010, DECIS SUPPORT SYST, V48, P354, DOI 10.1016/j.dss.2009.09.003 Lin YJ, 2007, J AM MED INFORM ASSN, V14, P651, DOI 10.1197/jamia.M2215 Monnier J, 2002, CANCER PRACT, V10, P305, DOI 10.1046/j.1523-5394.2002.106005.x Patterson O., 2011, AMIA ANN S P, P1099 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz Pamela N, 2003, Clin J Oncol Nurs, V7, P663, DOI 10.1188/03.CJON.663-667 Tyson T R, 2000, J Ambul Care Manage, V23, P1 Umefjord G, 2003, J MED INTERNET RES, V5, DOI 10.2196/jmir.5.4.e26 Yang C, 2010, INTELLIGENT SYSTEMS, V27, P44 NR 27 TC 8 Z9 9 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD FEB 15 PY 2013 VL 8 IS 2 AR e56221 DI 10.1371/journal.pone.0056221 PG 9 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 099EQ UT WOS:000315603700028 PM 23457530 ER PT J AU Wang, DQ Wu, JJ Zhang, H Xu, K Lin, MX AF Wang, Deqing Wu, Junjie Zhang, Hui Xu, Ke Lin, Mengxiang TI Towards enhancing centroid classifier for text classification-A border-instance approach SO NEUROCOMPUTING LA English DT Article DE Text classification/categorization; Centroid-based classifier; Border instance; Iterative adjustment; Support vector machines (SVMs) ID SUPPORT VECTOR MACHINES; CATEGORIZATION; RETRIEVAL; SETS AB Text classification/categorization (TC) is to assign new unlabeled natural language documents to the predefined thematic categories. Centroid-based classifier (CC) has been widely used for TC because of its simplicity and efficiency. However, it has also been long criticized for its relatively low classification accuracy compared with state-of-the-art classifiers such as support vector machines (SVMs). In this paper, we find that for CC using only border instances rather than all instances to construct centroid vectors can obtain higher generalization accuracy. Along this line, we propose Border-Instance-based Iteratively Adjusted Centroid Classifier (IACC_BI), which relies on the border instances found by some routines, e.g. 1-Nearest-and-1-Furthest-Neighbors strategy, to construct centroid vectors for CC. IACC_BI then iteratively adjusts the initial centroid vectors according to the misclassified training instances. Our extensive experiments on 11 real-world text corpora demonstrate that IACC_BI improves the performance of centroid-based classifiers greatly and obtains classification accuracy competitive to the well-known SVMs, while at significantly lower computational costs. (C) 2012 Elsevier B.V. All rights reserved. C1 [Wu, Junjie] Beihang Univ, Sch Econ & Management, Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China. [Wang, Deqing; Zhang, Hui; Xu, Ke; Lin, Mengxiang] Beihang Univ, State Key Lab Software Dev Environm, Sch Comp Sci & Engn, Beijing 100191, Peoples R China. RP Wu, JJ (reprint author), Beihang Univ, Sch Econ & Management, Beijing Key Lab Emergency Support Simulat Technol, Beijing 100191, Peoples R China. EM wujj02@gmail.com RI Wang, Deqing/E-4845-2013 FU State Key Laboratory of Software Development Environment [SKLSDE-2009ZX-12]; National Natural Science Foundation of China [70901002, 71171007, 70890080, 71031001]; Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201189]; Program for New Century Excellent Talents in University [NCET-11-0778]; Ph.D. Programs Foundation of Ministry of Education of China [20091102120014] FX This research was supported by the fund of the State Key Laboratory of Software Development Environment under Grant SKLSDE-2009ZX-12. Junjie Wu was supported in part by the National Natural Science Foundation of China under Grant 70901002, 71171007, 70890080 and 71031001, the Foundation for the Author of National Excellent Doctoral Dissertation of PR China under Grant 201189, the Program for New Century Excellent Talents in University under Grant NCET-11-0778, and the Ph.D. Programs Foundation of Ministry of Education of China under Grant 20091102120014. CR Cervantes J, 2008, NEUROCOMPUTING, V71, P611, DOI 10.1016/j.neucom.2007.07.028 Chang C.-C., 2001, LIBSVM LIB SUPPORT V Chen PC, 2010, NEUROCOMPUTING, V73, P1501, DOI 10.1016/j.neucom.2009.11.005 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Czarnowski I, 2012, KNOWL INF SYST, V30, P113, DOI 10.1007/s10115-010-0375-z Czarnowski Ireneusz, 2010, International Journal of Intelligent Information and Database Systems, V4, DOI 10.1504/IJIIDS.2010.034082 Dietterich Thomas, 1998, NEURAL COMPUT, V10, P273 Guan H., 2009, P 18 INT C WORLD WID, P201, DOI 10.1145/1526709.1526737 Guan NY, 2011, IEEE T NEURAL NETWOR, V22, P1218, DOI 10.1109/TNN.2011.2157359 Han Eui-Hong, 2000, P PKDD 00 Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims T., 1998, P ECML 98 10 EUR C M, V1398, P137 Joachims T., 1997, 23 U DORTM Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Lertnattee V, 2004, INFORM SCIENCES, V158, P89, DOI 10.1016/j.ins.2003.07.007 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Li Feng, 2003, P 20 INT C MACH LEAR, P203 Li GC, 2008, LECT NOTES ARTIF INT, V5032, P203 Liu Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 McCallum A., 1998, P AAAI WORKSH LEARN Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez VD, 2003, NEUROCOMPUTING, V55, P5, DOI 10.1016/S0925-2312(03)00373-4 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shankar Shrikanth, 2000, P KDD WORKSH TEXT MI Smola AJ, 2004, STAT COMPUT, V14, P199, DOI 10.1023/B:STCO.0000035301.49549.88 SPARCKJONES K, 1972, J DOC, V28, P11 Tam V, 2002, INT C PATT RECOG, P235 Tan P. N., 2005, INTRO DATA MINING Tan SB, 2011, EXPERT SYST APPL, V38, P10264, DOI 10.1016/j.eswa.2011.02.114 Tan SB, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P398 Tan Songbo, 2008, EXPERT SYST APPL, V35, P1279 Tao DC, 2006, IEEE T PATTERN ANAL, V28, P1088 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wann M., 1990, P INT JOINT C NEUR N, P137 Wu Haoran, 2002, P 8 ACM SIGKDD INT C, P207 Wu JJ, 2010, DATA MIN KNOWL DISC, V20, P191, DOI 10.1007/s10618-009-0146-1 Xiong H, 2009, IEEE T SYST MAN CY B, V39, P318, DOI 10.1109/TSMCB.2008.2004559 Xiong Hui, 2006, P 12 ACM SIGKDD INT, P779 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Zhou Tianyi, 2011, CORROSION Zhou TY, 2011, DATA MIN KNOWL DISC, V22, P340, DOI 10.1007/s10618-010-0182-x NR 47 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD FEB 4 PY 2013 VL 101 BP 299 EP 308 DI 10.1016/j.neucom.2012.08.019 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA 052AW UT WOS:000312171100030 ER PT J AU Chen, YL Liu, YH Ho, WL AF Chen, Yen-Liang Liu, Yi-Hung Ho, Wu-Liang TI A text mining approach to assist the general public in the retrieval of legal documents SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE text mining; information retrieval ID VECTOR-SPACE MODEL; PATENT RETRIEVAL; FEATURES AB Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text-mining-based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively. C1 [Chen, Yen-Liang; Liu, Yi-Hung] Natl Cent Univ, Dept Informat Management, Jhongli 32001, Taoyuan County, Taiwan. [Ho, Wu-Liang] Straits Exchange Fdn, Dept Legal Serv, Taipei 10465, Taiwan. RP Chen, YL (reprint author), Natl Cent Univ, Dept Informat Management, 300 Jhongda Rd, Jhongli 32001, Taoyuan County, Taiwan. EM ylchen@mgt.ncu.edu.tw; lyh0315@gmail.com; alainsouchan2005@yahoo.com.tw CR Aliguliyev RM, 2010, COMPUT INTELL, V26, P420, DOI 10.1111/j.1467-8640.2010.00365.x Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bergholz Andre, 2010, Journal of Computer Security, V18, DOI 10.3233/JCS-2010-0371 Calvo R. A, 2001, P 6 AUSTR DOC COMP S CAN F, 1987, J AM SOC INFORM SCI, V38, P171, DOI 10.1002/(SICI)1097-4571(198705)38:3<171::AID-ASI6>3.0.CO;2-S Chen YL, 2011, INFORM PROCESS MANAG, V47, P309, DOI 10.1016/j.ipm.2010.06.001 Chi Jeffery Y. P., 2010, PACIS 2010 P, P706 Chou SC, 2010, LECT NOTES COMPUT SC, V6122, P113 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 CKIP, 2011, ON LIN CHIN WORDS SE CKIP, 2013, On-line Chinese words segmented service, Patent No. 7257530 Cortez P, 2010, LECT NOTES ARTIF INT, V6171, P476, DOI 10.1007/978-3-642-14400-4_37 Evangelista A., 2006, FRONTIERS UNDERGRADU Feldman R, 2007, TEXT MINING HDB ADV He YL, 2011, INFORM PROCESS MANAG, V47, P606, DOI 10.1016/j.ipm.2010.11.003 HERLOCKER JL, 1999, P INT ACM SIGIR C RE, V22, P230 Hotho A., 2005, LDV FORUM, V20, P19 Judicial Yuan, 2011, LAW REG RETR SYST JU Kang IS, 2007, INFORM PROCESS MANAG, V43, P1173, DOI 10.1016/j.ipm.2006.11.006 Kaur Jaspal, 2010, 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), DOI 10.1109/ICCAE.2010.5451344 Kawai H, 2011, IEICE T INF SYST, VE94D, P552, DOI 10.1587/transinf.E94.D.552 LexisNexis, 2012, WEB BAS LEG DAT SYST Li N, 2010, DECIS SUPPORT SYST, V48, P354, DOI 10.1016/j.dss.2009.09.003 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 Moens M.-F., 2001, Artificial Intelligence and Law, V9, DOI 10.1023/A:1011297104922 Pons-Porrata A, 2007, INFORM PROCESS MANAG, V43, P752, DOI 10.1016/j.ipm.2006.06.001 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 Trappey AJC, 2008, IND MANAGE DATA SYST, V108, P245, DOI 10.1108/02635570810847608 Udn News Net, 2011, UNION DAILY NEWS Thomaidou S., 2011, Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), DOI 10.1109/ASONAM.2011.70 Velardi P., 2008, P IEEE INT C SEM COM, P18 Wang J, 2011, MULTIMEDIA TOOLS APP, V22 Westlaw, 2012, WEB BAS LEG INF DAT Wikipedia, 2012, DEF NORM GOOGL DIST Zhang CL, 2009, J AM SOC INF SCI TEC, V60, P2474, DOI 10.1002/asi.21206 Zheng R, 2006, J AM SOC INF SCI TEC, V57, P378, DOI 10.1002/asi.20316 Zheng WH, 2002, AMIA 2002 SYMPOSIUM, PROCEEDINGS, P949 NR 42 TC 1 Z9 1 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB PY 2013 VL 64 IS 2 BP 280 EP 290 DI 10.1002/asi.22767 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 204SC UT WOS:000323388100005 ER PT J AU Wang, GA Jiao, J Abrahams, AS Fan, WG Zhang, ZJ AF Wang, G. Alan Jiao, Jian Abrahams, Alan S. Fan, Weiguo Zhang, Zhongju TI ExpertRank: A topic-aware expert finding algorithm for online knowledge communities SO DECISION SUPPORT SYSTEMS LA English DT Article DE Expert finding; Online community; Ranking; Vector space model; Social network analysis; Social media analytics ID INFORMATION-RETRIEVAL; RANKING FUNCTIONS; WEB SEARCH; NETWORKS; MODEL; DISCOVERY; CONTEXT; ORGANIZATION; CENTRALITY; FRAMEWORK AB With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate one's expertise based on either the contents of authored documents or one's social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community. We modify the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered. (C) 2012 Elsevier B.V. All rights reserved. C1 [Wang, G. Alan; Abrahams, Alan S.] Virginia Tech, Pamplin Coll Business, Dept Business Informat Technol, Blacksburg, VA 24061 USA. [Jiao, Jian] Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA. [Fan, Weiguo] Virginia Tech, Pamplin Coll Business, Dept Accounting & Informat Syst, Blacksburg, VA 24061 USA. [Zhang, Zhongju] Univ Connecticut, Operat & Informat Management Dept, Sch Business, Storrs, CT 06269 USA. [Fan, Weiguo] Zhejiang Univ Finance & Econ, Sch Informat, Hangzhou 310018, Peoples R China. RP Wang, GA (reprint author), Virginia Tech, Pamplin Coll Business, Dept Business Informat Technol, 1007 Pamplin Hall, Blacksburg, VA 24061 USA. EM alanwang@vt.edu; jjiao@vt.edu; abra@vt.edu; wfan@vt.edu; john.zhang@business.uconn.edu RI Abrahams, Alan/B-3110-2013; Wang, Alan/L-8549-2013; Fan, Weiguo/E-6343-2012 OI Abrahams, Alan/0000-0002-4884-3192; Wang, Alan/0000-0002-5026-881X; Fan, Weiguo/0000-0003-1272-5538 FU Natural Science Foundation of China [70872089, 71072129]; National Science Foundation [DUE-0840719] FX This research is partly supported by the Natural Science Foundation of China (grant #70872089 and #71072129) and the National Science Foundation (grant #DUE-0840719). CR Ackerman M., 1990, P ACM C OFF INF SYST, DOI 10.1145/91474.91485 Ackerman M. S., 1996, P ACM C COMP SUPP CO, P97, DOI 10.1145/240080.240203 Ackerman MS, 2003, SHARING EXPERTISE: BEYOND KNOWLEDGE MANAGEMENT, P159 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balog K., 2007, P 30 ANN INT ACM SIG, P551, DOI DOI 10.1145/1277741.1277836 Balog K., 2006, P 29 ANN INT ACM SIG, P50 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brown JS, 2001, ORGAN SCI, V12, P198, DOI 10.1287/orsc.12.2.198.10116 Burt R. S., 1982, STRUCTURAL THEORY AC Campbell C. S., 2003, P 12 INT C INF KNOWL CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S Constant D, 1996, ORGAN SCI, V7, P119, DOI 10.1287/orsc.7.2.119 Cross R, 2001, IEEE T SYST MAN CY C, V31, P438, DOI 10.1109/5326.983927 D'Amore R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009089 Davenport T. H., 1998, WORKING KNOWLEDGE OR Fan W, 2006, DECIS SUPPORT SYST, V42, P362, DOI 10.1016/j.dss.2005.01.007 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 Fan WG, 2005, J MANAGE INFORM SYST, V21, P37 Fan WG, 2009, DECIS SUPPORT SYST, V47, P398, DOI 10.1016/j.dss.2009.04.005 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 Fu YP, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P77 Gu B, 2007, INFORM SYST RES, V18, P68, DOI 10.1287/isre.1070.0114 Hill S, 2011, INT J ELECTRON COMM, V15, P73, DOI 10.2753/JEC1086-4415150304 Jin R., 2001, P 24 ANN INT ACM SIG, P402, DOI 10.1145/383952.384039 Kameda T, 1997, J PERS SOC PSYCHOL, V73, P296, DOI 10.1037/0022-3514.73.2.296 Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 Krulwich B., 1996, 13 NAT C ART INT POR Krulwich B., 1996, P 13 NAT C ART INT P IBARRA H, 1993, ACAD MANAGE J, V36, P471, DOI 10.2307/256589 Lederer AL, 2000, DECIS SUPPORT SYST, V29, P269, DOI 10.1016/S0167-9236(00)00076-2 Lin H., 2009, INT J E COLLAB, V5, P39, DOI 10.4018/jec.2009070103 Lin H., 2006, P 16 BIENN C INT TEL Lin Hui, 2007, P 40 HAW INT C SYST Liu XM, 2012, LECT NOTES ARTIF INT, V7390, P1 Marrocco C, 2010, LECT NOTES COMPUT SC, V6218, P650, DOI 10.1007/978-3-642-14980-1_64 Mattox D., 1999, P 8 INT C HUM COMP I, P23 McCullagh P., 1989, GEN LINEAR MODELS MORRIS PA, 1977, MANAGE SCI, V23, P679, DOI 10.1287/mnsc.23.7.679 Nambisan S, 2007, J INTERACT MARK, V21, P42, DOI 10.1002/dir.20077 Reichling T, 2009, HUM-COMPUT INT-SPRIN, P139, DOI 10.1007/978-1-84800-332-3_14 ROMNEY AK, 1986, AM ANTHROPOL, V88, P313, DOI 10.1525/aa.1986.88.2.02a00020 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Serdyukov P., 2007, 16 TEXT RETR C TREC Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9 Streeter Lynn A., 1988, P RIAO 88, P380 Sussman SW, 2003, INFORM SYST RES, V14, P47, DOI 10.1287/isre.14.1.47.14767 Tax DMJ, 1997, P WORKSH STAT PATT R Torres RD, 2009, PATTERN RECOGN, V42, P283, DOI 10.1016/j.patcog.2008.04.010 Wang G.A., 2011, P 2011 INT C INF SYS WANG L, 2011, P 34 ANN INT ACM SIG, P105 Wasko M, 2004, J ASS INFORM SYST, V5, P494 Wasko MM, 2005, MIS QUART, V29, P35 Wiertz C, 2007, ORGAN STUD, V28, P347, DOI 10.1177/0170840607076003 Wikipedia, WIK FEAT ART STAT Wu S., 2011, DATA KHOWL ENG, V71, P114 Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yimam-Seid D., 2003, EXPERT FINDING SYSTE Zhang H, 2004, LECT NOTES COMPUT SC, V3243, P92 Zhang J, 2007, AM J OBSTET GYNECOL, V197, pS45, DOI 10.1016/j.ajog.2007.10.125 Zhang J., 2007, P 16 INT WORLD WID W Zhang W., 2003, SYSTEMES INFORM MANA, V9, P81 NR 62 TC 11 Z9 11 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 EI 1873-5797 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD FEB PY 2013 VL 54 IS 3 BP 1442 EP 1451 DI 10.1016/j.dss.2012.12.020 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA 111IW UT WOS:000316516000020 ER PT J AU Price, S Flach, PA Spiegler, S Bailey, C Rogers, N AF Price, Simon Flach, Peter A. Spiegler, Sebastian Bailey, Christopher Rogers, Nikki TI SubSift web services and workflows for profiling and comparing scientists and their published works SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE LA English DT Article DE Workflows; Web services; Ranking AB Scientific researchers, laboratories, organisations and research communities can be profiled and compared by analysing their published works, including documents ranging from academic papers to web sites, blog posts and Twitter feeds. This paper describes how the vector space model from information retrieval, more normally associated with full text search, has been employed in the open source SubSift software to support workflows to profile and compare such collections of documents. SubSift was originally designed to match submitted conference or journal papers to potential peer reviewers based on the similarity between the paper's abstract and the reviewer's publications as found in online bibliographic databases such as Google Scholar. The software is implemented as a family of RESTful web services that, composed into a re-useable workflow, have already been used to support several major data mining conferences. Alternative workflows and service compositions are now enabling other interesting applications, such as expert finding for the press and media, organisational profiling, and suggesting potential interdisciplinary research partners. This work is a useful generalisation and proof-of-concept realisation of an engineering solution to enable RESTful services to be assembled in workflows to analyse general content in a way that is not immediately available elsewhere. The challenges and lessons learned in the implementation and use of SubSift are discussed. (C) 2011 Elsevier B.V. All rights reserved. C1 [Price, Simon; Bailey, Christopher; Rogers, Nikki] Univ Bristol, Inst Learning & Res Technol, Bristol BS8 1HH, Avon, England. [Price, Simon; Flach, Peter A.; Spiegler, Sebastian] Univ Bristol, Intelligent Syst Lab, Bristol BS8 1UB, Avon, England. RP Price, S (reprint author), Univ Bristol, Inst Learning & Res Technol, 8-10 Berkeley Sq, Bristol BS8 1HH, Avon, England. EM simon.price@bristol.ac.uk; peter.flach@bristol.ac.uk; sebastian.spiegler@bristol.ac.uk; c.bailey@bristol.ac.uk; nikki.rogers@bristol.ac.uk RI Price, Simon/I-8495-2014 OI Price, Simon/0000-0002-9126-1980 FU UK's Joint Information Services Council (JISC) [2009-11] FX Funded by the UK's Joint Information Services Council (JISC) under the Rapid Innovation strand of the Information Environment Programme 2009-11. CR Altintas I., 2004, SSDBM, P423 Ananiadou S, 2010, PHILOS T R SOC A, V368, P3829, DOI 10.1098/rsta.2010.0152 Bhagat J, 2010, NUCLEIC ACIDS RES, V38, P689 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 De Roure D, 2009, FUTURE GENER COMP SY, V25, P561, DOI 10.1016/j.future.2008.06.010 Ding Y., WEAR COMP SYST AS PA, P36 Elmroth E, 2010, FUTURE GENER COMP SY, V26, P245, DOI 10.1016/j.future.2009.08.011 Fielding RT, 2002, ACM T INTERNET TECHN, V2, P115, DOI DOI 10.1145/514183.514185 Flach P.A., 2009, SIGKDD EXPLORATIONS, V11, P63 Frantzi K., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900023 Gaertner T., 2004, MACH LEARN, V57, P205 Hao YN, 2010, FUTURE GENER COMP SY, V26, P1053, DOI 10.1016/j.future.2010.04.012 Hull D., 2006, NUCLEIC ACIDS RES, V34, P729, DOI DOI 10.1093/NAR/GKL320 Kano Y, 2010, BIOINFORMATICS, V26, P2486, DOI 10.1093/bioinformatics/btq464 Lathem J., 2007, INT C SEM COMP, V11, P469 Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898 Okazaki N, 2006, BIOINFORMATICS, V22, P3089, DOI 10.1093/bioinformatics/btl534 Price S., 2010, WORKSH APPL PATT AN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Sheth AP, 2007, IEEE INTERNET COMPUT, V11, P91, DOI 10.1109/MIC.2007.133 van der Aalst WMP, 2005, INFORM SYST, V30, P245, DOI 10.1016/j.is.2004.02.002 Zhuge H., 2004, FUTURE GENER COMP SY, V20, P47, DOI 10.1016/S0167-739X(03)00164-X NR 23 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-739X J9 FUTURE GENER COMP SY JI Futur. Gener. Comp. Syst. PD FEB PY 2013 VL 29 IS 2 BP 569 EP 581 DI 10.1016/j.future.2011.10.016 PG 13 WC Computer Science, Theory & Methods SC Computer Science GA 063CU UT WOS:000312974500013 ER PT J AU Cunha, E Figueira, A Mealha, O AF Cunha, Elisabete Figueira, Alvaro Mealha, Oscar BE Rocha, A Reis, LP Cota, MP Painho, M Neto, MC TI Clustering Documents Using Tagging Communities and Semantic Proximity SO PROCEEDINGS OF THE 2013 8TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2013) SE Iberian Conference on Information Systems and Technologies LA English DT Proceedings Paper CT 8th Iberian Conference on Information Systems and Technologies (CISTI) CY JUN 19-22, 2013 CL Lisboa, PORTUGAL DE clustering; effectiveness; k-means; k-Communities; communitie detection; tagging; cosine similarity AB Euclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown. C1 [Cunha, Elisabete; Figueira, Alvaro] CRACS&INESC TEC, Oporto, Portugal. RP Cunha, E (reprint author), CRACS&INESC TEC, Oporto, Portugal. EM elisabetecunha@ese.ipvc.pt; arf@dcc.fc.up.pt; oem@ua.pt CR Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027 Cravino N., 2012, P 23 ACM C HYP SOC M Cunha E., 2012, 2012 IEEE 15 INT C C Feldman R., 2007, TEXT MINING HDB ADV, P410 Fortunato S., 2009, ENCY COMPLEXITY SYST, P1141, DOI DOI 10.1007/978-0-387-30440-3_76 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Manning C. D., 2009, INTRO INFORM RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Theodoridis S., 2009, PATTERN RECOGN, P961 Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2166-0727 J9 IBER CONF INF SYST PY 2013 PG 6 WC Computer Science, Information Systems SC Computer Science GA BB7LW UT WOS:000345737600054 ER PT J AU Figueira, AD da Silva, AG de Melo, BM Lino, AD Lobato, FRL de Morais, AS Favero, EL AF Figueira, Azauri dos Santos da Silva, Adriana Gomes de Melo, Bruno Machado Lino, Adriano Del Pino Lima Lobato, Fabricio Rossy de Morais, Andrei Santos Favero, Eloi Luiz BE Rocha, A Reis, LP Cota, MP Painho, M Neto, MC TI Module of evaluation Automatic essay questions on virtual learning environment LabSQL SO PROCEEDINGS OF THE 2013 8TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2013) SE Iberian Conference on Information Systems and Technologies LA Portuguese DT Proceedings Paper CT 8th Iberian Conference on Information Systems and Technologies (CISTI) CY JUN 19-22, 2013 CL Lisboa, PORTUGAL DE evaluation; automatomatic; similarity; n-grams; statistics AB The manual correction of discursive questions is not an easy task. In educational area is seen as a process that contributes to student learning. The time spent to correct an assessment is high and the teacher cannot maintain the same level of stiffness in corrections during all his work. This article presents a new approach for automatic corrections of discursive questions proposing use of a virtual learning environment, named in this study as "LabSQL". The approach is based on the use of n-grams and functions in PHP (soundex, similar_text and srt_compare) to measure the similarity between texts, with a system accuracy of 92.15%, compared to the result of a manual work. This result is higher than similar studies that reached up 82.10% of accuracy. C1 [Figueira, Azauri dos Santos; da Silva, Adriana Gomes; de Melo, Bruno Machado; Lino, Adriano Del Pino; Lima Lobato, Fabricio Rossy] Univ Fed Oeste Para UFOPA, Inst Engn & Geociencia, Santarem, PA, Brazil. RP Figueira, AD (reprint author), Univ Fed Oeste Para UFOPA, Inst Engn & Geociencia, Santarem, PA, Brazil. EM azaurifigueira@gmail.com; adriana_g.silva@hotmail.com; eloi.favero@gmail.com CR Bezerra M. A., 2008, QUESTOES DISCURSIVAS, V30 Caldas V. M., 2009, FERRAMENTA AVALIACAO Gadotti Moacir, 2000, PERSPECTIVAS ATUAIS Inocencio A. C. G., 2012, SCATEDI SISTEMA INTE Novak D. J., 1984, LEARNING LEARN Nozawa H. E., 2006, 17 SBIE DF Orengo V. M., 2001, S STRING PROC INF RE Page E. B., P 1966 INV C TEST, P87 Perrenoud P., 1999, AVALIACAO EXCELENCIA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos T. L., 2007, AVALIACAO AUTOMATICA NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2166-0727 J9 IBER CONF INF SYST PY 2013 PG 5 WC Computer Science, Information Systems SC Computer Science GA BB7LW UT WOS:000345737600030 ER PT J AU Sallaberry, C AF Sallaberry, C TI Geographical Information Retrieval in Textual Corpora SO GEOGRAPHICAL INFORMATION RETRIEVAL IN TEXTUAL CORPORA SE Focus Series LA English DT Book ID MULTICRITERIA DECISION-MAKING; DIGITAL LIBRARIES; RANKING METHODS; SEARCH ENGINE; WEB SEARCH; DOCUMENTS; AGGREGATION; SYSTEM; RELEVANCE; MODEL CR Abolhassani M, 2003, LECT NOTES COMPUT SC, V2818, P159 AGIRRE E., 2007, LECT NOTES COMPUTER, V5152, P908 ALLEN J., 1984, ARTIF INTELL, V23, P123 ALLEN J. F., 1991, P 2 INT C PRINC KNOW ALONSO O., 2011, P 4 ACM INT C WEB SE, P1, DOI 10.1145/1935826.1935831 Alonso Omar Rogelio, 2008, THESIS U CALIFORNIA ANDOGAH G., 2010, THESIS U GRONINGEN N ARSAC J., 1985, P 1 INT C COMP ASS I Aula A, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P35 AYACHE S., 2006, TREC WORKSH VID RETR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAST H., 2012, COMPUTING RES RE ABS BATTISTELLI D., 2011, LINGUISTIQUE RECHERC BAUER G., 1985, GERMANISTISCHE, V21, P356 BAZIZ M., 2005, THESIS P SABATIER U BAZIZ M., 2007, C COMP ASS INF RETR Beard K., 1997, International Journal on Digital Libraries, V1, DOI 10.1007/s007990050011 BESANCON R., 2004, SYSTEMES RECHERCHE I, P35 BILHAUT F., 2006, THESIS U CAEN BASSE BILHAUT F., 2007, CORIA 07 ACT 4 C REC, P65 BILHAUT F., 2006, EACL 06, P95 BILHAUT F., 2003, HLT NAACL 2003, P55 BORILLO A., 1998, SPACE SON EXPRESSION BOUAMOR H., 2009, C RECH INF APPL CORI, P519 BOUGHANEM M., 2008, RECHERCHE INFORM ETA BOUYSSOU D., 1993, J MULTICRITERIA DECI, V2, P125 Brisaboa NR, 2010, GEOINFORMATICA, V14, P307, DOI 10.1007/s10707-010-0106-3 BUCHER B., 2005, ICC 05 P 22 INT CART Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 BUSCALDI D., 2009, SEBD 09 P 17 IT S AD, P205 BUSCALDI D., 2008, LECT NOTES COMPUTER, V5706, P863 BUSCALDI D., 2009, P 32 INT ACM SIGIR C, P847, DOI 10.1145/1571941.1572168 Buyukozkan G, 2010, INFORM SCIENCES, V180, P441, DOI 10.1016/j.ins.2009.09.009 CABANAC G., 2011, DOCUMENT NUMERIQUE, V14, P169 CARDOSO N., 2011, SIGSPATIAL SPECIAL, V3, P46 CHALLITA K., 2005, THESIS U TOULOUSE 3 CHAROLLES M., 2005, S EXPL MOD MEAN SEM, P1 Charolles M., 1997, CAHIER RECHERCHE LIN, V6, P1 CHEN Y.-F.R., 2007, WWW 2007, P41 Chinchor N., 1998, P 7 MESS UND C MUC 7 Chinchor N., 1997, P 7 MESS UND C MUC 7 Chrisman N. R., 1990, International Journal of Geographical Information Systems, V4, DOI 10.1080/02693799008941537 CLEVERDON C., 1963, CRANFIELD NATURE, V197, P129 Clough P., 2005, P 2005 WORKSH GEOGR, P25, DOI 10.1145/1096985.1096992 Clough PD, 2006, LECT NOTES COMPUT SC, V3936, P548 Cohn AG, 2001, FUND INFORM, V46, P1 Cohn A. G., 1996, Artificial Intelligence and Symbolic Mathematical Computation. International Conference, AISMC-3. Proceedings CROFT B., 2000, ADV INFORM RETRIEVAL Cunningham H., 2002, ACL, P168 Daoud M, 2011, J INF SCI, V37, P614, DOI 10.1177/0165551511420220 Davies J., 2009, INFORM RETRIEVAL SEA de Borda J. C., 1781, HIST ACAD ROYALE SCI DE LOUPY C., 2004, SYSTEMES RECHERCHE I, P139 Denis M, 1997, CAH PSYCHOL COGN, V16, P409 DUDOGNON D., 2010, C VEILL STRAT SCI TE Eastman CM, 2003, ACM T INFORM SYST, V21, P383, DOI 10.1145/944012.944015 EGENHOFER M.J., 1994, 9 INTERSECTION FORMA Egozi Ofer, 2011, ACM Transactions on Information Systems, V29, DOI 10.1145/1961209.1961211 EZZAT M., 2011, RECENT ADV NATURAL L, P275 Farah M, 2008, INFORM RETRIEVAL, V11, P315, DOI 10.1007/s10791-008-9046-z FARHAT J., 2004, ARGUS, V33, P23 Fernandez M, 2011, J WEB SEMANT, V9, P434, DOI 10.1016/j.websem.2010.11.003 FERNANDEZ M, 2006, EUR C IR RES ECIR 06, V3936, P553 Ferrucci D., 2004, Natural Language Engineering, DOI 10.1017/S1351324904003523 Fox E. A., 1993, TREC, P243 FREKSA C, 1992, LECT NOTES COMPUTER, V639, P162 Fu GH, 2005, LECT NOTES COMPUT SC, V3761, P1466 GAIO M., 2001, TRAITEMENTS INFORM G Gaizauskas R, 2003, COMPUT LINGUIST, V29, P328, DOI 10.1162/coli.2003.29.2.328 Galton A, 2001, TOPOI-INT REV PHILOS, V20, P173, DOI 10.1023/A:1017913008827 GAN Q., 2008, LOCWEB 08, P49 Garcia-Cumbreras MA, 2009, INFORM PROCESS MANAG, V45, P605, DOI 10.1016/j.ipm.2009.04.006 GEY F.C., 2005, LECT NOTES COMPUTER, V4022, P908 Gospodnetic O., 2005, LUCENE ACTION GRISHMAN R., 1996, ASS COMPUT LINGUIST, V1, P466 HALPIN H., 2010, P INT WORKSH EV SEM HAMDADOU D., 2009, C INT GEOM AN SPAT S Hao Q., 2010, P 19 INT C WORLD WID, P401, DOI 10.1145/1772690.1772732 HILL L., 1990, THESIS U PITTUBURGH Hill L., 2006, GEOREFERENCING GEOGR Hoffart J, 2012, ARTIF INTELL, V194, P28 Hubert G, 2011, LECT NOTES COMPUT SC, V6966, P118, DOI 10.1007/978-3-642-24469-8_14 JENSEN C.S., 2001, LECT NOTES COMPUTER, V2121 JOLIVEAU T., 2011, CARTOGRAPHICA, V46, P160 JONES C.B., 2001, COSIT, V2205/2001, P322 Jones R, 2008, INT J GEOGR INF SCI, V22, P229, DOI 10.1080/13658810701626186 KANHABUA N, 2008, ECDL 08 P 12 EUR C R, V5173, P358 Kelly D, 2007, INFORM PROCESS MANAG, V43, P30, DOI 10.1016/j.ipm.2006.03.006 KERGOSIEN E., 2009, JFO 09 ACTES 3 JOURN, P11 KERGOSIEN E., 2011, THESIS U PAU PAYS AD KEVERS L., 2011, THESIS CATHOLIC U LO Kowalski G., 1997, INFORM RETRIEVAL SYS Kowalski JM, 2007, LECT NOTES COMPUT SC, V4736, P47 Labreuche C, 2003, FUZZY SET SYST, V137, P11, DOI 10.1016/S0165-0114(02)00429-3 Larson RR, 2004, LECT NOTES COMPUT SC, V3232, P45 Larson RR, 2009, LECT NOTES COMPUT SC, V5714, P461 LARSON R.R., 1996, GIS LIB PATRONS MAPS, P81 LATIRI C.C., 2012, LECT NOTES COMPUTER, V7603, P72 LE BER F., 2007, RAISONNEMENT ESPACE LE PARC-LACAYRELLE A., 2007, DOCUMENT NUMERIQUE, V10, P129, DOI 10.3166/dn.10.2.129-148 LE Y., 2004, STUDENT POSTER SESSI Le Grand B, 2010, ADV INFORM KNOWL PRO, P1, DOI 10.1007/978-1-84996-074-8_1 Leidner J.L., 2011, SIGSPATIAL SPECIAL, V3, P5 LEIDNER J.L., 2007, THESIS U EDINBURGH S LESBEGUERIES J., 2006, SAC 06 P 2006 ACM S, P83 LESBEGUERIES J., 2007, THESIS U PAU PAYS AD Gaio M, 2008, J VISUAL LANG COMPUT, V19, P3, DOI 10.1016/j.jvlc.2007.08.010 Li D, 2010, INTELL DATA ANAL, V14, P31, DOI 10.3233/IDA-2010-0407 LIBERGE L., 2008, MUSEUMS WEB 2008 P, P163 LIEBERMAN M.D., 2007, GIS 2007, P1 Liesaputra V, 2009, LECT NOTES COMPUT SC, V5714, P442 Ligozat G, 1998, J VISUAL LANG COMPUT, V9, P23, DOI 10.1006/jvlc.1997.9999 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 Lin J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148191 LIU X., 2010, GIS 10 P 18 SIGSPATI, P528 Liu Y, 2009, INT J GEOGR INF SCI, V23, P1471, DOI 10.1080/13658810802247114 Liu Z, 2006, CONSUM COMM NETWORK, P272 Longley P A, 2005, GEOGRAPHIC INFORM SY LOUSTAU P., 2008, THESIS U PAU PAYS AD LOUSTAU P., 2008, LECT NOTES GEOINFORM, P359 MALANDAIN N., 2000, THESIS U CASEN BASSE Malczewski J, 2003, ENVIRON PLANN A, V35, P1769, DOI 10.1068/a35156 MANDL T., 2011, SIGSPATIAL SPECIAL, V3, P42 MANGUINHAS H., 2009, EPERIMETRON, V4, P9 MANI I., 2003, RANLP CURRENT ISSUES, V260, P45 MANI I., 2008, P 6 INT LANG RES EVA Manning C.D., 2008, INTRO INFORM RETRIEV MARTEL J.-M., 1999, ACTES C CORS SCRO WI, P6 Martins B, 2007, GIR 2007, P39 MARTINS B., 2008, INT C SEM COMP ICSC, P19 MARTINS B., 2008, THESIS U LISBON PORT Martins B., 2005, GIR 2005, P31 MATA F., 2011, LECT NOTES COMPUTER, V6574, P520 MAUREL D, 2011, TRAITEMENT AUTOMATIQ, V52, P69 Meij E, 2010, INFORM PROCESS MANAG, V46, P448, DOI 10.1016/j.ipm.2009.09.005 MIKHEEV A., 1999, C EUR CHAPT ASS COMP, P1 MIRON A., 2007, C INT GEOM AN SPAT S MULLER P., 2004, P 20 INT C COMP LING, P50, DOI 10.3115/1220355.1220363 NGUYEN VAN T., 2011, LEX GRAMM C LGC 2011, P1 OGILVIE P., 2001, TREC 01 P 9 TEXT RET Ohta T., 2012, P DSSD 2012 ACL 12, P27 Ounis I, 2005, LECT NOTES COMPUT SC, V3408, P517 OVERELL S.E., 2009, THESIS IMPERIAL COLL PALACIO D., 2010, THESIS U PAU PAYS AD PALACIO D., 2011, AGILE 14 AGILE INT C PALACIO D., 2010, LECT NOTES GEOINFORM, P229 Palacio D, 2010, LECT NOTES COMPUT SC, V6273, P340, DOI 10.1007/978-3-642-15464-5_34 PALACIO D., 2012, BRIDG GEOGRC INF SCI, P267 Palacio Damien, 2010, International Journal on Digital Libraries, V11, DOI 10.1007/s00799-011-0070-z PALACIO D., 2012, LECT NOTES GEOINFORM, P65 PALACIO D., 2009, ECIR GIIW 31 EUR C I, P37 PEREAORTEGA JM, 2008, NLDB 08 P 13 INT C N, V5039, P142, DOI 10.1007/978-3-540-69858-6_15 PEREIRA CD, 2009, ECIR 09, V5478, P264 Pereira CD, 2012, INFORM PROCESS MANAG, V48, P340, DOI 10.1016/j.ipm.2011.07.001 PERRY M., 2007, GEOSPATIAL TEMPORAL, P1 PETERS C., 2001, LECT NOTES COMPUTER, V2069 Pfoser D, 2009, LECT NOTES COMPUT SC, V5886, P87, DOI 10.1007/978-3-642-10601-9_7 Pham TT, 2007, CIKM 07, P439 PISSINOU N., 1993, P 1 ACM WORKSH ADV G POIBEAU T., 2003, EXTRACTION AUTOMATIQ Poon H., 2010, ASS COMPUTER LINGUIS, P296 POWER D.J., 1997, ONLINE EXECUTIVE J D, V1 Purves R., 2011, SIGSPATIAL SPECIAL, V3, P2 PURVES R., 2004, SIGIR FORUM, V38, P53 Purves RS, 2007, INT J GEOGR INF SCI, V21, P717, DOI 10.1080/13658810601169840 PUSTEJOVSKY J., 2010, LANGUAGE RESOURCES E Pustejovsky J, 2005, LANG RESOUR EVAL, V39, P123, DOI 10.1007/s10579-005-7882-7 PUSTEJOVSKY J., 2003, NEW DIRECTIONS QUEST, P28 RANDELL DA, 1992, PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE (KR 92), P165 Rasolofo Y, 2003, INFORM PROCESS MANAG, V39, P581, DOI 10.1016/S0306-4573(02)00122-X Resnik P, 1995, INT JOINT CONF ARTIF, P448 REYMONET A., 2009, INT WORKSH PRINC DIA, P9 ROCHE M., 2009, REV NOUVELLES TECH E, VE-17, P1 Saaty T.L., 1980, ANAL HIERARCHY PROCE Sagot B., 2008, TRAITEMENT AUTOMATIQ, V49, P155 SALLABERRY C., 2009, OGRS 2009 INT OP GEO SALLABERRY C., 2007, ADV INFORM KNOWLEDGE SALLABERRY C., 2011, CORIA, P327 SALLABERRY C., 2008, P 2 INT WORKSH GEOGR, P1, DOI 10.1145/1460007.1460008 Sallaberry C, 2007, ICEIS 2007: PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, P190 SALLABERRY C., 2012, ACCES CONTENU GEOGRA SALLABERRY C., 2007, 4 FRENCH INF RETR C, P53 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDERSON M., 2004, SIGIR GIR 04 P WORKS Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 SCHARL A., 2007, ADV INFORM KNOWLEDGE Silverstein C., 1999, SIGIR Forum, V33 Smith B, 2001, INT J GEOGR INF SCI, V15, P591, DOI 10.1080/13658810110061199 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Stokes N, 2008, INT J GEOGR INF SCI, V22, P247, DOI 10.1080/13658810701626210 STROTGEN J., 2010, GIR 10 P 6 WORKSH GE, P16 SZMURLO M., 1998, ISPRS COMM 4 S GIS V, P1 TALMY L., 2000, COGNITIVE SEMANTICS, V1, P177 TEISSEIDRE C., 2011, TALN 11 ACT 18 C TRA Uren Victoria, 2010, International Journal of Metadata, Semantics and Ontologies, V5, DOI 10.1504/IJMSO.2010.033280 Usery EL, 1996, PHOTOGRAMM ENG REM S, V62, P833 Vaid S, 2005, LECT NOTES COMPUT SC, V3633, P218 VALCARTIER, 2006, GRID GEOSPATIAL RETR VANDELOISE C., 1986, ESPACE FRANCAIS van Kreveld M, 2005, GEOINFORMATICA, V9, P61, DOI 10.1007/s10707-004-5622-6 Verhagen M, 2009, LANG RESOUR EVAL, V43, P161, DOI 10.1007/s10579-009-9086-z Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 VOLK M., 2010, P 7 INT C LANG RES E, P19 VOORHEES E.M., 1999, TREC 8 P 8 TEXT RETR, P1 VOORHEES E.M., 2005, TEXT RETR C TREC EXP Voorhees EM, 2002, LECT NOTES COMPUT SC, V2406, P355 WALKER DRF, 1992, INT J GEOGR INF SYST, V6, P511, DOI 10.1080/02693799208901932 White R. W., 2007, SIGIR 2007, P255 WIDLOCHER A., 2004, RIAO 04 P 1 INT C CO, P555 WIDLOCHER A., 2005, TALN 05 ACT 12 C TRA, P517 WOLPERT DH, 1992, NEURAL NETWORKS, V5, P241, DOI 10.1016/S0893-6080(05)80023-1 WOODRUFF AG, 1994, J AM SOC INFORM SCI, V45, P645, DOI 10.1002/(SICI)1097-4571(199410)45:9<645::AID-ASI2>3.0.CO;2-8 Wu Z., 1994, ACL, P133 YAGER RR, 1988, IEEE T SYST MAN CYB, V18, P183, DOI 10.1109/21.87068 Zeleny M, 1982, MULTIPLE CRITERIA DE ZGHAL H.B., 2008, J DIGITAL INFORM MAN, V6, P375 NR 217 TC 0 Z9 0 PU BLACKWELL SCIENCE PUBL PI OXFORD PA OSNEY MEAD, OXFORD OX2 0EL, ENGLAND SN 2051-249X BN 978-1-84821-596-2 J9 FOCUS SER PY 2013 BP 1 EP 123 DI 10.1002/9781118790113 PG 136 WC Engineering, Electrical & Electronic; Geography, Physical SC Engineering; Physical Geography GA BB6SG UT WOS:000344949600007 ER PT S AU Musto, C Semeraro, G Lops, P de Gemmis, M AF Musto, Cataldo Semeraro, Giovanni Lops, Pasquale de Gemmis, Marco BE Huemer, C Lops, P TI Contextual eVSM: A Content-Based Context-Aware Recommendation Framework Based on Distributional Semantics SO E-COMMERCE AND WEB TECHNOLOGIES, EC-WEB 2013 SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 14th International Conference on Electronic Commerce and Web Technologies (EC-Web) CY AUG 27-28, 2013 CL Prague, CZECH REPUBLIC DE Context-aware Recommendations; Filtering; User Modeling; Content-based Recommenders ID SYSTEMS AB In several domains contextual information plays a key role in the recommendation task, since factors such as user location, time of the day, user mood, weather, etc., clearly affect user perception for a particular item. However, traditional recommendation approaches do not take into account contextual information, and this can limit the goodness of the suggestions. In this paper we extend the enhanced Vector Space Model (eVSM) framework in order to model contextual information as well. Specifically, we propose two different context-aware approaches: in the first one we adapt the microprofiling technique, already evaluated in collaborative filtering, to content-based recommendations. Next, we define a contextual modeling technique based on distributional semantics: it builds a context-aware user profile that merges user preferences with a semantic vector space representation of the context itself. In the experimental evaluation we carried out an extensive series of tests in order to determine the best-performing configuration among the proposed ones. We also evaluated Contextual eVSM against a state of the art dataset, and it emerged that our framework overcomes all the baselines in most of the experimental settings. C1 [Musto, Cataldo; Semeraro, Giovanni; Lops, Pasquale; de Gemmis, Marco] Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. RP Musto, C (reprint author), Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. EM cataldo.musto@uniba.it; giovanni.semeraro@uniba.it; pasquale.lops@uniba.it; marco.degemmis@uniba.it CR Adomavicius G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P217, DOI 10.1007/978-0-387-85820-3_7 Adomavicius G, 2011, AI MAG, V32, P67 Adomavicius G., 2001, LECT NOTES COMPUTER, V2232, P180 Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714 Baltrunas L., 2009, CONT AW REC SYST CAM Bazire M, 2005, LECT NOTES ARTIF INT, V3554, P29 Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 Cantador I, 2008, LECT NOTES COMPUT SC, V5149, P279, DOI 10.1007/978-3-540-70987-9_34 Codina V., 2013, P 3 WORKSH CONT AW R, P15 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Herlocker JL, 2001, IEEE INTERNET COMPUT, V5, P40, DOI 10.1109/4236.968830 Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6 Karatzoglou A., 2010, P 4 ACM C REC SYST R, P79, DOI 10.1145/1864708.1864727 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3 Mitchell T. M., 1997, MCGRAW HILL SERIES C Musto C, 2011, LECT NOTES BUS INF P, V85, P270 Musto C, 2012, LECT NOTES BUS INF P, V123, P112 Ostuni V.C., 2012, SERSY CEUR WS, V919, P37 Panniello U, 2012, ELECTRON COMMER RES, V12, P1, DOI 10.1007/s10660-012-9087-7 Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Widdows D, 2003, ACL 2003, P136 Zheng Y., 2012, LNBIP, V123, P88 NR 26 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-39878-0; 978-3-642-39877-3 J9 LECT NOTES BUS INF P PY 2013 VL 152 BP 125 EP 136 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BB6WB UT WOS:000345066600012 ER PT B AU Alahmadi, A Joorabchi, A Mahdi, AE AF Alahmadi, Alaa Joorabchi, Arash Mahdi, Abdulhussain E. GP IEEE TI A New Text Representation Scheme Combining Bag-of-Words and Bag-of-Concepts Approaches for Automatic Text Classification SO 2013 7TH IEEE GCC CONFERENCE AND EXHIBITION (GCC) LA English DT Proceedings Paper CT 7th IEEE GCC Conference and Exhibition (GCC) CY NOV 17-20, 2013 CL Doha, QATAR SP IEEE DE Text Classification; Bag-of-Words; Bag- of-Concepts; Wikipedia ID MODEL AB This paper introduces a new approach to creating text representations and apply it to a standard text classification collections. The approach is based on supplementing the well-known Bag-of-Words (BOW) representational scheme with a concept-based representation that utilises Wikipedia as a knowledge base. The proposed representations are used to generate a Vector Space Model, which in turn is fed into a Support Vector Machine classifier to categorise a collection of textual documents from two publically available datasets. Experimental results for evaluating the performance of our model in comparison to using a standard BOW scheme and a concept-based scheme, as well as recently reported similar text representations that are based on augmenting the standard BOW approach with concept-based representations. C1 [Alahmadi, Alaa; Joorabchi, Arash; Mahdi, Abdulhussain E.] Univ Limerick, Dept Elect & Comp Engn, Limerick, Ireland. RP Alahmadi, A (reprint author), Univ Limerick, Dept Elect & Comp Engn, Limerick, Ireland. EM alaa.alahmadi@ul.ie; arash.joorabchi@ul.ie; hussain.mahdi@ul.ie CR Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Gabrilovich E., 2006, P 21 NAT C ART INT, P1301 Gabrilovich E, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1048 Harris ZS, 1954, WORD, V10, P146 Hotho A., 2003, SIGIR WORKSH SEM WEB Hu Jian, 2008, P 31 ANN INT ACM SIG, P179, DOI 10.1145/1390334.1390367 Lang K, 1995, P 12 INT C MACH LEAR, P331 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Milne David N., 2009, P NZ COMP SCI RES ST, V9 Ricardo A., 1999, MODERN INFORM RETRIE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Yun JL, 2012, EXPERT SYST APPL, V39, P2035, DOI 10.1016/j.eswa.2011.08.027 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-0724-3; 978-1-4799-0722-9 PY 2013 BP 108 EP 113 PG 6 WC Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BB5GF UT WOS:000343832200022 ER PT J AU Zhang, L Li, Q Luo, XF Wei, X AF Zhang, Lun Li, Qing Luo, Xiangfeng Wei, Xiao BE Hsu, DF Wang, Y Rao, AR Zhang, D Kinsher, W Pedrycz, W Berwick, RC Zadeh, LA TI A MULTI-LEVEL TEXT REPRESENTATION MODEL WITHIN BACKGROUND KNOWLEDGE BASED ON HUMAN COGNITIVE PROCESS SO PROCEEDINGS OF THE 2013 12TH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI CC 2013) LA English DT Proceedings Paper CT 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI CC) CY JUL 16-18, 2013 CL New York, NY SP IEEE, ICIC, IEEE Comp Soc, IEEE Computat Intelligence Soc, IBM, IEEE ICCI CC Steering Comm, IEEE CS Press, Univ Calgary, Int Journal Cognit Informat & Nat Intelligence, Journal Adv Math & Applicat, Int Journal Software Sci & Computat Intelligence HO Fordham Univ DE Text comprehension; background knowledge; text representation; human cognitive process; surface code; text base; situational model; semantics ID MEMORY AB Text representation is one of the most fundamental works in text comprehension, processing, and search. Various works have been proposed to mine the semantics in texts and then to represent them. However, most of them only focus on how to mine semantics from the text itself while the background knowledge, which is very important to text understanding, is not taken into consideration. In this paper, on the basis of human cognitive process, we propose a multi-level text representation model within background knowledge, called TRMBK. It is composed of three levels, which are machine surface code (MSC), machine text base (MTB) and machine situational model (MSM). All of the three are able to be automatically constructed to acquire semantics both inside and outside of the text. Simultaneously, we also propose a method to automatically establish background knowledge and offer supports for the current text comprehension. Finally, experiments and comparisons have been presented to show the better performance of TRMBK. C1 [Zhang, Lun; Luo, Xiangfeng; Wei, Xiao] Shanghai Univ, Shanghai 200041, Peoples R China. RP Zhang, L (reprint author), Shanghai Univ, Shanghai 200041, Peoples R China. EM zhangjun_shu@shu.edu.cn; itgli@cityu.edu.hk; luoxf@shu.edu.cn; xwei@shu.edu.cn CR Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487 Anderson JR, 2004, PSYCHOL REV, V111, P1036, DOI 10.1037/0033-295x.111.4.1036 Atkinson R. C., 1968, PSYCHOL LEARN MOTIV, P89, DOI DOI 10.1016/S0079-7421(08)60422-3 Baddeley A, 2003, NAT REV NEUROSCI, V4, P829, DOI 10.1038/nrn1201 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fikes R., 2000, AXIOMATIC SEMANTICS Gu XJ, 2011, ADV INTEL SOFT COMPU, V104, P99 Hartigan J, 1975, CLUSTERING ALGORITHM Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Kinstch W., 1978, PSYCHOL REV, V85, P363 Lecue F., 2010, IEEE TKDE, V23, P942 Li Q, 2010, INFORM SCIENCES, V180, P4929, DOI 10.1016/j.ins.2010.08.044 Li YF, 2007, IEEE T KNOWL DATA EN, V19, P273, DOI 10.1109/TKDE.2007.18 Luo X., 2010, 9 INT C COGN INF, P383, DOI 10.1109/COGINF.2010.5599710 Luo XF, 2010, IEEE T LEARN TECHNOL, V3, P344, DOI 10.1109/TLT.2010.26 McCallum A., 2007, J ARTIF INTELL RES, V29, P249 MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037//0033-295X.101.2.343 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Snaider J., 2011, ARTIFICIAL GEN INTEL, V6830, P108 Wang Y., 2012, J ADV MATH APPL, V1, P275 Wang Y., 2012, J ADV MATH APPL, V1, P250 WAUGH NC, 1965, PSYCHOL REV, V72, P89, DOI 10.1037/h0021797 Wu C., 2011, IEEE WIC ACM INT C W, P288 NR 24 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4799-0781-6 PY 2013 BP 324 EP 331 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BB4BR UT WOS:000343133500043 ER PT S AU Rao, JY Jia, AX Feng, YS Zhao, DY AF Rao, Junyang Jia, Aixia Feng, Yansong Zhao, Dongyan BE Lin, X Manolopoulos, Y Srivastava, D Huang, G TI Taxonomy Based Personalized News Recommendation: Novelty and Diversity SO WEB INFORMATION SYSTEMS ENGINEERING - WISE 2013, PT I SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th International Conference on Web Information Systems Engineering (WISE) CY OCT 13-15, 2013 CL Nanjing, PEOPLES R CHINA SP Univ Chinese Acad Sci, Victoria Univ, Joint Lab Social Comp & E Hlth Res, Nanjing Univ Finance & Econ, Jiangsu Prov Key Lab E Business, Nanjing Sci & Technol Commiss DE Personalized Recommender System; Novelty and Diversity; Taxonomy; Online Encyclopedia ID SIMILARITY; RETRIEVAL AB Recommender systems are designed to help users quickly access large volumes of information according to their profiles. Most previous works in recommender systems have put their emphasis on the accuracy of finding the most similar items according to a user's profile, while often ignoring other aspects that may affect users' experiences in practice, e.g., the novelty and diversity issues within a recommendation list. In this paper, we focus on utilizing taxonomic knowledge extracted from an online encyclopedia to boost a content-based personalized news recommender system without much human involvement. Given a recommendation list, we improve a user's satisfaction by introducing the taxonomy based novelty and diversity metrics to include novel, but potentially related items into the list, and filter out redundant ones. The experimental results show that the coarse grained knowledge resources can help a content-based news recommender system provides accurate as well as user-oriented recommendations. C1 [Rao, Junyang; Jia, Aixia; Feng, Yansong; Zhao, Dongyan] Peking Univ, ICST, Beijing, Peoples R China. RP Rao, JY (reprint author), Peking Univ, ICST, Beijing, Peoples R China. EM raojunyang@pku.edu.cn; jiaaixia@pku.edu.cn; fengyansong@pku.edu.cn; zhaodongyan@pku.edu.cn CR Hliaoutakis A, 2006, INT J SEMANT WEB INF, V2, P55, DOI 10.4018/jswis.2006070104 Hurley N, 2008, PROC INT C TOOLS ART, P217, DOI 10.1109/ICTAI.2008.32 IJntema W., 2010, EDBT ICDT WORKSH Rao J., 2013, LNCS, V7923, P781 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smyth B, 2001, LECT NOTES ARTIF INT, V2080, P347 Vargas S, 2011, RECSYS, P109 Zhang M, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P508 Zhang M., 2009, RECSYS 09 P 3 ACM C, P397 Zhang M, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P123 Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 Ziegler C.-N., 2005, WWW 05, P22 Ziegler C.N., 2004, CIKM 04 P 13 ACM C I, P406 NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-41230-1; 978-3-642-41229-5 J9 LECT NOTES COMPUT SC PY 2013 VL 8180 BP 209 EP 218 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB3IQ UT WOS:000342806000018 ER PT J AU Xu, Z Luo, XF Liang, C Zhi, FL Mei, L AF Xu, Zheng Luo, Xiangfeng Liang, Chen Zhi, Fenglin Mei, Lin GP IEEE TI Using Association Link Network Model for Organizing Video Resources from Web SO 2013 NINTH INTERNATIONAL CONFERENCE ON SEMANTICS, KNOWLEDGE AND GRIDS (SKG) SE International Conference on Semantics, Knowledge and Grids LA English DT Proceedings Paper CT 9th International Conference on Semantics, Knowledge and Grids (SKG) CY OCT 03-04, 2013 CL Beijing, PEOPLES R CHINA SP IEEE DE Association Link Network; semantic link network; video representing; video organizing AB Recent research shows that videos "in the wild" are growing at a staggering rate. The rapid increase number of video resources has brought an urgent need to develop intelligent methods to organize the video events. In this paper, we use the Association Link Network model for organizing video resources from Web. Association Link Network is a kind of semantic link network, which is designed to establish associated relations among various resources (e.g., Web pages or documents in digital library) aiming at extending the loosely connected network of no semantics (e.g., the Web) to an association-rich network. Since the theory of cognitive science considers that the associated relations can make one resource more comprehensive to users, the motivation of ALN is to organize the associated resources loosely distributed in the Web for effectively supporting the Web intelligent activities such as browsing, knowledge discovery and publishing, etc. The tags and surrounding texts of video resources are used to represent the semantic content. The relatedness between tags and surrounding texts are implemented in the Association Link Network model. Two data sets from YouTube and Flickr(1) are used to evaluate the proposed method. The experimental results show that the proposed method can measure the association relations accurately and robustly. C1 [Xu, Zheng; Liang, Chen; Zhi, Fenglin; Mei, Lin] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, 339 Bisheng Rd, Shanghai, Peoples R China. EM xuzheng@shu.edu.cn; luoxf@shu.edu.cn; chenliang1982@163.com; Fenglinzhi1983@163.com; gasswlw@126.com CR [Anonymous], 2013, CISCO VIS NETW IND F Berners-Lee T, 2001, SCI AM, V284, P34 Bizer C., 2009, INT J SEMANT WEB INF, V5, P1 Liu L., 2009, IEEE T CIRCUITS SYST, V19, P452 Liu YH, 2010, IEEE T PARALL DISTR, V21, P405, DOI 10.1109/TPDS.2009.57 Liu YH, 2011, IEEE T PARALL DISTR, V22, P2100, DOI 10.1109/TPDS.2011.113 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Ma H, 2010, IEEE T MULTIMEDIA, V12, P462, DOI 10.1109/TMM.2010.2051360 Plebani P, 2009, IEEE T KNOWL DATA EN, V21, P1629, DOI 10.1109/TKDE.2009.35 Pons A. P., 2006, ACM SIGMIS DATABASE, V37, P97, DOI 10.1145/1120501.1120508 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN The official YouTube blog, 2013, GREAT SCOTT 35 HOURS Wu L., 2010, 2010 IEEE INT C SYST, P26 Xu CS, 2008, IEEE T MULTIMEDIA, V10, P1342, DOI 10.1109/TMM.2008.2004912 Xu Z, 2011, COMPUT SYST SCI ENG, V26, P153 Yu HQ, 2012, IEEE T LEARN TECHNOL, V5, P130, DOI 10.1109/TLT.2012.1 Zhang J, 2008, IEEE T SYST MAN CY C, V38, P649, DOI 10.1109/TSMCC.2008.923876 Zhuge H, 2008, IEEE T PARALL DISTR, V19, P145, DOI 10.1109/TC.2007.70725 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 Zhuge H., 2010, ARTIF INTELL, V174, P190 Zhuge H., 2012, KNOWLEDGE GRID CYBER NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2325-0623 BN 978-1-4799-3012-8 J9 INT CONF SEMANT PY 2013 BP 52 EP 57 DI 10.1109/SKG.2013.8 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BB2RX UT WOS:000342202300008 ER PT J AU Zhang, ZY Peng, L Huang, ZX AF Zhang, Zhiying Peng, Li Huang, Zhixing GP IEEE TI Designing a Hierarchical Graph Kernel for Semantic Link Network SO 2013 NINTH INTERNATIONAL CONFERENCE ON SEMANTICS, KNOWLEDGE AND GRIDS (SKG) SE International Conference on Semantics, Knowledge and Grids LA English DT Proceedings Paper CT 9th International Conference on Semantics, Knowledge and Grids (SKG) CY OCT 03-04, 2013 CL Beijing, PEOPLES R CHINA SP IEEE DE Semantic Link Network; Graph Kernel AB One of the most important abilities of human is cluster and classify similar things, which makes people could better understand the nature, easier establish and manage the social society. How to model things like people and how to compute the similarities between models are two major problems need to be solved to make the machine has this ability. For the first problem, the Semantic Link Network (SLN) could be a appropriate choice, however, the challenge is how to compute the similarities between SLNs. In this paper, we design a hierarchical graph kernel for SLNs to solve this challenge. We evaluate the practical performance of our kernels on a task of detecting semantic similar relationships between texts. The result show that the detection results of semantic node hierarchical kernel is most similar to human's. C1 [Zhang, Zhiying; Peng, Li; Huang, Zhixing] Southwest Univ, Sch Comp & Informat Sci, Semant Grid Lab, Chongqing 400715, Peoples R China. RP Zhang, ZY (reprint author), Southwest Univ, Sch Comp & Informat Sci, Semant Grid Lab, Chongqing 400715, Peoples R China. EM zzy8118@swu.edu.cn; hanjian@swu.edu.cn; huangzx@swu.edu.cn CR Bicer V, 2011, LECT NOTES COMPUT SC, V6643, P47, DOI 10.1007/978-3-642-21034-1_4 Borgwardt K. M., 2005, P 5 IEEE INT C DAT M, P74 Borgwardt KM, 2005, BIOINFORMATICS, V21, pI47, DOI 10.1093/bioinformatics/bti1007 Bunke H, 2011, PATTERN RECOGN, V44, P1057, DOI 10.1016/j.patcog.2010.11.015 Fanizzi N, 2006, LECT NOTES ARTIF INT, V4203, P322 Gartner T., 2003, GRAPH KERNELS HARDNE Gartner T., 2003, SIGKDD EXPLORATIONS, V5, P49, DOI DOI 10.1145/959242.959248 Grauman K, 2005, IEEE I CONF COMP VIS, P1458 Harchaoui Z., 2007, CVPR, P1 Huang ZX, 2010, FUTURE GENER COMP SY, V26, P400, DOI 10.1016/j.future.2009.07.006 Huang ZX, 2009, 2009 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, P219, DOI 10.1109/ASONAM.2009.6 Irfan Danish, 2007, Proceedings of the 2007 International Conference on Information & Knowledge Engineering. IKE 2007 Kashima H., 2003, INT C MACHINE LEARNI, V20, P321 Losch U., 2012, SEM WEB RES APPL 9 S, V7295, P134 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Shmilovici A, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P257, DOI 10.1007/0-387-25465-X_12 Suzuki J., 2003, P 41 ANN M ASS COMP, P32, DOI 10.3115/1075096.1075101 Vishwanathan SVN, 2010, J MACH LEARN RES, V11, P1201 Xu B., 2011, SKG, P245 Yu X., 2013, FUTURE GENERATION CO Zhuge H., 2007, IEEE T KNOWL DATA EN, V19, P759, DOI DOI 10.1109/TKDE.2007.190614 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H., 2003, WWW Zhuge H., 2004, KNOWLEDGE GRID WORLD Zhuge H., 2007, CONCURR COMP-PRACT E, V19, P1065, DOI 10.1002/cpe.1097 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 Zhuge H., 2010, ARTIF INTELL, V174, P190 Zhuge H., 2011, CONCURR COMP-PRACT E, V23, P924, DOI DOI 10.1002/CPE.1623 Zhuge H., 2008, IEEE INTELL SYST APP, V23, P63 NR 30 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2325-0623 BN 978-1-4799-3012-8 J9 INT CONF SEMANT PY 2013 BP 82 EP 89 DI 10.1109/SKG.2013.33 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BB2RX UT WOS:000342202300012 ER PT J AU Koulali, R El-Haj, M Meziane, A AF Koulali, Rim El-Haj, Mahmoud Meziane, Abdelouafi GP IEEE TI Arabic Topic Detection using Automatic Text Summarisation SO 2013 ACS INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA) LA English DT Proceedings Paper CT IEEE and Arab-Computing-Society (ACS) International Conference on Computer Systems and Applications (AICCSA) CY MAY 27-30, 2013 CL Ifrane, MOROCCO SP IEEE, Arab Comp Soc HO Al Akhawayn Univ DE Natural Language Processing; Topic Detection; Automatic Summarisation; TF-IDF; Cosine Similarity ID RETRIEVAL AB With the exponential growth of the online available Arabic documents, classifying and processing large Arabic corpora has became a challenging task. The presence of noisy information embedded in these documents has made it even more difficult to get accurate results when applying a Topic Detection (TD) process. To address this problem, a proper features selection approach is needed to enhance the topic detection accuracy. In this paper, we explore the impact of using automatic summarisation technique along with a feature-selection process to enhance Arabic Topic Detection. In our work we show that using automatic summarisation reduces noisy information and results in a significant enhancement to the topic detection process and therefore increases the performance of our TD system. This was achieved by the ability of our summariser system in reducing documents size to speed up the detection process. C1 [Koulali, Rim; Meziane, Abdelouafi] Mohammed I Univ, Coll Sci, LARI Lab, Oujda, Morocco. RP Koulali, R (reprint author), Mohammed I Univ, Coll Sci, LARI Lab, Oujda, Morocco. EM rim.koulali@gmail.com; m.el-haj@lancaster.ac.uk; abdelouafi_meziane@yahoo.fr CR Abbas M., 2011, J DIGITAL INFORM MAN, V9, P185 Allan J, 2002, TOPIC DETECTION TRAC, V12 Baeza-Yates R., 1999, MODERN INFORM RETRIE, V463 BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 Boudlal A., 2011, 7 INT COMP C AR El-Haj M., 2010, P LREC 2010 Fattah MA, 2008, PROC WRLD ACAD SCI E, V27, P192 Katragadda R., 2009, P 3 INT WORKSH CROSS, P46, DOI 10.3115/1572433.1572440 Koulali R., 2011, P ALTIC 2011, P54 Radev DR, 2004, INFORM PROCESS MANAG, V40, P919, DOI 10.1016/j.ipm.2003.10.006 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seymore K., 1997, P EUR, V97, P1987 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Yang Y., 1997, ICML, V97, P412 Yeh JY, 2008, EXPERT SYST APPL, V35, P1451, DOI 10.1016/j.eswa.2007.08.037 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-0792-2 PY 2013 PG 4 WC Computer Science, Theory & Methods SC Computer Science GA BB1FD UT WOS:000341079600045 ER PT S AU Xu, Z Mei, L Zhang, SX Ye, FY AF Xu, Zheng Mei, Lin Zhang, Shunxiang Ye, Feiyue BE Wang, JF Lau, R TI Temporal Faceted Learning of Concepts Using Web Search Engines SO ADVANCES IN WEB-BASED LEARNING - ICWL 2013 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th International Conference on Advances in Web-Based Learning (ICWL) CY OCT 06-09, 2013 CL TAIWAN SP Hong Kong Web Soc, Tajen Univ, City Univ Hong Kong DE Temporal facetedlearning; semantic annotation; content analysis; Web based learning AB In this paper, we propose the problem of generating temporal faceted learning of concepts. The goal of the proposed problem is to annotate a concept with semantic, temporal, faceted, concise, and structured information, which can release the cognitive burden of learning new concepts for users. The temporal faceted annotations can help users to learn and understand the unfamiliar or new emerged concepts. We propose a general method to generate temporal faceted annotation of a concept by constructing its learning words, learning sentences, learning graph, and learning communities. Empirical experiments on Linked In dataset show that the proposed algorithm is effective and accurate. Different from the manually generated annotation repository such as Linked In and Wikipedia, the proposed method can automatically generated the annotations and does not need any prior knowledge such as ontology or the hierarchical knowledge base such as Word Net. The proposed method uses Web search engines as a temporal faceted learning platform, which can add the new meaning and update the old meaning of concepts. C1 [Xu, Zheng; Mei, Lin] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China. EM Xuzheng@shu.edu.cn; l_mei72@hotmail.com; sxzhang@shu.edu.cn; fyye@shu.edu.cn CR Al-Khalifa H., 2006, P 2 INT IEEE C INN I, P1 Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Leung K, 2003, IEEE T KNOWL DATA EN, V20, P1505 Liu YH, 2010, IEEE T PARALL DISTR, V21, P405, DOI 10.1109/TPDS.2009.57 Liu YH, 2011, IEEE T PARALL DISTR, V22, P2100, DOI 10.1109/TPDS.2011.113 Luo XF, 2010, IEEE T LEARN TECHNOL, V3, P344, DOI 10.1109/TLT.2010.26 Luo XF, 2011, IEEE T AUTOM SCI ENG, V8, P482, DOI 10.1109/TASE.2010.2094608 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Radinsky K., 2011, P 20 INT C WORLD WID, P337, DOI DOI 10.1145/1963405.1963455 Ramage D., 2009, P 2 ACM INT C WEB SE, P54, DOI 10.1145/1498759.1498809 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenkel R., 2008, P 31 ANN INT ACM SIG, P523, DOI 10.1145/1390334.1390424 Sparrow B, 2011, SCIENCE, V333, P776, DOI 10.1126/science.1207745 Suchanek F. M., 2008, P 17 ACM C INF KNOWL, P223, DOI DOI 10.1145/1458082.1458114 White R. W., 2007, P 16 INT C WORLD WID, P21, DOI 10.1145/1242572.1242576 Xu S., 2007, P 15 ACM INT C INF K, P1003, DOI 10.1145/1321440.1321596 Zhou D, 2008, P 17 INT C WORLD WID, P715, DOI DOI 10.1145/1367497.1367594 Zhu H., 2010, P 10 INT C WEB BAS L, P320 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 NR 22 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-41174-8; 978-3-642-41175-5 J9 LECT NOTES COMPUT SC PY 2013 VL 8167 BP 254 EP 263 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BB0OX UT WOS:000340556100025 ER PT S AU Quezada, M Poblete, B AF Quezada, Mauricio Poblete, Barbara BE Antunes, P Gerosa, MA Sylvester, A Vassileva, J DeVreede, GJ TI Understanding Real-World Events via Multimedia Summaries Based on Social Indicators SO COLLABORATION AND TECHNOLOGY, CRIWG 2013 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th International Conference on Collaboration and Technology (CRIWG) CY OCT 30-NOV 01, 2013 CL Wellington, NEW ZEALAND DE Social Networks; Collaborative Summaries; Multimedia AB We present a novel methodology for creating multimedia summaries of real-world events through social media information. Summaries are generated using selected multimedia data disseminated through Twitter. The proposed summarization technique takes into account social indicators of relevance, which are used to select a set of representative multimedia objects for summarizing the event from a social perspective. In addition, our approach incorporates different news angles by extracting topics within each event. C1 [Quezada, Mauricio; Poblete, Barbara] Univ Chile, Dept Comp Sci, PRISMA Res Grp, Santiago, Chile. RP Quezada, M (reprint author), Univ Chile, Dept Comp Sci, PRISMA Res Grp, Santiago, Chile. EM mquezada@dcc.uchile.cl; bpoblete@dcc.uchile.cl CR Becker H, 2010, P 3 ACM INT C WEB SE Becker H., 2012, P 5 ACM INT C WEB SE Castillo C., 2011, P 20 INT C WORLD WID Chakrabarti D, 2011, P 5 INT AAAI C WEBL Del Fabro M., 2012, 4 INT C ADV MULT MME Del Fabro M, 2012, LECT NOTES COMPUT SC, V7131, P630 Duan Y., 2010, P 23 INT C COMP LING Karypis G., 2002, TR02017 U MN DEP COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sinha P., 2011, P 1 ACM INT C MULT R NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-41347-6; 978-3-642-41346-9 J9 LECT NOTES COMPUT SC PY 2013 VL 8224 BP 18 EP 25 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB0OU UT WOS:000340550200003 ER PT J AU Kryszkiewicz, M AF Kryszkiewicz, Marzena BE Selamat, A Nguyen, NT Haron, H TI Bounds on Lengths of Real Valued Vectors Similar with Regard to the Tanimoto Similarity SO INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2013), PT I, SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th Asian Conference on Intelligent Information and Database Systems (ACIIDS) CY MAR 18-20, 2013 CL Kuala Lumpur, MALAYSIA SP Univ Teknologi Malaysia, Wroclaw Univ Technol Poland, Nguyen Tat Thanh Univ DE the Tanimoto similarity; chemical substructure discovery; text mining; information filtering ID SPACE AB The Tanimoto similarity measure finds numerous applications in chemistry, bio-informatics, information retrieval and text mining. A typical task in these applications is finding most similar vectors. The task is very time consuming in the case of very large data sets. Thus methods that allow for efficient restriction of the number of vectors that have a chance to be sufficiently similar to a given vector are of high importance. To this end, recently, we have derived bounds on lengths of vectors similar with respect to the Tanimoto similarity. In this paper, we recall those results and derive new bounds on lengths of real valued vectors that have a chance to be Tanimoto similar to a given vector in a required degree. Finally, we compare the previous and current results and illustrate their usefulness. C1 Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland. RP Kryszkiewicz, M (reprint author), Warsaw Univ Technol, Inst Comp Sci, Nowowiejska 15-19, PL-00665 Warsaw, Poland. EM mkr@ii.pw.edu.pl CR Han J, 2012, MOR KAUF D, P1 Kristensen TG, 2010, J MATH CHEM, V48, P287, DOI 10.1007/s10910-010-9668-4 Kryszkiewicz M., 2012, 3 ISC WARS U TECHN Kryszkiewicz M., 2012, LNCS LNAI, V7345, P48 Lipkus AH, 1999, J MATH CHEM, V26, P263, DOI 10.1023/A:1019154432472 Rajaraman A, 2011, MINING MASSIVE DATAS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 Witten I. H., 1999, MANAGING GIGABYTES C Zezula P., 2006, SIMILARITY SEARCH ME NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-36546-1 J9 LECT NOTES COMPUT SC PY 2013 VL 7802 BP 445 EP 454 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB0PR UT WOS:000340589100046 ER PT J AU Song, CR Weng, SF Zhang, CS AF Song, Chengru Weng, Shifeng Zhang, Changshui GP IEEE TI Automatic Elements Extraction of Chinese Web News Using Prior Information of Content and Structure SO 2013 SECOND IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR 2013) LA English DT Proceedings Paper CT 2nd IAPR Asian Conference on Pattern Recognition (ACPR) CY NOV 05-08, 2013 CL Naha, JAPAN SP Int Assoc Pattern Recognit, IEEE, IEEE Comp Soc, KDDI Fdn, Support Ctr Adv Telecommunicat Technol Res, Pattern Recognit & Media Understanding, IEICE ISS, Comp Vis & Image Media, IPSJ, IEEE Comp Soc Fukuoka Chapter DE news extraction; term vector model; TF-IDF; LDR AB We propose a set of efficient processes for extracting all four elements of Chinese news webpages, namely news title, release date, news source and the main text. Our approach is based on a deep analysis of content and structure features of current Chinese news. We take content indicators as the key to recover tree structure of the main text. Additionally, we come up with the concept of Length-Distance Ratio to help improve performance. Our method rarely depends on selection of samples and has strong generalization ability regardless of training process, distinguishing itself from most existing methods. We have tested our approach on 1721 labeled Chinese news pages from 429 web sites. Results show that an 87% accuracy was achieved for news source extraction, and over 95% accuracy for other three elements. C1 [Song, Chengru; Zhang, Changshui] Tsinghtta Univ, Dept Automat, Beijing, Peoples R China. RP Song, CR (reprint author), Tsinghtta Univ, Dept Automat, Beijing, Peoples R China. EM scr.0731@gmail.com; wengsf@gmail.com; zes@mail.tsinghua.edu.cn CR Cai D., 2003, TECHNICAL REPORT Dong Y, 2008, P 2008 IEEE INT C IN, P179 Ferrara Emilio, 2012, ARXIV12070246 Kushmerick N., 1997, THESIS U WASHINGTON Lancaster F. W., 1974, J AM SOC INFORM SCI, V25 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Settles B., 2010, ACTIVE LEARNING LIT NR 8 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4799-2190-4 PY 2013 BP 340 EP 345 DI 10.1109/ACPR.2013.52 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BA9KJ UT WOS:000339501000068 ER PT J AU Duan, HH Pestov, VG Singla, V AF Duan, Hubert Haoyang Pestov, Vladimir G. Singla, Varun BE Brisaboa, N Pedreira, O Zezula, P TI Text Categorization via Similarity Search An Efficient and Effective Novel Algorithm SO SIMILARITY SEARCH AND APPLICATIONS (SISAP) SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Conference on Similarity Search and Applications (SISAP) CY OCT 02-04, 2013 CL A Coruna, SPAIN SP Fields Inst Res Math Sci, Univ A Coruna, Ctr Res & Dev Informat Technologies HO Univ A Coruna ID CLASSIFICATION AB We present a supervised learning algorithm for text categorization which has brought the team of authors the 2nd place in the text categorization division of the 2012 Cybersecurity Data Mining Competition (CDMC'2012) and a 3rd prize overall. The algorithm is quite different from existing approaches in that it is based on similarity search in the metric space of measure distributions on the dictionary. At the preprocessing stage, given a labeled learning sample of texts, we associate to every class label (document category) a point in the space of question. Unlike it is usual in clustering, this point is not a centroid of the category but rather an outlier, a uniform measure distribution on a selection of domain-specific words. At the execution stage, an unlabeled text is assigned a text category as defined by the closest labeled neighbour to the point representing the frequency distribution of the words in the text. The algorithm is both effective and efficient, as further confirmed by experiments on the Reuters 21578 dataset. C1 [Duan, Hubert Haoyang; Pestov, Vladimir G.] Univ Ottawa, Ottawa, ON K1N 6N5, Canada. RP Duan, HH (reprint author), Univ Ottawa, Ottawa, ON K1N 6N5, Canada. EM hduan065@uottawa.ca; vpest283@uottawa.ca; ee5080429@ee.iitd.ac.in CR Aas K., 1999, 941 NORW COMP CTR Bingham Ella, 2001, P 7 ACM SIGKDD INT C, P245, DOI 10.1145/502512.502546 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Cardoso-Cachopo A., DATASETS SINGLE LABE Church K., 1989, P ACL, P76, DOI DOI 10.3115/981623.981633 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dimitriadou E., 2011, E1071 MISCFUNCTIONS Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Ikonomakis M., 2005, WSEAS Transactions on Computers, V4 Joachims T., 1998, LNCS, V1398, P137 Johnson DE, 2002, IBM SYST J, V41, P428 Keim DA, 2010, LECT NOTES COMPUT SC, V5723, P154 Kim S. B, 2002, P LECT NOTES ARTIFIC, V2417, P414, DOI 10.1007/3-540-45683-X_45 Lewis D.D., REUTERS Liaw A, 2002, R NEWS, V2, P18, DOI DOI 10.1016/J.MEMSCI.2010.02.036 Lim HS, 2004, LECT NOTES COMPUT SC, V3316, P516 Pang P.S., 2012, 3 CYBERSECURITY DATA PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 R Development Core Team, 2008, R LANG ENV STAT COMP Radovanovic M., 2008, NOVI SAD J MATH, V38, P227 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B., 2003, LECT NOTES ARTIF INT, V2600, P41 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Torkkola K., 2001, PROCEEDINGS, P800 Weichold M., 2012, 19 INT C NEUR INF PR Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Zeng XQ, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P834 NR 32 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-41062-8 J9 LECT NOTES COMPUT SC PY 2013 VL 8199 BP 182 EP 193 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BA8HD UT WOS:000338111900019 ER PT J AU Li, H Xu, J AF Li, Hang Xu, Jun TI Semantic Matching in Search SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article ID LANGUAGE INFORMATION-RETRIEVAL; MATRIX FACTORIZATION; TOPIC MODELS; RELEVANCE; RANKING; TRANSLATION; DOCUMENTS; FRAMEWORK; THINKING; QUERIES AB Relevance is the most important factor to assure users' satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query "ny times" does not match well with a document only containing "New York Times"), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced can be generalized into more general machine learning techniques, which is referred to as learning to match in this survey. FU China National 973 project [2014CB340301] FX This work is supported in part by China National 973 project 2014CB340301. CR Abernethy J, 2009, J MACH LEARN RES, V10, P803 Agarwal D, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P19 Ahmad Farooq, 2005, P C HUM LANG TECHN E, P955, DOI 10.3115/1220575.1220695 Arguello Jaime, 2008, INT C WEBL SOC MED, P10 BaezaYates R, 2004, LECT NOTES ARTIF INT, V3034, P164 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE Bai B., 2009, P 18 ACM C INF KNOWL, P187, DOI 10.1145/1645953.1645979 Bai B, 2010, INFORM RETRIEVAL, V13, P291, DOI 10.1007/s10791-009-9117-9 Balakrishnan S., 2012, P 5 ACM INT C WEB SE, P143 Balasubramanian N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P571 Bast Hannah, 2012, CORR Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Beitzel S. M., 2005, P 28 ANN INT ACM SIG, P581, DOI 10.1145/1076034.1076138 Bendersky Michael, 2011, P 49 ANN M ASS COMP, P102 Bendersky Michael, 2010, P 3 ACM INT C WEB SE, P31, DOI DOI 10.1145/1718487.1718492 Bendersky Michael, 2012, P 5 ACM INT C WEB SE, P443 Bendersky Michael, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348408 Bendersky Michael, 2009, P 2009 WORKSH WEB SE, P8, DOI 10.1145/1507509.1507511 Bengio Y, 2006, STUD FUZZ SOFT COMP, V194, P137 Bennett P. N., 2010, P 19 INT C WORLD WID, P111, DOI 10.1145/1772690.1772703 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Berger A, 2000, P 23 ANN INT ACM SIG, P192, DOI 10.1145/345508.345576 Bergsma S., 2007, P JOINT C EMP METH N, P819 Bishop C. M., 2006, PATTERN RECOGNITION Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blei DM, 2012, COMMUN ACM, V55, P77, DOI 10.1145/2133806.2133826 Boldi P., 2008, P 17 ACM C INF KNOWL, P609, DOI DOI 10.1145/1458082.1458163 Brants T., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Brill E., 2000, P 38 ANN M ASS COMP, P286, DOI 10.3115/1075218.1075255 Broder A., 2009, P 18 INT C WORLD WID, P511, DOI 10.1145/1526709.1526778 Broder A. Z., 1997, P COMPR COMPL SEQ, P21, DOI DOI 10.1109/SEQUEN.1997.666900 Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Broder AZ, 2000, LECT NOTES COMPUT SC, V1848, P1 Brown P. F., 1993, Computational Linguistics, V19 Bu Fan, 2012, P 50 ANN M ASS COMP, V12, P449 Bu Fan, 2013, P 23 INT JOINT C ART, P2982 Burke RD, 1997, AI MAG, V18, P57 Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI 10.1145/1401890.1401995 CARPINETO C, 2012, ACM COMPUT SURV, V44, P50, DOI DOI 10.1145/2071389.2071390 Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 Chen Qing, 2007, P 2007 JOINT C EMP M, P181 Chen TianQi, 2013, JMLR P, P436 Chen Tianqi, 2011, CORR Chiang D, 2007, COMPUT LINGUIST, V33, P201, DOI 10.1162/coli.2007.33.2.201 Choi FYY, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P109 Cleverdon C.W., 1970, EFFECT VARIATIONS RE Collins M., 2002, P 40 ANN M ASS COMP, P263 Craswell N., 2007, P 30 ANN INT ACM SIG, P239, DOI 10.1145/1277741.1277784 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 2009, SEARCH ENGINES INFOR, V1st Croft W. Bruce, 2011, SIGIR FORUM, V44, P48 Cucerzan S, 2004, P EMNLP, V4, P293 Dagan I, 2006, LECT NOTES ARTIF INT, V3944, P177 Das D., 2009, P JOINT C 47 ANN M A, V1, P468, DOI 10.3115/1687878.1687944 Dasdan Ali, 2009, P CIKM 2009, P701, DOI 10.1145/1645953.1646043 De Bona Fabio, 2010, HUMAN LANGUAGE TECHN, P474 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200 Diaz Fernando, 2005, P 14 INT C INF KNOWL, P672, DOI 10.1145/1099554.1099722 Ding H., 2013, BRIEF BIOINFORM Dolan B., 2004, P 20 INT C COMP LING Duan H., 2011, P 20 INT C WORLD WID, P117 Dumais Susan T, 1997, AAAI SPRING S CROSS, V15, P21 Egozi Ofer, 2011, ACM T INFORM SYST, V29, P8, DOI DOI 10.1145/1961209 Fox Edward A., 1994, NIST SPECIAL PUBLICA, V500-215, P243 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Gao J., 2011, P 34 INT ACM SIGIR C, P675 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Gao JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P355, DOI 10.1145/1571941.1572003 Gao Jianfeng, 2012, P 21 ACM INT C INF K, P1 Gao Jianfeng, 2010, P CIKM 2010, P1139, DOI 10.1145/1871437.1871582 Giunchiglia F, 2004, LECT NOTES COMPUT SC, V3053, P61 Golding AR, 1999, MACH LEARN, V34, P107, DOI 10.1023/A:1007545901558 Gorla Jagadeesh, 2012, CORR Grangier D, 2008, IEEE T PATTERN ANAL, V30, P1371, DOI 10.1109/TPAMI.2007.70791 Guha R. V., 2003, P 12 INT C WORLD WID, P700 Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989 Guo Jiafeng, 2008, P 31 ANN INT ACM SIG, P379, DOI 10.1145/1390334.1390400 Hagen M., 2011, P 20 INT C WORLD WID, P97 Hagen Matthias, 2012, P 21 ACM INT C INF K, P1015 Haghighi Aria, 2009, P HUM LANG TECHN 200, P362, DOI 10.3115/1620754.1620807 Hardoon David R., 2003, 3 INT WORKSH CONT BA Hardoon DR, 2004, NEURAL COMPUT, V16, P2639, DOI 10.1162/0899766042321814 Heilman M., 2010, HUMAN LANGUAGE TECHN, P1011 Herbrich R, 1999, IEE CONF PUBL, P97, DOI 10.1049/cp:19991091 Hillard Dustin, 2010, P 3 ACM INT C WEB SE, P361, DOI 10.1145/1718487.1718532 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Huang Po-Sen, 2013, P 22 ACM INT C C INF, P2333 Huston Samuel, 2014, ACM T INFORM SYST, V32, P3 Islam A., 2009, P 2009 C EMP METH NA, V3, P1241, DOI 10.3115/1699648.1699670 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jiang Daxin, 2013, ACM T INTEL SYST TEC, V4, P57 Jin R., 2002, P 25 ANN INT ACM SIG, P42 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Karimzadehgan M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P323 Koehn P, 2003, P 2003 C N AM CHAPT, V1, P48, DOI DOI 10.3115/1073445.1073462 Koehn P., 2010, STAT MACHINE TRANSLA Koren Y., 2008, P 14 ACM SIGKDD INT, P426, DOI DOI 10.1145/1401890.1401944 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Koren Y., 2010, ACM T KNOWL DISCOV D, V4 Kotov A., 2012, P 5 ACM INT C WEB SE, P403 Krestel R., 2009, P 3 ACM C REC SYST, P61, DOI 10.1145/1639714.1639726 Kurland O., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009027 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 Lang H., 2010, P 19 ACM INT C INF K, P249, DOI 10.1145/1871437.1871473 Lara D.Catledge, 1995, COMPUT NETW ISDN SYS, V27, P1065 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lease M, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P476, DOI 10.1145/1571941.1572023 Lee DD, 2001, ADV NEUR IN, V13, P556 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lee U., 2005, P 14 INT C WORLD WID, P391, DOI DOI 10.1145/1060745.1060804 Levow GA, 2005, INFORM PROCESS MANAG, V41, P523, DOI 10.1016/j.ipm.2004.06.012 Li H, 2011, IEICE T INF SYST, VE94D, P1854, DOI 10.1587/transinf.E94.D.1854 Li Hang, 2012, P WORKSH WEB SEARCH Li Hang, 2011, SYNTHESIS LECT HUMAN, V4, P1 Li M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P1025 Li Xiao, 2008, P 31 ANN INT ACM SIG, P339, DOI 10.1145/1390334.1390393 Li Y., 2012, P 35 INT ACM SIGIR C, P611 Li Y., 2007, P 30 ANN INT ACM SIG, P797, DOI 10.1145/1277741.1277914 Li Yuan-hong, 2011, Journal of Natural Science of Hunan Normal University, V34 Liao Z., 2011, ACM T INTEL SYST TEC, V3 Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591 Lin D., 2001, NAT LANG ENG, V7, P343 Litkowski Kenneth C, 1999, P 8 TEXT RETR C TREC, P349 Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Lu Yumao, 2009, P 2009 C EMP METH NA, V2, P648, DOI 10.3115/1699571.1699597 Lu Zhengdong, 2013, ADV NEURAL INFORM PR, V26, P1367 Manning Christopher D., 2008, INTRO INFORM RETRIEV Maxwell K Tamsin, 2013, P ACM SIGIR C RES DE, P583 Mei Q., 2008, P 17 ACM C INF KNOWL, P469, DOI 10.1145/1458082.1458145 MENON AK, 2011, P EUR C MACH LEARN K, V6912, P437 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Metzler Donald, 2011, FEATURE CENTRIC VIEW Moschitti A, 2006, LECT NOTES COMPUT SC, V4212, P318 Moschitti A., 2007, P 24 INT C MACH LEAR, P649, DOI 10.1145/1273496.1273578 Nie Jian-Yun, 2010, SYNTHESIS LECT HUMAN, V3, P1, DOI 10.2200/S00266ED1V01Y201005HLT008 Oard Douglas W, 1998, ANN REV INFORM SCI A, V33 Och F. J., 2002, P 40 ANN M ASS COMP, P295 Page L., 1999, 199966 STANF INFOLAB Paranjpe D., 2009, P 18 ACM C INF KNOWL, P365, DOI 10.1145/1645953.1646002 Peng Fuchun, 2007, P 30 ANN INT ACM SIG, P639, DOI 10.1145/1277741.1277851 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Rendle S., 2012, ACM T INTEL SYST TEC, V3 Rendle Steffen, 2010, DAT MIN ICDM 2010 IE, P995 Riezler S, 2010, COMPUT LINGUIST, V36, P569, DOI 10.1162/coli_a_00010 Ristad ES, 1998, IEEE T PATTERN ANAL, V20, P522, DOI 10.1109/34.682181 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 Rosipal R, 2006, LECT NOTES COMPUT SC, V3940, P34 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P2126, DOI 10.1002/asi.20681 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Scholkopf B., 2001, LEARNING KERNELS SUP Schwartz WR, 2009, IEEE I CONF COMP VIS, P24, DOI 10.1109/ICCV.2009.5459205 Sheldon Daniel, 2011, P 4 ACM INT C WEB SE, P795, DOI DOI 10.1145/1935826.1935930 Shen D, 2006, ACM T INFORM SYST, V24, P320, DOI 10.1145/1165774.1165776 Shi L., 2010, P 19 ACM INT C INF K, P1493, DOI 10.1145/1871437.1871655 Silvestri Fabrizio, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000013 Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 Smith David A, 2011, P 20 ACM INT C INF K, P17 Socher Richard, 2011, ADV NEURAL INFORM PR, V24, P801 Song RH, 2008, LECT NOTES COMPUT SC, V4956, P346 Svore KM, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P154 Tan Bin, 2008, P 17 INT C WORLD WID, P347 Tao T, 2006, P MAIN C HUM LANG TE, P407, DOI 10.3115/1220835.1220887 Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794 Tombros A, 2005, J AM SOC INF SCI TEC, V56, P327, DOI 10.1002/asi.20106 Toutaniva K., 2002, P 40 ANN M ASS COMP, P144 Turney Peter D., 2001, P 12 EUR C MACH LEAR, P491 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Wang Chen, 2012, P 5 ACM INT C WEB SE, P343 Wang Hao, 2013, P 2013 C EMP METH NA, P935 Wang JQ, 2012, INFORM PROCESS MANAG, V48, P631, DOI 10.1016/j.ipm.2011.09.003 WANG K, 2009, P 32 ANN INT ACM, P187 Wang Q., 2013, ACM T INFORM SYSTEMS, V31, P1 Wang Q., 2011, P 34 INT ACM SIGIR C, P685 Wang Quan, 2012, P 35 INT ACM SIGIR C, P375 Wang XL, 2008, Proceedings of the 27th Chinese Control Conference, Vol 7, P479, DOI 10.1145/1458082.1458147 Wang Ziqi, 2011, P 49 ANN M ASS COMP, P52 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 Wen J. R., 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974 Wu Haocheng, 2013, CORR Wu W, 2013, J MACH LEARN RES, V14, P2519 Wu W, 2011, J MACH LEARN RES, V12, P1429 Wu Wei, 2013, P 6 ACM INT C WEB SE, P687 Xu G, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1365 Xu J, 2010, LECT NOTES COMPUT SC, V6458, P1 Xu Jingfang, 2011, P 4 ACM INT C WEB SE, P615, DOI 10.1145/1935826.1935912 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu Jun, 2011, P 20 INT C COMP WORL, P153, DOI [DOI 10.1145/1963192.1963270, 10.1145/1963192.1963270] Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Xue Xiaobing, 2012, P 50 ANN M ASS COMP, P187 Yamada K., 2001, P 39 ANN M ASS COMP, P523, DOI 10.3115/1073012.1073079 Yang Yin, 2009, P 2 ACM INT C WEB SE, P34, DOI DOI 10.1145/1498759.1498806 Yi X., 2011, P 10 ANN ACM WORKSH, P153, DOI 10.1145/2063576.2063604 Yi X, 2009, LECT NOTES COMPUT SC, V5478, P29 Yih W, 2011, P 15 C COMP NAT LANG, P247 Zanzotto FM, 2009, NAT LANG ENG, V15, P551, DOI 10.1017/S1351324909990143 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhang X, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P874 Zhao Le, 2010, P 19 ACM INT C INF K, P259, DOI 10.1145/1871437.1871474 Zhao Le, 2012, P 35 INT ACM SIGIR C, P515 NR 207 TC 1 Z9 1 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2013 VL 7 IS 5 BP 345 EP + DI 10.1561/1500000035 PG 127 WC Computer Science, Information Systems SC Computer Science GA AJ9LD UT WOS:000338031000001 ER PT J AU Shen, WW Lin, CL Marcus, A AF Shen, Wuwei Lin, Chung-Ling Marcus, Andrian BA Niu, N Mader, P BF Niu, N Mader, P TI Using Traceability Links to Identifying Potentially Erroneous Artifacts during Regulatory Reviews SO 2013 7TH INTERNATIONAL WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE) LA English DT Proceedings Paper CT 7th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE) CY MAY 19, 2013 CL San Francisco, CA DE Regulatory reviews; traceability; hazard analysis ID REQUIREMENTS AB Safety critical systems emphasize the high quality of both hardware and software of a product since the safety of the product to the public tops all the other considerations. In these domains, regulatory agencies are entitled to conduct reviews on the entire range of artifacts produced from system design to system performance/maintenance. Regulatory review is comprised of the pre-market review and the post-market review. Each aspect of the regulatory review is time-consuming and laborious. In this paper we target situations when errors are identified in the reviewed software, either during pre-or post-market review. We propose an automated mechanism, which utilizes traceability information to identify lists of related software artifacts that are similar to those involved in the reported errors. With the tool recommendations, regulators can quickly investigate these suspicious locations and avoid the occurrence of future hazardous events. C1 [Shen, Wuwei; Lin, Chung-Ling] Western Michigan Univ, Dept Comp Sci, Kalamazoo, MI 49008 USA. RP Shen, WW (reprint author), Western Michigan Univ, Dept Comp Sci, Kalamazoo, MI 49008 USA. EM wuwei.shen@wmich.edu; chung-ling.lin@wmich.edu; amarcus@wayne.edu CR Abrial Jean-Raymond, 1980, CONSTRUCTION PROGRAM Armbrust O, 2009, 2009 ICSE WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE), P36, DOI 10.1109/TEFSE.2009.5069581 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Breaux TD, 2008, IEEE T SOFTWARE ENG, V34, P5, DOI 10.1109/TSE.2007.70746 Breaux Travis D., 2006, 14 IEEE INT REQ ENG, P46 Chen Zhe, 2010, 2 ACM IEEE INT C SOF, P219 Cleland-Huang J., 2010, 32 ACM IEEE INT C SO, V1, P155 Cleland-Huang Jane, 2012, 18 INT C REQ ENG FDN, P179 Egyed A, 2002, ASE 2002: 17TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, P163 Gandhi Robin A., 2012, IEEE 2 INT WORKSH RE, P17 Gurevich Y, 1995, SPECIFICATION VALIDA, P9 Hill Janice, 2010, 18 IEEE INT REQ ENG, P297 Jackson D, 2002, ACM T SOFTW ENG METH, V11, P256, DOI 10.1145/505145.505149 Jean-Raymond Abrial, 1996, B BOOK ASSIGNING PRO Jetley R, 2006, COMPUTER, V39, P61, DOI 10.1109/MC.2006.113 Lucia Andrea De, 2007, ACM T SOFTWARE ENG M, V16 Mader P, 2010, LECT NOTES COMPUT SC, V6394, P226 McMillan C., 2009, 5 INT WORKSH TRAC EM Pohl K, 2010, REQUIREMENTS ENGINEERING: FUNDAMENTALS, PRINCIPLES, AND TECHNIQUES, P1, DOI 10.1007/978-3-642-12578-2 Ratanotayanon S., 2009, 5 INT WORKSH TRAC EM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sannier Nicolas, 2011, 1 MOD DRIV REQ ENG W, P64 Settimi R., 2004, 7 INT WORKSH PRINC S Spanoudakis G, 2004, J SYST SOFTWARE, V72, P105, DOI 10.1016/S0164-1212(03)00242-5 Zhang Yi, 2011, DIABETES SCI TECHNOL, V5, P1403 NR 25 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-0495-2 PY 2013 BP 19 EP 22 PG 4 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA4GM UT WOS:000335673000004 ER PT J AU Rempel, P Mader, P Kuschke, T AF Rempel, Patrick Maeder, Patrick Kuschke, Tobias BA Niu, N Mader, P BF Niu, N Mader, P TI Towards Feature-Aware Retrieval of Refinement Traces SO 2013 7TH INTERNATIONAL WORKSHOP ON TRACEABILITY IN EMERGING FORMS OF SOFTWARE ENGINEERING (TEFSE) LA English DT Proceedings Paper CT 7th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE) CY MAY 19, 2013 CL San Francisco, CA DE requirements traceability; graph clustering; trace retrieval; TraceLab experiment; ubiquitous challenge (GC-U) ID TRACEABILITY LINKS; DOCUMENTATION; REQUIREMENTS; EIGENVECTORS; MATRICES; RECOVERY; CODE AB Requirements traceability supports practitioners in reaching higher project maturity and better product quality. To gain this support, traces between various artifacts of the software development process are required. Depending on the number of existing artifacts, establishing traces can be a time-consuming and error-prone task. Additionally, the manual creation of traces frequently interrupts the software development process. In order to overcome those problems, practitioners are asking for techniques that support the creation of traces (see Grand Challenge: Ubiquitous (GC-U)). In this paper, we propose the usage of a graph clustering algorithm to support the retrieval of refinement traces. Refinement traces are traces that exist between artifacts created in different phases of a development project, e. g., between features and use cases. We assessed the effectiveness of our approach in several TraceLab experiments. These experiments employ three standard datasets containing differing types of refinement traces. Results show that graph clustering can improve the retrieval of refinement traces and is a step towards the overall goal of ubiquitous traceability. C1 [Rempel, Patrick; Maeder, Patrick; Kuschke, Tobias] Tech Univ Ilmenau, Dept Software Syst, Ilmenau, Germany. RP Rempel, P (reprint author), Tech Univ Ilmenau, Dept Software Syst, Ilmenau, Germany. EM patrick.rempel@tu-ilmenau.de; patrick.maeder@tu-ilmenau.de; tobias.kuschke@tu-ilmenau.de CR Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Bouillon E., 2013, LECT NOTES COMPUTER, V7830 Chen K, 2005, REQ ENG IEEE INT C, P31 Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195 Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78 Cleland-Huang J, 2005, PROC INT CONF SOFTW, P362 De Lucia A, 2004, PROC IEEE INT CONF S, P306, DOI 10.1109/ICSM.2004.1357816 FIEDLER M, 1975, CZECH MATH J, V25, P619 Gethers M., 2011, INT C SOFTW MAINT IC, P133 Gotel O., 2012, GRAND CHALLENGE TRAC, P343 Gotel O., 2012, 2012 IEEE 20th International Requirements Engineering Conference (RE 2012), DOI 10.1109/RE.2012.6345841 Hayes JH, 2006, IEEE T SOFTWARE ENG, V32, P4, DOI 10.1109/TSE.2006.3 Hendrickson B, 2007, LINEAR ALGEBRA APPL, V421, P345, DOI 10.1016/j.laa.2006.09.026 Keenan E, 2012, PROC INT CONF SOFTW, P1375, DOI 10.1109/ICSE.2012.6227244 Mader P, 2009, PROCEEDINGS OF THE 2009 17TH IEEE INTERNATIONAL REQUIREMENTS ENGINEERING CONFERENCE, P143, DOI 10.1109/RE.2009.23 Marcus A, 2005, INT J SOFTW ENG KNOW, V15, P811, DOI 10.1142/S0218194005002543 POTHEN A, 1990, SIAM J MATRIX ANAL A, V11, P430, DOI 10.1137/0611030 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-0495-2 PY 2013 BP 100 EP 104 PG 5 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA4GM UT WOS:000335673000018 ER PT S AU Viterbo, J AF Viterbo, Jose BA Cortez, E DaSilva, AS BF Cortez, E DaSilva, AS TI Exploiting Pre-Existing Datasets to Support IETS SO UNSUPERVISED INFORMATION EXTRACTION BY TEXT SEGMENTATION SE SpringerBriefs in Computer Science LA English DT Article; Book Chapter DE Information extraction; Unsupervised approach; Text segmentation; Databases; Structured data; Knowledge bases; Markov models ID DISCOVERY AB This chapter describes in detail a new approach for exploiting preexisting datasets to support Information Extraction by Text Segmentation methods. First, it presents a brief overview of the approach and introduces the concept of knowledge base. Next, it discusses all the steps involved in the unsupervised approach, including how to learn content-based features from knowledge bases, how to automatically induce structure-based features with no previous human-driven training, a feature that is unique to this approach, and how to effectively combine these features to label segments of a text input. C1 Brazilian Comp Soc, Porto Alegre, RS, Brazil. RP Viterbo, J (reprint author), Brazilian Comp Soc, Porto Alegre, RS, Brazil. CR Agichtein E., 2004, P 10 ACM SIGKDD INT, P20, DOI 10.1145/1014052.1014058 Agrawal S., 2003, P CIDR BIENN C INN D Borkar V, 2001, SIGMOD RECORD, V30, P175 Chiang F, 2012, PROC INT CONF DATA, P1277, DOI 10.1109/ICDE.2012.126 Cortez E, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P215, DOI 10.1145/1255175.1255219 Cortez E., 2010, P SIGMOD PHD WORKSH, P49, DOI 10.1145/1811136.1811145 Cortez E., 2011, P ACM SIGMOD INT C M, P541 Cortez E., 2010, P ACM SIGMOD INT C M, P807, DOI 10.1145/1807167.1807254 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 Joachims T., 1998, MACH LEARN ECML 98, V1398, P137, DOI DOI 10.1007/BFB0026683 Lafferty J., 2001, MACH LEARN, V951, P282 Mansuri I.R., 2006, P IEEE ICDE INT C DA, P29 Mesquita F, 2007, INFORM PROCESS MANAG, V43, P983, DOI 10.1016/j.ipm.2006.09.018 Pearl J., 1988, PROBABILISTIC REASON Porto A., 2011, UNSUPERVISED INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarawagi Sunita, 2008, Foundations and Trends in Databases, V1, DOI 10.1561/1500000003 Serra E., 2011, J INFORM DATA MANAGE, V2, P259 Toda G., 2010, P VLDB ENDOWMENT, V4, P151 Toda G., 2009, P WWW INT WORLD WID, P1163, DOI 10.1145/1526709.1526908 Toda G. A., 2006, MTODO PROBABILSTICO Zhao C., 2008, P SIAM INT C DAT MIN, P420 NR 22 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 2191-5768 BN 978-3-319-02597-1; 978-3-319-02596-4 J9 SPRINGERBRIEF COMPUT PY 2013 BP 19 EP 32 DI 10.1007/978-3-319-02597-1_3 D2 10.1007/978-3-319-02597-1 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BA1NW UT WOS:000332741100005 ER PT J AU Rodriguez, J Romero, M Bravo, M AF Rodriguez, Jose Romero, Manuel Bravo, Maricela BE Murgante, B Misra, S Carlini, M Torre, CM Nguyen, HQ Taniar, D Apduhan, BO Gervasi, O TI Dynamic Context for Document Search and Recovery SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, PT I SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 13th International Conference on Computational Science and Its Applications (ICCSA) CY JUN 24-27, 2013 CL Ho Chi Minh City, VIETNAM SP Ho Chi Minh City Int Univ, Univ Perugia, Monash Univ, Kyushu Sangyo Univ, Univ Basilicata, Off Naval Res DE document context; document search; dynamic context. ID MODEL AB From an Information Retrieval perspective there are many works which have been proposed to deal with the problem of retrieving and searching relevant documents. One of the main drawbacks of traditional approaches is related with their static context similarity evaluation, this issue has been addressed by manually refining the query. In this paper we describe a context-based method to dynamically improve the query during document search. A set of experiments were conducted to evaluate the precision and recall of the proposed method, evaluation of results show the benefits of this novel method. C1 [Rodriguez, Jose; Romero, Manuel] CINVESTAV IPN, Dept Comp, Mexico City 07300, DF, Mexico. RP Rodriguez, J (reprint author), CINVESTAV IPN, Dept Comp, Mexico City 07300, DF, Mexico. EM rodriguez@cs.cinvestav.mx; jmromero@computacion.cs.cinvestav.mx; mcbc@correo.azc.uam.mx CR Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Cheng Q, 2008, IEEE INT S IT MED ED, P1045 Hotho Andreas, 2002, J KUNSTLICHE INTELLI, V4, P48 Jing L., 2006, P TEXT MIN WORKSH Kalogeratos A, 2012, KNOWL INF SYST, V31, P455, DOI 10.1007/s10115-011-0412-6 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Manning C.D., 2008, INTRO INFORM RETRIEV Na SH, 2007, INFORM PROCESS MANAG, V43, P887, DOI 10.1016/j.ipm.2006.08.008 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Yu C.T., 1985, P 8 ANN INT ACM SIGI, P197, DOI 10.1145/253495.253525 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-39637-3 J9 LECT NOTES COMPUT SC PY 2013 VL 7971 BP 452 EP 463 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BA2ZY UT WOS:000334032900036 ER PT S AU Houy, C Niesen, T Fettke, P Loos, P AF Houy, Constantin Niesen, Tim Fettke, Peter Loos, Peter GP IEEE TI Towards Automated Identification and Analysis of Argumentation Structures in the Decision Corpus of the German Federal Constitutional Court SO 2013 7TH IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES (DEST) SE IEEE International Conference on Digital Ecosystems and Technologies LA English DT Proceedings Paper CT 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST) CY JUL 24-26, 2013 CL Menlo Park, CA SP Inst Elect Elect Engineers, IEEE Ind Elect Soc, UC Berkeley, Stanford Univ, Hochschule Furtwangen, Curtin Univ Technol, Univ Studi Milano DE argumentation mining; argument mining; NLP; jurisprudence; eHumanities; digital humanities; text mining AB Argumentation is an essential task in every scientific discipline. The development of strong and convincing argumentation as well as the analysis of existing argumentation structures is important in the field of humanities, and especially in the field of jurisprudence. Judicial argumentation requires sophisticated intellectual effort and the knowledge of as much potentially relevant background information as possible. Considering that the fulfillment of this task is limited by the natural human information processing capacity, the field of digital humanities investigates how such information-intensive and time-consuming tasks can be supported by computers. Against the background of the ever-growing availability of different corpora of jurisdiction in Germany, a software prototype supporting automated identification, analysis and recommendation of argumentation structures in electronically available corpora of jurisdiction is currently developed in the project ARGUMENTUM. In this article, we present the basic concept for the preparation and processing of the decision corpus of the German Federal Constitutional Court which shall provide the basis for the future ARGUMENTUM prototype. C1 [Houy, Constantin] German Res Ctr Artificial Intelligence DFKI, Inst Informat Syst IWi, Saarbrucken, Germany. RP Houy, C (reprint author), German Res Ctr Artificial Intelligence DFKI, Inst Informat Syst IWi, Saarbrucken, Germany. EM constantin.houy@iwi.dfki.de; tim.niesen@iwi.dfki.de; peter.fettke@iwi.dfki.de; peter.loos@iwi.dfki.de CR Hevner AR, 2004, MIS QUART, V28, P75 Hogenboom A, 2010, LECT NOTES COMPUT SC, V6413, P200 Houy C., 2012, KI 2012 35 GERM C AR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jurafsky D., 2009, SPEECH LANGUAGE PROC, V2nd Mochales R., 2009, P 12 INT C ART INT L MOCHALES R, 2008, P 2008 C LEG KNOWL I, V189, P11 Mochales Raquel, 2011, Artificial Intelligence and Law, V19, DOI 10.1007/s10506-010-9104-x Moens M.-F., 2007, P 11 INT C ART INT L Reed C, 2004, INT J AI TOOLS, V13, P961 Rice JA, 2007, MATH STAT DATA ANAL, V3rd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Toulmin S. E., 1958, USES ARGUMENT Walton D. N., 1996, ARGUMENTATION SCHEME Wyner A, 2010, LECT NOTES ARTIF INT, V6036, P60, DOI 10.1007/978-3-642-12837-0_4 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2150-4938 BN 978-1-4799-0786-1; 978-1-4799-0784-7 J9 I C DIGIT ECOSYST TE PY 2013 BP 72 EP 77 PG 6 WC Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA2RD UT WOS:000333812900013 ER PT J AU Kawase, R Nunes, BP Siehndel, P AF Kawase, Ricardo Nunes, Bernardo Pereira Siehndel, Patrick GP IEEE TI Content-based Movie Recommendation within Learning Contexts. SO 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2013) SE IEEE International Conference on Advanced Learning Technologies LA English DT Proceedings Paper CT 13th IEEE Annual International Conference on Advanced Learning Technologies (ICALT) CY JUL 15-18, 2013 CL Beijing, PEOPLES R CHINA SP IEEE, IEEE Comp Soc, IEEE Tech Comm Learning Technol HO Beijing Normal Univ AB A good movie is like a good book. As a good book can serve entertaining and learning purposes, so does a movie. In addition to that, movies are in general more engaging and reach a wider audience. In this work, we present and evaluate a method that overcomes the challenge of generating recommendations among heterogeneous resources. In our case, we recommend movies in the context of a learning object. We evaluate our method with 60 participants that judged the relevance of the recommendations. Results show that, in over 74% of the cases the recommendations are in fact related to the given learning object, outperforming a text-based recommendation approach. The implications of our work can take learning outside the classroom and invoke it during the joy of watching a movie. C1 [Kawase, Ricardo] Leibniz Univ Hannover, D-30167 Hannover, Germany. RP Kawase, R (reprint author), Leibniz Univ Hannover, Appelstr 9, D-30167 Hannover, Germany. EM kawase@L3S.de; nunes@L3S.de; siehndel@L3S.de CR Milne D., 2008, CIKM 08, P509, DOI DOI 10.1145/1458082.1458150 Niemann K, 2010, LECT NOTES COMPUT SC, V6383, P632, DOI 10.1007/978-3-642-16020-2_66 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siehndel P., 2012, INT SEM WEB C NR 5 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2161-3761 BN 978-0-7695-5009-1 J9 IEEE INT CONF ADV LE PY 2013 BP 171 EP 173 DI 10.1109/ICALT.2013.53 PG 3 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA2UD UT WOS:000333902700046 ER PT B AU Li, Y Li, TH Liu, R Yang, JH Lee, J AF Li, Ying Li, Ta-Hsin Liu, Rong Yang, Jeaha Lee, Juhnyoung GP IEEE TI Application Management Services Analytics SO 2013 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS (SOLI) LA English DT Proceedings Paper CT IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) CY JUL 28-30, 2013 CL Dongguan, PEOPLES R CHINA SP IEEE DE IT incidents; IT incident tickets; IT incident analysis; Application management services; incident management AB Enterprises often maintain many IT applications to support their business. Application Management Services (AMS) aim to maintain high levels of service quality and availability by restoring normal application service operations and minimizing negative business impact. In this paper, we present the AMS Analytics System for improving the productivity and quality of delivery for AMS practices. Issues regarding IT applications are formally referred as IT incidents or tickets, which are an important vehicle for measuring quality of AMS. IT incident ticket analytics, an important component of of the analytics system, measures workload variability, resource productivity and delivery performance using algorithms from statistics, queuing theory, data clustering and signal processing. The AMS Analytics System provides a standardized, integrated analytics platform supporting AMS delivery. It is built on a Web platform using a set of standard open stack software, enhanced with advanced analytics. Since its initial release, we have applied the AMS Analytics System to several dozens of real-world enterprise users, receiving very positive feedback. C1 [Li, Ying; Li, Ta-Hsin; Liu, Rong; Yang, Jeaha; Lee, Juhnyoung] IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA. RP Li, Y (reprint author), IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA. EM yingli@us.ibm.com; thl@us.ibm.com; rliu@us.ibm.com; jeaha@us.ibm.com; jyl@us.ibm.com CR Anand R., 2013, DIALOG MANAGER UNPUB Bishop C. M., 2006, PATTERN RECOGNITION Clifford David, 2008, IMPLEMENTING ISO IEC Mitchell T., 1997, MACH LEARN Ozgur A, 2007, COOCCURENCE NET 1215 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Van Bon J., 2002, GUIDE IT SERVICE MAN Van Bon J., 2002, IT SERVICE MANAGEMEN Van Bon Jan, 2006, FRAMEWORKS IT MANAGE NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-0530-0; 978-1-4799-0529-4 PY 2013 BP 366 EP 371 PG 6 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA2UB UT WOS:000333901500072 ER PT J AU Huang, YH Murphey, YL Ge, Y AF Huang, Yinghao Murphey, Yi L. Ge, Yao GP IEEE TI Machine Learning of Engineering Diagnostic Knowledge from Unstructured Verbatim Text Descriptions SO 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM) LA English DT Proceedings Paper CT IEEE Symposium on Computational Intelligence and Data Mining (CIDM) CY APR 16-19, 2013 CL Singapore, SINGAPORE SP IEEE, IEEE Computat Intelligence Soc DE machine learning; text mining; engineering diagnostics; important document detection ID MODEL AB This paper presents our research in text mining for discovering important engineering fault diagnostic knowledge from unstructured and verbatim text descriptions. In particular we focus on developing machine learning algorithms for detecting documents that contain descriptions of systematic failures and root causes to the faults. We developed a machine algorithm based on entropy analysis to extract an A-word list, a list of words that are important to characterize the documents of interests, a vector space model to represent features of important documents, and a constraint based k-means clustering algorithm to generate high purity clusters for use in detecting important documents. We applied the algorithms to automotive diagnostic text data, which are unstructured and verbatim descriptions by customers and technicians that contain many typos and self-invented terms. We were able to reduce a list of 2183 words to a list of 137 important words. The classification system generated by these machine learning algorithms showed high recall and accuracy in detecting important diagnostic descriptions. C1 [Huang, Yinghao; Murphey, Yi L.] Univ Michigan, Dept Elect & Comp Engn, Dearborn, MI 48128 USA. RP Huang, YH (reprint author), Univ Michigan, Dept Elect & Comp Engn, Dearborn, MI 48128 USA. EM yilu@umich.edu CR Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 Franz A., 1991, ACM COMPUT SURV, V23, P345 FRIGGE M, 1989, AM STAT, V43, P50, DOI 10.2307/2685173 Huan C.-R., 2007, P ACL 2007 DEM POST, P69, DOI 10.3115/1557769.1557791 Huang LiPing, 2006, 19 INT C IND ENG APP Huang Yinghao, 2012, ANN INFORM SYS UNPUB Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jing LP, 2005, LECT NOTES ARTIF INT, V3518, P802 Liu X., 2002, P 25 ANN INT ACM SIG, P191 Papapetrou O, 2012, IEEE T KNOWL DATA EN, V24, P1848, DOI 10.1109/TKDE.2011.120 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1996, INFORM PROCESSING MA, V32, P431 Zhang TP, 2012, IEEE T KNOWL DATA EN, V24, P1002, DOI 10.1109/TKDE.2011.49 Zhuang FZ, 2012, IEEE T KNOWL DATA EN, V24, P2025, DOI 10.1109/TKDE.2011.143 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-5895-8 PY 2013 BP 46 EP 52 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA2PH UT WOS:000333756100007 ER PT J AU Rao, S Medeiros, H Kak, A AF Rao, Shivani Medeiros, Henry Kak, Avinash BE Lammel, R Oliveto, R Robbes, R TI An Incremental Update Framework for Efficient Retrieval from Software Libraries for Bug Localization SO 2013 20TH WORKING CONFERENCE ON REVERSE ENGINEERING (WCRE) SE Working Conference on Reverse Engineering LA English DT Proceedings Paper CT 20th Working Conference on Reverse Engineering CY OCT 14-17, 2013 CL Koblenz, GERMANY SP Reengineering Forum, Tech Council Software Engn, IEEE Comp Soc, Univ Chile, Dept Ciencias Computac, Univ Studi Molise HO Univ Koblenz ID CONCEPT LOCATION AB Information Retrieval (IR) based bug localization techniques use a bug reports to query a software repository to retrieve relevant source files. These techniques index the source files in the software repository and train a model which is then queried for retrieval purposes. Much of the current research is focused on improving the retrieval effectiveness of these methods. However, little consideration has been given to the efficiency of such approaches for software repositories that are constantly evolving. As the software repository evolves, the index creation and model learning have to be repeated to ensure accuracy of retrieval for each new bug. In doing so, the query latency may be unreasonably high, and also, re-computing the index and the model for files that did not change is computationally redundant. We propose an incremental update framework to continuously update the index and the model using the changes made at each commit. We demonstrate that the same retrieval accuracy can be achieved but with a fraction of the time needed by current approaches. Our results are based on two basic IR modeling techniques - Vector Space Model (VSM) and Smoothed Unigram Model (SUM). The dataset we used in our validation experiments was created by tracking commit history of AspectJ and JodaTime software libraries over a span of 10 years. C1 [Rao, Shivani; Medeiros, Henry; Kak, Avinash] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. RP Rao, S (reprint author), Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. EM sgrao@purdue.edu; hmedeiro@purdue.edu; kak@purdue.edu CR Ali N., 2012, SOURC COD AN MAN IEE, P174 Brown E. W., 1994, VLDB 1994, p192 Buttcher S., 2005, CIKM, P317 CAN F, 1993, ACM T INFORM SYST, V11, P143, DOI 10.1145/130226.134466 Canfora G., 2006, MSR 06, P105 Charikar Moses, 1997, STOC 97, P626 Chiueh T., 1999, EFFICIENT REAL TIME Cleary B, 2009, EMPIR SOFTW ENG, V14, P93, DOI 10.1007/s10664-008-9095-3 Dallmeier V., 2007, ASE, P433 Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315 Haiduc S., 2011, P 2011 26 IEEE ACM I, P637 Haiduc S, 2012, PROC INT CONF SOFTW, P1273, DOI 10.1109/ICSE.2012.6227101 Haiduc S., 2013, SOFTW ENG ICSE 35 IN Jiang H, 2008, ASE 08, P59 Kawrykow D, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P351, DOI 10.1145/1985793.1985842 Lafferty J., 2001, 24 ACM SIGIR C RES D Lukins S., 2008, 15 WORK C REV ENG Manning C.D., 2008, INTRO INFORM RETRIEV Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 Middleton C., 2007, COMP OPEN SOURCE SEA PHILLIPS KF, 1990, J PHARMACOKINET BIOP, V18, P137, DOI 10.1007/BF01063556 Rao S., 2011, MSR 11, P43 Rao S., 2013, 04 PURD U SCH EL COM, V04 Runeson P, 2007, PROC INT CONF SOFTW, P499 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sisman B., 2012, MIN SOFTW REP MSR 20 Sisman B., 2013, MSR 13, P309 Smucker MD, 2007, CIKM 07, P623, DOI DOI 10.1145/1321440.1321528 Tao T., 2006, P HUM LANG TECHN C N Tomasic A., 1994, 19942 STANF INF Walker E., 2011, GEN INTERNAL MED FEB Wang S., 2011, REV ENG WCRE 2011 18, P92 Widyantoro D., 2002, DAT MIN 2002 ICDM 20, p705 Zhou J., 2012, P 2012 INT C SOFTW E Zimmermann T, 2004, PROC INT CONF SOFTW, P563, DOI 10.1109/ICSE.2004.1317478 NR 35 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1095-1350 BN 978-1-4799-2931-3 J9 WORK CONF REVERSE EN PY 2013 BP 62 EP 71 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BA1GO UT WOS:000332514300008 ER PT J AU Dit, B Moritz, E Linares-Vasquez, M Poshyvanyk, D AF Dit, Bogdan Moritz, Evan Linares-Vasquez, Mario Poshyvanyk, Denys GP IEEE TI Supporting and Accelerating Reproducible Research in Software Maintenance using TraceLab Component Library SO 2013 29TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE (ICSM) SE Proceedings-IEEE International Conference on Software Maintenance LA English DT Proceedings Paper CT 29th IEEE International Conference on Software Maintenance (ICSM) CY SEP 22-28, 2013 CL Eindhoven, NETHERLANDS SP IEEE, IEEE Comp Soc, Tech Council Software Engn, Software Improvement Grp, Lab Qual Software DE software maintenance; reproducible; experiments; case studies; TraceLab ID FEATURE LOCATION; INFORMATION-RETRIEVAL; EXECUTION AB Research studies in software maintenance are notoriously hard to reproduce due to lack of datasets, tools, implementation details (e. g., parameter values, environmental settings) and other factors. The progress in the field is hindered by the challenge of comparing new techniques against existing ones, as researchers have to devote a lot of their resources to the tedious and error-prone process of reproducing previously introduced approaches. In this paper, we address the problem of experiment reproducibility in software maintenance and provide a long term solution towards ensuring that future experiments will be reproducible and extensible. We conducted a mapping study of a number of representative maintenance techniques and approaches and implemented them as a library of experiments and components that we make publicly available with TraceLab, called the Component Library. The goal of these experiments and components is to create a body of actionable knowledge that would (i) facilitate future research and would (ii) allow the research community to contribute to it as well. In addition, to illustrate the process of using and adapting these techniques, we present an example of creating new techniques based on existing ones, which produce improved results. C1 [Dit, Bogdan; Moritz, Evan; Linares-Vasquez, Mario; Poshyvanyk, Denys] Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA. RP Dit, B (reprint author), Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23185 USA. EM bdit@cs.wm.edu; eamoritz@cs.wm.edu; mlinarev@cs.wm.edu; denys@cs.wm.edu CR Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30 [Anonymous], SIM [Anonymous], RAP MIN Asuncion H.U., 2010, ICSE, V1, P95 Barr E., 2010, FOSER, P23 BRIN S, 1998, 7 INT WORLD WID WEB, V30, P107 Capobianco G, 2009, INT C PROGRAM COMPRE, P148 Chen X., 2011, ICSE, P912 Cleland-Huang J., 2011, TEFSE Cleland-Huang J, 2012, PROC INT CONF SOFTW, P1329, DOI 10.1109/ICSE.2012.6227087 D'Ambros M, 2012, EMPIR SOFTW ENG, V17, P531, DOI 10.1007/s10664-011-9173-9 De Lucia A, 2012, ICPC, P193 DELUCIA A, 2011, ICPC, P21, DOI DOI 10.1109/ICPC.2011.34 DIT B, 2011, ICPC, P11, DOI DOI 10.1109/ICPC.2011.47 Dit B, 2013, EMPIR SOFTW ENG, V18, P277, DOI 10.1007/s10664-011-9194-4 Dit B., 2012, ICPC, P203 Dit B., 2013, TEFSE, P105 Dit B, 2013, J SOFTW-EVOL PROC, V25, P53, DOI 10.1002/smr.567 Dit B., 2013, MSR DATA TRACK, P131 Do HS, 2005, EMPIR SOFTW ENG, V10, P405, DOI 10.1007/s10664-005-3861-2 EGYED A, 2003, T SOFTWARE ENG TSE, V29, P116 EISENBARTH T, 2001, INT WORKSH PROGR COM, P300 ENSLEN E, 2009, MSR, P71 GAY G, 2009, IEEE INT C SOFTW MAI, P351 Gethers M., 2011, INT C SOFTW MAINT IC, P133 Gonzalez-Barahona JM, 2012, EMPIR SOFTW ENG, V17, P75, DOI 10.1007/s10664-011-9181-9 Haiduc S, 2010, INT C SOFTW ENG ICSE, P223 KAUSHIK N, 2012, CSMR, P159, DOI DOI 10.1109/CSMR.2012.78 Keenan E, 2012, PROC INT CONF SOFTW, P1375, DOI 10.1109/ICSE.2012.6227244 Kitchenham BA, 2011, INFORM SOFTWARE TECH, V53, P638, DOI 10.1016/j.infsof.2010.12.011 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Liu D., 2007, ASE, P234 MARCUS A, 2004, WCRE 04, P214 Menzies T., 2012, PROMISE REPOSITORY MYTKOWICZ T, 2010, IEEE COMPUT, V43, P62 Oliveto R, 2010, ICPC, P68 Panichella A., 2013, ICSE, P522 Panichella A., 2013, CSMR, P199 Petersen K., 2008, 12 INT C EV ASS SOFT Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016. Revelle M, 2009, INT C PROGRAM COMPRE, P218 Robles G., 2010, MSR, P171 Runeson P, 2007, PROC INT CONF SOFTW, P499 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sayyad S., 2005, PROMISE REPOSITORY S SCANNIELLO G, 2011, ICPC, P1, DOI DOI 10.1109/ICPC.2011.13 T. U. of Sheffield, GATE T. U. of Waikato, WEKA TIAN K, 2009, MSR 09, P163 U. of California, KEPL PROJ WANG XY, 2008, ICSE, P461 Wiese A., 2011, ICSM, P496 Yahoo, YAH PIP Zimmermann T, 2007, PROMISE NR 54 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1063-6773 J9 PROC IEEE INT CONF S PY 2013 BP 330 EP 339 DI 10.1109/ICSM.2013.44 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BA1PX UT WOS:000332836100034 ER PT J AU Senin, P Malinchik, S AF Senin, Pavel Malinchik, Sergey BE Xiong, H Karypis, G Thuraisingham, B Cook, D Wu, X TI SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model SO 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) SE IEEE International Conference on Data Mining LA English DT Proceedings Paper CT IEEE 13th International Conference on Data Mining (ICDM) CY DEC 07-10, 2013 CL Dallas, TX SP IEEE, IEEE Comp Soc, NSF, Toshiba, KNIME, TechMatrix, Univ Texas Dallas, Univ Texas Dallas, Erik Jonsson Sch Engn & Comp Sci, Dept Comp Sci DE time series analysis; classification algorithms ID REPRESENTATION; SIMILARITY AB In this paper, we propose a novel method for discovering characteristic patterns in a time series called SAX-VSM. This method is based on two existing techniques - Symbolic Aggregate approXimation and Vector Space Model. SAX-VSM automatically discovers and ranks time series patterns by their importance to the class, which not only facilitates well-performing classification procedure, but also provides an interpretable class generalization. The accuracy of the method, as shown through experimental evaluation, is at the level of the current state of the art. While being relatively computationally expensive within a learning phase, our method provides fast, precise, and interpretable classification. C1 [Senin, Pavel] Univ Hawaii Manoa, Informat & Comp Sci Dept, Honolulu, HI 96822 USA. RP Senin, P (reprint author), Univ Hawaii Manoa, Informat & Comp Sci Dept, Honolulu, HI 96822 USA. EM senin@hawaii.edu; sergey.b.malinchik@lmco.com CR Agrawal R., 1993, P 4 INT C FDN DAT OR, V730, P69 Bjorkman M., 1999, ADV MODELING OPTIMIZ, V1, P17 Briandet R, 1996, J AGR FOOD CHEM, V44, P170, DOI 10.1021/jf950305a Ding H., 2008, P VLDB ENDOWMENT, V1, P1542, DOI DOI 10.1145/1454159.1454226 Dirr MA, 2009, MANUAL WOODY LANDSCA Gandhi A., 2002, THESIS OREGON STATE Goldin D. Q., 1995, Principles and Practice of Constraint Programming - CP '95. First International Conference, CP'95. Proceedings Jones D., 1993, J OPTIM THEORY APPL, V79, P1 Keogh E., 2005, P 5 IEEE INT C DAT M, P226, DOI [DOI 10.1109/ICDM.2005.79, 10.1109/ICDM.2005.79] Keogh E., 2003, DMKD, V7, P4 Keogh EJ, 2000, LECT NOTES ARTIF INT, V1805, P122 Lin J, 2012, J INTELL INF SYST, V39, P287, DOI 10.1007/s10844-012-0196-5 Lin J, 2007, DATA MIN KNOWL DISC, V15, P107, DOI 10.1007/s10618-007-0064-z Lines J., 2012, P 18 ACM SIGKDD INT, P289 Manning C.D., 2008, INTRO INFORM RETRIEV Patel P., 2002, P ICDM Rakthanamanon T., 2013, P SDM Ratanamahatana C., 2004, SDM 04 Saito N, 1994, THESIS YALE U SAKOE H, 1978, IEEE T ACOUST SPEECH, V26, P43, DOI 10.1109/TASSP.1978.1163055 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salzberg SL, 1997, DATA MIN KNOWL DISC, V1, P317, DOI 10.1023/A:1009752403260 Wang XY, 2013, DATA MIN KNOWL DISC, V26, P275, DOI 10.1007/s10618-012-0250-5 Xi X., 2006, P ICML Ye LX, 2011, DATA MIN KNOWL DISC, V22, P149, DOI 10.1007/s10618-010-0179-5 NR 25 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1550-4786 J9 IEEE DATA MINING PY 2013 BP 1175 EP 1180 DI 10.1109/ICDM.2013.52 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BA1QI UT WOS:000332874200134 ER PT J AU Eddy, BP Robinson, JA Kraft, NA Carver, JC AF Eddy, Brian P. Robinson, Jeffrey A. Kraft, Nicholas A. Carver, Jeffrey C. BA Kagdi, H Poshyvanyk, D DiPenta, M BF Kagdi, H Poshyvanyk, D DiPenta, M TI Evaluating Source Code Summarization Techniques: Replication and Expansion SO 2013 IEEE 21ST INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION (ICPC) SE Conference Proceedings Annual International Symposium on Computer Architecture LA English DT Proceedings Paper CT IEEE 21st International Conference on Program Comprehension (ICPC) CY MAY 20-21, 2013 CL San Francisco, CA SP IEEE DE Program comprehension; text summarization; topic modeling; source code summarization; replication study AB During software evolution a developer must investigate source code to locate then understand the entities that must be modified to complete a change task. To help developers in this task, Haiduc et al. proposed text summarization based approaches to the automatic generation of class and method summaries, and via a study of four developers, they evaluated source code summaries generated using their techniques. In this paper we propose a new topic modeling based approach to source code summarization, and via a study of 14 developers, we evaluate source code summaries generated using the proposed technique. Our study partially replicates the original study by Haiduc et al. in that it uses the objects, the instruments, and a subset of the summaries from the original study, but it also expands the original study in that it includes more subjects and new summaries. The results of our study both support the findings of the original and provide new insights into the processes and criteria that developers use to evaluate source code summaries. Based on our results, we suggest future directions for research on source code summarization. C1 [Eddy, Brian P.; Robinson, Jeffrey A.; Kraft, Nicholas A.; Carver, Jeffrey C.] Univ Alabama, Dept Comp Sci, Tuscaloosa, AL 35487 USA. RP Eddy, BP (reprint author), Univ Alabama, Dept Comp Sci, Tuscaloosa, AL 35487 USA. EM bpeddy@ua.edu; jarobinson3@ua.edu; nkraft@cs.ua.edu; carver@cs.ua.edu CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Corley C., 2012, P IEEE INT C PROGR C, P173 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gethers M., 2010, P 2010 IEEE INT C SO, P1, DOI DOI 10.1109/ICSM.2010.5609687 Haiduc S., 2010, P WORK C REV ENG Haiduc S., 2010, P 32 ACM IEEE INT C, V2, P223, DOI 10.1145/1810295.1810335 Ko AJ, 2006, IEEE T SOFTWARE ENG, V32, P971, DOI 10.1109/TSE.2006.116 LAKHOTIA A, 1993, J SYST SOFTWARE, V23, P269, DOI 10.1016/0164-1212(93)90101-3 LaToza T., 2006, P INT C SOFTW ENG Li W., 2006, INT C MACH LEARN Lucia A. D., 2012, P INT C PROGR COMPR, P193 Lukins SK, 2010, INFORM SOFTWARE TECH, V52, P972, DOI 10.1016/j.infsof.2010.04.002 Mani I., 2001, NAACL WORKSH AUT SUM Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10 Marcus A., 2010, P FSE SDP WKSP FUT S, P229, DOI [10.1145/1882362.1882410, DOI 10.1145/1882362.1882410] Mimno D., 2007, INT C MACH LEARN Murphy G, 1996, THESIS U WASHINGTON Rastkar S., 2011, P IEEE INT C SOFTW M Rastkar S., 2010, P 32 ACM IEEE INT C, P505 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck-Jones K., 2007, INFORM PROCESSING MA, V43, P1449, DOI 10.1016/j.ipm.2007.03.009 Sridhara G., 2012, THESIS U DELAWARE Starke J., 2009, IEEE INT C SOFTW MAI Storey M., 2006, P COMP SUPP COLL WOR NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1063-6897 BN 978-1-4673-3092-3 J9 CONF PROC INT SYMP C PY 2013 BP 13 EP 22 PG 10 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA2DN UT WOS:000333336600003 ER PT J AU Ramos, JG Solorio, JC Campoy, L Ruiz, S Jasso, N AF Guadalupe Ramos, J. Solorio, Juan C. Campoy, Lourdes Ruiz, Sebastian Jasso, Nicolas GP IEEE TI Preparing Text Reports from Web Pages Employing Similarity Tests SO 2013 MEXICAN INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE (ENC 2013) SE Proceedings of the Mexican Conference on Computer Science LA English DT Proceedings Paper CT Mexican International Conference on Computer Science (ENC) CY OCT 30-NOV 01, 2013 CL Morelia, MEXICO SP Mexican Comp Sci Soc, Mexican Artificial Intelligence Soc, Univ Michoacana San Nicolas Hidalgo, Consejo Estatal Ciencia, Tecnologia & Innovac, Consejo Nacl Ciencia & Tecnologia, Mexican Assoc Human Comp Interact DE information retrieval; slicing; similarity; summarization AB The World Wide Web is the main source of information for many organizations and common users. However, the analysis and selection of the web content is still an arduous manual task in many cases. When a web query is sent towards a web search engine, a list of URLs is received, frequently ordered by popularity (such as Google's PageRank algorithm). Then, the user must read and analyze each URL in order to find out the convenient information. In this work a method that automatically constructs a text report induced by a web query from a set of URLs is presented. The method extracts text slices (excerpts) from web pages considering the most similar text w.r.t. a web query as slicing criterion. A slice is composed by document object model (DOM) nodes, whereas similarity is calculated using standard techniques employed in natural language processing. C1 [Guadalupe Ramos, J.; Solorio, Juan C.; Campoy, Lourdes; Ruiz, Sebastian; Jasso, Nicolas] Inst Tecnol La Piedad, La Piedad 59300, Mich, Mexico. RP Ramos, JG (reprint author), Inst Tecnol La Piedad, Av Tecnol 2000, La Piedad 59300, Mich, Mexico. EM jgramos@pricemining.com; jcsolorio@itlapiedad.edu.mx; division@itlapiedad.edu.mx; njasso@itlapiedad.edu.mx; lacampoy@itlapiedad.edu.mx CR Castillo Carlos, 2012, Computational Linguistics and Intelligent Text Processing. 13th International Conference (CICLing 2012). Proceedings, Part II, DOI 10.1007/978-3-642-28601-8_16 Das D., 2007, ENG TECHNOLOGY, V4, P192 TIP F, 1995, J PROGRAM LANG, V3, P121 Google Developers, 2013, GOOGL CUST SERARCH Gupta S, 2003, RE-READING HARRY POTTER, P3 Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 Jimenez S, 2010, LECT NOTES COMPUT SC, V6393, P297, DOI 10.1007/978-3-642-16321-0_31 Manning C.D., 2008, INTRO INFORM RETRIEV Nasukawa T., 2003, P 2 INT C KNOWL CAPT, P70, DOI DOI 10.1145/945645.945658 NetBeans.org, 2012, NETBEANS IDE Ramos JG, 2010, LECT NOTES COMPUT SC, V5947, P344, DOI 10.1007/978-3-642-11486-1_29 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Silva J, 2007, LECT NOTES COMPUT SC, V4362, P771 WEISER M, 1984, IEEE T SOFTWARE ENG, V10, P352 WorldWideWebSize.com, 2013, SIZ WORLD WID WEB X Qi, 2009, ACM COMPUT SURV, V41 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1550-4069 BN 978-0-7695-5087-9 J9 PROC MEX INT CONF CO PY 2013 BP 13 EP 19 DI 10.1109/ENC.2013.8 PG 7 WC Computer Science, Theory & Methods SC Computer Science GA BA1QN UT WOS:000332889700003 ER PT J AU Shi, CY Li, JH Chen, JY Chen, XZ AF Shi Chenye Li Jianhua Chen Jieyuan Chen Xiuzhen GP IEEE TI Chinese SNS Blog Classification Using Semantic Similarity SO 2013 FIFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL ASPECTS OF SOCIAL NETWORKS (CASON) SE International Conference on Computational Aspects of Social Networks LA English DT Proceedings Paper CT 5th International Conference on Computational Aspects of Social Networks (CASoN) CY AUG 12-14, 2013 CL Fargo, ND DE text classification; social network; semantic similarity; blog; multi-label classification ID TEXT CATEGORIZATION AB Social Network Services have become an important medium for people to communicate ideas and share interests in recent years. Blogs published and shared by users in this virtual world are one of the main sources of user-generated information. Classifying these freestyle blogs can help understand user interests and assist applications such as search and marketing. In this paper, we propose a new method of multi-label classification for Chinese blogs. By applying Dempster-Shafer theory on semantic word similarity algorithms, we achieve automatic classification without use of difficult-to-obtain training sets. Experiments were conducted on real world data from RENREN.com, the biggest SNS (Social Network Services) in China. Results show that the proposed method achieves satisfactory performance in multi-labeling real world SNS blogs as well as corpus. C1 [Shi Chenye; Li Jianhua; Chen Jieyuan; Chen Xiuzhen] Shanghai Jiao Tong Univ, Sch Informat Secur Engn, Shanghai 200030, Peoples R China. RP Shi, CY (reprint author), Shanghai Jiao Tong Univ, Sch Informat Secur Engn, Shanghai 200030, Peoples R China. EM chenye.shi@sjtu.edu.cn; ljh888@sjtu.edu.cn; chenxz@sjtu.edu.cn CR Balcan Maria-Florina, 2010, J ACM, V57, DOI 10.1145/1706591.1706599 DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950 Elgersma E., 2008, P ACM SIGIR, P723, DOI 10.1145/1390334.1390471 Hashimoto C., 2008, ANN M ASS COMP LING, P69 Husby S. D., 2012, P C EUR CHAPT ASS CO, P28 Ikeda D., 2008, P 23 AAAI C ART INT, P1156 Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Lacerda Yuri Almeida, 2012, P BRAZ S MULT WEB WE, P281, DOI [10.1145/2382636.2382696, DOI 10.1145/2382636.2382696] Lee Lam Hong, APPL INTELLIGENCE, V37, P80, DOI 10.1007/s10489-011-0314-z Luo X, 2011, PROC INT CONF DOC, P920, DOI 10.1109/ICDAR.2011.187 Rullo P., 2011, IEEE T KNOWL DATA EN, V21, P1118, DOI 10.1109/TKDE.2008.206 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck-Jones K, 2004, J DOC, V60, P521, DOI 10.1108/00220410410560591 Tan S, 2005, P 14 ACM INT C INF K, P469, DOI DOI 10.1145/1099554.1099687 Tseng Belle L., 2007, P INT WORKSH MULT DA, DOI [10.1145/1341920.1341923, DOI 10.1145/1341920.1341923] Wilson C, 2009, EUROSYS'09: PROCEEDINGS OF THE FOURTH EUROSYS CONFERENCE, P205 Wu X, 2011, P INT S DIG FOOTPR S, P37, DOI DOI 10.1145/2030066.2030077 Yang CH, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P275 Yen SJ, 2011, EXPERT SYST APPL, V38, P11581, DOI 10.1016/j.eswa.2011.03.036 Zhu X., 2003, P 20 INT C MACH LEAR, V20, P912 Zuo Min, 2010, P INT C MOD SIM VIS, P135, DOI 10.1109/WMSVM.2010.9. NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2155-7047 BN 978-1-4799-1409-8 J9 INT CONF COMPU ASPEC PY 2013 BP 1 EP 6 PG 6 WC Computer Science, Information Systems SC Computer Science GA BA1FT UT WOS:000332450200001 ER PT J AU Costa, R Lima, C Sarraipa, J Jardim-Goncalves, R AF Costa, Ruben Lima, Celson Sarraipa, Joao Jardim-Goncalves, Ricardo BE Simos, T Psihoyios, G Tsitouras, C TI Semantic Enrichment of Building and Construction Knowledge Sources using a Domain Ontology for Classification SO 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013, PTS 1 AND 2 (ICNAAM 2013) SE AIP Conference Proceedings LA English DT Proceedings Paper CT 11th International Conference of Numerical Analysis and Applied Mathematics (ICNAAM) CY SEP 21-27, 2013 CL GREECE SP European Soc Computat Methods Sci, Engn & Technol, Santilli Fdn DE Ontology Engineering; Unsupervised Document Classification; Vector Space Models; Semantic Vectors AB The paper introduces a new conceptual framework for representation of knowledge sources (whether they are web pages or documents), where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector (SV), which is enriched using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test domain for the assessment of the approach is the Building and Construction, using an appropriate available Ontology. Preliminary results were collected using a clustering algorithm for document classification where documents were assigned into a pre-defined set of categories. Such results indicate that the proposed approach does improve the precision and recall of classifications. RI Sarraipa, Joao/ OI Sarraipa, Joao/0000-0003-3102-3179 CR Costa R., 2012, 6 INT C ADV SEM PROC Figueiras P., 2012, KNOWL ENG ONT DEV C Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 MacQueen J., 1967, SOME METHODS CLASSIF Mladenic D., 1998, C AUT LEARN DISC Nagarajan M., 2007, 16 INT C WORLD WID W Rapid-I GmBH, 2012, RAPIDMINER SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1998, USE WORDNET NATURAL NR 10 TC 0 Z9 0 PU AMER INST PHYSICS PI MELVILLE PA 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA SN 0094-243X BN 978-0-7354-1185-2 J9 AIP CONF PROC PY 2013 VL 1558 BP 1381 EP 1384 DI 10.1063/1.4825773 PG 4 WC Mathematics, Applied; Physics, Applied SC Mathematics; Physics GA BA0IB UT WOS:000331472800327 ER PT J AU Subercaze, J Gravier, C Laforest, F AF Subercaze, Julien Gravier, Christophe Laforest, Frederique BE Raghavan, V Hu, X Liau, CJ Treur, J TI Towards an expressive and scalable Twitter's users profiles. SO 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 LA English DT Proceedings Paper CT 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) CY NOV 17-20, 2013 CL Atlanta, GA SP IEEE, Web Intelligence Consortium, Assoc Comp Machinery, IEEE Comp Soc, ebay, Facebook, IEEE Comp Soc Tech Comm Intelligent Informat, ACM SIGART ID RECOMMENDER SYSTEMS; EXTRACTION; WEB AB Microblogging websites such as Twitter produce tremendous amount of data each second. Consequently, real-time recommendation systems require very efficient algorithm to quickly proceed this massive amount of data. In this paper we propose a scalable and extensible way of building content-based user profiles. Scalability refers to the relative complexity of algorithms involved in building the users profiles with respect to state-of-the-art solutions. Extensibility consider avoiding to recompute the model for newcomers. We present a tractable algorithm to build user profiles out of their tweets. Our model is a graph of terms co-occurency, driven by the fact that user sharing similar interests will share similar terms. We then present how this model can be encoded as a binary footprint, hence boosting comparison of users. We provide an empirical study to measure how the distance between users in the hash space differs from distance between users using standard Information Retrieval techniques. This experiment is based on a Twitter dataset we crawled, and represents 25K users and 1 million tweets. Our approach is driven by real-time analysis requirements and is thus oriented on a trade-off between expressivity and efficiency. Experimental results shows that our approach outperforms vector space model by three orders of magnitude, with a precision of 58%. C1 [Subercaze, Julien; Gravier, Christophe; Laforest, Frederique] Telecom St Etienne, F-42023 St Etienne, France. RP Subercaze, J (reprint author), Telecom St Etienne, 20 Rue Docteur Remy Annino, F-42023 St Etienne, France. EM julien.subercaze@telecom-st-etienne.fr; christophe.gravier@telecom-st-etienne.fr; frederique.laforest@telecom-st-etienne.fr CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Bell RM, 2007, SIGKDD EXPLOR NEWSL, V9, P75, DOI DOI 10.1145/1345448.1345465 Bennett J., 2007, KDD CUP WORKSH CONJ Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Charikar M., 2000, APPROXIMATION ALGORI, P139 Dean J., 2004, OSDI 04, V6, P10 Debnath S., 2008, P 17 INT C WORLD WID, P1041, DOI 10.1145/1367497.1367646 Go A., 2009, ENTROPY, V2009, P17 Hannon J., 2010, RECSYS 10, P199 Hannon J., 2010, P 4 ACM C REC SYST R, P199, DOI 10.1145/1864708.1864746 Hartig O, 2009, LECT NOTES COMPUT SC, V5823, P293, DOI 10.1007/978-3-642-04930-9_19 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Java A., 2007, P JOINT 9 WEBKDD 1 S, P56, DOI DOI 10.1145/1348549.1348556 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Lafferty J.D., 2001, CONDITIONAL RANDOM F, V18, P282 Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3 man Au Yeung C., 2008, CEUR WORKSHOP P, V356 Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 Melville P, 2002, EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-02)/FOURTEENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-02), PROCEEDINGS, P187 Mika P, 2005, J WEB SEMANT, V3, P211, DOI 10.1016/j.websem.2005.05.006 MUNOZ S, 2007, SEMANTIC WEB RES APP, V4519, P53, DOI 10.1007/978-3-540-72667-8_6 Pak A., 2010, P LREC 2010 Papadimitriou Panagiotis, 2010, Journal of Internet Services and Applications, V1, DOI 10.1007/s13174-010-0003-x Peng FC, 2006, INFORM PROCESS MANAG, V42, P963, DOI 10.1016/j.ipm.2005.09.002 Pennacchiotti M., 2011, P 20 INT C COMP WORL, P101, DOI DOI 10.1145/1963192.1963244 Phelan O., 2009, P 3 ACM C REC SYST R, P385, DOI 10.1145/1639714.1639794 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer J. B., 2007, COLLABORATIVE FILTER, V4321, P291 Song Y, 2008, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2007, VOL 6, PTS A AND B, P515, DOI 10.1145/1390334.1390423 Stan J., 2011, ASONAM, P434 ter Horst HJ, 2005, J WEB SEMANT, V3, P79, DOI 10.1016/j.websem.2005.06.001 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wallach H. M., 2006, P 23 INT C MACH LEAR, P977, DOI DOI 10.1145/1143844.1143967 Wilson C, 2009, EUROSYS'09: PROCEEDINGS OF THE FOURTH EUROSYS CONFERENCE, P205 Wu W., 2010, HUM LANG TECHN 2010, P689 Zangerle E, 2011, LECT NOTES COMPUT SC, V6984, P113, DOI 10.1007/978-3-642-24704-0_16 Zesch T., 2009, PROC OF RANLP 2009, P484 NR 37 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2902-3 PY 2013 BP 101 EP 108 DI 10.1109/WI-IAT.2013.15 PG 8 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA0HL UT WOS:000331265000015 ER PT J AU Kose, G Tonta, Y Ahmadlouei, H Polatkan, AC AF Kose, Guven Tonta, Yasar Ahmadlouei, Hamid Polatkan, Aydin Can BE Raghavan, V Hu, X Liau, CJ Treur, J TI Story Link Detection in Turkish Corpus SO 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 LA English DT Proceedings Paper CT 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) CY NOV 17-20, 2013 CL Atlanta, GA SP IEEE, Web Intelligence Consortium, Assoc Comp Machinery, IEEE Comp Soc, ebay, Facebook, IEEE Comp Soc Tech Comm Intelligent Informat, ACM SIGART DE story link detection; topic detection and tracking; vector space model; relevance model ID INFORMATION-RETRIEVAL; MODELS AB Story Link Detection (SLD) is known as a sub-task of Topic Detection and Tracking (TDT). SLD aims to specify whether two randomly selected stories discuss the same topic or not. This sub-task drew special attention within the TDT research community as many tasks in TDT are thought to be solved automatically once SLD performs as expected. In this study, performance tests were carried out on the BilCol-2005 Turkish news corpus composed of approximately 209,000 news items using vector space model (VSM) and relevance model (RM) methods with respect to varied index term counts. Accordingly, best results obtained were as follows: the VSM method performed best with 30 terms (F-measure=0.2970) while RM method did with 4 terms (F-measure=0.1910). Furthermore, the combination of two methods using the AND and OR functions increased the precision ratio by 7.9% and recall ratio by 1.2%, respectively, indicating that retrieval performance of SLD algorithms can be increased to some extent by employing both VSM and RM models. C1 [Kose, Guven; Tonta, Yasar; Ahmadlouei, Hamid] Hacettepe Univ, Dept Informat Management, Ankara, Turkey. RP Kose, G (reprint author), Hacettepe Univ, Dept Informat Management, Ankara, Turkey. EM guvenkose@gmail.com; tonta@hacettepe.edu.tr; hamid2026@gmail.com; aydincan.polatkan@gmail.com RI Tonta, Yasar/A-9629-2011 OI Tonta, Yasar/0000-0003-0285-1338 CR Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954 Allan J, 1998, TOPIC DETECTION TRAC Allan J., 2002, TOPIC DETECTION TRAC, P1 Allan J., 2005, P HUM LANG TECHN C C, P121, DOI 10.3115/1220575.1220591 Allan J., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354843 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Can F, 2010, J AM SOC INF SCI TEC, V61, P802, DOI 10.1002/asi.21264 Chen F., 2003, P HLT NAACL, V2, P13, DOI 10.3115/1073483.1073488 Chen F., 2004, P HLT NAACL, P313 Croft W. B., 2000, ACM T INFORM SYST, V18, P79 Farahat A., 2003, P 41 ANN M ASS COMP, V1, P232, DOI 10.3115/1075096.1075126 Frakes W. B., 1992, INFORM RETRIEVAL DAT FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 Jones K. S., 2000, Information Processing & Management, V36 Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044 Lavrenko V., 2002, P 2 INT C HUM LANG T, P115, DOI 10.3115/1289189.1289268 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lavrenko V., 2009, GENERATIVE THEORY RE, V26 Leek T., 2002, TOPIC DETECTION TRAC, P67 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Meadow C. T., 1999, TEXT INFORM RETRIEVA Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Nomoto T., 2010, P 19 ACM INT C INF K, P789, DOI 10.1145/1871437.1871539 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rennie J. D. M., 2008, DERIVATION F MEASURE ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz J., 1999, P DARPA BROADC NEWS, P189 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Yang Y, 2000, P 23 ANN INT ACM SIG, P65, DOI 10.1145/345508.345550 NR 33 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2902-3 PY 2013 BP 154 EP 158 DI 10.1109/WI-IAT.2013.23 PG 5 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA0HL UT WOS:000331265000023 ER PT J AU Keivanloo, I Rilling, J AF Keivanloo, Iman Rilling, Juergen GP IEEE TI Semantic-enabled Clone Detection SO 2013 IEEE 37TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC) SE Proceedings International Computer Software and Applications Conference LA English DT Proceedings Paper CT IEEE 37th Annual Computer Software and Applications Conference (COMPSAC) CY JUL 22-26, 2013 CL Kyoto, JAPAN SP IEEE, IEEE Comp Soc, Informat Proc Soc Japan, Korean Inst Informat Scientists & Engineers, CCF, Tateisi Sci & Technol Fdn, Kyoto Univ, Iowa State Univ Sci & Technol DE Clone detection; Semantic Web; source code analysis ID GRAPH AB We introduce semantic-enabled clone detection, as an approach that emphasizes on importance of the token semantics during the pattern matching for clone detection. This approach can be realized using Semantic Web and its support for knowledge modeling. While the Semantic Web has found wide acceptance in various application and research domains, it still lacks the same acceptance in the source code analysis domain. In this paper we focus on how the Semantic Web and its support for semantic modeling and knowledge retrieval can be applied towards source code clone detection and search. We discuss both, open challenges in the source code clone detection domain, as well as both theoretical and practical aspects on how the Semantic Web can address some of these challenges. C1 [Keivanloo, Iman; Rilling, Juergen] Concordia Univ, Dept Comp Sci, Montreal, PQ H3G 1M8, Canada. RP Keivanloo, I (reprint author), Concordia Univ, Dept Comp Sci, Montreal, PQ H3G 1M8, Canada. EM i_keiv@cse.concordia.ca; rilling@cse.concordia.ca CR Baker B, 1995, WCRE 95, P86 Bellon S, 2007, IEEE T SOFTWARE ENG, V33, P577, DOI 10.1109/TSE.2007.70725 Berners-Lee T., 2001, SCI AM MAY, P28 Beyer D, 2005, IEEE T SOFTWARE ENG, V31, P137, DOI 10.1109/TSE.2005.23 Binkley D, 2007, FoSE 2007: Future of Software Engineering, P104, DOI 10.1109/FOSE.2007.27 Dar S., 1994, ACM SIGMOD INT C MAN Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Fowler M., 2002, LNCS, V2418, P256 Guha R., 2003, INT C WORLD WID WEB, P700 Higo Y, 2009, WORK CONF REVERSE EN, P315, DOI 10.1109/WCRE.2009.39 IOANNIDIS Y, 1993, ACM T DATABASE SYST, V18, P512, DOI 10.1145/155271.155273 Kapser CJ, 2006, J SOFTW MAINT EVOL-R, V18, P61, DOI 10.1002/smr.327 Keivanloo I., 2011, 18 WORK C REV ENG WC Keivanloo I., 2010, 6 INT WORKSH SEM WEB Keivanloo I., 2012, 6 INT WORKSH SOFTW C Koschke R, 2012, EUR CON SFTWR MTNCE, P309, DOI 10.1109/CSMR.2012.37 Liskov B., 1987, OOPSLA 87, P17 QUILLIAN MR, 1967, BEHAV SCI, V12, P410, DOI 10.1002/bs.3830120511 ROY B, 1959, CR HEBD ACAD SCI, V249, P216 Roy CK, 2009, SCI COMPUT PROGRAM, V74, P470, DOI 10.1016/j.scico.2009.02.007 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smullyan R. M., 1968, 1 ORDER LOGIC URBANI J, 2010, SEMANTIC WEB RES APP, V6088, P213, DOI 10.1007/978-3-642-13486-9_15 Welty C., 1997, IEEE INT C AUT SOFTW, P126 NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 0730-3157 BN 978-0-7695-4986-6 J9 P INT COMP SOFTW APP PY 2013 BP 393 EP 398 DI 10.1109/COMPSAC.2013.66 PG 6 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BA0HA UT WOS:000331216500061 ER PT S AU Djellali, C AF Djellali, Choukri GP IEEE TI Truncated Singular Value Decomposition for Semantic-Based Data Retrieval SO 2013 THIRD INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND INFORMATION TECHNOLOGY (ICCIT) SE International Conference on Communications and Information Technology LA English DT Proceedings Paper CT 3rd International Conference on Communications and Information Technology (ICCIT) CY JUN 19-21, 2013 CL Beirut, LEBANON DE semantic analysis; variable selection; clustering; indexation; learning; retrieval; truncated singular value decomposition ID FEATURE-SELECTION AB This paper addresses the increasingly encountered challenge of knowledge indexation. In the past decade, research on numerical schemes on knowledge indexation has been quite intensive. Vector space model is only based on the information contained in term weighting and does therefore not process the semantic contained in the sequence in which the words appear in a bag-of-words. This representation provides an abstraction of semantic relations between different linguistic units. A novel semantic-based method for knowledge indexation, which can provide improvement in both indexing and retrieval, is described. Despite a huge dimension in vector space model size, retrieval accuracies are seen to improve significantly when the proposed system is applied for indexing Reuters-21578 corpus. C1 Univ Quebec Montreal, LATECE, Lab Res Technol Ecommerce, Montreal, PQ H2X 3Y7, Canada. RP Djellali, C (reprint author), Univ Quebec Montreal, LATECE, Lab Res Technol Ecommerce, 201 President Kennedy, Montreal, PQ H2X 3Y7, Canada. EM djellali.choukri@courrier.uqam.ca CR Anderson MJ, 2001, AUSTRAL ECOL, V26, P32, DOI 10.1111/j.1442-9993.2001.01070.pp.x Arndt H, 2009, P INT COMP SOFTW APP, P460, DOI 10.1109/COMPSAC.2009.67 Attik M, 2004, IEEE IJCNN, P1371 Azzawi A A G, 2010, DEV E SYSTEM ENG DES, P199 Baena-Garcia M, 2011, INT SYST DES APPL IS, P1044 Braun A C, 2011, HYP IM SIGN PROC EV, P1 Cibas T, 1994, P ICANN 94 CIT Djellali Choukri, 2012, EGC M 2012 3 INT C E Duda R.O., 2001, PATTERN CLASSIFICATI Issac B, 2009, TENCON 2009 2009 IEE, P1, DOI 10.1109/TENCON.2009.5396056 JING YN, 2009, COMM SOFTW NETW 2009, P336 Kuo Chih-Hao, 2006, ANTENNAS PROPAGATION, V54, P2917 Perez Meir, 2012, EL EL ENG ISR IEEEI, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Stuhler E, 2011, IEEE NUCL SCI CONF R, P4398, DOI 10.1109/NSSMIC.2011.6153848 Sun Zhan-Li, 2005, GEOSCIENCE REMOTE SE, V2, P108 Tsymbal A., 2005, Information Fusion, V6, DOI 10.1016/j.inffus.2004.04.003 Verikas A, 2002, PATTERN RECOGN LETT, V23, P1323, DOI 10.1016/S0167-8655(02)00081-8 Zaman A N K, 2011, DIG INF MAN ICDIM 20, P133 Zhang Xingfu, 2011, MULT TECHN ICMT 2011, P934 NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2329-6372 BN 978-1-4673-5307-6; 978-1-4673-5306-9 J9 INT CONF COMM INF T PY 2013 BP 61 EP 66 PG 6 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BA0HN UT WOS:000331269900012 ER PT B AU Lutz, J Thonssen, B Witschel, HF AF Lutz, Jonas Thoenssen, Barbara Witschel, Hans Friedrich BE Gerber, A VanDeventer, P TI Breaking free from your information prison A recommender based on semantically enriched context descriptions SO 2013 ENTERPRISE SYSTEMS CONFERENCE (ES) LA English DT Proceedings Paper CT 1st International Conference on Enterprise Systems (ES) CY NOV 07-08, 2013 CL Cape Town, SOUTH AFRICA SP IEEE, Stand Bank, Capacity, Cape Peninsula Univ Technol, CSIR, Univ Pretoria, IEEE SMC Tech Comm Enterprise Informat Syst, IEEE SMC Tech Comm Enterprise Architecture & Engn DE information management; similarity; machine learning; context AB Information repositories, implemented as Enterprise Portals (EP) on the intranet, are increasingly popular in companies of all sizes. Enterprise Portals allow for structuring information in a way that resembles the organization of paper copies, i.e. simulating folders and registries and furthermore, provide simple routines for publishing and collaborating. Hence, in general, such kind of information management is not much different from paper management: electronic documents must be uploaded into the Enterprise Portal manually, filed into folders (which have to be created manually, too), tagged and related to other information objects if need be. With this approach information structuring remains subject to the individual user leading to the well-known problems of multiple filing, overlooking relevant information and incomprehensible folder structure. The SEEK!sem project aims at improving such kind of information system by automatically identifying and recommending related information resources to be added to a folder. The recommendations are based on rules, exploiting content and context similarity of information resources. Rules can be created upfront, based on explicitly defined relations between information objects. They can also be machine learned, i.e. the recommender exploits the existing linkage between documents, folders and other objects to learn "relatedness rules". In either case, potential new connections are inferred by applying the rules in a reasoning step. Recommended new connections are ranked by the sum of the scores of all applied rules - the rule scores, again, can either be provided by experts or machine-learned. The applied rules can serve as an explanation of a recommendation, i.e. they can assist users in understanding why a particular connection is suggested. C1 [Lutz, Jonas; Thoenssen, Barbara; Witschel, Hans Friedrich] Univ Appl Sci & Arts Northwestern Switzerland, FHNW, Olten, Switzerland. RP Lutz, J (reprint author), Univ Appl Sci & Arts Northwestern Switzerland, FHNW, Olten, Switzerland. EM jonas.lutz@fhnw.ch; barbara.thoenssen@fhnw.ch; hansfriedrich.witschel@fhnw.ch CR Al-Kofahi K., 2007, AAAI WORKSH, P9 ARMENGOL E, 2005, P 2005 C ART INT RES, V131, P239 Bertolazzi P., 2001, INT WORKSH OP ENT SO, P104 Chechik G, 2010, J MACH LEARN RES, V11, P1109 Fensel D., 1998, P 11 INT FLAIRS C FL Fouss F, 2007, IEEE T KNOWL DATA EN, V19, P355, DOI 10.1109/TKDE.2007.46 Fox M. S., 1996, SIMULATING ORG COMPU, P131 Geerts G.L., 2000, ONTOLOGICAL FDN REA Hevner A, 2010, INTEGR SER INFORM SY, V22, P1, DOI 10.1007/978-1-4419-5653-8 Hinkelmann K., 2010, P 2 INT WORKSH ADV E Leppanen M., 2005, LECT NOTES COMPUTER, P17 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Lu Q., 2012, KEOD, P76 Martin A., 2011, LINKED ENTERPRISE MO Michele M. S. Sebag, 1994, LECT NOTES COMPUTER, P119 Minkov E., 2007, P 9 WEBKDD 1 SNA KDD, P1, DOI 10.1145/1348549.1348550 Minkov E., 2008, EMNLP, P907 Minkov E, 2012, WORKSH P TEXTGRAPHS, P20 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stuckenschmidt H., 2001, Proceedings of the First International Conference on Knowledge Capture Tan P. N., 2005, INTRO DATA MINING The Open Group, 2012, ARCHIMATE 2 0 SPEC Thonssen B., 2011, P 5 INT C METH TECHN, P1 THONSSEN B, 2010, P 4 INT C MET SEM MT, V108, P195 Thonssen B., 2013, AUTOMATIC FORMAT IND Uren V, 2006, J WEB SEMANT, V4, P14, DOI 10.1016/j.websem.2005.10.002 Uschold M., 1997, KNOWLEDGE ENG REV, V13 NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-6411-9; 978-1-4673-6412-6 PY 2013 PG 9 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJU48 UT WOS:000330583500015 ER PT J AU Dey, L Verma, I AF Dey, Lipika Verma, Ishan BE Raghavan, V Hu, X Liau, CJ Treur, J TI Text-driven Multi-structured Data Analytics for Enterprise Intelligence SO 2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY - WORKSHOPS (WI-IAT), VOL 3 LA English DT Proceedings Paper CT 12th IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT) CY NOV 17-20, 2013 CL Atlanta, GA SP IEEE, Web Intelligence Consortium, Assoc Comp Machinery, IEEE Comp Soc, ebay, Facebook, IEEE Comp Soc Tech Comm Intelligent Informat, ACM SIGART DE Text Analytics; Information Fusion AB Text data constitutes the bulk of all enterprise data. Text repositories are not only tacit store-houses of knowledge about its people, projects and processes but also contain invaluable information about its customers, competitors, suppliers, partners and all other stakeholders. Mining this data can provide interesting and valuable insights provided it is appropriately integrated with other enterprise data. In this paper we propose a framework for text-driven analysis of multi-structured data. C1 [Dey, Lipika] Tata Consultancy Serv, TCS Innovat Labs, New Delhi, India. RP Dey, L (reprint author), Tata Consultancy Serv, TCS Innovat Labs, New Delhi, India. EM lipika.dey@tcs.com; ishan.verma@tcs.com CR Agarwal Puneet, 2012, CATCHING LONG TAIL E Benamara F., 2007, ICWSM BHIDE M, 2008, IEEE 24 INT C ONDATA, P1616 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Broder A. Z., COMPRESSION COMPLEXI, P21 Chakaravarthy Venkatesan T., 2006, P 32 INT C VER LARG DAVID N, 2007, LINGUISTICAE INVESTI, V30, P3 Dey L, 2009, INT J DOC ANAL RECOG, V12, P205, DOI 10.1007/s10032-009-0090-z Dey Lipika, 2013, FUSION 2013 IST TURK Dey Lipika, P 2011 JOINT WORKSH Esuli A., 2006, P 5 INT C LANG RES E, V6, P417 Finkel Jenny Rose, 2008, ACL HLT Huang LiGuo, 2010, ASE 10 SEPT 20 24 AN Khurdiya Arpit, 2011, P 22 INT JOINT C ART Kumar Akshi, 2012, IJCSI INT J COMPUTER, P372 Levy Roger, 2004, P 42 ANN M ASS COMP Liu Bing, 2012, SYNTHESIS LECT HUMAN, V5.1, P1 Manish A., 2007, P 2007 ACM SIGMOD IN Manning Christopher D., 1999, FDN STAT NATURAL LAN Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 O'Connor B, 2010, ICWSM, V11, P122 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Rahm E, 2007, LECT NOTES COMPUT SC, V4704, P14 Ritter A., 2012, KDD, P1104 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schumaker RP, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1462198.1462204 Shroff Gautam, 2011, P 14 INT C ONINFORMA Weikum Gerhard, 2010, PODS 10 JUN 6 11 IND ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5 NR 29 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2902-3 PY 2013 BP 213 EP 220 DI 10.1109/WI-IAT.2013.186 PG 8 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA0EJ UT WOS:000331078300050 ER PT J AU Ramezani, R Sheydaei, N Kahani, M AF Ramezani, Reza Sheydaei, Navid Kahani, Mohsen GP IEEE TI Evaluating the Effects of Textual Features on Authorship Attribution Accuracy SO PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE 2013) LA English DT Proceedings Paper CT 3rd International Conference on Computer and Knowledge Engineering (ICCKE) CY OCT 31-NOV 01, 2013 CL Mashhad, IRAN SP Ferdowsi Univ Mashhad, Comp Engn Dept HO Ferdowsi Univ Mashhad DE Authorship Attribution; Author Identification; Textual Features; Persian Corpus; Data Mining; Classification AB Authorship attribution (AA) or author identification refers to the problem of identifying the author of an unseen text. From the machine learning point of view, AA can be viewed as a multiclass, single-label text-categorization task. This task is based on this assumption that the author of an unseen text can be discriminated by comparing some textual features extracted from that unseen text with those of texts with known authors. In this paper the effects of 29 different textual features on the accuracy of author identification on Persian corpora in 30 different scenarios are evaluated. Several classification algorithms have been used on corpora with 2, 5, to, 20 and 40 different authors and a comparison is performed. The evaluation results show that the information about the used words and verbs are the most reliable criteria for AA tasks and also NLP based features are more reliable than BOW based features. C1 [Ramezani, Reza] Ferdowsi Univ Mashhad, Dept Comp Engn, DDEmS Lab, Mashhad, Iran. RP Ramezani, R (reprint author), Ferdowsi Univ Mashhad, Dept Comp Engn, DDEmS Lab, Mashhad, Iran. EM reza.ramezani@stu.um.ac.ir; n.sheydaei@ec.iut.ac.ir; kahani@um.ac.ir CR Gate, 2013, GEN ARCH TEXT ENG Gose E., 1996, PATTERN RECOGNITION Grieve J., 2007, LIT LINGUISTIC COMPU, V22, P251, DOI DOI 10.1093/LLC/FQM020> Koppel M., 2004, P 21 INT C MACH LEAR, P62 Lan M., 2009, PATTERN ANAL MACHINE, V31, P721 Li ZX, 2011, PATTERN RECOGN LETT, V32, P441, DOI 10.1016/j.patrec.2010.11.001 Rudman J, 1997, COMPUT HUMANITIES, V31, P351, DOI 10.1023/A:1001018624850 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sheydaei N., 2013, 21 IR C EL ENG ICEE2 Stamatatos E, 2009, J AM SOC INF SCI TEC, V60, P538, DOI 10.1002/asi.21001 Zhang W., 2011, EXPERT SYSTEMS APPL, V38, P2758 Zu Eissen S. M., 2007, ADV DATA ANAL, P359 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2093-8 PY 2013 BP 108 EP 113 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJU44 UT WOS:000330578800016 ER PT J AU Chen, KY Wang, HM Chen, B Chen, HH AF Chen, Kuan-Yu Wang, Hsin-Min Chen, Berlin Chen, Hsin-Hsi GP IEEE TI WEIGHTED MATRIX FACTORIZATION FOR SPOKEN DOCUMENT RETRIEVAL SO 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) SE International Conference on Acoustics Speech and Signal Processing ICASSP LA English DT Proceedings Paper CT IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) CY MAY 26-31, 2013 CL Vancouver, CANADA SP Inst Elect & Elect Engineers, Inst Elect & Elect Engineers Signal Proc Soc DE Spoken document retrieval; topic model; non-probabilistic; non-occurring words ID INFORMATION-RETRIEVAL; MODEL; WORD AB Since more and more multimedia data associated with spoken documents have been made available to the public, spoken document retrieval (SDR) has become an important research subject in the past two decades. Recently, topic models have been successfully used in SDR as well as general information retrieval (lR). These models fall into two categories: probabilistic topic models (PTM) and non-probabilistic topic models (NPTM). One major difference between PTM and NPTM is that the former only takes the words occurring in a document into account, whereas the latter, such as latent semantic analysis (LSA), explicitly models all the words in the vocabulary (including both occurring and non-occurring words). We believe that the non-occurring words can provide additional information that is also useful for SDR. However, to our best knowledge, there is a dearth of work investigating the effectiveness of the non-occurring words for SDR and IR. In order to make effective use of those non-occurring words of documents for semantic analysis, we propose a weighted matrix factorization (WMF) framework, in which the impact of the non-occurring words on the semantic analysis can be modulated properly. The results of SDR experiments conducted on the TDT-2 (Topic Detection and Tracking) collection highlight the performance merits of our proposed framework when compared to several existing topic models. C1 [Chen, Kuan-Yu; Wang, Hsin-Min] Acad Sinica, Inst Informat Sci, Taipei, Taiwan. RP Chen, KY (reprint author), Acad Sinica, Inst Informat Sci, Taipei, Taiwan. EM kychen@iis.sinica.edu.tw; whm@iis.sinica.edu.tw; berlin@ntnu.edu.tw; hhchen@ntu.edu.tw CR Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Blei D., 2009, TEXT MINING THEORY A Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992 Chen KY, 2012, IEICE T INF SYST, VE95D, P1195, DOI 10.1587/transinf.E95.D.1195 CHEN KY, 2010, P IEEE INT C AC SPEE, P5394 Chien J. T., 2004, INT J COMPUTATIONAL, V9, P29 Croft W. B., 2003, KLUWER INT SERIES IN, V13 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Garofolo J., 2000, P 8 TEXT RETR C, P107 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guo W., 2012, P ACL, P864 Hoffman T., 2001, MACH LEARN, V42, P177 Huang C. L., 2011, P INTERSPEECH, P717 Jones K. S., 2000, Information Processing & Management, V36 LDC, 2000, PROJ TOP DET TRACK Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42 Lv Y., 2012, P CIKM, P1799 Manning C.D., 2008, INTRO INFORM RETRIEV Naptali W., 2009, INT J COMPUTERS, P85 Naptali W, 2008, REC ADV COMPUT ENG, P275 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srebro N., 2003, P 20 INT C MACH LEAR, P720 Wang Q., 2011, P 34 INT ACM SIGIR C, P685 Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204 NR 26 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1520-6149 BN 978-1-4799-0356-6 J9 INT CONF ACOUST SPEE PY 2013 BP 8530 EP 8534 PG 5 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA BJQ19 UT WOS:000329611508140 ER PT S AU Wilhelm-Stein, T Eibl, M AF Wilhelm-Stein, Thomas Eibl, Maximilian BE Forner, P Muller, H Paredes, R Rosso, P Stein, B TI A Quantitative Look at the CLEF Working Notes SO INFORMATION ACCESS EVALUATION: MULTILINGUALITY, MULTIMODALITY, AND VISUALIZATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th International Conference of the CLEF Initiative (CLEF) CY SEP 23-26, 2013 CL Valencia, SPAIN SP Cross Language Evaluat Forum, Univ Politecnica Valencia, PROMISE Network Excellence, European Sci Fdn, ELIAS Project, Escola Tecnica Super Enginyeria Informatica, Dept Sistemas Informaticos & Computac, Web Informat Qual Evaluat Initiat, Corex, Bldg Knowledge Solut, ELIAS DE data mining; evaluation; retrospection; retrieval models; evaluation measures AB After seven years of participation in CLEF we take a look back at the developments and trends in different domains like evaluation measures and retrieval models. For that purpose a new collection containing all CLEF working notes including their metadata was created and analysed. C1 [Wilhelm-Stein, Thomas; Eibl, Maximilian] Tech Univ Chemnitz, D-09107 Chemnitz, Germany. RP Wilhelm-Stein, T (reprint author), Tech Univ Chemnitz, D-09107 Chemnitz, Germany. EM wilt@hrz.tu-chemnitz.de; eibl@hrz.tu-chemnitz.de CR Bogers T., 2012, CLEF 2012 EV LAB WOR Eibl M., 2009, WORKING NOTES CLEF Forner P., 2012, CLEF 2012 EV LAB WOR Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wilhelm T., 2011, SIGIR, P1291 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-40802-1; 978-3-642-40801-4 J9 LECT NOTES COMPUT SC PY 2013 VL 8138 BP 13 EP 16 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJR42 UT WOS:000329799900002 ER PT S AU Suominen, H Salantera, S Velupillai, S Chapman, WW Savova, G Elhadad, N Pradhan, S South, BR Mowery, DL Jones, GJF Leveling, J Kelly, L Goeuriot, L Martinez, D Zuccon, G AF Suominen, Hanna Salantera, Sanna Velupillai, Sumithra Chapman, Wendy W. Savova, Guergana Elhadad, Noemie Pradhan, Sameer South, Brett R. Mowery, Danielle L. Jones, Gareth J. F. Leveling, Johannes Kelly, Liadh Goeuriot, Lorraine Martinez, David Zuccon, Guido BE Forner, P Muller, H Paredes, R Rosso, P Stein, B TI Overview of the ShARe/CLEF eHealth Evaluation Lab 2013 SO INFORMATION ACCESS EVALUATION: MULTILINGUALITY, MULTIMODALITY, AND VISUALIZATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th International Conference of the CLEF Initiative (CLEF) CY SEP 23-26, 2013 CL Valencia, SPAIN SP Cross Language Evaluat Forum, Univ Politecnica Valencia, PROMISE Network Excellence, European Sci Fdn, ELIAS Project, Escola Tecnica Super Enginyeria Informatica, Dept Sistemas Informaticos & Computac, Web Informat Qual Evaluat Initiat, Corex, Bldg Knowledge Solut, ELIAS DE Information Retrieval; Evaluation; Medical Informatics; Test-set Generation; Text Classification; Text Segmentation ID NATURAL-LANGUAGE; CLINICAL TEXT; INFORMATION; TASKS AB Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) in clinical reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients' information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identified clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development. C1 [Suominen, Hanna] NICTA, Canberra, ACT, Australia. RP Suominen, H (reprint author), NICTA, Canberra, ACT, Australia. EM Hanna.Suominene@nicta.com.au; sansala@utu.fi; sumithra@dsv.su.se; wwchapman@ucsd.edu; Guergana.Savova@childrens.harvard.edu; noemie@dbmi.columbia.edu; Sameer.Pradhan@childrens.harvard.edu; brett.south@hsc.utah.edu; dlm31@pitt.edu; Gareth.Jones@computing.dcu.ie; Johannes.Leveling@computing.dcu.ie; Liadh.Kelly@computing.dcu.ie; Lorraine.Goeuriot@computing.dcu.ie; David.Martinez@nicta.com.au; Guido.Zuccon@csiro.au CR Allvin Helen, 2011, J Biomed Semantics, V2 Suppl 3, pS1, DOI 10.1186/2041-1480-2-S3-S1 Anderson B, 1975, AM J COMPUTATIONAL L, V2, P68 BECKER H, 1972, PATHOL EUR, V7, P193 Bodenreider O, 2003, J BIOMED INFORM, V36, P414, DOI 10.1016/j.jbi.2003.11.002 Boyer C., 2012, KHRESMOI EU PROJECT Chapman WW, 2011, J AM MED INFORM ASSN, V18, P540, DOI 10.1136/amiajnl-2011-000465 COLLEN MF, 1978, MED INSTRUM, V12, P222 Demner-Fushman D, 2009, J BIOMED INFORM, V42, P760, DOI 10.1016/j.jbi.2009.08.007 Experian Hitwise, 2008, GOOGL REC 87 81 PERC Fox S, 2011, TECHNICAL REPORT Goeuriot L., 2013, CLEF CLEF Goeuriot L., 2013, P 5 INT WORKSH EV IN Hanbury A., 2012, MIE VILL FUTURE Hirschman L., 1976, P SOC 1976 NAL COMP, V45, P267 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Kalpathy-Cramer J., 2011, CLEF 2011 CROSS LANG Koopman B., 2013, RELEVATION OPEN SOUR Kummervold PE, 2008, J MED INTERNET RES, V10, DOI 10.2196/jmir.1023 Mendonca EA, 2005, J BIOMED INFORM, V38, P314, DOI 10.1016/j.jbi.2005.02.003 Meystre SM, 2008, YB MED INFORM S1, V47, P128 Mowery D., 2013, CLEF CLEF Muller H, 2010, INFORM RETRIEVAL SER, V32, P1, DOI 10.1007/978-3-642-15181-1 Pakhomov SVS, 2006, J AM MED INFORM ASSN, V13, P516, DOI 10.1197/jamia.M2077 Pestian J., 2007, BIOL TRANSLATIONAL C, P97 Pestian J. P., 2012, BIOMEDICAL INFORMATI, V5, P3 Pradhan S., 2013, CLEF CLEF Reiner Bruce I, 2007, J Am Coll Radiol, V4, P313, DOI 10.1016/j.jacr.2007.01.015 Roberts PM, 2009, INFORM RETRIEVAL, V12, P81, DOI 10.1007/s10791-008-9072-x Robertson S., 2000, NIST SPECIAL PUBLICA, V500-249, P25 Robertson S.E., 1994, 356 U CAMBR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar IN, 2010, J TRANSL MED, V8, DOI 10.1186/1479-5876-8-22 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 South B, 2012, P 2012 WORKSH BIOM N, P130 Suominen H., 2012, P CLEFEHEALTH2012 CL Suominen Hanna, 2007, Int J Med Inform, V76 Suppl 2, pS293, DOI 10.1016/j.ijmedinf.2007.05.006 Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203 Voorhees E.M., 2011, P TREC NIST YEH A, 2000, PROCEEDINGS OF THE 1, V2, P947 Zweigenbaum P, 2007, BRIEF BIOINFORM, V8, P358, DOI 10.1093/bib/bbm045 NR 40 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-40802-1; 978-3-642-40801-4 J9 LECT NOTES COMPUT SC PY 2013 VL 8138 BP 212 EP 231 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJR42 UT WOS:000329799900024 ER PT J AU Chen, T Xue, LM Song, QS AF Chen, Tao Xue, Li-Min Song, Qing-Shuai BE Tan, D TI Research on Information Retrieval Based on Fuzzy Ontology SO 2013 INTERNATIONAL CONFERENCE ON CYBER SCIENCE AND ENGINEERING (CYBERSE 2013) LA English DT Proceedings Paper CT International Conference on Cyber Science and Engineering (CyberSE) CY DEC 14-15, 2013 CL Guangzhou, PEOPLES R CHINA SP Int Assoc Cyber Sci & Engn DE information retrieval; fuzzy class; feature word; fuzzy ontology; similarity AB Traditional method of information retrieval mainly obtains information wanted based on syntax match with keywords mechanically, which not only return much irrelevant information but also leaves out lots of relevant information, a method of information retrieval based on fuzzy ontology and focusing on intrinsic property of concepts is proposed then, which digs fuzzy semantic meaning and fuzzy relation of concepts and fulfills users' need in semantic understanding, at last, experiment result manifest the advantage of this method. C1 [Chen, Tao; Xue, Li-Min; Song, Qing-Shuai] Naval Command Coll, Dept Informat, Nanjing, Jiangsu, Peoples R China. RP Chen, T (reprint author), Naval Command Coll, Dept Informat, Nanjing, Jiangsu, Peoples R China. CR JIANG Jia-tao, 2010, RES FUZZY ONTOLOGY R [ē½—ę¬£ LUO Xin], 2005, [č®”ē®—ęœŗåŗ”ē”Ø, Computer Applications], V25, P2031 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P41 XU Zhong-xi, 2004, INTELLIGENCE MAGAZIN, V23, P7 Yu Yang-xin, 2009, Journal of Lanzhou University of Technology, V35 ZHAI Jun, 2010, INF SCI MAN ENG ISME ZHANG Li-li, 2011, FUZZY ONTOLOGY BASED NR 10 TC 0 Z9 0 PU DESTECH PUBLICATIONS, INC PI LANCASTER PA 439 DUKE STREET, LANCASTER, PA 17602-4967 USA BN 978-1-60595-134-8 PY 2013 BP 331 EP 339 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA BJN57 UT WOS:000329287400046 ER PT S AU Wang, Y Luo, C Li, X Liu, YQ Zhang, M Ma, SP AF Wang, Yan Luo, Cheng Li, Xin Liu, Yiqun Zhang, Min Ma, Shaoping BE Zhou, G Li, J Zhao, D Feng, Y TI Entity Linking from Microblogs to Knowledge Base Using ListNet Algorithm SO NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2013 SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 2nd Annual Conference of China-Computer-Federation Technical-Committee-of-Chinese-Information on Natural Language Processing and Chinese Computing (CCF TCCI NLPCC) CY NOV 15-19, 2013 CL Chongqing, PEOPLES R CHINA SP China Comp Federat, Tech Comm Chinese Informat, Peking Univ, Microsoft Res Asia, Tsinghua Univ, ACTA Scientiarum Naturalium Univ Pekinensis, Sogou Inc, Sina Weibo, Tencent Weibo, Sichuan Inst Comp Sci, Mingbo Educ Technol, Springer, China Comp Federat HO Chongqing Univ, State Key Lab Digital Publishing DE Entity Linking; Microblog; Learning to Rank AB Entity Linking (EL) is a fundamental technology in Natural Language Processing and Knowledge Engineering. Previous works mainly focus on linking mentioned names recognized in news or articles to knowledge base. However, in social network, user-generated content is quite different from typical news text. Users sometimes use words more informally, even create new words. One entity may have different aliases mentioned by web users, so identifying these aliases calls for more attention than before. Several methods are proposed to mine aliases and a learning-to-rank framework is applied to combine different types of feature together. A binary classifier based on SVM is trained to judge whether the top one candidate given by ranking algorithm is accepted. The evaluation results of NLP&CC 2013(1) Entity Linking Track shows the effectiveness of this framework. C1 [Wang, Yan; Luo, Cheng; Li, Xin; Liu, Yiqun; Zhang, Min; Ma, Shaoping] Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China. RP Wang, Y (reprint author), Tsinghua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China. EM yan-wang10@mails.thu.edu.cn; c-luo12@mails.thu.edu.cn; xli12@mails.thu.edu.cn; yiqunliu@mail.thu.edu.cn; z-m@mail.thu.edu.cn; msp@mail.thu.edu.cn CR Bagga A., 1998, P 17 INT C COMP LING, V1 Boser B. E., 1992, P 5 ANN WORKSH COMP Bunescu R.C., 2006, EACL, V6 Cao Zhe, 2007, P 24 INT C MACH LEAR Craswell N., 2007, P 30 ANN INT ACM SIG Mathioudakis M., 2010, P 2010 ACM SIGMOD IN McCallum A, 2003, P 7 C NAT LANG LEARN, V4 Mihalcea R., 2007, P 16 ACM C C INF KNO Sakaki T, 2010, P 19 INT C WORLD WID SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zheng Z., 2010, HUMAN LANGUAGE TECHN NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-41643-9; 978-3-642-41644-6 J9 COMM COM INF SC PY 2013 VL 400 BP 277 EP 287 PG 11 WC Computer Science, Theory & Methods; Logic SC Computer Science; Science & Technology - Other Topics GA BJQ08 UT WOS:000329580900026 ER PT J AU Jin, ZY Shi, DX Wu, QY Yan, HN AF Jin, Zhaoyan Shi, Dianxi Wu, Quanyuan Yan, Huining TI Random Walk Based Location Prediction in Wireless Sensor Networks SO INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS LA English DT Article ID MATRIX FACTORIZATION; RECOMMENDATION; SYSTEM AB With the development of wireless sensor network (WSN) technologies, WSNs have been applied in many areas. In all WSN technologies, localization is a crucial problem. Traditional localization approaches in WSNs mainly focus on calculating the current location of sensor nodes or mobile objects. In this paper, we study the problem of future location prediction in WSNs. We assume the location histories of mobile objects as a rating matrix and then use a random walk based social recommender algorithm to predict the future locations of mobile objects. Experiments show that the proposed algorithm has better prediction accuracy and can solve the rating matrix sparsity problem more effectively than related works. C1 [Jin, Zhaoyan; Shi, Dianxi; Wu, Quanyuan; Yan, Huining] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. RP Jin, ZY (reprint author), Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. EM jinzhaoyan@163.com FU National High Technology Research and Development Program ("863" Program) of China [2012AA011201] FX This work was supported by the National High Technology Research and Development Program ("863" Program) of China (no. 2012AA011201). CR Akther A., 2012, ADV ARTIFICIAL INTEL Alippi C, 2011, IEEE SENS J, V11, P45, DOI 10.1109/JSEN.2010.2051539 Balog K., 2013, P 10 C OP RES AR INF, P121 Baltrunas L., 2010, P 4 ACM C REC SYST R, P119, DOI DOI 10.1145/1864708.1864733 Barragans-Martinez AB, 2010, INFORM SCIENCES, V180, P4290, DOI 10.1016/j.ins.2010.07.024 Benesty J, 2008, IEEE T AUDIO SPEECH, V16, P757, DOI 10.1109/TASL.2008.919072 Berkovsky S., 2010, P 4 ACM C REC SYST R, P111, DOI DOI 10.1145/1864708.1864732 Bhatti S, 2009, ICWMC: 2009 FIFTH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMMUNICATIONS, P110 Breese J S, 1998, P 14 C UNC ART INT, P43 Chen Wen-Yen, 2009, P 18 INT C WORLD WID, P681, DOI 10.1145/1526709.1526801 Cobos C, 2013, INFORM PROCESS MANAG, V49, P607, DOI 10.1016/j.ipm.2012.12.002 da Silva A., 2005, P 1 ACM INT WORKSH Q, P16, DOI DOI 10.1145/1089761.1089765 Daly E. M., 2010, P 4 ACM REC SYST C R, P301, DOI 10.1145/1864708.1864772 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Golbeck J., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1183463.1183470 Golbeck J.A, 2005, THESIS U MARYLAND CO Guan NY, 2012, IEEE T SIGNAL PROCES, V60, P2882, DOI 10.1109/TSP.2012.2190406 Hannon J., 2010, P 4 ACM C REC SYST R, P199, DOI 10.1145/1864708.1864746 Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775 Jamali M, 2010, P 4 ACM C REC SYST 2, V2010, P135 JAMALI M, 2009, P 15 ACM SIGKDD INT, P397, DOI 10.1145/1557019.1557067 Jin R., 2004, P 27 ANN INT ACM SIG, P337, DOI 10.1145/1008992.1009051 Kim HN, 2013, USER MODEL USER-ADAP, V23, P249, DOI 10.1007/s11257-012-9130-3 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Lemire D, 2005, SIAM PROC S, P471 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu Nathan N., 2008, P 31 ANN INT ACM SIG, P83, DOI 10.1145/1390334.1390351 Ma H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/1571941.1571978 Mairal J, 2010, J MACH LEARN RES, V11, P19 Mao GQ, 2007, COMPUT NETW, V51, P2529, DOI 10.1016/j.comnet.2006.11.018 Massa P, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P17 Muscanell NL, 2012, COMPUT HUM BEHAV, V28, P107, DOI 10.1016/j.chb.2011.08.016 Nguyen H. V., 2010, 10 AS C COMP VIS ACC, P709 Pal A., 2010, NETWORK PROTOCOLS AL, V2, P45 Rendle Steffen, 2010, P 3 ACM INT C WEB SE, P81, DOI 10.1145/1718487.1718498 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Shinde SK, 2012, EXPERT SYST APPL, V39, P1381, DOI 10.1016/j.eswa.2011.08.020 Tsai CF, 2012, APPL SOFT COMPUT, V12, P1417, DOI 10.1016/j.asoc.2011.11.016 TUAROB S, 2013, P 13 ACM IEEE CS JOI NR 40 TC 0 Z9 0 PU HINDAWI PUBLISHING CORPORATION PI NEW YORK PA 410 PARK AVENUE, 15TH FLOOR, #287 PMB, NEW YORK, NY 10022 USA SN 1550-1329 EI 1550-1477 J9 INT J DISTRIB SENS N JI Int. J. Distrib. Sens. Netw. PY 2013 AR 691042 DI 10.1155/2013/691042 PG 9 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA 276NB UT WOS:000328755900001 ER PT S AU Sun, W Yu, Y Zhu, HV Wang, WD AF Sun Wei Yu Yang Zhu Huabo Wang Weidi GP IEEE TI A new construction method of the scientific and technological innovation team and its multi-agent simulation SO 2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) SE Chinese Control and Decision Conference LA English DT Proceedings Paper CT 25th Chinese Control and Decision Conference (CCDC) CY MAY 25-27, 2013 CL Guiyang, PEOPLES R CHINA SP IEEE, NE Univ, IEEE Ind Elect Chapter, IEEE Harbin Sect Control Syst Soc Chapter, Guizhou Univ, IEEE Control Syst Soc, Syst Engn Soc China, Chinese Assoc Artificial Intelligence, Chinese Assoc Automat, Tech Comm Control Theory, Chinese Assoc Aeronaut, Automat Control Soc, Chinese Assoc Syst Simulat, Simulat Methods & Modeling Soc, Intelligent Control & Management Soc DE scientific and technological innovation; team construction; multi-agent simulation AB For overcoming the shortcomings of the existing method of the construction of scientific and technological innovation team, this paper proposes a new method. In the new method, the selected candidate has the higher similarity not only with the knowledge that team needs, but also with the selected persons previously. Therefore, this method may increase the knowledge communication greatly. By the simulation based on the Repast platform to compare the old method and our proposed method of the construction of scientific and technological innovation team, we validate the novel approach of scientific and technology innovative has the advantage.. C1 [Sun Wei] Northeastern Univ, Scholl Business Adm, Shenyang 110136, Peoples R China. RP Sun, W (reprint author), Northeastern Univ, Scholl Business Adm, Shenyang 110136, Peoples R China. EM carmensunwei@yahoo.com.cn CR Ha Ding, 2002, J SYSTEM SIMULATION, V14 Hitt MA, 2000, J ENG TECHNOL MANAGE, V17, P231, DOI 10.1016/S0923-4748(00)00024-2 Lin BCA, 2006, ECOL ECON, V60, P324, DOI 10.1016/j.ecolecon.2006.01.012 Marshall B, 2006, DECIS SUPPORT SYST, V42, P1290, DOI 10.1016/j.dss.2005.10.009 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sordo Margarita, 2007, CLIN DECISION SUPPOR, P325 Sun W, 2011, ADV MATER RES-SWITZ, V204-210, P1771, DOI 10.4028/www.scientific.net/AMR.204-210.1771 Tobias Robert, 2004, JASSS-J ARTIF SOC S, V7, P1 NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1948-9439 BN 978-1-4673-5532-2; 978-1-4673-5533-9 J9 CHIN CONT DECIS CONF PY 2013 BP 1291 EP 1294 PG 4 WC Automation & Control Systems SC Automation & Control Systems GA BHY11 UT WOS:000326977301061 ER PT S AU Zhao, ZB Jia, YF Bao, YB AF Zhao Zhibin Jia Yanfeng Bao Yubin GP IEEE TI Research on Web Topic Detection Based on Domain Lexicon SO 2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) SE Chinese Control and Decision Conference LA Chinese DT Proceedings Paper CT 25th Chinese Control and Decision Conference (CCDC) CY MAY 25-27, 2013 CL Guiyang, PEOPLES R CHINA SP IEEE, NE Univ, IEEE Ind Elect Chapter, IEEE Harbin Sect Control Syst Soc Chapter, Guizhou Univ, IEEE Control Syst Soc, Syst Engn Soc China, Chinese Assoc Artificial Intelligence, Chinese Assoc Automat, Tech Comm Control Theory, Chinese Assoc Aeronaut, Automat Control Soc, Chinese Assoc Syst Simulat, Simulat Methods & Modeling Soc, Intelligent Control & Management Soc DE text classification; topic detection; vertical search; domain lexicon; data integration ID BAYESIAN NETWORK CLASSIFIERS AB Web topic detection is a crucial prerequisite to web-based data integration and also a key component for Vertical Search Engine. So, it attracts much attention from not only the industry but also the literature. In this paper, we proposed a domain-lexicon-based framework for Web topic detection. In our framework, we extracted the topical features from the web page first. Next, we employed Vector Space Model(VSM) and Support Vector Machine(SVM) to compute the topical relevance between the Web page features and the domain that the user prefers so as to conclude whether the web page satisfies the user's request. Vector Space Model is suitable for the domains where the corresponding domain lexicons need to be updated frequently. Oppositely, Support Vector Machine is suitable for the domains where the corresponding domain lexicons are relatively unchangeable. Moreover, in this work we also explored the mechanism of domain lexicon updating, which can guarantee the accuracy and freshness of the domain lexicon. Finally, we conducted extensive experiment to test our framework and analyze how the domain lexicon affects the judgement result. C1 [Zhao Zhibin; Jia Yanfeng; Bao Yubin] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China. RP Zhao, ZB (reprint author), Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China. EM zhaozb@mail.neu.edu.cn; jiayf@mail.neu.edu.cn; baoyb@mail.neu.edu.cn CR Douthat A., 1998, P 7 MESS UND C Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199 Jing YS, 2008, MACH LEARN, V73, P155, DOI 10.1007/s10994-008-5065-7 Joachims T., 1998, P 10 EUR C MACH LEAR, VECML-98, P137 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 NR 7 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1948-9439 BN 978-1-4673-5532-2; 978-1-4673-5533-9 J9 CHIN CONT DECIS CONF PY 2013 BP 3655 EP 3660 PG 6 WC Automation & Control Systems SC Automation & Control Systems GA BHY11 UT WOS:000326977303132 ER PT B AU Zheng, C Shen, WM Ghenniwa, HH AF Zheng, Cheng Shen, Weiming Ghenniwa, Hamada H. BE Shen, W Li, W Barthes, JP Luo, J Zhu, H Yong, J Li, X TI An Intents-based Approach for Service Discovery and Integration SO PROCEEDINGS OF THE 2013 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN (CSCWD) SE International Conference on Computer Supported Cooperative Work in Design LA English DT Proceedings Paper CT IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) CY JUN 27-29, 2013 CL Whistler, CANADA SP IEEE, Int Working Grp Comp Supported Cooperat Work Design, IEEE Syst, Man, & Cybernet Soc DE service computing; web service; service discovery and integration; intents AB Service discovery and integration has been a very active research field attracting many researchers around the world. Current technologies in service discovery like WSDL and UDDI have deficiencies which prevent their wide applications, especially outside of enterprise areas. Intents is an emerging technology aimed at interconnecting various services. Unlike its counterparts, services in Intents are single topic and given more informative descriptions. This paper examines the essentials of service discovery and integration applied in Intents, presents an architecture of Intents-based systems, and discusses the advantages of Intents over other existing technologies. C1 [Zheng, Cheng; Shen, Weiming; Ghenniwa, Hamada H.] Univ Western Ontario, Dept Elect & Comp Engn, London, ON N6A 3K7, Canada. RP Zheng, C (reprint author), Univ Western Ontario, Dept Elect & Comp Engn, 1151 Richmond St, London, ON N6A 3K7, Canada. EM czheng23@uwo.ca; wshen@uwo.ca; hghenniw@uwo.ca CR Al-Masri E, 2009, IEEE SYS MAN CYBERN, P3714, DOI 10.1109/ICSMC.2009.5346882 Al-Masri E., 2008, P 17 INT C WORLD WID, P795, DOI DOI 10.1145/1367497.1367605 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bell M., 2008, SERVICE ORIENTED MOD Billock G., 2012, WEB INTENTS Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 Fielding R., 2000, THESIS U CALIFORNIA Hang C., 2012, P IEEE 19 INT C WEB, p9 Hatzi O., 2012, Proceedings of the 2012 IEEE 19th International Conference on Web Services (ICWS), DOI 10.1109/ICWS.2012.73 Liu Fangfang, 2010, P 8 INT C WEB SERV I, P155, DOI DOI 10.1109/ICWS.2010.67 Nayak R, 2008, INT J WEB SERV RES, V5, P63, DOI 10.4018/jwsr.2008010104 Paolucci M, 2002, LECT NOTES COMPUT SC, V2342, P333 Ruby Sam, 2007, RESTFUL WEB SERVICES Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wikipedia contributors, 2012, UN DESCR DISC INT Winders J., 2012, DRIVING FUTURE W FRA Zheng Z., 2010, P 8 INT C WEB SERV I, P83, DOI DOI 10.1109/ICWS.2010.10 NR 19 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-6085-2; 978-1-4673-6084-5 J9 INT C COMP SUPP COOP PY 2013 BP 207 EP 212 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHY14 UT WOS:000326982300037 ER PT S AU Wu, LK Lin, WQ Xiao, XK Xu, YB AF Wu, Lingkun Lin, Wenqing Xiao, Xiaokui Xu, Yabo GP IEEE TI LSII: An Indexing Structure for Exact Real-Time Search on Microblogs SO 2013 IEEE 29TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT 29th IEEE International Conference on Data Engineering (ICDE) CY APR 08-12, 2013 CL Brisbane, AUSTRALIA SP IEEE, IEEE Comp Soc, IEEE Tech Comm Data Engn, SAP, Microsoft, Tourism & Events Queensland, HP, Univ Queensland, CSIRO, RMIT Univ, Oracle Labs, Renmin Univ, SA Ctr Big Data Res, Univ Melbourne, NICTA, Google, NEC, Facebook, Univ New S Wales AB Indexing microblogs for real-time search is challenging given the efficiency issue caused by the tremendous speed at which new microblogs are created by users. Existing approaches address this efficiency issue at the cost of query accuracy, as they either (i) exclude a significant portion of microblogs from the index to reduce update cost or (ii) rank microblogs mostly by their timestamps (without sufficient consideration of their relevance to the queries) to enable append-only index insertion. As a consequence, the search results returned by the existing approaches do not satisfy the users who demand timely and high-quality search results. To remedy this deficiency, we propose the Log-Structured Inverted Indices (LSII), a structure for exact real-time search on microblogs. The core of LSII is a sequence of inverted indices with exponentially increasing sizes, such that new microblogs are (i) first inserted into the smallest index and (ii) later moved into the larger indices in a batch manner. The batch insertion mechanism leads to a small amortize update cost for each new microblog, without significantly degrading query performance. We present a comprehensive study on LSII, exploring various design options to strike a good balance between query and update performance. In addition, we propose extensions of LSII to support personalized search and to exploit multi-threading for performance improvement. Extensive experiments demonstrate the efficiency of LSII with experiments on real data. C1 [Wu, Lingkun; Lin, Wenqing; Xiao, Xiaokui] Nanyang Technol Univ, Singapore 639798, Singapore. RP Wu, LK (reprint author), Nanyang Technol Univ, Singapore 639798, Singapore. EM wulingkun@ntu.edu.sg; wlin1@ntu.edu.sg; xkxiao@ntu.edu.sg; xuyabo@mail.sysu.edu.cn RI Xiao, Xiaokui/A-3739-2011 OI Xiao, Xiaokui/0000-0003-0914-4580 CR Adar E., 2007, WORKSH QUER LOG AN 1 [Anonymous], 2012, TWITTER DEV DOCUMENT [Anonymous], 2010, REPLIES RETWEETS TWI Busch M, 2012, PROC INT CONF DATA, P1360, DOI 10.1109/ICDE.2012.149 Chen C., 2011, SIGMOD C 11, P649 Corso G. M. D., 2005, WWW 05, P97 Dong A., 2010, WSDM, P11 Duan Yajuan, 2010, COLING, P295 Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Jagadish H. V., 1997, VLDB J, P16 Java A., 2007, WEBKDD SNA KDD, P118 Li Y., 2010, P VLDB ENDOWMENT, V3, P1195 Lin J., 2012, ICWSM Nagmoti R., 2010, WEB INTELLIGENCE, P153 ONeil P, 1996, ACTA INFORM, V33, P351, DOI 10.1007/s002360050048 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Teevan J., 2011, WSDM, P35 Weng J., 2010, WSDM 10, P261, DOI DOI 10.1145/1718487.1718520 Yan H., 2009, WWW, P401 Yao J., 2012, ICDE, P2183 Zhang J., 2008, WWW 08, P387 NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1084-4627 BN 978-1-4673-4909-3; 978-1-4673-4908-6 J9 PROC INT CONF DATA PY 2013 BP 482 EP 493 PG 12 WC Computer Science, Information Systems SC Computer Science GA BHV36 UT WOS:000326733500043 ER PT S AU Bouillot, F Hai, PN Bechet, N Bringay, S Ienco, D Matwin, S Poncelet, P Roche, M Teisseire, M AF Bouillot, Flavien Phan Nhat Hai Bechet, Nicolas Bringay, Sandra Ienco, Dino Matwin, Stan Poncelet, Pascal Roche, Mathieu Teisseire, Maguelonne BE Tanaka, Y Spyratos, N Yoshida, T Meghini, C TI How to Extract Relevant Knowledge from Tweets? SO INFORMATION SEARCH, INTEGRATION AND PERSONALIZATION SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 7th International Workshop on Information Search, Integration and Personalization CY OCT 11-13, 2012 CL Sapporo, JAPAN HO Hokkaido Univ, Meme Media Lab AB Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we investigate two different problems. The first one is related to the extraction of representative terms from a set of tweets. More precisely we address the following question: are traditional information retrieval measures appropriate when, dealing with tweets?. The second problem is related to the evolution of tweets over time for a set of users. With the development of data mining approaches, lots of very efficient methods have been defined to extract patterns hidden in the huge amount of data available More recently new spatio-temporal data mining approaches have specifically been defined for dealing with the huge amount of moving object data that can be obtained from the improvement in positioning technology. Due to particularity of tweets, the second question we investigate is the following: are spatio-temporal mining algorithms appropriate for better understanding the behavior of communities over time? These two problems are illustrated through real applications concerning both health and political tweets. C1 [Bouillot, Flavien; Phan Nhat Hai; Bringay, Sandra; Ienco, Dino; Poncelet, Pascal; Roche, Mathieu; Teisseire, Maguelonne] CNRS, LIRMM, Montpellier, France. RP Bouillot, F (reprint author), CNRS, LIRMM, 161 Rue Ada, Montpellier, France. EM bouillot@lirmm.fr; nhat-hai.phan@teledetection.fr; nicolas.bechet@unicaen.fr; bringay@lirmm.fr; dino.ienco@teledetection.fr; stan@site.uottawa.ca; poncelet@lirmm.fr; mroche@lirmm.fr; maguelonne.teisseire@teledetection.fr CR Barbosa Luciano, 2010, P COLING Bringay S., 2011, LNCS, V6861, P154 Codd E., 1993, PROVIDING OLAP ON LI, P3 Hai P.N., 2012, LNCS, V7619, P276 Jensen C, 2007, IEEE TKDE Jeung H., 2008, PVLDB, V1 Landauer T.K., 1998, DISCOURSE PROCESSES, V25 Li C., 2012, P SIGIR Li C, 2012, P CIKM Li Z, 2010, P SIGMOD Mathioudakis M., 2010, P 2010 INT C MAN DAT, P1155, DOI DOI 10.1145/1807167.180730 Nhat H.P., 2012, P SIGSPATIAL Perez-Martinez JM, 2008, DECIS SUPPORT SYST, V45, P77, DOI 10.1016/j.dss.2006.12.005 Sakaki T., 2010, P 19 INT C WORLD WID, P851, DOI DOI 10.1145/1772690.1772777 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang J., 2008, P ICDM Tsolmon B., 2012, LNCS, V7337, P265 Turney P.D., 2001, P 12 EUR C MACH LEAR, V2167, P491 Vieira M., 2009, P SIGSPATIAL Wang Y., 2006, DKE NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-40139-8 J9 COMM COM INF SC PY 2013 VL 146 BP 111 EP 120 PG 10 WC Computer Science, Information Systems SC Computer Science GA BHQ87 UT WOS:000326408300012 ER PT B AU Narayanan, N Judith, JE Jayakumari, J AF Narayanan, Neethi Judith, J. E. Jayakumari, J. GP IEEE TI Enhanced Distributed Document Clustering Algorithm Using Different Similarity Measures SO 2013 IEEE CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES (ICT 2013) LA English DT Proceedings Paper CT IEEE Conference on Information and Communication Technologies (ICT) CY APR 11-12, 2013 CL SOUTH KOREA SP IEEE, Noorul Islam Ctr Higher Educ, Noorul Islam Univ, Dept Elect & Commun Engn, IEEE India Council, IEEE Kerala Sect, EDS, CAS, ISRO, SSCS DE Distributed document clustering similarity measures; Cosine similarity; Jaccard coefficient; Pearson coefficient AB Many of the distributed environments like internets, intranets, local area networks and wireless networks have different distributed data sources. lnorder to analyze and monitor these distributed data sources specialized data mining technologies for distributed applications are required. A variety of distributed document clustering algorithms exists for this purpose. This paper presents an Enhanced Distributed Algorithm (FDA) for document clustering. This paper presents the performance analysis of the algorithm using different similarity measures like cosine similarity, Jaccard and Pearson coefficient. The test was performed on standard document corpora like 2ONG (News Group), Reuters, Web The performance of this proposed FDA algorithm is also evaluated using different performance factors in order to determine its accuracy and clustering quality. C1 [Narayanan, Neethi; Judith, J. E.; Jayakumari, J.] Noorul Islam Ctr Higher Educ Kumaracoil, Kumaracoil, Tamil Nadu, India. RP Narayanan, N (reprint author), Noorul Islam Ctr Higher Educ Kumaracoil, Kumaracoil, Tamil Nadu, India. EM neethi2nn@gmail.com; judithjegan@gmail.com; jkumaribharat@yahoo.com CR Aberer K, 2003, SIGMOD RECORD, V32, P29 Balakrishnan H., 2001, P SIGCOMM Bandyopadhyay S, 2006, INFORM SCIENCES, V176, P1952, DOI 10.1016/j.ins.2005.11.007 Datta S., 2006, P SIAM INT C DAT MIN Datta S, 2009, IEEE T KNOWL DATA EN, V21, P1372, DOI 10.1109/TKDE.2008.222 Datta S, 2006, IEEE INTERNET COMPUT, V10, P18, DOI 10.1109/MIC.2006.74 Eisenhardt M., 2003, INFORMATIK Forman G., 2000, SIGKDD EXPLORATIONS, V2, P34 Hammouda KM, 2009, IEEE T KNOWL DATA EN, V21, P681, DOI 10.1109/TKDE.2008.189 Hsiao H.C., 2003, P INT C PAR PROC Huang Anna, 2008, NZCSRSC JUDITH JE, 2012, EUR J SCI RES, V86, P283 Lewis DD, 1999, REUTERS 21578 TEXT C LUO P, 2007, P 13 ACM SIGKDD INT, P968, DOI 10.1145/1281192.1281296 Manning C.D., 2008, INTRO INFORM RETRIEV Papapetrou O, 2012, IEEE T KNOWL DATA EN, V24, P1848, DOI 10.1109/TKDE.2011.120 Porter M.F., 1980, ALGORITHM SUFFIX STR, P130 Rowstron A., 2001, P IFIP ACM MIDDL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M, 2000, P KDD WORKSH TEXT MI NR 20 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-5758-6; 978-1-4673-5759-3 PY 2013 BP 545 EP 550 PG 6 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BHE91 UT WOS:000325208700105 ER PT J AU Kryszkiewicz, M AF Kryszkiewicz, Marzena TI Using Non-Zero Dimensions for the Cosine and Tanimoto Similarity Search Among Real Valued Vectors SO FUNDAMENTA INFORMATICAE LA English DT Article DE sparse data sets; high dimensional data sets; the cosine similarity; the Tanimoto similarity; text mining; data mining; information retrieval; similarity joins; inverted indices ID TRIANGLE INEQUALITY; QUERIES; SPACE AB The cosine and Tanimoto similarity measures are typically applied in the area of chemical informatics, bio-informatics, information retrieval, text and web mining as well as in very large databases for searching sufficiently similar vectors. In the case of large sparse high dimensional data sets such as text or Web data sets, one typically applies inverted indices for identification of candidates for sufficiently similar vectors to a given vector. In this article, we offer new theoretical results on how the knowledge about non-zero dimensions of real valued vectors can be used to reduce the number of candidates for vectors sufficiently cosine and Tanimoto similar to a given one. We illustrate and discuss the usefulness of our findings on a sample collection of documents represented by a set of a few thousand real valued vectors with more than ten thousand dimensions. C1 Warsaw Univ Technol, Inst Comp Sci, PL-00665 Warsaw, Poland. RP Kryszkiewicz, M (reprint author), Warsaw Univ Technol, Inst Comp Sci, Nowowiejska 15-19, PL-00665 Warsaw, Poland. EM mkr@ii.pw.edu.pl CR Arasu A., 2006, P ICDE06 Bayardo R.J., 2007, P 16 INT C WORLD WID, P131, DOI 10.1145/1242572.1242591 Chaudhuri S, 2006, P ICDE06 Elkan C, 2003, LNCS LNAI, P147 Kristensen TG, 2010, J MATH CHEM, V48, P287, DOI 10.1007/s10910-010-9668-4 Kryszkiewicz M, 2010, LECT NOTES ARTIF INT, V6086, P60, DOI 10.1007/978-3-642-13529-3_8 Kryszkiewicz M., 2013, LNCS LNAI, V8132, P531 Kryszkiewicz M., 2013, LNCS LNAI, V7802, P445 Kryszkiewicz M, 2010, LECT NOTES COMPUT SC, V6283, P284, DOI 10.1007/978-3-642-15381-5_35 Kryszkiewicz M, 2012, LNCS LNAI, V7413, P229 Kryszkiewicz M, 2013, 43 INT SYST REF LIB, V43, P323 Kryszkiewicz M, 2012, 3 ICS WARS U TECHN Kryszkiewicz M., 2012, LNCS LNAI, V7345, P48 Moore Andrew W., 2000, P 12 C UNC ART INT, P397 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samet H, 2006, FDN MULTIDIMENSIONAL UHLMANN JK, 1991, INFORM PROCESS LETT, V40, P175, DOI 10.1016/0020-0190(91)90074-R Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 Witten I. H., 1999, MANAGING GIGABYTES C Xiao C., 2008, P 17 INT C WORLD WID, P131, DOI 10.1145/1367497.1367516 Zezula P., 2006, SIMILARITY SEARCH ME NR 21 TC 1 Z9 1 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0169-2968 EI 1875-8681 J9 FUND INFORM JI Fundam. Inform. PY 2013 VL 127 IS 1-4 BP 307 EP 323 DI 10.3233/FI-2013-911 PG 17 WC Computer Science, Software Engineering; Mathematics, Applied SC Computer Science; Mathematics GA 235UG UT WOS:000325745600023 ER PT S AU Joung, YJ Chen, SM Wu, CC Chiu, THY AF Joung, Yuh-Jzer Chen, Shy-Min Wu, Chih-Chang Chiu, Terry Hui-Ye BE Barolli, L Xhafa, F Takizawa, M Enokido, T Hsu, HH TI A Comparative Study of Expert Search Strategies in Online Social Networks SO 2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA) SE International Conference on Advanced Information Networking and Applications LA English DT Proceedings Paper CT IEEE 27th International Conference on Advanced Information Networking and Applications (IEEE AINA) CY MAR 25-28, 2013 CL Barcelona, SPAIN SP IEEE, IEEE Comp Soc, IEEE Comp Soc Tech Comm Distributed Proc (TCDP), Tech Univ Catalonia, Fukuoka Inst Technol (FIT), IEEE Tech Comm Distributed Proc (TCDP) DE Expert Seeking; Social Networks; Search Strategy; Profile-Based Search; Structure-Based Search; Hybrid-Based Search ID SMALL-WORLD PROBLEM AB Expert Seeking is a social network application that requires certain search strategies in the form of expert candidate-selection process. Assuming that no one in the network has the global knowledge, a query for expertise must be forwarded to, and between, candidates in order to reach the right person. During this candidate-selection process, a person determines whom to forward the query based only on the local information s/he knows. Two types of local information, actor profiles and structural attributes, may be used in devising search strategies. In this paper we conduct a comprehensive comparative study on search strategies using these types of information, as well as on search strategies using both types simultaneously to see if there is a synergy between them. C1 [Joung, Yuh-Jzer; Chen, Shy-Min; Wu, Chih-Chang; Chiu, Terry Hui-Ye] Natl Taiwan Univ, Dept Informat Management, Taipei 10764, Taiwan. RP Joung, YJ (reprint author), Natl Taiwan Univ, Dept Informat Management, Taipei 10764, Taiwan. EM joung@ntu.edu.tw; D93725002@ntu.edu.tw; R95725051@ntu.edu.tw; D92725003@ntu.edu.tw CR Adamic L, 2005, SOC NETWORKS, V27, P187, DOI 10.1016/j.spcnet.2005.01.007 Adamic LA, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.046135 Bornholdt S., 2003, HDB GRAPHS NETWORKS Collins JJ, 1998, NATURE, V393, P409, DOI 10.1038/30835 Dodds PS, 2003, SCIENCE, V301, P827, DOI 10.1126/science.1081058 Fu Y., 2007, P IEEE WIC ACM INT C GRANOVET.MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469 HAMMING RW, 1950, AT&T TECH J, V29, P147 Jun Z., 2005, P 2005 ACM SIGGROUP Liben-Nowell D, 2005, P NATL ACAD SCI USA, V102, P11623, DOI 10.1073/pnas.0503018102 Lin N, 2001, SOCIAL CAPITAL THEOR MILGRAM S, 1967, PSYCHOL TODAY, V1, P61 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck J., 1997, READINGS INFORM RETR Tang J., 2011, P 17 ACM SIGKDD, P769 TRAVERS J, 1969, SOCIOMETRY, V32, P425, DOI 10.2307/2786545 Wasserman S., 1994, SOCIAL NETWORK ANAL, P130 Yu B., 2003, P 2 INT JOINT C AUT, P65 Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1550-445X BN 978-1-4673-5550-6; 978-0-7695-4953-8 J9 INT CON ADV INFO NET PY 2013 BP 960 EP 967 DI 10.1109/AINA.2013.86 PG 8 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BGW73 UT WOS:000324398900127 ER PT J AU Kessler, C de Groot, RTA AF Kessler, Carsten de Groot, Rene Theodore Anton BE Vandenbroucke, D Bucher, B Crompvoets, J TI Trust as a Proxy Measure for the Quality of Volunteered Geographic Information in the Case of OpenStreetMap SO GEOGRAPHIC INFORMATION SCIENCE AT THE HEART OF EUROPE SE Lecture Notes in Geoinformation and Cartography LA English DT Proceedings Paper CT 16th AGILE Conference on Geographic Information Science CY MAY 14-17, 2013 CL Leuven, BELGIUM SP AGILE, ESRI, Intergraph, Geosparc, Natl Geograph Inst, Arenberg Doctoral Sch, Google ID SCIENCE; MODEL AB High availability and diversity make Volunteered Geographic Information (VGI) an interesting source of information for an increasing number of use cases. Varying quality, however, is a concern often raised when it comes to using VGI in professional applications. Recent research directs towards the estimation of VGI quality through the notion of trust as a proxy measure. In this chapter, we investigate which indicators influence trust, focusing on inherent properties that do not require any comparison with a ground truth dataset. The indicators are tested on a sample dataset extracted from OpenStreetMap. High numbers of contributors, versions and confirmations are considered as positive indicators, while corrections and revisions are treated as indicators that have a negative influence on the development of feature trustworthiness. In order to evaluate the trust measure, its results have been compared to the results of a quality measure obtained from a field survey. The quality measure is based on thematic accuracy, topological consistency, and information completeness. To address information completeness as a criterion of data quality, the importance of individual tags for a given feature type was determined based on a method adopted from information retrieval. The results of the comparison between trust assessments and quality measure show significant support for the hypothesis that feature-level VGI data quality can be assessed using a trust model based on data provenance. C1 [Kessler, Carsten; de Groot, Rene Theodore Anton] Univ Munster, Inst Geoinformat, D-48149 Munster, Germany. RP Kessler, C (reprint author), Univ Munster, Inst Geoinformat, D-48149 Munster, Germany. EM carsten.kessler@uni-muenster.de; rta.de.groot@gmail.com CR Artz D, 2007, J WEB SEMANT, V5, P58, DOI 10.1016/j.websem.2007.03.002 Bishr M., 2010, DIG EARTH SEARCH DIS Bishr M, 2007, LECT NOTES GEOINF CA, P365, DOI 10.1007/978-3-540-72385-1_22 Bishr Mohamed, 2008, GEOJOURNAL, V72, P229, DOI DOI 10.1007/S10708-008-9182-4 Flanagin A., 2008, GEOJOURNAL, V72, P137, DOI DOI 10.1007/S10708-008-9188-Y Golbeck J., 2005, THESIS U MARYLAND Goodchild M.F., 2007, GEOJOURNAL, V69, P211, DOI DOI 10.1007/S10708-007-9111-Y Haklay M, 2010, CARTOGR J, V47, P315, DOI 10.1179/000870410X12911304958827 Haklay M, 2010, ENVIRON PLANN B, V37, P682, DOI 10.1068/b35097 Helbich M, 2010, GISCIENCE 2010 WORKS International Organization for Standardization, 2002, 191132002 ISO, P29 Jakobsson A, 2009, EUROGRAPHICS 2009 Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226 Kessler C, 2011, ID OBJ PROC EV SPAT Kessler C, 2011, C SPAT INF THEOR 12 Koukoletsos T, 2012, T GIS, V16, P477, DOI 10.1111/j.1467-9671.2012.01304.x Mezzetti N, 2004, LECT NOTES COMPUT SC, V3093, P191 Mooney Peter, 2012, Future Internet, V4, DOI 10.3390/fi4010285 Mooney P, 2012, T GIS, V16, P561, DOI 10.1111/j.1467-9671.2012.01306.x Mooney P, 2012, P 20 ANN GIS RES UK Neis Pascal, 2012, Future Internet, V4, DOI 10.3390/fi4010001 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simmhan YL, 2005, SIGMOD RECORD, V34, P31, DOI 10.1145/1084805.1084812 Sztompka P., 1999, TRUST SOCIOLOGICAL T van Exel M, 2010, GISCIENC 2010 WORKSH Zielstra D., 2010, 13 AGILE INT C GEOGR NR 26 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1863-2246 BN 978-3-319-00615-4 J9 LECT NOTES GEOINF CA PY 2013 BP 21 EP 37 DI 10.1007/978-3-319-00615-4_2 PG 17 WC Computer Science, Information Systems; Geography, Physical SC Computer Science; Physical Geography GA BGW77 UT WOS:000324403600002 ER PT B AU Grudin, J AF Grudin, Jonathan BE Glushko, RJ TI The Discipline of Organizing Foreword SO DISCIPLINE OF ORGANIZING LA English DT Editorial Material; Book Chapter ID BIBLIOGRAPHIC RELATIONSHIPS; INFORMATION-SYSTEMS; CATEGORY COHERENCE; UNITED-STATES; SCIENCE; FUTURE; CATEGORIZATION; SIMILARITY; FRAMEWORK; RETRIEVAL CR AALBERSBERG Ijsbrand, 2011, D LIB MAGAZINE Agrawal R., 1989, SIGMOD C, P253 Allmendinger G., 2005, HARVARD BUSINESS REV ANDERSON James, 2006, CATALOGING CLASSIFIC, P7 Anderson JD, 2001, INFORM PROCESS MANAG, V37, P231, DOI 10.1016/S0306-4573(00)00026-1 ANDERSON John, 2003, ART HELD HOSTAGE BAT ANDERSON Stephen R., 2001, MIT ENCY COGNITIVE S, P562 [Anonymous], 2011, BUSINESSWEEK APTE UM, 1995, MANAGE SCI, V41, P1250, DOI 10.1287/mnsc.41.7.1250 ARASU Arvind, 2001, ACM T INTERNET TECHN, P2 ARGOTT Don, 2009, ART STEAL Arthur P., 1992, WAYFINDING PEOPLE SI AT&T, 2011, ACCUWEATHER CLOUDL A ATKINS Helen Barsky, 2000, WEB KNOWLEDGE FESTSC Atran S., 1987, MIND LANG, V2, P27, DOI 10.1111/j.1468-0017.1987.tb00107.x Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010 Aufderheide P., 2011, RECLAIMING FAIR USE Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE BAILEY Charles W., 2007, COLLECTION MANAGEMEN, P351 BAKER Keith M., 1962, ANN SCI, P99 BANZHAF Wolfgang, 2009, P ENC COMPL SYST SCI, P8040 Barbara TILLETT, 2003, TECHNICALITIES Bar-Ilan J, 2008, J INFORMETR, V2, P1, DOI 10.1016/j.joi.2007.11.001 BARSALOU LW, 1983, MEM COGNITION, V11, P211, DOI 10.3758/BF03196968 BARTA Patrick, 2009, WALL STREET J Bates M. J., 2005, INFORM RES Batley S., 2005, CLASSIFICATION THEOR Batt R, 2000, INT J HUM RESOUR MAN, V11, P540, DOI 10.1080/095851900339756 BATTEN W. E., 1951, PUNCHED CARDS, P169 Battistella Edwin, 1996, LOGIC MARKEDNESS Battles Matthew, 2003, LIB UNQUIET HIST Bean C. A., 2001, RELATIONSHIPS ORG KN BENTIVOGLI Luisa, 2000, P EUR 2000 INT C Bergmark D., 2002, P 6 EUR C RES ADV TE, P91 Berners-Lee T., 1998, COOL URIS DONT CHANG Berners-lee T., 2001, SCI AM BIASIOTTI Mariangela, 2008, LEGAL INFORM MANAGEM BITNER MJ, 1992, J MARKETING, V56, P57, DOI 10.2307/1252042 Bizer C, 2009, IEEE INTELL SYST, V24, P87, DOI 10.1109/MIS.2009.102 Bizer C, 2009, INT J SEMANT WEB INF, V5, P1, DOI 10.4018/jswis.2009081901 BLANZIERI Enrico, 2009, ARTIF INTELL, P63 Bleecker J., 2006, MANIFESTO NETWORKED [Board2002] BOARD ON ENERGY AND ENVIRONMENTAL SYSTEMS DIVISION ON ENGINEERING AND PHYSICAL SCIENCES and TRANSPORTATION RESEARCH BOARD, 2002, EFF IMP CORP AV FUEL BOLSHAKOV IA, 2004, P 9 INT C APPL NAT L, V3136, P312 Borgman C., 2011, J AM SOC INFORM SCI, P1 Borgman CL, 2000, GUTENBERG GLOBAL INF Boroditsky L., 2003, ENCY COGNITIVE SCI Boroditsky L., 2010, WALL STREET J BORODITSKY Lera, 2011, SCI AM BOWER Jim, 2001, DEV INT MUSEUM CULTU Bowker G., 2000, SORTING THINGS OUT C BRAILSFORD David F., 1999, ACM COMPUTING SURVEY BRAY Tim, 2005, XML 2005 BRIN Sergey, 2009, NEW YORK TIMES BROWN Bruce C., 2009, STOP E MAIL SPAM SPY Brown J. S., 2002, SOCIAL LIFE INFORM Bruner J. S., 1957, CONT APPROACHES COGN, p[41, 218] BUCKLAND MK, 1991, J AM SOC INFORM SCI, V42, P351, DOI 10.1002/(SICI)1097-4571(199106)42:5<351::AID-ASI5>3.0.CO;2-3 Buckland MK, 1997, J AM SOC INFORM SCI, V48, P804, DOI 10.1002/(SICI)1097-4571(199709)48:9<804::AID-ASI5>3.0.CO;2-V Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 BUETTCHER S., 2010, INFORM RETRIEVAL IMP BUHRMESTER Jason, 2007, WIRED Bulmer R., 1970, ECHANGES COMMUNICATI, P1069 Burrell J, 2004, IEEE PERVAS COMPUT, V3, P38, DOI 10.1109/MPRV.2004.1269130 Bush V., 1945, ATLANTIC Buttcher S., 2010, INFORM RETRIEVAL IMP BYYA Rajkumar, 2008, CONTENT DELIVERY NET Campbell JK, 2011, TOPICS CONTEMP PHIL, P1 CANO Pedro, 2005, COMPUTATIONAL INTELL CARNEY David, 2005, SOME CURRENT APPROAC CARR Patrick, 2010, AGAINST GRAIN, P16 Carroll E., 2010, YOUR DIGITAL AFTERLI Casson L, 2002, LIB ANCIENT WORLD CERF Vint, 1969, ASCII FORMAT NETWORK CHAFFIN R, 1984, MEM COGNITION, V12, P134, DOI 10.3758/BF03198427 CHAKRAVARTY S., 2010, 2 INT C MACH LEARN C, P32 Chan L.M., 2006, D LIB MAGAZINE Chandler A., 1977, VISIBLE HAND MANAGER CHAPMAN Nigel, 2009, DIGITAL MULTIMEDIA Charles HERBST, 2009, RES GESTAE, P16 CHEN Donglin, 2010, 2010 INT C E BUS E G, P2961, DOI 10.1109/ICEE.2010.748 Cherbakov L, 2005, IBM SYST J, V44, P653 CHILDRESS Eric, 2008, CODE 4 LIB Cho J, 2000, P 26 INT C VER LARG, P200 Chomsky N., 1957, SYNTACTIC STRUCTURES Chomsky N., 1965, ASPECTS THEORY SYNTA CHRISTEN Peter, 2006, COMP PERSONAL NAME M, P14 CLARK Stephen Watkins, 1868, PRACTICAL GRAMMAR WO CLARK Stephen Watkins, 2010, PRACTICAL GRAMMAR WO Coase RH, 1937, ECONOMICA-NEW SER, V4, P386, DOI 10.1111/j.1468-0335.1937.tb00002.x CODD E. F., 1970, COMM ACM JUN, P377 CONKLIN J, 1988, ACM T INFORM SYST, V6, P303, DOI 10.1145/58566.59297 Conklin Jeff, 1987, IEEE COMPUT, V20, P17 Constantin J. A., 1994, UNDERSTANDING RESOUR Cormen T. H., 2009, INTRO ALGORITHMS, V3rd COWAN John, 2004, XML INFORM SET RECOM Cox I., 2007, DIGITAL WATERMARKING Coyle K, 2006, J ACAD LIBR, V32, P428, DOI 10.1016/j.acalib.2006.04.004 COYLE Karen, 2010, LIB TECHNOLOGY REPOR, P5 COYLE Karen, 2010, LIB TECHNOLOGY REPOR, P26 Crow David, 2010, VISIBLE SIGNS INTRO CTEIN, 2010, DIGITAL RESTORATION Cutter Charles Ammi, 1876, RULES PRINTED DICT C Dabbish L., 2004, P SIGCHI C HUM FACT, P319, DOI DOI 10.1145/985692.985733.ISBN DARNTON Robert, 2011, NEW YORK REV BOOKS Das SK, 2002, IEEE WIREL COMMUN, V9, P77, DOI 10.1109/MWC.2002.1160085 Date C., 2003, INTRO DATABASE SYSTE Datta R., 2008, ACM COMPUTING SURVEY DE LEON David, 2003, LUND U COGNITIVE STU De Rose SJ, 1989, P HYPERTEXT 89, P249, DOI 10.1145/74224.74245 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMARTINI G, 2006, ADV INFORM RETRIEVAL, V3936, P488, DOI 10.1007/11735106_48 Denton W., 2007, UNDERSTANDING FRBR W, P35 DeRose S., 2010, XML LINKING LANGUAGE DESOKY Ashraf, 2010, MOSCOW PRIORITISATIO Deutscher G., 2011, LANGUAGE GLASS WHY W Dey A.K., 2001, PERS UBIQUIT COMPUT, P4 DIAZ Alejandro M., 2005, GOOGLE GOGGLES SOCIO Ding L., 2004, P 13 ACM INT C INF K, P652, DOI 10.1145/1031171.1031289 DOCTOROW Cory, 2001, METACRAP DOHERTY Paul, 2007, MUS WEB 2007 INT C C DONNELLAN Keith S., 1966, PHILOS REV, P281 DORAI C, 2002, MEDIA COMPUTING, V4, P1 Dougherty Janet W., 1985, DIRECTIONS COGNITIVE, P161 DRAPEAU Mark, 2010, OREILLY RADAR Dumais S, 2003, COGNITIVE SCI, V27, P491, DOI 10.1016/S0364-0213(03)00013-2 Efron M, 2011, J AM SOC INF SCI TEC, V62, P996, DOI 10.1002/asi.21512 EFTHYVOULOU George, 2008, J SOCIO-ECON, P1266 Elman JL, 2009, COGNITIVE SCI, V33, P547, DOI 10.1111/j.1551-6709.2009.01023.x Engelbart D., 1963, VISTAS INFORM HANDLI, P1 Erl T., 2005, SERVICE ORIENTED ARC Evans G. E., 2000, DEV LIB INFORM CTR C FARISH J. Brian, 2002, WHATS NAME VERTAASIS Feinberg M, 2012, INFORM SOC, V28, P329, DOI 10.1080/01972243.2012.708709 FETTERLY Dennis, 2003, P 12 INT WORLD WID W Few S., 2012, SHOW ME NUMBERS DESI Fidel R, 2012, HUMAN INFORMATION INTERACTION: AN ECOLOGICAL APPROACH TO INFORMATION BEHAVIOR, P1 Fillmore C.J., 2000, POLYSEMY, P91 Fishman Charles, 2003, FAST CO Fister Barbara, 2009, LIB J FLOREY Kitty Burns, 2012, NEW YORK TIMES Freeman Geoffrey T., 2005, LIB PLACE RETHINKING Frege Gottlob, 1952, TRANSLATIONS PHILOS, P56 FU Xiang, 2004, P 13 C WORLD WID WEB FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 FURNER Jonathan, 2008, KNOWL ORGAN, P3 FURNER Jonathan, 2007, INT CATALOGUING BIBL, P47 GARFIELD Eugene, 2000, WEB KNOWLEDGE FESTSC Gaukroger S., 2001, F BACON TRANSFORMATI Geller J, 1999, SALMAGUNDI, P175 Gelman R., 1991, EPIGENESIS MIND ESSA GENTNER D, 1983, COGNITIVE SCI, V7, P155, DOI 10.1207/s15516709cog0702_3 Gershenfeld N., 2004, SCI AM Gibson J. J., 1977, PERCEIVING ACTING KN GILL Tony, 2008, INTRO METADATA Gillian BYRNE, 2010, D LIB MAGAZINE Gillies James, 2000, WEB WAS BORN STORY W GILLILAND-SWETLAND AJ, 2000, COMMUNICATIONS Gladwell M, 1996, NEW YORKER Glushko R. J., 2005, DOCUMENT ENG ANAL DE GLUSHKO RJ, 2008, CATEGORIZATION WILD, V12, P129 Glushko RJ, 2013, J SERV RES-US, V16, P21, DOI 10.1177/1094670512463967 GLUSHKO Robert J., 1988, P ACM C DOC PROC SYS, P11, DOI 10.1145/62506.62508 GOLDBERG Kevin Howard, 2008, VISUAL QUICKSTART GU Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 GOLDSTONE RL, 1994, COGNITION, V52, P125, DOI 10.1016/0010-0277(94)90065-5 GOODWIN Richard, 2012, 2012 ANN SRII GLOB C, P682 GORMAN Michael, 2004, CONCISE AACR2 GRAPPONE Jennifer, 2011, SEARCH ENGINE OPTIMI GREAN Michael, 2005, SUPPLY CHAIN INTEGRA GRIMMELMANN James, 2009, J INTERNET LAW Gross D., 1990, INT J LEXICOGR, V3, P265, DOI DOI 10.1093/IJL/3.4265 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 GRUDIN J, 1994, COMMUN ACM, V37, P92, DOI 10.1145/175222.175230 Guarino N, 1998, FR ART INT, V46, P3 GUENTHER Rebecca, 2009, IPRES 2009 6 INT C P Guijarro L, 2007, GOV INFORM Q, V24, P89, DOI 10.1016/j.giq.2006.05.003 HALASZ F, 1994, COMMUN ACM, V37, P30, DOI 10.1145/175235.175237 Halvey M. J., 2007, P 16 INT C WORLD WID, P1313, DOI 10.1145/1242572.1242826 HAMILTON Kate, 2012, P BAL MARK C 2012 HAMMOND Tony, 2004, D LIB MAGAZINE Hansen M. T., 2009, COLLABORATION LEADER Harinarayana NS, 2010, ELECTRON LIBR, V28, P69, DOI 10.1108/02640471011023388 HARPRING Patricia, 2009, CATEGORIES DESCRIPTI HARRIS Roy, 1996, SIGNS WRITING Haspelmath Martin, 2010, UNDERSTANDING MORPHO, V2nd Haviland JB, 1998, ETHOS, V26, P25, DOI 10.1525/eth.1998.26.1.25 He B., 2007, COMMUN ACM, V50, P94, DOI 10.1145/1230819.1241670 Hearst M., 2009, P 2009 WORKSH TEXT C, P62, DOI 10.3115/1699750.1699760 Heath T., 2011, LINKED DATA EVOLVING Heller D, 2012, TOP COGN SCI, V4, P290, DOI 10.1111/j.1756-8765.2012.01182.x HEMERLY Jess, 2011, SSRN ELECT J HILLMANN Diane, 2005, USING DUBLIN CORE EL Hjorland B, 2001, J AM SOC INF SCI TEC, V52, P1193, DOI 10.1002/asi.1179 HJORLAND B, 1992, J DOC, V48, P172, DOI 10.1108/eb026895 HOFFMAN Michael, 2008, AIR FORCE TIMES HOLMAN G. Ken, 2001, DEFINITIVE XSLT XPAT Hori M, 2010, FUJITSU SCI TECH J, V46, P446 Howard J., 2011, CHRONICLE HIGHER ED Howe J., 2006, WIRED Howe J., 2008, CROWDSOURCING WHY PO, V1 Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Hutchins E, 2010, TOP COGN SCI, V2, P705, DOI 10.1111/j.1756-8765.2010.01089.x HUTCHINS W. John, 1977, J INFORM, P17 HYVONEN Eero, 2004, MUS WEB 2004 INT C C J. PAUL GETTY TRUST, 2006, CDWA LIT SPEC XML SC Jackendoff Ray, 1996, ARCHITECTURE LANGUAG JOHN Ajita, 2006, P 15 INT WORLD WID W JOHNSON Marilyn, 2010, LOS ANGELES TIMES Jones W, 2008, MKS MULTIMED INFORM, P1 Kaser O., 2007, WWW 07 WORKSH TAGG M KAYNAK Erdener, 1997, HDB CROSS CULTURAL M KENT William, 2012, DATA REALITY TIMELES Kilgour F. G., 1998, EVOLUTION BOOK Kim K., 2009, CORPORATE GOVERNANCE KIMBER Eliot, 2012, DITA PRACTITIONERS, V1 KIRSCHENBAUM Matthew G., 2009, IPRES 2009 6 INT C P Kirschenbaum MG, 2008, MECH NEW MEDIA FOREN KIRSH D, 1995, ARTIF INTELL, V73, P31, DOI 10.1016/0004-3702(94)00017-U KIRSH David, 2000, INTELLECTICA, P19 KLEPPE Anneke, 2003, EXPLAINED MODEL DRIV KNAPP Alex, 2011, FORBES Kogut B, 2003, GLOBAL INTERNET ECONOMY, P1 Kulish Nicholas, 2009, NEW YORK TIMES Kuniavsky M, 2010, SMART THINGS: UBIQUITOUS COMPUTING USER EXPERIENCE DESIGN, P1 Lackey DP, 1999, PHILOS FORUM, V30, P329, DOI 10.1111/0031-806X.00022 Lakoff G., 1990, WOMEN FIRE DANGEROUS LAMERSDORF Winfried, 2001, P 12 ACM C HYP HYP U, P19 Lancaster F. W., 1968, INFORM RETRIEVAL SYS LANGEVOORT Donald, 2006, GEORGETOWN LAW FACUL LANGVILLE Amy, 2012, GOOGLES PAGE RANK SC Laskey KJ, 2005, NET-CENTRIC APPROACHES TO INTELLIGENCE AND NATIONAL SECURITY, P29, DOI 10.1007/0-387-26176-1_3 LEE SM, 1993, ETHNIC RACIAL STUD, V16, P75 Leonardi P.M., 2010, 1 MONDAY Levitin D. J, 2002, FDN COGNITIVE PSYCHO LEVY Robert L., 2006, ELECT EVIDENCE LARGE Levy S, 2010, WIRED LEWIS MICHAEL, 2003, MONEYBALL ART WINNIN LINNAEUS C. von, 1758, SYSTEMA NATURAE Linthicum D. S., 1999, ENTERPRISE APPL INTE Lorch RF, 1989, EDUC PSYCHOL REV, V1, P209, DOI 10.1007/BF01320135 Loshin D, 2009, MASTER DATA MANAGEMENT, P1 Lovink G., 2011, CRITICAL POINT VIEW LUBETZKY Seymour, 2001, S LUBETZKY WRITINGS LUSCH Robert F., 2007, J SUPPLY CHAIN MANAG, P14 Helper S, 2003, GLOBAL INTERNET ECONOMY, P331 MACH Michelle, 2003, REAL JOB TITLES LIB MADRIGAL Alexis, 2009, WIRED Maglio PP, 2009, INF SYST E-BUS MANAG, V7, P395, DOI 10.1007/s10257-008-0105-1 Malaga RA, 2008, COMMUN ACM, V51, P147, DOI 10.1145/1409360.1409388 MALONE TW, 1983, ACM T OFF INF SYST, V1, P99, DOI 10.1145/357423.357430 MALT BC, 1995, COGNITIVE PSYCHOL, V29, P85, DOI 10.1006/cogp.1995.1013 MANESS Jack M., 2006, WEBOLOGY Mann W, 1988, TEXT, V8, P243, DOI 10.1515/text.1.1988.8.3.243 Manning C.D., 2008, INTRO INFORM RETRIEV Manyika J., 2011, BIG DATA NEXT FRONTI Mardia KV, 1980, MULTIVARIATE ANAL Margolis E., 1999, CONCEPTS CORE READIN Markoff J., 2011, NEW YORK TIMES Marlow C., 2006, P 17 C HYP HYP, P31, DOI DOI 10.1145/1149941.1149949 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 Marsh J, 2006, XML INCLUSIONS XINCL MARSHALL Catherine C., 2008, D LIB MAGAZINE MARSHALL Catherine C., 2007, P IS T ARCH 2007 MCBRIDE S., 2006, J AM HLTH INFORM MAN, P44 McCartney S., 2006, WALL STREET J Medin DL, 1997, COGNITIVE PSYCHOL, V32, P49, DOI 10.1006/cogp.1997.0645 MEDIN DL, 1993, PSYCHOL REV, V100, P254, DOI 10.1037/0033-295X.100.2.254 Merryman John Henry, 2006, IMPERIALISM ART REST MERVIS CB, 1981, ANNU REV PSYCHOL, V32, P89, DOI 10.1146/annurev.ps.32.020181.000513 Miksa Francis L., 1984, DEV CLASSIFICATION L MILLEN David, 2005, QUEUE, P28 Millen D.R., 2006, P SIGCHI C HUM FACT, P111, DOI 10.1145/1124772.1124792 Miller G., 1956, PSYCHOL REV, V62, P81 Miller G., 1998, WORDNET ELECT LEXICA Miller George A., 1976, LANGUAGE PERCEPTION Miller J., 2003, MDA GUIDE VERSION 1, V234, P51 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Mockapetris P., 1987, 1035 RFC MONTAGUE James, 2010, RISE FALL FANTASY SP Morgan Lewis Henry, 1871, SYSTEMS CONSANGUINIT Morgan Lewis Henry, 1997, SYSTEMS CONSANGUINIT Morville Peter, 2006, INFORM ARCHITECTURE, V29 MUNK Nina, 2004, FOOLS RUSH STEVE CAS National Information Standards Organization, 2004, UND MET NELSON Theodor H, 1981, REPORT PROJECT XANAD NELSON Theodor Holm, 1974, COMPUTER LIB Neuhaus C, 2008, J DOC, V64, P193, DOI 10.1108/00220410810858010 Newell A., 1972, HUMAN PROBLEM SOLVIN Norman D. A., 1988, PSYCHOL EVERYDAY THI NORMAN Donald A., 2006, INTERACTIONS, P45 Norman D. A., 1999, Interactions, V6, DOI 10.1145/301153.301168 Nunberg Geoffrey, 1996, FUTURE BOOK NUNBERG Geoffrey, 2011, NEW YORK TIMES Nunberg Geoffrey, 2009, CHRONICLE HIGHER ED O'Reilly T., 2005, DESIGN PATTERNS BUSI OASIS, 2006, UN BUS LANG UBL OASIS, 2003, XML COMM BIOM FORM Page L, 1999, STANFORD INFOLAB PANDEY Upasana, 2010, 2010 2 INT C MACH LE, P32, DOI 10.1109/ICMLC.2010.61 PANIZZI Anthony, 1841, RULES COMPILATION CA Panofsky E., 1972, STUDIES ICONOLOGY HU PARK Jung-Ran, 2009, CATALOGING CLASSIFIC, P213 Pathan M, 2008, LECT NOTES ELECTR EN, V9, P3 Pirolli P, 2007, INFORM FORAGING THEO POGUE David, 2009, NEW YORK TIMES POHS Wendi, 2013, B AM SOC INFORM SCI, P34 POOLE Erika Shehan, 2010, P 6 INT S WIK OP COL POPE Julia T., 2011, CATALOGING CLASSIFIC, P1 POUILLON Jean, 1970, ECHANGES COMMUNICATI PRALAHAD C. K., 1990, HARVARD BUSINESS REV PRATS Mario, 2008, INTELLIGENT SERVICE, P321 PREMIS EDITORIAL COMMITTEE, 2008, DAT DICT PRES MET PR PROCTOR Nancy, 2011, CURATOR MUSEUM J, P215 QUEENAN Joe, 2011, WALL STREET J Rahm E, 2006, SIGMOD RECORD, V35, P30 RANDALL Terri, 2011, PLUTO FILES RANGANATHAN Shiyali Ramamrita, 1967, PROLOGMENA LIB CLASS RDF WORKING GROUP, 2004, RES DESCR FRAM REC W Reaney P. H., 1997, DICT ENGLISH SURNAME Regazzoni CS, 2010, IEEE SIGNAL PROC MAG, V27, P16, DOI 10.1109/MSP.2010.937451 REGISTER R, 2012, METADATA HDB BOOK PU Rehder B, 2004, COGNITION, V91, P113, DOI 10.1016/S0010-0277(03)00167-7 RENEAR Allen, 2003, INT C DUBL COR MET A, P181 RESNICK P., 2001, INTERNET SOC Richard MACMANUS, 2009, TWEETING HOUSE TWITT Rips L. J., 2012, OXFORD HDB THINKING, P177 ROBERTSON Stephen, 2005, TREC EXPT EVALUATION, P287 ROBINSON David, 2008, YALE J LAW TECHNOLOG, P160 Rogers TT, 2008, BEHAV BRAIN SCI, V31, P689, DOI 10.1017/S0140525X0800589X Rosch E., 1999, CONCEPTS CORE READIN, P189 ROSCH E, 1975, J EXP PSYCHOL GEN, V104, P192, DOI 10.1037//0096-3445.104.3.192 ROSEN Jeffrey, 2012, NEW YORK TIMES MAGAZ Rosenthal A, 2004, SIGMOD RECORD, V33, P44 ROTHENBERG Jeff, 1999, RAND RUBINSKY Yuri, 1997, SGML WEB SMALL STEPS SAG Matthew, 2012, SSRN ELECT J SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samuelson P, 2011, WISC LAW REV, P479 Samuelson P, 2010, MINN LAW REV, V94, P1308 Samuelson Pamela, 2009, HUFFINGTON POST Sandler M, 2006, LIBR RESOUR TECH SER, V50, P239 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SATIJA MP, 2001, RELATIONSHIPS ORG KN, V2, P199 SAVODNIK Peter, 2011, BLOOMBERG BUSINESSWE Sayre R, 2005, IEEE INTERNET COMPUT, V9, P71, DOI 10.1109/MIC.2005.74 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C SCHATZ Bruce R., 1994, SCIENCE, P895 Schmandt-Besserat D., 1997, WRITING CAME SCHMIDT Desmond, 2009, P BAL MARK C 2009 SCHMITZ Patrick L., 2008, MUS WEB 2008 INT C C Scholl H.J., 2007, INT J PUBLIC ADMIN, V30, P889, DOI 10.1080/01900690701402668 Schwartz B, 2005, PARADOX CHOICE WHY M Sebald W. G., 1998, RINGS SATURN Sen A, 2004, DECIS SUPPORT SYST, V37, P151, DOI 10.1016/S0167-9236(02)00208-7 Sen S., 2006, CSCW 2006, P181 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 SHAH Rajiv C., 2006, P 8 ANN INT DIG GOV Shapiro C., 1998, INFORM RULES STRATEG Shirky C, 2005, ONTOLOGY IS OVERRATE Shneiderman B, 2008, SCIENCE, V319, P1349, DOI 10.1126/science.1153539 SIEMENS Ray, 2005, COMPANION DIGITAL HU SILMAN Roberta, 1998, NEW YORK TIMES SILVERSTON Len, 2000, DATA MODEL RESOURCE, V2 Simon H. A., 1997, ADM BEHAV, V4th Simon H. A., 1996, SCI ARTIFICIAL Simon Nina, 2010, PARTICIPATORY MUSEUM Simpson J. A., 1989, OXFORD ENGLISH DICT SINCLAIR James, 2007, J INF SCI, P15 SMIRAGLIA Richard P., 1994, NAVIGATING NETWORKS, P115 Smiraglia RP, 1999, J AM SOC INFORM SCI, V50, P493, DOI 10.1002/(SICI)1097-4571(1999)50:6<493::AID-ASI4>3.0.CO;2-U Smith E. E., 1981, CATEGORIES CONCEPTS Smith LB, 2003, TRENDS COGN SCI, V7, P343, DOI 10.1016/S1364-6613(03)00156-6 SPITERI Louis, 1998, CANADIAN J INFORM LI, P1 Srinivasan R, 2009, INFORM SOC, V25, P265, DOI 10.1080/01972240903028714 Stewart T., 1997, INTELLECTUAL CAPITAL STOREY Veda C., 1993, VDLB J, P455 Strohman T., 2009, SEARCH ENGINES INFOR STROUT Ruth French, 1956, LIB Q, P254 SUEHLE Ruth, 2012, WIRED Svenonius E., 2000, INTELLECTUAL FDN INF TAGLIABUE John, 2012, NEW YORK TIMES Taylor A. G., 2009, ORG INFORM TAYLOR Hugh, 2006, JOY SOX WHY SARBANES Taylor J., 2007, SMART ENOUGH SYSTEMS TAYLOR Maureen A., 2010, PRESERVING YOUR FAMI Teece DJ, 1998, CALIF MANAGE REV, V40, P55 Teper TH, 2005, LIBR RESOUR TECH SER, V49, P32 Thomas HOFWEBER, 2009, STANFORD ENCY PHILOS TIDWELL Doug, 2008, XSLT MASTERING XML T Tillett B. B., 2005, Cataloging & Classification Quarterly, V39, DOI 10.1300/J104v39n03_12 TILLETT BB, 1992, LIBR RESOUR TECH SER, V36, P162 TILLETT BB, 1991, LIBR RESOUR TECH SER, V35, P150 TILLETT BB, 2001, RELATIONSHIPS ORG KN, V2, P19 TRANT Jennifer, 2009, MUSEUM MANAGEMENT CU, P1 TRANT Jennifer, 2009, J DIGITAL INFORM, P1 TRAVERS J, 1969, SOCIOMETRY, V32, P425, DOI 10.2307/2786545 Tufte ER, 1983, VISUAL DISPLAY QUANT Turban E., 2010, DECISION SUPPORT BUS TURNBULL Giles, 2009, MORNING NEWS Underhill P, 2008, WHY WE BUY SCI SHOPP VAN DER VLIST Eric, 2007, SCHEMATRON VANDER WAL Thomas, 2007, FOLKSONOMY VANDERWAL VANDESOMPEL Herbert, 2010, IETF TOOLS Vargo SL, 2004, J MARKETING, V68, P1, DOI 10.1509/jmkg.68.1.1.24036 Lee JA, 2007, INFORM SCI STAT, P1 Viswanadham N, 2002, IEEE ROBOT AUTOM MAG, V9, P48, DOI 10.1109/MRA.2002.1019490 VON AHN Luis, 2008, COMMUN ACM, P57 VON RIEGEN Claus, 2006, OASIS S MAY 10 2006 Wakabayashi D., 2011, WALL STREET J WALKER Rob, 2009, NEW YORK TIMES Wallach A, 1998, EXHIBITING CONTRADIC Walsh N., 2010, DOCBOOK 5 DEFINITIVE Want Roy, 2006, PERVASIVE COMPUTING, V5, P25, DOI DOI 10.1109/MPRV.2006.2 Watson HJ, 2007, COMPUTER, V40, P96, DOI 10.1109/MC.2007.331 Watts DJ, 2004, ANNU REV SOCIOL, V30, P243, DOI 10.1146/annurev.soc.30.020404.104342 Weill P., 2004, IT GOVERNANCE TOP PE Weinberger D., 2007, EVERYTHING IS MISCEL WEINBERGER Jerry, 1985, SCI FAITH POLITICS F WHEATLEY Malcolm, 2004, CIO WILBERT Caroline, 2006, HOWSTUFFWORKS Wilde E, 2008, COMMUN ACM, V51, P43, DOI 10.1145/1400181.1400195 Wilde E., 2002, XPATH XLINK XPOINTER Pautasso C, 2011, REST: FROM RESEARCH TO PRACTICE, P1, DOI 10.1007/978-1-4419-8303-9_0 WILDE Erik, 2007, DOCENG 07 P 2007 ACM WILDE Erik, 2008, PLAIN WEB WILLIAMS Robin, 2012, NONDESIGNERS DESIGN Williamson O.E., 1998, EC I CAPITALISM Williamson OE, 1975, MARKETS HIERARCHIES Wilson P., 1968, 2 KINDS POWER ESSAY Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0 WINSTON ME, 1987, COGNITIVE SCI, V11, P417, DOI 10.1207/s15516709cog1104_2 Witten IH, 2011, MOR KAUF D, P1 WITTGENSTEIN Ludwig, 2002, FDN COGNITIVE PSYCHO, P271 WRIGHT Alex, 2010, NEW YORK TIMES WU Michael, 2012, SCI SOCIAL HYPE LIKE WYNHOLDS Laura, 2011, INT J DIGITAL CURATI, P214 Xu C, 2009, J ACAD LIBR, V35, P324 Yau Nathan, 2011, VISUALIZE THIS FLOWI YEE Martha M., 2009, CAN BIBLIO DATA BE P Yee R., 2008, PRO WEB 2 0 MASHUPS ZEITHAMI Valarie A., 2001, CALIFORNIA MANAGEMEN, P118 Zhou RR, 2003, J CONSUM RES, V29, P517, DOI 10.1086/346247 NR 443 TC 0 Z9 0 PU MIT PRESS PI CAMBRIDGE PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA BN 978-0-262-31396-4; 978-0-262-51850-5 PY 2013 PG 41 WC Information Science & Library Science SC Information Science & Library Science GA BGM36 UT WOS:000323496500001 ER PT S AU Teixeira, LFS Ribeiro, RA Falcao, A Lopes, GP Raminhos, R AF Teixeira, Luis F. S. Ribeiro, Rita A. Falcao, Antonio Lopes, Gabriel P. Raminhos, Ricardo BE CamarinhaMatos, LM Tomic, S Graca, P TI BrainMap - A Navigation Support System in a Tourism Case Study SO TECHNOLOGICAL INNOVATION FOR THE INTERNET OF THINGS SE IFIP Advances in Information and Communication Technology LA English DT Proceedings Paper CT 4th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013 CY APR 15-17, 2013 CL Costa de Caparica, PORTUGAL SP SOCOLNET, Int Federat Informat Process, IEEE Ind Elect Soc DE Document Correlation; Graph Exploration; Tf-Idf Metric; Jaccard Metric; Tourism AB Presently, the amount of documents in corporations can make searching and browsing for a specific topic or information a very hard task. Therefore, it is important to develop tools to ease the retrieval of specific information and to support the exploration by users on corporate intranets (composed of several hundreds of gigabytes of documents). Although not explicitly identified, many of these documents are related among themselves (directly or implicitly). In this paper we discuss a navigation support system to explore graphs applied to document correlations, using a tourism case study. C1 [Teixeira, Luis F. S.; Lopes, Gabriel P.] DI FCT UNL, P-2829516 Caparica, Portugal. RP Teixeira, LFS (reprint author), DI FCT UNL, P-2829516 Caparica, Portugal. EM lft13809@fct.unl.pt; rar@uninova.pt; ajf@uninova.pt; gpl@fct.unl.pt; rraminhos@viatecla.com CR Afrin T, 2001, EXTRACTION BASIC NOU Amit S, 1996, P 19 ANN INT ACM SIG Hulth A, 2003, EMPIRICAL METHODS NA, P216 Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 Mattmann Chris A., 2012, TIKA ACTION Maynard D, 2006, WWW 2006 ED UK MAY 2 Mihalcea R., 2011, GRAPH BASED NATURAL Miyamoto S, 1990, FUZZY SETS INFORM D, V4 Rocha LM, 2005, 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Proceedings, P565, DOI 10.1109/WI.2005.106 Salton G., 1987, TERM WEIGHTING APPRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shyi-Ming Chen, 1995, Fuzzy Sets and Systems, V72, DOI 10.1016/0165-0114(94)00284-E Teixeira L, 2011, IFIP ADV INF COMM TE, V349, P101 Teixeira L.F.S, 2012, P 4 INT C AG ART INT Van Kranenburg R., 2007, NETWORK NOTEBOOKS, V2 Viatecla, 2011, VAC PACK DAT ViaTecla, 2011, BRAINMAP Viji S, 2002, TERM DOCUMENT CORREL Zimmermann H. J., 2001, FUZZY SET THEORY ITS NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1868-4238 BN 978-3-642-37290-2 J9 IFIP ADV INF COMM TE PY 2013 VL 394 BP 99 EP 106 PG 8 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BGJ03 UT WOS:000323186100011 ER PT J AU Li, HQ Yan, ST AF Li, Huanqin Yan, Shi-Tao BE Zhu, Z TI Word Frequency Statistics by Tree-Structure Algorithm Research SO INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2012) SE Proceedings of SPIE LA English DT Proceedings Paper CT 4th International Conference on Graphic and Image Processing (ICGIP) CY OCT 06-07, 2012 CL Singapore, SINGAPORE SP Int Assoc Comp Sci & Informat Technol (IACSIT), Wuhan Univ DE word frequency statistics; redundancy; find tree; binary search; time complexity AB English articles for obtaining statistics is very convenient, Because there are space between words, While due to the characteristics of Chinese word-formation, Chinese articles statistical is very difficult. In the study on the basis of previous methods, put forward a new kind of more keywords matching method- Word frequency statistics by Tree-structure algorithm. In implementing this algorithm based on its performance is analyzed, The result shows that this method can realize a scan statistics shows all words of information, Consumed time is greatly reduced. C1 [Li, Huanqin] Zhengzhou Normal Univ, Dept Informat Technol, Zhengzhou, Henan Province, Peoples R China. RP Li, HQ (reprint author), Zhengzhou Normal Univ, Dept Informat Technol, Zhengzhou, Henan Province, Peoples R China. CR Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Elkan C ., 1997, CS97 U CAL SANDIEGO Kononerko I., 1991, P 6 EUR WORK SESS LE, P206 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P623 Stern Mia K, NATIVE BAYES CLASSIF NR 6 TC 0 Z9 0 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 978-0-8194-9566-2 J9 PROC SPIE PY 2013 VL 8768 AR 87680S DI 10.1117/12.2010653 PG 5 WC Optics; Imaging Science & Photographic Technology SC Optics; Imaging Science & Photographic Technology GA BET62 UT WOS:000318032900028 ER PT J AU Chang, CJ Tseng, JM Liu, CC AF Chang, Chia-Jung Tseng, Jui-Min Liu, Chen-Chung TI The effect of semantic technologies on the exploration of the web of knowledge by female and male users SO ELECTRONIC LIBRARY LA English DT Article DE Information searches; Men; Women; Technology; Semantics; Information seeking; Semantic technologies; Gender difference; Web of knowledge ID SELF-EFFICACY; HYPERMEDIA ENVIRONMENTS; SEARCHING STRATEGIES; COMPUTER ATTITUDES; GENDER DIFFERENCES; INTERNET; INFORMATION; STUDENTS; RECOMMENDATION; DISORIENTATION AB Purpose - Semantic technologies are a potential approach to enhancing the use of the web of knowledge. An experiment was conducted to investigate the roles of two semantic techniques, namely concept recommendation and mind maps, for females and males This paper aims to document this research. Design/methodology/approach - The study compared the searching behaviours and perceptions of searching strategies in the search environment with techniques to reveal the gender difference in the use of semantic technologies. The study further investigated how the techniques influenced female and male students' searching experiences by comparing their searching behaviours and strategies in two different environments, one developed with the semantic technologies and one without Findings - Although the techniques were helpful for both female and male students in improving their perceptions of searching strategies, there were gender differences in sense of disorientation, problem solving and searching patterns. In particular, the techniques facilitated females to search for information beyond similarity holes, but did not demonstrate such an effect on males. However, they supported males to organize information for better use. Research limitations/implications - The study is only a small-scale investigation. Further studies need to be conducted with a larger sample to obtain robust evidence. Originality/value - In this paper, the effects of semantic technologies are evaluated from the perspectives of education and the human factor, rather than only in terms of technical performance. C1 [Chang, Chia-Jung; Tseng, Jui-Min; Liu, Chen-Chung] Natl Cent Univ, Grad Inst Network Learning Technol, Jhongli, Taiwan. RP Liu, CC (reprint author), Natl Cent Univ, Grad Inst Network Learning Technol, Jhongli, Taiwan. EM ccliu@cl.ncu.edu.tw CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Ahuja JS, 2001, INTERACT COMPUT, V14, P15, DOI 10.1016/S0953-5438(01)00048-0 Bandura A, 1997, SELF EFFCACY EXERCIS Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 BERNERS-LEE T., 1999, WEAVING WEB PRESENT Berners-Lee T, 2001, SCI AM, V284, P34 Blanco-Fernandez Y, 2008, J SYST SOFTWARE, V81, P2371, DOI 10.1016/j.jss.2008.05.009 Buzan T, 1994, MIND MAP BOOK USE RA Chen CC, 2007, ELECTRON LIBR, V25, P711, DOI 10.1108/02640470710837137 Chen SY, 2010, INT J INFORM MANAGE, V30, P379, DOI 10.1016/j.ijinfomgt.2010.02.009 Chyan Yang, 2007, Electronic Library, V25, DOI 10.1108/02640470710741359 Ciravegna F, 2004, LECT NOTES COMPUT SC, V3053, P312 Comber C, 1997, EDUC RES, V39, P123 Conklin Jeff, 1987, IEEE COMPUT, V20, P17 Debowski S, 2001, ELECTRON LIBR, V19, P371, DOI 10.1108/02640470110411991 Dias P, 1999, J EDUC COMPUT RES, V20, P93 Dill S., 2003, P 12 INT C WORLD WID, P178 Doan A, 2003, VLDB J, V12, P303, DOI 10.1007/S00778-003-0104-2 Durndell A, 2002, COMPUT HUM BEHAV, V18, P521, DOI 10.1016/S0747-5632(02)00006-7 Durndell A, 1997, COMPUT EDUC, V28, P1, DOI 10.1016/S0360-1315(96)00034-6 Ford N., 1996, 3 ELVIRA C 30 APR 19, P87 Ford N, 2001, J AM SOC INF SCI TEC, V52, P1049, DOI 10.1002/asi.1165.abs Gery M., 1999, ADV CONCEPTUAL MODEL, P334 Han LX, 2009, J INF SCI, V35, P426, DOI 10.1177/0165551508101862 Hsu IC, 2009, EXPERT SYST APPL, V36, P3795, DOI 10.1016/j.eswa.2008.02.062 Jackson LA, 2001, SEX ROLES, V44, P363, DOI 10.1023/A:1010937901821 Jenkins C., 2003, IT SOC, V1, P64 Johnson R., 1994, LEARNING TOGETHER AL, V4a Klockner K., 2004, CHI 04 HUM FACT COMP, P1539 Koper R., 2004, J INTERACTIVE MEDIA Large A, 2002, INFORM PROCESS MANAG, V38, P427, DOI 10.1016/S0306-4573(01)00034-6 Leong S. C., 1999, INFORM RES, V5 Li N, 2007, COMPUT EDUC, V48, P301, DOI 10.1016/j.compedu.2005.01.007 Liu CC, 2011, COMPUT EDUC, V57, P1907, DOI 10.1016/j.compedu.2011.04.002 Liu CC, 2010, ONLINE INFORM REV, V34, P786, DOI 10.1108/14684521011084627 Liu CC, 2005, J INF SCI ENG, V21, P873 Madrid RI, 2009, COMPUT HUM BEHAV, V25, P66, DOI 10.1016/j.chb.2008.06.005 Maedche A., 2002, PRINCIPLES DATA MINI, P383 Makela E, 2005, P SEM KNOWL MAN SEM Mathes A., 2004, COMPUTER MEDIATED CO, V47 McNee SM, 2006, CHI 06, P1097, DOI DOI 10.1145/1125451.1125659 Peng HY, 2006, EDUC STUD, V32, P73, DOI 10.1080/03055690500416025 Qin ZX, 2004, LECT NOTES ARTIF INT, V3056, P145 Rockland RH, 2000, IEEE T EDUC, V43, P420, DOI 10.1109/13.883352 Roy M., 2003, Journal of Educational Computing Research, V29, DOI 10.2190/7BR8-VXA0-07A7-8AVN Roy M., 2003, Journal of Educational Computing Research, V29, DOI 10.2190/KCGA-3197-2V6U-WUTH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savolainen R, 2006, INFORM PROCESS MANAG, V42, P519, DOI 10.1016/j.ipm.2005.01.009 Schacter J, 1998, J AM SOC INFORM SCI, V49, P840, DOI 10.1002/(SICI)1097-4571(199807)49:9<840::AID-ASI9>3.3.CO;2-4 Scheiter K, 2007, EDUC PSYCHOL REV, V19, P285, DOI 10.1007/s10648-007-9046-3 Shashaani L, 1997, J EDUC COMPUT RES, V16, P37, DOI 10.2190/Y8U7-AMMA-WQUT-R512 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Tiropanis T, 2009, IEEE INTELL SYST, V24, P49, DOI 10.1109/MIS.2009.121 Tsai MJ, 2003, INNOV EDUC TEACH INT, V40, P43, DOI 10.1080/135500032000038822 Tsai MJ, 2009, COMPUT EDUC, V53, P473, DOI 10.1016/j.compedu.2009.03.006 Tseng J.-M., 2011, P 15 GLOB CHIN C COM Tu YW, 2008, COMPUT EDUC, V51, P1142, DOI 10.1016/j.compedu.2007.11.003 Walraven A, 2009, COMPUT EDUC, V52, P234, DOI 10.1016/j.compedu.2008.08.003 Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 Yang FY, 2009, COMPUT EDUC, V52, P848, DOI 10.1016/j.compedu.2008.12.008 Yu ZW, 2007, LECT NOTES COMPUT SC, V4611, P898 NR 61 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0264-0473 J9 ELECTRON LIBR JI Electron. Libr. PY 2013 VL 31 IS 3 BP 376 EP 398 DI 10.1108/EL-08-2011-0122 PG 23 WC Information Science & Library Science SC Information Science & Library Science GA 181PB UT WOS:000321679900008 ER PT J AU Borg, M Pfahl, D Runeson, P AF Borg, Markus Pfahl, Dietmar Runeson, Per BE Cleve, A Ricca, F Cerioli, M TI Analyzing Networks of Issue Reports SO PROCEEDINGS OF THE 17TH EUROPEAN CONFERENCE ON SOFTWARE MAINTENANCE AND REENGINEERING (CSMR 2013) SE European Conference on Software Maintenance and Reengineering LA English DT Proceedings Paper CT 17th European Conference on Software Maintenance and Reengineering (CSMR) CY MAR 05-08, 2013 CL Genova, ITALY SP IEEE, IEEE Comp Soc, Reengineering Forum (REF), Univ Studi Genova, Dipartimento Informatica, Bioingegneria, Robotica & Ingegneria Sistemi (DIBRIS), Tech Council Software Engn (TCSE) HO Univ Studi Genova, Dipartimento Informatica, Bioingegneria, Robotica & Ing DE issue reports; impact analysis; safety development; link mining; information retrieval ID TRACEABILITY LINKS; DOCUMENTATION; SYSTEMS; CODE AB Completely analyzed and closed issue reports in software development projects, particularly in the development of safety-critical systems, often carry important information about issue-related change locations. These locations may be in the source code, as well as traces to test cases affected by the issue, and related design and requirements documents. In order to help developers analyze new issues, knowledge about issue clones and duplicates, as well as other relations between the new issue and existing issue reports would be useful. This paper analyses, in an exploratory study, issue reports contained in two Issue Management Systems (IMS) containing approximately 20.000 issue reports. The purpose of the analysis is to gain a better understanding of relationships between issue reports in IMSs. We found that link-mining explicit references can reveal complex networks of issue reports. Furthermore, we found that textual similarity analysis might have the potential to complement the explicitly signaled links by recommending additional relations. In line with work in other fields, links between software artifacts have a potential to improve search and navigation in large software engineering projects. C1 [Borg, Markus; Runeson, Per] Lund Univ, Dept Comp Sci, S-22100 Lund, Sweden. RP Borg, M (reprint author), Lund Univ, Dept Comp Sci, Box 118, S-22100 Lund, Sweden. EM markus.borg@cs.lth.se; dietmarp@ifi.uio.no; per.runeson@cs.lth.se RI Pfahl, Dietmar/H-3084-2015; Runeson, Per/ OI Pfahl, Dietmar/0000-0003-2400-501X; Runeson, Per/0000-0003-2795-4851 CR Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Bastian M., 2009, GEPHI OPEN SOURCE SO BENBASAT I, 1987, MIS QUART, V11, P369, DOI 10.2307/248684 Bettenburg N, 2008, PROC IEEE INT CONF S, P337, DOI 10.1109/ICSM.2008.4658082 Borg Markus, 2012, Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2012) Brandes U., HDB GRAPH D IN PRESS Cleland-Huang J., 2007, P 2 INT WORKSH REQ E, P4 De Lucia A., 2007, T SOFTWARE ENG METHO, V16 De Lucia A, 2012, SOFTWARE SYSTEMS TRA Flick U., 2009, INTRO QUALITATIVE RE Getoor L., 2005, SIGKDD EXPLORATIONS, V7, P3 Herbsleb JD, 2003, IEEE T SOFTWARE ENG, V29, P481, DOI 10.1109/TSE.2003.1205177 International Electrotechnical Commission, 2003, 615111 IEC Johnson JN, 2003, COMPUT SCI ENG, V5, P71, DOI 10.1109/MCISE.2003.1238707 Kagdi H, 2007, INT C PROGRAM COMPRE, P145 Karabatis G, 2009, LECT NOTES COMPUT SC, V5850, P74, DOI 10.1007/978-3-642-10562-3_3 Kaushik N, 2012, EUR CON SFTWR MTNCE, P159, DOI 10.1109/CSMR.2012.78 Keim DA, 2002, IEEE T VIS COMPUT GR, V8, P1, DOI 10.1109/2945.981847 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Klevin A., 2012, THESIS LUND U Marcus A, 2003, PROC INT CONF SOFTW, P125, DOI 10.1109/ICSE.2003.1201194 Mierswa I., 2006, P 12 ACM SIGKDD INT, P935, DOI DOI 10.1145/1150402.1150531 Page L., 1999, SIDLWP19990120 STANF Robinson B., 2010, P ACM IEEE INT S EMP, P21 Runeson P, 2007, PROC INT CONF SOFTW, P499 Runeson P, 2012, CASE STUDY RES SOFTW SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scacchi W., 2002, IEE Proceedings-Software, V149, DOI 10.1049/ip-sen:20020202 Shihab E., 2012, P 9 WORK C MIN SOFTW, P112 Sowa J.F., 1991, PRINCIPLES SEMANTIC Tian Y, 2012, EUR CON SFTWR MTNCE, P385, DOI 10.1109/CSMR.2012.48 NR 31 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1944-2793 BN 978-0-7695-4948-4 J9 EUR CON SFTWR MTNCE PY 2013 BP 79 EP 88 DI 10.1109/CSMR.2013.18 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BFS23 UT WOS:000321127000010 ER PT J AU Uysal, AK Gunal, S Ergin, S Gunal, ES AF Uysal, A. K. Gunal, S. Ergin, S. Gunal, E. Sora TI The Impact of Feature Extraction and Selection on SMS Spam Filtering SO ELEKTRONIKA IR ELEKTROTECHNIKA LA English DT Article DE Feature extraction; feature selection; SMS; spam filter ID SUPPORT VECTOR MACHINE; TEXT CATEGORIZATION; CLASSIFICATION; RETRIEVAL; ALGORITHM AB This paper investigates the impact of several feature extraction and feature selection approaches on filtering of short message service (SMS) spam messages in two different languages, namely Turkish and English. The entire feature set of filtering framework consists of the features originated from the bag-of-words (BoW) model along with the ensemble of structural features (SF) specific to spam problem. The distinctive BoW features are identified using information theoretic feature selection methods. Various combinations of the BoW and SF are then fed into widely used pattern classification algorithms to classify SMS messages. The filtering framework is evaluated on both Turkish and English SMS message datasets. For this purpose, as part of the study, the first publicly available Turkish SMS message collection is constituted as well. Comprehensive experimental analysis on the respective datasets revealed that the combinations of BoW and SFs, rather than BoW features alone, provide better classification performance on both datasets. Effectiveness of the utilized feature selection methods however slightly differs in each language. C1 [Uysal, A. K.; Gunal, S.] Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. [Ergin, S.; Gunal, E. Sora] Eskisehir Osmangazi Univ, Dept Elect & Elect Engn, Eskisehir, Turkey. RP Uysal, AK (reprint author), Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. EM akuysal@anadolu.edu.tr FU Anadolu University [1103F054] FX This work was supported by Anadolu University, Fund of Scientific Research Projects under grant number 1103F054. CR Almeida TA, 2011, DOCENG 2011: PROCEEDINGS OF THE 2011 ACM SYMPOSIUM ON DOCUMENT ENGINEERING, P259 Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 [Anonymous], 1992, 0340 ETSI GSM Can F, 2008, J AM SOC INF SCI TEC, V59, P407, DOI 10.1002/asi.20750 Chen YT, 2011, EXPERT SYST APPL, V38, P3085, DOI 10.1016/j.eswa.2010.08.100 Cormack G.V., 2007, P 16 ACM C INF KNOWL, P313, DOI DOI 10.1145/1321440.1321486 Delany SJ, 2012, EXPERT SYST APPL, V39, P9899, DOI 10.1016/j.eswa.2012.02.053 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Gunal S, 2006, LECT NOTES COMPUT SC, V4105, P635 Gunal S, 2012, TURK J ELECTR ENG CO, V20, P1296, DOI 10.3906/elk-1101-1064 Gunal S, 2008, INFORM SCIENCES, V178, P3716, DOI 10.1016/j.ins.2008.06.001 Hidalgo J. M. G., 2006, ACM S DOC ENG AMST N, P107 Ogura H, 2011, EXPERT SYST APPL, V38, P4978, DOI 10.1016/j.eswa.2010.09.153 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joe I, 2010, LECT NOTES COMPUT SC, V6485, P577 Theodoridis S, 2009, PATTERN RECOGNITION, 4RTH EDITION, P1 Manning C.D., 2008, INTRO INFORM RETRIEV Metsis V., 2006, P 3 C EM ANT, P28 Nuruzzaman M. T., 2011, P IEEE 11 INT C COMP Ogura H, 2009, EXPERT SYST APPL, V36, P6826, DOI 10.1016/j.eswa.2008.08.006 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Puniskis D, 2007, ELEKTRON ELEKTROTECH, P35 Puniskis D., 2006, Elektronika ir Elektrotechnika SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 Sohn DN, 2012, PATTERN RECOGN LETT, V33, P364, DOI 10.1016/j.patrec.2011.10.017 Uysal A. K., 2012, P IEEE INT S INN INT Wang C, 2010, IBM J RES DEV, V54, DOI 10.1147/JRD.2010.2066050 Yadav K., 2011, 12 WORKSH MOB COMP S Yoon JW, 2010, COMPUT SECUR, V29, P446, DOI 10.1016/j.cose.2009.11.003 Yuanchun Zhu, 2011, IEEE Transactions on Information Forensics and Security, V6, DOI 10.1109/TIFS.2010.2103060 Zhang W, 2008, KNOWL-BASED SYST, V21, P879, DOI 10.1016/j.knosys.2008.03.044 NR 33 TC 0 Z9 0 PU KAUNAS UNIV TECHNOLOGY PI KAUNAS PA KAUNAS UNIV TECHNOL, DEPT ELECTRONICS ENGINEERING, STUDENTU STR 50, KAUNAS, LT-51368, LITHUANIA SN 1392-1215 J9 ELEKTRON ELEKTROTECH JI Elektron. Elektrotech. PY 2013 VL 19 IS 5 BP 67 EP 72 DI 10.5755/j01.eee.19.5.1829 PG 6 WC Engineering, Electrical & Electronic SC Engineering GA 165PW UT WOS:000320496800014 ER PT B AU Santos, I Laorden, C Sanz, B Bringas, PG AF Santos, Igor Laorden, Carlos Sanz, Borja Bringas, Pablo G. GP IEEE TI JURD: Joiner of Un-Readable Documents to Reverse Tokenization Attacks to Content-based Spam Filters SO 2013 IEEE CONSUMER COMMUNICATIONS AND NETWORKING CONFERENCE (CCNC) LA English DT Proceedings Paper CT 10th IEEE Consumer Communications and Networking Conference (CCNC) CY JAN 11-14, 2013 CL Las Vegas, NV SP IEEE ID CATEGORIZATION AB Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques like sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, there are attacks that can subvert the filtering capabilities of these methods. Tokenization attacks, in particular, insert characters that create divisions within words, causing incorrect representations of e-mails. In this paper, we introduce a new method that reverses the effects of tokenization attacks. Our method processes e-mails iteratively by considering possible words, starting from the first token and compares the word candidates with a common dictionary to which spam words have been previously added. We provide an empirical study of how tokenization attacks affect the filtering capability of a Bayesian classifier and we show that our method can reverse the effects of tokenization attacks. C1 [Santos, Igor; Laorden, Carlos; Sanz, Borja; Bringas, Pablo G.] Univ Deusto, Deusto Inst Technol, DeustoTech Comp, S3Lab, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, Deusto Inst Technol, DeustoTech Comp, S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; claorden@deusto.es; borja.sanz@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 Androutsopoulos I., 2000, P MACH LEARN TEXT IN Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bayes T., 1764, PHILOS T ROY SOC LON, V53, P370, DOI [10.1093/biomet/45.3-4.296, DOI 10.1098/RSTL.1763.0053] Blanzieri E., 2007, P FLAIRS 20, P441 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Choo KKR, 2011, COMPUT SECUR, V30, P719, DOI 10.1016/j.cose.2011.08.004 Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Laorden C, 2012, ELECTRON COMMER R A, V11, P290, DOI 10.1016/j.elerap.2011.11.004 Laorden C, 2013, LOG J IGPL, V21, P540, DOI 10.1093/jigpal/jzs030 Laorden C., LECT NOTES COMPUTER Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Polyvyanyy A., 2007, THESIS Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Sahami M., 1998, LEARNING TEXT CATEGO, V62, P98 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos I, 2012, EXPERT SYST APPL, V39, P437, DOI 10.1016/j.eswa.2011.07.034 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Vapnik V. N., 2000, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wittel G., 2004, P 1 C EM ANT CEAS Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 NR 33 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-3133-3; 978-1-4673-4735-8 PY 2013 BP 259 EP 264 PG 6 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BEZ77 UT WOS:000318943600038 ER PT J AU Fujita, O AF Fujita, Osamu TI Metrics based on average distance between sets SO JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS LA English DT Article DE Metric; Distance between sets; Average distance; Power mean; Hausdorff metric; Information retrieval AB This paper presents a distance function between sets based on an average of distances between their elements. The distance function is a metric if the sets are non-empty finite subsets of a metric space. It includes the Jaccard distance as a special case, and can be generalized by using the power mean so as to also include the Hausdorff metric on finite sets. It can be extended to deal with non-null measurable sets, and applied for measuring distances between fuzzy sets and between probability distributions. These distance functions are useful for measuring similarity between data in computer science and information science. In instructional systems design and information retrieval, for example, they are likely to be useful for analyzing and processing text documents that are modeled as hierarchical collections of sets of terms. A distance measure of learners' knowledge is also discussed in connection with quantities of information. C1 Osaka Kyoiku Univ, Osaka 5828582, Japan. RP Fujita, O (reprint author), Osaka Kyoiku Univ, Osaka 5828582, Japan. EM fuji@cc.osaka-kyoiku.ac.jp CR Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401 Bukatin M, 2009, AM MATH MON, V116, P708, DOI 10.4169/193009709X460831 Bullen P.S., 2003, MATH ITS APPL, V560 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Deza M. M., 2009, ENCY DISTANCES Duda R.O., 2001, PATTERN CLASSIFICATI Everitt B., 1980, CLUSTER ANAL Hart K. P, 2004, ENCY GEN TOPOLOGY Lowen R., 1997, APPROACH SPACES MISS Manning C.D., 2008, INTRO INFORM RETRIEV Nagata J., 1985, MODERN GEN TOPOLOGY Rubinstein R. Y., 2008, SIMULATION MONTE CAR Rucklidge W., 1996, LECT NOTES COMPUTER, V1173 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Searcoid M. O., 2007, METRIC SPACES Zimmermann H. J., 2001, FUZZY SET THEORY ITS NR 17 TC 0 Z9 0 PU KINOKUNIYA CO LTD PI TOKYO PA 13-11 HIGASHI 3-CHOME, SHINJUKU-KU, TOKYO, 150-8513, JAPAN SN 0916-7005 J9 JPN J IND APPL MATH JI Jpn. J. Ind. Appl. Math. PY 2013 VL 30 IS 1 BP 1 EP 19 DI 10.1007/s13160-012-0089-6 PG 19 WC Mathematics, Applied SC Mathematics GA 110HK UT WOS:000316433000001 ER PT J AU Gornitz, N Kloft, M Rieck, K Brefeld, U AF Goernitz, Nico Kloft, Marius Rieck, Konrad Brefeld, Ulf TI Toward Supervised Anomaly Detection SO JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH LA English DT Article ID NOVELTY DETECTION; SUPPORT; CLASSIFICATION; SYSTEM; TIME; SVMS AB Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies. C1 [Goernitz, Nico; Kloft, Marius] Tech Univ Berlin, Machine Learning Lab, Berlin, Germany. [Goernitz, Nico; Kloft, Marius] Mem Sloan Kettering Canc Ctr, Computat Biol Ctr, New York, NY 10021 USA. [Rieck, Konrad] Univ Gottingen, Dep Comp Sci, D-37077 Gottingen, Germany. [Brefeld, Ulf] Tech Univ Darmstadt, D-64289 Darmstadt, Germany. [Brefeld, Ulf] German Inst Int Educ Res, D-60486 Frankfurt, Germany. RP Gornitz, N (reprint author), Tech Univ Berlin, Machine Learning Lab, Franklinstr 28-29, Berlin, Germany. EM NICO.GOERNITZ@TU-BERLIN.DE; KLOFT@TU-BERLIN.DE; KONRAD.RIECK@UNI-GOETTINGEN.DE; BREFELD@KMA.INFORMATIK.TU-DARMSTADT.DE FU German Bundesministerium fur Bildung und Forschung (BMBF) under the project PROSEC [FKZ 01BY1145]; FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence; German National Science Foundation (DFG) [GA 1615/1-1, MU 987/6-1, MU 987/11-1, RA 1894/11]; German Academic Exchange Service (DAAD); German Research Foundation (DFG); Ministry of Education, Science, and Technology, through the National Research Foundation of Korea [R31-10008] FX The authors are very grateful to Klaus-Robert Muller for comments that helped improving the manuscript. This work was supported in part by the German Bundesministerium fur Bildung und Forschung (BMBF) under the project PROSEC (FKZ 01BY1145), by the FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence, and by the German National Science Foundation (DFG) under GA 1615/1-1, MU 987/6-1, MU 987/11-1 and RA 1894/11. Furthermore, Marius Kloft acknowledges a PhD scholarship by the German Academic Exchange Service (DAAD) and a postdoctoral fellowship by the German Research Foundation (DFG) as well as funding by the Ministry of Education, Science, and Technology, through the National Research Foundation of Korea under Grant R31-10008. A part of the work was done while Marius Kloft was with Computer Science Division and Department of Statistics, University of California, Berkeley, CA 94720-1758, USA. CR Almgren M, 2004, P IEEE CSFW, P88, DOI 10.1109/CSFW.2004.1310734 ANDREWS DF, 1978, J ROY STAT SOC B MET, V40, P85 Blanchard G, 2010, J MACH LEARN RES, V11, P2973 Blum A., 1998, COLT, P92 Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401 Campbell C, 2001, ADV NEUR IN, V13, P395 Chandola V, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541882 Chapelle O., 2006, SEMISUPERVISED LEARN Chapelle O., 2006, ICML, P185 Chapelle O., 2005, P INT WORKSH AI STAT CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Do T.-M.-T., 2010, THESIS PIERRE M CURI Eskin Eleazar, 2002, APPL DATA MINING COM Fogla P., 2006, P USENIX SEC S Goh KS, 2005, IEEE T KNOWL DATA EN, V17, P1333, DOI 10.1109/TKDE.2005.170 Gornitz N, 2009, LECT NOTES ARTIF INT, V5781, P407 Heller K. A., 2003, P WORKSH DAT MIN COM Hoi C.-H., 2003, P INT JOINT C NEUR N HUBER PJ, 1972, ANN MATH STAT, V43, P1041, DOI 10.1214/aoms/1177692459 Joachimss T., 1999, INT C MACH LEARN ICM, P200 Kloft M, 2011, J MACH LEARN RES, V12, P953 Kruegel C., 2005, COMPUTER NETWORKS, V48 Krueger T., 2010, P 25 ACM S APPL COMP, P1846, DOI 10.1145/1774088.1774480 Lai C, 2004, INT J PATTERN RECOGN, V18, P867, DOI 10.1142/S0218001404003459 Li X.L., 2005, ECML Liu B., 2003, INT C DAT MIN, P179 Liu Y, 2006, INT C PATT RECOG, P129 Manevitz LM, 2002, J MACH LEARN RES, V2, P139, DOI 10.1162/15324430260185574 Mao C.-H., 2009, SAC 2009, P2042 Markou M, 2003, SIGNAL PROCESS, V83, P2499, DOI 10.1016/j.sigpro.2003.07.019 Markou M, 2003, SIGNAL PROCESS, V83, P2481, DOI [10.1016/j.sigpro.2003.07.018, 10.1016/j.sigpro.2003.018] Maynor K., 2007, METASPLOIT TOOLKIT Muller KR, 2001, IEEE T NEURAL NETWOR, V12, P181, DOI 10.1109/72.914517 Munoz Mari J., 2010, IEEE T GEOSCI REMOTE, V48, P3188 Onoda T, 2006, 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS PROCEEDINGS, P393, DOI 10.1109/WI-IATW.2006.98 Park J, 2007, NEURAL COMPUT, V19, P1919, DOI 10.1162/neco.2007.19.7.1919 Paxson V, 1999, COMPUT NETW, V31, P2435, DOI 10.1016/S1389-1286(99)00112-7 Pelleg D., 2004, P NIPS 2004, P1073 Perdisci R, 2009, COMPUT NETW, V53, P864, DOI 10.1016/j.comnet.2008.11.011 Rieck K., 2007, J COMPUTER VIROLOGY, V2, P243, DOI 10.1007/s11416-006-0030-0 Rieck K., 2009, THESIS TU BERLIN Rieck K, 2006, LECT NOTES COMPUT SC, V4064, P74 Rieck K, 2008, J MACH LEARN RES, V9, P23 Rifkin RM, 2007, J MACH LEARN RES, V8, P441 Roeschs M, 1999, P 13 USENIX C SYST A, P229 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B, 2001, NEURAL COMPUT, V13, P1443, DOI 10.1162/089976601750264965 Scholkopf B., 2002, LEARNING KERNELS Shawe-Taylor J., 2004, KERNEL METHODS PATTE Sindhwani V., 2005, ICML, V119, P824 Sonnenburg S., 2008, THESIS FRAUNHOFER I Stokes J. W., 2008, ALADIN ACTIVE LEARNI Stolfo S. J., 2005, Journal of Computer Security, V13 Tax D., 2001, THESIS TU DELFT Tax DMJ, 2004, MACH LEARN, V54, P45, DOI 10.1023/B:MACH.0000008084.60811.49 Tong S., 2000, P 17 INT C MACH LEAR Vapnik V., 1998, STAT LEARNING THEORY Wang JG, 2005, LECT NOTES COMPUT SC, V3735, P241 Wang K, 2006, LECT NOTES COMPUT SC, V4219, P226 Warmuth MK, 2003, J CHEM INF COMP SCI, V43, P667, DOI 10.1021/ci025620t Yuan C., 2004, P INT JOINT C NEUR N Zhang D., 2005, SIMPLE PROBABILISTIC NR 62 TC 2 Z9 2 PU AI ACCESS FOUNDATION PI MARINA DEL REY PA USC INFORMATION SCIENCES INST, 4676 ADMIRALITY WAY, MARINA DEL REY, CA 90292-6695 USA SN 1076-9757 EI 1943-5037 J9 J ARTIF INTELL RES JI J. Artif. Intell. Res. PY 2013 VL 46 BP 235 EP 262 PG 28 WC Computer Science, Artificial Intelligence SC Computer Science GA 102RN UT WOS:000315861800001 ER PT S AU Bharti, KK Singh, PK AF Bharti, Kusum Kumari Singh, Pramod Kumar BE Bansal, JC Singh, PK Deep, K Pant, M Nagar, AK TI A Two-Stage Unsupervised Dimension Reduction Method for Text Clustering SO PROCEEDINGS OF SEVENTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS (BIC-TA 2012), VOL 2 SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT 7th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) CY DEC 14-16, 2012 CL Madhya Pradesh, INDIA HO ABV Indian Inst Informat Technol & Management Gwalior DE Feature selection; feature extraction; relevant; redundant; text clustering ID PRINCIPAL COMPONENT ANALYSIS; FEATURE-SELECTION METHOD; GENETIC ALGORITHM; INFORMATION GAIN; CATEGORIZATION; SPACE AB Feature selection is widely used in text clustering to reduce dimensions in the feature space. In this paper, we study and propose two-stage unsupervised feature selection methods to determine a subset of relevant features to improve accuracy of the underlying algorithm. We experiment with hybrid approach of feature selection-feature selection (FS-FS) and feature-selection feature extraction (FS-FE) methods. Initially, each feature in the document is scored on the basis of its importance for the clustering using two different feature selection methods individually Mean-Median (MM) and Mean Absolute Difference (MAD),In the second stage, in two different experiments, we hybridize them with a feature selection method absolute cosine (AC) and a feature extraction method principal component analysis (PCA) to further reduce the dimensions in the feature space. We perform comprehensive experiments to compare FS, FS-FS and FS-FE using k-mean clustering on Reuters-21578 dataset. The experimental results show that the two-stage feature selection methods are more effective to obtain good quality results by the underlying clustering algorithm. Additionally, we observe that FS-FE approach is superior to FS-FS approach. C1 [Bharti, Kusum Kumari; Singh, Pramod Kumar] ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & DataMin Res Lab, Gwalior, MP, India. RP Bharti, KK (reprint author), ABV Indian Inst Informat Technol & Management Gwa, Computat Intelligence & DataMin Res Lab, Morena Link Rd, Gwalior, MP, India. EM kkusum.bharti@gmail.com; pksingh@iiitm.ac.in CR Akadi A., 2011, KNOWL INF SYST, V26, P487 Church K., 1989, P ACL, P76, DOI DOI 10.3115/981623.981633 DEERWESTER S, 1988, P ASIS ANNU MEET, V25, P36 FERRE L, 1995, COMPUT STAT DATA AN, V19, P669, DOI 10.1016/0167-9473(94)00020-J Ferreira A., 2011, P EUR S ART NEUR NET, P339 Ferreira AJ, 2012, PATTERN RECOGN LETT, V33, P1794, DOI 10.1016/j.patrec.2012.05.019 Foithong S, 2012, EXPERT SYST APPL, V39, P574, DOI 10.1016/j.eswa.2011.07.048 Hsu HH, 2011, EXPERT SYST APPL, V38, P8144, DOI 10.1016/j.eswa.2010.12.156 Hyvarinen A., 1999, INDEPENDENT COMPONEN Jilliffe T., 1986, PRINCIPAL COMPONENT, P1 Kira K., 1992, FEATURE SELECTION PR, V21, P129 Kononenko I., 1994, P ECML, V784, P171 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 Liu L., 2005, IEEE INT C NAT LANG, P597 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Maldonado S, 2009, INFORM SCIENCES, V179, P2208, DOI 10.1016/j.ins.2009.02.014 Meng JN, 2011, COMPUT MATH APPL, V62, P2793, DOI 10.1016/j.camwa.2011.07.045 Pearson K, 1901, PHILOS MAG, V2, P559 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singh P.K., 2011, P INT C SOFT COMP PR, P967 Song W, 2009, COMPUT MATH APPL, V57, P1901, DOI 10.1016/j.camwa.2008.10.010 Uguz H, 2012, COMPUT METH PROG BIO, V107, P598, DOI 10.1016/j.cmpb.2011.03.013 Uguz H, 2011, KNOWL-BASED SYST, V24, P1024, DOI 10.1016/j.knosys.2011.04.014 Unler A, 2011, INFORM SCIENCES, V181, P4625, DOI 10.1016/j.ins.2010.05.037 Valle S, 1999, IND ENG CHEM RES, V38, P4389, DOI 10.1021/ie990110i Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 [å¼ č„ ZHANG Ying], 2008, [ä»Ŗč”ØꊀęœÆäøŽä¼ ę„Ÿå™Ø, Intstrument Technique and Sensor], P1 NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-81-322-1040-5 J9 ADV INTELL SYST PY 2013 VL 202 BP 529 EP 542 DI 10.1007/978-81-322-1041-2_45 PG 14 WC Computer Science, Artificial Intelligence; Mathematical & Computational Biology SC Computer Science; Mathematical & Computational Biology GA BDU89 UT WOS:000314999400045 ER PT J AU Bogdanov, D Haro, M Fuhrmann, F Xambo, A Gomez, E Herrera, P AF Bogdanov, Dmitry Haro, Martin Fuhrmann, Ferdinand Xambo, Anna Gomez, Emilia Herrera, Perfecto TI Semantic audio content-based music recommendation and visualization based on user preference examples SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Music information retrieval; Information systems; User modeling; Recommender system; Preference visualization; Evaluation ID INFORMATION-RETRIEVAL; FEATURES; SYSTEMS; MODEL AB Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user's musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user's musical preferences by creating a humanoid cartoon-like character - the Musical Avatar - automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-of-the-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Bogdanov, Dmitry; Haro, Martin; Fuhrmann, Ferdinand; Gomez, Emilia; Herrera, Perfecto] Univ Pompeu Fabra, Mus Technol Grp, Barcelona 08018, Spain. [Xambo, Anna] Open Univ, Mus Comp Lab, Milton Keynes MK7 6AA, Bucks, England. RP Bogdanov, D (reprint author), Univ Pompeu Fabra, Mus Technol Grp, Roc Boronat 138, Barcelona 08018, Spain. EM dmitry.bogdanov@upf.edu RI Gomez, Emilia/F-4485-2010; Herrera, Perfecto/C-4658-2012 OI Gomez, Emilia/0000-0003-4983-3989; Herrera, Perfecto/0000-0003-2799-7675 FU FI Grant of Generalitat de Catalunya (AGAUR); Buscamedia Project [CEN-20091026]; MITYC [TSI-070100-2009-407]; DRIMS (MICINN) [TIN2009-14247-C02-01]; MIRES [EC-FP7 ICT-2011.1.5, 287711] FX The authors thank all participants involved in the evaluation and Justin Salamon for proofreading. This research has been partially funded by the FI Grant of Generalitat de Catalunya (AGAUR) and the Buscamedia (CEN-20091026), Classical Planet (TSI-070100-2009-407, MITYC), DRIMS (TIN2009-14247-C02-01, MICINN), and MIRES (EC-FP7 ICT-2011.1.5 Networked Media and Search Systems, grant agreement No. 287711) Projects. CR ABDULLAH MB, 1990, J ROY STAT SOC D-STA, V39, P455 Ahmed N., 2005, ACM S VIRT REAL SOFT, P257 Amatriain X, 2009, LECT NOTES COMPUT SC, V5535, P247, DOI 10.1007/978-3-642-02247-0_24 [Anonymous], 2005, INT S MUS INF RETR I Aucouturier J.-J., 2009, LANGUAGE EVOLUTION B, P35 Baltrunas L, 2009, WORKSH CONT AW REC S Barrington L., 2007, MUS INF RETR EV EXCH Barrington L., 2009, ISMIR 2009, P357 Bishop C. M., 2006, PATTERN RECOGNITION Bogdanov D, 2011, IEEE T MULTIMEDIA, V13, P687, DOI 10.1109/TMM.2011.2125784 Bogdanov D., 2010, ACM C REC SYST WORKS Bogdanov D, 2009, 2009 11TH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2009), P453, DOI 10.1109/ISM.2009.72 Brossier P.M., 2007, THESIS QMUL LONDON Cano P., 2006, ISMIR 2004 AUDIO DES Casey MA, 2008, P IEEE, V96, P668, DOI 10.1109/JPROC.2008.916370 CELMA O, 2008, ACM C REC SYST RECSY, P179 Celma O., 2006, ESWC 2006 WORKSH MAS Celma O, 2008, J WEB SEMANT, V6, P250, DOI 10.1016/j.websem.2008.09.004 Celma O., 2008, THESIS UPF BARCELONA Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Cosley D., 2003, CHI 2003, P585 Cramer H, 2008, USER MODEL USER-ADAP, V18, P455, DOI 10.1007/s11257-008-9051-3 Downie JS, 2010, STUD COMPUT INTELL, V274, P93 Firan CS, 2007, LA-WEB 2007: 5TH LATIN AMERICAN WEB CONGRESS, PROCEEDINGS, P32 Gomez E, 2006, THESIS UPF BARCELONA Gomez E., 2008, EMPIRICAL MUSICOLOGY, V3, P140 Grimaldi M., 2004, ACM SIGMM INT WORKSH, P173 Grimmett G, 2001, PROBABILITY RANDOM P Hall M. A., 2000, INT C MACH LEARN, P359 Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Haro M., 2010, AUD MOSTL AM 10 Hoashi K., 2003, ACM MULTIMEDIA, P110 Holm J, 2009, J NEW MUSIC RES, V38, P87, DOI 10.1080/09298210902940094 Homburg H., 2005, INT C MUS INF RETR I, P528 Jawaheer G., 2010, INT WORKSH INF HET F, P47 Jones N., 2007, NETW EL COMM RES C Laurier C., 2009, INT WORKSH CONT BAS Levy M., 2010, WORKSH MUS REC DISC Li Q, 2005, LECT NOTES COMPUT SC, V3689, P72 Li Q, 2007, INFORM PROCESS MANAG, V43, P473, DOI 10.1016/j.ipm.2006.07.005 Logan B., 2004, INT C MUS INF RETR I, P425 Lu CC, 2009, EXPERT SYST APPL, V36, P10035, DOI 10.1016/j.eswa.2009.01.074 MacDonald R. A. R., 2002, MUSICAL IDENTITIES Maffesoli M., 1996, TIME TRIBES DECLINE Maltz D., 1995, SIGCHI C HUM FACT CO, P202 Mandel M. I., 2005, INT C MUS INF RETR I, P594 McCloud S., 2009, UNDERSTANDING COMICS Nanopoulos A, 2009, INFORM PROCESS MANAG, V45, P392, DOI 10.1016/j.ipm.2009.02.002 Pampalk E., 2006, THESIS VIENNA U TECH Peeters G., 2004, LARGE SET AUDIO FEAT Petajan E., 2005, REAL TIME VISION HUM Platt JC, 2000, ADV NEUR IN, P61 Pohle T., 2009, INT SOC MUS INF RETR, P525 Reas C., 2007, PROCESSING PROGRAMMI, P160 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B.M., 2001, P WWW 2010, P285 Sauer D, 2009, ACM T MULTIM COMPUT, V5, DOI 10.1145/1596990.1596991 Schedl M, 2011, INFORM PROCESS MANAG, V47, P426, DOI 10.1016/j.ipm.2010.09.002 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Sotiropoulos D. N., 2007, USER MODEL USER-ADAP, V18, P315 Streich S., 2007, THESIS UPF BARCELONA Su J. H., 2010, ACM S APPL COMP SAC, P1924 Tintarev N, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P153 Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560 Voong M., 2007, CHI 2007 HUM FACT CO, P1869, DOI 10.1145/1240866.1240913 West K., 2007, EURASIP J APPL SIG P, P149 Yoshii K., 2006, INT C MUS INF RETR I NR 67 TC 2 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2013 VL 49 IS 1 BP 13 EP 33 DI 10.1016/j.ipm.2012.06.004 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 069WA UT WOS:000313466600002 ER PT S AU Santos, I Galan-Garcia, P Santamaria-Ibirika, A Alonso-Isla, B Alabau-Sarasola, I Bringas, PG AF Santos, Igor Galan-Garcia, Patxi Santamaria-Ibirika, Aitor Alonso-Isla, Borja Alabau-Sarasola, Iker Garcia Bringas, Pablo BE Herrero, A Snasel, V Abraham, A Zelinka, I Baruque, B Quintian, H Calvo, JL Sedano, J Corchado, E TI Adult Content Filtering through Compression-Based Text Classification SO INTERNATIONAL JOINT CONFERENCE CISIS'12 - ICEUTE'12 - SOCO'12 SPECIAL SESSIONS SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT 7th Int Conf on Soft Comp Models in Industrial and Environm Applications/5th Computational Intelligence in Security for Information Syst/3rd Int Conf on EUropean Transnational Educ CY SEP 05-07, 2012 CL Ostrava, CZECH REPUBLIC SP IT4Innovations, VSB Techn Univ Ostrava, IEEE Syst Man & Cybernet Soc CzechoSlovakia, MIR Lab, IEEE Syst Man & Cybernet Soc Spain, Spanish Assoc Artificial Intelligence, IFCOLOG DE Content filtering; text-processing; compression-based text classification ID SELECTION; MODEL AB Internet is a powerful source of information. However, some of the information that is available in the Internet, cannot be shown to every type of public. For instance, pornography is not desirable to be shown to children. To this end, several algorithms for text filtering have been proposed that employ a Vector Space Model representation of the webpages. Nevertheless, these type of filters can be surpassed using different attacks. In this paper, we present the first adult content filtering tool that employs compression algorithms to represent data that is resilient to these attacks. We show that this approach enhances the results of classic VSM models. C1 [Santos, Igor; Galan-Garcia, Patxi; Santamaria-Ibirika, Aitor; Alonso-Isla, Borja; Alabau-Sarasola, Iker; Garcia Bringas, Pablo] Univ Deusto, S3Lab, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; patxigg@deusto.es; a.santamaria@deusto.es; borjaalonso@deusto.es; ialabau@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Bratko A, 2006, J MACH LEARN RES, V7, P2673 Cano JR, 2006, APPL SOFT COMPUT, V6, P323, DOI 10.1016/j.asoc.2005.02.006 Choi B, 2009, ICCIT: 2009 FOURTH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND CONVERGENCE INFORMATION TECHNOLOGY, VOLS 1 AND 2, P659 Cooper G.F., 1991, P 7 C UNC ART INT CORMACK GV, 1987, COMPUT J, V30, P541 Czarnowski I., 2006, P SCI SESS ORG 21 FA, P60 Dash M, 2003, ARTIF INTELL, V151, P155, DOI 10.1016/S0004-3702(03)00079-1 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Du R., 2003, 11 IEEE INT C NETW I, P325 Duan L., 2002, AS C COMP VIS MELB A, P797 Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Geiger D., 1997, MACH LEARN, P131 Hidalgo JMG, 2009, ADV COMPUT, V76, P257, DOI 10.1016/S0065-2458(09)01007-9 Ho WH, 2004, IEEE SYS MAN CYBERN, P4792 Ide N., 1998, COMPUTATIONAL LINGUI, V24, P2 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Kim Y., 2006, 8 INT C ADV COMM TEC, V1, P3 Lee JS, 2007, PATTERN RECOGN, V40, P2261, DOI 10.1016/j.patcog.2006.11.016 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Liu H., 2001, INSTANCE SELECTION C LOVINS JB, 1968, MECH TRANSL, V11, P22 Liu H, 2008, CH CRC DATA MIN KNOW, P3 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Pyle D., 1999, DATA PREPARATION DAT RISSANEN J, 1978, AUTOMATICA, V14, P465, DOI 10.1016/0005-1098(78)90005-5 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Torkkola K., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753742 Tsang ECC, 2003, IEEE T FUZZY SYST, V11, P202, DOI 10.1109/TFUZZ.2003.809895 Wittel G., 2004, P 1 C EM ANT CEAS Zheng H., 2004, ELECT LETT COMPUTER, V4, P1 NR 32 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-3-642-33017-9 J9 ADV INTELL SYST PY 2013 VL 189 BP 281 EP 288 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BDE89 UT WOS:000312969500029 ER PT J AU Yoon, J Park, H Kim, K AF Yoon, Janghyeok Park, Hyunseok Kim, Kwangsoo TI Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis SO SCIENTOMETRICS LA English DT Article DE Dynamic patent map; Subject-action-object (SAO) structure; Semantic patent similarity; Natural language processing (NLP); Research and development (R&D) strategy ID SCIENCE MAPS; REPRESENTATIONS AB Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (R&D) strategies. By introducing semantic patent analysis with advantages in representing technological objectives and structures, this paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps. The proposed maps are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text; the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted ("patent vacuums"), areas in which many patents have actively appeared ("technological hot spots"), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies. C1 [Kim, Kwangsoo] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 790784, South Korea. [Yoon, Janghyeok] Konkuk Univ, Dept Ind Engn, Seoul 143701, South Korea. [Park, Hyunseok] Pohang Univ Sci & Technol, Dept Technol & Innovat Management, Pohang 790784, South Korea. RP Kim, K (reprint author), Pohang Univ Sci & Technol, Dept Ind & Management Engn, 77 Cheongam Ro, Pohang 790784, South Korea. EM janghyoon@konkuk.ac.kr; howgood@postech.ac.kr; kskim@postech.ac.kr CR Bergmann I, 2008, R&D MANAGE, V38, P550 Bollobas B, 1983, EVOLUTION SPARSE GRA Cascini G., 2004, LECT NOTES COMPUTER, V3163, P89 Cascini G, 2008, INT FED INFO PROC, V277, P31 Chang PL, 2010, SCIENTOMETRICS, V82, P5, DOI 10.1007/s11192-009-0033-y Choi S, 2011, SCIENTOMETRICS, V88, P863, DOI 10.1007/s11192-011-0420-z Choi S., 2010, IAMOT 2010 CIAR EG Feldman R, 2007, TEXT MINING HDB ADV Fujii A, 2007, INFORM PROCESS MANAG, V43, P1149, DOI 10.1016/j.ipm.2006.11.004 Gerken J., 2010, R D MAN C 2010 MANCH Hair FJ, 2010, MULTIVARIATE DATA AN Jeong B., 2005, EXPERT SYST APPL, V34, P8 KIPO, 2010, K2E AUT TRANSL Kostoff RN, 1998, SCIENTOMETRICS, V43, P27, DOI 10.1007/BF02458392 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P115, DOI 10.1007/BF02289694 Lee S, 2009, TECHNOVATION, V29, P481, DOI 10.1016/j.technovation.2008.10.006 Lin D., 2010, MINIPAR MacQueen J, 1967, SOME METHODS CLASSIF, V1, P14 Mann D., 2002, HANDS ON SYSTEMATIC Miller G., 1995, COMMUN ACM, V38, P41 Moehrle M. G., 2004, P 13 INT C MAN TECHN, P1 Moehrle MG, 2005, R&D MANAGE, V35, P513, DOI 10.1111/j.1467-9310.2005.00408.x Park H, 2012, SCIENTOMETRICS, V90, P515, DOI 10.1007/s11192-011-0522-7 Park HS, 2009, EXPERT SYST APPL, V36, P3336, DOI 10.1016/j.eswa.2008.01.039 PETERS HPF, 1993, RES POLICY, V22, P47, DOI 10.1016/0048-7333(93)90032-D PETERS HPF, 1993, RES POLICY, V22, P23, DOI 10.1016/0048-7333(93)90031-C Resnik P., 1999, J ARTIFICIAL INTELLI, V11, P130 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savransky S. D., 2000, ENG CREATIVITY INTRO Schmoch U., 2009, INT J TECHNOL MANAGE, V10, P426 Simpson T., 2005, WORDNET BASED SEMANT Stanford, 2010, STANF PARS STAT PARS STOPWORDS, 2010, ENGL STOPW Wanner Leo, 2008, World Patent Information, V30, DOI 10.1016/j.wpi.2007.03.008 WIPO, 2010, PCT YEARL REV 2009 Yoon B., 2004, J HIGH TECHNOLOGY MA, V15, P37, DOI DOI 10.1016/J.HITECH.2003.09.003 Yoon BU, 2002, R&D MANAGE, V32, P291, DOI 10.1111/1467-9310.00261 Yoon J, 2012, SCIENTOMETRICS, V90, P445, DOI 10.1007/s11192-011-0543-2 Yoon J, 2011, SCIENTOMETRICS, V88, P213, DOI 10.1007/s11192-011-0383-0 Yoon J, 2011, SCIENTOMETRICS, V86, P687, DOI 10.1007/s11192-010-0303-8 NR 40 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD JAN PY 2013 VL 94 IS 1 BP 313 EP 331 DI 10.1007/s11192-012-0830-6 PG 19 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 063RA UT WOS:000313016300018 ER PT S AU Turchi, M Perrotta, D Riani, M Cerioli, A AF Turchi, Marco Perrotta, Domenico Riani, Marco Cerioli, Andrea BE Kruse, R Berthold, MR Moewes, C Gil, MA Grzegorzewski, P Hryniewicz, O TI Robustness Issues in Text Mining SO SYNERGIES OF SOFT COMPUTING AND STATISTICS FOR INTELLIGENT DATA ANALYSIS SE Advances in Intelligent Systems and Computing LA English DT Proceedings Paper CT 6th International Conference on Soft Methods in Probability and Statistics CY OCT 04-06, 2012 CL Konstanz, GERMANY SP CajAstur Bank, Univ Konstanz, Univ Magdeburg, German Soc Comp Sci, European Soc Fuzzy Logic & Technol DE Cosine similarity; document classification; forward search ID RETRIEVAL; MODEL AB We extend the Forward Search approach for robust data analysis to address problems in text mining. In this domain, datasets are collections of an arbitrary number of documents, which are represented as vectors of thousands of elements according to the vector space model. When the number of variables v is so large and the dataset size n is smaller by order of magnitudes, the traditional Mahalanobis metric cannot be used as a similarity distance between documents. We show that by monitoring the cosine (dis)similarity measure with the Forward Search approach it is possible to perform robust estimation for a document collection and order the documents so that the most dissimilar (possibly outliers, for that collection) are left at the end. We also show that the presence of more groups of documents in the collection is clearly detected with multiple starts of the Forward Search. C1 [Turchi, Marco; Perrotta, Domenico] Commiss European Communities, Joint Res Ctr, Brussels, Belgium. RP Turchi, M (reprint author), Commiss European Communities, Joint Res Ctr, Brussels, Belgium. EM marco.turchi@jrc.ec.europa.eu; domenico.perrotta@ec.europa.eu; mriani@unipr.it; andrea.cerioli@unipr.it CR Atkinson AC, 2007, COMPUT STAT DATA AN, V52, P272, DOI 10.1016/j.csda.2006.12.034 Atkinson AC, 2004, EXPLORING MULTIVARIA Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Garcia-Escudero LA, 2008, ANN STAT, V36, P1324, DOI 10.1214/07-AOS515 Huang A., 2008, P 6 NZ COMP SCI RES, P49 Hubert M, 2008, STAT SCI, V23, P92, DOI 10.1214/088342307000000087 MAO WL, 2002, AM 2002 S P, P489 Pouliquen B., 2003, P WORKSH ONT INF EXT Riani M, 2012, CHEMOMETR INTELL LAB, V116, P17, DOI 10.1016/j.chemolab.2012.03.017 Riani M, 2000, ROBUST DIAGNOSTIC RE SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinberger R., 2012, P 8 INT C LANG RES E Yates R. B., 1999, MODERN INFORM RETRIE NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2194-5357 BN 978-3-642-33041-4 J9 ADV INTELL SYST PY 2013 VL 190 BP 263 EP 272 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDE90 UT WOS:000312969600029 ER PT J AU Du, YJ Hai, YF AF Du, Yajun Hai, YuFeng TI Semantic ranking of web pages based on formal concept analysis SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE Web crawler; Crawling direction; Search engine; Formal concept analysis ID CONCEPT SIMILARITY; CONTEXT GRAPH; INFORMATION; CRAWLER; PERFORMANCE; KNOWLEDGE; SYSTEMS; MODELS AB A web crawler is an important research component in a search engine. In this paper, a new method for measuring the similarity of formal concept analysis (FCA) concepts and a new notion of a web page's rank are proposed that use an information content approach based on users' web logs. First, an extension similarity and an intension similarity that analyze a user's browsing pattern and their hyperlinks are proposed. Second, the information content similarity between two nouns is computed automatically by examining their ISA and Part-Of hierarchy and using a user's web log. A method for computing the semantic similarity between two concepts in two different concept lattices (the base concept lattice and the current concept lattice) and finding the semantic ranking of web pages is proposed. Last, our experiment demonstrates that our crawler is more suitable for crawling focused web pages. It proves that the semantic ranking of web pages is useful and efficient for making a web crawler's choice of a web page for continuing work. (C) 2012 Elsevier Inc. All rights reserved. C1 [Du, Yajun; Hai, YuFeng] Xihua Univ, Sch Math & Comp Sci, Chengdu 610039, Sichuan, Peoples R China. RP Du, YJ (reprint author), Xihua Univ, Sch Math & Comp Sci, Chengdu 610039, Sichuan, Peoples R China. EM duyajun@mail.xhu.edu.cn FU National Nature Science Foundation [60872089]; Cultivating Foundation of Science & Technology Leaders of Sichuan Province FX This work was supported by the National Nature Science Foundation (Grant No. 60872089), the Cultivating Foundation of Science & Technology Leaders of Sichuan Province. CR Allan B., 2001, P 10 INT WORLD WID W, P415 Almpanidis G, 2007, INFORM SYST, V32, P886, DOI 10.1016/j.is.2006.09.004 Bain M, 2003, LECT NOTES ARTIF INT, V2903, P88 Batsakis S, 2009, DATA KNOWL ENG, V68, P1001, DOI 10.1016/j.datak.2009.04.002 Beg MMS, 2005, INFORM SCIENCES, V169, P365, DOI 10.1016/j.ins.2004.07.003 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Cho J, 1998, COMPUT NETWORKS ISDN, V30, P161, DOI 10.1016/S0169-7552(98)00108-1 Cohn D., 2000, P 17 INT C MACH LEAR Conesa J, 2008, DATA KNOWL ENG, V66, P18, DOI 10.1016/j.datak.2007.07.009 Diligenti M, 2000, VLDB 2000, P527 Ding Y, 2002, DATA KNOWL ENG, V41, P205, DOI 10.1016/S0169-023X(02)00041-1 Du Y, 2007, LECT NOTES COMPUT SC, V4706, P434 Du Y.J., 2009, J COMPUTATIONAL INFO, V3, P47 Du Y.J., 2004, J COMPUTER APPL, V1, P33 Du Y.J., 2005, ECTI T COMPUTER INFO, V01, P40 Du YJ, 2010, APPL SOFT COMPUT, V10, P772, DOI 10.1016/j.asoc.2009.09.007 Formica A, 2006, INFORM SCIENCES, V176, P2624, DOI 10.1016/j.ins.2005.11.014 Formica A, 2008, KNOWL-BASED SYST, V21, P80, DOI 10.1016/j.knosys.2007.02.001 Hsu CC, 2006, INFORM SYST, V31, P232, DOI 10.1016/j.is.2005.02.007 Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 Jung JJ, 2009, EXPERT SYST APPL, V36, P3914, DOI 10.1016/j.eswa.2008.02.057 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Konstantin A., 2011, COMPUTER NETWORKS, V55, P1880 Lempel R, 2000, COMPUT NETW, V33, P387, DOI 10.1016/S1389-1286(00)00034-7 Lin D., 1998, P 15 INT C MACH LEAR, V1, P296 Liu HY, 2006, DATA KNOWL ENG, V59, P270, DOI 10.1016/j.datak.2006.01.012 Park J, 2011, EXPERT SYST APPL, V38, P5133, DOI 10.1016/j.eswa.2010.10.002 Resniks P., 1995, P 14 INT JOINT C ART, P448 Rungsawang A, 2005, J NETW COMPUT APPL, V28, P97, DOI 10.1016/j.inca.2004.01.001 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shaw WM, 1997, INFORM PROCESS MANAG, V33, P1, DOI 10.1016/S0306-4573(96)00043-X Uschold H., 1996, KNOWLEDGE ENG REV, V11 Wille R., 1982, ORDERED SETSAND I RI, V23, P445 WILLE R, 1992, COMPUT MATH APPL, V23, P493, DOI 10.1016/0898-1221(92)90120-7 Yang SY, 2010, EXPERT SYST APPL, V37, P5381, DOI 10.1016/j.eswa.2010.01.018 Yang YK, 2008, LECT NOTES COMPUT SC, V5227, P840 Zheng HT, 2008, INFORM SCIENCES, V178, P4512, DOI 10.1016/j.ins.2008.07.030 NR 37 TC 3 Z9 3 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 J9 J SYST SOFTWARE JI J. Syst. Softw. PD JAN PY 2013 VL 86 IS 1 BP 187 EP 197 DI 10.1016/j.jss.2012.07.040 PG 11 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 062NR UT WOS:000312927100015 ER PT J AU Machhour, H Kassou, I AF Machhour, Hamid Kassou, Ismail GP IEEE TI Dynamic Text Classifier based on Search Engine Features SO 2013 3RD INTERNATIONAL SYMPOSIUM ISKO-MAGHREB LA English DT Proceedings Paper CT ISKO-Maghreb 3rd International Symposium CY NOV 08-09, 2013 CL Marrakech, MOROCCO SP ISKO DE Search Engine; Text Categorization; Text Indexing; K Nearest Neighbors ID DOCUMENT CATEGORIZATION; MODEL AB Search engines and text categorization are two research areas almost inseparable. Where one is studied, the other is referred sooner or later. Automatic text categorization became more important with the enormous increase of the online information, and text classifiers are often there to help search engines classify indexed documents. The main idea presented in this paper consists of using a search engine as a text classifier. A search engine can take advantage of its scoring performances to categorize a new document without requiring building and using other categorization model. K Nearest Neighbors (KNN) principal based on search engine score as similarity measure was used. This approach is highly dependent on the scoring quality of the used search engine. It is a simple approach but can be competitive to other more complex categorization models. Also, this method is useful as a kind of categorization on the fly when indexing a new document. Through its evolving index, the search engine becomes a dynamic classifier of the fact that any document, recently joining the index, participate in the categorization of other new documents. C1 [Machhour, Hamid; Kassou, Ismail] Mohammed V Souissi Univ, ENSIAS, Dept Comp Sci & Decis Support, Rabat, Morocco. RP Machhour, H (reprint author), Mohammed V Souissi Univ, ENSIAS, Dept Comp Sci & Decis Support, Rabat, Morocco. CR Aggarwal CC, 2004, IEEE T KNOWL DATA EN, V16, P245, DOI 10.1109/TKDE.2004.1269601 Aggarwal CC, 2012, MINING TEXT DATA Aparicio R., 2013, INT J DATA MINING KN Batal I., 2009, P 18 ACM C INF KNOWL, P2041, DOI 10.1145/1645953.1646296 Bespalov D., 2011, P 20 ACM INT C INF K, P375 Bhatia Nitin, 2010, ARXIV10070085 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Cavnar W., 1995, NIST SPECIAL PUBLICA, P269 Cavnar W B, 1994, N GRAM BASED TEXT CA, V48113, P161 Chakrabarti S., 1997, VLDB 97, P446 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 ESULI A, 2009, ADV INFORM RETRIEVAL, V5766, P29, DOI 10.1007/978-3-642-04417-5_4 Fan LJ, 2003, PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PDCAT'2003, PROCEEDINGS, P472, DOI 10.1109/PDCAT.2003.1236347 Ghiassi M, 2012, EXPERT SYST APPL, V39, P10967, DOI 10.1016/j.eswa.2012.03.027 Han EH, 2000, CENTROID BASED DOCUM Heckerman D., 1998, AAAI 98 WORKSH LEARN Jiang SY, 2012, EXPERT SYST APPL, V39, P1503, DOI 10.1016/j.eswa.2011.08.040 Joachims T., 1996, PROBABILISTIC ANAL R Joachims T., 1998, TEXT CATEGORIZATION Joachims Thorsten, 2001, P 24 ANN INT ACM SIG, P128, DOI 10.1145/383952.383974 Kim H, 2009, PATTERN RECOGN, V42, P1777, DOI 10.1016/j.patcog.2009.01.020 Kulesza T., 2011, ACM T INTERACTIVE IN, V1, P1 Lam S. L., 1999, DASFAA 99 P 6 INT C, P195 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lee LH, 2012, APPL INTELL, V37, P80, DOI 10.1007/s10489-011-0314-z Lewis D., 1994, 3 ANN S DOC AN INF R, V33, P81 Lewis D., 1997, REUTERS 21578 TEXT C Lewis D.D., 1994, ICML, V94, P148 Li X., 2011, BUS MAN EL INF BMEI, P224 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Lin WR, 2011, APPL MECH MATER, V50-51, P700, DOI 10.4028/www.scientific.net/AMM.50-51.700 Machhour H., 2013, INT J DATA MINING KN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, RELEVANCE FEEDBACK I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi K., 2011, CLOUD COMP INT SYST, P113 Zhong S, 2005, NEURAL NETWORKS, V18, P790, DOI 10.1016/j.neunet.2005.06.008 Sidorov G., 2013, COMPUTATIONAL LINGUI, V7816, P13 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wang L., 2012, CONS EL COMM NETW CE, P1848 Wei ZH, 2009, INT J COMPUT INT SYS, V2, P365 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Xia T., 2011, COMP SCI ED ICCSE 20, P210 Xu H., 2013, P 2012 INT C INF TEC, P619 Xu ZQ, 2012, PHYSCS PROC, V33, P1986, DOI 10.1016/j.phpro.2012.05.312 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Zhang W, 2011, EXPERT SYST APPL, V38, P2758, DOI 10.1016/j.eswa.2010.08.066 [张孝飞 ZHANG Xiao-Fei], 2009, [ęؔ式čƆ别äøŽäŗŗå·„ę™ŗčƒ½, Pattern Recognition and Artificial Intelligence], V22, P936 Zhou Yong, 2009, Journal of Computers, V4 NR 51 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3392-1 PY 2013 PG 8 WC Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary SC Computer Science; Engineering GA BC1NM UT WOS:000350287800019 ER PT S AU De Silva, J Haddela, PS AF De Silva, Jenomi Haddela, Prasanna S. GP IEEE TI A Term Weighting Method for Identifying Emotions From Text Content SO 2013 8TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS (ICIIS) SE International Conference on Industrial and Information Systems LA English DT Proceedings Paper CT 8th IEEE International Conference on Industrial and Information Systems (ICIIS) CY DEC 17-20, 2013 CL Peradeniya, SRI LANKA DE Support Vector Machine; Term weighting; Text Classification; Vector Space Model AB Since the inception of the concept of social networking, communication patterns have shifted drastically with the unmitigated trend in socializing over the Internet, especially when people began connecting via mobile devices. Nowadays people tend to use these modern communication systems to share their emotions with each other. Human emotions play a vital role in human relationships and people share their emotions through facial expressions, gestures, speech and text messages. However, text messaging is the most common and widely accepted method to exchange information among peers through the Internet and mobile networks. In comparison to other methods, identifying emotions from text messages is rather difficult for the recipient. Therefore, the need of automating the emotion recognition from textual content has increased. Utilization of text classification techniques can be considered as the most common approach of identifying emotions from textual content. Prior to applying a text classifier, the textual data should be transformed into a data structure that the classifier understands by conforming to a document representation model and term weighting method. For this research Vector Space Model (VSM) is used as the document representation model. This paper proposes an extension to the Term Frequency - Inverse Document Frequency (TF-IDF) weighting method to increase classification accuracy and explains experiments conducted to discover the best term weighting method in vector space to be used in feature (text term) extraction from Aman's emotion text corpus. The text classification is done using Oracle's ODM SVM tool and LibSVM tool. C1 [De Silva, Jenomi] Sri Lanka Inst Informat Technol, Fac Comp, Dept Informat Technol, Malabe, Sri Lanka. RP De Silva, J (reprint author), Sri Lanka Inst Informat Technol, Fac Comp, Dept Informat Technol, New Kandy Rd, Malabe, Sri Lanka. EM dit10c1-0091@my.sliit.lk; prasanna@sliit.lk CR Aman S, 2007, LECT NOTES ARTIF INT, V4629, P196 Aman S., 2008, P 3 INT JOINT C NAT, P296 [Anonymous], 2011, ORACLE DATA MINING A Baby Shalini T., 2013, INT J ENG TRENDS TEC, V4, P1337 Balabantaray R. C., 2012, INT J APPL INFORM SY, V4, P48 Boser B, 1992, 5 ANN WORKSH COMP LE, V5, P144 Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199 Chuang Ze Jing, 2004, ASS COMPUTATIONAL LI, V9, P45 Cohen I., 2000, NEURAL INFORM PROCES Cohen I., 2002, MULT EXP 2002 ICME 0 Crammer K., J MACHINE LEARNING R, V2, P265 Han J., 2006, DATA MINING CONCEPTS Hsu C. W., PRACTICAL GUID UNPUB Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Kao ECC, 2009, INT C INF MAN ENG IC, P70 Kirange D. K., 2012, ASIAN J COMPUTER SCI, P104 Li Xiang, 2011, J COMPUTERS, V6 Liao CT, 2013, PATTERN RECOGN, V46, P3103, DOI 10.1016/j.patcog.2013.03.017 Milenova Boriana L., 2005, VER LARG DAT END 200 Mingyong Liu, 2012, INT C COMP TECHN SCI, V47 Opencv dev team, 2011, INTR SUPP VECT MACH EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quan C, 2010, INT J ADV INTELLIGEN, V2, P105 Rodrigues M., 2004, P 4 INT C REC ADV SO, P269 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tabatabaei TS, 2007, IEEE INT SYMP CIRC S, P345, DOI 10.1109/ISCAS.2007.378460 Wagner J., 2005, MULT EXP 2005 ICME 2 NR 28 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2164-7011 BN 978-1-4799-0908-7; 978-1-4799-0909-4 J9 INT CONF IND INF SYS PY 2013 BP 381 EP 386 PG 6 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC0WZ UT WOS:000349770400070 ER PT S AU Nathawitharana, N Matharage, S Alahakoon, D AF Nathawitharana, Nilupulee Matharage, Sumith Alahakoon, Damminda GP IEEE TI Feature Overlap-Based Dynamic Self Organizing Model for Hierarchical Text Clustering SO 2013 8TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL AND INFORMATION SYSTEMS (ICIIS) SE International Conference on Industrial and Information Systems LA English DT Proceedings Paper CT 8th IEEE International Conference on Industrial and Information Systems (ICIIS) CY DEC 17-20, 2013 CL Peradeniya, SRI LANKA DE Feature overlap; Growing Self-Organizing Map; Hierarchical clustering; Text document clustering ID MAPS; DISCOVERY AB In text document clustering documents are represented as feature vectors where features can be either words or phrases. Documents can belong to different topics when categorized by humans; however it is noted that obtaining one to one mapping between the features and the topics is almost impossible since the same features can and will be used in documents in different topics. Such common features results in overlap in text clustering, and as such traditional cluster purity measures may not be practical or meaningful. In this paper new methodology and algorithm is introduced which considers the feature overlap between the clusters when clustering text documents. Hierarchical clustering facilitated by the Growing Self-Organizing Map (GSOM) is used together with the calculated feature overlap to check the possibility of obtaining clusters with minimum feature overlap. We also present the experimental results obtained from following the proposed methodology with the new algorithm. C1 [Nathawitharana, Nilupulee; Matharage, Sumith; Alahakoon, Damminda] Deakin Univ, Sch Informat & Business Analyt, Geelong, Vic 3217, Australia. RP Nathawitharana, N (reprint author), Deakin Univ, Sch Informat & Business Analyt, Geelong, Vic 3217, Australia. EM mnathawi@deakin.edu.au; s.matharage@deakin.edu.au; d.alahakoon@deakin.edu.au CR Ahmad N, 2010, NEURAL COMPUT APPL, V19, P531, DOI 10.1007/s00521-009-0300-0 Alahakoon D, 2000, IEEE T NEURAL NETWOR, V11, P601, DOI 10.1109/72.846732 Alahakoon D., 2001, DATA MINING COMPUTAT, V68, P217 Bache K., 2013, MACHINE LEARNING REP DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Goldberger AL, 2000, CIRCULATION, V101, pE215 Gunasinghe U., 2012, 2012 INT JOINT C NEU Haiying Wang, 2004, IEEE Transactions on Information Technology in Biomedicine, V8, DOI 10.1109/TITB.2004.824727 Hsu AL, 2003, BIOINFORMATICS, V19, P2131, DOI 10.1093/bioinformatics/btg296 Iwayama M., 1995, P 18 ANN INT ACM SIG Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T, 1998, NEUROCOMPUTING, V21, P1, DOI 10.1016/S0925-2312(98)00030-7 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Matharage S. S., 2012, AUTONOMOUS INCREMENT Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sinka MP, 2005, J NETW COMPUT APPL, V28, P129, DOI 10.1016/j.jnca.2004.01.002 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2164-7011 BN 978-1-4799-0908-7; 978-1-4799-0909-4 J9 INT CONF IND INF SYS PY 2013 BP 393 EP 398 PG 6 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC0WZ UT WOS:000349770400072 ER PT J AU Wang, M Lin, LF Wang, F AF Wang, Meng Lin, Lanfen Wang, Feng GP IEEE TI Improving Short Text Classification through Better Feature Space Selection SO 2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) LA English DT Proceedings Paper CT 9th International Conference on Computational Intelligence and Security (CIS) CY DEC 14-15, 2013 CL PEOPLES R CHINA SP Leshan Normal Univ, Xidian Univ, Guangdong Univ Technol, Natl Nat Sci Fdn China, Beijing Normal Univ DE short text classification; feature space; feature words AB Nowadays people are overwhelmed by more and more short information from lots of different applications, especially with the rapid development of mobile systems. One way to alleviate this issue is an automatic classification of the short texts before they are delivered to users. Several methods have been proposed to classify the short texts, and they are largely based on expanding the short texts to longer ones with external resources to solve the sparseness problem. Different from these studies, we tackle the sparseness problem by selecting a better feature space in which the feature vectors of the short texts are denser, and our method needs no external resources at all. The experimental results on an open dataset show that this method can significantly improve the short text classification accuracy comparing with the baseline, especially when the dimension of the feature space is low. C1 [Wang, Meng; Lin, Lanfen; Wang, Feng] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310003, Zhejiang, Peoples R China. RP Wang, M (reprint author), Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310003, Zhejiang, Peoples R China. EM wangmeng@zju.edu.cn; llf@zju.edu.cn; wangfeng@zju.edu.cn CR Bai J., 2004, P AS INF RETR S AIRS Banerjee Somnath, 2007, P 30 ANN INT ACM SIG, P787, DOI 10.1145/1277741.1277909 Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Chen M, 2011, P 22 INT JOINT C ART, P1776, DOI DOI 10.5591/978-1-57735-516-8/IJCAI11-298 Hall M., 2009, ACM SIGKDD EXPLORATI, V11, P10, DOI DOI 10.1145/1656274.1656278 Hu X., 2009, P 18 ACM C INF KNOWL, P919, DOI DOI 10.1145/1645953.1646071 Phan Xuan-Hieu, 2008, P 17 INT C WORLD WID, P91, DOI DOI 10.1145/1367497.1367510 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sriram B, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P841, DOI 10.1145/1835449.1835643 Sun A., 2012, P 35 INT ACM SIGIR C, P1145, DOI 10.1145/2348283.2348511 Yih W. T., 2007, P AAAI, P1489 Yuan Q., 2012, P INT C WORLD WID WE, P645, DOI [10.1145/2187980.2188169, DOI 10.1145/2187980.2188169] NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-2548-3 PY 2013 BP 120 EP 124 DI 10.1109/CIS.2013.32 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC2PX UT WOS:000351209000025 ER PT J AU Uddin, M Stadler, R Clemm, A AF Uddin, Misbah Stadler, Rolf Clemm, Alexander GP IEEE TI Scalable Matching and Ranking for Network Search SO 2013 9TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) SE International Conference on Network and Service Management LA English DT Proceedings Paper CT 9th International Conference on Network and Service Management (CNSM) CY OCT 14-18, 2013 CL Zurich, SWITZERLAND SP IFIP, IEEE Comp Soc, ACM, Commun Syst Grp, DMTF, Orange, MAKI, IBM, Intracom Telecom, Canton Zurich, ACM SIGCOMM, SWISS HO Univ Zurich DE Network search; matching and ranking; distributed query processing; distributed management; in-network management ID WEB AB Network search makes operational data available in real-time to management applications. In contrast to traditional monitoring, neither the data location nor the data format needs to be known to the invoking process, which simplifies application development, but requires an efficient search plane inside the managed system. The search plane is realized as a network of search nodes that process search queries in a distributed fashion. This paper introduces matching and ranking for network search queries. We are proposing a semantic for matching and ranking, which is configurable to support different types of management applications-from exact matching for database-style queries to loose, approximate matching, which is appropriate for exploratory purposes. We describe an echo protocol for efficient distributed query processing that supports matching and ranking. Further, we present the design of a search node, which maintains a real-time database of operational information and allows for parallel processing of search queries. A prototype implementation on a cloud testbed shows that the network search system, on a 9-node cluster with 24 core servers, executes 200 global search queries/sec with the 75th percetile latency below 100 milliseconds and with a CPU utilization below 5%. The performance measurements, together with our design, suggest that a system of 100,000 servers processing the same load would exhibit the same overhead per server and a query latency of below 1 sec. C1 [Uddin, Misbah; Stadler, Rolf] KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden. RP Uddin, M (reprint author), KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden. EM ahmmud@kth.se; stadler@kth.se; alex@cisco.com CR Andrews M, 2012, IEEE SOFTWARE, V29, P13, DOI 10.1109/MS.2012.39 Tonon Alberto, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348304 [Anonymous], 2010, INTERNET THINGS IOT Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chen C., 2011, P ACM SIGMOD INT C M, P649 Easley D., 2010, NETWORKS CROWDS MARK, P397 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Gulli A, 2009, LECT NOTES COMPUT SC, V5427, P143, DOI 10.1007/978-3-540-95995-3_12 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lim K., 2001, IFIP IEEE INM SEATTL, P405 Manning C. D., 2008, COMMUN, P117 Neumayer R., 2012, EUR C INF RETR ECIR, P133 Robertson Stephen, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000019 Sadalage P., 2012, NOSQL DISTILLED BRIE, P20 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEGALL A, 1983, IEEE T INFORM THEORY, V29, P23, DOI 10.1109/TIT.1983.1056620 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Stadler R., 2012, 2012028 KTH Uddin A., 2013, IFIP IEEE INT NETW M Uddin M., 2013, IFIP IEEE INT S INT Uddin M., 2012, IFIP IEEE NETW OP MA NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2165-9605 BN 978-3-901882-53-1 J9 INT CONF NETW SER PY 2013 BP 251 EP 259 PG 9 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA BB7OT UT WOS:000345853200042 ER PT J AU Pavan, KM Ananthanarayana, VS AF Pavan, K. Madhu Ananthanarayana, V. S. GP IEEE TI An Approach for Mining Heterogeneous Data for Cross-Media Retrieval SO 2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT) LA English DT Proceedings Paper CT 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT) CY JUL 04-06, 2013 CL Tiruchengode, INDIA DE cross-media retrieval; cross-media correlation graph; image segmentation; multimedia document AB Due to the wide availability of huge amount of multimedia data in various modalities such as image and text documents, having a great amount of similarity among them is inevitable. In this paper, we present an efficient model which correlates the similarity among documents belonging to various modalities to achieve cross-media retrieval. Cross-media retrieval is a content based information retrieval system where heterogeneous data is mined to retrieve results of various modalities, i.e., input object and returned results may be of different modalities. For example, text objects can be retrieved as a result to image input. First, features are extracted from multimedia objects by which the objects are labeled. Using the labels, similar documents are grouped to generate Multimedia Documents. We construct a cross-media correlation graph with documents as vertices, where positive weight is assigned to every single edge according to the amount of similarity between vertices. The cross-media retrieval system identifies the input document and as a result returns required number of documents with highest weights. C1 [Pavan, K. Madhu; Ananthanarayana, V. S.] Natl Inst Technol Karnataka, Dept Informat Technol, Mangalore, Karnataka, India. RP Pavan, KM (reprint author), Natl Inst Technol Karnataka, Dept Informat Technol, Mangalore, Karnataka, India. EM kothapally.pavan@gmail.com; anvs@nitk.ac.in CR Cheng Y., 1995, PATTERN ANAL MACHINE, V17, P790 Comaniciu D., 2002, PAMI, V24, P603 Kailath T., 1967, COMMUNICATION TECHNO, V15, P52, DOI [DOI 10.1109/TCOM.1967.1089532, 10.1109/TCOM.1967.1089532] KIM HH, 2003, OBJECT ORIENTED INFO, V2817, P250, DOI 10.1007/978-3-540-45242-3_23 Kourosh Jafari-Khouzani, 2005, PATTERN ANAL MACHINE, V27, P1004 Liu J, 2007, P 15 INT C MULT Lowe David G., 1999, COMP VIS 1999 P 7 IE, V2 Martinec R., 2005, VISUAL COMMUNICATION, V4, P337, DOI 10.1177/1470357205055928 Rivest R., 1992, MD5 MESSAGE DIGEST A SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Xuehua, 2005, P 28 ANN INT ACM SIG Srivastava Anuj, 2005, PATTERN ANAL MACHINE, V27, P590 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 Yang Jun, 2002, P 11 INT C WORLD WID Yang Yi, 2008, MULTIMEDIA IEEE T, V10.3, P437 Zhang RF, 2007, IEEE T IMAGE PROCESS, V16, P562, DOI 10.1109/TIP.2006.888350 Zhuang YT, 2007, J VLSI SIG PROC SYST, V46, P153, DOI 10.1007/s11265-006-0020-y NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-3925-1 PY 2013 PG 5 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BB8FT UT WOS:000346508600332 ER PT J AU Chatterjee, A Sarkar, B Chandraghatgi, P Seal, K Ananthakrishnan, G AF Chatterjee, Abhranil Sarkar, Bijoy Chandraghatgi, Prateeksha Seal, Krishanu Ananthakrishnan, Girish BE Harit, G Das, S TI Search Based Video Recommendations SO 2013 FOURTH NATIONAL CONFERENCE ON COMPUTER VISION, PATTERN RECOGNITION, IMAGE PROCESSING AND GRAPHICS (NCVPRIPG) SE National Conference on Computer Vision Pattern Recognition Image Processing and Graphics LA English DT Proceedings Paper CT 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) CY DEC 18-21, 2013 CL Jodhpur, INDIA SP Indian Inst Technol Jodhpur, Indian Unit Pattern Recognit & Artificial Intelligence AB In this paper, we present a search powered approach we have used in building a Video Recommendations Engine for Yahoo hosted videos and Yahoo Video Search. The aim is to increase user engagement by recommending related videos and hence increase revenue by being able to show more advertisements as the user keeps consuming more videos. This system accepts an input context which provides information about the user and the video consumed and returns a set of related videos as recommended. We look at this problem as a multi-faceted problem since the intent of the user at a particular point in time cannot be known deterministically. So we generate the candidate set of recommendations using an ensemble of algorithms and available search signals. We discuss these algorithms and mechanisms for retrieving related videos in details along with an explore-exploit strategy to learn a near optimal ranking of the candidate recommendations, and provide the performance results. This system has been able to increase the number of video plays at Yahoo by 66%. C1 [Chatterjee, Abhranil; Sarkar, Bijoy; Chandraghatgi, Prateeksha; Seal, Krishanu; Ananthakrishnan, Girish] Yahoo Software Dev India, Bangalore, Karnataka, India. RP Chatterjee, A (reprint author), Yahoo Software Dev India, Bangalore, Karnataka, India. EM abhranil@yahoo-inc.com; bijoys@yahoo-inc.com; puday@yahoo-inc.com; krishanu@yahoo-inc.com; gkrishna@yahoo-inc.com CR Baluja S, 2008, P 17 INT C WORLD WID, P895, DOI DOI 10.1145/1367497.1367618 Bell R. M., 2007, BELLKOR SOLUTION NET Davidson James, 2010, P 4 ACM C REC SYST R, P293, DOI DOI 10.1145/1864708.1864770 GOLUB GH, 1970, NUMER MATH, V14, P403, DOI 10.1007/BF02163027 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mei T, 2011, ACM T INFORM SYST, V29, DOI 10.1145/1961209.1961213 Moon TK, 1996, IEEE SIGNAL PROC MAG, V13, P47, DOI 10.1109/79.543975 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 T. S. N. L. P. Group, STANF NAM ENT REC Yang B., 2007, P 6 ACM INT C IM VID, P73 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2372-658X BN 978-1-4799-1588-0 J9 NAT CONF COMPUT VIS PY 2013 PG 5 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BC4GJ UT WOS:000352485900039 ER PT J AU Xu, Z Luo, XF Wei, X Mei, L AF Xu, Zheng Luo, Xiangfeng Wei, Xiao Mei, Lin BE Chen, J Cuzzocrea, A Yang, LT TI Investigating the Impact Factors for Event Detection using Web Search Engines SO 2013 IEEE 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE 2013) SE IEEE International Conference on Computational Science and Engineering LA English DT Proceedings Paper CT IEEE 16th International Conference on Computational Science and Engineering (CSE) CY DEC 03-05, 2013 CL Sydney, AUSTRALIA SP IEEE, Univ Technol Sydney, CSIRO, IEEE Tech Comm Scalable Comp, IEEE Comp Soc DE Algorithms; Experimentation; Event detection; web mining; trends analysis ID NETWORKS; TIME AB Online popular events, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. Recently, the web is becoming an important event information provider and poster due to its real-time, open, and dynamic features. However, it is difficult to detect events since the huge scale and dynamics of the internet. In this paper, we define the novel problem of investigating impact factors for event detection. We give the definitions of five impact factors including the number of increased web pages, the number of increased keywords, the number of communities, the average clustering coefficient, and the average similarities of web pages. These five impact factors contain statistic and content information of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that that the number of web pages and the average clustering coefficient can be used to detect events. Some strategies integrating the number of web pages and the average clustering coefficient are also employed. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly. C1 [Xu, Zheng; Mei, Lin] Minist Publ Secur, Res Inst 3, Beijing, Peoples R China. [Luo, Xiangfeng; Wei, Xiao] Shanghai Univ, Shanghai, Peoples R China. RP Xu, Z (reprint author), Minist Publ Secur, Res Inst 3, Beijing, Peoples R China. EM xuzheng@shu.edu.cn; Luoxf@shu.edu.cn; Xwei@shu.edu.cn; gasswlw@126.com CR Abonyi J, 2005, FUZZY SET SYST, V149, P39, DOI 10.1016/j.fss.2004.07.008 Akritidis L., 2012, IEEE T SYST MAN CYB, V41, P759 Allan J., 1998, P BROADC NEWS TRANSC Allan J., 2000, TOPIC DETECTION TRAC Bollegala D, 2007, P 16 INT C WORLD WID, P757, DOI DOI 10.1145/1242572.1242675 Clayton M. ., 2006, CHRISTIAN SCI MONITO Fung GPC, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P300 Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799 Haddow D., 2010, INTRO EMERGENCY MANA He Q, 2010, IEEE T PATTERN ANAL, V32, P1795, DOI 10.1109/TPAMI.2009.203 Hristidis V., 2006, P ACM CIKM INT C INF, P802, DOI 10.1145/1183614.1183738 Jin X., 2010, P 19 INT C WORLD WID, P481, DOI 10.1145/1772690.1772740 Jo Y, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P370 Makkonen J., 2003, P 2003 C N AM CHAPT, P43 Mei Q., 2005, P 11 ACM SIGKDD INT, P198, DOI DOI 10.1145/1081870.1081895 Nallapati R., 2004, P 13 ACM INT C INF K, P446, DOI 10.1145/1031171.1031258 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strogatz SH, 2001, NATURE, V410, P268, DOI 10.1038/35065725 Sung C, 2012, IEEE T SYST MAN CY C, V42, P532, DOI 10.1109/TSMCC.2011.2135850 Wang CH., 2008, P 17 ACM C INF KNOWL, P1033, DOI 10.1145/1458082.1458219 Wei CP, 2007, IEEE T SYST MAN CY A, V37, P273, DOI 10.1109/TSMCA.2006.886377 Wu XA, 2011, IEEE MULTIMEDIA, V18, P38, DOI 10.1109/MMUL.2011.12 Yang C. C., 2006, P 15 INT C WORLD WID, P945, DOI 10.1145/1135777.1135957 Zhao Q., 2006, P 12 ACM SIGKDD INT, P484, DOI 10.1145/1150402.1150456 NR 26 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1949-0828 J9 IEEE INT C COMPUT PY 2013 BP 580 EP 587 DI 10.1109/CSE.2013.92 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BC3TK UT WOS:000351950300084 ER PT S AU Puszta, A Szule, J Laki, S AF Puszta, Adrian Szuele, Janos Laki, Sandor GP IEEE TI Near Real-Time Thematic Clustering of Web Documents and other Internet contents SO 2013 IEEE 4TH INTERNATIONAL CONFERENCE ON COGNITIVE INFOCOMMUNICATIONS (COGINFOCOM) SE International Conference on Cognitive Infocommunications LA English DT Proceedings Paper CT 4th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) CY DEC 02-05, 2013 CL Budapest, HUNGARY SP IEEE Hungary Sect, IEEE, IEEE Joint Chapter IES & RAS, IEEE Chapter SMC, IEEE Chapter CI, Obuda Univ, BME Dept Telecommunicat & Media Informat, 3D Internet Based Control & Commun Lab, Hungarian Acad Sci, Comp & Automat Res Inst AB In the past decade, Internet has radically changed our lives, enabling us to obtain information on everything (disasters, political decisions, ordinary events, etc.) we are interested in in almost real-time. Downloading a web page by a browser, instant messaging or file sharing generate huge amount of network traffic that carry valuable information on the most relevant topics that raise interest in individual users, user groups or the entire society. However, the analysis of this huge amount of unstructured textual data poses many challenges, especially if it is not possible to store the data off-line and real-time clustering is needed. In this paper, we propose a framework for real-time textual content clustering of different sources called documents over the Internet, including posts on Twitter and Facebook, blogs, web sites or other textual contents. To support real-time processing, we extend the spherical on-line K-means clustering algorithm with heuristic improvements: an adaptive dimension reduction technique is introduced to keep the dimension of the document space on a reasonable level, and the ability to open new and remove old clusters according to the actual demand is also added. The performance of our improved algorithm called ASKM (Adaptive Streaming K-Means) has been analyzed on a ground truth data set based on the catalog of Open Directory Project. Furthermore, we also consider a much more realistic scenario where only some parts of the Internet documents are available because of practical limitations of traffic capturing, resulting incomplete textual documents to be clustered. We also show that the proposed method can achieve reasonable good accuracy even in this practical case. C1 [Puszta, Adrian; Szuele, Janos] Interuniv Ctr Telecommun & Informat, Debrecen, Hungary. RP Puszta, A (reprint author), Interuniv Ctr Telecommun & Informat, Debrecen, Hungary. EM puszta@complex.elte.hu; szule@complex.elte.hu; laki@complex.elte.hu CR Baranyi P, 2012, ACTA POLYTECH HUNG, V9, P67 CALLAN J., 2006, P INT C DIG GOV RES, P167, DOI DOI 10.1145/1146598.1146650 Carmel D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P139, DOI 10.1145/1571941.1571967 GERACI F., 2006, INTERNET, V3, P413, DOI 10.1080/15427951.2006.10129133 LEWIS D. D, 1997, REUTERS 21 578 TEXT Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 LIU Y., CLUSTERING MASSIVE T, P389 LIU Y., BUILDING A DOCUMENT Muhr M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P178 PEDERSEN T., INFORMATION CONTENT SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zhong S, 2005, NEURAL NETWORKS, V18, P790, DOI 10.1016/j.neunet.2005.06.008 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2375-1312 BN 978-1-4799-1543-9; 978-1-4799-1546-0 J9 INT CONF COGN INFO PY 2013 BP 307 EP 312 PG 6 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BC0WY UT WOS:000349770000053 ER PT J AU Zunino, R Bisio, F Peretti, C Surlinelli, R Scillia, E Ottaviano, A Sangiacomo, F AF Zunino, Rodolfo Bisio, Federica Peretti, Chiara Surlinelli, Roberto Scillia, Eugenio Ottaviano, Augusto Sangiacomo, Fabio BE Ozyer, T Carrington, P TI An Analyst-Adaptive Approach to Focused Crawlers SO 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) LA English DT Proceedings Paper CT IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) CY AUG 25-28, 2013 CL Niagara Falls, CANADA SP IEEE, Assoc Comp Machinery, Assoc Comp Machinery SIGKDD, IEEE Comp Soc, IEEE TCDE, Global Univ, Hellen American Univ, Springer, Microsoft, EXtra, Springer Verlag, Assoc Comp Machinery SIGMOD DE Focused crawler; Analyst-adaptation; OSINT AB The paper presents a general methodology to implement a flexible Focused Crawler for investigation purposes, monitoring, and Open Source Intelligence (OSINT). The resulting tool is specifically aimed to fit the operational requirements of law-enforcement agencies and intelligence analyst. The architecture of the semantic Focused Crawler features static flexibility in the definition of desired concepts, used metrics, and crawling strategy; in addition, the method is capable to learn (and adapt to) the analyst's expectations at runtime. The user may instruct the crawler with a binary feedback (yes/no) about the current performance of the surfing process, and the crawling engine progressively refines the expected targets accordingly. The method implementation is based on an existing text-mining environment, integrated with semantic networks and ontologies. Experimental results witness the effectiveness of the adaptive mechanism. C1 [Zunino, Rodolfo; Bisio, Federica; Peretti, Chiara] Univ Genoa, DITEN, I-16145 Genoa, Italy. [Surlinelli, Roberto; Scillia, Eugenio; Ottaviano, Augusto] Polizia Postale & Comunicaz, I-16121 Genoa, Italy. [Sangiacomo, Fabio] CyberLabs Srl, I-20024 Garbagnate Milanese, Italy. RP Zunino, R (reprint author), Univ Genoa, DITEN, Via Opera Pia 11a, I-16145 Genoa, Italy. EM rodolfo.zunino@unige.it CR Aggarwal C.C., 2001, WORLD WIDE WEB, P96 [Anonymous], 2005, 14 INT C WORLD WID W Balsakis S., 2009, DATA KHOWL ENG, V68, P1001 Bay H., 2006, P EUR C COMP VIS Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Csurka G., 2004, INT WORKSH STAT LEAR, V1, P1 De Bra P., 1994, P 4 RIAO C NEW YORK, P481 Ehrig M, 2003, P 2003 ACM S APPL CO, P1174 Freeman RT, 2005, IEEE T NEURAL NETWOR, V16, P1256, DOI 10.1109/TNN.2005.853415 Fu TJ, 2010, J AM SOC INF SCI TEC, V61, P1213, DOI 10.1002/asi.21323 Gastaldo P., 2009, ADV ARTIFICIAL INTEL Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 Hhaoutakis A., 2006, INT J SEMANT WEB INF, V2, P55 Joachims T., 1998, P EUR C MACH LEARN Kumar J., 2013, INT J EMERGING TECHN, V3, P922 Leoncini A., 2012, THEORY APPL ADV TEXT Leoncini A., 2011, P IEEE INT C SEM COM, P75 LEONCINI A, 2011, P INT S NEUR NETW 3, V6677, P523 Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI 10.1145/1031114.1031117 Pant G, 2005, ACM T INFORM SYST, V23, P430, DOI 10.1145/1095872.1095875 Pant G, 2006, IEEE T KNOWL DATA EN, V18, P107 Radev DR, 2004, INFORM PROCESS MANAG, V40, P919, DOI 10.1016/j.ipm.2003.10.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sangiacomo Fabio, 2010, Proceedings 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), DOI 10.1109/ICSC.2010.48 Vossen P., 1998, EUROWORDNET MULTILIN Xu JJ, 2004, DECIS SUPPORT SYST, V38, P473, DOI 10.1016/S0167-9236(03)00117-9 Zhang HJ, 2011, IEEE T NEURAL NETWOR, V22, P1532, DOI 10.1109/TNN.2011.2161999 Zhang R., 2002, P 16 INT C PATT REC, V4, P289 NR 28 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4503-2240-9 PY 2013 BP 1073 EP 1077 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC5YC UT WOS:000353639700157 ER PT J AU Djellali, C AF Djellali, Choukri BE Ozyer, T Carrington, P TI Enhancing text Clustering model based on Truncated Singular Value Decomposition, Fuzzy ART and Cross Validation SO 2013 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM) LA English DT Proceedings Paper CT IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) CY AUG 25-28, 2013 CL Niagara Falls, CANADA SP IEEE, Assoc Comp Machinery, Assoc Comp Machinery SIGKDD, IEEE Comp Soc, IEEE TCDE, Global Univ, Hellen American Univ, Springer, Microsoft, EXtra, Springer Verlag, Assoc Comp Machinery SIGMOD DE Learning; Data Mining; NLP; TSVD; variable selection; semantic analysis; model selection AB Numerical schemes research on clustering model has been quite intensive in the past decade. The difficulties associated with curse of dimensionality and cost functions to reflect the general knowledge about internal structures and distributions of target data. Traditional computational clustering and variables selection schemes are struggling to estimate at high level of accuracy for this type of problem. Hence, in the present study, a novel semantic-based scheme was proposed to enhance the clustering accuracy. The results show that our conceptual model is automatic and optimal. Good comparisons with the experimental studies demonstrate the multidisciplinary applications of our approach. C1 Univ Quebec Montreal, Lab Res Technol Ecommerce, Montreal, PQ H2X 3Y7, Canada. RP Djellali, C (reprint author), Univ Quebec Montreal, Lab Res Technol Ecommerce, LATECE 201, Montreal, PQ H2X 3Y7, Canada. EM djellali.choukri@courrier.uqam.ca CR Alter O, 2000, P NATL ACAD SCI USA, V97, P10101, DOI 10.1073/pnas.97.18.10101 Anderson MJ, 2001, AUSTRAL ECOL, V26, P32, DOI 10.1111/j.1442-9993.2001.01070.pp.x Baena-Garcia M, 2011, INT SYST DES APPL IS, P1044 Berkhin Pavel, 2006, GROUPING MULTIDIMENS, V10, P25 Berzal F, 2002, ACM SIGMOD RECORD, V31, P66, DOI 10.1145/565117.565130 CARPENTER GA, 1991, NEURAL NETWORKS, V4, P759, DOI 10.1016/0893-6080(91)90056-B Duda R.O., 2001, PATTERN CLASSIFICATI Hall LO, 1999, IEEE T EVOLUT COMPUT, V3, P103, DOI 10.1109/4235.771164 Huang Zhijun, 2009, INT SYST 2009 GCIS 0, V2, P404 ISAWA H, 2008, NEUR NETW 2008 IJCNN, P3595 Issac B, 2009, TENCON 2009 2009 IEE, P1, DOI 10.1109/TENCON.2009.5396056 Khoshgoftaar T M, 2011, SYSTEMS MAN CYBERN A, V41, P552 Lei Meng, 2012, NEUR NETW IJCNN 2012, P1 LI HP, 2011, AC SPECCH SIGN PROC, P1297 Lingras P, 2002, FUZZ SYST 2002 FUZZ, V2, P1039 Lu Yu, 2008, COMP INT IND APPL 20, V2, P14 Meunier Jean-Guy, 2012, 3 INT C EXTR MAN KNO Rui Xu, 2005, NEURAL NETWORKS IEEE, V16, P645, DOI DOI 10.1109/TNN.2005.845141 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shavitt Y, 2010, EL EL ENG ISR IEEEI, P244 Shuhan Luan, 2012, COMM ICC 2012 IEEE I, P6744 Varan Nath Shyam, 2006, WEB INT INT AG TECHN, P41 Xu Baowen, 2003, IEEE INT C INF REUS, P273 Yang Xiaobing, 2004, INT CONTR AUT 2004 W, V5, P4245 Zaman A N K, 2011, DIG INF MAN ICDIM 20, P133 Zhang Yanchun, 2008, WEB INT INT AG TECHN, V1, P20 Zhang Yong, 2008, FUZZ SYST KNOWL DISC, V2, P528 Zhao J, 2002, STUDY TECHNOLOGIES F, V2, P689 Zhou Yingjie, 2010, SORUCES TAKING KUNLU, P1 Zhuang Weiwei, 2012, SYSTEMS MAN CYBERN C, V42, P1784 NR 30 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4503-2240-9 PY 2013 BP 1078 EP 1083 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC5YC UT WOS:000353639700158 ER PT J AU Shuai, B Li, HF Li, MJ Zhang, Q Tang, CJ AF Shuai, Bo Li, Haifeng Li, Mengjun Zhang, Quan Tang, Chaojing GP IEEE TI Automatic Classification for Vulnerability Based on Machine Learning SO 2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) LA English DT Proceedings Paper CT IEEE International Conference on Information and Automation (ICIA) CY AUG 26-28, 2013 CL Yinchuan, PEOPLES R CHINA SP IEEE, RA DE Vulnerability classification; Words location; Vulnerability distribution; LDA model; SVM AB In order to solve the problems of traditional machine learning methods for automatic classification of vulnerability, this paper presents a novel machine learning method based on LDA model and SVM. Firstly, word location information is introduced into LDA model called WL-LDA (Weighted Location LDA), which could acquire better effect through generating vector space on themes other than on words. Secondly, a multi-class classifier called HT-SVM (Huffman Tree SVM) is constructed, which could make a faster and more stable classification by making good use of the prior knowledge about distribution of the number of vulnerabilities. Experiments show that the method could obtain higher classification accuracy and efficiency. C1 [Shuai, Bo; Li, Haifeng; Li, Mengjun; Zhang, Quan; Tang, Chaojing] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China. RP Shuai, B (reprint author), Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China. EM shuaibo85@163.com; quanzhang@nudt.edu.cn CR Abbott R, 1976, SECURITY ANAL ENHANC Adam T, 1996, USE TAXONOMY SECURIT Aghila G., 2010, INT J COMPUTER SCI I, V7 Bisbey II R, 1978, PROTECTION ANAL Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOTTOU L, 1994, INT C PATT RECOG, P77, DOI 10.1109/ICPR.1994.576879 Braschler Martin, 2004, EFFECTIVE STEMMING D Chang C-C., 2011, ACM T INTELLIGENT SY, V2 Chen ZQ, 2010, COMPUT J, V53, P551, DOI 10.1093/comjnl/bxp040 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Grilfiths T, 2004, GIBBS SAMPLING GENER Hassan S., 2012, CORR Krebel U, 1999, PAIRWISE CLASSIFICAT, P255 Kuo T., 2010, INT J INTELL SYST, V25 Lei La, 2012, EURASIP J ADV SIGNAL Li Y I, 2007, APPROACH STANDARDISI Lu G., 2012, PATTERN RECOGNITION, V45 Platt JC, 2000, ADV NEUR IN, V12, P547 Rezarta I.D., 2012, J BIOMEDICAL SEMANTI, V3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Leeuwen J, 1976, P 3 INT C Van Rijsbergen K., 1979, INFORM RETRIEVAL Weston J., 1998, CSDTR9804 U LOND DEP Xu Z., 2012, PHYS PROCEDIA, V33 Zhong Xiu-yu, 2011, RES APPL WEB LOG MIN Zhu T., 2012, PROCEDIA ENG, V29 NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-1334-3 PY 2013 BP 312 EP 318 PG 7 WC Automation & Control Systems; Engineering, Electrical & Electronic SC Automation & Control Systems; Engineering GA BB8ES UT WOS:000346483800057 ER PT J AU Yousuf, S Ahmad, M Nasrullah, S AF Yousuf, Shameem Ahmad, Muzamil Nasrullah, Sheikh GP IEEE TI A Review of Plagiarism Detection Based On Lexical and Semantic Approach SO 2013 INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN COMMUNICATION, CONTROL, SIGNAL PROCESSING AND COMPUTING APPLICATIONS (IEEE-C2SPCA-2013) LA English DT Proceedings Paper CT International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications (C2SPCA) CY OCT 10-11, 2013 CL Bangalore, INDIA SP IEEE, Oxford Coll Engn, Dept MCA & ECE, IEEE Bangalore Sect PHO Chapter, India Institut Engineers, Comp Soc India, Bangalore Chapter, VTU, IETE, Oxford Educ Institut, Naval Res Board DRDO, IEEE Photon Soc Bangalore chapter HO Oxford Coll Engn ID N-GRAMS AB Due to easy availability of documents over the web, plagiarism has become serious problem to teachers, researchers and publishers. In this paper, we discuss about the plagiarism process, types and detection methodologies. Further, we have classified the different plagiarism detection techniques based on Lexical and Semantic Approach. Finally we present the brief study of the different tools (offline and online) available for detecting plagiarism. C1 [Yousuf, Shameem; Ahmad, Muzamil; Nasrullah, Sheikh] Cent Univ Kashmir, Dept Informat Technol, Srinagar, Jammu & Kashmir, India. RP Yousuf, S (reprint author), Cent Univ Kashmir, Dept Informat Technol, Srinagar, Jammu & Kashmir, India. EM bhat.shameem@gmail.com; muzamilahmad87@gmail.com; nasrullah@cukashmir.ac.in CR Alzahrani SM, 2012, IEEE T SYST MAN CY C, V42, P133, DOI 10.1109/TSMCC.2011.2134847 Barron-Cedeno A, 2009, LECT NOTES COMPUT SC, V5478, P696, DOI 10.1007/978-3-642-00958-7_69 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Brin S., 1995, SIGMOD Record, V24 Chen H.-H., 2006, ACL Chong M., 2011, RECENT ADV NATURAL L, P704 Clough P., 2003, NATL PLAGIARISM ADVI, P391 Clough P., 2000, PLAGIARISM NATURAL P Grman J., 2011, CLEF NOT PAP LABS WO Niezgoda S., 2006, SIGCSE 06, P51 Pera MS, 2010, INT J ARTIF INTELL T, V19, P465, DOI 10.1142/S0218213010000285 Peter Bulychev M. M., 2009, P 3 INT WORKSH SOFTW Prechelt L., 2000, JPLAG FINDING PLAGIA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shivakumar N., 1995, P 2 ANN C THEOR PRAC Stamatatos E, 2011, J AM SOC INF SCI TEC, V62, P2512, DOI 10.1002/asi.21630 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 White D. R., 2004, ACM J ED RESOURCES C, V4, P1 Yoshiki Higo S. K., 2011, 2011 15 EUR C SOFTW, P75 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4799-1085-4 PY 2013 PG 5 WC Automation & Control Systems; Engineering, Electrical & Electronic; Telecommunications SC Automation & Control Systems; Engineering; Telecommunications GA BB8ZZ UT WOS:000347903000069 ER PT J AU Xu, DH Qu, ZY AF Xu, Donghua Qu, Zhiyi GP IEEE TI An Image Classification Method Based on Matching Similarity and TF-IDF Value of Region SO 2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2 SE International Symposium on Computational Intelligence and Design LA English DT Proceedings Paper CT 6th International Symposium on Computational Intelligence and Design (ISCID) CY OCT 28-29, 2013 CL Hangzhou, PEOPLES R CHINA SP IEEE Nanjing Computat Intelligence Chapter, Univ Bristol, Zhejiang Univ, Zhejiang Sci Tech Univ, Zhejiang Univ, Coll Comp Sci DE image classification; matching similarity; TF-IDF; feature vector ID COLOR HISTOGRAMS AB Traditional image classification methods are mainly based on the overall color statistics and content semantics of the image itself. However, due to the poor distinctiveness of overall color statistics and content semantics, traditional methods often cannot acquire very accurate classification results. In this study, an image classification method based on matching similarity and TF-IDF value of region is proposed. First, an image is divided into some fixed-size regions according to its size, and features of each region are extracted and stored. Then, the matching similarity and TF-IDF value of each region are obtained by comparing with and matching regions in the standard image region dataset. Finally, constructing corresponding feature vectors based on features of each region to get the final image classification result. Experimental results show that the proposed method can be used for fast classification of vast images and gains good classification results. C1 [Xu, Donghua; Qu, Zhiyi] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China. RP Xu, DH (reprint author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China. EM 807831808@qq.com; quzy@lzu.edu.cn CR Berens J, 2000, IEE P-VIS IMAGE SIGN, V147, P349, DOI 10.1049/ip-vis:20000630 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Fei B, 2006, IEEE T NEURAL NETWOR, V17, P696, DOI 10.1109/TNN.2006.872343 Gong YH, 1996, MULTIMED TOOLS APPL, V2, P133, DOI 10.1007/BF00672252 Liu Guixiong, 2008, 4 INT C NAT COMP, P103 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sheikholesiami G., 1998, Proceedings ACM Multimedia 98, DOI 10.1145/290747.290749 Smith JR, 1995, IEEE P ICIP, V3, P528 Tan SB, 2005, EXPERT SYST APPL, V28, P667, DOI 10.1016/j.eswa.2004.12.023 Yin Yong, 2012, Computer Engineering and Applications, V48, DOI 10.3778/j.issn.1002-8331.2012.12.038 Zhao R, 2002, IEEE T MULTIMEDIA, V4, P189 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2165-1701 BN 978-0-7695-5079-4 J9 INT SYM COMPUT INTEL PY 2013 BP 112 EP 115 DI 10.1109/ISCID.2013.142 PG 4 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BC7XJ UT WOS:000355287400026 ER PT J AU Lu, YJ AF Lu, Yingjie TI Automatic topic identification of health-related messages in online health community using text classification SO SPRINGERPLUS LA English DT Article DE Online health community; Text classification; Topic identification; Topic classification AB To facilitate patient involvement in online health community and obtain informative support and emotional support they need, a topic identification approach was proposed in this paper for identifying automatically topics of the health-related messages in online health community, thus assisting patients in reaching the most relevant messages for their queries efficiently. Feature-based classification framework was presented for automatic topic identification in our study. We first collected the messages related to some predefined topics in a online health community. Then we combined three different types of features, n-gram-based features, domain-specific features and sentiment features to build four feature sets for health-related text representation. Finally, three different text classification techniques, C4.5, Naive Bayes and SVM were adopted to evaluate our topic classification model. By comparing different feature sets and different classification techniques, we found that n-gram-based features, domain-specific features and sentiment features were all considered to be effective in distinguishing different types of health-related topics. In addition, feature reduction technique based on information gain was also effective to improve the topic classification performance. In terms of classification techniques, SVM outperformed C4.5 and Naive Bayes significantly. The experimental results demonstrated that the proposed approach could identify the topics of online health-related messages efficiently. C1 Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China. RP Lu, YJ (reprint author), Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China. EM luyingjie982@163.com FU National Natural Science Foundation of China [71171131] FX This research was supported by the National Natural Science Foundation of China under Grant 71171131. CR Abbasi A, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361685 Anderson JG, 2003, J MED SYST, V27, P67, DOI 10.1023/A:1021061229743 Bansil P, 2006, PREVENTING CHRONIC D, V3, P1 Castleton K, 2011, SUPPORT CARE CANCER, V19, P1183, DOI 10.1007/s00520-010-0935-5 Dickerson SS, 2006, ONCOL NURS FORUM, V33, P8 Fox S., 2009, SOCIAL LIFE HLTH INF Frank E, 2005, DATA MINING PRACTICA Gerber BS, 2001, J MED INTERNET RES, V3, DOI 10.2196/jmir.3.2.e15 Kandula Sasikiran, 2011, AMIA Annu Symp Proc, V2011, P674 Kinnane N, 2010, SUPPORT CARE CANCER, V18, P1126 Monnier J, 2002, CANCER PRACT, V10, P305, DOI 10.1046/j.1523-5394.2002.106005.x Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tyson T R, 2000, J Ambul Care Manage, V23, P1 Umefjord G, 2003, MED INTERNET RES, V5, pe26 Vapnik V., 1995, NATURE STAT LEARNING Yetisgen-Yildiz M., 2005, P AMIA S, V2005, P849 Zhu SF, 2009, BIOINFORMATICS, V25, P1944, DOI 10.1093/bioinformatics/btp338 NR 18 TC 2 Z9 2 PU SPRINGER INTERNATIONAL PUBLISHING AG PI CHAM PA GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND SN 2193-1801 J9 SPRINGERPLUS JI SpringerPlus PY 2013 VL 2 AR 309 DI 10.1186/2193-1801-2-309 PG 7 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA V40FR UT WOS:000209465000107 PM 23961389 ER PT J AU Zheng, HT Chen, JY Jiang, Y AF Zheng, Hai-Tao Chen, Jin-Yuan Jiang, Yong TI An ontology-based approach to Chinese semantic advertising SO INFORMATION SCIENCES LA English DT Article DE Contextual advertising; Sponsored search; Chinese semantic advertising; Taobao Ontology; Distance function ID SEARCH AB In the web advertising domain, contextual advertising and sponsored search are two of the main advertising channels used to display related advertisements on web pages. A major challenge for contextual advertising is to match advertisements and web pages based on their semantics. When a web page and its semantically related advertisements contain many different words, the performance of the traditional methods can be very poor. In particular, there are few studies presented for Chinese contextual advertising that are based on semantics. To address these issues, we propose an ontology-based approach to Chinese semantic advertising. We utilize an ontology called the Taobao Ontology and populate it by automatically adding related phrases as instances. The ontology is used to match web pages and advertisements on a conceptual level. Based on the Taobao Ontology, the proposed method exploits seven distance functions to measure the similarities between concepts and web pages or advertisements. Then, the similarities between web pages and advertisements are calculated by considering the ontology-based similarities as well as term-based similarities. The empirical experiments indicate that our method is able to match Chinese web pages and advertisements with a relatively high accuracy. Among the seven distance functions, Cosine distance and Tanimoto distance show the best performance in terms of precision, recall, and F-measure. In addition, our method outperforms two contextual advertising methods, i.e., the impedance coupling method and the SVM-based method. (C) 2012 Elsevier Inc. All rights reserved. C1 [Zheng, Hai-Tao; Chen, Jin-Yuan; Jiang, Yong] Tsinghua Univ, Grad Sch Shenzhen, Tsinghua Southampton Web Sci Lab Shenzhen, Shenzhen 518055, Peoples R China. RP Jiang, Y (reprint author), Tsinghua Univ, Grad Sch Shenzhen, Tsinghua Southampton Web Sci Lab Shenzhen, Shenzhen 518055, Peoples R China. EM jiangy@sz.tsinghua.edu.cn FU 973 Project of China [2012CB315803]; National Natural Science Foundation of China [61003100, 60972011]; Research Fund for the Doctoral Program of Higher Education of China [20100002120018, 20100002110033] FX We thank Charles Borchert for his helpful English revision and the anonymous reviewers for their valuable comments. This research is supported by the 973 Project of China (2012CB315803), National Natural Science Foundation of China (Grant Nos. 61003100 and 60972011) and Research Fund for the Doctoral Program of Higher Education of China (Grant Nos. 20100002120018 and 20100002110033). CR Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Castellanos-Nieves D, 2011, INFORM SCIENCES, V181, P1517, DOI 10.1016/j.ins.2011.01.010 Chen JY, 2011, LECT NOTES COMPUT SC, V7063, P169 Chien LF, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/258525.258534 Ciaramita M, 2008, J ELECTRON COMMER RE, V9, P1 DEMAIO C, 2009, IEEE INT C FUZZ SYST, P1044 Fan TK, 2009, LECT NOTES COMPUT SC, V5478, P202 Ghose A., 2008, P INT C WEB SEARCH W, P241, DOI 10.1145/1341531.1341563 Harris ZS, 1954, WORD, V10, P146 Hillard Dustin, 2010, P 3 ACM INT C WEB SE, P361, DOI 10.1145/1718487.1718532 Jang Y., 2007, P 6 INT C ADV LANG P, P440 Jiang X, 2009, INFORM SCIENCES, V179, P2794, DOI 10.1016/j.ins.2009.04.005 Kazienko P, 2007, INFORM SCIENCES, V177, P2269, DOI 10.1016/j.ins.2007.01.002 Khan S., 2009, P 7 INT C FRONT INF, P69 Li T., 2009, PPAR RES, P1, DOI 10.1145/1592748.1592750 Loia V, 2008, J INTERNET TECHNOL, V9, P355 Lou Y, 2012, INFORM SCIENCES, V190, P127, DOI 10.1016/j.ins.2011.12.011 Mei T, 2008, P 16 ACM INT C MULT, P439, DOI 10.1145/1459359.1459418 Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 Rossignol M, 2005, INTELL DATA ANAL, V9, P105 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sculley D, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P1325 Serrano-Guerrero J, 2011, INFORM SCIENCES, V181, P1503, DOI 10.1016/j.ins.2011.01.012 Tsang MM, 2004, INT J ELECTRON COMM, V8, P65 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wang C., 8 AM C INF SYST, P1143 Wojnar A, 2010, INFORM SCIENCES, V180, P1817, DOI 10.1016/j.ins.2009.12.024 Yih Wen-tau, 2006, P 15 INT C WORLD WID, P213, DOI 10.1145/1135777.1135813 Zhang C., 2008, J COMPUTATIONAL INFO, V4, P1169 Zhang H.-P., 2003, P 2 SIGHAN WORKSH CH, V17, P184, DOI 10.3115/1119250.1119280 Zheng HT, 2010, J BIOMED INFORM, V43, P31, DOI 10.1016/j.jbi.2009.07.006 Zheng HT, 2008, INFORM SCIENCES, V178, P4512, DOI 10.1016/j.ins.2008.07.030 Zheng HT, 2009, INFORM SCIENCES, V179, P2249, DOI 10.1016/j.ins.2009.02.019 Zhu YZ, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P588, DOI 10.1145/1571941.1572042 NR 37 TC 5 Z9 5 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD DEC 20 PY 2012 VL 216 BP 138 EP 154 DI 10.1016/j.ins.2012.06.012 PG 17 WC Computer Science, Information Systems SC Computer Science GA 000JR UT WOS:000308383100007 ER PT J AU Tilahun, G Feuerverger, A Gervers, M AF Tilahun, Gelila Feuerverger, Andrey Gervers, Michael TI DATING MEDIEVAL ENGLISH CHARTERS SO ANNALS OF APPLIED STATISTICS LA English DT Article DE Bandwidth selection; cross-validation; medieval charters; DEEDS data set; generalized linear models; kernel smoothing; local log-likelihood; maximum prevalence method; nearest neighbor methods (kNN); quantile regression; text mining ID DISTANCE AB Deeds, or charters, dealing with property rights, provide a continuous documentation which can be used by historians to study the evolution of social, economic and political changes. This study is concerned with charters (written in Latin) dating from the tenth through early fourteenth centuries in England. Of these, at least one million were left undated, largely due to administrative changes introduced by William the Conqueror in 1066. Correctly dating such charters is of vital importance in the study of English medieval history. This paper is concerned with computer-automated statistical methods for dating such document collections, with the goal of reducing the considerable efforts required to date them manually and of improving the accuracy of assigned dates. Proposed methods are based on such data as the variation over time of word and phrase usage, and on measures of distance between documents. The extensive (and dated) Documents of Early England Data Set (DEEDS) maintained at the University of Toronto was used for this purpose. C1 [Tilahun, Gelila; Feuerverger, Andrey] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada. [Gervers, Michael] Univ Toronto, Dept Hist, Toronto, ON M5S 3G3, Canada. RP Tilahun, G (reprint author), Univ Toronto, Dept Stat, 100 St George St, Toronto, ON M5S 3G3, Canada. EM gelila@utstat.toronto.edu; andrey@utstat.toronto.edu; m.gervers@utoronto.ca FU Natural Sciences and Engineering Research Council of Canada; Google Inc. FX Supported by a grant from the Natural Sciences and Engineering Research Council of Canada and by a grant from Google Inc. CR Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164 BRODER A. Z., 1998, IEEE SEQUENCES 97, P21 de Jong F, 2005, P 16 INT C ASS HIST, P161 DJERABA C., 2003, MULTIMEDIA MINING HI Domingos P., 1996, P 13 INT C MACH LEAR, P105 Fan JQ, 2000, WILEY SER PROB STAT, P229 Feuerverger A, 2012, STAT SCI, V27, P202, DOI 10.1214/11-STS368 FEUERVERGER A., 2008, I MATH STAT COLLECT, V1, P321 Feuerverger A, 2005, J COMPUT GRAPH STAT, V14, P255, DOI 10.1198/106186005X47291 FIALLOS R., 2000, DATING UNDATED MEDIE GERVERS M., 2000, DATING UNDATED MEDIE GERVERS M., 2010, PROAMORE DIPLOMATIC Hastie T., 2009, ELEMENTS STAT LEARNI KANHABUA N., 2008, LECT NOTES COMPUTER, V5173 KANHABUA N., 2009, LECT NOTES COMPUTER, V5782 Koenker R., 2005, ECONOMETRIC SOC MONO, V38 Loader C., 1999, LOCAL REGRESSION LIK LUHN HP, 1958, IBM J RES DEV, V2, P159 Manning C.D., 2008, INTRO INFORM RETRIEV MCGILL M., 1979, TECHNICAL REPORT MOSTELLER F, 1963, J AM STAT ASSOC, V58, P275, DOI 10.2307/2283270 Nadaraya EN, 1965, THEOR PROBAB APPL, V10, P186 QUANG P. X., 1999, 995 NSASAG, P99 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simonoff J.S., 1996, SMOOTHING METHODS ST Tan P. N., 2005, INTRO DATA MINING Wand M. P., 1995, MONOGRAPHS STAT APPL, V60 Watson G. S., 1964, SANKHYA A, V26, P359 Zhang J, 1999, J AM SOC INFORM SCI, V50, P772, DOI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E NR 29 TC 1 Z9 1 PU INST MATHEMATICAL STATISTICS PI CLEVELAND PA 3163 SOMERSET DR, CLEVELAND, OH 44122 USA SN 1932-6157 J9 ANN APPL STAT JI Ann. Appl. Stat. PD DEC PY 2012 VL 6 IS 4 BP 1615 EP 1640 DI 10.1214/12-AOAS566 PG 26 WC Statistics & Probability SC Mathematics GA 083JB UT WOS:000314458400012 ER PT J AU Bouras, C Tsogkas, V AF Bouras, Christos Tsogkas, Vassilis TI A clustering technique for news articles using WordNet SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE News clustering; k-Means; W-k means; Cluster labeling; Partitional clustering AB The Web is overcrowded with news articles, an overwhelming information source both with its amount and diversity. Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed which, however, suffer from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. In this work, we are investigating the application of a great spectrum of clustering algorithms, as well as similarity measures, to news articles that originate from the Web. Also, we are proposing the enhancement of standard k-means algorithm using the external knowledge from WordNet hypernyms in a twofold manner: enriching the "bag of words" used prior to the clustering process and assisting the label generation procedure following it. Furthermore, we are examining the effect that text preprocessing has on clustering. Operating on a corpus of news articles derived from major news portals, our comparison of the existing clustering methodologies revealed that k-means, gives better aggregate results when it comes to efficiency. This is amplified when the algorithm is accompanied with preliminary steps for data cleaning and normalizing, despite its simple nature. Moreover, the proposed WordNetenabled W-k means clustering algorithm significantly improves standard k-means generating also useful and high quality cluster tags by using the presented cluster labeling process. (C) 2012 Elsevier B.V. All rights reserved. C1 [Bouras, Christos] Comp Technol Inst, GR-26110 Patras, Greece. Press Diophantus, Patras, Greece. Univ Patras, Comp Engn & Informat Dept, Patras 26500, Greece. RP Bouras, C (reprint author), Comp Technol Inst, GR-26110 Patras, Greece. EM bouras@cti.gr; tsogkas@ceid.upatras.gr FU European Union (European Social Fund - ESF); Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II FX This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. CR Abdelmalek A.A., 2010, INT ARAB J INFORM TE, V7 Al-Omary AY, 2006, KNOWL-BASED SYST, V19, P248, DOI 10.1016/j.knosys.2005.10.011 Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027 Arthur D., 2005, TECHNICAL REPORT Bouras C, 2008, LECT NOTES ARTIF INT, V5178, P593, DOI 10.1007/978-3-540-85565-1_73 Bouras C, 2008, DATA KNOWL ENG, V64, P330, DOI 10.1016/j.datak.2007.07.007 Bradley P. S., 1998, P 15 INT C MACH LEAR, P91 Carmel D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P139, DOI 10.1145/1571941.1571967 Chen CL, 2009, LECT NOTES ARTIF INT, V5476, P147 Day W.H.E., 1984, J CLASSIF, P7 El-Hamdouchi A., 1989, COMPUT J, V32, P220 Gharib TF, 2010, ADVANCES TECHNIQUES IN COMPUTING SCIENCES AND SOFTWARE ENGINEERING, P181, DOI 10.1007/978-90-481-3660-5_31 Hoon M., 2003, C CLUSTERING LIB Karypis G., 2002, 02017 U MINN DEP COM Kiran GVR, 2010, LECT NOTES ARTIF INT, V6277, P11 Purandare A., 2004, P 19 NAT C ART INT A, P25 Rodpongpun S., 2012, KNOWLEDGE BASED SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sedding J., 2004, P COLLING WORKSH ROB Steinbach M., 2000, COMP DOCUMENT CLUSTE Taeho J., 2007, ADV NEURAL NETWORKS, V4492, P871 Treeratpituk P., 2006, P 2006 INT C DIG GOV Tseng Y.H., 2009, EXPERT SYSTEMS APPL, V37, P2247 Varelas G., 2005, WIDM, P10 Yanjun Li, 2007, Journal of Supercomputing, V39, DOI 10.1007/s11227-006-0002-7 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 26 TC 6 Z9 7 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD DEC PY 2012 VL 36 BP 115 EP 128 DI 10.1016/j.knosys.2012.06.015 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 046OV UT WOS:000311775200011 ER PT J AU Torkestani, JA AF Torkestani, Javad Akbari TI An adaptive focused Web crawling algorithm based on learning automata SO APPLIED INTELLIGENCE LA English DT Article DE Web crawling; Focused Web crawler; Search engine; Learning automata ID AD-HOC NETWORKS; SEMANTIC WEB; INFORMATION; RETRIEVAL; SYSTEM; GRAPH AB The recent years have witnessed the birth and explosive growth of the Web. The exponential growth of the Web has made it into a huge source of information wherein finding a document without an efficient search engine is unimaginable. Web crawling has become an important aspect of the Web search on which the performance of the search engines is strongly dependent. Focused Web crawlers try to focus the crawling process on the topic-relevant Web documents. Topic oriented crawlers are widely used in domain-specific Web search portals and personalized search tools. This paper designs a decentralized learning automata-based focused Web crawler. Taking advantage of learning automata, the proposed crawler learns the most relevant URLs and the promising paths leading to the target on-topic documents. It can effectively adapt its configuration to the Web dynamics. This crawler is expected to have a higher precision rate because of construction a small Web graph of only on-topic documents. Based on the Martingale theorem, the convergence of the proposed algorithm is proved. To show the performance of the proposed crawler, extensive simulation experiments are conducted. The obtained results show the superiority of the proposed crawler over several existing methods in terms of precision, recall, and running time. The t-test is used to verify the statistical significance of the precision results of the proposed crawler. C1 Islamic Azad Univ, Arak Branch, Arak, Iran. RP Torkestani, JA (reprint author), Islamic Azad Univ, Arak Branch, Arak, Iran. EM j-akbari@iau-arak.ac.ir CR Akbari Torkestani J, 2012, J NETW COMP IN PRESS Akbari Torkestani J, 2012, J CLUST COM IN PRESS Akbari Torkestani J, 2012, INF SCI IN PRESS Akbari Torkestani J, 2012, WIREL PERS IN PRESS Akbari Torkestani J, 2012, J INTELL IN IN PRESS Torkestani JA, 2012, CYBERNET SYST, V43, P1, DOI 10.1080/01969722.2012.637012 Akbari Torkestani J, 2012, TELECOMMUN IN PRESS Akbari Torkestani J, 2012, COMPUT COMM IN PRESS BARTO AG, 1985, IEEE T SYST MAN CYB, V15, P360 Batsakis S, 2009, DATA KNOWL ENG, V68, P1001, DOI 10.1016/j.datak.2009.04.002 Batzios A, 2008, EXPERT SYST APPL, V35, P524, DOI 10.1016/j.eswa.2007.07.054 Billard E, 1999, IEEE T SYST MAN CY B, V29, P329, DOI 10.1109/3477.764864 Camacho D, 2006, APPL INTELL, V25, P59, DOI 10.1007/s10489-006-8866-z Diligenti M., 2000, P 26 INT C VER LARG, P527 Ehrig M., 2003, P S APPL COMP SAC 20 Hashim A. A., 1986, ADAPTIVE LEARNING SY, P229 Hliaoutakis A, 2006, INT J SEMANT WEB INF, V2, P55, DOI 10.4018/jswis.2006070104 Hsu CC, 2006, INFORM SYST, V31, P232, DOI 10.1016/j.is.2005.02.007 Jung JJ, 2009, NEURAL COMPUT APPL, V18, P213, DOI 10.1007/s00521-008-0173-7 Kim S, 2003, APPL INTELL, V18, P243, DOI 10.1023/A:1023293820057 LAKSHMIVARAHAN S, 1976, IEEE T SYST MAN CYB, V6, P756 Liu HY, 2006, DATA KNOWL ENG, V59, P270, DOI 10.1016/j.datak.2006.01.012 Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI 10.1145/1031114.1031117 Meybodi M. R., 1983, THESIS U OKLAHOMA NO Narendra K., 1989, LEARNING AUTOMATA IN OOMMEN BJ, 1987, SIAM J COMPUT, V16, P705, DOI 10.1137/0216047 Pant G, 2005, ACM T INFORM SYST, V23, P430, DOI 10.1145/1095872.1095875 Patel A, 2011, COMPUT STAND INTER, V33, P325, DOI 10.1016/j.csi.2010.08.002 Rungsawang A, 2005, J NETW COMPUT APPL, V28, P97, DOI 10.1016/j.inca.2004.01.001 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos E, 2011, APPL INTELL, V35, P375, DOI 10.1007/s10489-010-0229-0 Symeonidis AL, 2005, IEEE T SYST MAN CY A, V35, P420, DOI 10.1109/TSMCA.2005.846406 THATHACHAR MAL, 1987, IEEE T SYST MAN CYB, V17, P1095 Torkestani JA, 2011, J NETW COMPUT APPL, V34, P1429, DOI 10.1016/j.jnca.2011.03.026 Torkestani JA, 2011, COMPUT ELECTR ENG, V37, P461, DOI 10.1016/j.compeleceng.2011.05.006 Torkestani JA, 2010, COMPUT INFORM, V29, P447 Torkestani JA, 2010, J NETW COMPUT APPL, V33, P477, DOI 10.1016/j.jnca.2010.01.004 Unsal C, 1999, IEEE T SYST MAN CY A, V29, P120, DOI 10.1109/3468.736368 Xiao L, 2004, APPL INTELL, V21, P195, DOI 10.1023/B:APIN.0000033637.51909.04 Zhang HX, 2010, APPL SOFT COMPUT, V10, P490, DOI 10.1016/j.asoc.2009.08.017 NR 40 TC 12 Z9 13 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-669X J9 APPL INTELL JI Appl. Intell. PD DEC PY 2012 VL 37 IS 4 BP 586 EP 601 DI 10.1007/s10489-012-0351-2 PG 16 WC Computer Science, Artificial Intelligence SC Computer Science GA 035YL UT WOS:000310989900011 ER PT J AU Spanakis, G Siolas, G Stafylopatis, A AF Spanakis, Gerasimos Siolas, Georgios Stafylopatis, Andreas TI DoSO: a document self-organizer SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Document representation; Document clustering; SOM; Wikipedia ID NONLINEAR DIMENSIONALITY REDUCTION; TEXT CLASSIFICATION; CATEGORIZATION; WIKIPEDIA; MODEL; VISUALIZATION; COLLECTIONS; CONSTRAINTS; INFORMATION; FRAMEWORK AB In this paper, we propose a Document Self Organizer (DoSO), an extension of the classic Self Organizing Map (SOM) model, in order to deal more efficiently with a document clustering task. Starting from a document representation model, based on important "concepts" exploiting Wikipedia knowledge, that we have previously developed in order to overcome some of the shortcomings of the Bag-of-Words (BOW) model, we demonstrate how SOM's performance can be boosted by using the most important concepts of the document collection to explicitly initialize the neurons. We also show how a hierarchical approach can be utilized in the SOM model and how this can lead to a more comprehensive final clustering result with hierarchical descriptive labels attached to neurons and clusters. Experiments show that the proposed model (DoSO) yields promising results both in terms of extrinsic and SOM evaluation measures. C1 [Spanakis, Gerasimos; Siolas, Georgios; Stafylopatis, Andreas] Natl Tech Univ Athens, Athens, Greece. RP Spanakis, G (reprint author), Intelligent Syst Lab, Athens, Greece. EM gspana@ece.ntua.gr; gsiolas@islab.ntua.gr; andreas@cs.ntua.gr CR A. Carnegie Group Inc. & B. Reuters Ltd., 1997, REUT 21578 TEXT CAT Alias-i, 2008, LINGPIPE 4 1 0 Amigo E, 2009, INFORM RETRIEVAL, V12, P461, DOI 10.1007/s10791-008-9066-8 Banerjee S., 2007, SIGIR, P787 Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002 Bloehdorn S, 2006, ST CLASS DAT ANAL, P334, DOI 10.1007/3-540-31314-1_40 Breaux T. D., 2005, HICSS 05, P1112 Bunescu R. C., 2007, EACL CARPENTER GA, 1991, NEURAL NETWORKS, V4, P759, DOI 10.1016/0893-6080(91)90056-B Chen HC, 1996, J VIS COMMUN IMAGE R, V7, P88, DOI 10.1006/jvci.1996.0008 Cucerzan S., 2007, P EMNLP CONLL, V6, P708 Davison M., 1983, MULTIDIMENSIONAL SCA Demartines P, 1997, IEEE T NEURAL NETWOR, V8, P148, DOI 10.1109/72.554199 Francis W. N., 1964, MANUAL INFORM ACCOMP Fung BCM, 2003, SIAM PROC S, P59 Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606 Gabrilovich E., 2006, AAAI 06, P1301 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 He J, 2002, IEEE IJCNN, P2550 Himberg J., 2000, IEEE INNS ENNS INT J, V3, P3587 Hofmann T, 1999, IJCAI-99: PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 & 2, P682 Hotho A., 2002, P FGML WORKSH, P37 Hotho A., 2003, P SEM WEB WORKSH 26 Hu J., 2008, SIGIR 08, P179 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Huang A, 2009, LECT NOTES ARTIF INT, V5476, P628, DOI 10.1007/978-3-642-01307-2_62 Jin HD, 2005, IEEE T PATTERN ANAL, V27, P1710 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Junker M., 2000, Learning Language in Logic (Lecture Notes in Artificial Intelligence Vol.1925) Kangas J A, 1990, IEEE Trans Neural Netw, V1, P93, DOI 10.1109/72.80208 Karypis G., 2002, TECHNICAL REPORT, V02-017 Kiran G. V. R., 2010, P 15 AUSTR DOC COMP Kohonen T., 1989, SELF ORG ASS MEMORY Kohonen T., 2001, SELF ORG MAPS Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 KRAAIJVELD MA, 1992, 11TH IAPR INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, PROCEEDINGS, VOL II, P41 Lagus K, 2004, INFORM SCIENCES, V163, P135, DOI 10.1016/j.ins.2003.03.017 Lang K., 1995, P INT C MACH LEARN Larsen B., 1999, KDD 99, P16 Li Y., 2007, SIGIR 07 P 30 ANN IN, P797 Lin X., 1991, SIGIR 91, P262 Liu X., 2002, SPECIAL INTEREST GRO, P191 Marcus M.P., 1993, COMPUTATIONAL LINGUI, V19, P313 Mendes P. N., 2011, P 7 INT C SEM SYST I Merkl D., 1997, P WORKSH SELF ORG MA, P106 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Mihalcea R., 2007, CIKM 2007, V23, P233, DOI [10.1145/1321440.1321475, DOI 10.1145/1321440.1321475] Miikkulainen R., 1990, Connection Science, V2 Milne D., 2008, P 17 ACM C INF KNOWL, V9, P509 Mitra P, 2002, IEEE T PATTERN ANAL, V24, P301, DOI 10.1109/34.990133 Moutarde F., 2005, WORKSH SELF ORG MAPS Navigli Roberto, 2010, P 48 ANN M ASS COMP, P216 Pampalk E, 2002, LECT NOTES COMPUT SC, V2415, P871 Polzlbauer G., 2004, P 5 WORKSH DAT AN WD, P67 Pullwitt D, 2002, NEURAL NETWORKS, V15, P1099, DOI 10.1016/S0893-6080(02)00082-5 Ratinov L., 2011, P 49 ANN M ASS COMP, V1, P1375 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 Rauber A., 1999, INT JOINT C NEUR NET, V5, P3527, DOI 10.1109/IJCNN.1999.836235 Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMMON JW, 1969, IEEE T COMPUT, VC 18, P401, DOI 10.1109/T-C.1969.222678 Schmid H., 1994, P INT C NEW METH LAN Sedding J., 2004, ROMAND 04 P 3 WORKSH, P104 Shehata S, 2010, IEEE T KNOWL DATA EN, V22, P1360, DOI 10.1109/TKDE.2009.174 Slonim N., 2002, SIGIR 02, P129 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Spanakis G, 2012, COMPUT J, V55, P299, DOI 10.1093/comjnl/bxr024 Stanford, 2009, NAM ENT REC Steinbach Michael, 2000, KDD WORKSH TEXT MIN, P109 Stumme G, 2002, DATA KNOWL ENG, V42, P189, DOI 10.1016/S0169-023X(02)00057-5 Suchanek FM, 2008, J WEB SEMANT, V6, P203, DOI 10.1016/j.websem.2008.06.001 Talavera L, 2001, IEEE T PATTERN ANAL, V23, P196, DOI 10.1109/34.908969 Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Toral A., 2006, EACL Ultsch A., 1990, P INT NEUR NETW C IN, P305 Vesanto J, 2000, IEEE T NEURAL NETWOR, V11, P586, DOI 10.1109/72.846731 Vinokourov A, 2002, J INTELL INF SYST, V18, P153, DOI 10.1023/A:1013677411002 Wang B. B., 2003, ACSC 03 P 26 AUSTR C, P69 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wang P., 2008, KDD 08, P713, DOI DOI 10.1145/1401890.1401976 Wikipedia, 2011, WIK API WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Xiong H, 2004, SIAM PROC S, P279 Xue XB, 2009, IEEE T KNOWL DATA EN, V21, P428, DOI 10.1109/TKDE.2008.166 Yin HJ, 2002, IEEE T NEURAL NETWOR, V13, P237, DOI 10.1109/72.977314 NR 87 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD DEC PY 2012 VL 39 IS 3 BP 577 EP 610 DI 10.1007/s10844-012-0204-9 PG 34 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 036MG UT WOS:000311029800002 ER PT J AU Alam, MH Ha, J Lee, S AF Alam, Md. Hijbul Ha, JongWoo Lee, SangKeun TI Novel approaches to crawling important pages early SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Web crawler; Crawl ordering; PageRank; Fractional PageRank ID CLASSIFICATION AB Web crawlers are essential to many Web applications, such as Web search engines, Web archives, and Web directories, which maintain Web pages in their local repositories. In this paper, we study the problem of crawl scheduling that biases crawl ordering toward important pages. We propose a set of crawling algorithms for effective and efficient crawl ordering by prioritizing important pages with the well-known PageRank as the importance metric. In order to score URLs, the proposed algorithms utilize various features, including partial link structure, inter-host links, page titles, and topic relevance. We conduct a large-scale experiment using publicly available data sets to examine the effect of each feature on crawl ordering and evaluate the performance of many algorithms. The experimental results verify the efficacy of our schemes. In particular, compared with the representative RankMass crawler, the FPR-title-host algorithm reduces computational overhead by a factor as great as three in running time while improving effectiveness by 5 % in cumulative PageRank. C1 [Alam, Md. Hijbul; Ha, JongWoo; Lee, SangKeun] Korea Univ, Dept Comp Sci & Engn, Seoul 136701, South Korea. RP Lee, S (reprint author), Korea Univ, Dept Comp Sci & Engn, Seoul 136701, South Korea. EM hijbul@korea.ac.kr; okcomputer@korea.ac.kr; yalphy@korea.ac.kr FU National Research Foundation of Korea (NRF); Ministry of Education, Science and Technology [2012M3C4A7033344, 2011-0010325] FX This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2012M3C4A7033344) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0010325). CR Abiteboul S., 2003, P 12 INT C WORLD WID, P280, DOI DOI 10.1145/775152.775192 Alam MH, 2009, LECT NOTES COMPUT SC, V5463, P590, DOI 10.1007/978-3-642-00887-0_52 Almpanidis G, 2007, INFORM SYST, V32, P886, DOI 10.1016/j.is.2006.09.004 Baeza YR, 2005, P SPEC INT TRACKS PO, P864 Bai X, 2011, P 20 ACM INT C INF K, P77 Boldi P., 2004, P 13 INT WORLD WID W, P595, DOI DOI 10.1145/988672.988752 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brinkmeier M., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1151087.1151090 Castillo C., 2006, ACM SIGIR FORUM, V40, P11, DOI 10.1145/1189702.1189703 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Cho J, 2000, P 26 INT C VER LARG, P200 Cho J, 1998, COMPUT NETWORKS ISDN, V30, P161, DOI 10.1016/S0169-7552(98)00108-1 Cho J., 2005, P ACM SIGMOD, P551, DOI DOI 10.1145/1066157.1066220 Cho J., 2007, P 33 INT C VER LARG, P375 Dasgupta Anirban, 2007, P 16 INT C WORLD WID, P421, DOI 10.1145/1242572.1242630 Deerwester S., 1988, P 51 ANN M AM SOC IN, P36 Fan RE, 2008, J MACH LEARN RES, V9, P1871 Fetterly D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P580, DOI 10.1145/1571941.1572041 Gery M, 2012, KNOWL INF SYST, V32, P217, DOI 10.1007/s10115-011-0426-0 Groza T, 2013, KNOWL INF SYST, V34, P1, DOI 10.1007/s10115-011-0473-6 Gyongi Z., 2004, P 30 INT C VER LARG, P576, DOI 10.1016/B978-012088469-8/50052-8 Kamvar S. D., 2003, P 12 INT WORLD WID W, P261 Lee HJ, 2009, GEOL SOC AM SPEC PAP, V454, P1, DOI 10.1130/2009.2454(00) Lin ZJ, 2012, KNOWL INF SYST, V32, P141, DOI 10.1007/s10115-011-0427-z Mei JP, 2012, KNOWL INF SYST, V31, P527, DOI 10.1007/s10115-011-0437-x Najork M., 2001, P 10 INT WORLD WID W, P114, DOI DOI 10.1145/371920.371965 Ntoulas A., 2004, P 13 INT C WORLD WID, P1, DOI 10.1145/988672.988674 Olston C., 2008, P 17 INT C WORLD WID, P437, DOI DOI 10.1145/1367497.1367557 Orlandi Alessio, 2008, SIGIR Forum, V42, DOI 10.1145/1480506.1480512 Pandey S, 2008, P WSDM, P3, DOI 10.1145/1341531.1341535 Pant G, 2005, ACM T INFORM SYST, V23, P430, DOI 10.1145/1095872.1095875 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shchekotykhin K, 2009, KNOWL INF SYST, V25, P303 Wan M, 2012, KNOWL INF SYST, V33, P89, DOI 10.1007/s10115-011-0453-x NR 34 TC 4 Z9 4 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD DEC PY 2012 VL 33 IS 3 BP 707 EP 734 DI 10.1007/s10115-012-0535-4 PG 28 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 034KM UT WOS:000310871700009 ER PT J AU Goyal, P Behera, L McGinnity, TM AF Goyal, Pawan Behera, Laxmidhar McGinnity, Thomas Martin TI Query Representation through Lexical Association for Information Retrieval SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Information retrieval; lexical association; query expansion; language model ID RELEVANCE; EXPANSION; SCIENCE; MODEL; WEB AB A user query for information retrieval (IR) applications may not contain the most appropriate terms (words) as actually intended by the user. This is usually referred to as the term mismatch problem and is a crucial research issue in IR. Using the notion of relevance, we provide a comprehensive theoretical analysis of a parametric query vector, which is assumed to represent the information needs of the user. A lexical association function has been derived analytically using the system relevance criteria. The derivation is further justified using an empirical evidence from the user relevance criteria. Such analytical derivation as presented in this paper provides a proper mathematical framework to the query expansion techniques, which have largely been heuristic in the existing literature. By using the generalized retrieval framework, the proposed query representation model is equally applicable to the vector space model (VSM), Okapi best matching 25 (Okapi BM25), and Language Model (LM). Experiments over various data sets from TREC show that the proposed query representation gives statistically significant improvements over the baseline Okapi BM25 and LM as well as other well-known global query expansion techniques. Empirical results along with the theoretical foundations of the query representation confirm that the proposed model extends the state of the art in global query expansion. C1 [Goyal, Pawan; Behera, Laxmidhar; McGinnity, Thomas Martin] Univ Ulster, Sch Comp & Intelligent Syst, Intelligent Syst Res Ctr, Coleraine BT48 7JL, Londonderry, North Ireland. [Behera, Laxmidhar] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India. [Goyal, Pawan; McGinnity, Thomas Martin] Univ Ulster, Intelligent Syst Res Ctr, Fac Comp & Engn, Coleraine BT48 7JL, Londonderry, North Ireland. RP Goyal, P (reprint author), Univ Ulster, Sch Comp & Intelligent Syst, Intelligent Syst Res Ctr, Northland Rd, Coleraine BT48 7JL, Londonderry, North Ireland. EM goyal-p@email.ulster.ac.uk; lbehera@iitk.ac.in; tm.mcginnity@ulster.ac.uk FU Centre of Excellence in Intelligent Systems (CoEIS) project; Northern Ireland Integrated Development Fund; InvestNI FX This research is supported under the Centre of Excellence in Intelligent Systems (CoEIS) project, funded by the Northern Ireland Integrated Development Fund and InvestNI. CR ANICK P, 2003, P 26 ANN INT ACM SIG, P88 Bai J, 2008, INFORM PROCESS MANAG, V44, P1901, DOI 10.1016/j.ipm.2008.07.006 Bai J, 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725 Barry CL, 1998, J AM SOC INFORM SCI, V49, P1293, DOI 10.1002/(SICI)1097-4571(1998)49:14<1293::AID-ASI7>3.0.CO;2-E BENESTY J, 2009, SPRINGER TOP SIGN PR, V2, P1 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Buckley C., 1994, P TREC Cao G, 2005, P 28 ANN INT ACM SIG, P298 Cao Guihong, 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Cho BH, 2003, INFORM PROCESS MANAG, V39, P505, DOI 10.1016/S0306-4573(02)00078-X Collins-Thompson K., 2007, P SIGIR 2007, P303, DOI 10.1145/1277741.1277795 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C Custis T., 2007, P 30 ANN INT ACM SIG, P575, DOI 10.1145/1277741.1277840 Downey D., 2008, P 17 ACM C INF KNOWL, P449, DOI 10.1145/1458082.1458143 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q Hsu MH, 2008, LECT NOTES COMPUT SC, V4993, P213 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jones K. S., 2000, Information Processing & Management, V36 Kalt T., 1998, TECHNICAL REPORT Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 MacKay David J.C., 1994, NAT LANG ENG, V1, P1 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Na SH, 2007, INFORM PROCESS MANAG, V43, P302, DOI 10.1016/j.ipm.2006.07.003 Navigli R., 2003, P WORKSH AD TEXT EXT, P42 Okabe M., 2005, P HUM LANG TECHN C C, P963, DOI 10.3115/1220575.1220696 Park LAF, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1462198.1462200 PARK TK, 1993, LIBR QUART, V63, P318 Pinto F.J., 2009, INT J COMPUTER APPL, V33, P271 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Robertson S., 1981, P 3 ANN ACM C RES DE, P35 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracevic T., 1996, P 2 C CONC LIB INF S, P201 Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P1915, DOI 10.1002/asi.20682 Schutz A., 1970, REFLECTIONS PROBLEM Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 Song M, 2007, DATA KNOWL ENG, V63, P63, DOI 10.1016/j.datak.2006.10.010 SWANSON DR, 1986, LIBR QUART, V56, P389 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Voorhees E. M., 1998, P 6 TEXT RETR C TREC, P83 Wang PL, 2003, J AM SOC INF SCI TEC, V54, P743, DOI 10.1002/asi.10262 WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X Wollersheim D., 2005, INT J WEB INFORM SYS, V1, P101 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang JL, 2009, AST: 2009 INTERNATIONAL E-CONFERENCE ON ADVANCED SCIENCE AND TECHNOLOGY, PROCEEDINGS, P52, DOI 10.1109/AST.2009.24 NR 58 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD DEC PY 2012 VL 24 IS 12 BP 2260 EP 2273 DI 10.1109/TKDE.2011.171 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 021VV UT WOS:000309914400011 ER PT J AU Smirnov, S Reijers, HA Weske, M AF Smirnov, Sergey Reijers, Hajo A. Weske, Mathias TI From fine-grained to abstract process models: A semantic approach SO INFORMATION SYSTEMS LA English DT Article DE Business process modeling; Process model management; Business process model abstraction ID BUSINESS PROCESS MODELS; LANGUAGE AB Organizations actively managing their business processes face a rapid growth of the number of process models that they maintain. Business process model abstraction has proven to be an effective means to generate readable, high-level views on business process models by showing coarse-grained activities and leaving out irrelevant details. In this way, abstraction facilitates a more efficient management of process models, as a single model can provide for many relevant views. Yet, it is an open question how to perform abstraction in the same skillful way as experienced modelers combine activities into more abstract tasks. This paper presents an approach that uses semantic information of a process model to decide on which activities belong together, which extends beyond existing approaches that merely exploit model structural characteristics. The contribution of this paper is twofold: we propose a novel activity aggregation method and suggest how to discover the activity aggregation habits of human modelers. In an experimental validation, we use an industrial process model repository to compare the developed activity aggregation method with actual modeling decisions, and observe a strong correlation between the two. The presented work is expected to contribute to the development of modeling support for the effective process model abstraction. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Smirnov, Sergey; Weske, Mathias] Hasso Plattner Inst, D-14482 Potsdam, Germany. [Reijers, Hajo A.] Eindhoven Univ Technol, Sch Ind Engn, NL-5600 MB Eindhoven, Netherlands. RP Smirnov, S (reprint author), Hasso Plattner Inst, Prof Dr Helmert Str 2-3, D-14482 Potsdam, Germany. EM sergey.smirnov@hpi.uni-potsdam.de; h.a.reijers@tue.nl; mathias.weske@hpi.uni-potsdam.de CR Aho A.V., 1972, THEORY PARSING TRANS Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbier F, 2003, IEEE T SOFTWARE ENG, V29, P459, DOI 10.1109/TSE.2003.1199074 Becker J., 2003, PROCESS MANAGEMENT G Becker J., 2003, BIS 2003, P7 BERTHELOT G, 1987, ADV PETRI NETS, V1986, P359 Bobrik R, 2007, LECT NOTES COMPUT SC, V4714, P88 BOC Group, 2012, ADONIS PROC PORT Casati F., 2002, EDBT, P287 de Medeiros A. K. A., 2008, ECIS 2008 GALW IR, P1953 Di Francescomarino C., 2010, J SOFTWARE MAINTENAN Dijkman R, 2011, INFORM SYST, V36, P498, DOI 10.1016/j.is.2010.09.006 Dijkman R, 2009, LECT NOTES COMPUT SC, V5701, P48 Dumas M., 2009, IEEE DATA ENG B, V32, P23 Eshuis R, 2008, DATA KNOWL ENG, V64, P419, DOI 10.1016/j.datak.2007.07.003 Franzblau A, 1958, PRIMER STAT NONSTATI Gcorgakopoulos D., 1995, DISTRIBUTED PARALLEL, V3, P119 Gerstman B.B., 2010, STATPRIMER VERSION 6 Giinther C.W., 2006, BETA WORKING PAPER S Guizzardi G, 2007, LECT NOTES COMPUT SC, V4495, P5 Gunther CW, 2007, LECT NOTES COMPUT SC, V4714, P328 Hallerbach A., 2009, SOFTWARE PROCESS IMP Hammer M., 1994, HARPERBUSINESS APR Hartigan J, 1975, CLUSTERING ALGORITHM Henderson-Sellers B, 2010, LECT NOTES COMPUT SC, V6412, P219 Hepp M, 2005, INT CONF E BUS ENG, P535, DOI 10.1109/ICEBE.2005.110 Indulska M, 2009, LECT NOTES COMPUT SC, V5565, P501, DOI 10.1007/978-3-642-02144-2_39 Jin T, 2010, LECT NOTES COMPUT SC, V6426, P402 Keller G., 1998, SAP R 3 PROCESS ORIE Keller G., 1992, VEROFFENTLICHUNGEN I Kiihne Th., 2009, CRPIT, V96 Kuhne T., 2006, SOFTWARE SYSTEMS MOD, V5, P369, DOI 10.1007/s10270-006-0017-9 KUNZE M, 2011, LNCS, V6896, P166 Kuropka D., 2004, MODELLE REPRASENTATI La Rosa M., 2009, THESIS QUEENSLAND U Leymann F., 2000, PRODUCTION WORKFLOW Mendling J, 2008, DATA KNOWL ENG, V64, P312, DOI 10.1016/j.datak.2007.06.019 Object Management Group(OMG), 2010, OMG UN MOD LANG OMG OMG, 2011, BUS PROC MOD NOT Polyvyanyy A, 2009, LECT NOTES COMPUT SC, V5701, P229 Reijers H.A., 2008, LECT NOTES COMPUTER, P20 Rosemann M., 2006, BUSINESS PROCESS MAN, V12, P249, DOI 10.1108/14637150610657567 Rosemann M, 2007, INFORM SYST, V32, P1, DOI 10.1016/j.is.2005.05.003 Sadiq W, 2000, INFORM SYST, V25, P117, DOI 10.1016/S0306-4379(00)00012-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAEFFER S.E., 2007, COMPUTER SCI REV, V1, P27, DOI DOI 10.1016/J.C0SREV.2007.05.001 Sharp A., 2008, WORKFLOW MODELING TO Siegel S, 1956, NONPARAMETRIC STAT B Smirnov S, 2010, LECT NOTES COMPUT SC, V6470, P1, DOI 10.1007/978-3-642-17358-5_1 Smirnov S., 2012, DISTRIBUTED PARALLEL Smirnov S, 2010, LECT NOTES COMPUT SC, V6412, P1 Stachowiak Herbert, 1973, ALLGEMEINE MODELLTHE Uba R., 2011, LNCS, V6896, P248, DOI DOI 10.1007/978-3-642-23059-2-20 van der Aalst WMP, 1997, LECT NOTES COMPUT SC, V1248, P62 Vanhatalo J., 2008, LECT NOTES COMPUT SC, P100 Weidlich M, 2010, LECT NOTES COMPUT SC, V6051, P483, DOI 10.1007/978-3-642-13094-6_37 Wong S. K. Michael, 1985, SIGIR, P18, DOI DOI 10.1145/253495.253506 NR 57 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD DEC PY 2012 VL 37 IS 8 SI SI BP 784 EP 797 DI 10.1016/j.is.2012.05.007 PG 14 WC Computer Science, Information Systems SC Computer Science GA 964WQ UT WOS:000305727800006 ER PT J AU Rieck, K Wressnegger, C Bikadorov, A AF Rieck, Konrad Wressnegger, Christian Bikadorov, Alexander TI Sally: A Tool for Embedding Strings in Vector Spaces SO JOURNAL OF MACHINE LEARNING RESEARCH LA English DT Article DE string embedding; bag-of-words models; learning with sequential data ID SIMILARITY; KERNELS; TEXT AB Strings and sequences are ubiquitous in many areas of data analysis. However, only few learning methods can be directly applied to this form of data. We present Sally, a tool for embedding strings in vector spaces that allows for applying a wide range of learning methods to string data. Sally implements a generalized form of the bag-of-words model, where strings are mapped to a vector space that is spanned by a set of string features, such as words or n-grams of words. The implementation of Sally builds on efficient string algorithms and enables processing millions of strings and features. The tool supports several data formats and is capable of interfacing with common learning environments, such as Weka, Shogun, Matlab, or Pylab. Sally has been successfully applied for learning with natural language text, DNA sequences and monitored program behavior. C1 [Rieck, Konrad] Univ Gottingen, D-37077 Gottingen, Germany. [Wressnegger, Christian] Idalab GmbH, D-10977 Berlin, Germany. [Bikadorov, Alexander] Tech Univ Berlin, D-10587 Berlin, Germany. RP Rieck, K (reprint author), Univ Gottingen, Goldschmidtstr 7, D-37077 Gottingen, Germany. EM KONRAD.RIECK@UNI-GOETTINGEN.DE; WRESSNEGGER@IDALAB.DE; ABIKU@CS.TU-BERLIN.DE CR DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Jawurek M., 2011, P 27 ANN COMP SEC AP, P227 Klimt B., 2004, P C EM ANT CEAS Leslie C, 2002, PAC S BIOCOMPUT, V7, P564, DOI DOI 10.1142/9789812799623_0053 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 O'Donovan Claire, 2002, Brief Bioinform, V3, P275, DOI 10.1093/bib/3.3.275 Rieck Konrad, 2011, Journal of Computer Security, V19, DOI 10.3233/JCS-2010-0410 Rieck K, 2008, J MACH LEARN RES, V9, P23 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Shi QF, 2009, J MACH LEARN RES, V10, P2615 Sonnenburg S, 2006, BIOINFORMATICS, V22, pE472, DOI 10.1093/bioinformatics/btl250 Sonnenburg S., 2007, LARGE SCALE KERNEL M, P73 Wahl S, 2009, BELL LABS TECH J, V14, P243, DOI 10.1002/bltj.20365 NR 14 TC 0 Z9 0 PU MICROTOME PUBL PI BROOKLINE PA 31 GIBBS ST, BROOKLINE, MA 02446 USA SN 1532-4435 J9 J MACH LEARN RES JI J. Mach. Learn. Res. PD NOV PY 2012 VL 13 BP 3247 EP 3251 PG 5 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 066DL UT WOS:000313200200004 ER PT J AU Zhou, D Truran, M Brailsford, T Wade, V Ashman, H AF Zhou, Dong Truran, Mark Brailsford, Tim Wade, Vincent Ashman, Helen TI Translation Techniques in Cross-Language Information Retrieval SO ACM COMPUTING SURVEYS LA English DT Article DE Algorithms; Languages; Cross-language information retrieval; query translation; document translation; bilingual dictionary; parallel corpora; machine translation; semantic model ID TRANSITIVE DICTIONARY TRANSLATION; LATENT SEMANTIC ANALYSIS; TEXT RETRIEVAL; WEB; MODELS; WORDS; QUERY; CLIR; TRANSLITERATION; PERFORMANCE AB Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments. C1 [Zhou, Dong] Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan, Hunan, Peoples R China. [Zhou, Dong; Wade, Vincent] Trinity Coll Dublin, Dublin, Ireland. [Truran, Mark] Univ Teesside, Middlesbrough, Cleveland, England. [Brailsford, Tim] Univ Nottingham, Nottingham NG7 2RD, England. [Ashman, Helen] Univ S Australia, Adelaide, SA 5001, Australia. RP Zhou, D (reprint author), Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan, Hunan, Peoples R China. EM dongzhou1979@hotmail.com FU University of Nottingham; Science Foundation Ireland as part of the Centre for Next Generation Localisation at the University of Dublin, Trinity Collage [07/CE/1142] FX This research was partially supported by a PhD scholarship from the University of Nottingham and funding from the Science Foundation Ireland (Grant 07/CE/1142) as part of the Centre for Next Generation Localisation (www.cngl.ie) at the University of Dublin, Trinity Collage. CR Abduljaleel N. L., 2003, P 12 INT C INF KNOWL, P139 ADRIANI M., 2000, INFORM RETRIEVAL, V2, P71, DOI 10.1023/A:1009989801965 ADRIANI M., 2005, CLEF 2005 Agirre E, 2009, LECT NOTES COMPUT SC, V5706, P15 Alfonseca E., 2008, P 46 ANN M ASS COMP, P253, DOI 10.3115/1557690.1557763 Aljlayl M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 ANDEKKA M., 2009, P 10 CROSS LANG EV F, P50 Anderka M, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P670, DOI 10.1145/1571941.1572070 [Anonymous], 2003, P ACL 2003 WORKSH MU, V15, P57, DOI 10.3115/1119384.1119392 Bacchin M, 2005, INFORM PROCESS MANAG, V41, P121, DOI 10.1016/j.ipm.2004.04.006 Baeza-Yates R., 2008, MODERN INFORM RETRIE BALLESTEROS L., 2003, P 12 INT C INF KNOWL, P147 Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 BENCZUR A., 2003, P 12 INT WORLD WID W Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boughanem M, 2002, LECT NOTES COMPUT SC, V2406, P158 Braschler M, 2004, INFORM RETRIEVAL, V7, P291, DOI 10.1023/B:INRT.0000011208.60754.a1 BROGLIO J., 1993, P TIPSTER TEXT PROGR, P47, DOI 10.3115/1119149.1119159 Brown P. F., 1993, Computational Linguistics, V19 Buckley C, 2000, INFORM PROCESS MANAG, V36, P109, DOI 10.1016/S0306-4573(99)00047-3 Callan James, 1992, P 3 INT C DAT EXP SY, P78 CAO G., 2007, P MT SUMM 11, P57 Cao G., 2007, P 16 ACM C INF KNOWL, P351, DOI 10.1145/1321440.1321491 Carbonell JG, 1997, INT JOINT CONF ARTIF, P708 CHEN A., 2002, P CLEF 2002, P28 Chen J., 2004, P 2 WORKSH AUSTR INF, V32, P157 Chen J., 2000, P RIAO 2000 CONT BAS, P188 Cheng P.-J., 2004, P 27 ANN INT ACM SIG, P146, DOI 10.1145/1008992.1009020 Chen-Yu Su T.-C. L., 2007, P 6 NTCIR WORKSH M E, P109 Chew P. A., 2006, P WORKSH MULT LANG R, P68, DOI 10.3115/1613162.1613171 Cimiano P, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1513 Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Darwish K., 2003, P 26 ANN INT ACM SIG, P338 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMNER-FUSHMAN D., 2003, P 36 ANN HAW INT C S Dolamic L, 2010, J AM SOC INF SCI TEC, V61, P2266, DOI 10.1002/asi.21337 DUMAIS S. T., 1995, P TEXT RETR C TREC 3, P219 DUMAIS S. T., 1993, P 2 TEXT RETR C TREC, P105 DUMAIS S. T., 1997, P AAAI SPRING S SERI, P18 Fautsch C, 2009, J AM SOC INF SCI TEC, V60, P1616, DOI 10.1002/asi.21093 Federico M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval FERRO N., 2009, P 10 CROSS LANG EV F, P13 Fox C., 1990, SIGIR Forum, V24 Franz M., 1999, P 7 TEXT RETR C TREC, P157 Fujii A, 2001, COMPUT HUMANITIES, V35, P389, DOI 10.1023/A:1011856202986 Gao J, 2006, P 29 ANN INT ACM SIG, P194, DOI 10.1145/1148170.1148207 Gao J., 2001, P 24 ANN INT ACM SIG, P96, DOI 10.1145/383952.383966 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Gao J., 2002, P 25 ANN INT ACM SIG, P183 GAO W, 2005, P 1 INT JOINT C NAT, V3248, P110 GEY F., 2007, P 6 NTCIR WORKSH M E, P73 GEY F., 1998, P 9 TEXT RETR C TREC, P15 Gollins T., 2001, P 24 ANN INT ACM SIG, P90, DOI 10.1145/383952.383965 GOTO I., 2004, P 20 INT C COMP LING Goutte C, 2009, NEURAL INF PROCESS S, pXI HE D., 2008, P 17 ACM C INF KNOWL, P729, DOI 10.1145/1458082.1458180 HEDLUND T., 2002, INFORM RES, V7, P2 Hollink V, 2004, INFORM RETRIEVAL, V7, P33, DOI 10.1023/B:INRT.0000009439.19151.4c Huang F., 2005, P HUM LANG TECHN C C, P483, DOI 10.3115/1220575.1220636 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 HULL D. A., 1997, AAAI S CROSS LANG TE, P84 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 JANG M.-G., 1999, P 37 ANN M ASS COMP, P223, DOI 10.3115/1034678.1034718 Jeong KS, 1999, INFORM PROCESS MANAG, V35, P523, DOI 10.1016/S0306-4573(98)00055-7 Jin R., 2002, P 25 ANN INT ACM SIG, P42 JONES G. J. F, 2008, P 2 INT WORKSH CROSS, P34 Kang Byung-Ju, 2000, P 5 INT WORKSH INF R, P133, DOI 10.1145/355214.355234 KANG I.-H., 2000, P 18 C COMP LING, V1, P418, DOI 10.3115/990820.990881 KANG I.-S., 2004, P 4 NTCIR WORKSH NAT KASHIOKA H., 2003, P MACH TRANSL SUMM M, P216 KESKUSTALO H, 2003, P STRING PROC INF RE, V2857, P252 Kishida K, 2008, LIBR INFORM SCI RES, V30, P138, DOI 10.1016/j.lisr.2007.09.003 KISHIDA K., 2005, P WORKSH CROSS LANG Knight K, 1998, COMPUT LINGUIST, V24, P599 KOHN M., 2005, P 28 ANN INT ACM SIG, P528 Kraaij W, 2003, COMPUT LINGUIST, V29, P381, DOI 10.1162/089120103322711587 KRAAIJ W., 2003, P 4 DUTCH BELG INF R Kuriyama K, 2002, INFORM RETRIEVAL, V5, P41, DOI 10.1023/A:1012778807438 KWOK K. L., 1996, P TREC 5, P133 Kwok K. L., 1999, P WORKSH MACH TRANSL, P8 Kwok K. L., 2000, P 5 INT WORKSH INF R, P173, DOI 10.1145/355214.355239 Lancaster F. W., 1973, INFORM RETRIEVAL ON Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lavrenko V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee CJ, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P651 LEEK T., 2000, 3 TOP DET TRACK WORK Lehtokangas R, 2004, INFORM PROCESS MANAG, V40, P973, DOI 10.1016/j.ipm.2003.10.005 Lehtokangas R, 2008, J AM SOC INF SCI TEC, V59, P476, DOI 10.1002/asi.20762 Leveling J., 2009, P 10 CROSS LANG EV F, P58 Levow GA, 2005, INFORM PROCESS MANAG, V41, P523, DOI 10.1016/j.ipm.2004.06.012 LEVOW G.-A., 2000, 3 TOP DET TRACK WORK Lin MC, 2010, P 8 NTCIR WORKSH M E, P101 Liu X., 2005, ANNU REV INFORM SCI, V39, P1, DOI 10.1002/aris.1440390108 Liu Y., 2005, P 28 ANN INT ACM SIG, P536, DOI 10.1145/1076034.1076125 Lopez A, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1380584.1380586 Loponen A, 2010, LECT NOTES COMPUT SC, V6360, P3, DOI 10.1007/978-3-642-15998-5_3 Lu W.-H., 2002, ACM T ASIAN LANGUAGE, V2, P159, DOI 10.1145/568954.568958 Lu WH, 2004, ACM T INFORM SYST, V22, P242, DOI 10.1145/984321.984324 Maeda A, 2000, P 5 INT WORKSH INF R, P25, DOI 10.1145/355214.355218 MAJUMDER P., 2007, ACM T INFORM SYST, V25 Manning C.D., 2008, INTRO INFORM RETRIEV Mayfield J., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009085 McCarley J. S., 1999, P 37 ANN M ASS COMP, P208, DOI 10.3115/1034678.1034716 MCEWAN CJA, 2002, ADV INFORM RETRIEVAL, V2291, P303, DOI 10.1007/3-540-45886-7_20 McNamee P, 2004, INFORM RETRIEVAL, V7, P73, DOI 10.1023/B:INRT.0000009441.78971.be MCNAMEE P., 2004, P WORKSH CROSS LANG MCNAMEE P., 2002, J COMPUT SMALL COLL, V17, P8 Melamed ID, 2000, COMPUT LINGUIST, V26, P221, DOI 10.1162/089120100561683 Melucci M., 2003, P 12 INT C INF KNOWL, P131 MENG H., 2000, P NAACL ANLP WORKSH, V5, P23 MENG H., 2001, P NSF WORKSH KUAL LU, P1, DOI 10.3115/1072133.1072202 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Monz C., 2005, P 28 ANN INT ACM SIG, P520, DOI 10.1145/1076034.1076123 MOREAU F, 2007, P 29 EUR C IR RES SP, V4425, P222 Mori T., 2001, P 2 NTCIR WORKSH M E Nie J. -Y., 2010, SYNTHESIS LECT HUMAN Nie Jian-Yun, 1999, P 22 ANN INT ACM SIG, P74, DOI 10.1145/312624.312656 Nie JY, 1999, INFORM PROCESS MANAG, V35, P443, DOI 10.1016/S0306-4573(98)00051-X NIE J.-Y., 1998, P 6 WORKSH VER LARG OAED D. W., 2000, P EV CROSS LANG INF OAED D. W., 1996, UMIACSTR9619 OAED D. W., 1999, P DARPA BROADC NEWS, P209 OAHD D. W., 2001, P 2 NTCIR WORKSH RES Oard D. W., 1997, P 6 TEXT RETR C TREC, P687 Oard DW, 1998, LECT NOTES ARTIF INT, V1529, P472 OARD DW, 2002, ADV INFORM RETRIEVAL, V2291, P324, DOI 10.1007/3-540-45886-7_21 Och F. J., 2003, Computational Linguistics, V29, DOI 10.1162/089120103321337421 PARTON K., 2008, P 17 ACM C INF KNOWL, P719, DOI 10.1145/1458082.1458179 Peters C., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980) PETERS C., 1996, ERCIM NEWS, P27 PIRKOLA A., 2003, P 26 ANN INT ACM SIG, P345 Pirkola A, 2003, INFORM PROCESS MANAG, V39, P391, DOI 10.1016/S0306-4573(02)00091-2 Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957 PIRKOLA A., 2002, INFORM RES, V7, P2 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 POTTHAST M, 2008, P 30 EUR C ADV INF R, V4956, P522 Qu Y., 2003, P 26 ANN INT ACM SIG, P353 Resnik P., 1999, P 37 ANN M ASS COMP, P527, DOI 10.3115/1034678.1034757 Resnik P, 2003, COMPUT LINGUIST, V29, P349, DOI 10.1162/089120103322711578 Resnik P, 1998, LECT NOTES ARTIF INT, V1529, P72 Resnik P., 2001, P 1 INT C HUM LANG T, P1, DOI 10.3115/1072133.1072185 Robertson AM, 1998, J DOC, V54, P48, DOI 10.1108/EUM0000000007161 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RUIZ M., 1999, P 8 TEXT RETR C TREC SAKAI T., 2010, P 8 NTCIR WORKSH M E, P63 SAKAI T., 2008, P 7 NTCIR WORKSH M E, P77 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J, 2009, COMMUN ACM, V52, P139, DOI 10.1145/1562764.1562799 Savoy J, 2004, INFORM RETRIEVAL, V7, P121, DOI 10.1023/B:INRT.0000009443.51912.e7 Savoy J, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P872, DOI 10.1145/1244002.1244193 Savoy J., 2005, ACM T ASIAN LANGUAGE, V4, P163, DOI 10.1145/1105696.1105701 Schauble P., 1993, P 1993 ACM SIGIR C R, P318, DOI 10.1145/160688.160756 SCHONHOFEN P, 2008, P 8 WORKSH CROSS LAN, V5152, P72 Shannon C. E., 1963, MATH THEORY COMMUNIC Shaw J.A., 1994, P 2 TEXT RETR C TREC, P243 Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Shi L, 2010, LECT NOTES COMPUT SC, V5993, P471 Shi L, 2007, P 2 INT C SCAL INF S, P1, DOI [10.1109/OCEANSE.2007.4302485, DOI 10.1109/OCEANSE.2007.4302485] Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 Snajder J, 2008, INFORM PROCESS MANAG, V44, P1720, DOI 10.1016/j.ipm.2008.03.006 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 SORG P., 2008, CLEF WORKSH Sparck Jones K., 1988, STAT INTERPRETATION, P132 SUN L., 2002, P 3 WORKSH AS LANG R, V12, P1, DOI 10.3115/1118759.1118770 Voorhees E.M., 2000, P 9 TEXT RETRIEVAL C, P1 Wang J., 2006, P 29 ANN INT ACM SIG, P202, DOI 10.1145/1148170.1148208 WEISCHEDEL R., 2000, P JOINT SIGDAT C EMP, V13, P95 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Xu J., 2001, P 24 ANN INT ACM SIG, P105, DOI 10.1145/383952.383968 Xu JX, 2005, INFORM PROCESS MANAG, V41, P475, DOI 10.1016/j.ipm.2004.06.009 Yang C. C., 2002, P 11 INT WORLD WID W, P188 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 ZHAI C., 2009, SYNTHESIS LECT HUMAN Zhang Y., 2004, P 27 ANN INT ACM SIG, P162, DOI 10.1145/1008992.1009022 Zhang Y., 2005, ACM T ASIAN LANGUAGE, V4, P57, DOI 10.1145/1105696.1105697 ZHANG Y., 2005, P 10 MACH TRANSL SUM, P71 ZHOU D., 2008, ACM T ASIAN LANG INF, V7, P1 ZHOU D., 2007, P 6 NTCIR WORKSH M E, P145 ZHOU D., 2008, P 23 ANN ACM S APPL, P1566, DOI 10.1145/1363686.1364054 Zhu J, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P593 ZOBEL J, 1995, SOFTWARE PRACT EXPER, V25, P331, DOI 10.1002/spe.4380250307 NR 189 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD NOV PY 2012 VL 45 IS 1 AR 1 DI 10.1145/2379776.2379777 PG 44 WC Computer Science, Theory & Methods SC Computer Science GA 052PY UT WOS:000312211700001 ER PT J AU Ma, BJ Wei, Q AF Ma, Baojun Wei, Qiang TI Measuring the coverage and redundancy of information search services on e-commerce platforms SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS LA English DT Article DE Information coverage; Information redundancy; Information search; Information structure ID RECOMMENDER SYSTEMS; WEB SEARCH; RETRIEVAL; MARKET; ALGORITHMS; TOPICALITY; DIVERSITY; CONSUMERS; REVIEWS; NOVELTY AB Today's widespread e-commerce applications pose a new challenge to information search services. They must extract a useful small set of search or recommendation results from a larger set that preserves information diversity. This paper proposes a novel metric setting to measure two important aspects of information diversity, information coverage and information redundancy. In addition to content coverage, we consider another important measure of information coverage called structure coverage, and model it using information entropy. This approach can better convey the information coverage of the extracted small set with respect to the original large set. The proposed metrics are effective and have various useful properties, which are demonstrated by theoretical and experimental analysis. We also designed a calculation method that shows good computational efficiency. Finally, we conducted an experiment using real data from online customer reviews to further emphasize the effectiveness of the proposed metrics. (C) 2012 Elsevier B.V. All rights reserved. C1 [Ma, Baojun; Wei, Qiang] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China. RP Wei, Q (reprint author), Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China. EM weiq@sem.tsinghua.edu.cn RI Ma, Baojun/I-5395-2014 FU National Natural Science Foundation of China [70890083/71072015/71110107027]; Tsinghua University Initiative Scientific Research Program [20101081741] FX The work was partially supported by the National Natural Science Foundation of China (70890083/71072015/71110107027) and the Tsinghua University Initiative Scientific Research Program (20101081741). CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148175 Agrawal R., 2009, P 2 ACM INT C WEB SE, P5, DOI 10.1145/1498759.1498766 Aliguliyev RM, 2009, EXPERT SYST APPL, V36, P7904, DOI 10.1016/j.eswa.2008.11.017 Aliguliyev RM, 2009, INFORM SCIENCES, V179, P3583, DOI 10.1016/j.ins.2009.06.012 Allan J., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Atlam ES, 2000, INFORM PROCESS MANAG, V36, P717, DOI 10.1016/S0306-4573(00)00009-1 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bernstein Y., 2005, P 14 ACM INT C INF K, P736, DOI 10.1145/1099554.1099733 Boldi P, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1629096.1629097 BOYCE B, 1982, INFORM PROCESS MANAG, V18, P105, DOI 10.1016/0306-4573(82)90033-4 Broder A., 2002, SIGIR Forum, V36 Brynjolfsson E, 2003, MANAGE SCI, V49, P1580, DOI 10.1287/mnsc.49.11.1580.20580 Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000 Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Carpineto C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541884 Carterette B., 2009, P 18 ACM C INF KNOWL, P1287, DOI 10.1145/1645953.1646116 Chehreghani MH, 2009, DECIS SUPPORT SYST, V47, P374, DOI 10.1016/j.dss.2009.04.002 Chen CL, 2010, DATA KNOWL ENG, V69, P1208, DOI 10.1016/j.datak.2010.08.003 Chen H., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148245 Chen YL, 2009, ELECTRON COMMER R A, V8, P241, DOI 10.1016/j.elerap.2009.03.002 Clarke C L, 2008, P 31 ANN INT ACM SIG, P659, DOI 10.1145/1390334.1390446 Clarke C. L. A., 2010, 18 TEXT RETRIEVAL C De P, 2010, MANAGE SCI, V56, P1930, DOI 10.1287/mnsc.1100.1233 Duan WJ, 2008, DECIS SUPPORT SYST, V45, P1007, DOI 10.1016/j.dss.2008.04.001 Dupret G., 2007, P WORKSH QUER LOG AN Fleder D, 2009, MANAGE SCI, V55, P697, DOI 10.1287/mnsc.1080.0974 Fox S, 2005, ACM T INFORM SYST, V23, P147, DOI 10.1145/1059981.1059982 Ghose A, 2011, IEEE T KNOWL DATA EN, V23, P1498, DOI 10.1109/TKDE.2010.188 GOFFMAN W, 1964, INFORM STORAGE RET, V2, P73, DOI 10.1016/0020-0271(64)90006-3 Gollapudi S., 2009, P 18 INT C WORLD WID, P381, DOI 10.1145/1526709.1526761 Grabmeier J, 2002, DATA MIN KNOWL DISC, V6, P303, DOI 10.1023/A:1016308404627 Han J., 2006, DATA MINING CONCEPTS Hastie T., 2009, ELEMENTS STAT LEARNI He J., 2011, J AM SOC INFORM SCI, V62, p[550, 3] Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Huang A., 2008, P 6 NZ COMP SCI RES, P49 Hurley N, 2011, ACM T INTERNET TECHN, V10, DOI 10.1145/1944339.1944341 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jie Z., 2006, J MANAGEMENT INFORM, V23, P71 Joachims T, 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063 Koh NS, 2010, ELECTRON COMMER R A, V9, P374, DOI 10.1016/j.elerap.2010.04.001 Korenius T, 2007, INFORM SCIENCES, V177, P4893, DOI 10.1016/j.ins.2007.05.027 KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 Kumar N, 2007, DECIS SUPPORT SYST, V44, P159, DOI 10.1016/j.dss.2007.03.010 Kuruzovich J, 2010, MANAGE SCI, V56, P1702, DOI 10.1287/mnsc.1100.1216 LATHIA N, 2010, P 33 INT ACM SIGIR C, P210, DOI 10.1145/1835449.1835486 Li DH, 2008, ELECTRON COMMER R A, V7, P55, DOI 10.1016/j.elerap.2007.02.010 Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 Liu Bing, 2007, WEB DATA MINING EXPL MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Malik HH, 2010, DATA MIN KNOWL DISC, V21, P153, DOI 10.1007/s10618-010-0172-z Mudambi SM, 2010, MIS QUART, V34, P185 Mulpuru S, 2008, STATE RETAILING ONLI, VMA, P2008 Pan F, 2005, P 5 IEEE INT C DAT M, P338 Radlinski F, 2008, P 25 INT C MACH LEAR, P784, DOI DOI 10.1145/1390156.1390255 Radlinski F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148320 Rafiei D, 2010, P 19 INT C WORLD WID, P781, DOI 10.1145/1772690.1772770 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos R L, 2010, P 19 ACM INT C INF K, P1179, DOI 10.1145/1871437.1871586 Santos R.L., 2010, P 19 INT C WORLD WID, P881, DOI 10.1145/1772690.1772780 Santos R.L.T, 2011, P 34 INT ACM SIGIR C, P1185 Sen R, 2006, J MANAGE INFORM SYST, V23, P211, DOI 10.2753/MIS0742-1222230107 SHANNON CE, 1948, AT&T TECH J, V27, P379 Silverstein C., 1999, SIGIR Forum, V33 Siwicki B, 2007, INTERNET RETAILER HO Sparck-Jones K., 2007, SIGIR FORUM, V41, P8, DOI DOI 10.1145/1328964.1328965 Spink A, 2002, COMPUTER, V35, P107, DOI 10.1109/2.989940 Spink A., 2002, SIGIR Forum, V36 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Tsaparas P., 2011, P 17 ACM SIGKDD INT, P168 Turban E., 2010, ELECT COMMERCE MANAG Vargas S., 2011, P 34 INT ACM SIGIR C, P1211 Vee E, 2008, PROC INT CONF DATA, P228, DOI 10.1109/ICDE.2008.4497431 Voorhees E. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291017 Vuylsteke A, 2010, J INTERACT MARK, V24, P309, DOI 10.1016/j.intmar.2010.02.010 Wang J, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P115, DOI 10.1145/1571941.1571963 Xu YJ, 2008, J AM SOC INF SCI TEC, V59, P201, DOI 10.1002/asi.20709 You WJ, 2011, ELECTRON COMMER R A, V10, P510, DOI 10.1016/j.elerap.2011.06.001 Yue Y, 2008, P 25 INT C MACH LEAR, P1224, DOI 10.1145/1390156.1390310 Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003 Zhang B, 2005, P 28 ANN INT ACM SIG, P504, DOI 10.1145/1076034.1076120 Zhang M, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P123 Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 Zhong S, 2005, KNOWL INF SYST, V8, P374, DOI 10.1007/s10115-004-0194-1 Zhuang J, 2008, P 2 WORKSH AN NOIS U, P55, DOI 10.1145/1390749.1390759 NR 96 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1567-4223 J9 ELECTRON COMMER R A JI Electron. Commer. Res. Appl. PD NOV-DEC PY 2012 VL 11 IS 6 BP 560 EP 569 DI 10.1016/j.elerap.2012.09.001 PG 10 WC Business; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Business & Economics; Computer Science GA 042UT UT WOS:000311497500004 ER PT J AU Chen, YL Chang, YC AF Chen, Yen-Liang Chang, Yuan-Che TI A three-phase method for patent classification SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Patent classification; Vector space model (VSM); IPC taxonomy; Support vector machines (SVM); K-means; K nearest neighbors (KNN) ID VECTOR-SPACE MODEL; FEATURE-SELECTION; TEXT CATEGORIZATION; RETRIEVAL; INFORMATION AB An automatic patent categorization system would be invaluable to individual inventors and patent attorneys, saving them time and effort by quickly identifying conflicts with existing patents. In recent years, it has become more and more common to classify all patent documents using the International Patent Classification (IPC), a complex hierarchical classification system comprised of eight sections, 128 classes, 648 subclasses, about 7200 main groups, and approximately 72,000 subgroups. So far, however, no patent categorization method has been developed that can classify patents down to the subgroup level (the bottom level of the IPC). Therefore, this paper presents a novel categorization method, the three phase categorization (TPC) algorithm, which classifies patents down to the subgroup level with reasonable accuracy. The experimental results for the TPC algorithm, using the WIPO-alpha collection, indicate that our classification method can achieve 36.07% accuracy at the subgroup level. This is approximately a 25,764-fold improvement over a random guess. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Chen, Yen-Liang; Chang, Yuan-Che] Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. RP Chen, YL (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. EM ylchen@mgt.ncu.edu.tw CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Cai L., 2004, CIKM 04, P78 cai L., 2003, 26 ANN INT ACM SIGIR, P182 Chakrabarti S, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P446 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Chang C.-C., 2001, LIBSVM LIB SUPPORT V Fall C. J., 2003, ACM SIGIR FORUM, V37, P10, DOI 10.1145/945546.945547 Fall CJ, 2004, EXPERT SYST APPL, V26, P269, DOI 10.1016/S0957-4174(03)00141-6 Feldman R, 2007, TEXT MINING HDB ADV Godbole S., 2004, P 8 PAC AS C KNOWL D Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hotho A., 2005, LDV FORUM, V20, P19 Khan L., 2002, MULTIMEDIA INFORM SY, V56, P65 Kim JH, 2007, INFORM PROCESS MANAG, V43, P1200, DOI 10.1016/j.ipm.2007.02.002 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Larkey L. S., 1998, AAAI 98 Li Y., 2008, ARCH BIOCHEM BIOPHYS, V7, P1, DOI 10.1145/1362782.1362786 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Magerman T, 2010, SCIENTOMETRICS, V82, P289, DOI 10.1007/s11192-009-0046-6 Mitchell TM, 1997, MACHINE LEARNING Ozgur A, 2005, LECT NOTES COMPUT SC, V3733, P606 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Ribeiro M. N., 2008, 15 ICONIP, P45 Salton G., 1983, TEXT ANAL AUTOMATIC SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Shehata S, 2006, ICDM WORKSH, P39 Tikk D., 2007, EMERGING TECHNOLOGIE, P244 Trappey AJC, 2006, EXPERT SYST APPL, V31, P755, DOI 10.1016/j.eswa.2006.01.013 Wanner Leo, 2008, World Patent Information, V30, DOI 10.1016/j.wpi.2007.03.008 WIPO, 2011, READM INF WIPO ALPH Yang Y., 2002, CIKM 02, P659 NR 37 TC 1 Z9 2 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2012 VL 48 IS 6 BP 1017 EP 1030 DI 10.1016/j.ipm.2011.11.001 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 020FI UT WOS:000309794000001 ER PT J AU Alencar, AB de Oliveira, MCF Paulovich, FV AF Alencar, Aretha B. de Oliveira, Maria Cristina F. Paulovich, Fernando V. TI Seeing beyond reading: a survey on visual text analytics SO WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article ID DOCUMENT COLLECTIONS; TAG CLOUDS; VISUALIZATION; EXPLORATION; PATTERNS; TRENDS; SPACE; MODEL; MAPS AB We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input materialeither single texts or collections of textsand their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics. (c) 2012 Wiley Periodicals, Inc. C1 [Alencar, Aretha B.; de Oliveira, Maria Cristina F.; Paulovich, Fernando V.] Univ Sao Paulo, ICMC, Sao Carlos, SP, Brazil. RP Paulovich, FV (reprint author), Univ Sao Paulo, ICMC, Sao Carlos, SP, Brazil. EM paulovic@icmc.usp.br CR Alencar AB, 2012, ACM S APPL COMP MULT, P997 Alsakran J, 2012, IEEE COMPUT GRAPH, V32, P34, DOI 10.1109/MCG.2011.91 Andrews K., 2002, Information Visualization, V1, DOI 10.1057/palgrave.ivs.9500023 [Anonymous], SCI SCI SCI 2 TOOL Becks A., 2002, P ACM S APPL COMP NE, P621 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Cao N, 2010, IEEE T VIS COMPUT GR, V16, P1172, DOI 10.1109/TVCG.2010.154 Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317 Collins C, 2009, COMPUT GRAPH FORUM, V28, P1039 Cui WW, 2010, IEEE COMPUT GRAPH, V30, P42, DOI 10.1109/MCG.2010.102 Cui WW, 2011, IEEE T VIS COMPUT GR, V17, P2412, DOI 10.1109/TVCG.2011.239 de Pinho RD, 2010, MULTIMED TOOLS APPL, V50, P533, DOI 10.1007/s11042-010-0483-5 Dunne C, 2012, J AM SOC IN IN PRESS Eler DM, 2009, INFORMATION VISUALIZATION, IV 2009, PROCEEDINGS, P149, DOI 10.1109/IV.2009.31 Hassan-Montero Y., 2006, INT C MULT INF SCI T Havre S, 2002, IEEE T VIS COMPUT GR, V8, P9, DOI 10.1109/2945.981848 Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Hearst MA, 1995, C HUM FACT COMP SYST Heimonen T, 2005, INT C INF VIS, P877 Herr BW, 2008, IEEE INT CON INF VIS, P421 Hoeber O, 2006, ACM C WEB INT Hoeber O, 2006, Information Visualization, P157 JabRef Development Team, 2010, JABREF JIAO BX, 2010, NEW YORK ACM C RES D, P499 Jing TY, 2010, SUPPORTING WEB SEARC, P183 Keim DA, 2007, IEEE S VIS ANAL, P115 Kuo BY-L, 2007, INT C WORLD WID WEB, P1203 Kyle Koh, 2010, IEEE Transactions on Visualization and Computer Graphics, V16, DOI 10.1109/TVCG.2010.175 Lam H, 2005, CHI 05, P681 Lee B, 2010, IEEE T VIS COMPUT GR, V16, P1182, DOI 10.1109/TVCG.2010.194 Leydesdorff L, 2008, J AM SOC INF SCI TEC, V59, P1810, DOI 10.1002/asi.20891 Li Z, 2008, INT C WORLD WID WEB, P21 Lopes AA, 2007, COMPUT GRAPH-UK, V31, P316, DOI 10.1016/j.cag.2007.01.023 Luo DN, 2012, IEEE T VIS COMPUT GR, V18, P93, DOI 10.1109/TVCG.2010.225 Mao Y, 2007, IEEE T VIS COMPUT GR, V13, P1208, DOI 10.1109/TVCG.2007.70592 Miller NE, 1998, IEEE VISUALIZATION 9, P189 Nguyen TN, 2006, IEEE T VIS COMPUT GR, V12, P981, DOI 10.1109/TVCG.2006.111 Nizamee MR, 2010, IEEE S WEB SOC, P5 Paulovich FV, 2008, IEEE T VIS COMPUT GR, V14, P564, DOI 10.1109/TVCG.2007.70443 Paulovich FV, 2008, IEEE T VIS COMPUT GR, V14, P1229, DOI 10.1109/TVCG.2008.138 Perer A, 2006, IEEE T VIS COMPUT GR, V12, P693, DOI 10.1109/TVCG.2006.122 PNNL, IN SPIRE VIS DOC AN Rusu D, 2009, INFORMATION VISUALIZATION, IV 2009, PROCEEDINGS, P292, DOI 10.1109/IV.2009.57 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seifert C, 2008, IEEE INT CON INF VIS, P17 Skupin A, 2002, IEEE COMPUT GRAPH, V22, P50, DOI 10.1109/38.974518 Spoerri A, 2004, INT C INF VIS, P208 Strobelt H, 2009, IEEE T VIS COMPUT GR, V15, P1145, DOI 10.1109/TVCG.2009.139 TEEVAN J, 2009, INT C HUM FACT COMP, P2023 Teh YW, 2006, J AM STAT ASSOC, V101, P1566, DOI 10.1198/016214506000000302 van Ham F, 2009, IEEE T VIS COMPUT GR, V15, P1169, DOI 10.1109/TVCG.2009.165 Viegas F. B., 2004, CHI 04, P575 Viegas FB, 2009, IEEE T VIS COMPUT GR, V15, P1137, DOI 10.1109/TVCG.2009.171 Wattenberg M, 2008, IEEE T VIS COMPUT GR, V14, P1221, DOI 10.1109/TVCG.2008.172 Wei F, 2010, ACM INT C KNOWL DISC, P153 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 NR 58 TC 3 Z9 4 PU WILEY PERIODICALS, INC PI SAN FRANCISCO PA ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA SN 1942-4787 J9 WIRES DATA MIN KNOWL JI Wiley Interdiscip. Rev.-Data Mining Knowl. Discov. PD NOV-DEC PY 2012 VL 2 IS 6 BP 476 EP 492 DI 10.1002/widm.1071 PG 17 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA 023KE UT WOS:000310030500003 ER PT J AU Chung, WY Tseng, TL AF Chung, Wingyan Tseng, Tzu-Liang (Bill) TI Discovering business intelligence from online product reviews: A rule-induction framework SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE E-commerce; Online reviews; Data mining; Text mining; Association rule mining; Rough set theory; Business intelligence; Online reputation ID CUSTOMER RELATIONSHIP MANAGEMENT; ROUGH SET-THEORY; RECOMMENDER SYSTEMS; KNOWLEDGE; CLASSIFICATION; INFORMATION; WEB; MODEL AB Online product reviews are a major source of business intelligence (BI) that helps managers and marketers understand customers' concerns and interests. The large volume of review data makes it difficult to manually analyze customers' concerns. Automated tools have emerged to facilitate this analysis, however most lack the capability of extracting the relationships between the reviews' rich expressions and the customer ratings. Managers and marketers often resort to manually read through voluminous reviews to find the relationships. To address these challenges, we propose the development of a new class of BI systems based on rough set theory, inductive rule learning, and information retrieval methods. We developed a new framework for designing BI systems that extract the relationship between the customer ratings and their reviews. Using reviews of different products from Amazon.com, we conducted both qualitative and quantitative experiments to evaluate the performance of a BI system developed based on the framework. The results indicate that the system achieved high accuracy and coverage related to rule quality, and produced interesting and informative rules with high support and confidence values. The findings have important implications for market sentiment analysis and e-commerce reputation management. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Chung, Wingyan] UNC Fayetteville State Univ, Sch Business & Econ, Fayetteville, NC 28301 USA. [Tseng, Tzu-Liang (Bill)] Univ Texas El Paso, Dept Ind Mfg & Syst Engn, El Paso, TX 79968 USA. RP Chung, WY (reprint author), UNC Fayetteville State Univ, Sch Business & Econ, 1200 Murchison Rd, Fayetteville, NC 28301 USA. EM wchung@uncfsu.edu FU UNC Fayetteville State University Title III/CCRA Grant FX This research is part of the outcome of the project titled "Establishment of the Knowledge Systems Laboratory" (http://ksl.uncfsu.edu/) funded by UNC Fayetteville State University Title III/CCRA Grant. We thank all the project participants for their help. CR Aciar S, 2007, IEEE INTELL SYST, V22, P39, DOI 10.1109/MIS.2007.55 Agrawal R., 1994, P 20 INT C VER LARG, P487 Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961 [Anonymous], 2010, MERRIAM WEBSTER ONLI Bazan JG, 2000, STUD FUZZ SOFT COMP, V56, P49 Berzal F., 2005, ACT TALL MIN DAT APR, P135 BOWMAN CM, 1994, COMMUN ACM, V37, P98, DOI 10.1145/179606.179704 Brown MPS, 2000, P NATL ACAD SCI USA, V97, P262, DOI 10.1073/pnas.97.1.262 Carvalho R., 2001, INFORM RES, V7, P23 Chang CW, 2009, EXPERT SYST APPL, V36, P1433, DOI 10.1016/j.eswa.2007.11.027 Chen H., 2009, MIS CENTRAL, V2009 Chen HC, 2004, COMPUTER, V37, P50, DOI 10.1109/MC.2004.1297301 Chen MC, 2005, EXPERT SYST APPL, V28, P453, DOI 10.1016/j.eswa.2004.12.006 Chen P, 2008, ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS, P153, DOI 10.1109/ICNC.2008.443 Cheng Y., 2008, 5 INT C FUZZ SYST KN, V2, P301 Chung W, 2005, J MANAGE INFORM SYST, V21, P57 Chung W., 2004, THESIS U ARIZONA TUC Chung W., 2008, INT J ELECT BUSINESS, V6, P25, DOI 10.1504/IJEB.2008.017343 Chung WY, 2009, J AM SOC INF SCI TEC, V60, P59, DOI 10.1002/asi.20948 Chung WY, 2009, HANDB INFO SYST, V3, P373 Clark P., 1989, Machine Learning, V3, DOI 10.1007/BF00116835 Cohens W, 1995, P 12 INT C MACH LEAR, P115 Dai LL, 2008, PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 1, P182, DOI 10.1109/ISCID.2008.178 Dang Y, 2010, IEEE INTELL SYST, V25, P46 Davies PHJ, 2002, ANNU REV INFORM SCI, V36, P313 Demiriz A, 2004, DATA MIN KNOWL DISC, V9, P147, DOI 10.1023/B:DAMI.0000031629.31935.ac Ding X., 2007, P 30 ANN INT ACM SIG, P811, DOI 10.1145/1277741.1277921 Dong L, 2008, ELECTR POW SYST RES, V78, P129, DOI 10.1016/j.epsr.2006.12.013 Doumpos M, 2009, EUR J OPER RES, V199, P496, DOI 10.1016/j.ejor.2008.11.035 Drucker P., 2003, DAILY DRUCKER Gaudreault J, 2009, COMPUT IND, V60, P441, DOI 10.1016/j.compind.2009.02.006 Grzymala-Busse J. W., 1997, Fundamenta Informaticae, V31 Hevner AR, 2004, MIS QUART, V28, P75 Hsinchun Chen, 2001, KNOWLEDGE MANAGEMENT Huang CC, 2008, IEEE INT CONF FUZZY, P818 Huang HH, 2009, HIS 2009: 2009 NINTH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS, VOL 1, PROCEEDINGS, P168, DOI 10.1109/HIS.2009.41 Huang Z, 2004, J AM SOC INF SCI TEC, V55, P259, DOI 10.1002/asi.10372 Jerzy W. G., 1988, J INTELLIGENT ROBOTI, V1, P3 Jindal N, 2007, IEEE DATA MINING, P547 Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 Lawton G, 2006, COMPUTER, V39, P14, DOI 10.1109/MC.2006.318 Li Q., 2004, 2004 INT C MACH LEAR, V3, P1464 Liang WY, 2006, DECIS SUPPORT SYST, V42, P390, DOI 10.1016/j.dss.2005.01.009 Liao SH, 2010, EXPERT SYST APPL, V37, P4212, DOI 10.1016/j.eswa.2009.11.081 Liu Bing, 2007, WEB DATA MINING EXPL Malcolm J. B., 2001, OMEGA, V29, P561 Mitchell TM, 1997, MACHINE LEARNING Nga E. W. T., 2009, EXPERT SYSTEMS APPL, V36, P2592 PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 Predki B., 2008, LECT NOTES COMPUTER, V1424 Qian YH, 2008, COMPUT MATH APPL, V55, P1754, DOI 10.1016/j.camwa.2007.08.031 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shyng JY, 2007, EXPERT SYST APPL, V32, P56, DOI 10.1016/j.eswa.2005.11.002 Simoudis E., 1996, P 2 INT C KNOWL DISC Teng Z., 2007, 2007 INT C NAT LANG, P36, DOI 10.1109/NLPKE.2007.4368008 Trybula WJ, 1999, ANNU REV INFORM SCI, V34, P385 Tseng TL, 2007, OMEGA-INT J MANAGE S, V35, P365, DOI 10.1016/j.omega.2005.07.006 Tseng TL, 2004, J MANUF SYST, V23, P56, DOI 10.1016/S0278-6125(04)80007-4 Vidhya K. A., 2010, 2 INT C COMP AUT ENG, V1, P210 Wang C. G. F. L. Y., 2009, 5 INT C WIR COMM NET Xiao H., 2008, WSES T COMPUTERS, V7, P552 Zhang Z, 2008, IEEE INTELL SYST, V23, P42 Zhang ZW, 2009, EXPERT SYST APPL, V36, P8932, DOI 10.1016/j.eswa.2008.11.007 Zhu F., 2010, J MARKETING, V74, P23 Zhuang L., 2006, P 15 ACM INT C INF K, P43, DOI 10.1145/1183614.1183625 NR 68 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD NOV 1 PY 2012 VL 39 IS 15 BP 11870 EP 11879 DI 10.1016/j.eswa.2012.02.059 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 980GI UT WOS:000306881200008 ER PT J AU Bronselaer, A De Tre, G AF Bronselaer, Antoon De Tre, Guy TI Concept-relational text clustering SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article AB The ongoing exponential growth of online information sources has led to a need for reliable and efficient algorithms for text clustering. In this paper, we propose a novel text model called the relational text model that represents each sentence as a binary multirelation over a concept space \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{C}}$\end{document}. Through usage of the smart indexing engine (SIE), a patented technology of the Belgian company i.Know, the concept space adopted by the text model can be constructed dynamically. This means that there is no need for an a priori knowledge base such as an ontology, which makes our approach context independent. The concepts resulting from SIE possess the property that frequency of concepts is a measure for relevance. We exploit this property with the development of the CR-algorithm. Our approach relies on the representation of a data set \documentclass{article}\usepackage{amssymb}\pagestyle{empty}\begin{document}${\mathcal{D}}$\end{document} as a multirelation, of which k-cuts can be taken. These cuts can be seen as sets of relevant patterns with respect to the topics that are described by documents. Analysis of dependencies between patterns allows to produce clusters, such that precision is sufficiently high. The best k-cut is the one that best approximates the estimated number of clusters to ensure recall. Experimental results on Dutch news fragments show that our approach outperforms both basic and advanced methods. (c) 2012 Wiley Periodicals, Inc. C1 [Bronselaer, Antoon; De Tre, Guy] Univ Ghent, Dept Telecommun & Informat Proc, B-9000 Ghent, Belgium. RP Bronselaer, A (reprint author), Univ Ghent, Dept Telecommun & Informat Proc, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium. EM antoon.bronselaer@ugent.be; guy.detre@ugent.be RI De Tre, Guy/C-6374-2014 CR Andrews N.O., 2007, TR0735 DEP COMP SCI Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bronselaer A, 2010, IEEE T FUZZY SYST, V18, P312, DOI 10.1109/TFUZZ.2010.2041353 Bronselaer A, 2009, CONTROL CYBERN, V38, P341 Bronselaer A, 2009, IEEE T FUZZY SYST, V17, P208, DOI 10.1109/TFUZZ.2008.2008025 Bronselaer Antoon, 2010, NATURAL LANGUAGE BAS, P31 De Cooman G., 1995, FUZZY SET THEORY ADV, P89 Deerwester S., 1988, P 51 ANN M AM SOC IN, P36 Dubois D, 1988, POSSIBILITY THEORY Hisdal E., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90019-2 HOPCROFT J, 1973, COMMUN ACM, V16, P372, DOI 10.1145/362248.362272 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/TIT.1982.1056489 McKeown K., 2005, P 28 ANN INT ACM SIG, P210, DOI 10.1145/1076034.1076072 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shackle G. L. S., 1961, DECISION ORDER TIME WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 YAGER RR, 1986, INT J GEN SYST, V13, P23, DOI 10.1080/03081078608934952 ZADEH LA, 1983, COMPUT MATH APPL, V9, P149, DOI 10.1016/0898-1221(83)90013-5 Zadeh L. A., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90029-5 NR 20 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 0884-8173 J9 INT J INTELL SYST JI Int. J. Intell. Syst. PD NOV PY 2012 VL 27 IS 11 BP 970 EP 993 DI 10.1002/int.21557 PG 24 WC Computer Science, Artificial Intelligence SC Computer Science GA 004YL UT WOS:000308717300002 ER PT J AU Wang, J Liu, ZJ Zhao, H AF Wang, Jing Liu, Zhijing Zhao, Hui TI A probabilistic model with multi-dimensional features for object extraction SO FRONTIERS OF COMPUTER SCIENCE LA English DT Article DE feature extraction; conditional random fields (CRFs); information extraction (IE) ID RANDOM-FIELDS AB To identify recruitment information in different domains, we propose a novel model of hierarchical tree-structured conditional random fields (HT-CRFs). In our approach, first, the concept of a Web object (WOB) is discussed for the description of special Web information. Second, in contrast to traditional methods, the Boolean model and multi-rule are introduced to denote a one-dimensional text feature for a better representation of Web objects. Furthermore, a two-dimensional semantic texture feature is developed to discover the layout of a WOB, which can emphasize the structural attributes and the specific semantics term attributes of WOBs. Third, an optimal WOB information extraction (IE) based on HT-CRF is performed, addressing the problem of a model having an excessive dependence on the page structure and optimizing the efficiency of the model's training. Finally, we compare the proposed model with existing decoupled approaches for WOB IE. The experimental results show that the accuracy rate of WOB IE is significantly improved and that time complexity is reduced. C1 [Wang, Jing; Liu, Zhijing] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China. [Zhao, Hui] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China. RP Wang, J (reprint author), Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China. EM wangjing@mail.xidian.edu.cn FU National Science & Technology Pillar Program [2007BAH08B02] FX This research project was promoted by the National Science & Technology Pillar Program (2007BAH08B02). CR Cai D., 2003, MSRTR200379 Chen MM, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P189 Cohn Trevor, 2005, P 9 C COMP NAT LANG, P169, DOI 10.3115/1706543.1706573 Cui H, 2007, ACM T INFORM SYSTEMS, V25 Finn A, 2004, P EUR C MACH LEARN, P111 Freitag D., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000) Haileong C, 2002, P AM ASS ART INT AAA, P786 Jinlin C, 2006, P IEEE 5 INT C MACH, P279 Kobayashi N, P AAAI CAAW 06 Kristjansson T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P412 Lafferty J, 2004, P 21 INT C MACH LEAR LAFFERTY J, 2001, P 18 INT C MACH LEAR, P282 Li W, 2003, J ACM T ASIAN LANGUA, V2, P290 Loth R, 2010, P 9 INT C AD PERS FU Mooney R. J., 2005, ACM SIGKDD EXPLORATI, V7, P3, DOI 10.1145/1089815.1089817 Nie Z., 2005, P 14 INT C WORLD WID, P567, DOI 10.1145/1060745.1060828 Nyberg E, 2003, P 12 TEXT RETR C Peng F., 2004, P HUM LANG TECHN C N, P329 Peng F., 2004, P 20 INT C COMP LING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang J, 2006, LECT NOTES COMPUT SC, V4273, P640 Trevor C, 2005, P 9 C COMP NAT LANG, P169 Truyen T T, 2008, P 22 ANN C NEUR INF Wallach H, 2004, MSCIS0421 U PENNS CI Xiao J H, 2007, PROCESSING ACM T ASI, V6 Xu Z T, 2007, HIERARCHICAL CONDITI Yang XY, 2002, PATTERN RECOGN LETT, V23, P93, DOI 10.1016/S0167-8655(01)00092-7 Ye SR, 2006, IEEE T KNOWL DATA EN, V18, P334, DOI 10.1109/TKDE.2006.47 Zhu J., 2005, P 22 INT C MACH LEAR, P1044, DOI 10.1145/1102351.1102483 Zhu J, 2008, J MACH LEARN RES, V9, P1583 Zhu J, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P903 Zhu Z, 2004, P 13 ACM C INF KNOWL, P581 NR 32 TC 0 Z9 0 PU HIGHER EDUCATION PRESS PI BEIJING PA SHATANHOU ST 55, BEIJING 100009, PEOPLES R CHINA SN 2095-2228 J9 FRONT COMPUT SCI-CHI JI Front.. Comput. Sci. PD OCT PY 2012 VL 6 IS 5 BP 513 EP 526 DI 10.1007/s11704-012-1093-3 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 019EZ UT WOS:000309721200003 ER PT J AU Wang, QJ Wang, JR Yu, J Yu, M Zhang, Y AF Wang, Qingjie Wang, Jianrong Yu, Jian Yu, Mei Zhang, Yan TI Trust-aware query routing in P2P social networks SO INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS LA English DT Article DE semantic-based community; P2P social network; vector space model (VSM); inverted index; Bloom filter; community-based; trust mechanism ID CENTRALITY AB Query efficiency is a significant challenge in peer-to-peer (P2P) social networks. In this paper, we build a trust-aware semantic-based query routing strategy to improve the searching efficiency and enhance the security. Based on the vector space model, similar semantic ordinary peers set up an unstructured semantic social community. Super peers in each community constitute a structured P2P network and store the keywords information in a global inverted file. We exploit Bloom filters to complete multikeyword query. Furthermore, malicious behaviors may take place in P2P social networks. We then build a community-based trust mechanism. The intracommunity reputations of ordinary peers in a semantic social community are calculated, and the intercommunity reputations of super peers in the structured P2P network are evaluated. This mechanism combines the local trust and the global trust. The results show that the proposed query scheme is able to increase query efficiency and also decrease query cost with significantly reduced malicious behaviors. Copyright (c) 2011 John Wiley & Sons, Ltd. C1 [Wang, Qingjie; Wang, Jianrong; Yu, Jian; Yu, Mei] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. [Zhang, Yan] Univ Oslo, Dept Informat, N-0316 Oslo, Norway. EM yanzhang@ieee.org CR American National Standards Institute, X93021997 AM NAT STA, P2 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Buchegger S, 2009, P SOCIALNETS Buchegger S, 2009, P WONS 2009 6 INT C Buchmann E, 2004, LECT NOTES COMPUT SC, V3290, P337 Buragohain C, 2003, P P2P SWED Costa P, 2008, IEEE J SEL AREA COMM, V26, P748, DOI 10.1109/JSAC.2008.080602 Dou W, 2003, THESIS NATL DEFENSE Ford B, 2006, P OSDI 06 7 USENIX S FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543 Gao W, 2009, P MOBIHOC 09 NEW ORL HUI P, 2008, P 9 ACM INT S MOB AD, P241, DOI DOI 10.1145/1374618.1374652 Hui P, 2007, P 2 ACM IEEE INT WOR, P1, DOI DOI 10.1145/1366919.1366929 Ioannidis S., 2009, P INFOCOM Joachims T., 1997, MACH LEAM P 14 INT C Kalofonos Dimitris N, 2008, P PERCOM 2008 6 ANN Kalofonos Dimitris N, 2008, P PIMRC 08 19 IEEE P Kronfol AZ, 2002, FASD FAULT TOLERANT Li F, 1998, P ACM SIGCOMM 98, P254 Li JT, 2006, THESIS FUDAN U CHINA Marsden PV, 2002, SOC NETWORKS, V24, P407, DOI 10.1016/S0378-8733(02)00016-3 MULLIN JK, 1990, IEEE T SOFTWARE ENG, V16, P558, DOI 10.1109/32.52778 Reynolds P, 2003, LECT NOTES COMPUTER, V2672 Ribeiro-Neto B, 1999, P 22 ACM C RES DEV I SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton RG, 1983, INTRO MODERN INFORM Saroius S, 2002, P MULT COMP NETW 200 SCHNEIDER O., 2009, THESIS Stoica I., 2001, P ACM SIGCOMM 01 AUG Tang C, 2003, PEER TO PEER INFORM USA Department of Commerce/NIST, 1995, 1801 NIST FIPS US DE Witten IH, 1999, DATA MINING Yoneki E., 2007, P MSWIM Yu YQ, 2008, JOUNAL COMPUTERS, V31, P1 NR 37 TC 2 Z9 2 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1074-5351 J9 INT J COMMUN SYST JI Int. J. Commun. Syst. PD OCT PY 2012 VL 25 IS 10 SI SI BP 1260 EP 1280 DI 10.1002/dac.1320 PG 21 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA 014XA UT WOS:000309407900003 ER PT J AU Lin, J Khade, R Li, Y AF Lin, Jessica Khade, Rohan Li, Yuan TI K Rotation-invariant similarity in time series using bag-of-patterns representation SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Time series; Similarity search; Feature extraction; Representation; Classification; Structural similarity ID MODEL AB For more than a decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search relies on shape-based similarity matching. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We performed extensive experiments and show that our approach outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets. We further demonstrate that the representation allows rotation-invariant matching in shape datasets. C1 [Lin, Jessica; Khade, Rohan; Li, Yuan] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. RP Lin, J (reprint author), George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. EM jessica@cs.gmu.edu; rkhade@gmu.edu; ylif@gmu.edu CR Agrawal R., 1993, P 4 INT C FDN DAT OR, P69 Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Chan KP, 1999, PROC INT CONF DATA, P126 Chen L., 2004, P 30 INT C VER LARG, V30, P792 CROCHEMORE M, 1994, ALGORITHMICA, V12, P247, DOI 10.1007/BF01185427 Deng K., 1997, IEEE INT S COMP INT, V1, P246 Ding H., 2008, P VLDB ENDOWMENT, V1, P1542, DOI DOI 10.1145/1454159.1454226 Faloutsos C., 1994, SIGMOD Record, V23 Gavrilov M., 2000, P 6 ACM SIGKDD Ge X., 2000, P 6 ACM SIGKDD INT C, P81, DOI 10.1145/347090.347109 Geurts P., 2001, P 5 EUR C PRINC DAT, V2168, P115, DOI 10.1007/3-540-44794-6_10 Goldberger AL, 2000, CIRCULATION, V101, pE215 JOHNSON SC, 1967, PSYCHOMETRIKA, V32, P241, DOI 10.1007/BF02289588 Keogh E., 2004, DATA MINING MACHINE Keogh E., 2004, P 10 ACM SIGKDD INT Keogh E., 2002, P 28 INT C VER LARG Keogh E, 2002, P 8 ACM SIGKDD INT C, P102 Keogh E, 2001, SIGMOD RECORD, V30, P151 Keogh E., 2006, KNOWLEDGE INFORM SYS Keogh E., 2006, P 32 INT C VER LARG Keogh E. J., 2006, UCR TIME SERIES CLAS Ko M.H., 2005, INT SENS SENS NETW I, P283 Kriegel HP, 2008, LECT NOTES COMPUT SC, V4947, P364, DOI 10.1007/978-3-540-78568-2_27 Li M., 1997, INTRO KOLMOGOROV COM Lin J., 2004, 9 C EXT DAT TECHN ED Lin J, 2009, LECT NOTES COMPUT SC, V5566, P461, DOI 10.1007/978-3-642-02279-1_33 Lin J, 2007, DATA MIN KNOWL DISC, V15, P107, DOI 10.1007/s10618-007-0064-z Manning C.D., 2008, INTRO INFORM RETRIEV Marteau PF, 2009, IEEE T PATTERN ANAL, V31, P306, DOI 10.1109/TPAMI.2008.76 McQueen J., 1967, P 5 BERK S MATH STAT, V1, P281 Mueen A., 2011, P 17 ACM SIGKDD INT Nanopoulos A, 2001, INFORMATION PROCESSING AND TECHNOLOGY, P49 Olszewski R., 2001, THESIS CARNEGIE MELL Radovanovic M., 2010, TIME SERIES CLASSIFI, P677 Ratanamahatana Chotirat Ann, 2004, P SIAM INT C DAT MIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sart Doruk, 2010, Proceedings 2010 10th IEEE International Conference on Data Mining (ICDM 2010), DOI 10.1109/ICDM.2010.21 Vlachos M., 2002, P 18 INT C DAT ENG Wang XZ, 2006, DATA MIN KNOWL DISC, V13, P335, DOI 10.1007/s10618-005-0039-x Wei L., 2006, P IEEE INT C DAT MIN Wei L., 2006, P 12 ACM SIGKDD INT, P748, DOI 10.1145/1150402.1150498 Xing Z., 2011, P SDM 2011 Ye L, 2009, P 15 ACM SIGKDD INT NR 43 TC 10 Z9 10 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD OCT PY 2012 VL 39 IS 2 BP 287 EP 315 DI 10.1007/s10844-012-0196-5 PG 29 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 008KC UT WOS:000308954800001 ER PT J AU Liu, DR Lai, CH Chen, YT AF Liu, Duen-Ren Lai, Chin-Hui Chen, Ya-Ting TI Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE data mining; collaborative filtering ID INFORMATION-RETRIEVAL; MODEL; SYSTEMS; MANAGEMENT; DISCOVERY; WEB AB Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods. C1 [Liu, Duen-Ren; Chen, Ya-Ting] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu, Taiwan. [Lai, Chin-Hui] Chung Yuan Christian Univ, Dept Informat Management, Chungli, Taoyuan County, Taiwan. RP Lai, CH (reprint author), Chung Yuan Christian Univ, Dept Informat Management, Chungli, Taoyuan County, Taiwan. EM dliu@iim.nctu.edu.tw; chlai@cycu.edu.tw; yavicky13@gmail.com FU National Science Council of the Taiwan [NSC 96-2416-H-009-007-MY3, NSC 99-2410-H-009-034-MY3] FX This research was supported by the National Science Council of the Taiwan under Grants NSC 96-2416-H-009-007-MY3 and NSC 99-2410-H-009-034-MY3. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Aggarwal C. C., 1999, P 5 ACM SIGKDD INT C, P352, DOI 10.1145/312129.312279 Agrawal R., 1998, P ACM SIGMOD INT C M, P94, DOI 10.1145/276304.276314 Ardissono L, 2003, APPL ARTIF INTELL, V17, P687, DOI 10.1080/08839510390225050 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Basu C., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bordogna G, 2010, SOFT COMPUT, V14, P799, DOI 10.1007/s00500-009-0476-3 Breese J S, 1998, P 14 C UNC ART INT, P43 Charter K., 2000, Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences. METMBS'00 Cole C, 2011, J AM SOC INF SCI TEC, V62, P1216, DOI 10.1002/asi.21541 Feldman R., 2007, TEXT MINING HDB ADV, V34 Furner J, 2002, J AM SOC INF SCI TEC, V53, P747, DOI 10.1002/asi.10080 Garcia I, 2009, LECT NOTES COMPUT SC, V5692, P26, DOI 10.1007/978-3-642-03964-5_4 GLANCE N, 1998, P 5 DELOS WORKSH FIL, P83 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Huang Z, 2004, J AM SOC INF SCI TEC, V55, P259, DOI 10.1002/asi.10372 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jameson A., 2004, P INT WORK C ADV VIS, P48, DOI DOI 10.1145/989863.989869 JOHNSON SC, 1967, PSYCHOMETRIKA, V32, P241, DOI 10.1007/BF02289588 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kim JK, 2010, INT J INFORM MANAGE, V30, P212, DOI 10.1016/j.ijinfomgt.2009.09.006 Kim S., 2003, KNOWLEDGE PROCESS MA, V10, P260, DOI 10.1002/kpm.186 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Lai CH, 2009, J SYST SOFTWARE, V82, P2023, DOI 10.1016/j.jss.2009.06.044 Lang K, 1995, P 12 INT C MACH LEAR, P331 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu DR, 2008, J SYST SOFTWARE, V81, P1377, DOI 10.1016/j.jss.2007.10.027 Liu DR, 2008, DECIS SUPPORT SYST, V44, P524, DOI 10.1016/j.dss.2007.06.015 Liu DR, 2011, DECIS SUPPORT SYST, V50, P370, DOI 10.1016/j.dss.2010.09.004 Lorenzi F, 2008, LECT NOTES ARTIF INT, V5249, P103, DOI 10.1007/978-3-540-88190-2_16 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P1791, DOI 10.1002/cpe.1319 Masthoff J, 2008, STUD COMPUT INTELL, V104, P157 McCarthy JF, 1998, P 1998 ACM C COMP SU, P363, DOI DOI 10.1145/289444.289511 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 O'Connor M, 2001, P 7 EUR C COMP SUPP, P199 Oguducu SG, 2006, DISTRIB PARALLEL DAT, V19, P5, DOI [10.1007/s10619-006-6284-1, 10.1007/s10619-006-6248-1] Pazzani M.J., 2007, LNCS, V4321, P325, DOI DOI 10.1007/978-3-540-72079-9_10 Rucker J, 1997, COMMUN ACM, V40, P73, DOI 10.1145/245108.245125 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B.M., 2001, P WWW 2010, P285 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Shin C., 2009, IEEE T CONSUM ELECTR, V55, P928 Steinbach M, 2000, KDD WORKSH TEXT MIN, V400, P525 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Pasi G., 2005, Proceedings. Sixteenth International Workshop on Database and Expert Systems Applications Wu IC, 2009, J AM SOC INF SCI TEC, V60, P2430, DOI 10.1002/asi.21201 Xi J, 2009, P INT C MAN SERV SCI, P1, DOI [10.1109/ICMSS.2009.5302121, DOI 10.1109/ICMSS.2009.5302121] Yu ZW, 2006, USER MODEL USER-ADAP, V16, P63, DOI 10.1007/s11257-006-9005-6 Zhuge H, 2006, COMMUN ACM, V49, P101, DOI 10.1145/1125944.1125948 Zhuge H, 2002, EXPERT SYST APPL, V23, P23, DOI 10.1016/S0957-4174(02)00024-6 Zhuge H, 2006, DECIS SUPPORT SYST, V42, P571, DOI 10.1016/j.dss.2005.03.007 NR 56 TC 6 Z9 6 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD OCT PY 2012 VL 63 IS 10 BP 2100 EP 2117 DI 10.1002/asi.22705 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 007KY UT WOS:000308888400016 ER PT J AU Guo, J Chen, G Xu, WR AF Guo, Jun Chen, Guang Xu, Weiran TI Word Activation Forces: Distinctive Statistics Revealing Word Associations SO WIRELESS PERSONAL COMMUNICATIONS LA English DT Article DE Word activation; Universal gravitation; Similarity of words; Text statistics ID SPREADING ACTIVATION; BRAIN; REPRESENTATIONS; CONTEXT; NOUNS AB Word activation has caused ample investigations in many different scientific areas. Various theories have long been debated in predicting and interpreting the fundamental language phenomenon. From a perspective of mechanics, this study considers the word activations as imaginary forces and quantifies their amount by adapting the formula of the universal gravitation to the corresponding imaginary masses and distance that are estimated via the statistics of language experience. In large scale experiments, we found that the word activation forces not only straightforwardly predict various kinds of word activations, but also lead to a simple and human-comparably accurate measure to identify word closest associates including synonyms, near-synonyms, antonyms and similar functional words. The plausibility of identified closest associates with over 10,000 popular English words is highly inspiring. C1 [Guo, Jun; Chen, Guang; Xu, Weiran] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Pattern Recognit & Intelligent Syst Lab, Beijing 100088, Peoples R China. RP Guo, J (reprint author), Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Pattern Recognit & Intelligent Syst Lab, Beijing 100088, Peoples R China. EM guojun@bupt.edu.cn; chenguang@bupt.edu.cn; xuweiran@bupt.edu.cn FU Chinese 111 program of 'Advanced Intelligence and Network Service' [B08004]; Ministry of Science and Technology of China [2011ZX03002-005-01] FX This work was supported by Chinese 111 program of 'Advanced Intelligence and Network Service' under grant no. B08004 and a key project of the Ministry of Science and Technology of China under grant no. 2011ZX03002-005-01. CR Aziz-Zadeh L, 2006, CURR BIOL, V16, P1818, DOI 10.1016/j.cub.2006.07.060 BALOTA DA, 1986, J EXP PSYCHOL LEARN, V12, P336, DOI 10.1037//0278-7393.12.3.336 Boulenger V, 2006, J COGNITIVE NEUROSCI, V18, P1607, DOI 10.1162/jocn.2006.18.10.1607 Livesay K, 1998, PROCEEDINGS OF THE TWENTIETH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P609 Church K.W., 1990, J COMPUTATIONAL LING, V16, P22 Crinion J, 2006, SCIENCE, V312, P1537, DOI 10.1126/science.1127761 Erk K., 2008, P C EMP METH NAT LAN, P897, DOI 10.3115/1613715.1613831 Gennari SP, 2007, NEUROIMAGE, V35, P1278, DOI 10.1016/j.neuroimage.2007.01.015 Haxby JV, 2001, SCIENCE, V293, P2425, DOI 10.1126/science.1063736 Henson R, 2000, SCIENCE, V287, P1269, DOI 10.1126/science.287.5456.1269 Hinrich S., 1998, J COMPUTATIONAL LING, V24, P97 Hoeks JCJ, 2004, COGNITIVE BRAIN RES, V19, P59, DOI 10.1016/j.cogbrainres.2003.10.022 Jun G., 2011, NATURE ONLINE MAGAZI KUTAS M, 1984, NATURE, V307, P161, DOI 10.1038/307161a0 Lin D., 1998, P 17 INT C COMP LING, P768 Livesay K., 1997, P 19 ANN C COGN SCI, P436 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 McCarthy D., 2004, P 42 ANN M ASS COMP, P280 MCKOON G, 1992, J EXP PSYCHOL LEARN, V18, P1155, DOI 10.1037/0278-7393.18.6.1155 Mitchell T. M., 2008, SCIENCE, V320 Mitchell TM, 2008, SCIENCE, V320, P1191, DOI 10.1126/science.1152876 Neininger B, 2003, NEUROPSYCHOLOGIA, V41, P53, DOI 10.1016/S0028-3932(02)00126-4 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Polyn SM, 2005, SCIENCE, V310, P1963, DOI 10.1126/science.1117645 Pulvermuller F, 1996, EUR J NEUROSCI, V8, P937, DOI 10.1111/j.1460-9568.1996.tb01580.x RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seidenberg MS, 1997, SCIENCE, V275, P1599, DOI 10.1126/science.275.5306.1599 Seidenberg MS, 2002, SCIENCE, V298, P553, DOI 10.1126/science.1078094 Will L., 2001, P 23 ANN C COGN SCI, P576 Will L., 2000, P 22 ANN C COGN SCI, P675 NR 31 TC 4 Z9 4 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0929-6212 J9 WIRELESS PERS COMMUN JI Wirel. Pers. Commun. PD OCT PY 2012 VL 66 IS 3 SI SI BP 511 EP 521 DI 10.1007/s11277-012-0740-1 PG 11 WC Telecommunications SC Telecommunications GA 000AR UT WOS:000308358800003 ER PT J AU Felizardo, KR Andery, GF Paulovich, FV Minghim, R Maldonado, JC AF Felizardo, Katia R. Andery, Gabriel F. Paulovich, Fernando V. Minghim, Rosane Maldonado, Jose C. TI A visual analysis approach to validate the selection review of primary studies in systematic reviews SO INFORMATION AND SOFTWARE TECHNOLOGY LA English DT Review DE Systematic Literature Review (SLR); Visual Text Mining (VTM); Information visualization; Content document map; Citation document map ID INFORMATION VISUALIZATION; ABSTRACTS AB Context: Systematic Literature Reviews (SLRs) are an important component to identify and aggregate research evidence from different empirical studies. Despite its relevance, most of the process is conducted manually, implying additional effort when the Selection Review task is performed and leading to reading all studies under analysis more than once. Objective: We propose an approach based on Visual Text Mining (VTM) techniques to assist the Selection Review task in SLR. It is implemented into a VTM tool (Revis), which is freely available for use. Method: We have selected and implemented appropriate visualization techniques into our approach and validated and demonstrated its usefulness in performing real SLRs. Results: The results have shown that employment of VTM techniques can successfully assist in the Selection Review task, speeding up the entire SLR process in comparison to I he conventional approach. Conclusions: VTM techniques are valuable tools to be used in the context of selecting studies in the SLR process, prone to speed up some stages of SIRS. (C) 2012 Elsevier B.V. All rights reserved. C1 [Felizardo, Katia R.; Andery, Gabriel F.; Paulovich, Fernando V.; Minghim, Rosane; Maldonado, Jose C.] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13560970 Sao Carlos, SP, Brazil. RP Felizardo, KR (reprint author), Univ Sao Paulo, Inst Ciencias Matemat & Computacao, POB 668, BR-13560970 Sao Carlos, SP, Brazil. EM katiarf@icmc.usp.br; gfandery@icmc.usp.br; paulovich@icmc.usp.br; rminghim@icmc.usp.br; jcmaldon@icmc.usp.br RI Paulovich, Fernando /G-1329-2010; Minghim, Rosane/E-5703-2011; Isec, Inct/I-2409-2013 OI Paulovich, Fernando /0000-0002-2316-760X; CR Ananiadou S, 2009, SOC SCI COMPUT REV, V27, P509, DOI 10.1177/0894439309332293 Andery G.F., 2009, 2 INT WORKSH WEB TEX, P1, DOI 10.1109/AERO.2009.4839571 Andrews K., 2002, Information Visualization, V1, DOI 10.1057/palgrave.ivs.9500023 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Brereton P, 2007, J SYST SOFTWARE, V80, P571, DOI 10.1016/j.jss.2006.07.009 Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309 Cohen KB, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-492 Cruzes D.S., 2010, 4 ACM IEEE INT S EMP, P1 da Silva F.Q.B., 2010, 32 INT C SOFTW ICSE, P1 Dieste O., 2007, P 1 INT S EMP SOFTW, P215 Dybs T., 2008, 2 ACM IEEE INT S EMP, P178 Dybs T., 2007, 1 INT S EMP SOFTW EN, P225 Eades P., 1984, C NUMERANTIUM, V42, P149 Ellis G, 2007, IEEE T VIS COMPUT GR, V13, P1216, DOI 10.1109/TVCG.2007.70535 Emam K.E., 2009, J MED INTERNET RES, V11, P1 Felizardo K.R., 2011, J SOFTWARE IN PRESS Felizardo K.R., 2010, 14 INT C EV ASS SOFT Garcia R.E., 2004, 10 INT C DISTR MULT, P225 GENNARI JH, 1989, ARTIF INTELL, V40, P11, DOI 10.1016/0004-3702(89)90046-5 Jorgensen M, 2007, IEEE T SOFTWARE ENG, V33, P33, DOI 10.1109/TSE.2007.256943 Keim DA, 2002, IEEE T VIS COMPUT GR, V8, P1, DOI 10.1109/2945.981847 Kitchenham B, 2009, INFORM SOFTWARE TECH, V51, P7, DOI 10.1016/j.infsof.2008.09.009 Kitchenham B., 2004, PROCEDURES PERFORMIN Kitchenham B, 2010, INFORM SOFTWARE TECH, V52, P792, DOI 10.1016/j.infsof.2010.03.006 Kitchenham B., 2007, 2007001 EBSE KEEL U Kitchenham BA, 2008, IET SOFTW, V2, P37, DOI 10.1049/iet-sen:20070044 Lopes AA, 2007, COMPUT GRAPH-UK, V31, P316, DOI 10.1016/j.cag.2007.01.023 MacQueen JB, 1967, 5 BERK S MATH STAT P, P281 Malheiros V., 2007, 1 INT S EMP SOFTW EN, P245, DOI 10.1109/ESEM.2007.21 Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480 Oliveira M., 2003, IEEE T VISUALIZATION, V9, P378 Pacific Northwest National Laboratory (PNL), 2008, IN SPIRE VIS DOC AN Paulovich FV, 2008, IEEE T VIS COMPUT GR, V14, P1229, DOI 10.1109/TVCG.2008.138 Riaz M., 2010, 14 INT C EV ASS SOFT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sjoberg DIK, 2007, FOSE 2007: FUTURE OF SOFTWARE ENGINEERING, P358, DOI 10.1109/FOSE.2007.30 Skoglund M., 2009, 13 INT C EV ASS SOFT, P10 Tan A., 1999, P PAC AS C KNOWL DIS, P65 Tan P., 2006, INTRO DATA MINING NR 39 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-5849 J9 INFORM SOFTWARE TECH JI Inf. Softw. Technol. PD OCT PY 2012 VL 54 IS 10 BP 1079 EP 1091 DI 10.1016/j.infsof.2012.04.003 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 983QK UT WOS:000307133600003 ER PT J AU Dragoni, M Pereira, CD Tettamanzi, AGB AF Dragoni, Mauro Pereira, Celia da Costa Tettamanzi, Andrea G. B. TI A conceptual representation of documents and queries for information retrieval systems by using light ontologies SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Conceptual representation; Lexica; Ontologies; Intelligent information retrieval ID VECTOR-SPACE MODEL; EXPANSION AB This article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-Hoc collections demonstrate the effectiveness of the proposed approach. (C) 2012 Elsevier Ltd. All rights reserved. C1 [Dragoni, Mauro] Fdn Bruno Kessler, I-38123 Trento, Italy. [Pereira, Celia da Costa] Univ Nice Sophia Antipolis, CNRS, UMR 6070, Lab I3S, F-06903 Sophia Antipolis, France. [Tettamanzi, Andrea G. B.] Univ Milan, DTI, I-26013 Crema, CR, Italy. RP Dragoni, M (reprint author), Fdn Bruno Kessler, Via Sommar 18 Povo, I-38123 Trento, Italy. EM dragoni@fbk.eu; celia.pereira@unice.fr; andrea.tettamanzi@unimi.it RI Tettamanzi, Andrea G. B./ OI Tettamanzi, Andrea G. B./0000-0002-8877-4654 CR Aggarwal C., 2001, IEEE INT C DAT MIN, P3 Andreasen T, 2009, LECT NOTES ARTIF INT, V5822, P323, DOI 10.1007/978-3-642-04957-6_28 Arguello J., 2008, NIST SPECIAL PUBLICA Azzini A., 2008, P HIS 2008 BARC SPAI, P332 Baziz M., 2007, RIAO Baziz M, 2005, LECT NOTES COMPUT SC, V3507, P171 Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 Billerbeck B, 2004, LECT NOTES COMPUT SC, V3246, P30 Boughanem M., 1998, TREC, P355 Bulskov H., 2009, IFSA EUSFLAT C, P1618 Cai D., 2001, CIKM, P419 Calegari S., 2007, URSW CEUR WORKSH P, V327 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Cronen-Townsend S., 2004, CIKM 2004, P236 Cruse A., 1986, LEXICAL SEMANTICS da Costa Pereira C., 2006, ONTOLOGY BASED METHO, P211 Diamantini C., 1999, DEXA WORKSH, P193 Dridi O., 2008, P IEEE INT C RES CHA, P421 Dumais S. T., 1988, CHI 88, P281, DOI DOI 10.1145/57167.57214 Efthimiadis E., 1996, ANNU REV INFORM SCI, V31 Endres-Niggemeyer B., 1998, SUMMARIZING INFORM Evens M., 1986, RELATIONAL MODELS LE FELLBAUM C, 1990, PSYCHOL REV, V97, P565, DOI 10.1037//0033-295X.97.4.565 Fellbaum C, 1998, WORDNET ELECT LEXICA Gangemi A, 2002, LECT NOTES ARTIF INT, V2473, P166 Gao LQ, 2006, LECT NOTES COMPUT SC, V4182, P158 Gave P., 1973, WEBSTERS NEW DICT SY Gomez-Perez A., 2006, WWW 06 P 15 INT C WO, P1007 Gonzalo J., 1998, CMPLG9808002 CORR Green R., 2002, SEMANTICS RELATIONSH Grootjen FA, 2006, DATA KNOWL ENG, V56, P174, DOI 10.1016/j.datak.2005.03.006 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Guan J., 2005, SKG, P63 Guarino N, 1995, VERY LARGE KNOWLEDGE, P25 Hattori T, 2006, LECT NOTES COMPUT SC, V3841, P1197 Holub M., 2003, ISICT ACM INT C P SE, V49, P310 Kayed A., 2002, AUSTR DAT C CRPIT, V5 Kilgarriff A, 1997, COMPUT HUMANITIES, V31, P91, DOI 10.1023/A:1000583911091 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 Lakoff George, 1987, WOMEN FIRE DANGEROUS Lau RYK, 2009, LECT NOTES ARTIF INT, V5589, P239, DOI 10.1007/978-3-642-02962-2_30 Levow G.-A., 2003, IRAL-INT REV APPL LI, P77 Li Y, 2004, LECT NOTES ARTIF INT, V3066, P699 Li ZJ, 2007, AI EDAM, V21, P137, DOI 10.1017/S0890060407070199 Lyons John, 1977, SEMANTICS Maisonnasse L, 2007, LECT NOTES COMPUT SC, V4592, P240 Mandala R, 2000, INFORM PROCESS MANAG, V36, P361, DOI 10.1016/S0306-4573(99)00068-0 Manjula D, 2003, LECT NOTES COMPUT SC, V2690, P685 Navigli R., 2009, ACM COMPUTING SURVEY, V41 Peters C., 2008, LECT NOTES COMPUTER, V5152 Qiu Y., 1993, SIGIR, P160 Ruhl Charles, 1989, MONOSEMY STUDY LINGU Salton G., 1975, DYNAMIC INFORM LIB P Salton G., 1968, AUTOMATIC INFORM ORG Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schtitze H., 1995, P 4 ANN S DOC AN INF, P161 Spink A, 2006, INFORM PROCESS MANAG, V42, P1379, DOI 10.1016/j.ipm.2005.11.001 Staab S., 2004, HDB ONTOLOGIES INT H Stojanovic N, 2004, PROC INT C TOOLS ART, P450 Stojanovic N., 2005, WEB INTELLIGENCE, P359 Stojanovic N, 2004, LECT NOTES ARTIF INT, V3336, P414 Stokoe Christopher, 2003, SIGIR, P159 Talmy Leonard, 1985, LANGUAGE TYPOLOGY SY, P57 Tao T., 2006, HLT NAACL Tomassen SL, 2006, LECT NOTES COMPUT SC, V4278, P1460 Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455 Varga P, 2003, LECT NOTES ARTIF INT, V2718, P359 Voorhees E., 1997, OV 5 TEXT RETR C TRE, P1 Voorhees E. M., 1993, SIGIR Forum Wan X., 2007, AAAI, P931 Wang J., 2005, LECT NOTES COMPUTER, V4022, P800 Wu F., 2007, IFIP, V254, P293 Xu J., 1996, SIGIR 96, P4 Zhai J., 2008, INT FEDERATION INFOR, V288, P58, DOI 10.1007/978-0-387-87685-6_9 Zhai J., 2008, P INT S INF PROC, P173 Zhai Jun, 2008, J SOFTWARE, V3, P20 NR 77 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD SEP 15 PY 2012 VL 39 IS 12 BP 10376 EP 10388 DI 10.1016/j.eswa.2012.01.188 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 966UQ UT WOS:000305863300007 ER PT J AU Hornik, K Feinerer, I Kober, M Buchta, C AF Hornik, Kurt Feinerer, Ingo Kober, Martin Buchta, Christian TI Spherical k-Means Clustering SO JOURNAL OF STATISTICAL SOFTWARE LA English DT Article DE spherical; clustering; text mining; cosine dissimilarity; R ID ALGORITHM AB Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype-based partitioning of term weight representations of the documents. This paper presents the theory underlying the standard spherical k-means problem and suitable extensions, and introduces the R extension package skmeans which provides a computational environment for spherical k-means clustering featuring several solvers: a fixed-point and genetic algorithm, and interfaces to two external solvers (CLUTO and Gmeans). Performance of these solvers is investigated by means of a large scale benchmark experiment. C1 [Hornik, Kurt; Kober, Martin; Buchta, Christian] WU Wirtschaftsuniv Wien, Inst Stat & Math, A-1090 Vienna, Austria. [Feinerer, Ingo] Vienna Univ Technol, Inst Informat Syst, A-1040 Vienna, Austria. RP Hornik, K (reprint author), WU Wirtschaftsuniv Wien, Inst Stat & Math, Augasse 2-6, A-1090 Vienna, Austria. EM Kurt.Hornik@R-project.org; Ingo.Feinerer@tuwien.ac.at; Martin.Kober@wu.ac.at; Christian.Buchta@wu.ac.at CR Banerjee A, 2005, J MACH LEARN RES, V6, P1345 Bates D, 2011, MATRIX SPARSE DENSE Belacel N, 2002, PATTERN RECOGN, V35, P2193, DOI 10.1016/S0031-3203(01)00193-5 Bezdek J. C., 1981, PATTERN RECOGNITION Blei D., 2009, TEXT MINING CLASSIFI, P71 D'haeseleer P, 2005, NAT BIOTECHNOL, V23, P1499, DOI 10.1038/nbt1205-1499 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dhillon IS, 2001, MASSIVE COMP, V2, P357 Dhillon IS, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P131, DOI 10.1109/ICDM.2002.1183895 Feinerer I, 2008, J STAT SOFTW, V25, P1 Frank A., 2010, UCI MACHINE LEARNING Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Guan Y, 2006, THESIS U TEXAS AUSTI Hahsler M, 2011, J COMPUT GRAPH STAT, V20, P335, DOI 10.1198/jcgs.2010.09139 Han E.-H., 1998, AGENTS 98, P408 Hersh W., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Hornik K, 2005, J STAT SOFTW, V14, P1 Hornik K, 2011, SLAM SPARSE LIGHTWEI Karatzoglou A, 2010, COMPUT STAT DATA AN, V54, P290, DOI 10.1016/j.csda.2009.09.023 Karypis G., 2003, 02017 U MINN DEP COM Krishna K, 1999, IEEE T SYST MAN CY B, V29, P433, DOI 10.1109/3477.764879 Leisch F, 2006, COMPUT STAT DATA AN, V51, P526, DOI 10.1016/j.csda.2005.10.006 Lewis D., 1997, REUTERS 21578 TEXT C Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Maitra R, 2010, J COMPUT GRAPH STAT, V19, P377, DOI 10.1198/jcgs.2009.08155 Maitra R, 2012, KMNDIRS K MEAN DIREC Manning C.D., 2008, INTRO INFORM RETRIEV Mitchell T, 1999, 20 NEWSGROUPS R Development Core Team, 2012, R LANG ENV STAT COMP, V2.15th SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tan PN, 2004, INFORM SYST, V29, P293, DOI 10.1016/S0306-4379(03)00072-3 Tunali V, 2010, 1 INT S COMP SCI ENG Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 33 TC 9 Z9 9 PU JOURNAL STATISTICAL SOFTWARE PI LOS ANGELES PA UCLA DEPT STATISTICS, 8130 MATH SCIENCES BLDG, BOX 951554, LOS ANGELES, CA 90095-1554 USA SN 1548-7660 J9 J STAT SOFTW JI J. Stat. Softw. PD SEP PY 2012 VL 50 IS 10 BP 1 EP 22 PG 22 WC Computer Science, Interdisciplinary Applications; Statistics & Probability SC Computer Science; Mathematics GA 007TA UT WOS:000308909900001 ER PT J AU Reitsma, R Marshall, B Chart, T AF Reitsma, Rene Marshall, Byron Chart, Trevor TI Can intermediary-based science standards crosswalking work? Some evidence from mining the standard alignment tool (SAT) SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE computerized cataloging; automatic classification; data mining ID OF-THE-LITERATURE; INFORMATION-SCIENCE; FRAMEWORK; RELEVANCE; NETWORKS; BEHAVIOR; THINKING; NOTION; MODEL AB We explore the feasibility of intermediary-based crosswalking and alignment of K-12 science education standards. With the increasing availability of K-12 science, technology, engineering, and mathematics (STEM) digital library content, alignment of that content with educational standards is a significant and continuous challenge. Whereas direct, one-to-one alignment of standards is preferable but currently unsustainable in its resource demands, less resource-intensive intermediary-based alignment offers an interesting alternative. But will it work? We present the results from an experiment in which the machine-based Standard Alignment Tool (SAT)incorporated in the National Science Digital Library (NSDL)was used to collect over half a million direct alignments between standards from different standard-authoring bodies. These were then used to compute intermediary-based alignments derived from the well-known AAAS Project 2061 Benchmarks and NSES standards. The results show strong variation among authoring bodies in their success at crosswalking, with the best results for those who modeled their standards on the intermediaries. The results furthermore show a strong inverse relationship between recall and precision when both intermediates were involved in the crosswalking. C1 [Reitsma, Rene; Marshall, Byron] Oregon State Univ, Coll Business, Corvallis, OR 97331 USA. [Chart, Trevor] Oregon State Univ, Coll Sci, Corvallis, OR 97331 USA. RP Reitsma, R (reprint author), Oregon State Univ, Coll Business, Corvallis, OR 97331 USA. EM reitsmar@bus.oregonstate.edu; byron.marshall@bus.oregonstate.edu; chartt@onid.oregonstate.edu FU National Science Foundation [0532709]; Center for Natural Language Processing at Syracuse University; JESCo. FX This work was supported in part by National Science Foundation grant #0532709. We also thank the Center for Natural Language Processing at Syracuse University for its support of the Standard Alignment Tool (SAT) and JES&Co. for its support of the Achievement Standards Network (ASN). CR American Association for the Advancement of Science, 1989, SCI ALL AM PROJ 2061 American Association for the Advancement of Science, 2001, ATL SCI LIT American Association for the Advancement of Science, 1989, SCI ALL AM SUMM PROJ Anderson N., 2010, WASHINGTON POST Arentze TA, 2005, TRANSPORT RES A-POL, V39, P125, DOI 10.1016/j.tra.2004.08.002 Blumenthal D., 2003, EVALUATION APPROACHE Bologna Secretariat, 2010, WELC WEBS EUR HIGH E Breen C., 2008, REV NETTREKKER Cai XY, 2011, J AM SOC INF SCI TEC, V62, P2067, DOI 10.1002/asi.21593 Cunningham C. M., 2007, P ASEE ANN C HON HI Devaul H, 2011, J AM SOC INF SCI TEC, V62, P395, DOI 10.1002/asi.21437 Diekema AR, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P223, DOI 10.1145/1065385.1065436 Diekema A. R., 2009, J LIB METADATA, V9, P239 Diekema AR, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P398, DOI 10.1145/1255175.1255253 Doan A, 2005, AI MAG, V26, P83 Felix K., 2004, SPOTLIGHT Gateway, 2007, NSDL ASN ACH STAND N International Technology and Engineering Educators Association, 2011, OPP TECHN ENG SCI FR Jay M., 2003, DEATH TAXES CORRELAT Johnson A., 2003, SUMMATIVE EVAL UNPUB Kendall J. S., 1997, CONTENT KNOWLEDGE CO Kubilinskiene S., 2009, INFORM ED, V9, P217 Lachapelle C. P., 2007, P ASEE ANN C HON HI Lin S., 2003, SIGKDD EXPLORATIONS, V5, P173, DOI 10.1145/980972.981000 Marshall B., 2007, P JOINT C DIG LIB JC Marshall B, 2008, J AM SOC INF SCI TEC, V59, P2099, DOI 10.1002/asi.20924 Mason B, 2006, COMPUT SCI ENG, V8, P62, DOI 10.1109/MCSE.2006.65 McAnear A., 2007, LEARNING LEADING TEC, V5 Melnik S, 2002, PROC INT CONF DATA, P117, DOI 10.1109/ICDE.2002.994702 National Research Council, 2011, FRAM K 12 SCI ED PRA National Research Council, 1996, NAT SCI ED STAND NGA-CBP: National Governors Association Center for Best Practices, 2011, COMM COR STAT STAND Reitsma R, 2010, INFORM PROCESS MANAG, V46, P362, DOI 10.1016/j.ipm.2009.08.006 Reitsma R., 2011, INT J DIGITAL LIB, V11, P209 Richards L. M., 2010, REFERENCE REV, V24, P38 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P2126, DOI 10.1002/asi.20681 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P1915, DOI 10.1002/asi.20682 Saylor J. M., 2006, Science & Technology Libraries, V26, DOI 10.1300/J122v26n03_05 Schamber L., 1994, INFORM PROCESSING MA, V26, P3 Sullivan J. F., 2005, P ASEE ANN C PORTL O Sumner T., 2005, INT J DIGITAL LIB, V5, P18 Sutton S. A., 2008, P 2008 INT C DUBL CO, P69 Teacher's Domain, 2011, STAND CORR LEX TERMS TIMMERMANS HJP, 1984, ENVIRON PLANN A, V16, P377, DOI 10.1068/a160377 TRILL J. C., 1999, SPATIAL MULTICRITERI Wallis C., 2006, TIME 0207 White HD, 2003, J AM SOC INF SCI TEC, V54, P423, DOI 10.1002/asi.10228 Xu JJ, 2004, DECIS SUPPORT SYST, V38, P473, DOI 10.1016/S0167-9236(03)00117-9 Yilmazel O., 2007, IEEE 23 INT C DAT EN, P755 Yilmazel O., 2007, P 40 HAW INT C SYST, DOI [10.1109/HICSS.2007.517, DOI 10.1109/HICSS.2007.517] Zia L., 2002, D LIB MAGAZINE, V8 Zia L., 2005, LECT NOTES COMPUTER, V3334, P45 NR 54 TC 0 Z9 0 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD SEP PY 2012 VL 63 IS 9 BP 1843 EP 1858 DI 10.1002/asi.22712 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 991VE UT WOS:000307730000011 ER PT J AU Balachandran, V Deepak, P Khemani, D AF Balachandran, Vipin Deepak, P. Khemani, Deepak TI Interpretable and reconfigurable clustering of document datasets by deriving word-based rules SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Data clustering; Text clustering; Interpretability AB Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein. C1 [Deepak, P.] IBM Res Corp, Informat Management Grp, Bangalore, Karnataka, India. [Balachandran, Vipin] VMware, R&D, Storage Div, Bangalore, Karnataka, India. [Khemani, Deepak] IIT Madras, Madras, Tamil Nadu, India. RP Deepak, P (reprint author), IBM Res Corp, Informat Management Grp, Bangalore, Karnataka, India. EM vbala@vmware.com; deepak.s.p@in.ibm.com; khemani@iitm.ac.in RI P, Deepak/B-7387-2014 OI P, Deepak/0000-0002-1336-2356 CR Agrawal R., 1998, SIGMOD ALSBERG BK, 1995, J COMPUT CHEM, V16, P414, DOI 10.1002/jcc.540160404 Balachandran VPD, 2009, CIKM Basak J, 2005, IEEE T KNOWL DATA EN Basu S., 2004, SIAM INT C DAT MIN Beil F., 2002, KDD 02, P436, DOI DOI 10.1145/775047.775110 Bilenko M., 2004, ICML Boley D, 1998, 20041068 USGS Chen KK, 2009, KNOWL INF SYST, V20, P1, DOI 10.1007/s10115-008-0159-x Cho YH, 2002, EXPERT SYST APPL, V23, P329, DOI 10.1016/S0957-4174(02)00052-0 Cohen W. W., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Cohens W, 1995, P 12 INT C MACH LEAR, P115 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Deepak P, 2006, INT WORKSH FEAT SEL Dhillon IS, 2003, P 9 ACM SIGKDD INT C, P89 Farahat AK, 2011, KNOWL INF SYST, V26 Fisher DH, 1987, MACH LEARN, P139 Gao B, 2006, SDM Greene D, 2005, LECT NOTES ARTIF INT, V3721, P486 Halvey M, 2007, WORLD WID WEB C Hamerly G., 2003, NEURAL INFORM PROCES, P2003 Hotho A., 2003, ICDM, P541 Ishibuchi H, 2007, INT J APPROX REASON Jain A. K., 1999, ACM COMPUT SURV Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jing LP, 2010, KNOWL INF SYST, V25, P35, DOI 10.1007/s10115-009-0256-5 Johnson S. C., 1967, PSYCHOMETRIKA Krishnapuram R, 2003, LECT NOTES ARTIF INT, V2715, P52 Lakshmanan L, 2002, VLDB Liu B, 2000, CIKM Liu T, 2003, INT C MACH LEARN ICM MacQueen JB, 1967, 5 S MATH STAT PROB Mamdani EH, 1975, INT J MAN MACH STUD, P1 Mandhani B, 2003, WWW 03, P511 Mendelzon AO, 2003, PODS Michalski R, 1983, MACHINE LEARNING ART Nauc D.D., 2002, P IEEE INT C FUZZ SY, P196 Oikonomakou N, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P921, DOI 10.1007/0-387-25465-X_43 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, COMP DOCUMENT CLUSTE Steinbach M, 2003, NEW VISTAS STAT PHYS Tagaki S, 1983, IFAC S FUZZ INF KNOW Tan P. N., 2005, INTRO DATA MINING Veronis J, 2002, COMPUT LINGUIST, V28, P90, DOI 10.1162/coli.2000.28.1.90 Wei FR, 2010, KNOWL INF SYST, V22, P245, DOI 10.1007/s10115-009-0194-2 Weiss S. M., 2000, INT C MACH LEARN, P1135 Zamir O., 1997, KDD 97, P287 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zeng YJ, 2002, CSB2002: IEEE COMPUTER SOCIETY BIOINFORMATICS CONFERENCE, P276 Zhao Y, 2001, TR Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 NR 53 TC 5 Z9 5 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD SEP PY 2012 VL 32 IS 3 BP 475 EP 503 DI 10.1007/s10115-011-0446-9 PG 29 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 986HA UT WOS:000307331500001 ER PT J AU El Sayad, I Martinet, J Urruty, T Djeraba, C AF El Sayad, Ismail Martinet, Jean Urruty, Thierry Djeraba, Chabane TI Toward a higher-level visual representation for content-based image retrieval SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE SURF; Content-based image retrieval; Visual words; Visual phrases; Gaussian mixture model; Spatial weighting; pLSA ID FEATURES; RECOGNITION; PHRASES; WORDS AB Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques. C1 [El Sayad, Ismail; Martinet, Jean; Urruty, Thierry; Djeraba, Chabane] Univ Lille 1, LIFL CNRS UMR 8022, Lille, France. RP El Sayad, I (reprint author), Univ Lille 1, LIFL CNRS UMR 8022, Lille, France. EM ismail.elsayad@lifl.fr; jean.martinet@lifl.fr; thierry.urruty@lifl.fr; chabane.djeraba@lifl.fr CR Agrawal R., 1993, SIGMOD, P207 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014 Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 Bilmes J, 1997, TR97021ICSI CANNY J, 1986, IEEE T PATTERN ANAL, V8, P679 Carson C., 1999, LECT NOTES COMPUTER, V1614, P509 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Fei-Fei L, 2004, COMPUTER VISION IMAG, V106, P59, DOI DOI 10.1016/J.CVIU.2005.09.012 Haddad M, 2000, 4 EUR C PRINC PRACT Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hoing N.V., 2010, PATTERN RECOGN, V43, P3013 Jegou H., 2010, IEEE C COMP VIS PATT Jeon J, 2004, P INT C IM VID RETR, P24 Jing F, 2003, P INT C IM VID RETR, P206 Jurie F, 2005, IEEE I CONF COMP VIS, P604 Lienhart R., 2009, CIVR 09 P ACM INT C, P1, DOI DOI 10.1145/1646396.1646408 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Martinet J, 2007, CONT BAS MULT IND 20, P344, DOI [10.1109/CBMI.2007.385432, DOI 10.1109/CBMI.2007.385432] Morand C, 2010, SIGNAL PROCESS-IMAGE, V25, P450, DOI 10.1016/j.image.2010.04.004 Nister D, 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264 Quelhas P, 2005, IEEE I CONF COMP VIS, P883 Quelhas P, 2007, IEEE T PATTERN ANAL, V29, P1575, DOI 10.1109/TPAMI.2007.1155 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sivic J., 2003, ICCV, P1470, DOI 10.1109/ICCV.2003.1238663 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Tirilly P., 2008, CIVR 08, P249 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Viola P.A., 2001, CVPR, V1, P511, DOI DOI 10.1109/CVPR.2001.990517 Willamowski J., 2004, ICPR WORKSH LEARN AD Witten I. H., 1999, MANAGING GIGABYTES C Yang J., 2007, MIR 2007, P197 Yang Y., 1997, ICML 97, P412 Yuan J, 2007, PROC CVPR IEEE, P1930, DOI 10.1109/CVPR.2007.383222 Zhang J, 2007, INT J COMPUT VISION, V73, P213, DOI 10.1007/s11263-006-9794-4 Zheng QF, 2008, ACM T MULTIM COMPUT, V5, DOI 10.1145/1404880.1404887 Zhou X, 2008, ACM MULTIMEDIA, P229 NR 39 TC 4 Z9 4 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 EI 1573-7721 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD SEP PY 2012 VL 60 IS 2 BP 455 EP 482 DI 10.1007/s11042-010-0596-x PG 28 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 958YH UT WOS:000305276400012 ER PT J AU Lee, C Huang, CH AF Lee, Chih Huang, Chun-Hsi TI Searching for transcription factor binding sites in vector spaces SO BMC BIOINFORMATICS LA English DT Article ID UCSC GENOME BROWSER; MOTIF DISCOVERY; WEIGHT MATRICES; SEQUENCES; PREDICTION; MODEL; ALGORITHMS; DATABASE; TOOL AB Background: Computational approaches to transcription factor binding site identification have been actively researched in the past decade. Learning from known binding sites, new binding sites of a transcription factor in unannotated sequences can be identified. A number of search methods have been introduced over the years. However, one can rarely find one single method that performs the best on all the transcription factors. Instead, to identify the best method for a particular transcription factor, one usually has to compare a handful of methods. Hence, it is highly desirable for a method to perform automatic optimization for individual transcription factors. Results: We proposed to search for transcription factor binding sites in vector spaces. This framework allows us to identify the best method for each individual transcription factor. We further introduced two novel methods, the negative-to-positive vector (NPV) and optimal discriminating vector (ODV) methods, to construct query vectors to search for binding sites in vector spaces. Extensive cross-validation experiments showed that the proposed methods significantly outperformed the ungapped likelihood under positional background method, a state-of-the-art method, and the widely-used position-specific scoring matrix method. We further demonstrated that motif subtypes of a TF can be readily identified in this framework and two variants called the kNPV and kODV methods benefited significantly from motif subtype identification. Finally, independent validation on ChIP-seq data showed that the ODV and NPV methods significantly outperformed the other compared methods. Conclusions: We conclude that the proposed framework is highly flexible. It enables the two novel methods to automatically identify a TF-specific subspace to search for binding sites. Implementations are available as source code at: http://biogrid.engr.uconn.edu/tfbs_search/. C1 [Lee, Chih; Huang, Chun-Hsi] Univ Connecticut, Dept Comp Sci & Engn, Storrs, CT 06269 USA. RP Huang, CH (reprint author), Univ Connecticut, Dept Comp Sci & Engn, Fairfield Rd, Storrs, CT 06269 USA. EM huang@engr.uconn.edu FU National Science Foundation [CCF-0755373, OCI-1156837] FX This work was supported in part by National Science Foundation [grant numbers CCF-0755373 and OCI-1156837]. CR Balla S, 2006, NAT METHODS, V3, P175, DOI 10.1038/nmeth856 Barash Y., 2001, WABI 01, V2149, P278 Bertsekas D., 1999, NONLINEAR PROGRAMMIN Buhler J., 2001, RECOMB, P69 Chekmenev DS, 2005, NUCLEIC ACIDS RES, V33, pW432, DOI 10.1093/nar/gki441 de Hoon MJL, 2004, BIOINFORMATICS, V20, P1453, DOI 10.1093/bioinformatics/bth078 Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Fujita PA, 2011, NUCLEIC ACIDS RES, V39, pD876, DOI 10.1093/nar/gkq963 Gama-Castro S, 2008, NUCLEIC ACIDS RES, V36, pD120, DOI 10.1093/nar/gkm994 Georgi B, 2006, BIOINFORMATICS, V22, pE166, DOI 10.1093/bioinformatics/btl249 Georgiev S, 2010, GENOME BIOL, V11, DOI 10.1186/gb-2010-11-2-r19 Hannenhalli S, 2005, BIOINFORMATICS, V21, pI204, DOI 10.1093/bioinformatics/bti1001 Hu JJ, 2005, NUCLEIC ACIDS RES, V33, P4899, DOI 10.1093/nar/gki791 Jain AK, 2010, PATTERN RECOGN LETT, V31, P651, DOI 10.1016/j.patrec.2009.09.011 Kel AE, 2003, NUCLEIC ACIDS RES, V31, P3576, DOI 10.1093/nar/gkg585 Kroshko DL, 2011, OPENOPT 0 36 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Li N, 2006, ALGORITHM MOL BIOL, V1, DOI 10.1186/1748-7188-1-8 Osada R, 2004, BIOINFORMATICS, V20, P3516, DOI 10.1093/bioinformatics/bth438 Portales-Casamar E, 2010, NUCLEIC ACIDS RES, V38, pD105, DOI 10.1093/nar/gkp950 Rajasekaran S, 2005, J COMPUT BIOL, V12, P1117, DOI 10.1089/cmb.2005.12.1117 Rosenbloom KR, 2010, NUCLEIC ACIDS RES, V38, pD620, DOI 10.1093/nar/gkp961 Salama RA, 2010, NUCLEIC ACIDS RES, V38, DOI 10.1093/nar/gkq274 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandelin A, 2004, NUCLEIC ACIDS RES, V32, pW249, DOI 10.1093/nar/gkh372 Sandve GK, 2006, BIOL DIRECT, V1, DOI 10.1186/1745-6150-1-11 Schug J, 2003, CURR PROTOC BIOINF Sinha S, 2002, RECOMB, P291 STADEN R, 1984, NUCLEIC ACIDS RES, V12, P505, DOI 10.1093/nar/12.1Part2.505 Takusagawa K T, 2004, Pac Symp Biocomput, P360 Tompa M, 2005, NAT BIOTECHNOL, V23, P137, DOI 10.1038/nbt1053 Turatsinze JV, 2008, NAT PROTOC, V3, P1578, DOI 10.1038/nprot.2008.97 Vilo J., 2000, Proceedings. Eighth International Conference on Intelligent Systems for Molecular Biology WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Yanover C, 2009, BIOINFORMATICS, V25, P868, DOI 10.1093/bioinformatics/btp090 Zambelli F, 2009, NUCLEIC ACIDS RES, V37, pW247, DOI 10.1093/nar/gkp464 Zaslavsky E, 2006, ALGORITHM MOL BIOL, V1, DOI 10.1186/1748-7188-1-13 NR 37 TC 2 Z9 2 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD AUG 27 PY 2012 VL 13 AR 215 DI 10.1186/1471-2105-13-215 PG 12 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 069IO UT WOS:000313428700001 PM 23244338 ER PT J AU Shi, SK Li, LM AF Shi, Steve Kansheng Li, Lemin TI A Close-to-linear Topic Detection Algorithm using Relative Entropy based Relevance Model and Inverted Indices Retrieval SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS LA English DT Article DE Topic Detection; Link Topic Detection; Retrospective Event Detection; Information Retrieval; Relevance Models; Inverted Indices AB Although timely access to information is becoming increasingly important and gaining such access is no longer a problem, the capacity for humans to assimilate such huge amounts of information is limited. Topic Detection(TD) is then a promising research area that addresses speedy access of desired information. However, ironically, the time complexity of existing TD algorithms themselves is usually O(n(3)) or up to the x-th power of e. Linear performance requirement of real world topic detection has not been significantly addressed. This paper reveals a new patented topic detection algorithm called RMIR that combines relevance model with information retrieval technique to improve on time efficiency. Relevance Model(RM) is a theoretical extension of statistical language modeling that was developed for the task of document retrieval. To reduce the costs of fetching RM, we reduce the number of comparisons for stories by a query-based approach that makes similar stories exist in the top-k query results. We also build our query based on inverted indices, which have the complexity close to linear. The time cost of rest of operations in the RMIR topic detection process is a constant. Hence, the total complexity of RMIR topic detection algorithm should be close to linear as shown in experimental results. In addition, RMIR also gains better detection rates and robustness by relative entropy based topic model design. C1 [Shi, Steve Kansheng] Shanghai Jiao Tong Univ, Coll Elect & Elect Engn, Shanghai 200240, Peoples R China. [Li, Lemin] Chinese Acad Engn, Beijing 100088, Peoples R China. RP Shi, SK (reprint author), Shanghai Jiao Tong Univ, Coll Elect & Elect Engn, Shanghai 200240, Peoples R China. EM steve@joinvc.com; lml@uestc.edu.cn FU National Natural Science Foundation [60970107, 61073150]; National Incubating Center FX This research is supported by National Natural Science Foundation under the grant numbers 60970107 and 61073150. Topic tracking application project for Business Intelligence (www.wasuo.com) is sponsored by National Incubating Center. We would like to express our sincere thanks to all of them. CR Allan J., 2000, P 9 INT C INF KNOWL AlSumait Loulwah, 2008, 8 IEEE INT C DAT MIN, P1550 [Anonymous], 2011, The 2004 Topic Detection and Tracking (TDT '04) Task Definition and Evaluation, Patent No. 2008100632951 [Anonymous], 2004, 2004 TOPIC DETECTION Brants T., 2003, P ACM SIGIR 03 Brossard A, 2011, INFORM SOFTWARE TECH, V53, P1349, DOI 10.1016/j.infsof.2011.06.011 Bun K.K., 2002, P 3 INT C WEB INF SY, P73 Chen KY, 2007, IEEE T KNOWL DATA EN, V19, P1016, DOI 10.1109/TKDE.2007.1040 Chieu H. L., 2004, P 27 ANN INT ACM SIG, P425, DOI 10.1145/1008992.1009065 Clarke Charles L. A., 1994, 9440 CS U WAT DEP CO Colomo-Palacios R, 2010, INT J INFORM MANAGE, V30, P465, DOI 10.1016/j.ijinfomgt.2010.05.012 Croft W.B., 2001, P DELOS NSF WORKSH P, P49 Garcia J, 2011, J UNIVERS COMPUT SCI, V17, P1299 Garcia-Crespo A, 2010, FUTURE GENER COMP SY, V26, P374, DOI 10.1016/j.future.2009.07.008 Garcia-Crespo A, 2010, J INF TECHNOL, V25, P178, DOI 10.1057/jit.2010.1 Qi He, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence, V32, DOI 10.1109/TPAMI.2009.203 Hisamitsu T., 2002, P 19 INT C COMP LING, V1, P320 Hong Yu, 2008, Journal of Software, V19, DOI 10.3724/SP.J.1001.2008.02265 Kumaran G., 2004, P ACM SIGIR 04 Lavrenko V., 2001, P ACM SIGIR 98 C, P267 Lavrenko V, 2002, P HUM LANG TECHN C H, P104 Lee C, 2007, INFORM PROCESS MANAG, V43, P1249, DOI 10.1016/j.ipm.2006.02.007 Li Z., 2005, P 28 ANN INT ACM SIG, P106, DOI DOI 10.1145/1076034.1076055 Lo Y., 2001, TOP DET TRACK WORKSH Luo W.H., 2003, P JSCL 2003 BEIJ CHI, P560 Maeno Y, 2007, IEEE T IND ELECTRON, V54, P1184, DOI 10.1109/TIE.2007.891661 Michalski R, 2011, COMPUT HUM BEHAV, V27, P2308, DOI 10.1016/j.chb.2011.07.010 Okazaki N., 2003, P 16 INT FLOR ART IN, P407 Penalvo Francisco, 2011, J UNIVERS COMPUT SCI, V17, P1222 Ramadan Q. H., 2011, 2011 International Conference on Semantic Technology and Information Retrieval (STAIR 2011), DOI 10.1109/STAIR.2011.5995790 Robertson S. E., 1997, PROBABILITY RANKING, P281 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi K, 2011, P IEEE BROADB MULT C, P675 Shi K, 2011, J CHINA U POSTS T S1, V18, P131 Shi K, 2011, P IEEE CLOUD COMP IN, P113 Shi K, 2011, P IEEE CLOUD COMP IN, P108 Stokes N., 2001, P ACM SIGIR 01 TDT, 2004, ANN MAN VERS 1 2 Tomasic Anthony, 1992, STANCS921434 STANF U Yang Y., 2002, P ACM SIGKDD 02 Yang Y., 1998, P ACM SIGIR 98 Zhang ZF, 2011, EXPERT SYST APPL, V38, P6848, DOI 10.1016/j.eswa.2010.12.052 NR 44 TC 0 Z9 0 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAUMIERE, PARIS, 75019, FRANCE SN 1875-6883 J9 INT J COMPUT INT SYS JI Int. J. Comput. Intell. Syst. PD AUG PY 2012 VL 5 IS 4 BP 735 EP 744 DI 10.1080/18756891.2012.718156 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 990PJ UT WOS:000307642700010 ER PT J AU Huang, L Milne, D Frank, E Witten, IH AF Huang, Lan Milne, David Frank, Eibe Witten, Ian H. TI Learning a concept-based document similarity measure SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article DE content analysis; text mining; semantic analysis ID WIKIPEDIA; WORDNET; RETRIEVAL; MODEL AB Document similarity measures are crucial components of many text-analysis tasks, including information retrieval, document classification, and document clustering. Conventional measures are brittle: They estimate the surface overlap between documents based on the words they mention and ignore deeper semantic connections. We propose a new measure that assesses similarity at both the lexical and semantic levels, and learns from human judgments how to combine them by using machine-learning techniques. Experiments show that the new measure produces values for documents that are more consistent with people's judgments than people are with each other. We also use it to classify and cluster large document sets covering different genres and topics, and find that it improves both classification and clustering performance. C1 [Huang, Lan; Milne, David; Frank, Eibe; Witten, Ian H.] Univ Waikato, Dept Comp Sci, Hamilton 3240, New Zealand. RP Huang, L (reprint author), Univ Waikato, Dept Comp Sci, Private Bag 3105, Hamilton 3240, New Zealand. EM lh92@cs.waikato.ac.nz; dnk2@cs.waikato.ac.nz; eibe@cs.waikato.ac.nz; ihw@cs.waikato.ac.nz RI Witten, Ian/A-3366-2012 OI Witten, Ian/0000-0001-6428-8988 CR AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470 ALTMANN G, 1988, COGNITION, V30, P191, DOI 10.1016/0010-0277(88)90020-0 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bloehdorn S, 2004, P 6 INT WORKSH KNOWL, P149 Britannica, 2011, ENCY BRIT BRON C, 1973, COMMUN ACM, V16, P575, DOI 10.1145/362342.362367 Budanitsky A., 2001, P 2 WORKSH WORDNET O Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Gabrilovich E, 2009, J ARTIF INTELL RES, V34, P443 GABRILOVICH E, 2005, P 19 INT JOINT C ART, P1048 Gabrilovich E, 2004, P 21 INT C MACH LEAR, P41 Gonzalo J, 1998, P COLING ACL 98 WORK, P38 Hartigan J, 1975, CLUSTERING ALGORITHM Hartmann R. R. K., 1998, DICT LEXICOGRAPHY Hastie T., 2009, BOOSTING ADDITIVE TR Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Hu Jian, 2008, P 31 ANN INT ACM SIG, P179, DOI 10.1145/1390334.1390367 Huang A., 2011, THESIS U WAIKATO HAM Huang A, 2008, IEEE DATA MINING, P839, DOI 10.1109/ICDM.2008.80 International Organization for Standardization, 2009, ISO704 Leacock C., 1997, WORDNET ELECT LEXICA, P265 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Manning C.D., 2008, INTRO INFORM RETRIEV Mihalcea R., 2006, P 21 NAT C ART INT A, P775 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150 Milne D., 2008, P AAAI WORKSH WIK AR, P25 Milne D, 2007, P 16 ACM C INF KNOWL, P445, DOI 10.1145/1321440.1321504 Mohler M., 2009, P 12 C EUR CHAPT ASS, P567, DOI 10.3115/1609067.1609130 Nadeau C, 2003, MACH LEARN, V52, P239, DOI 10.1023/A:1024068626366 Page L, 1999, PAGERANK CITATION RA Pincombe B., 2004, COMP HUMAN LATENT SE PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Potthast M, 2008, LECT NOTES COMPUT SC, V4956, P522 Rasmussen CE, 2005, ADAPT COMPUT MACH LE, P1 Recupero DR, 2007, INFORM RETRIEVAL, V10, P563, DOI 10.1007/s10791-007-9035-7 Resnik P., 1995, P 14 INT JOINT C ART, P448 Rorvig M, 1999, J AM SOC INFORM SCI, V50, P639, DOI 10.1002/(SICI)1097-4571(1999)50:8<639::AID-ASI2>3.0.CO;2-C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1999, P 16 INT C MACH LEAR, P379 Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Smola AJ, 2004, STAT COMPUT, V14, P199, DOI 10.1023/B:STCO.0000035301.49549.88 Strube M., 2006, P NAT C ART INT AAAI, P1419 Tatar D, 2009, J RES PRACT INF TECH, V41, P53 Voorhees EM, 1998, LANG SPEECH & COMMUN, P285 Wang Pu, 2008, P 14 ACM SIGKDD INT, P713, DOI 10.1145/1401890.1401976 Westerman SJ, 2010, J AM SOC INF SCI TEC, V61, P1535, DOI 10.1002/asi.21361 WILLETT P, 1983, INT CLASSIF, V10, P138 Yeh E., 2009, P 2009 WORKSH GRAPH, P41, DOI 10.3115/1708124.1708133 Zhu SF, 2009, BIOINFORMATICS, V25, P1944, DOI 10.1093/bioinformatics/btp338 NR 53 TC 9 Z9 9 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD AUG PY 2012 VL 63 IS 8 BP 1593 EP 1608 DI 10.1002/asi.22689 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 978PX UT WOS:000306758600009 ER PT J AU Kou, G Lou, CW AF Kou, Gang Lou, Chunwei TI Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data SO ANNALS OF OPERATIONS RESEARCH LA English DT Article DE Information retrieval; Web page clustering; Multiple criteria decision making; Multiple factor hierarchical algorithm; Clickstream analysis; K-means algorithm ID SOFTWARE; AGENT; MODEL AB The developments in World Wide Web and the advances in digital data collection and storage technologies during the last two decades allow companies and organizations to store and share huge amounts of electronic documents. It is hard and inefficient to manually organize, analyze and present these documents. Search engine helps users to find relevant information by present a list of web pages in response to queries. How to assist users to find the most relevant web pages from vast text collections efficiently is a big challenge. The purpose of this study is to propose a hierarchical clustering method that combines multiple factors to identify clusters of web pages that can satisfy users' information needs. The clusters are primarily envisioned to be used for search and navigation and potentially for some form of visualization as well. An experiment on Clickstream data from a processional search engine was conducted to examine the results shown that the clustering method is effective and efficient, in terms of both objective and subjective measures. C1 [Kou, Gang; Lou, Chunwei] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China. RP Kou, G (reprint author), Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China. EM kougang@yahoo.com FU National Natural Science Foundation of China [70901015, 70621001]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry FX A short 4-page version of this paper appeared previously at the NCM 2009, the 5th International Joint Conference on INC, IMS and IDC, Aug. 25-27, 2009, Seoul, Korea. The authors would like to thank three anonymous referees for the valuable suggestions and comments. This research has been partially supported by grants from the National Natural Science Foundation of China under the Grant No. 70901015, No. 70621001; the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry. CR Al-Aomar R, 2008, INT J INF TECH DECIS, V7, P35, DOI 10.1142/S0219622008002806 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bush V., 1945, ATLANTIC MONTHLY, P101 Cooley R., 1999, Knowledge and Information Systems, V1 CRISP-DM, 1996, CROSS IND STAND PROC Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DHILLON I., 2001, DATA MINING SCI ENG Foss A., 2001, 1 SIAM INT C DAT MIN, P41 Gomez SA, 2008, INT J INF TECH DECIS, V7, P71, DOI 10.1142/S021962200800282X Han J., 2006, DATA MINING CONCEPTS Hearst M. A., 1999, P ACL 99 37 ANN M AS HEER J., 2001, 1 SIAM ICDM WORKSH W, P51 Hong A., 2008, INT J INFORM TECHNOL, V7, P491 Hu J, 2008, INT J INF TECH DECIS, V7, P291, DOI 10.1142/S0219622008002971 Kou G, 2003, OPTIM METHOD SOFTW, V18, P453, DOI 10.1080/10556780310001600953 Kou G, 2005, ANN OPER RES, V135, P261, DOI 10.1007/s10479-005-6245-5 Kumar M., 2008, ANN OPERATIONS RES Lee J, 2008, INT J INF TECH DECIS, V7, P309, DOI 10.1142/S0219622008002983 Manning C.D., 2008, INTRO INFORM RETRIEV Mathiak B., 2004, P 2 EUR WORKSH DAT M, P47 McQueen J., 1967, P 5 BERK S MATH STAT, V1, P281 Nasraoui O., 2003, P KDD WORKSH WEB MIN Pantel P., 2003, THESIS U ALBERTA Park SS, 2007, INT J INF TECH DECIS, V6, P213, DOI 10.1142/S0219622007002496 Peng Y, 2008, DECIS SUPPORT SYST, V44, P1016, DOI 10.1016/j.dss.2007.12.001 Peng Y, 2008, INT J INF TECH DECIS, V7, P639 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi Y, 2009, INT J INF TECH DECIS, V8, P1 Shi Y, 2005, INT J INF TECH DECIS, V4, P1 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Steinbach M., 2000, 6 ACM SIGKDD WORLD T van Rijsbergen C. J., 1979, INFORM RETRIEVAL Zhang QY, 2008, INT J INF TECH DECIS, V7, P683 Zhang W, 2009, INT J INF TECH DECIS, V8, P249 NR 35 TC 23 Z9 23 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0254-5330 J9 ANN OPER RES JI Ann. Oper. Res. PD AUG PY 2012 VL 197 IS 1 BP 123 EP 134 DI 10.1007/s10479-010-0704-3 PG 12 WC Operations Research & Management Science SC Operations Research & Management Science GA 985NC UT WOS:000307272400008 ER PT J AU Prados-Suarez, B Molina, C Yanez, CP de Reyes, MP AF Prados-Suarez, Belen Molina, Carlos Pena Yanez, Carmen Prados de Reyes, Miguel TI Improving electronic health records retrieval using contexts SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Electronic Health Records (EHR); Context; Pertinence; Contextualized access; Fuzzy logic; Access improvement ID KNOWLEDGE; CLINICIAN; SYSTEMS; MODEL AB This paper aims to solve a recently arose problem, related to the access to the Electronic Health Records (EHR) in the Hospitals. Due to the digitalization of the information contained in the medical records, and the growing availability of devices that directly generate digital documents to include in it, the EHR are becoming unmanageable. Even more, to find a concrete item of information relevant for a given assistance act is a very hard, difficult and time-consuming task. To solve it we propose here the definition of contexts of access to the EHR, to exploit the logical division of the information inside each document in the EHR into data groups, and the computation of the pertinence of each data group to each context. It allows us to prioritize, the information in the EHR, even at a concrete data item level, according to the situation from which it is acceded. This way when the medical personnel is involved in an assistance act, the most relevant information for it is the one first showed, being able to widen the search but always according to the relevance. With it we not only improve the accessibility to the EHR and make easier the work of the doctors, but also enable other applications like the ubiquitous computation or the mobility, using devices like tablet PC's and PDA's. (c) 2012 Elsevier Ltd. All rights reserved. C1 [Molina, Carlos] Univ Jaen, Dept Comp Sci, Jaen, Spain. [Prados-Suarez, Belen] Univ Granada, Dept Languages & Comp Syst, Granada, Spain. [Pena Yanez, Carmen; Prados de Reyes, Miguel] San Cecilio Hosp, Dept Comp Sci, Granada, Spain. RP Molina, C (reprint author), Univ Jaen, Dept Comp Sci, Jaen, Spain. EM belenps@ugr.es; carlosmo@ujaen.es; carmenpy@decsai.ugr.es; prados@decsai.ugr.es RI Prados Suarez, Belen/E-1020-2012 FU Andalusian Government (Junta de Andalucia) [P07-TIC03175]; Spanish Government (Science and Innovation Department) [TIN2009-08296] FX The research reported in this paper was partially supported by the Andalusian Government (Junta de Andalucia) under project P07-TIC03175 "Representacin y Manipulacion de Objetos Imperfectos en Problemas de Integracin de Datos: Una Aplicacion a los Almacenes de Objetos de Aprendizaje" and also by the Spanish Government (Science and Innovation Department) under project TIN2009-08296. We would also like to thank their collaboration to the medical personnel that is participating in the development of the system. CR Adams Ann, 2007, Inform Prim Care, V15, P103 [Anonymous], 2008, ISO13606 [Anonymous], 2006, SIGIR 06 Bisbal J., 2009, METHODS INFORM MED, V48 Bobillo F, 2008, EXPERT SYST APPL, V35, P1899, DOI 10.1016/j.eswa.2007.08.090 Bohm K., 2010, 43 HAW INT C SYST SC, P1 Buchholz T, 2007, ELECTRON COMMER R A, V6, P40, DOI 10.1016/j.elerap.2006.04.002 Cayir Sinan, 2008, PICMET '08 - 2008 Portland International Conference on Management of Engineering & Technology, DOI 10.1109/PICMET.2008.4599692 Chaker H., 2010, 3 INT C ADV HUM OR P, P117 Charters K, 2009, ST HEAL T, V146, P197, DOI 10.3233/978-1-60750-024-7-197 Cho I, 2010, CIN-COMPUT INFORM NU, V28, P95, DOI 10.1097/NCN.0b013e3181cd8240 Cho I, 2010, INT J MED INFORM, V79, P611, DOI 10.1016/j.ijmedinf.2010.06.002 Chu WW, 2000, IEEE T INF TECHNOL B, V4, P97, DOI 10.1109/4233.845202 Cohens W, 1995, P 12 INT C MACH LEAR, P115 Collins B., 2008, ANN S P AMIA S AMIA, P914 Data exchange standard, 2011, DATA EXCHANGE STANDA de Reyes M. Amparo Vila Miranda Miguel Prados, 2006, GENERATION USE ONE O Del Fiol G, 2009, J BIOMED INFORM, V42, P82, DOI 10.1016/j.jbi.2008.07.001 Ehsan M., 2009, INT C ADV INF NETW A, P103 El Fadly Abdennaji, 2010, Stud Health Technol Inform, V160, P1324 Erdal S, 2009, J DIGIT IMAGING, V22, P166, DOI 10.1007/s10278-007-9086-8 Gagnon Marie-Pierre, 2010, Inform Prim Care, V18, P31 Garcia-Morchon O., 2010, 8 IEEE INT C PERV CO, P322 Gil-Leyva I., 1966, REV ESP DOC CIENT, V19, P273 Ginsburg M., 2007, 40 ANN HAW INT C SYS, P139 Image storage standard, 2011, IMAGE STORAGE STANDA Jarvelin K., 2000, SIGIR 00, P41 Jerding DF, 1998, IEEE T VIS COMPUT GR, V4, P257, DOI 10.1109/2945.722299 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Judd G., 2003, IEEE INT C PERV COMP, P133, DOI 10.1109/PERCOM.2003.1192735 Jung JJ, 2009, INFORM PROCESS MANAG, V45, P280, DOI 10.1016/j.ipm.2008.11.003 Kang Dong-Oh, 2006, Conf Proc IEEE Eng Med Biol Soc, V1, P5192 Kanoulas E., 2010, ADV INFORM RETRIEVAL, P152 Karahoca A, 2010, J BIOMED INFORM, V43, P224, DOI 10.1016/j.jbi.2009.09.002 Lahteenmaki H., 2009, C P ANN INT C IEEE E, P1726 Mao JY, 2001, INT J HUM-COMPUT ST, V55, P787, DOI 10.1006/ijhc.2001.0507 McAlearney AS, 2010, INT J MED INFORM, V79, P807, DOI 10.1016/j.ijmedinf.2010.09.002 Nagy M, 2010, METHOD INFORM MED, V49, P186, DOI 10.3414/ME09-02-0018 Nagy M, 2010, STUD HEALTH TECHNOL, V155, P122, DOI 10.3233/978-1-60750-563-1-122 Nomenclature medical standard S, 2011, NOMENCLATURE MED STA Open-EHR open electronical health records, 2011, OPEN EHR OPEN ELECT Prados M., 2003, SISTEMAS INFORM HOSP Prados M., 2010, ELECT HLTH RECORD EH Prados-Suarez B., 2008, P ICEIS 2008, P300 Pung HK, 2009, IEEE J SEL AREA COMM, V27, P510, DOI 10.1109/JSAC.2009.090513 Ray J., 2006, C P ANN INT C IEEE E, V1, P4686 Ross Susan, 2009, J Med Pract Manage, V24, P254 Ruland CM, 2008, ST HEAL T, V136, P57 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Svanaes D, 2008, ST HEAL T, V136, P541 Vest JR, 2010, J AM MED INFORM ASSN, V17, P302, DOI 10.1136/jamia.2009.000471 Vishwanath A, 2010, INT J MED INFORM, V79, P778, DOI 10.1016/j.ijmedinf.2010.09.006 WEINSTOCK M, 2010, HOSP HEALTH NETWORK, V84, P20 Zuniga AEF, 2010, INT J TECHNOL ASSESS, V26, P382, DOI 10.1017/S0266462310001121 NR 54 TC 1 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD AUG PY 2012 VL 39 IS 10 BP 8522 EP 8536 DI 10.1016/j.eswa.2012.01.016 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 932HR UT WOS:000303281800003 ER PT J AU Granados, A Camacho, D Rodriguez, FB AF Granados, Ana Camacho, David Borja Rodriguez, Francisco TI Is the contextual information relevant in text clustering by compression? SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Contextual information; Word removal; Compression-based text clustering ID CATEGORIZATION; IDENTIFICATION; RECOMMENDATION; RECOGNITION; WORDS AB Usually, when analyzing data that have not been processed or filtered yet, it can be observed that not all the data have equal importance. Thus, it is common to find relevant data surrounded by non relevant one. This occurs when analyzing textual information due to its intrinsic nature: texts contain words that provide a lot of information about the subject matter, whereas they contain other words with a little meaning or relevance. We believe that although in principle the non-relevant words are not as important as the relevant ones, the former constitute the substrate that supports the last. Since this substrate is the context that surrounds the relevant information, we call it the contextual information. In this paper, we analyze the relevance that the contextual information has in textual data, in a clustering by compression scenario. We generate the contextual information applying a distortion technique previously developed by the authors. One of the main characteristics of this technique is that it maintains the contextual information. In this paper we compare this technique with three new distortion techniques that destroy the contextual information in different ways. The experimental results support our hypothesis that the contextual information is relevant at least in the area of text clustering by compression. (c) 2012 Elsevier Ltd. All rights reserved. C1 [Granados, Ana; Camacho, David; Borja Rodriguez, Francisco] Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain. RP Camacho, D (reprint author), Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain. EM ana.granadosf@uam.es; david.camacho@uam.es; f.rodriguez@uam.es RI Rodriguez, Francisco de Borja/F-7812-2013; Camacho, David/B-5779-2014 OI Rodriguez, Francisco de Borja/0000-0003-4053-099X; Camacho, David/0000-0002-5051-3475 FU Spanish Ministry of Education and Science [TIN2010-19607, TIN 2010-19872, CAM S-SEM-0255-2006] FX This work was supported by the Spanish Ministry of Education and Science under TIN2010-19607, TIN 2010-19872, and CAM S-SEM-0255-2006. CR [Anonymous], INF COMP SCI U CAL I Bouras C, 2010, DATA KNOWL ENG, V69, P664, DOI 10.1016/j.datak.2010.02.005 Brown P. F., 1992, Computational Linguistics, V18 Chen BL, 2008, PATTERN RECOGN LETT, V29, P426, DOI 10.1016/j.patrec.2007.10.022 Chi YL, 2008, COMPUT VIS IMAGE UND, V112, P324, DOI 10.1016/j.cviu.2008.05.009 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 Cilibrasi R., COMPLEARN TOOLKIT Cimiano P., 2005, J ARTIFICIAL INTELLI, V24 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 Consortium B. N. C., BRIT NAT CORP Dagan I., 1993, P 31 ANN M ASS COMP, P164, DOI 10.3115/981574.981596 Driver Cormac, 2008, Pervasive and Mobile Computing, V4, DOI 10.1016/j.pmcj.2008.04.009 Eronen AJ, 2006, IEEE T AUDIO SPEECH, V14, P321, DOI 10.1109/TSA.2005.854103 Goodall JR, 2006, IEEE COMPUT GRAPH, V26, P72, DOI 10.1109/MCG.2006.31 Granados A., 2010, P 11 C INT DAT ENG A, P259 Granados A, 2011, IEEE T KNOWL DATA EN, V23, P1090, DOI 10.1109/TKDE.2010.173 Granados A, 2008, LECT NOTES COMPUT SC, V5228, P69, DOI 10.1007/978-3-540-87448-5_8 GuoDong Z., 2004, P 20 INT C COMP LING, P92, DOI 10.3115/1220355.1220369 Hegde RM, 2009, IEEE T MULTIMEDIA, V11, P645, DOI 10.1109/TMM.2009.2017631 Hu M., 2008, P 31 ANN INT ACM SIG, P291, DOI 10.1145/1390334.1390385 Huang CL, 2007, IEEE T COMPUT, V56, P1225, DOI 10.1109/TC.2007.1064 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Kozima H., 1993, P 31 ANN M ASS COMP, P286, DOI DOI 10.1016/S0306-4573(02)00035-3 Lau R., 2008, ACM T INFORM SYSTEMS, V26, P1, DOI DOI 10.1145/1344411.1344414 Li M, 2004, IEEE T INFORM THEORY, V50, P3250, DOI 10.1109/TIT.2004.838101 Liu R.-L., 2008, LECT NOTES ARTIF INT, P1004 Ma Z., 2007, ACM Transactions on Information Systems, V25, DOI 10.1145/1198296.1198301 MOONEY R, 2005, SIGKDD EXPLORATIONS, V7, P3 Mori G, 2006, IEEE T PATTERN ANAL, V28, P1052, DOI 10.1109/TPAMI.2006.149 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Rocha L., 2008, P 17 ACM C INF KNOWL, P243, DOI 10.1145/1458082.1458117 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shah C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1008996 SHANNON CE, 1948, AT&T TECH J, V27, P623 Silva C., 2003, P INT JOINT C NEUR N Su JH, 2010, IEEE INTELL SYST, V25, P16 Tamine-Lechani L, 2010, KNOWL INF SYST, V24, P1, DOI 10.1007/s10115-009-0231-1 Verdu S, 2008, IEEE T INFORM THEORY, V54, P5030, DOI 10.1109/TIT.2008.929968 Weng SS, 2009, EXPERT SYST APPL, V36, P1268, DOI 10.1016/j.eswa.2007.11.056 Xiong H, 2006, IEEE T KNOWL DATA EN, V18, P304, DOI 10.1109/TKDE.2006.46 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Yang YM, 1996, J AM SOC INFORM SCI, V47, P357 NR 43 TC 4 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD AUG PY 2012 VL 39 IS 10 BP 8537 EP 8546 DI 10.1016/j.eswa.2012.01.215 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 932HR UT WOS:000303281800004 ER PT J AU Zhuge, H Xing, YP AF Zhuge, Hai Xing, Yunpeng TI Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society SO IEEE TRANSACTIONS ON SERVICES COMPUTING LA English DT Article DE Cyber-physical society; faceted navigation; nonrelational data model; resource management; resource space model; semantic link network; cyber-physical-socio services ID RELATIONAL MODEL; DATABASE SYSTEM; INFORMATION; XML; ALGEBRA; WEB AB Classification is the most basic method for organizing resources in the physical space, cyber space, socio space, and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multidimensional classification space. It supports generalization and specialization on multidimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM's normal forms, operations, and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered, and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced. C1 [Zhuge, Hai; Xing, Yunpeng] Chinese Acad Sci, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China. RP Zhuge, H (reprint author), Chinese Acad Sci, Key Lab Intelligent Informat Proc, 6 Sci S Rd, Beijing 100190, Peoples R China. EM zhuge@ict.ac.cn; ypxing@kg.ict.ac.cn FU National Science Foundation of China [61075074]; Natural Science Foundation of Chongqing [cstc2012jjB40012]; Southwest University [NSKD11013]; Chongqing Municipal Government; Nanjing University of Posts and Telecommunications FX This research was supported by the National Science Foundation of China (61075074), the Natural Science Foundation of Chongqing (Project No. cstc2012jjB40012), the Key Discipline Fund of National 211 Project (Southwest University: NSKD11013), the scholar program of the Chongqing Municipal Government, and funding from the Nanjing University of Posts and Telecommunications. The authors thank the reviewers for their helpful comments on the earlier version of this paper. CR Abiteboul S., 1995, FDN DATABASES Abiteboul S, 2006, ACM T DATABASE SYST, V31, P208, DOI 10.1145/1132863.1132869 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbara D., 1992, IEEE T KNOWL DATA EN, V4, P437 Benjelloun O., 2006, P 32 INT C VER LARG, P953 Cavallo R., 1987, Proceedings of the Thirteenth International Conference on Very Large Data Bases: 1987 13th VLDB Chang CK, 2009, IEEE T SERV COMPUT, V2, P261, DOI [10.1007/s12245-009-0129-4, 10.1109/TSC.2009.21] CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/362384.362685 Dalvi N., 2007, PODS, P1, DOI 10.1145/1265530.1265531 Dalvi N. N., 2004, P VLDB, P864, DOI 10.1016/B978-012088469-8/50076-0 Dalvi N.N., 2005, P 31 INT C VER LARG, P805 Dey D, 1996, ACM T DATABASE SYST, V21, P339, DOI 10.1145/232753.232796 Dong X., 2007, P ACM SIGMOD INT C M, P43, DOI DOI 10.1145/1247480.1247487 Fuhr N, 1997, ACM T INFORM SYST, V15, P32, DOI 10.1145/239041.239045 Halevy A.Y., 2006, PODS, P1, DOI 10.1145/1142351.1142352 Han J., 2005, DATA MINING CONCEPTS Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983 Hung E, 2007, ACM T COMPUT LOG, V8, DOI 10.1145/1276920.1276926 Jiang HF, 2002, PROC INT CONF DATA, P335 Keulen M., 2005, P ICDE, P459 Kimelfeld B., 2007, P 33 INT C VER LARG, P27 Lakshmanan LVS, 1997, ACM T DATABASE SYST, V22, P419, DOI 10.1145/261124.261131 Madhavan J, 2005, PROC INT CONF DATA, P57 Nierman A., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases Re C, 2007, P VLDB, P51 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Senellart P, 2007, PODS, P283, DOI 10.1145/1265530.1265570 TAKAHASHI Y, 1993, IEEE T KNOWL DATA EN, V5, P122, DOI 10.1109/69.204096 Tao Y., 2005, P 31 INT C VER LARG, P922 Tho QT, 2006, IEEE T KNOWL DATA EN, V18, P842, DOI 10.1109/TKDE.2006.87 WANG F, 1990, IEEE T GEOSCI REMOTE, V28, P194, DOI 10.1109/36.46698 Widom J., 2005, P C INN DAT SYST RES, P262 Yee K.-P., 2003, P SIGCHI C HUM FACT, P401, DOI DOI 10.1145/642611.642681 Zheng G, 2009, IEEE T SERV COMPUT, V2, P65, DOI 10.1109/TSC.2009.2 Zhuge H, 2010, J AM SOC INF SCI TEC, V61, P1824, DOI 10.1002/asi.21353 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H., 2010, P IEEE 24 INT C ADV, P20 Zhuge H., 2012, KNOWLEDGE GRID Zhuge H, 2008, ACM T INTERNET TECHN, V8, DOI 10.1145/1391949.1391954 Zhuge H, 2008, WEB INF SYST ENG INT, V4, P1, DOI 10.1007/978-0-387-72772-1 Zhuge H, 2011, ARTIF INTELL, V175, P988, DOI 10.1016/j.artint.2010.09.009 Zhuge H., 2010, ARTIF INTELL, V174, P190 NR 42 TC 12 Z9 12 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1939-1374 J9 IEEE T SERV COMPUT JI IEEE Trans. Serv. Comput. PD JUL-SEP PY 2012 VL 5 IS 3 BP 404 EP 421 DI 10.1109/TSC.2011.12 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 992OW UT WOS:000307789100009 ER PT J AU Martins, RM Andery, GF Heberle, H Paulovich, FV Lopes, AD Pedrini, H Minghim, R AF Martins, Rafael Messias Andery, Gabriel Faria Heberle, Henry Paulovich, Fernando Vieira Lopes, Alneu de Andrade Pedrini, Helio Minghim, Rosane TI Multidimensional Projections for Visual Analysis of Social Networks SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY LA English DT Article DE social network; visual exploration; multidimensional visualization ID VISUALIZATION; EXPLORATION; SYSTEM; SPACE AB Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented. C1 [Martins, Rafael Messias; Heberle, Henry; Paulovich, Fernando Vieira; Lopes, Alneu de Andrade; Minghim, Rosane] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP, Brazil. [Andery, Gabriel Faria] CPM Braxis, BR-04543900 Sao Paulo, Brazil. [Pedrini, Helio] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil. RP Martins, RM (reprint author), Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP, Brazil. EM rmartins@gmail.com; gfandery@gmail.com; henryhbrl@gmail.com; paulovic@icmc.usp.br; alneu@icmc.usp.br; helio@ic.unicamp.br; rminghim@icmc.usp.br RI Paulovich, Fernando /G-1329-2010; Minghim, Rosane/E-5703-2011 OI Paulovich, Fernando /0000-0002-2316-760X; FU FAPESP; CNPq; CAPES FX The authors are thankful to FAPESP, CNPq and CAPES for their financial support. They are also grateful to IBOPE Media and Innovation team for the Orkut data. CR [Anonymous], 2011, COLLECTION COMPUTER Archambault D, 2008, IEEE T VIS COMPUT GR, V14, P900, DOI 10.1109/TVCG.2008.34 Bezerianos A, 2010, COMPUT GRAPH FORUM, V29, P863, DOI 10.1111/j.1467-8659.2009.01687.x Brandes U, 2007, LECT NOTES COMPUT SC, V4372, P42 COX TF, 1991, COMMUN STAT THEORY, V20, P2943, DOI 10.1080/03610929108830679 FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102 Gloor R.A., 2009, P INT C COMP SCI ENG, P215 Heer J., 2005, P IEEE S INF VIS, P32 Henry N, 2007, IEEE T VIS COMPUT GR, V13, P1302, DOI 10.1109/TVCG.2007.70582 Henry N, 2006, IEEE T VIS COMPUT GR, V12, P677, DOI 10.1109/TVCG.2006.160 Huisman M., 2005, MODELS METHODS SOCIA, P270 Ingram S, 2009, IEEE T VIS COMPUT GR, V15, P249, DOI 10.1109/TVCG.2008.85 Jolliffe I.T., 2002, PRINCIPAL COMPONENT, P487 LI CT, 2009, P INT C ADV SOC NETW, P255 Minghim R, 2006, P SPIE VIS DAT AN SA Namata G. M., 2007, P 16 ACM C INF KNOWL, P939, DOI 10.1145/1321440.1321580 Navarro G, 2001, ACM COMPUT SURV, V33, P31, DOI 10.1145/375360.375365 Paulovich FV, 2008, IEEE T VIS COMPUT GR, V14, P564, DOI 10.1109/TVCG.2007.70443 PAULOVICH FV, 2007, SIBGRAPI 07, P27 Perer A, 2006, IEEE T VIS COMPUT GR, V12, P693, DOI 10.1109/TVCG.2006.122 Pretorius AJ, 2006, IEEE T VIS COMPUT GR, V12, P685, DOI 10.1109/TVCG.2006.192 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen ZQ, 2006, IEEE T VIS COMPUT GR, V12, P1427, DOI 10.1109/TVCG.2006.107 Shneiderman B, 2006, IEEE T VIS COMPUT GR, V12, P733, DOI 10.1109/TVCG.2006.166 Smith M, 2009, INFORM TECHNOL MANAG, V10, P123, DOI 10.1007/s10799-009-0057-2 Telles GP, 2007, COMPUT GRAPH-UK, V31, P327, DOI 10.1016/j.cag.2007.01.024 Velardi P., 2008, P IEEE INT C SEM COM, P18 Wattenberg M., 2006, P SIGCHI C HUM FACT, P811, DOI 10.1145/1124772.1124891 Zhang ZK, 2011, J COMPUT SCI TECH-CH, V26, P767, DOI 10.1007/s11390-011-0176-1 NR 30 TC 2 Z9 3 PU SCIENCE PRESS PI BEIJING PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA SN 1000-9000 J9 J COMPUT SCI TECH-CH JI J. Comput. Sci. Technol. PD JUL PY 2012 VL 27 IS 4 BP 791 EP 810 DI 10.1007/s11390-012-1265-5 PG 20 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science GA 974DS UT WOS:000306410600013 ER PT J AU Wu, JW Tseng, JCR Yu, WD Yang, JB Lee, SM Tsai, WN AF Wu, Ji-Wei Tseng, Judy C. R. Yu, Wen-Der Yang, Jyh-Bin Lee, Shun-Min Tsai, Wen-Nung TI An integrated proactive knowledge management model for enhancing engineering services SO AUTOMATION IN CONSTRUCTION LA English DT Article DE Engineering consulting; Knowledge management; Proactive problem-solving; Business intelligence ID SYSTEM AB More and more construction organizations have adopted Knowledge Management (KM) to enhance their engineering services. However, most of the traditional KM methods suffer from their "reactive mode" of problem solving. To cope with this problem, a newly developed model, the Integrated Proactive Knowledge Management Model (IPKMM), is proposed in this paper. A leading engineering consulting firm in Taiwan was selected as a case study to implement the proposed model. The system implementation of IPKMM, the Integrated Proactive Knowledge Management System (IPKMS), is verified with real world cases. A novel Business Intelligence Index (BII) is also proposed in this paper to evaluate the relative competitiveness of different KM models. It is confirmed from the case study that IPKMM can significantly improve the efficiency of problem-solving and the competitiveness of an engineering consulting firm in the service market. This study demonstrates that IPKMM has great potential in enhancing emergent problem-solving for engineering consultants. (C) 2012 Elsevier B.V. All rights reserved. C1 [Yu, Wen-Der] Chung Hua Univ, Dept Const Management, Hsinchu, Taiwan. [Wu, Ji-Wei] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan. [Tseng, Judy C. R.] Chung Hua Univ, Dept Comp Sci & Informat Engn, Hsinchu, Taiwan. [Yang, Jyh-Bin] Natl Cent Univ, Grad Inst Const Engg & Management, Jhongli, Taoyuan, Taiwan. [Tsai, Wen-Nung] Yu Da Univ, Dept Multimedia & Game Sci, Miaoli, Taiwan. RP Yu, WD (reprint author), Chung Hua Univ, Dept Const Management, Hsinchu, Taiwan. EM wenderyu@chu.edu.tw CR AAMODT A, 1994, AI COMMUN, V7, P39 Chinowsky P, 2007, J MANAGE ENG, V23, P27, DOI [10.1061/(ASCE)0742-597X(2007)23:1(27), 10.1016/(ASCE)0742-597X] Dave B, 2009, AUTOMAT CONSTR, V18, P894, DOI 10.1016/j.autcon.2009.03.015 H Li, 1998, CONSTRUCTION MANAGEM, V16, P721, DOI 10. 1080/014461998372015 Hopcroft J. E., 2006, INTRO AUTOMATA THEOR Johansson F., 2002, MED EFFECT BREAKTHRO Mezher T, 2005, J MANAGE ENG, V21, P138, DOI 10.1061/(ASCE)0742-597X(2005)21:3(138) Mezher Toufic, 2009, Journal of Cases on Information Technology, V11, DOI 10.4018/jcit.2009070101 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 Runeson G., 1994, AUSTR I BUILDING PAP, V5, P3 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMITH GF, 1992, DECIS SUPPORT SYST, V8, P29, DOI 10.1016/0167-9236(92)90035-N Sun Q.F., 2010, P 3 INT C INF MAN IN, P235 Tseng JCR, 2007, ELECTRON COMMER R A, V6, P19, DOI 10.1016/j.elerap.2006.04.009 Wenger E.C., 2000, HARVARD BUSINESS JAN, P139 Wu J.W., 2007, P 2007 INT C ENT SYS Yu W., 2009, CONSTR MANAGE EC, V27, P733, DOI DOI 10.1080/01446190903074978 Yu W. D., 2006, P INT S AUT ROB CONS, P124 Yu W.D., 2007, P INT S AUT ROB CONS, P493 Yu W.D., 2007, INTRO PISP MODEL CEC Yu W.D., 2006, P 5 INT C ENG COMP T, P70 Yu W.D., 2007, P CME 25 C JUL 16 18 Yu W.D., 2007, P 4 INT C CONSTR 21, P574 Yu WD, 2010, AUTOMAT CONSTR, V19, P808, DOI 10.1016/j.autcon.2010.05.003 NR 25 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0926-5805 J9 AUTOMAT CONSTR JI Autom. Constr. PD JUL PY 2012 VL 24 BP 81 EP 88 DI 10.1016/j.autcon.2012.02.006 PG 8 WC Construction & Building Technology; Engineering, Civil SC Construction & Building Technology; Engineering GA 948HS UT WOS:000304494800009 ER PT J AU Gemmell, J Schimoler, T Mobasher, B Burke, R AF Gemmell, Jonathan Schimoler, Thomas Mobasher, Bamshad Burke, Robin TI Resource recommendation in social annotation systems: A linear-weighted hybrid approach SO JOURNAL OF COMPUTER AND SYSTEM SCIENCES LA English DT Article DE Resource recommendation; Social annotation system; Hybrid recommenders ID FOLKSONOMIES; MODEL AB Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation - personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility. (C) 2011 Elsevier Inc. All rights reserved. C1 [Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad; Burke, Robin] Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA. RP Gemmell, J (reprint author), Depaul Univ, Ctr Web Intelligence, Sch Comp, 243 S Wabash Ave, Chicago, IL 60604 USA. EM jgemmell@cs.depaul.edu; tschimoler@cs.depaul.edu; mobasher@cs.depaul.edu; rburke@cs.depaul.edu CR Begelman G., 2006, COLL WEB TAGG WORKSH BOGERS T, 2008, P 2008 ACM C REC SYS, P287, DOI 10.1145/1454008.1454053 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Felfernig A., 2008, P 10 INT C EL COMM, P3 Gemmell J., 2008, LNCS, V5182, P196 Gemmell J, 2009, P 3 ACM C REC SYST R, P45, DOI 10.1145/1639714.1639724 Gemmell J., 2009, RECOMMENDER SYSTEMS Gemmell J., 2010, P 19 ACM INT C INF K, P829, DOI 10.1145/1871437.1871543 Gemmell J., 2010, RECOMMENDER SYSTEMS Gemmell J., 2009, EUR C MACH LEARN PRI Gemmell J., 2009, 7 WORKSH INT TECHN W Goyal A, 2010, WSDM 10 Guan Z., 2010, P 19 INT C WORLD WID, P391, DOI 10.1145/1772690.1772731 Hayes C., 2007, P 1 INT C WEBL SOC M Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Hotho A., 2006, P CONC STRUCT TOOL I Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Lipczak M., 2008, P ECML PKDD 2008 DIS Macgregor G., 2006, Library Review, V55, DOI 10.1108/00242530610667558 Marlow C., 2006, P 17 C HYP HYP, P31, DOI DOI 10.1145/1149941.1149949 Mathes A., 2004, COMPUT MEDIAT COMMUN, V47 Mika P, 2005, LECT NOTES COMPUT SC, V3729, P522 Niwa S., 2006, 3 INT C INF TECHN NE Page L., PAGERANK CITATION RA Plangprasopchok A., 2007, AAAI WORKSH INF INT Rendle S., 2009, ECML PKDD 2008 DISC, P235 Rendle Steffen, 2010, P 3 ACM INT C WEB SE, P81, DOI 10.1145/1718487.1718498 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Sen S., 2009, P 18 INT C WORLD WID, P671, DOI 10.1145/1526709.1526800 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 SHEPITSEN A, 2008, P 2008 ACM C REC SYS, P259, DOI 10.1145/1454008.1454048 SYMEONIDIS P, 2008, P 2008 ACM C REC SYS, P43, DOI 10.1145/1454008.1454017 Tso-Sutter K., 2008, P 2008 ACM S APPL CO Vander Wal T, FOLKSONOMY INFORM AR Voss J., 2007, 10 INT S INF SCI COL Wetzker R., 2009, P WSDM 09 WORKSH EXP, P25, DOI 10.1145/1506250.1506255 Wetzker R., 2010, P 3 ACM INT C WEB SE, P71, DOI 10.1145/1718487.1718497 Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 NR 42 TC 7 Z9 8 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 0022-0000 J9 J COMPUT SYST SCI JI J. Comput. Syst. Sci. PD JUL PY 2012 VL 78 IS 4 SI SI BP 1160 EP 1174 DI 10.1016/j.jcss.2011.10.006 PG 15 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA 920HX UT WOS:000302396000010 ER PT J AU Olvera, EP Godoy, D AF Olvera, E. P. Godoy, D. TI Evaluating Term Weighting Schemes for Content-based Tag Recommendation in Social Tagging Systems SO IEEE LATIN AMERICA TRANSACTIONS LA Spanish DT Article DE Folksonomy; Social Tagging Systems; Term Weighting Schemes ID WEB; FOLKSONOMIES; BOOKMARKING; RETRIEVAL; SEARCH AB Social tagging systems allow users to publish different type of resources, such as Web pages or pictures, annotate them using keywords or tags and share their resources with other users. These systems achieved widespread success on the Web on account of the simplicity for organizing resources using open-ended tags. Recently, tag recommendation strategies have been proposed to alleviate the problems of ambiguity, syntactic variations and noise in tags cause by the inherent characteristics of natural language. In this work we proposed a content-based approach that generates a list of suggested tags for annotating a given resource starting from an analysis of its textual content exclusively. Thus, the proposed method can be used in situations in which there is not enough information for creating a tag-based user profile or compare the user with others. For extracting the more relevant words different term weighting approaches were evaluated, particularly considering the HTML structure of Web pages and the grammatical category of words in order to determine promising tag candidates. Experimental results of applying this technique to tag recommendation using several term weighting approaches are reported and compared. C1 [Olvera, E. P.] Univ Tecn Estatal Quevedo, Inst Informat, Quevedo, Los Rios, Ecuador. [Godoy, D.] Univ Nacl Ctr Prov Buenos Aires, ISISTAN Res Inst, Buenos Aires, DF, Argentina. [Godoy, D.] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Olvera, EP (reprint author), Univ Tecn Estatal Quevedo, Inst Informat, Quevedo, Los Rios, Ecuador. EM eliaspo@uteq.edu.ec; daniela.godoy@isistan.unicen.edu.ar CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Astrain JJ, 2010, IEEE LAT AM T, V8, P88, DOI 10.1109/TLA.2010.5453951 Choi B, 2005, STUD FUZZ SOFT COMP, V180, P221 Diederich J., 2006, P 1 INT WORKSH BUILD Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Hong L., 2008, P AVI 08, P65, DOI 10.1145/1385569.1385582 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Huang Y-C., 2008, AAAI SPRING S SOC IN, P36 Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 Kim HN, 2010, ELECTRON COMMER R A, V9, P73, DOI 10.1016/j.elerap.2009.08.004 Lipczak M., 2008, P ECML PKDD DISC CHA, P84 Mathes A., 2004, COMPUTER MEDIATED CO Michlmayr E., 2007, P WORKSH TAGG MET SO Nakamoto R., 2008, ADV COMMUNICATION SY, P309 Noll MG, 2007, LECT NOTES COMPUT SC, V4825, P367 Oliveira ADR, 2012, IEEE LAT AM T, V10, P1661, DOI 10.1109/TLA.2012.6187612 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun J. T., 2005, P 14 INT C WORLD WID, P382, DOI 10.1145/1060745.1060803 Symeonidis P, 2010, IEEE T KNOWL DATA EN, V22, P179, DOI 10.1109/TKDE.2009.85 Tonkin E., 2006, D LIB, V12 Tso-Sutter KHL, 2008, APPLIED COMPUTING 2008, VOLS 1-3, P1995 Xu YF, 2006, LECT NOTES COMPUT SC, V3841, P733 Yeung C. M. A., 2008, SOC WEB KNOWL MAN SO ZHANG N., 2009, P 2 ACM WORKSH SOC W, P9, DOI 10.1145/1651437.1651440 Zorzo SD, 2011, IEEE LAT AM T, V9, P152, DOI 10.1109/TLA.2011.5765568 NR 26 TC 0 Z9 3 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1548-0992 J9 IEEE LAT AM T JI IEEE Latin Am. Trans. PD JUN PY 2012 VL 10 IS 4 BP 1973 EP 1980 DI 10.1109/TLA.2012.6272482 PG 8 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 047QC UT WOS:000311854600017 ER PT J AU Chen, WQ Persen, R AF Chen, Weiqin Persen, Richard TI Recommending Collaboratively Generated Knowledge SO COMPUTER SCIENCE AND INFORMATION SYSTEMS LA English DT Article DE Recommender systems; collaborative knowledge; knowledge building; emerging learning objects ID VECTOR-SPACE MODEL; LEARNING OBJECTS; INFORMATION-RETRIEVAL; SYSTEMS; WEB; HYPERMEDIA; RESOURCES AB With the development and adoption of information technologies in education, learners become active producer of knowledge. There is an increasing amount of content generated by learners in their learning process. These emerging learning objects (ELOs) could potentially be valuable as learning resources as well as for assessment purpose. However, the potentials also give rise to new challenges for indexing, sharing, retrieval and recommendation of such learning objects. In this research we have developed a recommender system for emerging learning objects generated in a collaborative knowledge building process and studied the implications and added values of the recommendations. We conducted two evaluations with learners to assess and improve the system's design and study the quality and effects of the recommendations. From the evaluations, we received generally positive feedback and the results confirm the added values of the recommendations for the knowledge building process. C1 [Chen, Weiqin; Persen, Richard] Univ Bergen, Dept Informat Sci & Media Studies, N-5020 Bergen, Norway. [Chen, Weiqin] Oslo & Akershus Univ Coll Appl Sci, Fac Technol Art & Design, NO-0130 Oslo, Norway. RP Chen, WQ (reprint author), Univ Bergen, Dept Informat Sci & Media Studies, POB 7802, N-5020 Bergen, Norway. EM weiqin.chen@infomedia.uib.no; richard.persen@infomedia.uib.no CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Al-Khalifa H. S., 2008, P 10 INT C INF INT W Anjewierden A., 2009, WORKSH INT INN SUPP Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Basu C., 1998, 15 NAT C ART INT Behama G., 2010, PROCEDIA COMPUTER SC Bruns A., 2005, 2005 INT S WIK Brusilovsky P., 2007, ADAPTIVE WEB Brusilovsky P, 2001, USER MODEL USER-ADAP, V11, P87, DOI 10.1023/A:1011143116306 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chen GD, 2008, COMPUT EDUC, V50, P77, DOI 10.1016/j.compedu.2006.03.004 Chen W., 2008, 3 EUR C TECHN ENH LE Cheng C. K., 2004, ONTOLOGY BASED SEMAN Craven M., 1998, PROF 15 NAT C ART IN Drachsler H., 2009, P 4 EUR C TECHN ENH Drachsler Hendrik, 2008, International Journal of Learning Technology, V3, DOI 10.1504/IJLT.2008.019376 Falloon G, 2010, AUSTRALAS J EDUC TEC, V26, P626 Garrison D. R., 2000, INTERNET HIGH EDUC, V2, P87, DOI DOI 10.1016/S1096-7516(00)00016-6 Hanauer DI, 2006, SCIENCE, V314, P1880, DOI 10.1126/science.1136796 Hartmann B., 2010, P 28 INT C HUM FACT Helic D., 2004, ADV TECHNOLOGY LEARN, V1, P8 Henze P. B. a. N., 2007, ADAPTIVE WEB Hoppe H. U., 2007, INTEGRATING LEARNIN, V2, P31 Janssen J, 2007, COMPUT EDUC, V49, P781, DOI 10.1016/j.compedu.2005.11.022 Khribi MK, 2009, EDUC TECHNOL SOC, V12, P30 Klamma R., 2006, P 1 EUR C TECHN ENH Klebl M, 2010, BRIT J EDUC TECHNOL, V41, P869 Kobsa A, 2001, KNOWL ENG REV, V16, P111, DOI 10.1017/S0269888901000108 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Linden G., 2003, IEEE INTERNET COMPUT Littlejohn A., 2003, REUSING ONLINE RESOU Manouselis N., 2007, P 1 WORKSH SOC INF R Manouselis N, 2010, J COMPUT ASSIST LEAR, V26, P227, DOI 10.1111/j.1365-2729.2010.00362.x McGreal R., 2004, ONLINE ED USING LEAR McNee S. M., 2002, ACM INT C COMP SUPP Melville P., 2002, 18 NAT C ART INT Modritscher F, 2010, PROCEDIA COMPUT SCI, V1, P2775, DOI 10.1016/j.procs.2010.08.002 Muukkonen H., 1999, P INT C COMP SUPP CO Nitchot A., 2010, P 9 INT C INF TECHN Northrup P. T., 2007, LEARNING OBJECTS INS Ochoa X., 2006, P 1 INT WORKSH CONT Recker MM, 2003, INSTR SCI, V31, P299, DOI 10.1023/A:1024686010318 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Rucker J, 1997, COMMUN ACM, V40, P73, DOI 10.1145/245108.245125 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos O. C., 2008, P 2008 ACM C REC SYS Scardamalia M., 2003, ENCY ED Singhal A., 2001, B IEEE COMPUTER SOC, P24 Tang T., 2005, INT J E LEARNING, V4, P105 Tsai K. H., 2006, P 6 IEEE INT C ADV L Walker A., 2004, INT J ARTIFICIAL INT, V14, P1 Wang TI, 2007, EDUC TECHNOL SOC, V10, P84 WILEY D, 2001, INSTRUCTIONAL USE LE Ye Y., 2001, SUPPORTING COMPONENT NR 56 TC 1 Z9 1 PU COMSIS CONSORTIUM PI NOVI SAD PA UNIV NOVI SAD, FAC TECH SCI, TRG DOSITEJA OBRADOVICA 6, NOVI SAD, 21000, SERBIA SN 1820-0214 J9 COMPUT SCI INF SYST JI Comput. Sci. Inf. Syst. PD JUN PY 2012 VL 9 IS 2 BP 871 EP 892 DI 10.2298/CSIS111129017C PG 22 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 975WO UT WOS:000306542400018 ER PT J AU Chen, YL Chiu, YT AF Chen, Yen-Liang Chiu, Yu-Ting TI Vector space model for patent documents with hierarchical class labels SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE document classification; feature selection; hierarchical class label; vector space model (VSM) ID RETRIEVAL; CLASSIFICATION; INFORMATION AB A vector space model (VSM) composed of selected important features is a common way to represent documents, including patent documents. Patent documents have some special characteristics that make it difficult to apply traditional feature selection methods directly: (a) it is difficult to find common terms for patent documents in different categories; and (b) the class label of a patent document is hierarchical rather than flat. Hence, in this article we propose a new approach that includes a hierarchical feature selection (HFS) algorithm which can be used to select more representative features with greater discriminative ability to present a set of patent documents with hierarchical class labels. The performance of the proposed method is evaluated through application to two documents sets with 2400 and 9600 patent documents, where we extract candidate terms from their titles and abstracts. The experimental results reveal that a VSM whose features are selected by a proportional selection process gives better coverage, while a VSM whose features are selected with a weighted-summed selection process gives higher accuracy. C1 [Chen, Yen-Liang] Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. RP Chen, YL (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. EM ylchen@mgt.ncu.edu.tw CR Baeza-Yates R, 1999, MODERN INFORMATION R, P163 Chen YL, 2011, INFORM PROCESS MANAG, V47, P309, DOI 10.1016/j.ipm.2010.06.001 Fall CJ, 2003, P ACMS SPEC INT GROU, V37, P10, DOI DOI 10.1145/945546.945547 Feldman R, 2007, TEXT MINING HDB ADV Han J, 2006, DATA MINING CONCEPTS, P89 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hotho A, 2005, J LANGUAGE TECHNOLOG, V20, P19 Jiang CT, 2010, KNOWL-BASED SYST, V23, P302, DOI 10.1016/j.knosys.2009.11.010 Kang IS, 2007, INFORM PROCESS MANAG, V43, P1173, DOI 10.1016/j.ipm.2006.11.006 Karanikolas NN, 2010, J INF SCI, V36, P421, DOI 10.1177/0165551510368620 Kim JH, 2007, INFORM PROCESS MANAG, V43, P1200, DOI 10.1016/j.ipm.2007.02.002 Lai KK, 2006, J AM ACAD BUSINESS, V8, P248 Larkey L., 1999, P 4 ACM C DIG LIB, P79 Larkey LS, 1998, AAAI 98 WORKSH LEARN, P87 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 Manning C. D., 2008, INTRO INFORM RETRIEV, P234 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Tan PN, 2005, INTRO DATA MINING, P19 Tikk D., 2007, EMERGING TECHNOLOGIE, P244 Trappey A.J.C., 2006, J MANAGE, V23, P413 Trappey AJC, 2006, EXPERT SYST APPL, V31, P755, DOI 10.1016/j.eswa.2006.01.013 Trappey AJC, 2008, IND MANAGE DATA SYST, V108, P245, DOI 10.1108/02635570810847608 Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 WIPO, 2010, FREQ ASK QUEST INT P Xue C, 2010, 2010 7 INT C FUZZ SY, P1497, DOI 10.1109/FSKD.2010.5569326 NR 29 TC 2 Z9 2 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD JUN PY 2012 VL 38 IS 3 BP 222 EP 233 DI 10.1177/0165551512437635 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 957KE UT WOS:000305159500002 ER PT J AU Schedl, M AF Schedl, Markus TI #nowplaying Madonna: a large-scale evaluation on estimating similarities between music artists and between movies from microblogs SO INFORMATION RETRIEVAL LA English DT Article DE Social media mining; Microblog analysis; Vector space model; Term weighting; Information extraction; Evaluation ID INFORMATION; SYSTEM AB Different term weighting techniques such as or BM25 have been used intensely for manifold text-based information retrieval tasks. Their use for modeling term profiles for named entities and subsequent calculation of similarities between these named entities have been studied to a much smaller extent. The recent trend of microblogging made available massive amounts of information about almost every topic around the world. Therefore, microblogs represent a valuable source for text-based named entity modeling. In this paper, we present a systematic and comprehensive evaluation of different term weighting measures, normalization techniques, query schemes, index term sets, and similarity functions for the task of inferring similarities between named entities, based on data extracted from microblog posts. We analyze several thousand combinations of choices for the above mentioned dimensions, which influence the similarity calculation process, and we investigate in which way they impact the quality of the similarity estimates. Evaluation is performed using three real-world data sets: two collections of microblogs related to music artists and one related to movies. For the music collections, we present results of genre classification experiments using as benchmark genre information from allmusic.com . For the movie collection, we present results of multi-class classification experiments using as benchmark categories from IMDb . We show that microblogs can indeed be exploited to model named entity similarity with remarkable accuracy, provided the correct settings for the analyzed aspects are used. We further compare the results to those obtained when using Web pages as data source. C1 Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. RP Schedl, M (reprint author), Johannes Kepler Univ Linz, Dept Computat Percept, Altenberger Str 69, A-4040 Linz, Austria. EM markus.schedl@jku.at FU Austrian Science Funds (FWF) [P22856-N23] FX The author would like to express his gratitude to Tim Pohle for providing parts of the source code for evaluation. He further wishes to acknowledge the reviewers for their profound and very helpful comments. This research is supported by the Austrian Science Funds (FWF): P22856-N23. CR Armentano M.G., 2011, P IJCAI 2011 INT WOR Aucouturier JJ, 2002, P IEEE INT C MULT EX Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE Baumann S., 2003, P 3 INT C WEB DEL MU Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Brill E., 1992, P 3 C APPL NAT LANG, P152, DOI 10.3115/974499.974526 Buckley C., 2000, P 23 ANN INT ACM SIG Celma O., 2008, THESIS U POMPEU FABR Celma O., 2006, P 7 INT C MUS INF RE Cheng Z, 2010, P 19 ACM INT C INF K Cimiano P., 2004, ACM SIGKDD EXPLORATI, V6, P24, DOI 10.1145/1046456.1046460 Cimiano P, 2004, P 13 INT C WORLD WID Cohen W. W., 2000, WWW9 COMPUTER NETWOR, V33, P1 Cohen W. W., 1995, P 12 INT C MACH LEAR Debole F., 2003, P 18 ACM S APPL COMP Dong A., 2010, P 19 INT C WORLD WID, P331, DOI DOI 10.1145/1772690.1772725 Duan Y., 2010, P 23 INT C COMP LING, P295 Evans M., 2011, TWITTER ENJOYS MAJOR Geleijnse G., 2006, P 7 INT C MUS INF RE Phillips A., 1985, SIGKDD EXPLORATIONS, V31, P329, DOI DOI 10.1145/1656274.1656278 Hu X., 2007, P 8 INT C MUS INF RE Hu X., 2005, P 6 INT C MUS INF RE Java A., 2007, P 9 WEBKDD 1 SNA KDD Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Knees P., 2008, P 2 WORKSH LEARN SEM Knees P., 2007, P 30 ANN INT ACM SIG Knees P., 2004, P 5 INT S MUS INF RE Knees P, 2007, IEEE MULTIMEDIA, V14, P46, DOI 10.1109/MMUL.2007.48 KOREN Y, 2009, P 15 ACM SIGKDD INT, P447, DOI DOI 10.1145/1557019.1557072 Kwak H., 2010, P 19 INT C WORLD WID Lan M., 2005, SPEC INT TRACK 14 IN LIN JH, 1991, IEEE T INFORM THEORY, V37, P145, DOI 10.1109/18.61115 LUHN HP, 1957, IBM J RES DEV, V1, P309 Pachet F., 2000, P CONT BAS MULT INF Pampalk E., 2007, P 8 INT C MUS INF RE Pampalk E., 2005, P 9 EUR C RES ADV TE Perez-Iglesias J., 2009, ABS09115046 CORR Pohle T, 2007, IEEE T MULTIMEDIA, V9, P567, DOI 10.1109/TMM.2006.887991 Robertson S., 1999, P 7 TEXT RETR C TREC ROBERTSON SE, 1995, INFORM PROCESS MANAG, V31, P345, DOI 10.1016/0306-4573(94)00051-4 Sakaki T, 2010, P 19 INT C WORLD WID SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, P 28 ANN INT ACR SIG Sankaranarayanan J, 2009, P 17 ACM SIGSPATIAL, P42, DOI 10.1145/1653771.1653781 Schedl M, 2011, INFORM PROCESS MANAG, V47, P426, DOI 10.1016/j.ipm.2010.09.002 Schedl M., 2005, P 4 INT WORKSH CONT Schedl M., 2008, THESIS J KEPLER U LI Schedl M, 2011, P IJCAI 2011 INT WOR Schedl M., 2008, P 2 INT WORKSH LEARN Schedl M., 2011, ACM T INFORM SYSTEMS, V29 Schedl M., 2007, P 9 EUR IEEE VGTC S Schedl M., 2010, P 11 INT SOC MUS INF Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sharifi B., 2010, HUM LANT TECH 2010 A SHESKIN D., 2004, HDB PARAMETRIC NONPA, V3rd Sriram B, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P841, DOI 10.1145/1835449.1835643 Teevan J, 2011, P 4 ACM INT C WEB SE Turnbull D., 2007, P 30 ANN INT ACM SIG Vapnik V., 1995, NATURE STAT LEARNING Weng J. S., 2010, P 3 ACM INT C WEB SE Whitman B., 2002, P INT COMP MUS C ICM Yarow J., 2011, TWITTER FINNALY REVE Zadel M., 2004, P 5 INT S MUS INF RE ZOBEL, 1998, ACM SIGIR FORUM, V32, P18 Zobel J., 2006, ACM COMPUT SURV, V38, P1 NR 67 TC 2 Z9 2 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 EI 1573-7659 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2012 VL 15 IS 3-4 SI SI BP 183 EP 217 DI 10.1007/s10791-012-9187-y PG 35 WC Computer Science, Information Systems SC Computer Science GA 945WT UT WOS:000304307800002 ER PT J AU Kalogeratos, A Likas, A AF Kalogeratos, Argyris Likas, Aristidis TI Text document clustering using global term context vectors SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Text mining; Document clustering; Semantic matrix; Data projection ID MODEL; BAG AB Despite the advantages of the traditional vector space model (VSM) representation, there are known deficiencies concerning the assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to define the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words-BOW) document vectors onto a '' feature space where problems such as text document clustering can be solved more efficiently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches. C1 [Kalogeratos, Argyris; Likas, Aristidis] Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece. RP Likas, A (reprint author), Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece. EM akaloger@cs.uoi.gr; arly@cs.uoi.gr CR AlSumait L, 2008, SURVEY TEXT MINING, P219 Apte C., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Beil F., 2002, KDD 02, P436, DOI DOI 10.1145/775047.775110 Beyer K., 1999, INT C DAT THEOR, P217 Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Chen CL, 2011, KNOWL INF SYST, V28, P687, DOI 10.1007/s10115-010-0364-2 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Farahat AK, 2011, KNOWL INF SYST, V28, P365, DOI 10.1007/s10115-010-0367-z Fung B.C., 2003, P SIAM INT C DAT MIN Ghosh J, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P73, DOI 10.1007/3-540-28349-8_3 Grauman K, 2007, J MACH LEARN RES, V8, P725 Hotho A, 2001, KNSTLICHE INTELL, V4, P48 Hu X., 2009, P 18 ACM C INF KNOWL, P919, DOI DOI 10.1145/1645953.1646071 Jing J, 2006, P SIAM SDM WORKSH TE Karypis G, 2000, TR000016 U MINN Keikha M, 2008, SURVEY TEXT MINING, P219 Lebanon G, 2007, J MACH LEARN RES, V8, P2405 LEWIS DD, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P37 Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 McQueen J., 1967, P 5 BERK S MATH STAT, P281 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Mladenic D., 1998, THESIS U LJUBLJANA Ng A.Y, 2001, ADV NEURAL INFORM PR, V14, P849 Ni XL, 2011, KNOWL INF SYST, V27, P345, DOI 10.1007/s10115-010-0299-7 Porter M. F., 1997, READINGS INFORM RETR, P313 Pu W, 2007, IEEE DATA MINING, P625 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang P., 2008, KDD 08, P713, DOI DOI 10.1145/1401890.1401976 Wikipedia, 2004, WIK FREE ENC Wong S. K. Michael, 1985, SIGIR, P18, DOI DOI 10.1145/253495.253506 NR 31 TC 9 Z9 9 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD JUN PY 2012 VL 31 IS 3 BP 455 EP 474 DI 10.1007/s10115-011-0412-6 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 943JC UT WOS:000304116100003 ER PT J AU Bock, J Hettenhausen, J AF Bock, Juergen Hettenhausen, Jan TI Discrete particle swarm optimisation for ontology alignment SO INFORMATION SCIENCES LA English DT Article DE Discrete particle swarm optimisation; Ontology alignment; Ontology mapping; Ontology matching; Semantic integration; Semantic Web AB Particle swarm optimisation (PSO) is a biologically-inspired, population-based optimisation technique that has been successfully applied to various problems in science and engineering. In the context of semantic technologies, optimisation problems also occur but have rarely been considered as such. This work addresses the problem of ontology alignment, which is the identification of overlaps in heterogeneous knowledge bases backing semantic applications. To this end, the ontology alignment problem is revisited as an optimisation problem. A discrete particle swarm optimisation algorithm is designed in order to solve this optimisation problem and compute an alignment of two ontologies. A number of characteristics of traditional PSO algorithms are partially relaxed in this article, such as fixed dimensionality of particles. A complex fitness function based on similarity measures of ontological entities, as well as a tailored particle update procedure are presented. This approach brings several benefits for solving the ontology alignment problem, such as inherent parallelisation, anytime behaviour, and flexibility according to the characteristics of particular ontologies. The presented algorithm has been implemented under the name MapPSO (ontology mapping using particle swarm optimisation). Experiments demonstrate that applying PSO in the context of ontology alignment is a feasible approach. (C) 2010 Elsevier Inc. All rights reserved. C1 [Bock, Juergen] FZI Res Ctr Informat Technol, Dept Informat Proc Engn IPE, D-76131 Karlsruhe, Germany. [Hettenhausen, Jan] Griffith Univ, Inst Integrated & Intelligent Syst, Brisbane, Qld 4111, Australia. RP Bock, J (reprint author), FZI Res Ctr Informat Technol, Dept Informat Proc Engn IPE, Haid & Neu Str 10-14, D-76131 Karlsruhe, Germany. EM bock@fzi.de; j.hettenhausen@griffith.edu.au FU German Federal Ministry of Economics (BMWi) [01MQ07019] FX The presented research was partially funded by the German Federal Ministry of Economics (BMWi) under the project "Theseus" (No. 01MQ07019). The authors extend their gratitude to Florian Berghoff for his contributions to the implementation of the method. CR Baader F., 2003, DESCRIPTION LOGIC HD Correa E. S., 2006, P GEN EV COMP C GECC, P35, DOI 10.1145/1143997.1144003 Correa Elon S., 2007, P GEN EV COMP C, P2651, DOI 10.1145/1274000.1274081 Doshi Prashant, 2006, P 21 NAT C ART INT 1 Ehrig M., 2005, P WORKSH INT ONT, V156, P72 Ehrig Marc, 2005, P K CAP 2005 WORKSH, V156, P25 Engelbrecht A., 2005, FUNDAMENTALS COMPUTA Euzenat J, 2004, FR ART INT, V110, P333 Euzenat J, 2004, LECT NOTES COMPUT SC, V3298, P698 Gruber T., 1993, FORMAL ONTOLOGY CONC Hettenhausen Jan, 2007, THESIS GRIFFITH U Hitzler P., 2009, OWL 2 WEB ONTOLOGY L Ji Q., 2008, P 12 INT C INF PROC Jung JJ, 2010, INFORM SCIENCES, V180, P3248, DOI 10.1016/j.ins.2010.04.018 Kennedy J., 1995, P IEEE INT C NEUR NE, V4 Kennedy J., 1997, IEEE INT C SYST MAN, V5, P4104, DOI DOI 10.1109/ICSMC.1997.637339 Martinez-Gil J., 2008, CEUR WORKSHOP P, P419 Melnik S, 2002, P 18 INT C DAT ENG I Miller E., 2004, RDF PRIMER W3C RECOM Nagy Miklos, 2008, CEUR WORKSHOP P, V431 Noy N. F., 2001, WORKSH ONT INF SHAR Oren E, 2008, LECT NOTES COMPUT SC, V5318, P98, DOI 10.1007/978-3-540-88564-1_7 Paulhelm Heiko, 2008, CEUR WORKSHOP P, V431 Qin BA, 2008, INFORM SCIENCES, V178, P3525, DOI 10.1016/j.ins.2008.05.026 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi Y, 1998, EV COMP P 1998 IEEE, P69 Shvaiko P., 2007, ONTOLOGY MATCHING Shvaiko Pavel, 2008, CEUR WORKSHOP P, V431 Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624 Tawil AR, 2008, INFORM SCIENCES, V178, P1011, DOI 10.1016/j.ins.2007.09.010 Wang J, 2006, P 2006 IEEE AS PAC C, P617 Yu XM, 2004, ELECTR POW SYST RES, V71, P27, DOI 10.1016/j.epsr.2004.01.002 Zhang GF, 2010, INFORM SCIENCES, V180, P3140, DOI 10.1016/j.ins.2010.04.028 NR 33 TC 20 Z9 21 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JUN 1 PY 2012 VL 192 BP 152 EP 173 DI 10.1016/j.ins.2010.08.013 PG 22 WC Computer Science, Information Systems SC Computer Science GA 921XR UT WOS:000302511900012 ER PT J AU Kara, S Alan, O Sabuncu, O Akpinar, S Cicekli, NK Alpaslan, FN AF Kara, Soner Alan, Ozgur Sabuncu, Orkunt Akpinar, Samet Cicekli, Nihan K. Alpaslan, Ferda N. TI An ontology-based retrieval system using semantic indexing SO INFORMATION SYSTEMS LA English DT Article DE Semantic web; Keyword-based; Query; Semantic indexing; Ontology ID INFORMATION; EXTRACTION; MODEL; TEXT; WEB AB In this paper, we present an ontology-based information extraction and retrieval system and its application in the soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inferencing and rules. Scalability is achieved by adapting a semantic indexing approach and representing the whole world as small independent models. The system is implemented using the state-of-the-art technologies in Semantic Web and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inferencing. Finally, we show how we use semantic indexing to solve simple structural ambiguities. (C) 2011 Elsevier Ltd All rights reserved. C1 [Kara, Soner; Alan, Ozgur; Sabuncu, Orkunt; Akpinar, Samet; Cicekli, Nihan K.; Alpaslan, Ferda N.] METU, Dept Comp Engn, Ankara, Turkey. RP Cicekli, NK (reprint author), METU, Dept Comp Engn, Ankara, Turkey. EM soner.kara85@gmail.com; alan@ceng.metu.edu.tr; orkunt@ceng.metu.edu.tr; samet@ceng.metu.edu.tr; nihan@ceng.metu.edu.tr; alpaslan@ceng.metu.edu.tr FU Scientific and Technical Council of Turkey [TUBITAK EEEAG-107E234, TUBITAK TEYDEB-3080231] FX This work is partially supported by The Scientific and Technical Council of Turkey Grant TUBITAK EEEAG-107E234 and by TUBITAK TEYDEB-3080231. CR Banko Michele, 2008, P 46 ANN M ASS COMP, P28 Casado R.M., 2005, 10 INT C APPL NAT LA, P67 Celino I, 2007, LECT NOTES COMPUT SC, V4607, P485 Cimiano P., 2004, 13 INT C WORLD WID W, P462 Ciravegna F, 2004, LECT NOTES COMPUT SC, V3053, P312 Davies J, 2004, HAW INT C SYST SCI, V4, p[112, 40] Etzioni O., 2004, WWW 04, P100 Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001 Ferrucci D., 2004, Natural Language Engineering, DOI 10.1017/S1351324904003523 Freitag D, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P577 Gonzalo J., 1998, COL ACL 98 WORKSH US, P38 Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Hunter J., 2001, P 1 SEM WEB WORK S S, P261 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kiryakov A., 2005, J WEB SEMANTICS Kiyavitskaya N, 2009, DATA KNOWL ENG, V68, P1470, DOI 10.1016/j.datak.2009.07.012 Lei Y., 2006, SEMSEARCH SEARCH ENG, P238 Liao M., 1999, P WORKSH LEARN SOFTW, P11 Mihalcea R., 2000, P ACL 2000 WORKSH RE, P35 MOONEY R, 2005, SIGKDD EXPLORATIONS, V7, P3 Muller HM, 2004, PLOS BIOL, V2, P1984, DOI 10.1371/journal.pbio.0020309 Muslea I., 2003, IJCAI 2003, P415 Nitta N., 2000, P 15 INT C PATT REC, V4, P718, DOI 10.1109/ICPR.2000.903018 Oberle D, 2007, J WEB SEMANT, V5, P156, DOI 10.1016/j.websem.2007.06.002 Pisanelli D.M., 2004, ONTOLOGIES MED RAMAKRISHNAN C, 2006, INT SEM WEB C, P583 Saggion H, 2004, DATA KNOWL ENG, V48, P247, DOI 10.1016/S0169-023X(03)00108-3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutz A, 2005, LECT NOTES COMPUT SC, V3729, P593 Shah U, 2002, C INF KNOWL MGMT, P461 Sonntag D., 2007, P INT WORKSH AL HUM Surdeanu M., P NIST 2007 AUT CONT Tsinaraki C, 2007, IEEE T KNOWL DATA EN, V19, P219, DOI 10.1109/TKDE.2007.33 Tunaoglu Doruk, 2009, Proceedings of the 2009 IEEE International Conference on Semantic Computing (ICSC), DOI 10.1109/ICSC.2009.16 Uren V, 2007, KNOWL ENG REV, V22, P361, DOI 10.1017/S0269888907001233 Wang H., 2008, ESWC, P584 Wessman A., 2005, 4 INT C INF SYST TEC, P239 Xu C., 2006, MULTIMEDIA 06 P 14 A, P221 Yang Yongchuan, 2007, INT C NAT LANG PROC, P96 Zhou Q., 2007, LECT NOTES COMPUTER, V4825, P687 NR 43 TC 16 Z9 18 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD JUN PY 2012 VL 37 IS 4 SI SI BP 294 EP 305 DI 10.1016/j.is.2011.09.004 PG 12 WC Computer Science, Information Systems SC Computer Science GA 904PB UT WOS:000301209300002 ER PT J AU Sanderson, M Croft, WB AF Sanderson, Mark Croft, W. Bruce TI The History of Information Retrieval Research SO PROCEEDINGS OF THE IEEE LA English DT Article DE History; information retrieval; ranking algorithms ID PROBABILITY RANKING PRINCIPLE; SYSTEMS; IR AB This paper describes a brief history of the research and development of information retrieval systems starting with the creation of electromechanical searching devices, through to the early adoption of computers to search for items that are relevant to a user's query. The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. The paper closes with speculation on where the future of information retrieval lies. C1 [Sanderson, Mark] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia. [Croft, W. Bruce] Univ Massachusetts, Dept Comp Sci, Amherst, MA 01003 USA. RP Sanderson, M (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic 3001, Australia. EM mark.sanderson@rmit.edu.au; croft@cs.umass.edu RI Sanderson, Mark/ OI Sanderson, Mark/0000-0003-0487-9609 CR [Anonymous], 1954, Chem. Eng. News, V32, P891, Patent No. 1838389291931 [Anonymous], 1954, Chem. Eng. News, V32, P866, Patent No. 001351692311920 Baeza-Yates Ricardo, 2011, MODERN INFORM RETRIE BJORNER S, 2003, SEARCHER, V11, P36 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BROWNSON HL, 1960, SCIENCE, V132, P1922, DOI 10.1126/science.132.3444.1922 Buckland Michael, 2006, E GOLDBERG HIS KNOWL Bush V., 1945, ATLANTIC MONTHLY, P101 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Cleverdon C. W., 1959, P INT C SCI INF, P687 Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 2009, SEARCH ENGINES INFOR, V1st Cucerzan Silviu, 2004, P EMNLP 04, P293 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENNIS BK, 1962, J CHEM DOC, V2, P234, DOI 10.1021/c160007a014 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Eliot Simon, 2009, COMPANION HIST BOOK FUHR N, 1989, ACM T INFORM SYST, V7, P183, DOI 10.1145/65943.65944 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Goldberg E., 1931, CHEM ENG NEWS, Patent No. U.S. Patent 183 838 929-1931 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Hiemstra D, 1998, LNCS, V1513, P569 Holmstrom J. E., 1948, P ROY SOC SCI INF C, P77 Jahoda G., 1961, STATE LIB ART, V4, P139 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1958, IBM J RES DEV, V2, P159 Manning C.D., 2008, INTRO INFORM RETRIEV Maron M. E., 1959, 3 THOMPS RAM WOOLDR McBryan O., 1994, P 1 INT WORLD WID WE Meister D., 1967, CR918 NASA BUNK RAM0 Mitchell H. F., 1953, AM DOC, V4, P16, DOI 10.1002/asi.5090040105 Mooers C. N., 1950, P ASS COMP MACH C Mooers C. N., 1959, P W JOINT COMP C, P81, DOI 10.1145/1457838.1457853 NANUS B, 1960, B MED LIBR ASSOC, V48, P278 Peng Fuchun, 2007, P 30 ANN INT ACM SIG, P639, DOI 10.1145/1277741.1277851 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Radlinski F., 2005, P 11 ACM SIGKDD INT, P239, DOI 10.1145/1081870.1081899 ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J Robertson S. E., 1990, NIST SPECIAL PUBLICA, P109 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J. J., 1965, ISR9 HARV U SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1973, 73173 TR CORN U DEP Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAW RS, 1949, J DOC, V5, P164, DOI 10.1108/eb026148 Soper H. E., 1920, CHEM ENG NEWS, Patent No. U.S. Patent US00 135 169 231-1920 Sparck Jones K., 1981, INFORM RETRIEVAL EXP SPARCKJONES K, 1972, J DOC, V28, P11 Stevens M. E., 1964, P S STAT ASS METH ME Switzer P., 1963, VECTOR IMAGES DOCUME Taube M., 1952, AM DOC, V3, P213, DOI 10.1002/asi.5090030404 Turtle H., 1994, P 17 ANN INT ACM SIG, P212 van Rijsbergen C. J., 1979, INFORM RETRIEVAL VERHOEFF J, 1961, COMMUN ACM, V4, P557, DOI 10.1145/366853.366861 Voorhees E. M., 2001, NAT LANG ENG, V7, P361 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Walter C., 2005, INSIGHTS KRYDERS LAW NR 64 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 0018-9219 J9 P IEEE JI Proc. IEEE PD MAY PY 2012 VL 100 SI SI BP 1444 EP 1451 DI 10.1109/JPROC.2012.2189916 PG 8 WC Engineering, Electrical & Electronic SC Engineering GA 020TR UT WOS:000309838000032 ER PT J AU Laorden, C Santos, I Sanz, B Alvarez, G Bringas, PG AF Laorden, Carlos Santos, Igor Sanz, Borja Alvarez, Gonzalo Bringas, Pablo G. TI Word sense disambiguation for spam filtering SO ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS LA English DT Article DE Spam filtering; Word sense disambiguation; Secure e-commerce; Computer security ID CATEGORIZATION; KERNEL AB Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, these methods are merely syntactic and are unable to account for the underlying semantics of terms within messages. In this paper, we explore the use of semantics in spam filtering by introducing a preprocessing step of Word Sense Disambiguation (WSD). Based upon this disambiguated representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages. (C) 2011 Elsevier B. V. All rights reserved. C1 [Laorden, Carlos; Santos, Igor; Sanz, Borja; Bringas, Pablo G.] Univ Deusto, S3Lab, Bilbao 48007, Spain. [Alvarez, Gonzalo] CSIC, Inst Fis Aplicada, E-28006 Madrid, Spain. RP Laorden, C (reprint author), Univ Deusto, S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM claorden@deusto.es; isantos@deusto.es; borja.sanz@deusto.es; gonzalo@iec.csic.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Allan J., 1998, P DARPA BROADCAST NE, V1998 Amari S, 1999, NEURAL NETWORKS, V12, P783, DOI 10.1016/S0893-6080(99)00032-5 Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 Androutsopoulos I., 2000, P MACH LEARN TEXT IN Bar-Hillel Y., 1960, PRESENT STATUS AUTOM, V1, P91 Bates M., 1993, CHALLENGES NATURAL L Bishop C. M., 2006, PATTERN RECOGNITION Blanzieri E., 2007, P FLAIRS 20, P441 Bratko A, 2006, J MACH LEARN RES, V7, P2673 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Carpinter J, 2006, COMPUT SECUR, V25, P566, DOI 10.1016/j.cose.2006.06.001 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Carreras X., 2004, P 4 LREC, V4 Carreras X., 2002, P LREC, V2 Cashell B., 2004, ECONOMIC IMPACT CYBE Cooper G.F., 1991, P 7 C UNC ART INT Cormack G., 1991, 16 TEX RETR C TREC 2 Edmonds P., 2007, WORD SENSE DISAMBIGU Fellbaum C, 1998, WORDNET ELECT LEXICA Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Geiger D., 1997, MACH LEARN, P131 Gonzalo J., 1999, P EMNLP VLC, V99 Gopal R., 2011, EC ISSUES ADVERTISIN Hansell S., 2003, DIVERGING ESTIMATES Heron S., 2009, NETWORK SECURITY, V1, P11 Holmes G., 1994, MACHINE LEARNING WOR, P357 Ide N, 1998, COMPUT LINGUIST, V24, P1 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Jung J., 2004, P 4 ACM SIGCOMM C IN, P370, DOI 10.1145/1028788.1028838 Karlberger C., 2007, WOOT 07 P 1 USENIX W, P1 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Kolcz A., 2004, P 1 C EMAIL ANT CEAS Krovetz B., 2002, P LREC WORKSH CREAT Krovetz R., 1997, P 8 C EUR CHAPT ASS, P72 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 Leung C., 2009, THESIS HKUTO Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Lin H-T, 2003, STUDY SIGMOID KERNEL LOVINS JB, 1968, MECH TRANSL, V11, P22 Mallery J. C., 1988, THESIS MIT POLITICAL Mavroeidis D, 2005, LECT NOTES ARTIF INT, V3721, P181 Mihalcea R., 2005, P ACL 2005 INT POST, P56 Miller G. A., 1993, P ARPA WORKSH HUM LA, P303, DOI 10.3115/1075671.1075742 Mishne Gilad, 2005, P 1 INT WORKSH ADV I, P1 Mostafa Raad N., 2010, AFRICAN J BUSINESS M, V4, P2362 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Nelson B., 2009, MACHINE LEARNING CYB, P17, DOI 10.1007/978-0-387-88735-7_2 Platt J., 1999, ADV KERNEL METHODS S, V208 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN QUINLAN JR, 1986, MACH LEARN, V1, P1, DOI DOI 10.1023/A:1022643204877 Ramachandran A., 2006, DNS BASED BLACKLISTS Russell S.J., 2003, NORVIG ARTIFICIAL IN Sahami M., 1998, 1998 WORKSH MAD WISC, V62, P98 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Santos I, 2012, EXPERT SYST APPL, V39, P437, DOI 10.1016/j.eswa.2011.07.034 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Smith K.T., 2011, ACAD MARKETING STUDI Ustun B, 2006, CHEMOMETR INTELL LAB, V81, P29, DOI 10.1016/j.chemolab.2005.09.003 Vapnik V. N., 2000, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Voorhees E., 1999, INFORM EXTRACTION, P724 Weaver W, 1949, TECHNOLOGY PRESS MIT, P15 WILKINSON YA, 1990, MUTAGENESIS, V5, P99, DOI 10.1007/BF00393758 Xu H, 2010, EXPERT SYST APPL, V37, P18, DOI 10.1016/j.eswa.2009.02.059 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 NR 75 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1567-4223 J9 ELECTRON COMMER R A JI Electron. Commer. Res. Appl. PD MAY-JUN PY 2012 VL 11 IS 3 BP 290 EP 298 DI 10.1016/j.elerap.2011.11.004 PG 9 WC Business; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Business & Economics; Computer Science GA 965HL UT WOS:000305758100008 ER PT J AU Chen, AT AF Chen, Annie T. TI Exploring online support spaces: Using cluster analysis to examine breast cancer, diabetes and fibromyalgia support groups SO PATIENT EDUCATION AND COUNSELING LA English DT Article DE Support groups; Interface design; Cluster analysis; Fibromyalgia; Diabetes; Breast cancer ID INFORMATION NEEDS; INTERNET; PARTICIPATION; PROSTATE; HEALTH; WOMEN; EXPERIENCE; EMOTIONS; ILLNESS; SYMPTOM AB Objective: This study sought to characterize and compare online discussion forums for three conditions: breast cancer, type 1 diabetes and fibromyalgia. Though there has been considerable work examining online support groups, few studies have considered differences in discussion content between health conditions. In addition, in contrast to the extant literature, this study sought to employ a semi-automated approach to examine health-related online communities. Methods: Online discussion content for the three conditions was compiled, pre-processed, and clustered at the thread level using the bisecting k-means algorithm. Results: Though the clusters for each condition differed, the clusters fell into a set of common categories: Generic, Support, Patient-Centered, Experiential Knowledge, Treatments/Procedures, Medications, and Condition Management. Conclusion: The cluster analyses facilitate an increased understanding of various aspects of patient experience, including significant emotional and temporal aspects of the illness experience. Practice implications: The clusters highlighted the changing nature of patients' information needs. Information provided to patients should be tailored to address their needs at various points during their illness. In addition, cluster analysis may be integrated into online support groups or other types of online interventions to assist patients in finding information. (C) 2011 Elsevier Ireland Ltd. All rights reserved. C1 Univ N Carolina, Sch Informat & Lib Sci, Chapel Hill, NC 27599 USA. RP Chen, AT (reprint author), Univ N Carolina, Sch Informat & Lib Sci, 216 Lenoir Dr,CB 3360,100 Manning Hall, Chapel Hill, NC 27599 USA. EM atchen@email.unc.edu CR Alpers GW, 2005, COMPUT HUM BEHAV, V21, P361, DOI 10.1016/j.chb.2004.02.008 Antonina Bambina, 2007, ONLINE SOCIAL SUPPOR Arnold LM, 2008, PATIENT EDUC COUNS, V73, P114, DOI 10.1016/j.pec.2008.06.005 Barker KK, 2008, J HEALTH SOC BEHAV, V49, P20 Bartlett YK, PATIENT ED COUNS, V83, P113 Bennett RM, 2007, BMC MUSCULOSKEL DIS, V8, DOI 10.1186/1471-2474-8-27 Bennett RM, 2010, BMC MUSCULOSKEL DIS, V11, DOI 10.1186/1471-2474-11-134 Blank TO, 2010, COMPUT HUM BEHAV, V26, P1400, DOI 10.1016/j.chb.2010.04.016 Carpineto C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541884 Chen C, DATA KNOWL ENG, P1208 Funnel MM, 2004, CLIN DIABETES, V22, P123, DOI DOI 10.2337/DIACLIN.22.3.115 Gooden RJ, 2007, J HEALTH PSYCHOL, V12, P103, DOI 10.1177/1359105307071744 Greene JA, 2010, J GEN INTERN MED Han JY, 2008, J HEALTH PSYCHOL, V13, P1002, DOI 10.1177/1359105308097963 Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983 Herxheimer A, 2000, LANCET, V355, P1540, DOI 10.1016/S0140-6736(00)02174-7 Karypis G, 2003, 02017 CS U MINN Keselman A, 2008, J AM MED INFORM ASSN, V15, P496, DOI 10.1197/jamia.M2599 Klemm P, 1998, Comput Nurs, V16, P31 Klemm P, 2003, CIN-COMPUT INFORM NU, V21, P136, DOI 10.1097/00024665-200305000-00010 Lieberman MA, 2006, PSYCHO-ONCOL, V15, P160, DOI 10.1002/pon.932 Lin YJ, 2007, J AM MED INFORM ASSN, V14, P651, DOI 10.1197/jamia.M2215 Ma C, 2005, INT J MED INFORM, V75, P577 Madden S, 2006, SOC SCI MED, V63, P2962, DOI 10.1016/j.socscimed.2006.06.020 Pennebaker JW, 1996, COGNITION EMOTION, V10, P601 Rankin D, 2001, PATIENT EDUC COUNS, V83, P87 Ravert DR, 2004, DIABETES ED, V30, P827 Rozmovits L, 2004, PATIENT EDUC COUNS, V53, P57, DOI 10.1016/S0738-3991(03)00116-2 Rutledge DN, 2009, NURS RES, V58, P359, DOI 10.1097/NNR.0b013e3181b499d2 Rutten LJF, 2005, PATIENT EDUC COUNS, V57, P250, DOI 10.1016/j.pec.2004.06.006 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seale C, 2006, SOC SCI MED, V62, P2577, DOI 10.1016/j.socscimed.2005.11.016 Sillence E, 2004, CHI, V6, P663 Sim J, 2008, SOC SCI MED, V67, P57, DOI 10.1016/j.socscimed.2008.03.003 Steinbach M., 2000, KDD WORKSH TEXT MIN Sylvain H, 2002, J ADV NURS, V38, P264, DOI 10.1046/j.1365-2648.2002.02176.x van Uden-Kraan CF, 2009, PATIENT EDUC COUNS, V74, P61, DOI 10.1016/j.pec.2008.07.044 van Uden-Kraan CF, 2008, QUAL HEALTH RES, V18, P405, DOI 10.1177/1049732307313429 van Uden-Kraan CF, 2008, COMPUT HUM BEHAV, V24, P309, DOI 10.1016/j.chb.2007.01.014 Wise M, 2008, PATIENT EDUC COUNS, V70, P348, DOI 10.1016/j.pec.2007.11.009 Yamamoto Y, 2007, J BIOMED INFORM, V40, P114, DOI 10.1016/j.jbi.2006.07.004 Zhou Li, 2005, AMIA Annu Symp Proc, P874 Zhu M, 2008, WEB INTELLIGENCE INT, P77 Zrebiec JF, 2005, DIABETES EDUCATOR, V31, P825, DOI 10.1177/0145721705282162 NR 45 TC 10 Z9 10 PU ELSEVIER IRELAND LTD PI CLARE PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND SN 0738-3991 J9 PATIENT EDUC COUNS JI Patient Educ. Couns. PD MAY PY 2012 VL 87 IS 2 BP 250 EP 257 DI 10.1016/j.pec.2011.08.017 PG 8 WC Public, Environmental & Occupational Health; Social Sciences, Interdisciplinary SC Public, Environmental & Occupational Health; Social Sciences - Other Topics GA 946UY UT WOS:000304384200020 PM 21930359 ER PT J AU Prados-Suarez, B Molina, C Yanez, CP de Reyes, MP AF Prados-Suarez, Belen Molina, Carlos Pena Yanez, Carmen Prados de Reyes, Miguel TI Contextualized Access to Electronical Health Records in Cardiology SO IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE LA English DT Article DE Context awareness; electronic health record; fuzzy logic ID ONTOLOGIES; MODEL AB In this paper, we propose a new approach for accessing the electronical health records (EHR), and we apply it to the cardiology medical specialty. Though the use of EHR improves the storage and access to the information in it regarding the previous health records in papers, it entails the risk of having the same problems of huge size and of becoming inoperative and really difficult to handle, especially if the user is looking for a specific data item. Our proposal is based on the contextualization of the access, providing the user with the most important information for the assistance act in which he/she is involved. To do this, we define the set of possible contexts and consider different aspects of the pertinence of the documents to each context. We do it by using fuzzy logic and pay special attention to the efficiency, due to the huge size of the involved databases. Our proposal does not limit the access to the EHR, but establishes a prioritization based on the access needs, which provides the system with an additional advantage, easily enabling the use of new terminals and devices like tablet PCs and PDAs, which have great limitations in the interfaces. C1 [Prados-Suarez, Belen] Univ Granada, Dept Software Engn, E-18014 Granada, Spain. [Molina, Carlos] Univ Jaen, Dept Comp Sci, Jaen 23071, Spain. [Pena Yanez, Carmen; Prados de Reyes, Miguel] San Cecilio Hosp, Dept Informat, Granada 18012, Spain. [Prados de Reyes, Miguel] Univ Granada, Dept Comp Sci, E-18014 Granada, Spain. RP Prados-Suarez, B (reprint author), Univ Granada, Dept Software Engn, E-18014 Granada, Spain. EM belenps@ugr.es; carlosmo@ujaen.es; carmenpy@decsai.ugr.es; prados@decsai.ugr.es RI Prados Suarez, Belen/E-1020-2012 FU Andalusian Government (Junta de Andalucia) [P07-TIC03175]; Spanish Government (Science and Innovation Department) [TIN2009-08296]; [UJA2011/12/56] FX Manuscript received January 10, 2011; revised May 6, 2011 and September 6, 2011; accepted November 17, 2011. Date of publication December 20, 2011; date of current version May 4, 2012. This work was supported in part by the Andalusian Government (Junta de Andalucia) under Project P07-TIC03175 "Representacion y Manipulacion de Objetos Imperfectos en Problemas de Integracion de Datos: Una Aplicacion a los Almacenes de Objetos de Aprendizaje" by the Spanish Government (Science and Innovation Department) under Project TIN2009-08296 and also by project UJA2011/12/56. CR [Anonymous], 2010, OP EHR OP EL HLTH RE [Anonymous], 2008, 13606 ISO [Anonymous], 2010, HL7 DAT EXCH STAND [Anonymous], 2010, DICOM IMAGE STORAGE [Anonymous], 2010, SNOMED CT NOMENCLATU Bobillo F, 2008, EXPERT SYST APPL, V35, P1899, DOI 10.1016/j.eswa.2007.08.090 Bohm K., 2010, P 43 HAW INT C SYST, P1 Buchholz T, 2007, ELECTRON COMMER R A, V6, P40, DOI 10.1016/j.elerap.2006.04.002 Cao Y., 2006, P 29 ANN INT ACM SIG, P186, DOI 10.1145/1148170.1148205 Cayir Sinan, 2008, PICMET '08 - 2008 Portland International Conference on Management of Engineering & Technology, DOI 10.1109/PICMET.2008.4599692 Chaker Hamdi, 2010, P 3 INT C ADV HUM OR, P117 Chu WW, 2000, IEEE T INF TECHNOL B, V4, P97, DOI 10.1109/4233.845202 de Reyes M. A. V. M. Miguel Prados, 2008, P INT C ENT INF SYST Ehsan M., 2009, P INT C ADV INF NETW, P103 Garcia-Morchon O., 2010, P 8 IEEE INT C PERV, P322 Gil-Leyva I., 1966, REV ESP DOC CIENT, V19, P273 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Judd G, 2003, PROCEEDINGS OF THE FIRST IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS (PERCOM 2003), P133, DOI 10.1109/PERCOM.2003.1192735 Jung JJ, 2009, INFORM PROCESS MANAG, V45, P280, DOI 10.1016/j.ipm.2008.11.003 Kang Dong-Oh, 2006, Conf Proc IEEE Eng Med Biol Soc, V1, P5192 Kanoulas E., 2010, P 2 INT C THEOR INF, P152 Lahteenmaki J. L. H., 2009, P ANN INT C IEEE ENG, V2009, P1726 Lezcano L, 2011, J BIOMED INFORM, V44, P343, DOI 10.1016/j.jbi.2010.11.005 McAlearney AS, 2010, INT J MED INFORM, V79, P807, DOI 10.1016/j.ijmedinf.2010.09.002 Nagy M, 2010, STUD HEALTH TECHNOL, V155, P122, DOI 10.3233/978-1-60750-563-1-122 Prados M., 2003, SISTEMAS INFORM HOSP Prados M., 2010, P EUR FED MED INF C Prados-Suarez B., 2008, P ICEIS 2008, P300 Ray J., 2006, P ANN INT C IEEE ENG, V1, P4686 Salton G., 1994, P SIGIR 94, P292 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 World Health Organization, INT CLASS DIS ICD ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 34 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1089-7771 J9 IEEE T INF TECHNOL B JI IEEE T. Inf. Technol. Biomed. PD MAY PY 2012 VL 16 IS 3 BP 401 EP 412 DI 10.1109/TITB.2011.2178033 PG 12 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics SC Computer Science; Mathematical & Computational Biology; Medical Informatics GA 941WF UT WOS:000303997700013 PM 22194247 ER PT J AU Shih, CW Chen, MY Chu, HC Chen, YM AF Shih, Cho-Wei Chen, Ming-Yen Chu, Hui-Chuan Chen, Yuh-Min TI Enhancement of information seeking using an information needs radar model SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Information seeking; Information needs; Information needs radar model; Information filtering; Recommendation system ID VECTOR-SPACE; SEARCH; USERS; WEB; RECOMMENDATION; SYSTEMS; AGENT AB Information seeking is the act of obtaining information from existing resources in both human and technological contexts, and past studies have applied the behavior of users to determine the user needs. Search engines, information retrieval, and recommendation systems are the major solutions of information seeking. However, these techniques lack a description method for overall information needs and other limitations. Information seeking behavior is related to the content and concepts in content, and this study proposes an information needs radar model, which consists of users, content and concepts to describe information needs. The information seeking architecture based on this model is used to evaluate and obtain information about users' needs. The experimental results indicated that our proposed architecture has stable and better performance irrespective of data size, which demonstrates the applicability and effectiveness of the architecture. Furthermore, the information needs the radar model to be able to satisfy customer demands; it is not only helpful in the development of information filtering, recommendation systems, and knowledge-based systems, but also enhances the reliance and loyalty of users towards the system. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Shih, Cho-Wei; Chen, Yuh-Min] Natl Cheng Kung Univ, Inst Mfg Informat & Syst, Tainan 70101, Taiwan. [Chen, Ming-Yen] Ind Technol Res Inst, Informat & Commun Res Labs, Hsinchu, Taiwan. [Chu, Hui-Chuan] Nation Univ Tainan, Dept Special Educ, Tainan, Taiwan. RP Chen, YM (reprint author), Natl Cheng Kung Univ, Inst Mfg Informat & Syst, Tainan 70101, Taiwan. EM cwshih@imis.ncku.edu.tw; benjamin@itri.org.tw; huichu@mail.nutn.edu.tw; ymchen@mail.ncku.edu.tw FU National Science Council of Taiwan [NSC 100-2221-E-006-115] FX The authors would like to thank the National Science Council of Taiwan for financially supporting this research under Contract No. NSC 100-2221-E-006-115. CR Balabanovic M., 1995, LEARNING INFORM RETR, P13 Belkin N. J., 2003, P 26 ANN INT ACM SIG, P205 Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 Bezerra BLD, 2004, INFORM PROCESS LETT, V92, P45, DOI 10.1016/j.ipl.2004.06.003 Boiko B., 2001, CONTENT MANAGEMENT B Burkey J, 2003, IEEE T PROF COMMUN, V46, P81, DOI 10.1109/TPC.2003.813167 Chang YS, 2000, COMPUT NETW, V34, P467, DOI 10.1016/S1389-1286(00)00131-6 Chen G, 2009, INFORM PROCESS MANAG, V45, P368, DOI 10.1016/j.ipm.2008.12.004 Chen YL, 2009, ELECTRON COMMER R A, V8, P241, DOI 10.1016/j.elerap.2009.03.002 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Golovchinsky G., 2009, P 1 INT WORKSH COLL Herrera-Viedma E, 2004, FUZZY SET SYST, V148, P61, DOI 10.1016/j.fss.2004.03.006 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Kelly D, 2007, INFORM PROCESS MANAG, V43, P30, DOI 10.1016/j.ipm.2006.03.006 Kohonen T, 2001, SELF ORG MAPS, V3rd, P501 Kohonen Teuvo, 1988, NEUROCOMPUTING FDN R, P509 Lancster F., 1995, J INFORMATION COMMUN, V1, P3 Li Y, 2005, EXPERT SYST APPL, V28, P67, DOI 10.1016/j.eswa.2004.08.013 Li YF, 2004, KNOWL-BASED SYST, V17, P207, DOI 10.1016/j.knosys.2004.05.002 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu RL, 2003, INFORM SYST, V28, P815, DOI 10.1016/S0306-4379(02)00100-X London R. C., 2000, EUROPEAN J CANC CARE, V9, P41 Meng WY, 2002, ACM COMPUT SURV, V34, P48, DOI 10.1145/505282.505284 Nouali O, 2004, EXPERT SYST APPL, V26, P171, DOI 10.1016/S0957-4174(03)00118-0 Pitkow J, 2002, COMMUN ACM, V45, P50 Pivk A, 2002, EXPERT SYST APPL, V23, P207, DOI 10.1016/S0957-4174(02)00040-4 Porter M. F., 1997, READINGS INFORM RETR, P313 Salter J, 2006, IEEE INTELL SYST, V21, P35, DOI 10.1109/MIS.2006.4 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shenton AK, 2004, LIBR INFORM SCI RES, V26, P296, DOI 10.1016/j.lisr.2004.01.006 Shtykh RY, 2008, P 3 INT C SYST NETW, P365 Su X., 2009, ADV ARTIFICIAL INTEL, V2009, P1 Tan B, 2006, P 12 ACM SIGKDD INT, P718, DOI DOI 10.1145/1150402.1150493 Tax D. M. J., 2004, MACHINE LEARNING Tax DMJ, 1999, PATTERN RECOGN LETT, V20, P1191, DOI 10.1016/S0167-8655(99)00087-2 Yang Y., 2002, P 8 ACM SIGKDD INT C, P688, DOI DOI 10.1145/775047.775150 Zhang J., 2004, 18 ANN C NEUR INF PR NR 37 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2012 VL 48 IS 3 BP 524 EP 536 DI 10.1016/j.ipm.2011.08.010 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 931MI UT WOS:000303223600012 ER PT J AU Callaert, J Grouwels, J Van Looy, B AF Callaert, Julie Grouwels, Joris Van Looy, Bart TI Delineating the scientific footprint in technology: Identifying scientific publications within non-patent references SO SCIENTOMETRICS LA English DT Article; Proceedings Paper CT 13th International Conference on Scientometrics and Informetrics CY JUL 04-07, 2011 CL Durban, SOUTH AFRICA SP Int Soc Scientometr & Informetr (ISSI), Durban Univ Technol HO Univ Zululand DE Science-technology interaction; Non-patent references; Indicators; Machine learning ID SCIENCE; PATENTS; CITATIONS; LINKAGES AB Indicators based on non-patent references (NPRs) are increasingly being used for measuring and assessing science-technology interactions. But NPRs in patent documents contain noise, as not all of them can be considered 'scientific'. In this article, we introduce the results of a machine-learning algorithm that allows identifying scientific references in an automated manner. Using the obtained results, we analyze indicators based on NPRs, with a focus on the difference between NPR- and scientific non-patent references-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological domain. These results signal the relevancy of delineating scientific references when using NPRs to assess the occurrence and impact of science-technology interactions. C1 [Callaert, Julie; Grouwels, Joris; Van Looy, Bart] Katholieke Univ Leuven, ECOOM, B-3000 Louvain, Belgium. [Callaert, Julie; Grouwels, Joris; Van Looy, Bart] Katholieke Univ Leuven, Res Div INCENTIM, Fac Business & Econ, B-3000 Louvain, Belgium. RP Callaert, J (reprint author), Katholieke Univ Leuven, ECOOM, Waaistraat 6,Bus 3536, B-3000 Louvain, Belgium. EM Julie.Callaert@econ.kuleuven.be CR Bishop C. M., 2006, PATTERN RECOGNITION Callaert J, 2006, SCIENTOMETRICS, V69, P3, DOI 10.1007/s11192-006-0135-8 Guan JC, 2007, SCIENTOMETRICS, V72, P403, DOI 10.1007/s11192-007-1741-1 Harhoff D, 2003, RES POLICY, V32, P1343, DOI 10.1016/S0048-7333(02)00124-5 Hastie T., 2009, ELEMENTS STAT LEARNI He ZL, 2007, SCIENTOMETRICS, V72, P149, DOI 10.1007/s11192-007-1702-3 Kotsiantis SB, 2007, FRONT ARTIF INTEL AP, V160, P3 Lo SCS, 2010, SCIENTOMETRICS, V82, P109, DOI 10.1007/s11192-009-0036-8 Magerman T, 2010, SCIENTOMETRICS, V82, P289, DOI 10.1007/s11192-009-0046-6 Meyer M, 2000, RES POLICY, V29, P409, DOI 10.1016/S0048-7333(99)00040-2 Meyer M, 2000, SCIENTOMETRICS, V49, P93, DOI 10.1023/A:1005613325648 Michel J, 2001, SCIENTOMETRICS, V51, P185, DOI 10.1023/A:1010577030871 NARIN F, 1985, SCIENTOMETRICS, V7, P369, DOI 10.1007/BF02017155 Nelson AJ, 2009, RES POLICY, V38, P994, DOI 10.1016/j.respol.2009.01.023 Noyons E., 2011, P ISSI 2011 13 INT C, P13 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmoch U., 2008, CONCEPT TECHNOLOGY C Tijssen RJW, 2000, SCIENTOMETRICS, V47, P389 Van Looy B., 2003, J TECHNOLOGY TRANSFE, V28, P53, DOI 10.1023/A:1021630803637 Van Looy B, 2003, SCIENTOMETRICS, V57, P355 Van Looy B, 2007, SCIENTOMETRICS, V70, P441, DOI 10.1007/s11192-007-0211-8 VANVIANEN BG, 1990, RES POLICY, V19, P61, DOI 10.1016/0048-7333(90)90034-4 Verbeek A, 2002, INT J MANAG REV, V4, P179, DOI 10.1111/1468-2370.00083 Verspagen B, 2008, ECON SYST RES, V20, P266 NR 24 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD MAY PY 2012 VL 91 IS 2 BP 383 EP 398 DI 10.1007/s11192-011-0573-9 PG 16 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 921LD UT WOS:000302478200007 ER PT J AU Lin, HT Chi, NW Hsieh, SH AF Lin, Hsien-Tang Chi, Nai-Wen Hsieh, Shang-Hsien TI A concept-based information retrieval approach for engineering domain-specific technical documents SO ADVANCED ENGINEERING INFORMATICS LA English DT Article DE Information retrieval; Passage retrieval; Ontology; Reference collection; Knowledge management ID KNOWLEDGE; ONTOLOGY; SYSTEMS; MODEL AB Technical documents, which often have complicated structures, are often produced during Architecture/Engineering/Construction (A/E/C) projects and research. Applying information retrieval (IR) techniques directly to long or multi-topic documents often does not lead to satisfactory results. One way to address the problem is to partition each document into several "passages", and treat each passage as an independent document. In this research, a novel passage partitioning approach is designed. It generates passages according to domain knowledge, which is represented by base domain ontology. Such a passage is herein defined as an OntoPassage. In order to demonstrate the advantage of the OntoPassage partitioning approach, this research implements a concept-based IR system to illustrate the application of such an approach. The research also compares the OntoPassage partitioning approach with several conventional passage partitioning approaches to verify its IR effectiveness. It is shown that, with the proposed OntoPassage approach, IR effectiveness on domain-specific technical reports is as good as conventional passage partitioning approaches. In addition, the OntoPassage approach provides the possibility to display the concepts in each passage, and concept-based IR may thus be implemented. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Lin, Hsien-Tang; Chi, Nai-Wen; Hsieh, Shang-Hsien] Natl Taiwan Univ, Dept Civil Engn, Taipei 10617, Taiwan. RP Hsieh, SH (reprint author), Natl Taiwan Univ, Dept Civil Engn, 1 Roosevelt Rd,Sec 4, Taipei 10617, Taiwan. EM shhsieh@ntu.edu.tw CR Barzilay Regina, 2004, P 2 HUM LANG TECHN C, P113 Caldas CH, 2005, J COMPUT CIVIL ENG, V19, P25, DOI 10.1061/(ASCE)0887-3801(2005)19:1(25) Caldas CH, 2003, AUTOMAT CONSTR, V12, P395, DOI 10.1016/S0926-5805(03)00004-9 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Chen Y., 2002, P CIB W78 C DISTR KN, P37 Chi N.W., 2010, P 13 INT C COMP CIV, P195 Clarke C.L., 2000, P TEXT RETR C TRECT, P65 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 Day R. W., 2001, GEOTECHNICAL EARTHQU Greengrass E., 2000, INFORM RETRIEVAL SUR Gruber T.R., 1993, D91 SECURESCM Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 Hsieh SH, 2011, ADV ENG INFORM, V25, P288, DOI 10.1016/j.aei.2010.08.004 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 Kosovac B., 2000, P CONSTR INF TECHN 2, P556 Lee J.H., 1995, P 18 ANN INT ACM SIG, P180, DOI 10.1145/215206.215358 Lin H.T., 2009, THESIS NATL TAIWAN U Lin K.Y., 2007, ADV ENG INFORM, V22, P350 Manning C.D., 2008, INTRO INFORM RETRIEV Mizzaro S., 1998, INTERACTING COMPUTER, V10, P305 Noy N., 2001, SMI20010880 STANF U Rezgui Y, 2006, J COMPUT CIVIL ENG, V20, P261, DOI 10.1061/(ASCE)0887-3801(2006)20:4(261) Robertson SE, 1998, P 7 TEXT RETR C TREC, P253 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 White M., 2001, P HLT C, P263 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 NR 29 TC 3 Z9 3 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1474-0346 J9 ADV ENG INFORM JI Adv. Eng. Inform. PD APR PY 2012 VL 26 IS 2 BP 349 EP 360 DI 10.1016/j.aei.2011.12.003 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Multidisciplinary SC Computer Science; Engineering GA 996VH UT WOS:000308122400015 ER PT J AU Villatoro, E Juarez, A Montes, M Villasenor, L Sucar, LE AF Villatoro, Esau Juarez, Antonio Montes, Manuel Villasenor, Luis Sucar, L. Enrioue TI Document ranking refinement using a Markov random field model SO NATURAL LANGUAGE ENGINEERING LA English DT Article ID INFORMATION-RETRIEVAL; RELEVANCE FEEDBACK; IMAGES AB This paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine. C1 [Villatoro, Esau; Juarez, Antonio; Montes, Manuel; Villasenor, Luis; Sucar, L. Enrioue] Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Puebla 72840, Mexico. RP Villatoro, E (reprint author), Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Luis Enrique Erro 1 Tonantzintla, Puebla 72840, Mexico. EM villatoroe@inaoep.mx; antjug@inaoep.mx; mmontesg@inaoep.mx; villasen@inaoep.mx; esucar@inaoep.mx FU CONACyT [61335, CB-2008-106013, CB-2009-134186] FX This work was done under partial support of CONACyT project grants: 61335, CB-2008-106013 and CB-2009-134186. CR Baeza-Yates R., 1999, MODERN INFORM RETRIV Balinski J, 2005, INFORM PROCESS MANAG, V41, P759, DOI 10.1016/j.ipm.2004.01.006 Bear J., 1997, P 6 TEXT RETR C Bendersky M., 2008, P SIGIR, P853, DOI 10.1145/1390334.1390539 BESAG J, 1986, J ROY STAT SOC B MET, V48, P259 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Carbonetto P, 2004, LECT NOTES COMPUT SC, V3021, P350 Chavez O., 2010, P FLAIRS C DAYT BEAC Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Crouch C., 2002, INFORM PROCESSING MA, V38 DENG H., 2009, P 2 ACM INT C WEB SE, P212, DOI 10.1145/1498759.1498829 Diaz Fernando, 2005, P 14 INT C INF KNOWL, P672, DOI 10.1145/1099554.1099722 Di Nunzio GM, 2008, LECT NOTES COMPUT SC, V5152, P13, DOI 10.1007/978-3-540-85760-0_2 Escalante H. J., 2007, P 18 BRIT MACH VIS C, V2, P600 Garafolo J. S., 2000, P RIAO 2000 C CONT B, P1 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 Grossman D. A., 2004, INFORM RETRIEVAL ALG Grubinger Michael, 2007, THESIS VICTORIA U ME Held K, 1997, IEEE T MED IMAGING, V16, P878, DOI 10.1109/42.650883 Hernandez-Gracidas C, 2007, LECT NOTES COMPUT SC, V4872, P879 Kamps J, 2004, LECT NOTES COMPUT SC, V2997, P283 Kemeny J., 1976, DENUMERABLE MARKOV C Kurland Oren, 2005, P SIGIR, P306, DOI 10.1145/1076034.1076087 Lauritzen S. L., 1996, GRAPHICAL MODELS Lease M, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P476, DOI 10.1145/1571941.1572023 Lee KS, 2001, INFORM PROCESS MANAG, V37, P1, DOI 10.1016/S0306-4573(00)00017-0 Li S. Z., 1994, LECT NOTES COMPUTER, V18, P361 Li SZ, 2001, MARKOV RANDOM FIELD Luk R. W. P., 2004, P 4 NTCIR WORKSH M C Mallows C., 1975, BIOMEDIKA, V44, P114 Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Pearl J., 1988, PROBABILISTIC REASON Porter MF, 1997, ALGORITHM SUFFIX STR, P313 Qu Y., 2000, P 2 NTCIR WORKSH RES SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar P., 2009, P 18 INT C WORLD WID, P31, DOI 10.1145/1526709.1526715 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 Villatoro-Tello E, 2009, LECT NOTES ARTIF INT, V5845, P165 Villatoro-Tello E., 2009, LECT NOTES COMPUTER, V5822, P239 Winkler G., 2006, SPRINGER SERIES APPL, V27, P179 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Yang L., 2004, COLING 2004, P480 Yang L., 2006, P 15 ACM INT C INF K, P690, DOI 10.1145/1183614.1183713 Yang L. P., 2005, ACM T ASIAN LANGUAGE, V4, P357 Yang LP, 2005, LECT NOTES COMPUT SC, V3689, P299 Zhang B, 2005, P 28 ANN INT ACM SIG, P504, DOI 10.1145/1076034.1076120 Zhou D., 2010, COLING 10 P 23 INT C, P1524 Zhou D., 2010, P ACM CIKM 2010 INT, P1433, DOI 10.1145/1871437.1871640 NR 50 TC 0 Z9 0 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 J9 NAT LANG ENG JI Nat. Lang. Eng. PD APR PY 2012 VL 18 SI SI BP 155 EP 185 DI 10.1017/S1351324912000010 PN 2 PG 31 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 988CU UT WOS:000307466600002 ER PT J AU Paliwal, AV Shafiq, B Vaidya, J Xiong, H Adam, N AF Paliwal, Aabhas V. Shafiq, Basit Vaidya, Jaideep Xiong, Hui Adam, Nabil TI Semantics-Based Automated Service Discovery SO IEEE TRANSACTIONS ON SERVICES COMPUTING LA English DT Article DE Web services publishing; web services discovery; services discovery process and methodology ID WEB SERVICES AB A vast majority of web services exist without explicit associated semantic descriptions. As a result many services that are relevant to a specific user service request may not be considered during service discovery. In this paper, we address the issue of web service discovery given nonexplicit service description semantics that match a specific service request. Our approach to semantic-based web service discovery involves semantic-based service categorization and semantic enhancement of the service request. We propose a solution for achieving functional level service categorization based on an ontology framework. Additionally, we utilize clustering for accurately classifying the web services based on service functionality. The semantic-based categorization is performed offline at the universal description discovery and integration (UDDI). The semantic enhancement of the service request achieves a better matching with relevant services. The service request enhancement involves expansion of additional terms (retrieved from ontology) that are deemed relevant for the requested functionality. An efficient matching of the enhanced service request with the retrieved service descriptions is achieved utilizing Latent Semantic Indexing (LSI). Our experimental results validate the effectiveness and feasibility of the proposed approach. C1 [Paliwal, Aabhas V.] Rutgers State Univ, CIMIC, N Brunswick, NJ 08902 USA. [Shafiq, Basit; Vaidya, Jaideep; Xiong, Hui; Adam, Nabil] Rutgers State Univ, MSIS Dept, Newark, NJ 07102 USA. [Shafiq, Basit; Vaidya, Jaideep; Xiong, Hui; Adam, Nabil] Rutgers State Univ, CIMIC, Newark, NJ 07102 USA. RP Paliwal, AV (reprint author), Rutgers State Univ, CIMIC, 1110 Stony Brook Way, N Brunswick, NJ 08902 USA. EM aabhas@cimic.rutgers.edu; basit@cimic.rutgers.edu; jsvaidya@business.rutgers.edu; hxiong@rutgers.edu; adam@adam.rutgers.edu FU US National Science Foundation [IIS-0306838]; SAP Labs, LLC. FX This work was supported in part by the US National Science Foundation under grant IIS-0306838 and SAP Labs, LLC. CR Adcock J., 2004, P TRECVID Agrawal R., 1993, P ACM SIGMOD INT C M Al-Masri Eyhab, 2008, P 17 INT C WORLD WID Antonie M.-L., 2002, P IEEE INT C DAT MIN Anyanwu K., 2005, P 14 INT C WORLD WID Baldi P., 2003, PROBABILISTIC METHOD Bruno M., 2005, P IEEE INT C E TECHN SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Corella M.A., 2006, P INT C BUS PROC MAN FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Han E., 1997, P ACM SIGMOD INT C M Heb A., 2003, P IJCAI WORKSH INF I Johnson H.L., 2006, P PAC S BIOC Kher M., 2004, P 17 INT C PATT REC Klusch M., 2008, P IEEE INT C SEM COM Lu J., 2007, P 8 INT C WEB INF SY Martin D., 2004, P 1 INT WORKSH SEM W McIlraith SA, 2003, IEEE INTELL SYST, V18, P90, DOI 10.1109/MIS.2003.1179199 McIlraith SA, 2001, IEEE INTELL SYST APP, V16, P46, DOI 10.1109/5254.920599 Niles I., 2003, P IEEE INT C INF KNO Oldham N, 2005, LECT NOTES COMPUT SC, V3387, P137 Paliwal A. V., 2007, P IEEE INT C SERV CO Paliwal A.V., 2006, P IEEE WIC ACM INT C Sajjanhar A., 2004, P 6 AS PAC WEB C, P665 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Verma K., 2005, Information Technology & Management, V6, DOI 10.1007/s10799-004-7773-4 Xiong H., 2003, P IEEE 3 INT C DAT M NR 27 TC 15 Z9 18 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1939-1374 J9 IEEE T SERV COMPUT JI IEEE Trans. Serv. Comput. PD APR-JUN PY 2012 VL 5 IS 2 BP 260 EP 275 DI 10.1109/TSC.2011.19 PG 16 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 950TN UT WOS:000304672100010 ER PT J AU Li, G Liu, F AF Li, Gang Liu, Fei TI Application of a clustering method on sentiment analysis SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE sentiment analysis; opinion mining; clustering; semantic web ID WEB AB This article introduces a novel approach for sentiment analysis - the clustering-based sentiment analysis approach. By applying a TF-IDF weighting method, a voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. The methodology has competitive advantages over the two existing types of approaches: symbolic techniques and supervised learning methods. It is a well-performed, efficient and non-human participating approach to solving sentiment analysis problems. C1 [Liu, Fei] La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia. RP Liu, F (reprint author), La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia. EM F.Liu@latrobe.edu.au CR Andrews N. O., 2007, RECENT DEV DOCUMENT Aue A, 2005, P INT C REC ADV NAT, P207 Benamara F, 2007, P INT C WEBL SOC MED, P203 Boiy E, 2007, INT C EL PUBL PAG VI, P349 Cesarano C, 2004, AAAI SPRING S COMP A Chaovalit P., 2005, P 38 HAW INT C SYST Chiu CM, 2004, J INF SCI, V30, P60, DOI 10.1177/0165551504041679 Das SR, 2007, MANAGE SCI, V53, P1375, DOI 10.1287/mnsc.1070.0704 Harary F., 1969, GRAPH THEORY Hartigan J, 1975, CLUSTERING ALGORITHM Hatzivassiloglou V, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P174 Kamps J, 2004, INT C LANG RES EV, P1115 Manthey B, 2009, 20 ANN ACM SIAM S DI, P461 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Pang B., 2005, P 43 ANN M ASS COMP, P115, DOI DOI 10.3115/1219840.1219855 Pang B, 2002, C EMP METH NAT LANG, P79 Pang B., 2004, P 42 ANN M ASS COMP, P271, DOI DOI 10.3115/1218955.1218990 Porter M., 1993, PROGRAM ELECT LIB IN, V14, P130 Ross S, 1994, 1 COURSE PROBABILITY SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tan S., 2007, P 16 ACM C INF KNOWL, P979, DOI 10.1145/1321440.1321590 Thet TT, 2010, J INF SCI, V36, P823, DOI 10.1177/0165551510388123 Toutanova K., 2000, 2000 JOINT SIGDAT C, P63 Turney PD, 2002, 40TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P417 Turney P.D., 2001, ECML 2001, P491 Wiebe JM, 2000, C ART INT MENL PARK, P735 Yu H, 2003, C EMP METH NAT LANG, P129, DOI [10.3115/1119355.1119372, DOI 10.3115/1119355.1119372] NR 28 TC 3 Z9 3 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD APR PY 2012 VL 38 IS 2 BP 127 EP 139 DI 10.1177/0165551511432670 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 923OT UT WOS:000302629300003 ER PT J AU Chua, WWK Kim, JJ AF Chua, Watson Wei Khong Kim, Jung-jae TI BOAT: Automatic alignment of biomedical ontologies using term informativeness and candidate selection SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Article DE Ontology alignment; Ontology matching; Candidate selection; Information retrieval; Semantic web; Knowledge sharing ID PHENOTYPE ONTOLOGY; GENE-ONTOLOGY; TOOL AB The biomedical sciences is one of the few domains where ontologies are widely being developed to facilitate information retrieval and knowledge sharing, but there still remains the problem that applications using different ontologies cannot share knowledge without explicit references between overlapping concepts. Ontology alignment is the task of identifying such equivalence relations between concepts across ontologies. Its application to the biomedical domain should address two open issues: (1) determining the equivalence of concept-pairs which have overlapping terms in their names, and (2) the high run-time required to align large ontologies which are typical in the biomedical domain. To address them, we present a novel approach, named the Biomedical Ontologies Alignment Technique (BOAT), which is state-of-the-art in terms of F-measure, precision and speed. A key feature of BOAT is that it considers the informativeness of each component word in the concept labels, which has significant impact on biomedical ontologies, resulting in a 12.2% increase in F-measure. Another important feature of BOAT is that it selects for comparison only concept pairs that show high likelihoods of equivalence, based on the similarity of their annotations. BOATs F-measure of 0.88 for the alignment of the mouse and human anatomy ontologies is on par with that of another state-of-the-art matcher, AgreementMaker, while taking a shorter time. (C) 2011 Elsevier Inc. All rights reserved. C1 [Chua, Watson Wei Khong; Kim, Jung-jae] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. RP Chua, WWK (reprint author), Nanyang Technol Univ, Sch Comp Engn, Block N4,02A-32,Nanyang Ave, Singapore 639798, Singapore. EM watsonchua@pmail.ntu.edu.sg; jungjae.kim@ntu.edu.sg RI Kim, Jung-jae/A-3738-2011 CR Aranguren ME, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-57 Ashburner M, 2000, NAT GENET, V25, P25 Batzoglou S, 2000, GENOME RES, V10, P950, DOI 10.1101/gr.10.7.950 Bodenreider Olivier, 2005, AMIA Annu Symp Proc, P61 Chua WWK, 2010, 5 INT WORKSH ONT MAT, P150 Chua Watson Wei Khong, 2010, Proceedings of the Fourth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2010), DOI 10.1109/CISIS.2010.47 Cruz I, 2009, 4 INT WORKSH ONT MAT, P49 Cruz I, 2010, 5 INT WORKSH ONT MAT, P118 Cruz I.F., 2008, T GIS, V12, P683, DOI 10.1111/j.1467-9671.2008.01126.x Ehrig M., 2004, P ISWC, P683 Gene Ontology C., 2004, NUCL ACIDS RES S1, V32, pD258 Ghazvinian A, 2010, P BIOONT 2010 SEM AP, P164 Ghazvinian A, 2009, P AMIA ANN S AMIA 20 Gracia J, 2007, P 6 INT SEM WEB C 2 Hamdi F, 2010, STUD COMP INTELL, P251 Hamdi F., 2009, P 4 INT WORKSH ONT M, P230 Hanif M.S., 2009, J WEB SEMANT, V7, P344 Hu W, 2008, DATA KNOWL ENG, V67, P140, DOI 10.1016/j.datak.2008.06.003 Hu W, 2006, P 5 INT SEM WEB C IS, P300 Hu W, 2008, J WEB SEMANT, V6, P237, DOI 10.1016/j.websem.2008.02.006 Jean-Mary YR, 2009, J WEB SEMANT, V7, P235, DOI 10.1016/j.websem.2009.04.001 Lu Y, 2009, BIOINFORMATICS, V25, P1476, DOI 10.1093/bioinformatics/btp247 Mao M, 2010, J WEB SEMANT, V8, P14, DOI 10.1016/j.websem.2009.11.002 Meilicke C, 2006, P 1 INT WORKSH ONT M, P61 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mukherjea S, 2005, BRIEF BIOINFORM, V6, P252, DOI 10.1093/bib/6.3.252 Noy Natalya F., 2001, P WORKSH ONT INF SHA, P63 Parmentier G, 2010, BIOINFORMATICS, V26, P1766, DOI 10.1093/bioinformatics/btq283 Porter M. F., 1997, READINGS INFORM RETR, P313 Qu Y., 2006, P 15 INT C WORLD WID, P23, DOI DOI 10.1145/1135777.1135786 Ramanand J, 2008, P 4 GLOB WORDN C GWC, P360 Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017 Rubin DL, 2008, BRIEF BIOINFORM, V9, P75, DOI 10.1093/bib/bbm059 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shvaiko P., 2007, ONTOLOGY MATCHING Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346 Smith CL, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2004-6-1-r7 Winkler W. E., 1999, STATE RECORD LINKAGE Zhi-biao Wu, 1994, P 32 ANN M ASS COMP NR 39 TC 1 Z9 1 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD APR PY 2012 VL 45 IS 2 BP 337 EP 349 DI 10.1016/j.jbi.2011.11.010 PG 13 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 917WG UT WOS:000302208700013 PM 22155335 ER PT J AU Huang, JM Zhou, B Wu, QY Wang, XW Jia, Y AF Huang, Jiuming Zhou, Bin Wu, Quanyuan Wang, Xiaowei Jia, Yan TI Contextual correlation based thread detection in short text message streams SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Text stream; Thread detection; Short text; Contextual correlation ID SEGMENTATION AB Short text message streams are produced by Instant Messaging and Short Message Service which are wildly used nowadays. Each stream contains more than one thread usually. Detecting threads in the streams is helpful to various applications, such as business intelligence, investigation of crime and public opinion analysis. Existing works which are mainly based on text similarity encounter many challenges including the sparse eigenvector and anomaly of short text message. This paper introduces a novel concept of contextual correlation instead of the traditional text similarity into single-pass clustering algorithm to cover the challenges of thread detection. We firstly analyze the contextually correlative nature of conversations in short text message streams, and then propose an unsupervised method to compute the correlative degree. As a reference, a single-pass algorithm employing the contextual correlation is developed to detect threads in massive short text stream. Experiments on large real-life online chat logs show that our approach improves the performance by 11% when compared with the best similarity-based algorithm in terms of F1 measure. C1 [Huang, Jiuming; Zhou, Bin; Wu, Quanyuan; Wang, Xiaowei; Jia, Yan] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. RP Huang, JM (reprint author), Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China. EM huangjiuming@nudt.edu.cn; binzhou@nudt.edu.cn; quanyuan.wu@gmail.com; 31830397@qq.com; yanjia@nudt.edu.cn FU National Natural Science Foundation of China [60933005, 60873204]; National High-Tech Research and Development Plan of China [2001AA012505] FX This work is supported in part by the Key Project of National Natural Science Foundation of China under Grant No. 60933005; the National Natural Science Foundation of China under Grant No. 60873204; the National High-Tech Research and Development Plan of China under Grant No. 2001AA012505. The authors are grateful to the text mining team in Lab 613 of NUDT, which contributes a lot to this paper. CR Allan J., 1998, P DARPA BROADC NEWS Atkinson J. M., 1984, STRUCTURES SOCIAL AC, P53, DOI 10.1017/CBO9780511665868 Beeferman D, 1999, MACH LEARN, V34, P177, DOI 10.1023/A:1007506220214 Galley M., 2003, DISCOURSE SEGMENTATI Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Heringer J., 1977, SO CALIFORNIA OCCASI, V5, P169 HowNet, 2010, HOWNET Java A., 2007, P 9 WEBKDD 1 SNA KDD, P56, DOI DOI 10.1145/1348549.1348556 Levinson S., 1983, PRAGMATICS CAMBRIDGE Lin C, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P131, DOI 10.1145/1571941.1571966 Lucene, 2010, LUCENE Olney Andrew, 2005, P HUM LANG TECHN C C, P971, DOI 10.3115/1220575.1220697 Passonneau RJ, 1997, COMPUT LINGUIST, V23, P103 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen D., 2006, P 29 ANN INT ACM SIG, P35, DOI 10.1145/1148170.1148180 Simpson C., 1999, INTERNET RELAY CHAT Tianhao W., 2002, LUCSE02008 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wang L., 2007, P 2 INT C ADV COMP I, P435 Wang Le, 2008, J COMPUTERS, V3, P86 Wang Y., 2008, P 2 INT C WEBL SOC M Xia Y., 2006, P NEW TEXT WORKSH 11, P48 Yang Yiming, 1998, P 21 ANN INT ACM SIG, P28, DOI DOI 10.1145/290941.290953 NR 23 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD APR PY 2012 VL 38 IS 2 BP 449 EP 464 DI 10.1007/s10844-011-0162-7 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 918HU UT WOS:000302240800006 ER PT J AU Wu, ZD Xu, GD Zhang, YC Dolog, P Lu, CL AF Wu, ZongDa Xu, GuanDong Zhang, YanChun Dolog, Peter Lu, ChengLang TI An Improved Contextual Advertising Matching Approach based on Wikipedia Knowledge SO COMPUTER JOURNAL LA English DT Article DE Wikipedia knowledge; document modeling; contextual advertising AB The current boom of the Web is associated with the revenues originated from Web advertising. As one prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial textual ads within the content of a Web page, so as to provide a better user experience and thereby increase the revenues of Web site owners and an advertising platform. Therefore, in contextual advertising, the relevance of selected ads with a Web page is essential. However, some problems, such as homonymy and polysemy, low intersection of keywords and context mismatch, can lead to the selection of irrelevant textual ads for a Web page, making that a simple keyword matching technique generally gives poor accuracy. To overcome these problems and thus to improve the relevance of contextual ads, in this paper we propose a novel Wikipedia-based matching technique which, using selective matching strategies, selects a certain amount of relevant articles from Wikipedia as an intermediate semantic reference model for matching Web pages and textual ads. We call this technique SIWI: Selective Wikipedia Matching, which, instead of using the whole Wikipedia articles, only matches the most relevant articles for a page (or a textual ad), resulting in the effective improvement of the overall matching performance. An experimental evaluation is conducted, which runs over a set of real textual ads, a set of Web pages from the Internet and a dataset of more than 260 000 articles from Wikipedia. The experimental results show that our method performs better than existing matching strategies, which can deal with the matching over the large dataset of Wikipedia articles efficiently, and achieve a satisfactory contextual advertising effect. C1 [Wu, ZongDa; Lu, ChengLang] Wenzhou Univ, Oujiang Coll, Wenzhou, Zhejiang, Peoples R China. [Xu, GuanDong; Zhang, YanChun] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia. [Dolog, Peter] Univ Aalborg, Dept Comp Sci, Aalborg, Denmark. RP Wu, ZD (reprint author), Wenzhou Univ, Oujiang Coll, Wenzhou, Zhejiang, Peoples R China. EM zongda1983@163.com RI Dolog, Peter/C-7732-2014 OI Dolog, Peter/0000-0003-1842-9131 FU National Science Foundation of the Zhejiang Province of China [Y1100137]; Educational Research Program of the Zhejiang Province of China [Y201016197]; Science and Technology Program of the Wenzhou city of China [2009G0339, S20100055] FX This work is supported by the National Science Foundation of the Zhejiang Province of China under Grant Nos. Y1100137, the Educational Research Program of the Zhejiang Province of China under Grant Nos. Y201016197, the Science and Technology Program of the Wenzhou city of China under Grant Nos. 2009G0339, and the Science and Technology Program of the Wenzhou city of China under Grant Nos. S20100055 CR Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906 David HA, 1997, AM STAT, V51, P9, DOI 10.2307/2684684 Fan TK, 2010, KNOWL INF SYST, V23, P321, DOI 10.1007/s10115-009-0222-2 Gupta S, 2005, WORLD WIDE WEB, V8, P179, DOI 10.1007/s11280-004-4873-3 Hu J, 2009, P 18 INT C WORLD WID, P471, DOI 10.1145/1526709.1526773 Hu Jian, 2008, P 31 ANN INT ACM SIG, P179, DOI 10.1145/1390334.1390367 HU X, 2009, P 15 ACM SIGKDD INT, P389, DOI 10.1145/1557019.1557066 Jun Z., 2007, COMPREHENSIVE PERL A Jung JJ, 2009, EXPERT SYST APPL, V36, P10627, DOI 10.1016/j.eswa.2009.02.052 Lacerda A., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148265 Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 Pak AN, 2010, WORLD WIDE WEB, V13, P251, DOI 10.1007/s11280-010-0084-2 Papadopoulos S., 2009, P 31 EUR C INF RETR, P216 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sullivan D., 2003, SEARCH ENGINE WATCH Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wu H.C., 2008, ACM T INFORM SYST, V26, P78 Yih Wen-tau, 2006, P 15 INT C WORLD WID, P213, DOI 10.1145/1135777.1135813 Zhang Y, 2004, P 10 INT C THEOR MET, P4 NR 22 TC 3 Z9 3 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 J9 COMPUT J JI Comput. J. PD MAR PY 2012 VL 55 IS 3 BP 277 EP 292 DI 10.1093/comjnl/bxr056 PG 16 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 905SS UT WOS:000301295000003 ER PT J AU Spanakis, G Siolas, G Stafylopatis, A AF Spanakis, Gerasimos Siolas, Georgios Stafylopatis, Andreas TI Exploiting Wikipedia Knowledge for Conceptual Hierarchical Clustering of Documents SO COMPUTER JOURNAL LA English DT Article DE document clustering; document representation; Wikipedia knowledge; conceptual clustering AB In this paper, we propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. The proposed method overcomes the classic bag-of-words models disadvantages through the exploitation of Wikipedia textual content and link structure. A robust and compact document representation is built in real-time using the Wikipedia application programmer's interface, without the need to store locally any Wikipedia information. The clustering process is hierarchical and extends the idea of frequent items by using Wikipedia article titles for selecting cluster labels that are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach, both in terms of F-measure and entropy on the one hand and computational cost on the other. C1 [Spanakis, Gerasimos; Siolas, Georgios; Stafylopatis, Andreas] Natl Tech Univ Athens, Sch Elect & Comp Engn, Intelligent Syst Lab, Athens 15780, Greece. RP Spanakis, G (reprint author), Natl Tech Univ Athens, Sch Elect & Comp Engn, Intelligent Syst Lab, Athens 15780, Greece. EM gspana@ece.ntua.gr CR Banerjee S., 2007, SIGIR, P787 Bloehdorn S., 2006, STUDIES CLASSIFICATI, V30, P334 Breaux T.D., 2005, HICSS 05 P 38 ANN HA, P111 [Carnegie Group I. Reuters L.], 1997, REUT 21578 TEXT CAT Francis W. N., 1964, STANDARD CORPUS PRES Fung BCM, 2003, SIAM PROC S, P59 Gabrilovich E, 2007, IJCAI 07, P1606 Gabrilovich E., 2006, AAAI 06, P1301 Hotho A., 2002, P FGML WORKSH, P37 Hu J., 2008, SIGIR 08, P179 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Karypis G, 2002, CLUTO CLUSTERING TOO Lang K., 1995, P INT C MACH LEARN T Li Y., 2007, SIGIR 07 P 30 ANN IN, P797 Marcus M. P., 1994, COMPUTATIONAL LINGUI, V19, P313 Mihalcea R., 2007, CIKM 07, P233 Pullwitt D, 2002, NEURAL NETWORKS, V15, P1099, DOI 10.1016/S0893-6080(02)00082-5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmid H., 1994, P INT C NEW METH LAN Sedding J., 2004, ROMAND 04 P 3 WORKSH, P104 Steinbach Michael, 2000, KDD WORKSH TEXT MIN, P109 Stumme G, 2002, DATA KNOWL ENG, V42, P189, DOI 10.1016/S0169-023X(02)00057-5 Wang B. B., 2003, ACSC 03 P 26 AUSTR C, P69 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wang P., 2008, KDD 08, P713, DOI DOI 10.1145/1401890.1401976 Xue XB, 2009, IEEE T KNOWL DATA EN, V21, P428, DOI 10.1109/TKDE.2008.166 NR 26 TC 8 Z9 8 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 J9 COMPUT J JI Comput. J. PD MAR PY 2012 VL 55 IS 3 BP 299 EP 312 DI 10.1093/comjnl/bxr024 PG 14 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 905SS UT WOS:000301295000005 ER PT J AU Tu, YN Seng, JL AF Tu, Yi-Ning Seng, Jia-Lang TI Indices of novelty for emerging topic detection SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Topic detection and tracking; Text mining; Information retrieval; Novelty index; Published volume index; Aging theory ID SCIENCE-AND-TECHNOLOGY; CITATION NETWORKS; DISCOVERY LRD; IDENTIFICATION; INFORMATION; TRACKING; INFRASTRUCTURE; INTELLIGENCE; EXTRACTION; CHINESE AB Emerging topic detection is a vital research area for researchers and scholars interested in searching for and tracking new research trends and topics. The current methods of text mining and data mining used for this purpose focus only on the frequency of which subjects are mentioned, and ignore the novelty of the subject which is also critical, but beyond the scope of a frequency study. This work tackles this inadequacy to propose a new set of indices for emerging topic detection. They are the novelty index (NI) and the published volume index (PVI). This new set of indices is created based on time, volume, frequency and represents a resolution to provide a more precise set of prediction indices. They are then utilized to determine the detection point (DP) of new emerging topics. Following the detection point, the intersection decides the worth of a new topic. The algorithms presented in this paper can be used to decide the novelty and life span of an emerging topic in a specific field. The entire comprehensive collection of the ACM Digital Library is examined in the experiments. The application of the NI and PVI gives a promising indication of emerging topics in conferences and journals. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved. C1 [Tu, Yi-Ning] Fu Jen Catholic Univ, Dept Stat & Informat Sci, Coll Management, New Taipei 242, Taiwan. [Tu, Yi-Ning] Fu Jen Catholic Univ, Grad Sch Stat & Informat Sci, Coll Management, New Taipei 242, Taiwan. [Seng, Jia-Lang] Natl Chengchi Univ, Dept Accounting, Coll Commerce, Taipei 116, Taiwan. [Seng, Jia-Lang] Natl Chengchi Univ, Grad Sch Accounting, Coll Commerce, Taipei 116, Taiwan. RP Tu, YN (reprint author), Fu Jen Catholic Univ, Dept Stat & Informat Sci, Coll Management, New Taipei 242, Taiwan. EM 082435@mail.fju.edu.tw; seng@nccu.edu.tw CR Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954 Allan J., 1998, P DARPA BROADC NEWS Aurora P. P., 2007, INFORM PROCESSING MA, V43, P742 Berry M. W., 2004, SURVEY TEXT MINING C, P185 Braun T, 2000, CHEM REV, V100, P23, DOI 10.1021/cr990096j Chen C. C., 2003, P 14 EUR C MACH LEAR, P47 Chen KY, 2007, IEEE T KNOWL DATA EN, V19, P1016, DOI 10.1109/TKDE.2007.1040 Chou TC, 2008, IEEE T KNOWL DATA EN, V20, P289, DOI 10.1109/TKDE.2007.190702 Clifton C, 2004, IEEE T KNOWL DATA EN, V16, P949, DOI 10.1109/TKDE.2004.32 Cui C., 2005, P 2005 ACM S APPL CO, P1089, DOI 10.1145/1066677.1066924 Cunningham SW, 2006, TECHNOL FORECAST SOC, V73, P915, DOI 10.1016/j.techfore.2006.06.004 Daim TU, 2006, TECHNOL FORECAST SOC, V73, P981, DOI 10.1016/j.techfore.2006.04.004 Manmatha R., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Franz M., 2001, P 24 ANN INT ACM SIG, P310, DOI 10.1145/383952.384013 Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 Jin Y, 2007, INFORM PROCESS MANAG, V43, P365, DOI 10.1016/j.ipm.2006.07.007 JO Y, 2007, P 13 ACM SIGKDD INT, P370, DOI 10.1145/1281192.1281234 Joachims T., 1998, P EMNLP C Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI DOI 10.1023/A:1024940629314 Kollios G, 2003, IEEE T KNOWL DATA EN, V15, P1170, DOI 10.1109/TKDE.2003.1232271 Kostoff R. N., 2007, TECHNOLOGICAL FORECA, V74, P1538 Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P186, DOI 10.1016/j.techfore.2007.11.010 Kostoff RN, 2007, TECHNOL FORECAST SOC, V74, P1609, DOI 10.1016/j.techfore.2007.02.007 Kostoff RN, 2007, TECHNOL FORECAST SOC, V74, P1574, DOI 10.1016/j.techfore.2007.02.009 Kostoff RN, 2007, TECHNOL FORECAST SOC, V74, P1539, DOI 10.1016/j.techfore.2007.02.008 Kostoff RN, 2008, TECHNOL FORECAST SOC, V75, P165, DOI 10.1016/j.techfore.2007.11.004 Kuramochi M, 2004, IEEE T KNOWL DATA EN, V16, P1038, DOI 10.1109/TKDE.2004.33 Lee C, 2007, INFORM PROCESS MANAG, V43, P1249, DOI 10.1016/j.ipm.2006.02.007 Lee Z, 1999, INFORM MANAGE, V36, P233, DOI 10.1016/S0378-7206(99)00022-1 Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86 Malone J, 2006, EXPERT SYST APPL, V30, P24, DOI 10.1016/j.eswa.2005.09.047 Morinaga S., 2004, P 10 ACM SIGKDD INT, P811, DOI 10.1145/1014052.1016919 Nallapati R, 2008, P 14 ACM SIGKDD INT, P542, DOI DOI 10.1145/1401890.1401957 Ontrup J., 2008, IEEE T KNOWLEDGE DAT, V20 Ozmutlu HC, 2005, INFORM PROCESS MANAG, V41, P1243, DOI 10.1016/j.ipm.2004.04.018 Ozmutlu S, 2006, INFORM PROCESS MANAG, V42, P934, DOI 10.1016/j.ipm.2005.10.002 Porter A. L, 2005, TECH MINING EXPLOITI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz J. M., 1999, P DARPA BROADC NEWS Shibata N, 2008, TECHNOVATION, V28, P758, DOI 10.1016/j.technovation.2008.03.009 Shibata N, 2007, J AM SOC INF SCI TEC, V58, P872, DOI 10.1002/asi.20529 Shibata N., 2010, TECHNOLOGICAL FORECA Steyvers M., 2004, P 10 ACM SIGKDD INT, P306, DOI DOI 10.1145/1014052.1014087 Stokes N., 2001, P 24 ANN INT ACM SIG, P424, DOI 10.1145/383952.384068 Swan R., 2000, P 23 ANN INT ACM SIG, P49, DOI 10.1145/345508.345546 Tran TA, 2008, TECHNOL FORECAST SOC, V75, P1396, DOI 10.1016/j.techfore.2008.04.004 Tu YN, 2009, EXPERT SYST APPL, V36, P12151, DOI 10.1016/j.eswa.2009.03.015 Walls F., 1999, P DARPA BROADC NEWS WANG X, 2007, P 13 ACM SIGKDD INT, P784, DOI 10.1145/1281192.1281276 Wu KJ, 2004, INFORM PROCESS MANAG, V40, P239, DOI 10.1016/S0306-4573(03)00039-6 Yang Y., 2002, P 8 ACM SIGKDD INT C, P688, DOI DOI 10.1145/775047.775150 Yang Y, 2000, P 23 ANN INT ACM SIG, P65, DOI 10.1145/345508.345550 Yang Y., 2005, P 28 ANN INT ACM SIG, P98, DOI 10.1145/1076034.1076054 Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 Zhang Y., 2008, P 14 INT C KNOWL DIS, P1051, DOI 10.1145/1401890.1402015 Zhu DH, 2002, TECHNOL FORECAST SOC, V69, P495, DOI 10.1016/S0040-1625(01)00157-3 NR 56 TC 9 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2012 VL 48 IS 2 BP 303 EP 325 DI 10.1016/j.ipm.2011.07.006 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 910GT UT WOS:000301625900009 ER PT J AU Jiang, L Sorensen, P Thomsen, B Edwards, SM Skarman, A Rontved, CM Lund, MS Workman, CT AF Jiang, Li Sorensen, Peter Thomsen, Bo Edwards, Stefan M. Skarman, Axel Rontved, Christine M. Lund, Mogens S. Workman, Christopher T. TI Gene prioritization for livestock diseases by data integration SO PHYSIOLOGICAL GENOMICS LA English DT Article DE protein complexes; phenotype similarity; gene expression ID QUANTITATIVE TRAIT LOCI; ONLINE MENDELIAN INHERITANCE; PROTEIN-INTERACTION NETWORKS; BAYESIAN REGRESSION APPROACH; AFFECTING CLINICAL MASTITIS; ESCHERICHIA-COLI-MASTITIS; BOVINE MAMMARY-GLAND; LINKAGE ANALYSIS; DAIRY-COWS; CATTLE AB Jiang L, Sorensen P, Thomsen B, Edwards SM, Skarman A, Rontved CM, Lund MS, Workman CT. Gene prioritization for livestock diseases by data integration. Physiol Genomics 44: 305-317, 2012. First published January 10, 2012; doi:10.1152/physiolgenomics.00047.2011.-Identifying causal genes that underlie complex traits such as susceptibility to disease is a primary aim of genetic and biomedical studies. Genetic mapping of quantitative trait loci (QTL) and gene expression profiling based on high-throughput technologies are common first approaches toward identifying associations between genes and traits; however, it is often difficult to assess whether the biological function of a putative candidate gene is consistent with a particular phenotype. Here, we have implemented a network-based disease gene prioritization approach for ranking genes associated with quantitative traits and diseases in livestock species. The approach uses ortholog mapping and integrates information on disease or trait phenotypes, gene-associated phenotypes, and protein-protein interactions. It was used for ranking all known genes present in the cattle genome for their potential roles in bovine mastitis. Gene-associated phenome profile and transcriptome profile in response to Escherichia coli infection in the mammary gland were integrated to make a global inference of bovine genes involved in mastitis. The top ranked genes were highly enriched for pathways and biological processes underlying inflammation and immune responses, which supports the validity of our approach for identifying genes that are relevant to animal health and disease. These gene-associated phenotypes were used for a local prioritization of candidate genes located in a QTL affecting the susceptibility to mastitis. Our study provides a general framework for prioritizing genes associated with various complex traits in different species. To our knowledge this is the first time that gene expression, ortholog mapping, protein interactions, and biomedical text data have been integrated systematically for ranking candidate genes in any livestock species. C1 [Jiang, Li; Sorensen, Peter; Thomsen, Bo; Edwards, Stefan M.; Skarman, Axel; Lund, Mogens S.] Aarhus Univ, Dept Mol Biol & Genet, DK-8830 Tjele, Denmark. [Rontved, Christine M.] Aarhus Univ, Dept Anim Sci, DK-8830 Tjele, Denmark. [Workman, Christopher T.] Tech Univ Denmark, Dept Syst Biol, Ctr Biol Sequence Anal, DK-2800 Lyngby, Denmark. RP Jiang, L (reprint author), Aarhus Univ, Dept Mol Biol & Genet, Blichers Alle 20,POB 50, DK-8830 Tjele, Denmark. EM Li.Jiang@agrsci.dk RI Navntoft, Anne Mette/G-8527-2013; Workman, Christopher/K-1066-2015; Hoj-Edwards, Stefan/ OI Workman, Christopher/0000-0002-2210-3743; Hoj-Edwards, Stefan/0000-0002-4628-8148 FU Cutting Edge Genomics for Sustainable Animal Breeding (SABRE); European commission [FOOD-CT-2006-016250]; Quantomics FX The work was supported by Cutting Edge Genomics for Sustainable Animal Breeding (SABRE) research project that has been cofinanced by the European commission within the 6th Framework Programme, contract no. FOOD-CT-2006-016250. Additional financial support was kindly granted by Quantomics, a collaborative project under the 7th Framework Programme (FP7). CR Adler P, 2009, GENOME BIOL, V10, DOI 10.1186/gb-2009-10-12-r139 Ali W, 2010, MOL BIOSYST, V6, P2296, DOI 10.1039/c004430j Bannerman DD, 2009, J ANIM SCI, V87, P10, DOI 10.2527/jas.2008-1187 Berruyer C, 2004, MOL CELL BIOL, V24, P7214, DOI 10.1128/MCB.24.16.7214-7224.2004 Buitenhuis B, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-130 Burvenich C, 2003, VET RES, V34, P521, DOI 10.1051/vetres:2003023 Chen J, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-73 Elsik CG, 2009, SCIENCE, V324, P522, DOI 10.1126/science.1169588 Erten S, 2011, BIODATA MIN, V4, DOI 10.1186/1756-0381-4-19 Erten S, 2011, J COMPUT BIOL, V18, P1561, DOI 10.1089/cmb.2011.0154 Giles J, 2005, NATURE, V438, P900, DOI 10.1038/438900a Hamosh A, 2002, NUCLEIC ACIDS RES, V30, P52, DOI 10.1093/nar/30.1.52 Huang DW, 2009, NUCLEIC ACIDS RES, V37, P1, DOI 10.1093/nar/gkn923 Huang DW, 2009, NAT PROTOC, V4, P44, DOI 10.1038/nprot.2008.211 Irizarry RA, 2003, BIOSTATISTICS, V4, P249, DOI 10.1093/biostatistics/4.2.249 Jensen LJ, 2009, NUCLEIC ACIDS RES, V37, pD412, DOI 10.1093/nar/gkn760 Jiang L, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-443 Jiang QH, 2010, BMC SYST BIOL, V4, DOI 10.1186/1752-0509-4-S1-S2 Jouanguy E, 1999, NAT GENET, V21, P370 Lage K, 2007, NAT BIOTECHNOL, V25, P309, DOI 10.1038/nbt1295 Lindberg C, 1990, J Am Med Rec Assoc, V61, P40 Lund MS, 2008, J DAIRY SCI, V91, P4028, DOI 10.3168/jds.2007-0290 Lund MS, 2003, GENETICS, V163, P405 Martin F, 2004, J CLIN INVEST, V113, P591, DOI 10.1172/JCI200419557 McGary KL, 2010, P NATL ACAD SCI USA, V107, P6544, DOI 10.1073/pnas.0910200107 Mitchell Joyce A, 2003, AMIA Annu Symp Proc, P460 Newton MA, 2007, ANN APPL STAT, V1, P85, DOI 10.1214/07-AOAS104 Nicholas FW, 2003, NUCLEIC ACIDS RES, V31, P275, DOI 10.1093/nar/gkg074 Osborne John D., 2007, V408, P153 Qian WF, 2011, P NATL ACAD SCI USA, V108, P8725, DOI 10.1073/pnas.1104695108 Rainard P, 2006, VET RES, V37, P369, DOI 10.1051/vetres:2006007 Riollet C, 2000, ADV EXP MED BIOL, V480, P247 Sahana G, 2008, ANIM GENET, V39, P354, DOI 10.1111/j.1365-2052.2008.01729.x Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smyth G. K., 2004, STAT APPL GENET MOL, V3, DOI DOI 10.2202/1544-6115.1027 Sorensen LP, 2008, J DAIRY SCI, V91, P2493, DOI 10.3168/jds.2007-0583 Sorensen P, 2003, GENET SEL EVOL, V35, P605, DOI 10.1051/gse:2003042 Thye T, 2003, AM J HUM GENET, V72, P448, DOI 10.1086/367714 von Mering C, 2005, NUCLEIC ACIDS RES, V33, pD433, DOI 10.1093/nar/gki005 Wu XB, 2008, MOL SYST BIOL, V4, DOI 10.1038/msb.2008.27 Yang P, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0021502 Zhang WS, 2011, BMC SYST BIOL, V5, DOI 10.1186/1752-0509-5-55 Zhang WS, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S1-S11 NR 45 TC 7 Z9 7 PU AMER PHYSIOLOGICAL SOC PI BETHESDA PA 9650 ROCKVILLE PIKE, BETHESDA, MD 20814 USA SN 1094-8341 J9 PHYSIOL GENOMICS JI Physiol. Genomics PD MAR PY 2012 VL 44 IS 5 BP 305 EP 317 DI 10.1152/physiolgenomics.00047.2011 PG 13 WC Cell Biology; Genetics & Heredity; Physiology SC Cell Biology; Genetics & Heredity; Physiology GA 904JZ UT WOS:000301192500003 PM 22234994 ER PT J AU Tasi, CS Huang, YM Liu, CH Huang, YM AF Tasi, Cheng-Shiun Huang, Yong-Ming Liu, Chien-Hung Huang, Yueh-Min TI Applying VSM and LCS to develop an integrated text retrieval mechanism SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Text retrieval; Integrated text retrieval mechanism; Vector space models; Longest common subsequence ID VECTOR-SPACE MODEL; SEQUENCE ALIGNMENT; ALGORITHM AB Text retrieval has received a lot of attention in computer science. In the text retrieval field, the most widely-adopted similarity technique is using vector space models (VSM) to evaluate the weight of terms and using Cosine, Jaccard or Dice to measure the similarity between the query and the texts. However, these similarity techniques do not consider the effect of the sequence of the information. In this paper, we propose an integrated text retrieval (ITR) mechanism that takes the advantage of both VSM and longest common subsequence (ICS) algorithm. The key idea of the ITR mechanism is to use LCS to re-evaluate the weight of terms, so that the sequence and weight relationships between the query and the texts can be considered simultaneously. The results of mathematical analysis show that the ITR mechanism can increase the similarity on Jaccard and Dice similarity measurements when a sequential relationship exists between the query and the texts. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Tasi, Cheng-Shiun; Huang, Yong-Ming; Huang, Yueh-Min] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan. [Liu, Chien-Hung] Hsing Kuo Univ Management, Dept Network Multimedia Design, Tainan 709, Taiwan. RP Huang, YM (reprint author), Natl Cheng Kung Univ, Dept Engn Sci, 1 Ta Hsueh Rd, Tainan 701, Taiwan. EM alen566@gmail.com; huang@mail.ncku.edu.tw; chliu@mail.hku.edu.tw; huang@mail.ncku.edu.tw FU National Science Council of the Republic of China [NSC 97-2511-S-006-001-MY3, NSC 98-2631-S-024-001, NSC 99-2631-S-006-001] FX The authors thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 97-2511-S-006-001-MY3, NSC 98-2631-S-024-001, and NSC 99-2631-S-006-001. CR Berry MW, 2007, SURVEY TEXT MINING C BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Brodzik AK, 2006, IEEE T SIGNAL PROCES, V54, P2456, DOI 10.1109/TSP.2006.873717 Cormen T.H., 2001, INTRO ALGORITHMS Cunningham P, 2009, IEEE T KNOWL DATA EN, V21, P1532, DOI 10.1109/TKDE.2008.227 Delen D, 2008, EXPERT SYST APPL, V34, P1707, DOI 10.1016/j.eswa.2007.01.035 Gupta Vishal, 2009, Journal of Emerging Technologies in Web Intelligence, V1, DOI 10.4304/jetwi.1.1.60-76 Huang C. R., 2003, P INT C NAT LANG PRO, P825 Huang L., 2008, P ACM C INF KNOWL MA, P63, DOI 10.1145/1458082.1458094 Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 Lavenier D., 2005, RECONFIGURABLE COMPU Manning C.D., 2008, INTRO INFORM RETRIEV Manning Christopher D., 2001, FDN STAT NATURAL LAN Nanni L, 2008, EXPERT SYST APPL, V35, P1463, DOI 10.1016/j.eswa.2007.08.028 NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 Tan A., 1999, P PAC AS C KNOWL DIS, P65 VINGRON M, 1994, J MOL BIOL, V235, P1, DOI 10.1016/S0022-2836(05)80006-3 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 Xiao Y., 2005, J CHINESE LANGUAGE C, V15, P45 Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 NR 28 TC 1 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAR PY 2012 VL 39 IS 4 BP 3974 EP 3982 DI 10.1016/j.eswa.2011.09.039 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 882SH UT WOS:000299583700008 ER PT J AU Paukkeri, MS Garcia-Plaza, AP Fresno, V Unanue, RM Honkela, T AF Paukkeri, Mari-Sanna Perez Garcia-Plaza, Alberto Fresno, Victor Martinez Unanue, Raquel Honkela, Timo TI Learning a taxonomy from a set of text documents SO APPLIED SOFT COMPUTING LA English DT Article DE Taxonomy learning; Knowledge representation; Document clustering; Keyphrase extraction; Fuzzy logic; Multilinguality; Self-Organizing Map ID MODEL AB We present a methodology for learning a taxonomy from a set of text documents that each describes one concept. The taxonomy is obtained by clustering the concept definition documents with a hierarchical approach to the Self-Organizing Map. In this study, we compare three different feature extraction approaches with varying degree of language independence. The feature extraction schemes include fuzzy logic-based feature weighting and selection, statistical keyphrase extraction, and the traditional tf-idf weighting scheme. The experiments are conducted for English, Finnish, and Spanish. The results show that while the rule-based fuzzy logic systems have an advantage in automatic taxonomy learning, taxonomies can also be constructed with tolerable results using statistical methods without domain-or style-specific knowledge. (C) 2011 Elsevier B.V. All rights reserved. C1 [Paukkeri, Mari-Sanna; Honkela, Timo] Aalto Univ, Sch Sci, Adapt Informat Res Ctr, FI-00076 Aalto, Finland. [Perez Garcia-Plaza, Alberto; Fresno, Victor; Martinez Unanue, Raquel] UNED, ETSI Informat, NLP & IR Grp, Madrid 28040, Spain. RP Paukkeri, MS (reprint author), Aalto Univ, Sch Sci, Adapt Informat Res Ctr, POB 15400, FI-00076 Aalto, Finland. EM mari-sanna.paukkeri@tkk.fi; alpgarcia@lsi.uned.es; vfresno@lsi.uned.es; raquel@lsi.uned.es; timo.honkela@tkk.fi FU Finnish Graduate School in Language Studies (Langnet); Finnish Funding Agency for Technology and Innovation (Tekes); Academy of Finland; MA2VICMR consortium [S2009/TIC-1542]; Madrid Regional Government FX We acknowledge the financial support for our research from Finnish Graduate School in Language Studies (Langnet), Content-Factory project funded by Finnish Funding Agency for Technology and Innovation (Tekes), Academy of Finland through the Centre of Excellence program, and the Spanish research project MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government. CR Amsler R. A., 1981, P 19 ANN M ASS COMP, P133, DOI 10.3115/981923.981959 Bakus J., 2002, P 9 INT C NEUR INF P, P2212 Benz D., 2007, LWA 2007, P109 Brank J., 2005, P C DAT MIN DAT WAR, P166 Brewster C., 2007, RECENT ADV NATURAL L BREWSTER C, 2004, P USE COMPUTATIONAL BUITELAAR P, 2000, WORKSH ONT LEX KNOWL, P16 Caraballo S., 1999, P 37 ANN M ASS COMP, P120, DOI 10.3115/1034678.1034705 Ciaramita M., 2005, P 19 INT JOINT C ART, P659 Cimiano P., 2005, P WORKSH LEARN EXT L Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 CREUTZ M, 2007, ACM T SPEECH LANGUAG, V4, P34 Creutz Mathias, 2007, ACM T SPEECH LANGUAG, V4, P3, DOI 10.1145/1217098.1217101 Creutz Mathis, 2007, ACM T SPEECH LANGUAG, V4, P1 Dellschaft K, 2006, LECT NOTES COMPUT SC, V4273, P228 Dittenbach M., 2000, P INT JOINT C NEUR N, V6, P15 Duran N., 2007, P 29 ANN M COGN SCI, P233 Fallucchi F, 2011, NAT LANG ENG, V17, P71, DOI 10.1017/S1351324910000197 Faure D., 1998, LREC WORKSH AD LEX C, P5 Fernandez-Lopez M, 2002, KNOWL ENG REV, V17, P129, DOI 10.1017/S0269888902000462 FRESNO V, 2006, THESIS URJC GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Gardenfors P., 2004, FORMAL ONTOLOGY INFO, V114, P17 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Harris ZS, 1954, WORD, V10, P146 Hearst M. A., 1992, P 14 INT C COMP LING, P539 Honkela T., 2007, P IJCNN INT JOINT C, P2881 Honkela T., 2008, J EC METHODOLOGY, V15, P245, DOI 10.1080/13501780802321350 Hung C., 2004, INT J HYBRID INTELLI, V1, P127 Kim KJ, 2007, APPL SOFT COMPUT, V7, P398, DOI 10.1016/j.asoc.2005.09.002 Koehn Philipp, 2005, P 10 MACH TRANSL SUM, P79 Kohonen T., 2001, SELF ORGANIZING MAPS KOIKKALAINEN P, 1990, NEURAL NETWORKS, V2, P279 Kosko B, 1998, IEEE T SYST MAN CY C, V28, P441, DOI 10.1109/5326.704584 KOZAREVA Z, 2009, P AAAI 2009 SPR S LE, P50 Liu YC, 2008, NEUROCOMPUTING, V71, P857, DOI 10.1016/j.neucom.2007.03.006 Maedche A., 2002, P WEB INT C, P301 Paukkeri Mari-Sanna, 2010, Proceedings 2010 International Multiconference on Computer Science and Information Technology (IMCSIT 2010) Paukkeri M.S., 2008, COLING 2008 COMPANIO, P83 PENNACCHIOTTI M, 2006, P INF COMP SEM ICOS, P87 PLAZA AG, 2008, WEB INTELLIGENCE, P851 Ponzetto S. P., 2007, P 22 NAT C ART INT A, P1440 RIBEIRO A, 2003, INTELLIGENT EXPLORAT, P19 Ruotsalo T, 2009, IEEE INTELL SYST, V24, P64, DOI 10.1109/MIS.2009.32 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZ D, 2005, P WORKSH EXT LEARN L, P53 Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679 SIMPERL EPB, 2006, INT SEM WEB C, P625 SNOW R, 2006, P 21 INT C COMP LING, P801, DOI 10.3115/1220175.1220276 Specia L., 2006, P 2 WORKSH ONT LEARN, P57 Steinberger R, 2008, MINING MASSIVE DATA, P295 Velardi P, 2007, IEEE T KNOWL DATA EN, V19, P180, DOI 10.1109/TKDE.2007.21 Wilks Yorick, 2006, Foundations and Trends in Web Science, V1, DOI 10.1561/1800000002 Wong W., 2009, THESIS U W AUSTR Yuret D, 2010, COMPUT LINGUIST, V36, P111, DOI 10.1162/coli.2010.36.1.36103 NR 55 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1568-4946 J9 APPL SOFT COMPUT JI Appl. Soft. Comput. PD MAR PY 2012 VL 12 IS 3 BP 1138 EP 1148 DI 10.1016/j.asoc.2011.11.009 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 879IZ UT WOS:000299324200014 ER PT J AU Becker, M Laue, R AF Becker, Michael Laue, Ralf TI A comparative survey of business process similarity measures SO COMPUTERS IN INDUSTRY LA English DT Article DE Survey; Business process model; Similarity measures ID PROCESS MODELS; SEARCH; DISCOVERY; DISTANCE; FEATURES; TREE AB Similarity measures for business process models have been suggested for different purposes such as measuring compliance between reference and actual models, searching for related models in a repository, or locating services that adhere to a specification given by a process model. The aim of our article is to provide a comprehensive survey on techniques to define and calculate such similarity measures. As the measures differ in many aspects, it is an interesting question how different measures rank "similarity" within the same set of models. We investigated, how different kinds of changes in a model influence the values of different similarity measures that have been published in academic literature. Furthermore, we identified eight properties that a similarity measure should have from a theoretical point of view and analysed how these properties are fulfilled by the different measures. Our results show that there are remarkable differences among existing measures. We give some recommendations which type of measure is useful for which kind of application. (C) 2011 Elsevier B.V. All rights reserved. C1 [Becker, Michael] Univ Leipzig, Dept Business Informat Syst, Leipzig, Germany. [Laue, Ralf] Univ Appl Sci Zwickau, Zwickau, Germany. RP Becker, M (reprint author), Univ Leipzig, Dept Business Informat Syst, Leipzig, Germany. EM michael.becker@uni-leipzig.de; ralf.laue@fh-zwickau.de CR Akkiraju R, 2010, LECT NOTES COMPUT SC, V6470, P515, DOI 10.1007/978-3-642-17358-5_35 de Medeiros AKA, 2008, DATA KNOWL ENG, V64, P55, DOI 10.1016/j.datak.2007.06.010 [Anonymous], 2011, OBJECT MANAGEMENT GR Bae J, 2007, INT J WEB SERV RES, V4, P1, DOI 10.4018/jwsr.2007100101 Bae J., 2006, P IEEE INT C WEB SER, P479 Bae J, 2004, IEEE T KNOWL DATA EN, V16, P1010 Bae J, 2006, LECT NOTES COMPUT SC, V4103, P141 Bergroth L., 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, DOI 10.1109/SPIRE.2000.878178 Bille P, 2005, THEOR COMPUT SCI, V337, P217, DOI 10.1016/j.tcs.2004.12.030 Brocke J. V., 2009, P 17 EUR C INF SYST, P2206 Dijkman R, 2011, INFORM SYST, V36, P498, DOI 10.1016/j.is.2010.09.006 Dijkman R, 2009, LECT NOTES COMPUT SC, V5701, P48 Dijkman R, 2007, P 11 IEEE INT ENT DI, P37 Dumas M., 2009, IEEE DATA ENG B, V32, P23 Ehrig M, 2007, P 4 AS PAC C CONC MO, V67, P71 Fettke P, 2006, LECT NOTES COMPUT SC, V3812, P469 Gerke K., 2009, P CONF INT C 2009 1, P76 Gerth C., 2010, P 2010 IEEE INT C SE, P57 Grigori D, 2010, IEEE T SERV COMPUT, V3, P178, DOI 10.1109/TSC.2010.6 Gulla J. A., 2000, IEEE INT C REQ ENG, P17 Huang K., 2004, LECT NOTES COMPUT SC, P107 IDEF0, 1993, DRAFT FED INF PROC S Jung J.Y., 2009, INT J INNOV COMPUT I, V5, P1 Kunze M., 2010, P 1 INT WORKSH PROC Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Li C., 2008, P 27 INT C CONC MOD, P248 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 Lu R., 2007, DISCOVERY PREFERRED, P165 Lu R., 2006, MANAGING PROCESS VAR Mahleko B., 2005, Proceedings. The 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service Melcher J., 2008, INT S SYMB NUM ALG S, P572 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Minor M., 2007, P 7 INT C CAS BAS RE, P224 Niemann M., 2010, P 1 INT WORKSH PROC Reichert M, 1998, J INTELL INF SYST, V10, P93, DOI 10.1023/A:1008604709862 Reiss SP, 2009, PROC INT CONF SOFTW, P243, DOI 10.1109/ICSE.2009.5070525 Rosa M.L., 2010, LNCS, V6426, P96 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 1999, IEEE T PATTERN ANAL, V21, P871, DOI 10.1109/34.790428 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 van Dongen B., 2005, LECT NOTES COMPUTER, V3536, P1105 van Dongen B, 2008, LECT NOTES COMPUT SC, V5074, P450 Vanhatalo J, 2008, LECT NOTES COMPUT SC, V5240, P100, DOI 10.1007/978-3-540-85758-7_10 Wang JM, 2010, LECT NOTES COMPUT SC, V6426, P394 Weber B, 2008, DATA KNOWL ENG, V66, P438, DOI 10.1016/j.datak.2008.05.001 Weidlich M, 2010, LECT NOTES COMPUT SC, V6128, P63, DOI 10.1007/978-3-642-13675-7_6 Weidlich M, 2009, LECT NOTES BUS INF, V29, P71 Weidlich M, 2010, LECT NOTES COMPUT SC, V6051, P483, DOI 10.1007/978-3-642-13094-6_37 Weidlich M., 2010, IEEE T SOFTWARE ENG, V99 Wombacher A., 2006, SERVICE ORIENTED ELE, V80, P51 Yan Z., 2009, WORKING PAPERS TU EI Yan ZQ, 2010, LECT NOTES COMPUT SC, V6426, P60 Zha HP, 2010, COMPUT IND, V61, P463, DOI 10.1016/j.compind.2010.01.001 NR 53 TC 11 Z9 16 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0166-3615 J9 COMPUT IND JI Comput. Ind. PD FEB PY 2012 VL 63 IS 2 SI SI BP 148 EP 167 DI 10.1016/j.compind.2011.11.003 PG 20 WC Computer Science, Interdisciplinary Applications SC Computer Science GA 913QI UT WOS:000301892100005 ER PT J AU Jonnalagadda, S Cohen, T Wu, S Gonzalez, G AF Jonnalagadda, Siddhartha Cohen, Trevor Wu, Stephen Gonzalez, Graciela TI Enhancing clinical concept extraction with distributional semantics SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Article DE NLP; Information extraction; NER; Distributional semantics; Clinical informatics ID SPACE MODELS; FREE-TEXT; INFORMATION; DOCUMENTS AB Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type "clinical trials" to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged F-score for exact match increased from 80.3% to 82.3% and the micro-averaged F-score based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the boot-strap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. (C) 2011 Elsevier Inc. All rights reserved. C1 [Jonnalagadda, Siddhartha; Wu, Stephen] Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA. [Jonnalagadda, Siddhartha; Gonzalez, Graciela] Arizona State Univ, Dept Biomed Informat, Phoenix, AZ USA. [Cohen, Trevor] Univ Texas Hlth Sci Ctr, Sch Biomed Informat, Houston, TX USA. RP Jonnalagadda, S (reprint author), Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA. EM siddhartha@mayo.edu FU NLM [HHSN276201000031C]; Elmer V Bernstam's group at the School of Biomedical Informatics at University of Texas Houston [3UL1RR024148, NCRR 1RC1RR028254, NSF 0964613] FX We thank NLM for making us available the Medline abstracts to create semantic resources and for partially funding all authors for this work (Contract# HHSN276201000031C), Elmer V Bernstam's group at the School of Biomedical Informatics at University of Texas Houston (NCRR Grant 3UL1RR024148, NCRR 1RC1RR028254, NSF 0964613 and the Brown Foundation) and Mayo Clinic for testing the process using their respective clinical notes, the developers of BANNER, MALLET and Semantic Vectors for the software packages and the organizers of the i2b2/VA 2010 NLP challenge for sharing the corpus. CR Ando RK, 2007, P 2 BIOCR CHALL [Anonymous], MALLET MACHINE LEARN Aronson AR, 2001, AMIA Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chuang JH, 2002, AMIA 2002 SYMPOSIUM, PROCEEDINGS, P160 Cohen Trevor, 2010, Stud Health Technol Inform, V160, P661 Cohen T, 2009, J BIOMED INFORM, V42, P390, DOI 10.1016/j.jbi.2009.02.002 De Vine L, 2010, 2010 AAA FALL S SERI DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fiszman M, 1999, J AM MED INFORM ASSN, P67 Friedman C, 1997, AMIA Friedman C, 2002, J BIOMED INFORM, V35, P222, DOI 10.1016/S1532-0464(03)00012-1 Harris Zellig Sabbettai, 1991, THEORY LANGUAGE INFO HAUG PJ, 1990, RADIOLOGY, V174, P543 Haug P J, 1995, Proc Annu Symp Comput Appl Med Care, P284 Hirschman L, 1976, P NAT COMP C EXP NEW, P267, DOI 10.1145/1499799.1499842 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Johnson W. B., 1984, CONT MATH, V26, P189 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Jonnalagadda S, 2010, COMPUT LINGUIST INTE, V6008/2010 Jonnalagadda S, 2011, THESIS ARIZONA STATE Jurgens D, 2009, P WORKSH EV EM TEXT, P9 Kanerva P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P1036 Kanerva P., 2008, P 30 ANN M COGN SCI, P23 Karlgren J., 2001, FDN REAL WORLD INTEL, P294 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Leaman R, 2008, PAC S BIOINF Lin D., 1998, P 17 INT C COMP LING, P768 Liu C., 2007, P 45 ANN M ASS COMP, P208 Lund K, 1996, LANG COGNIT PROCESS Lussier Y. A., 2007, SEMANTIC WEB REVOLUT, P101, DOI 10.1007/978-0-387-48438-9_6 Meystre Stephane, 2005, BMC Med Inform Decis Mak, V5, P30, DOI 10.1186/1472-6947-5-30 Meystre S, 2005, ST HEAL T, V116, P823 Meystre S, 2006, J BIOMED INFORM, V39, P589, DOI 10.1016/j.jbi.2005.11.004 Meystre S M, 2008, Yearb Med Inform, P128 Mika S., P 1999 IEEE SIGN PRO, P41 Minka T., 2005, MSRTR2005144 Noreen Eric W., 1989, COMPUTER INTENSIVE M Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Plate TA, 2002, NEURAL NETWORKS IEEE, V6, P623 Rapp R, 2002, P 19 INT C COMP LING, V1, P7 Rau LF, 1991, 7 IEEE C ART INT APP, P1 SAGER N, 1975, J AM SOC INFORM SCI, V26, P10, DOI 10.1002/asi.4630260104 Sahlgren M, 2006, WORD SPACE MODEL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sang E.F.T.K., 2003, P 7 C NAT LANG LEARN, P142 Saussure F, 1922, COURS LINGUISTIQUE G Savova GK, 2008, LREC Schadow Gunther, 2003, AMIA Annu Symp Proc, P584 Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Kabiljo R, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-233 Stanfill MH, 2010, J AM MED INFORM ASSN, V17, P646, DOI 10.1136/jamia.2009.001024 Sutton C., 2007, INTRO STAT RELATIONA, DOI Cambridge, MA, USA Taira RK, 1999, J AM MED INFORM ASSN, P970 Traugott Elizabeth Closs, 2002, REGULARITY SEMANTIC Trefethen L.N., 1997, NUMERICAL LINEAR ALG Turian J, 2010, ACL, V51, P61801 Turney P., 2001, P 12 EUR C MACH LEAR Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Uzuner O, 2011, J AM MED INFORM ASS Wiceintowski R, 2008, J AM MED INFORM ASSN, V15, P29, DOI 10.1197/jamia.M2440 Widdows D., 2008, 6 INT C LANG RES EV Widdows D, 2010, 4 IEEE INT C SEM COM, V1, P43 WILBUR J, 2007, P 2 BIOCREATIVE CHAL, P7 Xu Hua, 2003, AMIA Annu Symp Proc, P1057 Zeng Qing T, 2006, BMC Med Inform Decis Mak, V6, P30, DOI 10.1186/1472-6947-6-30 NR 66 TC 13 Z9 16 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD FEB PY 2012 VL 45 IS 1 BP 129 EP 140 DI 10.1016/j.jbi.2011.10.007 PG 12 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 895EK UT WOS:000300478800013 PM 22085698 ER PT J AU Evangelopoulos, N Visinescu, L AF Evangelopoulos, Nicholas Visinescu, Lucian TI Text-Mining the Voice of the People SO COMMUNICATIONS OF THE ACM LA English DT Article ID E-GOVERNMENT RESEARCH; PERSPECTIVE; DEMOCRACY C1 [Evangelopoulos, Nicholas; Visinescu, Lucian] Univ N Texas, Dept Informat Technol & Decis Sci, Coll Business, Denton, TX 76203 USA. RP Evangelopoulos, N (reprint author), Univ N Texas, Dept Informat Technol & Decis Sci, Coll Business, Denton, TX 76203 USA. EM Nick.Evangelopoulos@unt.edu; Lucian.Visinescu@unt.edu RI Evangelopoulos, Nicholas/ OI Evangelopoulos, Nicholas/0000-0001-9866-6828 CR Anttiroiko AV, 2003, COMMUN ACM, V46, P121, DOI 10.1145/903893.903926 Bimber B, 1999, POLIT COMMUN, V16, P409, DOI 10.1080/105846099198569 Coussement K, 2008, DECIS SUPPORT SYST, V44, P870, DOI 10.1016/j.dss.2007.10.010 Evangelopoulos N, 2012, EUR J INFORM SYST, V21, P70, DOI 10.1057/ejis.2010.61 Fang Z., 2002, INT J COMPUTER INTER, V10, P1 Fung A., 2009, WORKING TOGETHER STR Gil-Garcia R., 2007, GOVT INFORM Q, V24, P266 Gronlund A., 2001, COMMUN ACM, V44, P23 Heeks R., 2002, Information Polity, V7 Heeks R, 2007, GOV INFORM Q, V24, P243, DOI 10.1016/j.giq.2006.06.005 Irani Z, 2007, EUR J INFORM SYST, V16, P103, DOI 10.1057/palgrave.ejis.3000678 Kane J., 2005, GRIFFITH REV, P203 Kuechler WL, 2007, COMMUN ACM, V50, P86, DOI 10.1145/1290958.1290967 Kushchu I, 2004, E GOVT M GOVT FACING Landauer T., 2007, HDB LATENT SEMANTIC, P3 Ndou V., 2004, ELECT J INFORM SYSTE, V18, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sidorova A, 2008, MIS QUART, V32, P467 Smith A., 2010, GOVT ONLINE INTERNET *UN, 2008, UN E GOVT SURV 2008 Watson RT, 2001, COMMUN ACM, V44, P27, DOI 10.1145/357489.357499 Watson RT, 1999, COMMUN ACM, V42, P58, DOI 10.1145/322796.322810 Yildiz M, 2007, GOV INFORM Q, V24, P646, DOI 10.1016/j.giq.2007.01.002 NR 23 TC 5 Z9 5 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PD FEB PY 2012 VL 55 IS 2 BP 55 EP 62 DI 10.1145/2076450.2076467 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 884UK UT WOS:000299734100024 ER PT J AU Escalante, HJ Montes, M Sucar, E AF Escalante, Hugo Jair Montes, Manuel Sucar, Enrique TI Multimodal indexing based on semantic cohesion for image retrieval SO INFORMATION RETRIEVAL LA English DT Article DE Multimedia image retrieval; Image annotation; Distributional term representations; Semantic cohesion modeling ID PHOTOGRAPHIC RETRIEVAL; INFORMATION-RETRIEVAL; RELEVANCE FEEDBACK; TEXT QUERIES; ANNOTATION; EXPANSION; TASK AB This paper introduces two novel strategies for representing multimodal images with application to multimedia image retrieval. We consider images that are composed of both text and labels: while text describes the image content at a very high semantic level (e.g., making reference to places, dates or events), labels provide a mid-level description of the image (i.e., in terms of the objects that can be seen in the image). Accordingly, the main assumption of this work is that by combining information from text and labels we can develop very effective retrieval methods. We study standard information fusion techniques for combining both sources of information. However, whereas the performance of such techniques is highly competitive, they cannot capture effectively the content of images. Therefore, we propose two novel representations for multimodal images that attempt to exploit the semantic cohesion among terms from different modalities. Such representations are based on distributional term representations widely used in computational linguistics. Under the considered representations the content of an image is modeled by a distribution of co-occurrences over terms or of occurrences over other images, in such a way that the representation can be considered an expansion of the multimodal terms in the image. We report experimental results using the SAIAPR TC12 benchmark on two sets of topics used in ImageCLEF competitions with manually and automatically generated labels. Experimental results show that the proposed representations outperform significantly both, standard multimodal techniques and unimodal methods. Results on manually assigned labels provide an upper bound in the retrieval performance that can be obtained, whereas results with automatically generated labels are encouraging. The novel representations are able to capture more effectively the content of multimodal images. We emphasize that although we have applied our representations to multimedia image retrieval the same formulation can be adopted for modeling other multimodal documents (e.g., videos). C1 [Escalante, Hugo Jair; Montes, Manuel; Sucar, Enrique] Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Puebla 72840, Mexico. RP Escalante, HJ (reprint author), Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Luis Enrique Erro 1, Puebla 72840, Mexico. EM hugojair@inaoep.mx; mmontesg@inaoep.mx; esucar@inaoep.mx RI Escalante, Hugo Jair/ OI Escalante, Hugo Jair/0000-0003-4603-3513 FU CONACyT [61335, 205834] FX The authors are grateful with the reviewers for their comments that have helped to improve the content of the article. We also thank the valuable support of Thamar Solorio in the preparation of the final manuscript. This project was supported by CONACyT under project grant 61335 and scholarship 205834. CR ADRIANI M, 2005, CLEF WORKSH VIENN AU AHPINE J, 2008, 2008 CLEF WORKSH AAR Ah-Pine J, 2009, MULTIMED TOOLS APPL, V42, P31, DOI 10.1007/s11042-008-0246-8 AHPINE J, 2009, CLEF WORKSH CORF GRE ALLAN M, 2009, P 20 BRIT MACH VIS C ALY R, 2007, P SIGIR MULT INF RET, P40 ALY R, 2009, P INT C CONT BAS IM, P44 Arni T, 2009, LECT NOTES COMPUT SC, V5706, P500 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Barnard K, 2008, INT J COMPUT VISION, V77, P199, DOI 10.1007/s11263-007-0068-6 BESANCON R, 2006, CLEF WORKSH AL SPAIN Boldareva L, 2004, LECT NOTES COMPUT SC, V3115, P308 Bradshaw B., 2000, Proceedings ACM Multimedia 2000, DOI 10.1145/354384.354456 Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61 Carrillo M, 2009, LECT NOTES ARTIF INT, V5729, P24, DOI 10.1007/978-3-642-04208-9_7 CHANG Y, 2005, CLEF WORKSH VIENN AU CHANG Y, 2006, CLEF WORKSH AL SPAIN CHEN HC, 1995, J AM SOC INFORM SCI, V46, P175, DOI 10.1002/(SICI)1097-4571(199504)46:3<175::AID-ASI3>3.0.CO;2-U Chua T., 1994, P 27 ANN HAW INT C S, V3, P590 CLINCHANT S, 2007, 2007 CLEF WORKSH BUD Clough P, 2007, LECT NOTES COMPUT SC, V4730, P579 COX J, 2000, IEEE T IMAGE PROCESS, V9, P20 CURTONI P, 2006, CLEF WORKSH AL SPAIN Datta R., 2008, ACM COMPUT SURV, V40, P40 Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248 Elworthy D., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354850 Escalante HJ, 2008, LECT NOTES COMPUT SC, V5152, P546, DOI 10.1007/978-3-540-85760-0_68 Escalante HJ, 2010, COMPUT VIS IMAGE UND, V114, P419, DOI 10.1016/j.cviu.2009.03.008 Escalante H.J., 2008, P 1 ACM INT C MULT I, P172, DOI 10.1145/1460096.1460125 ESCALANTE HJ, 2009, P CIMAT PI 09 WORKSH ESCALANTE HJ, 2011, COMPUTER VI IN PRESS Escalante HJ, 2009, LECT NOTES COMPUT SC, V5706, P669, DOI 10.1007/978-3-642-04447-2_84 Farah M., 2007, P 30 ANN INT ACM SIG, P591, DOI 10.1145/1277741.1277843 FOX EA, 1994, NIST PUBLICATION GALE W, 1993, COMPUT HUMANITIES, V26, P415 Goodrum A. A., 2000, Informing Science, V3 Grangier D, 2006, LECT NOTES COMPUT SC, V4132, P24 Grangier D, 2006, LECT NOTES COMPUT SC, V4212, P162 Grubinger M, 2008, LECT NOTES COMPUT SC, V5152, P433, DOI 10.1007/978-3-540-85760-0_57 Grubinger Michael, 2007, THESIS VICTORIA U ME Hanbury A, 2008, J VISUAL LANG COMPUT, V19, P617, DOI 10.1016/j.jvlc.2008.01.002 HANBURY A, 2006, 102 PRIP VIENN U TEC Hare J. S., 2006, P MULT CONT AN MAN R, V6073, P1 HOI SCH, 2005, CLEF WORKSH VIENN AU INOUE M, 2005, SAC 05 P 2005 ACM S, P1031 Ishikawa Y., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases IZQUIERDOBEVIA R, 2005, CLEF WORKSH VIENN AU Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 JONES GJF, 2005, CLEF WORKSH VIENN AU KRAAIJ W, 2006, TREC VID RETR EV ONL La Cascia M., 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173), DOI 10.1109/IVL.1998.694480 LARSON M, 2009, CLEF WORKSH CORF GRE LAVELLI A, 2005, P INT C INF KNOWL MA, P615 LESTARIPARAMITA M, 2009, CLEF WORKSH CORF GRE Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Lewis D., 1990, P 13 ANN INT ACM SIG, P385, DOI 10.1145/96749.98244 Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045 MAILLOT N, 2006, CLEF WORKSH AL SPAIN MARTINEZFERNAND.J, 2005, CLEF WORKSH VIENN AU MARTINEZFERNAND.J, 2006, CLEF WORKSH AL SPAIN MARTINVALDIVIA MT, 2005, CLEF WORKSH VIENN AU Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 PEINADO V, 2005, CLEF WORKSH VIENN AU RAHMAN MM, 2006, CLEF WORKSH AL SPAIN Raicu DS, 2006, EURASIP J APPL SIG P, DOI 10.1155/ASP/2006/63124 RAUTIAINEN M, 2004, P 6 ACM SIGMM INT WO, P197, DOI 10.1145/1026711.1026744 RAUTIAINEN M, 2005, P ICME, P932 REYES A, 2011, LNCS IN PRESS Rui Y, 1999, J VIS COMMUN IMAGE R, V10, P39, DOI 10.1006/jvci.1999.0413 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 Salton G., 1987, TR87881 CORN U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sclaroff S, 1999, COMPUT VIS IMAGE UND, V75, P86, DOI 10.1006/cviu.1999.0765 SHU DF, 2005, INFORM RETRIEVAL, V8, P449 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 SMUCKER D, 2009, P 32 INT ACM SIGIR C Snoek C., 2005, P 13 ANN ACM INT C M, P399, DOI 10.1145/1101149.1101236 SNOEK CGM, 2006, P 14 ANN ACM C MULT VANGEMERT J, 2003, THESIS U AMSTERDAM N WESTERVELD T, 2000, P EUR W MIL IT, P33 WESTERVELD T, 2004, THESIS TWENTE U NETH Westerveld T, 2000, P CONT BAS MULT INF, P276 Winn J., 2005, Proceedings. Tenth IEEE International Conference on Computer Vision ZHANG C., 2005, P 28 ANN INT ACM SIG, P51, DOI 10.1145/1076034.1076046 Zhou ZH, 2006, ACM T INFORM SYST, V24, P219, DOI 10.1145/1148020.1148023 NR 85 TC 2 Z9 2 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD FEB PY 2012 VL 15 IS 1 BP 1 EP 32 DI 10.1007/s10791-011-9170-z PG 32 WC Computer Science, Information Systems SC Computer Science GA 887IN UT WOS:000299920200001 ER PT J AU Ramirez, F Lawyer, G Albrecht, M AF Ramirez, Fidel Lawyer, Glenn Albrecht, Mario TI Novel search method for the discovery of functional relationships SO BIOINFORMATICS LA English DT Article ID GENE ONTOLOGY ANNOTATIONS; SEMANTIC SIMILARITY; HUMAN GENOME; DATABASE; RESOURCE; PRIORITIZATION; PREDICTION; NETWORKS; PROTEINS; MODEL AB Results: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision. C1 [Ramirez, Fidel; Lawyer, Glenn; Albrecht, Mario] Max Planck Inst Informat, D-66123 Saarbrucken, Germany. RP Albrecht, M (reprint author), Max Planck Inst Informat, Campus E1-4, D-66123 Saarbrucken, Germany. EM mario.albrecht@mpi-inf.mpg.de RI Albrecht, Mario/B-6694-2008 OI Albrecht, Mario/0000-0002-3103-9624 FU German National Genome Research Network (NGFN); German Research Foundation (DFG) [KFO 129/1-2] FX German National Genome Research Network (NGFN); German Research Foundation (DFG) contract number KFO 129/1-2. The research was conducted in the context of the DFG-funded Cluster of Excellence for Multimodal Computing and Interaction. CR Aerts S, 2006, NAT BIOTECHNOL, V24, P537, DOI 10.1038/nbt1203 ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 Amberger J, 2009, NUCLEIC ACIDS RES, V37, pD793, DOI 10.1093/nar/gkn665 Bahcall O, 2007, NAT REV GENET, V8, pS14 Bairoch A, 2000, NUCLEIC ACIDS RES, V28, P304, DOI 10.1093/nar/28.1.304 Benabderrahmane S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-588 Berman H, 2003, NAT STRUCT BIOL, V10, P980, DOI 10.1038/nsb1203-980 Blake JA, 2011, NUCLEIC ACIDS RES, V39, pD842, DOI 10.1093/nar/gkq1008 Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Buffa L, 2008, EUR J CELL BIOL, V87, P197, DOI 10.1016/j.ejcb.2007.11.003 Camon E., 2004, IN SILICO BIOL, V4, P5 Chabalier J, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-235 Chan JNY, 2010, TRENDS PHARMACOL SCI, V31, P82, DOI 10.1016/j.tips.2009.11.002 Chatr-Aryamontri A, 2007, NUCLEIC ACIDS RES, V35, pD572, DOI 10.1093/nar/gkl950 Chen F, 2006, NUCLEIC ACIDS RES, V34, pD363, DOI 10.1093/nar/gkj123 Consortium U, 2010, NUCLEIC ACIDS RES, V38, pD142 del Pozo A, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-50 Finn RD, 2008, NUCLEIC ACIDS RES, V36, pD281, DOI 10.1093/nar/gkm960 Flicek P, 2008, NUCLEIC ACIDS RES, V36, pD707, DOI 10.1093/nar/gkm988 Friedberg I, 2006, BRIEF BIOINFORM, V7, P225, DOI 10.1093/bib/bbl004 Prasad TSK, 2009, NUCLEIC ACIDS RES, V37, pD767, DOI 10.1093/nar/gkn892 Gribskov M, 1996, COMPUT CHEM, V20, P25, DOI 10.1016/S0097-8485(96)80004-0 Huang DW, 2007, GENOME BIOL, V8, DOI 10.1186/gb-2007-8-9-r183 Hunter S, 2009, NUCLEIC ACIDS RES, V37, pD211, DOI 10.1093/nar/gkn785 Huttenhower C, 2009, GENOME RES, V19, P1093, DOI 10.1101/gr.082214.108 Jensen LJ, 2009, NUCLEIC ACIDS RES, V37, pD412, DOI 10.1093/nar/gkn760 Kanehisa M, 2008, NUCLEIC ACIDS RES, V36, pD480, DOI 10.1093/nar/gkm882 Kerrien S, 2007, NUCLEIC ACIDS RES, V35, pD561, DOI 10.1093/nar/gkl958 Lerman G, 2007, P NATL ACAD SCI USA, V104, P11334, DOI 10.1073/pnas.0702965104 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 Lord PW, 2003, BIOINFORMATICS, V19, P1275, DOI 10.1093/bioinformatics/btg153 Matthews L, 2009, NUCLEIC ACIDS RES, V37, pD619, DOI 10.1093/nar/gkn863 MCMAHON HT, 1995, CELL, V83, P111, DOI 10.1016/0092-8674(95)90239-2 Mistry M, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-327 Pesquita C, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-S5-S4 Pesquita C, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000443 Popescu M, 2006, IEEE ACM T COMPUT BI, V3, P263, DOI 10.1109/TCBB.2006.37 Ramirez F, 2007, PROTEOMICS, V7, P2541, DOI 10.1002/pmic.200600924 Reeves GA, 2008, BIOINFORMATICS, V24, P2767, DOI 10.1093/bioinformatics/btn528 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 Rhee SY, 2008, NAT REV GENET, V9, P509, DOI 10.1038/nrg2363 Rhodes DR, 2005, NAT BIOTECHNOL, V23, P951, DOI 10.1038/nbt1103 Romero P, 2005, GENOME BIOL, V6 Ruepp A, 2008, NUCLEIC ACIDS RES, V36, pD646, DOI 10.1093/nar/gkm936 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salwinski L, 2004, NUCLEIC ACIDS RES, V32, pD449, DOI 10.1093/nar/gkh086 Schlicker A, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-302 Schlicker A, 2010, BIOINFORMATICS, V26, pi561, DOI 10.1093/bioinformatics/btq384 Schnoes AM, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000605 Seal RL, 2011, NUCLEIC ACIDS RES, V39, pD514, DOI 10.1093/nar/gkq892 Sevilla JL, 2005, IEEE ACM T COMPUT BI, V2, P330, DOI 10.1109/TCBB.2005.50 Sing T, 2005, BIOINFORMATICS, V21, P3940, DOI 10.1093/bioinformatics/bti623 Speer N, 2004, PROCEEDINGS OF THE 2004 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, P252 Spiegelman BM, 2001, CELL, V104, P531, DOI 10.1016/S0092-8674(01)00240-9 Su AI, 2002, P NATL ACAD SCI USA, V99, P4465, DOI 10.1073/pnas.012025199 Suzek BE, 2007, BIOINFORMATICS, V23, P1282, DOI 10.1093/bioinformatics/btm098 Vassilev LT, 2004, SCIENCE, V303, P844, DOI 10.1126/science.1092472 Velankar S, 2005, NUCLEIC ACIDS RES, V33, pD262, DOI 10.1093/narl/gki058 Vidal M, 2011, CELL, V144, P986, DOI 10.1016/j.cell.2011.02.016 Wang J, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-290 Wang PI, 2010, J PROTEOMICS, V73, P2277, DOI 10.1016/j.jprot.2010.07.005 Warde-Farley D, 2010, NUCLEIC ACIDS RES, V38, pW214, DOI 10.1093/nar/gkq537 Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 NR 64 TC 5 Z9 5 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 1367-4803 J9 BIOINFORMATICS JI Bioinformatics PD JAN 15 PY 2012 VL 28 IS 2 BP 269 EP 276 DI 10.1093/bioinformatics/btr631 PG 8 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics GA 880NN UT WOS:000299414200016 PM 22180409 ER PT J AU Jen, TH Chen, KM Lee, CD Fu, HH Chang, CY AF Jen, Tsung-Hau Chen, Kuan-Ming Lee, Che-Di Fu, Hsieh-Hai Chang, Chun-Yen BE Chova, LG Martinez, AL Torres, IC TI USE ITEM RESPONSE THEORY AND EXPECTATION MAXIMIZATION ALGORITHM TO IMPROVE THE RELIABILITY OF AUTOMATED SCORING FOR SHORT ANSWER QUESTIONS IN EARTH SCIENCE SO INTED2012: INTERNATIONAL TECHNOLOGY, EDUCATION AND DEVELOPMENT CONFERENCE SE INTED Proceedings LA English DT Proceedings Paper CT 6th International Conference of Technology, Education and Development (INTED) CY MAR 05-07, 2012 CL Valencia, SPAIN DE Automated scoring; expectation maximization algorithm; Bayesian inference; computer-based assessment ID EM ALGORITHM AB The testing style of multiple-choice has become prevalent among high-stake examinations due to its easiness and fairness for scoring, but whether all abilities can be assessed by fixed answers or by closed-form solution has been under debate for years. Other forms of testing such as open-ended questions, hands-on tasks, free response essay questions, etc. are thus adopted to comprehensively assess examinees' abilities of high-level thinking, scientific reasoning, or argumentation skills, and however, inter-rater reliability and time-efficiency of scoring need to be considered for these forms of testing. As well-defined guidelines for scoring increases the inter-rater reliability and the efficiency of computer science and information technology decreases the scoring time, an automated scoring system to integrate both of them ensures a more reliable and time-saving scoring. The inter-rater reliability has been used to indicate the degree of agreement on the guideline for scoring among raters. However, different raters weight certain information of a response differently, especially when the response cannot fit in any scoring criterion, causing different scores for the same response across raters. Usually such variability among raters is taken as error variance, and the more the raters' scoring results, the less the error variance and the better the ability estimate by their average. Lee et al. in [10] proved such idea by comparing the reliability among four automated scoring systems. Three of them were trained separately by three human raters' scores, and the fourth one was trained by the average scores of the three human raters. Accordingly, the fourth system made the most reliable prediction. By separating randomly the same examinees as in Lee et al.'s study into two groups in the current study, the scores of one group were obtained from the three human raters and their average, from the four automated scoring systems, and from the information of the IRT scores on other test items. For the other group, their scores from the four automated scoring systems and the IRT scores remained whereas the ones from the three human raters and their average were treated as missing data. A system trained by two groups implementing the missing value analysis module in SPSS 19 and the expectation maximization algorithm was used to impute the scores of the three human raters and their average for the second group. The imputed scores were then treated as the adjusted scores and compared with the scores predicted by the four automated scoring systems. The 10-fold cross validation method was used to replicate the procedure of random separation for the two groups and to estimate the standard errors of the reliabilities of the predicted scores. The results suggested that for each one of the four automatic scoring systems the adjusted scores are more reliable and have less biases across the score points than the unadjusted scores predicted by the automatic scoring system. C1 [Jen, Tsung-Hau; Chen, Kuan-Ming; Lee, Che-Di] Natl Taiwan Normal Univ, Sci Educ Ctr, Taipei, Taiwan. EM tsunghau@ntnu.edu.tw; kmchen@ntnu.edu.tw; chedi.lee@ntnu.edu.tw; HHfu@ntnu.edu.tw; changcy@ntnu.edu.tw CR DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dessus P., 2000, P 3 INT C HUM SYST L, P61 Frank E, 2005, DATA MINING PRACTICA Herman J. L., 2005, ANN M AM ED RES ASS Lee C. D., BRIT J ED T IN PRESS Lee C.-D., 2009, P 17 INT C COMP ED Perez D., 2004, P LANG RES EV C LREC Perez D., 2004, P 30 INT ASS ED ASS Press WH, 2007, NUMERICAL RECIPES AR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shermis M. D., 2003, AUTOMATED ESSAY SCOR Taylor A. R., 2005, FUTURE PROCESS ARRIV Wang HC, 2008, COMPUT EDUC, V51, P1450, DOI 10.1016/j.compedu.2008.01.006 Wang H.C., 2005, P 13 INT C COMP ED I WU CFJ, 1983, ANN STAT, V11, P95, DOI 10.1214/aos/1176346060 Yang YW, 2002, APPL MEAS EDUC, V15, P391, DOI 10.1207/S15324818AME1504_04 NR 16 TC 0 Z9 0 PU IATED-INT ASSOC TECHNOLOGY EDUCATION A& DEVELOPMENT PI VALENICA PA LAURI VOLPI 6, VALENICA, BURJASSOT 46100, SPAIN SN 2340-1079 BN 978-84-615-5563-5 J9 INTED PROC PY 2012 BP 6767 EP 6773 PG 7 WC Education & Educational Research SC Education & Educational Research GA BHQ76 UT WOS:000326396406102 ER PT J AU Mendes, PN Jakob, M Bizer, C AF Mendes, Pablo N. Jakob, Max Bizer, Christian BE Calzolari, N Choukri, K Declerck, T Dogan, MU Maegaard, B Mariani, J Odijk, J Piperidis, S TI DBpedia: A Multilingual Cross-Domain Knowledge Base SO LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION LA English DT Proceedings Paper CT 8th International Conference on Language Resources and Evaluation (LREC) CY MAY 21-27, 2012 CL Istanbul, TURKEY SP Nuance, CELI Language & Informat Technol, European Media Lab GmbH (EML), IMMI, META-Net, Quaero, K-dictionaries DE Knowledge Base; Semantic Web; Ontology AB The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using a structured query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and extended data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides. C1 [Mendes, Pablo N.; Bizer, Christian] Free Univ Berlin, Web Based Syst Grp, Berlin, Germany. EM pablo.mendes@fu.berlin.de; max.jakob@fu.berlin.de; christian.bizer@neofonie.de CR [Anonymous], 2011, P 1 WORKSH QUEST ANS, P6 Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002 Fellbaum C, 1998, WORDNET ELECT LEXICA Ferragina Paolo, 2010, P 19 ACM INT C INF K, P1625, DOI 10.1145/1871437.1871689 Garcia-Bryce A, 2011, PENN ST ROMA STUD, P137 Giuliano C, 2009, COMPUT LINGUIST, V35, P513, DOI 10.1162/coli.2009.35.4.35407 Han X., 2011, P 49 ANN M ASS COMP, P945 Harris ZS, 1954, WORD, V10, P146 Ji Heng, 2011, P TEXT AN C TAC 2011 LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745 Lin C.-Y., 2000, COLING, P495 McNamee Paul, 2010, LREC Mendes P. N., 2011, P 7 INT C SEM SYST I Mendes P.N., 2011, P TACKBP 2011 WORKSH Nastase V., 2008, P C EMP METH NAT LAN, P763, DOI 10.3115/1613715.1613812 Olston C., 2008, P 2008 ACM SIGMOD IN, P1099, DOI 10.1145/1376616.1376726 Orchestr8 LLC, 2009, ORCHESTR8 LLC Ratinov Lev-Arie, 2011, ACL, P1375 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandhaus Evan, 2008, NEW YORK TIMES ANNOT Tacchini Eugenio, 2009, CEUR WORKSHOP P, V449 Thomson Reuters, 2008, OPENCALAIS CONN EV Zemanta Ltd, 2009, ZEM AP OV NR 23 TC 3 Z9 4 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE BN 978-2-9517408-7-7 PY 2012 BP 1813 EP 1817 PG 5 WC Linguistics; Language & Linguistics SC Linguistics GA BGR95 UT WOS:000323927701147 ER PT J AU Boella, G di Caro, L Humphreys, L Robaldo, L van der Torre, L AF Boella, Guido di Caro, Luigi Humphreys, Llio Robaldo, Livio van der Torre, Leendert BE Calzolari, N Choukri, K Declerck, T Dogan, MU Maegaard, B Mariani, J Odijk, J Piperidis, S TI NLP Challenges for Eunomos, a Tool to Build and Manage Legal Knowledge SO LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION LA English DT Proceedings Paper CT 8th International Conference on Language Resources and Evaluation (LREC) CY MAY 21-27, 2012 CL Istanbul, TURKEY SP Nuance, CELI Language & Informat Technol, European Media Lab GmbH (EML), IMMI, META-Net, Quaero, K-dictionaries DE ontology; legislative XML; classification; pattern matching; information extraction ID MODEL AB In this paper, we describe how NLP can semi-automate the construction and analysis of knowledge in Eunomos, a legal knowledge management service which enables users to view legislation from various sources and find the right definitions and explanations of legal concepts in a given context. NLP can semi-automate some routine tasks currently performed by knowledge engineers, such as classifying norms, or linking key terms within legislation to ontological concepts. This helps overcome the resource bottleneck problem of creating specialist knowledge management systems. While accuracy is of the utmost importance in the legal domain, and the information should be verified by domain experts as a matter of course, a semi-automated approach can result in considerable efficiency gains. C1 [Boella, Guido; di Caro, Luigi; Humphreys, Llio; Robaldo, Livio] Univ Turin, I-10124 Turin, Italy. EM guido@di.unito.it; dicaro@di.unito.it; humphreys@di.unito.it; robaldo@di.unito.it; leon.vandertorre@uni.lu CR Biagioli C., 2005, THE TENTH INTERNATIO, P133 Boella G., 2012, PROCEEDINGS OF INFOR Boella G., 2012, SEMANTIC PROCESSING Boella G., 2011, AI APPROACHES TO THE Candan K.S., 2008, P 2008 ACM WORKSH SE, P75, DOI DOI 10.1145/1458583.1458597 Cataldi M., 2009, PROCEEDINGS OF THE I, V33, p[218, 225] Cataldi M., 2009, PROCEEDINGS OF THE I, P33 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Greene J., 2001, PROCEEDINGS OF ANNUA Hall M., 2009, ACM SIGKDD EXPLORATI, V11, P10, DOI DOI 10.1145/1656274.1656278 Joachims T., 1998, MACH LEARN ECML 98, V1398, P137, DOI DOI 10.1007/BFB0026683 Kohavi R., 1995, P 14 INT JOINT C ART, V2, P1137 Lesmo L., 2007, INTELLIGENZA ARTIFIC, V2, P46 Lesmo L., 2009, P EVALITA, P9 Lesmo L, 2009, 20TH ACM CONFERENCE ON HYPERTEXT AND HYPERMEDIA (HYPERTEXT 2009), P167 Manning CD, 2008, INTRODUCTION TO INFO MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Palmirani M, 2006, LECT NOTES COMPUT SC, V4084, P207 Platt J., 1999, ADV KERNEL METHODS S, V208, P98 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Szpektor Idan, 2004, P EMP METH NAT LANG, P41 Yang Y., 1997, MACH LEARN INT WORKS, P412 NR 23 TC 0 Z9 0 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE BN 978-2-9517408-7-7 PY 2012 BP 3672 EP 3678 PG 7 WC Linguistics; Language & Linguistics SC Linguistics GA BGR95 UT WOS:000323927703121 ER PT J AU Yang, Y Chen, YQ AF Yang, Yang Chen, YuQuan BE Shen, CW Kuo, SY DalKwack, K Chen, YW Hsu, PY Ko, F TI Online System for Detection of Chinese Near-Duplicate Documents SO 2012 6TH INTERNATIONAL CONFERENCE ON NEW TRENDS IN INFORMATION SCIENCE, SERVICE SCIENCE AND DATA MINING (ISSDM2012) LA English DT Proceedings Paper CT 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM) CY OCT 23-25, 2012 CL Taipei, TAIWAN SP Inst Elect & Elect Engineers (IEEE), Inst Elect & Elect Engineers (IEEE), Taipei Sect, Inst Elect & Elect Engineers (IEEE), Korea Council, AICIT, Natl Cent Univ DE near-duplicate; simhash; Hamming distance; online detection; Chinese document AB In various types of information retrieval systems, searching engines, and some data-mining systems, there is one task cannot be avoided-how to detect the large-scale duplicate and near-duplicate documents rapidly. Too many duplicates will influence our systems in many aspects malignantly. For example it reduces the computational performance, cuts down the user experience and so on. On the other hand, if quantity of documents increases dynamically, we should take another way to tackle this problem. This paper aims to construct a practical online detection system under the guidance of the fingerprint extraction technique based on simhash. Our contribution is that we develop a system running online, which means we don't know the accurate quantity of the documents before, and the system is able to accept new documents anytime. It requires efficiency and flexibility, and we propose a favorable solution. C1 [Yang, Yang; Chen, YuQuan] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. EM yangyang339113@gmail.com; chen-yq@cs.sjtu.edu.cn CR Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3 Chang P. C., OPTIMIZING CHINESE W, P224 Charikar M. S., SIMILARITY ESTIMATIO, P380 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Denning PJ, 2005, COMMUN ACM, V48, P19, DOI 10.1145/1070838.1070856 Gang Zhang, 2001, CHINESE INFORM PROCE, V11, pr25 Hatcher Erik, 2004, LUCENE ACTION Manku G. S., DETECTING NEAR DUPLI, P141 Matsumoto M., 1998, ACM Transactions on Modeling and Computer Simulation, V8, DOI 10.1145/272991.272995 Micali S., 1990, GOOGLE PATENTS Muthmann K., NEAR DUPLICATE DETEC, P142 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shivakumar N, 1999, LECT NOTES COMPUT SC, V1590, P204 Steane AM, 1996, PHYS REV LETT, V77, P793, DOI 10.1103/PhysRevLett.77.793 Stravers P., 2002, GOOGLE PATENTS Wu PingBo, 2003, J CHINESE INFORM PRO, V17, P28 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-0876-2 PY 2012 BP 726 EP 731 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGW31 UT WOS:000324371900140 ER PT J AU Huang, J Etzioni, O Zettlemoyer, L Clark, K Lee, C AF Huang, Jeff Etzioni, Oren Zettlemoyer, Luke Clark, Kevin Lee, Christian GP ACM TI RevMiner: An Extractive Interface for Navigating Reviews on a Smartphone SO UIST'12: PROCEEDINGS OF THE 25TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY LA English DT Proceedings Paper CT 25th Annual ACM Symposium on User Interface Software and Technology (UIST) CY OCT 07-10, 2012 CL Cambridge, MA SP ACM, ACM, Special Interest Grp Comp Human Interact, ACM, Special Interest Grp Comp Graph DE opinion mining; user reviews; information extraction; information retrieval ID WEB AB Smartphones are convenient, but their small screens make searching, clicking, and reading awkward. Thus, perusing product reviews on a smartphone is difficult. In response, we introduce REVMINER-a novel smartphone interface that utilizes Natural Language Processing techniques to analyze and navigate reviews. REVMINER was run over 300K Yelp restaurant reviews extracting attribute-value pairs, where attributes represent restaurant attributes such as sushi and service, and values represent opinions about the attributes such as fresh or fast. These pairs were aggregated and used to: 1) answer queries such as "cheap Indian food", 2) concisely present information about each restaurant, and 3) identify similar restaurants. Our user studies demonstrate that on a smartphone, participants preferred REVMINER's interface to tag clouds and color bars, and that they preferred REVMINER's results to Yelp's, particularly for conjunctive queries (e.g., "great food and huge portions"). Demonstrations of REVMINER are available at revminer . com. C1 [Huang, Jeff; Etzioni, Oren; Zettlemoyer, Luke; Clark, Kevin; Lee, Christian] Univ Washington, Seattle, WA 98195 USA. RP Huang, J (reprint author), Univ Washington, Seattle, WA 98195 USA. EM uist@jeffhuang.com; etzioni@cs.washington.edu; lsz@cs.washington.edu; kevinc22@cs.washington.edu; clee@cs.washington.edu CR Agichtein E., 2000, P 5 ACM INT C DIG LI, P85, DOI 10.1145/336597.336644 Breese J S, 1998, P 14 C UNC ART INT, P43 Brin S, 1999, LECT NOTES COMPUT SC, V1590, P172 Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152 Esuli A., 2006, P 5 INT C LANG RES E, V6, P417 Guha R. V., 2003, P 12 INT C WORLD WID, P700 Hearst M, 2002, COMMUN ACM, V45, P42 Hoffmann R, 2007, UIST 2007: PROCEEDINGS OF THE 20TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P13 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Karlson AK, 2006, P SIGCHI C HUM FACT, P711, DOI 10.1145/1124772.1124878 KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Popescu A. M., 2005, P HLT EMNLP, V5, P339 Probst K, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2838 Riloff E., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Rivadeneira AW, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P995 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Su X., 2009, ADV ARTIF INTELL, V2009 von Reischach F., 2010, P 12 INT C HUM COMP, P199, DOI 10.1145/1851600.1851635 von Reischach F., 2009, P 2009 IEEE INT C PE, P1, DOI 10.1145/1658550.1658552 Yatani K., 2011, P SIGCHI C HUM FACT, P1541 NR 21 TC 2 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-4503-1580-7 PY 2012 BP 3 EP 12 PG 10 WC Computer Science, Cybernetics; Computer Science, Software Engineering SC Computer Science GA BHA39 UT WOS:000324815300002 ER PT J AU Gunal, S AF Gunal, Serkan TI Hybrid feature selection for text classification SO TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES LA English DT Article DE Feature extraction; feature selection; pattern recognition; text classification ID SUPPORT VECTOR MACHINES; LINEAR DISCRIMINANT-ANALYSIS; FEATURE SUBSET-SELECTION; PATTERN-RECOGNITION; SEMANTIC ANALYSIS; CATEGORIZATION; ALGORITHM; INFORMATION AB Feature selection is vital in the field of pattern classification due to accuracy and processing time considerations. The selection of proper features is of greater importance when the initial feature set is considerably large. Text classification is a typical example of this situation, where the size of the initial feature set may reach to hundreds or even thousands. There are numerous research studies in the literature offering different feature selection strategies for text classification, mostly focused on filters. In spite of the extensive number of these studies, there is no significant work investigating the efficacy of a combination of features, which are selected by different selection methods, under different conditions. In this study, a hybrid feature selection strategy, which consists of both filter and wrapper feature selection steps, is proposed to comprehensively analyze the redundancy or relevancy of the text features selected by different methods in the case of different feature set sizes, dataset characteristics, classifiers, and success measures. The results of the experimental study reveal that a combination of the features selected by various methods is more effective than the features selected by the single selection method. The profile of the combination is, however, influenced by characteristics of the dataset, choice of the classification algorithm, and the success measure. C1 Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. RP Gunal, S (reprint author), Anadolu Univ, Dept Comp Engn, Eskisehir, Turkey. EM serkangunal@anadolu.edu.tr CR Asuncion A., 2007, UCI MACHINE LEARNING Bellman R. E., 1961, ADAPTIVE CONTROL PRO Chen YT, 2011, EXPERT SYST APPL, V38, P3085, DOI 10.1016/j.eswa.2010.08.100 Das S., 2001, P 18 INT C MACH LEAR, P74 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Goldberg D. E., 1989, GENETIC ALGORITHMS S Gunal S, 2009, EXPERT SYST APPL, V36, P10266, DOI 10.1016/j.eswa.2009.01.051 Gunal S, 2008, INFORM SCIENCES, V178, P3716, DOI 10.1016/j.ins.2008.06.001 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Huang CL, 2006, EXPERT SYST APPL, V31, P231, DOI 10.1016/j.eswa.2005.09.024 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims T, 1999, ADV KERNEL METHODS S Johnson DE, 2002, IBM SYST J, V41, P428 Kohavi G, 1997, ARTIF INTELL, V97, P273 Theodoridis S, 2009, PATTERN RECOGNITION, 4RTH EDITION, P1 Kumar MA, 2010, PATTERN RECOGN LETT, V31, P1437, DOI 10.1016/j.patrec.2010.02.015 Lee CH, 2009, EXPERT SYST APPL, V36, P2400, DOI 10.1016/j.eswa.2007.12.052 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Li ZX, 2011, PATTERN RECOGN LETT, V32, P441, DOI 10.1016/j.patrec.2010.11.001 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Liu HW, 2009, PATTERN RECOGN, V42, P1330, DOI 10.1016/j.patcog.2008.10.028 Manning C.D., 2008, INTRO INFORM RETRIEV Mladenic D, 2003, DECIS SUPPORT SYST, V35, P45, DOI 10.1016/S0167-9236(02)00097-0 NARENDRA P, 1977, IEEE T COMPUT, V26, P917 Park CH, 2008, PATTERN RECOGN LETT, V29, P878, DOI 10.1016/j.patrec.2008.01.003 Pehlivan I, 2011, TURK J ELECTR ENG CO, V19, P59, DOI 10.3906/elk-1002-10 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Selamat A, 2004, INFORM SCIENCES, V158, P69, DOI 10.1016/j.ins.2003.03.003 SIEDLECKI W, 1989, PATTERN RECOGN LETT, V10, P335, DOI 10.1016/0167-8655(89)90037-8 Torkkola K, 2003, PATTERN ANAL APPL, V6, P301, DOI 10.1007/s10044-003-0196-8 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Yang JH, 1998, IEEE INTELL SYST APP, V13, P44, DOI 10.1109/5254.671091 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Ye JP, 2005, IEEE T PATTERN ANAL, V27, P929 Yu B, 2008, KNOWL-BASED SYST, V21, P900, DOI 10.1016/j.knosys.2008.03.045 Zhang Y, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S2-S27 NR 42 TC 4 Z9 4 PU TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY PI ANKARA PA ATATURK BULVARI NO 221, KAVAKLIDERE, ANKARA, 00000, TURKEY SN 1300-0632 J9 TURK J ELECTR ENG CO JI Turk. J. Electr. Eng. Comput. Sci. PY 2012 VL 20 SU 2 BP 1296 EP 1311 DI 10.3906/elk-1101-1064 PG 16 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 195ZC UT WOS:000322741500007 ER PT B AU Ikbal, S Joshi, S Verma, A Deshmukh, OD AF Ikbal, Shajith Joshi, Sachindra Verma, Ashish Deshmukh, Om D. GP International Speech Communications Association TI Spoken Document Clustering Using Word Confusion Networks SO 13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3 LA English DT Proceedings Paper CT 13th Annual Conference of the International-Speech-Communication-Association CY SEP 09-13, 2012 CL Portland, OR SP Int Speech Commun Assoc DE spoken document clustering; word confusion network; posterior weighted TF-IDF vector; k-means clustering AB In this paper, we propose a word confusion network (WCN) based approach to perform clustering of the spoken documents and analyze its ability to handle the influence of speech recognition errors. WCN compactly represents multiple confidence weighted recognition hypotheses. Thus it provides scope for improving the clustering accuracy as a result of the likely presence of the correct transcription in the alternative hypotheses for those cases where 1-best transcripts are erroneous. On the other hand, several of the remaining hypotheses are incorrect and hence could pose a challenge during the clustering. In our approach, we extract TF-IDF vectors from the WCNs to perform clustering using K-Means algorithm. The components of TF-IDF vectors are further weighted with the word posterior probabilities. This is to potentially down-weight those vector components that are contributed by the incorrect hypotheses of low posterior probabilities. The experimental results obtained using switchboard data illustrate the usefulness of rich information in the WCN for clustering, showing upto 4% absolute improvement in normalized mutual information metric. EM shajmoha@in.ibm.com; jsachind@in.ibm.com; vashish@in.ibm.com; odeshmuk@in.ibm.com CR Chaudhari U. V., 2007, P IEEE ASRU 07 Clements M, 2002, PROCEEDINGS OF THE 2002 IEEE 10TH DIGITAL SIGNAL PROCESSING WORKSHOP & 2ND SIGNAL PROCESSING EDUCATION WORKSHOP, P186, DOI 10.1109/DSPWS.2002.1231100 Cortes C., 2003, P IEEE INT C AC SPEE, P628 Favre B., 2008, P ICASSP 08 LAS VEG Garofolo J. S., 2000, NIST TREXT RETR C TR Jain A.K., 1999, ACM COMPUTING SURVEY, V31 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Mangu L, 2000, COMPUT SPEECH LANG, V14, P373, DOI 10.1006/csla.2000.0152 Matusov E., 2006, P EACL Palmer D. D., 1999, P DARPA BROADC NEWS, P41 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soltau H., 2010, P IEEE SLT 10 BERK C Tur D. H., 2006, COMPUTER SPEECH LANG, V20, P495 Wolfel M., 2005, P INT 05 LISB PROT Xie S., 2010, P HLT 2010 ANN C N A, P46 Xu W., 2003, P ACM SIGIR TOR CAN NR 17 TC 0 Z9 0 PU ISCA-INT SPEECH COMMUNICATION ASSOC PI BAIXAS PA C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE BN 978-1-62276-759-5 PY 2012 BP 1378 EP 1381 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BFO97 UT WOS:000320827200345 ER PT J AU Demiroz, G Yanikoglu, B Tapucu, D Saygin, Y AF Demiroz, Gulsen Yanikoglu, Berrin Tapucu, Dilek Saygin, Yucel BE Vreeken, J Ling, C Zaki, MJ Siebes, A Yu, JX Goethals, B Webb, G Wu, X TI Learning Domain-Specific Polarity Lexicons SO 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012) LA English DT Proceedings Paper CT 12th IEEE International Conference on Data Mining (ICDM) CY DEC 10-13, 2012 CL Brussels, BELGIUM SP IEEE, IEEE Comp Soc, Natl Sci Fdn (NSF), FWO, IBM, SAS, Yahoo, NASA, Biside, KNIME, Univ Antwerpen, Univ Libre Bruxelles (ULB) DE sentiment analysis; lexicon adaptation; polarity detection; machine learning; natural language processing AB Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time-consuming, researchers often use a general purpose or domain-independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset. C1 [Demiroz, Gulsen; Yanikoglu, Berrin; Tapucu, Dilek; Saygin, Yucel] Sabanci Univ, Fac Engn & Nat Sci, Istanbul, Turkey. RP Demiroz, G (reprint author), Sabanci Univ, Fac Engn & Nat Sci, Istanbul, Turkey. EM gulsend@sabanciuniv.edu; berrin@sabanciuniv.edu; dilektapucu@sabanciuniv.edu; ysaygin@sabanciuniv.edu CR Baccianella S., 2010, P LREC Choi Y., 2009, P 2009 C EMP METH NA, P590 Das S., 2001, 8 AS PAC FIN ASS ANN Dragut E. C., 2010, P 19 ACM INT C INF K, P1761, DOI 10.1145/1871437.1871723 Fellbaum C, 1998, WORDNET ELECT LEXICA Hatzivassiloglou Vasileios, 1997, P 35 ANN M ASS COMP, P174, DOI DOI 10.3115/976909.979640 Hussaini M., 2012, ECMLPKDD Ikeda D., 2008, P 3 INT JOINT C NAT Kim S., 2004, P 20 INT C COMP LING, P1367, DOI DOI 10.3115/1220355.1220555 Li S., 2010, P 23 INT C COMP LING, P635 Liu B., 2012, MINING TEXT DATA, P415 Lu Y., 2011, P 20 INT C WORLD WID, P347, DOI DOI 10.1145/1963405.1963456 Martineau J., 2009, ICWSM Paltoglou G., 2010, SENTIMENT ANAL INFOR Pang B., 2004, P ACL, V04, P271 Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 Qiu GA, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1199 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney P. D., 2002, THUMBS THUMBS SEMANT, P417 Wang H., 2010, P 16 ACM SIGKDD INT, P783, DOI DOI 10.1145/1835804.1835903 Wiebe J., 2000, AAAI IAAI, P735 Wilson T, 2009, COMPUT LINGUIST, V35, P399, DOI 10.1162/coli.08-012-R1-06-90 NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-0-7695-4925-5 PY 2012 BP 674 EP 679 DI 10.1109/ICDMW.2012.120 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BFQ26 UT WOS:000320946500089 ER PT J AU Freitas, JL da Cruz, D Henriques, PR AF Freitas, Jose Luis da Cruz, Daniela Henriques, Pedro Rangel GP IEEE TI A Comment Analysis approach for Program Comprehension SO PROCEEDINGS OF THE 2012 IEEE 35TH SOFTWARE ENGINEERING WORKSHOP (SEW 2012) SE IEEE Annual IEEE Software Engineering Workshop LA English DT Proceedings Paper CT 35th IEEE Software Engineering Workshop (SEW) CY OCT 12-13, 2012 CL Heraklion, GREECE SP IEEE, IEEE Comp Soc DE program comprehension; comment analysis; concept location; problem domain; program domain ID SOFTWARE; MODEL AB Comments are interspersed by the Programmer among code lines, at software development phase, with two main purposes: to help himself during the development phase; to help other programmers later on, during the maintenance phase. The former are memos to help him remembering to do something; they are not useful for those willing to understand code. The latter are explanations about the ideas he has in mind when he wrote the code; they can be a relevant aid for others and should be taken into consideration as a first step in program comprehension. Comments are scattered all over the source code, sometimes wrapping a block of code (placed at the beginning or at its end), other times complementing a single statement. If comments are inserted to help in understanding the programmer ideas, they will contain for sure concepts associated with problem domain In this paper we discuss an approach to locate a relevant code chunk (one where the programmer should focus the attention for software maintenance), using information retrieval techniques to locate problem domain concepts within comments. In our approach, comments are isolated marking their type (inline, block or javadoc comment) and keeping their context (code lines to which they are associated). Picking up concepts from the ontology that describes the problem, it is possible to find all the comments that contain that concept (similar words) and rate them. Reading comments from the retrieved list, the programmer can select those that seem to him meaningful and dive directly into the associated chunk. In the paper, we also survey Comment Analysis techniques and describe an environment, Darius, that aims at automatizing the approach proposed. Moreover, Darius provides functionality to study comments frequency in the source files of a given project, to support the discussion weather it is worthwhile or not to apply this program comprehension step. C1 [Freitas, Jose Luis; da Cruz, Daniela; Henriques, Pedro Rangel] Univ Minho, Dept Informat, Braga, Portugal. RP Freitas, JL (reprint author), Univ Minho, Dept Informat, Braga, Portugal. EM freitas.jlf@gmail.com; danieladacruz@di.uminho.pt; prh@di.uminho.pt RI Henriques, Pedro Rangel/M-5587-2013; Cruz, Daniela/N-2588-2013 OI Henriques, Pedro Rangel/0000-0002-3208-0207; Cruz, Daniela/0000-0002-3672-7119 CR Aman H, 2008, FR ART INT, V180, P415, DOI 10.3233/978-1-58603-900-4-415 Antoniol G., 2000, P INT C SOFTW MAINT Arafat O., 2009, OOPSLA 09, P857 Beron M., 2007, OPENCERT 07 Beron M., 2010, ICPC 10 Brooks R., 1978, P 3 INT C SOFTW ENG COLEMAN D, 1994, COMPUTER, V27, P44, DOI 10.1109/2.303623 da Cruz D., 2009, ACTA ELECTROTECHNICA, V9, P32 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Erlikh L., 2000, IT Professional, V2, DOI 10.1109/6294.846201 Etzkorn L. H., 1999, NATURAL LANGUAGE ENG, V5, P219, DOI 10.1017/S1351324999002120 Etzkorn LH, 2001, J PRAGMATICS, V33, P1731, DOI 10.1016/S0378-2166(00)00068-0 Fjeldstad R. K., 1983, P GUIDE 48 APR Fluri B., 2009, SOFTWARE QUALITY CON, V17 Fluri B, 2007, P 14 WORK C REV ENG, P70, DOI 10.1109/WCRE.2007.21 Freitas J. L., 2011, THESIS Jiang ZM, 2006, MSR 06, P179 Kernighan Brian W., 1978, ELEMENTS PROGRAMMING Maletic J. I., 2000, P 12 IEEE INT C TOOL Maletic J. I., 1999, P 14 IEEE INT C AUT MALETIC JI, 2001, ICSE 01, P103 Marcus A., 2004, P 11 WORK C REV ENG Marcus A., 2003, P 25 INT C SOFTW ENG Marcus A., 2005, P 13 INT WORKSH PROG Nurvitadhi E., 2003, FRONTIERS ED ANN, V1, pT3C13 Poshyvanyk D., 2007, P 15 IEEE INT C PROG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stamelos I, 2002, INFORM SYST J, V12, P43, DOI 10.1046/j.1365-2575.2002.00117.x Storey MAD, 1999, J SYST SOFTWARE, V44, P171, DOI 10.1016/S0164-1212(98)10055-9 TENNY T, 1988, IEEE T SOFTWARE ENG, V14, P1271, DOI 10.1109/32.6171 Tenny T., 1985, SIGCSE Bulletin, V17 Vinz BL, 2008, KNOWL-BASED SYST, V21, P813, DOI 10.1016/j.knosys.2008.03.033 VONMAYRHAUSER A, 1995, COMPUTER, V28, P44, DOI 10.1109/2.402076 Woodfield S.N., 1981, P 5 INT C SOFTW ENG, P215 NR 34 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1550-6215 BN 978-0-7695-4947-7 J9 IEEE ANNU SOFTW ENG PY 2012 BP 11 EP 20 DI 10.1109/SEW.2012.8 PG 10 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BFM03 UT WOS:000320453500002 ER PT J AU Zhang, CQ Shang, WQ Li, YF AF Zhang, Chengqi Shang, Wenqian Li, Yafeng BE Wu, B TI An Improvement Method of Duplicate Webpage Detection SO PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ELECTRONIC & MECHANICAL ENGINEERING AND INFORMATION TECHNOLOGY (EMEIT-2012) SE Advances in Intelligent Systems Research LA English DT Proceedings Paper CT 2nd International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT) CY 2012 CL Shenyang, PEOPLES R CHINA DE search engine; duplicate detection; BloomFilter; Fuzzy Hamming distance AB As Internet is very easy to implement the diffusing and sharing of resources, duplication of pages on the Internet is very large. The search engine as an index tool of Internet resources is facing a serious repeat testing, its crawler will encounter a large number of links of duplicate content. If these links are all added to the download queue, it will cause a serious drop in performance and this would seriously affect the user experience. In this paper, we adopt an improved duplicate detection method-using BloomFilter combining with fuzzy Hamming distance. This will not only meet the detection of duplicate content, but also it will meet the needs of users. C1 [Zhang, Chengqi; Shang, Wenqian; Li, Yafeng] Commun Univ China, Dept Comp Sci, Beijing, Peoples R China. RP Zhang, CQ (reprint author), Commun Univ China, Dept Comp Sci, Beijing, Peoples R China. EM 18201017652@163.com; shangwenqian@cuc.edu.cn CR BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Broder A., 2005, INTERNET MATH, P485 Chowdhury A., ACM T INFORM SYSTEM, V20, P171 Narayana V. A., 2009, IEEE T J MAGN INDIA, P1945 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shivakumar Narayanan, 1996, 1 ACM INT C DIG LIB, P160 NR 6 TC 0 Z9 0 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE SN 1951-6851 BN 978-90-78677-60-4 J9 ADV INTEL SYS RES PY 2012 VL 23 PG 4 WC Computer Science, Artificial Intelligence SC Computer Science GA BFK90 UT WOS:000320317700006 ER PT B AU Pushpalatha, KP Raju, G AF Pushpalatha, K. P. Raju, G. GP IEEE TI Compactness - A Useful Feature for Generating Search Index SO 2012 IEEE INTERNATIONAL CONFERENCE ON TECHNOLOGY ENHANCED EDUCATION (ICTEE 2012) LA English DT Proceedings Paper CT IEEE International Conference on Technology Enhanced Education (ICTEE) CY JAN 03-05, 2012 CL INDIA SP IEEE, IEEE Educ Soc, IEEE India Council, IEEE Kerala Sect, Int Assoc Journals & Conf (IAJC) HO Amrita Univ, Amritapuri Campus DE index; compactness; TDVs; Term Discrimination Value; distributional features; document search ID DISCRIMINATION VALUES AB Generating meaningful or relevant keywords for information retrieval using Data Mining techniques is a highly relevant field. Term Discrimination Values (TDVs) are better measures compared to frequency or term weights to select the keywords. Terms with high TDVs will generate good keywords. Hamdouchi, P. Willet and Carolyn J Crouch have developed various algorithms to generate TDVs. In earlier days frequency or weighted frequency was used to compute TDVs. But these simple or weighted frequencies are not enough for retrieving relevant documents. Here we use some new features, connected with the distribution of terms within the document, called distributional features, to compute the TDVs. Distributional features such as First Appearance, Last Appearance, Compactness on number of parts, distance between first and last occurrence and on variance of positions of term occurrences etc. are pointers to the importance of the term in a document. Experiments have shown that combination of various features give much improved results than individual features in the case of Text Categorization. Through this work we also could prove that it is correct in the case of generating keywords. An additional overhead in storage and time is compensated by this efficient output. This work will add a narrow light towards text document search in education for both teaching and research. C1 [Pushpalatha, K. P.] Mahatma Gandhi Univ, Sch Comp Sci, Kottayam 686560, Kerala, India. RP Pushpalatha, KP (reprint author), Mahatma Gandhi Univ, Sch Comp Sci, Kottayam 686560, Kerala, India. EM pushpalatha.kp@gmail.com; kurupgraju@gmail.com CR Bekkerman R., 2003, J MACHINE LEARNING R, V3, P1182 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Carolyn J., 1988, INFORM PROCESSING MA, V24 CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 Lan M, 2005, IEEE IJCNN, P546 Pushpalatha K. P, 2010 IEEE INT C COMP, P1 Salton G., 1988, INFORM PROCESSING MA, V24 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 Willet P, 1988, INFORM PROCESSING MA, V24, P17 Xue Xiao-Bing, 2009, IEEE T KNOWLEDGE DAT, V21 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4577-0726-1 PY 2012 PG 6 WC Computer Science, Information Systems SC Computer Science GA BFM62 UT WOS:000320562200024 ER PT J AU Tryfou, G Theodosiou, Z Tsapatsoulis, N AF Tryfou, Georgina Theodosiou, Zenonas Tsapatsoulis, Nicolas GP IEEE TI Web image context extraction based on semantic representation of web page visual segments SO 2012 SEVENTH INTERNATIONAL WORKSHOP ON SEMANTIC AND SOCIAL MEDIA ADAPTATION AND PERSONALIZATION (SMAP 2012) LA English DT Proceedings Paper CT 7th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) CY DEC 03-04, 2012 CL Luxembourg, LUXEMBOURG SP IEEE, IEEE Comp Soc, Publ Res Ctr Henri Tudor, Serv Sci & Innovat Dept (Tudor), IEEE Syst, Man, & Cybernet Soc, IEEE Computat Intelligence Soc, Fonds Natl Rech Luxemborg DE web image context extraction; visual segmentation; semantic representation; vocabulary reduction; WordNet AB Among the most challenging scientific interests of the past years, special attention has been given to the task of web image information mining. Web images exist in huge amounts on the web and several methods for their efficient description and representation have been proposed so far. In many of the exploited algorithms, web image information is extracted from textual sources such as image file names, anchor texts, existing keywords and, of course, surrounding text. However, the systems that attempt to mine information for images using surrounding text suffer from several problems, such as the inability to correctly assign all relevant text to an image and discard the irrelevant text at the same time. A novel method for indexing web images is discussed in the present paper. The proposed system uses visual cues in order to obtain a web page segmentation. The segments are represented with semantic metrics and a k-means clustering assigns these segments to the web image they refer to. The evaluation procedure indicates that the semantic representation method of the visual segments delivers a good description for the web images. C1 [Tryfou, Georgina; Theodosiou, Zenonas; Tsapatsoulis, Nicolas] Tech Univ Cyprus, Dept Commun & Internet Studies, Limassol, Cyprus. RP Tryfou, G (reprint author), Tech Univ Cyprus, Dept Commun & Internet Studies, Limassol, Cyprus. EM georgia.tryfou@cut.ac.cy; zenonas.theodosiou@cut.ac.cy; nicolas.tsapatsoulis@cut.ac.cy CR Alcic S., 2011, MMEDIA 11 Alcic S., 2010, P 10 INT WORKSH MULT, P1, DOI 10.1145/1814245.1814253 del Bimbo A., 1999, VISUAL INFORM RETRIE Deng Cai, 2003, VIPS VISION BASED PA Fauzi F., 2009, MM 09 Fellbaum C, 1998, WORDNET ELECT LEXICA Feng H., 2004, MULTIMEDIA 04 He X., 2007, ACM T MULTIMEDIA COM, V3 Leacock C., 1998, WORDNET ELECT LEXICA Pedersen T., 2004, WORDNET SIMILARITY M SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tryfou G., 2012, 8 ART INT A IN PRESS Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-0-7695-4887-6 PY 2012 BP 63 EP 67 DI 10.1109/SMAP.2012.13 PG 5 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BFQ19 UT WOS:000320931700010 ER PT S AU Kimbrough, SO Lee, TY Oktem, U AF Kimbrough, Steven O. Lee, Thomas Y. Oktem, Ulku BE Dolk, D Granat, J TI On Deriving Indicators from Texts SO MODELING FOR DECISION SUPPORT IN NETWORK-BASED SERVICES: THE APPLICATION OF QUANTITATIVE MODELING TO SERVICE SCIENCE SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 12th Conference of the IFIP Working Group 7.6 on Advanced Analytics CY AUG 29-31, 2012 CL Aachen, GERMANY DE text mining; text data mining; data mining; mashing; economic indicators; social indicators AB This paper presents and explores the idea of deriving numerical indicators from texts, that is, converting text data to numerical data that has predictive or diagnostic value. One application of such a general capability is to the provisional identification of networks, or rather, of associations within networks. Conversely, given a network structure among entities that are associated with various texts, the network structure can itself contribute usefully to construction of indicators derived from texts. The focus of the paper is on basic concepts and methods for deriving indicators from texts. Much research remains to be done. C1 [Kimbrough, Steven O.; Lee, Thomas Y.; Oktem, Ulku] Univ Penn, Philadelphia, PA 19104 USA. RP Kimbrough, SO (reprint author), Univ Penn, Philadelphia, PA 19104 USA. EM kimbrough@wharton.upenn.edu CR Archak N., 2007, ACM SIGKDD INT C KNO Balakrishnan K., 2008, WORKSH EC INF SEC WE BEINHOCKER ERIC D., 2006, ORIGIN WEALTH EVOLUT Blair DC, 2002, INFORM PROCESS MANAG, V38, P363, DOI 10.1016/S0306-4573(01)00027-9 Cecchini M., 2005, THESIS U FLORIDA GAI Dworman G. O., 1999, THESIS U PENNSYLVANI Dworman GO, 2000, J AM SOC INFORM SCI, V51, P14, DOI 10.1002/(SICI)1097-4571(2000)51:1<14::AID-ASI4>3.0.CO;2-N Feldman R, 2007, TEXT MINING HDB ADV Fulcuyama F., 1995, TRUST Glickman T. S., 1994, USING NEWS DEV WORLD Hu M., 2004, P KNOWL DISC DAT KDD Jackson P., 2002, NATURAL LANGUAGE PRO Kimbrough S.O., 2007, United States Patent Application, Patent No. 20070106662 Kimbrough SO, 2007, United States Patent, Patent No. [7,257,568, 7257568] Konchady M., 2006, TEXT MINING APPL PRO LAUW HW, 2007, P 22 ANN ACM S APPL, P824, DOI 10.1145/1244002.1244185 Lee T, 2004, P 14 ANN WORKSH INF, P128 Lee T., 2008, IEEE C EL COMM JUL Lee T, 2008, WINT INF SYST C U UT Lee T. Y., 2004, P 2004 WORKSH INF TE Li F., 2006, 898181 SSRN U MICH Liu B., 2005, P WWW 2005 Mieszkowski K, 2005, STEAL THIS BOOKMARK Moens M.-F., 2000, INFORM RETRIEVAL SER, V6 Nasukawa T., 2003, P 2 INT C KNOWL CAPT Popescu A.-M., 2005, P HLTEMNLP Putnam R., 2000, BOWLING ALONE COLLAP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scaffidi C., 2007, ACM C EL COMM JUN Voss J., 2007, P 10 INT S INF SCI C, P234 Yi J., 2003, 3 IEEE INT C DAT MIN NR 31 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-27611-8 J9 LECT NOTES BUS INF P PY 2012 VL 42 BP 196 EP 225 PG 30 WC Computer Science, Information Systems SC Computer Science GA BFD23 UT WOS:000319240600009 ER PT J AU Yao, MY Pi, DC Cong, XX AF Yao, Mingyu Pi, Dechang Cong, Xiangxiang BE Yang, D TI Chinese text clustering algorithm based k-means SO 2012 INTERNATIONAL CONFERENCE ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING (ICMPBE2012) SE Physics Procedia LA English DT Proceedings Paper CT International Conference on Medical Physics and Biomedical Engineering (ICMPBE) CY 2012 CL Qingdao, PEOPLES R CHINA DE text cluster; k-means; Chinese text AB Text clustering is an important means and method in text mining. The process of Chinese text clustering based on k-means was emphasized, we found that new center of a cluster was easily effected by isolated text after some experiments. Average similarity of one cluster was used as a parameter, and multiplied it with a modulus between 0.75 and 1.25 to get the similarity threshold value, the texts whose similarity with original cluster center was greater than or equal to the threshold value ware collected as a candidate collection, then updated the cluster center with center of candidate collection. The experiments show that improved method averagely increased purity and F value about 10 percent over the original method. (C) 2012 Published by Elsevier B.V. Selection and/or peer review under responsibility of ICMPBE International Committee. C1 [Yao, Mingyu; Pi, Dechang] Nanjing Univ Aeronaut & Astronaut, Coll Informat Sci & Technol, Nanjing 210016, Jiangsu, Peoples R China. RP Yao, MY (reprint author), Nanjing Univ Aeronaut & Astronaut, Coll Informat Sci & Technol, Nanjing 210016, Jiangsu, Peoples R China. EM dc.pi@nuaa.edu.cn CR Barzilay Regina, 2001, P WORKSH SUMM NAACL Chen Zheng, 2004, P 27 ANN INT ACM SIG, P210 CUTTING DR, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P318 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Fang Y.C., 2002, P IEEE ICDM WORKSH T Li Xiao-Guang, 2008, Journal of Software, V19, DOI 10.3724/SPJ.1001.2008.02276 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, P KDD WORKSH TEXT MI, P1 Zhao Ying, 2005, P DAT MIN KNOWL DISC, V10, P141 NR 9 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1875-3892 J9 PHYSCS PROC PY 2012 VL 33 BP 301 EP 307 DI 10.1016/j.phpro.2012.05.066 PG 7 WC Biophysics; Engineering, Biomedical; Physics, Applied SC Biophysics; Engineering; Physics GA BDQ64 UT WOS:000314472700045 ER PT J AU O'Shea, J Bandar, Z Crockett, K AF O'Shea, James Bandar, Zuhair Crockett, Keeley BE Nguyen, NT TI A Multi-classifier Approach to Dialogue Act Classification Using Function Words SO TRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE VII SE Lecture Notes in Computer Science LA English DT Article; Book Chapter DE Dialogue Act; Speech Act; Classification; Semantic Similarity; Decision Tree ID SPEECH AB This paper extends a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. Initial experiments on classifying questions in the presence of a mix of straightforward and "difficult" non-questions yielded promising results, with classification accuracy approaching 90%. However, this initial dataset does not fully represent the various permutations of natural language in which sentences may occur. Also, a higher Classification Accuracy is desirable for real-world applications. Following an analysis of categorisation of sentences, we present a series of experiments that show improved performance over the initial experiment and promising performance for categorising more complex combinations in the future. C1 [O'Shea, James; Bandar, Zuhair; Crockett, Keeley] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England. RP O'Shea, J (reprint author), Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England. EM j.d.oshea@mmu.ac.uk; z.bandar@mmu.ac.uk; k.crockett@mmu.ac.uk CR Aleksander I., 1995, INTRO NEURAL COMPUTI Andernach T., 1997, ECML MLNET WORKSH EM, P85 Bickmore T, 2006, J BIOMED INFORM, V39, P556, DOI 10.1016/j.jbi.2005.12.004 Biro S, 2007, ACTA PSYCHOL, V124, P1, DOI 10.1016/j.actpsy.2006.09.003 Bollacker K., 1998, 2 INT ACM C AUT AG, P116 Bui T.H., 2007, INT JOINT C AI IJCAI Christensen C, 1992, ED JUDGMENT ARTISTRY Clark A., 2004, 5 SIGDIAL WORKSH DIS Crockett K., 2009, KNOWLEDGE REASONING, P1 Deerwester S., 1989, COMPUTER INFORM RETR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fernandez R., 2002, SPEECH PROSODY Flynn R., 2002, QUESTION TYPES GLOSS Frank E, 2005, DATA MINING PRACTICA IRS, 2009, IRS FREQ ASK TAX QUE Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 Jokinen K., 2001, 2 WORKSH NAT LANG PR, P77 Keizer S., 2001, BI DIALOG 2001 5 WOR Keizer S. A. R., 2002, 3 ACL SIGDIAL WORKSH, P88 Kral Pavel, 2007, Journal of Multimedia, V2, DOI 10.4304/jmm.2.3.1-8 Langle T., 1995, INTELLIGENT AUTONOMO, P357 Lesch S., 2005, DIAL 2005 9 WORKSH S Levin L., 2003, 4 SIGDIAL WORKSH DIS Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 O'Shea J, 2011, LECT NOTES ARTIF INT, V6682, P26, DOI 10.1007/978-3-642-22000-5_4 O'Shea J, 2010, LECT NOTES ARTIF INT, V6071, P82, DOI 10.1007/978-3-642-13541-5_9 Prasad R., 2002, 3 SIGDIAL WORKSH DIS, V2, P162 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Quirk Randolph, 1985, COMPREHENSIVE GRAMMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 1994, STOP WORD LIST Saygin AP, 2002, J PRAGMATICS, V34, P227, DOI 10.1016/S0378-2166(02)80001-7 Searle J., 1999, MIND LANGUAGE SOC Serafin R., 2003, 2003 C N AM ASS COMP SPARCKJONES K, 1972, J DOC, V28, P11 Stolcke A, 2000, COMPUT LINGUIST, V26, P339, DOI 10.1162/089120100561737 van Rijsbergen C.J., 1980, INFORM RETRIEVAL Venkataraman A., 2002, 9 AUSTR INT C SPEECH Verbree D., 2006, IEEE SPOK LANG TECHN, P70 Vytelingum P., 2010, 9 INT C AUT AG MULT, V1, P1649 Webb N, 2005, LECT NOTES ARTIF INT, V3658, P451 Webb N., 2005, AAAI 2005 Wermter S., 1996, 16 INT C COMP LING C, P740 NR 44 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-32066-8 J9 LECT NOTES COMPUT SC PY 2012 VL 7270 BP 119 EP 143 D2 10.1007/978-3-642-32066-8 PG 25 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BES07 UT WOS:000317916500007 ER PT S AU Pienaar, JA Chakradhar, S Raghunathan, A AF Pienaar, Jacques A. Chakradhar, Srimat Raghunathan, Anand GP IEEE TI Automatic Generation of Software Pipelines for Heterogeneous Parallel Systems SO 2012 INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS (SC) SE International Conference for High Performance Computing Networking Storage and Analysis LA English DT Proceedings Paper CT International Conference for High Performance Computing, Networking, Storage and Analysis (SC) CY NOV 10-16, 2012 CL Salt Lake City, UT SP IEEE Comp Soc, Assoc Comp Machinery (ACM) ID PROGRAMMING-MODEL; TASK; PERFORMANCE; PLATFORM; OPENCL; CUDA AB Pipelining is a well-known approach to increasing parallelism and performance. We address the problem of software pipelining for heterogeneous parallel platforms that consist of different multi-core and many-core processing units. In this context, pipelining involves two key steps-partitioning an application into stages and mapping and scheduling the stages onto the processing units of the heterogeneous platform. We show that the inter-dependency between these steps is a critical challenge that must be addressed in order to achieve high performance. We propose an Automatic Heterogeneous Pipelining framework (AHP) that generates an optimized pipelined implementation of a program from an annotated unpipelined specification. Across three complex applications (image classification, object detection, and document retrieval) and two heterogeneous platforms (Intel Xeon multi-core CPUs with Intel MIC and NVIDIA GPGPU accelerators), AHP achieves a throughput improvement of up to 1.53x (1.37x on average) over a heterogeneous baseline that exploits data and task parallelism. C1 [Pienaar, Jacques A.; Raghunathan, Anand] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. RP Pienaar, JA (reprint author), Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA. EM jpienaar@purdue.edu; chak@nec-labs.com; raghunathan@purdue.edu CR AIKEN A, 1988, LECT NOTES COMPUT SC, V300, P221 Arabnia H. R., 1996, P 1996 INT C PAR DIS, P230 Augonnet C, 2009, LECT NOTES COMPUT SC, V5704, P863, DOI 10.1007/978-3-642-03869-3_80 Bellens P, 2009, SCI PROGRAMMING-NETH, V17, P77, DOI 10.3233/SPR-2009-0272 Bienia C., 2010, PROCEEDINGS OF THE 3 Blumofe R.D., 1996, IPPS 96, P132 Buck I., 2001, BROOK A STREAMING PR Buck I, 2004, ACM T GRAPHIC, V23, P777, DOI 10.1145/1015706.1015800 Byna S., 2010, GPGPU10, P86 CAPS, 2010, HMPP WORKBENCH Chakradhar S, 2010, CONF PROC INT SYMP C, P247 Che S, 2008, J PARALLEL DISTR COM, V68, P1370, DOI 10.1016/j.jpdc.2008.05.014 Cole M, 2004, PARALLEL COMPUT, V30, P389, DOI 10.1016/j.parco.2004.12.002 Daily W.J., 2003, SC 03, P35 Danelutto M, 2002, LECT NOTES COMPUT SC, V2330, P844 Das A., 2006, P 15 INT C PAR ARCH, P33, DOI DOI 10.1145/1152154.1152164 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dezso B, 2011, ELECT NOTES THEOR CO, V264, P23, DOI DOI 10.1016/J.ENTCS.2011.06.003 Diamos G., 2008, HPDC 08, P197 Garland M, 2008, IEEE MICRO, V28, P13, DOI 10.1109/MM.2008.57 Gelado I, 2010, ACM SIGPLAN NOTICES, V45, P347 Gordon M. I., 2002, INTERNATIONAL CONFER Gordon MI, 2006, ACM SIGPLAN NOTICES, V41, P151, DOI 10.1145/1168918.1168877 Hill MD, 2008, COMPUTER, V41, P33, DOI 10.1109/MC.2008.209 IBM, THE CELL PROJECT Intel, LANGUAGE EXTENSION F Intel, 2012, INTEL THREADING BUIL Intel, MANY INTEGRATED CORE Kwok YK, 1996, IEEE T PARALL DISTR, V7, P506 LAM M, 1988, SIGPLAN NOTICES, V23, P318 LeCun Y., 2010, PROC INTERNATIONAL S Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791 Lee S.H., 2010, SC 10, P1, DOI DOI 10.1109/SC.2010.36 Lee VW, 2010, CONF PROC INT SYMP C, P451 Leijen D, 2009, ACM SIGPLAN NOTICES, V44, P227 LEISERSON CE, 1991, ALGORITHMICA, V6, P5, DOI 10.1007/BF01759032 Li S.Q., 2011, COMPUTER ENG APPL, V3, P3 Linderman MD, 2008, ACM SIGPLAN NOTICES, V43, P287, DOI 10.1145/1353536.1346318 Luk C., INTEL SOFTWARE AUTOT Luk C.-K., 2009, MICRO 42, P45 MacDonald S, 2002, PARALLEL COMPUT, V28, P1663, DOI 10.1016/S0167-8191(02)00190-4 Mahajan N., 2011, PROCEEDINGS OF THE 1 Makhorin A, GLPK GNU LINEAR PROG Massingill BL, 2007, CONCURR COMP-PRACT E, V19, P503, DOI 10.1002/cpe.1147 Mattson T., 2004, PATTERNS FOR PARALLE McCool M. D., 2010, HOTPAR10, P5 Nickolls John, 2008, ACM Queue, V6, DOI 10.1145/1365490.1365500 NVIDIA, CUDA ZONE OpenACC, 2012, OPENACC DIRECTIVES F Ottoni G., 2005, MICROARCHITECTURE 20, P12 Pienaar J. A., 2011, ICS 11, P225, DOI DOI 10.1145/1995896.1995933 Raman E, 2008, CGO 2008: SIXTH INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION, PROCEEDINGS, P114 Rangan R., 2004, Proceedings. 13th International Conference on Parallel Architecture and Compilation Techniques (PACT 2004), DOI 10.1109/PACT.2004.1342552 Saha B, 2009, ACM SIGPLAN NOTICES, V44, P431 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez D., 2011, Proceedings 2011 International Conference on Parallel Architectures and Compilation Techniques (PACT), DOI 10.1109/PACT.2011.9 Spafford K, 2010, LECT NOTES COMPUT SC, V6272, P275, DOI 10.1007/978-3-642-15291-7_26 Stone JE, 2010, COMPUT SCI ENG, V12, P66, DOI 10.1109/MCSE.2010.69 Suleman MA, 2010, PACT 2010: PROCEEDINGS OF THE NINETEENTH INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES, P147, DOI 10.1145/1854273.1854296 Tan K., 2003, P ACM SIGPLAN S PRIN, P203 Thies W., 2007, INTERNATIONAL SYMPOS Topcuoglu H, 2002, IEEE T PARALL DISTR, V13, P260, DOI 10.1109/71.993206 Udupa A, 2009, CGO 2009: INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION, PROCEEDINGS, P200, DOI 10.1109/CGO.2009.20 Vandierendonck H., 2011, HOTPAR 11, P13 Wang X., 2010, COMPUTER APPLICATION, V11, pV11 NR 66 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2167-4329 BN 978-1-4673-0805-2 J9 INT CONF HIGH PERFOR PY 2012 PG 12 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BEJ46 UT WOS:000316911000048 ER PT J AU Har-Peled, S Kumar, N AF Har-Peled, Sariel Kumar, Nirman GP IEEE TI Down the Rabbit Hole: Robust Proximity Search and Density Estimation in Sublinear Space SO 2012 IEEE 53RD ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS) SE Annual IEEE Symposium on Foundations of Computer Science LA English DT Proceedings Paper CT IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS) CY OCT 20-23, 2012 CL New Brunswick, NJ SP IEEE, IEEE Comp Soc, Techn Comm Math Fdn Comp, Microsoft, DIMACS ID APPROXIMATE NEAREST-NEIGHBOR; VORONOI DIAGRAMS; DIMENSIONS; ALGORITHM AB For a set of n points in R-d, and parameters k and epsilon, we present a data structure that answers (1 + epsilon)-approximate k nearest neighbor queries in logarithmic time. Surprisingly, the space used by the data-structure is (O) over tilde (n/k); that is, the space used is sublinear in the input size if k is sufficiently large. Our approach provides a novel way to summarize geometric data, such that meaningful proximity queries on the data can be carried out using this sketch. Using this we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) sum of distances of k closest points to the query point, and (ii) sum of squared distances of k closest points to the query point. Our approach generalizes to other distance based estimation of densities of similar flavor. C1 [Har-Peled, Sariel; Kumar, Nirman] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Har-Peled, S (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM sariel@uiuc.edu; nkumar5@illinois.edu CR Agarwal P. K., 1998, ADV DISCRETE COMPUTA Agarwal P. K., 1993, SIAM J COMPUT, V22, P540 Andoni A, 2008, COMMUN ACM, V51, P117, DOI 10.1145/1327452.1327494 Aronov B, 2008, SIAM J COMPUT, V38, P899, DOI 10.1137/060669474 Arya S, 1998, J ACM, V45, P891, DOI 10.1145/293347.293348 Arya S, 2002, SIAM PROC S, P147 Arya S, 2005, PROCEEDINGS OF THE SIXTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P535 Arya S, 2009, J ACM, V57, DOI 10.1145/1613676.1613677 AURENHAMMER F, 1991, COMPUT SURV, V23, P345 Bernstein M., 2000, GRAPH APPROXIMATIONS CALLAHAN PB, 1995, J ASSOC COMPUT MACH, V42, P67, DOI 10.1145/200836.200853 Carmi P, 2005, ALGORITHMICA, V41, P233, DOI 10.1007/s00453-004-1130-1 Chan TM, 2010, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL SYMPOSIUM ON COMPUTATIONAL GEOMETRY (SCG'10), P1 Chazelle B, 2008, COMMUN ACM, V51, P115, DOI 10.1145/1327452.1327493 CLARKSON KL, 1988, SIAM J COMPUT, V17, P830, DOI 10.1137/0217052 Clarkson K. L., 2006, NEAREST NEIGHBOR MET, P15 CLARKSON KL, 1989, DISCRETE COMPUT GEOM, V4, P387, DOI 10.1007/BF02187740 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 de Berg M, 2010, COMP GEOM-THEOR APPL, V43, P493, DOI 10.1016/j.comgeo.2009.11.001 Devroye L., 1982, NEAREST NEIGHBOR MET, V2 Duda R.O., 2001, PATTERN CLASSIFICATI Fix E., 1949, 4 USAF SCH AV MED Gersho A., 1991, VECTOR QUANTIZATION Guibas L, 2011, COMPUTATIONAL GEOMETRY (SCG 11), P57 Har-Peled S., 2011, CORR Har-Peled S., 2011, GEOMETRIC APPROXIMAT Har-Peled S, 2011, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P854 Har-Peled S., 2001, Proceedings 42nd IEEE Symposium on Foundations of Computer Science, DOI 10.1109/SFCS.2001.959884 Har-Peled S., 2012, THEORY COMP IN PRESS Har-Peled S, 1999, SIAM J COMPUT, V28, P1182, DOI 10.1137/S0097539797325223 Har-Peled S, 2006, LECT NOTES COMPUT SC, V4337, P33 Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 Kohonen T., 2001, SPRINGER SERIES INFO, V30 Kushilevitz E, 2000, SIAM J COMPUT, V30, P457, DOI 10.1137/S0097539798347177 MARTINETZ T, 1994, NEURAL NETWORKS, V7, P507, DOI 10.1016/0893-6080(94)90109-0 MATOUSEK J, 1992, DISCRETE COMPUT GEOM, V8, P315, DOI 10.1007/BF02293051 Matousek J., 2002, LECT DISCRETE GEOMET MEISER S, 1993, INFORM COMPUT, V106, P286, DOI 10.1006/inco.1993.1057 Minsky M. L., 1969, PERCEPTRONS RUPPERT J, 1995, J ALGORITHM, V18, P548, DOI 10.1006/jagm.1995.1021 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shakhnarovich G., 2006, NEAREST NEIGHBOR MET Silverman B. W., 1986, MONOGRAPHS STAT APPL Tenenbaum JB, 1998, ADV NEUR IN, V10, P682 Weinberger KQ, 2006, INT J COMPUT VISION, V70, P77, DOI 10.1007/s11263-005-4939-z NR 45 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 0272-5428 BN 978-1-4673-4383-1 J9 ANN IEEE SYMP FOUND PY 2012 BP 430 EP 439 DI 10.1109/FOCS.2012.31 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA BEJ77 UT WOS:000316999700046 ER PT J AU Xiao, J He, L AF Xiao, Jie He, Liang BE Tan, Y Shi, Y Ji, Z TI An Expandable Recommendation System on IPTV SO ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 3rd International Conference on Swarm Intelligence (ICSI) CY JUN 17-20, 2012 CL Shenzhen, PEOPLES R CHINA SP Shenzhen Univ, Peking Univ, Xian Jiaotong-Liverpool Univ, IEEE Computat Intelligence Soc, World Federat Soft Comp, Int Neural Network Soc, Nat Sci Fdn China DE IPTV; Recommendation Framework; Recommendation Algorithm; tag; template AB IPTV's mass resources, high quality service and its open and free interactive service mode attract billons of user. It has brilliant potential and also has great challenge. Traditional one algorithm supported recommendation system is no longer content IPTV's huge service demand. In order to solve this problem, an expandable recommendation system is proposed in this paper. It contains new flexible system framework and basic ideas of applicable algorithms. And it has been implemented in real IPTV system and works well. C1 [Xiao, Jie; He, Liang] E China Normal Univ, Inst Comp Aplicat, Shanghai 200241, Peoples R China. RP He, L (reprint author), E China Normal Univ, Inst Comp Aplicat, Dongchuan Rd 500, Shanghai 200241, Peoples R China. EM jxiao@ica.stc.sh.cn; lhe@ica.stc.sh.cn CR Banfield RE, 2007, IEEE T PATTERN ANAL, V29, P173, DOI 10.1109/TPAMI.2007.250609 Pazzani M.J., 2007, LNCS, V4321, P325, DOI DOI 10.1007/978-3-540-72079-9_10 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandvig JJ, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P105 Schafer J. B., 2007, LNCS, V4321, P291, DOI DOI 10.1007/978-3-540-72079-9_ Shparlinski IE, 2009, B AUST MATH SOC, V79, P183, DOI 10.1017/S0004972708001020 Xiao J., 2010, 3 IEEE INT C COMP SC, P221 Xiao Y, 2007, IEEE COMMUN MAG, V45, P126, DOI 10.1109/MCOM.2007.4378332 Zhen C., 2002, J SOFTWARE, V13, P1953 NR 9 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-31020-1 J9 LECT NOTES COMPUT SC PY 2012 VL 7332 BP 33 EP 40 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEK56 UT WOS:000317101700005 ER PT J AU Ying, LZ Zhou, XH Yuan, J Huang, YF AF Ying, Lizhi Zhou, Xinhao Yuan, Jian Huang, Yongfeng BE Tan, Y Shi, Y Ji, Z TI A Novel Focused Crawler Based on Breadcrumb Navigation SO ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 3rd International Conference on Swarm Intelligence (ICSI) CY JUN 17-20, 2012 CL Shenzhen, PEOPLES R CHINA SP Shenzhen Univ, Peking Univ, Xian Jiaotong-Liverpool Univ, IEEE Computat Intelligence Soc, World Federat Soft Comp, Int Neural Network Soc, Nat Sci Fdn China DE Breadcrumb Navigation; focused crawler; W2T similarity; semantic forest AB In this paper, a novel focused crawler based on Breadcrumb Navigation (BN) is proposed. It mainly leverages Breadcrumb Navigation in the webpages to reconstruct the website structures and resolve focused crawling problems. Different from some previous focused crawlers which use prediction models, the BN crawler firstly samples the web to construct the semantic forest for websites based on Breadcrumb Navigation, and then searches the forest to find the sub-trees relevant to the given topic. After sampling, the BN crawler only needs to download the webpages belonging to the relevant sub-forest. By using this method, the BN crawler costs less time to analyze the Webpage-to-Topic (W2T) similarity but results in a highly efficient performance. The experimental evidences show that the BN crawler significantly outperforms Breadth-First and Best-First crawlers in harvest ratio and can be widely used for most websites. C1 [Ying, Lizhi; Zhou, Xinhao; Yuan, Jian; Huang, Yongfeng] Tsinghua Univ, Inst Informat Cognit & Intelligence Syst, Dept Elect Engn, Beijing 100084, Peoples R China. RP Ying, LZ (reprint author), Tsinghua Univ, Inst Informat Cognit & Intelligence Syst, Dept Elect Engn, Beijing 100084, Peoples R China. EM lzying05@gmail.com; zhouxh07@vip.qq.com; hydrogenleobob@gmail.com; yfhuang@tsinghua.edu.cn CR Batsakis S., 2008, IMPROVING PERFORMANC Blanco L., 2011, WWW Chakrabarti S., 1999, FOCUSED CRAWLING NEW Chang Y.-C., 2009, INT JOINT C WEB INT Diligenti M., 2000, P 26 INT C VER LARG, P527 Feng S., 2010, CIKM 2010 Gao Z., 2010, J COMPUTATIONAL INFO, V6, P9 Hati D., 2010, COMPUTER SCI INFORM Hati D., 2010, INT J COMPUTER APPL, V2 Kan M.-Y., 2005, CIKM 2005 Kumar M., 2010, J ADV INFORM TECHNOL Liu H., 2004, WIDM 2004 WASH DC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Uemura Y, 2010, LECT NOTES ARTIF INT, V6279, P220, DOI 10.1007/978-3-642-15384-6_24 Yang S.-Y., 2010, EXPERT SYSTEMS APPL NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-31020-1 J9 LECT NOTES COMPUT SC PY 2012 VL 7332 BP 264 EP 271 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEK56 UT WOS:000317101700031 ER PT J AU Sitarz, R Kraslawski, A AF Sitarz, Robert Kraslawski, Andrzej BE Bogle, IDL Fairweather, M TI Application of semantic and lexical analysis to technology forecasting by trend analysis - thematic clusters in separation processes SO 22 EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING SE Computer-Aided Chemical Engineering LA English DT Proceedings Paper CT 22nd European Symposium on Computer Aided Process Engineering (ESCAPE) CY JUN 17-20, 2012 CL London, ENGLAND SP Comp Aided Proc Engn (CAPE), European Federat Chemical Engineers Working Party HO Univ Coll London DE technology forecasting; knowledge management; semantic and lexical analysis; distillation; trend analysis AB The development trends in a given field of research. are very important factors influencing decisions on R&D funding. The analysis of the thematic clusters of the publications allows for the identification of the structure of given field of research. The number of the scientific publications and patents as well as their change in time are the measures of dynamics of development trends. The existing approach of structuring a research field, based on clustering of the sets of high co-occurrence words, does not take into account semantic and lexical similarities between words and requires an intervention by the experts in a given discipline. The paper presents an improved method for identification of thematic groups of papers, based on clustering of sets of high co-occurrence words, as well as application of financial analysis techniques for prediction of development trends. The method takes into account semantic and lexical similarity factors between words as well as clustering of the sets of words with the automatic rejection of the redundant groups of terms. The material presented in this paper is limited to the journal papers. It covers publications in the field of distillation presented in ISI Web of Science database for the period 1990-2010. The identified 26 thematic clusters of research have shown the diversified patterns of development, e. g. stagnation, revival, slow development or intensive growth. C1 [Sitarz, Robert; Kraslawski, Andrzej] Lappeenranta Univ Technol, Lappeenranta, Finland. RP Sitarz, R (reprint author), Lappeenranta Univ Technol, Lappeenranta, Finland. EM robs@prz.edu.pl CR Fabry B., 2006, World Patent Information, V28, DOI 10.1016/j.wpi.2005.10.004 Mierswa I., 2006, P 12 ACM SIGKDD INT MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Murphy J., 1999, TECHNICAL ANAL FINAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sitarz R, 2010, COMPUT-AIDED CHEM EN, V28, P583 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X Zapata JC, 2008, COMPUT CHEM ENG, V32, P307, DOI 10.1016/j.compchemeng.2007.03.024 NR 8 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-7946 BN 978-0-444-59431-0 J9 COMPUT-AIDED CHEM EN PY 2012 VL 30 BP 437 EP 441 PG 5 WC Computer Science, Interdisciplinary Applications; Engineering, Chemical; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA BEI14 UT WOS:000316669100088 ER PT S AU Santos, I Laorden, C Ugarte-Pedrero, X Sanz, B Bringas, PG AF Santos, Igor Laorden, Carlos Ugarte-Pedrero, Xabier Sanz, Borja Bringas, Pablo G. BE Obaidat, MS Sevillano, JL Filipe, J TI Spam Filtering through Anomaly Detection SO E-BUSINESS AND TELECOMMUNICATIONS SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 8th International Joint Conference on e-Business and Telecommunications (ICETE) CY JUL 18-21, 2011 CL Seville, SPAIN DE Computer security; Spam filtering; Anomaly detection; Text classification ID SELECTION; CATEGORIZATION; MODELS AB More than 85% of received e-mails are spam. Spam is an important issue for computer security because it is used to spread other threats such as computer viruses, worms or phishing. Classic techniques to fight spam, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Machine-learning techniques trained using statistical representations of the terms that usually appear in the e-mails are widely used in the literature. However, these methods demand a time-consuming training step with labelled data. Dealing with the situation where the availability of labelled training instances is limited slows down the progress of filtering systems and offers advantages to spammers. In this paper, we present the first spam filtering method based on anomaly detection that reduces the necessity of labelling spam messages and only uses the representation of legitimate e-mails. This approach represents legitimate e-mails as word frequency vectors. Thereby, an email is classified as spam or legitimate by measuring its deviation to the representation of these legitimate e-mails. This method achieves high accuracy rates detecting spam and maintains a low false positive rate, reducing the effort produced by labelling spam. C1 [Santos, Igor; Laorden, Carlos; Ugarte-Pedrero, Xabier; Sanz, Borja; Bringas, Pablo G.] Univ Deusto, Deusto Inst Technol, S3Lab, DeustoTech Comp, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, Deusto Inst Technol, S3Lab, DeustoTech Comp, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; claorden@deusto.es; xabier.ugarte@deusto.es; borja.sanz@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 Androutsopoulos I., 2000, P MACH LEARN TEXT IN Awad A, 2008, IEEE EDOC CONF, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bates M., 1993, CHALLENGES NATURAL L Becker J, 2003, P 6 INT C BUS INF SY, P7 Blanzieri E., 2007, P FLAIRS 20, P441 Bratko A, 2006, J MACH LEARN RES, V7, P2673 Cano JR, 2006, APPL SOFT COMPUT, V6, P323, DOI 10.1016/j.asoc.2005.02.006 Carnap Rudolf, 1955, PHILOS STUD, VVI, P33, DOI DOI 10.1007/BF02330951 Carpinter J, 2006, COMPUT SECUR, V25, P566, DOI 10.1016/j.cose.2006.06.001 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Cohen D., 1974, EXPLAINING LINGUISTI Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 Cruse D., 1975, ARCH LINGUIST, V6, P26 Czarnowski I., 2006, P SCI SESS ORG 21 FA, P60 Dash M, 2003, ARTIF INTELL, V151, P155, DOI 10.1016/S0004-3702(03)00079-1 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Elkan C, 2001, INT JOINT C ART INT, V17, P973 Heron S., 2009, TECHNOLOGIES SPAM DE, P11 Ide N., 1998, J COMPUTATIONAL LING, V24, P2 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Jung J., 2004, P 4 ACM SIGCOMM C IN, P370, DOI 10.1145/1028788.1028838 Karlberger C., 2007, P 1 USENIX WORKSH OF, P1 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R., 1995, P 14 INT JOINT C ART, V2, P1137 Kolcz A., 2004, P 1 C EM ANT CEAS 20 Kuropka D., 2004, ADV INFORM SYSTEMS M, V10 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Liu H., 2001, INSTANCE SELECTION C LOVINS JB, 1968, MECH TRANSL, V11, P22 Maron O, 1998, ADV NEUR IN, V10, P570 McGill M. J., 1983, INTRO MODERN INFORM Ming-Tzu KW, 2004, APPL LINGUIST, V25, P291, DOI 10.1093/applin/25.3.291 Mishne Gilad, 2005, P 1 INT WORKSH ADV I, P1 Liu H, 2008, CH CRC DATA MIN KNOW, P3 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Polyvyanyy A., 2007, THESIS Pyle D., 1999, DATA PREPARATION DAT Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Radden G., 1999, HUMAN COGNITIVE PROC, P17 Ramachandran A., 2006, C EM ANT CIT Sahami M., 1998, LEARNING TEXT CATEGO, V62 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Torkkola K., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753742 Tsang ECC, 2003, IEEE T FUZZY SYST, V11, P202, DOI 10.1109/TFUZZ.2003.809895 Vapnik V. N., 2000, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wittel G., 2004, P 1 C EM ANT CEAS Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 Zhou Y, 2007, PROC INT C TOOLS ART, P298, DOI 10.1109/ICTAI.2007.120 NR 59 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-35754-1 J9 COMM COM INF SC PY 2012 VL 314 BP 203 EP 216 PG 14 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BEB33 UT WOS:000315973800015 ER PT J AU Delaere, I De Sutter, G Plevoets, K AF Delaere, Isabelle De Sutter, Gert Plevoets, Koen TI Is translated language more standardized than non-translated language? Using profile-based correspondence analysis for measuring linguistic distances between language varieties SO TARGET-INTERNATIONAL JOURNAL OF TRANSLATION STUDIES LA English DT Article DE profile-based correspondence analysis; translated versus non-translated language; standardization; Belgian Dutch; text type AB With this article, we seek to support the law of growing standardization by showing that texts translated into Belgian Dutch make more use of standard language than non-translated Belgian Dutch texts. Additionally, we want to examine whether the use of standard vs. non-standard language can be attributed to the variables text type and source language. In order to achieve that goal, we gathered a diverse set of linguistic variables and used a 10-million-word corpus that is parallel, comparable and bidirectional (the Dutch Parallel Corpus; Macken et al. 2011). The frequency counts for each of the variables are used to determine the differences in standard language use by means of profile-based correspondence analysis (Plevoets 2008). The results of our analysis show that (i) in general, there is indeed a standardizing trend among translations and (ii) text types with a lot of editorial control (fiction, non-fiction and journalistic texts) contain more standard language than the less edited text types (administrative texts and external communication) which adds support for the idea that the differences between translated and non-translated texts are text type dependent. C1 [Delaere, Isabelle] Univ Ghent, Ghent, Belgium. [Delaere, Isabelle; De Sutter, Gert; Plevoets, Koen] Univ Coll Ghent, Fac Appl Language Studies, B-9000 Ghent, Belgium. RP Delaere, I (reprint author), Univ Ghent, Ghent, Belgium. EM isabelle.delaere@hogent.be; gert.desutter@hogent.be; koen.plevoets@hogent.be CR Baker M., 1996, TERMINOLOGY LSP TRAN, P175 Baker Mona, 1993, TEXT TECHNOLOGY HONO, P233 Ben-Shahar Rina, 1994, TARGET, V6, P195, DOI 10.1075/target.6.2.05ben Bernardini S, 2011, META, V56, P226 Bush P., 2001, ROUTLEDGE ENCY TRANS, P127 Chesterman Andrew, 1997, MEMES TRANSLATION SP De Sutter G, 2012, STUD CORPUS LINGUIST, V51, P325 Greenacre M, 2007, CORRES ANAL PRACTICE, V2nd Kenny D, 1998, META, V43, P515 Kenny Dorothy, 2000, INTERCULTURAL FAULTL, P93 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Macken L, 2011, META, V56, P374 Malmkjaer Kirsten, 1997, NONVERBAL COMMUNICAT, P151 Mauranen A., 2008, INCORPORATING CORPOR, P32 May Rachel, 1997, TRANSLATOR, V3, P1 Neumann Stella, 2011, CONTRASTIVE REGISTER Olohan Maeve, 2008, ROUTLEDGE ENCY TRANS, P240 Olohan Maeve, 2000, LANGUAGES CULTURES, V1, P141, DOI 10.1556/Acr.1.2000.2.1 Overas L., 1998, META, V43, P557, DOI DOI 10.7202/003775AR Penninckx Willy, 2001, CORRECT TAALGEBRUIK Plevoets K., 2008, THESIS KATHOLIEKE U Plevoets Koen, CORRES ANAL IN PRESS Plevoets Koen, 2007, VARIATIONAL PRAGMATI, P181 Puurtinen T., 2003, Literary & Linguistic Computing, V18, DOI 10.1093/llc/18.4.389 Pym A., 2008, DESCRIPTIVE TRANSLAT, P311 Reiczigel J, 1996, APPL STOCH MODEL D A, V12, P107, DOI 10.1002/(SICI)1099-0747(199606)12:2<107::AID-ASM280>3.0.CO;2-I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott Nelia, 1998, THESIS U LIVERPOOL Speelman D, 2003, COMPUT HUMANITIES, V37, P317, DOI 10.1023/A:1025019216574 Speelman Dirk, 1999, CONVERGENTIE DIVERGE Steiner Erich, 2001, SPRIK REPORTS, V7, P1 Stewart Dominic, 2000, INTERCULTURAL FAULTL, P73 Strang G, 2009, INTRO LINEAR ALGEBRA Teich E., 2003, CROSS LINGUISTIC VAR TOURY Gideon, 1995, DESCRIPTIVE TRANSLAT Vanderauwera R., 1985, DUTCH NOVELS TRANSLA NR 36 TC 0 Z9 0 PU JOHN BENJAMINS PUBLISHING COMPANY PI AMSTERDAM PA PO BOX 36224, 1020 ME AMSTERDAM, NETHERLANDS SN 0924-1884 J9 TARGET-NETH JI Target PY 2012 VL 24 IS 2 BP 203 EP 223 DI 10.1075/target.24.2.01del PG 21 WC Linguistics; Language & Linguistics SC Linguistics GA 105XX UT WOS:000316107100001 ER PT J AU Zhang, Y Bres, S Chen, LM AF Zhang, Yu Bres, Stphane Chen, Liming BE Yetongnon, K Chbeir, R Dipanda, A Gallo, L TI Semantic Bag-of-Words Models for Visual Concept Detection and Annotation SO 8TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY & INTERNET BASED SYSTEMS (SITIS 2012) LA English DT Proceedings Paper CT 8th International Conference on Signal Image Technology and Internet Based Systems (SITIS) CY NOV 25-29, 2012 CL Sorrento, ITALY SP IEEE Comp Soc, INCAR HO Univ Bourgogne DE multi-model; Multiple Kernel Learning; WordNet ID CLASSIFICATION AB This paper presents a novel method for building textual feature defined on semantic distance and describes multi-model approach for Visual Concept Detection and Annotation(VCDA). Nowadays, the tags associated with images have been popularly used in the VCDA task, because they contain valuable information about image content that can hardly be described by low-level visual features. Traditionally the term frequencies model is used to capture this useful text information. However, the shortcoming in the term frequencies model lies that the valuable semantic information can not be captured. To solve this problem, we propose the semantic bag-of-words(BoW) model which use WordNet-based distance to construct the codebook and assign the tags. The advantages of this approach are two-fold: (1) It can capture tags semantic information that is hardly described by the term frequencies model. (2) It solves the high dimensionality issue of the codebook vocabulary construction, reducing the size of the tags representation. Furthermore, we employ a strong Multiple Kernel Learning (MKL) classifier to fuse the visual model and the text model. The experimental results on the ImageCLEF 2011 show that our approach effectively improves the recognition accuracy. C1 [Zhang, Yu; Chen, Liming] Univ Lyon, CNRS, Ecole Cent Lyon, LIRIS,UMR5205, F-69134 Lyon, France. RP Zhang, Y (reprint author), Univ Lyon, CNRS, Ecole Cent Lyon, LIRIS,UMR5205, F-69134 Lyon, France. EM Yu.Zhang@ec-lyon.fr; stephane.bres@insa-lyon.fr; liming.chen@ec-lyon.fr CR Binder A., 2011, CLEF NOTEBOOK PAPERS Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chakravarti R, 2009, PROCEEDINGS OF THE 2009 SIXTH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: NEW GENERATIONS, VOLS 1-3, P1323 Csurka G., 2004, ECCV WORKSH STAT LEA, P1 Daroczy B., 2011, CLEF NOTEBOOK PAPERS DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fellbaum C, 1998, WORDNET ELECT LEXICA Guillaumin M., 2010, MULTIMODAL SEMISUPER Haralick R. M., 1973, IEEE T SYSTEM MAN CY, V6 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Lin Y.Y., 2007, 2012 IEEE C COMP VIS, P1 Liu N., 2011, CLEF NOTEBOOK PAPERS Mikolajczyk K., 2001, IEEE INT C COMP VIS, V1, P525 Nagel K., 2011, CLEF NOTEBOOK PAPERS Ojala T, 2002, IEEE T PATTERN ANAL, V24, P971, DOI 10.1109/TPAMI.2002.1017623 Ramos J., 1999, USING TF IDF DETERMI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shih J.-L., 2002, LECT NOTES COMPUTER, V2314, P88 Su Y., 2011, CLEF NOTEBOOK PAPERS van de Sande K. E. A., 2011, CLEF NOTEBOOK PAPERS van de Sande KEA, 2010, IEEE T PATTERN ANAL, V32, P1582, DOI 10.1109/TPAMI.2009.154 Wang G, 2009, PROC CVPR IEEE, P1367 Xioufis E. S., 2011, CLEF NOTEBOOK PAPERS Yue Y., 2007, SIGIR 07, P271 Zhu C., 2010, INT C PATT REC ICPR, P3065 Zhu C., 2011, MULT EXP IEEE INT C, V0, P1 NR 26 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-0-7695-4911-8 PY 2012 BP 289 EP 295 PG 7 WC Engineering, Electrical & Electronic SC Engineering GA BDW60 UT WOS:000315360300044 ER PT J AU Lichtnow, D Alves, R Palazzo, J de Oliveira, M Levin, A Pastor, O Castello, IM Dopazo, J AF Lichtnow, Daniel Alves, Ronnie Palazzo, Jose de Oliveira, Moreira Levin, Ana Pastor, Oscar Medina Castello, Ignacio Dopazo, Joaquin GP IEEE TI Using Papers Citations for Selecting the Best Genomic Databases SO 2011 30TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC) SE Proceedings of the International Conference of the Chilean Computer Science Society LA English DT Proceedings Paper CT 30th International Conference of the Chilean-Computer-Science-Society (SCCC) CY NOV 09-11, 2011 CL Curico, CHILE SP Chilean Comp Sci Soc (SCCC) HO Univ Talca DE Database selection; database catalogs; quality indicators ID COLLECTION; QUALITY; SYSTEMS; ISSUE AB Selecting the right data is an essential activity in Genomic-related Information Systems. This work aims to analyze if it is possible to select the best genomic databases from a catalog using information about papers citations related to these genomic databases. The motivation for using information about citations has to do with the fact that it is not easy to obtain proper metadata with respect to these databases. Thus, in this work, information related to papers citations is used for measuring three distinct data quality dimensions: believability, timeliness, and relevancy. Believability is evaluated through the inspection of the number of citations. The variation of the number of citations over time is useful for determining the recency of a database and it is related to the timeliness dimension. Regarding to relevancy, the keywords of papers are useful to indicate the main context of application of these databases. C1 [Lichtnow, Daniel; Alves, Ronnie; Palazzo, Jose; de Oliveira, Moreira] Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil. RP Lichtnow, D (reprint author), Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil. EM dlichtnow@inf.ufrgs.br; ralves@inf.ufrgs.br; palazzo@inf.ufrgs.br; alevin@pros.upv.es; opastor@pros.upv.es; imedina@cipf.es; jdopazo@cipf.es RI Dopazo, Joaquin/A-9270-2014 OI Dopazo, Joaquin/0000-0003-3318-120X CR Amento B, 2000, P 23 ANN INT ACM SIG, P296, DOI 10.1145/345508.345603 BALAKRISHNAN R., 2010, P 19 INT C WORLD WID, P1055, DOI 10.1145/1772690.1772801 Ballou D, 1998, MANAGE SCI, V44, P462, DOI 10.1287/mnsc.44.4.462 Barbosa L, 2007, LECT N BIOINFORMAT, V4544, P6 Bizer C., 2007, THESIS FREIE U BERLI Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Bry F, 2003, DISTRIB PARALLEL DAT, V13, P7, DOI 10.1023/A:1021540705916 Buneman P., 2006, P 2006 ACM SIGMOD IN, P539, DOI 10.1145/1142473.1142534 Cochrane GR, 2010, NUCLEIC ACIDS RES, V38, pD1, DOI 10.1093/nar/gkp1077 Cohen-Boulakia S, 2007, BIOINFORMATICS, V23, P1301, DOI 10.1093/bioinformatics/btm088 Devignes M. D., 2008, P 10 INT C INF INT W, P456, DOI 10.1145/1497308.1497392 Do HH, 2003, LECT NOTES COMPUT SC, V2593, P221 Galperin MY, 2011, NUCLEIC ACIDS RES, V39, pD1, DOI 10.1093/nar/gkq1243 Gaudet P, 2011, NUCLEIC ACIDS RES, V39, pD7, DOI 10.1093/nar/gkq1173 Golbeck J, 2004, LECT NOTES COMPUT SC, V3257, P116 Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278 Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102 Lichtnow D., 2011, P IADIS WWW INT 2011, P253 Manning C.D., 2008, INTRO INFORM RETRIEV Naumann F., 1999, P INT C INF QUAL IQ, P99 Naumann F, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P447 Naumann Felix, 2002, LECT NOTES COMPUTER, V2261 Pernici B, 2003, LECT NOTES COMPUT SC, V2800, P48 Robertson S, 2008, J INF SCI, V34, P439, DOI 10.1177/0165551507086989 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siegel S, 1956, NONPARAMETRIC STAT B Smedley D., 2010, DATABASE Wang R. Y., 1996, Journal of Management Information Systems, V12 Witten I. H., 2000, DATA MINING PRACTICA NR 29 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1522-4902 BN 978-0-7695-4689-6 J9 P INT C CHIL COMPUT PY 2012 BP 33 EP 42 DI 10.1109/SCCC.2011.6 PG 10 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BDT37 UT WOS:000314774100005 ER PT S AU Wagner, H Dlotko, P Mrozek, M AF Wagner, Hubert Dlotko, Pawel Mrozek, Marian BE Ferri, M Frosini, P Landi, C Cerri, A DiFabio, B TI Computational Topology in Text Mining SO COMPUTATIONAL TOPOLOGY IN IMAGE CONTEXT (CTIC) SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th Annual International Workshop on Computational Topology in Image Context (CTIC) CY MAY 28-30, 2012 CL Bertinoro, ITALY SP Univ Studi Bologna, European Sci Fdn (ESF), Rotary Club Bologna, Gruppo Nazl Strutture Algebriche, Geometriche Applicazioni (GNSAGA), Ist Nazl Alta Matematica Francesco Severi, Univ Bologna, Alma Mater Studiorum, Univ Studi Modena & Reggio Emilia, Soc Italiana Matematica Applicata & Ind (SIMAI), Gruppo Italiano Ricercatori Pattern Recognit (GIRPR), Univ Bologna, Adv Res Ctr Elect Syst Informat & Commun Technologies E De Castro (ARCES) DE Computational topology; Computational homology; Flag Complex; Discrete Morse theory; Text mining; Vector space model AB In this paper we present our ongoing research on applying computational topology to analysis of structure of similarities within a collection of text documents. Our work is on the fringe between text mining and computational topology, and we describe techniques from each of these disciplines. We transform text documents to the so-called vector space model, which is often used in text mining. This representation is suitable for topological computations. We compute homology, using discrete Morse theory, and persistent homology of the Flag complex built from the point-cloud representing the input data. Since the space is high-dimensional, many difficulties appear. We describe how we tackle these problems and point out what challenges are still to be solved. C1 [Wagner, Hubert; Dlotko, Pawel; Mrozek, Marian] Jagiellonian Univ, Inst Comp Sci, PL-31007 Krakow, Poland. RP Wagner, H (reprint author), Jagiellonian Univ, Inst Comp Sci, PL-31007 Krakow, Poland. EM hubert.wagner@ii.uj.edu.pl; pawel.dlotko@ii.uj.edu.pl; marian.mrozek@ii.uj.edu.pl RI Mrozek, Marian/K-7209-2014 CR Baeza-Yates Ricardo, 1999, MODERN INFORM RETRIE, P192 Beygelzimer A., 2006, P ICML 2006 Carlsson G, 2009, B AM MATH SOC, V46, P255 CHEN C, 2011, 27 EUR WORKSH COMP G Edelsbrunner H., 2010, COMPUTATIONAL TOPOLO Feng A.-X., 2011, SCIENCE Forman R., 2002, SEMINAIRE LOTHARINGI, V48, pB48c Gunther D., 2011, SIBGRAPI 2011 Kozlov D., 2007, COMBINATORIAL ALGEBR LEWINER T, 2005, THESIS Lewiner T, 2003, EXP MATH, V12, P271 POLANCO X, 2006, P INSCIT, P586 Robins V, 2011, IEEE T PATTERN ANAL, V33, P1646, DOI 10.1109/TPAMI.2011.95 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL Zomorodian A, 2010, COMPUT GRAPH-UK, V34, P263, DOI 10.1016/j.cag.2010.03.007 NR 16 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-30237-4 J9 LECT NOTES COMPUT SC PY 2012 VL 7309 BP 68 EP 78 PG 11 WC Computer Science, Theory & Methods SC Computer Science GA BDO26 UT WOS:000314173900008 ER PT J AU Ahmed, MU Begum, S Funk, P AF Ahmed, Mobyen Uddin Begum, Shahina Funk, Peter GP IEEE TI Case Studies on the Clinical Applications using Case-Based Reasoning SO 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS) LA English DT Proceedings Paper CT Federated Conference on Computer Science and Information Systems (FedCSIS) CY SEP 09-12, 2012 CL Wroclaw, POLAND SP IEEE, IEEE Reg 8, Polskie Towarzystwo Informatyczne (PTI), Polish Acad Sci, Syst Res Inst, Wroclaw Univ Econ, Wroclaw Univ Technol, Fac Comp Sci & Management, Republ Poland, Minist Sci & Higher Educ, IEEE Gdansk Comp Soc Chapter, ACM Special Interest Grp Appl Comp (SIGAPP), Lodz ACM Chapter, Sci & Engn Res Support Soc (SERSC), Informat Europe, Asociacion Tecnicos Informatica, Polish Assoc Informat Syst (PLAIS), Polish Artificial Intelligence Soc, Polish Soc Business Informat, Naukowe Towarzystwo Informatyki Ekonomicznej (NTIE), Polish Chamber Commerce High Technol (IZTECH), IEEE Computer Society Chapter, Wroclaw Univ Technol, Polish Acad Sci (PAN) ID DECISION-SUPPORT; SYSTEM; DIAGNOSIS; STRESS; KNOWLEDGE; THERAPY; PAIN AB Case-Based Reasoning (CBR) is a promising Artificial Intelligence (AI) method that is applied for problem solving tasks. This approach is widely used in order to develop Clinical Decision Support System (CDSS). A CDSS for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. This paper presents the case studies on 3 clinical Decision Support Systems as an overview of CBR research and development. Two medical domains are used here for the case studies: case_study_1) CDSS for stress diagnosis case_study_2) CDSS for stress treatment and case_study_3) CDSS for postoperative pain treatment. The observation shows the current developments, future directions and pros and cons of the CBR approach. Moreover, the paper shares the experiences of developing 3CDSS in medical domain in terms of case study. C1 [Ahmed, Mobyen Uddin; Begum, Shahina; Funk, Peter] Malardalen Univ, Sch Innovat Design & Engn, SE-72123 Vasteras, Sweden. RP Ahmed, MU (reprint author), Malardalen Univ, Sch Innovat Design & Engn, SE-72123 Vasteras, Sweden. EM mobyen.ahmed@mdh.se; shahina.begum@mdh.se; peter.funk@mdh.se RI Funk, Peter/ OI Funk, Peter/0000-0002-5562-1424 CR AAMODT A, 1994, AI COMMUN, V7, P39 Ahmed M., 2009, P INT C ART INT APPL, P225 Ahmed M. U., 2010, INT J ARTIFICIAL INT, V51, P107 Ahmed M. U., 2011, P INT WORKSH CAS BAS, P30 Ahmed M. U., 2008, INT J T CASE BASED R, V1, P3 Ahmed M. U., 2011, IEEE INT S IN PRESS Ahmed M. U., 2012, IEEEEMBS IN IN PRESS Ahmed M. U., 2006, P 23 ANN WORKSH SWED, P45 ALAYON S, 2007, BIOMEDICAL INFORM, V40, P221, DOI DOI 10.1016/J.JBI.2006.11.002 Begum S, 2011, IEEE T SYST MAN CY C, V41, P421, DOI 10.1109/TSMCC.2010.2071862 Begum S., 2006, P 8 EUR WORKSH CAS B, P113 Begum S, 2009, COMPUT INTELL, V25, P180 Begum S., 2011, 11 IND C ICDM 2011 A, P42 Buchanan B.G., 1984, RULE BASED EXPERT SY Carol BRADBUM, 1994, P TOP CAS BAS REAS 1, P365 Casas F, 2004, EXPERT SYST APPL, V26, P357, DOI 10.1016/j.eswa.2003.09.006 Charles E. K., 1997, COMPUT BIOL MED, V27, P19 Charlton E., 1997, MANAGEMENT POSTOPERA Clark WC, 2002, PAIN, V98, P241 Cleeland C. S., 1994, Annals Academy of Medicine Singapore, V23, P129 Corchado JM, 2008, IEEE INTELL SYST, V23, P19, DOI 10.1109/MIS.2008.27 D'Aquin M, 2006, COMPUT INTELL, V22, P161, DOI 10.1111/j.1467-8640.2006.00281.x De Paz J. F., 2009, INT J HYBRID INTELLI, V6, P97 Demirali J., 2011, REASON INTRO CRITICA Diaz F, 2006, COMPUT INTELL, V22, P254, DOI 10.1111/j.1467-8640.2006.00287.x Gardner RM, 1999, INT J MED INFORM, V54, P169, DOI 10.1016/S1386-5056(99)00013-1 Glez-Pena D., 2009, BMC BIOINFORMATICS, V10 Hartrick Craig T, 2003, Pain Pract, V3, P310, DOI 10.1111/j.1530-7085.2003.03034.x Hawthorn J., 1999, J PSYCHIATR MENT HLT, V6, P409, DOI 10.1046/j.1365-2850.1999.00227-8.x HUSKISSO.EC, 1974, LANCET, V2, P1127 Jose D. A., POSTOPERATIVE PAIN M Kappes B., 2008, APPL PSYCHOPHYS BIOF, V33, P173 Kolodner J. L., 1996, CASE BASED REASONING, P31 Koton P., 1989, THESIS MIT Kumar Sanjay, 2002, Indian J Physiol Pharmacol, V46, P396 Lehrer P, 2000, APPL PSYCHOPHYS BIOF, V25, P193, DOI 10.1023/A:1009506909815 Lindgaard G, 2008, J UNIVERS COMPUT SCI, V14, P2720 MARLING C, 2001, 4 INT C CAS BAS REAS, V2080, P702 Montani S, 2006, ARTIF INTELL MED, V37, P31, DOI 10.1016/j.artmed.2005.06.003 Nilsson M, 2006, ARTIF INTELL MED, V36, P159, DOI 10.1016/j.artmed.2005.04.004 Noble RE, 2002, METABOLISM, V51, P37, DOI 10.1053/meta.2002.33190 Podgorelec V., 1999, 12 IEEE S COMP BAS M, P202 Salton G., 1988, INFORM PROCESSING MA, V24, P513 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt R, 2006, KNOWL-BASED SYST, V19, P333, DOI 10.1016/j.knosys.2005.11.016 Selye H., 1956, STRESS LIFE Watson I., 1997, APPL CASE BASED REAS Yang J. B., 2008, INT J COMPUTATIONAL, V1, P159 NR 48 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-83-60810-48-4 PY 2012 BP 3 EP 10 PG 8 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BDC97 UT WOS:000312714400001 ER PT J AU Laorden, C Ugarte-Pedrero, X Santos, I Sanz, B Nieves, J Bringas, PG AF Laorden, Carlos Ugarte-Pedrero, Xabier Santos, Igor Sanz, Borja Nieves, Javier Bringas, Pablo G. GP IEEE TI On the Study of Anomaly-based Spam Filtering Using Spam as Representation of Normality SO 2012 IEEE CONSUMER COMMUNICATIONS AND NETWORKING CONFERENCE (CCNC) SE IEEE Consumer Communications and Networking Conference LA English DT Proceedings Paper CT IEEE Consumer Communications and Networking Conference (CCNC) CY JAN 14-17, 2012 CL Las Vegas, NV SP IEEE AB In previous work, we presented the first spam filtering method based on anomaly detection that reduces the necessity of labelling spam messages and only employs the representation of legitimate e-mails. This method achieved high accuracy rates detecting spam while maintaining a low false positive rate and reducing the effort produced by labelling spam. In this paper, we study the performance of our previous method when using spam messages to represent normality. C1 [Laorden, Carlos; Ugarte-Pedrero, Xabier; Santos, Igor; Sanz, Borja; Nieves, Javier; Bringas, Pablo G.] Univ Deusto, Deusto Inst Technol, DeustoTech Comp, S3Lab, Bilbao 48007, Spain. RP Laorden, C (reprint author), Univ Deusto, Deusto Inst Technol, DeustoTech Comp, S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM claorden@deusto.es; xabier.ugartge@deusto.es; isantos@deusto.es; borja.sanz@deusto.es; jnieves@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Burton B., 2003, P SPAM C CHIRITA PA, 2005, P 14 ACM INT C INF K, P373, DOI 10.1145/1099554.1099671 Chiu Y., 2007, NAT COMP 2007 ICNC 2, V4, P203 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R., 1995, INT JOINT C ART INT, V2, P1137 Mason J., 2002, HEANET ANN C McGill M. J., 1983, INTRO MODERN INFORM Raymond E., 2005, BOGOFILTER FAST OPEN Robinson G., 2003, LINUX J, V2003, P3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos I., 2011, P 6 INT C S IN PRESS Schryen G., 2006, SYST SCI 2006 HICSS, V6, P129 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4577-2071-0 J9 CONSUM COMM NETWORK PY 2012 BP 693 EP 695 PG 3 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BDD68 UT WOS:000312834500156 ER PT J AU Oba, T Hori, T Nakamura, A Ito, A AF Oba, Takanobu Hori, Takaaki Nakamura, Atsushi Ito, Akinori GP IEEE TI SPOKEN DOCUMENT RETRIEVAL BY DISCRIMINATIVE MODELING IN A HIGH DIMENSIONAL FEATURE SPACE SO 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) LA English DT Proceedings Paper CT IEEE International Conference on Acoustics, Speech and Signal Processing CY MAR 25-30, 2012 CL Kyoto, JAPAN SP Inst Elect & Elect Engineers, Signal Processing Soc, IEEE DE Spoken document retrieval; Discriminative model; Linear model AB This paper proposes discriminative modeling in a high dimensional feature space for spoken document retrieval (SDR). To estimate the parameters of a high dimensional model properly, a large quantity of data is necessary, but there is no such large corpus for document retrieval. This paper employs two approaches to overcome this problem. One is a reranking approach. A baseline system first gives each document a score and then the score is compensated by employing a high dimensional model. The other approach is automatic query generation. A large number of queries are automatically generated and used for parameter estimation. Our experimental result shows that our proposed method can greatly improve SDR performance. EM oba.takanobu@lab.ntt.co.jp; hori.t@lab.ntt.co.jp; nakamura.atsushi@lab.ntt.co.jp; aito@spcom.ecei.tohoku.ac.jp CR Akiba T., 2009, J INFORM PROCESSING, V17, P82 Cao Y., 2006, P 29 ANN INT ACM SIG, P186, DOI 10.1145/1148170.1148205 Collins M, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P1 Croft WB, 2003, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.2003.1260777 Hu Xinhui, 2009, P 3 INT UN COMM S, P400, DOI 10.1145/1667780.1667862 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 LAFFERTY J, 2001, P 18 INT C MACH LEAR, P282 Li JF, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P1001 LIU DC, 1989, MATH PROGRAM, V45, P503, DOI 10.1007/BF01589116 Maekawa K, 2000, P LREC2000, V2, P947 Mamou J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148183 MENG CH, 2009, P ICASSP, P4893 Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 Oba T., 2010, P INTERSPEECH, p2446 Och F. J., 2002, P 40 ANN M ASS COMP, P295 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Roark B, 2007, COMPUT SPEECH LANG, V21, P373, DOI 10.1016/j.csl.2006.06.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stolcke A., 1998, P DARPA BROADC NEWS, P270 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-0046-9 PY 2012 BP 5153 EP 5156 PG 4 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA BDA84 UT WOS:000312381405057 ER PT J AU Mohammadzadeh, H Paknia, O Schweiggert, F Gottron, T AF Mohammadzadeh, Hadi Paknia, Omid Schweiggert, Franz Gottron, Thomas BE Hameurlain, A Tjoa, AM Wagner, RR TI Revealing Trends Based on Defined Queries in Biological Publications Using Cosine Similarity SO 2012 23RD INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS (DEXA) SE International Workshop on Database and Expert Systems Applications-DEXA LA English DT Proceedings Paper CT 23rd International Workshop on Database and Expert Systems Applications (DEXA) CY SEP 03-07, 2012 CL Vienna, AUSTRIA HO Vienna Univ Technol DE Trends in Biological Publications; Information Retrieval; Queries; Cosine Similarity; Vector Space Model AB Extracting valuable information in terms of number and content of published papers in any field of research will simplify decision making for future researches and investments. A novel and simple text mining approach, called TrendFinder, has been developed in this paper to reveal the content-based trends of expert-defined queries in selected biological published papers during the last five decades. So, in order to evaluate the results, three different data sets were collected and four vectors of selected keywords were considered as the four queries. Title, Published date and the Abstract were downloaded for three series of journals namely, "Conservation Biology", "Ecology", and "The American Naturalist" as data sets, including total number of 19,010 papers. In order to show the trend between each query and the Abstract of each paper, Cosine similarity method was used by TrendFinder. Afterwards, three diagrams were demonstrated content-based trends of the four defined queries on the three provided data sets. C1 [Mohammadzadeh, Hadi; Schweiggert, Franz] Univ Ulm, Inst Appl Informat Proc, D-89069 Ulm, Germany. RP Mohammadzadeh, H (reprint author), Univ Ulm, Inst Appl Informat Proc, D-89069 Ulm, Germany. EM hadi.mohammadzadeh@uni-ulm.de; omid.paknia@ecolevol.de; franz.schweiggert@uni-ulm.de; gottron@uni-koblenz.de CR Chen W, 2009, LECT NOTES COMPUT SC, V5691, P402 Feldman R, 1998, J INTELL INF SYST, V10, P281, DOI 10.1023/A:1008623632443 Kim Su Nam, 2010, P 5 INT WORKSH SEM E, P21 Manning C. D., 2009, INTRO INFORM RETRIEV Mei Q., 2005, P ACM SIGKDD INT C K, P285 Naveed N, 2011, LECT NOTES COMPUT SC, V6611, P733, DOI 10.1007/978-3-642-20161-5_82 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 7 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1529-4188 BN 978-0-7695-4801-2 J9 INT WORKSHOP DATABAS PY 2012 BP 218 EP 222 DI 10.1109/DEXA.2012.16 PG 5 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BDC67 UT WOS:000312658400035 ER PT J AU Ensan, A Biletskiy, Y AF Ensan, Alireza Biletskiy, Yevgen GP IEEE TI MATCHMAKING THROUGH SEMANTIC ANNOTATION AND SIMILARITY MEASUREMENT SO 2012 25TH IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE) LA English DT Proceedings Paper CT 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) CY APR 29-MAY 02, 2012 CL Montreal, CANADA SP IEEE, IEEE Canada DE Computer Applications; Ontology; Information Extraction; Semantic Annotation; Matchmaking; Semantic Similarity ID INFORMATION EXTRACTION AB The proposed work briefly describes an approach to automatically extract structured information from semi-structured documents to match the document creators and users in order to find the best similarities between them and connect them for further collaborations. The general idea is to employ a semantic annotation technique and similarity measurement approach by using the ontology to find best matches between web documents. The proposed approach uses ontologies to annotate the extracted information and for the measuring the similarity between each pair of documents. GATE (General Architecture for Text Engineering) as one of the most famous annotation tools has been utilized to annotate semi-structure documents. A novel algorithm is proposed to update the supported ontology for extraction purpose in GATE by using a training data set. Furthermore, specific domain-based metrics are also utilized to measure semantic similarities between documents with regard to semantic annotations which are implemented in an ontology-based approach. These metrics can be used in order to find the most similar web documents among documents corpus. C1 [Ensan, Alireza; Biletskiy, Yevgen] Univ New Brunswick, Fredericton, NB, Canada. RP Ensan, A (reprint author), Univ New Brunswick, Fredericton, NB, Canada. EM alireza.ensan@unb.ca; biletski@unb.ca CR Appelt D., 1999, INTRO INFORM EXTRACT Biletskiy Y, 2009, EXPERT SYST APPL, V36, P4508, DOI 10.1016/j.eswa.2008.05.011 Laender AHF, 2002, SIGMOD REC, V31, P84 Leacock L C., 1998, WORDNET ELECT LEXICA, P305 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Lin D., 1998, P INT C MACH LEARN I McGuinness D., 2002, ONTOLOGIES COME AGE Navigli R, 2003, IEEE INTELL SYST, V18, P22, DOI 10.1109/MIS.2003.1179190 Resnik P, 1995, P 14 INT JOINT C ART Saiiuguet A., 2001, DATA KHOWL ENG, V36, P283 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wimalasuriya DC, 2010, J INF SCI, V36, P306, DOI 10.1177/0165551509360123 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-1433-6 PY 2012 PG 5 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BDA83 UT WOS:000312379300153 ER PT J AU Cerqueus, T Cazalens, S Lamarre, P AF Cerqueus, Thomas Cazalens, Sylvie Lamarre, Philippe GP IEEE TI An Approach to Manage Semantic Heterogeneity in Unstructured P2P Information Retrieval Systems SO 2012 IEEE 12TH INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING (P2P) SE IEEE International Conference on Peer-to-Peer Computing LA English DT Proceedings Paper CT 12th IEEE International Conference on Peer-to-Peer Computing (P2P) CY SEP 03-05, 2012 CL Tarragona, SPAIN ID NETWORK AB In unstructured information retrieval P2P systems, semantic heterogeneity comes from the use of different ontologies. Semantic interoperability refers to the ability of peers to communicate with each others. We take into account these notions separately, as raising two different problems. Hence we propose two independent and complementary solutions. The GoOD-TA protocol aims at reducing heterogeneity through ontology-driven topology adaptation. DiQuESh is a top-k algorithm for distributed information retrieval that is intended to ensure interoperability. This distinction enables highlighting their respective benefits on the IR performances and leads to a modular architecture. For our experiments we obtained a set of actively used real-world ontologies through the NCBO BioPortal. We implemented GoOD-TA and DiQuESH in Java and used the PeerSim simulator. We first show that GoOD-TA nicely reduces the semantic heterogeneity related to the system topology, handles the evolution of peers' descriptors, and is suitable for dynamic systems. Then, GoOD-TA and DiQuESh are run simultaneously, with a significant increase of precision and recall. This enables to identify the indirect contribution of heterogeneity reduction obtained with GoOD-TA to improving interoperability. C1 [Cerqueus, Thomas] Univ Nantes, LINA, UMR 6241, F-44035 Nantes, France. RP Cerqueus, T (reprint author), Univ Nantes, LINA, UMR 6241, F-44035 Nantes, France. EM thomas.cerqueus@univ-nantes.fr; sylvie.cazalens@univ-nantes.fr; philippe.lamarre@liris.cnrs.fr CR Aberer Karl, 2003, WWW, P197 Akbarinia R, 2006, DISTRIB PARALLEL DAT, V19, P67, DOI 10.1007/s10619-006-8313-5 Antoniou G., 2009, HDB ONTOLOGIES, P91, DOI DOI 10.1007/978-3-540-92673-3_4 Bertier M, 2010, LECT NOTES COMPUT SC, V6452, P191, DOI 10.1007/978-3-642-16955-7_10 Bonifati A, 2010, VLDB J, V19, P231, DOI 10.1007/s00778-009-0159-9 Cerqueus Thomas, 2011, 10 IEEE WIC ACM INT Cerqueus Thomas, 2011, 4 INT C DAT MAN GRID, P37 Comito Carmela, 2006, 11 IEEE S COMP COMM, P88, DOI 10.1109/ISCC.2006.19 Cudre-Mauroux P, 2007, IEEE INTERNET COMPUT, V11, P36, DOI 10.1109/MIC.2007.108 Halevy Alon, 2003, 12 INT WORLD WID WEB, P556 Hartung M, 2011, DATA CENTRIC SYST AP, P149, DOI 10.1007/978-3-642-16518-4_6 JELASITY M, 2004, 5 ACM IFIP USENIX IN, V3231, P79 JELASITY M, 2005, 3 INT WORKSH ENG SEL, V3910, P1 Jelasity M, 2009, COMPUT NETW, V53, P2321, DOI 10.1016/j.comnet.2009.03.013 Kantere V, 2009, INFORM SYST, V34, P62, DOI 10.1016/j.is.2008.04.002 Kermarrec Anne-Marie, 2007, Operating Systems Review, V41, DOI 10.1145/1317379.1317381 Loser Alexander, 2003, 1 INT WORKSH DAT INF, P33 MONTRESOR A, 2009, 2009 IEEE 9 INT C, P99 Nedos Andronikos, 2007, 3 IEEE INT C WIR MOB, P53, DOI 10.1109/WIMOB.2007.4390847 Nejdl Wolfgang, 2004, J WEB SEM, V1, P177 Noy N.F., 2009, NUCLEIC ACIDS RES, V37, P170, DOI [DOI 10.1093/NAR/GKP440, 10.1093/nar/gkp440] Penzo Wilma, 2008, EDBT 08, P26 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saroiu S, 2003, MULTIMEDIA SYST, V9, P170, DOI 10.1007/s00530-003-0088-1 Shvaiko P., 2007, ONTOLOGY MATCHING Souza Damires, 2011, T LARGE SCALE DATA K, V3, P56 Voulgaris S, 2004, INT WKSHP FUT TR DIS, P238 NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2161-3567 BN 978-1-4673-2862-3 J9 IEEE INT CONF PEER PY 2012 BP 179 EP 190 PG 12 WC Telecommunications SC Telecommunications GA BDC79 UT WOS:000312674500025 ER PT J AU Shu, LC Lin, C Meng, WY Han, Y Yu, CT Smalheiser, NR AF Shu, Liangcai Lin, Can Meng, Weiyi Han, Yue Yu, Clement T. Smalheiser, Neil R. BE Zhang, C Joshi, J Bertino, E Thuraisingham, B TI A Framework for Entity Resolution with Efficient Blocking SO 2012 IEEE 13TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI) LA English DT Proceedings Paper CT 13th IEEE International Conference on Information Reuse and Integration (IEEE IRI) / DIM / WICSOC / IEEE EM- RITE / IRI-HI CY AUG 08-10, 2012 CL Las Vegas, NV SP IEEE Syst Man & Cybernet Soc (IEEE SMC), Soc Informat Reuse & Integrat (SIRI), IEEE ID LINKAGE AB In applications of Web data integration, we frequently need to identify whether data objects in different data sources represent the same entity in the real world. This problem is known as entity resolution. In this paper, we propose a generic framework for entity resolution for relational data sets, called BARM, consisting of the Blocker, Attribute matchers and the Record Matcher. BARM is convenient for different blocking and matching algorithms to fit into it. For the blocker, we apply the SPectrAl Neighborhood (SPAN), a state-of-the-art blocking algorithm, to our data sets and show that SPAN is effective and efficient. For attribute matchers, we propose the Context Sensitive Value Matching Library (CSVML) for matching attribute values and also an approach to evaluate the goodness of matching functions. CSVML takes the meaning and context of attribute values into consideration and therefore has good performance, as shown in experimental results. We adopt Bayesian network as the record matcher in the framework and propose a method of inference from Bayesian network based on Markov blanket of the network. As a comparison, we also apply three other classifiers, including Decision Tree, Support Vector Machines, and the Naive Bayes classifier to our data sets. Experiments show that Bayesian network is advantageous in the book domain. C1 [Shu, Liangcai; Lin, Can; Meng, Weiyi; Han, Yue; Yu, Clement T.] SUNY Binghamton, Dept Comp Sci, Binghamton, NY 13902 USA. RP Shu, LC (reprint author), SUNY Binghamton, Dept Comp Sci, Binghamton, NY 13902 USA. EM lshu@cs.binghamton.edu; clin27@binghamton.edu; meng@cs.binghamton.edu; yhan1@binghamton.edu; yu@cs.uic.edu; nsmalheiser@psych.uic.edu CR Batini C., 2006, DATA QUALITY CONCEPT Bhattacharya I., 2004, ACM SIGMOD WORKSH RE Bhattacharya I., 2004, ACM SIGKDD WORKSH LI Bhattacharya I., 2006, SDM Bilenko M., 2003, KDD Chapman A.D., 2005, PRINCIPLES METHODS D Churches T., 2005, FEBRL FREELY EXTENSI Cohen W., 2002, KDD Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061 Frank E, 2005, DATA MINING PRACTICA Garcia-Molina H., 2009, SIGMOD Gravano L., 2003, WWW, P90 Guha S., 2004, VLDB Hassanzadeh O., 2009, VLDB Hernandez MA, 1998, DATA MIN KNOWL DISC, V2, P9, DOI 10.1023/A:1009761603038 Jia X., 2009, VLDB Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Manning C.D., 2008, INTRO INFORM RETRIEV Marthi B., 2002, NIPS McCallum A., 2000, KNOWLEDGE DISCOVERY, P169 NEWCOMBE HB, 1959, SCIENCE, V130, P954, DOI 10.1126/science.130.3381.954 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Pearl J., 1988, PROBABILISTIC REASON SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarawagi Sunita, 2002, KDD, P269 Shu L., 2009, ICDE Shu LC, 2011, PROC INT CONF DATA, P1067 Sun D. X., 1998, KDD UKKONEN E, 1992, THEOR COMPUT SCI, V92, P191, DOI 10.1016/0304-3975(92)90143-4 Verykios VS, 2000, INFORM SCIENCES, V126, P83, DOI 10.1016/S0020-0255(00)00013-X Williamson J., 2005, BAYESIAN NETS CAUSAL, P14 Winkler W. E., 2006, TECHNICAL REPORT NR 33 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-2284-3 PY 2012 BP 431 EP 440 PG 10 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BDC08 UT WOS:000312540300063 ER PT J AU Granada, RL Vieira, R de Lima, VLS AF Granada, Roger L. Vieira, Renata Strube de Lima, Vera Lucia BE Zhang, C Joshi, J Bertino, E Thuraisingham, B TI Evaluating co-occurrence order for automatic thesaurus construction SO 2012 IEEE 13TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI) LA English DT Proceedings Paper CT 13th IEEE International Conference on Information Reuse and Integration (IEEE IRI) / DIM / WICSOC / IEEE EM- RITE / IRI-HI CY AUG 08-10, 2012 CL Las Vegas, NV SP IEEE Syst Man & Cybernet Soc (IEEE SMC), Soc Informat Reuse & Integrat (SIRI), IEEE DE Syntactic dependency; Vector Space Model; Semantic similarity; Automatic Thesaurus construction ID LATENT SEMANTIC ANALYSIS; KNOWLEDGE AB Identifying the best semantically similar terms in an automatic thesaurus construction task is still an open problem in natural language processing. Many methods have been proposed to solve this problem. In this work we present a comparison between three corpus based methods for automatically build thesaurus. These methods look for related terms using the relations between terms and they differ among themselves in the co-occurrences order of these relations. The evaluation process was carried out by domain specialists who evaluated the related terms generated by each method in an experiment applied to the data privacy domain. C1 [Granada, Roger L.; Vieira, Renata; Strube de Lima, Vera Lucia] Pontifical Catholic Univ Rio Grande Sul PUCRS, BR-90169900 Porto Alegre, RS, Brazil. RP Granada, RL (reprint author), Pontifical Catholic Univ Rio Grande Sul PUCRS, Ipiranga Av,6681 FACIN, BR-90169900 Porto Alegre, RS, Brazil. EM roger.granada@acad.pucrs.br; renata.vieira@pucrs.br; vera.strube@pucrs.br CR Anick V.M.P., 2008, P LREC 08, P1209 Bruckschen M., 2010, LREC 10, P16 Budanitsky A., 2001, P WORKSH WORDNET OTH, P29 Chen L., 2007, INT J INFORM MANAGE, V18, P299 Church K. W., 1990, COMPUTATIONAL LINGUI, V16, P22 Crouch C. J., 1992, P 15 ANN INT ACM SIG, P77, DOI 10.1145/133160.133180 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fellbaum C., 1998, WORDNET ELECT LEXICA Firth J. R., 1957, STUDIES LINGUISTIC A, P1 FLEISS JL, 1971, PSYCHOL BULL, V76, P378, DOI 10.1037/h0031619 Gamallo P, 2011, LANG RESOUR EVAL, V45, P95, DOI 10.1007/s10579-010-9129-5 Gamallo P, 2005, COMPUT LINGUIST, V31, P107, DOI 10.1162/0891201053630318 GASPERIN CV, 2003, P 4 INT C COMP LING, V2588, P314 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Hanks P., 1987, DEFINITIONS EXPLANAT Harris ZS, 1954, WORD, V10, P146 Heilman M., 2007, P SLATE WORKSH SPEEC, P65 Kaji H., 2000, P 18 INT C COMP LING, P404 Kilgarriff A., 2000, P 2 INT C LANG RES E, P1371 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lemaire B., 2006, CURRENT PSYCHOL LETT, V18, P1 Lenci A., 2008, ITALIAN J LINGUISTIC, V20, P55 Michelbacher L., 2010, P LREC 10 Pantel Patrick, 2002, P ACM SIGKDD C KNOWL, P613 Pearson Siani, 2009, Proceedings of the 2009 13th Enterprise Distributed Object Computing Conference Workshops, EDOCW. IEEE EDOC 2009 Workshops and Short Papers, DOI 10.1109/EDOCW.2009.5331996 Rao P., 2011, ONTOBRAS MOST 2011, P85 Rapp R., 2004, P 4 LANG RES EV C, P395 Ruiz-Casado M., 2005, P RANLP 2005 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sears B., 1868, THESAURUS ENGLISH WO Tanimoto T.T., 1958, TECHNICAL REPORT Toutanova K., 2003, P HLT NAACL, P252 Turney P.D., 2001, P 12 EUR C MACH LEAR, V2167, P491 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Xu H, 2010, EXPERT SYST APPL, V37, P18, DOI 10.1016/j.eswa.2009.02.059 Yang D., 2008, P 31 AUSTR C COMP SC, V74, P147 Yang Dongqiang, 2005, P 28 AUSTR COMP SCI, P315 NR 38 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-2284-3 PY 2012 BP 474 EP 481 PG 8 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BDC08 UT WOS:000312540300068 ER PT J AU Bouillot, F Poncelet, P Roche, M Ienco, D Bigdeli, E Matwin, S AF Bouillot, Flavien Poncelet, Pascal Roche, Mathieu Ienco, Dino Bigdeli, Elnaz Matwin, Stan GP ACM TI French Presidential Elections: What are the Most Efficient Measures for Tweets? SO PROCEEDINGS OF THE FIRST EDITION WORKSHOP ON POLITICS, ELECTIONS AND DATA LA English DT Proceedings Paper CT ACM Workshop on Politics, Elections and Data Co-located with 21st ACM International Conference on Information and Knowledge Management (CIKM) CY NOV 02, 2012 CL Maui, HI SP Assoc Comp Machinery (ACM), Assoc Comp Machinery Special Interest Grp Informat Retrieval (ACM SIGIR), Assoc Comp Machinery SIGWEB (ACM SIGWEB) DE Tweet Analysis; Information RetrievalMeasure; Community Detection; Political Data AB Tweets exchanged over the Internet are an important source of information even if their characteristics make them difficult to analyze (e.g., a maximum of 140 characters; noisy data). In this paper, we address the problem of extracting relevant topics through tweets coming from different communities. More precisely we are interested to address the following question: which are the most relevant terms given a community. To answer this question we define and evaluate new variants of the traditional TF-IDF. Furthermore we also show that our measures are well suited to recommend a community affiliation to a new user. Experiments have been conducted on tweets collected during French Presidential and Legislative elections in 2012. The results underline the quality and the usefulness of our proposal. C1 [Bouillot, Flavien; Poncelet, Pascal; Roche, Mathieu] LIRMM, Montpellier, France. RP Bouillot, F (reprint author), LIRMM, 16 Rue Ada, Montpellier, France. EM bouillot@lirmm.fr; poncelet@lirmm.fr; mroche@lirmm.fr; dino.ienco@teledetection.fr; elnaz@site.uottawa.ca; stan@site.uottawa.ca CR Barbosa, 2010, P 23 INT C COMP LING, P36 Benhardus J., 2010, NATL SCI FDN REU ART Bringay S, 2011, LNCS, P154 Joshi A., 2011, P ACL SYST DEM, P127 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mathioudakis M., 2010, P 2010 INT C MAN DAT, P1155, DOI DOI 10.1145/1807167.180730 Pennacchiotti M., 2011, P 17 ACM SIGKDD INT, P430, DOI DOI 10.1145/2020408.2020477 Sakaki T., 2010, P 19 INT C WORLD WID, P851, DOI DOI 10.1145/1772690.1772777 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang J, 2008, IEEE DATA MINING, P1055, DOI 10.1109/ICDM.2008.71 Tsolmon B, 2012, LNCS Turney P.D., 2001, LNCS LNAI, V2167, P491 Yvon F., 2008, COLING, P441 NR 13 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-4503-1713-9 PY 2012 BP 23 EP 30 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDC55 UT WOS:000312634300005 ER PT J AU Mohammadzadeh, H Gottron, T Schweiggert, F Heyer, G AF Mohammadzadeh, Hadi Gottron, Thomas Schweiggert, Franz Heyer, Gerhard GP ACM TI TitleFinder: Extracting the Headline of News Web Pages based on Cosine Similarity and Overlap Scoring Similarity SO PROCEEDINGS OF THE TWELFTH INTERNATIONAL WORKSHOP ON WEB INFORMATION AND DATA MANAGEMENT LA English DT Proceedings Paper CT 12th ACM International Workshop on Web Information and Data Management (WIDM) Co-located with 21st ACM International Conference on Information and Knowledge Management (CIKM) CY NOV 02, 2012 CL Maui, HI SP Assoc Comp Machinery Special Interest Grp Informat Retrieval (ACM SIGIR), Assoc Comp Machinery SIGWEB (ACM SIGWEB) DE Vector Space Model; Cosine Similarity; Overlap Scoring Similarity; HTML Web Pages; Headline Extraction; Title Extraction; Information Retrieval AB Automatically extracting the headline of online web articles has many applications in web mining and information retrieval. In this paper, we developed a content-based and domain- and language-independent approach, TitleFinder, for unsupervised extraction of the headline of web articles. TitleFinder starts by using a heuristic to select a candidate headline. In a second step the contents of each text fragment in the HTML file are compared to the candidate headline. We implemented four types of similarity for this comparison: two variations of the cosine similarity based on tf and tf-idf weighting schemata, an overlap scoring similarity and an aggregated metric combining the scores of the previous three similarities. Our method achieves high performance in terms of effectiveness and efficiency and outperforms approaches operating on structural and visual features on a test set consisting of 11,218 news web pages from 15 different domains. C1 [Mohammadzadeh, Hadi; Schweiggert, Franz] Univ Ulm, Inst Appl Informat Proc, D-89081 Ulm, Germany. RP Mohammadzadeh, H (reprint author), Univ Ulm, Inst Appl Informat Proc, D-89081 Ulm, Germany. EM hadi.mohammadzadeh@uni-ulm.de; gottron@uni-koblenz.de; franz.schweiggert@uni-ulm.de; heyer@informatik.uni-leipzig.de CR CHANGUEL S, 2009, 6 INT C MACH LEARN D, V5632, P704 Fairon C, 2007, WAC3 P 3 WEB CORP WO Fan J, 2011, P 17 ACM SIGKDD INT, P743 Fan J, 2011, PROC SPIE, V7879, DOI 10.1117/12.876708 Gottron T, 2007, ITA 07, P123 Gottron T, 2008, INT WORKSHOP DATABAS, P29 Gottron T, 2008, EUROIMSA 08, P66 Hu Y, 2005, ACM IEEE JOINT C DIG, P145 Hu Y, 2005, SIGIR 2005, P250 Ibrahim H, 2008, P INT C LANG RES EV Manning C. D., 2009, INTRO INFORM RETRIEV Mohammadzadeh H, 2011, TIR 11, P167 Mohammadzadeh H, 2012, WEBIST 12, P677 Moreno J, 2009, P 9 DUTCH BELG INF R, P50 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun Fei, 2011, P 34 INT ACM SIGIR C, P245 Weninger T., 2010, P 19 INT C WORLD WID, P971, DOI 10.1145/1772690.1772789 Xue YW, 2007, INFORM PROCESS MANAG, V43, P1332, DOI 10.1016/j.ipm.2006.11.007 Zhang ZC, 2005, Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'05), P553 NR 19 TC 0 Z9 1 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-4503-1720-7 PY 2012 BP 65 EP 71 PG 7 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDC59 UT WOS:000312647600010 ER PT J AU Dong, T Cheng, WN Shang, WQ AF Dong, Tao Cheng, Weinan Shang, Wenqian GP IEEE TI The Research of kNN Text Categorization Algorithm Based On Eager Learning SO 2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE) LA English DT Proceedings Paper CT International Conference on Industrial Control and Electronics Engineering (ICICEE) CY AUG 23-25, 2012 CL Xian, PEOPLES R CHINA SP IEEE Comp Soc, XianTechnol Univ, Missouri Western State Univ, IEEE Kansas City Sect DE Text categorization; kNN; Eager learning AB Text categorization is a fundamental methodology of text mining and it is also a hot topic of the research of data mining and web mining in recent years. It plays an important role in business, government decision-making management, scientific research, and so on. This paper presents an improved algorithm of text categorization which combines eager learning with kNN classification. Experimental results show that the improved algorithm not only improve the efficiency of categorization, but also significantly increase the accuracy of categorization and produce a qualitative leap on the practical value of the sensitive information system. C1 [Dong, Tao; Cheng, Weinan; Shang, Wenqian] Commun Univ China, Sch Comp, Beijing, Peoples R China. RP Dong, T (reprint author), Commun Univ China, Sch Comp, Beijing, Peoples R China. EM victor666.ok@163.com; shangwenqian@163.com CR Kamber M., 2007, DATA MINGING CONCEPT Liu Bin, 2009, WEB DATA MINING SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Liu Zhen-yan, 2002, Mini-Micro Systems, V23 Jiang Tao, 2009, Computer Engineering and Applications, V45, DOI 10.3778/j.issn.1002-8331.2009.07.046 NR 7 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-4792-3 PY 2012 BP 1120 EP 1123 DI 10.1109/ICICEE.2012.297 PG 4 WC Engineering, Electrical & Electronic SC Engineering GA BCJ89 UT WOS:000310339200290 ER PT S AU Guha, S Lamichhane, AR AF Guha, Sumanta Lamichhane, Ananta Raj BE Venugopal, KR Patnaik, LM TI Document Classification after Dimension Reduction through a Modified Gram-Schmidt Process SO WIRELESS NETWORKS AND COMPUTATIONAL INTELLIGENCE, ICIP 2012 SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 6th International Conference on Information Processing (ICIP 2012) CY AUG 10-12, 2012 CL Bangalore, INDIA SP Soc Informat Process, Bangalore Univ,Univ Visvesvaraya Coll Engn, IEEE, UVCE, Alpha Coll Engn, Vijaya Vittala Inst Technol DE Centroid; Document Classification; Dimension Reduction; Gram-Schmidt; LSI; Vector Space Model ID LATENT SEMANTIC ANALYSIS AB This paper proposes a modified Gram-Schmidt algorithm for dimension reduction of a document vector space in order to do classification. We also evaluate the performance of the proposed algorithm by comparing with commonly known algorithms such as the centroid-based algorithm and latent semantic indexing. Further, to measure efficiency, a modified Gram-Schmidt training set is applied in the centroid-based algorithm and latent semantic indexing as well. Performance measurement was based on two different parameters, viz., classification accuracy and closeness of similarity with the corresponding training set. The results shows Chat modified Gram-Schmidt algorithm is indeed an effective method for dimension reduction prior classification. Moreover, it is easy to code and computationally inexpensive. C1 [Guha, Sumanta; Lamichhane, Ananta Raj] Asian Inst Technol, Pathum Thani 12120, Thailand. RP Guha, S (reprint author), Asian Inst Technol, Pathum Thani 12120, Thailand. EM guha@ait.ac.th CR Biro Istvan, 2009, THESIS EOTVOS LORAND DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Goller C., 2000, MACH LEARN, V1, P1 Han EH, 2000, CENTROID BASED DOCUM Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Jollife IT, 1986, PRINCIPAL COMPONENT Li J., 2007, COMPUTATIONAL LINGUI, P774 Rorres C., ELEMENTARY LINEAR AL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Torkkola K., 2001, P IEEE ICDM WORKSH T Ye JP, 2005, IEEE T KNOWL DATA EN, V17, P1208 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-31685-2 J9 COMM COM INF SC PY 2012 VL 292 BP 236 EP 243 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture SC Computer Science GA BDB17 UT WOS:000312432900028 ER PT S AU Aghassi, H Sheykhlar, Z AF Aghassi, Hadi Sheykhlar, Zahra BE Lukose, D Ahmad, AR Suliman, A TI Extending Information Retrieval by Adjusting Text Feature Vectors SO KNOWLEDGE TECHNOLOGY SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 3rd Conference of Knowledge Technology Week (KTW 2011) CY JUL 18-22, 2011 CL Kajang, MALAYSIA SP Minist Sci Technol & Innovat, MIMOS, Univ Tenaga Nas, Leibniz Informat Ctr Econom, Franz Inc, Sabah Econom Dev & Investment Author, Infovalley Sdn, NOTA ASIA Sdn HO Univ Tenaga Nas, Coll Informat Technol DE Text mining; Information retrieval; Semantic relationship; Social network analysis AB Automatic detection of text scope is now crucial for information retrieval tasks owing to semantic, linguistic, and unexpressive content problems, which has increased the demand for uncomplicated, language-independent, and scope-based strategies. In this paper, we extend the vector of documents with exerting impressive words to simplify expressiveness of each document from extracted essential words of related documents and then analyze the network of these words to detect words that share meaningful concepts related to exactly our document. In other words, we analyze each document in only one topic: the topic of that document. We changed measures of social network analysis according to weights of the document words. The impression of these new words to the document can be exerted as changing the document vector weights or inserting these words as metadata to the document. As an example, we classified documents and compared effectiveness of our Intelligent Information Retrieval (IIR) model. C1 [Aghassi, Hadi; Sheykhlar, Zahra] Iran Univ Sci & Technol, Tehran, Iran. RP Aghassi, H (reprint author), Iran Univ Sci & Technol, Tehran, Iran. EM hadi.aghassi@gmail.com; za_sheikhlar@comp.iust.ac.ir CR Aldous D., 1987, PROBAB ENG INFORM SC, V1, P33 Bar-Yossef Z., 2000, VLDB Bar-Yossef Z., 2006, WWW Das G., 2008, P 2008 ACM WORKSH SE Gkantsidis C, 2006, PERFORM EVALUATION, V63, P241, DOI 10.1016/j.peva.2005.01.002 Hirst G., 2001, EUROLAN Isak T., 2007, P 40 ANN HAW INT C S Jarmasz M., 2003, P INT C REC ADV NAT, P212 Kumar R., 2003, CLUSTER ANAL BASIC C Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lang K., 20000 MESSAGES TAKEN Mislove A., 2006, EXPLOITING SOCIAL NE Pedersen T., 2004, AAAI, P1024 Ricardo A., 1999, MODERN INFORM RETRIE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Taheri Malchsoos P., 2010, INT C IT THAIL MARCH Teevan J., 2005, PERSONALIZING SEARCH Voorhees EM, 2008, INFORM PROCESS MANAG, V44, P1879, DOI 10.1016/j.ipm.2007.12.011 Wang W., 2007, P 6 INT C ADV LANG P Wasserman S., 1994, SOCIAL NETWORK ANAL Zhou D., 2008, P 17 INT C WORLD WID NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-32825-1 J9 COMM COM INF SC PY 2012 VL 295 BP 133 EP 142 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA BCY29 UT WOS:000311978400014 ER PT S AU Nuipian, V Meesad, P Boonrawd, P AF Nuipian, Vatinee Meesad, Phayung Boonrawd, Pudsadee BE Yuan, L TI Improve Abstract Data with Feature Selection for Classification Techniques SO MEMS, NANO AND SMART SYSTEMS, PTS 1-6 SE Advanced Materials Research LA English DT Proceedings Paper CT 7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011) CY NOV 04-06, 2011 CL Kuala Lumpur, MALAYSIA SP Int Assoc Comp Sci & Informat Technol, Singapore Inst Elect DE digital library; text classification; feature selection; support vector machine; SVM Attribute ID HYBRID FEATURE-SELECTION; SYMMETRICAL UNCERTAINTY; ALGORITHM AB A universal problem with text classification has a problem due to the high dimensionality of feature space, e.g. word frequency vectors. To overcome this problem, this paper proposed a feature selection which focuses on statistical pattern based on SVM Attribute. Experiments have shown that the determination of word importance may increase the speed of the classification algorithm and save their resource used significantly. The proposed method was studied by comparing classification performance among Decision Tree, Naive Bayes, and Support Vector Machine. The results showed that Support Vector Machine was found to be the best algorithm with F-measure 93.6%. It is found that the feature selection can reduce dimensionality of data significantly. C1 [Nuipian, Vatinee] King Mongkuts Univ Technol N Bangkok, Inst Comp & Informat Technol, Bangkok, Thailand. RP Nuipian, V (reprint author), King Mongkuts Univ Technol N Bangkok, Inst Comp & Informat Technol, Bangkok, Thailand. EM vtn@kmutnb.ac.th; pym@kmutnb.ac.th; pudsadee@kmutnb.ac.th CR Ahmad A, 2005, PATTERN RECOGN LETT, V26, P43, DOI 10.1016/j.patrec.2004.08.015 Baeza-Yate R., 1999, MODERN INFORM RETRIE Jiang BN, 2008, LECT NOTES OPER RES, V9, P152 Joachims Thorsten, TEXT CATEGORIZATION Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Meesad P, 2011, INT C INF EL ENG, V6, P110 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Kannan SS, 2010, KNOWL-BASED SYST, V23, P580, DOI 10.1016/j.knosys.2010.03.016 Soucy P., TFIDF WEIGHTING TEXT Vapnik V., 1995, NATURE STAT LEARNING Zhang S., 2008, FEATURE SELECTION FI NR 13 TC 1 Z9 1 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1022-6680 BN 978-3-03785-312-2 J9 ADV MATER RES-SWITZ PY 2012 VL 403-408 BP 3699 EP 3703 DI 10.4028/www.scientific.net/AMR.403-408.3699 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary SC Computer Science; Engineering; Science & Technology - Other Topics; Materials Science GA BCN35 UT WOS:000310764702017 ER PT B AU Tan, GI AF Tan Guo-Iv BE Zhu, M TI An Information Retrieval Method Based on Matrix Singular Value Decomposition SO CEIT 2012: 2012 INTERNATIONAL CONFERENCE ON CIVIL ENGINEERING AND INFORMATION TECHNOLOGY LA English DT Proceedings Paper CT 2nd International Conference on Civil Engineering and Information Technology CY JUN 01-02, 2012 CL Los Angeles, CA DE Information retrieval; Vector space model; Singular value decomposition AB Vector space model is one of the widely used information retrieval models. Aiming at it's exist problems, here use singular value decomposition to reduces the dimension of document vector. A decomposition parameter was put forward. It can also be consider as a retrieval precision. Using the decomposition parameters, not only improve the retrieval recall and precision, but also more importantly, greatly speed up the retrieval speed. C1 [Tan Guo-Iv] Shangrao Normal Univ, Dept Math & Comp, Shangrao 334001, Peoples R China. RP Tan, GI (reprint author), Shangrao Normal Univ, Dept Math & Comp, Shangrao 334001, Peoples R China. EM tan-gl@163.com CR CAI-Hua, 2011, J TIBET U NATURAL SC, V26, P118 Dong Xing, 2011, IND CONTROL COMPUTER, V24, P66 Golub G. H., 2001, MATRIX COMPUTATION, V3rd LI Chun-sheng, 2010, SCI TECHNOLOGY ENG, V10, P8164 [ęŽé«˜ę˜Ž Li Gaoming], 2011, [ę•°å­¦ēš„å®žč·µäøŽč®¤čƆ, Mathematics in Practice and Theory], V41, P212 Li Qing-hua, 2005, Mini-Micro Systems, V26 Li Yu-jian, 2008, Journal of Beijing University of Technology, V34 Ma Hui-nan, 2008, Journal of Harbin Institute of Technology, V40 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 9 TC 0 Z9 0 PU HONG KONG EDUCATION SOC PI TSEUNG KWAN O PA FLAT 16E BLOCK 3 THE PINNACLE, 8 WANG HANG RD, TSEUNG KWAN O, NEW TERRITORIES 00000, PEOPLES R CHINA BN 978-988-19750-2-7 PY 2012 BP 90 EP 94 PG 5 WC Computer Science, Interdisciplinary Applications; Engineering, Civil SC Computer Science; Engineering GA BCL43 UT WOS:000310556100016 ER PT J AU Yang, B Sun, JH AF Yang, Bo Sun, Jian-hong GP IEEE TI Information Extraction from BBS Data for Enhancing the Service Quality and Efficiency of University SO 2012 7TH INTERNATIONAL CONFERENCE ON SYSTEM OF SYSTEMS ENGINEERING (SOSE) LA English DT Proceedings Paper CT 7th International Conference on System of Systems Engineering (SoSE) CY JUL 16-19, 2012 CL Genoa, ITALY SP IEEE Syst, Man, & Cybernet Soc (SMC), IEEE Reliabil Soc, Univ Genoa, Dept Commun Comp & Syst Sci (dist), ACE, MABL DE Information extraction; Chinese Automatic Word Segmentation; text classification; Find-Rate; Feature Selection AB For enhancing the service quality and efficiency of university, a feedback extraction method from BBS is employed. For reduce workload of data analysis, some commonly used technology of web data capture and Chinese automatic word-segment were employed in the process of data. F or classification the data, a classification algorithm based on Find-Rate of feature words is presented. The results of experiment show that it works well in this task. C1 [Yang, Bo; Sun, Jian-hong] Honghe Univ, Coll Engn, Mengzi, Yunnan, Peoples R China. RP Yang, B (reprint author), Honghe Univ, Coll Engn, Mengzi, Yunnan, Peoples R China. EM piao_snow_2008@163.com; sparkhonghe@gmail.com CR Boser B, 1992, COLT 92, V5, P144 Feng D., 2006, IUI 2006, P171 Gao Cong, SIGIR 2008, P467 Glance N., 2005, P 11 ACM SIGKDD INT Kim Jihie, 2007, P AAAI 2007 SPRING S Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Lin Sun, 2010, FUZZ SYST KNOWL DISC, P2422 Raghavan Preethi, 2010, 16 INT C MAN DAT COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sondhi Parikshit, 2010, 23 INT C COMP LING B, P1158 Wang BX, 2009, IEEE SYS MAN CYBERN, P1159, DOI 10.1109/ICSMC.2009.5345956 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4673-2975-0 PY 2012 BP 801 EP 804 PG 4 WC Engineering, Electrical & Electronic SC Engineering GA BCK14 UT WOS:000310375800224 ER PT J AU Kitto, K Bruza, P Gabora, L AF Kitto, Kirsty Bruza, Peter Gabora, Liane GP IEEE TI A Quantum Information Retrieval Approach to Memory SO 2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI) CY JUN 10-15, 2012 CL Brisbane, AUSTRALIA SP IEEE ID REPRESENTATION; CONTEXT; SPACE AB As computers approach the physical limits of information storable in memory, new methods will be needed to further improve information storage and retrieval. We propose a quantum inspired vector based approach, which offers a contextually dependent mapping from the subsymbolic to the symbolic representations of information. If implemented computationally, this approach would provide exceptionally high density of information storage, without the traditionally required physical increase in storage capacity. The approach is inspired by the structure of human memory and incorporates elements of Gardenfors' Conceptual Space approach and Humphreys et al.'s matrix model of memory. C1 [Kitto, Kirsty; Bruza, Peter] Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia. RP Kitto, K (reprint author), Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia. EM kirsty.kitto@qut.edu.au; p.bruza@qut.edu.au; liane.gabora@ubc.ca CR Aerts D., 2005, KYBERNETES, V34, P151, DOI DOI 10.1108/03684920510699972 Aerts D, 2005, KYBERNETES, V34, P192, DOI 10.1108/03684920510575807 Aerts D, 2011, NEW IDEAS PSYCHOL, V29, P136, DOI 10.1016/j.newideapsych.2010.06.002 Aerts S, 2011, LECT NOTES COMPUT SC, V7052, P13, DOI 10.1007/978-3-642-24971-6_3 Brezillon P, 1999, KNOWL ENG REV, V14, P47, DOI 10.1017/S0269888999141018 Bruza P., 2007, HDB QUANTUM LOGIC QU, V2 Bruza P., NONDECOMPOS IN PRESS Bruza P. D., 2005, WE WILL SHOW THEM ES, V1, P339 Churchland P. S., 1992, COMPUTATIONAL BRAIN Edelman G., 1993, BRIGHT AIR BRILLIANT Edelman G. M., 1989, NEURAL DARWINISM THE Feldman J., 1982, COGNITIVE SCI, V6, P204 Gabora L, 2010, CREATIVITY RES J, V22, P1, DOI 10.1080/10400410903579494 Gabora L., 2001, THESIS VRIJE U BRUSS Gardenfors P., 2000, CONCEPTUAL SPACES GE Guha R, 2003, LECT NOTES ARTIF INT, V2680, P164 Hebb D. O., 1949, ORG BEHAV Hopfield J. J., 1983, NATURE, V304, P159 HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554 HUBEL DH, 1965, J NEUROPHYSIOL, V28, P229 HUMPHREYS MS, 1989, PSYCHOL REV, V96, P208, DOI 10.1037/0033-295X.96.2.208 Isham C., 1995, LECT QUANTUM THEORY Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kitto K., 2012, P COGN SCI IN PRESS Kitto K, 2011, AXIOMATHES, V21, P331, DOI 10.1007/s10516-010-9144-6 Kitto K., 2010, P AAAI FALL S QUANT Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lee DD, 1999, NATURE, V401, P788 Loftus E. F., 1980, MEMORY SURPRISING NE MARR D, 1969, J PHYSIOL-LONDON, V202, P437 McClelland J. L., 1986, PARALLEL DISTRIBUTED, V2 Melucci M., 2008, ACM T INFORM SYSTEMS, V26, P1 Mikkulainen, 1997, NATURAL LANGUAGE PRO Paterson HM, 2009, PSYCHIAT PSYCHOL LAW, V16, pS112, DOI 10.1080/13218710802620380 Ranjan A., NEUROSCIENCE CREATIV Roy A., 2011, NATURAL INTELLIGENCE, V1, P23 Saab A., 2011, P ANN M COGN SCI SOC, P3506 Sahlgren M., 2008, P 30 ANN M COGN SCI, P11 Sahlgren M, 2005, P METH APPL SEM IND SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schacter D., 2001, 7 SINS MEMORY MIND F Schutze H, 1998, COMPUT LINGUIST, V24, P97 SMOLENSKY P, 1988, BEHAV BRAIN SCI, V11, P1 Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 TVERSKY A, 1982, PSYCHOL REV, V89, P123, DOI 10.1037/0033-295X.89.2.123 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Widdows Dominic, 2004, GEOMETRY AND MEANING Wiles J., 1994, ARTIF INTELL, P145 NR 48 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4673-1490-9 J9 IEEE IJCNN PY 2012 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BCA06 UT WOS:000309341300130 ER PT J AU Liu, ZM Liu, L Li, H AF Liu, Zhiming Liu, Lu Li, Hong TI Determinants of information retweeting in microblogging SO INTERNET RESEARCH LA English DT Article DE Microblogging; Information retweeting; Emergency event; Heuristic-Systematic Model (HSM); Sentiment mining; Social media; Blogs; China ID WORD-OF-MOUTH; SOURCE CREDIBILITY; COMMUNITIES; PERSUASION; ADOPTION; TWITTER AB Purpose - The purpose of this paper is to propose a conceptual model to investigate the determinants of information retweeting in microblogging based on Heuristic-Systematic Model. Design/methodology/approach - Microblogging data about emergency events from Sina microblogging (http://weibo.com) are collected and analyzed with text mining technology. The proposed hypotheses are tested with logistic and multiple linear regressions. Findings - The results show that source trustworthiness, source expertise, source attractiveness, and the number of multimedia have significant effects on the information retweeting. In addition, source expertise moderates the effects of user trustworthiness and content objectivity on the information retweeting in microblogging. Practical implications - This study provides an in-depth understanding of what makes information about emergency events in microblogging diffuse so rapidly. Based on these findings the emergency management organizations in China can apply the microblogging to spread useful information, and these findings also provide practical implications for microblogging system designers. Originality/value - The primary value of this paper lies in providing a better understanding of information retweeting in microblogging based on Heuristic-Systematic Model. Organizations that would like to adopt the microblogging platform in emergency situations to improve the ability of emergency response can benefit from the findings of this study. C1 [Liu, Zhiming; Liu, Lu; Li, Hong] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China. RP Liu, ZM (reprint author), Beihang Univ, Sch Econ & Management, Beijing, Peoples R China. EM wylzm@126.com FU National Natural Science Foundation of China [90924020]; PhD Program Foundation of Education Ministry of China [200800060005] FX The research is supported by the National Natural Science Foundation of China under Grant No. 90924020 and the PhD Program Foundation of Education Ministry of China under Contract No. 200800060005. CR Ajzen I., 1992, PERSUASIVE COMMUNICA Alden DL, 2000, J ADVERTISING, V29, P1 Ali Y., 2010, SOCIAL MEDIA WORKS D Andranik T., 2010, WORLD J INT LINGUIST, V280, P175 Aron C., 2010, P KDD WORKSH SOC MED Bongwon S., 2010, IEEE INT C PRIV SEC, P177 Brendan O'Connor, 2010, P INT AAAI C WEBL SO Bristor J.M., 1990, RES CONSUMER BEHAV, V4, P51 Chae M., 2002, ELECTRON MARK, V12, P38, DOI 10.1080/101967802753433254 CHAIKEN S, 1980, J PERS SOC PSYCHOL, V39, P752, DOI 10.1037//0022-3514.39.5.752 CHAIKEN S, 1989, P212 Chen C.C., 2010, DECIS SUPPORT SYST, V50, P755 Chen Colin, 2002, P 27 ANN SAS US GROU Chen S, 1999, PSYCHOL INQ, V10, P44, DOI 10.1207/s15327965pli1001_6 Cheung CMK, 2008, INTERNET RES, V18, P229, DOI 10.1108/10662240810883290 Dan Zarrella, 2009, SCI RETWEETS Eagly A. H., 1993, PSYCHOL ATTITUDE EAGLY AH, 1975, J PERS SOC PSYCHOL, V32, P136, DOI 10.1037/h0076850 Eisend M, 2006, INT J RES MARK, V23, P187, DOI 10.1016/j.ijresmar.2005.11.001 Fan RE, 2008, J MACH LEARN RES, V9, P1871 Frewer L. J., 1998, Agriculture and Human Values, V15, P15, DOI 10.1023/A:1007465730039 Haewoon Kwak, 2010, P 19 INT C WORLD WID, P591 Hansen L.K., 2011, COMPUTING RES REPOSI, Vabs/1101.0, P1 HERR PM, 1991, J CONSUM RES, V17, P454, DOI 10.1086/208570 Hovland C. I., 1953, COMMUNICATION PERSUA Hovland CI, 1951, PUBLIC OPIN QUART, V15, P635, DOI 10.1086/266350 Hughes A.L., 2009, P 6 INT ISCRAM C MAY Hunt J.M., 1987, J ACAD MARKET SCI, V15, P11, DOI 10.1007/BF02721949 Jansen BJ, 2009, J AM SOC INF SCI TEC, V60, P2169, DOI 10.1002/asi.21149 Java A., 2007, P 9 WEKKDD 1 SNA KDD Jiang Y., 2010, INT C WEBL SOC MED, P355 Johan B., 2011, J COMPUTATIONAL SCI, Vabs/1010.3, P1 JOHNSON EJ, 1985, MANAGE SCI, V31, P395, DOI 10.1287/mnsc.31.4.395 Joshua R., 2009, FORECAST, V809996, P1 Kate S., 2010, P 7 INT ISCRAM C SEA Kate S., 2010, P ACM 2010 C COMP SU, P241 Katz E., 1955, PERSONAL INFLUENCE P Man Y.C., 2009, INT J ELECTRON COMM, V13, P9 Marc C.V., 2010, INFORM SYSTEMS FRONT, V13, P1 Mcleod J.M., 1996, MEDIA EFFECTS ADV TH, P126 Mcleod J.M., 1981, MASS COMMUNICATION R, P245 Meeyoung C., 2010, 4 INT C WEBL SOC MED Ostrow A., 2009, SWINE FLU HYSTERIA 1 Pang B., 2002, P C EMP METH NAT LAN, P8 Petty R. E., 1986, COMMUNICATION PERSUA Petty Richard E, 1986, ADV EXPT SOCIAL PSYC, V19, P123, DOI DOI 10.1016/S0065-2601(08)60214-2 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarah V., 2010, CHI2010 ATL GA, P1079 Scanfeld D, 2010, AM J INFECT CONTROL, V38, P182, DOI 10.1016/j.ajic.2009.11.004 Shamma David A., 2009, P 1 SIGMM WORKSH SOC Sitaram A., 2010, 2010 IEEE WIC ACM IN, P492 Songbo Tan, 2008, Expert Systems With Applications, V34, DOI 10.1016/j.eswa.2007.05.028 STIGLER GJ, 1961, J POLIT ECON, V69, P213, DOI 10.1086/258464 Sutton J., 2008, P 5 INT C INF SYST C Takeshi S., 2010, WWW2010 RAL NC 26 AP Thomas H., 2010, P 7 INT ISCRAM C MAY Thorsten J, 1998, P 10 EUR C MACH LEAR, P137 Timm O.S., 2010, TWEETS TRADES INFORM Todorov A., 2002, PERSUASION HDB DEV T, P195 Vadileios L., 2010, 2 IAPR WORKSH COGN I, P411 Wang R. Y., 1996, Journal of Management Information Systems, V12 WHITEHEAD JL, 1968, Q J SPEECH, V54, P59 Wilson Elizabeth J., 1993, J ACAD MARKET SCI, V21, P101, DOI DOI 10.1007/BF02894421 Yaffee R.A., 2002, ROBUST REGRESSION AN Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Ye SZ, 2010, LECT NOTES COMPUT SC, V6430, P216 [å¼ é‡‘ę³¢ Zhang Jinbo], 2011, [作ē‰©ę‚åæ—, Crops], P10 Zhang W, 2008, J ASSOC INF SYST, V9, P73 Zhu X., 2000, P 23 ANN INT ACM SIG, P8 NR 69 TC 12 Z9 12 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 1066-2243 J9 INTERNET RES JI Internet Res. PY 2012 VL 22 IS 4 BP 443 EP 466 DI 10.1108/10662241211250980 PG 24 WC Business; Computer Science, Information Systems; Telecommunications SC Business & Economics; Computer Science; Telecommunications GA 022HP UT WOS:000309949000003 ER PT J AU Montalvo, S Pardo, EG Martinez, R Fresno, V AF Montalvo, Soto Pardo, Eduardo G. Martinez, Raquel Fresno, Victor GP IEEE TI Automatic Cognate Identification based on a Fuzzy Combination of String Similarity Measures SO 2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) SE IEEE International Conference on Fuzzy Systems LA English DT Proceedings Paper CT IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI) CY JUN 10-15, 2012 CL Brisbane, AUSTRALIA SP IEEE ID LOGIC AB Cognates are words in different languages that have similar spelling and meaning. The identification of cognates is very useful for many different Natural Language Processing tasks, and also in the process of learning a second language. This paper presents a new approach to classify pairs of words into cognates/false friends or not related classes. The proposed approach uses a fuzzy system to combine complementary string similarity measures in order to improve the cognate identification task. The underlying hypothesis is that the combination of different string measures by applying heuristic knowledge, can outperform those measures working separately. The results obtained by the proposed system confirm the previous hypothesis, and furthermore it also outperforms other systems that combine string measures by using a supervised approach. As an additional contribution, we have created a bilingual test data set which include pairs of cognates, false friends and unrelated words in Spanish and English, that is freely available for research purposes. C1 [Montalvo, Soto; Pardo, Eduardo G.] Univ Rey Juan Carlos, Madrid, Spain. RP Montalvo, S (reprint author), Univ Rey Juan Carlos, Madrid, Spain. EM soto.montalvo@urjc.es; eduardo.pardo@urjc.es; raquel@lsi.uned.es; vfresno@lsi.uned.es CR ADAMSON GW, 1974, INFORM STORAGE RET, V10, P253, DOI 10.1016/0020-0271(74)90020-5 Al-Onaizan Y., 1999, STAT MACHINE TRANSLA Bergsma S., 2007, P INT WORKSH ACQ MAN, P11 Bilenko M., 2003, P IJCAI 2003 WORKSH, P67 Brew Chris, 1996, P 2 INT C NEW METH L, P45 Camacho H., 2006, RES COMPUTER SCI NUM, V19, P171 COCHRAN WG, 1950, BIOMETRIKA, V37, P256, DOI 10.2307/2332378 Cohen W.W., 2003, P IJCAI 03 WORKSH IN, P73 Derrac J, 2011, SWARM EVOL COMPUT, V1, P3, DOI 10.1016/j.swevo.2011.02.002 Frunza O., 2009, INT J LINGUISTICS, V1 Gass S. M., 1987, STUDIES 2 LANGUAGE A, V9 Gomes L., 2011, EPIA 11, P624 Guy J. B. M., 1984, ACL 84, P448 Hall D., 2010, ACL 10, P1030 Hall Mark, 2009, SIGKDD EXPLORATIONS, V11 Hansen B. K., 2000, P 2 C ART INT AM MET Inkpen Diana, 2005, P INT C REC ADV NAT, P251 Isermann R, 1998, IEEE T SYST MAN CY A, V28, P221, DOI 10.1109/3468.661149 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Koehn P., 2000, P 17 NAT C ART INT 1 Koehn Philipp, 2002, P ACL 02 WORKSH UNS, P9 Kondrak, 2009, TRAITEMENT AUTOMATIQ, V50, P201 Kondrak G., 2004, COLING 04 Kondrak G, 2004, LECT NOTES ARTIF INT, V3060, P44 Kondrak G., 2001, NAACL 2001, P1 Kondrak G, 2005, LECT NOTES COMPUT SC, V3772, P115 Kondrak Grzegorz, 2003, P HLT NAACL 2003 HUM, P46 Leblanc R., 1989, ENSEIGNEMENT LANGUES Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Makin R., 2007, P INT WORKSH CROSS L MAMDANI EH, 1977, IEEE T COMPUT, V26, P1182 Mann G. S., 2001, NAACL 01, P1 Martinez R., 1998, P 6 WORKSH VER LARG, P102 Mccallum A., 2005, C UNC AI UAI McNemar Q, 1947, PSYCHOMETRIKA, V12, P153, DOI 10.1007/BF02295996 Melamed D.I., 1999, COMPUTATIONAL LINGUI, V25, P107 Monge A. E., 1997, P WORKSH DAT MIN KNO Montalvo S, 2007, PATTERN RECOGN LETT, V28, P2305, DOI 10.1016/j.patrec.2007.07.011 Piantadosi S., 2011, P NATL ACAD SCI Ringbom H., 1987, ROLE 1 LANGUAGE FORE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schulz S., 2004, COLING 04 Simard Michel, 1992, P 4 INT C THEOR METH Tufis D., 2002, P 19 INT C COMP LING, V1, P1, DOI 10.3115/1072228.1072230 Zadeh L., 1975, CONCEPT LINGUISTIC V, V8 Zipf G., 1936, PSYCHOBIOLOGY LANGUA NR 46 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7584 BN 978-1-4673-1506-7 J9 IEEE INT CONF FUZZY PY 2012 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BBZ47 UT WOS:000309188200037 ER PT B AU Li, R He, KQ Chen, HF Liang, P AF Li, Rong He, Keqing Chen, Huafeng Liang, Peng GP ASME TI ONTOLOGY-BASED PROCESS DESCRIPTION AND REASONING AT MULTIPLE LEVELS OF ABSTRACTION FOR SERVICE DISCOVERY SO 2011 3RD INTERNATIONAL CONFERENCE ON COMPUTER TECHNOLOGY AND DEVELOPMENT (ICCTD 2011), VOL 2 LA English DT Proceedings Paper CT 3rd International Conference on Computer Technology and Development (ICCTD 2011) CY NOV 25-27, 2011 CL Chengdu, PEOPLES R CHINA DE Process description; Service discovery; Ontology; Multiple levels of abstraction ID VECTOR-SPACE MODEL AB Discovering proper web services to satisfy user requirements is the key issue for service reuse. A main obstacle in service reuse is the different levels of abstraction between requirements and service descriptions. In this paper, we simplify requirements by regarding process as a type of requirements and focus on the issue of matching process into services. We propose an ontology-based method to organize processes according to their functions, and reasoning rules are defined basing on the ontology. In this method, a service can be determined whether it can satisfy a process by the vector space model and cosine similarity of function descriptions, and reasoning rules are employed to discover services which have no relationships with the process literally. This method provides more efficient services discovery for the implementation of process. C1 [Li, Rong; He, Keqing; Chen, Huafeng; Liang, Peng] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China. RP Li, R (reprint author), Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China. EM lirongwow@yahoo.com.cn CR Adam S., 2008, COMP SOFTW APPL 2008, P631 KRITIKOS K, 2007, 31 ANN INT COMP SOFT, P467, DOI 10.1109/COMPSAC.2007.181 Liu GZ, 1997, J AM SOC INFORM SCI, V48, P395 Liu XZ, 2009, IEEE T SERV COMPUT, V2, P167, DOI 10.1109/TSC.2009.11 Papazoglou MP, 2007, COMPUTER, V40, P38, DOI 10.1109/MC.2007.400 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang J, 2008, P 3 INT WORKSH ADV A, P155 Zhang LJ, 2008, IBM SYST J, V47, P397 NR 8 TC 0 Z9 0 PU AMER SOC MECHANICAL ENGINEERS PI NEW YORK PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA BN 978-0-7918-5991-9 PY 2012 BP 519 EP 524 PG 6 WC Computer Science, Interdisciplinary Applications SC Computer Science GA BBN38 UT WOS:000307481500085 ER PT S AU Handaga, B Deris, MM AF Handaga, Bana Deris, Mustafa Mat BE Murgante, B Gervasi, O Misra, S Nedjah, N Rocha, AMAC Taniar, D Apduhan, BO TI Text Categorization Based on Fuzzy Soft Set Theory SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2012, PT IV SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th International Conference on Computational Science and Its Applications (ICCSA) CY JUN 18-21, 2012 CL Salvador de Bahia, BRAZIL SP Univ Fed Bahia (UFBA), Univ Fed Reconcavo Bahia (UFRB), Univ Estadual Feira Santana (UEFS), Univ Perugia, Univ Basilicata (UB), Monash Univ, Kyushu Sangyo Univ (KSU), Brazilian Comp Soc, Coordenacao Aperfeicoamento Pessoal Nivel Super (CAPES), Natl Council Sci & Technol Dev (CNPq), Fundacao Amparo Pesquisa Estado Bahia (FAPESB), Bahia Governo, Secretaria Ciencia, Tecnologia & Inovacao DE Fuzzy soft set theory; bag-of-words; Text Classification ID DECISION-MAKING; CLASSIFICATION; ALGORITHM AB In this paper, we proposed a new method for Text Categorization based on fuzzy soft set theory so called fuzzy soft set classifier (FSSC). We use fuzzy soft set representation that derived from the bag-of-words representation and define each term as a distinct word in the set of words of the document collection. The FSSC categorize each document by using fuzzy c-means formula for classification, and use fuzzy soft set similarity to measure distance between two documents. We perform the experiments with the standard Reuters-21578 dataset, and using three kind of weigthing such as boolean, term frequency, and term frequency-invert document frequency to compare the performance of FSSC with others four classifier such as kNN, Bayesian, Rocchio, and SVM. We are using precision, recall, F-measure, retun-size, and the running time as a performance evaluation. Result shown that there is no absolute winner. The FSSC has precision, recall, and F-measure lower than SVM, and kNN but FSSC can work faster than both. When compared with the Bayesian and Rocchio, the FSSC works more slowly but has a higher precision and F-measure. C1 [Handaga, Bana; Deris, Mustafa Mat] Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Parit Raja, Malaysia. RP Handaga, B (reprint author), Univ Tun Hussein Onn Malaysia, Fac Comp Sci & Informat Technol, Parit Raja, Malaysia. EM handaga.bana@gmail.com; mmustafa@uthm.edu.my RI Mat Deris, Mustafa/D-4662-2013 CR BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 Duan QG, 2007, LECT NOTES ARTIF INT, V4482, P240 Feng F, 2010, J COMPUT APPL MATH, V234, P10, DOI 10.1016/j.cam.2009.11.055 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Joachims T, TEXT CATEGORIZATION, P137 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Lewis D. D., 2011, REUTERS 21578 DOCUME Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Maji P. K., 2001, J FUZZY MATH, V9, P589 Maji PK, 2003, COMPUT MATH APPL, V45, P555, DOI 10.1016/S0898-1221(03)00016-6 Majumdar P, 2010, COMPUT MATH APPL, V59, P1425, DOI 10.1016/j.camwa.2009.12.006 Manning C.D., 2008, INTRO INFORM RETRIEV McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Molodtsov D, 1999, COMPUT MATH APPL, V37, P19, DOI 10.1016/S0898-1221(99)00056-5 Mushrif MM, 2006, LECT NOTES COMPUT SC, V3851, P246 Ozgur A, 2005, LECT NOTES COMPUT SC, V3733, P606 Ozgur L, 2010, PATTERN RECOGN LETT, V31, P1598, DOI 10.1016/j.patrec.2010.05.005 Ozgur L, 2004, PATTERN RECOGN LETT, V25, P1819, DOI 10.1016/j.patrec.2004.07.004 Porter MF, 1997, ALGORITHM SUFFIX STR, P313 Roy AR, 2007, J COMPUT APPL MATH, V203, P412, DOI 10.1016/j.cam.2006.04.008 Salton G., TERM WEIGHTING APPRO, P513 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Szmidt E, 2008, LECT NOTES ARTIF INT, V5097, P351, DOI 10.1007/978-3-540-69731-2_35 Widyantoro D. H., FUZZY SIMILARITY APP, V2, P653 Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 Yin S., WEB TEXT FUZZY CLASS, P1114 NR 27 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-31127-7 J9 LECT NOTES COMPUT SC PY 2012 VL 7336 BP 340 EP 352 PG 13 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BBU89 UT WOS:000308289700025 ER PT J AU Zhang, H Wang, DQ Wu, WJ Hu, HP AF Zhang, Hui Wang, Deqing Wu, Wenjun Hu, Hongping TI Term frequency - function of document frequency: a new term weighting scheme for enterprise information retrieval SO ENTERPRISE INFORMATION SYSTEMS LA English DT Article DE enterprise information retrieval; term weighting scheme; term frequency; function of document frequency; relevance ranking ID MANAGEMENT; SYSTEMS; ARCHITECTURE; PERFORMANCE; INTEGRATION; FRAMEWORK AB In today's business environment, enterprises are increasingly under pressure to process the vast amount of data produced everyday within enterprises. One method is to focus on the business intelligence (BI) applications and increasing the commercial added-value through such business analytics activities. Term weighting scheme, which has been used to convert the documents as vectors in the term space, is a vital task in enterprise Information Retrieval (IR), text categorisation, text analytics, etc. When determining term weight in a document, the traditional TF-IDF scheme sets weight value for the term considering only its occurrence frequency within the document and in the entire set of documents, which leads to some meaningful terms that cannot get the appropriate weight. In this article, we propose a new term weighting scheme called Term Frequency Function of Document Frequency (TF-FDF) to address this issue. Instead of using monotonically decreasing function such as Inverse Document Frequency, FDF presents a convex function that dynamically adjusts weights according to the significance of the words in a document set. This function can be manually tuned based on the distribution of the most meaningful words which semantically represent the document set. Our experiments show that the TF-FDF can achieve higher value of Normalised Discounted Cumulative Gain in IR than that of TF-IDF and its variants, and improving the accuracy of relevance ranking of the IR results. C1 [Zhang, Hui; Wang, Deqing; Wu, Wenjun; Hu, Hongping] Beihang Univ, Sch Comp Sci, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China. RP Zhang, H (reprint author), Beihang Univ, Sch Comp Sci, State Key Lab Software Dev Environm, Beijing 100191, Peoples R China. EM hzhang@nlsde.buaa.edu.cn RI Wang, Deqing/E-4845-2013 FU State Key Laboratory of Software Development Environment, Beihang University [SKLSDE-2009ZX-12]; Chinese Ministry of Education; National Natural Science Foundation of China [70971005]; Key Project of The National Social Science Fund [11AZD096] FX This work is supported by a grant from the State Key Laboratory of Software Development Environment, Beihang University (No. SKLSDE-2009ZX-12), the Changjiang Scholar Program, Chinese Ministry of Education, National Natural Science Foundation of China 70971005 and Key Project of The National Social Science Fund Grant No. 11AZD096. CR Aldin L, 2011, ENTERP INF SYST-UK, V5, P359, DOI 10.1080/17517575.2011.557443 Beheshti HM, 2010, ENTERP INF SYST-UK, V4, P445, DOI 10.1080/17517575.2010.511276 Buckley C., 1996, NIST PUBLICATION, P25 Chiang DMH, 2011, ENTERP INF SYST-UK, V5, P219, DOI 10.1080/17517575.2010.537784 Duan L, 2009, ANN OPER RES, V168, P151, DOI 10.1007/s10479-008-0371-9 Duan L, 2011, ENTERP INF SYST-UK, V5, P169, DOI 10.1080/17517575.2010.541287 Duan L, 2007, INFORM SYST, V32, P978, DOI 10.1016/j.is.2006.10.006 Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 Feng S, 2001, KNOWL-BASED SYST, V14, P225, DOI 10.1016/S0950-7051(01)00100-9 Gong ZG, 2010, ENTERP INFORM SYST, V4, P1, DOI 10.1080/17517570903502856 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jeong OR, 2009, EXPERT SYST APPL, V36, P11204, DOI 10.1016/j.eswa.2009.02.074 Jiang, 2009, SYSTEMS RES BEHAV SC, V26, P661 Jin R., 2001, P 24 ACM INT C RES D, P83, DOI 10.1145/383952.383964 Li L, 2007, INFORM SYST, V32, P941, DOI 10.1016/j.is.2006.10.001 Li LLX, 1999, EXPERT SYST APPL, V16, P33 Narman P, 2011, ENTERP INF SYST-UK, V5, P37, DOI 10.1080/17517575.2010.507878 Pantel P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Perez-Castillo R, 2011, ENTERP INF SYST-UK, V5, P301, DOI 10.1080/17517575.2011.587545 Reed J., 2006, P MACH LEARN APPL IC, P258 Ricardo B.Y., 1999, MODERN INFORM RETRIE Roelleke T, 2003, P 26 ANN INT ACM SIG, P227 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi ZZ, 2007, DECIS SUPPORT SYST, V42, P2016, DOI 10.1016/j.dss.2004.11.006 Tokunga T, 1994, TR0001 TOK I TECHN D Wang K, 2010, ENTERP INF SYST-UK, V4, P225, DOI 10.1080/17517575.2010.498525 Wang L, 2009, SYST RES BEHAV SCI, V26, P357, DOI 10.1002/sres.973 Wang L, 2011, INFORM TECHNOL MANAG, V12, P111, DOI 10.1007/s10799-011-0092-7 Wetzstein B, 2011, ENTERP INF SYST-UK, V5, P79, DOI 10.1080/17517575.2010.493956 Xu E, 2011, ENTERP INF SYST-UK, V5, P385, DOI 10.1080/17517575.2011.566630 Xu LD, 2011, INT J PROD RES, V49, P183, DOI 10.1080/00207543.2010.508944 Xu LD, 2006, SYST RES BEHAV SCI, V23, P147, DOI 10.1002/sres.750 Xu LD, 2008, IEEE T SYST MAN CY C, V38, P590, DOI 10.1109/TSMCC.2007.913894 Xu LD, 2011, IEEE T IND INFORM, V7, P630, DOI 10.1109/TII.2011.2167156 Xu SB, 2011, INFORM TECHNOL MANAG, V12, P51, DOI 10.1007/s10799-011-0090-9 NR 36 TC 8 Z9 8 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 1751-7575 J9 ENTERP INF SYST-UK JI Enterp. Inf. Syst. PY 2012 VL 6 IS 4 BP 433 EP 444 DI 10.1080/17517575.2012.665945 PG 12 WC Computer Science, Information Systems SC Computer Science GA 990OP UT WOS:000307640700004 ER PT J AU Tsinakos, A Kazanidis, I AF Tsinakos, Avgoustos Kazanidis, Ioannis TI Identification of Conflicting Questions in the PARES System SO INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTANCE LEARNING LA English DT Article DE Computer adaptive testing; CAT; conflict detection algorithm AB Student testing and knowledge assessment is a significant aspect of the learning process. In a number of cases, it is expedient not to present the exact same test to all learners all the time (Pritchett, 1999). This may be desired so that cheating in the exam is made harder to carry out or so that the learners can take several practice tests on the same subject as part of the course. This study presents an e-testing platform, namely PARES, which aims to provide assessment services to academic staff by facilitating the creation and management of question banks and powering the delivery of nondeterministically generated test suites. PARES uses a conflict detection algorithm based on the vector space model to compute the similarity between questions and exclude questions which are deemed to have an unacceptably large similarity from appearing in the same test suite. The conflict detection algorithm and a statistical evaluation of its accuracy are presented. Evaluation results show that PARES succeeds in detecting question types at about 90% and its efficiency can be further increased through continuing education and enrichment of the system's correlation vocabulary. C1 [Tsinakos, Avgoustos; Kazanidis, Ioannis] Kavala Inst Technol, Thessaloniki, Greece. RP Tsinakos, A (reprint author), Kavala Inst Technol, Thessaloniki, Greece. CR Bilenko M, 2003, P 9 ACM SIGKDD INT C, P39, DOI [DOI 10.1145/956750.956759, 10.1145/956750.956759] Hage H., 2006, P IEEE INT C COMP SY, P980 IMS GLC, 2011, IMS QUEST TEST INT S Jiang H., 2009, P IEEE INT S INT UB, P436 Kaburlasos V. G., 2004, Proceedings. IEEE International Conference on Advanced Learning Technologies Kikusawa M., 2006, P WORLD C E LEARN CO, P661 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Plateau, 2011, PLAT TAL MAN SYST Pritchett N., 1999, COMPUTER ASSISTED AS, P29 Romero C., 2005, P 5 INT C HUM SYST L, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seet A.M., 2010, P 10 INT C ADV LEARN, P86 Triantafillou E, 2008, COMPUT EDUC, V50, P1319, DOI 10.1016/j.compedu.2006.12.005 NR 13 TC 0 Z9 0 PU ATHABASCA UNIV PRESS PI ATHABASCA PA 1 UNIVERSITY DR, ATHABASCA, AB T9S 3A3, CANADA SN 1492-3831 J9 INT REV RES OPEN DIS JI Int. Rev. Res. Open Distance Learn. PY 2012 VL 13 IS 3 BP 298 EP 314 PG 17 WC Education & Educational Research SC Education & Educational Research GA 984VU UT WOS:000307221300017 ER PT J AU Jimenez, S Gelbukh, A AF Jimenez, Sergio Gelbukh, Alexander TI BASELINES FOR NATURAL LANGUAGE PROCESSING TASKS BASED ON SOFT CARDINALITY SPECTRA SO APPLIED AND COMPUTATIONAL MATHEMATICS LA English DT Article DE Text Similarity; Soft Cardinality; SC Spectra; q-Grams; NLP Baselines; Entity Resolution; Information Retrieval; Paraphrase Recognition; Textual Entailment Recognition ID APPROXIMATION AB Soft-cardinality spectra (SC spectra) is a new method of approximation for text strings in linear time, which divides text strings into character q-grams of different sizes. The method allows simultaneous use of weighting at term and q-gram levels. SC spectra in combination with resemblance coefficients allows the construction of a family of text similarity functions that only use the surface information of the texts and weights obtained in the same text collection. These similarity measures can be used in various tasks of natural language processing as baseline for other methods that exploit the hidden syntactic and/or semantic structure using resources based on knowledge, inference of large corpora. The proposed method was evaluated in 30 data sets to address the tasks of information retrieval, entity matching, paraphrase and textual entailment recognition. The results raised the bar near to the best published results in the used data sets. We claim that any method that uses any resource or information external to a particular data set should outperform our method. We found that our method is an effective and challenging baseline for the evaluated tasks. C1 [Jimenez, Sergio] Univ Nacl Colombia, Bogota, Colombia. [Gelbukh, Alexander] Inst Politecn Nacl, CIC, Mexico City 07738, DF, Mexico. RP Jimenez, S (reprint author), Univ Nacl Colombia, Ciudad Univ,Ed 453,220, Bogota, Colombia. EM sgjimenez@unal.edu.co; gelbukh@gelbukh.com RI Gelbukh, Alexander/A-8979-2008 OI Gelbukh, Alexander/0000-0001-7845-9039 FU Systems and Industrial Engineering Department; Office of Student Wellfare of the National University of Colombia, Bogota; Colombian Department for Science, Technology and Innovation Colciencias [110152128465]; Mexican Government (SNI); Mexican Government (COFAA-IPN); Mexican Government (SIP) [20121823]; Mexican Government (CONACYT) [50206-H]; CONACYT-DST India [122030 2011-2014] FX This research was funded in part by the Systems and Industrial Engineering Department, the Office of Student Wellfare of the National University of Colombia, Bogota, and throught a grant from the Colombian Department for Science, Technology and Innovation Colciencias, project 110152128465. The second author acknowledges support from Mexican Government (SNI, COFAA-IPN, SIP 20121823, CONACYT 50206-H) and CONACYT-DST India project 122030 2011-2014 "Answer Validation through Textual Entailment". CR Alvaro E., 1996, P 2 INT C KNOWL DISC, P267 Androutsopoulos I, 2010, J ARTIF INTELL RES, V38, P135 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Batyrshin I, 2007, STUD COMPUT INTELL, V36, P55 Bilenko M, 2003, IEEE INTELL SYST, V18, P16, DOI 10.1109/MIS.2003.1234765 Chaudhuri S., 2003, P ACM SIGMOD INT C M, P313 Christen P., 2006, DAT MIN WORKSH INT C, P290 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 Cohen W.W., 2003, P IJCAI 03 WORKSH IN, P73 Corley Courtney, 2005, P ACL WORKSH EMP MOD, P13, DOI 10.3115/1631862.1631865 Das D., 2009, P JOINT C 47 ANN M A, V1, P468, DOI 10.3115/1687878.1687944 de la Higuera Colin, 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop (ICDE Workshop), DOI 10.1109/ICDEW.2008.4498345 DICE LR, 1945, ECOLOGY, V26, P297, DOI 10.2307/1932409 Dietterich TG, 2000, LECT NOTES COMPUT SC, V1857, P1 Do Quang Xuan, 2010, ROBUST LIGHTWEIGHT A Dolan B., 2004, P 20 INT C COMP LING Duma Susan, 2004, LATENT SEMANTIC ANAL Finch Andrew, 2005, P 3 INT WORKSH PAR J Giampiccolo D., 2007, P ACL PASCAL WORKSH, P1, DOI 10.3115/1654536.1654538 Gravano L., 2003, TEXT JOINS RDBMS WEB Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Jimenez S, 2010, LECT NOTES COMPUT SC, V6393, P297, DOI 10.1007/978-3-642-16321-0_31 Jimenez Sergio, 2012, P 6 INT WORKSH SEM E Vargas SJ, 2011, LECT NOTES ARTIF INT, V7095, P213 Keskustalo H, 2003, LECT NOTES COMPUT SC, V2857, P252 KUKICH K, 1992, ACM COMPUT SURV, V24, P377, DOI 10.1145/146370.146380 Ledeneva Yulia, 2010, Informatica, V34 Leslie C., 2001, BIOCOMPUTING 2002, P564, DOI 10.1142/9789812799623_0053 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 Lintean Mihai, 2009, P 22 INT FLAIRS C MALAKASIOTIS P., 2009, P ACL IJCNLP 2009 ST, P27, DOI 10.3115/1667884.1667889 Moreau Erwan, 2008, P 22 INT C COMP LING, V1, P593, DOI 10.3115/1599081.1599156 Pedersen Ted, 2004, P HLT NAACL DEM PAP Piskorski Jakub, 2008, USABILITY STRING DIS Porter M., 1980, PROGRAM, V3, P130 Qiu L., 2006, P 2006 C EMP METH NA, P18, DOI 10.3115/1610075.1610079 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarker BR, 1996, COMPUT IND ENG, V30, P103, DOI 10.1016/0360-8352(95)00024-0 Sidorov G., 2010, P 7 INT C LANG RES E, P277 ULLMANN JR, 1977, COMPUT J, V20, P141, DOI 10.1093/comjnl/20.2.141 Wan S., 2006, P AUSTR LANG TECHN W Westerman SJ, 2010, J AM SOC INF SCI TEC, V61, P1535, DOI 10.1002/asi.21361 Winkler W. E., 1999, STATE RECORD LINKAGE Zhang Y., 2005, P AUSTR LANG TECHN W NR 45 TC 1 Z9 2 PU AZERBAIJAN NATIONAL ACAD SCI PI BAKU PA BAKU STATE UNIV, INST APPLIED MATHEMATICS, Z KHALILOV STR 23, BAKU, AZ 1148, AZERBAIJAN SN 1683-3511 J9 APPL COMPUT MATH-BAK JI Appl. Comput. Math. PY 2012 VL 11 IS 2 SI SI BP 180 EP 199 PG 20 WC Mathematics, Applied SC Mathematics GA 970RI UT WOS:000306149600004 ER PT S AU Ziegler, CN AF Ziegler, Cai-Nicolas BA Ziegler, CN BF Ziegler, CN TI Mining for Strategic Competitive Intelligence Foundations and Applications Introduction SO MINING FOR STRATEGIC COMPETITIVE INTELLIGENCE: FOUNDATIONS AND APPLICATIONS SE Studies in Computational Intelligence LA English DT Editorial Material; Book Chapter ID SMALL-WORLD; RECOMMENDER SYSTEMS; COMPLEX NETWORKS; SPACE; TEXT; WEBFOUNTAIN; POINTS; WEB C1 Univ Freiburg, PAYBACK GmbH, Munich, Germany. RP Ziegler, CN (reprint author), Univ Freiburg, PAYBACK GmbH, Munich, Germany. CR Agrawal R., 2003, P 12 INT C WORLD WID, P529, DOI DOI 10.1145/775152.775227 Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Albert R, 2000, NATURE, V406, P378, DOI 10.1038/35019019 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barabasi AL, 2009, SCIENCE, V325, P412, DOI 10.1126/science.1173299 Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 Barabasi A.-L., 2003, Scientific American (International Edition), V288 Barabasi Albert-Laszlo, 2002, LINKED NEW SCI NETWO Berners-Lee T, 2001, SCI AM, V284, P34 Berry M. J. A., 2000, MASTERING DATA MININ Berry Michael J, 2004, DATA MINING TECHNIQU Boclendorf F., 2010, P THIRS INT C KNOWL, P24 Breese J S, 1998, P 14 C UNC ART INT, P43 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Budanitsky A., 2000, P WORKSH WORDNET OTH Chakrabarti S., 2002, MINING WEB DISCOVERI Cselle G., 2007, P 12 INT C INT US IN, P190, DOI 10.1145/1216295.1216331 Cunningham H., 2001, ACL 2002, P168 Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226 Domingos P., 2001, P 7 ACM SIGKDD INT C, P57, DOI 10.1145/502512.502525 Dunning T., 1993, Computational Linguistics, V19 Eiken UC, 2006, LECT NOTES ARTIF INT, V4139, P512 Erdos P, 1959, PUBL MATH-DEBRECEN, V6, P290 Ester M., 1996, P 2 INT C KNOWL DISC, P226 Esuli A., 2005, P 14 ACM INT C INF K, P617, DOI 10.1145/1099554.1099713 Ferrucci D., 2004, Natural Language Engineering, DOI 10.1017/S1351324904003523 Field A., 2009, DISCOVERING STAT USI Fox W.F.C., 2003, SIGIR FORUM, V37, P26 FREDKIN E, 1960, COMMUN ACM, V3, P490, DOI 10.1145/367390.367400 Gamon M, 2005, LECT NOTES COMPUT SC, V3646, P121 Gamon Michael, 2005, P ACL 2005 WORKSH FE Glance N., 2005, P 11 ACM SIGKDD INT, P419, DOI DOI 10.1145/1081870.1081919 Glance N., 2004, P WWW 2004 WORKSH WE Gruhl D, 2004, IBM SYST J, V43, P64 Guha R., 2004, 1 WORKSH FRIEND FRIE Han J., 2006, DATA MINING CONCEPTS Hatzivassiloglou Vasileios, 1997, P 35 ANN M ASS COMP, P174, DOI DOI 10.3115/976909.979640 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Holme P., 2002, PHYS REV E, V65 Kammergruber W., 2009, P INT C COMP ASP SOC Kamvar S. D., 2003, P 12 INT C WORLD WID, P640, DOI DOI 10.1145/775152.775242 Kennedy A, 2006, COMPUT INTELL-US, V22, P110, DOI 10.1111/j.1467-8640.2006.00277.x KENNEDY J, 1995, P IEEE INT C NEUR NE, P1942, DOI DOI 10.1109/ICNN.1995.488968 Kleinberg J., 2000, Proceedings of the Thirty Second Annual ACM Symposium on Theory of Computing, DOI 10.1145/335305.335325 Kleinberg JM, 2000, NATURE, V406, P845, DOI 10.1038/35022643 Kleinberg J.M., 2004, SIAM NEWS, V37 Kleinfeld JS, 2002, SOCIETY, V39, P61, DOI 10.1007/BF02717530 Knuth D. E., 1998, ART COMPUTER PROGRAM, V3 Kohonen T., 2001, SELF ORG MAPS, V3rd Konstan J., 2006, P 21 NAT AAAI C ART Konstan JA, 2004, ACM T INFORM SYST, V22, P1, DOI 10.1145/963770.963771 Kullback S., 1951, ANN MATH STAT, V22, P49 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 MacQueen JB, 1967, 5 BERK S MATH STAT P, P281 Manning C. D., 1999, FDN STAT NATURAL LAN Martinez-Arroyo M, 2006, INT C PATT RECOG, P1236 Menon A, 2004, LONG RANGE PLANN, V37, P153, DOI 10.1016/j.lrp.2004.01.008 Milgram Stanley, 1992, INDIVIDUAL SOCIAL WO, V2nd MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Morinaga S., 2002, P 8 ACM SIGKDD INT C, P341, DOI DOI 10.1145/775047.775098 Najork M., 2001, P 10 INT WORLD WID W, P114, DOI DOI 10.1145/371920.371965 Newman M., 2006, STRUCTURE DYNAMICS N Newman MEJ, 2003, SIAM REV, V45, P167, DOI 10.1137/S003614450342480 Nigam K., 2006, INFORM RETRIEVAL SER Nigam K, 2004, P AAAI SPRING S EXPL Page L., 1998, PAGE RANK CITATION R Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Pang B., 2004, P 42 ANN M ASS COMP, P271, DOI DOI 10.3115/1218955.1218990 Pearson K, 1901, PHILOS MAG, V2, P559 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QUINLAN R., 1996, J ARTIFICIAL INTELLI, V4, P77, DOI [10.1613/jair.279, DOI 10.1613/JAIR.279] Riloff E, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P105 Russell S. J., 2003, ARTIFICIAL INTELLIGE, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott J., 2000, SOCIAL NETWORK ANAL, V2nd Shaikh MAM, 2008, APPL ARTIF INTELL, V22, P558, DOI 10.1080/08839510802226801 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768 Shneiderman B, 2001, IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2001, PROCEEDINGS, P73, DOI 10.1109/INFVIS.2001.963283 Simons K, 2005, P 14 ACM INT C INF K, P381, DOI DOI 10.1145/1099554.1099672 Skubacz M., 2008, P IEEE WIC ACM INT C, P932 Spangler S, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P11 Spangler S., 2008, P 2008 IEEE WIC ACM, P258 Tang H., 2009, EXPERT SYSTEMS APPL, V36, P10 Tomokiyo T., 2003, WORKSH MULT EXPR ACL Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 van Rijsbergen K., 1975, INFORM RETRIEVAL Vapnik V., 1995, NATURE STAT LEARNING Viermetz M, 2008, I W ADV ISS E COMMER, P215, DOI 10.1109/CEC/EEE.2008.33 Wall M. E., 2003, PRACTICAL APPROACH M, P91 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Witten I, 2005, DATA MINING Yi J, 2003, P 3 IEEE INT C DAT M, P427 Zhai Y., 2005, P 14 INT C WORLD WID, P76, DOI 10.1145/1060745.1060761 Ziegler C.- N., 2006, P 2006 IEEE WIC ACM, P1066 Ziegler C. N., 2006, P 15 ACM INT C INF K, P465, DOI 10.1145/1183614.1183682 Ziegler C. N., 2004, P IEEE INT C E TECHN Ziegler Cai-Nicolas, 2004, P 2 ACM SIGIR SEM WE Ziegler C.N., 2006, IX MAGAZIN PROFESSIO, V18, P106 Ziegler C.N., 2006, IX MAGAZIN PROFESSIO, V8, P116 Ziegler C.N., 2009, P IEEE WIC ACM INT C, P707 Ziegler C.N., 2009, ENCY DATABASE SYSTEM, P1191 Ziegler CN, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P242, DOI 10.1109/WI.2007.137 Ziegler C.N., 2007, IX MAGAZIN PROFESSIO, V19, P132 Ziegler C.N., 2005, INFORM SYST IN PRESS Ziegler CN, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P548, DOI 10.1145/1571941.1572035 Ziegler C.N., 2006, IX MAGAZIN PROFESSIO, V18, P54 Ziegler C.-N., 2005, THESIS ALBERT LUDWIG NR 111 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-27713-9 J9 STUD COMPUT INTELL PY 2012 VL 406 BP 1 EP + D2 10.1007/978-3-642-27714-6 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BAD73 UT WOS:000303852500001 ER PT J AU Jouhet, V Defossez, G Burgun, A le Beux, P Levillain, P Ingrand, P Claveau, V AF Jouhet, V. Defossez, G. Burgun, A. le Beux, P. Levillain, P. Ingrand, P. Claveau, V. TI Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer SO METHODS OF INFORMATION IN MEDICINE LA English DT Article DE Medical Informatics; neoplasm; pathology; free text; automated classification ID MEDICAL INFORMATICS; HIGH AGREEMENT; LOW KAPPA; RETRIEVAL; BIOINFORMATICS; PARADOXES AB Objective: Our study aimed to construct and evaluate functions called "classifiers", produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content. Methods: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-03 code. The ability to correctly attribute a precise ICD-03 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures. Results: 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-03 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology. Conclusion: These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports. C1 [Jouhet, V.; Defossez, G.; Ingrand, P.] Univ Poitiers, Ctr Hosp Univ Poitiers, Fac Med, Unite Epiderniol Biostat & Registre Canc Poitou C, F-86034 Poitiers, France. [Burgun, A.; le Beux, P.] Univ Rennes 1, Fac Med, INSERM, U936, Rennes, France. [Levillain, P.] Univ Poitiers, Fac Med, Ctr Regrp Informat & Stat Anatomopathol Poitou Ch, F-86034 Poitiers, France. [Ingrand, P.] INSERM, CIC 802, Poitiers, France. [Claveau, V.] IRISA, CNRS, UMR 6074, Rennes, France. RP Jouhet, V (reprint author), Univ Poitiers, Ctr Hosp Univ Poitiers, Fac Med, Unite Epiderniol Biostat & Registre Canc Poitou C, 6 Rue Miletrie,BP 199, F-86034 Poitiers, France. EM vianney.jouhet@gmail.com CR Aronson A R, 2001, Proc AMIA Symp, P17 Aronson AR, 2004, ST HEAL T, V107, P268 Belot A, 2008, REV EPIDEMIOL SANTE, V56, P159, DOI 10.1016/j.respe.2008.03.117 Berg JW, 1996, CANC EPIDEMIOLOGY PR Boser BE, 1992, TRAINING ALGORITHM O, P144 Buemi A., 2008, PATHOLOGY TUMOURS CA Carrell David, 2007, AMIA Annu Symp Proc, P889 CICCHETTI DV, 1990, J CLIN EPIDEMIOL, V43, P551, DOI 10.1016/0895-4356(90)90159-M Clech J, 2003, 35 JOURN STAT JUIN, P933 Coden A, 2009, J BIOMED INFORM, V42, P937, DOI 10.1016/j.jbi.2008.12.005 Crowley RS, 2010, J AM MED INFORM ASSN, V17, P253, DOI 10.1136/jamia.2009.002295 Curado M, 2004, INT RULES MULTIPLE P FEINSTEIN AR, 1990, J CLIN EPIDEMIOL, V43, P543, DOI 10.1016/0895-4356(90)90158-L Fleiss JL, 1981, STAT METHODS RATES P Friedman C, 2004, J AM MED INFORM ASSN, V11, P392, DOI 10.1197/jamia.M1552 Garcia-Remesal M, 2010, METHOD INFORM MED, V49, P337, DOI 10.3414/ME0614 Phillips A., 1985, SIGKDD EXPLORATIONS, V31, P329, DOI DOI 10.1145/1656274.1656278 Hanauer DA, 2007, J AM COLL SURGEONS, V205, P690, DOI 10.1016/j.jamcollsurg.2007.05.014 Hripcsak G, 2005, J AM MED INFORM ASSN, V12, P296, DOI 10.1197/jamia.M1733 John G. H., 1995, 11 C UNC ART INT, P338 Kohane IS, 2000, J AM MED INFORM ASSN, V7, P512 Laroum S, 2009, RNTI REV NOUVELLES T, V1, P25 Lertnattee V., 2004, IEEE INT S COMM INF, V2, P1171, DOI 10.1109/ISCIT.2004.1413903 Li Y.J., 2010, AUSTRALASIAN LANGUAG, P41 MacLennan R, 1991, IARC SCI PUBL, V1, P43 Maojo V, 2003, J AM MED INFORM ASSN, V10, P515, DOI 10.1197/jamia.M1305 McCowan Ian, 2006, Conf Proc IEEE Eng Med Biol Soc, V1, P5153 McCowan IA, 2007, J AM MED INFORM ASSN, V14, P736, DOI 10.1197/jamia.M2130 Murphy Shawn N, 2007, AMIA Annu Symp Proc, P548 Percy C, 2000, INT CLASSIFICATION D Platt J. C., 1999, FAST TRAINING SUPPOR, P185 Prokosch HU, 2009, METHOD INFORM MED, V48, P38, DOI 10.3414/ME9132 Safran C, 2007, J AM MED INFORM ASSN, V14, P1, DOI 10.1197/jamia.M2273 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Viera A, 2005, FAM MED, V37, P543 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 39 TC 5 Z9 5 PU SCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN PI STUTTGART PA HOLDERLINSTRASSE 3, D-70174 STUTTGART, GERMANY SN 0026-1270 J9 METHOD INFORM MED JI Methods Inf. Med. PY 2012 VL 51 IS 3 BP 242 EP 251 DI 10.3414/ME11-01-0005 PG 10 WC Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics SC Computer Science; Health Care Sciences & Services; Medical Informatics GA 948JV UT WOS:000304500400007 PM 21792466 ER PT J AU Fersini, E Sartori, F AF Fersini, E. Sartori, F. TI Semantic storyboard of judicial debates: a novel multimedia summarization environment SO PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS LA English DT Article DE Multimedia summarization; Ontological query expansion; Multimedia; Justice; Information retrieval ID QUERY EXPANSION AB Purpose - The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and with low cost. This paper seeks to address these issues. Design/methodology/approach - In this context the JUMAS system, stem from the homonymous European Project (www.jumasproject.eu), takes up the challenge of exploiting semantics and machine learning techniques towards a better usability of multimedia judicial folders. Findings - In this paper one of the most challenging issues addressed by the JUMAS project is described: extracting meaningful abstracts of given judicial debates in order to efficiently access salient contents. In particular, the authors present an ontology enhanced multimedia summarization environment able to derive a synthetic representation of judicial media contents by a limited loss of meaningful information while overcoming the information overload problem. Originality/value - The adoption of ontology-based query expansion has made it possible to improve the performance of multimedia summarization algorithms with respect to the traditional approaches based on statistics. The effectiveness of the proposed approach has been evaluated on real media contents, highlighting a good potential for extracting key events in the challenging area of judicial proceedings. C1 [Fersini, E.; Sartori, F.] Univ Milano Bicocca, DISCo, Milan, Italy. RP Sartori, F (reprint author), Univ Milano Bicocca, DISCo, Milan, Italy. EM sartorig@disco.unimib.it FU European Community [214306]; grant "Dote Ricercatori": FSE, Regione Lombardia FX This work has been partially supported by the European Community FP-7 under the JUMAS Project (ref.: 214306) and by the grant "Dote Ricercatori": FSE, Regione Lombardia. CR Archetti F., 2006, P INT C FLEX QUER AN Avgerinakis K., 2009, P 2 INT C ICT SOL JU Bo-Wei Chen, 2009, IEEE Transactions on Multimedia, V11, DOI 10.1109/TMM.2008.2009703 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Chen YT, 2009, IEEE T AUDIO SPEECH, V17, P95, DOI 10.1109/TASL.2008.2005031 Durao F.A., 2008, SAC 2008, P1151 Eldib M.Y., 2009, P 16 IEEE INT C IM P Evangelopoulos G., 2009, P 2009 IEEE INT C AC Falavigna D., 2009, P 2 INT C ICT SOL JU Fendri E., 2010, P 2 INT C MULT INF T Fersini E., 2009, P 6 INT C MACH LEARN Fersini E., 2010, P 10 IND C DAT MIN B Jiang RM, 2009, IEEE T CONSUM ELECTR, V55, P1551, DOI 10.1109/TCE.2009.5278026 Kashyap V., 2005, International Journal of Web and Grid Services, V1, DOI 10.1504/IJWGS.2005.008322 Kovacs L., 2009, P 11 INT C ADV CONC Loof J., 2009, INTERSPEECH, P88 Manzato M.G., 2009, P 7 EUR INT TEL C LE Navigli R., 2003, P WORKSH AD TEXT EXT, P42 Neto J. L., 2000, Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Ruthven I., 2001, P 23 BCS EUR ANN C I SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sartori F, 2010, LECT NOTES BUS INF, V62, P107 Sartori F, 2009, COMM COM INF SC, V46, P203 Tuominen J., 2009, P 5 WORKSH SCRIPT DE Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Wielinga B. J., 2001, Proceedings of the First International Conference on Knowledge Capture Zorn H.P., 2009, CLASSIFYING TAG RELA NR 29 TC 0 Z9 0 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0033-0337 J9 PROGRAM-ELECTRON LIB JI Program-Electron. Libr. Inf. Syst. PY 2012 VL 46 IS 2 BP 199 EP 219 DI 10.1108/00330331211221846 PG 21 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 950DP UT WOS:000304629800003 ER PT J AU Kim, JW Kashyap, A Bhamidipati, S AF Kim, Jong Wook Kashyap, Ashwin Bhamidipati, Sandilya TI Wikipedia-Based Semantic Interpreter Using Approximate Top-k Processing and Its Application SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE LA English DT Article DE Wikipedia; concept; semantic interpretation ID DOCUMENT-RETRIEVAL; INFORMATION; RELEVANCE AB Proper representation of the meaning of texts is crucial for enhancing many data mining and information retrieval tasks, including clustering, computing semantic relatedness between texts, and searching. Representing of texts in the concept-space derived from Wikipedia has received growing attention recently. This concept-based representation is capable of extracting semantic relatedness between texts that cannot be deduced with the bag of words model. A key obstacle, however, for using Wikipedia as a semantic interpreter is that the sheer size of the concepts derived from Wikipedia makes it hard to efficiently map texts into concept-space. In this paper, we develop an efficient and effective algorithm which is able to represent the meaning of a text by using the concepts that best match it. In particular, our approach first computes the approximate top-k Wikipedia concepts that are most relevant to the given text. We then leverage these concepts for representing the meaning of the given text. The experimental results show that the proposed technique provides significant gains in execution time without causing significant reduction in precision. We then explore the effectiveness of the proposed algorithm on a real world problem. In particular, we show that this novel scheme could be leveraged to boost the effectiveness in finding topic boundaries in a news video. C1 [Kim, Jong Wook] Teradata Corp, El Segundo, CA USA. [Kashyap, Ashwin; Bhamidipati, Sandilya] Technicolor, Palo Alto, CA USA. RP Kim, JW (reprint author), Teradata Corp, El Segundo, CA USA. EM jongwook.kim@teradata.com; ashwin.kashyap@technicolor.com; sandilya.bhamidipati@technicolor.com CR Allan J., 2003, P 26 ACM SIGIR INT C Arai B., 2007, P 33 INT C VERY LARG Bansal N., 2008, P 28 ACM SIGMOD INT Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boreczky J.S., 1996, P STOR RETR IM VID D Carmel D., 2009, P 32 INT ACM SIGIR C Chakrabarti K., 2006, P 26 ACM SIGMOD INT Cheng T., 2007, P 33 INT C VERY LARG Choi B., 2000, P 1 N AM CHAPT ASS C CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Devore J. L., 1999, PROBABILITY STAT ENG Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Fagin R, 1999, J COMPUT SYST SCI, V58, P83, DOI 10.1006/jcss.1998.1600 Gabrilovich E., 2007, P 20 INT JOINT C ART Gabrilovich E, 2009, J ARTIF INTELL RES, V34, P443 Guntzer U., 2000, P 26 INT C VERY LARG Hu X., 2009, P 15 ACN SIGKDD INT Ilyas I.F., 2003, P 29 INT C VERY LARG Kim J.W., 2009, P 29 ACM SIGMOD INT Kim J.W., 2005, P INT WORLD WID WEB Kim J.W., 2010, P 19 C INF KNOWL MAN Lee DD, 2000, P NIPS, ppp Miller G., 1990, INT J LEXICOGR, V3, P235 Milne D., 2008, P 17 ACM C INF KNOWL Nepal S., 1999, P 15 INT C DAT ENG OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Qi Y., 2006, P ACM C HYP HYP Ramanathan K., 2008, P 28 INT C CONC MOD SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schonhofen P., 2006, P IEEE WIC ACM INT C Theobald M., 2004, P 30 INT C VERY LARG Tsaparas P., 2003, P 19 INT C DAT ENG Wang P, 2008, P 14 ACM SIGKDD INT Zhu D, 2009, J UNIVERS COMPUT SCI, V15, P1685 NR 36 TC 0 Z9 0 PU GRAZ UNIV TECHNOLGOY, INST INFORMATION SYSTEMS COMPUTER MEDIA-IICM PI GRAZ PA INFFELDGASSE 16C, GRAZ, A-8010, AUSTRIA SN 0948-695X J9 J UNIVERS COMPUT SCI JI J. Univers. Comput. Sci. PY 2012 VL 18 IS 5 BP 650 EP 675 DI 10.1145/1871437.1871736 PG 26 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 946LZ UT WOS:000304354400004 ER PT J AU Huang, LB Balakrishnan, V Raj, RG AF Huang, Lim Bee Balakrishnan, Vimala Raj, Ram Gopal TI IMPROVING THE RELEVANCY OF DOCUMENT SEARCH USING THE MULTI-TERM ADJACENCY KEYWORD-ORDER MODEL SO MALAYSIAN JOURNAL OF COMPUTER SCIENCE LA English DT Article DE Information retrieval; keyword-order; vector space model; adjacency terms relationship ID VECTOR-SPACE MODEL; INFORMATION; RANKING AB This paper presents an enhanced vector space model, Multi-Term Adjacency Keyword-Order Model, to improve the relevancy of search results, specifically document search. Our model is based on the concept of keyword grouping. The keyword-order relationship in the adjacency terms is taken into consideration in measuring a term's weight. Assigning more weights to adjacency terms in a query order results in the document vector being moved closer to the query vector, and hence increases the relevancy between the two vectors and thus eventually results in documents with better relevancy being retrieved. The performance of our model is measured based on precision metrics against the performance of a classic vector space model and the performance of a Multi-Term Vector Space Model. Results show that our model performs better in retrieving more relevant results based on a particular search query compared to both the other models. C1 [Huang, Lim Bee; Balakrishnan, Vimala] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur 50603, Malaysia. [Raj, Ram Gopal] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Artificial Intelligence, Kuala Lumpur 50603, Malaysia. RP Huang, LB (reprint author), Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur 50603, Malaysia. EM bhlimum@um.edu.my; vimala_balakrishnan@um.edu.my; gopalraj@tm.net.my RI Raj, Ram Gopal/B-3399-2010 OI Raj, Ram Gopal/0000-0002-8627-1113 CR Bidoki AMZ, 2010, INFORM PROCESS MANAG, V46, P159, DOI 10.1016/j.ipm.2009.12.005 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Raj RG, 2011, MALAYS J COMPUT SCI, V24, P160 Salton G., 1983, COMMUNICATIONS ACM, V26 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Silva R., 2004, P INT DAT ENG APPL S Tai XY, 2002, INFORM PROCESS MANAG, V38, P749, DOI 10.1016/S0306-4573(01)00053-X Wang L.S., 2009, RELEVANCE WEIGHTING Wang L.S., 2006, U.S. Patent Application Publication, Patent No. [US 2007/0179930 A1, 20070179930] Yates R. B., 1999, MODERN INFORM RETRIE NR 11 TC 6 Z9 6 PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH PI KUALA LUMPUR PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR, 50603, MALAYSIA SN 0127-9084 J9 MALAYS J COMPUT SCI JI Malayas. J. Comput. Sci. PY 2012 VL 25 IS 1 BP 1 EP 10 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA 936UU UT WOS:000303616300001 ER PT J AU Perez-Suarez, A Martinez-Trinidad, JF Carrasco-Ochoa, JA Medina-Pagola, JE AF Perez-Suarez, Airel Fco Martinez-Trinidad, Jose Carrasco-Ochoa, Jesus A. Medina-Pagola, Jose E. TI A dynamic clustering algorithm for building overlapping clusters SO INTELLIGENT DATA ANALYSIS LA English DT Article DE Data mining; overlapping clustering; graph-based algorithms ID TOPIC DETECTION AB Clustering is a Data Mining technique which has been widely used in many practical applications. In some of these applications like, medical diagnosis, categorization of digital libraries, topic detection and others, the objects could belong to more than one cluster. However, most of the clustering algorithms generate disjoint clusters. Moreover, processing additions, deletions and modifications of objects in the clustering built so far, without having to rebuild the clustering from the beginning is an issue that has been little studied. In this paper, we introduce DCS, a clustering algorithm which includes a new graph-cover strategy for building a set of clusters that could overlap, and a strategy for dynamically updating the clustering, managing multiple additions and/or deletions of objects. The experimental evaluation conducted over different collections demonstrates the good performance of the proposed algorithm. C1 [Perez-Suarez, Airel; Fco Martinez-Trinidad, Jose; Carrasco-Ochoa, Jesus A.] Natl Inst Astrophys, Dept Comp Sci, Puebla 72840, Mexico. [Perez-Suarez, Airel; Medina-Pagola, Jose E.] Adv Technol Applicat Ctr, Havana, Cuba. RP Perez-Suarez, A (reprint author), Natl Inst Astrophys, Dept Comp Sci, Opt & Elect Luis Enrique Erro 1, Puebla 72840, Mexico. EM airel@inaoep.mx FU National Council of Science and Technology of Mexico [CB-2008-01-106443, 32040] FX This work was partly supported by the National Council of Science and Technology of Mexico under the project CB-2008-01-106443 and grant 32040. CR Abella-Perez R., 2010, P INT WORKSH HANDL C, P65 Aggarwal CC, 2003, P 29 INT C VER LARG, V29, P81 Aslam J., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354833 Aslam J., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288659 Aslam J. A., 2004, J GRAPH ALGORITHMS A, V8, P95 Banerjee A., 2005, P 11 ACM SIGKDD INT, P532, DOI 10.1145/1081870.1081932 Berry M.W., 2004, SURVEY TEXT MINING C Bloy GJ, 2008, IEEE T PATTERN ANAL, V30, P532, DOI 10.1109/TPAMI.2007.1183 Chen L., 2006, P ICMLC 2005, P169 Cselle G., 2007, P 12 INT C INT US IN, P446 Dunn J. C., 1974, Journal of Cybernetics, V4 Elghazel H, 2007, LECT NOTES ARTIF INT, V4755, P78 Ester M., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Gil-Garcia RJ, 2003, LECT NOTES COMPUT SC, V2905, P480 Gil-Garcia R.J., 2008, LECT NOTES COMPUT SC, P187 Greengrass E, 2001, TRR52008001 DOD Guha S, 2000, ANN IEEE SYMP FOUND, P359 Halkidi M, 2008, INTELL DATA ANAL, V12, P587 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 He Y, 2005, INTELL DATA ANAL, V9, P253 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Khy S., 2006, P 22 INT C DAT ENG W, P40 Kuncheva LI, 2004, IEEE SYS MAN CYBERN, P1214 Lazarevic A., 2001, Intelligent Data Analysis, V5 Liu Y.G., 2004, INTELL DATA ANAL, V8, P325 Mahata P., 2008, IEEE ACM T COMPUTATI Nayak R, 2008, KNOWL INF SYST, V14, P197, DOI 10.1007/s10115-007-0080-8 O'Callaghan L, 2002, PROC INT CONF DATA, P685, DOI 10.1109/ICDE.2002.994785 Omran MGH, 2007, INTELL DATA ANAL, V11, P583 Perez-Suarez A., 2007, LNCS LNAI, V4571, P248 Pons-Porrata A, 2002, J INTELL FUZZY SYST, V12, P185 Pons-Porrata A., 2002, RECONOCIMIENTO PATRO, P405 Pons-Porrata A, 2004, LECT NOTES COMPUT SC, V3287, P446 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez-Diaz G., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989590 Sanchez-Diaz G., 2000, P 5 SIARP LISB PORT, P475 Sia W, 2005, LECT NOTES ARTIF INT, V3587, P163 Vignes M., 2007, IEEE ACM T COMPUT BI, P260 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 40 TC 2 Z9 2 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 1088-467X J9 INTELL DATA ANAL JI Intell. Data Anal. PY 2012 VL 16 IS 2 BP 211 EP 232 DI 10.3233/IDA-2012-0520 PG 22 WC Computer Science, Artificial Intelligence SC Computer Science GA 906RY UT WOS:000301366100005 ER PT J AU Velasquez, JD AF Velasquez, Juan D. TI Web site keywords: A methodology for improving gradually the web site text content SO INTELLIGENT DATA ANALYSIS LA English DT Article DE Web site keywords; web site text content; web usage mining; web content mining ID VISITOR BEHAVIOR; KNOWLEDGE; FRAMEWORK; SYSTEMS AB The construction of a web site is a great challenge that integrates different elements such as the hyperlink structure, colors, pictures, movies and textual contents. In the latter, the correct textual content can be the key to attracting users to visit the site. In fact, many users visit a web site by using a web search engine such as, Google or Yahoo!, and continue exploring the site if it contains the information that they are looking for. In this paper, a methodology to extract the main words in a static web site is proposed. Furthermore, one of the key elements in this methodology is to determine which pages in a web site can further attract the users attention when they are browsing the site. These words are called web site keywords and by using them in the site textual content, significant improvements, from the point of view of the user, can be achieved. A web user's browsing behaviour can be classified in two categories: those of amateurs and experienced. The former is a user with little or no experience in using web-based systems. Their browsing behaviour is normally erratic and it can take them a considerable amount of time to find what they are looking for. The latter is a user with a greater amount of experience with web-based systems whose behaviour is more controlled and purpose driven, and thus takes them less time in determining whether the site contains worthwhile information. What is important, regarding the experienced web users is that there is a correlation between the amount of time spent on a webpage during a session and the extent to which they are interested in the page content. By using this characteristic, a feature vector is created in relation to the time spent on each page during a user's session. The described vectors are the input for two clustering algorithms: SOFM and K-means, which enables the extraction of significant patterns about users with similar or identical browsing behaviour and content preferences. Then, these patterns form the basis in identification of the web site keywords. In order to validate the proposed methodology, web data originated in a complex static web site belonging to a Chilean bank was used. From the clusters identified, a set of web site keywords were identified and their utility was tested on a group of real users, thus illustrating the effectiveness of the proposed methodology. C1 Univ Chile, Dept Ind Engn, Santiago, Chile. RP Velasquez, JD (reprint author), Univ Chile, Dept Ind Engn, Santiago, Chile. EM jvelasqu@dii.uchile.cl RI Velasquez, Juan/H-3859-2013 OI Velasquez, Juan/0000-0003-4819-9680 FU FONDEF [DO8I-1015]; Millennium Institute on Complex Engineering Systems [ICM: P-05-004-F, CONICYT: FBO16] FX This work was supported partially by the FONDEF project DO8I-1015 and the Millennium Institute on Complex Engineering Systems (ICM: P-05-004-F, CONICYT: FBO16). CR Amitay E., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354816 Baeza-Yates R., 2004, WEB MINING APPL TECH, P307 Berendt B., 2002, P 1 INT SEM WEB C IS, P264 Berendt B, 2000, VLDB J, V9, P56, DOI 10.1007/s007780050083 Buttler D., 2004, P INT C INT COMP LAS, P3 Buyukkokten O, 2000, COMPUT NETW, V33, P213, DOI 10.1016/S1389-1286(00)00060-8 CATLEDGE LD, 1995, COMPUT NETWORKS ISDN, V27, P1065, DOI 10.1016/0169-7552(95)00043-7 Chakrabarti S., 1999, IEEE COMPUTER, V32 Chang G., 2003, MINING WORLD WIDE WE Chuang WT, 2000, P 23 ANN INT ACM SIG, P152, DOI 10.1145/345508.345566 COOLEY R, 1999, J KNOWLEDGE INFORMAT, V1, P1 Hahn U, 2000, COMPUTER, V33, P29, DOI 10.1109/2.881692 Joshi A., 2000, P ACM SIGMOD WORKSH, P63 Kohonen T., 2001, SELF ORGANIZING MAPS Lawrie D, 2001, P 24 ANN INT ACM SIG, P349, DOI 10.1145/383952.384022 Liddy E.D., 1993, P HUM LANG TECHN WOR, DOI 10.3115/1075671.1075707 Linoff G. S., 2001, MINING WEB Loh S., 2000, SIGKDD EXPLORATIONS, V2, P29 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Mani I., 1999, ADV AUTOMATIC TEXT S Mitra S, 2002, IEEE T NEURAL NETWOR, V13, P3, DOI 10.1109/72.977258 Mobasher B., 1999, P INT C IEEE KNOWL D Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Pereira Jr A.R., 2004, J WEB ENG, V2, P247 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rios S.A., 2006, INT J COMPUTATIONAL, V1, P159 van Wel L., 2004, Ethics and Information Technology, V6, DOI 10.1023/B:ETIN.0000047476.05912.3d Runkler TA, 2003, INT J APPROX REASON, V32, P217, DOI 10.1016/S0888-613X(02)00084-1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spiliopoulou M., 1999, LECT NOTES ARTIF INT, P588 Spiliopoulou M, 2003, INFORMS J COMPUT, V15, P171, DOI 10.1287/ijoc.15.2.171.14445 SRIVASTAVA, 2000, SIGKDD EXPLOR NEWSL, V1, P12, DOI DOI 10.1145/846183.846188 Tonella P., 2001, Proceedings 3rd International Workshop on Web Site Evolution. WSE 2001, DOI 10.1109/WSE.2001.988780 Tonella P, 2002, PROC IEEE INT CONF S, P290, DOI 10.1109/ICSM.2002.1167784 Velasquez J. D., 2004, Proceedings. 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 Velasquez J. D., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 Velasquez JD, 2008, FRONT ARTIF INTEL AP, V170, P1 Velasquez JD, 2003, LECT NOTES ARTIF INT, V2773, P951 Velasquez J.D., 2003, P 3 IEEE INT C DAT M, P669 Velasquez JD, 2007, KNOWL-BASED SYST, V20, P238, DOI 10.1016/j.knosys.2006.05.015 Wong C., 2001, WORKSH SOFT COMP CAS Xiao J., 2001, ADC 01, P107 Zechner K., 1996, P 16 INT C COMP LING, P986 NR 45 TC 3 Z9 3 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 1088-467X J9 INTELL DATA ANAL JI Intell. Data Anal. PY 2012 VL 16 IS 2 BP 327 EP 348 DI 10.3233/IDA-2012-0526 PG 22 WC Computer Science, Artificial Intelligence SC Computer Science GA 906RY UT WOS:000301366100011 ER PT J AU Stewart, SA Abidi, SSR AF Stewart, Samuel Alan Abidi, Syed Sibte Raza TI An Infobutton For Web 2.0 Clinical Discussions: The Knowledge Linkage Framework SO IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE LA English DT Article DE Infobutton; information seeking behavior; knowledge management; mesh; Web 2.0 ID INFORMATION NEEDS; CREATION AB This paper aims to develop an infobutton to automatically retrieve published papers corresponding to a topic-specific online clinical discussion. The knowledge linkages infobutton is designed to supplement online clinical conversations with pertinent medical literature from Pubmed. The project involves three distinct steps: 1) Clinical messages around a specific problem are grouped together into a thread. 2) These threads are processed using Metamap to link the conversations to keywords from the MeSH lexicon. 3) These keywords are used in a novel search strategy to retrieve a set of papers from Pubmed, which are then returned to the user. A pilot study using the messages from 2007 and 2008, was conducted to compare the knowledge linkage search strategy to a vector space model and extended Boolean model. The knowledge linkage model proved to be significantly better in terms of precision (p = 0.013 and 0.003, respectively) and recall (p = 0.351 and 0.013). Pertinent papers were returned to over 55% of the threads. This approach has demonstrated how clinicians can supplement their peer communications with evidence based research. Future work should focus on how to improve the threading and keyword-mapping strategies. C1 [Stewart, Samuel Alan; Abidi, Syed Sibte Raza] Fac Comp Sci, NICHE Res Grp, Halifax, NS B3H 4R2, Canada. RP Stewart, SA (reprint author), Fac Comp Sci, NICHE Res Grp, 6050 Univ Ave, Halifax, NS B3H 4R2, Canada. EM sam.stewart@dal.ca; sraza@cs.dal.ca FU Global Health Research Initiative (GHRI), Canadian Institutes of Health Research; Canadian International Development Agency, Health Canada; International Development Research Centre; Public Health Agency of Canada FX This work was supported by the Global Health Research Initiative (GHRI), a collaborative research funding partner of the Canadian Institutes of Health Research, the Canadian International Development Agency, Health Canada, the International Development Research Centre, and the Public Health Agency of Canada. CR Abidi SSR, 2005, IEEE T INF TECHNOL B, V9, P193, DOI 10.1109/TITB.2005.847188 Aronson A. R., P AMIA ANN S 2001, P17 Baorto D., P AMIA ANN S, P47 Chapman WW, 2004, ST HEAL T, V107, P487 Chase HS, 2009, J AM MED INFORM ASSN, V16, P387, DOI 10.1197/jamia.M2940 Chen K, 2002, J TELEMED TELECARE, V8, P5, DOI 10.1258/13576330260440673 Chueh H, 1997, ACAD MED, V72, P512, DOI 10.1097/00001888-199706000-00016 Cimino J., P AMIA ANN FALL S, P528 Cimino J. J., 2008, P AMIA ANN S, P116 Cimino J. J., P AMIA ANN S 2003, P815 CIMINO JJ, 1993, B MED LIBR ASSOC, V81, P195 Clarke SJ, 1997, ASLIB PROC, V49, P184, DOI 10.1108/eb051463 CONSORTIUM GO, 2004, NUCLEIC ACIDS RES, V32, pD258, DOI DOI 10.1093/NAR/GKH036 COVELL DG, 1985, ANN INTERN MED, V103, P596 Curran Janet A, 2007, Health Informatics J, V13, P255, DOI 10.1177/1460458207079834 Curran-Smith J., 2005, HLTH INFORM J, V11, P19, DOI 10.1177/1460458205050682 Del Fiol G., P AMIA ANN S 2006, P180 Del Fiol G, 2008, J AM MED INFORM ASSN, V15, P752, DOI 10.1197/jamia.M2725 Doms A., 2005, NUCLEIC ACIDS RES, V1, P783 Stewart Sam, 2010, Stud Health Technol Inform, V160, P1184 Foong Deborah P S, 2010, Indian J Plast Surg, V43, P195, DOI 10.4103/0970-0358.73448 Haynes B., 1994, J AM MED INFORM ASSN, V1, P447 Kahn C., 2009, J AM MED INFORM ASSN, V16, P280 Leroy G, 2001, IEEE T INF TECHNOL B, V5, P261, DOI 10.1109/4233.966101 Muller HM, 2004, PLOS BIOL, V2, P1984, DOI 10.1371/journal.pbio.0020309 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 OSHEROFF JA, 1991, ANN INTERN MED, V114, P576 Perez-Iratxeta C, 2003, NUCLEIC ACIDS RES, V31, P3866, DOI 10.1093/nar/gkg538 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TIMPKA T, 1989, Scandinavian Journal of Primary Health Care, V7, P105, DOI 10.3109/02813438909088656 NR 31 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1089-7771 J9 IEEE T INF TECHNOL B JI IEEE T. Inf. Technol. Biomed. PD JAN PY 2012 VL 16 IS 1 BP 129 EP 135 DI 10.1109/TITB.2011.2177097 PG 7 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics SC Computer Science; Mathematical & Computational Biology; Medical Informatics GA 892AX UT WOS:000300260000016 PM 22128010 ER PT J AU Cao, TH Ngo, VM AF Cao, Tru H. Ngo, Vuong M. TI Semantic Search by Latent Ontological Features SO NEW GENERATION COMPUTING LA English DT Article DE Named Entities; Ontology; Semantic Annotation; Vector Space Model; Information Retrieval ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL AB Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries. C1 [Cao, Tru H.] Ho Chi Minh City Univ Technol, Ho Chi Minh City, Vietnam. John von Neumann Inst VNU HCM, Ho Chi Minh City, Vietnam. RP Cao, TH (reprint author), Ho Chi Minh City Univ Technol, Ho Chi Minh City, Vietnam. EM tru@cse.hcmut.edu.vn; vuong@cse.hcmut.edu.vn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bast H, 2007, P 30 ANN INT ACM SIG, P671, DOI 10.1145/1277741.1277856 Buckley C., 1985, IMPLEMENTATION SMART, P85 Cao T., 2008, P 17 IEEE INT C FUZZ, P2028 Cao TH, 2008, LECT NOTES COMPUT SC, V5367, P479, DOI 10.1007/978-3-540-89704-0_33 Castells P., 2006, IEEE T KNOWL DATA EN, P261 Cheng G., 2008, P WWW2008 WORKSH LIN Cheng Tao, 2007, P 33 INT C VER LARG, P387 Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 Gonalves A., 2006, P 7 INT C WEB AG INF Gospodnetic O., 2003, J IBM DEV WORKS Guha R. V., 2003, P 12 INT C WORLD WID, P700 Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jing L., 2009, KNOWLEDGE INFORM SYS Jones K. S., 2000, Information Processing & Management, V36 Jones Rosie, 2008, P 2 INT WORKSH GEOGR, P57, DOI 10.1145/1460007.1460023 Khalid MA, 2008, LECT NOTES COMPUT SC, V4956, P705 Kiryakov A., 2005, J WEB SEMANTICS, V2 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Manning C.D., 2008, INTRO INFORM RETRIEV Meij E., 2007, P 16 TEXT RETR C Mihalcea R., 2001, STUDIES INFORM CONTR, V10 Nguyen V. T. T., 2007, J NEW GENERATION COM, V25, P277 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sekine S., 2004, PROTEUS PROJECT REPO Small H., 1973, INFORM PROCESSING MA, V13, P277 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 Sparck Jones K., 2000, INFORM PROCESSING MA, V36, P623 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Varelas G., 2005, P 7 ANN ACM INT WORK, P10, DOI 10.1145/1097047.1097051 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Zhou W., 2006, P 15 TEXT RETR C NR 33 TC 1 Z9 1 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0288-3635 EI 1882-7055 J9 NEW GENERAT COMPUT JI New Gener. Comput. PD JAN PY 2012 VL 30 IS 1 BP 53 EP 71 DI 10.1007/s00354-012-0104-0 PG 19 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA 892NJ UT WOS:000300292600004 ER PT J AU Krstajic, M Najm-Araghi, M Mansmann, F Keim, DA AF Krstajic, Milos Najm-Araghi, Mohammad Mansmann, Florian Keim, Daniel A. BE Wong, PC Kao, DL Hao, MC Chen, C Kosara, R Livingston, MA Park, J Roberts, I TI Incremental Visual Text Analytics of News Story Development SO VISUALIZATION AND DATA ANALYSIS 2012 SE Proceedings of SPIE LA English DT Proceedings Paper CT 19th SPIE Conference on Visualization and Data Analysis (VDA) CY JAN 23-25, 2012 CL Burlingame, CA SP Soc Imaging Sci & Technol (IS&T), SPIE, Hewlett Packard Co, Kitware Inc, Pacific NW Natl Lab, SAGE Publicat Ltd, U.S. Dept Homeland Secur DE News Stream Analysis; Topic Evolution; Dynamic Visualization; Text Analytics ID EVENTS AB Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. Additionally, the stories have very complex relationships and characteristics that are difficult to model: they can be weakly or strongly connected, or they can merge or split over time. In this paper, we present a visual analytics system for exploration of news topics in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. We employ text clustering techniques to automatically extract stories from online news streams and present a visualization that: 1) shows temporal characteristics of stories in different time frames with different level of detail; 2) allows incremental updates of the display without recalculating the visual features of the past data; 3) sorts the stories by minimizing clutter and overlap from edge crossings. By using interaction, stories can be filtered based on their duration and characteristics in order to be explored in full detail with details on demand. To demonstrate the usefulness of our system, case studies with real news data are presented and show the capabilities for detailed dynamic text stream exploration. C1 [Krstajic, Milos; Najm-Araghi, Mohammad; Mansmann, Florian; Keim, Daniel A.] Univ Konstanz, Constance, Germany. RP Krstajic, M (reprint author), Univ Konstanz, Constance, Germany. EM milos.krstajic@uni-konstanz.de; mohammad.najm-araghi@uni-konstanz.de; florian.mansmann@uni-konstanz.de; daniel.keim@uni-konstanz.de CR Aigner W, 2011, HUM-COMPUT INT-SPRIN, P1, DOI 10.1007/978-0-85729-079-3 Allan J., 1998, P DARPA BROADC NEWS, P194 Atkinson M, 2009, WWW 09, P1153 Blei D. M., 2006, P 23 INT C MACH LEAR, P113, DOI DOI 10.1145/1143844.1143859 Byron L, 2008, IEEE T VIS COMPUT GR, V14, P1245, DOI 10.1109/TVCG.2008.166 Dork M, 2010, IEEE T VIS COMPUT GR, V16, P1129, DOI 10.1109/TVCG.2010.129 Fisher D, 2008, IEEE S VIS ANAL, P115 Harrower M, 2011, COLORBREWER ORG ONLI, P261 Havre S., 2000, P IEEE S INF VIS INF, P115 Kleinberg J., 2006, DATA STREAM MANAGEME Krstajic M, 2010, 1 INT WORKSH DAT ENG Krstajic M, 2010, STREAMKDD 10, P39 Luo D., 2010, IEEE T VISUALIZATION, V99 Osinski S, 2004, ADV SOFT COMP, P359 Osinski S., 2005, LNCS, P439 Rose SJ, 2009, IEEE S VIS AN SCI TE, P99 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stefanowski J, 2003, LECT NOTES ARTIF INT, V2663, P240 SUGIYAMA K, 1981, IEEE T SYST MAN CYB, V11, P109, DOI 10.1109/TSMC.1981.4308636 Wei F., 2010, P 16 ACM SIGKDD INT, P153, DOI DOI 10.1145/1835804.1835827 Wong P. C., 2003, IEEE S INF VIS INFOV, V0, P13 Zamir O. E., 1999, CLUSTERING WEB DOCUM NR 22 TC 1 Z9 1 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 978-0-81948-941-8 J9 PROC SPIE PY 2012 VL 8294 AR 829407 DI 10.1117/12.912456 PG 12 WC Engineering, Electrical & Electronic; Optics SC Engineering; Optics GA BYT62 UT WOS:000300179600005 ER PT J AU Bentham, J Hand, DJ AF Bentham, James Hand, David J. TI Data mining from a patient safety database: the lessons learned SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article DE Knowledge Discovery; Data Mining; Information Extraction; Patient Safety ID SPACE; MODEL AB The issue of patient safety is an extremely important one; each year in the UK, hundreds of thousands of people suffer due to some sort of incident that occurs whilst they are in National Health Service care. The National Patient Safety Agency (NPSA) works to try to reduce the scale of the problem. One of its major projects is to collect a very large dataset, the Reporting and Learning System (RLS), which describes several million of these incidents. The RLS is used as the basis for research by the NPSA. However, the NPSA has identified a gap in their work between high-level quantitative analysis and detailed, manual analysis of small samples. This paper describes the lessons learned from a knowledge discovery process that attempted to fill this gap. The RLS contains a free text description of each incident. A high dimensional model of the text is calculated, using the vector space model with term weighting applied. Dimensionality reduction techniques are used to produce the final models of the text. These models are examined using an anomaly detection tool to find groups of incidents that should be coherent in meaning, and that might be of interest to the NPSA. A three stage process is developed for assessing the results. The first stage uses a quantitative measure based on the use of planted groups of known interest, the second stage involves manual filtering by a non-expert, and the third stage is assessment by clinical experts. C1 [Bentham, James] Kings Coll London, Dept Med & Mol Genet, London WC2R 2LS, England. [Hand, David J.] Univ London Imperial Coll Sci Technol & Med, Dept Math, London, England. [Hand, David J.] Univ London Imperial Coll Sci Technol & Med, Inst Math Sci, London, England. RP Bentham, J (reprint author), Kings Coll London, Dept Med & Mol Genet, London WC2R 2LS, England. EM james.bentham@kcl.ac.uk RI Bentham, James/ OI Bentham, James/0000-0001-5733-9417 FU EPSRC; NPSA; Royal Society FX James Bentham would like to thank the EPSRC and NPSA for funding his CASE award. The work of David Hand was partially supported by a Royal Society Wolfson Research Merit Award. We would also like to thank two anonymous reviewers and an editor for their helpful comments. CR Aggarwal CC, 2001, LECT NOTES COMPUT SC, V1973, P420 Blum A., 1998, COLT, P92 Department of Health Expert Group, 2000, ORG MEM Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining Fayyad U, 1996, COMMUN ACM, V39, P27, DOI 10.1145/240455.240464 FOSTER J, 2008, P 46 ANN M ASS COMP, P221, DOI 10.3115/1557690.1557753 Hinneburg A., 2000, P 26 INT C VER LARG, P506 HONNIBAL M, 2009, P 2009 WORKSH PEOPL, P38, DOI 10.3115/1699765.1699771 Hyvarinen A., 2001, INDEPENDENT COMPONEN LEASE M, 2005, 2 INT JOINT C NAT LA, P58 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Mannila H., 1996, Proceedings. Eighth International Conference on Scientific and Statistical Database Management (Cat. No.96TB100051), DOI 10.1109/SSDM.1996.505910 Manning C.D., 2008, INTRO INFORM RETRIEV Rasmussen CE, 2000, ADV NEUR IN, V12, P554 Ripley B. D., 1996, PATTERN RECOGNITION Saad F. H., 2006, P 2006 INT C DAT MIN Salakhutdinov R, 2009, INT J APPROX REASON, V50, P969, DOI 10.1016/j.ijar.2008.11.006 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zhang X., 2008, INT J DATA WAREHOUS, V4, P62, DOI DOI 10.4018/JDWM.2008010104 ZHANG Z, 2005, P LNCS, P509 NR 21 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1384-5810 J9 DATA MIN KNOWL DISC JI Data Min. Knowl. Discov. PD JAN PY 2012 VL 24 IS 1 BP 195 EP 217 DI 10.1007/s10618-011-0225-y PG 23 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 874OA UT WOS:000298966700007 ER PT J AU Evangelopoulos, N Zhang, XN Prybutok, VR AF Evangelopoulos, Nicholas Zhang, Xiaoni Prybutok, Victor R. TI Latent Semantic Analysis: five methodological recommendations SO EUROPEAN JOURNAL OF INFORMATION SYSTEMS LA English DT Article DE text mining; analysis of textual data; singular value decomposition; clustering; factor analysis ID NONNEGATIVE MATRIX FACTORIZATION; INFORMATION-SYSTEMS; UNSTRUCTURED TEXT; SELECTION; ADOPTION; PROFILE; MODEL; CATEGORIZATION; DIMENSIONALITY; RETRIEVAL AB The recent influx in generation, storage, and availability of textual data presents researchers with the challenge of developing suitable methods for their analysis. Latent Semantic Analysis (LSA), a member of a family of methodological approaches that offers an opportunity to address this gap by describing the semantic content in textual data as a set of vectors, was pioneered by researchers in psychology, information retrieval, and bibliometrics. LSA involves a matrix operation called singular value decomposition, an extension of principal component analysis. LSA generates latent semantic dimensions that are either interpreted, if the researcher's primary interest lies with the understanding of the thematic structure in the textual data, or used for purposes of clustering, categorization, and predictive modeling, if the interest lies with the conversion of raw text into numerical data, as a precursor to subsequent analysis. This paper reviews five methodological issues that need to be addressed by the researcher who will embark on LSA. We examine the dilemmas, present the choices, and discuss the considerations under which good methodological decisions are made. We illustrate these issues with the help of four small studies, involving the analysis of abstracts for papers published in the European Journal of Information Systems. European Journal of Information Systems (2012) 21, 70-86. doi:10.1057/ejis.2010.61; published online 21 December 2010 C1 [Evangelopoulos, Nicholas; Prybutok, Victor R.] Univ N Texas, Coll Business, Informat Technol & Decis Sci Dept, Denton, TX 76203 USA. [Zhang, Xiaoni] No Kentucky Univ, Dept Business Informat, Coll Informat, Highland Hts, KY USA. RP Evangelopoulos, N (reprint author), Univ N Texas, Coll Business, Informat Technol & Decis Sci Dept, POB 305249,BUSI 336, Denton, TX 76203 USA. EM nick.evangelopoulos@unt.edu RI Evangelopoulos, Nicholas/; Prybutok, Victor/ OI Evangelopoulos, Nicholas/0000-0001-9866-6828; Prybutok, Victor/0000-0003-3810-9039 CR Abbasi A, 2008, MIS QUART, V32, P811 Bajwa I.S., 2009, EUROPEAN J SCI RES, V35, P22 Barrett MI, 1999, EUR J INFORM SYST, V8, P1, DOI 10.1057/palgrave.ejis.3000313 Berry MW, 2007, COMPUT STAT DATA AN, V52, P155, DOI 10.1016/j.csda.2006.11.006 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bondarouk TV, 2006, EUR J INFORM SYST, V15, P42, DOI 10.1057/palgrave.ejis.3000608 Bradford R., 2008, CIKM 08 P 17 ACM C I, P153 CHEW PA, 2007, P 13 ACM SIGKDD INT, P143, DOI 10.1145/1281192.1281211 Cho SY, 2007, EUR J INFORM SYST, V16, P738, DOI 10.1057/palgrave.ejis.3000718 Couger JD, 1994, EUR J INFORM SYST, V3, P285, DOI 10.1057/ejis.1994.33 Coussement K, 2008, DECIS SUPPORT SYST, V44, P870, DOI 10.1016/j.dss.2007.10.010 Dam G, 2008, BEHAV RES METHODS, V40, P8, DOI 10.3758/BRM.40.1.8 Damsgaard J, 2000, EUR J INFORM SYST, V9, P173, DOI 10.1057/palgrave.ejis.3000368 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Doxas I, 2010, P NATL ACAD SCI USA, V107, P4866, DOI 10.1073/pnas.0908315107 Dumais S., 2007, HDB LATENT SEMANTIC, P293 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Dwivedi YK, 2008, EUR J INFORM SYST, V17, P678, DOI 10.1057/ejis.2008.57 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 Frank E, 2005, DATA MINING PRACTICA Franzosi R., 2004, WORDS NUMBERS NARRAT Galliers RD, 2007, EUR J INFORM SYST, V16, P20, DOI 10.1057/palgrave.ejis.3000662 Ghose A, 2009, MIS QUART, V33, P263 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 HALEY DT, 2007, P22 Han J., 2006, DATA MINING CONCEPTS HOVORKA D, 2009 Hovorka DS, 2006, EUR J INFORM SYST, V15, P159, DOI 10.1057/palgrave.ejis.3000606 Hu X., 2007, HDB LATENT SEMANTIC, P401 Husbands P, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P145 Jensen TB, 2007, EUR J INFORM SYST, V16, P672, DOI 10.1057/palgrave.ejis.3000713 Johnson R. A., 2007, APPL MULTIVARIATE ST Kuechler WL, 2007, COMMUN ACM, V50, P86, DOI 10.1145/1290958.1290967 Landauer T., 2007, HDB LATENT SEMANTIC, P3 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 Larsen KR, 2004, SOCIOL METHODOL, V34, P349, DOI 10.1111/j.0081-1750.2004.00156.x Larsen KR, 2008, DECIS SUPPORT SYST, V45, P884, DOI 10.1016/j.dss.2008.02.009 Lifchitz A, 2009, BEHAV RES METHODS, V41, P1201, DOI 10.3758/BRM.41.4.1201 Manning C.D., 2008, INTRO INFORM RETRIEV Martin D. I., 2007, HDB LATENT SEMANTIC, P33 McDonald M. P., 2004, WILEY SERIES PROBABI Merono-Cerdan AL, 2007, EUR J INFORM SYST, V16, P66, DOI 10.1057/palgrave.ejis.3000656 Moore G. C., 1991, INFORMATION SYSTEMS, V2, P192, DOI DOI 10.1287/ISRE.2.3.192 Ord TJ, 2005, ANIM BEHAV, V69, P1399, DOI 10.1016/j.anbehav.2004.08.020 Palvia P., 2004, COMMUNICATIONS AIS, V14, P526 Panteli A, 1999, EUR J INFORM SYST, V8, P170, DOI 10.1057/palgrave.ejis.3000326 PARK L, 2009, ACM Transactions on Information Systems, V27, P8 Penumatsa P, 2006, INT J ARTIF INTELL T, V15, P767, DOI 10.1142/S021821300600293X PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahnaz F, 2006, INFORM PROCESS MANAG, V42, P373, DOI 10.1016/j.ipm.2004.11.005 Sidorova A, 2008, MIS QUART, V32, P467 Spomer J. E, 2009, THESIS CENTRAL CONNE Steyvers M., 2007, HDB LATENT SEMANTIC, P427 Teh YW, 2006, J AM STAT ASSOC, V101, P1566, DOI 10.1198/016214506000000302 Valle-Lisboa JC, 2007, INFORM SCIENCES, V177, P4122, DOI 10.1016/j.ins.2007.04.007 Weber R. P., 1990, BASIC CONTENT ANAL Wei C., 2008, J MANAGE INFORM SYST, V24, P269 Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Willcocks L, 2008, EUR J INFORM SYST, V17, P163, DOI 10.1057/ejis.2008.9 Zhu M, 2006, COMPUT STAT DATA AN, V51, P918, DOI 10.1016/j.csda.2005.09.010 NR 63 TC 11 Z9 11 PU PALGRAVE MACMILLAN LTD PI BASINGSTOKE PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND SN 0960-085X J9 EUR J INFORM SYST JI Eur. J. Inform. Syst. PD JAN PY 2012 VL 21 IS 1 SI SI BP 70 EP 86 DI 10.1057/ejis.2010.61 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 871MV UT WOS:000298743100005 ER PT S AU Antunes, B Cordeiro, J Gomes, P AF Antunes, Bruno Cordeiro, Joel Gomes, Paulo BE DeRaedt, L Bessiere, C Dubois, D Doherty, P Frasconi, P Heintz, F Lucas, P TI Context-Based Search in Software Development SO 20TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE (ECAI 2012) SE Frontiers in Artificial Intelligence and Applications LA English DT Proceedings Paper CT 20th European Conference on Artificial Intelligence (ECAI) CY AUG 27-31, 2012 CL Montpellier, FRANCE SP European Coordinating Comm Artificial Intelligence, Assoc Francaise Intelligence Artificielle, US AF Res Lab, AF Off Sci Res, European Off Aerosp Res & Dev, Artificial Intelligence, IBM Res, IOS Press, Mines Telecom, Montpellier Agglomerat, Reg Languedoc Roussillon, Univ Montpellier 2, Lab Informatique Robotique Microelectronique Montpellier HO CNRS AB Software developers usually spend a large amount of time navigating their own source code or searching for a specific source code artifact they need to work with. We propose a context-based search approach that focuses in the source code artifacts that exist in the workspace of the developer. These artifacts are stored in a knowledge base and represented using ontologies. The contextual information of the developer is used to rank the search results according to their relevance for the developer. The results of an experiment performed with two groups of developers show that our approach has a positive impact in retrieving relevant artifacts for the developer, helping them find what they need more quickly and easily. C1 [Antunes, Bruno; Cordeiro, Joel; Gomes, Paulo] Univ Coimbra, Ctr Informat & Syst, Coimbra, Portugal. RP Antunes, B (reprint author), Univ Coimbra, Ctr Informat & Syst, Coimbra, Portugal. CR Brown PJ, 1997, IEEE PERS COMMUN, V4, P58, DOI 10.1109/98.626984 Dey A., 2000, CHI 2000 WORKSH WHAT Harris ZS, 1954, WORD, V10, P146 Henrich A, 2003, 14TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, P249, DOI 10.1109/DEXA.2003.1232031 Holmes R, 2005, PROC INT CONF SOFTW, P117 Kersten M., 2006, P 14 ACM SIGSOFT INT, P1, DOI 10.1145/1181775.1181777 Mostefaoui G. K., 2004, Proceedings. The IEEE/ACS International Conference on Pervasive Services (IEEE Cat. No.04EX892) Parnin C, 2006, INT C PROGRAM COMPRE, P13, DOI 10.1109/ICPC.2006.14 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHILIT BN, 1994, IEEE NETWORK, V8, P22, DOI 10.1109/65.313011 Ye YW, 2002, ICSE 2002: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P513 Zuniga Gloria L., 2001, P INT C FORM ONT INF, P187 NR 12 TC 0 Z9 0 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-61499-098-7; 978-1-61499-097-0 J9 FRONT ARTIF INTEL AP PY 2012 VL 242 BP 937 EP 942 DI 10.3233/978-1-61499-098-7-937 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BC0FF UT WOS:000349006300173 ER PT J AU Kawase, R Siehndel, P Nunes, BP Fisichella, M Nejdl, W AF Kawase, Ricardo Siehndel, Patrick Nunes, Bernardo Pereira Fisichella, Marco Nejdl, Wolfgang BE Ravenscroft, A Lindstaedt, S Kloos, CD HernandezLeo, D TI Towards Automatic Competence Assignment of Learning Objects SO 21ST CENTURY LEARNING FOR 21ST CENTURY SKILLS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th European Conference on Technology Enhanced Learning (EC-TEL) CY SEP 18-21, 2012 CL Saarbrucken, GERMANY SP European Assoc Technol Enhanced Learning, TELspain, eMadrid, Springer, Informat Multimedia Commun AG HO Saarland Univ, Ctr eLearning Technol DE Metadata Generation; Competences; e-Learning; Automatic Competence Classification AB Competence-annotations assist learners to retrieve and better understand the level of skills required to comprehend learning objects. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence topics. To solve this problem, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. Results show that automatically assigned competences are coherent and may be applied to automatically enhance learning objects metadata. C1 [Kawase, Ricardo; Siehndel, Patrick; Nunes, Bernardo Pereira; Fisichella, Marco; Nejdl, Wolfgang] Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany. RP Kawase, R (reprint author), Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany. EM kawase@L3S.de; siehndel@L3S.de; nunes@L3S.de; fisichella@L3S.de; nejdl@L3S.de CR Diaz-Aviles E., 2010, P 4 ACM C REC SYST R, P309, DOI 10.1145/1864708.1864774 Niemann K, 2010, LECT NOTES COMPUT SC, V6383, P632, DOI 10.1007/978-3-642-16020-2_66 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 4 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-33263-0 J9 LECT NOTES COMPUT SC PY 2012 VL 7563 BP 401 EP 406 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB7QB UT WOS:000345910800034 ER PT S AU Munoz, R Rios, SA AF Munoz, Ricardo Rios, Sebastian A. BE Grana, M Toro, C Posada, J Howlett, RJ Jain, LC TI Overlapping Community Detection in VCoP using Topic Models SO ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS SE Frontiers in Artificial Intelligence and Applications LA English DT Proceedings Paper CT 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems CY SEP 10-12, 2012 CL San Sebastian, SPAIN SP Univ Basque Country, Vicomtech IJ4, World Federat Soft Comp, KES Int DE Overlapping Community Detection; Label Propagation; Social Network Analysis; Latent Dirichlet Allocation AB Identification of communities in social networks has become a hot research topic in recent years. Many algorithms have been designed to discover networks' community structure. Most of these algorithms detect disjoint communities, which means that every community member belongs to a single community. These models do not consider that a person may have more than one interest. Thus, lately, a few methods have been designed to find overlapping communities. But most researchers have either emphasize to solve this problem on computing network's structural properties, or using graphical models for the community extraction process, where structural properties of networks are not considered. However, when end users are connected with each other by documents, posts or comments it is not possible to ignore underlying informations' semantics from these texts. In this paper, we propose a novel approach to combine traditional network analysis methods for overlapping community detection with topic-model based text mining techniques. C1 [Munoz, Ricardo; Rios, Sebastian A.] Univ Chile, Dept Ind Engn, Santiago, Chile. RP Munoz, R (reprint author), Univ Chile, Dept Ind Engn, Santiago, Chile. EM rimunoz@dii.uchile.cl; srios@dii.uchile.cl RI munoz, ricardo/ OI munoz, ricardo/0000-0003-2001-8008 CR Adamcsek B, 2006, BIOINFORMATICS, V22, P1021, DOI 10.1093/bioinformatics/btl039 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOBROW DG, 2002, REFLECTIONS, V4, P47, DOI 10.1162/152417302762251336 Cazabet Remy, 2010, Proceedings of the 2010 IEEE Second International Conference on Social Computing (SocialCom 2010). the Second IEEE International Conference on Privacy, Security, Risk and Trust (PASSAT 2010), DOI 10.1109/SocialCom.2010.51 Gregory S, 2009, STUD COMPUT INTELL, V207, P47 Gregory Steve, 2010, NEW J PHYS Lancichinetti A, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018961 Nicosia V, 2009, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2009/03/P03024 Palla Gergely, 2005, NATURE, V435 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wenger E., 2002, CULTIVATING COMMUNIT Xie J., 2011, ICDM 2011 WORKSH DMC NR 12 TC 1 Z9 1 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-61499-105-2; 978-1-61499-104-5 J9 FRONT ARTIF INTEL AP PY 2012 VL 243 BP 736 EP 745 DI 10.3233/978-1-61499-105-2-736 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BA1RD UT WOS:000332936700074 ER PT B AU Lin, J Williamson, S Borne, KD DeBarr, D AF Lin, Jessica Williamson, Sheri Borne, Kirk D. DeBarr, David BE Way, MJ Scargle, JD Ali, KM Srivastava, AN TI Pattern Recognition in Time Series SO ADVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY SE Chapman & Hall-CRC Data Mining and Knowledge Discovery Series LA English DT Article; Book Chapter ID ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINES; PHOTOMETRIC REDSHIFTS; EM ALGORITHM; CLASSIFICATION; ENSEMBLE; DISTANCE; MODEL; SDSS C1 [Lin, Jessica; Williamson, Sheri] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. [Borne, Kirk D.] George Mason Univ, Dept Computat & Data Sci, Fairfax, VA 22030 USA. [DeBarr, David] Microsoft Corp, Seattle, WA USA. RP Lin, J (reprint author), George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. CR Agrawal R, 1995, P 21 INT C VER LARG, P502 Agrawal R., 1993, P 4 INT C FDN DAT OR, P69 Aitkenhead MJ, 2003, ENG APPL ARTIF INTEL, V16, P167, DOI 10.1016/S0952-1976(03)00042-3 Alcock C, 2001, ASTROPHYS J SUPPL S, V136, P439, DOI 10.1086/322529 Amini M, 2006, COMPUT SECUR, V25, P459, DOI 10.1016/j.cose.2006.05.003 Bakalov P., 2005, P ACM INT S ADV GEOG Bloom JS, 2008, ASTRON NACHR, V329, P284, DOI 10.1002/asna.200710957 Borne KD, 2008, ASTRON NACHR, V329, P255, DOI 10.1002/asna.200710946 Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Bramich DM, 2008, MON NOT R ASTRON SOC, V386, P887, DOI 10.1111/j.1365-2966.2008.13053.x Camerra A., 2010, IEEE INT C DAT MIN, P58 Castro N., 2010, SDM, P665 Chan KP, 1999, PROC INT CONF DATA, P126 Chandola V., 2009, 09004 U MINN COMP SC Chaovalitwongse W A, 2008, Cybernetics and Systems Analysis, V44, DOI 10.1007/s10559-008-0012-y Chen J. S., 2005, P 5 INT C AUD VID BA Chen L., 2004, P 30 INT C VER LARG, V30, P792 Chen L., 2005, THESIS U WATERLOO WA Chiu B., 2003, P 9 ACM SIGKDD INT C, P493 Collister AA, 2004, PUBL ASTRON SOC PAC, V116, P345, DOI 10.1086/383254 Cortes C., 2000, AC SIGKDD INT P C KN, P9 CROCHEMORE M, 1994, ALGORITHMICA, V12, P247, DOI 10.1007/BF01185427 Dasgupta D., 1999, P 8 INT C INT SYST D Dede G, 2010, DIGIT SIGNAL PROCESS, V20, P763, DOI 10.1016/j.dsp.2009.10.004 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deng K, 1997, 1997 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION - CIRA '97, PROCEEDINGS, P246 Desai AA, 2010, PATTERN RECOGN, V43, P2582, DOI 10.1016/j.patcog.2010.01.008 Ding H., 2008, P VLDB ENDOWMENT, V1, P1542, DOI DOI 10.1145/1454159.1454226 Duda R. O., 2002, PATTERN CLASSIFICATI Eads D, 2002, P SOC PHOTO-OPT INS, V4787, P74, DOI 10.1117/12.453526 Faloutsos C., 1994, SIGMOD Record, V23 Garg A., 2008, THESIS HARVARD U Ge X., 2000, P 6 ACM SIGKDD INT C, P81, DOI 10.1145/347090.347109 Geurts P., 2001, P 5 EUR C PRINC DAT, P115 Goldberger A. L., 1997, DISCOVERY, V1 Han J, 2000, DATA MINING CONCEPTS Hugueney B, 2006, LECT NOTES ARTIF INT, V4213, P545 Kadous M., 2002, UCI KDD ARCH 0226 Kaiser N, 2004, P SOC PHOTO-OPT INS, V5489, P11, DOI 10.1117/12.552472 Kalpakis K, 2001, 2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P273 Kampouraki A, 2009, IEEE T INF TECHNOL B, V13, P512, DOI 10.1109/TITB.2008.2003323 Keogh E., 2004, DATA MINING MACHINE Keogh E., 2005, P 5 IEEE INT C DAT M, P226, DOI [DOI 10.1109/ICDM.2005.79, 10.1109/ICDM.2005.79] Keogh E., 2004, P 10 ACM SIGKDD INT Keogh E., 2002, P 8 ACM SIGKDD INT C, P550 Keogh E., 2002, P 28 INT C VER LARG Keogh E., CLUSTERING TIME SERI Keogh E, 2002, P 8 ACM SIGKDD INT C, P102 Keogh E, 2001, SIGMOD RECORD, V30, P151 Keogh E. J., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Keogh E., 2006, KNOWLEDGE INFORM SYS Keogh E. J., 2006, UCR TIME SERIES CLAS Kim KJ, 2008, NEUROCOMPUTING, V71, P1604, DOI 10.1016/j.neucom.2007.04.008 Krasnopolsky VM, 2007, NEURAL NETWORKS, V20, P454, DOI 10.1016/j.neunet.2007.04.008 LAWRENCE CE, 1990, PROTEINS, V7, P41, DOI 10.1002/prot.340070105 Li M., 1997, INTRO KOLMOGOROV COM Liao TW, 2005, PATTERN RECOGN, V38, P1857, DOI 10.1016/j.patcog.2005.01.025 Lin JL, 2002, 2002 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS, P53 Lin J., 2004, 9 C EXT DAT TECHN ED Lin J., 2009, P 21 INT C SCI STAT Lin J, 2007, DATA MIN KNOWL DISC, V15, P107, DOI 10.1007/s10618-007-0064-z Lippmann R.P., 1987, ASSP MAGAZINE IEEE, V4, P4, DOI 10.1109/MASSP.1987.1165576. Lkhagva B., 2006, P 22 INT C DAT ENG W, P115 LSST Science Collaboration, 2009, ARXIV09120201 LSST S Marteau PF, 2009, IEEE T PATTERN ANAL, V31, P306, DOI 10.1109/TPAMI.2008.76 McGovern A, 2011, DATA MIN KNOWL DISC, V22, P232, DOI 10.1007/s10618-010-0193-7 McQueen J., P 5 BERK S MATH STAT, V1, P281 Minku F.L., 2008, NEURAL NETWORKS, V21 Minnen D., 2007, 22 C ART INT AAAI 07 Minnen D., 2006, SNOWB LEARN WORKSH S Morchen F., 2005, P 11 ACM SIGKDD INT, P660, DOI 10.1145/1081870.1081953 Mueen A., 2010, P 16 ACM SIGKDD INT Mueen A., 2009, P 2009 SIAM INT C DA Murakami K., 2004, P JSME C ROB MECH NA Nanopoulos A, 2001, INFORMATION PROCESSING AND TECHNOLOGY, P49 Olszewski R., 2001, THESIS CARNEGIE MELL Pomerleau D. A., 1989, CMUCS89107 Pouget F., 2006, P 18 ANN 1 C BALT MD Preston D., 2009, SDM 2009 Radovanovic M., 2010, SDM, P677 Ratanamahatana CA, 2004, SIAM PROC S, P11 Rau A, 2009, PUBL ASTRON SOC PAC, V121, P1334 Rebbapragada U, 2009, MACH LEARN, V74, P281, DOI 10.1007/s10994-008-5093-3 Rodrigues PP, 2006, SIAM PROC S, P499 Rodriguez JJ, 2005, KNOWL-BASED SYST, V18, P171, DOI 10.1016/j.knosys.2004.10.007 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shieh J., 2008, P 14 ACM SIGKDD INT, P623, DOI 10.1145/1401890.1401966 Starr D., 2008, P 7 PYTH SCI C Szymanski MK, 2005, ACTA ASTRONOM, V55, P43 Tang H, 2008, KNOWL-BASED SYST, V21, P666, DOI 10.1016/j.knosys.2008.03.022 Vapnik V., 1998, STAT LEARNING THEORY Vlachos M., 2002, P 18 INT C DAT ENG I Wang T, 2009, RES ASTRON ASTROPHYS, V9, P390, DOI 10.1088/1674-4527/9/4/002 Wang XZ, 2006, DATA MIN KNOWL DISC, V13, P335, DOI 10.1007/s10618-005-0039-x Wei L., 2006, P 12 ACM SIGKDD INT, P748, DOI 10.1145/1150402.1150498 Yankov D, 2008, KNOWL INF SYST, V17, P241, DOI 10.1007/s10115-008-0131-9 Yi B.K., 2000, P 26 INT C VER LARG, P385 Zhou ZH, 2002, ARTIF INTELL, V137, P239, DOI 10.1016/S0004-3702(02)00190-X Zhu X, 2005, 1530 U WISC MAD COMP NR 100 TC 1 Z9 1 PU CRC PRESS-TAYLOR & FRANCIS GROUP PI BOCA RATON PA 6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA BN 978-1-4398-4174-7; 978-1-4398-4173-0 J9 CH CRC DATA MIN KNOW PY 2012 BP 617 EP 645 PG 29 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC2TP UT WOS:000351309800030 ER PT S AU Musto, C Semeraro, G Lops, P de Gemmis, M Narducci, F AF Musto, Cataldo Semeraro, Giovanni Lops, Pasquale de Gemmis, Marco Narducci, Fedelucio BE Huemer, C Lops, P TI Leveraging Social Media Sources to Generate Personalized Music Playlists SO E-COMMERCE AND WEB TECHNOLOGIES, EC-WEB 2012 SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 13th International Conference on Electronic Commerce and Web Technologies (EC-Web) CY SEP 03-07, 2012 CL Vienna, AUSTRIA DE Music Recommendation; Social Media; Personalization; DBPedia ID RECOMMENDATION AB This paper presents MyMusic, a system that exploits social media sources for generating personalized music playlists. This work is based on the idea that information extracted from social networks, such as Facebook and Last. fm, might be effectively exploited for personalization tasks. Indeed, information related to music preferences of users can be easily gathered from social platforms and used to define a model of user interests. The use of social media is a very cheap and effective way to overcome the classical cold start problem of recommender systems. In this work we enriched social media-based playlists with new artists related to those the user already likes. Specifically, we compare two different enrichment techniques: the first leverages the knowledge stored on DBpedia, the structured version of Wikipedia, while the second is based on the content-based similarity between descriptions of artists. The final playlist is ranked and finally presented to the user that can listen to the songs and express her feedbacks. A prototype version of MyMusic was made available online in order to carry out a preliminary user study to evaluate the best enrichment strategy. The preliminary results encouraged keeping on this research. C1 [Musto, Cataldo; Semeraro, Giovanni; Lops, Pasquale; de Gemmis, Marco; Narducci, Fedelucio] Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. RP Musto, C (reprint author), Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. EM cataldomusto@di.uniba.it; semeraro@di.uniba.it; lops@di.uniba.it; degemmis@di.uniba.it; narducci@di.uniba.it CR Baltrunas L, 2011, LECT NOTES BUS INF P, V85, P89 Bu J., 2010, P ACM INT C MULT, P391, DOI 10.1145/1873951.1874005 Celma O., 2011, P 5 ACM C REC SYST R, P7 Celma O, 2008, J WEB SEMANT, V6, P250, DOI 10.1016/j.websem.2008.09.004 Celma O., 2005, 6 INT C MUS INF RETR, P464 CELMA O, 2010, MUS REC DISC, P1 Eppler MJ, 2004, INFORM SOC, V20, P325, DOI 10.1080/01972240490507974 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Hahn C, 2010, LECT NOTES COMPUT SC, V6389, P427 Ho J., 2001, P 2001 INT ACM SIGGR, P91 Kobilarov G., 2009, P DEV TRACK 18 INT W Lamere P, 2008, J NEW MUSIC RES, V37, P101, DOI 10.1080/09298210802479284 Lee JS, 2007, LECT NOTES COMPUT SC, V4836, P45 Levy M, 2008, J NEW MUSIC RES, V37, P137, DOI 10.1080/09298210802479292 Li Q., 2004, SIGIR 04, P532 Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3 Mulvenna M. D., 2000, Communications of the ACM, V43, DOI 10.1145/345124.345165 Passant A., 2008, SOC DAT WEB WORKSH 7 Reddy Sasank, 2006, P 1 ACM INT WORKSH H, P25, DOI 10.1145/1178745.1178754 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schedl M., 2012, P 2 WORKSH CONT AW R Shardanand U., 1994, THESIS MASSACHUSETTS Wang D., 2010, ISMIR, P57 NR 23 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-32273-0; 978-3-642-32272-3 J9 LECT NOTES BUS INF P PY 2012 VL 123 BP 112 EP 123 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BB6WD UT WOS:000345068100010 ER PT J AU Balog, K Fang, Y de Rijke, M Serdyukov, P Si, L AF Balog, Krisztian Fang, Yi de Rijke, Maarten Serdyukov, Pavel Si, Luo TI Expertise Retrieval SO FOUNDATIONS AND TRENDS IN INFORMATION RETRIEVAL LA English DT Article AB People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains, such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research. C1 [Balog, Krisztian] NTNU, Dept Comp & Informat Sci, N-7491 Trondheim, Norway. [Fang, Yi] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. [de Rijke, Maarten] Univ Amsterdam, Intelligent Syst Lab Amsterdam, NL-1098 XII Amsterdam, Netherlands. [Serdyukov, Pavel] Yandex, Moscow 119021, Russia. [Si, Luo] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. RP Balog, K (reprint author), NTNU, Dept Comp & Informat Sci, Sem Saelandsvei 7-9, N-7491 Trondheim, Norway. EM krisztian.balog@idi.ntnu.no; yfang.purdue@gmail.com; derijke@uva.nl; pavser@yandex-team.ru; lsi@purdue.edu RI Balog, Krisztian/ OI Balog, Krisztian/0000-0003-2762-721X FU COMIDOR project - Norwegian Research Council; NSF [IIS-0746830, CNS-1012208, IIS-1017837]; Purdue Cyber Center; Center for Science of Information (CSoI), an NSF Science and Technology Center [CCF-0939370]; European Union's ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme, CIP ICT-PSP [250430]; European Community's Seventh Framework Programme (FP7/2007-2013) [258191]; (LiMoSINe project) [288024]; Netherlands Organisation for Scientific Research (NWO) [612.061.814, 612.061.815, 640.004.802, 380-70-011, 727.011.005, 612.001.116]; Center for Creation, Content and Technology (CCCT); Hyperlocal Service Platform project - Service Innovation ICT program; WAHSP; BILAND projects - CLARIN-nl program; Dutch national program COMMIT; ESF Research Network Program ELIAS FX Krisztian Balog was supported by the COMIDOR project, funded by the Norwegian Research Council.The research of Yi Fang and Luo Si was partially supported by the NSF research grants IIS-0746830, CNS-1012208, IIS-1017837 and research grants from Purdue Cyber Center as well as Google. This work is also partially supported by the Center for Science of Information (CSoI), an NSF Science and Technology Center, under grant agreement CCF-0939370.Maarten de Rijke was partially supported by the European Union's ICT Policy Support Programme as part of the Competitiveness and Innovation Framework Programme, CIP ICT-PSP under grant agreement nr 250430 (Galateas), the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements nr 258191 (PROMISE Network of Excellence) and 288024 (LiMoSINe project), the Netherlands Organisation for Scientific Research (NWO) under project nrs 612.061.814, 612.061.815, 640.004.802, 380-70-011, 727.011.005, 612.001.116, the Center for Creation, Content and Technology (CCCT), the Hyperlocal Service Platform project funded by the Service Innovation & ICT program, the WAHSP and BILAND projects funded by the CLARIN-nl program, the Dutch national program COMMIT, and the ESF Research Network Program ELIAS. CR Ackerman M. S., 2000, Information Systems Frontiers, V2, DOI 10.1023/A:1026572712714 Adafre S., 2005, P 3 INT WORKSH LINK, P90, DOI 10.1145/1134271.1134284 Adafre S. Fissaha, 2007, REC ADV NAT LANG PRO Adamic LA, 2008, P 17 INT C WORLD WID, P665, DOI [10.1145/1367497.1367587, DOI 10.1145/1367497.1367587] AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855 Amati Giambattista, 2003, THESIS U GLASGOW [Anonymous], 1999, ECSCW 99 WORKSH KNOW Artiles J., 2005, P 28 ANN INT ACM SIG, P569, DOI 10.1145/1076034.1076132 Artiles J., 2010, CROSS LANGUAGE EVALU Artiles J., 2009, WEB PEOPL SEARCH EV Artiles Javier, 2007, P 4 INT WORKSH SEM E, P64, DOI 10.3115/1621474.1621486 Asmussen S, 1987, APPL PROBABILITY QUE Bailey P., 2007, ACM SIGIR FORUM, V41, P42, DOI 10.1145/1328964.1328969 Bailey P., 2008, P TEXT RETRIEVAL C T Balog K., 2008, THESIS U AMSTERDAM Balog K, 2009, INFORM PROCESS MANAG, V45, P1, DOI 10.1016/j.ipm.2008.06.003 BALOG K, 2008, P EUR C IR RES ECIR, V4956, P296 Balog K., 2007, P 30 ANN INT ACM SIG, P551, DOI DOI 10.1145/1277741.1277836 Balog K., 2011, ACM T INFORM SYSTEMS, V29 Balog K., 2007, P SIGIR 07, P821, DOI 10.1145/1277741.1277926 BALOG K., 2008, P 31 ANN INT ACM SIG, P753, DOI 10.1145/1390334.1390486 Balog K., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148181 Balog K., 2007, P TEXT RETRIEVAL C T Balog K., 2006, P 15 INT C WORLD WID, P1035, DOI DOI 10.1145/1135777.1136002 Balog K., 2011, P TEXT RETRIEVAL C T Balog K., 2010, P TEXT RETRIEVAL C T Balog K., 2008, P TEXT RETRIEVAL C T Balog K, 2009, WEAVING SERVICES AND PEOPLE ON THE WORLD WIDE WEB, P301, DOI 10.1007/978-3-642-00570-1_15 Balog K, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2657 Balog K., 2009, P TEXT RETRIEVAL C T Balog K., 2008, P 17 ACM C INF KNOWL, P489, DOI DOI 10.1145/1458082.1458148 Balog Krisztian, 2008, P 31 ANN INT ACM SIG, P371, DOI 10.1145/1390334.1390399 Bansal B., 2011, TALK Bao S., 2007, P TEXT RETRIEVAL C T Baumard P., 2001, TACIT KNOWLEDGE ORG Becerra-Fernandez I., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1183463.1183464 Becerra-Fernandez I, 2000, KNOWL-BASED SYST, V13, P315, DOI 10.1016/S0950-7051(00)00091-5 Bennett J., 2007, KDD 07, P35 Berendsen R., 2012, P EUR C INF RETR ECI Berendsen R., 2012, CLEF 2012 C LABS EV Blanco R., 2011, P INT WORKSH ENT OR, P65 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borgatti SP, 2003, MANAGE SCI, V49, P432, DOI 10.1287/mnsc.49.4.432.14428 Bron M., 2011, P INT C THEOR PRACT Bron M., 2010, P 19 ACM INT C INF K, P1079, DOI 10.1145/1871437.1871574 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Callan J., 2002, ADV INFORM RETRIEVAL, V7, P127, DOI DOI 10.1007/0-306-47019-5_ Campbell C. S., 2003, P 12 INT C INF KNOWL, P528, DOI DOI 10.1145/956863.956965 Cao Y., 2006, P TEXT RETRIEVAL C T Cetintas S., 2011, P INT ACM SIGIR C RE, P1209 Chapelle O, 2010, INFORM RETRIEVAL, V13, P216, DOI 10.1007/s10791-009-9110-3 Charlin L., 2011, P C UNC ART INT UAI, P86 Chen H., 2006, P TEXT RETRIEVAL C T Constant D, 1996, ORGAN SCI, V7, P119, DOI 10.1287/orsc.7.2.119 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 Craswell N., 2003, TREC 02, P78 Craswell N, 2005, P 28 ANN INT ACM SIG, P416, DOI 10.1145/1076034.1076106 Craswell N., 2001, AUSW P Craswell N., 2006, P TEXT RETRIEVAL C T Craswell N., 2002, P TEXT RETRIEVAL C P Csomai A, 2008, IEEE INTELL SYST, V23, P34, DOI 10.1109/MIS.2008.86 Cucerzan S., 2007, P 2007 JOINT C EMP M, P708 CUMMINS R, 2010, P EUR C ART INT ECAI, V215, P535 Das S., 2011, INT WORKSH ENT OR SE, P53 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Demartini G., 2010, INEX 09, P254 DEMARTINI G, 2009, P IN EV XML RETR INE, V5631, P243 Demartini G., 2008, P INT SEM WEB C ISWC Demartini G, 2009, LECT NOTES COMPUT SC, V5478, P189, DOI 10.1007/978-3-642-00958-7_19 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deng H, 2009, P 18 ACM C INF KNOWL, P1733, DOI 10.1145/1645953.1646217 Deng HB, 2008, IEEE DATA MINING, P163, DOI 10.1109/ICDM.2008.29 Dennis J.E., 1996, NUMERICAL METHODS UN DERIJKE M, 2010, P 2010 C MULT MULT I, V6360, P94 DEVRIES AP, 2008, P IN EV XML RETR INE, V4862, P245 Doan A, 2005, AI MAG, V26, P83 Dom B., 2003, P 8 ACM SIGMOD WORKS, P42, DOI DOI 10.1145/882082.882093 Dong X., 2005, P ACM SIGMOD INT C M, P85, DOI 10.1145/1066157.1066168 Duan H., 2007, P TEXT RETRIEVAL C T EHRLICH K, 2008, P ANN SIGCHI C HUM F, P1093, DOI 10.1145/1357054.1357224 EHRLICH K, 2007, P 2007 INT ACM C SUP, P117, DOI 10.1145/1316624.1316642 Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 FANG H, 2007, P EUR C IR RES ECIR, V4425, P418 Fang Y, 2011, INFORM RETRIEVAL, V14, P158, DOI 10.1007/s10791-010-9139-3 Fang Y., 2009, SIGIR WORKSH LEARN R Fang Y., 2008, SERCTR294 PURD U DEP Fang Y, 2010, INFORM RETRIEVAL, V13, P618, DOI 10.1007/s10791-010-9127-7 Fang Yang, 2011, Proceedings of the 2011 9th World Congress on Intelligent Control and Automation (WCICA 2011), DOI 10.1109/WCICA.2011.5970704 FANG Y, 2010, P ANN INT ACM SIGIR, P683, DOI 10.1145/1835449.1835563 Fox EA, 1994, TEXT RETRIEVAL C TRE, V500-215, P243 Fu YP, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P77 Guan Z., 2012, IEEE T KNOWLEDGE DAT HALPIN H., 2010, P INT WORKSH EV SEM Heath T., 2006, P CHI2006 WORKSH REI Herbrich R., 2000, ADV LARGE MARGIN CLA, V88, P115 Hertzum M., 2000, New Review of Information Behaviour Research, V1 Hertzum M, 2000, INFORM PROCESS MANAG, V36, P761, DOI 10.1016/S0306-4573(00)00011-X Hofmann K, 2010, J AM SOC INF SCI TEC, V61, P994, DOI 10.1002/asi.21292 Hofmann K., 2008, P SIGIR WORKSH FUT C Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Hogan A., 2007, P INT EXPERTFINDER W Horowitz D., 2010, P 19 INT C WORLD WID, P431, DOI DOI 10.1145/1772690.1772735 Jiang J., 2007, P TEXT RETRIEVAL C T Jiang J., 2008, P SIGIR WORKSH FUT C JIANG JP, 2009, P IN EV XML RETR INE, V5631, P264 Jijkoun V., 2008, P 2 WORKSH AN NOIS U, P23, DOI 10.1145/1390749.1390755 KAPTEIN R, 2009, P IN EV XML RETR INE, V5631, P273 Kaptein Rianne, 2010, P 19 ACM C INF KNOWL, P69, DOI 10.1145/1871437.1871451 Karimzadehgan M., 2008, P 17 ACM C INF KNOWL, P1113, DOI 10.1145/1458082.1458230 Karimzadehgan M., 2009, CIKM, P1697 KARIMZADEHGAN M, 2009, P EUR C IR RES ECIR, V5478, P177 Khalid M., 2008, P EUR C IR RES ECIR, P705 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Ko J., 2007, P 30 ANN INT ACM SIG, P343, DOI 10.1145/1277741.1277801 Kukla G, 2012, COMPUT J, V55, P258, DOI 10.1093/comjnl/bxr093 Kuncheva LI, 2003, MACH LEARN, V51, P181, DOI 10.1023/A:1022859003006 Lappas T., 2009, P 15 ACM SIGKDD INT, P467, DOI 10.1145/1557019.1557074 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Lee J. H., 1995, SIGIR 95 P 18 ANN IN, P180 Li J.-Z., 2007, P WWW 2007, P1271, DOI 10.1145/1242572.1242803 Liebregts R., 2009, P 31 EUR C IR RES AD, P587 Liker J., 2007, TOYOTA TALENT DEV YO Lin B., 2011, P INT WORKSH ENT OR, P14 Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Liu X., 2005, P 14 ACM INT C INF K, P315, DOI DOI 10.1145/1099554.1099644 MACDONALD C, 2007, P EUR C IR RES ECIR, V4425, P431 Macdonald C., 2009, THESIS U GLASGOW Macdonald Craig, 2010, SIGIR Forum, V44, DOI 10.1145/1842890.1842899 Macdonald C., 2009, P ACM INT C INF KNOW, P1983 Macdonald C., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148345 Macdonald C, 2009, COMPUT J, V52, P729, DOI 10.1093/comjnl/bxm112 Macdonald C, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P816, DOI 10.1145/1571941.1572143 Macdonald C., 2008, P 17 ACM C INF KNOWL, P1043, DOI 10.1145/1458082.1458221 Macdonald C., 2010, INFORM PROCESSING MA, V74, P376 MACDONALD C, 2011, P EUR C IR RES ECIR, V6611, P517 MACDONALD C, 2008, P EUR C IR RES ECIR, V4956, P283 Macdonald C., 2007, P ACM INT C INF KNOW, P341, DOI 10.1145/1321440.1321490 Macdonald C., 2006, P 15 ACM INT C INF K, P387, DOI 10.1145/1183614.1183671 MACDONALD C, 2008, P EUR C IR RES ECIR, V4956, P555 Maclin R., 1999, J ARTIFICIAL INTELLI, V11, P169 Mann G. S., 2003, P 7 C NAT LANG LEARN, P33 Manning C.D., 2008, INTRO INFORM RETRIEV Maybury M., 2006, MTR06B000040 McDonald D. W., 2001, P ACM C SUPP GROUP W, P214 McDonald D. W., 2000, P ACM C COMP SUPP CO, P231, DOI DOI 10.1145/358916.3589941 Meij E, 2011, J WEB SEMANT, V9, P418, DOI 10.1016/j.websem.2011.04.001 Meij E., 2009, P INT SEM WEB C ISWC, P415 Meij E., 2012, P ACM INT C WEB SEAR MENZEL H, 1966, ANNU REV INFORM SCI, V1, P41 Menzel H., 1960, REV STUDIES FLOW INF Mihalcea Rada, 2007, P 16 ACM C INF KNOWL, P233, DOI DOI 10.1145/1321440.1321475 Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150 MIMNO D, 2007, P 13 ACM SIGKDD INT, P500, DOI 10.1145/1281192.1281247 Mockus A, 2002, ICSE 2002: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P503 Moreira C., 2011, THESIS TU LISBON Moreira C, 2011, LECT NOTES ARTIF INT, V7026, P431, DOI 10.1007/978-3-642-24769-9_32 Morris M. R., 2010, P 4 INT AAAI C WEBL, P291 Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 Neumayer R., 2012, EUR C INF RETR ECIR, P133 NG A, 2001, P ADV NEUR INF PROC, P841 Noll MG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P612, DOI 10.1145/1571941.1572046 Page L, 1999, PAGERANK CITATION RA Pal A., 2012, P INT AAAI C WEBL SO, P274 Peacock M., 2009, CMSWIRE Pearl J., 1988, PROBABILISTIC REASON Pehcevski J, 2008, LECT NOTES COMPUT SC, V4956, P258 Petkova D, 2006, PROC INT C TOOLS ART, P599 Petkova D., 2007, P 16 ACM C INF KNOWL, P731, DOI 10.1145/1321440.1321542 Plachouras V., 2011, P WORKSH DIV DOC RET, P63 Pound J., 2010, P 19 INT C WORLD WID, P771, DOI DOI 10.1145/1772690.1772769 Prusak L, 2000, WORKING KNOWLEDGE OR Riahi F., 2012, P 21 INT C COMP WORL, P791 Richardson M., 2011, P INT WORLD WID WEB, P755 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J.J., 1971, RELEVANCE FEEDBACK I ROSENBER.V, 1967, INFORM STORAGE RET, V3, P119, DOI 10.1016/0020-0271(67)90005-8 Rosen-Zvi M., 2004, P 20 C UNC ART INT, P487 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 Santos R. L. T., 2010, AD PERS FUS HET INF, P1 Santos RLT, 2011, INFORM PROCESS MANAG, V47, P467, DOI 10.1016/j.ipm.2010.11.009 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SCHWARTZ MF, 1993, COMMUN ACM, V36, P78, DOI 10.1145/163381.163402 Seid D., 2000, DEMOIR HYBRID ARCHIT SERDYUKOV P, 2007, P EUR C IR RES ECIR, V4425, P737 Serdyukov P., 2009, P TEXT RETRIEVAL C T SERDYUKOV P, 2011, P EUR C IR RES ECIR, V6611, P399 Serdyukov P., 2008, P 17 ACM C INF KNOWL, P1133, DOI 10.1145/1458082.1458232 SERDYUKOV P, 2008, P EUR C IR RES ECIR, V4956, P309 Serdyukov P., 2008, P 31 ANN INT ACM SIG, P17 Shami S. N., 2008, P ACM C HUM FACT COM, P1089 Shami S. N., 2007, P ACM C SUPP GROUP W, P379 Shokouhi M., 2011, FDN TRENDS INFORM RE, V5 SMIRNOVA E, 2011, P EUR C IR RES ECIR, V6611, P580 Soboroff I., 2007, P TEXT RETRIEVAL C T Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 Song Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P342, DOI 10.1145/1255175.1255243 Sorg P., 2011, P INT C KNOWL DISC I Sorg P., 2010, WORKSH CROSS LANG EV Su H., 2012, P PAC AS C KNOWL DIS Sutton C., 2007, INTRO STAT RELATIONA, P93 TANG J, 2009, P 15 ACM SIGKDD INT, P807, DOI DOI 10.1145/1557019.1557108>(2009) Tang J., 2008, P 14 ACM SIGKDD INT, P990, DOI 10.1145/1401890.1402008 Tang J, 2011, MACH LEARN, V82, P211, DOI 10.1007/s10994-010-5212-9 Tang WB, 2012, NEUROCOMPUTING, V76, P71, DOI 10.1016/j.neucom.2011.04.039 Tax DMJ, 2000, PATTERN RECOGN, V33, P1475, DOI 10.1016/S0031-3203(99)00138-7 Terveen L., 2005, ACM Transactions on Computer-Human Interaction, V12, DOI 10.1145/1096737.1096740 Tsikrika T., 2008, INEX 07, P306 Tu Y, 2010, P 23 INT C COMP LING, P1265 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Vapnik V. N., 2000, NATURE STAT LEARNING Weerkamp W., 2008, P ASS COMP LING HUM, P923 Weerkamp W, 2011, INFORM RETRIEVAL, V14, P515, DOI 10.1007/s10791-011-9165-9 Weng J., 2010, P 3 ACM INT C WEB SE, P261, DOI DOI 10.1145/1718487.1718520 Wiig KM, 1997, EXPERT SYST APPL, V13, P1, DOI 10.1016/S0957-4174(97)00018-3 Winkler W., 1999, TECHNICAL REPORT Woudstra L, 2008, INFORM PROCESS MANAG, V44, P1267, DOI 10.1016/j.ipm.2007.07.004 Xue Y., 2008, P TEXT RETRIEVAL C T YAHYAEI S, 2008, P EUR C IR RES ECIR, V4956, P406 Yang Zhang, 2009, Proceedings of the 2009 International Conference on Computational Intelligence and Security (CIS 2009), DOI 10.1109/CIS.2009.263 Yarosh S., 2012, P 2012 ACM ANN C HUM, P2247 Yimam-Seid D, 2003, J ORG COMP ELECT COM, V13, P1, DOI 10.1207/S15327744JOCE1301_1 Yom-Tov Elad, 2005, P 28 ANN INT ACM SIG, P512, DOI 10.1145/1076034.1076121 Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang J., 2007, P 16 INT C WORLD WID, P221, DOI DOI 10.1145/1242572.1242603 Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 ZHANG J, 2008, P ADV KNOWL DISC DAT, V5012, P466 Zhang J., 2005, P 2005 INT ACM SIGGR, P71, DOI 10.1145/1099203.1099214 Zhang M., 2002, P TEXT RETR C TREC, P586 Zhu J., 2008, P CIKM 2008, P1421, DOI 10.1145/1458082.1458312 Zhu J., 2007, P TEXT RETRIEVAL C T Zhu JH, 2010, KNOWL INF SYST, V23, P29, DOI 10.1007/s10115-009-0202-6 Zhu J., 2008, P 10 ACM WORKSH WEB, P25, DOI 10.1145/1458502.1458507 ZHU JH, 2008, P IN EV XML RETR INE, V4862, P336 Zhu JH, 2009, J AM SOC INF SCI TEC, V60, P694, DOI 10.1002/asi.21012 NR 239 TC 12 Z9 13 PU NOW PUBLISHERS INC PI HANOVER PA PO BOX 1024, HANOVER, MA 02339 USA SN 1554-0669 EI 1554-0677 J9 FOUND TRENDS INF RET JI Found. Trends Inf. Retr. PY 2012 VL 6 IS 2-3 BP 127 EP 256 DI 10.1561/1500000024 PG 130 WC Computer Science, Information Systems SC Computer Science GA V32VI UT WOS:000208978300001 ER PT S AU Boella, G Di Caro, L Lesmo, L Daniele, R Robaldo, L AF Boella, Guido Di Caro, Luigi Lesmo, Leonardo Daniele, Rispoli Robaldo, Livio BE Schafer, B TI Multi-label Classification of Legislative Text into EuroVoc SO Legal Knowledge and Information Systems (JURIX 2012) SE Frontiers in Artificial Intelligence and Applications LA English DT Proceedings Paper CT 25th Annual International Conference on Legal Knowledge and Information Systems (JURIX) CY DEC 17-19, 2012 CL Amsterdam, NETHERLANDS SP Dutch Fdn Legal Knowledge Syst HO Univ Amsterdam DE Multi-label Text Classification; EuroVoc AB In this paper we present a novel method for the automatic classification of multi-label text documents. In principle, automatic classification of text is usually tackled by supervised Machine Learning techniques like Support Vector Machines (SVM), that typically achieve state-of-the-art accuracy in several domains. Nevertheless, SVM can not handle multi-labeled documents, thus a specific preprocessing of the data is needed. In this paper we present a novel technique for the transformation of multi-label data into mono-label that is able to maintain all the information, allowing the use of standard approaches like SVM. We then evaluate our system using JRC-Acquis-it, a large dataset of italian legislation that has been manually annotated according to EuroVoc, demonstrating the potential of our approach compared to the current state of the art. C1 [Boella, Guido; Di Caro, Luigi; Lesmo, Leonardo; Daniele, Rispoli; Robaldo, Livio] Univ Turin, Dipartimento Informat, I-10124 Turin, Italy. RP Boella, G (reprint author), Univ Turin, Dipartimento Informat, I-10124 Turin, Italy. CR Aggarwal C. C., 2012, MINING TEXT DATA, P163, DOI 10.1007/978-1-4614-3223-4_6 Boutell MR, 2004, PATTERN RECOGN, V37, P1757, DOI 10.1016/j.patcog.2004.03.009 Clare A., 2001, LNCS LNAI, V2168, P42, DOI DOI 10.1007/3-540-44794-6_ CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Diplaris S, 2005, LECT NOTES COMPUT SC, V3746, P448 Fan RE, 2008, J MACH LEARN RES, V9, P1871 Freund Y., 1995, COMPUTATIONAL LEARNI, V904, P23, DOI DOI 10.1007/3-540-59119-2_166 Godbole S, 2004, LECT NOTES ARTIF INT, V3056, P22 Pouliquen Bruno, 2006, CORR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Steinberger R., 2012, P 8 INT C LANG RES E Thabtah FA, 2004, P 4 IEEE INT C DAT M, P217, DOI DOI 10.1109/ICDM.2004.10117 Tsoumakas G., 2007, INT J DATA WAREHOUS, V3, P1, DOI DOI 10.4018/JDWM.2007070101 Zhang M.L., 2005, 2005 IEEE INT C GRAN, V2, P718 NR 15 TC 0 Z9 0 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-61499-167-0; 978-1-61499-166-3 J9 FRONT ARTIF INTEL AP PY 2012 VL 250 BP 21 EP 30 DI 10.3233/978-1-61499-167-0-21 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BC0DS UT WOS:000348936600003 ER PT B AU Schedl, M AF Schedl, Markus BE Li, T Ogihara, M Tzanetakis, G TI Web-Based and Community-Based Music Information Extraction SO MUSIC DATA MINING SE Chapman & Hall-CRC Data Mining and Knowledge Discovery Series LA English DT Article; Book Chapter ID RETRIEVAL; SIGNALS C1 Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. RP Schedl, M (reprint author), Johannes Kepler Univ Linz, Dept Computat Percept, Altenberger Str 69, A-4040 Linz, Austria. EM markus.schedl@jku.at CR Aucouturier J.-J., 2004, J NEGATIVE RESULTS S, V1 Aucouturier J.-J., 2002, Proceedings 2002 IEEE International Conference on Multimedia and Expo (Cat. No.02TH8604), DOI 10.1109/ICME.2002.1035729 Aucouturier JJ, 2005, IEEE T MULTIMEDIA, V7, P1028, DOI 10.1109/TMM.2005.858380 Baccigalupo C., 2008, P 9 INT C MUS INF RE Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baumann S., 2003, P 3 INT C WEB DEL MU Berenzweig A., 2003, P 4 INT C MUS INF RE Brill E., 1992, P 3 C APPL NAT LANG, P152, DOI 10.3115/974499.974526 Callan J., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Cano P, 2004, P 5 INT S MUS INF RE, P466 Casey MA, 2008, P IEEE, V96, P668, DOI 10.1109/JPROC.2008.916370 Celma O., 2008, THESIS U POMPEU FABR Celma O., 2007, ISMIR 2007 TUTORIAL Celma O., 2006, P 7 INT C MUS INF RE Cohen WW, 2000, COMPUT NETW, V33, P685, DOI 10.1016/S1389-1286(00)00057-8 Cox T.F., 1994, MULTIDIMENSIONAL SCA DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DiNucci D., 1999, DESIGN NEW MEDIA, V53 Ellis D. P. W., 2002, P 3 INT C MUS INF RE Feng David Dagan, 2003, MULTIMEDIA INFORM RE Geleijnse G., 2007, P 8 INT C MUS INF RE Govaerts S., 2009, P 10 INT SOC MUS INF Hofmann T., 1999, P UNCERTAINTY ARTIFI Hu X., 2009, P 10 INT SOC MUS INF Hu X., 2005, P 6 INT C MUS INF RE Kleedorfer F., 2008, P 9 INT C MUS INF RE, P287 Knees P., 2008, P 2 WORKSH LEARN SEM Knees P., 2004, P 5 INT S MUS INF RE, P517 Knees P., 2005, P 6 INT C MUS INF RE, P564 Knees P., 2007, P 30 ANN INT ACM SIG Knees P, 2007, IEEE MULTIMEDIA, V14, P46, DOI 10.1109/MMUL.2007.48 Knees P., 2006, P 14 ACM INT C MULT Kohonen T., 2001, SELF ORG MAPS, V30 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Korst J., 2006, P 3 PHIL S INT ALG S, P205 Kruskal J.B., 1978, PAPER SERIES QUANTIT Laurier C., 2008, P INT C MACH LEARN A Law E., 2007, P 8 INT C MUS INF RE Levy M., 2007, P 8 INT C MUS INF RE Linden G., 2003, IEEE INTERNET COMPUT, V4 Logan B., 2003, P 26 ANN INT ACM SIG Logan B., 2004, P IEEE INT C MULT EX Lubbers D., 2009, P 10 INT SOC MUS INF LUHN HP, 1957, IBM J RES DEV, V1, P309 Macqueen J. B., 1967, SOME METHODS CLASSIF, V1, P281 Mahedero J. P. G., 2005, P 13 ACM INT C MULT, P475, DOI 10.1145/1101149.1101255 Mandel M. I., 2007, P 8 INT C MUS INF RE Mayer R., 2008, P 9 INT C MUS INF RE NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 O'Reilly T., WHAT IS WEB 2 0 DESI Pachet F., 2001, P 1 INT C WEB DEL MU Pampalk E., 2007, P 8 INT C MUS INF RE Pohle T, 2007, IEEE T MULTIMEDIA, V9, P567, DOI 10.1109/TMM.2006.887991 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2001, WORLD WIDE WEB, P285, DOI DOI 10.1145/371920.372071 Schedl M., 2006, P 28 EUR C INF RETR Schedl M., 2007, P 5 WORKSH AD MULT R Schedl M., 2005, P 4 INT WORKSH CONT Schedl M., 2008, THESIS J KEPLER U LI Schedl M., 2010, P IEEE INT C MULT EX Schedl M., 2010, P 33 ANN INT ACM SI Schedl M., 2010, P 11 INT SOC MUS INF Shavitt Y., 2009, P IEEE INT S MULT IS Stober S., 2010, P 11 INT SOC MUS INF Turnbull D., 2007, P 8 INT C MUS INF RE Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560 Vapnik V., 1995, NATURE STAT LEARNING Whitman Brian, 2002, P 2002 INT COMP MUS, P591 Zadel M., 2004, P 5 INT S MUS INF RE Zobel J., 2006, ACM COMPUT SURV, V38, P1 NR 71 TC 2 Z9 2 PU CRC PRESS-TAYLOR & FRANCIS GROUP PI BOCA RATON PA 6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA BN 978-1-4398-3555-5; 978-1-4398-3552-4 J9 CH CRC DATA MIN KNOW PY 2012 BP 219 EP 249 PG 31 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BC8DA UT WOS:000355553300008 ER PT J AU Santos, I Laorden, C Sanz, B Bringas, PG AF Santos, Igor Laorden, Carlos Sanz, Borja Bringas, Pablo G. TI Enhanced Topic-based Vector Space Model for semantics-aware spam filtering SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Spam detection; Information Retrieval; Semantics; Computer security; Machine-learning ID CATEGORIZATION; KERNEL AB Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combat these messages including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Currently, many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods are merely syntactic and are unable to account for the underlying semantics of terms within the messages. In this paper, we explore the use of semantics in spam filtering by representing e-mails with a recently introduced Information Retrieval model: the enhanced Topic-based Vector Space Model (eTVSM). This model is capable of representing linguistic phenomena using a semantic ontology. Based upon this representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Santos, Igor; Laorden, Carlos; Sanz, Borja; Bringas, Pablo G.] Univ Deusto, Lab Smartness Semant & Secur S3Lab, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, Lab Smartness Semant & Secur S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; claorden@deusto.es; borja.sanz@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Amari S, 1999, NEURAL NETWORKS, V12, P783, DOI 10.1016/S0893-6080(99)00032-5 Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 Androutsopoulos I., 2000, P MACH LEARN TEXT IN Awad A, 2008, IEEE EDOC CONF, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bates M., 1993, CHALLENGES NATURAL L Bayes T., 1764, PHILOS T ROY SOC LON, V53, P370, DOI [10.1093/biomet/45.3-4.296, DOI 10.1098/RSTL.1763.0053] Becker J, 2003, P 6 INT C BUS INF SY, P7 Bishop C. M., 2006, PATTERN RECOGNITION Blanzieri E., 2007, P FLAIRS 20, P441 Bratko A, 2006, J MACH LEARN RES, V7, P2673 Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324 Carnap Rudolf, 1955, PHILOS STUD, VVI, P33, DOI DOI 10.1007/BF02330951 Carpinter J, 2006, COMPUT SECUR, V25, P566, DOI 10.1016/j.cose.2006.06.001 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Castillo E., 1996, EXPERT SYSTEMS PROBA Chapelle O., 2006, SEMISUPERVISED LEARN Cohen D., 1974, EXPLAINING LINGUISTI Cooper G.F., 1991, P 7 C UNC ART INT Cormack G.V., 2007, ACM T INFORM SYSTEMS, V25 Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 Cruse D., 1975, ARCH LINGUIST, V6, P26 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Fellbaum C, 1998, WORDNET ELECT LEXICA Fix E., 1952, 2149004 USAF SCH AV Floridi L., 2003, BLACKWELL GUIDE PHIL Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Geiger D., 1997, MACH LEARN, P131 Gross J. L., 2004, SERIES DISCRETE MATH Heron S., 2009, NETWORK SECURITY JAN, V2009, P11 Holmes G., 1994, WEKA MACHINE LEARNIN, P357 Ide N., 1998, COMPUTATIONAL LINGUI, V24, P2 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Jung J., 2004, P 4 ACM SIGCOMM C IN, P370, DOI 10.1145/1028788.1028838 Karlberger C., 2007, P 1 USENIX WORKSH OF, P1 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Kolcz A., 2004, P 1 C EM ANT CEAS 20 Kotsiantis S., 2007, P 2007 C EM ART INT, P3 Kotsiantis S. B., 2004, WSEAS T INFORM SCI A, V1, P73 Kuropka D., 2004, ADV INFORM SYSTEMS M, V10 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Lin H-T, 2003, STUDY SIGMOID KERNEL LOVINS JB, 1968, MECH TRANSL, V11, P22 Maron O, 1998, ADV NEUR IN, V10, P570 Ming-Tzu KW, 2004, APPL LINGUIST, V25, P291, DOI 10.1093/applin/25.3.291 Mishne Gilad, 2005, P 1 INT WORKSH ADV I, P1 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Nigam K, 1998, AAAI 98 WORKSH LEARN, V752 Pearl J., 1982, P AM ASS ART INT NAT, P133 Platt J., 1999, ADV KERNEL METHODS S, V208 Polyvyanyy A., 2007, THESIS Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN QUINLAN JR, 1986, MACH LEARN, V1, P1, DOI DOI 10.1023/A:1022643204877 Radden Gunther, 1999, THEORY METONYMY, P17 Ramachandran A., 2006, C EM ANT Russell S. J., 2003, ARTIFICIAL INTELLIGE, V2nd Sahami M., 1998, LEARNING TEXT CATEGO, V62, P98 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Sakkis G, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P44 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Ustun B, 2006, CHEMOMETR INTELL LAB, V81, P29, DOI 10.1016/j.chemolab.2005.09.003 Vapnik V. N., 2000, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wittel G., 2004, P 1 C EM ANT CEAS WOLPERT DH, 1992, NEURAL NETWORKS, V5, P241, DOI 10.1016/S0893-6080(05)80023-1 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 Zhou Y., 2007, P 19 IEEE INT C TOOL, V02, P298 NR 76 TC 9 Z9 9 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JAN PY 2012 VL 39 IS 1 BP 437 EP 444 DI 10.1016/j.eswa.2011.07.034 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 837PE UT WOS:000296214900047 ER PT J AU Jiang, SY Pang, GS Wu, ML Kuang, LM AF Jiang, Shengyi Pang, Guansong Wu, Meiling Kuang, Limin TI An improved K-nearest-neighbor algorithm for text categorization SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Text categorization; KNN text categorization; One-pass clustering; Spam filtering ID CLASSIFICATION; MODEL AB Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it has great scalability in many real-word applications. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Jiang, Shengyi; Pang, Guansong; Wu, Meiling; Kuang, Limin] Guangdong Univ Foreign Studies, Sch Informat, Guangzhou 510420, Guangdong, Peoples R China. RP Jiang, SY (reprint author), Guangdong Univ Foreign Studies, Sch Informat, Guangzhou 510420, Guangdong, Peoples R China. EM jiangshengyi@163.com FU National Natural Science Foundation of China [60673191]; Guangdong Province's Institutes of Higher Education [06Z012]; National Natural Science Foundation of Guangdong Province of China [9151026005000002] FX This work was supported by the National Natural Science Foundation of China (No. 60673191), the Natural Science Research Programs of Guangdong Province's Institutes of Higher Education (No. 06Z012) and the National Natural Science Foundation of Guangdong Province of China (No. 9151026005000002). CR Aghbari Z. A., 2005, DATA KHOWL ENG, V52, P33 Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P WORKSH MACH LEARN, P1 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Blanzieri E, 2008, ARTIF INTELL REV, V29, P63, DOI 10.1007/s10462-009-9109-6 Chen RC, 2006, EXPERT SYST APPL, V31, P427, DOI 10.1016/j.eswa.2005.09.079 Esuli A, 2008, INFORM RETRIEVAL, V11, P287, DOI 10.1007/s10791-008-9047-y Frank, 2006, LECT NOTES COMPUTER, P503 Guo GD, 2004, LECT NOTES COMPUT SC, V2945, P559 Hao PY, 2007, EXPERT SYST APPL, V33, P627, DOI 10.1016/j.eswa.2006.06.009 [郝ē§€å…° Hao Xiulan], 2009, [č®”ē®—ęœŗē ”ē©¶äøŽå‘展, Journal of Computer Research and Development], V46, P52 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jiang SY, 2006, Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Vols 1-7, P1190 Jiang SY, 2006, PATTERN RECOGN LETT, V27, P802, DOI 10.1016/j.patrec.2005.11.007 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Koprinska I, 2007, INFORM SCIENCES, V177, P2167, DOI 10.1016/j.ins.2006.12.005 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 [ęŽč£é™† Li Ronglu], 2004, [č®”ē®—ęœŗē ”ē©¶äøŽå‘展, Journal of Computer Research and Development], V41, P539 Rocchio J., 1971, SMART SYSTEM, P67 Ruiz ME, 2002, INFORM RETRIEVAL, V5, P87, DOI 10.1023/A:1012782908347 Sakkis G, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P44 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 [苏金ꠑ SU JinShu], 2006, [č½Æä»¶å­¦ęŠ„, Journal of Software], V17, P1848, DOI 10.1360/jos171848 Tan SB, 2005, EXPERT SYST APPL, V28, P667, DOI 10.1016/j.eswa.2004.12.023 Tan SB, 2008, EXPERT SYST APPL, V35, P279, DOI 10.1016/j.eswa.2007.06.028 Vries A. d., 2002, P 2002 ACM SIGMOD IN, P322 Wang Y, 2007, PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P3436 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Ye Q, 2009, EXPERT SYST APPL, V36, P6527, DOI 10.1016/j.eswa.2008.07.035 NR 31 TC 18 Z9 19 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JAN PY 2012 VL 39 IS 1 BP 1503 EP 1509 DI 10.1016/j.eswa.2011.08.040 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 837PE UT WOS:000296214900156 ER PT J AU Wei, DP Wang, T Wang, J Bernstein, A AF Wei, Dengping Wang, Ting Wang, Ji Bernstein, Abraham TI SAWSDL-iMatcher: A customizable and effective Semantic Web Service matchmaker SO JOURNAL OF WEB SEMANTICS LA English DT Article DE Semantic Web Service discovery; SAWSDL; Matching strategy AB As the number of publicly available services grows, discovering proper services becomes an important issue and has attracted amount of attempts. This paper presents a new customizable and effective matchmaker, called SAWSDL-iMatcher. It supports a matchmaking mechanism, named iXQuery, which extends XQuery with various similarity joins for SAWSDL service discovery. Using SAWSDL-iMatcher, users can flexibly customize their preferred matching strategies according to different application requirements. SAWSDL-iMatcher currently supports several matching strategies, including syntactic and semantic matching strategies as well as several statistical-model-based matching strategies which can effectively aggregate similarity values from matching on various types of service description information such as service name, description text, and semantic annotation. Besides, we propose a semantic matching strategy to measure the similarity among SAWSDL semantic annotations. These matching strategies have been evaluated in SAWSDL-iMatcher on SAWSDL-TC2 and Jena Geography Dataset (JGD). The evaluation shows that different matching strategies are suitable for different tasks and contexts, which implies the necessity of a customizable matchmaker. In addition, it also provides evidence for the claim that the effectiveness of SAWSDL service matching can be significantly improved by statistical-model-based matching strategies. Our matchmaker is competitive with other matchmakers on benchmark tests at S3 contest 2009. (C) 2011 Elsevier B.V. All rights reserved. C1 [Wei, Dengping; Wang, Ting] Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China. [Wei, Dengping; Bernstein, Abraham] Univ Zurich, CH-8050 Zurich, Switzerland. [Wang, Ji] Natl Lab Parallel & Distributed Proc, Changsha 410073, Hunan, Peoples R China. RP Wang, T (reprint author), Natl Univ Def Technol, Changsha 410073, Hunan, Peoples R China. EM tingwang@nudt.edu.cn FU National Grand Fundamental Research 973 Program of China [2005 CB321802, 2011CB302603]; National Natural Science Foundation of China [60873097] FX This research is supported by the National Grand Fundamental Research 973 Program of China under Grant Nos. 2005 CB321802,2011CB302603, and the National Natural Science Foundation of China under Grant No. 60873097. CR Akkiraju R., 2005, WEB SERVICE SEMANTIC Al-Masri E., 2007, P 16 INT C WORLD WID, P1257, DOI DOI 10.1145/1242572.1242795 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Becker J., 2010, 18 EUR C INF SYST EC Berners-Lee T., 2001, SEMANTIC WEB Bernstein A., 2005, WIRTSCHAFTSINF, P1347, DOI 10.1007/3-7908-1624-8_71 Bernstein A., 2005, SIMPACK GENERIC JAVA de Bruijn J., 2006, D2V1 3 WEB SERVICE M Farrell J., 2007, SEMANTIC ANNOTATIONS Frank E, 2005, DATA MINING PRACTICA Grishman R., 1986, ANAL LANGUAGE RESTRI Phillips A., 1985, SIGKDD EXPLORATIONS, V31, P329, DOI DOI 10.1145/1656274.1656278 Hsu C.-W., 2003, PRACTICAL GUIDE SUPP Hull D., 2006, 21 NAT C ART INT 18, P1319 Jaeger M.C., 2005, KIVS 2005, P42 JARO MA, 1989, J AM STAT ASSOC, V84, P414, DOI 10.2307/2289924 Kiefer C., 2007, ISWC, P295 Kiefer C, 2008, LECT NOTES COMPUT SC, V5021, P463 Klusch M., 2009, ESWC 2009 HERAKLION, P550 Klusch M., 2008, CASCOM INTELLIGENT S KLUSCH M, 2009, IEEE INT C WEB SERV, P335 Klusch M, 2009, J WEB SEMANT, V7, P121, DOI 10.1016/j.websem.2008.10.001 Kluscha Matthias, 2009, Web Intelligence and Agent Systems, V7, DOI 10.3233/WIA-2009-0153 LECESSIE S, 1992, J R STAT SOC C-APPL, V41, P191, DOI DOI 10.2307/2347628 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Lin C-J., 2001, LIBSVMA LIB SUPPORT Maedche E., 2002, P EUR C KNOWL ACQ MA, P251 Martin David, 2004, OWL S SEMANTIC MARKU Paolucci M, 2002, LECT NOTES COMPUT SC, V2342, P333 Plebani P, 2009, IEEE T KNOWL DATA EN, V21, P1629, DOI 10.1109/TKDE.2009.35 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quinlan R., 1993, C4 5 PROGRAMS MACHIN Raghavan V.V., 1989, SIGIR, P59 Sabou M, 2005, J WEB SEMANT, V3, P340, DOI 10.1016/j.websem.2005.09.008 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan N., 2006, 39 HAW INT C SYST SC, V6 Stollberg M, 2007, LECT NOTES COMPUT SC, V4519, P99 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wang P., 2008, OM Wei D., 2008, 7 INT SEM WEB C, P146 Winkler W.E., 1987, APPL FELLEGI SUNTER NR 41 TC 9 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-8268 J9 J WEB SEMANT JI J. Web Semant. PD DEC PY 2011 VL 9 IS 4 BP 402 EP 417 DI 10.1016/j.websem.2011.08.001 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 890UI UT WOS:000300170600004 ER PT J AU Aerts, S AF Aerts, Sven TI Dimensional Reduction in Vector Space Methods for Natural Language Processing: Products and Projections SO INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS LA English DT Article; Proceedings Paper CT 10th Biennial Meeting of the International-Quantum-Structure-Association CY JUN 20-28, 2011 CL Boston, MA SP Int Quantum Struct Assoc DE Natural language processing; Quantum theory; Subspace; Tensor product; Convolution product ID REPRESENTATION AB We introduce vector space based approaches to natural language processing and some of their similarities with quantum theory when applied to information retrieval. We explain how dimensional reduction is called for from both a practical and theoretical point of view and how this can be achieved through choice of product or through projectors onto subspaces. C1 Free Univ Brussels, Ctr Leo Apostel Interdisciplinary Studies CLEA, B-1050 Brussels, Belgium. RP Aerts, S (reprint author), Free Univ Brussels, Ctr Leo Apostel Interdisciplinary Studies CLEA, Pl Laan 2, B-1050 Brussels, Belgium. EM saerts@vub.ac.be CR Aerts D, 2009, J MATH PSYCHOL, V53, P314, DOI 10.1016/j.jmp.2009.04.005 Aerts D., 2007, ARXIV07051740 CZACHOR M, 2004, J PHYS A, P37 Dumais S.T., 1988, P CHI 88 C HUM FACT, P281, DOI 10.1145/57167.57214 Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367 FRIEZE AM, 1998, IEEE S FDN COMP SCI, P370 Gardenfors P., 2000, CONCEPTUAL SPACES GE Golub G., 1965, J SOC IND APPL MAT B, V2, P205, DOI DOI 10.1137/0702016 Johnson W. B., 1984, CONT MATH, V26, P189 Kanerva P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P1036 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 NERLICH B, 2003, POLYSEMY FLEXIBILITY Plate T. A., 2003, CSLI LECT NOTES, V150 ROSCH E., 1983, NEW TRENDS CONCEPTUA Rosch Eleanor, 1978, COGNITION CATEGORIZA SAHLGREN M, 2001, ACQUISITION REPRESEN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMOLENSKY P, 1990, ARTIF INTELL, V46, P159, DOI 10.1016/0004-3702(90)90007-M van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR WIDDOWS D, 2004, COMPOSITIONAL CONNEC Widdows D., 2003, MATH LANGUAGE, V8, P141 NR 23 TC 0 Z9 0 PU SPRINGER/PLENUM PUBLISHERS PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0020-7748 J9 INT J THEOR PHYS JI Int. J. Theor. Phys. PD DEC PY 2011 VL 50 IS 12 BP 3646 EP 3653 DI 10.1007/s10773-011-0851-6 PG 8 WC Physics, Multidisciplinary SC Physics GA 846SM UT WOS:000296923500002 ER PT J AU Velasquez, JD Dujovne, LE L'Huillier, G AF Velasquez, Juan D. Dujovne, Luis E. L'Huillier, Gaston TI Extracting significant Website Key Objects: A Semantic Web mining approach SO ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE LA English DT Article DE Website Key Objects; Web user preferences; Web usage mining; Semantic Web mining; Self Organizing Feature Maps; Ontology Engineering; Web Intelligence; Clickstream analysis; Web personalization ID ONTOLOGY AB Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Velasquez, Juan D.; Dujovne, Luis E.; L'Huillier, Gaston] Univ Chile, Dept Ind Engn, Santiago, Chile. RP Velasquez, JD (reprint author), Univ Chile, Dept Ind Engn, Republ 701,POB 8370439, Santiago, Chile. EM jvelasqu@dii.uchile.cl; dujovne@dcc.uchile.cl; glhuilli@dii.uchile.cl RI Velasquez, Juan/H-3859-2013; L'Huillier, Gaston/ OI Velasquez, Juan/0000-0003-4819-9680; L'Huillier, Gaston/0000-0002-3248-4228 FU FONDEF [DO8I-1015]; Millennium Institute on Complex Engineering Systems (ICM) [P-05-004-F]; CONICYT [FBO16] FX This work was supported partially by the FONDEF project DO8I-1015 and the Millennium Institute on Complex Engineering Systems (ICM: P-05-004-F, CONICYT: FBO16). Authors would like to thank anonymous reviewers for their interesting and useful comments on the preliminary version of this article. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BERENDT B, 2002, INT SEM WEB C ISWC, P264 Berendt B, 2000, VLDB J, V9, P56, DOI 10.1007/s007780050083 BERRY R, 1998, IBM SYST J, V3, P281 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BURGET R, 2009, P 1 AS C INT INF DAT CHAMBERS N, 2006, AAAI 06, P1259 Fayyad U, 1996, AI MAG, V17, P37 GAO XZ, 2005, P IEEE INT C E BUS E, P332 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 JR AP, 2004, J WEB ENG, V2, P247 Khare R., 2006, P 15 INT C WORLD WID, P865, DOI DOI 10.1145/1135777.1135917 Klyne G., 2004, RESOURCE DESCRIPTION Kohonen T., 2001, SELF ORG MAPS LEVENSHTEIN VI, 1966, SOV PHYS DOKL, P705 LI Y, 2003, WI 03 Li YF, 2007, LECT NOTES ARTIF INT, V4845, P406 Li YF, 2006, IEEE T KNOWL DATA EN, V18, P554 Loranger H., 2006, PRIORITIZING WEB USA MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 PAASS G, 2004, LWA, P193 Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 Plumbaum T, 2009, LECT NOTES COMPUT SC, V5535, P391, DOI 10.1007/978-3-642-02247-0_42 POON H, ACL 10 IN PRESS PORTER MF, 1980, AUTOMATED LIB INFORM, V3, P130 SAHAMI M, 2006, FLAIRS C, P14 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stumme G, 2006, J WEB SEMANT, V4, P124, DOI 10.1016/j.websem.2006.02.001 TIWARY U, 2009, P 1 INT C INT HUM CO Tsoi LC, 2009, J BIOMED INFORM, V42, P824, DOI 10.1016/j.jbi.2009.03.009 URBANSKY D, 2008, P 13 AUSTR DOC COMP VELASQUEZ JD, 2005, J WEB INFORM SYSTEMS, V1, P11 VELASQUEZ JD, INTELLIGENT IN PRESS, V15 Velasquez JD, 2008, FRONT ARTIF INTEL AP, V170, P1 Wang JB, 2005, PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, P3770 Zavitsanos Elias, 2010, Web Intelligence and Agent Systems, V8, DOI 10.3233/WIA-2010-0178 NR 37 TC 9 Z9 9 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0952-1976 J9 ENG APPL ARTIF INTEL JI Eng. Appl. Artif. Intell. PD DEC PY 2011 VL 24 IS 8 SI SI BP 1532 EP 1541 DI 10.1016/j.engappai.2011.02.001 PG 10 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA 841WM UT WOS:000296545900019 ER PT J AU Qiao, YN Qi, Y Hou, D AF Qiao Ya-nan Qi Yong Hou Di TI Tensor Field Model for higher-order information retrieval SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE Information retrieval; Text representation; Text mining AB There is an implied assumption for keywords in the traditional Information Retrieval (IR) models: keywords are parallel to each other. In fact, there are some relations between terms in quite a few queries, and in these queries, perhaps one term is subordinate to another term according to the inner meanings of information needs. This is "Higher-order IR"(HIR) defined in this paper, and we call traditional IR "first-order IR" instead. Some research fields such as Public Opinion Analysis, Chain of Events Analysis and Trend Analysis which reflect the vague concept of HIR are all special form of HIR. Apparently, traditional IR models cannot deal with HIR directly. We need a new HIR model to represent and organize the documents, queries and relevance relationship between them. In this paper, we propose "Tensor Field Model" (TFM), and its perspectives are field theory in physics and multilinear algebra in maths. We construct the tensor representations of documents and queries in TFM, presenting some key concepts such as term field, tensor product of term array and term field constant. Empirical results show that TFM is appropriate for HIR theoretically and formally compared with traditional models which simplify the HIR problems as first-order IR problems to some extent. (C) 2011 Elsevier Inc. All rights reserved. C1 [Qiao Ya-nan; Qi Yong; Hou Di] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China. RP Qiao, YN (reprint author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China. EM qiaoyanan@mail.xjtu.edu.cn; qiy@mail.xjtu.edu.cn; houdi@mail.xjtu.edu.cn FU National Natural Science Foundation of China [60933003]; National High Technology Research and Development Program of China (863 Program) [2009AA01Z116]; Science and Technology Research and Development Program of Shaanxi Province [2008KW-02]; State Key Laboratory of High-end Server & Storage Technology [2009HSSA09] FX This work is supported by Major Program of National Natural Science Foundation of China (No. 60933003), National High Technology Research and Development Program of China (863 Program) (No. 2009AA01Z116), the Science and Technology Research and Development Program of Shaanxi Province under Grant No. 2008KW-02, IBM Joint Project and the State Key Laboratory of High-end Server & Storage Technology (No. 2009HSSA09). CR [Anonymous], 2009, PEOPLES DAILY ONLINE Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BEIGBEDER M, 2005, SAC 05, P1018 CAL D, 2006, SIGIR 06, P625 Chen Y., 2008, P INT C WEB SEARCH W, P251, DOI 10.1145/1341531.1341564 Croft WB, 2003, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.2003.1260777 Dolan B., 2004, P 20 INT C COMP LING FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Gao J., 2002, SIGIR 02, P183 Gospodnetic O., 2005, LUCENE ACTION Greub W H, 1967, MULTILINEAR ALGEBRA Hartmann E., 1984, INTRO CRYSTAL PHYS LIU N, 2005, TEXT REPRESENTATION, P725 Metzler D., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 Petkova Desislava, 2007, CIKM 07, P731 RASOLOFO Y, 2003, ECIR 2003 P, P207 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHI S, 2005, SIGIR 05, P488 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Yoshida K., 1980, FUNCTIONAL ANAL 2003, TREC WEB CORPUS WT 1 2010, LEMUR TOOLKIT NR 25 TC 1 Z9 1 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 J9 J SYST SOFTWARE JI J. Syst. Softw. PD DEC PY 2011 VL 84 IS 12 BP 2303 EP 2313 DI 10.1016/j.jss.2011.06.057 PG 11 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 840CE UT WOS:000296415100021 ER PT J AU Mesleh, AM AF Mesleh, Abdelwadood Moh'd TI Feature sub-set selection metrics for Arabic text classification SO PATTERN RECOGNITION LETTERS LA English DT Article DE Feature selection; SVM; Arabic text classification ID CATEGORIZATION; MODEL; KNN AB Feature sub-set selection (FSS) is an important step for effective text classification (TC) systems. This paper presents an empirical comparison of seventeen traditional FSS metrics for TC tasks. The TC is restricted to support vector machine (SVM) classifier and only for Arabic articles. Evaluation used a corpus that consists of 7842 documents independently classified into ten categories. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F(1) measures. Results reveal that Chi-square and Fallout FSS metrics work best for Arabic TC tasks. (C) 2011 Elsevier B.V. All rights reserved. C1 Al Blaqa Appl Univ, Fac Engn Technol, Dept Comp Engn, Amman, Jordan. RP Mesleh, AM (reprint author), Al Blaqa Appl Univ, Fac Engn Technol, Dept Comp Engn, Amman, Jordan. EM wadood@fet.edu.jo CR Al-Fedagi SS, 1989, P 11 NAT COMP C KING, P4 Al-Harbi S., 2008, P 9 INT C STAT AN TE, P77 ALJLAYL M, 2001, 10 TEXT RETR C GAITH, P265 ALSHALABI R, 2006, P 4 INT MULT COMP SC, V4 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bawaneh M. J., 2008, J COMPUT SCI, V4, P600 Beesley K, 1996, P COLING 96, P89, DOI 10.3115/992628.992647 Bishop C. M., 1995, NEURAL NETWORKS PATT Cavalli-Sforza V., 2000, P 6 APPL NAT LANG PR, P86 Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 DARWISH K, 2002, P ACL WORKSH SEM LAN Dash M, 1997, INTELL DATA ANAL, V1, P131, DOI DOI 10.1016/S1088-467X(97)00008-5 El-Halees A., 2007, ISLAMIC U J, V15, P157 El-Kourdi M., 2004, P COLING 20 WORKSH C, P51, DOI 10.3115/1621804.1621819 Forman G., 2008, ACM C INF KNOWL MAN, P263 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Galavotti L., 2000, P ECDL 00 4 EUR C RE, P59 GOWEDER A, 2001, AR NAT LANG PROC WOR Guo GD, 2004, LECT NOTES COMPUT SC, V2945, P559 He J, 2003, APPL INTELL, V18, P311, DOI 10.1023/A:1023202221875 Hmeidi I, 2008, ADV ENG INFORM, V22, P106, DOI 10.1016/j.aei.2007.12.001 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Kanaan G, 2009, J AM SOC INF SCI TEC, V60, P1836, DOI 10.1002/asi.20832 KANAAN G, 2006, P 4 INT MULT COMP SC, V4 Khoja S, 2001, P STUD WORKSH NAACL, P20 Khreisat L, 2009, J INFORMETR, V3, P72, DOI 10.1016/j.joi.2008.11.005 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A., 1998, 15 NAT C ART INT AAA MESLEH A, 2010, SUPPORT VECTOR MACHI Mesleh AM, 2008, ADVANCES IN COMPUTER AND INFORMATIOM SCIENCES AND ENGINEERING, P11, DOI 10.1007/978-1-4020-8741-7_3 Mesleh AMA, 2007, J COMPUTER SCI, V3, P430 Mesleh A.M.d., 2008, P 2008 INT C COMP EN, P143 Mladenic D., 1999, P 16 INT C MACH LEAR, P258 MOEN M, 2006, INFORM EXTRACTION AL Liu H, 2008, CH CRC DATA MIN KNOW, P3 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Ogura H, 2009, EXPERT SYST APPL, V36, P6826, DOI 10.1016/j.eswa.2008.08.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sawaf H., 2001, AR NAT LANG PROC WOR Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shang WQ, 2007, EXPERT SYST APPL, V33, P1, DOI 10.1016/j.eswa.2006.04.001 Syiam M. M., 2006, INT J INTELLIGENT CO, V6, P1 Vapnik V., 1995, NATURE STAT LEARNING Wenqian Shang, 2007, Expert Systems with Applications, V33, DOI 10.1016/j.eswa.2006.04.001 YAHYA A, 1989, 1 GREAT LAK COMP SCI Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y., 2003, 26 ACM SIGIR C RES D, P96 ZHANG YX, 2006, THESIS U MIAMI ZHENG ZH, 2003, P 3 IEEE INT C DAT M, P705 NR 51 TC 6 Z9 7 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-8655 J9 PATTERN RECOGN LETT JI Pattern Recognit. Lett. PD OCT 15 PY 2011 VL 32 IS 14 BP 1922 EP 1929 DI 10.1016/j.patrec.2011.07.010 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA 841XB UT WOS:000296547400025 ER PT J AU Algergawy, A Mesiti, M Nayak, R Saake, G AF Algergawy, Alsayed Mesiti, Marco Nayak, Richi Saake, Gunter TI XML Data Clustering: An Overview SO ACM COMPUTING SURVEYS LA English DT Article DE Documentation; Algorithms; Performance; XML data; clustering; tree similarity; schema matching; semantic similarity; structural similarity; documentation ID STRUCTURAL SIMILARITY; SEMANTIC SIMILARITY; GENE-EXPRESSION; ALGORITHM; INFORMATION; DOCUMENTS; DATABASES; INTEGRATION; EFFICIENT; PATTERNS AB In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, structures, and semantics. In this article, we first outline the application contexts in which clustering is useful, then we survey approaches so far proposed relying on the abstract representation of data (instances or schema), on the identified similarity measure, and on the clustering algorithm. In this presentation, we aim to draw a taxonomy in which the current approaches can be classified and compared. We aim at introducing an integrated view that is useful when comparing XML data clustering approaches, when developing a new clustering algorithm, and when implementing an XML clustering component. Finally, the article moves into the description of future trends and research issues that still need to be faced. C1 [Saake, Gunter] Univ Magdeburg, Dept Comp Sci, D-39106 Magdeburg, Germany. [Mesiti, Marco] Univ Milan, DICO, I-20135 Milan, Italy. [Nayak, Richi] Queensland Univ Technol, Fac Sci & Technol, Brisbane, Qld 4001, Australia. [Algergawy, Alsayed] Tanta Univ, Dept Comp Engn, Tanta, Egypt. RP Algergawy, A (reprint author), Tanta Univ, Dept Comp Engn, Tanta, Egypt. EM algergawy@informatik.uni-leipzig.de; mesiti@dico.unimi.it; r.nayak@qut.edu.au; saake@cs.unimagdeburg.de FU Egyptian Ministry of Higher Education; Tanta University, Egypt FX A. Algergawy has been supported by the Egyptian Ministry of Higher Education and Tanta University, Egypt. CR AGGARWAL CC, 2007, P 13 ACM SIGKDD INT, P46, DOI 10.1145/1281192.1281201 Algergawy A, 2009, DATA KNOWL ENG, V68, P728, DOI 10.1016/j.datak.2009.01.001 Algergawy A, 2008, COMM COM INF SC, V8, P19 Algergawy A, 2008, P 10 INT C INF INT W, P131, DOI 10.1145/1497308.1497337 Andreopoulos B, 2009, BRIEF BIOINFORM, V10, P297, DOI 10.1093/bib/bbn058 Antonellis P, 2008, P ACM S APPL COMP SA, P1081, DOI 10.1145/1363686.1363940 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BATINI C, 1986, COMPUT SURV, V18, P323 Berkhin P., 2002, SURVEY CLUSTERING DA Bertino E, 2001, IEEE INTERNET COMPUT, V5, P75, DOI 10.1109/4236.968835 Bertino E, 2008, J INTELL INF SYST, V30, P55, DOI 10.1007/s10844-006-0023-y Bertino E, 2004, INFORM SYST, V29, P23, DOI 10.1016/S0306-4379(03)00031-0 Bille P, 2005, THEOR COMPUT SCI, V337, P217, DOI 10.1016/j.tcs.2004.12.030 BOLSHAKOVA N, 2005, TCDCS200523 U DUBL Bonifati A, 2008, P 11 INT C EXT DAT T, P85 Boukottaya Aida, 2005, P 2005 ACM S DOC ENG, P101, DOI 10.1145/1096601.1096629 Bourret R., 2009, XML DATABASE PRODUCT Buttler D., 2004, P INT C INT COMP LAS, P3 CAN F, 1993, ACM T INFORM SYST, V11, P143, DOI 10.1145/130226.134466 Candillier L., 2005, P WORKSH IN EV XML R, P469 CERAMI E, 2005, XML BIOINFORMATICS Charikar M, 2004, SIAM J COMPUT, V33, P1417, DOI 10.1137/S0097539702418498 Chawathe SS, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P90 Choi I, 2007, DATA KNOWL ENG, V60, P361, DOI 10.1016/j.datak.2006.02.004 Cohen W.W., 2003, P IJCAI 03 WORKSH IN, P73 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Dalamagas T, 2006, INFORM SYST, V31, P187, DOI 10.1016/j.is.2004.11.009 Do H., 2002, P 28 INT C VER LARG, P610 Doan A.H., 2004, HDB ONTOLOGIES, P385 Dorneles C.F., 2004, P 6 ACM INT WORKSH W, P56, DOI 10.1145/1031453.1031465 Eisen MB, 1998, P NATL ACAD SCI USA, V95, P14863, DOI 10.1073/pnas.95.25.14863 Flesca S, 2005, IEEE T KNOWL DATA EN, V17, P160, DOI 10.1109/TKDE.2005.27 Florescu D., 1999, IEEE DATA ENG B, V22, P27 Giannotti F., 2002, Principles of Data Mining and Knowledge Discovery. 6th European Conference, PKDD 2002. Proceedings (Lecture Notes in Artificial Intelligence Vol.2431) Giunchilia F, 2007, J DATA SEMANTICS, VIX, P1 Gou G, 2007, IEEE T KNOWL DATA EN, V19, P1381, DOI 10.1109/TKDE.2007.1060 GUERRINI G, 2007, OVERVIEW SIMILARITY Guha S., 1998, P ACM SIGMOD INT C M, P73, DOI 10.1145/276304.276312 Guha S, 2000, INFORM SYST, V25, P345, DOI 10.1016/S0306-4379(00)00022-3 Hanisch Daniel, 2002, In Silico Biology, V2, P313 Huang ZX, 1998, DATA MIN KNOWL DISC, V2, P283, DOI 10.1023/A:1009769707641 Hucka M, 2003, BIOINFORMATICS, V19, P524, DOI 10.1093/bioinformatics/btg015 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jeong B, 2008, EXPERT SYST APPL, V34, P1651, DOI 10.1016/j.eswa.2007.01.025 JEONG J, 2006, LECT NOTES COMPUTER, V4277 KADE AM, 2008, P 8 ACM S DOC ENG SA, P191, DOI 10.1145/1410140.1410178 LE DX, 2006, P INT C COMP SCI ITS, P207 Lee D, 2000, SIGMOD RECORD, V29, P76 Lee M. L., 2002, P 11 INT C INF KNOWL, P292 Lee Y, 2007, VLDB J, V16, P97, DOI 10.1007/s00778-006-0024-z Leitao L., 2007, P INT C INF KNOWL MA, P293, DOI 10.1145/1321440.1321483 Leung HP, 2005, KNOWL INF SYST, V7, P476, DOI 10.1007/s10115-004-0156-7 Li WS, 2000, DATA KNOWL ENG, V33, P49, DOI 10.1016/S0169-023X(99)00044-0 Lian W, 2004, IEEE T KNOWL DATA EN, V16, P82 Liang W., 2005, P 22 BRIT NAT C DAT, P82 LIANG W, 2006, IPSJ DIGITAL COURIER, V2, P382, DOI 10.2197/ipsjdc.2.382 Madhavan J., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Manning C.D., 2008, INTRO INFORM RETRIEV Melnik S, 2002, P 18 INT C DAT ENG I Melton J., 2006, QUERYING XML XQUERY MULLER T, 2005, 7 INT TAG WIRSCH MULLER T, 2005, P 2 JOINT M I MATH S Nayak R, 2007, INT J PATTERN RECOGN, V21, P723, DOI 10.1142/S0218001407005648 Nayak R, 2008, KNOWL INF SYST, V14, P197, DOI 10.1007/s10115-007-0080-8 Nayak R, 2007, KNOWL-BASED SYST, V20, P336, DOI 10.1016/j.knosys.2006.08.006 Nierman A., 2002, P 5 INT WORKSH WEB D, P61 Ordonez C, 2004, IEEE T KNOWL DATA EN, V16, P909, DOI 10.1109/TKDE.2004.25 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 R Srikant, 1996, P 5 INT C EXT DAT TE, ppp RAFIEI D, 2006, P 17 INT C DAT EXP S, P512 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 Saleem K, 2008, INFORM SYST, V33, P637, DOI 10.1016/j.is.2008.01.010 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANZ I, 2008, P 3 INT WORKSH XML D, P238 SANZ I, 2006, P 10 INT C EXT DAT T, P1186 Sasson O., 2002, BIOINFORMATICS S1, V18, P14 Shanmugasundaram J, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P302 Shasha D., 1995, PATTERN MATCHING STR Singhal A., 2001, IEEE DATA ENG B, V24, P35 Somervuo P., 2000, P 3 INT C DISC SCI, P76 Tagarelli A, 2006, SIAM PROC S, P188 TAI KC, 1979, J ACM, V26, P422, DOI 10.1145/322139.322143 Tamayo P, 1999, P NATL ACAD SCI USA, V96, P2907, DOI 10.1073/pnas.96.6.2907 TEKLI J, 2009, COMPUT SCI REV, V3, P3 Tekli Joe, 2007, Proceedings 8th International Conference on Web Information Systems Engineering - WISE 2007. (Lecture Notes in Computer Science vol. 4831) Tran T., 2008, P 7 AUSTR DAT MIN C, P219 VAKALI A, 2004, P EDBT WORKSH 2004 H, P597 VIYANON W, 2008, P OTM 2008 CONF INT, P484 Vutukuru V, 2002, P INT WORLD WID WEB Wang GR, 2004, J COMPUT SCI TECHNOL, V19, P224, DOI 10.1007/BF02944801 Wang JY, 2009, DATA MIN KNOWL DISC, V18, P1, DOI 10.1007/s10618-008-0100-7 Wilde E, 2008, COMMUN ACM, V51, P40, DOI 10.1145/1364782.1364795 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Yang J, 2005, INT J ARTIF INTELL T, V14, P771, DOI 10.1142/S0218213005002387 Yang JW, 2009, KNOWL INF SYST, V19, P53, DOI 10.1007/s10115-008-0138-2 Yang R., 2005, P 2005 ACM SIGMOD IN, P754, DOI 10.1145/1066157.1066243 Yoon JP, 2001, J INTELL INF SYST, V17, P241, DOI 10.1023/A:1012861931139 ZHANG KZ, 1989, SIAM J COMPUT, V18, P1245, DOI 10.1137/0218082 Zhang T., 1996, P 1996 ACM SIGMOD IN, P103, DOI DOI 10.1145/235968.233324 ZHAO Y, 2002, 0140 U MINN DEP COMP Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 NR 101 TC 4 Z9 4 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD OCT PY 2011 VL 43 IS 4 AR 25 DI 10.1145/1978802.1978804 PG 41 WC Computer Science, Theory & Methods SC Computer Science GA 849SN UT WOS:000297145500002 ER PT J AU Maghsoodi, N Homayounpour, MM AF Maghsoodi, Nooshin Homayounpour, Mohammad Mehdi TI Improving Farsi Multiclass Text Classification Using a Thesaurus and Two-Stage Feature Selection SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID SUPPORT VECTOR MACHINES; CATEGORIZATION AB The progressive increase of information content has recently made it necessary to create a system for automatic classification of documents. In this article, a system is presented for the categorization of multiclass Farsi documents that requires fewer training examples and can help to compensate the shortcoming of the standard training dataset. The new idea proposed in the present article is based on extending the feature vector by adding some words extracted from a thesaurus and then filtering the new feature vector by applying secondary feature selection to discard inappropriate features. In fact, a phase of secondary feature selection is applied to choose more appropriate features among the features added from a thesaurus to enhance the effect of using a thesaurus on the efficiency of the classifier. To evaluate the proposed system, a corpus is gathered from the Farsi Wikipedia website and some articles in the Hamshahri newspaper, the Roshd periodical, and the Soroush magazine. In addition to studying the role of a thesaurus and applying secondary feature selection, the effect of a various number of categories, size of the training dataset, and average number of words in the test data also are examined. As the results indicate, classification efficiency improves by applying this approach, especially when available data is not sufficient for some text categories. C1 [Maghsoodi, Nooshin; Homayounpour, Mohammad Mehdi] Amir Kabir Univ Technol, Fac Comp Engn, Lab Intelligent Signal & Speech Proc, Tehran, Iran. RP Maghsoodi, N (reprint author), Amir Kabir Univ Technol, Fac Comp Engn, Lab Intelligent Signal & Speech Proc, Tehran, Iran. EM n_maghsoodi@aut.ac.ir; homayoun@aut.ac.ir CR Aizerman A., 1964, AUTOMAT REM CONTR, V25, P821 Aqaee N., 2008, P 13 INT COMP C COMP, P383 BIJANKHAN M, 2008, 100 MILLION WORD FAR Bina Bahareh, 2008, Proceedings of the 2008 International Conference on Data Mining Bloehdorn S., 2004, WORKSH TEXT BAS INF, P149 BLUM A, 1997, ARTIF INTELL, V1, P245 Campos L., 2009, APPROXIMATE REASONIN, V50, P932 DEHDARI J, 2008, PERSTEM STEMMER VERS Diaz I, 2004, J AM SOC INF SCI TEC, V55, P579, DOI 10.1002/asi.10409 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 FARARUY J, 2004, P 1 WORKSH FARS LANG, P210 Feili H., 2006, P 2 WORKSH PERS LANG, P245 GABRILOVICH E, 2005, P 19 INT JOINT C ART, P1048 Guyon, 2003, J MACH LEARN RES, V3, P1157 Hotho A., 2003, P SEM WEB WORKSH 26, P61 Huang Y.L., 1998, J LIBR INF SCI, V24, P1023 INO Y, 2005, P 23 IASTED INT MULT, P656 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 John G., 1994, P 11 INT C MACH LEAR, P121 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Liu Y, 2006, PATTERN RECOGN, V39, P1333, DOI 10.1016/j.patcog.2005.10.006 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Pilevar M. T., 2009, P INT C NAT LANG PRO, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SONG X, 2009, P INT FOR COMP SCI T, P187 Vapnik V., 1995, NATURE STAT LEARNING Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wang TY, 2007, INFORM PROCESS MANAG, V43, P914, DOI 10.1016/j.ipm.2006.09.011 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 NR 35 TC 2 Z9 2 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD OCT PY 2011 VL 62 IS 10 BP 2055 EP 2066 DI 10.1002/asi.21592 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 827VZ UT WOS:000295458700016 ER PT J AU Stone, B Dennis, S AF Stone, Benjamin Dennis, Simon TI Semantic models and corpora choice when using Semantic Fields to predict eye movement on web pages SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES LA English DT Article DE Semantic Fields model; Semantic salience; Web navigation; Semantic models; LSA; SpNMF; Vectorspace; Eye tracking; Pupil dilation ID VISUAL-SEARCH; PUPILLARY RESPONSES; INFORMATION; CONTEXT; COLOR; SPACE; TASK AB Ten models are compared in their ability to predict eye-tracking data that was collected from 49 participants' goal-oriented search tasks on a total of 1809 Web pages. Forming the basis of six of these models, three semantic models and two corpus types are compared as components for the Semantic Fields model (Stone and Dennis, 2007) that estimates the semantic salience of different areas displayed on Web pages. Latent Semantic Analysis, Sparse Nonnegative Matrix Factorization, and Vectorspace were used to generate similarity comparisons of goal and Web page text in the semantic component of the Semantic Fields model. Overall, Vectorspace was the best performing semantic model in this study. Two types of corpora or knowledge-bases were used to inform the semantic models, the well known TASA corpus and other corpora that were constructed from the Wikipedia encyclopedia. In all cases the Wikipedia corpora outperformed the TASA corpora. A non-corpus-based Semantic Fields model that incorporated word overlap performed more poorly at these tasks. Three baseline models were also included as a point of comparison to evaluate the effectiveness of the Semantic Fields models. In all cases the corpus-based Semantic Fields models outperformed the baseline models when predicting the participants' eye-tracking data. Both final destination pages and pupil data (dilation) indicated that participants' were actively performing goal-oriented search tasks. Published by Elsevier Ltd. C1 [Stone, Benjamin] Univ Adelaide, Sch Psychol, Adelaide, SA 5005, Australia. [Dennis, Simon] Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA. RP Stone, B (reprint author), Univ Adelaide, Sch Psychol, Level 4,Hughes Bldg, Adelaide, SA 5005, Australia. EM benjamin.stone@gmail.com; simon.dennis@gmail.com CR AHERN S, 1979, SCIENCE, V205, P1289, DOI 10.1126/science.472746 Blackmon M. H., 2005, C HUM FACT COMP SYST, P31 Blackmon M. H., 2002, Conference Proceedings. Conference on Human Factors in Computing Systems. CHI 2002 Blackmon M. H., 2003, CHI 03, P497 Blackmon M. H., 2007, HDB LATENT SEMANTIC, P345 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOERSMA F, 1970, J EXP CHILD PSYCHOL, V9, P142, DOI 10.1016/0022-0965(70)90079-2 BRUMBY DP, 2003, P 25 ANN C COGN SCI, V25, P1320 BUDIU R, 2007, P 26 ANN C RECH INF Burgess C, 1997, LANG COGNITIVE PROC, V12, P177 Cai ZQ, 2004, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P180 Chi E. H., 2001, P ACM CHI 2001 C HUM, P490, DOI 10.1145/365024.365325 Chi E. H., 2003, CHI 03, P505 Cox AL, 2004, PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON COGNITIVE MODELING, P82 Dennis S., 2007, HDB LATENT SEMANTIC, P57 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 FARADAY P, 2000, P 6 C HUM FACT WEB FARADAY P, 2001, HUM FACT COMP SYST, P159 Fitts P. M., 1950, AERONAUTICAL ENG REV, V9, P24 Foltz P. W., 1999, INTERACTIVE MULTIMED, V1 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Giles J, 2005, NATURE, V438, P900, DOI 10.1038/438900a Graesser A. C., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT129 GRIER RA, 2004, THESIS U CINCINNATI Griffiths TL, 2002, P 24 ANN C COGN SCI, P381 HABUCHI Y, 2006, EY TRACK REAS APPL S, P47 Habuchi Yoshiko, 2008, ETRA 2008. Eye Tracking Research & Applications, DOI 10.1145/1344471.1344505 Hess E. H., 1972, HDB PSYCHOPHYSIOLOGY, P491 HESS EH, 1964, SCIENCE, V143, P1190, DOI 10.1126/science.143.3611.1190 KAUR I, 2005, C HUM FACT COMP SYST, P51 KITAJIMA M, 2000, PEOPLE COMPUTERS, V14, P357 KITAJIMA M, 2005, HC1 INT 2005, V4 Kwantes PJ, 2005, PSYCHON B REV, V12, P703, DOI 10.3758/BF03196761 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Ling J, 2004, ERGONOMICS, V47, P907, DOI 10.1080/00140130410001670417 Ling J, 2002, DISPLAYS, V23, P223, DOI 10.1016/S0141-9382(02)00041-0 McCarthy J., 2003, PEOPLE COMPUTERS, P401 Miller CS, 2004, HUM-COMPUT INTERACT, V19, P225, DOI 10.1207/s15327051hci1903_2 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 NAKAYAMA M, 2002, ETRA 02, P37 NAMATAME M, 2008, ASSETS 08, P247 Nielsen J, 2006, FSHAPED PATTERN READ Nielsen J., 1997, BE SUCCINCT WRITING NIELSEN JAKOB, 2010, SCROLLING ATTENTION Pan B., 2004, ETRA 04, P147 Pearson R, 2003, INT J HUM-COMPUT ST, V59, P327, DOI 10.1016/S1071-5819(03)00045-4 Pirolli P, 2003, LECT NOTES ARTIF INT, V2702, P45 Pirolli P., 1997, CHI 97, P3 Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643 Rayner K, 1998, PSYCHOL BULL, V124, P372, DOI 10.1037/0033-2909.124.3.372 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 RI WT, 2007, HUMAN COMPUTER INTER, V22, P355 Rigutti S, 2004, PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON COGNITIVE MODELING, P378 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAEFER T, 1968, PSYCHON SCI, V12, P137 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 Shashua A, 2005, P 22 INT C MACH LEAR, P792, DOI 10.1145/1102351.1102451 SPOOL JM, 1998, CHI 98 P LOS ANG CA, P147, DOI 10.1145/286498.286626 Steinhauer SR, 2000, INT J PSYCHOPHYSIOL, V39, P21, DOI 10.1016/S0167-8760(00)00119-7 STONE B, 2004, PUPIL SIZE MENTAL LO Stone B., 2007, P 29 ANN C COGN SOC, P665 STONE B, 2011, TOP COGN SCI, V31, P92 Turney P.D., 2001, ECML 2001, P491 WU SC, 2007, CHI 07 EXTENDED ABST, P2765, DOI 10.1145/1240866.1241076 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 NR 66 TC 3 Z9 3 PU ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD PI LONDON PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND SN 1071-5819 J9 INT J HUM-COMPUT ST JI Int. J. Hum.-Comput. Stud. PD OCT PY 2011 VL 69 IS 11 BP 720 EP 740 DI 10.1016/j.ijhcs.2011.06.007 PG 21 WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary SC Computer Science; Engineering; Psychology GA 824DM UT WOS:000295182300004 ER PT J AU Usui, S Kamiji, NL Taniguchi, T Ueda, N AF Usui, Shiro Kamiji, Nilton Liuji Taniguchi, Tatsuki Ueda, Naonori TI RAST: finding related documents based on triplet similarity SO NEURAL COMPUTING & APPLICATIONS LA English DT Article DE Text data mining; Knowledge visualization; Neuroinformatics; Bipartite graph; Dimensionality reduction; Document similarity AB With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only searches abstracts by the given keyword(s) but also displays abstracts related to a single or multiple selection. It also displays highly relevant abstracts together with possible keywords to help users refine their search. Analysis of the conditional similarity algorithm proposed here has shown that it does provide better output compared to ordinary cosine similarity, as well as the list of possible keywords reflects results of latent topic analysis. An interface for storing and sorting selected abstracts for future review and/or printing is also provided. C1 [Usui, Shiro; Kamiji, Nilton Liuji] RIKEN Brain Sci Inst, Lab Neuroinformat, Wako, Saitama 3510198, Japan. [Taniguchi, Tatsuki] IVIS Inc, Bunkyo Ku, Tokyo 1130033, Japan. [Ueda, Naonori] NTT Commun Sci Labs, Seika, Kyoto 6190237, Japan. RP Usui, S (reprint author), RIKEN Brain Sci Inst, Lab Neuroinformat, 2-1 Hirosawa, Wako, Saitama 3510198, Japan. EM usuishiro@riken.jp CR Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 IWATA T, 2007, NEURAL COMPUT, V19, P254 Lariviere V, 2008, J AM SOC INF SCI TEC, V59, P288, DOI 10.1002/asi.20744 Naud Antoine, 2007, Front Neuroinform, V1, P7, DOI 10.3389/neuro.11.007.2007 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SAITO K, 2004, 14 JAP NEUR NETW SOC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 USUI S, 2008, 15 INT C NEUR INF PR Usui S, 2007, IEEE IJCNN, P1103, DOI 10.1109/IJCNN.2007.4371112 USUI S, 2008, SFN 2008 WASH DC NEUR M PLANN SOC NEU 2009, MY SCHED JAP NEUR SO NR 12 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0941-0643 J9 NEURAL COMPUT APPL JI Neural Comput. Appl. PD OCT PY 2011 VL 20 IS 7 SI SI BP 993 EP 999 DI 10.1007/s00521-010-0392-6 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA 820MQ UT WOS:000294910400009 ER PT J AU Costa-jussa, MR Banchs, RE AF Costa-jussa, Marta R. Banchs, Rafael E. TI A vector-space dynamic feature for phrase-based statistical machine translation SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE Statistical machine translation; Source context information; Vector-space model AB In this paper, we propose and evaluate a novel dynamic feature function for log-linear model combinations in phrase-based statistical machine translation. The feature function is inspired on the popularly known vector-space model which is typically used in information retrieval and text mining applications, and it aims at improving translation unit selection at decoding time by incorporating context information from the source language. Significant improvements on an English-Spanish experimental corpus are presented and discussed. C1 [Costa-jussa, Marta R.] Barcelona Media Innovat Ctr, Speech & Language Dept, Barcelona 08018, Spain. [Banchs, Rafael E.] Inst Infocomm Res, Human Language Technol Dept, Singapore 138632, Singapore. RP Costa-jussa, MR (reprint author), Barcelona Media Innovat Ctr, Speech & Language Dept, Av Diagonal 177,9th Floor, Barcelona 08018, Spain. EM marta.ruiz@barcelonamedia.org; rembanchs@i2r.a-star.edu.sg RI Costa-jussa, Marta/M-7886-2013 OI Costa-jussa, Marta/0000-0002-5703-520X FU Spanish Department of Education and Science; Spanish Government [TEC2009-14094-C04-01]; Barcelona Media Innovation Center and Institute for Infocomm Research FX The authors would like to thank Barcelona Media Innovation Center and Institute for Infocomm Research for its support and permission to publish this research. We would also like to thank Bart Mellebeek for his helpful contribution. We would like to give credit to the anonymous reviewers of this paper for their valuable suggestions.This work has been partially funded by the Spanish Department of Education and Science through the Juan de la Cierva fellowship program and the Spanish Government under the BUCEADOR project (TEC2009-14094-C04-01). CR Carpuat M, 2007, EMPIRICAL METHODS NA, P61 Chew P. A., 2006, P WORKSH MULT LANG R, P68, DOI 10.3115/1613162.1613171 Costa-Jussa MR, 2009, IEICE T INF SYST, VE92D, P2179, DOI 10.1587/transinf.E92.D.2179 HAQUE R, 2009, 13 ANN C EUR ASS MAC, P234 Koehn K., 2003, P 10 C EUR CHAPT ASS, P347 Koehn P., 2007, P 45 ANN M ASS COMP, P177, DOI 10.3115/1557769.1557821 Och F. J., 1999, P 9 C EUR CHAPT ASS, P71, DOI 10.3115/977035.977046 Och F. J., 2002, P 40 ANN M ASS COMP, P295 Och Franz J., 2003, P 41 ANN M ASS COMP, P160 Och Franz Josef, 2000, P 18 INT C COMP LING, P1086 Papineni Kishore, 2002, P 40 ANN M ASS COMP, P311, DOI DOI 10.3115/1073083.1073135 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWENK H, 2007, EMPIRICAL METHODS NA, P430 Stolcke A., 2002, P INT C SPOK LANG PR, V2, P901 STROPPA N, 2007, 11 C THEOR METH ISS, P231 NR 16 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD OCT PY 2011 VL 37 IS 2 BP 139 EP 154 DI 10.1007/s10844-010-0130-7 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 819JV UT WOS:000294822100002 ER PT J AU Bruno, E Faessel, N Glotin, H Le Maitre, J Scholl, M AF Bruno, Emmanuel Faessel, Nicolas Glotin, Herve Le Maitre, Jacques Scholl, Michel TI Indexing and querying segmented web pages: the BlockWeb Model SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE web page segmentation; block importance; block permeability; web image indexing; document indexing; document retrieval AB We present in this paper a model for indexing and querying web pages, based on the hierarchical decomposition of pages into blocks. Splitting up a page into blocks has several advantages in terms of page design, indexing and querying such as (i) blocks of a page most similar to a query may be returned instead of the page as a whole (ii) the importance of a block can be taken into account, as well as (iii) the permeability of the blocks to neighbor blocks: a block b is said to be permeable to a block b' in the same page if b' content (text, image, etc.) can be (partially) inherited by b upon indexing. An engine implementing this model is described including: the transformation of web pages into blocks hierarchies, the definition of a dedicated language to express indexing rules and the storage of indexed blocks into an XML repository. The model is assessed on a dataset of electronic news, and a dataset drawn from web pages of the ImagEval campaign where it improves by 16% the mean average precision of the baseline. C1 [Bruno, Emmanuel; Glotin, Herve; Le Maitre, Jacques] Univ Sud Toulon Var, LSIS, F-83957 La Garde, France. [Faessel, Nicolas] Univ Paul Cezanne, LSIS, F-13397 Marseille 20, France. [Scholl, Michel] CNAM, F-75141 Paris 03, France. RP Bruno, E (reprint author), Univ Sud Toulon Var, LSIS, BP 20132, F-83957 La Garde, France. EM bruno@univ-tln.fr; nicolas.faessel@lsis.org; glotin@univ-tln.fr; lemaitre@univ-tln.fr; michel.scholl@cnam.fr FU ANR AVEIR [ANR-06-MDCA-002] FX We would like to thank CEA and TRIBVN society for providing the IMAGEVAL web image Task data set. We also thank ANR AVEIR ANR-06-MDCA-002 which partially supports this research. CR BRUNO E, 2009, P 9 ACM S DOC ENG DO, P70, DOI 10.1145/1600193.1600209 BRUNO E, 2009, P 7 INT WORKSH CONT, P219 Cai D., 2004, P 12 ANN ACM INT C M, P952, DOI 10.1145/1027527.1027747 CUI H, 2003, P 25 EUR C IR RES, P73 Debnath S, 2005, IEEE T KNOWL DATA EN, V17, P1233, DOI 10.1109/TKDE.2005.138 Deng Cai, 2003, VIPS VISION BASED PA Duda R., 2000, PATTERN CLASSIFICATI, V2nd Ha J., 1995, P 3 INT C DOC AN REC, V2, P952 Lin Shian-Hua, 2002, P 8 ACM SIGKDD INT C, P588, DOI DOI 10.1145/775047.775134 MOELLIC PA, 2006, IMAGEVAL 2006 OFFICI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 Tollari S, 2008, INT CONF ACOUST SPEE, P1229, DOI 10.1109/ICASSP.2008.4517838 Tollari S., 2007, P ACM INT C IM VID R, P65 Vadrevu S, 2007, WORLD WIDE WEB, V10, P157, DOI 10.1007/s11280-007-0021-1 Yi L., 2003, P 9 ACM SIGKDD INT C, P296 Zou J., 2006, P JOINT C DIG LIB, P119, DOI 10.1145/1141753.1141777 NR 17 TC 3 Z9 3 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD OCT PY 2011 VL 14 IS 5-6 SI SI BP 623 EP 649 DI 10.1007/s11280-011-0124-6 PG 27 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 815VT UT WOS:000294558800007 ER PT J AU Kilinc, D Alpkocak, A AF Kilinc, Deniz Alpkocak, Adil TI An expansion and reranking approach for annotation-based image retrieval from Web SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Information retrieval; Image retrieval; Query expansion; Document expansion; Reranking; WordNet ID DATABASES; SIMILARITY; QUERY; MODEL AB In this paper, we introduce an expansion and reranking approach for annotation based image retrieval from Web pages. Our suggestion considers an image retrieval system using the surrounding texts nearby the image in a Web page as annotations. However, annotations may include too much and uninformative text such as copyright notice, date, author. In order to choose indexing terms effectively, we propose a term selection approach, which first expands the document using WordNet, and then selects descriptive terms among them. Notably, we applied this term selection methodology to both document and query. This is because applying either of documents or query does not help to increase retrieval performance. On the other hand, term selection process increases the number of terms per documents, and both documents and queries become more exhaustive than original. Consequently, this results high recall with low precision in retrieval. Thus, we also proposed a two-level reranking approach. In order to evaluate our approaches we have participated ImageCLEF2009 WikipediaMM subtask. The results we obtained are superior to any participating approaches and our approach has obtained the best four ranks, in text-only image retrieval. The results also showed that document expansion and effective term selection to annotations plays an important role in text-based image retrieval. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Kilinc, Deniz; Alpkocak, Adil] Dokuz Eylul Univ, Dept Comp Engn, TR-35160 Izmir, Turkey. RP Alpkocak, A (reprint author), Dokuz Eylul Univ, Dept Comp Engn, TR-35160 Izmir, Turkey. EM deniz.kilinc@univera.com.tr; alpkocak@cs.deu.e-du.tr RI Alpkocak, Adil/F-3388-2013 FU Turkish National Science Foundation (TUBITAK) [107E217] FX This work is supported by Turkish National Science Foundation (TUBITAK) under project number 107E217. CR Allan J, 2001, INFORM PROCESS MANAG, V37, P435, DOI 10.1016/S0306-4573(00)00056-X BALANSKI J, 2005, INFORM PROCESSING MA, V41, P759 Billerbeck B., 2005, 10 AUSTR DOC COMP S, P34 BUCKLEY C, 1993, HUMAN LANGUAGE TECHN, P349 Callan James, 1992, P 3 INT C DAT EXP SY, P78 CAN F, 1990, ACM T DATABASE SYST, V15, P483, DOI 10.1145/99935.99938 Carbonell J. G., 1998, SIGIR 1998, P335 CHANG NS, 1980, IEEE T SOFTWARE ENG, V6, P519 CHANG NS, 1979, RELATIONAL DATABASE CHANG SK, 1981, COMPUTER, V14, P13 El-Kwae EA, 2000, ACM T INFORM SYST, V18, P171, DOI 10.1145/348751.348762 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 FRANKEL C, 1996, TR9614 U CHIC COMP S GARCIA E, 2006, IMPLEMENTATION APPL Lee KS, 2001, INFORM PROCESS MANAG, V37, P1, DOI 10.1016/S0306-4573(00)00017-0 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 LEW MS, 1997, WEBCRAWLING USING SK Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Manning C. D., 2009, INTRO INFORM RETRIEV Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 OGLE VE, 1995, COMPUTER, V28, P40, DOI 10.1109/2.410150 RESNIK O, 1999, J ARTIFICIAL INTELLI, V11, P95 Richardson R., 1994, CA1294 DUBL CIT U SC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 2000, IEEE MULTIMEDIA, V7, P26, DOI 10.1109/93.879766 Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 TSIKRIKA T, 2009, CLEF WORKING NOTES 2 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 Wu JK, 1997, IEEE T KNOWL DATA EN, V9, P978 Wu QS, 2001, J AM SOC INF SCI TEC, V52, P868, DOI 10.1002/asi.1134.abs Wu Z., 1994, ACL, P133 Yang L., 2006, P 15 ACM INT C INF K, P690, DOI 10.1145/1183614.1183713 Yang LP, 2005, LECT NOTES COMPUT SC, V3408, P169 NR 34 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD SEP 15 PY 2011 VL 38 IS 10 BP 13121 EP 13127 DI 10.1016/j.eswa.2011.04.118 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 784PP UT WOS:000292169500121 ER PT J AU Sohngen, C Chang, A Schomburg, D AF Soehngen, Carola Chang, Antje Schomburg, Dietmar TI Development of a classification scheme for disease-related enzyme information SO BMC BIOINFORMATICS LA English DT Article ID THERAPEUTIC TARGET; MUTATIONS; CANCER AB Background: BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods. Results: Currently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 +/- 0.032 and 0.738 +/- 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall. Conclusions: The DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA. C1 [Soehngen, Carola; Chang, Antje; Schomburg, Dietmar] Tech Univ Carolo Wilhelmina Braunschweig, Dept Bioinformat & Biochem, D-38106 Braunschweig, Germany. RP Schomburg, D (reprint author), Tech Univ Carolo Wilhelmina Braunschweig, Dept Bioinformat & Biochem, Langer Kamp 19 B, D-38106 Braunschweig, Germany. EM D.Schomburg@tu-bs.de FU European Union [021902 (RII3), 226073] FX This work was supported by the European Union: (FELICS: Free European Life-Science Information and Computational Services: 021902 (RII3) and SLING: Serving Life-science Information for the Next Generation: 226073). CR DEBOUCK C, 1992, AIDS RES HUM RETROV, V8, P153, DOI 10.1089/aid.1992.8.153 Dosaka-Akita H, 2002, BRIT J CANCER, V87, P751, DOI 10.1038/sj.bjc.6600536 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Germain DP, 2010, ORPHANET J RARE DIS, V5, DOI 10.1186/1750-1172-5-30 Hoffmann R, 2004, NAT GENET, V36, P664, DOI 10.1038/ng0704-664 Hofmann O, 2005, BIOINFORMATICS, V21, P2059, DOI 10.1093/bioinformatics/bti284 Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768 Jirapongsananuruk O, 2002, CLIN IMMUNOL, V104, P73, DOI 10.1006/clim.2002.5230 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Kim JJ, 2008, BIOINFORMATICS, V24, P1410, DOI 10.1093/bioinformatics/btn117 Krallinger M, 2010, METHODS MOL BIOL, V593, P341, DOI 10.1007/978-1-60327-194-3_16 Krallinger M, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S8-S1 Kwon KH, 2007, ACTA PHARMACOL SIN, V28, P1409, DOI 10.1111/j.1745-7254.2007.00694.x Pastores GM, 2010, BIODRUGS, V24, P41, DOI 10.2165/11318540-000000000-00000 Perrone MG, 2010, CURR MED CHEM, V17, P3769 Saif MW, 2005, J APPL RES, V5, P88 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saw S, 2000, PATHOLOGY, V32, P245 Sayers EW, 2010, NUCLEIC ACIDS RES, V38, pD5, DOI 10.1093/nar/gkp967 Scagliotti GV, 2004, CLIN CANCER RES, V10, p4227S, DOI 10.1158/1078-0432.CCR-040007 SCHEER M, 2010, NUCL ACIDS RES SEWELL W, 1964, B MED LIBR ASSOC, V52, P164 Sing T, 2005, BIOINFORMATICS, V21, P3940, DOI 10.1093/bioinformatics/bti623 Whitfield JB, 2001, CRIT REV CL LAB SCI, V38, P263 Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958 Yeh KC, 1998, ANTIMICROB AGENTS CH, V42, P332 Yeniterzi S, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S8-S2 NR 27 TC 6 Z9 7 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD AUG 9 PY 2011 VL 12 AR 329 DI 10.1186/1471-2105-12-329 PG 13 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 814TN UT WOS:000294480200001 PM 21827651 ER PT J AU Alfred, R AF Alfred, Rayner TI FEATURE TRANSFORMATION: A GENETIC-BASED FEATURE CONSTRUCTION METHOD FOR DATA SUMMARIZATION SO COMPUTATIONAL INTELLIGENCE LA English DT Article DE feature construction; feature transformation; data summarization; genetic algorithm; clustering ID ATTRIBUTES AB The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic-based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features. C1 Univ Malaysia Sabah, Sch Engn & Informat Technol, Kota Kinabalu 88999, Sabah, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Sch Engn & Informat Technol, Locked Bag 2073, Kota Kinabalu 88999, Sabah, Malaysia. EM ralfred@ums.edu.my RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR AHA DW, 1991, MACHINE LEARNING, P117 Alfred R, 2006, LECT NOTES ARTIF INT, V4093, P889 Amaldi E, 1998, THEOR COMPUT SCI, V209, P237, DOI 10.1016/S0304-3975(97)00115-1 BENSUSAN H, 1996, ICML 96, P96 BLOCKEEL H, 2000, LECT NOTES ARTIF INT, V1866, P66 Cubero JC, 1999, INFORM SCIENCES, V121, P233, DOI 10.1016/S0020-0255(99)00104-8 DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DIETTERICH TG, 1981, ARTIF INTELL, V16, P257, DOI 10.1016/0004-3702(81)90002-3 Frank E., 1999, DATA MINING PRACTICA Freitas AA, 2001, ARTIF INTELL REV, V16, P177, DOI 10.1023/A:1011996210207 Holland J. H., 1975, ADAPTATION NATURAL A HU YJ, 1996, GENERATION ATTRIBUTE, V1, P806 Hu Y-J, 1998, P 3 ANN GEN PROGR C, P146 KIRSTEN M, 1998, 8 INT C IND LOG PROG, P261 Koller D, 1996, INT C MACH LEARN, P284 Koza J. R., 1994, STAT COMPUT, V4, P191 Kramer S, 2000, AI COMMUN, V13, P275 Krawiec K., 2002, Genetic Programming and Evolvable Machines, V3, DOI 10.1023/A:1020984725014 Lavrac N., 2001, ACM T COMPUT LOG, V2, P458, DOI 10.1145/383779.383781 Michalewicz Z, 1994, GENETIC ALGORITHMS P, V2nd OTERO FEB, 2003, EUROGP, P384 PAGALLO G, 1990, MACH LEARN, V5, P71, DOI 10.1023/A:1022611825350 Perlich C, 2006, MACH LEARN, V62, P65, DOI 10.1007/s10994-006-6064-1 Quinlan R. J., 1993, MORGAN KAUFMANN SERI Raschia G, 2002, FUZZY SET SYST, V129, P137, DOI 10.1016/S0165-0114(01)00197-X SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAFTI LS, 2003, IDA, P599 Shannon Claude E., BELL SYSTEM TECHNICA, V27, P379 Srinivasan A, 1996, ARTIF INTELL, V85, P277, DOI 10.1016/0004-3702(95)00122-0 Vafaie H, 1998, IEEE INTELL SYST APP, V13, P57, DOI 10.1109/5254.671093 Wiener N., 1965, CYBERNETICS CONTROL ZHENG Z, 1996, ICTAI, P254 Zheng ZJ, 2000, MACH LEARN, V40, P35, DOI 10.1023/A:1007626017208 NR 33 TC 0 Z9 0 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 0824-7935 J9 COMPUT INTELL-US JI Comput. Intell. PD AUG PY 2011 VL 27 IS 3 BP 315 EP 335 DI 10.1111/j.1467-8640.2011.00381.x PG 21 WC Computer Science, Artificial Intelligence SC Computer Science GA 821ZY UT WOS:000295014200001 ER PT J AU Farahat, AK Kamel, MS AF Farahat, Ahmed K. Kamel, Mohamed S. TI Statistical semantics for enhancing document clustering SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Document clustering; Statistical semantics; Semantic similarity; Term-term correlations ID ALGORITHMS AB Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. This paper proposes new models for document representation that capture semantic similarity between documents based on measures of correlations between their terms. The paper uses the proposed models to enhance the effectiveness of different algorithms for document clustering. The proposed representation models define a corpus-specific semantic similarity by estimating measures of term-term correlations from the documents to be clustered. The corpus of documents accordingly defines a context in which semantic similarity is calculated. Experiments have been conducted on thirteen benchmark data sets to empirically evaluate the effectiveness of the proposed models and compare them to VSM and other well-known models for capturing semantic similarity. C1 [Farahat, Ahmed K.; Kamel, Mohamed S.] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. RP Farahat, AK (reprint author), Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. EM afarahat@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 Carbonell J.G., 1997, P 15 INT JOINT C ART, P708 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Dhillon I., 2001, ACM SIGKDD INT C KNO, P269 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dhillon I.S., 2003, SURVEY TEXT MINING, P73 Ding C, 2010, IEEE T PATTERN ANAL, V32, P45, DOI 10.1109/TPAMI.2008.277 Dongen S, 2000, PERFORMANCE CRITERIA Drineas P, 2006, SIAM J COMPUT, V36, P132, DOI 10.1137/S0097539704442684 FARAHAT AK, 2009, P 9 IEEE INT C DAT M, P459 FARAHAT AK, 2010, P 8 WORKSH TEXT MIN, P83 Fung BCM, 2003, SIAM PROC S, P59 FURNAS GW, 1983, AT&T TECH J, V62, P1753 Gabrilovich E, 2007, P 20 INT JOINT C ART, P6 Han E.-H., 1998, P 2 INT C AUT AG, P408, DOI DOI 10.1145/280765.280872 He X, 2002, COMPUT STAT DATA AN, V41, P19, DOI 10.1016/S0167-9473(02)00070-1 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 HU X, 2009, P 15 ACM SIGKDD INT, P389, DOI 10.1145/1557019.1557066 Huang A., 2009, P 13 PAC AS C ADV KN, P628 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jing LP, 2010, KNOWL INF SYST, V25, P35, DOI 10.1007/s10115-009-0256-5 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Karypis G., 2003, 02017 U MINN DEP COM Lee DD, 1999, NATURE, V401, P788 Lewis DD, 1999, REUTERS 21578 TEXT C Luxburg U.V., 2007, STAT COMPUT, V17, P395, DOI DOI 10.1007/S11222-007-9033-Z Meila M, 2003, LECT NOTES ARTIF INT, V2777, P173, DOI 10.1007/978-3-540-45167-9_14 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Pessiot JF, 2010, INFORM PROCESS MANAG, V46, P180, DOI 10.1016/j.ipm.2009.09.007 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B., 1997, LECT NOTES COMPUTER, V1327, P583, DOI DOI 10.1007/BFB0020217 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Wu J, 2009, P 15 ACM SIGKDD INT, P877, DOI 10.1145/1557019.1557115 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 40 TC 11 Z9 11 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD AUG PY 2011 VL 28 IS 2 SI SI BP 365 EP 393 DI 10.1007/s10115-010-0367-z PG 29 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 805EW UT WOS:000293710200006 ER PT J AU Dong, H Hussain, FK AF Dong, Hai Hussain, Farookh Khadeer TI Semantic service matchmaking for Digital Health Ecosystems SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE Digital Ecosystems; Digital Health Ecosystems; Health service metadata; Health service ontology; Semantic search; Semantic service matchmaking; Service evaluation and ranking ID WEB SEARCH; INFORMATION; RELEVANCE AB The vision of Digital Ecosystems was initiated by the European Commission, with the purpose of constructing an information and communication technology environment to facilitate the sustainable development of small and medium enterprises. As a key sub-domain of Digital Ecosystems, Digital Health Ecosystems provide crucial services to maintain the health of the main participants of Digital Ecosystems. We are concerned with the large-scale, ambiguous, heterogeneous, and untrustworthy nature of health service information in Digital Health Ecosystems. An intensive survey found that current research cannot support accurate and trustworthy matchmaking between health service requests and health service advertisements in Digital Health Ecosystems. Therefore, in this paper, we propose a framework for a semantic service matchmaker that takes into account the ambiguous, heterogeneous nature of service information in Digital Health Ecosystems. This framework is designed to make four major contributions, which are health service domain knowledge modeling, online health service information disambiguation, health service query disambiguation and health service quality evaluation and ranking. In order to thoroughly evaluate this framework, we implement a prototype - a Semantic Health Service Search Engine, and conduct a series of experiments on the prototype using a functional testing and simulation approach. (C) 2011 Elsevier B.V. All rights reserved. C1 [Dong, Hai; Hussain, Farookh Khadeer] Curtin Univ, Digital Ecosyst & Business Intelligence Inst, Enterprise Unit 4, Bentley, WA 6102, Australia. RP Hussain, FK (reprint author), Curtin Univ, Digital Ecosyst & Business Intelligence Inst, Enterprise Unit 4, De Laeter Way,Technol Pk, Bentley, WA 6102, Australia. EM hai.dong@cbs.curtin.edu.au; farookh.hussain@cbs.curtin.edu.au RI Dong, Hai/E-9101-2010 OI Dong, Hai/0000-0002-7033-5688 CR Andreassen HK, 2007, BMC PUBLIC HEALTH, V7, DOI 10.1186/1471-2458-7-53 BERGENTI F, 2007, WORKSH SEM WEB UB HE Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612 Caceres C, 2006, IEEE INTELL SYST, V21, P11, DOI 10.1109/MIS.2006.107 Can AB, 2007, COMPUT METH PROG BIO, V86, P73, DOI 10.1016/j.cmpb.2007.01.007 Champin PA, 2010, KNOWL-BASED SYST, V23, P287, DOI 10.1016/j.knosys.2009.11.011 Chang E., 2006, P IIWAS YOG IND, P3, DOI 10.1145/1178677.1178679 Chang E., 2006, WSEAS Transactions on Environment and Development, V2 Deerwester SC, 1988, Patent, U. S., Patent No. 4839853 Dini P, 2005, DIGITAL ECOSYSTEMS R Dong H, 2011, J COMPUT SYST SCI, V77, P687, DOI 10.1016/j.jcss.2010.02.009 Dong H, 2009, INT J WEB GRID SERV, V5, P261 Dong H, 2011, IEEE T IND ELECTRON, V58, P2183, DOI 10.1109/TIE.2009.2031186 Dong H, 2010, WORLD WIDE WEB, V13, P75, DOI 10.1007/s11280-009-0081-5 DUMAS M, 2004, ELECTRON COMMER R A, V3, P95, DOI 10.1016/j.elerap.2003.09.003 Fenza G, 2008, INT J APPROX REASON, V48, P808, DOI 10.1016/j.ijar.2008.01.005 Grimes S., 2010, INFORMATIONWEEK HADZIC M, 2008, 2 IEEE INT C DIG EC Hawking D, 2006, COMPUTER, V39, P88, DOI 10.1109/MC.2006.286 Hevner AR, 2004, MIS QUART, V28, P75 Holi M, 2006, LECT NOTES COMPUT SC, V4185, P351 HYVONEN E, 2007, 5 INT SEM WEB C ISWC, P778 Jin H, 2008, KNOWL-BASED SYST, V21, P655, DOI 10.1016/j.knosys.2008.03.040 KANNAN SS, 2010, KNOWLEDGE BASED SYST Klusch M, 2009, J WEB SEMANT, V7, P121, DOI 10.1016/j.websem.2008.10.001 KUSTER U, 2007, 16 INT WORLD WID WEB, P1033 Nachira F., 2007, DIGITAL BUSINESS ECO, P1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 Suominen O, 2007, LECT NOTES COMPUT SC, V4519, P356 VALLE ED, 2005, W3C WORKSH FRAM SEM, P1 VALLE ED, 2005, 1 INT WORKSH MED SEM, P35 NR 34 TC 9 Z9 10 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD AUG PY 2011 VL 24 IS 6 BP 761 EP 774 DI 10.1016/j.knosys.2011.02.005 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 785JH UT WOS:000292224800004 ER PT J AU Liu, YC Wu, C Liu, M AF Liu, Yuan-chao Wu, Chong Liu, Ming TI Research of fast SOM clustering for text information SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Self organizing maps; Text mining; Clustering efficiency; Feature coding; Similarity computation ID HIGH-DIMENSIONAL DATA; SELF-ORGANIZING MAP; DOCUMENT COLLECTION; RANDOM PROJECTIONS; CATEGORIZATION; ALGORITHM; MODEL; SIMILARITY; HIERARCHY; SEARCH AB The state-of-the-art text clustering methods suffer from the huge size of documents with high-dimensional features. In this paper, we studied fast SUM clustering technology for Text Information. Our focus is on how to enhance the efficiency of text clustering system whereas high clustering qualities are also kept. To achieve this goal, we separate the system into two stages: offline and online. In order to make text clustering system more efficient, feature extraction and semantic quantization are done offline. Although neurons are represented as numerical vectors in high-dimension space, documents are represented as collections of some important keywords, which is different from many related works, thus the requirement for both time and space in the offline stage can be alleviated. Based on this scenario, fast clustering techniques for online stage are proposed including how to project documents onto output layers in SOM, fast similarity computation method and the scheme of Incremental clustering technology for real-time processing, We tested the system using different datasets, the practical performance demonstrate that our approach has been shown to be much superior in clustering efficiency whereas the clustering quality are comparable to traditional methods. (C) 2011 Elsevier Ltd. All rights reserved. C1 [Liu, Yuan-chao; Wu, Chong; Liu, Ming] Harbin Inst Technol, Dept Comp Sci & Technol, Harbin 150001, Peoples R China. RP Liu, YC (reprint author), Harbin Inst Technol, Dept Comp Sci & Technol, Harbin 150001, Peoples R China. EM ycliu@hit.edu.cn FU Chinese 863 program [2007AA01Z172]; National Natural Science Foundation of China [70773029, 60603092] FX This work was supported by Chinese 863 program (2007AA01Z172) and the National Natural Science Foundation of China(70773029 and 60603092). CR Aas K., 1999, 941 NORW COMP CTR AGGARWAL YCC, 2006, P 6 SIAM INT C DAT M, P479 Agrawal C C, 2003, P 29 INT C VER LARG, P81 AGRAWAL CC, 2004, P 30 INT C VER LARG, P852 Aguado D, 2008, ENG APPL ARTIF INTEL, V21, P919, DOI 10.1016/j.engappai.2007.08.001 Aliguliyev RM, 2009, EXPERT SYST APPL, V36, P7904, DOI 10.1016/j.eswa.2008.11.017 Asharaf S, 2003, PATTERN RECOGN, V36, P3015, DOI 10.1016/S0031-3203(03)00081-5 Avogadri R, 2009, ARTIF INTELL MED, V45, P173, DOI 10.1016/j.artmed.2008.07.014 Chow TWS, 2009, EXPERT SYST APPL, V36, P12023, DOI 10.1016/j.eswa.2009.03.008 Cios K. J., 1998, DATA MINING METHODS Fritzke B., 1995, NEURAL PROCESS LETT, P9 Gonzalez-Barrios JM, 2003, STAT PROBABIL LETT, V62, P23, DOI 10.1016/S0167-7152(02)00421-2 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Han Sang il, 2006, Biosystems, V84, P175, DOI 10.1016/j.biosystems.2005.11.001 Hsu CC, 2008, EXPERT SYST APPL, V35, P1177, DOI 10.1016/j.eswa.2007.08.049 Huang SH, 2008, EXPERT SYST APPL, V34, P2290, DOI 10.1016/j.eswa.2007.03.012 Hung CL, 2009, EXPERT SYST APPL, V36, P7064, DOI 10.1016/j.eswa.2008.08.037 Isa D, 2009, EXPERT SYST APPL, V36, P9584, DOI 10.1016/j.eswa.2008.07.082 Karkkainen I, 2007, PATTERN RECOGN, V40, P784, DOI 10.1016/j.patcog.2006.06.023 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Keskin GA, 2010, EXPERT SYST APPL, V37, P1235, DOI 10.1016/j.eswa.2009.06.004 Kifer D, 2004, P 30 INT C VER LARG, P180, DOI 10.1016/B978-012088469-8/50019-X KOHONEN T, 2000, P 23 ACM SIGIR, P224 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Lee CH, 2009, EXPERT SYST APPL, V36, P2400, DOI 10.1016/j.eswa.2007.12.052 LIU Y, 2009, J COMPUTATIONAL INFO, V5, P141 LIU Y, 2006, 7 INT C WEB AG INF M LOIS R, 2007, INFORM PROCESSING MA, V43, P1260 Lonardi S, 2006, THEOR COMPUT SCI, V368, P217, DOI 10.1016/j.tcs.2006.09.023 LOUIS M, 2005, P IASTED INT C COMP, P85 Martin-Guerrero JD, 2006, EXPERT SYST APPL, V30, P299, DOI [10.1016/j.eswa.2005.07.025, 10.1016/j.eswwa.2005.07.025] Morita K, 2004, INFORM PROCESS MANAG, V40, P957, DOI 10.1016/j.ipm.2003.08.009 O'Callaghan L, 2002, PROC INT CONF DATA, P685, DOI 10.1109/ICDE.2002.994785 Pullwitt D, 2002, NEURAL NETWORKS, V15, P1099, DOI 10.1016/S0893-6080(02)00082-5 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 SAHOO N, 2006, INCREMENTAL HIERARCH, P357 Salton G., 1983, MCGRAWHILL COMPUTER SALTON G, 1989, TRANSFORMATION ANAL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracoglu R, 2008, EXPERT SYST APPL, V34, P2545, DOI 10.1016/j.eswa.2007.04.003 Saracoglu R, 2007, EXPERT SYST APPL, V33, P600, DOI 10.1016/j.eswa.2006.06.002 SASCHA H, 2006, LECT NOTES COMPUTER, P332 Sebzalli YM, 2001, ENG APPL ARTIF INTEL, V14, P607, DOI 10.1016/S0952-1976(01)00032-X Shi Zhong, 2005, Neural Networks, V18, DOI 10.1016/j.neunet.2005.06.008 Sinka MP, 2005, J NETW COMPUT APPL, V28, P129, DOI 10.1016/j.jnca.2004.01.002 Song W, 2009, EXPERT SYST APPL, V36, P9095, DOI 10.1016/j.eswa.2008.12.046 Taeho Jo, 2005, Proceedings of the International Joint Conference on Neural Networks 2005 (IEEE Cat. No. 05CH37663C) TANG B, 2005, LECT NOTES COMPUTER, P292 Tjhi WC, 2008, FUZZY SET SYST, V159, P371, DOI 10.1016/j.fss.2007.10.003 Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Wu LH, 2005, EXPERT SYST APPL, V29, P757, DOI 10.1016/j.eswa.2005.06.003 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 ZHONGHUI F, 2007, J XIAN JIAOTONG U, V41, P398 ZHOU J, 2005, LECT NOTES COMPUTER, P63 NR 56 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD AUG PY 2011 VL 38 IS 8 BP 9325 EP 9333 DI 10.1016/j.eswa.2011.01.126 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 759JV UT WOS:000290237500033 ER PT J AU Aerts, D Broekaert, J Gabora, L AF Aerts, Diederik Broekaert, Jan Gabora, Liane TI A case for applying an abstracted quantum formalism to cognition SO NEW IDEAS IN PSYCHOLOGY LA English DT Article DE Concepts; Consciousness; Contextuality; Decision-making; Liar paradox; Quantum ID LATENT SEMANTIC ANALYSIS; PROBABILITY; MECHANICS; MODEL; REPRESENTATION; COMBINATIONS; FALLACY; SPACE AB The contextual nature of experience suggests that in order to formally model it we should look to the domain of science where contextuality has been most seriously addressed: quantum mechanics. As in quantum mechanics, conscious experience consists of segments that are not contextual and do not involve resolution of ambiguity or decision, which can be modeled as dynamic evolution, and segments that are context-dependent and involve a decision or the resolution of ambiguity, which can be modeled as collapse events. An abstracted quantum mechanical representation of the entity-context interaction, with its creation of new states, is adapted to describe the process by which situations are contextualized. We show that Bell inequalities the definitive test for quantum structure are violated in the relationship between an abstract concept and instances of that concept. We summarize work on a theory of concepts that focuses on how the potentiality of a concept gets actualized through interaction with a context, and how the mathematics of entanglement can be applied to concept combination. A full quantum model is presented for the description of (1) contextually elicited opinions (as in an opinion-poll situation), and (2) the alternating changes of cognitive state in the Liar Paradox. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Aerts, Diederik; Broekaert, Jan] Free Univ Brussels, Lejo Apostel Ctr Interdisciplinary Studies CLEA, B-1160 Brussels, Belgium. [Gabora, Liane] Univ British Columbia, Dept Psychol, Kelowna, BC V1V 1V7, Canada. RP Broekaert, J (reprint author), Free Univ Brussels, Lejo Apostel Ctr Interdisciplinary Studies CLEA, Krijgskundestr 33, B-1160 Brussels, Belgium. EM diraerts@vub.ac.be; jbroekae@vub.ac.be; liane.gabora@ubc.ca CR ACCARDI L, 1982, LETT NUOVO CIMENTO, V34, P161, DOI 10.1007/BF02817051 Accardi L., 1982, REND SEM MATH U POLI, P249 Aerts D., 1999, FOUND SCI, V4, P115, DOI 10.1023/A:1009610326206 Aerts D, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.012316 Aerts D, 2009, LECT NOTES ARTIF INT, V5494, P128 Aerts D, 2004, J PHYS A-MATH GEN, V37, pL123, DOI 10.1088/0305-4470/37/12/L01 Aerts D., 1995, FOUND SCI, V1, P85 Aerts D, 1999, INT J THEOR PHYS, V38, P3231, DOI 10.1023/A:1026686316673 Aerts D, 2009, J MATH PSYCHOL, V53, P314, DOI 10.1016/j.jmp.2009.04.005 Aerts D., 1997, TOPICS FDN STAT, P85 AERTS D, 1986, J MATH PHYS, V27, P202, DOI 10.1063/1.527362 Aerts D, 2005, KYBERNETES, V34, P192, DOI 10.1108/03684920510575807 Aerts D, 2009, LECT NOTES ARTIF INT, V5494, P59 Aerts D, 2000, FOUND PHYS, V30, P1387, DOI 10.1023/A:1026449716544 Aerts D., 1987, INFORM COMPLEXITY CO, P77 Aerts D, 1999, INT J THEOR PHYS, V38, P407, DOI 10.1023/A:1026662013986 AERTS D, 1983, J MATH PHYS, V24, P2441, DOI 10.1063/1.525626 AERTS D, 1994, FOUND PHYS, V24, P1353, DOI 10.1007/BF02283037 AERTS D, 1994, S FDN MOD PHYS GIFS AERTS D, 1982, FOUND PHYS, V12, P1131, DOI 10.1007/BF00729621 Aerts D, 2005, KYBERNETES, V34, P167, DOI 10.1108/03684920510575799 Aerts D., 2002, Probing the Structure of Quantum Mechanics. Nonlinearity, Nonlocality, Computation, Axiomatics, DOI 10.1142/9789812778024_0004 AERTS D, 1995, INT J THEOR PHYS, V34, P1165, DOI 10.1007/BF00676227 Aerts D, 2009, J MATH PSYCHOL, V53, P389, DOI 10.1016/j.jmp.2009.02.005 Aerts D, 1999, INT J THEOR PHYS, V38, P289, DOI 10.1023/A:1026605829007 Aerts D., 1993, TATRA MOUNTAINS MATH, V1, P5 AERTS D, 1993, INT J THEOR PHYS, V32, P2207, DOI 10.1007/BF00672993 BARHILLEL M, 1993, J PERS SOC PSYCHOL, V65, P1119 Barrett LF, 2004, PSYCHOL BULL, V130, P553, DOI 10.1037/0033-2909.130.4.553 Bell J.S., 1964, Physics, V1 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BROEKAERT J, 2002, FOUND SCI, V11, P399 Bruza P. D., 2005, WE WILL SHOW THEM ES Bruza P. D., 2007, P AAAI SPRING S QUAN Bruza P. D., 2009, LECT NOTES ARTIFICIA, V5494 Bruza P.D., 2008, QUANTUM INTERACTION Busemeyer JR, 2006, J MATH PSYCHOL, V50, P220, DOI 10.1016/j.jmp.2006.01.003 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Edelman GM, 2000, UNIVERSE CONSCIOUSNE Gabora L, 2005, INTERDISCIPL SCI REV, V30, P69, DOI 10.1179/030801805X25873 Gabora L, 2008, ECOL PSYCHOL, V20, P84, DOI 10.1080/10407410701766676 Gabora L, 2001, THESIS FREE U BRUSSE Gabora L, 2002, J EXP THEOR ARTIF IN, V14, P327, DOI 10.1080/09528130210162253 GAUSSIER E, 2002, LECT NOTES COMPUTER, V2291 GRIFFITHS TL, 2002, ADV NEURAL INFORM PR Hofmann Thomas, 1999, P UNC ART INT STOCKH Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Li Y., 2008, ACM SIGIR, V42, P22 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Mogiliansky AL, 2009, J MATH PSYCHOL, V53, P349, DOI 10.1016/j.jmp.2009.01.001 Paivio A, 2007, MIND ITS EVOLUTION D Pitowsky I., 1989, QUANTUM PROBABILITY Plate T.A., 2003, HOLOGRAPHIC REDUCED PLATE TA, 1995, IEEE T NEURAL NETWOR, V6, P623, DOI 10.1109/72.377968 Pothos E.M., 2009, P ROYAL SOC B SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sloman SA, 1996, PSYCHOL BULL, V119, P3, DOI 10.1037//0033-2909.119.1.3 Sun R., 2002, DUALITY MIND TVERSKY A, 1992, PSYCHOL SCI, V3, P305, DOI 10.1111/j.1467-9280.1992.tb00678.x TVERSKY A, 1983, PSYCHOL REV, V90, P293, DOI 10.1037/0033-295X.90.4.293 Tversky Amos, 1982, JUDGMENT UNCERTAINTY Van Rijsbergen K., 2004, GEOMETRY INFORM RETR Vinokourov A, 2002, J INTELL INF SYST, V18, P153, DOI 10.1023/A:1013677411002 Widdows D., 2003, MATH LANGUAGE, V8, P141 Widdows D., 2003, P 41 ANN M ASS COMP, P136 NR 66 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0732-118X J9 NEW IDEAS PSYCHOL JI New Ideas Psychol. PD AUG PY 2011 VL 29 IS 2 BP 136 EP 146 DI 10.1016/j.newideapsych.2010.06.002 PG 11 WC Psychology, Multidisciplinary; Psychology, Experimental SC Psychology GA 734DG UT WOS:000288312800008 ER PT J AU Anand, P Escuadro, H Gera, R Martell, C AF Anand, Pranav Escuadro, Henry Gera, Ralucca Martell, Craig TI Triangular line graphs and word sense disambiguation SO DISCRETE APPLIED MATHEMATICS LA English DT Article DE H-line graphs; Triangular line graph; Line graph; Connectivity ID SEQUENCES AB Linguists often represent the relationships between words in a collection of text as an undirected graph G = (V, E), where V is the vocabulary and vertices are adjacent in G if and only if the words that they represent co-occur in a relevant pattern in the text. Ideally, the words with similar meanings give rise to the vertices of a component of the graph. However, many words have several distinct meanings, preventing components from characterizing distinct semantic fields. This paper examines how the structural properties of triangular line graphs motivate the use of a clustering coefficient on the triangular line graph, thereby helping to identify polysemous words. The triangular line graph of G, denoted by T(G), is the subgraph of the line graph of G where two vertices are adjacent if the corresponding edges in G belong to a K(3). (C) 2011 Elsevier B.V. All rights reserved. C1 [Escuadro, Henry] Juniata Coll, Dept Math, Huntingdon, PA 16652 USA. [Anand, Pranav] Univ Calif Santa Cruz, Dept Linguist, Santa Cruz, CA 95064 USA. [Gera, Ralucca] USN, Postgrad Sch, Dept Appl Math, Monterey, CA 93943 USA. [Martell, Craig] USN, Postgrad Sch, Dept Comp Sci, Monterey, CA 93943 USA. RP Escuadro, H (reprint author), Juniata Coll, Dept Math, Huntingdon, PA 16652 USA. EM panand@ucsc.edu; escuadro@juniata.edu; rgera@nps.edu; cmartell@nps.edu FU Department of the Navy PEO Integrated Warfare Systems FX The authors would like to thank the referees, as well as Derrick Stolee and Stephen Hartke, for their comments and suggestions regarding this publication. Also P. Anand, R. Gera and C. Martell thank the Department of the Navy PEO Integrated Warfare Systems for funding that partially supported this work. CR Chartrand G., 2004, INTRO GRAPH THEORY Chartrand G, 1995, DISCRETE MATH, V147, P73, DOI 10.1016/0012-365X(94)00258-K DOROW B, 2004, USING CURVATURE MARK Dorrough D, 1996, DISCRETE MATH, V161, P79, DOI 10.1016/0012-365X(95)00220-Q Fellbaum C, 1998, WORDNET ELECT LEXICA Ide N, 1998, COMPUT LINGUIST, V24, P1 Jarrett E.B., 1995, P 7 INT KAL C GRAPH, P589 Le VB, 1996, DISCRETE MATH, V159, P179, DOI 10.1016/0012-365X(95)00109-A MIHALCEA R, 2005, ADV WORD SENSE DISAM Rilof E., 1997, P 2 C EMP METH NAT L, P117 Roark B, 1998, P 36 ANN M ASS COMP, P1110 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sproat R., 1992, P INT C SPOK LANG PR Weaver W, 1955, MACHINE TRANSLATION, P15 NR 14 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0166-218X J9 DISCRETE APPL MATH JI Discret Appl. Math. PD JUL 6 PY 2011 VL 159 IS 11 BP 1160 EP 1165 DI 10.1016/j.dam.2011.03.019 PG 6 WC Mathematics, Applied SC Mathematics GA 780GP UT WOS:000291842800010 ER PT J AU Rieck, K AF Rieck, Konrad TI Similarity measures for sequential data SO WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Review ID NATURAL-LANGUAGE; STRING KERNELS; TEXT; RECOGNITION; DISTANCES; CODES AB Expressive comparison of strings is a prerequisite for analysis of sequential data in many areas of computer science. However, comparing strings and assessing their similarity is not a trivial task and there exists several contrasting approaches for defining similarity measures over sequential data. In this paper, we review three major classes of such similarity measures: edit distances, bag-of-word models, and string kernels. Each of these classes originates from a particular application domain and models similarity of strings differently. We present these classes and underlying comparisons in detail, highlight advantages, and differences as well as provide basic algorithms supporting practical applications. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 296-304 DOI:10.1002/widm.36 C1 Tech Univ Berlin, Machine Learning Grp, Berlin, Germany. RP Rieck, K (reprint author), Tech Univ Berlin, Machine Learning Grp, Berlin, Germany. EM konrad.rieck@tu-berlin.de FU Bundesministerium fur Bildung und Forschung [FKZ 01-IS07007A] FX The author would like to thank Tammo Krueger, Soren Sonnenburg, and Klaus-Robert Muller for fruitful discussions and support. Moreover, the author acknowledges funding from the Bundesministerium fur Bildung und Forschung under the project REMIND (FKZ 01-IS07007A). CR CHANG WI, 1994, ALGORITHMICA, V12, P327, DOI 10.1007/BF01185431 Collins M, 2002, ADV NEUR IN, V14, P625 Cuturi M, 2007, P INT C AC SPEECH SI DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Doolittle RF, 1986, URFS ORFS PRIMER ANA Gartner T, 2004, MACH LEARN, V57, P205, DOI 10.1023/B:MACH.0000039777.23772.30 Gusfield D., 1997, ALGORITHMS STRINGS T HAMMING RW, 1950, AT&T TECH J, V29, P147 Haussler D., 1999, UCSCCRL9910 Hofmeyr S. A., 1998, Journal of Computer Security, V6 Jaakkola T, 2000, J COMPUT BIOL, V7, P95, DOI 10.1089/10665270050081405 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Leslie Christina, 2002, Pac Symp Biocomput, P564 Leslie C, 2004, J MACH LEARN RES, V5, P1435 Leslie C.S., 2003, BIOINFORMATICS, V1, P1 LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845 Liao Y, 2002, P 11 USENIX SEC S, P51 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 MASEK WJ, 1980, J COMPUT SYST SCI, V20, P18, DOI 10.1016/0022-0000(80)90002-1 Muller KR, 2001, IEEE T NEURAL NETWOR, V12, P181, DOI 10.1109/72.914517 NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 Ratsch G, 2005, BIOINFORMATICS, V21, pI369, DOI 10.1093/bioinformatics/bti1053 Rieck K, 2010, J MACH LEARN RES, V11, P555 Rieck K., 2007, J COMPUTER VIROLOGY, V2, P243, DOI 10.1007/s11416-006-0030-0 Rieck K, 2008, J MACH LEARN RES, V9, P23 Robertson A, 1998, J DOC, V58, P48 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sankoff D., 1983, TIME WRAPS STRING ED Scholkopf B., 2002, LEARNING KERNELS Shawe-Taylor J., 2004, KERNEL METHODS PATTE SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 Sonnenburg S, 2006, BIOINFORMATICS, V22, pE472, DOI 10.1093/bioinformatics/btl250 Sonnenburg S., 2007, LARGE SCALE KERNEL M, P73 SUEN CY, 1979, IEEE T PATTERN ANAL, V1, P164 Tsuda K, 2002, NEURAL COMPUT, V14, P2397, DOI 10.1162/08997660260293274 Vert J. P., 2004, KERNEL METHODS COMPU, P131 Vishwanathan S, 2004, KERNELS BIOINFORMATI, P113 Vishwanathan SVN, 2010, J MACH LEARN RES, V11, P1201 Watkins C, 2000, ADV NEUR IN, P39 Zien A, 2000, BIOINFORMATICS, V16, P799, DOI 10.1093/bioinformatics/16.9.799 NR 42 TC 1 Z9 1 PU WILEY PERIODICALS, INC PI SAN FRANCISCO PA ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA SN 1942-4787 J9 WIRES DATA MIN KNOWL JI Wiley Interdiscip. Rev.-Data Mining Knowl. Discov. PD JUL-AUG PY 2011 VL 1 IS 4 BP 296 EP 304 DI 10.1002/widm.36 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA 945FK UT WOS:000304258000003 ER PT J AU Aral, S Van Alstyne, M AF Aral, Sinan Van Alstyne, Marshall TI The Diversity-Bandwidth Trade-off SO AMERICAN JOURNAL OF SOCIOLOGY LA English DT Article ID EVOLVING SOCIAL NETWORK; STRUCTURAL HOLES; AUTO-CORRELATION; WORK GROUPS; WEAK TIES; KNOWLEDGE; PERFORMANCE; INFORMATION; MARKET; ORGANIZATIONS AB The authors propose that a trade-off between network diversity and communications bandwidth regulates access to novel information because a more diverse network structure increases novelty at a cost of reducing information flow. Received novelty then depends on whether (a) the information overlap is small enough, (b) alters' topical knowledge is shallow enough, and (c) alters' knowledge stocks refresh slowly enough to justify bridging structural holes. Social network and e-mail content from an executive recruiting firm show that bridging ties can actually offer less novelty for these reasons, suggesting that the strength of weak ties and structural holes depend on brokers' information environments. C1 [Aral, Sinan] NYU, Stern Sch Business, New York, NY 10027 USA. [Van Alstyne, Marshall] Boston Univ, Sch Management, Boston, MA 02215 USA. RP Aral, S (reprint author), NYU, Stern Sch Business, New York, NY 10027 USA. EM sinan@stern.nyu.edu CR Allen TJ, 1977, MANAGING FLOW TECHNO ANCONA DG, 1992, ORGAN SCI, V3, P321, DOI 10.1287/orsc.3.3.321 Aral S, 2011, MARKET SCI, V30, P217, DOI 10.1287/mksc.1100.0596 ARAL S, MANAGEMENT IN PRESS Aral S, 2009, P NATL ACAD SCI USA, V106, P21544, DOI 10.1073/pnas.0908800106 Argote L., 1999, ORG LEARNING CREATIN BAKER WE, 1984, AM J SOCIOL, V89, P775, DOI 10.1086/227944 BAKER WE, 1990, AM J SOCIOL, V96, P589, DOI 10.1086/229573 Bell D., 1973, COMING POSTINDUSTRIA BERNARD HR, 1981, CONNECTIONS, V4, P11 Blau P.M., 1986, EXCHANGE POWER SOCIA, V2nd Brynjolfsson E, 2007, P 28 ANN INT C INF S Van Alstyne M, 2004, NETWORK SOCIETY: A CROSS-CULTURAL PERSPECTIVE, P145 Burt R. S., 1992, STRUCTURAL HOLES SOC Burt RS, 2007, ACAD MANAGE J, V50, P119 Burt RS, 2004, AM J SOCIOL, V110, P349, DOI 10.1086/421787 Burt RS, 2000, RES ORGAN BEHAV, V22, P345, DOI 10.1016/S0191-3085(00)22009-1 Burt RS, 2008, IND CORP CHANGE, V17, P953, DOI 10.1093/icc/dtn033 Burt RS, 2005, BROKERAGE CLOSURE IN Centola D, 2007, AM J SOCIOL, V113, P702, DOI 10.1086/521848 COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553 COLEMAN JS, 1988, AM J SOCIOL, V94, pS95, DOI 10.1086/228943 Cramton CD, 2001, ORGAN SCI, V12, P346, DOI 10.1287/orsc.12.3.346.10098 Cummings JN, 2003, SOC NETWORKS, V25, P197, DOI 10.1016/S0378-8733(02)00049-7 Cummings JN, 2004, MANAGE SCI, V50, P352, DOI 10.1287/mnsc.1030.0134 Currarini S, 2010, P NATL ACAD SCI USA, V107, P4857, DOI 10.1073/pnas.0911793107 Currarini S, 2009, ECONOMETRICA, V77, P1003, DOI 10.3982/ECTA7528 DOREIAN P, 1980, SOCIOL METHOD RES, V9, P29, DOI 10.1177/004912418000900102 DOW MM, 1982, SOC NETWORKS, V4, P169, DOI 10.1016/0378-8733(82)90031-4 Drew P., 1992, TALK WORK INTERACTIO Driscoll JC, 1998, REV ECON STAT, V80, P549, DOI 10.1162/003465398557825 EISENHARDT KM, 1989, ACAD MANAGE J, V32, P543, DOI 10.2307/256434 Eliasoph N, 1996, SOCIOL THEOR, V14, P262, DOI 10.2307/3045389 Finlay W, 2000, WORK OCCUPATION, V27, P377, DOI 10.1177/0730888400027003006 FRIEDKIN NE, 1984, SOCIOL METHOD RES, V12, P235, DOI 10.1177/0049124184012003001 GALBRAIT.JR, 1974, INTERFACES, V4, P28, DOI 10.1287/inte.4.3.28 Gibson DR, 2005, AM J SOCIOL, V110, P1561, DOI 10.1086/428689 GLAZER R, 1993, J MARKETING RES, V30, P509, DOI 10.2307/3172694 GLAZER R, 1991, J MARKETING, V55, P1, DOI 10.2307/1251953 Goffman Erwin, 1961, ENCOUNTERS GRANOVETTER M, 1992, NETWORKS AND ORGANIZATIONS : STRUCTURE, FORM, AND ACTION, P25 GRANOVET.MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469 GRANOVETTER M, 1985, AM J SOCIOL, V91, P1420 GRANOVETTER M, 1978, AM J SOCIOL, V83, P1420, DOI 10.1086/226707 Hansen MT, 1999, ADMIN SCI QUART, V44, P82, DOI 10.2307/2667032 Hansen MT, 2002, ORGAN SCI, V13, P232, DOI 10.1287/orsc.13.3.232.2771 Hargadon A, 1997, ADMIN SCI QUART, V42, P716, DOI 10.2307/2393655 HAUSMAN JA, 1978, ECONOMETRICA, V46, P1251, DOI 10.2307/1913827 Heider Fritz, 1958, PSYCHOL INTERPERSONA Helper S., 2000, IND CORP CHANGE, V9, P443, DOI 10.1093/icc/9.3.443 Hinds P., 2002, DISTRIBUTED WORK HUBER GP, 1984, MANAGE SCI, V30, P928, DOI 10.1287/mnsc.30.8.928 Kempe D, 2003, P 9 ACM SIGKDD INT C, P137, DOI DOI 10.1145/956750.956769 Kossinets G, 2006, SCIENCE, V311, P88, DOI 10.1126/science.1116869 Kossinets G, 2009, AM J SOCIOL, V115, P405 Krackhardt D, 1999, J PERS SOC PSYCHOL, V76, P770, DOI 10.1037/0022-3514.76.5.770 KRACKHARDT D, 1987, SOC NETWORKS, V9, P109, DOI 10.1016/0378-8733(87)90009-8 KRACKHARDT D, 1990, ADMIN SCI QUART, V35, P342, DOI 10.2307/2393394 KUMBASAR E, 1994, AM J SOCIOL, V100, P477, DOI 10.1086/230544 Kutner M. H., 2004, APPL LINEAR REGRESSI Lazer D, 2007, ADMIN SCI QUART, V52, P667 LIANG DW, 1995, PERS SOC PSYCHOL B, V21, P384, DOI 10.1177/0146167295214009 Lichterman Paul, 1996, SEARCH POLITICAL COM Lin N, 2002, SOCIAL CAPITAL THEOR Lingo EL, 2010, ADMIN SCI QUART, V55, P47 LOFTIN C, 1983, AM SOCIOL REV, V48, P121, DOI 10.2307/2095150 March JG, 1991, ORGAN SCI, V2, P71, DOI 10.1287/orsc.2.1.71 MARSDEN PV, 1990, ANNU REV SOCIOL, V16, P435 MARSDEN PV, 1993, SOCIOL METHOD RES, V22, P127, DOI 10.1177/0049124193022001006 McLean PD, 1998, AM J SOCIOL, V104, P51, DOI 10.1086/210002 McPherson M, 2001, ANNU REV SOCIOL, V27, P415, DOI 10.1146/annurev.soc.27.1.415 Mische A, 1998, SOC RES, V65, P695 MISCHE A, 2000, SOCIAL MOVEMENTS NET, P258 MONTGOMERY JD, 1991, AM ECON REV, V81, P1408 Moreno JL, 1940, SOCIOMETRY, V3, P62, DOI 10.2307/2785546 Newcomb T. M., 1961, ACQUAINTANCE PROCESS Obstfeld D, 2005, ADMIN SCI QUART, V50, P100 ORD K, 1975, J AM STAT ASSOC, V70, P120, DOI 10.2307/2285387 PADGETT JF, 1993, AM J SOCIOL, V98, P1259, DOI 10.1086/230190 Pfeffer J., 1983, RES ORGAN BEHAV, V5, P299, DOI DOI 10.2307/2392635 Podolny JM, 2001, AM J SOCIOL, V107, P33, DOI 10.1086/323038 Podolny JM, 1997, AM SOCIOL REV, V62, P673, DOI 10.2307/2657354 PUTNAM RD, 1995, J DEMOCRACY, V0006 Reagans R, 2003, ADMIN SCI QUART, V48, P240, DOI 10.2307/3556658 Reagans R, 2001, ORGAN SCI, V12, P502, DOI 10.1287/orsc.12.4.502.10637 Reagans RE, 2008, IND CORP CHANGE, V17, P979, DOI 10.1093/icc/dtn037 Reagans RE, 2008, IND CORP CHANGE, V17, P903, DOI 10.1093/icc/dtn036 REYNOLDS M, 2009, P 8 ANN SEC C DISC S, P41 Rodan S, 2004, STRATEGIC MANAGE J, V25, P541, DOI 10.1002/smj.398 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schelling Thomas C, 1978, MICROMOTIVES MACROBE Simmel G., 1955, CONFLICT WEB GROUP A Simon HA, 1991, ORGAN SCI, V2, P125, DOI 10.1287/orsc.2.1.125 Sparrowe RT, 2001, ACAD MANAGE J, V44, P316, DOI 10.2307/3069458 Steinberg MW, 1999, AM J SOCIOL, V105, P736, DOI 10.1086/210359 SWEDBERG R., 1990, EC SOCIOLOGY Szulanski G, 1996, STRATEGIC MANAGE J, V17, P27, DOI 10.1002/smj.4250171105 Tsai WP, 1998, ACAD MANAGE J, V41, P464, DOI 10.2307/257085 Uzzi B, 1997, ADMIN SCI QUART, V42, P35, DOI 10.2307/2393808 Uzzi B, 2005, AM J SOCIOL, V111, P447, DOI 10.1086/432782 Uzzi B, 1996, AM SOCIOL REV, V61, P674, DOI 10.2307/2096399 Van Alstyne M., 2007, 13172 NAT BUR EC RES Van Alstyne M, 2005, MANAGE SCI, V51, P851, DOI 10.1287/mnsc.1050.0363 VANALSTYNE M, 2003, M N AM ASS COMP SOC Walker G, 1997, ORGAN SCI, V8, P109, DOI 10.1287/orsc.8.2.109 Watts DJ, 1999, AM J SOCIOL, V105, P493, DOI 10.1086/210318 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 WEGNER E, 1987, THEORIES GROUP BEHAV, P185 Weick K. E., 1979, SOCIAL PSYCHOL ORG, V2nd Weitzman ML, 1998, Q J ECON, V113, P331, DOI 10.1162/003355398555595 Wenger E., 1991, SITUATED LEARNING LE WHITE H, 1980, ECONOMETRICA, V48, P817, DOI 10.2307/1912934 White HC, 2000, POETICS, V27, P117, DOI 10.1016/S0304-422X(99)00027-3 White HC, 1995, SOC RES, V62, P1035 Wu F, 2004, PHYSICA A, V337, P327, DOI 10.1016/j.physa.2004.01.030 NR 115 TC 52 Z9 52 PU UNIV CHICAGO PRESS PI CHICAGO PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA SN 0002-9602 J9 AM J SOCIOL JI Am. J. Sociol. PD JUL PY 2011 VL 117 IS 1 BP 90 EP 171 PG 82 WC Sociology SC Sociology GA 816EP UT WOS:000294581800003 ER PT J AU Wang, CM Chiang, MC Yang, CS Tsai, CW AF Wang, Chih-Ming Chiang, Ming-Chao Yang, Chu-Sing Tsai, Chun-Wei TI AIDED CONTENTS SUPPORTING SERVICE FOR E-LEARNING SYSTEMS SO INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL LA English DT Article DE Information retrieval; e-Learning; Aided contents ID ALGORITHM; MODEL AB In this paper, we present a novel method for automatically augmenting the learning contents of an e-Learning system, called Aided Contents Supporting Service (or ACSS for short). The proposed method starts off with the examination subsystem of which the exam items are the main learning contents. Then, motivated by the desire to provide the users of such a system with more contents, ACSS will automatically extract concepts associated with each exam item when it is being read. The key concepts associated with the exam items can even be sent as queries to content search engines to get more contents to aid learning. Moreover, the aided contents can be accumulated, ranked, and shared by the users of such a system. Our simulation results indicate that the proposed method can effectively find and integrate many more relevant contents into an e-Learning system. Besides, as a test bed, we not only apply the proposed method to an e-Learning system we develop, but we also use the grid computing technology to speed up the performance of the proposed method. C1 [Wang, Chih-Ming; Chiang, Ming-Chao] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan. [Yang, Chu-Sing; Tsai, Chun-Wei] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 70101, Taiwan. [Tsai, Chun-Wei] Chia Nan Univ Pharm & Sci, Dept Appl Geoinformat, Tainan 71710, Taiwan. RP Wang, CM (reprint author), Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, 70 Lienhai Rd, Kaohsiung 80424, Taiwan. EM wangcm@cse.nsysu.edu.tw; mcchiang@cse.nsysu.edu.tw; csyang@ee.ncku.edu.tw; cwtsai87@gmail.com FU National Science Council, Taiwan [NSC98-2811-E-006-078, NSC98-2219-E-006-002] FX The authors would like to thank the editor and anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by National Science Council, Taiwan, under Contact Nos. NSC98-2811-E-006-078 and NSC98-2219-E-006-002. CR BAEZE-YATES R., 1999, MODERN INFORM RETRIE CHANG SY, 2005, THESIS NATL TAIWAN N DOWNES S, 2005, E LEARN MAGAZINE Duval E, 2008, IEEE T LEARN TECHNOL, V1, P229, DOI 10.1109/TLT.2009.6 Fu F, 2008, PHYSICA A, V387, P675, DOI 10.1016/j.physa.2007.10.006 *GLOB ALL, GRIDFTP *GLOB ALL, GRAM HAYTHORNTHWAITE C, 2005, E LEARN SEM HOEGG R, 2006, P GENEME Joachims T, 1996, P 14 INT C MACH LEAR, P143 KUO PX, 2005, P 2005 IEEE INT C E, P582 LIAW S, 2003, P 2003 INT C COMP AS, P24 LU YH, 2002, THESIS NATL CHUNG CH Matsumoto Y, 2009, INT J INNOV COMPUT I, V5, P4885 Morita Y, 2009, INT J INNOV COMPUT I, V5, P3565 O'Reilly T., 2005, WHAT IS WEB 2 0 DESI PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PURWITASARI D, 2008, P 16 INT C COMP ED, P557 Richardson W., 2006, BLOGS WIKIS PODCASTS ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SAFRAN C, 2007, P WORLD C E LEARN CO Safran C., 2007, P INT C INT COMP AID SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shih WC, 2009, FUTURE GENER COMP SY, V25, P687, DOI 10.1016/j.future.2009.01.005 Su Z, 2009, INT J INNOV COMPUT I, V5, P4275 Sun X, 2009, INT J INNOV COMPUT I, V5, P4523 Tsai CW, 2008, J INTERNET TECHNOL, V9, P297 Tsai CW, 2010, INT J INNOV COMPUT I, V6, P1843 Tseng JCR, 2009, INT J INNOV COMPUT I, V5, P567 Tseng SP, 2010, INT J INNOV COMPUT I, V6, P4319 WANG CM, 2009, INT C DISTR FRAM APP CHINESE WORDS SEGMEN WIKI TF IDF NR 33 TC 3 Z9 3 PU ICIC INT PI KUMAMOTO PA TOKAI UNIV, 9-1-1, TOROKU, KUMAMOTO, 862-8652, JAPAN SN 1349-4198 J9 INT J INNOV COMPUT I JI Int. J. Innov. Comp. Inf. Control PD JUL PY 2011 VL 7 IS 7A BP 4005 EP 4026 PG 22 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 790YN UT WOS:000292626800031 ER PT J AU Park, B Hong, JWK Won, YJ AF Park, Byungchul Hong, James Won-Ki Won, Young J. TI Toward Fine-Grained Traffic Classification SO IEEE COMMUNICATIONS MAGAZINE LA English DT Article AB A decade of research on traffic classification has provided various methodologies to investigate the traffic composition in data communication networks. Many variants or combinations of such methodologies have been introduced continuously to improve the classification accuracy and efficiency. However, the level of classification details is often bounded to identifying protocols or applications in use. In this article, we propose a fine-grained traffic classification scheme based on the analysis of existing classification methodologies. This scheme allows to classify traffic according to the functionalities in an application. In particular, we present a traffic classifier which utilizes a document retrieval technique and applies multiple signatures to detect the peer-to-peer application traffic according to different functionalities in it. We show that the proposed scheme can provide more in-depth classification results for analyzing user contexts. C1 [Won, Young J.] Internet Initiat Japan, Res Lab, Tokyo, Japan. EM fates@postech.ac.kr; jwkhong@postech.ac.kr; young@iijlab.net FU Ministry of Education, Science and Technology [R31-2010-000-10100-0]; Korea Communications Commission (KCC) [KCA-2011-10921-05003] FX This research was supported by the World Class University (WCU) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R31-2010-000-10100-0) and the Korea Communications Commission (KCC) under the Novel Study on Highly Manageable Network and Service Architecture for the New Generation support program supervised by the Korea Communications Agency (KCA; KCA-2011-10921-05003). CR Borgnat P, 2009, IEEE INFOCOM SER, P711, DOI 10.1109/INFCOM.2009.5061979 Chung JY, 2010, IEEE IFIP NETW OPER, P286 HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 Karagiannis T, 2005, ACM SIGCOMM, p229 240 Kim H, 2008, ACM CONEXT C MADR SP, P1 Kim MS, 2006, COMPUT COMMUN, V29, P1639, DOI 10.1016/j.comcom.2005.07.015 McGregor A, 2004, LECT NOTES COMPUT SC, V3015, P205 MOORE D, 2001, 15 USENIX C SYST ADM, P133 Park BC, 2008, IEEE IFIP NETW OPER, P160 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sen S., 2004, WWW 04, P512 Szabo Geza, 2007, IEEE INT S WORLD WIR, P1 Thompson K, 1997, IEEE NETWORK, V11, P10, DOI 10.1109/65.642356 NR 13 TC 2 Z9 2 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 0163-6804 J9 IEEE COMMUN MAG JI IEEE Commun. Mag. PD JUL PY 2011 VL 49 IS 7 BP 104 EP 111 PG 8 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA 787KW UT WOS:000292376000013 ER PT J AU White, K Sutcliffe, RFE AF White, Kieran Sutcliffe, Richard F. E. TI Butcher, Baker, or Candlestick Maker? Predicting Occupations Using Predicate-Argument Relations SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; NATURAL-LANGUAGE AB In a previous question answering study, we identified nine semantic-relationship types, including synonyms, hypernyms, word chains, and holonyms, that exist between terms in Text Retrieval Conference queries and those in their supporting sentences in the Advanced Question Answering for Intelligence (Graff, 2002) corpus. The most frequently occurring relationship type was the hypernym(e. g., Katherine Hepburn is an actress). The aim of the present work, therefore, was to develop a method for determining a person's occupation from syntactic data in a text corpus. First, in the P-System, we compared predicate-argument data involving a proper name for different occupations using Okapi's BM25 weighting algorithm. When classifying actors and using sufficiently frequent names, an accuracy of 0.955 was attained. For evaluation purposes, we also implemented a standard apposition-based classifier (A-System). This performs well, but only if a particular name happens to appear in apposition with the corresponding occupation. Last, we created a hybrid (H-System) which combines the strengths of P with those of A. Using data with a minimum of 100 predicate-argument pairs, H performed best with an overall lenient accuracy of 0.750 while A and P scored 0.615 and 0.656, respectively. We therefore conclude that a hybrid approach combining information from different sources is the best way to predict occupations. C1 [White, Kieran; Sutcliffe, Richard F. E.] Univ Limerick, Dept Comp Sci & Informat Syst, Limerick, Ireland. RP White, K (reprint author), Univ Limerick, Dept Comp Sci & Informat Syst, Limerick, Ireland. EM richard.sutcliffe@ul.ie CR ALFONSECA E, 2002, P 13 INT C KNOWL ENG, P1 ALMUHAREB A, 2005, ANN M COGN SCI SOC S Almuhareb A., 2004, P EMNLP, P158 Bernard J. R. L., 1990, MACQUARIE ENCY THESA Caraballo S., 1999, P 37 ANN M ASS COMP, P120, DOI 10.3115/1034678.1034705 Caraballo S. A., 1999, P JOINT SIGDAT C EMP, P63 Curran J., 2003, P 7 C NAT LANG LEARN, V4, P164, DOI 10.3115/1119176.1119200 Curran James R., 2003, P 10 C EUR CHAPT ASS, V1, P91, DOI 10.3115/1067807.1067821 Curran James R., 2002, P WORKSH UNS LEX ACQ, P59, DOI DOI 10.3115/1118627.1118635 Fleischman M., 2003, P 41 ANN M ASS COMP, P1 GAMALLO P, 2001, P 4 INT C TEXT SPEEC, P116 GASPERIN CV, 2002, P WORKSH MULT INF AC, P55 GIRJU R, 2001, P N AM CHAPT ASS COM Graff David, 2002, AQUAINT CORPUS ENGLI Grefenstette G., 1994, EXPLORATIONS AUTOMAT Hahn U., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence HARMAN D, 1994, P 2 TEXT RETR C TREC, P1 Hearst M. A., 1992, P 14 INT C COMP LING, P539 Hindle D., 1990, P 28 ANN M ASS COMP, P268, DOI 10.3115/981823.981857 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 Jijkoun V., 2004, P 20 INT C COMP LING, P1284, DOI 10.3115/1220355.1220543 Lee Lillian, 1997, THESIS HARVARD U CAM Lin D., 1998, P 17 INT C COMP LING, P768 Lin D., 1998, P WORKSH EV PARS SYS Lin D., 2001, P ACM SIGKDD C KNOWL, P317, DOI 10.1145/502512.502558 Lin Dekang, 1997, P 35 ANN M ASS COMP, P64 Mann Gideon S., 2002, P COLING 2002 SEMANE, P1 Manning C. D., 1999, FDN STAT NATURAL LAN Marcus M., 1980, THEORY SYNTACTIC REC RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G. A., 1990, J LEXICOGRAPHY, V3, P235 MILOSAVLJEVIC M, 2007, P 8 C RECH INF ASS O, P653 Moldovan D., 2000, P 6 C APPL NAT LANG, P268, DOI 10.3115/974147.974184 Morin E, 2004, COMPUT HUMANITIES, V38, P363, DOI 10.1007/s10579-004-1926-2 Pantel P., 2004, P HUM LANG TECHN N A, P321 PEKAR V, 2003, EUR CHAPT ASS COMP L, P147 Penas A., 2009, P 10 CROSS LANG EV F, P174 Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] PEREZ J, 2009, P CROSS LANG EV FOR, P253 POLLOCK SM, 1968, AM DOC, V19, P387, DOI 10.1002/asi.5090190406 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 Richardson R., 1995, CA0395 DUBL CIT U Rilof E., 1997, P 2 C EMP METH NAT L, P117 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 ROGET P, 1911, THESAURUS ENGLISH WO RUGE G, 1992, INFORM PROCESS MANAG, V28, P317, DOI 10.1016/0306-4573(92)90078-E SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679 SANDERSON M, 2002, ADV INFORM RETRIEVAL, P235 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Snow R., 2005, ADV NEURAL INFORM PR, V17, P1297 STRZALKOWSKI T, 1995, INFORM PROCESS MANAG, V31, P397, DOI 10.1016/0306-4573(94)00055-8 Sussna M, 1993, P 2 INT C INF KNOWL, P67, DOI 10.1145/170088.170106 SUTCLIFFE RFE, 2006, CROSS LANG EV FOR CL SUTCLIFFE RFE, 2006, 15 TEXT RETR C TREC TAKENOBU T, 1997, P ASS COMP LING EUR, P16 Tanimoto T., 1958, ELEMENTARY MATH THEO TJONG E, 2009, P 13 C COMP NAT LANG, P174 *TREC, 2004, TEXT RETR C TREC QUE *TREC, 2002, TEXT RETR C TREC QUE *TREC, 2003, TEXT RETR C TREC QUE Voorhees Ellen M., 2005, TREC EXPT EVALUATION WARD G, 1996, MOBY WORD LISTS WHITE KJ, 2004, P INF RETR QUEST ANS, P59 WHITE KJ, 2008, THESIS U LIMERICK LI YU LC, 2007, P 45 ANN M ASS COMP, P1024 1997, J DOCUMENTATION, V53, P3 NR 71 TC 0 Z9 0 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUL PY 2011 VL 62 IS 7 BP 1325 EP 1344 DI 10.1002/asi.21547 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 781SL UT WOS:000291953800010 ER PT J AU Phan, XH Nguyen, CT Le, DT Nguyen, LM Horiguchi, S Ha, QT AF Xuan-Hieu Phan Cam-Tu Nguyen Dieu-Thu Le Le-Minh Nguyen Horiguchi, Susumu Quang-Thuy Ha TI A Hidden Topic-Based Framework toward Building Applications with Short Web Documents SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Web mining; hidden topic analysis; sparse data; classification; matching; ranking; contextual advertising ID LATENT; MODEL AB This paper introduces a hidden topic-based framework for processing short and sparse documents (e. g., search result snippets, product descriptions, book/movie summaries, and advertising messages) on the Web. The framework focuses on solving two main challenges posed by these kinds of documents: 1) data sparseness and 2) synonyms/homonyms. The former leads to the lack of shared words and contexts among documents while the latter are big linguistic obstacles in natural language processing (NLP) and information retrieval (IR). The underlying idea of the framework is that common hidden topics discovered from large external data sets (universal data sets), when included, can make short documents less sparse and more topic-oriented. Furthermore, hidden topics from universal data sets help handle unseen data better. The proposed framework can also be applied for different natural languages and data domains. We carefully evaluated the framework by carrying out two experiments for two important online applications (Web search result classification and matching/ranking for contextual advertising) with large-scale universal data sets and we achieved significant results. C1 [Xuan-Hieu Phan; Horiguchi, Susumu] Tohoku Univ, Grad Sch Informat Sci, Dept Comp Sci, Sendai, Miyagi 980, Japan. [Dieu-Thu Le] Univ Trent, Dept Informat Engn & Comp Sci, Trento, Italy. [Le-Minh Nguyen] Japan Adv Inst Sci & Technol, Grad Sch Informat Sci, Nomi, Ishikawa 9231292, Japan. [Quang-Thuy Ha] Vietnam Natl Univ, Coll Technol, Hanoi, Vietnam. [Horiguchi, Susumu] Tohoku Univ, Dept Informat Engn, Fac Engn, Sendai, Miyagi 980, Japan. RP Phan, XH (reprint author), Tohoku Univ, Grad Sch Informat Sci, Dept Comp Sci, Sendai, Miyagi 980, Japan. EM hieuxuan@ecei.tohoku.ac.jp; ncamtu@ecei.tohoku.ac.jp; dle@disi.unitn.it; nguyenml@jaist.ac.jp; susumu@ecei.tohoku.ac.jp; thuyhq@vnu.edu.vn FU Japan Society for Promotion of Science (JSPS) [P06366] FX This work is fully supported by the research grant No. P06366 from Japan Society for Promotion of Science (JSPS). The authors would also like to say special thanks to the Editor-in-Chief, the Associate Editor, and the anonymous reviewers for reviewing their manuscript and giving them a lot of useful comments and suggestions. This paper is an extension of a shorter version at WWW2008 [31]. CR Baker L, 1998, P ACM SIGIR Baldi P., 2003, MODELING INTERNET WE BANERJEE S, 2007, P ACM SIGIR Bekkerman R., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753625 Berger AL, 1996, COMPUT LINGUIST, V22, P39 Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Blum A., 1998, P 11 ANN C COMP LEAR BOLLEGALA D, 2007, P 16 INT C WORLD WID BRODER A, 2007, P ACM SIGIR CAI J, 2007, P JOINT C EMP METH N CAI L, 2003, P ACM SIGIR Chatterjee P, 2003, MARKET SCI, V22, P520, DOI 10.1287/mksc.22.4.520.24906 Ciaramita M, 2008, J ELECTRON COMMER RE, V9, P1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENOYER L, 2006, P ACM SIGIR FOR Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Gabrilovich E., 2007, P 20 INT JOINT C ART GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Heinrich G, 2005, PARAMETER ESTIMATION Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 IAB, 2008, IAB INT ADV REV REP Joachims T., 1998, P 10 EUR C MACH LEAR LACERDA A, 2006, P ACM SIGIR Letsche TA, 1997, INFORM SCIENCES, V100, P105, DOI 10.1016/S0020-0255(97)00044-3 LIU DC, 1989, MATH PROGRAM, V45, P503, DOI 10.1007/BF01589116 Manning C.D., 2008, INTRO INFORM RETRIEV Metzler D., 2007, P 29 EUR C IR RES EC Phan X., 2008, P 17 INT C WORLD WID RIBEIRONETO B, 2005, P ACM SIGIR Sahami M., 2006, P 15 INT C WORLD WID SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schonhofen P., 2006, P IEEE WIC ACM INT C Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 WANG R, 2002, P 8 AM C INF SYST AM WEI X, 2006, P ACM SIGIR Yih W., 2007, P 22 NAT C ART INT A Yih W.T., 2006, P 15 INT C WORLD WID ZAMIR O, 1999, P 8 INT C WORLD WID ZENG H, 2004, P ACM SIGIR NR 41 TC 7 Z9 7 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JUL PY 2011 VL 23 IS 7 BP 961 EP 976 DI 10.1109/TKDE.2010.27 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 765CQ UT WOS:000290681400001 ER PT J AU Rochimah, S Kadir, WMNW Abdullah, AH AF Rochimah, Siti Kadir, Wan M. N. Wan Abdullah, Abdul H. TI UTILIZING MULTIFACETED REQUIREMENT TRACEABILITY APPROACH: A CASE STUDY SO INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING LA English DT Article DE Software evolution; requirement traceability; multifaceted traceability approach ID MODEL AB Software evolution is inevitable. When a system evolves, there are certain relationships among software artifacts that must be maintained. Requirement traceability is one of the important factors in facilitating software evolution since it maintains the artifacts relationship before and after a change is performed. Requirement traceability can be expensive activities. Many researchers have addressed the problem of requirement traceability, especially to support software evolution activities. Yet, the evaluation results of these approaches show that most of them typically provide only limited support to software evolution. Based on the problems of requirement traceability, we have identified three directions that are important for traceability to support software evolution, i.e. process automation, procedure simplicity, and best results achievement. Those three directions are addressed in our multifaceted approach of requirement traceability. This approach utilizes three facets to generate links between artifacts, i.e. syntactical similarity matching, link prioritization, and heuristic-list based processes. This paper proposes the utilization of multifaceted approach to traceability generation and recovery in facilitating software evolution process. The complete experiment has been applied in a real case study. The results show that utilization of these three facets in generating the traceability among artifacts is better than the existing approach, especially in terms of its accuracy. C1 [Rochimah, Siti] Fac Informat Technol, Inst Teknol Sepuluh Nopember, Dept Informat, Surabaya 60111, Indonesia. [Kadir, Wan M. N. Wan; Abdullah, Abdul H.] Univ Teknol Malaysia, Dept Software Engn, Fac Comp Sci & Informat Syst, Skudai Johor 81310, Malaysia. RP Rochimah, S (reprint author), Fac Informat Technol, Inst Teknol Sepuluh Nopember, Dept Informat, Surabaya 60111, Indonesia. EM siti@its-sby.edu; wnasir@cs.utm.my; hanan@cs.utm.my RI Wan Kadir, Wan Mohd Nasir/ OI Wan Kadir, Wan Mohd Nasir/0000-0003-4459-4050 FU Islamic Development Bank; National Ministry of Higher Education Indonesia FX This work is funded by Islamic Development Bank and National Ministry of Higher Education Indonesia. CR [Anonymous], OXFORD DICT ONLINE [Anonymous], 1998, 8301998 IEEE Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 ANTONIOL G, 2000, IEEE INT C SOFTW MAI *AP, AP LUC BELADY LA, 1976, IBM SYST J, V15, P225 Bennett K.H., 2000, P C FUT SOFTW ENG Buckley J, 2005, J SOFTW MAINT EVOL-R, V17, P309, DOI 10.1002/smr.319 CARROL PB, 1988, WALL STREET J CHAPIN N, 2001, J SOFTWARE MAINTENAN, P3 Cleland-Huang J., 2005, P 13 IEEE INT C REQ Cleland-Huang J, 2003, IEEE T SOFTWARE ENG, V29, P796, DOI 10.1109/TSE.2003.1232285 CLELANDHUANG J, 2002, IEEE P INT COMP SOFT CLELANDHUANG J, 2002, P IEEE JOINT INT C R DELUCIA A, 2005, 9 EUR C SOFTW MAINT EGYED A, 2002, 17 IEEE INT C AUT SO EGYED A, 23 INT C SOFTW ENG 2 EGYED A, 2005, INT J SOFTWARE ENG K, V15 GOTEL O, 1997, 3 IEEE INT S REQ ENG GOTEL O, 1994, P 1 INT C REQ ENG GROSSMAN DA, 2004, INFROM RETRIEVAL ALG HANNA M, 1993, DATAMATION APR, P53 Hayes J. H., 2003, P 11 IEEE INT REQ EN Hayes J. H., 2006, IEEE T SOFTWARE ENG, V32 Hayes JH, 2005, IEEE SOFTWARE, V22, P30, DOI 10.1109/MS.2005.156 HAYES JH, 2004, WORKSH EMP STUD SOFT HAYES JH, 2004, P 12 IEEE INT REQ EN HEINDL M, 2005, INT C EMP SOFT ENG E IBRAHIM S, 2005, IASTED INT C SOFTW E IBRAHIM S, 2006, DOCUMENT BASED SOFTW *IEEE, 1999, 12191988 IEEE LEHMAN MM, 1997, 4 INT S SOFTW METR M LIN J, 2006, 14 IEEE INT REQ ENG LORMANS M, 2004, INT WORKSH PRINC SOF Maletic J. I., 2003, P 2 INT WORKSH TRAC MALETIC JI, 2005, P 4 INT WORKSH TRAC MARCUS A, 2003, P 25 IEEE INT C SOFT PASHOV I, 2004, P 11 IEEE INT C WORK Pfleeger S. L., 1998, SOFTWARE ENG THEORY RIEBISCH M, 2004, P 11 IEEE INT C WORK ROCHIMAH S, 2007, 2 INT C SOFTW ENG AD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SETTIMI R, 2004, P 7 INT WORKSH PRINC Sherba S.A., 2003, 2 INT WORKSH TRAC EM SHERBA SA, 2005, AUTOMATING TRACEABIL Sommerville Ian, 2004, SOFTWARE ENG Spanoudakis G, 2004, J SYST SOFTWARE, V72, P105, DOI 10.1016/S0164-1212(03)00242-5 SPANOUDAKIS G, 2002, P 14 INT C SOFTW ENG ZEMONT G, 2005, VALUE BASED REQUIREM, P80 ZOU X, 2006, P 30 ANN INT COMP SO ONLINE FREE DICT MIRIAM WEBSTER ONLIN NR 52 TC 1 Z9 1 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-1940 J9 INT J SOFTW ENG KNOW JI Int. J. Softw. Eng. Knowl. Eng. PD JUN PY 2011 VL 21 IS 4 BP 571 EP 603 DI 10.1142/S0218194011005372 PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 817II UT WOS:000294664900004 ER PT J AU Huang, XD Zheng, XD Yuan, W Wang, F Zhu, SF AF Huang, Xiaodi Zheng, Xiaodong Yuan, Wei Wang, Fei Zhu, Shanfeng TI Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization SO INFORMATION SCIENCES LA English DT Article DE Biomedical document clustering; Non-negative matrix factorization; Ensemble clustering ID DISCOVERY; IDENTIFICATION; INFORMATION; ALGORITHMS; MODEL; TEXT AB Searching and mining biomedical literature databases are common ways of generating scientific hypotheses by biomedical researchers. Clustering can assist researchers to form hypotheses by seeking valuable information from grouped documents effectively. Although a large number of clustering algorithms are available, this paper attempts to answer the question as to which algorithm is best suited to accurately cluster biomedical documents. Non-negative matrix factorization (NMF) has been widely applied to clustering general text documents. However, the clustering results are sensitive to the initial values of the parameters of NMF. In order to overcome this drawback, we present the ensemble NMF for clustering biomedical documents in this paper. The performance of ensemble NMF was evaluated on numerous datasets generated from the TREC Genomics track dataset. With respect to most datasets, the experimental results have demonstrated that the ensemble NMF significantly outperforms classical clustering algorithms of bisecting K-means, and hierarchical clustering. We compared four different methods for constructing an ensemble NMF. For clustering biomedical documents, this research is the first to compare ensemble NMF with typical classical clustering algorithms, and validates ensemble NMF constructed from different graph-based ensemble algorithms. This is also the first work on ensemble NMF with Hybrid Bipartite Graph Formulation for clustering biomedical documents. (C) 2011 Elsevier Inc. All rights reserved. C1 [Zheng, Xiaodong; Yuan, Wei; Wang, Fei; Zhu, Shanfeng] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China. [Huang, Xiaodi] Charles Sturt Univ, Sch Comp & Math, Albury, NSW 2640, Australia. [Zheng, Xiaodong; Yuan, Wei; Wang, Fei; Zhu, Shanfeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China. [Huang, Xiaodi; Zhu, Shanfeng] Wuhan Univ, State Key Lab Software Engn, Wuhan 430072, Peoples R China. RP Zhu, SF (reprint author), Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China. EM zhushanfeng@gmail.com RI Huang, Xiaodi/J-8224-2013 OI Huang, Xiaodi/0000-0002-6084-1851 FU National Nature Science Foundation of China [60903076]; State Key Lab of Software Engineering, Wuhan University FX The authors thank anonymous reviewers for their helpful comments and advices. This project has been partly supported by, the National Nature Science Foundation of China (Grant No. 60903076) and The State Key Lab of Software Engineering, Wuhan University. CR Andreopoulos B, 2009, BRIEF BIOINFORM, V10, P297, DOI 10.1093/bib/bbn058 Chagoyen M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-41 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Devarajan K, 2008, PLOS COMPUT BIOL, V4, DOI 10.1371/journal.pcbi.1000029 D'hondt J, 2010, INFORM SCIENCES, V180, P2341, DOI 10.1016/j.ins.2010.02.021 Duda R.O., 2001, PATTERN CLASSIFICATI Dudoit S, 2003, BIOINFORMATICS, V19, P1090, DOI 10.1093/bioinformatics/btg038 Fern X.Z., 2004, 21 INT C MACH LEARN, P281 Fogel P, 2007, BIOINFORMATICS, V23, P44, DOI 10.1093/bioinformatics/btl550 Ghosh J., 2003, HDB DATA MINING Greene D, 2008, BIOINFORMATICS, V24, P1722, DOI 10.1093/bioinformatics/btn286 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Heger A, 2003, BIOINFORMATICS, V19, pi130, DOI 10.1093/bioinformatics/btg1017 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jensen LJ, 2006, NAT REV GENET, V7, P119, DOI 10.1038/nrg1768 Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997 Kim PM, 2003, GENOME RES, V13, P1706, DOI 10.1101/gr.903503 Lee DD, 2001, ADV NEUR IN, V13, P556 Lee DD, 1999, NATURE, V401, P788 Lee M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-140 Lin CJ, 2007, NEURAL COMPUT, V19, P2756, DOI 10.1162/neco.2007.19.10.2756 Pehkonen P, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-162 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Shinnou H., P ACL 2007, P77 Strehl A., 2002, J MACHINE LEARNING R, V3, P583, DOI DOI 10.1162/153244303321897735 Tamayo P, 2007, P NATL ACAD SCI USA, V104, P5959, DOI 10.1073/pnas.0701068104 Vavasis S.A., 2007, COMPLEXITY NONNEGATI Wang RS, 2008, NEUROCOMPUTING, V72, P134, DOI 10.1016/j.neucom.2007.12.043 Wheeler DL, 2008, NUCLEIC ACIDS RES, V36, pD13, DOI 10.1093/nar/gkm1000 Xu W., 2003, 26 ANN INT ACM SIGIR Yoo I., 2006, ACM IEEE JOINT C DIG, P220 Zheng HT, 2009, INFORM SCIENCES, V179, P2249, DOI 10.1016/j.ins.2009.02.019 Zhong S, 2003, J MACHINE LEARNING R, V4, P1001, DOI 10.1162/jmlr.2003.4.6.1001 Zhong S, 2005, KNOWL INF SYST, V8, P374, DOI 10.1007/s10115-004-0194-1 Zhu S., 2008, P 2 INT S OPT SYST B, P358 NR 36 TC 7 Z9 8 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 EI 1872-6291 J9 INFORM SCIENCES JI Inf. Sci. PD JUN 1 PY 2011 VL 181 IS 11 BP 2293 EP 2302 DI 10.1016/j.ins.2011.01.029 PG 10 WC Computer Science, Information Systems SC Computer Science GA 790WS UT WOS:000292622100017 ER PT J AU Jiang, Y Zheng, HT Wang, XM Lu, BG Wu, KH AF Jiang, Yong Zheng, Hai-Tao Wang, Xinmin Lu, Binggan Wu, Kaihua TI Affiliation Disambiguation for Constructing Semantic Digital Libraries SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID NORMALIZED COMPRESSION DISTANCE; MODEL AB With increasing digital information availability, semantic web technologies have been employed to construct semantic digital libraries in order to ease information comprehension. The use of semantic web enables users to search or visualize resources in a semantic fashion. Semantic web generation is a key process in semantic digital library construction, which converts metadata of digital resources into semantic web data. Many text mining technologies, such as keyword extraction and clustering, have been proposed to generate semantic web data. However, one important type of metadata in publications, called affiliation, is hard to convert into semantic web data precisely because different authors, who have the same affiliation, often express the affiliation in different ways. To address this issue, this paper proposes a clustering method based on normalized compression distance for the purpose of affiliation disambiguation. The experimental results show that our method is able to identify different affiliations that denote the same institutes. The clustering results outperform the well-known k-means clustering method in terms of average precision, F-measure, entropy, and purity. C1 [Jiang, Yong; Zheng, Hai-Tao; Wang, Xinmin] Tsinghua Univ, Grad Sch Shenzhen, Tsinghua Southampton Web Sci Lab Shenzhen, Shenzhen 518055, Peoples R China. RP Jiang, Y (reprint author), Tsinghua Univ, Grad Sch Shenzhen, Tsinghua Southampton Web Sci Lab Shenzhen, Shenzhen 518055, Peoples R China. EM jiangy@sz.tsinghua.edu.cn; zheng.haitao@sz.tsinghua.edu.cn; aero.wxm@gmail.com; nickforthink@gmail.com; wukh@sz.tsinghua.edu.cn FU National Natural Science Foundation of China [61003100] FX We thank the anonymous reviewers for valuable comments. This research is supported by the National Natural Science Foundation of China under the Grant 61003100. CR Bainbridge D, 2004, JCDL 2004: PROCEEDINGS OF THE FOURTH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P416 Benedetto D, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.048702 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOLELLI L, 2009, P 9 ACM IEEE CS JOIN, P69, DOI 10.1145/1555400.1555411 Cebrian M, 2007, IEEE T INFORM THEORY, V53, P1895, DOI 10.1109/TIT.2007.894669 Cernian A, 2009, REC ADV COMPUT ENG, P133 Cilibrasi R, 2004, COMPUT MUSIC J, V28, P49, DOI 10.1162/0148926042728449 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 CLEARY JG, 1984, IEEE T COMMUN, V32, P396, DOI 10.1109/TCOM.1984.1096090 Huang SH, 2005, INFORM PROCESS MANAG, V41, P891, DOI 10.1016/j.ipm.2004.04.012 ITO K, 2009, LECT NOTES ARTIF INT, V997, P56 Klyne G., 2004, RESOURCE DESCRIPTION Kruk SR, 2005, LECT NOTES COMPUT SC, V3588, P716 Lagoze C, 2006, INT J DIGITAL LIB, V6, P124, DOI DOI 10.1007/S00799-005-0130-3 Li M, 2003, SIAM PROC S, P863 Li N., 2010, P 10 ANN JOINT C DIG, P245, DOI 10.1145/1816123.1816160 LU X, 2007, P INT WORLD WID WEB, P1229, DOI 10.1145/1242572.1242780 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 McCarthy D., 2004, P 42 M ASS COMP LING, P279, DOI 10.3115/1218955.1218991 McCarthy D, 2007, COMPUT LINGUIST, V33, P553, DOI 10.1162/coli.2007.33.4.553 McGuinness D. L., 2004, OWL WEB ONTOLOGY LAN MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mortensen J, 2009, COMM COM INF SC, V61, P106 Navigli R., 2010, P 2010 C EMP METH NA, P116 Pant G., 2004, P 4 ACM IEEE CS JOIN, P142, DOI 10.1145/996350.996384 Pantel P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Prud'hommeaux Eric, 2008, SPARQL QUERY LANGUAG RAUBER A, 1999, P EUR C DIG LIB, P323 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shum S. B., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007990000034 Steinbach M., 2000, KDD WORKSH TEXT MIN Sun Y., 2008, P 10 ACM WORKSH WEB, P133, DOI 10.1145/1458502.1458524 SUN Y, 2008, P 17 ACM C INF KNOWL, P1497, DOI 10.1145/1458082.1458353 Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455 Vitanyi PMB, 2008, INFORM THEORY STAT L, P45 Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 ZIV J, 1977, IEEE T INFORM THEORY, V23, P337, DOI 10.1109/TIT.1977.1055714 NR 38 TC 5 Z9 5 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2011 VL 62 IS 6 BP 1029 EP 1041 DI 10.1002/asi.21538 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 770XW UT WOS:000291122800003 ER PT J AU Cao, YB Duan, HZ Lin, CY Yu, Y AF Cao, Yunbo Duan, Huizhong Lin, Chin-Yew Yu, Yong TI Re-Ranking Question Search Results by Clustering Questions SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID WEB SEARCH; DOCUMENTS AB In this article, we address the problem of question clustering and study its use for re-ranking question search results. In question clustering we have to organize question search results into certain meaningful and condensed groups. Specifically, we propose to use a data structure consisting of question topic and question focus for modeling questions, and then cluster questions on the basis of the data structure. Experimental results show that our approach to question clustering improves the performance of question search significantly better than the approach not utilizing the topic focus structure. C1 [Cao, Yunbo; Yu, Yong] Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China. [Cao, Yunbo; Lin, Chin-Yew] Microsoft Res Asia, Web Intelligence Grp, Beijing 100190, Peoples R China. [Duan, Huizhong] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Cao, YB (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci, Shanghai 200240, Peoples R China. EM yunbo.cao@microsoft.com RI Cong , Gao/A-3726-2011 CR Aslam J. A., 2004, J GRAPH ALGORITHMS A, V8, P95 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Berger A, 2000, P 23 ANN INT ACM SIG, P192, DOI 10.1145/345508.345576 Bian J, 2008, P 17 INT C WORLD WID, P467, DOI 10.1145/1367497.1367561 Broder A. Z., 1997, P COMPR COMPL SEQ, P21, DOI DOI 10.1109/SEQUEN.1997.666900 Broder AZ, 2000, LECT NOTES COMPUT SC, V1848, P1 Burke R.D., 1997, QUESTION ANSWERING F Cao X, 2010, P 19 INT C WORLD WID, P201, DOI 10.1145/1772690.1772712 Cao Y., 2002, P 19 INT C COMP LING, P127 Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 Demartini G, 2008, LECT NOTES COMPUT SC, V4956, P564 Duan Huizhong, 2008, P 46 ANN M ASS COMP, P156 Fellbaum C, 1998, WORDNET ELECT LEXICA FREDKIN E, 1960, COMMUN ACM, V3, P490, DOI 10.1145/367390.367400 Henzinger M., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148222 Hoad TC, 2003, J AM SOC INF SCI TEC, V54, P203, DOI 10.1002/asi.10170 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jeon J., 2005, P 28 ANN INT ACM SIG, P617, DOI 10.1145/1076034.1076156 Jeon J., 2005, P 14 ACM INT C INF K, P84, DOI DOI 10.1145/1099554.1099572.ISBN:1-59593-140-6 Jijkoun V., 2005, P 14 ACM INT C INF K, P76, DOI 10.1145/1099554.1099571 LAI YS, 2002, P 2002 C MULT SUMM Q, P1 Li H, 1998, COMPUT LINGUIST, V24, P217 MANBER U, 1994, PROCEEDINGS OF THE WINTER 1994 USENIX CONFERENCE, P1 Manku G. S., 2007, P 16 INT C WORLD WID, P141, DOI 10.1145/1242572.1242592 Page L, 1998, PAGERANK CITATION RA Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 RIEZLER S, 2007, P 45 ANN M ASS COMP, P461 Rissanen J., 1978, AUTOMATICA, V14, P445 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIVAKUMAR N, 1995, 2 ANN C THEOR PRACT Wang K., 2009, P 32 ANN INT ACM SIG, P187 Wang Xuanhui, 2007, P 30 ANN INT ACM SIG, P87, DOI http://dx.doi.org/10.1145/1277741.1277759 Xue Xiaobing, 2008, P 31 ANN INT ACM SIG, P475, DOI DOI 10.1145/1390334.1390416 Yunbo Cao, 2008, P 17 INT C WORLD WID, P81, DOI 10.1145/1367497.1367509 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 NR 37 TC 3 Z9 3 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2011 VL 62 IS 6 BP 1177 EP 1187 DI 10.1002/asi.21529 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 770XW UT WOS:000291122800015 ER PT J AU Cribbin, T AF Cribbin, Timothy TI Discovering Latent Topical Structure by Second-Order Similarity Analysis SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID VISUALIZATION; FRAMEWORK; ALGORITHM; SPACE AB Computing document similarity directly from a "bag of words" vector space model can be problematic because term independence causes the relationships between synonymous terms and the contextual influences that determine the sense of polysemous terms to be ignored. This study compares two methods that potentially address these problems by deriving the higher order relationships that lie latent within the original first-order space. The first is latent semantic analysis (LSA), a dimension reduction method that is a well-known means of addressing the vocabulary mismatch problem in information retrieval systems. The second is the lesser known yet conceptually simple approach of second-order similarity (SOS) analysis, whereby latent similarity is measured in terms of mutual first-order similarity. Nearest neighbour tests show that SOS analysis derives similarity models that are superior to both first-order and LSA-derived models at both coarse and fine levels of semantic granularity. SOS analysis has been criticized for its computational complexity. A second contribution is the novel application of vector truncation to reduce run-time by a constant factor. Speed-ups of 4 to 10 times are achievable without compromising the structural gains achieved by full-vector SOS analysis. C1 Brunel Univ, Dept Informat Syst & Comp, Uxbridge UB8 3PH, Middx, England. RP Cribbin, T (reprint author), Brunel Univ, Dept Informat Syst & Comp, Uxbridge UB8 3PH, Middx, England. EM timothy.cribbin@brunel.ac.uk RI Cribbin, Timothy/C-1497-2010 OI Cribbin, Timothy/0000-0001-9737-4727 CR AGGARWAL CC, 2001, 20 ACM SIGMOD SIGACT Ahlgren P, 2009, J INFORMETR, V3, P49, DOI 10.1016/j.joi.2008.11.003 Ando R. K., 2000, P 23 ANN INT ACM SIG, P216, DOI 10.1145/345508.345579 Cavanagh J., 2009, P 11 ANN C COMP GEN, P2505, DOI 10.1145/1570256.1570352 Chen CH, 2002, STAT SINICA, V12, P7 Chen CM, 1998, INTERACT COMPUT, V10, P107, DOI 10.1016/S0953-5438(98)00015-0 Cribbin T, 2010, INFORM VISUAL, V9, P83, DOI 10.1057/ivs.2009.3 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 He X, 2004, P 27 ANN INT ACM SIG, P96, DOI 10.1145/1008992.1009012 Hornbaek K., 1999, P 7 IFIP C HUM COMP, P179 Janssens F., 2007, THESIS KATHOLIEKE U Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 LANDAUER T, 1994, P ED TEST SERV C NAT Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 MADSEN RE, 2004, P 17 INT C PATT REC, V2, P483 Martin-Merino M, 2004, LECT NOTES COMPUT SC, V3316, P860 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q Mecca G, 2007, DATA KNOWL ENG, V62, P504, DOI 10.1016/j.datak.2006.10.006 MONTELLO DR, 2003, LECT NOTES COMPUTER, V2825, P335 Muresan G, 2004, J AM SOC INF SCI TEC, V55, P892, DOI 10.1002/asi.20034 RAND WM, 1971, J AM STAT ASSOC, V66, P846, DOI 10.2307/2284239 Rorvig M, 1998, INFORM PROCESS MANAG, V34, P135, DOI 10.1016/S0306-4573(97)00058-7 Rorvig M, 1998, INFORM PROCESS MANAG, V34, P151, DOI 10.1016/S0306-4573(97)00057-5 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Venna J, 2006, NEURAL NETWORKS, V19, P889, DOI 10.1016/j.neunet.2006.05.014 VOORHEES E, 1997, NIST SPECIAL PUBLICA, P1 Voorhees E., 1998, NIST SPECIAL PUBLICA, P1 Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 Westerman SJ, 2010, J AM SOC INF SCI TEC, V61, P1535, DOI 10.1002/asi.21361 WOORHEES E. M., 1999, NIST SPECIAL PUBLICA, P1 NR 37 TC 3 Z9 3 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2011 VL 62 IS 6 BP 1188 EP 1207 DI 10.1002/asi.21519 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 770XW UT WOS:000291122800016 ER PT J AU Cabanac, G AF Cabanac, Guillaume TI Accuracy of inter-researcher similarity measures based on topical and social clues SO SCIENTOMETRICS LA English DT Article DE Similarity among researchers; Topical clues; Social clues; Literature review; Recommendation; Experiment; Human perception; Measurement ID COMBINING FULL-TEXT; SMALL-WORLD PROBLEM; RECOMMENDER SYSTEMS; INFORMATION-RETRIEVAL; NETWORK ANALYSIS; INDEX AB Scientific literature recommender systems (SLRSs) provide papers to researchers according to their scientific interests. Systems rely on inter-researcher similarity measures that are usually computed according to publication contents (i.e., by extracting paper topics and citations). We highlight two major issues related to this design. The required full-text access and processing are expensive and hardly feasible. Moreover, clues about meetings, encounters, and informal exchanges between researchers (which are related to a social dimension) were not exploited to date. In order to tackle these issues, we propose an original SLRS based on a threefold contribution. First, we argue the case for defining inter-researcher similarity measures building on publicly available metadata. Second, we define topical and social measures that we combine together to issue socio-topical recommendations. Third, we conduct an evaluation with 71 volunteer researchers to check researchers' perception against socio-topical similarities. Experimental results show a significant 11.21% accuracy improvement of socio-topical recommendations compared to baseline topical recommendations. C1 Univ Toulouse, CNRS, IRIT, Comp Sci Dept,UMR 5505, F-31062 Toulouse 9, France. RP Cabanac, G (reprint author), Univ Toulouse, CNRS, IRIT, Comp Sci Dept,UMR 5505, 118 Route Narbonne, F-31062 Toulouse 9, France. EM guillaume.cabanac@irit.fr RI Cabanac, Guillaume/C-5913-2011 OI Cabanac, Guillaume/0000-0003-3060-6241 CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Agarwal N, 2005, LECT NOTES COMPUT SC, V3739, P475 Alonso Omar, 2008, SIGIR Forum, V42, DOI 10.1145/1480506.1480508 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BENJABEUR L, 2010, RIAO 10 Biryukov M, 2008, COMM COM INF SC, V14, P399 Bogers T, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P287 Buckley C., 2005, TREC EXPT EVALUATION, P53 Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 Cazella SC, 2005, LECT NOTES ARTIF INT, V3538, P327 Cleverdon C. W., 1962, REPORT TESTING ANAL Deng HB, 2008, IEEE DATA MINING, P163, DOI 10.1109/ICDM.2008.29 Dolamic L, 2010, J AM SOC INF SCI TEC, V61, P200, DOI 10.1002/asi.21186 Easley D, 2010, NETWORKS CROWDS MARK Elmacioglu E, 2005, SIGMOD REC, V34, P33, DOI 10.1145/1083784.1083791 Fox C., 1990, SIGIR Forum, V24 Fox E. A., 1993, TREC, P243 Garfield E, 2006, JAMA-J AM MED ASSOC, V295, P90, DOI 10.1001/jama.295.1.90 Garfield E, 1996, SCIENTIST, V10, P11 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 Glenisson P, 2005, SCIENTOMETRICS, V63, P163, DOI 10.1007/s11192-005-0208-0 Glenisson P, 2005, INFORM PROCESS MANAG, V41, P1548, DOI 10.1016/j.ipm.2005.03.021 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Gori M., 2006, WI, P778, DOI DOI 10.1109/WI.2006.149 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102 Hirsch JE, 2010, SCIENTOMETRICS, V85, P741, DOI 10.1007/s11192-010-0193-9 Huang ZX, 2009, 2009 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, P219, DOI 10.1109/ASONAM.2009.6 Hubert G, 2009, J AM SOC INF SCI TEC, V60, P1625, DOI 10.1002/asi.21091 Hull D., 1993, SIGIR Forum JANAS JM, 1977, INFORM PROCESS MANAG, V13, P205, DOI 10.1016/0306-4573(77)90001-2 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 Karoui H, 2006, LECT NOTES ARTIF INT, V4106, P76 Kelly Diane, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000012 KLAS CP, 2000, P 22 BCS IRSG C IR R LEE J, 1997, HLA 1997, P267 Ley M., 2002, LNCS, V2476, P1, DOI DOI 10.1007/3-540-45735-6_1 Likert R., 1932, ARCH PSYCHOL, V22, P5 Manning C.D., 2008, INTRO INFORM RETRIEV Martin GH, 2010, LECT NOTES COMPUT SC, V6273, P457 Martin GH, 2009, PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, P408 McNee S. M., 2002, CSCW 02, P116, DOI DOI 10.1145/587078.587096 McNee S.M., 2006, CSCW 06, P171, DOI [http://doi.acm.org/10.1145/1180875.1180903, DOI 10.1145/1180875.1180903] MICARELLI A, 2007, LNCS, V4321, P155, DOI DOI 10.1007/978-3-540-72079-9_5 MILGRAM S, 1967, PSYCHOL TODAY, V1, P61 Mimno D, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P105, DOI 10.1145/1255175.1255196 MITTELBACH F, 2005, LATEX COMPANION Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159 Naak A, 2009, LECT NOTES BUS INF, V26, P25 Porcel C, 2009, EXPERT SYST APPL, V36, P5173, DOI 10.1016/j.eswa.2008.06.038 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 POWLEY B, 2007, RIAO 07 Reips UD, 2002, EXP PSYCHOL, V49, P243, DOI 10.1027//1618-3169.49.4.243 Reips UD, 2005, BEHAV RES METHODS, V37, P287, DOI 10.3758/BF03192696 Reips U.-D., 2007, OXFORD HDB INTERNET, P373 Reitz F, 2010, LECT NOTES COMPUT SC, V6273, P216, DOI 10.1007/978-3-642-15464-5_23 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Rosen-Zvi M., 2004, UAI, P487 Rosen-Zvi M, 2010, ACM T INFORM SYST, V28, DOI 10.1145/1658377.1658381 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009 Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 Student, 1908, BIOMETRIKA, V6, P1, DOI DOI 10.2307/2331554 Tang J., 2008, KDD 08, P990, DOI DOI 10.1145/1401890.1402008 TRAVERS J, 1969, SOCIOMETRY, V32, P425, DOI 10.2307/2786545 TSATSARONIS G, 2009, WIDM 09, P87, DOI DOI 10.1145/1651587.1651607 Voorhees Ellen M., 2005, TREC EXPT EVALUATION Voorhees EM, 2002, LECT NOTES COMPUT SC, V2406, P355 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Yan EJ, 2009, J AM SOC INF SCI TEC, V60, P2107, DOI 10.1002/asi.21128 Yang Z., 2010, RIAO 10 ZAMPARELLI R, 1998, LNCS, V1513, P635, DOI DOI 10.1007/3-540-49653-X_38 Zhou D, 2007, IEEE DATA MINING, P739 NR 77 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD JUN PY 2011 VL 87 IS 3 BP 597 EP 620 DI 10.1007/s11192-011-0358-1 PG 24 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 753PS UT WOS:000289792600011 ER PT J AU Viana, W Miron, AD Moisuc, B Gensel, J Villanova-Oliver, M Martin, H AF Viana, Windson Miron, Alina Dia Moisuc, Bogdan Gensel, Jerome Villanova-Oliver, Marlene Martin, Herve TI Towards the semantic and context-aware management of mobile multimedia SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article; Proceedings Paper CT International Conference on Advances in Mobile Computing and Multimedia CY NOV 24-26, 2008 CL Linz, AUSTRIA SP ACM SIGMM DE Semantic web; Spatial reasoning; Context awareness; Multimedia annotation; Semantic image retrieval; Index expansion ID IMAGE RETRIEVAL; ANNOTATION AB Users of mobile devices can nowadays easily create large quantities of mobile multimedia documents tracing significant events attended, places visited or, simply, moments of their everyday life. However, they face the challenge of organizing these documents in order to facilitate searching through them at a later time and sharing them with other users. We propose using context awareness and semantic technologies in order to improve and facilitate the organization, annotation, retrieval and sharing of personal mobile multimedia documents. Our approach combines metadata extracted and enriched automatically from the users' context with annotations provided manually by the users and with annotations inferred by applying user-defined rules to context features. These new contextual metadata are integrated into the processes of annotation, sharing and keyword-based retrieval. C1 [Viana, Windson; Miron, Alina Dia; Moisuc, Bogdan; Gensel, Jerome; Villanova-Oliver, Marlene; Martin, Herve] STEAMER Team, LIG, F-38402 St Martin Dheres, France. RP Viana, W (reprint author), STEAMER Team, LIG, 681 Rue Passerelle, F-38402 St Martin Dheres, France. EM carvalho@imag.fr; Alina-Dia.Miron@imag.fr; Bogdan.Moisuc@imag.fr; Jerome.Gensel@imag.fr; Marlene.Villanova-Oliver@imag.fr; Herve.Martin@imag.fr RI DE CARVALHO, WINDSON/E-8998-2015 CR Aizawa K., 2004, PERV 2004 WORKSH MEM, P15 Aleksy M, 2008, MOB INF SYST, V4, P105 Ames M, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P971 BLOEHDORN S, 2005, P 2 EUR SEM WEB Buscaldi D., 2006, P 3 SIGIR WORKSH GEO Carvalho RF, 2009, MULTIMED TOOLS APPL, V42, P73, DOI 10.1007/s11042-008-0249-5 Christensen CB, 2001, MIND, V110, P789, DOI 10.1093/mind/110.439.789 DEY AK, 2000, HUC 99 P 1 INT S HAN, P304 FILHO JB, 2008, P SIGSPATIAL ACM GIS, V8, P30 Frank AU, 1996, INT J GEOGR INF SYST, V10, P269, DOI 10.1080/02693799608902079 GOYAL RK, 2001, IEEE T KNOW IN PRESS Gulliver SR, 2007, MOB INF SYST, V3, P71 Guo B, 2008, MOB INF SYST, V4, P81 Hammiche S, 2008, STUD COMPUT INTELL, V116, P351 Heesch D, 2008, MULTIMED TOOLS APPL, V40, P261, DOI 10.1007/s11042-008-0207-2 Hendler J, 2008, COMPUTER, V41, P106, DOI 10.1109/MC.2008.34 Hollink L, 2004, P 4 INT WORKSH KNOWL Jones C.B., 2001, LECT NOTES COMPUTER, V2205, P322 KOOLWAAIJ J, 2006, CONTEXT WATCHER SHAR Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045 LUX M, 2004, P 19 INT CODATA C IN MATELLANES A, 2006, P 1 INT WORKSH SEM W MIRON AD, 2007, 7 INT S WEB WIR GIS MOISUC B, 2005, GEOMATICA, P59 MONAGHAN F, 2006, P IEEE INT C MOB DAT, P79, DOI DOI 10.1109/MDM.2006.39 NAAMAN M, 2004, MULTIMEDIA 04 P 12 A OHARE N, 2007, P ACM SAC 2007 Raento M, 2005, IEEE PERVAS COMPUT, V4, P51, DOI 10.1109/MPRV.2005.29 RESSLER J, 2007, GEOSPATIAL ONTOLOGY RILEY M, 2008, P 9 INT C MUSIC INFO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarvas R., 2004, MOBISYS 2004, P36 SCHROETER R, 2003, P K CAP 2003 WORKSH SCHWEER A, 2007, WORKSH SUPP HUM MEM Tuffleld M., 2006, 1 INT WORKSH SEM WEB Uren V, 2006, J WEB SEMANT, V4, P14, DOI 10.1016/j.websem.2005.10.002 VIANA W, 2004, LECT NOTES COMPUTER, V3124, P1015 Viana Windson, 2008, Journal of Location Based Services, V2, DOI 10.1080/17489720802487956 Viana W, 2008, J SYST SOFTWARE, V81, P382, DOI 10.1016/j.jss.2007.04.045 Viana W, 2007, LECT NOTES COMPUT SC, V4857, P187 Voorhees E. M., 1999, P TREC 8, P77 WANG S, P SIGCHI C HUM FACT Yamabe T., 2005, Proceedings. 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications NR 43 TC 5 Z9 5 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JUN PY 2011 VL 53 IS 2 BP 391 EP 429 DI 10.1007/s11042-010-0502-6 PG 39 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 753BS UT WOS:000289739800004 ER PT J AU Kumar, H Park, PS Yang, S Kim, HG AF Kumar, Harshit Park, Pil Seong Yang, SooMi Kim, Hong Gee TI An ontological approach to partnership establishment SO INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL LA English DT Article DE Ontology; Semantic Similarity; Partnership Establishment; Supplier Selection; E-Commerce ID SUPPLIER SELECTION; MANAGEMENT; MODEL AB In order to maximize the advantages and minimize the negative effects of globalization and growing interdependence, it is imperative for SMEs (Small and Medium Enterprises) in developing countries to forge partnerships with big enterprises in developed regions. However, the partnership establishment process is a rough ride; it comes with its own set of hurdles. A survey by PricewaterhouseCoopers (PwC) reveals that 44% of the partnerships were unsuccessful. In this paper, we refer to research literature to find out various features that are involved during partnership establishment process. Based upon a review, we select features that form core concepts in a partnership establishment process. These concepts along with their related properties are modeled as an ontology, termed as Partnership Ontology. Big enterprises and SME (Small and Medium Enterprises) can use the partnership ontology to lay down their requirements as a buyer profiles and seller profiles respectively. A semantic similarity measure is defined to compute a ranked list of matching seller profiles given a buyer profile. We illustrate the devised methodology of partnership establishment process by an example using a case study. C1 [Kumar, Harshit; Park, Pil Seong; Yang, SooMi] Univ Suwon, Dept Comp Sci, Suwon, South Korea. [Kumar, Harshit; Kim, Hong Gee] Seoul Natl Univ, Biomed Knowledge Engn Lab, Seoul 151, South Korea. [Park, Pil Seong] Univ Suwon, Dept Comp, Suwon, South Korea. RP Park, PS (reprint author), Univ Suwon, Dept Comp Sci, Suwon, South Korea. EM pspark@suwon.ac.kr FU GRRC project of Gyeonggi Province Province [GRRC Suwon2010-A2] FX The authors would like to thank Trade Investment Agency for providing us with the data for this work. This research has been funded by the GRRC project of Gyeonggi Province Province [GRRC Suwon2010-A2, Research on knowledge-based context-aware information system] CR Basnet C, 2005, COMPUT OPER RES, V32, P1, DOI 10.1016/S0305-0548(03)00199-0 Bayazit O., 2006, BENCHMARKING INT J, V13, P566, DOI 10.1108/14635770610690410 Cakravastia A, 2004, INT J PROD RES, V42, P4457, DOI 10.1080/00207540410001727622 Chen CT, 2006, INT J PROD ECON, V102, P289, DOI 10.1016/j.ijpe.2005.03.009 Chen SH, 2008, MANAGE DECIS, V46, P449, DOI 10.1108/00251740810863889 Choy KL, 2003, SUPPLY CHAIN MANAG, V8, P140, DOI 10.1108/13598540310468742 Crama Y, 2004, EUR J OPER RES, V159, P364, DOI 10.1016/j.ejor.2003.08.021 Cullen J. B., 1999, MULTINATIONAL MANAGE Dacin MT, 1997, J WORLD BUS, V32, P3, DOI 10.1016/S1090-9516(97)90022-5 Dickson G. W., 1996, J PURCHASING, V2, P5 Dulmin R, 2003, J PURCH SUPPLY MANAG, V9, P177, DOI DOI 10.1016/S1478-4092(03)00032-3 Dyer J., 2000, COLLABORATIVE ADVANT Fensel D., 2001, ONTOLOGIES SILVER BU, P1 Geringer J. M., 1988, JOINT VENTURE PARTNE Giachetti RE, 2004, INT J PROD RES, V42, P1147, DOI 10.1080/00207540310001622430 Hans Carl, 2008, Journal of Manufacturing Technology Management, V19, DOI 10.1108/17410380810869932 HEPP M, 2008, 1 INT C KNOWL ENG KN, V5268, P332 Jagersma P. K., 2003, J BUS STRAT, V24, P27, DOI 10.1108/02756660310504942 Jang SH, 2010, INFORMATION-TOKYO, V13, P1639 Kaplan N. J., 2002, J BUS STRAT, V23, P38, DOI 10.1108/eb040250 Lemke F., 2003, INT J PHYS DISTRIB, V33, P12, DOI 10.1108/09600030310460981 Li L, 2004, LECT NOTES COMPUT SC, V3007, P834 LI Y, 2001, SYSTEMS MAN CYBERNET, V3, P2041 Lin J, 2010, INFORMATION-TOKYO, V13, P1449 Liu FHF, 2005, INT J PROD ECON, V97, P308, DOI 10.1016/j.ijpe.2004.09.005 Maheshwari B., 2006, J ENTERPRISE INFORM, V19, P277, DOI 10.1108/17410390610658469 Min H., 1994, INT J PHYS DISTRIB, V24, P24, DOI 10.1108/09600039410064008 PETERSEN SA, 2002, AGENT ORIENTED INFOR, P98 PHAM SHP, 2010, INFORM INT INTERDISC, V13, P1467 Pidduck A.B., 2006, J ENTERPRISE INFORM, V19, P262, DOI 10.1108/17410390610658450 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMITZ J, 2004, PRODUCTION EC, V89, P231 Shekhar Sandhya, 2008, Benchmarking: An International Journal, V15, DOI 10.1108/14635770810876575 SUI Z, 2009, INFORM INT INTERDISC, V12 Sun Q, 2009, INFORMATION, V12, P13 Wang L., 2006, INT J PHYS DISTRIB, V36, P466, DOI 10.1108/09600030610677410 WEBER CA, 1991, EUR J OPER RES, V50, P2, DOI 10.1016/0377-2217(91)90033-R NR 37 TC 0 Z9 0 PU INT INFORMATION INST PI TOKYO PA FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN SN 1343-4500 J9 INFORMATION-TOKYO JI Information PD MAY PY 2011 VL 14 IS 5 SI SI BP 1803 EP 1824 PG 22 WC Engineering, Multidisciplinary SC Engineering GA 804UA UT WOS:000293678300026 ER PT J AU Tomaszewski, B Blanford, J Ross, K Pezanowski, S MacEachren, AM AF Tomaszewski, Brian Blanford, Justine Ross, Kevin Pezanowski, Scott MacEachren, Alan M. TI Supporting geographically-aware web document foraging and sensemaking SO COMPUTERS ENVIRONMENT AND URBAN SYSTEMS LA English DT Article DE Visual analytics; Sensemaking; Information foraging; Geographic information retrieval; Infectious disease dynamics; Niger ID INFORMATION; DYNAMICS; SYSTEMS; MODEL AB This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst's or researcher's ability to judge information relevancy. Determining information relevancy is ultimately a process of sensemaking. In this paper, we present our research on visually supporting web document foraging and sensemaking. In particular, we present the Sense-of-Place (SensePlace) analytic environment. The scientific goal of SensePlace is to visually and computationally support analyst sensemaking with text artifacts that have potential place, time, and thematic relevance to an analytical problem through identification and visual highlighting of named entities (people, places, times, and organizations) in documents, automated inference to determine document relevance using stored knowledge, and a visual interface with coupled geographic map, timeline, and concept graph displays that are used to contextualize the contexts of potentially relevant documents. We present the results of a case study analysis using SensePlace to uncover potential population migration, geopolitical, and other infectious disease dynamics drivers for measles and other epidemics in Niger. Our analysis allowed us to demonstrate how our approach can support analysis of complex situations along (a) multi-scale geographic dimensions (i.e., vaccine coverage areas), (b) temporal dimensions (i.e., seasonal population movement and migrations), and (c) diverse thematic dimensions (effects of political upheaval, food security, transient movement, etc.). (C) 2011 Elsevier Ltd. All rights reserved. C1 [Tomaszewski, Brian] Rochester Inst Technol, Dept Informat Sci & Technol, Rochester, NY 14623 USA. [Blanford, Justine; Ross, Kevin; Pezanowski, Scott; MacEachren, Alan M.] Penn State Univ, Dept Geog, University Pk, PA 16801 USA. [Blanford, Justine; Ross, Kevin; Pezanowski, Scott; MacEachren, Alan M.] Penn State Univ, GeoVISTA Ctr, University Pk, PA 16801 USA. RP Tomaszewski, B (reprint author), Rochester Inst Technol, Dept Informat Sci & Technol, 31 Lomb Mem Dr, Rochester, NY 14623 USA. EM bmtski@rit.edu; jib18@psu.edu; kross@psu.edu; spezanowski@psu.edu; maceachren@psu.edu RI MacEachren, Alan/E-9444-2011 CR ALFA S, 1999, CHILD LABOUR SMALL S *ALLAFRICA, 2009, AFR MIGR MEN MOV THR [Anonymous], 1997, BBC NEWS [Anonymous], 2001, NY TIMES [Anonymous], 2005, KHALEEJ TIMES [Anonymous], 1994, NY TIMES [Anonymous], 2002, NY TIMES [Anonymous], 2004, BOSTON GLOBE [Anonymous], 2005, GUARDIAN [Anonymous], 2009, BBC NEWS [Anonymous], 1996, NY TIMES [Anonymous], 2005, FOX NEWS [Anonymous], 2006, NY TIMES [Anonymous], 2001, INDEPENDENT [Anonymous], 2005, INDEPENDENT [Anonymous], 2004, MED NEWS [Anonymous], 2004, BBC NEWS *AS AFR INT WIR, 2004, LOC INV AGR AR NIG *AS AFR INT WIR, 2002, MORE 200 CHILD MIN W Baader F., 2003, DESCRIPTION LOGIC HD BEX F, 2007, SENSE MAKING SOFTWAR Bharti N, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001941 Bier E. A., 2008, IEEE S VIS AN SCI TE BLISS J, 2008, EXODE HIV AIDS *CIA, 2009, CIA WORLD FACTB NIG *CNN, 2003, 3 NIG STAT HALT POL Cunningham H, 2002, COMPUT HUMANITIES, V36, P223, DOI 10.1023/A:1014348124664 *ECOWAS SWAC OECD, 2006, ATL REG INT W AFR PO Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 *ENV SYST RES I CA, 1993, DIG CHART WORLD DCW *ENV SYST RES I CA, 1993, ESRI DAT MAPS Ferrari MJ, 2008, NATURE, V451, P679, DOI 10.1038/nature06509 *FEWSNET, 2009, LIV ZON MAP *FEWSNET, 2005, NIG LIV PROF FRANKENBERGER T, 2002, HOUSEHOLD LIVELIHOOD Gahegan M. N., 2005, EXPLORING GEOVISUALI, P83, DOI 10.1016/B978-008044531-1/50422-X *GMACL, 2006, REV CHILD LAB ED POV HAAN AD, 2002, J DEV STUDIES, V37 *IOL, 2006, MEN SPREAD CHECK NIG *IRIN AFR, 2008, W AFR VOIC CLAND *IRIN AFR, 2007, NIGER MIGR BEN STILL *IRIN AFR, 2007, NIGER NIGERIA NIG MA *IRIN AFR, 2008, W AFR MIGR RISK ALL JOHNSON D, 2005, EXODUS AFRICA SEARCH KIRWIN M, 2004, SOCIOPOLITICAL EFFEC Klein G, 2006, IEEE INTELL SYST, V21, P70, DOI 10.1109/MIS.2006.75 MacEachren AM, 2004, P NATL ACAD SCI USA, V101, P5279, DOI 10.1073/pnas.0307755101 Manning C.D., 2008, INTRO INFORM RETRIEV MOSOLO C, 2002, D17 LADSEBCNR *MSF, 2005, OUR PROGR NIGER *MSF, 2003, BEAUT BEAST NIG *OECD, 2009, REG CHALL W AFR MIGR, P199 Overell S, 2007, LECT NOTES COMPUT SC, V4730, P930 Peuquet D. J., 2002, REPRESENTATIONS SPAC PEUQUET DJ, 1994, ANN ASSOC AM GEOGR, V84, P441, DOI 10.1111/j.1467-8306.1994.tb01869.x Pirolli P., 2005, INT C INT AN MCLEAN Pirolli P., 1995, P SIGCHI C HUM FACT, P51, DOI 10.1145/223904.223911 Pirolli P, 2007, INFORM FORAGING THEO Pirolli P, 2009, COMPUTER, V42, P33, DOI 10.1109/MC.2009.94 Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643 PROULX P, 2006, IEEE S VIS AN SCI TE Rain D., 1999, EATERS DRY SEASON CI RAUCH E, 2003, P HTL NAACL WORKSH A *REUT, 2002, PROST BORD BOOMT STR *REUT, 2009, NIG OUTBR SETB POL E Robinson A. C., 2005, CARTOGR GEOGR INF SC, V32, P243, DOI 10.1559/152304005775194700 RUSSEL D, 1993, P INTERACT 93 CHI 93 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneiderman B., 1997, DESIGNING USER INTER, Vthird *SIMILE PROJ, TIM STASKO J, 2007, VAST SACR CA STASKO J, 2008, SENS WORKSH CHI 2008 Tergan SO, 2005, LECT NOTES COMPUT SC, V3426, P185 Thomas J.J., 2005, ILLUMINATING PATH RE Tomaszewski B, 2011, COMPUT GEOSCI-UK, V37, P86, DOI 10.1016/j.cageo.2010.03.009 TOMASZEWSKI B, P 4 UN INT UN SPIDER TOMASZEWSKI B, 2010, IEEE C VIS AN SCI TE Tomaszewski B, 2008, CARTOGR J, V45, P165, DOI 10.1179/000870408X311369 TOMASZEWSKI B, 2009, GEOVISUAL ANAL APPRO TUAN Y, 1990, TOPOPHILLIA STUDY EN Turhan AY, 2006, FOURTH ANNUAL IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS, PROCEEDINGS, P13, DOI 10.1109/PERCOMW.2006.114 *USGS CART, NIG MIN MAP Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455 WANG X, 2008, COMP GRAP FOR WEICK KE, 1988, J MANAGE STUD, V25, P305, DOI 10.1111/j.1467-6486.1988.tb00039.x *WHO, 2006, WEEKLY EPIDEMIOLOGIC, V10, P89 *WHO, 2002, WHO LAUNCH NEW VACC *XINH, 2006, UNICEF AL NIG POL CA ZHANG X, 2008, CHI FLOR IT 2008, DAILY TRUST 2005, OXFORD ANAL 2004, 2 MORE NIGERIAN STAT 2008, HERALD TRIBUNE NR 93 TC 4 Z9 4 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0198-9715 J9 COMPUT ENVIRON URBAN JI Comput. Environ. Urban Syst. PD MAY PY 2011 VL 35 IS 3 BP 192 EP 207 DI 10.1016/j.compenvurbsys.2011.01.003 PG 16 WC Computer Science, Interdisciplinary Applications; Engineering, Environmental; Environmental Studies; Geography; Operations Research & Management Science SC Computer Science; Engineering; Environmental Sciences & Ecology; Geography; Operations Research & Management Science GA 757EH UT WOS:000290067500002 ER PT J AU Wu, JN Xuan, ZG Pan, DH AF Wu, Jiangning Xuan, Zhaoguo Pan, Donghua TI ENHANCING TEXT REPRESENTATION FOR CLASSIFICATION TASKS WITH SEMANTIC GRAPH STRUCTURES SO INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL LA English DT Article; Proceedings Paper CT 6th International Symposium on Management Engineering (ISME 2009) CY AUG 05-07, 2009 CL Dalian, PEOPLES R CHINA HO Univ Technol China, Sch Management DE Text representation; Graph structure; Maximum common subgraph; Classification ID MAXIMUM COMMON SUBGRAPH; ALGORITHMS; RETRIEVAL AB To represent the textual knowledge more expressively, a kind of semantic-based graph structure is proposed, in which more semantic and ordering information among terms as well as the structural information of the text are incorporated. Such model can be constructed by extracting representative terms from texts and their mutually semantic relationships. Afterward, it is represented as a graph, whose nodes are the selected terms and whose edges are the corresponding relationships respectively. Moreover, the weight is assigned to each edge so that the strength of relationship between two terms can be measured. Furthermore, for this weighted directed graph structure, a novel graph similarity algorithm is developed by extracting the maximum common subgraph between two concerned graphs, which can therefore be used to measure the distance between two graph structures, i.e., two texts, and further be applied to classification tasks. Finally, some experiments have been conducted with the Chinese benchmark corpus for validation. The experimental results have proved the better performance of the proposed textual knowledge representation model in terms of its precision and recall. C1 [Wu, Jiangning; Xuan, Zhaoguo; Pan, Donghua] Dalian Univ Technol, Inst Syst Engn, Dalian 116023, Peoples R China. RP Wu, JN (reprint author), Dalian Univ Technol, Inst Syst Engn, 2 Linggong Rd, Dalian 116023, Peoples R China. EM jnwu@dlut.edu.cn; xzg@dl.cn; gyise@dlut.edu.cn CR BORGATTI S, 2008, SOCIAL NETWORK ANAL Bunke H, 1997, PATTERN RECOGN LETT, V18, P689, DOI 10.1016/S0167-8655(97)00060-3 Bunke H, 2000, PATTERN RECOGN LETT, V21, P163, DOI 10.1016/S0167-8655(99)00143-9 Chow TWS, 2009, EXPERT SYST APPL, V36, P12023, DOI 10.1016/j.eswa.2009.03.008 Hsieh SM, 2008, DATA KNOWL ENG, V65, P401, DOI 10.1016/j.datak.2007.12.004 Jiang CT, 2010, KNOWL-BASED SYST, V23, P302, DOI 10.1016/j.knosys.2009.11.010 Jiang Shaohua, 2006, Journal of the China Society for Scientific and Technical Information, V25 Levi G., 1973, CALCOLO, V9, P341 MCGREGOR JJ, 1982, SOFTWARE PRACT EXPER, V12, P23, DOI 10.1002/spe.4380120103 Raju GT, 2008, INT J INNOV COMPUT I, V4, P381 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A, 2004, LECT NOTES COMPUT SC, V3138, P190 Schenker A., 2003, THESIS U S FLORIDA Schenker A., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition WEI ZH, 2008, P 9 INT C STAT AN TE, P1175 Wu Chun-ying, 2008, Journal of Computer Applications, V28, DOI 10.3724/SP.J.1087.2008.00764 NR 16 TC 0 Z9 0 PU ICIC INTERNATIONAL PI KUMAMOTO PA TOKAI UNIV, 9-1-1, TOROKU, KUMAMOTO, 862-8652, JAPAN SN 1349-4198 J9 INT J INNOV COMPUT I JI Int. J. Innov. Comp. Inf. Control PD MAY PY 2011 VL 7 IS 5B SI SI BP 2689 EP 2698 PG 10 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 764AS UT WOS:000290601400011 ER PT J AU Chen, YL Chiu, YT AF Chen, Yen-Liang Chiu, Yu-Ting TI An IPC-based vector space model for patent retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Patent mining; Patent retrieval; Vector space model (VSM) ID INFORMATION-RETRIEVAL; DOCUMENTS AB Determining requirements when searching for and retrieving relevant information suited to a user's needs has become increasingly important and difficult, partly due to the explosive growth of electronic documents. The vector space model (VSM) is a popular method in retrieval procedures. However, the weakness in traditional VSM is that the indexing vocabulary changes whenever changes occur in the document set, or the indexing vocabulary selection algorithms, or parameters of the algorithms, or if wording evolution occurs. The major objective of this research is to design a method to solve the afore-mentioned problems for patent retrieval. The proposed method utilizes the special characteristics of the patent documents, the International Patent Classification (IPC) codes, to generate the indexing vocabulary for presenting all the patent documents. The advantage of the generated indexing vocabulary is that it remains unchanged, even if the document sets, selection algorithms, and parameters are changed, or if wording evolution occurs. Comparison of the proposed method with two traditional methods (entropy and chi-square) in manual and automatic evaluations is presented to verify the feasibility and validity. The results also indicate that the IPC-based indexing vocabulary selection method achieves a higher accuracy and is more satisfactory. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Chen, Yen-Liang; Chiu, Yu-Ting] Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. RP Chen, YL (reprint author), Natl Cent Univ, Dept Informat Management, Chungli 320, Taiwan. EM ylchen@mgt.ncu.edu.tw CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chen YL, 2006, J INF SCI, V32, P449, DOI 10.1177/0165551506065814 Fall C. J., 2003, SIGIR FORUM, V37, P10 Feldman R, 2007, TEXT MINING HDB ADV Fox B, 2002, INFORM PROCESS MANAG, V38, P547, DOI 10.1016/S0306-4573(01)00047-4 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hotho A., 2005, LDV FORUM, V20, P19 HUANG SH, 2004, INT COMP S 2004, P15 Huang SH, 2008, EXPERT SYST APPL, V34, P2290, DOI 10.1016/j.eswa.2007.03.012 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kang IS, 2007, INFORM PROCESS MANAG, V43, P1173, DOI 10.1016/j.ipm.2006.11.006 Kim JH, 2007, INFORM PROCESS MANAG, V43, P1200, DOI 10.1016/j.ipm.2007.02.002 Kim YG, 2008, EXPERT SYST APPL, V34, P1804, DOI 10.1016/j.eswa.2007.01.033 Lai KK, 2006, J AM ACAD BUSINESS, V8, P248 Larkey L. S., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354830 LARKEY LS, 1999, 4 ACM C DIG LIB, P79 LARKEY LS, 1998, AAAI 98 WORKSH LEARN, P87 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 MILICFRYLING N, 2005, TEXT MINING ITS APPL, P1 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 *STANF U NLP GROUP, 2009, STANF LOG LIN PART O Trappey AJC, 2006, EXPERT SYST APPL, V31, P755, DOI 10.1016/j.eswa.2006.01.013 Trappey AJC, 2008, IND MANAGE DATA SYST, V108, P245, DOI 10.1108/02635570810847608 Tseng YH, 2007, J INF SCI, V33, P718, DOI 10.1177/0165551507077406 *WIPO, 2009, INTR IPC INT *WIPO, 2009, GUID INT PAT CLASS Wong S., 1984, P 7 ANN INT ACM SIGI, P167 NR 32 TC 9 Z9 12 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2011 VL 47 IS 3 BP 309 EP 322 DI 10.1016/j.ipm.2010.06.001 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 755HM UT WOS:000289919700001 ER PT J AU Martinet, J Chiaramella, Y Mulhem, P AF Martinet, Jean Chiaramella, Yves Mulhem, Philippe TI A relational vector space model using an advanced weighting scheme for image retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Information retrieval; Vector space model; Conceptual graph; Image indexing; Weighting scheme; Evaluation ID SCENE ANALYSIS; CLASSIFICATION; SEGMENTATION; QUERY AB In this paper, we lay out a relational approach for indexing and retrieving photographs from a collection. The increase of digital image acquisition devices, combined with the growth of the World Wide Web, requires the development of information retrieval (IR) models and systems that provide fast access to images searched by users in databases. The aim of our work is to develop an IR model suited to images, integrating rich semantics for representing this visual data and user queries, which can also be applied to large corpora. Our proposal merges the vector space model of IR - widely tested in textual IR - with the conceptual graph (CG) formalism, based on the use of star graphs (i.e. elementary CGs made up of a single relation connected to some concepts representing image objects). A novel weighting scheme for star graphs, based on image objects size, position, and image heterogeneity is outlined. We show that integrating relations into the vector space model through star graphs increases the system's precision, and that the results are comparable to those from graph projection systems, and also that they shorten processing time for user queries. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Martinet, Jean] Fondamentale Lille, Lab Informat, F-59655 Villeneuve Dascq, France. [Chiaramella, Yves; Mulhem, Philippe] Lab Informat Grenoble, F-38400 St Martin Dheres, France. RP Martinet, J (reprint author), Fondamentale Lille, Lab Informat, Batiment M3, F-59655 Villeneuve Dascq, France. EM Jean.Martinet@lifl.fr; Yves.Chiaramella@imag.fr; Philippe.Mulhem@imag.fr CR AYACHE S, 2007, EUR C INF RETR ECIR, P494 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BASTAN M, 2006, P CIVR 2006 PHOEN AZ Bertino E, 1998, MULTIMEDIA SYST, V6, P2, DOI 10.1007/s005300050072 Bouch A., 2000, CHI 2000 Conference Proceedings. Conference on Human Factors in Computing Systems. CHI 2000. The Future is Here Carson C., 1999, BLOBWORLD SYSTEM REG Chein M., 1992, REV INTELLIGENCE ART, V6, P365 CZUNI L, 2003, LECT NOTES COMPUTER, P76 Deerwester S., 1990, INDEXING LATENT SEMA Di Sciascio E, 2002, J ARTIF INTELL RES, V16, P209 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 Francois E, 1997, IEEE T CIRC SYST VID, V7, P237, DOI 10.1109/76.554436 Haar Romeny B., 2003, FRONT END VISION MUL HOERSTER E, 2007, P CIVR 2007 AMST NET, P17 Hollink L, 2004, INT J HUM-COMPUT ST, V61, P601, DOI 10.1016/j.ijhcs.2004.03.002 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Itti L, 1998, IEEE T PATTERN ANAL, V20, P1254, DOI 10.1109/34.730558 LEE JH, 1997, SIGIR 97 P 20 ANN IN, P267 Li LY, 2004, IEEE T IMAGE PROCESS, V13, P1459, DOI 10.1109/TIP.2004.836169 Lim JH, 2001, PATTERN ANAL APPL, V4, P125, DOI 10.1007/PL00014574 Lim JH, 2000, NEW GENERAT COMPUT, V18, P147 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 MAHER PE, 1991, NAFIPS 91, P106 Malik AS, 2008, PATTERN RECOGN, V41, P2200, DOI 10.1016/j.patcog.2007.12.014 MARTINET J, 2005, ACM CIKM, P760 MARTINET J, 2003, P ECIR 03 25 BCS IRS, P546 MARTINET J, 2003, P CBMI 03, P335 MARTINET J, 2007, ECIR 07 ROM IT MARTINET J, 2008, MULTIMEDIA TOOLS APP Mechkour M., 1995, Database and Expert Systems Applications. 6th International Conference, DEXA '95. Proceedings MECHKOUR M, 1995, THESIS U J FOURIER G Merchan P, 2008, PATTERN RECOGN LETT, V29, P1105, DOI 10.1016/j.patrec.2007.06.014 Morton S., 1987, THESIS U BRISTOL Mugnier M.-L., 1996, REV INTELLIGENCE ART, V10, P7 MULHEM P, 2003, MULTIMEDIA SYSTEMS C Mulhem P., 2002, CIKM, P94 NIBLACK W, 1993, 920381511 RJ IBM ALM Nielsen J., 1994, USABILITY ENG Ounis I, 1998, SIGIR, P266 QUELHAS P, 2006, CIVR 2006 PHOEN AZ Quelhas P, 2007, IEEE T PATTERN ANAL, V29, P1575, DOI 10.1109/TPAMI.2007.1155 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RODDEN K, 1999, P 25 BCS IRSG C IR RODDEN K, 2003, ACM C HUM FACT COMP, P409 ROJER AS, 1990, 10TH INT C PATT REC, V2, P278 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P379 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Snoek CGM, 2005, MULTIMED TOOLS APPL, V25, P5, DOI 10.1023/B:MTAP.0000046380.27575.a5 Sowa J. F., 1984, CONCEPTUAL STRUCTURE van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vapnik V., 1995, NATURE STAT LEARNING Vogel J, 2007, INT J COMPUT VISION, V72, P133, DOI 10.1007/s11263-006-8614-1 Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109 WANG JZ, 2001, ICIAP, P380 WUWONGSE V, 1993, ICCS 93, P430 ZHENG QF, 2006, ACM MULT 2006 SANT B NR 61 TC 4 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2011 VL 47 IS 3 BP 391 EP 414 DI 10.1016/j.ipm.2010.10.003 PG 24 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 755HM UT WOS:000289919700007 ER PT J AU Schedl, M Widmer, G Knees, P Pohle, T AF Schedl, Markus Widmer, Gerhard Knees, Peter Pohle, Tim TI A music information system automatically generated via Web content mining techniques SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Music information retrieval; Web content mining; Information systems; Application; Evaluation ID RETRIEVAL AB This article deals with the problem of mining music-related information from the Web and representing this information via a music information system. Novel techniques have been developed as well as existing ones refined in order to automatically gather information about music artists and bands. After searching, retrieval, and indexing of Web pages that are related to a music artist or band. Web content mining and music information retrieval techniques were applied to capture the following categories of information: similarities between music artists or bands, prototypicality of an artist or a band for a genre, descriptive properties of an artist or a band, band members and instrumentation, images of album cover artwork. Approaches to extracting these pieces of information are presented and evaluation experiments are described that investigate the proposed approaches' performance. From the insights gained by the various experiments an Automatically Generated Music Information System (AGMIS) providing Web-based access to the extracted information has been developed. AGMIS demonstrates the feasibility of automated music information systems on a large collection of more than 600,000 music artists. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Schedl, Markus; Widmer, Gerhard; Knees, Peter; Pohle, Tim] Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. RP Schedl, M (reprint author), Johannes Kepler Univ Linz, Dept Computat Percept, Altenberger Str 69, A-4040 Linz, Austria. EM markus.schedl@jku.at FU Austrian Science Fund (FWF) [L511-N15, Z159, P22856-N23] FX This research is supported by the Austrian Science Fund (FWF) under Project Numbers L511-N15, Z159, and P22856-N23. The authors would further like to thank Julien Carcenac from exalead for his support in the querying process. CR Alani H, 2003, IEEE INTELL SYST, V18, P14, DOI 10.1109/MIS.2003.1179189 ANDREWS K, 1998, P IEEE INF VIS 1998 Aucouturier J.-J., 2004, J NEGATIVE RESULTS S, V1 Aucouturier J.-J., 2002, Proceedings 2002 IEEE International Conference on Multimedia and Expo (Cat. No.02TH8604), DOI 10.1109/ICME.2002.1035729 Aucouturier JJ, 2005, IEEE T MULTIMEDIA, V7, P1028, DOI 10.1109/TMM.2005.858380 BAYER R, 1971, P ACM SIG FIDET WORK Cano P, 2004, P 5 INT S MUS INF RE, P466 Celma O., 2007, ISMIR 2007 TUTORIAL Celma Oscar, 2008, THESIS U P FABRA BAR Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Cohen WW, 2000, COMPUT NETW, V33, P685, DOI 10.1016/S1389-1286(00)00057-8 Cohen W.W., 2003, P IJCAI 03 WORKSH IN, P73 Dixon S., 2004, P 5 INT C MUS INF RE, P509 Downie JS, 2003, ANNU REV INFORM SCI, V37, P295, DOI 10.1002/aris.1440370108 ECK D, 2007, P 8 INT C MUS INF RE Ellis D. P. W., 2002, P 3 INT C MUS INF RE Friedman M, 1940, ANN MATH STAT, V11, P86, DOI 10.1214/aoms/1177731944 Geleijnse G., 2006, P 7 INT C MUS INF RE Geleijnse G., 2007, P 8 INT C MUS INF RE Hu X., 2007, P 8 INT C MUS INF RE Knees P., 2004, P 5 INT S MUS INF RE, P517 Knees P., 2005, P 6 INT C MUS INF RE, P564 Knees P., 2007, P 30 ANN INT ACM SIG Knees P, 2007, IEEE MULTIMEDIA, V14, P46, DOI 10.1109/MMUL.2007.48 Knees P., 2006, P 14 ACM INT C MULT Korst J., 2006, P 3 PHIL S INT ALG S, P205 Lamere P, 2008, J NEW MUSIC RES, V37, P101, DOI 10.1080/09298210802479284 Law E., 2007, P 8 INT C MUS INF RE Mandel M. I., 2007, P 8 INT C MUS INF RE Mandel M. I., 2005, P 6 INT C MUS INF RE Pachet F., 2001, P 1 INT C WEB DEL MU Pampalk E., 2007, P 8 INT C MUS INF RE Pampalk E., 2002, P 10 ACM INT C MULT, P570, DOI [DOI 10.1145/641007.641121, 10.1145/641007.641121] Pampalk E., 2005, P 9 EUR C RES ADV TE Pohle T, 2007, IEEE T MULTIMEDIA, V9, P567, DOI 10.1109/TMM.2006.887991 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schedl M., 2006, P 28 EUR C INF RETR Schedl M., 2007, P 5 WORKSH AD MULT R SCHEDL M, 2005, P 6 INT C MUS INF RE SCHEDL M, 2006, P 7 INT C MUS INF RE Schedl M., 2005, P 4 INT WORKSH CONT Schedl M, 2010, MULTIMED TOOLS APPL, V49, P101, DOI 10.1007/s11042-009-0396-3 SCHEDL M, 2006, P 1 IEEE INT C DIG I SCHEDL M, 2007, P 18 IEEE VIS 2007 C SHESKIN D., 2004, HDB PARAMETRIC NONPA, V3rd STASKO J, 2000, P IEEE INF VIS 2000 Turnbull D., 2007, P 8 INT C MUS INF RE von Ahn L., 2004, CHI 04 Whitman Brian, 2002, P 2002 INT COMP MUS, P591 WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 Zadel M., 2004, P 5 INT S MUS INF RE ZOBEL J, 2006, ACM COMPUT SURV, V38, pNI236, DOI DOI 10.1145/1132956.1132959 NR 52 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2011 VL 47 IS 3 BP 426 EP 439 DI 10.1016/j.ipm.2010.09.002 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 755HM UT WOS:000289919700009 ER PT J AU Cui, JW Liu, HY He, J Li, PY Du, XY Wang, PW AF Cui, Jianwei Liu, Hongyan He, Jun Li, Pei Du, Xiaoyong Wang, Puwei TI TagClus: a random walk-based method for tag clustering SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Tag clustering; Tag relevance; Social tag; Random walk AB Tagging behavior on the Internet has seen dramatic increase in recent years, and social tagging has become a popular way to organize and share resources. However, ambiguity and large quantities of tags restrict its effective use for resource searching and classifying. Tag clustering can group tags with similar semantics together, thus helping alleviate these problems. In this paper, we introduce a random walk-based method to measure relevance between tags by exploiting the relationship between tags and resources. Based on this, we also develop a novel clustering method, TagClus, which can address several challenges in tag clustering. Experimental results on a real dataset show that our methods achieve good accuracy and acceptable performance for tag clustering. C1 [Liu, Hongyan] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China. [Cui, Jianwei; He, Jun; Li, Pei; Du, Xiaoyong; Wang, Puwei] Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China. RP Liu, HY (reprint author), Minist Educ, Key Labs Data Engn & Knowledge Engn, Beijing, Peoples R China. EM cjwruc@ruc.edu.cn; hyliu@tsinghua.edu.cn; hejun@ruc.edu.cn; lp@ruc.edu.cn; duyong@ruc.edu.cn; wangpuwei@ruc.edu.cn RI ruc, comp_xinxi/E-4212-2012 FU Microsoft Research Asia Request for Proposals (RFP); National Natural Science Foundation of China [70871068, 70890083, 60873017]; State High-Tech Research and Development Program of China (863 program) [2009AA01Z149] FX This work is supported in part by Microsoft Research Asia Request for Proposals (RFP) in 2009, the National Natural Science Foundation of China under Grant. Nos. 70871068, 70890083, 60873017, and State High-Tech Research and Development Program of China (863 program) No. 2009AA01Z149. We would like to thank the anonymous reviewers for their valuable and constructive comments. CR ADAMIC L, 2009, ZIPF POWER LAWS PARE Ben-Dor A, 1999, J COMPUT BIOL, V6, P281, DOI 10.1089/106652799318274 BORKUR S., 2008, P 17 INT C WORLD WID, P327 CAMERON M, 2006, P 17 C HYP HYP OD DE, P31 CELINE VD, 2007, P ESWC WORKSH BRIDG, P57 CHRISTOPHER HB, 2006, P 15 WORLD WID WEB C DARIUS P, 2009, KNOWL INF SYST, V19, P361 Ester M, 1996, P 2 INT C KNOWL DISC, V96, P226 FABIAN MS, 2008, P 17 ACM C INF KNOWL, P223 Gabriela M., 2009, KNOWL INF SYST, V21, P299 Gerard S., 1986, INTRO MODERN INFORM Glen J., 2002, P 8 ACM SIGKDD INT C, P538 GRIGORY B, 2006, COLL WEB TAGG WORKSH Kallenberg O., 1997, FDN MODERN PROBABILI KERSTIN B, 2008, P 17 ACM C INF KNOWL, P193 Leonard Kaufman, 1990, FINDING GROUPS DATA Li P, 2009, LECT NOTES COMPUT SC, V5446, P455 Motwani R., 1995, RANDOMIZED ALGORITHM Ng R. T., 1994, VLDB, P144 Page L, 1998, PAGERANK CITATION RA PAUL H, 2006, 200610 STANF INFOLAB PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Reed WJ, 2001, ECON LETT, V74, P15, DOI 10.1016/S0165-1765(01)00524-9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIMPSON E, 2008, CLUSTERING TAGS ENTE Song W, 2010, KNOWL INF SYST, V22, P347, DOI 10.1007/s10115-009-0191-5 SONG Y, 2008, P 31 ANN INT ACM SIG, P512 Sudipto G., 1998, P ACM SIGMOD INT C M, P73 *WIK, 2009, STOCH MATR Xiong H, 2009, KNOWL INF SYST, V19, P311, DOI 10.1007/s10115-008-0148-0 Zhang Tian, 1996, P 1996 ACM SIGMOD IN, P103 2009, STOP WORDS LIST NR 32 TC 9 Z9 9 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 EI 0219-3116 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD MAY PY 2011 VL 27 IS 2 BP 193 EP 225 DI 10.1007/s10115-010-0307-y PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 750QF UT WOS:000289562400003 ER PT J AU Brown, SV Tucker, JW AF Brown, Stephen V. Tucker, Jennifer Wu TI Large-Sample Evidence on Firms' Year-over-Year MD&A Modifications SO JOURNAL OF ACCOUNTING RESEARCH LA English DT Article ID INFORMATION-CONTENT; EARNINGS ANNOUNCEMENTS; RETURN VOLATILITY; DISCLOSURES; MANAGEMENT; STATEMENTS; FORECASTS; FILINGS AB The Securities and Exchange Commission (SEC) has expressed concern about the informativeness of firms' Management Discussion and Analysis (MD&A) disclosures. A firm's MD&A is potentially uninformative if it does not change appreciably from the previous year after significant economic changes at the firm. We introduce a measure for narrative disclosure-the degree to which the MD&A differs from the previous disclosure-and provide three findings on the usefulness of MD&A disclosure. First, firms with larger economic changes modify the MD&A more than those with smaller economic changes. Second, the magnitude of stock price responses to 10-K filings is positively associated with the MD&A modification score, but analyst earnings forecast revisions are unassociated with the score, suggesting that investors-but not analysts-use MD&A information. Finally, MD&A modification scores have declined in the past decade even as MD&A disclosures have become longer; the price reaction to MD&A modification scores has also weakened, suggesting a decline in MD&A usefulness. C1 [Brown, Stephen V.; Tucker, Jennifer Wu] Univ Florida, Fisher Sch Accounting, Gainesville, FL 32611 USA. RP Brown, SV (reprint author), Univ Florida, Fisher Sch Accounting, Gainesville, FL 32611 USA. CR ATIASE RK, 1985, J ACCOUNTING RES, V23, P21, DOI 10.2307/2490905 Barron O. E., 1999, CONTEMP ACCOUNT RES, V16, P75, DOI DOI 10.1111/J.1911-3846.1999.TB00575.X Bartov E, 2004, ACCOUNT REV, V79, P889, DOI 10.2308/accr.2004.79.4.889 Bryan SH, 1997, ACCOUNT REV, V72, P285 Bushee BJ, 2000, J ACCOUNTING RES, V38, P171, DOI 10.2307/2672914 Bushee BJ, 2003, J ACCOUNT ECON, V34, P149, DOI 10.1016/S0165-4101(02)00073-3 Carter D. A., 2004, Q REV EC FINANCE, V44, P539, DOI DOI 10.1016/J.GREF.2003.10.001 CHOUDHARY P, 2009, TIMELINESS FORM 10 K Clarkson P. M., 1999, CONTEMP ACCOUNT RES, V16, P111, DOI DOI 10.1111/J.1911-3846.1999.TB00576.X Cole C. J., 2005, J ACCOUNTING LIT, V24, P135 DAVIS AK, 2009, MANAGERS USE LANGUAG Feldman R, 2010, REV ACCOUNT STUD, V15, P915, DOI 10.1007/s11142-009-9111-x Francis J, 2002, ACCOUNT REV, V77, P515, DOI 10.2308/accr.2002.77.3.515 Frankel R, 2010, REV ACCOUNT STUD, V15, P220, DOI 10.1007/s11142-009-9089-4 Gerard Salton C.B, 1988, INFORM PROCESSING MA, V24, P513 GRANT EB, 1980, J ACCOUNTING RES, V18, P255, DOI 10.2307/2490401 Griffin PA, 2003, REV ACC STUD, V8, P433, DOI 10.1023/A:1027351630866 Hanley KW, 2010, REV FINANC STUD, V23, P2821, DOI 10.1093/rfs/hhq024 HOBERG G, 2010, PRODUCT MARKET SYNER Hutton AP, 2003, J ACCOUNTING RES, V41, P867, DOI 10.1046/j.1475-679X.2003.00126.x *INT ACC STAND BOA, 2009, MAN COMM EXP DRAFT FRANCIS J, 1994, J ACCOUNTING RES, V32, P137, DOI 10.2307/2491279 Kothari SP, 2009, ACCOUNT REV, V84, P1639, DOI 10.2308/accr.2009.84.5.1639 Lerman A, 2010, REV ACCOUNT STUD, V15, P752, DOI 10.1007/s11142-009-9114-7 Leuz Christian, 2009, DISCLOSURE COST CAPI Li EX, 2009, ACCOUNT REV, V84, P1171, DOI 10.2308/accr.2009.84.4.1171 Li F, 2008, J ACCOUNT ECON, V45, P221, DOI 10.1016/j.jacceco.2008.02.003 Li F, 2010, J ACCOUNT RES, V48, P1049, DOI 10.1111/j.1475-679X.2010.00382.x LOUGHRAN T, 2008, PLAIN ENGLISH WORKIN Manning C. D., 1999, FDN STAT NATURAL LAN Marquardt C, 2005, J ACCOUNTING RES, V43, P205, DOI 10.1111/j.1475-679x.2005.00168.x MATSUMOTO D, 2007, C CALL DISCL FIRM PE MUSLU V, 2009, FORWARD LOOKING DISC Nelson K., 2007, LITIGATION RISK VOLU Petersen MA, 2009, REV FINANC STUD, V22, P435, DOI 10.1093/rfs/hhn053 Rogers R. K., 1997, J FINANCIAL STATEMEN, V3, P17 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *SEC EXCH COMM, 1987, CONC REL MAN DISC AN *SEC EXCH COMM, 1989, MAN DISC AN FIN COND *SEC EXCH COMM, 2003, COMM GUID REG MAN DI Securities and Exchange Commission, 1998, PLAIN ENGL HDB CREAT Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 Teoh SH, 1998, J FINANC ECON, V50, P63, DOI 10.1016/S0304-405X(98)00032-4 You HF, 2009, REV ACCOUNT STUD, V14, P559, DOI 10.1007/s11142-008-9083-2 NR 44 TC 21 Z9 21 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 0021-8456 J9 J ACCOUNT RES JI J. Account. Res. PD MAY PY 2011 VL 49 IS 2 BP 309 EP 346 DI 10.1111/j.1475-679X.2010.00396.x PG 38 WC Business, Finance SC Business & Economics GA 744RO UT WOS:000289112300001 ER PT J AU Serrano-Guerrero, J Herrera-Viedma, E Olivas, JA Cerezo, A Romero, FP AF Serrano-Guerrero, Jesus Herrera-Viedma, Enrique Olivas, Jose A. Cerezo, Andres Romero, Francisco P. TI A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0 SO INFORMATION SCIENCES LA English DT Article DE Recommender system; University Digital Library; Google wave; 2-Tuple fuzzy linguistic modeling ID DECISION-MAKING PROBLEMS; LINGUISTIC INFORMATION; RESEARCH RESOURCES; RETRIEVAL SYSTEM; MODEL; WORDS; METHODOLOGY; ENVIRONMENT; OPERATORS; QUALITY AB Nowadays Digital Libraries 2.0 are mainly based on the interaction between users through collaborative applications such as wikis, blogs, etc. or new possible paradigms like the waves proposed by Google. This new concept, the wave, represents a common space where resources and users can work together. The problem arises when the number of resources and users is high, then tools for assisting the users in their information needs are necessary. In this case a fuzzy linguistic recommender system based on the Google Wave capabilities is proposed as tool for communicating researchers interested in common research lines. The system allows the creation of a common space by means a wave as a way of collaborating and exchanging ideas between several researchers interested in the same topic. In addition, the system suggests, in an automatic way, several researchers and useful resources for each wave. These recommendations are computed following several previously defined preferences and characteristics by means of fuzzy linguistic labels. Thus the system facilitates the possible collaborations between multi-disciplinar researchers and recommends complementary resources useful for the interaction. In order to test the effectiveness of the proposed system, a prototype of the system has been developed and tested with several research groups from the same university achieving successful results. (C) 2011 Elsevier Inc. All rights reserved. C1 [Serrano-Guerrero, Jesus; Olivas, Jose A.; Cerezo, Andres; Romero, Francisco P.] Univ Castilla La Mancha, Dept Informat Technol & Syst, E-13071 Ciudad Real, Spain. [Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain. RP Serrano-Guerrero, J (reprint author), Univ Castilla La Mancha, Dept Informat Technol & Syst, E-13071 Ciudad Real, Spain. EM jesus.serrano@uclm.es RI HERRERA-VIEDMA, ENRIQUE/C-2704-2008; Serrano-Guerrero, Jesus/A-4137-2013; Herrera-Viedma, Enrique/ OI Serrano-Guerrero, Jesus/0000-0002-6177-8188; Herrera-Viedma, Enrique/0000-0002-7922-4984 FU MEC-FEDER (Spain) [TIN2007-67494 F-META]; JCCM (Spain) [PEIC09-0196-3018 SCAIWEB-2]; Spanish Ministry of Public Works [90/07]; Spanish Ministry of Science and Technology (Spain) [TIN2007-61079, PET2007-0460, TIN2010-17876]; Andalusian Government (Spain) [TIC5299] FX Supported by TIN2007-67494 F-META project, MEC-FEDER (Spain), PEIC09-0196-3018 SCAIWEB-2 excellence project, JCCM (Spain), 90/07 Project, Spanish Ministry of Public Works, TIN2007-61079, PET2007-0460 and TIN2010-17876 projects, Spanish Ministry of Science and Technology (Spain) and TIC5299 excellence project, Andalusian Government (Spain). CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 ALSPECTOR J, 1998, INT C DIG LIB ACM PI, P11 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Barragans-Martinez AB, 2010, INFORM SCIENCES, V180, P4290, DOI 10.1016/j.ins.2010.07.024 BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I Bordogna G, 2004, FUZZY SET SYST, V148, P105, DOI 10.1016/j.fss.2004.03.008 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Cabrerizo FJ, 2009, INT J INF TECH DECIS, V8, P109 Cabrerizo FJ, 2010, KNOWL-BASED SYST, V23, P169, DOI 10.1016/j.knosys.2009.11.019 Cao YK, 2007, EXPERT SYST APPL, V33, P230, DOI 10.1016/j.eswa.2006.04.012 Carchiolo V, 2010, INFORM SCIENCES, V180, P1893, DOI 10.1016/j.ins.2009.12.023 Casey M. E., 2007, LIB 2 0 GUIDE PARTIC Chen LS, 2008, INFORM SCIENCES, V178, P1032, DOI 10.1016/j.ins.2007.09.027 Cornelis C, 2007, INFORM SCIENCES, V177, P4906, DOI [10.1016/j.ins.2007.07.001, 10.1016/i.ins.2007.07.001] Hastings R, 2007, LIBR J, V132, P36 Herrera F, 2008, IEEE T FUZZY SYST, V16, P354, DOI 10.1109/TFUZZ.2007.896353 Herrera F, 2000, FUZZY SET SYST, V115, P67, DOI 10.1016/S0165-0114(99)00024-X Herrera F, 2001, IEEE T SYST MAN CY B, V31, P227, DOI 10.1109/3477.915345 Herrera F, 2000, IEEE T FUZZY SYST, V8, P746, DOI 10.1109/91.890332 Herrera F, 1996, FUZZY SET SYST, V79, P175, DOI 10.1016/0165-0114(95)00162-X Herrera F, 1997, IEEE T SYST MAN CY A, V27, P646, DOI 10.1109/3468.618263 Herrera-Viedma E, 2001, INT J UNCERTAIN FUZZ, V9, P77, DOI 10.1142/S0218488501001009 Herrera-Viedma E, 2005, IEEE T FUZZY SYST, V13, P644, DOI 10.1109/TFUZZ.2005.856561 Herrera-Viedma E, 2006, J AM SOC INF SCI TEC, V57, P538, DOI 10.1002/asi.20308 Herrera-Viedma E, 2003, INT J APPROX REASON, V34, P221, DOI 10.1016/j.ijar.2003.07.009 Herrera-Viedma E, 2003, INFORM PROCESS MANAG, V39, P233, DOI 10.1016/S0306-4573(02)00049-3 Herrera-Viedma E, 2001, J AM SOC INF SCI TEC, V52, P460, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1087>3.0.CO;2-Q Herrera-Viedma E, 2007, INT J INTELL SYST, V22, P1197, DOI 10.1002/int.20244 Li Q, 2010, INFORM SCIENCES, V180, P4929, DOI 10.1016/j.ins.2010.08.044 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu ZM, 2006, INFORM PROCESS MANAG, V42, P583, DOI 10.1016/j.ipm.2004.12.002 Martinez L, 2009, INFORM SCIENCES, V179, P2297, DOI 10.1016/j.ins.2008.12.017 Mata F, 2009, IEEE T FUZZY SYST, V17, P279, DOI 10.1109/TFUZZ.2009.2013457 Mirza BJ, 2003, J INTELL INF SYST, V20, P131, DOI 10.1023/A:1021819901281 MOONEY RJ, 2000, INT C DIG LIB ACM SA, P195 Moreno JM, 2010, SOFT COMPUT, V14, P887, DOI 10.1007/s00500-009-0472-7 O'Reilly T., 2005, WHAT IS WEB 2 0 DESI Perugini S, 2004, J INTELL INF SYST, V23, P107, DOI 10.1023/B:JIIS.0000039532.05533.99 Porcel C, 2010, KNOWL-BASED SYST, V23, P32, DOI 10.1016/j.knosys.2009.07.007 Porcel C, 2009, EXPERT SYST APPL, V36, P5173, DOI 10.1016/j.eswa.2008.06.038 Porcel C, 2009, EXPERT SYST APPL, V36, P12520, DOI 10.1016/j.eswa.2009.04.038 Renda ME, 2005, INFORM PROCESS MANAG, V41, P5, DOI 10.1016/j.ipm.2004.04.007 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Ross L, 2008, J ACAD LIBR, V34, P145, DOI 10.1016/j.acalib.2007.12.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singh MP, 2001, COMMUN ACM, V44, P49, DOI 10.1145/367211.367255 Smeaton A.F., 2005, INT J DIGITAL LIB, V5, P299, DOI 10.1007/s00799-004-0100-1 Stephens M, 2007, SERIALS REV, V33, P253, DOI 10.1016/j.serrev.2007.08.002 Su JH, 2010, INFORM SCIENCES, V180, P113, DOI 10.1016/j.ins.2009.08.005 Wallis Richard, 2007, New Review of Information Networking, V13, DOI 10.1080/13614570701571484 Xu C, 2009, J ACAD LIBR, V35, P324 ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5 Zhang GQ, 2009, INFORM SCIENCES, V179, P2299, DOI 10.1016/j.ins.2009.01.043 NR 53 TC 51 Z9 51 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD MAY 1 PY 2011 VL 181 IS 9 BP 1503 EP 1516 DI 10.1016/j.ins.2011.01.012 PG 14 WC Computer Science, Information Systems SC Computer Science GA 740EP UT WOS:000288774700001 ER PT J AU Lim, SCJ Liu, Y Lee, WB AF Lim, Soon Chong Johnson Liu, Ying Lee, Wing Bun TI A methodology for building a semantically annotated multi-faceted ontology for product family modelling SO ADVANCED ENGINEERING INFORMATICS LA English DT Article DE Product family modeling; Ontology development; Semantic annotation; Multi-faceted ID INFORMATION-MANAGEMENT; TO-ORDER; CONFIGURATION; DESIGN; INTEGRATION; RETRIEVAL; FRAMEWORK; LANGUAGE; SUPPORT; SYSTEM AB Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Lim, Soon Chong Johnson; Liu, Ying; Lee, Wing Bun] Natl Univ Singapore, Dept Mech Engn, Singapore 117576, Singapore. RP Liu, Y (reprint author), Natl Univ Singapore, Dept Mech Engn, 9 Engn Dr 1,Block EA,07-08, Singapore 117576, Singapore. EM mpeliuy@nus.edu.sg RI Lee, Wing Bun/P-6257-2014 OI Lee, Wing Bun/0000-0002-3413-4029 FU Hong Kong Polytechnic University, Hong Kong SAR, China [A-PD0C, G-YF59]; Research Grants Council, Hong Kong SAR, China (RGC) [520208] FX The work described in this paper was supported by two research Grants from the Hong Kong Polytechnic University, Hong Kong SAR, China (Grant No: A-PD0C and G-YF59) and was supported by one GRF Grant from the Research Grants Council, Hong Kong SAR, China (RGC Ref: 520208). CR Ahmed S, 2007, J COMPUT INF SCI ENG, V7, P132, DOI 10.1115/1.2720879 AHONEN H, 1999, P IJCAI 99 WORKSH TE, P1 Callahan S, 2006, J COMPUT INF SCI ENG, V6, P263, DOI 10.1115/1.2218361 Cho J, 2006, COMPUT AIDED DESIGN, V38, P713, DOI 10.1016/j.cad.2006.03.002 Dartigues C, 2007, CONCURRENT ENG-RES A, V15, P237, DOI 10.1177/1063293X07079312 Du XH, 2002, CONCURRENT ENG-RES A, V10, P113, DOI 10.1106/106329302027635 Du XH, 2001, CONCURRENT ENG-RES A, V9, P309, DOI 10.1177/106329302023735 Du XH, 2002, AI EDAM, V16, P103, DOI 10.1017/S0890060402020097 Felfernig A, 2003, AI EDAM, V17, P31, DOI 10.1017/S0890060403171041 Fernandez M., 1997, AAAI SPRING S ONT EN, P33 Frutos JD, 2004, CONCURRENT ENG-RES A, V12, P131, DOI 10.1177/1063293X04044382 Gershenson JK, 2004, J ENG DESIGN, V15, P33, DOI 10.1080/0954482032000101731 Grosse IR, 2005, AI EDAM, V19, P1, DOI 10.1017/S0890060405050018 Gruber T. R., 1993, 9304 KSL STANF U Gruninger Michael, 1995, P WORKSH BAS ONT ISS, P1 Gupta D, 2004, IIE TRANS, V36, P529, DOI 10.1080/07408170490438519 Church K. W., 1990, Computational Linguistics, V16 HEGGE HMH, 1991, INT J PROD ECON, V23, P117, DOI 10.1016/0925-5273(91)90055-X Hicks BJ, 2006, INT J INFORM MANAGE, V26, P267, DOI 10.1016/j.ijinfomgt.2006.03.006 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jiao J, 2000, INTEGRATED MANUFACTU, V11, P469, DOI 10.1108/09576060010349776 Li ZJ, 2007, AI EDAM, V21, P137, DOI 10.1017/S0890060407070199 Lim S. C. J., 2009, INFORM PROCESSING MA, V46, P479 Lin HK, 2004, INT J PROD RES, V42, P5099, DOI 10.1080/002075440412331281999 Liu S, 2006, ADV ENG INFORM, V20, P401, DOI 10.1016/j.aei.2006.05.008 LIU Y, 2007, EMERGING TECHNOLOGIE, P99 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Liu Y, 2009, HDB RES TEXT WEB MIN, P1 McGuinness DL, 1998, AI EDAM, V12, P333, DOI 10.1017/S089006049812406X Meyer M. H., 1997, POWER PRODUCT PLATFO Nanda J., 2004, P DETC 04 SALT LAK C, P1 Nanda J, 2006, J COMPUT INF SCI ENG, V6, P103, DOI 10.1115/1.2190237 Nanda J, 2007, AI EDAM, V21, P173, DOI 10.1017/S0890060407070217 Nanda J., 2005, P DETC 05 ASME 2005, P1 NOY NF, 01200105 KNOWL SYST Orlicky J., 1975, MAT REQUIREMENTS PLA SAAKSVOURI A, 2005, PRODUCT LIFECYCLE MA, P99 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarder MB, 2007, PORTL INT CONF MANAG, P1011, DOI 10.1109/PICMET.2007.4349422 SHIN I, 2006, INT C HYBR INF TECHN, P433 Simpson T. W., 2005, ARTIF INTELL, V18, P3 Soininen T, 1998, AI EDAM, V12, P357 Uschold M., 1995, P INT JOINT C ART IN Yang D, 2009, EXPERT SYST APPL, V36, P4399, DOI 10.1016/j.eswa.2008.05.026 YU W, 2008, P 6 INT C MAN RES IC Zhang JS, 2005, INT J ADV MANUF TECH, V25, P41, DOI 10.1007/s00170-003-1871-z NR 46 TC 11 Z9 11 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 1474-0346 J9 ADV ENG INFORM JI Adv. Eng. Inform. PD APR PY 2011 VL 25 IS 2 BP 147 EP 161 DI 10.1016/j.aei.2010.07.005 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Multidisciplinary SC Computer Science; Engineering GA 758UY UT WOS:000290193700004 ER PT J AU Zhuge, H AF Zhuge, Hai TI Semantic linking through spaces for cyber-physical-socio intelligence: A methodology SO ARTIFICIAL INTELLIGENCE LA English DT Review DE Cyber-physical society; Cyber-physical-socio intelligence; Cyber-physical system; Complex intelligence; Future interconnection environment; Interactive semantics; Resource space model; Semantic link network ID SELF-SIMILARITY; KNOWLEDGE; NETWORKS; LANGUAGE; MODEL; COMMUNITIES; PREDICTION; RETRIEVAL; HYPERTEXT; DISCOVERY AB Humans consciously and subconsciously establish various links, emerge semantic images and reason in mind, learn linking effect and rules, select linked individuals to interact, and form closed loops through links while co-experiencing in multiple spaces in lifetime. Machines are limited in these abilities although various graph-based models have been used to link resources in the cyber space. The following are fundamental limitations of machine intelligence: (1) machines know few links and rules in the physical space, physiological space, psychological space, socio space and mental space, so it is not realistic to expect machines to discover laws and solve problems in these spaces; and, (2) machines can only process pre-designed algorithms and data structures in the cyber space. They are limited in ability to go beyond the cyber space, to learn linking rules, to know the effect of linking, and to explain computing results according to physical, physiological, psychological and socio laws. Linking various spaces will create a complex space - the Cyber-Physical-Physiological-Psychological-Socio-Mental Environment (CPSME)-S-3. Diverse spaces will emerge, evolve, compete and cooperate with each other to extend machine intelligence and human intelligence. From multi-disciplinary perspective, this paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the (CPSME)-S-3 including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyberphysical-socio intelligence. The methodology includes new models, principles, mechanisms, scientific issues, and philosophical explanation. The (CPSME)-S-3 aims at an ideal environment for humans to live and work. Exploration will go beyond previous ideals on intelligence and computing. (C) 2011 Elsevier B.V. All rights reserved. C1 Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Cyber Phys Socio Knowledge Grid Res Grp, Beijing 100190, Peoples R China. RP Zhuge, H (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Cyber Phys Socio Knowledge Grid Res Grp, Beijing 100190, Peoples R China. EM zhuge@ict.ac.cn FU National Science Foundation of China [61075074]; National Basic Research Program of China [2003CB317001] FX This work was supported by the National Science Foundation of China (61075074) and the National Basic Research Program of China (2003CB317001). CR Bachman CW, 1969, ACM SIGMIS DATABASE, V1, P4, DOI 10.1145/1017466.1017467 Berger P. L., 1966, SOCIAL CONSTRUCTION Berners-Lee T, 2001, SCI AM, V284, P34 Bernier PM, 2010, NEURON, V68, P776, DOI 10.1016/j.neuron.2010.11.002 Birbaumer N, 1999, NATURE, V398, P297, DOI 10.1038/18581 BOOCH G, 1991, OBJECT ORIENTED DESI BROWN PJ, 1986, INTERACTIVE DOCUMENT, P291 Bush V., 1945, ATLANTIC MONTHLY, P101 Cerf M, 2010, NATURE, V467, P1104, DOI 10.1038/nature09510 CHAKRABARTI S, 1999, COMPUTER Clauset A, 2008, NATURE, V453, P98, DOI 10.1038/nature06830 CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/362384.362685 COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 CONKLIN J, 1987, COMPUTER, V20, P17 Copeland B. J., 1999, SCI AM, V280, P99 Crovella ME, 1997, IEEE ACM T NETWORK, V5, P835, DOI 10.1109/90.650143 Delvenne JC, 2010, P NATL ACAD SCI USA, V107, P12755, DOI 10.1073/pnas.0903215107 Doeller C. F., 2010, NATURE JAN, DOI [10.1038/nature08704, DOI 10.1038/NATURE08704] Dreyfus H., 1992, WHAT COMPUTERS CANT Eagle N, 2009, P NATL ACAD SCI USA, V106, P15274, DOI 10.1073/pnas.0900282106 Edwards E, 2010, NEUROIMAGE, V50, P291, DOI 10.1016/j.neuroimage.2009.12.035 ENGELBART DC, 1963, VISTAS INFORM HANDLI, V1 Fauconnier Gilles, 1994, MENTAL SPACES, V2nd Fodor JA, 1975, LANGUAGE THOUGHT FREI HP, 1995, INFORM PROCESS MANAG, V31, P1, DOI 10.1016/0306-4573(94)E0005-M GLASHOW SL, 1961, NUCL PHYS, V22, P579, DOI 10.1016/0029-5582(61)90469-2 Gonzalez MC, 2008, NATURE, V453, P779, DOI 10.1038/nature06958 GRAEDEL T, 1995, IND ECOLOGY DEFINITI Gray J, 2003, J ACM, V50, P41, DOI 10.1145/602382.602401 Guinard D., 2009, 18 INT WORLD WID WEB Hai Zhuge, 2010, P 6 INT C SEM KNOWL Han J., 2006, DATA MINING CONCEPTS HARNAD S, 1990, PHYSICA D, V42, P335, DOI 10.1016/0167-2789(90)90087-6 HARRIS M, 2010, IEEE SPECTRUM AUG Harte J, 1999, SCIENCE, V284, P334, DOI 10.1126/science.284.5412.334 HAWKING S, 2010, SCI AM OCT HEAD GH, 1934, MIND SELF SOC Hendler J, 2010, ARTIF INTELL, V174, P156, DOI 10.1016/j.artint.2009.11.010 HERZOG G, 1994, ARTIF INTELL REV, V8, P175, DOI 10.1007/BF00849073 Holt-Lunstad J, 2010, PLOS MED, V7, DOI 10.1371/journal.pmed.1000316 Jacobson I., 1992, OBJECT ORIENTED SOFT Kellis S, 2010, J NEURAL ENG, V7, DOI 10.1088/1741-2560/7/5/056007 Kelly G. A., 1955, PSYCHOL PERSONAL CON, V2 Kelly G. A., 1955, PSYCHOL PERSONAL CON Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LELAND WE, 1994, IEEE ACM T NETWORK, V2, P1, DOI 10.1109/90.282603 Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591 Macedo A. A., 2002, P 13 ACM C HYP HYP, P107 Marshall C., 2003, P 14 ACM C HYP HYP, P57 MCCARTHY J, 1969, SOME PHILOS PROBLEMS McCarthy J, 2007, ARTIF INTELL, V171, P1174, DOI 10.1016/j.artint.2007.10.009 MCCARTHY J, 1986, ARTIF INTELL, V28, P89, DOI 10.1016/0004-3702(86)90032-9 Minsky M., 2006, EMOTION MACHINE COMM MINSKY M, 1988, SOC MIND SIMON SCHUS Montanari A, 2010, P NATL ACAD SCI USA, V107, P20196, DOI 10.1073/pnas.1004098107 Newell A., 1990, UNIFIED THEORIES COG NEWELL A, 1982, ARTIF INTELL, V18, P87, DOI 10.1016/0004-3702(82)90012-1 Ng Andrew Y., 2001, INT JOINT C ART INT Nowak MA, 2005, NATURE, V437, P1291, DOI 10.1038/nature04131 ROSCH EH, 1973, COGNITIVE PSYCHOL, V4, P328, DOI 10.1016/0010-0285(73)90017-0 Roy D, 2005, ARTIF INTELL, V167, P170, DOI 10.1016/j.artint.2005.04.007 Rumbaugh J., 1990, OBJECT ORIENTED MODE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHANK RC, 1972, COGNITIVE PSYCHOL, V3, P552, DOI 10.1016/0010-0285(72)90022-9 Schecter G., 1967, INFORM RETRIEVAL CRI SHI X, 2010, CYBER PHYS SOCIO ECO, DOI DOI 10.1002/CPE.1625 SIEGELMANN HT, 1995, SCIENCE, V268, P545, DOI 10.1126/science.268.5210.545 Sun XP, 2010, FUTURE GENER COMP SY, V26, P389, DOI 10.1016/j.future.2009.07.007 Trigg R. H., 1983, THESIS U MARYLAND Turing A. M., 1950, MIND, P433, DOI DOI 10.1093/MIND/LIX.236.433 Turing AM, 1937, P LOND MATH SOC, V42, P230 Wegner P, 1997, COMMUN ACM, V40, P80, DOI 10.1145/253769.253801 Weick K. E., 1979, SOCIAL PSYCHOL ORGAN WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 Zhuge H., 2007, IEEE T KNOWL DATA EN, V19, P759, DOI DOI 10.1109/TKDE.2007.190614 Zhuge H, 2010, J AM SOC INF SCI TEC, V61, P1824, DOI 10.1002/asi.21353 Zhuge H, 2008, IEEE T KNOWL DATA EN, V20, P276, DOI 10.1109/TKDE.2007.190688 Zhuge H., 2004, COMMUN ACM, V47, P78, DOI 10.1145/1015864.1015867 ZHUGE H, 2010, AUTOMATICALLY CONSTR, DOI DOI 10.1002/CPE.1624 Zhuge H, 2008, IEEE T KNOWL DATA EN, V20, P1712, DOI 10.1109/TKDE.2008.102 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 ZHUGE H, 2010, SCHEMA THEORY SEMANT, V26, P408 Zhuge H., 2008, ACM T INTERNET TECHN, V8 Zhuge H., 2005, COMPUTER, V38, P27 ZHUGE H, 2010, P IEEE AINA 2010 PER, P19 ZHUGE H, WWW2004, P278 Zhuge H., 2004, KNOWLEDGE GRID Zhuge H, 2008, WEB INF SYST ENG INT, V4, P1, DOI 10.1007/978-0-387-72772-1 ZHUGE H, 2010, BASIC OPERATIONS COM, DOI DOI 10.1002/CPE.1623 Zhuge H, 2006, COMMUN ACM, V49, P101, DOI 10.1145/1125944.1125948 Zhuge H., 2010, ARTIF INTELL, V174, P190 Zhuge H, 2005, COMPUT INTELL-US, V21, P197, DOI 10.1111/j.0824-7935.2005.00271.x Zhuge H, 2001, J SYST SOFTWARE, V55, P231, DOI 10.1016/S0164-1212(00)00073-X NR 93 TC 44 Z9 44 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0004-3702 EI 1872-7921 J9 ARTIF INTELL JI Artif. Intell. PD APR PY 2011 VL 175 IS 5-6 SI SI BP 988 EP 1019 DI 10.1016/j.artint.2010.09.009 PG 32 WC Computer Science, Artificial Intelligence SC Computer Science GA 753ZB UT WOS:000289818600006 ER PT J AU Louwerse, MM AF Louwerse, Max M. TI Symbol Interdependency in Symbolic and Embodied Cognition SO TOPICS IN COGNITIVE SCIENCE LA English DT Review DE Symbolic; Embodied; Amodal; Modal; Perceptual simulations; Symbol interdependency; LSA; Semantic knowledge ID LATENT SEMANTIC ANALYSIS; LANGUAGE COMPREHENSION; WORD-ASSOCIATION; AMBIGUITY RESOLUTION; COMPUTATIONAL THEORY; PICTURE-RECOGNITION; EYE-MOVEMENTS; MEMORY; INFORMATION; REPRESENTATIONS AB Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. C1 Univ Memphis, Dept Psychol, Inst Intelligent Syst, Memphis, TN 38152 USA. RP Louwerse, MM (reprint author), Univ Memphis, Dept Psychol, Inst Intelligent Syst, 202 Psychol Bldg, Memphis, TN 38152 USA. EM mlouwerse@memphis.edu CR Anderson J. R., 2007, CAN HUMAN MIND OCCUR Bargh JA, 1996, J PERS SOC PSYCHOL, V71, P230, DOI 10.1037//0022-3514.71.2.230 Barsalou L. W., 2008, SYMBOLS EMBODIMENT M, P245, DOI DOI 10.1093/ACPROF:0S0/9780199217274.003.0013 Barsalou LW, 1999, BEHAV BRAIN SCI, V22, P577 BARTON SB, 1993, MEM COGNITION, V21, P477, DOI 10.3758/BF03197179 Benor SB, 2006, LANGUAGE, V82, P233, DOI 10.1353/lan.2006.0077 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Borg I., 1997, MODERN MULTIDIMENSIO Borsboom D, 2008, BEHAV BRAIN SCI, V31, P714, DOI 10.1017/S0140525X08005906 Brants T., 2006, WEB 1T 5 GRAM VERSIO BROWN R, 1960, CHILD DEV, V31, P1 Budiu R., 2007, P 8 ANN C RECH INF A Cai ZQ, 2004, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P180 Campanario J., 2002, THEMATICS INTERDISCI, P55 CAPLAN D, 1996, LANGUAGE STRUCTURE P Chomsky N., 1980, RULES REPRESENTATION Christiansen MH, 2008, BEHAV BRAIN SCI, V31, P489, DOI 10.1017/S0140525X08004998 COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 de Vega M., 2008, SYMBOLS EMBODIMENT D Deacon T. W., 1997, SYMBOLIC SPECIES COE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dehaene-Lambertz G, 1998, LANG SPEECH, V41, P21 Dennis S, 2004, P NATL ACAD SCI USA, V101, P5206, DOI 10.1073/pnas.0307758101 Dijkstra K, 2004, PSYCHOL AGING, V19, P352, DOI 10.1037/0882-7974.19.2.352 Dumais S, 2003, COGNITIVE SCI, V27, P491, DOI 10.1016/S0364-0213(03)00013-2 ERVIN SM, 1961, AM J PSYCHOL, V74, P361, DOI 10.2307/1419742 Ferreira F, 2002, CURR DIR PSYCHOL SCI, V11, P11, DOI 10.1111/1467-8721.00158 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Glenberg AM, 2000, J MEM LANG, V43, P379, DOI 10.1006/jmla.2000.2714 Glenberg AM, 2002, PSYCHON B REV, V9, P558, DOI 10.3758/BF03196313 Glenberg AM, 1997, BEHAV BRAIN SCI, V20, P1 Goldstone RL, 2002, COGNITION, V84, P295, DOI 10.1016/S0010-0277(02)00053-7 Graesser AC, 2001, AI MAG, V22, P39 Graesser AC, 2004, BEHAV RES METH INS C, V36, P193, DOI 10.3758/BF03195564 Graesser AC, 2004, BEHAV RES METH INS C, V36, P180, DOI 10.3758/BF03195563 Greenberg Joseph, 1963, UNIVERSALS LANGUAGE HARNAD S, 1990, PHYSICA D, V42, P335, DOI 10.1016/0167-2789(90)90087-6 HINSON JM, 1983, J EXP ANAL BEHAV, V40, P321, DOI 10.1901/jeab.1983.40-321 Honsa V., 1978, PAPERS LINGUISTICS C, P203 Hurford J.R., 2007, ORIGINS MEANING LANG Jones K. S., 1997, READINGS INFORM RETR JURAFSKY D, 2001, SPEECH LANGUAGE PROC JUST MA, 1992, PSYCHOL REV, V99, P122, DOI 10.1037/0033-295X.99.1.122 Kaschak MP, 2005, COGNITION, V94, pB79, DOI 10.1016/j.cognition.2004.06.005 Kaschak MP, 2008, Q J EXP PSYCHOL, V61, P883, DOI 10.1080/17470210701623852 Kintsch E., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT087 Kintsch W., 1998, COMPREHENSION PARADI Kintsch W, 2000, PSYCHON B REV, V7, P257, DOI 10.3758/BF03212981 Kintsch W., 2002, THEMATICS INTERDISCI, P157 KLATZKY RL, 1989, J MEM LANG, V28, P56, DOI 10.1016/0749-596X(89)90028-4 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1999, DISCOURSE PROCESS, V27, P303 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LEWIS RL, 1999, MIT ENCY COGNITIVE S, P525 Louwerse M, 2009, APPL COGN LINGUIST, V10, P423 Louwerse MM, 2009, DISCOURSE, OF COURSE: AN OVERVIEW OF RESEARCH IN DISCOURSE STUDIES, P213 Louwerse M, 2006, INT J ARTIF INTELL T, V15, P1021, DOI 10.1142/S0218213006003090 Louwerse MM, 2004, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P843 Louwerse M. M., 2008, EMBODIMENT MEANING D, P309, DOI 10.1093/acpronoso/9780199217274.003.0015 Louwerse MM, 2004, COMPUT HUMANITIES, V38, P207, DOI 10.1023/B:CHUM.0000031185.88395.b1 LOUWERSE MM, 2008, NEW BEGINNINGS LIT S, P108 Louwerse MM, 2008, PSYCHON B REV, V15, P838, DOI 10.3758/PBR.15.4.838 Louwerse MM., 2007, HDB LATENT SEMANTIC, P107 Louwerse MM, 2010, COGNITION, V114, P96, DOI 10.1016/j.cognition.2009.09.002 Louwerse MM, 2009, COGNITIVE SCI, V33, P51, DOI 10.1111/j.1551-6709.2008.01003.x Louwerse M.M., 2006, TIJDSCHRIFT NEDERLAN, V122, P21 Madden CJ, 2006, J EXP PSYCHOL LEARN, V32, P1291, DOI 10.1037/0278-7393.32.6.1291 McNamara D. S., 2007, HDB LATENT SEMANTIC, P379 McNamara D.S., 2007, HDB LATENT SEMANTIC, P227 MCNEILL D, 1966, J VERB LEARN VERB BE, V5, P548, DOI 10.1016/S0022-5371(66)80090-7 Mehl MR, 2007, SCIENCE, V317, P82, DOI 10.1126/science.1139940 Meyer DE, 1997, PSYCHOL REV, V104, P3, DOI 10.1037//0033-295X.104.1.3 Miller GA, 2003, TRENDS COGN SCI, V7, P141, DOI 10.1016/S1364-6613(03)00029-9 Monaghan P, 2005, COGNITION, V96, P143, DOI 10.1016/j.cognition.2004.09.001 MORDKOFF JT, 1991, J EXP PSYCHOL HUMAN, V17, P520, DOI 10.1037/0096-1523.17.2.520 Musso M, 2003, NAT NEUROSCI, V6, P774, DOI 10.1038/nn1077 Newell A., 1990, UNIFIED THEORIES COG Paivio A., 1971, IMAGERY VERBAL PROCE Paivio A., 1986, MENTAL REPRESENTATIO Pecher D, 2003, PSYCHOL SCI, V14, P119, DOI 10.1111/1467-9280.t01-1-01429 Pecher D., 2005, GROUNDING COGNITION Pecher D, 2009, Q J EXP PSYCHOL, V62, P1108, DOI 10.1080/17470210802633255 Pecher D, 2007, EXP PSYCHOL, V54, P173, DOI 10.1027/1618-3169.54.3.173 PEIRCE CS, 1923, COLLECTED PAPERS CHA Polk TA, 2002, J EXP PSYCHOL GEN, V131, P65, DOI 10.1037//0096-3445.131.1.65 Pulvermuller F, 2005, J COGNITIVE NEUROSCI, V17, P884, DOI 10.1162/0898929054021111 Ramus F, 2000, SCIENCE, V288, P349, DOI 10.1126/science.288.5464.349 RECCHIA GL, 2009, BEHAV RES METHODS, V41, P657 Richardson D, 2007, COGNITION, V102, P129, DOI 10.1016/j.cognition.2005.12.004 Rogers T. T., 2004, SEMANTIC COGNITION P Rogers TT, 2008, BEHAV BRAIN SCI, V31, P689, DOI 10.1017/S0140525X0800589X Rumelhart D. E., 1986, PARALLEL DISTRIBUTED Saffran JR, 1997, PSYCHOL SCI, V8, P101, DOI 10.1111/j.1467-9280.1997.tb00690.x Saffran JR, 1996, SCIENCE, V274, P1926, DOI 10.1126/science.274.5294.1926 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEARLE JR, 1980, BEHAV BRAIN SCI, V3, P417 Semin GR, 2008, EMBODIED GROUNDING: SOCIAL, COGNITIVE, AFFECTIVE, AND NEUROSCIENTIFIC APPROACHES, P1 Snedeker J, 2008, BEHAV BRAIN SCI, V31, P727, DOI 10.1017/S0140525X08006043 Spivey MJ, 2001, PSYCHOL RES-PSYCH FO, V65, P235, DOI 10.1007/s004260100059 Spivey MJ, 2002, COGNITIVE PSYCHOL, V45, P447, DOI 10.1016/S0010-0285(02)00503-0 Stanfield RA, 2001, PSYCHOL SCI, V12, P153, DOI 10.1111/1467-9280.00326 STONE B, 2010, TOPICS, DOI DOI 10.1111/J.1756-8765.2010.01108.X Sun R, 1997, CONNECTIONIST-SYMBOLIC INTEGRATION, P113 Taylor HA, 1996, J MEM LANG, V35, P371, DOI 10.1006/jmla.1996.0021 Thumb A., 1901, EXPT UNTERSUCHUNGEN WARRINGTON EK, 1980, BRAIN, V103, P99, DOI 10.1093/brain/103.1.99 Widdows Dominic, 2004, GEOMETRY AND MEANING Wolford G, 2000, J NEUROSCI, V20, part. no. Zipf G. K., 1935, PSYCHOBIOLOGY LANGUA Zwaan RA, 2006, J EXP PSYCHOL GEN, V135, P1, DOI 10.1037/0096-3445.135.1.1 Zwaan RA, 2002, PSYCHOL SCI, V13, P168, DOI 10.1111/1467-9280.00430 Zwaan RA, 2004, PSYCHOL LEARN MOTIV, V44, P35 Zwaan RA, 2003, PSYCHON B REV, V10, P954, DOI 10.3758/BF03196557 NR 114 TC 36 Z9 38 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1756-8757 J9 TOP COGN SCI JI Top. Cogn. Sci. PD APR PY 2011 VL 3 IS 2 BP 273 EP 302 DI 10.1111/j.1756-8765.2010.01106.x PG 30 WC Psychology, Experimental SC Psychology GA 746MC UT WOS:000289250000015 PM 25164297 ER PT J AU Cao, YG Liu, FF Simpson, P Antieau, L Bennett, A Cimino, JJ Ely, J Yu, H AF Cao, YongGang Liu, Feifan Simpson, Pippa Antieau, Lamont Bennett, Andrew Cimino, James J. Ely, John Yu, Hong TI AskHERMES: An online question answering system for complex clinical questions SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Article DE Clinical question answering; Question analysis; Passage retrieval; Summarization; Answer presentation ID INFORMATION NEEDS; HEALTH INFORMATION; BIOMEDICAL TEXT; MEDICAL LITERATURE; QUERY EXPANSION; INTERNET; KNOWLEDGE; QUALITY; PHYSICIANS; SEARCH AB Objective: Clinical questions are often long and complex and take many forms. We have built a clinical question answering system named AskHERMES to perform robust semantic analysis on complex clinical questions and output question-focused extractive summaries as answers. Design: This paper describes the system architecture and a preliminary evaluation of AskHERMES, which implements innovative approaches in question analysis, summarization, and answer presentation. Five types of resources were indexed in this system: MEDLINE abstracts, PubMed Central full-text articles, eMedicine documents, clinical guidelines and Wikipedia articles. Measurement: We compared the AskHERMES system with Google (Google and Google Scholar) and UpToDate and asked physicians to score the three systems by ease of use, quality of answer, time spent, and overall performance. Results: AskHERMES allows physicians to enter a question in a natural way with minimal query formulation and allows physicians to efficiently navigate among all the answer sentences to quickly meet their information needs. In contrast, physicians need to formulate queries to search for information in Google and UpToDate. The development of the AskHERMES system is still at an early stage, and the knowledge resource is limited compared with Google or UpToDate. Nevertheless, the evaluation results show that AskHERMES' performance is comparable to the other systems. In particular, when answering complex clinical questions, it demonstrates the potential to outperform both Google and UpToDate systems. Conclusions: AskHERMES, available at http://www.AskHERMES.org, has the potential to help physicians practice evidence-based medicine and improve the quality of patient care. (C) 2011 Elsevier Inc. All rights reserved. C1 [Cao, YongGang; Liu, Feifan; Antieau, Lamont; Yu, Hong] Univ Wisconsin, Dept Hlth Sci, Milwaukee, WI 53201 USA. [Simpson, Pippa] Med Coll Wisconsin, Dept Pediat, Milwaukee, WI 53226 USA. [Bennett, Andrew] Med Coll Wisconsin, Dept Psychiat, Milwaukee, WI 53226 USA. [Bennett, Andrew] Vet Affairs Hosp, Milwaukee, WI USA. [Cimino, James J.] NIH, Ctr Clin, Bethesda, MD 20892 USA. [Ely, John] Univ Iowa Hosp & Clin, Dept Family Med, Iowa City, IA 52242 USA. [Yu, Hong] Univ Wisconsin, Dept Elect Engn & Comp Sci, Milwaukee, WI 53201 USA. RP Yu, H (reprint author), 2400 E Hartford Ave,Room 939, Milwaukee, WI 53211 USA. EM hongyu@uwm.edu RI Cimino, James/ OI Cimino, James/0000-0003-4101-1622 FU National Institute of Health (NIH) [2R01LM009836] FX The authors acknowledge support from the National Institute of Health (NIH), Grant Number 2R01LM009836. Any opinions, findings, or recommendations are those of the authors and do not necessarily reflect the views of the NIH. CR Abdou S, 2008, INFORM PROCESS MANAG, V44, P781, DOI 10.1016/j.ipm.2007.03.013 Aronson AR, 2001, J AM MED INFORM ASSN, P17 Benigeri M, 2003, HEALTH PROMOT INT, V18, P381, DOI 10.1093/heapro/dag409 Bergus GR, 2000, ARCH FAM MED, V9, P541, DOI 10.1001/archfami.9.6.541 CAO YG, 2009, BIONLP Chase HS, 2009, J AM MED INFORM ASSN, V16, P387, DOI 10.1197/jamia.M2940 Childs S., 2005, Performance Measurement and Metrics, V6, DOI 10.1108/14678040510607803 CIMINO JJ, 1993, B MED LIBR ASSOC, V81, P195 Cimino James J, 2003, AMIA Annu Symp Proc, P175 Cimino James J, 2002, Proc AMIA Symp, P170 Cimino James J, 2003, AMIA Annu Symp Proc, P815 Cimino James J, 2008, AMIA Annu Symp Proc, P1203 Cimino JJ, 1997, J AM MED INFORM ASSN, P528 Cimino James J, 2006, AMIA Annu Symp Proc, P151 Cimino James J, 2008, AMIA Annu Symp Proc, P116 Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671 COLLINS SA, 2008, J AM MED INFORM ASS COVELL DG, 1985, ANN INTERN MED, V103, P596 Cullen RJ, 2002, J MED LIBR ASSOC, V90, P370 D'Alessandro DM, 2004, PEDIATRICS, V113, P64, DOI 10.1542/peds.113.1.64 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Del Fiol G, 2008, J AM MED INFORM ASSN, V15, P752, DOI 10.1197/jamia.M2725 Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63 Demner-Fushman D., 2006, P 21 INT C COMP LING, P945 Elhadad N, 2005, ARTIF INTELL MED, V33, P179, DOI 10.1016/j.artmed.2004.07.018 Elhadad Noemie, 2005, AMIA Annu Symp Proc, P226 Ely JW, 1999, BRIT MED J, V319, P358 Ely JW, 2005, J AM MED INFORM ASSN, V12, P217, DOI 10.1197/jamia.M1608 Ely JW, 2000, BRIT MED J, V321, P429, DOI 10.1136/bmj.321.7258.429 ELY JW, 1992, J FAM PRACTICE, V35, P265 Ely JW, 1997, J FAM PRACTICE, V45, P382 Fletcher RH, 1997, J GEN INTERN MED, V12, pS5, DOI 10.1046/j.1525-1497.12.s2.1.x Frakes W. B., 1992, INFORM RETRIEVAL DAT Freeman MK, 2009, ANN PHARMACOTHER, V43, P478, DOI 10.1345/aph.1L223 GLENNIE E, 2006, J DIAGN RADIOGR IMAG, V6, P25, DOI 10.1017/S1460472806000034 GOBEILL J, 2007, 16 TEXT RETR C TREC Goodyear-Smith F, 2008, AUST FAM PHYSICIAN, V37, P878 Griffiths KM, 2005, J MED INTERNET RES, V7, P80, DOI 10.2196/jmir.7.5.e59 Griffiths KM, 2000, BRIT MED J, V321, P1511, DOI 10.1136/bmj.321.7275.1511 HEARST M, 1996, 19 ANN INT ACM C RES Hearst MA, 1997, COMPUT LINGUIST, V23, P33 HERSH W, 2007, TREC GEN TRACK C HERSH W, 2006, TREC GEN TRACK C Hersh W, 1999, J Am Coll Dent, V66, P43 Hersh WR, 2002, J AM MED INFORM ASSN, V9, P283, DOI 10.1197/jamia.M0996 HIRSCHBERG DS, 1977, J ACM, V24, P664, DOI 10.1145/322033.322044 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Hoogendam A, 2008, J MED INTERNET RES, V10, DOI 10.2196/jmir.1012 HOVY E, 2001, TREC 2001, P644 Huang Xiaoli, 2006, AMIA Annu Symp Proc, P359 HUMPHREYS BL, 1993, B MED LIBR ASSOC, V81, P170 Ide NC, 2007, J AM MED INFORM ASSN, V14, P253, DOI 10.1197/jamia.M2233 Jadad AR, 1998, JAMA-J AM MED ASSOC, V279, P611, DOI 10.1001/jama.279.8.611 JOACHIMS T, 1997, LECT NOTES COMPUTER, P137 Kitchin DR, 2007, ACAD RADIOL, V14, P1113, DOI 10.1016/j.acra.2007.06.002 Lafferty JD, 2001, CONDITIONAL RANDOM F, P282 Lee M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-140 Lee Minsuk, 2006, AMIA Annu Symp Proc, P469 Lei Jianbo, 2003, AMIA Annu Symp Proc, P906 De Leo G, 2006, AMIA Annu Symp Proc, P902 Lin C.-Y., 2004, P WORKSH TEXT SUMM B, P74 Lin J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148191 Liu FF, 2010, IEEE T AUDIO SPEECH, V18, P187, DOI 10.1109/TASL.2009.2025096 Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 McCord G, 2007, ACAD MED, V82, P298, DOI 10.1097/ACM.0b013e3180307fed MCKEOWN K, 2001, P 1 ACM IEEE CS JOIN MENDONCA EA, 2005, P 9 WORLD C HLTH INF Muller HM, 2004, PLOS BIOL, V2, P1984, DOI 10.1371/journal.pbio.0020309 NG RT, 2005, ACM SIGKDD 05, P544 NIU Y, 2004, ACL 2004 WORKSH QUES NIU Y, 2003, ACL WORKSH NAT LANG OSHEROFF JA, 1991, ANN INTERN MED, V114, P576 Osinski S, 2004, ADV SOFT COMP, P359 Paterson M., 1994, LNCS, V841, P127 Phua J, 2008, BMC MED EDUC, V8, DOI 10.1186/1472-6920-8-39 Ponte J.M., 1998, RES DEV INFORM RETRI, P275 Pratt W, 1997, J AM MED INFORM ASSN, P480 Purcell GP, 2002, BRIT MED J, V324, P557, DOI 10.1136/bmj.324.7337.557 RINALDI F, 2007, ACL 2004 WORKSH QUES ROBERTSON S, 2004, ACM CIKM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEOL YH, 2004, MEDINFO, V11, P306 Silberg WM, 1997, JAMA-J AM MED ASSOC, V277, P1244, DOI 10.1001/jama.277.15.1244 Smith R, 1996, BRIT MED J, V313, P1062 Sneiderman CA, 2007, J AM MED INFORM ASSN, V14, P772, DOI 10.1197/jamia.M2407 Srinivasan Padmini, 2002, Proc AMIA Symp, P722 Stephens MB, 2009, MIL MED, V174, P259 Stokes N, 2009, INFORM RETRIEVAL, V12, P17, DOI 10.1007/s10791-008-9073-9 Tang HW, 2006, BRIT MED J, V333, P1143, DOI 10.1136/bmj.39003.640567.AE Tellex S., 2003, P 26 ANN INT ACM SIG, P41 TIMPKA T, 1990, METHOD INFORM MED, V29, P23 VOORHEES EM, 1999, P TREC Wyatt JC, 1997, BRIT MED J, V314, P1879 YU H, 2005, P 19 INT JOINT C ART Yu H., 2007, PAC S BIOCOMPUT, V12, P328 Yu Hong, 2008, AMIA Annu Symp Proc, P96 Yu Hong, 2006, AMIA Annu Symp Proc, P834 YU H, 2009, IEEE INT C BIOINF BI YU H, 2009, AMIA SUMM TRANSL BIO Yu H, 2007, J BIOMED INFORM, V40, P236, DOI 10.1016/j.jbi.2007.03.002 ZARAGOZA H, 2004, P TREC 2004 Zhang Y., 2005, P AUSTR LANG TECHN W Zhu H., 2005, P AAAI 2005 WORKSH Q Zighelnic L., 2008, P 31 ANN INT ACM SIG, P825, DOI 10.1145/1390334.1390524 ZWEIGENBAUM P, 2003, EACL WORKSH NAT LANG, P1 ZWEIGENBAUM P, 2005, MIE 2005 WORKSH NR 106 TC 18 Z9 18 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD APR PY 2011 VL 44 IS 2 SI SI BP 277 EP 288 DI 10.1016/j.jbi.2011.01.004 PG 12 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 743PF UT WOS:000289030100009 PM 21256977 ER PT J AU Ozel, SA AF Ozel, Selma Ayse TI A Web page classification system based on a genetic algorithm using tagged-terms as features SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Web mining; Classification; Classifier learning; Genetic algorithms ID TEXT CLASSIFICATION; DOCUMENT-RETRIEVAL; FEATURE-SELECTION AB The incredible increase in the amount of information on the World Wide Web has caused the birth of topic specific crawling of the Web. During a focused crawling process, an automatic Web page classification mechanism is needed to determine whether the page being considered is on the topic or not. In this study, a genetic algorithm (GA) based automatic Web page classification system which uses both HTML tags and terms belong to each tag as classification features and learns optimal classifier from the positive and negative Web pages in the training dataset is developed. Our system classifies Web pages by simply computing similarity between the learned classifier and the new Web pages. In the existing GA-based classifiers, only HTML tags or terms are used as features, however in this study both of them are taken together and optimal weights for the features are learned by our GA. It was found that, using both HTML tags and terms in each tag as separate features improves accuracy of classification, and the number of documents in the training dataset affects the accuracy such that if the number of negative documents is larger than the number of positive documents in the training dataset, the classification accuracy of our system increases up to 95% and becomes higher than the well known Naive Bayes and k nearest neighbor classifiers. (C) 2010 Elsevier Ltd. All rights reserved. C1 Cukurova Univ, Dept Comp Engn, TR-01330 Adana, Turkey. RP Ozel, SA (reprint author), Cukurova Univ, Dept Comp Engn, TR-01330 Adana, Turkey. EM saozel@cu.edu.tr FU Cukurova University Academic Research Project Unit [MMF2008BAP6] FX This work was supported by Cukurova University Academic Research Project Unit under the Grant No. MMF2008BAP6. CR Altingovde IS, 2001, LECT NOTES COMPUT SC, V2113, P699 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bai RJ, 2007, LECT NOTES COMPUT SC, V4476, P256 Boughanem M., 1999, Information Retrieval, V1, DOI 10.1023/A:1009931404333 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chen CM, 2009, EXPERT SYST APPL, V36, P260, DOI 10.1016/j.eswa.2007.09.008 CHEN H, 1995, J MANAGEMENT INFORMA, V11, P7 Chen RC, 2006, EXPERT SYST APPL, V31, P427, DOI 10.1016/j.eswa.2005.09.079 Craven M, 1998, FIFTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-98) AND TENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICAL INTELLIGENCE (IAAI-98) - PROCEEDINGS, P509 DEBRA PME, 1994, COMPUT NETWORKS ISDN, V27, P183, DOI 10.1016/0169-7552(94)90132-5 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 Han J., 2006, DATA MINING CONCEPTS Holland JH, 1992, ADAPTATION NATURAL A Kim S, 2003, APPL INTELL, V18, P243, DOI 10.1023/A:1023293820057 Liu H, 2003, LECT NOTES COMPUT SC, V2762, P173 Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI 10.1145/1031114.1031117 Michalewicz Z., 1996, GENETIC ALGORITHM DA, V3rd Ozalp SA, 2006, LECT NOTES COMPUT SC, V3743, P535, DOI 10.1007/11666806_61 Ozel SA, 2008, 23RD INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, P441 Pietramala A, 2008, LECT NOTES ARTIF INT, V5212, P188, DOI 10.1007/978-3-540-87481-2_13 PINKERTON B, 1994, 2 INT WWW C PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QI D, 2004, IRI, P241 Qi XG, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459357 Ribeiro A, 2003, LECT NOTES ARTIF INT, V2663, P103 Robertson AM, 1996, J DOC, V52, P405, DOI 10.1108/eb026973 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Selamat A, 2004, INFORM SCIENCES, V158, P69, DOI 10.1016/j.ins.2003.03.003 Trotman A, 2005, INFORM PROCESS MANAG, V41, P243, DOI 10.1016/j.ipm.2003.10.003 Wakaki T., 2006, Web Intelligence and Agent Systems, V4 NR 30 TC 13 Z9 15 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD APR PY 2011 VL 38 IS 4 BP 3407 EP 3415 DI 10.1016/j.eswa.2010.08.126 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 715RY UT WOS:000286904600053 ER PT J AU Dijkman, R Dumas, M van Dongen, B Kaarik, R Mendling, J AF Dijkman, Remco Dumas, Marlon van Dongen, Boudewijn Kaeaerik, Reina Mendling, Jan TI Similarity of business process models: Metrics and evaluation SO INFORMATION SYSTEMS LA English DT Article DE Business process management; Process similarity; Process model repository; Process model search ID SERVICE RETRIEVAL; VIEW INTEGRATION; PROCESS DESIGN; LANGUAGE; BEHAVIOR; ALGORITHM; WORKFLOWS; SYSTEMS; BPMN AB It is common for large organizations to maintain repositories of business process models in order to document and to continuously improve their operations. Given such a repository, this paper deals with the problem of retrieving those models in the repository that most closely resemble a given process model or fragment thereof. Up to now, there is a notable research gap on comparing different approaches to this problem and on evaluating them in the same setting. Therefore, this paper presents three similarity metrics that can be used to answer queries on process repositories: (i) node matching similarity that compares the labels and attributes attached to process model elements; (ii) structural similarity that compares element labels as well as the topology of process models; and (iii) behavioral similarity that compares element labels as well as causal relations captured in the process model. These metrics are experimentally evaluated in terms of precision and recall. The results show that all three metrics yield comparable results, with structural similarity slightly outperforming the other two metrics. Also, all three metrics outperform text-based search engines when it comes to searching through a repository for similar business process models. (C) 2010 Elsevier B.V. All rights reserved. C1 [Dijkman, Remco] Eindhoven Univ Technol, Sch Ind Engn, NL-5600 MB Eindhoven, Netherlands. [Dumas, Marlon; Kaeaerik, Reina] Univ Tartu, Inst Comp Sci, EE-50409 Tartu, Estonia. [van Dongen, Boudewijn] Eindhoven Univ Technol, Dept Math & Comp Sci, NL-5600 MB Eindhoven, Netherlands. [Mendling, Jan] Humboldt Univ, Inst Informat Syst, D-10099 Berlin, Germany. RP Dijkman, R (reprint author), Eindhoven Univ Technol, Sch Ind Engn, POB 513, NL-5600 MB Eindhoven, Netherlands. EM r.m.dijkman@tue.nl; marlon.dumas@ut.ee; b.f.v.dongen@tue.nl; reinak@ut.ee; contact@mendling.com RI van Dongen, Boudewijn/C-2133-2011; Dijkman, Remco/B-2721-2014; Dumas, Marlon/H-2757-2015; Mendling, Jan/ OI Dumas, Marlon/0000-0002-9247-7476; Mendling, Jan/0000-0002-7260-524X FU Beta Research School for Operations Management and Logistics at TU Eindhoven; Estonian Centre of Excellence in Computer Science FX The research that led to this paper is partly funded by the Beta Research School for Operations Management and Logistics at TU Eindhoven, and by the Estonian Centre of Excellence in Computer Science. CR AALST W, 2006, LNCS, V4102, P129 Aalst W, 2007, INFORM SYST, V32, P713 Aalst W.M.P.V.D., 2003, DISTRIBUTED PARALLEL, V14, P5 Awad A, 2008, LECT NOTES COMPUT SC, V5240, P326, DOI 10.1007/978-3-540-85758-7_24 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Basten T, 2001, J LOGIC ALGEBR PROGR, V47, P47, DOI 10.1016/S1567-8326(00)00004-7 Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Dijkman R, 2009, IEEE EDOC CONF, P45, DOI 10.1109/EDOC.2009.11 Dijkman R, 2009, LECT NOTES COMPUT SC, V5701, P48 Dijkman RM, 2008, INFORM SOFTWARE TECH, V50, P1281, DOI 10.1016/j.infsof.2008.02.006 DONGEN B, 2006, P EDOC 2006, P116 Eertink H, 1999, LECT NOTES COMPUT SC, V1708, P76 Ehrig M., 2007, APCCM 2007, P71 Eshuis R, 2007, ECOWS 07: PROCEEDING OF THE 5TH IEEE EUROPEAN CONFERENCE ON WEB SERVICES, P171 Esparza J., 1998, LECT NOTES COMPUTER, V1491, P374 FAUVET M, ICSOC 2010 IN PRESS Greco G, 2005, IEEE T KNOWL DATA EN, V17, P519, DOI 10.1109/TKDE.2005.63 Grigori D, 2008, INFORM SYST, V33, P681, DOI 10.1016/j.is.2008.02.004 GROOTE JF, 1990, 17 INT C AUT LANG PR, P626 Grossmann G, 2005, LECT NOTES COMPUT SC, V3649, P186 HART PE, 1968, IEEE T SYST SCI SSC, V4, P100, DOI DOI 10.1109/TSSC.1968.300136 Hornung T, 2008, LECT NOTES COMPUT SC, V5231, P265, DOI 10.1007/978-3-540-87877-3_20 Klein M, 2004, IEEE INTERNET COMPUT, V8, P30, DOI 10.1109/MIC.2004.1260701 KUANG L, 2007, IEEE INT C SERV COMP, P506, DOI 10.1109/SCC.2007.60 Levenshtein I. V., 1966, CYBERNETICS CONTROL, V10, P707 LI C, 2007, TRCTIT0789 U TWENT LOHMANN N, 2009, T PETRI NETS OTHER M, V2, P46 Lu R, 2007, LECT NOTES COMPUT SC, V4801, P165 Madhusudan T, 2004, DATA KNOWL ENG, V50, P87, DOI 10.1016/j.datak.2004.01.005 Manna Z., 1991, TEMPORAL LOGIC REACT Melnik S, 2002, PROC INT CONF DATA, P117, DOI 10.1109/ICDE.2002.994702 Mendling J, 2006, LECT NOTES COMPUT SC, V4103, P55 Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Minor M, 2007, LECT NOTES COMPUT SC, V4626, P224 Momotko M, 2004, LECT NOTES COMPUT SC, V3255, P306 Nejati S, 2007, PROC INT CONF SOFTW, P54 PANKRATIUS V, 2005, CRPIT, V43 Pesic M, 2007, LECT NOTES COMPUT SC, V4803, P77 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Preuner G, 2001, DATA KNOWL ENG, V36, P153, DOI 10.1016/S0169-023X(00)00043-4 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 Rosemann M., 2006, BUSINESS PROCESS MAN, V12, P249, DOI 10.1108/14637150610657567 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEN Z, 2005, IEEE INT C SERV COMP, P279 Stahl C, 2009, DATA KNOWL ENG, V68, P819, DOI 10.1016/j.datak.2009.02.012 Valmari A., 1998, LECT NOTES COMPUTER, V1491, P429 van Glabbeek R, 2001, ACTA INFORM, V37, P229, DOI 10.1007/s002360000041 VANDONGEN B, 2007, WP233 BETA EINDH U T van Dongen B, 2008, LECT NOTES COMPUT SC, V5074, P450 Weber B, 2008, DATA KNOWL ENG, V66, P438, DOI 10.1016/j.datak.2008.05.001 WEIDLICH M, 2010, P 22 INT C ADV INF S, P483 Wombacher A, 2006, LECT NOTES COMPUT SC, V4275, P255 NR 53 TC 79 Z9 93 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD APR PY 2011 VL 36 IS 2 SI SI BP 498 EP 516 DI 10.1016/j.is.2010.09.006 PG 19 WC Computer Science, Information Systems SC Computer Science GA 695JP UT WOS:000285366700020 ER PT J AU Chen, JG He, TT AF Chen, Jinguang He, Tingting TI Query-focused Multi-document Summarization Using Cloud Model SO INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL LA English DT Article; Proceedings Paper CT 2011 International Conference on Intelligent Computing and Information Science (ICICIS 2011) CY JAN 09-10, 2011 CL Chonqqing, PEOPLES R CHINA DE Cloud model; Query-focused multi-document summarization; Uncertainty; Text summarization AB This paper presents a method called CloudSum which improves effectiveness of query-focused multi-document summarization by handling uncertainties with cloud model. In CloudSum, three cloud models are defined corresponding to the key phases of summarization, contributions of words and sentences are obtained by taking into account both fuzziness and randomness of their basic distribution, instead of considering the later only as in statistical method. Experiments on the DUC2005, DUC2006, DUC2007, TAC2008, TAC2009 corpuses show that our method achieves results comparable to the best available systems. CloudSum also achieves good results in participating the just ended TAC 2010. C1 [Chen, Jinguang; He, Tingting] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China. [He, Tingting] Huazhong Normal Univ, Dept Comp Sci & Technol, Wuhan 430079, Peoples R China. [Chen, Jinguang] Huzhou Teachers Coll, Sch Teacher Educ, Huzhou 313000, Peoples R China. RP He, TT (reprint author), Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China. EM cjg2003@hutc.zj.cn; tthe@ccnu.edu.cn CR Binwahlan Mohammed Salem, 2009, Journal of Computer Sciences, V5 Carbonell J. G., P SIGIR 1998, P335 Deyi Li, 2007, ARTIFICIAL INTELLIGE FERRIER L, 2001, MAXIMUM ENTROPY APPR Haghighi, P NAACL HLT 2009, P362 Isfahani F.R., IADIS INT C INF 2008, P347 JAGARLAMUDI J, 2005, P DOC UND C Kiani B.A., P IEEE INT C FUZZ SY, P977 Kyoomarsi F., P 7 IEEE ACIS INT C, P347 Li D.Y., 1996, LPSC 1996 GERM, P38 Long H., P CCIR 2009, P9 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Neto J.L., P PADD 2000, P41 Pham XH, 2010, INFORMATION-TOKYO, V13, P1467 Ravindra G., P SOFSEM 2004, P289 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Toutanova K., 2007, P DOC UND C 2007 Wan X.J., P HLT NAACL 2006, P181 Witte R., P ARQAS 2003, P43 NR 19 TC 0 Z9 0 PU INT INFORMATION INST PI TOKYO PA FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN SN 1343-4500 J9 INFORMATION-TOKYO JI Information PD MAR PY 2011 VL 14 IS 3 SI SI BP 951 EP 956 PG 6 WC Engineering, Multidisciplinary SC Engineering GA 773SJ UT WOS:000291330300045 ER PT J AU Liu, KP Fang, BX Zhang, WZ AF Liu, Kaipeng Fang, Binxing Zhang, Weizhe TI Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS LA English DT Article DE social tagging systems; personalized tag recommendation; social relations; collaborative filtering ID GRAPH AB With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG. C1 [Liu, Kaipeng; Zhang, Weizhe] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China. [Fang, Binxing] Chinese Acad Sci, Inst Comp Technol, Key Lab Network Sci & Technol, Beijing 100190, Peoples R China. RP Zhang, WZ (reprint author), Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China. EM wzzhang@hit.edu.cn CR Batagelj V., 2002, CORR, Vcs.DS/0202039 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bonhard P, 2006, BT TECHNOL J, V24, P84, DOI 10.1007/s10550-006-0080-3 Chebotarev PY, 1997, AUTOMAT REM CONTR+, V58, P1505 Chebotarev PY, 1998, AUTOMAT REM CONTR+, V59, P1443 Chirita P.A., 2007, P 16 INT C WORLD WID, P845, DOI 10.1145/1242572.1242686 Fouss F, 2007, IEEE T KNOWL DATA EN, V19, P355, DOI 10.1109/TKDE.2007.46 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Guan ZY, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P540, DOI 10.1145/1571941.1572034 Guy I., 2009, P 3 ACM C REC SYST R, P53, DOI 10.1145/1639714.1639725 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Heymann P., 2006, 200610 STANF U Hotho A., 2006, P 3 EUR SEM WEB C, P411 JASCHKE R., 2007, P 11 EUR C PRINC PRA, P506 Jaschke R, 2008, AI COMMUN, V21, P231, DOI 10.3233/AIC-2008-0438 KATZ L, 1953, PSYCHOMETRIKA, V18, P39 Krestel R., 2009, P 3 ACM C REC SYST, P61, DOI 10.1145/1639714.1639726 Lathauwer LD, 2000, SIAM J MATRIX ANAL A, V21, P1253 LU YT, 2009, P 21 INT JOINT C ART, P2064 Marinho L.B., 2007, P 31 ANN C GERM CLAS, P533 Markines B., 2009, P 18 INT C WORLD WID, P641, DOI 10.1145/1526709.1526796 Page L., 1999, 199966 STANF INFOLAB Rao C.R., 1971, GEN INVERSE MATRIX I Rendle S, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P727 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2001, 10 INT WORLD WID WEB, P285 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Song Y, 2008, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2007, VOL 6, PTS A AND B, P515, DOI 10.1145/1390334.1390423 Symeonidis P, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P43 Tang J, 2008, IEEE DATA MINING, P1055, DOI 10.1109/ICDM.2008.71 Wu L., 2007, P INT WORKSH MULT IN, P115, DOI 10.1145/1290082.1290101 Wu L., 2009, P 18 INT C WORLD WID ZHANG N., 2009, P 2 ACM WORKSH SOC W, P9, DOI 10.1145/1651437.1651440 NR 34 TC 0 Z9 1 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG MINATO-KU SHIBAKOEN 3 CHOME, TOKYO, 105, JAPAN SN 0916-8532 J9 IEICE T INF SYST JI IEICE Trans. Inf. Syst. PD MAR PY 2011 VL E94D IS 3 BP 542 EP 551 DI 10.1587/transinf.E94.D.542 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 757YS UT WOS:000290125900017 ER PT J AU Kalogeratos, A Likas, A AF Kalogeratos, Argyris Likas, Aristidis TI Document clustering using synthetic cluster prototypes SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE Clustering methods; Document clustering; Text mining; Term selection; Subspace clustering ID ALGORITHM; MODEL AB The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods. (c) 2010 Elsevier B.V. All rights reserved. C1 [Kalogeratos, Argyris; Likas, Aristidis] Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece. RP Likas, A (reprint author), Univ Ioannina, Dept Comp Sci, GR-45110 Ioannina, Greece. EM akaloger@cs.uoi.gr; arly@cs.uoi.gr CR Arthur D., 2007, SODA 07, P1027 Beyer K., 1999, INT C DAT THEOR, P217 Bishop C. M., 2006, PATTERN RECOGNITION Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bradley P. S., 1998, P 15 INT C MACH LEAR, P91 Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 Cal D., 2010, P 16 ACM SIGKDD INT, P333, DOI 10.1145/1835804.1835848 Cheng H., 2008, P VLDB END, P90 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DHILLON I., 2001, DATA MINING SCI ENG Dhillon IS, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P131, DOI 10.1109/ICDM.2002.1183895 DHILLON I.S., 2002, P WORKSH CLUST HIGH, P71 DHILLON LS, 2001, MACH LEARN, V45, P143 Ding C., 2004, P 2004 ACM S APPL CO, P584, DOI 10.1145/967900.968021 Domeniconi C, 2007, DATA MIN KNOWL DISC, V14, P63, DOI 10.1007/s10618-006-0060-8 Friedman JH, 2004, J ROY STAT SOC B, V66, P815, DOI 10.1111/j.1467-9868.2004.02059.x Ghosh J, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P73, DOI 10.1007/3-540-28349-8_3 GRIRA N, 2007, P 24 INT C MACH LEAR, P313, DOI 10.1145/1273496.1273536 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Heaps H. S., 1978, INFORM RETRIEVAL COM Hinneburg A., 2000, P 26 INT C VER LARG, P506 Huang RZ, 2009, DATA KNOWL ENG, V68, P49, DOI 10.1016/j.datak.2008.08.008 Jing L, 2007, IEEE T KNOWL DATA EN, V19, P1026, DOI 10.1109/TKDE.2007.1048 Jing L, 2005, P PAC AS C KNOWL DIS, P802 KALOGERATOS A, 2006, P 4 HELL C AI SETN 0, P515 Kanungo T., 2004, Computational Geometry: Theory and Applications, V28, DOI 10.1016/j.comgeo.2004.03.003 Kaufman L, 1990, FINDING GROUPS DATA Kriegel HP, 2009, ACM T KNOWL DISCOV D, V3, DOI 10.1145/1497577.1497578 Kumar V, 2000, P 6 ACM SIGKDD INT C, P20 LEEA JW, 2009, DATA KHOWL ENG, V68, P362 LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Liu YB, 2008, J COMPUT SCI TECHNOL, V23, P112, DOI 10.1007/s11390-008-9115-1 Maitra R, 2009, IEEE ACM T COMPUT BI, V6, P144, DOI 10.1109/TCBB.2007.70244 McQueen J., 1967, P 5 BERK S MATH STAT, P281 Mitra P, 2002, IEEE T PATTERN ANAL, V24, P301, DOI 10.1109/34.990133 Modha DS, 2003, MACH LEARN, V52, P217, DOI 10.1023/A:1024016609528 Ng AY, 2002, ADV NEUR IN, V14, P849 Parsons L., 2004, SIGKDD EXPLORATIONS, V6, P90, DOI DOI 10.1145/1007730.1007731 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A., 2003, WEB DOCUMENT ANAL CH, P3 Strehl A, 2000, P AAAI WORKSH AI WEB, P58 TEST JMP, 1999, PATTERN RECOGN, V20, P1027 Tsai CY, 2008, COMPUT STAT DATA AN, V52, P4658, DOI 10.1016/j.csda.2008.03.002 WANAS NM, 2006, P 5 AUSTR DAT MIN AN, P91 WIRATUNGA N, 2006, P 8 EUR C CAS BAS RE, P340 Wu QY, 2010, LECT NOTES ARTIF INT, V6230, P292 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Yang Y., 1997, COMP STUDY FEATURE S, P412 Zanette D. H., 2005, J QUANT LINGUIST, V12, P29, DOI DOI 10.1080/09296170500055293 Zhang Y., 2010, ACM T KNOWL DISCOV D, V4, P1 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 Zhong S, 2005, KNOWL INF SYST, V8, P374, DOI 10.1007/s10115-004-0194-1 Zipf G., 1936, PSYCHOBIOLOGY LANGUA NR 54 TC 11 Z9 13 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X EI 1872-6933 J9 DATA KNOWL ENG JI Data Knowl. Eng. PD MAR PY 2011 VL 70 IS 3 BP 284 EP 306 DI 10.1016/j.datak.2010.12.002 PG 23 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 736XS UT WOS:000288528900003 ER PT J AU Huang, CH Liu, Y Xia, SZ Yin, JA AF Huang, Chenghui Liu, Yan Xia, Shengzhong Yin, Jian TI A Text Similarity Measure Based on Suffix Tree SO INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL LA English DT Article DE Similarity measure; Suffix tree; Document model; Text clustering AB It is well known that most text clustering algorithms use the bag-of-words model, which represents a document as a vector. These methods ignored the word sequence information, and the good clustering result limited to some special domains. This paper presents a new text similarity algorithm (STSM) by applying the TF-IDF method to weight word sequences of a document that modeled as a suffix tree. Experimental results on standard document benchmark corpus RUTERS and BBC show that the new text similarity is effective. Comparing with the results of the state-of-art similarity measure, our proposed method brings an improvement of about 10% on the average of F-measure score. C1 [Huang, Chenghui; Yin, Jian] Sun Yat Sen Univ, Dept Comp Sci, Guangzhou 510275, Guangdong, Peoples R China. [Huang, Chenghui] Guangdong Univ Finance, Dept Comp Sci & Technol, Guangzhou 510520, Guangdong, Peoples R China. [Liu, Yan] Guangdong Univ Finance, Dept Appl Math, Guangzhou 510520, Guangdong, Peoples R China. [Xia, Shengzhong] Guangdong AIB Coll, Guangzhou 510507, Guangdong, Peoples R China. RP Huang, CH (reprint author), Sun Yat Sen Univ, Dept Comp Sci, Guangzhou 510006, Guangdong, Peoples R China. EM hch.gduf@163.com FU National Natural Science Foundation of China [60573097, 60773198, 60703111, 10971234, 11001088]; Program for New Century Excellent Talents in University of China [NCET-06-0727] FX This work is supported by the National Natural Science Foundation of China (60573097, 60773198, 60703111, 10971234, 11001088), Program for New Century Excellent Talents in University of China (NCET-06-0727). CR *AL I INC, LINGPIPE Chim H., 2007, P 16 INT C WORLD WID, P121, DOI 10.1145/1242572.1242590 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Karypis G., CLUTO A CLUSTERING T Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 *MACH LEARN GROUP, BBC DAT Maguitman A G., 2005, INT WORLD WID WEB C, P107 Meadow C., 2000, TEXT INFORM RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shehata S, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P629 Theobald M, 2008, P 31 ANN INT ACM SIG, P563, DOI 10.1145/1390334.1390431 Wang D., 2008, P 31 ANN INT ACM SIG, P307, DOI 10.1145/1390334.1390387 Li Y, 2008, ACTA CRYSTALLOGR E, V64, pM50, DOI 10.1107/S1600536807063301 Zamir O., 1998, RES DEV INFORM RETRI, P46 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining 1997, REUTERS 21578 TEXT C NR 16 TC 0 Z9 0 PU INT INFORMATION INST PI TOKYO PA FAC ENG, HOSEI UNIV, KOGANEI, TOKYO, 184-8584, JAPAN SN 1343-4500 J9 INFORMATION-TOKYO JI Information PD FEB PY 2011 VL 14 IS 2 BP 583 EP 592 PG 10 WC Engineering, Multidisciplinary SC Engineering GA 747LV UT WOS:000289322200025 ER PT J AU Ulmer, C Gokhale, M Gallagher, B Top, P Eliassi-Rad, T AF Ulmer, Craig Gokhale, Maya Gallagher, Brian Top, Philip Eliassi-Rad, Tina TI Massively parallel acceleration of a document-similarity classifier to detect web attacks SO JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING LA English DT Article DE Cybersecurity; Document classification; Machine learning; Multi-core; Reconfigurable computing AB This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms. The TFIDF classifier is used to detect web attacks in HTTP data. In our parallel hardware approaches, we design streaming, real time classifiers by simplifying the sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. Parallel implementations on the Tilera 64-core System on Chip and the Xilinx Virtex 5-LX FPGA are presented. For the Tilera, we employ a reduced state machine to recognize dictionary terms without requiring explicit tokenization, and achieve throughput of 37 MB/s at a slightly reduced accuracy. For the FPGA, we have developed a set of software tools to help automate the process of converting training data to synthesizable hardware and to provide a means of trading off between accuracy and resource utilization. The Xilinx Virtex 5-LX implementation requires 0.2% of the memory used by the original algorithm. At 166 MB/s (80X the software) the hardware implementation is able to achieve Gigabit network throughput at the same accuracy as the original algorithm. (C) 2010 Elsevier Inc. All rights reserved. C1 [Gokhale, Maya; Gallagher, Brian; Eliassi-Rad, Tina] Lawrence Livermore Natl Lab, CASC, Livermore, CA 94550 USA. [Ulmer, Craig] Sandia Natl Labs, Livermore, CA USA. RP Gokhale, M (reprint author), Lawrence Livermore Natl Lab, CASC, Livermore, CA 94550 USA. EM maya@llnl.gov FU US Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344] FX This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. CR Baker Z.K., 2004, FPGA 04, P223 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 CHEN T, 2008, FUZZ SYST KNOWL DISC, V5, P353 DAS A, 2008, DES AUT TEST EUR C E, P1160 DENOYER L, 2007, ECML PKDD 2007 DISCO EXBRAYAT M, 2007, P ECML PKDD 2007 DIS, P53 Frank E, 2005, DATA MINING PRACTICA GALLAGHER B, TR414570 *LLNL, 2009, DAT CTR COMP ARCH Manning C.D., 2008, INTRO INFORM RETRIEV Olson D.L., 2008, ADV DATA MINING TECH *OTOC, 2009, W POINT DAT SET PACHOPOULOS K, 2007, P ECML PKDD 2007 DIS, P65 PEARSON PK, 1990, COMMUN ACM, V33, P677, DOI 10.1145/78973.78978 Rassi C., 2007, P DISC CHALL ECML PK, P47 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song H., 2005, FPGA 05, P238 NR 18 TC 4 Z9 4 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 0743-7315 J9 J PARALLEL DISTR COM JI J. Parallel Distrib. Comput. PD FEB PY 2011 VL 71 IS 2 SI SI BP 225 EP 235 DI 10.1016/j.jpdc.2010.07.005 PG 11 WC Computer Science, Theory & Methods SC Computer Science GA 708LS UT WOS:000286363900008 ER PT J AU Omerovic, S Babovic, Z Tafa, Z Milutinovic, V Tomazic, S AF Omerovic, Sanida Babovic, Zoran Tafa, Zhilbert Milutinovic, Veljko Tomazic, Saso TI Concept modeling: From origins to multimedia SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Concepts; Knowledge; Ontology; Semantics; Multimedia; Retrieval; Understanding; Data; Relations; Representation AB The origins of concept modeling are in the field of artificial intelligence. This is where the initial algorithms were introduced first. With the emerging developments in the field of multimedia systems, a strong need is generated to examine and implement concepts-based retrieval of multimedia-contents, from large data bases or from the Internet. The early works were based on appropriate modifications of classical approaches. The latest developments utilize the algorithms that make sense only in the case of multimedia systems. This paper presents a number of classical approaches to concept modeling and their applicability to multimedia. Then it discusses a number of approaches introduced specifically for multimedia. Finally it presents an approach which was fully implemented and tested in an academic environment for industry needs. C1 [Babovic, Zoran; Milutinovic, Veljko] Univ Belgrade, Sch Elect Engn, Belgrade, Serbia. [Omerovic, Sanida; Tomazic, Saso] Univ Ljubljana, Ljubljana, Slovenia. [Tafa, Zhilbert] Univ Podgor & Telekom Montenegro, Podgorica, Montenegro. [Milutinovic, Veljko] Singidunum Univ, Belgrade, Serbia. RP Babovic, Z (reprint author), Univ Belgrade, Sch Elect Engn, Belgrade, Serbia. EM zbabovic@gmail.com FU Purdue University, West Lafayette, Indiana, USA [2588-1314] FX This research was conceptualized at Purdue University, West Lafayette, Indiana, USA, as a part of the grant # 2588-1314. CR BABOVIC Z, 2010, P VIPSI 2010 AMALFI BALLAN L, 2010, IEEE MULTIM IN PRESS Bryant R. E., 2007, DATA INTENSIVE SUPER CHAN C, 2004, CAN C EL COMP ENG 2, V3, P1353 Chein M., 1992, REV INTELLIGENCE ART, V6, P365 CHEN H, 2003, ONTOLOGY CONTEXT AWA, V18 CHEN P, 2007, ACM T DATABASE SYST, V1, P9 CHUA TS, 1994, P 27 ANN HAW INT C S, V3, P590 COOK D, 2007, SUBDUE GRAPH BASED K Deacon TW, 1998, SYMBOLIC SPECIES COE DILIGENTI M, 2010, IPSI T INTERNET RES, V6, P18 DJORDJEVIC N, 2010, IPSI T INTERNET RES, V6, P27 Dou D., 2004, ONTOLOGY TRANSLATION FRAWLEY WJ, 1992, KNOWLEDGE DISCOVERY Fujihara H, 1997, IEEE T KNOWL DATA EN, V9, P209, DOI 10.1109/69.591447 GAUCH S, 2002, ITTCFY2004TR864637 U GIUGNO R, 2007, GRAPHGREP Gomez-Perez A, 2002, IEEE INTELL SYST, V17, P54, DOI 10.1109/5254.988453 HALLADAY S, 2004, P 37 ANN HAW INT C S HALPIN T, 2007, OBJECT ROLE MODELING Han JW, 1996, IEEE T KNOWL DATA EN, V8, P373 HAWKINS J, 2007, IEEE SPECTRUM ON APR Hollink L., 2005, P 13 ANN ACM INT C M, P479, DOI 10.1145/1101149.1101256 HOOGS A, 2003, P C COMP VIS PATT RE HUNTER J, 2001, INT SEM WEB WORK S JUNG MY, 2008, ITC CSCC 2008 JAP, P45 MILUTINOVIC V, 2010, P VIPSI 2010 AMALFI NAKABASAMI C, 2002, INT SEM WEB C ISWC20 Novak J., 2005, 200601 FLOR I HUM MA QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105 Rubin DL, 2009, IEEE INTELL SYST, V24, P57, DOI 10.1109/MIS.2009.3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIEGEL, 1985, BASICS IMAGE UNDERST Smeulders A.M.W., 2000, IEEE T PATTERN ANAL, V22 SOWA J, 2000, P LING CONC STRUCT L, P55 SOWA J, 1999, LECT NOTES COMPUTER, P1 *STANF U, 2006, CONC MOD SOFTW PACK VARGA E, 2010, IPSI T INTERNET RES, V6, P5 VIOLA P, 2001, 2000101 CRL VOSS A, 1999, INT WORKSH EN TECHN, P245 Vrochidis Stefanos, 2008, International Journal of Metadata, Semantics and Ontologies, V3, DOI 10.1504/IJMSO.2008.023566 WAN X, 2009, BOOK ADV INFORM RETR, P749 WOODS W, 1997, TR9761 SUN MICR Yan R., 2009, P 1 ACM WORKSH LARG, P35, DOI DOI 10.1145/1631058.1631067 Zellweger P., 2003, P INT C INT KNOWL IN, P747 2007, DAML ONTOLOGIES NR 46 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD FEB PY 2011 VL 51 IS 3 BP 1175 EP 1200 DI 10.1007/s11042-010-0642-8 PG 26 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 717XL UT WOS:000287081100015 ER PT J AU Li, ZX Xiong, ZY Zhang, YF Liu, CY Li, KA AF Li Zhixing Xiong Zhongyang Zhang Yufang Liu Chunyong Li Kuan TI Fast text categorization using concise semantic analysis SO PATTERN RECOGNITION LETTERS LA English DT Article DE Text representation; Semantic analysis; Text categorization; Dimensionality reduction ID DECOMPOSITION; COMBINATION AB Text representation is a necessary procedure for text categorization tasks. Currently, bag of words (BOW) is the most widely used text representation method but it suffers from two drawbacks. First, the quantity of words is huge; second, it is not feasible to calculate the relationship between words. Semantic analysis (SA) techniques help BOW overcome these two drawbacks by interpreting words and documents in a space of concepts. However, existing SA techniques are not designed for text categorization and often incur huge computing cost. This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks. CSA extracts a few concepts from category labels and then implements concise interpretation on words and documents. These concepts are small in quantity and great in generality and tightly related to the category labels. Therefore, CSA preserves necessary information for classifiers with very low computing cost. To evaluate CSA, experiments on three data sets (Reuters-21578, 20-News-Group and Tancorp) were conducted and the results show that CSA reaches a comparable micro- and macro-F(1) performance with BOW, if not better one. Experiments also show that CSA helps dimension sensitive learning algorithms such as k-nearest neighbor (kNN) to eliminate the "Curse of Dimensionality" and as a result reaches a comparable performance with support vector machine (SVM) in text categorization applications. In addition, CSA is language independent and performs equally well both in Chinese and English. (C) 2010 Elsevier B.V. All rights reserved. C1 [Li Zhixing; Xiong Zhongyang; Zhang Yufang; Liu Chunyong; Li Kuan] Chongqing Univ, Dept Comp Sci, Chongqing, Peoples R China. RP Li, ZX (reprint author), Chongqing Univ, Dept Comp Sci, Chongqing, Peoples R China. EM adam0730@126.com; zyxiong@cqu.edu.cn; yfzhang@cqu.edu.cn; lcy@cqu.edu.cn; sloweater@163.com CR Brand M, 2006, LINEAR ALGEBRA APPL, V415, P20, DOI 10.1016/j.laa.2005.07.021 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Chen FL, 2010, EXPERT SYST APPL, V37, P4902, DOI 10.1016/j.eswa.2009.12.025 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gabrilovich E, 2009, J ARTIF INTELL RES, V34, P443 Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110 Lang K., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 LEWIS DD, 1995, P SIGIR Lewis DD, 2004, J MACH LEARN RES, V5, P361 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1999, P 16 INT C MACH LEAR, P379 Tan S. B., 2005, ACM CIKM 2005, P469 TAN SB, 2010, CHINESE TEXT CATEGOR TONG Y, 2009, 2009 INT WORKSH INT Wang M, 2010, NEUROCOMPUTING, V73, P1562, DOI 10.1016/j.neucom.2009.08.023 Wang W, 2009, NEURAL COMPUT APPL, V18, P875, DOI 10.1007/s00521-008-0193-3 Xue XB, 2009, IEEE T KNOWL DATA EN, V21, P428, DOI 10.1109/TKDE.2008.166 Yang Y., 1999, ACM SIGIR C RES DEV, P42 NR 19 TC 7 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-8655 J9 PATTERN RECOGN LETT JI Pattern Recognit. Lett. PD FEB 1 PY 2011 VL 32 IS 3 BP 441 EP 448 DI 10.1016/j.patrec.2010.11.001 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA 711BL UT WOS:000286560600005 ER PT J AU Aseervatham, S Antoniadis, A Gaussier, E Burlet, M Denneulin, Y AF Aseervatham, Sujeevan Antoniadis, Anestis Gaussier, Eric Burlet, Michel Denneulin, Yves TI A sparse version of the ridge logistic regression for large-scale text categorization SO PATTERN RECOGNITION LETTERS LA English DT Article DE Logistic regression; Model selection; Text categorization; Large scale ategorization ID SELECTION; REGULARIZATION; LASSO; MODEL AB The ridge logistic regression has successfully been used in text categorization problems and It has been shown to reach the same performance as the Support Vector Machine but with the main advantage of computing a probability value rather than a score However the dense solution of the ridge makes its use unpractical for large scale categorization On the other side LASSO regularization is able to produce sparse solutions but its performance is dominated by the ridge when the number of features is larger than the number of observations and/or when the features are highly correlated In this paper we propose a new model selection method which tries to approach the ridge solution by a sparse solution The method first computes the ridge solution and then performs feature selection The experimental evaluations show that our method gives a solution which is a good trade-off between the ridge and LASSO solutions (C) 2010 Elsevier B V All rights reserved C1 [Aseervatham, Sujeevan; Gaussier, Eric] Univ Grenoble 1, LIG, F-38041 Grenoble 9, France. [Antoniadis, Anestis] Univ Grenoble 1, LJK, F-38041 Grenoble 9, France. [Burlet, Michel] Univ Grenoble 1, Lab Leibniz, F-38031 Grenoble 1, France. [Denneulin, Yves] ENSIMAG, LIG, F-38330 Montbonnot St Martin, France. RP Aseervatham, S (reprint author), Univ Grenoble 1, LIG, 385 Rue Bibliotheque BP 53, F-38041 Grenoble 9, France. FU LASCAR project Univ J Fourier Grenoble FX This work was partly supported by the LASCAR project Univ J Fourier Grenoble CR Antoniadis A, 2001, J AM STAT ASSOC, V96, P939, DOI 10.1198/016214501753208942 Bach F, 2008, ICML 08, P33 Chu Cheng-Tao, 2006, NIPS, P281 Efron B, 2004, ANN STAT, V32, P407 Friedman J., 2008, REGULARIZATION PATHS Genkin A, 2007, TECHNOMETRICS, V49, P291, DOI 10.1198/004017007000000245 Hastie T., 1990, GEN ADDITIVE MODELS Hersh William, 1994, P 17 ANN INT ACM SIG, P192 HOERL AE, 1970, TECHNOMETRICS, V12, P55, DOI DOI 10.1080/00401706.1970.10488634 Joachims T, 1998, EUR C MACH LEARN ECM, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE LECESSIE S, 1992, J R STAT SOC C-APPL, V41, P191, DOI DOI 10.2307/2347628 McCullagh P., 1989, GEN LINEAR MODELS Minka T. P., 2003, COMP NUMERICAL OPTIM Porter M.F., 1980, PROGRAM, V14 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck Jones K., 1988, DOCUMENT RETRIEVAL S, P132 Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vapnik V., 1995, NATURE STAT LEARNING Zhang T, 2001, INFORM RETRIEVAL, V4, P5, DOI 10.1023/A:1011441423217 Zhao P, 2006, J MACH LEARN RES, V7, P2541 Zou H, 2005, J ROY STAT SOC B, V67, P301, DOI 10.1111/j.1467-9868.2005.00503.x NR 23 TC 3 Z9 3 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-8655 EI 1872-7344 J9 PATTERN RECOGN LETT JI Pattern Recognit. Lett. PD JAN 15 PY 2011 VL 32 IS 2 BP 101 EP 106 DI 10.1016/j.patrec.2010.09.023 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA 700AF UT WOS:000285703800001 ER PT S AU Blank, D Fuhr, N Henrich, A Mandl, T Rolleke, T Schutze, H Stein, B AF Blank, Daniel Fuhr, Norbert Henrich, Andreas Mandl, Thomas Roelleke, Thomas Schuetze, Hinrich Stein, Benno BE Efthimiadis, E FernandezLuna, JM Heute, JF MacFarlane, A TI Teaching IR: Curricular Considerations SO TEACHING AND LEARNING IN INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Article; Book Chapter ID MODELING INFORMATION-RETRIEVAL; SEARCH INTERFACE; DESIGN C1 [Blank, Daniel; Henrich, Andreas] Univ Bamberg, Bamberg, Germany. [Fuhr, Norbert] Univ Duisburg Essen, Duisburg, Germany. [Mandl, Thomas] Univ Hildesheim, D-31141 Hildesheim, Germany. [Roelleke, Thomas] Queen Mary Univ London, London, England. [Schuetze, Hinrich] Univ Stuttgart, Stuttgart, Germany. [Stein, Benno] Bauhaus Univ Weimar, D-99423 Weimar, Germany. RP Blank, D (reprint author), Univ Bamberg, Bamberg, Germany. EM daniel.blank@uni-bamberg.de CR Al-Maskari A., 2007, P 30 ANN INT ACM SIG, P773, DOI 10.1145/1277741.1277902 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BATES MJ, 1990, INFORM PROCESS MANAG, V26, P575, DOI 10.1016/0306-4573(90)90103-9 BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320 Bawden D, 2007, 1 INT BCS WORKSH TEA Belew R.K., 2000, FINDING OUT COGNITIV BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Buckley C, 2005, DIGITAL LIB ELECT PU, P53 Buckley C., 1985, P 8 ANN INT ACM SIGI, P97, DOI 10.1145/253495.253515 Cacheda F, 2008, 2 INT BCS WORKSH TEA Croft B, 2003, INFORM RETRIEVAL SER, V13 Croft B, 1995, D LIB MAG, V1, P5 ELLIS D, 1989, J DOC, V45, P171, DOI 10.1108/eb026843 Fernandez-Luna J, 2009, INFORM RETRIEVAL, V12, P201, DOI 10.1007/s10791-009-9089-9 Grossman DA, 2004, INFORM RETRIEVAL SER, V15 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Ingwersen P., 1992, INFORM RETRIEVAL INT Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Jurafsky D., 2008, SPEECH LANGUAGE PROC KUHLTHAU CC, 1988, RQ, V28, P232 Lieberman H, 1995, INT JOINT CONF ARTIF, P924 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Mandl Thomas, 2008, Informatica, V32 Mann TM, 2002, THESIS U CONSTANCE Manning C. D., 1999, FDN STAT NATURAL LAN Manning C.D., 2008, INTRO INFORM RETRIEV Melucci M, 2006, INFORM RETRIEVAL, V9, P119, DOI 10.1007/s10791-006-7145-2 Middleton Christian, 2007, COMPARISON OPEN SOUR Moffat A, 2005, SIGIR FORUM, V39, P3 Riggs KR, 2002, J COMPUT SCI COLL, V17, P179 Rijsbergen C. J. v., 1979, INFORM RETRIEVAL Robertson S., 1994, NIST SPECIAL PUBLICA, P109 Robertson S, 2008, J INF SCI, V34, P439, DOI 10.1177/0165551507086989 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schaefer A, 2005, 9 EUR C DIG LIB VIEN, P414 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shneiderman B, 1998, DESIGNING USER INTER Shneiderman B, 1997, ACM INTERACTIONS, V4, P42 Sparck Jones K, 1997, MORGAN KAUFMANN SERI Stein B., 2007, P 30 ANN INT ACM SIG, P527, DOI 10.1145/1277741.1277832 Strohman T., 2009, SEARCH ENGINES INFOR Witten I. H., 1999, MANAGING GIGABYTES C WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 47 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-642-22511-6 J9 INFORM RETRIEVAL SER PY 2011 VL 31 BP 31 EP 46 DI 10.1007/978-3-642-22511-6_3 D2 10.1007/978-3-642-22511-6 PG 16 WC Computer Science, Information Systems SC Computer Science GA BAG48 UT WOS:000304080200003 ER PT S AU Luo, JG Yuan, CA Luo, JK AF Luo, Jin'guang Yuan, Chang'an Luo, Jinkun BE Deng, H Miao, D Wang, FL Lei, JS TI A Model of a GEP-Based Text Clustering on Counter Propagation Networks SO EMERGING RESEARCH IN ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 3rd International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011) CY SEP 23-25, 2011 CL Taiyuan, PEOPLES R CHINA SP Taiyuan Univ Technol DE Text Clustering; Weight Optimization; Neural Network; Gene Expression Programming; Counter Propagation Networks AB In this paper, we present a model of a GEP-based test clustering research on counter propagation networks. The idea of the model is that it optimize the link weight vector by using the advantage of the GEP(Gene Expression Programming, GEP) in combinatorial optimization. We investigate how the value of weight in the network affect the performance of the text clustering by comparing it to a based on genetic algorithm and SOM network and the method of the traditional CPN(Counter Propagation Networks, CPN). Furthermore, we improve and optimize the weight in the CPN network by the method of GEP, thus raise the quality of the text clustering in the network. Finally, in this paper, we demonstrated the validity and superiority of the presented model. C1 [Luo, Jin'guang; Yuan, Chang'an; Luo, Jinkun] Guangxi Teachers Educ Univ, Sch Comp & Informat Engn, Nanning 530023, Peoples R China. RP Luo, JG (reprint author), Guangxi Teachers Educ Univ, Sch Comp & Informat Engn, Nanning 530023, Peoples R China. EM yca@gxtc.edu.cn CR Ayad H., 2002, LNCS LNAI, V2338, P161 Chen H.-G, 2010, COMPUTER ENG, V36, P79 Ferreira C., 2001, Complex Systems, V13 Gao H.-s., 1999, MICROCOMPUTER DEV, P28 Han J, 2000, DATA MINING CONCEPTS Heaton J., 2007, JAVA NEURAL NETWORKS Kohonen T, 2001, SOLF ORG MAPS Na T., 2008, STAT DECISION, P155 Peng Z., 2008, NEW TECHNOLOGY LIB I, V11, P65 Qin X., 2009, J INFORM COMPUTATION, V6, P1303 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wasserman P. D., 1989, NEURAL COMPUTING THE Xiao Q, 2008, COMPUTER APPL, V28, P757 [č®ø厚金 Xu Houjin], 2010, [č®”ē®—ęœŗå·„ē؋äøŽč®¾č®”, Computer Engineering and Design], V31, P1802 [å…ƒę˜Œå®‰ Yuan Changan], 2004, [å››å·å¤§å­¦å­¦ęŠ„. å·„ē؋ē§‘å­¦ē‰ˆ, Journal of Sichuan University.Engineering Science Edition], V36, P100 Zuo J, 2002, LNCS, VLNCS 2419, P92 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-24281-6 J9 COMM COM INF SC PY 2011 VL 237 BP 214 EP 221 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BCP01 UT WOS:000310936100029 ER PT S AU Zaoui, I Wadjinny, F Chiadmi, D Benhlima, L AF Zaoui, Imane Wadjinny, Faouzia Chiadmi, Dalila Benhlima, Laila BE Pichappan, P Ahmadi, H Ariwa, E TI A Model Driven Approach for Personalizing Data Source Selection in Mediation Systems SO INNOVATIVE COMPUTING TECHNOLOGY SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 1st International Conference on Innovative Computing Technology CY DEC 13-15, 2011 CL Tehran, IRAN DE Personalization; User profile; Mediation systems; Source selection ID WEB AB Nowadays, there is a real trend to personalize mediation systems to improve user satisfaction. The mediator answers should be adapted to the user needs and preferences. In this paper, we propose a solution for this problem. Our solution is based on models for users and data sources. These models are used to perform a content matching and a quality matching between user profile and data sources profiles. The objective of matching is to rank sources according to user preferences and needs, and to select the most relevant ones. Our solution for personalizing data source selection provides the mediator with a set of relevant data sources. These are then involved in the rewriting process to give more satisfying response. By reducing the number of integrated data sources, the mediator performances are also optimized. C1 [Zaoui, Imane; Wadjinny, Faouzia; Chiadmi, Dalila; Benhlima, Laila] Mohammadia Engn Sch, Dept Comp Sci, Rabat, Morocco. RP Zaoui, I (reprint author), Mohammadia Engn Sch, Dept Comp Sci, BP 765, Rabat, Morocco. EM zaoui@emi.ac.ma; wadjinny@emi.ac.ma; chiadmi@emi.ac.ma; benhlima@emi.ac.ma CR Amato G, 1999, LECT NOTES COMPUT SC, V1696, P184 Berkovsky S, 2008, USER MODEL USER-ADAP, V18, P245, DOI 10.1007/s11257-007-9042-9 Bouzeghoub M., 2004, INT WORKSH INF QUAL Bradley Keith, 2000, LECT NOTES COMPUTER, V1892, P62 Burgess M, 2002, LECT NOTES COMPUT SC, V2405, P103 Callan J, 2000, KLUW S INF, V7, P127 Egghe L, 2010, J AM SOC INF SCI TEC, V61, P2151, DOI 10.1002/asi.21380 Fink J, 2000, USER MODEL USER-ADAP, V10, P209, DOI 10.1023/A:1026597308943 Gowan J., 2003, THESIS U COLL DUBLIN Gravano L., 1997, P ACM SIGMOD INT C M Gravano Luis, 1995, P 21 INT C VER LARG, P78 Hardy M, 2010, MATH INTELL, V32, P38, DOI 10.1007/s00283-010-9159-2 Hwang CL, 1981, MULTIPLE ATTRIBUTE D Kobsa A, 1989, USER MODELS DIALOG S Mobasher B, 2002, DATA MIN KNOWL DISC, V6, P61, DOI 10.1023/A:1013232803866 Naumann F, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P447 Naumann F, 1998, P NEW TECHN TECHN ST Nguyen A.T., 2006, COCOFIL2 NOUVEAU SYS Nottelmann H., 2003, P 26 ANN INT ACM SIG Paltoglou G, 2009, THESIS U MACEDONIA Peralta V., 2006, THESIS VERSAILLE U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Segev A, 1990, P 6 INT C DAT ENG IC Si L., 2003, P 26 ANN INT ACM SIG Tanudjaja F., 2002, P 35 HAW INT C SYST, P53 Wadjinny F, 2010, THESIS MOHAMMADIA EN Wadjinny F., 2009, P 7 ACS IEEE INT C C Walters-Williams J, 2010, ADVANCES TECHNIQUES IN COMPUTING SCIENCES AND SOFTWARE ENGINEERING, P79, DOI 10.1007/978-90-481-3660-5_14 Wang R. Y., 1996, Journal of Management Information Systems, V12 Wiederhold Gio, 1992, IEEE COMPUT, V25, P38 Xu J., 1999, P 22 ANN INT ACM SIG Zaoui I., 2009, P WOTIC AG MOR Zaoui I., 2011, IJEST INT J ENG SCI, V3 NR 33 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-27336-0 J9 COMM COM INF SC PY 2011 VL 241 BP 130 EP 145 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCN38 UT WOS:000310767300013 ER PT S AU Xiang, HM He, WS AF Xiang, Hongmei He, Weisong BE Zhu, M TI Mining Wireless Sensor Network Data Based on Vector Space Model SO INFORMATION AND MANAGEMENT ENGINEERING, PT VI SE Communications in Computer and Information Science LA English DT Proceedings Paper CT International Conference on Computing, Information and Control (ICCIC 2011) CY SEP 17-18, 2011 CL Wuhan, PEOPLES R CHINA DE Vector space model; Wireless sensor network; Feature Analysis AB In this letter, in order to explore wireless sensor network data, a vector-space-model is applied. With application of the method, the similarity between query features and wireless sensor network features is calculated, which facilitates detecting anomalies. Experiments are conducted and, together with application of SIGCOMM 2008 trace data, show some positive results. C1 [Xiang, Hongmei; He, Weisong] Chongqing Coll Elect Engn, Chongqing, Peoples R China. RP Xiang, HM (reprint author), Chongqing Coll Elect Engn, Chongqing, Peoples R China. EM xhm1989879@sina.com CR Akyildiz, 2002, COMPUTER NETWORKS, V38, P393 da Silva A., 2005, P 1 ACM INT WORKSH Q, P16, DOI DOI 10.1145/1089761.1089765 Djenouri D, 2005, IEEE COMMUN SURV TUT, V7, P2, DOI 10.1109/COMST.2005.1593277 Hodge VJ, 2004, ARTIF INTELL REV, V22, P85, DOI 10.1023/B:AIRE.0000045502.10941.a9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi E, 2004, IEEE WIREL COMMUN, V11, P38, DOI 10.1109/MWC.2004.1368895 Zhao F, 2003, P IEEE, V91, P1199, DOI 10.1109/JPROC.2003.814921 NR 7 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-24096-6 J9 COMM COM INF SC PY 2011 VL 236 BP 100 EP 104 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCL18 UT WOS:000310543700017 ER PT S AU Taranto, C Di Mauro, N Esposito, F AF Taranto, Claudio Di Mauro, Nicola Esposito, Floriana BE Agosti, M Esposito, F Meghini, C Orio, N TI Probabilistic Inference over Image Networks SO DIGITAL LIBRARIES AND ARCHIVES SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 7th Italian Research Conference on Digital Libraries CY JAN 20-21, 2011 CL Pisa, ITALY SP Italian Natl Res Council, Inst Informat Sci Technologies, Univ Padua, Dept Informat Engn HO Italian Natl Res Council, Inst Informat Sci & Technol ID CLASSIFICATION; MODELS AB Digital Libraries contain collections of multimedia objects providing services for the management, sharing and retrieval. Involved objects have two levels of complexity: the former refers to the inner object complexity while the latter takes into account the implicit/explicit relationships among objects. Traditional machine learning classifiers do not consider the relationships among objects assuming them independent and identically distributed. Recently, link-based classification methods have been proposed, that try to classify objects exploiting their relationships (links). In this paper, we deal with objects corresponding to digital images, even if the proposed approach can be naturally applied to different kind of multimedia objects. Relationships can be expressed among the features of the same image or among features belonging to different images. The aim of this work is to verify whether a link-based classifier based on a Statistical Relational Learning (SRL) language can improve the accuracy of a classical k-nearest neighbour approach. Experiments will show that the modelling of the relationships in a real-word dataset using a SRL model reduces the classification error. C1 [Taranto, Claudio; Di Mauro, Nicola; Esposito, Floriana] Univ Bari Aldo Moro, Dept Comp Sci, I-70125 Bari, Italy. EM claudio.taranto@di.uniba.it; ndm@di.uniba.it; esposito@di.uniba.it CR Bishop C. M., 2006, PATTERN RECOGNITION Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Boiman O., 2008, CVPR, P1 Burl Michael C., 1998, LNCS, VII, P628 Candela L., 2007, DELOS DIGITAL LIB RE Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Conte D, 2004, INT J PATTERN RECOGN, V18, P265, DOI 10.1142/S0218001404003228 Fei-Fei L., 2004, IEEE COMP VIS PATT R FERGUS R, 2003, IEEE C COMP VIS PATT, P264 Getoor L., 2005, ADV METHODS KNOWLEDG Getoor L., 2007, INTRO STAT RELATIONA Getoor L., 2005, SIGKDD EXPLORATIONS, V7, P3 Hofmann T., 1995, P 15 C UNC ART INT Huang J, 1997, PROC CVPR IEEE, P762 Kameya Y, 1999, LECT NOTES ARTIF INT, V1721, P264 Kimmig A, 2008, LECT NOTES COMPUT SC, V5366, P175, DOI 10.1007/978-3-540-89982-2_22 Koller D., 2009, PROBABILISTIC GRAPHI Lu Q., 2003, P 20 INT C MACH LEAR, P496 Macskassy S. A., 2003, P 2 WORKSH MULT DAT, P64 Macskassy SA, 2007, J MACH LEARN RES, V8, P935 Nowak E, 2006, LECT NOTES COMPUT SC, V3954, P490 Poole D, 2000, J LOGIC PROGRAM, V44, P5, DOI 10.1016/S0743-1066(99)00071-0 Raedt L.D., 2007, P 12 INT JOINT C ART, P2468 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santos Costa V., 2003, P 19 C UNC ART INT, P517 Sato T., 1995, P 12 INT C LOG PROGR, P715 Sen P, 2008, AI MAG, V29, P93 Shterionov D., 2010, INT C IMPL CONSTR LO Taranto C., 2010, P INT C MULT MM 2010, P1127, DOI 10.1145/1873951.1874167 NR 29 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-27301-8 J9 COMM COM INF SC PY 2011 VL 249 BP 1 EP 13 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BCD81 UT WOS:000309892900001 ER PT S AU Mendes, D Rodrigues, I AF Mendes, David Rodrigues, Irene BE CruzCunha, MM Varajao, J Powell, P Martinho, R TI A Semantic Web Pragmatic Approach to Develop Clinical Ontologies, and Thus Semantic Interoperability, Based in HL7 v2.XML Messaging SO ENTERPRISE INFORMATION SYSTEMS, PT 3 SE Communications in Computer and Information Science LA English DT Proceedings Paper CT International Conference on Enterprise Information Systems CY OCT 05-07, 2011 CL Vilamoura, PORTUGAL SP Polytechn Inst Cavado & Ave, Univ Tras os Montes & Alto Douro ID TEXT; SENSE AB The ISO/HL7 27931:2009 standard intends to establish a global interoperability framework for Healthcare applications. However, being a messaging related protocol. it lacks a semantic foundation for interoperability at a machine treatable level has intended through the Semantic Web. There is no alignment between the HL7 V2.xml message payloads and a meaning service like a suitable ontology. Careful application of Semantic Web tools and concepts can ease extremely the path to the fundamental concept of Shared Semantics. In this paper the Semantic Web and Artificial Intelligence tools and techniques that allow aligned ontology population are presented and their applicability discussed. We present the coverage of HL7 RIM inadequacy for ontology mapping and how to circumvent it. NLP techniques for semi automated ontology population and discuss the current trends about knowledge representation and reasoning that concur to the proposed achievement. C1 [Mendes, David; Rodrigues, Irene] Univ Evora, Evora, Portugal. RP Mendes, D (reprint author), Univ Evora, Evora, Portugal. RI Pimenta Rodrigues, Irene/ OI Pimenta Rodrigues, Irene/0000-0003-2370-3019 CR Batet Montserrat, 2011, J Biomed Inform, V44, P118, DOI 10.1016/j.jbi.2010.09.002 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Cimino JJ, 2011, METHOD INFORM MED, V50, P101 Cornet R, 2006, METHOD INFORM MED, V45, P253 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Demner-Fushman D, 2010, J BIOMED INFORM, V43, P587, DOI 10.1016/j.jbi.2010.02.005 Friedman C, 2006, BIOINFORMATICS, V22, P2421, DOI 10.1093/bioinformatics/btl405 Guarino N, 2000, FRONT ARTIF INTEL AP, V54, P219 Hofmann T., 1999, P 15 C UNC ART INT Jolliffe I, 2005, ENCY STAT BEHAV SCI, P1580 KIFER M, 1995, J ASSOC COMPUT MACH, V42, P741, DOI 10.1145/210332.210335 Liu KH, 2011, J BIOMED INFORM, V44, P163, DOI 10.1016/j.jbi.2010.07.006 Meystre S. M., 2008, EXTRACTING INFORM TE Navigli R, 2005, IEEE T PATTERN ANAL, V27, P1075, DOI 10.1109/TPAMI.2005.149 Ng V., 2001, P 40 ANN M ACL ACL P Rodrigues J.M., 2009, MED INFORM ICD, P255, DOI [10.3233/978-1-60750-044-5-255, DOI 10.3233/978-1-60750-044-5-255] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith B., 2008, ESTABLISHING HARMONI SMITH B, 2006, MED INFORM, V124, P133 Smith B, 2010, METHOD INFORM MED, V49, P135, DOI 10.3414/ME9302 Soon WM, 2001, COMPUT LINGUIST, V27, P521, DOI 10.1162/089120101753342653 Spasic I, 2005, BRIEF BIOINFORM, V6, P239, DOI 10.1093/bib/6.3.239 Teufel S., 1997, P ACL WORKSH OP FACT, P1, DOI 10.3115/1598819.1598820 Yoo I., 2010, J MED SYSTEMS NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-24351-6 J9 COMM COM INF SC PY 2011 VL 221 BP 297 EP 306 PG 10 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA BCD88 UT WOS:000309894100032 ER PT S AU Zuo, JL Wang, MW Ye, H AF Zuo, Jiali Wang, Mingwen Ye, Hao BE Deng, H Miao, DQ Lei, JS Wang, FL TI Markov Graphic Method for Information Retrieval SO ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT II SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 3rd International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011) CY SEP 24-25, 2011 CL Taiyuan, PEOPLES R CHINA SP Taiyuan Univ Technol DE Information retrieval model; Markov Network; term relationship AB Information retrieval model is central in information retrieval, which have been studied by many researchers. But over the decade, no single retrieval model has proven to be most effective. One of the reasons is the term independent assumption. Research have shown that adding useful information to retrieval model can improve the performance of retrieval model. As graphical model can model information effectively, we use Markov network to construct the term relationship, and model the term relationship and information retrieval model in a unified framework. Experimental results show that our model can improve the retrieval performance. C1 [Zuo, Jiali] Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330032, Peoples R China. RP Zuo, JL (reprint author), Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330032, Peoples R China. EM August813cn@hotmail.com; mwwang@jxnu.edu.cn; yehao21cn@gmail.com CR Bai J., 2005, 14 ACM INT C INF KNO, P688 Brants T., 2003, 14 M COMP LING NETH, P1 Cao G., 2005, 28 ANN INT ACM SIGIR, P298 Gao J., 2004, 27 ANN INT ACM SIGIR, P170 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Iwayama M., 2003, 26 ANN INT ACM SIGIR, P251 Karimzadehgan M, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P323 Lease M, 2007, 1 PH D WORKSH ACM C, P1 Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579 Metzler D, 2005, 28 ACM SIGIR C RES D, P472 Metzler Jr DA, 2007, BAGS WORDS EFFECTIVE Ponte J. M., 1998, 21 ACM SIGIR C RES D, P275 Rijsbergen C.J.V., 1977, J DOC, V33, P106, DOI 10.1108/eb026637 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Xu J., 1996, 19 ANN INT ACM SIGIR, P4 Zhai C, 2008, 7 FDN TRENDS INFORM, V2, P137 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23886-4 J9 LECT NOTES ARTIF INT PY 2011 VL 7003 BP 487 EP 494 PN II PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BBZ54 UT WOS:000309192100062 ER PT S AU Kubek, M Unger, H AF Kubek, Mario Unger, Herwig BE Unger, H Kyamaky, K Kacprzyk, J TI Search Word Extraction Using Extended Page Rank Calculations SO AUTONOMOUS SYSTEMS: DEVELOPMENTS AND TRENDS SE Studies in Computational Intelligence LA English DT Proceedings Paper CT Workshop on Autonomous Systems CY 2011 CL Palma de Mallorca, SPAIN SP Gesell Informat, German Comp Soc AB This paper describes a new method to determine characteristic terms from texts by weighting them using extended Page Rank calculations. Additionally, this method clusters found semantic term relations to assign each term a level of specifity to be able to distinguish between general and specific terms. This way, it is also possible to differentiate between terms of different semantic orientations in the same specifity level. In the experiments, it is shown which terms can be used for the automatic retrieval of semantically similar documents from large corpora like the World Wide Web through automatic query formulation. The selection of query terms of a different specifity level is also a useful instrument in interactive document retrieval to express the intended similarity of documents to be found. An added advantage of this method is, that it does not rely on third-party datasets and works on single texts. C1 [Kubek, Mario; Unger, Herwig] Fernuniv, Fac Math & Comp Sci, Hagen, Germany. RP Kubek, M (reprint author), Fernuniv, Fac Math & Comp Sci, Hagen, Germany. EM mario.kubek@fernuni-hagen.de; herwig.unger@fernuni-hagen.de; herwig.unger@fernuni-hagen.de CR Buechler M., 2006, THESIS U LEIPZIG Dunning T., 1994, J COMPUTATIONAL LING, V19, P61 Heyer Gerhard, 2006, TEXT MINING WISSENSR Kubek M., 2010, P MALL WORKSH 2010 A Kubek M., 2011, 7 INT C COMP INF TEC Mihalcea R., 2004, P 20 INT C COMP LING Page L., 1998, ORAL HLTH STATUS ORA Quasthoff U., 2002, P 2 INT WORKSH COMP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sodsee S., 2010, COMPUTER SCI ED INNO Wang JH, 2007, LECT NOTES COMPUT SC, V4426, P857 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-24805-4 J9 STUD COMPUT INTELL PY 2011 VL 391 BP 325 EP 337 PG 13 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA BBW77 UT WOS:000308509200025 ER PT S AU Deshpande, N Chakravarthy, S Adaikkalavan, R AF Deshpande, Nikhil Chakravarthy, Sharma Adaikkalavan, Raman BE Fernandes, AAA Gray, AJG Belhajjame, K TI Searching for Complex Patterns over Large Stored Information Repositories SO ADVANCES IN DATABASES SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 28th British National Conference on Databases CY JUL 12-14, 2011 CL Manchester, ENGLAND HO Univ Manchester, Sch Comp Sci DE Information retrieval; Complex patterns; Document search ID MODEL AB Although Information Retrieval (IR) systems, including search engines, have been effective in locating documents that contain specified patterns from large repositories, they support only keyword searches and queries/patterns that use Boolean operators. Expressive search for complex text patterns is important in many domains such as patent search, search on incoming news, and web repositories. In this paper, we first present the operators and their semantics for specifying an expressive search. We then investigate the detection of complex patterns currently not supported by search engines using a pre-computed index, and the type of information needed as part of the index to efficiently detect such complex patterns. We use an expressive pattern specification language and a pattern detection graph mechanism that allows sharing of common sub-patterns. Algorithms have been developed for all the pattern operators using the index to detect complex patterns efficiently. Experiments have been performed to illustrate the scalability of the proposed approach, and its efficiency as compared to a streaming approach. C1 [Deshpande, Nikhil; Chakravarthy, Sharma] Univ Texas Arlington, CSE Dept, Arlington, TX USA. RP Deshpande, N (reprint author), Univ Texas Arlington, CSE Dept, Arlington, TX USA. EM sharma@cs.iusb.edu; raman@cs.iusb.edu CR Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Callan James, 1992, P 3 INT C DAT EXP SY, P78 Chalcravarthy S., 2006, IC AI, P17 Deshpande N., 2005, THESIS U TEXAS ARLIN Elkhalifa L., 2005, P ACM SAC SANT FE NM Mauldin M.L., 1997, IEEE EXPERT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Witten I. H., 1999, MANAGING GIGABYTES C NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24576-3 J9 LECT NOTES COMPUT SC PY 2011 VL 7051 BP 68 EP 82 PG 15 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBW78 UT WOS:000308509400008 ER PT S AU Shi, XL Zhao, Y AF Shi, Xuelin Zhao, Ying BE Chiu, DKW Bellatreche, L Sasaki, H Leung, HF Cheung, SC Hu, H Shao, J TI Dynamic Resource Scheduling and Workflow Management in Cloud Computing SO WEB INFORMATION SYSTEMS ENGINEERING - WISE 2010 WORKSHOPS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 11th International Conference on Web Information Systems Engineering CY DEC 12-14, 2010 CL Hong Kong, PEOPLES R CHINA SP Croucher Fdn, KC Wong Educ Fdn DE cloud computing; dynamic scheduling; workflow management; QoS AB Cloud computing is a new trend after grids. Many market-based resource management strategies are been brought out to implement resource scheduling in cloud computing environment. More and more consumers rely on cloud providers to supply computing service, so economic effectiveness become crucial decisive factor for scheduling policy. In this paper we designed an economic scheduling model with business parameters. And a dynamic scheduling algorithm was presented, which made a trade-off between economic effectiveness and performance. Based on the model and algorithm, we brought out market-oriented workflow management architecture for cloud, in which QoS based resource allocation mechanism was introduced to meet different consumers' demands and improve scheduling efficiency. C1 [Shi, Xuelin; Zhao, Ying] Beijing Univ Chem Technol, Sch Informat Sci & Technol, Beijing 100029, Peoples R China. RP Shi, XL (reprint author), Beijing Univ Chem Technol, Sch Informat Sci & Technol, Beijing 100029, Peoples R China. EM shixl@mail.buct.edu.cn; zhaoy@mail.buct.edu.cn CR BUYYA R, 2009, FUTURE GENER COMP SY, V25, P599, DOI DOI 10.1016/j.future.2008.12.001 Chakrabart S., 2000, SIGKDD EXPLORATIONS, V1, P1 Duan R., 2007, P 2007 ACM IEEE C SU Finger M., 2008, MGC 2008 LEUV BELG D Goldberg D.E., 1988, OPTIMIZATION MACHINE Kumar S, 2009, EUR J OPER RES, V194, P856, DOI 10.1016/j.ejor.2007.12.024 lbarra O., 1977, J ACM, V77, P280 Li CL, 2006, J COMPUT SYST SCI, V72, P706, DOI 10.1016/j.jcss.2006.01.003 Michalewicz Z, 1994, GENETIC ALGORITHMS D, V2nd Nascimento A.P., 2008, P 6 INT WORKSH MIDDL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 11 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24395-0 J9 LECT NOTES COMPUT SC PY 2011 VL 6724 BP 440 EP 448 PG 9 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBL16 UT WOS:000307265000035 ER PT S AU Szymanski, J AF Szymanski, Julian BE Yin, H Wang, W RaywardSmith, V TI Categorization of Wikipedia Articles with Spectral Clustering SO INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2011 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2011) CY SEP 07-09, 2011 CL Norwich, UNITED KINGDOM SP IEEE Syst, Man & Cybernet Soc, Norwich Res Park, Univ E Anglia, SYS CONSULTING, CMP HO Univ E Anglia AB The article reports application of clustering algorithms for creating hierarchical groups within Wikipedia articles. We evaluate three spectral clustering algorithms based on datasets constructed with usage of Wikipedia categories. Selected algorithm has been implemented in the system that categorize Wikipedia search results in the fly. C1 Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. RP Szymanski, J (reprint author), Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. EM julian.szymanski@eti.pg.gda.pl CR Cvetkovic D. M., 1995, SPECTRA GRAPHS THEOR Eldridge S, 2008, BRIT MED J, V336, P876, DOI 10.1136/bmj.39517.495764.25 Kannan R, 2004, J ACM, V51, P497, DOI 10.1145/990308.990313 Kriegel H., 2005, P 11 ACM SIGKDD INT, V677 Manning C. D., 2008, INTRO INFORM RETRIEV, V1 Ng AY, 2002, ADV NEUR IN, V14, P849 Provost F., 1998, P 15 INT C MACH LEAR, V445 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Steinbach M, 2000, KDD WORKSH TEXT MIN, V400, P525 Szymanski J, 2010, LECT NOTES COMPUT SC, V6283, P102, DOI 10.1007/978-3-642-15381-5_13 Vazirani V., 2005, ALGORYTMY APROKSYMAC Verma D., 2003, UWCSE030501 Wagstaff K., 2001, P 18 INT C MACH LEAR, V577, P584 Yeung KY, 2001, BIOINFORMATICS, V17, P309, DOI 10.1093/bioinformatics/17.4.309 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23877-2 J9 LECT NOTES COMPUT SC PY 2011 VL 6936 BP 108 EP 115 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBD23 UT WOS:000306498500014 ER PT S AU Hasan, MM AF Hasan, Md Maruf BE Theeramunkong, T Kunifuji, S Sornlertlamvanich, V Nattee, C TI Experiments in Automatic Assessment Using Basic Information Retrieval Techniques SO KNOWLEDGE, INFORMATION, AND CREATIVITY SUPPORT SYSTEMS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 5th International Conference on Knowledge, Information, and Creativity Support Systems CY NOV 25-27, 2010 CL Chiang Mai, THAILAND SP Thammasat Univ, Sirindhorn Int Inst Technol, Natl Elect & Comp Technol Ctr, Japan Adv Inst Sci & Technol, Chiang Mai Univ, Air Force Off Sci Res, Asian Off Aerospace Res & Dev, Japan Creat Soc, Thailand Convent & Exhibit Bur DE Computer Aided Assessment (CAA); Information Retrieval (IR); Intelligent Text Analysis; Natural Language Processing AB In Information Retrieval (IR), the similarity scores between a query and a set of documents are calculated, and the relevant documents are ranked based on their similarity scores. IR systems often consider queries as short documents containing only a few words in calculating document similarity score. In Computer Aided Assessment (CAA) of narrative answers, when model answers are available, the similarity score between Students' Answers and the respective Model Answer may be a good quality-indicator. With such an analogy in mind, we applied basic IR techniques in the context of automatic assessment and discussed our findings. In this paper, we explain the development of a web-based automatic assessment system that incorporates 5 different text analysis techniques for automatic assessment of narrative answers using vector space framework. The experimental results based on 30 narrative questions with 30 model answers, and 300 student's answers (from 10 students) show that the correlation of automatic assessment with human assessment is higher when advanced text processing techniques such as Keyphrase Extraction and Synonym Resolution are applied. C1 Shinawatra Univ, Sch Technol, Bangkok, Thailand. RP Hasan, MM (reprint author), Shinawatra Univ, Sch Technol, Bangkok, Thailand. EM maruf@siu.ac.th CR Frank E, 1999, P 16 INT JOINT C ART, P668 Jarmasz M., 2003, P INT C REC ADV NAT, P212 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 P'erez D., 2005, P REC ADV NAT LANG P, P380 Perez D., REV SIGNOS, V38, P325 Rose C. P., 2003, HLT NAACL WORKSH BUI, P68 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Witten I. H., 1999, P 4 ACM C DIG LIB, P254, DOI 10.1145/313238.313437 Zhang Y., 2004, IMPROVED TF IDF APPR, P49 NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24787-3 J9 LECT NOTES ARTIF INT PY 2011 VL 6746 BP 13 EP 21 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBI29 UT WOS:000306943800002 ER PT S AU Zhang, Q Li, JW Zhang, ZP AF Zhang, Qing Li, Jianwu Zhang, Zhiping BE Lu, BL Zhang, L Kwok, J TI Efficient Semantic Kernel-Based Text Classification Using Matching Pursuit KFDA SO NEURAL INFORMATION PROCESSING, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 18th International Conference on Neural Information Processing (ICONIP 2011) CY NOV 13-17, 2011 CL Shanghai, PEOPLES R CHINA SP Asia Pacific Neural Network Assembly, Shanghai Jiao Tong Univ, Natl Nat Sci Fdn China, Shanghai Hyron Software Co Ltd, Microsoft Res Asia, Hitachi Res & Dev Corp, Fujitsu Res & Dev Ctr Co Ltd DE Kernel Method; Efficient Text Classification; Matching Pursuit KFDA; Semantic Kernel AB A number of powerful kernel-based learning machines, such as support vector machines (SVMs). kernel Fisher discriminant analysis (KFDA), have been proposed with competitive performance. However, directly applying existing attractive kernel approaches to text classification (TC) task will suffer semantic related information deficiency and incur huge computation costs hindering their practical use in numerous large scale and real-time applications with fast testing requirement. To tackle this problem. this paper proposes a novel semantic kernel-based framework for efficient TC which offers a sparse representation of the final optimal prediction function while preserving the semantic related information in kernel approximate subspace. Experiments on 20-Newsgroup dataset demonstrate the proposed method compared with SVM and KNN (K-nearest neighbor) can significantly reduce the computation costs in predicating phase while maintaining considerable classification accuracy. C1 [Zhang, Qing; Zhang, Zhiping] Inst Sci & Tech Informat China, Beijing 100038, Peoples R China. RP Li, JW (reprint author), Beijing Inst Technol, Beijing Lab Intelligent Informat Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China. EM zqbit@tom.com; ljw@bit.edu.cn; zhangzp@istic.ac.cn CR Burges C.J.C., 1996, INT C MACH LEARN, P71 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diethe T., 2009, AISTATS, P121 Fine S, 2001, J MACHINE LEARNING R, V2, P243 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Joachims T., 1998, LNCS, V1398, P137 Kandola J., 2002, NIPS, P657 Miltsakaki E., 2008, 3 WORKSH INN US NLP, P89 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Smola A.J., 2000, ICML, P911 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 Wang HY, 2005, LECT NOTES COMPUT SC, V3739, P81 Wang P., 2008, KDD 08, P713, DOI DOI 10.1145/1401890.1401976 Wu M., 2005, ICML, P996 Zhang Q, 2010, LECT NOTES COMPUT SC, V6444, P658 NR 18 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24957-0 J9 LECT NOTES COMPUT SC PY 2011 VL 7063 BP 382 EP 390 PN II PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BBI81 UT WOS:000306990600045 ER PT S AU Aerts, S Kitto, K Sitbon, L AF Aerts, Sven Kitto, Kirsty Sitbon, Laurianne BE Song, D Melucci, M Frommholz, I Zhang, P Wang, L Arafat, S TI Similarity Metrics within a Point of View SO QUANTUM INTERACTION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th International Symposium on Quantum Interaction CY JUN 26-29, 2011 CL Aberdeen, SCOTLAND SP Scottish Informat & Comp Sci Alliance DE Similarity; Semantic Space; Triangle Inequality; Metric; Context ID LATENT SEMANTIC ANALYSIS; SPACE; REPRESENTATIONS; MODEL AB Vector space based approaches to natural language processing are contrasted with human similarity judgements to show the manner in which human subjects fail to produce data which satisfies all requirements for a metric space. This result would constrains the validity and applicability vector space based (and hence also quantum inspired) approaches to the modelling of cognitive processes. This paper proposes a resolution to this problem, by arguing that pairs of words imply a context which in turn induces a point of view, so allowing a subject to estimate semantic similarity. Context is here introduced as a point of view vector (POVV) and the expected similarity is derived as a measure over the POVV's. Different pairs of words will invoke different contexts and different POVV's. We illustrate the proposal on a few triples of words and outline further research. C1 [Aerts, Sven] Vrije Univ Brussel, Ctr Interdisciplinary Studies CLEA, Brussels, Belgium. RP Aerts, S (reprint author), Vrije Univ Brussel, Ctr Interdisciplinary Studies CLEA, Brussels, Belgium. EM saerts@vub.ac.be; kirsty.kitto@qut.edu.au; laurianne.sitbon@qut.edu.au RI Sitbon, Laurianne/J-1225-2012 CR Aerts D., 2005, KYBERNETES, V34, P151, DOI DOI 10.1108/03684920510699972 Aerts D, 2009, J MATH PSYCHOL, V53, P314, DOI 10.1016/j.jmp.2009.04.005 Aerts D, 2005, KYBERNETES, V34, P192, DOI 10.1108/03684920510575807 Boyd-Graber Jordan, 2006, P 3 INT WORDNET C Bruza P, 2009, J MATH PSYCHOL, V53, P362, DOI 10.1016/j.jmp.2009.04.004 Bruza P.D., 2011, NONDECOMPOS IN PRESS Bruza P.D., 2010, LOGIC J IGP IN PRESS Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Firth J. R., 1957, PAP LINGUIST Harris ZS, 1954, WORD, V10, P146 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lee DD, 1999, NATURE, V401, P788 NOSOFSKY RM, 1986, J EXP PSYCHOL GEN, V115, P39, DOI 10.1037/0096-3445.115.1.39 Rosch E., J EXPT PSYCHOL, V104, P192 Rosch Eleanor, 1978, COGNITION CATEGORIZA Sahlgren M, 2005, P METH APPL SEM IND Sahlgren M., 2008, P 30 ANN M COGN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 SMITH EE, 1988, COGNITIVE SCI, V12, P485, DOI 10.1207/s15516709cog1204_1 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 TVERSKY A, 1982, PSYCHOL REV, V89, P123, DOI 10.1037/0033-295X.89.2.123 Veksler V.D., 2008, 30 ANN M COGN SCI SO, P1282 Wooters W.K., 1980, THESIS U TEXAS AUSTI NR 27 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24970-9 J9 LECT NOTES COMPUT SC PY 2011 VL 7052 BP 13 EP 24 PG 12 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Physics, Multidisciplinary SC Computer Science; Physics GA BBI79 UT WOS:000306990400003 ER PT S AU Wittek, P Daranyi, S AF Wittek, Peter Daranyi, Sandor BE Song, D Melucci, M Frommholz, I Zhang, P Wang, L Arafat, S TI Spectral Composition of Semantic Spaces SO QUANTUM INTERACTION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th International Symposium on Quantum Interaction CY JUN 26-29, 2011 CL Aberdeen, SCOTLAND SP Scottish Informat & Comp Sci Alliance AB Spectral theory in mathematics is key to the success of as diverse application domains as quantum mechanics and latent semantic indexing, both relying on eigenvalue decomposition for the localization of their respective entities in observation space. This points at some implicit "energy" inherent in semantics and in need of quantification. We show how the structure of atomic emission spectra, and meaning in concept space, go back to the same compositional principle, plus propose a tentative solution for the computation of term, document and collection "energy" content. C1 [Wittek, Peter] Gothenburg Univ, Swedish Sch Lib & Informat Sci, S-50190 Boras, Sweden. RP Wittek, P (reprint author), Gothenburg Univ, Swedish Sch Lib & Informat Sci, Allegatan 1, S-50190 Boras, Sweden. EM peterwittek@acm.org; sandor.daranyi@hb.se CR Aerts D, 2004, J PHYS A-MATH GEN, V37, pL123, DOI 10.1088/0305-4470/37/12/L01 Baker A., 2008, LANGUAGE LINGUISTICS, V2, P289 Beaver D., 2001, PRESUPPOSITION ASSER Beeferman D., 1997, P 35 ANN M ASS COMP, P373 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bruza P., 2008, P QI 2008 2 INT S QU Bruza P. D., 2005, WE WILL SHOW THEM ES DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dorrer C., 2001, P QELS 2001 QUANT EL, P149 Frege G., 1948, PHILOS REV, V57, p[209, 25], DOI 10.2307/2181485 Gardenfors P., 2000, CONCEPTUAL SPACES GE Harris Z., 1970, PAPERS STRUCTURAL TR, P775 Kanerva P., 2000, P COGSCI 2000 22 ANN, V1036 Kitto K, 2009, LECT NOTES ARTIF INT, V5494, P17 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LeCun Y., 2006, PREDICTING STRUCTURE, P1 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Lyons John, 1968, INTRO THEORETICAL LI PETTERSEN BR, 1989, ASTRON ASTROPHYS, V217, P187 Salton G., 1975, DYNAMIC INFORM LIB P SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi S., 2005, P 28 ANN INT ACM SIG, P488, DOI 10.1145/1076034.1076117 Visser A., 2010, STANFORD ENCY PHILOS NR 24 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24970-9 J9 LECT NOTES COMPUT SC PY 2011 VL 7052 BP 60 EP 70 PG 11 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Physics, Multidisciplinary SC Computer Science; Physics GA BBI79 UT WOS:000306990400007 ER PT S AU Lin, YL Brusilovsky, P AF Lin, Yi-ling Brusilovsky, Peter BE Konstan, JA Conejo, R Marzo, JL Oliver, N TI Towards Open Corpus Adaptive Hypermedia: A Study of Novelty Detection Approaches SO USER MODELING, ADAPTATION, AND PERSONALIZATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th International Conference on User Modeling, Adaptation and Personalization (UMAP 2011) CY JUL 11-15, 2011 CL Girona, SPAIN SP User Modeling Inc, ACM SIGART, SIGCHI & SIGIR, Chen Family Fdn, Microsoft Res, US Natl Sci Fdn, Springer, Telefon Espana, Univ Girona DE Novelty detection; knowledge modeling; personalization AB Classic adaptive hypermedia systems are able to track a user's knowledge of the subject and use it to evaluate the novelty and difficulty of content encountered by the user. Our goal is to implement this functionality in an open corpus context where a domain model is not available nor is the content indexed with domain concepts. We examine methods for novelty measurement based on automatic text analysis. To compare these methods, we use an evaluation approach based on knowledge encapsulated in the structure of a textbook. Our study shows that a knowledge accumulation method adopted from the domain of intelligent tutoring systems offers a more meaningful novelty measurement than methods adapted from the area of personalized information retrieval. C1 [Lin, Yi-ling; Brusilovsky, Peter] Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15206 USA. RP Lin, YL (reprint author), Univ Pittsburgh, Sch Informat Sci, 135 N Bellefield Ave, Pittsburgh, PA 15206 USA. EM yil54@pitt.edu; peterb@pitt.edu RI Brusilovsky, Peter/ OI Brusilovsky, Peter/0000-0002-1902-1464 CR Atkinson R. C., 1965, INTRO MATH LEARNING Brusilovsky P., 1998, J UNIVERS COMPUT SCI, V4, P429 Brusilovsky P., 1998, Journal of Computing and Information Technology - CIT, V6 Brusilovsky P, 2001, USER MODEL USER-ADAP, V11, P87, DOI 10.1023/A:1011143116306 Brusilovsky P., 2007, LNCS, V4321, P671 Jones W.P., 1987, J AM SOC INFORM SCI Kraaij W., 1999, 8 TEXT RETRIEVAL C T Miller D.R.H., 2001, 22 ANN INT ACM SIGIR, P214 Newman W., 1995, INTERACTIVE SYSTEM D SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weber G., 1997, 6 INT C US MOD SPRIN, P289 Zhai C., 2001, 10 INT C INF KNOWL M, P403 NR 12 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-22361-7 J9 LECT NOTES COMPUT SC PY 2011 VL 6787 BP 353 EP 358 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBI76 UT WOS:000306990100032 ER PT S AU Wu, JN Shi, YF Guo, CH AF Wu, Jiangning Shi, Yunfei Guo, Chonghui BE Xiong, H Lee, WB TI A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies SO KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 5th International Conference on Knowledge Science, Engineering and Management (KSEM 2011) CY DEC 12-14, 2011 CL Irvine, CA HO Univ Calif-Irvine DE Personalized recommendation; User taste diffusion; Tripartite hypergraph; Folksonomy ID SYSTEMS AB To deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation, we propose a user taste diffusion model based on the tripartite hypergraph to recommend resources for users. Through the defined tri-relation model and diffusion probability matrix, the user's taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user's direct neighbors but also cover the ones which are collected by his/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods. C1 [Wu, Jiangning; Shi, Yunfei; Guo, Chonghui] Dalian Univ Technol, Inst Syst Engn, Dalian 116023, Peoples R China. RP Wu, JN (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116023, Peoples R China. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Gibbins N., 2008, P SOC WEB KNOWL MAN Gourab G., 2009, PHYS REV E, V9, P1 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775 Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 Liang H., 2008, P 2008 IEEE WIC ACM, P59 Liu JG, 2009, INT J MOD PHYS C, V20, P285 Marinho LB, 2011, RECOMMENDER SYSTEMS HANDBOOK, P615, DOI 10.1007/978-0-387-85820-3_19 Millen D.R., 2006, WORKSH SOC NAV COMM Niwa S., 2006, P 3 INT C INF TECHN, P388 Peng J., 2010, P 19 ACM INT C INF K, P809, DOI DOI 10.1145/1871437.1871541 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TSOSUTTER KHL, 2008, P 2008 ACM S APPL CO, P1995, DOI 10.1145/1363686.1364171 Zhang Y.C., 2007, EPL-EUROPHYS LETT, V80, P1 Zhang ZK, 2010, PHYSICA A, V389, P179, DOI 10.1016/j.physa.2009.08.036 Zhou T., 2007, PHYS REV E, V76, P1 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25974-6 J9 LECT NOTES ARTIF INT PY 2011 VL 7091 BP 112 EP 123 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Logic SC Computer Science; Science & Technology - Other Topics GA BBD85 UT WOS:000306579100011 ER PT S AU Dang, YZ Yu, LT Yang, GF Wang, MZ AF Dang, Yanzhong Yu, Litao Yang, Guangfei Wang, Mingzheng BE Xiong, H Lee, WB TI A New Domain Adaptation Method Based on Rules Discovered from Cross-Domain Features SO KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 5th International Conference on Knowledge Science, Engineering and Management (KSEM 2011) CY DEC 12-14, 2011 CL Irvine, CA HO Univ Calif-Irvine DE Transfer learning; domain adaptation; rule discovery AB Traditional classification methods in machine learning assume that training data and testing data should share the same feature space and have the same data distribution. In real world applications, however, this assumption often does not hold. If there are very few labeled instances in the target domain for training, it is time-consuming to label them manually. In this case, a source domain which has semantic relationships with the target domain but has the different feature space or distribution can be used to assist the classification. In this paper, we propose a new method using rules to help the domain adaptation, which can well represent the knowledge relationships between source domain and target domain. In this algorithm we first discover term-term rules according to the term relationships in target domain to build the knowledge bridge, then we reconstruct the source domain using these rules and get a better classifier to improve the cross-domain classification performance. We conduct several cross-domain data sets and demonstrate that the proposed method is easy to understand and it has a better performance compared to state-of-art transfer algorithms. C1 [Dang, Yanzhong; Yu, Litao; Yang, Guangfei; Wang, Mingzheng] Dalian Univ Technol, Inst Syst Engn, Dalian 116023, Peoples R China. RP Dang, YZ (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116023, Peoples R China. CR Blitzer J., 2006, P C EMP METH NAT LAN, P120, DOI 10.3115/1610075.1610094 Blitzer J., 2007, P 45 ANN M ASS COMP, P432 DAI W, 2007, P 13 ACM SIGKDD INT, P210, DOI 10.1145/1281192.1281218 Dai W., 2007, P 24 INT C MACH LEAR, P193, DOI DOI 10.1145/1273496.1273521 Joachims T, 2003, P 20 INT C MACH LEAR, P290 Joachims T, 1999, ADV KERNEL METHODS S Li S., 2008, P 46 ANN M ASS COMP, P257, DOI 10.3115/1557690.1557765 Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191 Pan SJ, 2010, P 19 INT C WORLD WID, P751, DOI 10.1145/1772690.1772767 Pedrycz W, 2009, AUTOMATICA, V45, P449, DOI 10.1016/j.automatica.2008.07.010 Rakesh Agrawal, 1993, P 1993 ACM SIGMOD IN, P207 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnik V., 1998, STAT LEARNING THEORY Xiang EW, 2010, IEEE T KNOWL DATA EN, V22, P770, DOI 10.1109/TKDE.2010.31 Zhuang FZ, 2010, IEEE T KNOWL DATA EN, V22, P1664, DOI 10.1109/TKDE.2009.205 NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25974-6 J9 LECT NOTES ARTIF INT PY 2011 VL 7091 BP 425 EP 436 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Logic SC Computer Science; Science & Technology - Other Topics GA BBD85 UT WOS:000306579100038 ER PT S AU Gong, JB Sun, ST AF Gong, Jibing Sun, Shengtao BE Tang, J King, I Chen, L Wang, JY TI Individual Doctor Recommendation Model on Medical Social Network SO ADVANCED DATA MINING AND APPLICATIONS, PT II SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 7th International Conference on Advanced Data Mining and Applications CY DEC 17-19, 2011 CL Beijing, PEOPLES R CHINA SP IBM Res, China Samsung Telecom R&D Ctr, Tsinghua Univ AB It is difficult for patients to find the most appropriate doctor/physician to diagnose. In most cases, just considering Authority Degrees of Candidate Doctors(AD-CDs) cannot satisfy this need due to some objective preferences such as economic affordability of a patient, commuting distance for visiting doctors and so on. In this paper, we try to systematically investigate the problem and propose a novel method to enable patients access such intelligent medical service like this. In the method, we first mine patient-doctor relationships via Time-constraint Probability Factor Graph mode(TPFG) from a medical social network, and then extract four essential features for AD-CDs that would be subsequently sorted via Ranking SVM. At last, combining AD-CDs and patients' preferences together, we propose a novel Individual Doctor Recommendation Model, namely IDR-Model, to compute doctor recommendation success rate based on weighted average method. We conduct experiments to verify the method on a real medical data set. Experimental results show that we obtain the better accuracies of mining patient-doctor relationship from the network, AD-CDs ranking is also better than the traditional Reduced SVM method, and doctor recommendation results of IDR-Model is reasonable and satisfactory. C1 [Gong, Jibing; Sun, Shengtao] Yanshan Univ, Dept Comp Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China. RP Gong, JB (reprint author), Yanshan Univ, Dept Comp Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China. EM gongjibing@gmail.com CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Herbrich Ralf, 2000, LARGE MARGIN RANK BO Joachims T, 2006, P ACM C KNOWL DISC D Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kschischang FR, 2001, IEEE T INFORM THEORY, V47, P498, DOI 10.1109/18.910572 Lee YJ, 2007, IEEE T NEURAL NETWOR, V18, P1, DOI 10.1109/TNN.2006.883722 Macdonald C., 2006, CIKM 2006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang J., 2008, SIGKDD 2008 Wang C., 2010, SIGKDD 2010 Wang Z., WE CALCULATE ACAD ST Wu T., 2010, DATA MINING KNOWLEDG Yang Z., 2009, SIGKDD 2009 Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 NR 14 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25855-8 J9 LECT NOTES ARTIF INT PY 2011 VL 7121 BP 69 EP 81 PN II PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBD34 UT WOS:000306504100006 ER PT S AU Crouch, CJ Crouch, DB Vadlamudi, S Cherukuri, R Mahule, A AF Crouch, Carolyn J. Crouch, Donald B. Vadlamudi, Sandeep Cherukuri, Ramakrisha Mahule, Abhijeet BE Geva, S Kamps, J Schenkel, R Trotman, A TI A Useful Method for Producing Competitive Ad Hoc Task Results SO COMPARATIVE EVALUATION OF FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2010 CY DEC 13-15, 2010 CL Vught, NETHERLANDS ID RETRIEVAL; ELEMENTS AB This paper reports the final results of our experiments involving the 2009 INEX Ad-Hoc Track and describes the methodology upon which our current, 2010 experiments are built. In 2009, our INEX investigations centered on indentifying a methodology for producing what we have referred to as improved focused elements-i.e., elements which when evaluated are competitive with others in the upper ranges of the official rankings. Our 2009 INEX paper [5] describes our approach to producing such elements, which is based on the combination of traditional document retrieval (to identify the document set of interest to the query) with our method of dynamic element retrieval (to generate and retrieve the elements of the document(s) so identified) and the subsequent application of a specific focusing technique for the Focused and Relevant-in-Context tasks (to select the focused elements). The system is based on the Vector Space Model [101; basic functions are performed using the Smart experimental retrieval system [9]. In this paper, we report the final results of these experiments, applied to the INEX 2009 Thorough, Focused and Relevant-in-Context tasks. Results show that this approach produces highly ranked results for all three of these Ad Hoc tasks. Significance tests, applied to these results as compared to the top-ranked runs, show in which cases statistically significant results are obtained. Our 2010 work is ongoing at present. C1 [Crouch, Carolyn J.; Crouch, Donald B.; Vadlamudi, Sandeep; Cherukuri, Ramakrisha; Mahule, Abhijeet] Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. EM ccrouch@d.umn.edu CR Bapat S, 2008, THESIS U MINNESOTA D Cherukuri R, 2010, THESIS U MINNESOTA D Crouch CJ, 2010, LECT NOTES COMPUT SC, V6203, P70, DOI 10.1007/978-3-642-14556-8_8 Crouch CJ, 2006, ACM T INFORM SYST, V24, P437, DOI 10.1145/1185877.1185880 Crouch CJ, 2009, LECT NOTES COMPUT SC, V5631, P33 Kamps J, 2010, LECT NOTES COMPUT SC, V6203, P26, DOI 10.1007/978-3-642-14556-8_5 Khanna S, 2005, THESIS U MINNESOTA D Mahule A, 2010, THESIS U MINNESOTA D Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Vadlamudi S, 2009, THESIS U MINNESOTA D NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23576-4 J9 LECT NOTES COMPUT SC PY 2011 VL 6932 BP 63 EP 70 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBD22 UT WOS:000306498300005 ER PT S AU Tovar, M Cruz, A Vazquez, B Pinto, D Vilarino, D Montes, A AF Tovar, Mireya Cruz, Adrian Vazquez, Blanca Pinto, David Vilarino, Darnes Montes, Azucena BE Geva, S Kamps, J Schenkel, R Trotman, A TI An Iterative Clustering Method for the XML-Mining Task of the INEX 2010 SO COMPARATIVE EVALUATION OF FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2010 CY DEC 13-15, 2010 CL Vught, NETHERLANDS AB In this paper we propose two iterative clustering methods for grouping Wikipedia documents of a given huge collection into clusters. The recursive method clusters iteratively subsets of the complete collection. In each iteration, we select representative items for each group, which are then used for the next stage of clustering. The presented approaches are scalable algorithms which may be used with huge collections that in other way (for instance, using the classic clustering methods) would be computationally expensive of being clustered. The obtained results outperformed the random baseline presented in the INEX 2010 clustering task of the XML-Mining track. C1 [Tovar, Mireya; Pinto, David; Vilarino, Darnes] Benemerita Univ Autonoma Puebla, Puebla, Mexico. RP Tovar, M (reprint author), Benemerita Univ Autonoma Puebla, Puebla, Mexico. EM mtovar@cs.buap.mx; abadrector@gmail.com; blanca_tec@hotmail.com; dpinto@cs.buap.mx; darnes@cs.buap.mx; amr@cenidet.edu.mx CR MacKay D. J. C., 2003, INFORM THEORY INFERE Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Mirkin B., 1996, MATH CLASSIFICATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 4 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23576-4 J9 LECT NOTES COMPUT SC PY 2011 VL 6932 BP 377 EP 382 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBD22 UT WOS:000306498300036 ER PT J AU Laorden, C Sanz, B Santos, I Galan-Garcia, P Bringas, PG AF Laorden, Carlos Sanz, Borja Santos, Igor Galan-Garcia, Patxi Bringas, Pablo G. BE Herrero, A Corchado, E TI Collective Classification for Spam Filtering SO COMPUTATIONAL INTELLIGENCE IN SECURITY FOR INFORMATION SYSTEMS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th International Conference on Computational Intelligence in Security for Information Systems (CISIS)/IWANN CY JUN 08-10, 2011 CL Torremolinos, SPAIN DE Spam filtering; collective classification; semi-supervised learning ID MODEL AB Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. Many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods require a training step with labelled data. Dealing with the situation where the availability of labelled training instances is limited slows down the progress of filtering systems and offers advantages to spammers. Currently, many approaches direct their efforts into Semi-Supervised Learning (SSL). SSL is a halfway method between supervised and unsupervised learning, which, in addition to unlabelled data, receives some supervision information such as the association of the targets with some of the examples. Collective Classification for Text Classification poses as an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, Collective Classification algorithms for spam filtering to overcome the amount of unclassified e-mails that are sent every day. C1 [Laorden, Carlos; Sanz, Borja; Santos, Igor; Galan-Garcia, Patxi; Bringas, Pablo G.] Univ Deusto, DeustoTech Comp S3Lab, Bilbao 48007, Spain. RP Laorden, C (reprint author), Univ Deusto, DeustoTech Comp S3Lab, Ave Univ 24, Bilbao 48007, Spain. EM claorden@deusto.es; borja.sanz@deusto.es; isantos@deusto.es; patxigg@deusto.es; pablo.garcia.bringas@deusto.es RI Santos, Igor/L-3854-2013 OI Santos, Igor/0000-0002-9511-8612 CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Burton B., 2003, P SPAM C CHIRITA PA, 2005, P 14 ACM INT C INF K, P373, DOI 10.1145/1099554.1099671 Chiu Y., 2007, 3 INT C ICNC 2007, V4, P203 Dengel A., 1995, INT C DOC AN REC, V2, P587 Denoyer L, 2004, INFORM PROCESS MANAG, V40, P807, DOI 10.1016/j.ipm.2004.04.009 FUJISAWA H, 1992, P IEEE, V80, P1079, DOI 10.1109/5.156471 Garner S.R., 1995, P NZ COMP SCI RES ST, P57 Holmes G., 1994, WEKA MACHINE LEARNIN, P357 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 KENT JT, 1983, BIOMETRIKA, V70, P163 Mason J., 2002, HEANET ANN C Namata G., 2009, COLLECTIVE CLASSIFIC, P51 Raymond E., 2005, BOGOFILTER FAST OPEN Robinson G., 2003, LINUX J, V3 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schryen G., 2006, P 39 AN HAW INT C SY, V6, P129 Singh Yogesh, 2009, International Journal of Computer Applications in Technology, V35, DOI 10.1504/IJCAT.2009.026595 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21323-6 J9 LECT NOTES COMPUT SC PY 2011 VL 6694 BP 1 EP 8 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBA72 UT WOS:000306312300001 ER PT B AU Chen, CM AF Chen, Chaomei BA Chen, CM BF Chen, CM TI Knowledge Domain Analysis SO TURNING POINTS: THE NATURE OF CREATIVITY LA English DT Article; Book Chapter ID COMBINING BIBLIOMETRICS; INFORMATION-RETRIEVAL; SCIENTIFIC LITERATURE; RELEVANCE THEORY; RESEARCH FRONTS; CENTRALITY; ALGORITHM; NETWORKS; VISUALIZATION; BETWEENNESS C1 Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Chen, CM (reprint author), Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA. EM chaomei.chen@drexel.edu; chaomei.chen@drexel.edu CR BONACICH P, 1987, AM J SOCIOL, V92, P1170, DOI 10.1086/228631 Brandes U, 2001, J MATH SOCIOL, V25, P163 Bursill H., 1859, HAND SHADOWS BE THRO Carmel D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P139, DOI 10.1145/1571941.1571967 Chen-Lung Chan, 2005, Proceedings. 11th International Conference on Parallel and Distributed Systems, DOI 10.1145/1040830.1040859 Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309 Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004 Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100 Chen CM, 2009, IEEE INTELL SYST, V24, P74 Chen CM, 2003, J AM SOC INF SCI TEC, V54, P435, DOI 10.1002/asi.10229 Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317 CRONIN B, 1981, J INFORM SCI, V3, P27, DOI 10.1177/016555158100300104 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dunning T., 1993, Computational Linguistics, V19 Fiszman M, 2009, J BIOMED INFORM, V42, P801, DOI 10.1016/j.jbi.2008.10.002 Frank E., 1999, DATA MINING PRACTICA FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543 French BM, 2010, EARTH-SCI REV, V98, P123, DOI 10.1016/j.earscirev.2009.10.009 Garfield E., 1979, CITATION INDEXING IT Garfield E, 2004, J INF SCI, V30, P119, DOI 10.1177/0165551504042802 Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 KAMADA T, 1989, INFORM PROCESS LETT, V31, P7, DOI 10.1016/0020-0190(89)90102-6 Kiss C, 2008, DECIS SUPPORT SYST, V46, P233, DOI 10.1016/j.dss.2008.06.007 Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI DOI 10.1023/A:1024940629314 Kumar R., 2003, P 12 INT C WORLD WID, P568, DOI 10.1145/775152.775233 Luxburg U. v., 2009, LIMITS SPECTRAL CLUS Morris SA, 2008, ANNU REV INFORM SCI, V42, P213 Morris SA, 2003, J AM SOC INF SCI TEC, V54, P413, DOI 10.1002/asi.10227 Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103 Ng AY, 2002, ADV NEUR IN, V14, P849 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneider J.W., 2009, P 12 INT C SCI INF I, P443 Schvaneveldt R. W., 1990, PATHFINDER ASS NETWO Schwarz J.H., 1996, 2 SUPERSTRING REVOLU SCHWARZ JH, 1982, PHYS REP, V89, P224 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Shibata N, 2008, TECHNOVATION, V28, P758, DOI 10.1016/j.technovation.2008.03.009 SMALL HG, 1977, SOC STUD SCI, V7, P139, DOI 10.1177/030631277700700202 SMALL H, 1986, J AM SOC INFORM SCI, V37, P97, DOI 10.1002/asi.4630370302 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 Sparck Jones K., 1999, ADV AUTOMATIC TEXT S, P2 Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936 von Luxburg U., 2006, TUTORIAL SPECTRAL CL White HD, 2007, J AM SOC INF SCI TEC, V58, P583, DOI 10.1002/asi.20542 White HD, 2007, J AM SOC INF SCI TEC, V58, P536, DOI 10.1002/asi.20543 Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P2070, DOI 10.1002/asi.20910 NR 47 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-3-642-19159-6 PY 2011 BP 139 EP 175 PG 37 WC Information Science & Library Science SC Information Science & Library Science GA BZJ45 UT WOS:000301757800006 ER PT S AU Varadharajalu, A Liu, W Wong, W AF Varadharajalu, Arvin Liu, Wei Wong, Wilson BE Wang, DH Reynolds, M TI Author Name Disambiguation for Ranking and Clustering PubMed Data Using NetClus SO AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 24th Australasian Joint Conference on Artificial Intelligence (AI) CY DEC 05-08, 2011 CL Perth, AUSTRALIA DE Author Disambiguation; Clustering; NetClus; Heterogeneous Information Network ID MODEL AB The ranking and clustering of publication databases are often used to discover useful information about research areas. NetClus is an iterative algorithm for clustering heterogenous star-schema information network that incorporates the ranking information of individual data types. The algorithm has been evaluated using the DBLP database. In this paper, we apply NetClus on PubMed, a free database of articles on life sciences and biomedical topics to discover key aspects of cancer research. The absence of unique identifiers for authors in PubMed introduces additional challenges. To address this, we introduce an improved author disambiguation technique using affiliation string normalisation based on vector space model together with co-author networks. Our technique for disambiguating authors, which offers a. higher accuracy than existing techniques, significantly improves NetClus clustering results. C1 [Varadharajalu, Arvin; Liu, Wei] Univ Western Australia, Sch Comp Sci & Software Engn, Nedlands, WA 6009, Australia. RP Liu, W (reprint author), Univ Western Australia, Sch Comp Sci & Software Engn, Nedlands, WA 6009, Australia. EM wei@csse.uwa.edu.au CR Boyack KW, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018029 Golic J. D., 1991, Journal of Cryptology, V3, DOI 10.1007/BF00196912 Jonnalagadda Siddhartha Reddy, 2010, J Biomed Discov Collab, V5, P50 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun Y., 2009, EDBT, P565 Sun Y., 2009, P 15 ACM SIGKDD INT, P797, DOI 10.1145/1557019.1557107 Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] Wong W., 2006, P AUSDM2006 5 AUSTR, V61, P83 Yu W, 2007, BMC MED INFORM DECIS, V7, DOI 10.1186/1472-6947-7-17 NR 9 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25831-2 J9 LECT NOTES ARTIF INT PY 2011 VL 7106 BP 152 EP 161 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BAS34 UT WOS:000305356500016 ER PT J AU Callaert, J Van Looy, B Joris, G AF Callaert, Julie Van Looy, Bart Joris, Grouwels BE Noyons, E Ngulube, P Leta, J TI Delineating the scientific footprint in technology: Identifying science within non-patent references SO PROCEEDINGS OF ISSI 2011: THE 13TH CONFERENCE OF THE INTERNATIONAL SOCIETY FOR SCIENTOMETRICS AND INFORMETRICS, VOLS 1 AND 2 SE Proceedings of the International Conference on Scientometrics and Informetrics LA English DT Proceedings Paper CT 13th Conference of the International-Society-for-Scientometrics-and-Informetrics (ISSI) CY JUL 04-07, 2011 CL Durban, SOUTH AFRICA SP Int Soc Scientometr & Informetr (ISSI) ID PATENT CITATIONS AB Indicators based on non-patent references (NPRs) are increasingly being used for measuring and monitoring science-technology relations. But NPRs in patent documents contain noise, as not all of them can be considered 'scientific'. In this paper, we introduce the results of a machine-learning algorithm that allows to identify scientific references in an automated way. The obtained outcomes are used to analyze indicators based on non-patent references, with a focus on the difference between NPR-and SNPR-based indicators. Differences between both indicators are significant and dependent on the considered patent system, the applicant country and the technological field. These results indicate the relevance of delineating scientific references when developing and employing indicators of science-technology relations. Furthermore, the revealed sensitivity of these indicators to technological, national and patent system characteristics urges for a contextualized interpretation. C1 Katholieke Univ Leuven, ECOOM, Fac Business & Econ, Louvain, Belgium. CR Callaert J, 2006, SCIENTOMETRICS, V69, P3, DOI 10.1007/s11192-006-0135-8 Harhoff D, 2003, RES POLICY, V32, P1343, DOI 10.1016/S0048-7333(02)00124-5 Magerman T, 2010, SCIENTOMETRICS, V82, P289, DOI 10.1007/s11192-009-0046-6 Meyer M, 2000, RES POLICY, V29, P409, DOI 10.1016/S0048-7333(99)00040-2 Meyer M, 2000, SCIENTOMETRICS, V49, P93, DOI 10.1023/A:1005613325648 Michel J, 2001, SCIENTOMETRICS, V51, P185, DOI 10.1023/A:1010577030871 NARIN F, 1985, SCIENTOMETRICS, V7, P369, DOI 10.1007/BF02017155 Nelson AJ, 2009, RES POLICY, V38, P994, DOI 10.1016/j.respol.2009.01.023 Nomaler O, 2008, ECON SYST RES, V20, P339, DOI 10.1080/09535310802551315 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tijssen RJW, 2000, SCIENTOMETRICS, V47, P389 Van Looy B., 2003, J TECHNOLOGY TRANSFE, V28, P53, DOI 10.1023/A:1021630803637 Van Looy B, 2003, SCIENTOMETRICS, V57, P355 Van Looy B, 2007, SCIENTOMETRICS, V70, P441, DOI 10.1007/s11192-007-0211-8 VANVIANEN BG, 1990, RES POLICY, V19, P61, DOI 10.1016/0048-7333(90)90034-4 Verbeek A., 2001, LINKING SCI TECHNOLO Verbeek A, 2002, INT J MANAG REV, V4, P179, DOI 10.1111/1468-2370.00083 NR 17 TC 0 Z9 0 PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI PI LEUVEN PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN, B-3000, BELGIUM SN 2175-1935 BN 978-90-817527-0-1 J9 PRO INT CONF SCI INF PY 2011 BP 142 EP 154 PG 13 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BAR88 UT WOS:000305337100016 ER PT J AU Magerman, T Van Looy, B Debackere, K AF Magerman, Tom Van Looy, Bart Debackere, Koenraad BE Noyons, E Ngulube, P Leta, J TI In search of anti-commons: Patent-Paper Pairs in Biotechnology. An Analysis of Citation Flows SO PROCEEDINGS OF ISSI 2011: THE 13TH CONFERENCE OF THE INTERNATIONAL SOCIETY FOR SCIENTOMETRICS AND INFORMETRICS, VOLS 1 AND 2 SE Proceedings of the International Conference on Scientometrics and Informetrics LA English DT Proceedings Paper CT 13th Conference of the International-Society-for-Scientometrics-and-Informetrics (ISSI) CY JUL 04-07, 2011 CL Durban, SOUTH AFRICA SP Int Soc Scientometr & Informetr (ISSI) ID UNIVERSITIES; INNOVATIONS; ANTICOMMONS; PROPERTY AB In this paper, we examine the possible presence of anti-commons dynamics in biotechnology by comparing citation patterns of patent-paper pairs, i.e. scientific publications from which the contents (subject, methodology, findings, discovery) is part of a patent application. Patent-paper pairs have been detected by relying on text mining algorithms. Starting from a dataset consisting of 948,432 scientific biotechnology publications and 88,994 EPO and USPTO biotechnology patent documents, a total of 584 patent-paper pairs have been identified. Forward citations patterns of those patent-paper pairs have been compared with biotechnology patents and publications that are not part of a patent-paper pairs. In terms of scientific citations, patent-paper pairs receive considerable more citations than publication without a patent counterpart. This is not the case for the technological impact of patent-paper pairs; patent citation rates do not differ significantly between patents with or without a scientific counterpart. As such our findings do not provide evidence for the presence of anti-commons effects stemming from the introduction of IP within scientific activities in the field of biotechnology. C1 [Magerman, Tom; Van Looy, Bart; Debackere, Koenraad] Katholieke Univ Leuven, Fac Business & Econ, Louvain, Belgium. EM tom.magerman@econ.kuleuven.be CR Archibugi D., 1992, SCI PUBL POLICY, V19, P357 Argyres NS, 1998, J ECON BEHAV ORGAN, V35, P427, DOI 10.1016/S0167-2681(98)00049-3 BASBERG BL, 1987, RES POLICY, V16, P131, DOI 10.1016/0048-7333(87)90027-8 Boitani A., 1990, SPATIAL CONTEXT TECH Branscomb L. M., 1999, IND KNOWLEDGE U IND Czarnitzki D., 2007, RES EVALUAT, V164, P311 David P. A., 2000, WORKING PAPERS STANF Dosi G., 2000, INNOVATION ORG EC DY Etzkowitz H., 1998, CAPITALIZING KNOWLED Fabrizio KR, 2008, RES POLICY, V37, P914, DOI 10.1016/j.respol.2008.01.010 Freeman C., 1987, TECHNOLOGY POLICY EC FREEMAN C, 1994, CAMBRIDGE J ECON, V18, P463 Heller MA, 1998, SCIENCE, V280, P698, DOI 10.1126/science.280.5364.698 Heller MA, 1998, HARVARD LAW REV, V111, P621, DOI 10.2307/1342203 Henderson R, 1998, REV ECON STAT, V80, P119, DOI 10.1162/003465398557221 Krimsky, 2003, SCI PRIVATE INTEREST Lissoni F, 2008, RES EVALUAT, V17, P87, DOI 10.3152/095820208X287171 Lundvall B., 1992, NATL SYSTEMS INNOVAT Magerman T, 2010, SCIENTOMETRICS, V82, P289, DOI 10.1007/s11192-009-0046-6 Magerman T., 2011, ASSESSMENT IN PRESS MANSFIELD E, 1995, REV ECON STAT, V77, P55, DOI 10.2307/2109992 Mansfield E, 1996, RES POLICY, V25, P1047, DOI 10.1016/S0048-7333(96)00893-1 Meyer M, 2003, SCIENTOMETRICS, V58, P321, DOI 10.1023/A:1026240727851 Montobbio F., 2007, EC INNOVATION NEW TE, V16, P101, DOI DOI 10.1080/10438590600982830 Mowery D. C., 1999, SOURCES IND LEADERSH Murray F, 2007, J ECON BEHAV ORGAN, V63, P648, DOI 10.1016/j.jebo.2006.05.017 Nelson R., 1993, NATL INNOVATION SYST Nelson R.R., 1993, TECHNICAL INNOVATION OECD, 2005, FRAM BIOT STAT, P29 Rosenberg N., 1998, GEN PURPOSE TECHNOLO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stephan P., 2007, EC INNOVATION NEW TE, V61, P71, DOI 10.1080/10438590600982806 Thursby JG, 2002, MANAGE SCI, V48, P90, DOI 10.1287/mnsc.48.1.90.14271 TRAJTENBERG M, 1990, RAND J ECON, V21, P172, DOI 10.2307/2555502 NR 34 TC 0 Z9 0 PU INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI PI LEUVEN PA KATHOLIEKE UNIV LEUVEN, FACULTEIT E T E W, DEKENSTRAAT 2, LEUVEN, B-3000, BELGIUM SN 2175-1935 BN 978-90-817527-0-1 J9 PRO INT CONF SCI INF PY 2011 BP 521 EP 533 PG 13 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BAR88 UT WOS:000305337100054 ER PT J AU Sitbon, L Bruza, P Iannella, R Indrakanti, S AF Sitbon, Laurianne Bruza, Peter Iannella, Renato Indrakanti, Sarath BE Foth, M Forlano, L Satchell, C Gibbs, M TI Empowering Urban Communities through Social Commonalities SO FROM SOCIAL BUTTERFLY TO ENGAGED CITIZEN: URBAN INFORMATICS, SOCIAL MEDIA, UBIQUITOUS COMPUTING, AND MOBILE TECHNOLOGY TO SUPPORT CITIZEN ENGAGEMENT LA English DT Article; Book Chapter C1 [Iannella, Renato] Natl ICT Australia NICTA Res Lab, Eveleigh, NSW, Australia. [Iannella, Renato] Queensland Univ Technol, Brisbane, Qld 4001, Australia. [Sitbon, Laurianne; Iannella, Renato; Indrakanti, Sarath] Univ Queensland, Brisbane, Qld 4072, Australia. [Iannella, Renato] LiveEvents Wireless, IPR Syst, Godalming, Surrey, England. [Iannella, Renato] DSTC, Brisbane, Qld, Australia. [Indrakanti, Sarath] NICTA, Brisbane Res Lab, Eveleigh, NSW, Australia. RP Sitbon, L (reprint author), Univ Queensland, Brisbane, Qld 4072, Australia. RI Foth, Marcus/I-9914-2012; Sitbon, Laurianne/J-1225-2012 OI Foth, Marcus/0000-0001-9892-0208; CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Brooke J., 2009, P 1 INT CIKM WORKSH, P1, DOI 10.1145/1651461.1651463 CHEN J, 2009, P 27 INT C HUM FACT, P201, DOI 10.1145/1518701.1518735 Chunyan X., 2006, P COMP VIS PATT REC, P46 DIMICCO JM, 2007, P 2007 INT ACM C SUP, P383, DOI 10.1145/1316624.1316682 Iriberri A, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459356 Jung J. J., 2007, P 4 EUR SEM WEB C IN, P267 Koh J., 2007, COMMUN ACM, V50, P68, DOI 10.1145/1216016.1216023 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Laverack G, 2006, J HEALTH POPUL NUTR, V24, P113 Lee DD, 1999, NATURE, V401, P788 Liben-Nowell D, 2007, J AM SOC INF SCI TEC, V58, P1019, DOI 10.1002/asi.20591 Maciel C., 2009, P 10 ANN INT C DIG G, P25 Mei Q., 2007, P 16 INT C WORLD WID, P171, DOI DOI 10.1145/1242572.1242596 Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Puddifoot JE, 1995, J COMMUNITY APPL SOC, V5, P357, DOI 10.1002/casp.2450050507 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wiebe J., 2003, AAAI SPRING S NEW DI, P12 Zuccon G., 2009, P 3 INT S QUANT INT, P225 NR 20 TC 0 Z9 0 PU MIT PRESS PI CAMBRIDGE PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA BN 978-0-26229-934-3 PY 2011 BP 375 EP 392 PG 18 WC Communication; Sociology SC Communication; Sociology GA BYJ06 UT WOS:000298998100025 ER PT S AU Sieminski, A AF Sieminski, Andrzej BE Jedrzejowicz, P Nguyen, NT Hoang, K TI WordNet Based Word Sense Disambiguation SO COMPUTATIONAL COLLECTIVE INTELLIGENCE: TECHNOLOGIES AND APPLICATIONS, PT II: THIRD INTERNATIONAL CONFERENCE, ICCCI 2011 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 3rd International Conference on Computational Collective Intelligence (ICCCI 2011) CY SEP 21-23, 2011 CL Gdynia, POLAND HO Gdynia Maritime Univ DE word sense disambiguation; WordNet; tf-idf; Ant Colony Optimization; SynPath AB Due to the intrinsic ambiguity of a natural language the word sense disambiguation or WSD is a challenging task. The paper uses Word Net for (WSD) for that purpose. Unlike many others approaches on that area it exploits the structure of Word Net in an indirect manner. To disambiguate the words it measures the semantic similarity of the words glosses. The similarity is calculated using the SynPath algorithm. Its essence is the replacement of each word by a sequence of Word Net synset identifiers that describe related concepts. To measure the similarity of such sequence the standard tf-idf formula is used. At the last stage a modification of Ant Colony Optimization for the Traveling Salesman Problem is responsible for word disambiguation. C1 Wroclaw Univ Technol, Inst Informat, PL-50370 Wroclaw, Poland. RP Sieminski, A (reprint author), Wroclaw Univ Technol, Inst Informat, Wybrzeze St Wyspianskiego 27, PL-50370 Wroclaw, Poland. EM Andrzej.Sieminski@pwr.wroc.pl CR Agirre E, 1999, COMBINING SUPERVISED Bhattacharya I, 2004, P ANN M ACL Chirico U, 2004, ANTS2004 Dorigo M, 1992, THESIS POLITECNICO M Dorigo M., 2009, IRIDIA TECHNICAL REP Fellbaum C., 2001, P NAACL 2001 WORKSH Fellbaum C., 1998, WORD NET ELECT LEXIC Hastings P, 1998, P 29 ANN C COGN SCI Kwong O.Y, 2000, THESIS U CAMBRIDGE Magnini B., 2000, INT C LANG RES EV LR Manning C.D., 2008, INTRO INFORM RETRIEV Mihalcea R, 2006, CORPUS BASED KNOWLED, P775 Mihalcea R., 2000, P FLOR ART INT RES S, P219 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Ng H. T., 1999, P ACL SIGLEX WORKSH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seco N, 2004, P EUR C ART INT Sieminski A, 2010, ADV INTEL SOFT COMPU, V80, P245, DOI 10.1007/978-3-642-14989-4_23 Stevenson M, 2001, COMPUT LINGUIST, V27, P321, DOI 10.1162/089120101317066104 Xiaohua Z., 2005, 18 FLAIRS C CLEARW B NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23937-3 J9 LECT NOTES ARTIF INT PY 2011 VL 6923 BP 405 EP 414 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BAF62 UT WOS:000304027900041 ER PT S AU Dong, YQ Zhao, XJ Zhang, GJ AF Dong, Yongquan Zhao, Xiangjun Zhang, Gongjie BE Luo, Q TI Web Data Extraction with Hierarchical Clustering and Rich Features SO RECENT TRENDS IN MATERIALS AND MECHANICAL ENGINEERING MATERIALS, MECHATRONICS AND AUTOMATION, PTS 1-3 SE Applied Mechanics and Materials LA English DT Proceedings Paper CT International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2011) CY JAN 27-28, 2011 CL Shenzhen, PEOPLES R CHINA SP Intelligent Informat Technol Appl Res Assoc, So Illinoic Univ Carbondale DE Data Extraction; Hierarchical Clustering; Feature; Deep Web AB A novel approach is proposed to automatically extract data records from detail pages using hierarchical clustering techniques. The approach uses the information of the listing pages to identify the content blocks in detail pages, which narrows the scopes of Web data extraction. Meanwhile, it also makes full use of the structure and content features to cluster content feature vectors. Finally, it aligns data elements of multiple details pages to extract the data records. Experiment results on test beds of real web pages show that the approach can achieve high extraction accuracy and outperforms the existing techniques substantially. C1 [Dong, Yongquan; Zhao, Xiangjun; Zhang, Gongjie] Xuzhou Normal Univ, Xuzhou, Jiangsu, Peoples R China. RP Dong, YQ (reprint author), Xuzhou Normal Univ, Xuzhou, Jiangsu, Peoples R China. EM tomdyq@gmail.com; xjzhao@xznu.edu.cn; zhanggongjie@xznu.edu.cn CR Arasu A., 2003, P 2003 ACM SIGMOD IN Bergman M. K., 2001, DEEP WEB SURFACING H Cohen W., 2003, P 2 INT WORKSH INF I Crescenzi V., 2001, P 27 INT C VER LARG Liu B., 2003, P 9 ACM SIGKDD INT C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simon K., 2005, P 14 ACM INT C INF K Yi L., 2003, P 9 ACM SIGKDD INT C NR 8 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1660-9336 BN 978-3-03785-099-2 J9 APPL MECH MATER PY 2011 VL 55-57 BP 1003 EP 1008 DI 10.4028/www.scientific.net/AMM.55-57.1003 PN 1-3 PG 6 WC Engineering, Mechanical; Materials Science, Multidisciplinary; Mechanics SC Engineering; Materials Science; Mechanics GA BAC05 UT WOS:000303778600198 ER PT S AU Zhao, Y Du, YJ Peng, QQ AF Zhao, Ying Du, Yajun Peng, Qiangqiang BE Luo, Q TI Clustering Chinese Web Search Results based on Association Calculation SO RECENT TRENDS IN MATERIALS AND MECHANICAL ENGINEERING MATERIALS, MECHATRONICS AND AUTOMATION, PTS 1-3 SE Applied Mechanics and Materials LA English DT Proceedings Paper CT International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2011) CY JAN 27-28, 2011 CL Shenzhen, PEOPLES R CHINA SP Intelligent Informat Technol Appl Res Assoc, So Illinoic Univ Carbondale DE Chinese Web Search Results; Suffix Tree Clustering; Association Calculation AB Clustering web search results is a kind of solution which help user to find the interested topic by grouping the search results. This paper presents an improved method for clustering search results focused on Chinese web pages. The main contributions of this paper are the following: First, in this paper, a method which identifies the complete semantic information phrase by comparing the attributes of base clusters in the suffix tree document model and the overlap of their document sets is presented. Second, by analyzing the content and structure of title and snippet of Chinese web search results, one way of sentence segmentation is designed and implemented to constructing suffix tree. Third, In order to better respond to the associate degree of terms, a novel method is proposed which compute the distance in sentence-grain of terms' co-occurrences. Finally, the experiment illustrates that the new clustering method provides an efficient and effective way for user browsing and locating sought information. C1 [Zhao, Ying; Du, Yajun; Peng, Qiangqiang] Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. RP Zhao, Y (reprint author), Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. EM zjfc.zy@gmail.com; duyajun@mail.xhu.edu.cn; twinkle_pqq@126.com CR Chen L.-C., 2010, INFORM PROCESSING MA Chim H, 2008, IEEE T KNOWL DATA EN, V20, P1217, DOI 10.1109/TKDE.2008.50 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Li Z.S., 2005, WEB CLUSTERING TECHN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang J., 2008, RES WEB SEARCH RESUL Weiner, 1973, 14 ANN IEEE S SWITCH, P1 Wu Jiangning, 2010, Journal of the China Society for Scientific and Technical Information, V29, DOI 10.3772/j.issn.1000-0135.2010.01.012 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhang D, 2004, LECT NOTES COMPUT SC, V3007, P69 Zhang Y., 2009, J XIAN JIAOTONG U, V43 NR 11 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1660-9336 BN 978-3-03785-099-2 J9 APPL MECH MATER PY 2011 VL 55-57 BP 1418 EP 1423 DI 10.4028/www.scientific.net/AMM.55-57.1418 PN 1-3 PG 6 WC Engineering, Mechanical; Materials Science, Multidisciplinary; Mechanics SC Engineering; Materials Science; Mechanics GA BAC05 UT WOS:000303778600277 ER PT B AU Piech, C Sahami, M Koller, D Cooper, S Blikstein, P AF Piech, Chris Sahami, Mehran Koller, Daphne Cooper, Stephen Blikstein, Paulo GP ACM TI Modeling How Students Learn to Program SO SIGCSE 12: PROCEEDINGS OF THE 43RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION LA English DT Proceedings Paper CT 43rd ACM Technical Symposium on Computer Science Education (SIGCSE 2012) CY FEB 29-MAR 03, 2012 CL Raleigh, NC SP ACM SIGCSE DE Probabilistic Graphical Models; Hidden Markov Model; Program Dissimilarity Metric; Intelligent Tutor; Student Progress Model AB Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class. C1 [Piech, Chris; Sahami, Mehran; Koller, Daphne; Cooper, Stephen] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA. RP Piech, C (reprint author), Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA. EM piech@cs.stanford.edu; sahami@cs.stanford.edu; koller@cs.stanford.edu; coopers@cs.stanford.edu; paulob@stanford.edu CR Berland M., 2011, CLUSTERS PATTERNS NO Blikstein P., 2008, THESIS NW U EVANSTON Blikstein P., 2011, LEARNING ANAL ASSESS Blikstein P., 2011, P LEARN AN KNOWL C B Booth S., 1992, LEARNING PROGRAM PHE BOWMAN M, 1993, ACM T PROGR LANG SYS, V15, P795, DOI 10.1145/161468.161471 Bruce C., 2004, J INF TECHNOL, V3, P143 Brusilovsky Peter, 2000, LNCS Conati C., 1997, P 7 INT C US MOD, P231 Cristianini N., 2000, INTRO SUPPORT VECTOR Cutting D., 1992, P 15 SIGIR 1992 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Gall Harald, 2009, SOFTWARE IEEE Hoc J-M., 1984, P 2 EUR C READ COGN, P92 Kaufman L, 1990, FINDING GROUPS DATA Kessler C., 1988, STUDYING NOVICE PROG, V26, P229 Kurland D., 1983, P 5 ANN C COGN SCI S, P1 Needleman S.B., 1970, J MOL BIOL Paine Carina, 2001, P IEEE INT C ADV LEA Parlante N., 2006, SIGCSE Bulletin, V38, DOI 10.1145/1124706.1121516 Rabiner L. R., 1986, IEEE ASSP Magazine, V3, DOI 10.1109/MASSP.1986.1165342 Rabiner L.R., 1993, FUNDAMENTALS SPEECH Reiser B.J., 1985, P INT JOINT C ART IN, P8 Roberts Eric, KAREL LEARNS JAVA Sagar Tobias, 2006, P 2006 INT WORKSH MI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samurcay R., 1988, STUDYING NOVICE PROG, V26, P161 Smyth P, 1997, ADV NEUR IN, V9, P648 Soloway E., 1988, STUDYING NOVICE PROG SOLOWAY E, 1983, COMMUN ACM, V26, P853, DOI 10.1145/182.358436 Spohrer J. G., 1986, 1 WORKSH EMP STUD PR, P230 NR 31 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-4503-1098-7 PY 2011 BP 153 EP 158 PG 6 WC Computer Science, Theory & Methods SC Computer Science GA BAH03 UT WOS:000304130200031 ER PT S AU El-Haj, M Kruschwitz, U Fox, C AF El-Haj, Mahmoud Kruschwitz, Udo Fox, Chris BE Salem, MVM Shaalan, K Oroumchian, F Shakery, A Khelalfa, H TI Exploring Clustering for Multi-document Arabic Summarisation SO INFORMATION RETRIEVAL TECHNOLOGY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th Asia Information Retrieval Societies Conference (AIRS 2011) CY DEC 18-20, 2011 CL Dubai, U ARAB EMIRATES SP Dubai Knowledge Village, Microsoft Middle E AB In this paper we explore clustering for multi-document Arabic summarisation. For our evaluation we use an Arabic version of the DUC-2002 dataset that we previously generated using Google Translate. We explore how clustering (at the sentence level) can be applied to multi-document summarisation as well as for redundancy elimination within this process. We use different parameter settings including the cluster size and the selection model applied in the extractive summarisation process. The automatically generated summaries are evaluated using the ROUGE metric, as well as precision and recall. The results we achieve are compared with the top five systems in the DUC-2002 multi-document summarisation task. C1 [El-Haj, Mahmoud; Kruschwitz, Udo; Fox, Chris] Univ Essex, Colchester CO4 3SQ, Essex, England. RP El-Haj, M (reprint author), Univ Essex, Colchester CO4 3SQ, Essex, England. EM melhaj@essex.ac.uk; udo@essex.ac.uk; foxcj@essex.ac.uk CR [Anonymous], 2002, DOC UND C DUC DAT Berger A., 2000, P 38 ANN M ASS COMP, P294, DOI 10.3115/1075218.1075256 BRANDOW R, 1995, INFORM PROCESS MANAG, V31, P675, DOI 10.1016/0306-4573(95)00052-I Douzidia F.S., 2004, P DUC 2004, P128 DunlaVy DM, 2007, INFORM PROCESS MANAG, V43, P1588, DOI 10.1016/j.ipm.2007.01.003 El-Haj M., 2011, P 3 COMP SCI EL ENG Fattah MA, 2008, PROC WRLD ACAD SCI E, V27, P192 Fiszman M, 2009, J BIOMED INFORM, V42, P801, DOI 10.1016/j.jbi.2008.10.002 Funk A., 2007, MULT MULT INF EXTR S Gong Y., 2001, P 24 ANN INT ACM SIG, P19, DOI DOI 10.1145/383952.383955 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Koehn P., 2005, 10 MACH TRANSL SUMM, P79 Lin C., 2004, P WORKSH TEXT SUMM B, P25 Liu S., 2006, P 2006 IEEE WIC ACM, P978 McKeown K., 1995, P 18 ANN INT ACM SIG, P74, DOI 10.1145/215206.215334 Mihalcea R, 2005, 1 INT C INT AN METH Papineni Kishore, 2002, P 40 ANN M ASS COMP, P311, DOI DOI 10.3115/1073083.1073135 Radev D., 2000, P 2000 NAACL ANLP WO, P21 Radev DR, 2004, INFORM PROCESS MANAG, V40, P919, DOI 10.1016/j.ipm.2003.10.006 SALHI Hammouda, 2010, GLOBALISATION ASPECT, P53 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar K., 2009, TECHNIA INT J COMPUT, V2 Schlesinger JD, 2008, LECT NOTES COMPUT SC, V4919, P568, DOI 10.1007/978-3-540-78135-6_49 Steinberger R., 2006, P 5 LANG RES EV C LR, P24 Turchi M, 2010, LECT NOTES COMPUT SC, V6360, P52, DOI 10.1007/978-3-642-15998-5_7 Wan Xiaojun, 2008, P 31 ANN INT ACM SIG, P299, DOI 10.1145/1390334.1390386 Yell J.Y., 2008, EXPERT SYSTEMS APPL, V35, P1451 Zhao L, 2009, INFORM PROCESS MANAG, V45, P35, DOI 10.1016/j.ipm.2008.07.001 NR 29 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-25630-1 J9 LECT NOTES COMPUT SC PY 2011 VL 7097 BP 550 EP 561 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BZY36 UT WOS:000303361100050 ER PT J AU Sun, ZC Ye, YM Deng, WR Huang, ZX AF Sun, Zhaocai Ye, Yunming Deng, Weiru Huang, Zhexue BE Ran, C Yang, G TI A Cluster Tree Method For Text Categorization SO CEIS 2011 SE Procedia Engineering LA English DT Proceedings Paper CT International Conference on Advanced in Control Engineering and Information Science (CEIS) CY AUG 18-19, 2011 CL Dali, PEOPLES R CHINA DE Text categorixzation; cluster tree; decision tree ID MODEL AB The decision tree is a flexible and useful classification tool. But on the data with high dimensionality, it meets problems. For most of current decision tree algorithms, when splitting a node of a tree, only the "best" one feature is selected and used. Since more features are ignored, the classification accuracy is not high. To solve the problem, this paper uses a cluster tree for text categorization. Unlike familiar decision trees (e. g. CART, C4.5), clustering results are used as the splitting rule and more features are considered. Obviously, the used clustering algorithm is an very important to the cluster tree. For better performance, a text clustering algorithm is proposed to enhance the cluster tree. Experiments show that the cluster tree solves the high-dimensionality problem and outperforms C4.5 and CART on text data. Sometimes, it may do better than LibSVM, which may be the most powerful tool for text categorization. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011] C1 [Sun, Zhaocai; Ye, Yunming; Deng, Weiru; Huang, Zhexue] Harbin Inst Technol, Shenzhen Grad Schoo, Shenzhen, Peoples R China. RP Sun, ZC (reprint author), Harbin Inst Technol, Shenzhen Grad Schoo, Shenzhen, Peoples R China. EM sunnykiller@126.com CR Breiman L., 1984, CLASSIFICATION REGRE Han EH, 2000, PRINCIPLES DATA MINI, P116 Joachims T., 1998, Machine Learning: ECML-98. 10th European Conference on Machine Learning. Proceedings Lewis D., 1994, 3 ANN S DOC AN INF R, V33, P81 Li Y, 2008, LECT NOTES ARTIF INT, V5360, P337 Liu Bing, 2002, KDD 02 P 8 ACM SIGKD Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Wang Q, 2007, APPL ENVIRON MICROB, V73, P5261, DOI 10.1128/AEM.00062-07 NR 10 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1877-7058 J9 PROCEDIA ENGINEER PY 2011 VL 15 DI 10.1016/j.proeng.2011.08.709 PG 6 WC Automation & Control Systems SC Automation & Control Systems GA BYZ94 UT WOS:000300876503155 ER PT S AU Armenska, J Tomovski, A Zdravkova, K Pehcevski, J AF Armenska, Jasmina Tomovski, Aleksandar Zdravkova, Katerina Pehcevski, Jovan BE Gusev, M Mitrevski, P TI Information Retrieval Using a Macedonian Test Collection for Question Answering SO ICT INNOVATIONS 2010 SE Communications in Computer and Information Science LA English DT Proceedings Paper CT ICT Innovations Conference 2010 CY SEP 12-15, 2010 CL Ohrid, MACEDONIA SP Macedonian Soc Informat & Commun Technol, UKIM, Univ Ss Cyril & Methodius, UKIM, Inst Informat, Fac Nat Sci & Math, UKIM, Fac Elect Engn & Informat Technol, Asseco S E Europe, Duna Comp, Lancom Comp, Netcetera, Neocom DE Information retrieval; Question answering; Macedonian test collection AB Question answering systems solve many of the problems that users encounter when searching for focused information on the web and elsewhere. However, these systems cannot always adequately understand the user's question posed in a natural language, primarily because any particular language has its own specifics that have to be taken into account in the search process. When designing a system for answering questions posed in a natural language. there is a need of creating an appropriate test collection that will be used for testing the system's performance, as well as using an information retrieval method that will effectively answer questions for that collection. In this paper, we present a test collection we developed for answering questions in Macedonian language. We use this collection to test the performance of the vector space model with pivoted document length normalization. Preliminary experimental results show that our test collection can be effectively used to answer multiple-choice questions in Macedonian language. C1 [Armenska, Jasmina; Pehcevski, Jovan] European Univ, Fac Informat, Skopje, Macedonia. RP Armenska, J (reprint author), European Univ, Fac Informat, Skopje, Macedonia. EM jasmina.armenska@eurm.edu.mk; aleksandar.tomovski@gmail.com; keti@ii.edu.mk; jovan.pehcevski@eurm.edu.mk CR Belkin N., 1985, INTERACTION INFORM S Chowdhury A., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Dang H.T., 2007, NIST SPECIAL PUBLICA Jones K. S., 2000, Information Processing & Management, V36 Kwok C, 2001, ACM T INFORM SYST, V19, P242, DOI 10.1145/502115.502117 Magnini B., 2002, P DAT MIN 2002 BOL I Manning C.D., 2008, INTRO INFORM RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Sultan M., 2006, THESIS U SHEFFIELD Witten I. H., 1999, MANAGING GIGABYTES C Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-19324-8 J9 COMM COM INF SC PY 2011 VL 83 BP 205 EP 214 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BZQ46 UT WOS:000302390700021 ER PT J AU Patton, RM Beaver, JM Steed, CA Potok, TE Treadwell, JN AF Patton, Robert M. Beaver, Justin M. Steed, Chad A. Potok, Thomas E. Treadwell, Jim N. GP IEEE TI Hierarchical Clustering and Visualization of Aggregate Cyber Data SO 2011 7TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) LA English DT Proceedings Paper CT 7th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC) CY JUL 04-08, 2011 CL Istanbul, TURKEY SP IEEE, IEEE Turkey sect, Huawei DE hierarchical clustering; sunburst visualization; IDS analysis; vector space model AB Most commercial intrusion detections systems (IDS) can produce a very high volume of alerts, and are typically plagued by a high false positive rate. The approach described here uses Splunk to aggregate IDS alerts. The aggregated IDS alerts are retrieved from Splunk programmatically and are then clustered using text analysis and visualized using a sunburst diagram to provide an additional understanding of the data. The equivalent of what the cluster analysis and visualization provides would require numerous detailed queries using Splunk and considerable manual effort. C1 [Patton, Robert M.; Beaver, Justin M.; Steed, Chad A.; Potok, Thomas E.; Treadwell, Jim N.] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA. RP Patton, RM (reprint author), Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA. EM pattonrm@ornl.gov; beaverjm@ornl.gov; steedca@ornl.gov; potokte@ornl.gov; treadwelljn@ornl.gov CR Andrews K., 1998, P IEEE S INF VIS INF, P9 Collins Christopher, 2009, P EUR IEEE VGTC S VI, P1039 Heer J., 2005, P SIGCHI C HUM FACT, P421, DOI DOI 10.1145/1054972.1055031 Hendry G.R., 2007, THESIS ROCHESTER I T Kamber M., 2001, DATA MINING CONCEPTS King B., 1967, J AM STAT ASSOC, P86 MIT Lincoln Laboratory, 1999, 1999 DARPA INTR DET Portnoy L., 2001, P ACM CSS WORKSH DAT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stasko J., 2000, P IEEE S INF VIS, P57 Yang J. C., 2002, P 13 EUR WORKSH REND, P77 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-9537-5 PY 2011 BP 1287 EP 1291 PG 5 WC Telecommunications SC Telecommunications GA BYV01 UT WOS:000300570200214 ER PT S AU Bellogin, A Wang, J Castells, P AF Bellogin, Alejandro Wang, Jun Castells, Pablo BE Clough, P Foley, C Gurrin, C Jones, GJF Kraaij, W Lee, H Murdoch, V TI Text Retrieval Methods for Item Ranking in Collaborative Filtering SO ADVANCES IN INFORMATION RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 33rd European Conference on Information Retrieval CY APR 18-21, 2011 CL Dublin, IRELAND DE Collaborative Filtering; Text Retrieval; Unified Models ID INFORMATION-RETRIEVAL AB Collaborative Filtering (CF) aims at predicting unknown ratings of a user from other similar users. The uniqueness of the problem has made its formulation distinctive to other information retrieval problems. While the formulation has proved to be effective in rating prediction tasks, it has limited the potential connections between these algorithms and Information Retrieval (IR) models. In this paper we propose a common notational framework for IR and rating-based CF, as well as a technique to provide CF data with a particular structure, in order to be able to use any IR weighting function with it. We argue that; the flexibility of our approach may yield to much better performing algorithms. In fact, in this work we have found that IR models perform well in item ranking tasks, along with different normalization strategies. C1 [Bellogin, Alejandro; Castells, Pablo] Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain. RP Bellogin, A (reprint author), Univ Autonoma Madrid, Escuela Politecn Super, Francisco Tomas & Valiente 11, E-28049 Madrid, Spain. EM alejandro.bellogin@uam.es; jun.wang@cs.ucl.ac.uk; pablo.castells@uam.es RI Castells, Pablo/B-5020-2010; Bellogin, Alejandro/G-2025-2010 OI Castells, Pablo/0000-0003-0668-6317; Bellogin, Alejandro/0000-0001-6368-2510 CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Breese J.S., 1998, 14 C UNC ART INT, P43 Coster R., 2002, 25 ACM SIGIR C, P246 Herlocker J. L., 1999, 22 ANN INT ACM SIGIR, P230 Manning C.D., 2008, INTRO INFORM RETRIEV Metzler D, 2009, LECT NOTES COMPUT SC, V5766, P42, DOI 10.1007/978-3-642-04417-5_5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang J, 2008, INFORM RETRIEVAL, V11, P477, DOI 10.1007/s10791-008-9060-1 NR 9 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-20160-8 J9 LECT NOTES COMPUT SC PY 2011 VL 6611 BP 301 EP 306 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BZL87 UT WOS:000301968000030 ER PT S AU Maghsoodi, N Homayounpour, MM AF Maghsoodi, Nooshin Homayounpour, Mohammad Mehdi BE Gelbukh, A TI Using Thesaurus to Improve Multiclass Text Classification SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 12th Annual Conference on Intelligent Text Processing and Computational Linguistics CY FEB 20-26, 2011 CL Tokyo, JAPAN SP Kayamori Fdn Informat Sci Adv DE Text classification; Support vector machine; Thesaurus; Farsi ID CATEGORIZATION; MACHINE AB With the growing amount of textual information available on the Internet, the importance of automatic text classification has been increasing in the last decade. In this paper, a system was presented for the classification of multi-class Farsi documents which uses Support Vector Machine (SVM) classifier. The new idea proposed in the present paper. is based on extending the feature vector by adding some words extracted from a thesaurus. The goal is to assist classifier when training dataset is not comprehensive for some categories. For corpus preparation. Farsi Wikipedia website and articles of some archived newspapers and magazines are used. As the results indicate, classification efficiency improves by applying this approach. 0.89 micro F-measure were achieved for classification of 10 categories of Farsi texts. C1 [Maghsoodi, Nooshin; Homayounpour, Mohammad Mehdi] Amir Kabir Univ Technol, Lab Intelligent Signal & Speech Proc, Fac Comp Engn, Tehran, Iran. RP Maghsoodi, N (reprint author), Amir Kabir Univ Technol, Lab Intelligent Signal & Speech Proc, Fac Comp Engn, Tehran, Iran. EM n_maghsoodi@aut.ac.ir; homayoun@aut.ac.ir CR Bijankhan M., 2008, 100 MILLIONS WORD FA Bloehdorn S., 2004, WORKSH TEXTB INF RET, P149 Campos L., 2009, APPROXIMATE REASONIN, V50, P932 Diaz I, 2004, J AM SOC INF SCI TEC, V55, P579, DOI 10.1002/asi.10409 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fararuy J., 2004, P 1 WORKSH FARS LANG GABRILOVICH E, 2005, P 19 INT JOINT C ART, P1048 Hotho A., 2003, P SEM WEB WORKSH 26, P61 Huang Y.L., 1998, J LIBR INF SCI, V24, P1023 Joachims T., 1998, LNCS, V1398, P137 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Song X., 2009, INT FOR COMP SCI TEC, P187 Wang P, 2009, KNOWL INF SYST, V19, P265, DOI 10.1007/s10115-008-0152-4 Wang TY, 2007, INFORM PROCESS MANAG, V43, P914, DOI 10.1016/j.ipm.2006.09.011 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 21 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-19436-8 J9 LECT NOTES COMPUT SC PY 2011 VL 6609 BP 244 EP 253 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BZM17 UT WOS:000302000800020 ER PT S AU Chevalier, M Dattolo, A Hubert, G Pitassi, E AF Chevalier, Max Dattolo, Antonina Hubert, Gilles Pitassi, Emanuela BE Huemer, C Setzer, T TI Information Retrieval and Folksonomies together for Recommender Systems SO E-COMMERCE AND WEB TECHNOLOGIES SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 12th International Conference on Electronic Commerce and Web Technologies (EC Web 2011) CY AUG 29-SEP 02, 2011 CL Toulouse, FRANCE HO IRIT, P Sabatier Univ DE Information Retrieval; Folksonomy; Recommendation; e-commerce ID SEARCH; MODEL; USER; TAGS AB The powerful and democratic activity of social tagging allows the wide set of Web users to add free annotations on resources. Tags express user interests, preferences and needs, but also automatically generate folksonomies. They can be considered as gold mine, especially for e-commerce applications, in order to provide effective recommendations. Thus, several recommender systems exploit folksonomies in this context. Folksonomies have also been involved in many information retrieval approaches. In considering that information retrieval and recommender systems are siblings, we notice that few works deal with the integration of their approaches, concepts and techniques to improve recommendation. This paper is a first attempt in this direction. We propose a trail through recommender systems, social Web, e-commerce and social commerce, tags and information retrieval: an overview on the methodologies, and a survey on folksonorny-based information retrieval from recommender systems point of view, delineating a set of open and new perspectives. C1 [Chevalier, Max; Hubert, Gilles] Univ Toulouse 3, IRIT, F-31062 Toulouse, France. RP Chevalier, M (reprint author), Univ Toulouse 3, IRIT, 118 Rte Narbonne, F-31062 Toulouse, France. EM Max.Chevalier@irit.fr; antonina.dattolo@uniud.it; Gilles.Hubert@irit.fr; emanuela.pitassi@uniud.it CR Aciar S., 2005, CCIA 2005, P421 Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 Bar-Ilan J., 2010, P 3 S INF INT CONT I, P85, DOI 10.1145/1840784.1840798 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Bellogin A, 2010, LECT NOTES COMPUT SC, V5993, P382, DOI 10.1007/978-3-642-12275-0_34 Bender M., 2008, DAT ENG BLOGS SOC ME, P501 Biancalana C., 2009, P INT C COMP SCI ENG, P1060, DOI DOI 10.1109/CSE.2009.492 Burke R., 2007, The Adaptive Web. Methods and Strategies of Web Personalization Carmagnola F, 2007, LECT NOTES ARTIF INT, V4511, P445 Costa A., 2010, CORR Dattolo A., 2002, HUMAN COMPU IN PRESS, V2 Durao F., 2009, ADAPTATION PERSONALI, P40 Gemmell J, 2010, LECT NOTES BUS INF, V61, P1 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 Hassan-Montero Y., 2006, INSCIT 2006 INT C MU Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Felfernig A, 2011, RECOMMENDER SYSTEMS HANDBOOK, P187, DOI 10.1007/978-0-387-85820-3_6 Jeon H., 2010, AISS ADV INFORM SCI, V2, P134 Jiang F, 2010, LECT NOTES COMPUT SC, V6377, P597 Jiao YY, 2007, I C WIREL COMM NETW, P3609 KIM J, 2010, P 33 INT ACM SIGIR C, P50, DOI 10.1145/1835449.1835461 KUMAR A., 2010, GLOBAL J COMPUTER SC, V9, P30 Lam X N, 2008, P 2 INT C UB INF MAN, P208, DOI 10.1145/1352793.1352837 Linda ling Lai S, 2010, WORLD ACAD SCI ENG T, P39 Liu D., 2009, P 2009 IEEE INT C MU, P1636 Lu C., 2010, P 16 ACM SIGKDD INT, P683, DOI 10.1145/1835804.1835891 MALONE TW, 1987, COMMUN ACM, V30, P390, DOI 10.1145/22899.22903 Manning C.D., 2008, INTRO INFORM RETRIEV Milicevic AK, 2010, ARTIF INTELL REV, V33, P187, DOI 10.1007/s10462-009-9153-2 Niwa S., 2006, P 3 INT C INF TECHN, P388 Peters I, 2010, LIBR HI TECH, V28, P81, DOI 10.1108/07378831011026706 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Ramezani Maryam, 2008, INT WORKSH REC COLL Rendle Steffen, 2010, P 3 ACM INT C WEB SE, P81, DOI 10.1145/1718487.1718498 Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1 Robertson S.E., 1998, TREC 7 7 TEXT RETR C, P253 ROBERTSON SE, 1977, INFORM PROCESS MANAG, V13, P247, DOI 10.1016/0306-4573(77)90005-X SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer B.J., 1999, ACM C EL COMM, P158 Shen C.X, 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747 SIERSDORFER S, 2009, P 20 ACM C HYP HYP H, P261, DOI 10.1145/1557914.1557959 Simpson E., 2009, COLLABORATIVE SOCIAL, P43 Sprague Ralph H., 2010, P 2010 43 HAW INT C, P1 Tin S., 2009, 2009 IEEE INT C GRAN, P300 Vallet D, 2010, LECT NOTES COMPUT SC, V5993, P420, DOI 10.1007/978-3-642-12275-0_37 Wang J., 2008, P 2008 ACM WORKSH SE, P43, DOI 10.1145/1458583.1458592 Wei H., 2010, 2010 7 INT C FUZZ SY, V5, P2489 Weisberg J., 2011, INTERNET RES, V21 Wetzker R., 2010, WSDM 10, P71 Xia X., 2010, DBTA 2010, P1 Zhang L., 2009, J COMPUTERS, V4, P436 Zimmermann H.D., 2010, P LIB INF FOR NOV 4, P171 NR 55 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-23013-4 J9 LECT NOTES BUS INF P PY 2011 VL 85 BP 172 EP 183 PG 12 WC Computer Science, Information Systems SC Computer Science GA BZO22 UT WOS:000302190000015 ER PT S AU Gemmell, J Schimoler, T Mobasher, B Burke, R AF Gemmell, Jonathan Schimoler, Thomas Mobasher, Bamshad Burke, Robin BE Huemer, C Setzer, T TI Recommendation by Example in Social Annotation Systems SO E-COMMERCE AND WEB TECHNOLOGIES SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 12th International Conference on Electronic Commerce and Web Technologies (EC Web 2011) CY AUG 29-SEP 02, 2011 CL Toulouse, FRANCE HO IRIT, P Sabatier Univ DE Social Annotation Systems; Resource Recommendation; Recommendation by Example; Personalization; Hybrid Recommendation ID RETRIEVAL; MODEL AB Recommendation by example is common in contemporary Internet applications providing resources similar to a user-selected example. In this paper this task is considered as a function available within a social annotation system offering new ways to model both users and resources. Using three real-world datasets we motivate several conclusions. First, a personalized approach outperforms non-personalized approaches suggesting that users perceive the similarity between resources differently. Second, the manner in which users interact with social annotation systems vary producing datasets with variable characteristics and requiring different recommendation strategies to best satisfy their needs. Third, a hybrid recommender constructed from several component recommenders can produce superior results by exploiting multiple dimensions of the data. The hybrid remains powerful, flexible and extensible despite the underlying characteristics of the data. C1 [Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad; Burke, Robin] Depaul Univ, Sch Comp, Ctr Web Intelligence, Chicago, IL 60604 USA. RP Gemmell, J (reprint author), Depaul Univ, Sch Comp, Ctr Web Intelligence, Chicago, IL 60604 USA. EM jgemmell@cdm.depaul.edu; tschimloer@cdm.depaul.edu; mobasher@cdm.depaul.edu; rburke@cdm.depaul.edu CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Basu C., 1998, AAAI IAAI, P714 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Gemmell J., 2008, 10 INT C DAT WAR KNO Gemmell J., 2010, E COMMERCE WEB TECHN Gemmell J., 2010, 19 ACM INT C INF KNO Gemmell J., 2010, RECOMMENDER SYSTEMS Gemmell J., 2009, EUR C MACH LEARN PRI Guan Z., 2010, P 19 INT C WORLD WID, P391, DOI 10.1145/1772690.1772731 Guy I., 2010, P 33 INT ACM SIGIR C, P194, DOI 10.1145/1835449.1835484 Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506 Konstan J., 1997, COMMUN ACM, V40, P87 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Liang H., 2010, P 21 ACM C HYP HYP, P51, DOI 10.1145/1810617.1810628 Markines B., 2009, P 18 INT C WORLD WID, P641, DOI 10.1145/1526709.1526796 Mika P, 2007, J WEB SEMANT, V5, P5, DOI 10.1016/j.websem.2006.11.002 Mobasher B., 2011, USER MODELING ADAPTA Pennock D.M., 2001, P 17 C UNC ART INT U, P437 Plangprasopchok A., 2007, P AAAI WORKSH INF IN Rendle S., 2010, P 3 ACM INT C WEB SE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2001, 10 INT C WORLD WID W Schafer J. B., 2001, Data Mining and Knowledge Discovery, V5, DOI 10.1023/A:1009804230409 Sen S, 2009, WWW 2009, P671 Shardanand U., 1995, SIGCHI C HUM FACT CO Shepitsen A., 2008, ACM C REC SYST LAUS Wu X., 2006, P 15 INT C WORLD WID YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 31 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-23013-4 J9 LECT NOTES BUS INF P PY 2011 VL 85 BP 209 EP 220 PG 12 WC Computer Science, Information Systems SC Computer Science GA BZO22 UT WOS:000302190000018 ER PT S AU Musto, C Semeraro, G Lops, P de Gemmis, M AF Musto, Cataldo Semeraro, Giovanni Lops, Pasquale de Gemmis, Marco BE Huemer, C Setzer, T TI Random Indexing and Negative User Preferences for Enhancing Content-Based Recommender Systems SO E-COMMERCE AND WEB TECHNOLOGIES SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 12th International Conference on Electronic Commerce and Web Technologies (EC Web 2011) CY AUG 29-SEP 02, 2011 CL Toulouse, FRANCE HO IRIT, P Sabatier Univ DE Content-based Recommender Systems; Dimensionality Reduction; Personalization; Vector Space Models ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL AB The vector space model (VSM) emerged for almost three decades as one of the most effective approaches in the area of Information Retrieval (IR), thanks to its good compromise between expressivity, effectiveness and simplicity. Although Information Retrieval and Information Filtering (IF) undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analyzed, especially for content-based recommender systems. The goal of this work is twofold: first, we investigate the impact of VSM in the area of content-based recommender systems; second, since VSM suffer from well-known problems, such as its high dimensionality and the inability to manage information coming from negative user preferences, we propose techniques able to effectively tackle these drawbacks. Specifically we exploited Random Indexing for dimensionality reduction and the negation operator implemented in the Semantic Vectors open source package to model negative user preferences. Results of an experimental evaluation performed on these enhanced vector space models (eVSM) and the potential applications of these approaches confirm the effectiveness of the model and lead us to further investigate these techniques. C1 [Musto, Cataldo; Semeraro, Giovanni; Lops, Pasquale; de Gemmis, Marco] Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. RP Musto, C (reprint author), Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. EM cataldomusto@di.uniba.it; semeraro@di.uniba.it; lops@di.uniba.it; degemmis@di.uniba.it RI Semeraro, Giovanni /A-2180-2012; Lops, Pasquale/E-7509-2014; de Gemmis, Marco/E-9490-2014 OI Semeraro, Giovanni /0000-0001-6883-1853; CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Basile P., 2010, IIR 2010 P 1 IT INF, P1 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Bingham E., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Cohen W.W., 1998, KDD 1998, P169 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Hecht-Nielsen R., 1994, COMPUT INTELL, P43 Hofmann T., 1999, P 22 ANN INT SIGIR C Joachims T., 1998, LNCS, V1398 Johnson W., 1984, CONT MATH Kim SB, 2006, IEEE T KNOWL DATA EN, V18, P1457 Lops P, 2009, STUD COMPUT INTELL, V229, P27 Lops P, 2011, RECOMMENDER SYSTEMS HANDBOOK, P73, DOI 10.1007/978-0-387-85820-3_3 Musto C, 2010, P 4 ACM C REC SYST S, P361, DOI 10.1145/1864708.1864791 Nouali O, 2004, EXPERT SYST APPL, V26, P171, DOI 10.1016/S0957-4174(03)00118-0 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tan P., 2006, INTRO DATA MINING Turney PD, 2010, J ARTIF INTELL RES, V37, P141 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR Widdows D, 2003, ACL 2003, P136 NR 24 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-23013-4 J9 LECT NOTES BUS INF P PY 2011 VL 85 BP 270 EP 281 PG 12 WC Computer Science, Information Systems SC Computer Science GA BZO22 UT WOS:000302190000023 ER PT S AU Wang, XL Zhang, M AF Wang, Xiaolong Zhang, Ming BE Du, XY Fan, WF Wang, JM Peng, ZY Sharaf, MA TI Let Other Users Help You Find Answers: A Collaborative Question-Answering Method with Continuous Markov Chain Model SO WEB TECHNOLOGIES AND APPLICATIONS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 13th Asia-Pacific Conference on Web Technology CY APR 18-20, 2011 CL Beijing, PEOPLES R CHINA DE Question Answering; Collaborative Ranking; Continuous Markov Model ID WEB AB The answering communities, such as Yahoo! Answers, offer great intelligence to help people solve questions. Participants can express their judgements towards answers and the system also keeps a record for every user. Retrieving Question-Answer pairs (QA pairs) extracted from these forums can improve the quality of Question-Answering (QA) systems. In this paper, we propose a Collaborative Ranking (Col Rank) algorithm employing the Continuous Markov Chain Model (CMCM) to combine the quality of QA pairs and relationships among them. Empirical results show that the innovative algorithm is effective and outperform the state of art Question-Answering baselines. C1 [Wang, Xiaolong; Zhang, Ming] Peking Univ, Sch EECS, Inst Network Comp, Beijing 100871, Peoples R China. RP Wang, XL (reprint author), Peking Univ, Sch EECS, Inst Network Comp, Beijing 100871, Peoples R China. EM dragonxlwang@pku.edu.cn; mzhang_cs@pku.edu.cn RI Cong , Gao/A-3726-2011 CR Brill E., 2002, P ACL 2002 C EMP MET, V10, P291 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Clarke C.L.A., 2002, MULTITEXT EXPT TREC Ding S, 2008, P 46 ANN M ASS COMP, P710 Erkan G, 2004, P EMNLP, P365 Hermjakob U., 2001, P WORKSH OP DOM QUES, V12 Ilse I., 7 IMACS INT S IT MET Jeon J., 2006, P 29 ANN INT ACM SIG, P228, DOI 10.1145/1148170.1148212 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Li Xiao, 2008, P 31 ANN INT ACM SIG, P339, DOI 10.1145/1390334.1390393 Lin D., 2001, NAT LANG ENG, V7, P734 Lopez V, 2005, LECT NOTES COMPUT SC, V3532, P546 Otterbacher J, 2009, INFORM PROCESS MANAG, V45, P42, DOI 10.1016/j.ipm.2008.06.004 Paranjpe D., 2003, 12 TEXT RETRIEVAL C, P305 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Voorhees E., 2001, P 10 TEXT RETR C TRE, P157 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-20290-2 J9 LECT NOTES COMPUT SC PY 2011 VL 6612 BP 264 EP 270 PG 7 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BZM18 UT WOS:000302001600027 ER PT S AU Nawaz, A Khanum, A AF Nawaz, Asif Khanum, Aasia BE Liu, D Zhang, HG Polycarpou, M Alippi, C He, H TI Ranked Neuro Fuzzy Inference System (RNFIS) for Information Retrieval SO ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT I SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Symposium on Neural Networks CY MAY 29-JUN 01, 2011 CL Guilin, PEOPLES R CHINA SP Natl Nat Sci Fdn China, Chinese Acad Sci, Inst Automat, Chinese Univ Hong Kong, Univ Illinois Chicago DE Information Retrieval; Fuzzy Logic; Natural Language Processing AB The paper presents a novel approach to informational retrieval based on a synergy of knowledge-based models, set theoretic models, and vector space models of domain within a Fuzzy Logic framework. An input query is expanded to multiple synonym queries based on query semantics. Each document in the collection is divided into different zones with different relative importance assigned to each zone indicating its role in the query. Fuzzy rule bases are applied to each zone with parameters derived from vector space models and semantic query expansion. Fuzzy inference procedure outputs the relevance rank of each zone in satisfying the query. The relevance ranks of different zones are aggregated using the Ordered Weighted Averaging (OWA) operator to get the overall relevance rank of the complete document. The documents are ranked according to their relevance. The system has been tested on a standard dataset and has been demonstrated to show improved performance over typical vector space based approaches. C1 [Nawaz, Asif; Khanum, Aasia] NUST, Coll Elect & Mech Engn, Islamabad, Pakistan. RP Nawaz, A (reprint author), NUST, Coll Elect & Mech Engn, Islamabad, Pakistan. EM aasifnawaz@gmail.com; aasia@ceme.nust.edu.pk CR [Anonymous], 2004, FUZZ LOG TOOLB US GU Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baldwin J. F., 1995, FRIL FUZZY EVIDENTIA Baldwin JF, 1996, FUZZY SET SYST, V83, P353, DOI 10.1016/0165-0114(95)00297-9 Baziz M., 2005, EUSFLAT LFA Bordogna G., 1995, Proceedings. 1995. Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, DOI 10.1109/ANNES.1995.499452 Kraft D.H., 2006, P 2006 INT WORKSH RE Lancaster Frederick Wilfrid, 1973, INFORM RETRIEVAL Larocca Neto J., 2000, P 4 INT C PRACT APPL, P41 Murad M.A.A., 2005, SIMILARITY BASED EST, V1 Raehavan, 2008, INTRO INFORM RETRIEV Rubens N. O., 2006, COMPUTER MODELLING N, V10, P20 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shaw, 1991, LISR, P347 Stavrianou A, 2007, SIGMOD RECORD, V36, P23, DOI 10.1145/1324185.1324190 NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21104-1 J9 LECT NOTES COMPUT SC PY 2011 VL 6675 BP 578 EP 586 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BZK00 UT WOS:000301802600067 ER PT S AU Leoncini, A Sangiacomo, F Decherchi, S Gastaldo, P Zunino, R AF Leoncini, Alessio Sangiacomo, Fabio Decherchi, Sergio Gastaldo, Paolo Zunino, Rodolfo BE Liu, D Zhang, H Polycarpou, M Alippi, C He, H TI Semantic Oriented Clustering of Documents SO ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT III SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Symposium on Neural Networks CY MAY 29-JUN 01, 2011 CL Guilin, PEOPLES R CHINA SP Natl Nat Sci Fdn China, Chinese Acad Sci, Inst Automat, Chinese Univ Hong Kong, Univ Illinois Chicago DE Semantic Orientation; EuroWordNet; Clustering AB Semantic web-based approaches and computational intelligence can be merged in order to gel useful tools for several data mining issues. In this work a web-based tagging process followed by a validation step is carried to tag Word Net adjectives with positive, neutral or negative moods. This tagged Word Net is used to define a semantic metric for text documents clustering. Experimental results on movie reviews prove that the introduced semantically oriented metric is extremely fast and gives improved results with respect to the classical frequency based text mining metric from the accuracy point of view. C1 [Leoncini, Alessio; Sangiacomo, Fabio; Decherchi, Sergio; Gastaldo, Paolo; Zunino, Rodolfo] Univ Genoa, DIBE, SeaLab, I-16135 Genoa, Italy. RP Leoncini, A (reprint author), Univ Genoa, DIBE, SeaLab, Via Opera Pia 11, I-16135 Genoa, Italy. EM alessio.leoncini@unige.it; fabio.sangiacomo@unige.it; sergio.decherchi@unige.it; rodolfo.zunino@unige.it CR Bo Pang, 2002, C EMP METH NAT LANG Decherchi S., 2009, ADV ARTIFICIAL INTEL Hatsivassiloglou V., 1997, P 35 ANN M ACL, P174 Lehrer Adrienne, 1974, SEMANTIC FIELDS LEXI MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sangiacomo Fabio, 2010, Proceedings 2010 IEEE Fourth International Conference on Semantic Computing (ICSC), DOI 10.1109/ICSC.2010.48 Vossen P., 1998, EUROWORDNET MULTILIN Zhang R, 2002, INT C PATT RECOG, P289 NR 9 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21110-2 J9 LECT NOTES COMPUT SC PY 2011 VL 6677 BP 523 EP 529 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BZL62 UT WOS:000301947300059 ER PT J AU Chan, NN Gaaloul, W Tata, S AF Nguyen Ngoc Chan Gaaloul, Walid Tata, Samir GP IEEE TI A Web Service Recommender System Using Vector Space Model and Latent Semantic Indexing SO 25TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS (AINA 2011) SE International Conference on Advanced Information Networking and Applications LA English DT Proceedings Paper CT 25th IEEE International Conference on Advanced Information Networking and Applications (AINA) CY MAR 22-25, 2011 CL Singapore, SINGAPORE SP IEEE, IEEE Comp Soc, Singapore Chapter ACM, Inst Infocomm Res, IEEE Comp Soc TCDP DE web service; recommender system; vector space model; latent semantic indexing ID DISCOVERY AB The tremendous growth in the amount of available web services (WS) impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, text based recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we propose to take into account user's behaviors instead of text based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement two algorithms based on Vector Space Model and Latent Semantic Indexing to validate our approach. We also provide evaluation methods with different datasets in order to compare and assert the efficiency of our two algorithms. C1 [Nguyen Ngoc Chan; Gaaloul, Walid; Tata, Samir] TELECOM & Management SudParis, UMR CNRS Samovar 5157, Dept Informat, Paris, France. RP Chan, NN (reprint author), TELECOM & Management SudParis, UMR CNRS Samovar 5157, Dept Informat, Paris, France. EM chan.nguyen_ngoc@it-sudparis.eu; walid.gaaloul@it-sudparis.eu; samir.tata@it-sudparis.eu CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Birukou A, 2007, IEEE SOFTWARE, V24, P47, DOI 10.1109/MS.2007.169 Blake MB, 2007, ICWS, P575, DOI 10.1109/ICWS.2007.28 Daniel B., 2006, 3 EUR SEM WEB C BUDV DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong X., 2004, VLDB, P372 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 James B., 2007, KDDCUP 07, V0 Kokash N, 2007, LECT NOTES COMPUT SC, V4439, P95 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Ma J., 2008, IEEE INT C WEB SERV, P740 Manikrao US, 2005, NWESP 2005, P117 Manning C.D., 2008, INTRO INFORM RETRIEV Paliwal A. V., 2007, SERV COMP IEEE INT C, V0, P106 Paolucci M., 2002, INT SEM WEB C, P333 Paterek A., 2007, KDDCUP 07, V0 Platzer C., 2005, WEB SERV 2005 ECOWS, P9 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2000, EC'00. Proceedings of the 2nd ACM Conference on Electronic Commerce Uniprot ftp (nd), 1989, [No title captured], Patent No. [4839853, 4,839,853] Wang ZQ, 2007, ALPIT 2007: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON ADVANCED LANGUAGE PROCESSING AND WEB INFORMATION TECHNOLOGY, P429 Wu C., 2009, ADV WEB SEMANTICS, P346 Wu C.-T., 2007, IEEE INT C IND ENG E, P228 Wu S., 2009, IUCE 09, P414 Zhang S, 2005, CEC 2005: Seventh IEEE International Conference on E-Commerce Technology, Proceedings, P257 ZHENG Z, 2009, ICWS 09, P437 NR 30 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1550-445X BN 978-0-7695-4337-6 J9 INT CON ADV INFO NET PY 2011 BP 602 EP 609 DI 10.1109/AINA.2011.99 PG 8 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BYK19 UT WOS:000299083800080 ER PT S AU Draszawka, K Szymanski, J AF Draszawka, Karol Szymanski, Julian BA Ryzko, D Rybinski, H Gawrysiak, P Kryszkiewicz, M BF Ryzko, D Rybinski, H Gawrysiak, P Kryszkiewicz, M TI External Validation Measures for Nested Clustering of Text Documents SO EMERGING INTELLIGENT TECHNOLOGIES IN INDUSTRY SE Studies in Computational Intelligence LA English DT Article; Book Chapter DE Clustering validation; Rand index; Hubert's index; F-measure; OPTICS; reachability plot ID NUMBER; MODEL AB This article handles the problem of validating the results of nested (as opposed to "flat") clusterings. It shows that standard external validation indices used for partitioning clustering validation, like Rand statistics, Hubert Gamma statistic or F-measure are not applicable in nested clustering cases. Additionally to the work, where F-measure was adopted to hierarchical classification as hF-measure, here some methods to get desired hRand and h Gamma indices for nested clustering are presented. Introduced measures are evaluated and, as an exemplary application, a validation of nested clustering methods for Wikipedia articles using OPTICS algorithm is shown. C1 [Draszawka, Karol; Szymanski, Julian] Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. RP Draszawka, K (reprint author), Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. EM kadr@eti.pg.gda.pl; julian.szymanski@eti.pg.gda.pl CR Aliguliyev RM, 2009, INFORM SCIENCES, V179, P3583, DOI 10.1016/j.ins.2009.06.012 Ankerst M., 1999, SIGMOD Record, V28 BRECHEISEN S, 2004, P 4 SIAM INT C DAT M Brun M, 2007, PATTERN RECOGN, V40, P807, DOI 10.1016/j.patcog.2006.06.026 CHRISTAINE F, 1998, WORDNET ELECT LEXICA Dimitriadou E, 2002, PSYCHOMETRIKA, V67, P137, DOI 10.1007/BF02294713 GORDAN DA, 1987, REV HIERARCHICAL CLA, P119 Halkidi M, 2008, PATTERN RECOGN LETT, V29, P773, DOI 10.1016/j.patrec.2007.12.011 Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075 JAMES RF, 1974, ANNU REV ECOL SYST, V5, P101 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Kiritchenko S, 2006, LECT NOTES ARTIF INT, V4013, P395 Manning C.D., 2008, INTRO INFORM RETRIEV Menardi G, 2011, STAT COMPUT, V21, P295, DOI 10.1007/s11222-010-9169-0 MILLIGAN GW, 1985, PSYCHOMETRIKA, V50, P159, DOI 10.1007/BF02294245 MILLIGAN GW, 1983, IEEE T PATTERN ANAL RAGHAVAN VV, 1983, LECT NOTES COMPUT SC, V146, P209 RAND WM, 1971, J AM STAT ASSOC, V66, P846, DOI 10.2307/2284239 ROY S, 2006, P 2006 SIAM INT C DA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDER J, 2003, LNCS, V2637, P567 SHALOM MW, 1997, PREDICTIVE DATA MINI Zalik KR, 2011, PATTERN RECOGN LETT, V32, P221, DOI 10.1016/j.patrec.2010.08.007 NR 24 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-22731-8 J9 STUD COMPUT INTELL PY 2011 VL 369 BP 207 EP 225 PG 19 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BWZ15 UT WOS:000295385300018 ER PT S AU Marinai, S Miotti, B Soda, G AF Marinai, Simone Miotti, Beatrice Soda, Giovanni BE Biba, M Xhafa, F TI Digital Libraries and Document Image Retrieval Techniques: A Survey SO LEARNING STRUCTURE AND SCHEMAS FROM DOCUMENTS SE Studies in Computational Intelligence LA English DT Article; Book Chapter ID SHAPE; RECOGNITION; DATABASES; FEEDBACK; FEATURES AB Nowadays, Digital Libraries have become a widely used service to store and share both digital born documents and digital versions of works stored by traditional libraries. Document images are intrinsically non-structured and the structure and semantic of the digitized documents is in most part lost during the conversion. Several techniques related to the Document Image Analysis research area have been proposed in the past to deal with document image retrieval applications. In this chapter a survey about the more recent techniques applied in the field of recognition and retrieval of text and graphical documents is presented. In particular we describe techniques related to recognition-free approaches. C1 [Marinai, Simone; Miotti, Beatrice; Soda, Giovanni] Univ Florence, I-50121 Florence, Italy. [Marinai, Simone; Miotti, Beatrice; Soda, Giovanni] Dipartimento Sistemi & Informat, I-50139 Florence, Italy. RP Marinai, S (reprint author), Univ Florence, I-50121 Florence, Italy. EM simone.marinai@unifi.it; miotti@dsi.unifi.it; soda@dsi.unifi.it CR Alajlan N, 2008, IEEE T PATTERN ANAL, V30, P1003, DOI 10.1109/TPAMI.2008.37 Bai S., 2009, P 10 INT C DOC AN RE, P331 Balasubramanian A., 2006, P INT WORKSH DOC AN, P1 Banerjee S., 2007, P IEEE ICDAR, V2, P1208 Barbu E., 2005, P INT WORKSH GRAPH R, P195 Belaid A., 2004, Proceedings. First Workshop on Document Image Analysis for Libraries Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 Cao H., J PATTERN RECOGNITIO, V42, P3374 Cao H., 2007, P 9 INT C DOC AN REC, V1, P88 Chellapilla K., 2007, P INT C DOC AN REC, V1, P387 Choisy C, 2007, P INT C DOC AN REC, P242 Curtis J.D., 1995, P SPIE DOCUMENT RECO, VII, P270 Delalandre M, 2008, LECT NOTES COMPUT SC, V5046, P135, DOI 10.1007/978-3-540-88188-9_14 Doermann D, 1998, COMPUT VIS IMAGE UND, V70, P287, DOI 10.1006/cviu.1998.0692 FATAICHA Y, 2006, INT J DOC ANAL RECOG, V8, P1 Fonseca M.J., 2005, P 6 IAPR INT WORKSH, P291 GATOS B, 2009, P INT C DOC AN REC, P271 Gordo A., 2009, P INT C DOC AN REC, P481 Govindaswamy Visvasuresh Victor, 2009, Proceedings of the 2009 IEEE Conference on the History of Technical Societies, DOI 10.1109/HTS.2009.5337864 Harris ZS, 1954, WORD, V10, P146 Hu J., 2000, Information Retrieval, V2, DOI 10.1023/A:1009910911387 Jain A. K., 2003, P 7 INT C DOC AN REC, P655 Jones G., 1995, INT C AC SPEECH SIGN, V1, P309 Journet N., 2007, P INT C DOC AN REC, P1053 Joutel G., 2007, P INT C DOC AN REC, P649 Karray A., 2008, P INT WORKSH GRAPH R, P126 KESIDIS A, 2009, P WORKSH AN NOIS UNS, P55, DOI 10.1145/1568296.1568307 Khurshid K., 2009, P INT C DOC AN REC, P266 Kise K., 2003, P ICDAR 2003, P488 Kogler M., 2010, P INT WORKSH MULT DA, P3 Konidaris T, 2007, INT J DOC ANAL RECOG, V9, P167, DOI 10.1007/s10032-007-0042-4 Latecki LJ, 2000, PROC CVPR IEEE, P424, DOI 10.1109/CVPR.2000.855850 Leydier Y, 2007, PATTERN RECOGN, V40, P3552, DOI 10.1016/j.patcog.2007.04.024 Li L., 2007, P INT C DOC AN REC, P68 Liang S, 2008, PATTERN RECOGN LETT, V29, P1733, DOI 10.1016/j.patrec.2008.05.004 Licata A., 2009, P INT C DOC AN REC, P818 Llados J., 2007, P 9 INT C DOC AN REC, P362 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Lu S., 2007, P 9 INT C DOC AN REC, P994 Lu S., 2008, J PATTERN RECOGNITIO, V41, P1816 Lu SJ, 2008, IEEE T PATTERN ANAL, V30, P1913, DOI 10.1109/TPAMI.2008.89 Lu Y., 2004, P 1 INT WORKSH DOC I, P174 Manmatha R., 1996, INT C COMP VIS PATT, P631 Marinai S., 2006, IEEE T PATTERN ANAL, V28 Marinai S., 2006, P DIAL 06, P243, DOI DOI 10.1109/DIAL.2006.44 Marinai S., 2005, Proceedings. Eighth International Conference on Document Analysis and Recognition MARINAI S, 2009, P INT C DOC AN REC, P1041 Marinai S, 2011, INT J DOC ANAL RECOG, V14, P117, DOI 10.1007/s10032-010-0146-0 Marinai S, 2006, LECT NOTES COMPUT SC, V3872, P336 Marinai S, 2005, IEEE T PATTERN ANAL, V27, P23, DOI 10.1109/TPAMI.2005.4 Marinai Simone, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), DOI 10.1109/ICPR.2010.534 Meshesha M, 2008, INT J DOC ANAL RECOG, V11, P29, DOI 10.1007/s10032-008-0067-3 Mitra M., 2000, Information Retrieval, V2, DOI 10.1023/A:1009950525500 Moghaddam R. F., 2009, P 10 INT C DOC AN RE, P511 Nakai T., 2009, P INT C DOC AN REC, P146 Nguyen TO, 2008, PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, P191, DOI 10.1109/DAS.2008.58 Perronnin F, 2008, IEEE T PATTERN ANAL, V30, P1243, DOI 10.1109/TPAMI.2007.70755 Qureshi RJ, 2008, LECT NOTES COMPUT SC, V5046, P91, DOI 10.1007/978-3-540-88188-9_10 Rath T. M., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition Rath TM, 2007, INT J DOC ANAL RECOG, V9, P139, DOI [10.1007/s10032-006-0027-8, 10.1007/s10032-007-0027-8] Rodriguez J. A., 2008, P INT C HANDWR REC Rodriguez-Serrano J., 2009, P INT C DOC AN REC, P351 Rusinol M, 2010, SYMBOL SPOTTING IN DIGITAL LIBRARIES: FOCUSED RETRIEVAL OVER GRAPHIC-RICH DOCUMENT COLLECTIONS, P1 Rusinol M, 2008, PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, P489, DOI 10.1109/DAS.2008.24 Rusinol M, 2006, LECT NOTES COMPUT SC, V3926, P35 Rusinol M, 2008, LECT NOTES COMPUT SC, V5046, P104, DOI 10.1007/978-3-540-88188-9_11 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schomaker L., 2007, P INT C DOC AN REC, V2, P594 Sivic J., 2003, ICCV, V2, P1470, DOI DOI 10.1109/1CCV.2003.1238663 Smeaton AF, 1997, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, P974, DOI 10.1109/ICDAR.1997.620655 Sulem L.L., 2006, ACT 9 C INT FRANC EC, P193 Super BJ, 2006, INT J PATTERN RECOGN, V20, P1117, DOI 10.1142/S0218001406005174 Tahmasebi N., 2010, P JOINT C DIG LIB, P89, DOI 10.1145/1816123.1816137 TAN G, 2009, P INT C DOC AN REC, P336 Terasawa K., 2005, Proceedings. Eighth International Conference on Document Analysis and Recognition TZACHEVA A, 2002, P SPIE DOC REC RETR, V9, P121 Uttama S, 2006, LECT NOTES COMPUT SC, V3926, P88 Wan G, 2008, INFORM TECHNOL LIBR, V27, P41 Waters D., 1996, PRESERVING DIGITAL I Wei CH, 2009, PATTERN RECOGN, V42, P386, DOI 10.1016/j.patcog.2008.08.019 Witten I. H., 2002, BUILD DIGITAL LIB Wong WT, 2007, INT J PATTERN RECOGN, V21, P995, DOI 10.1142/S0218001407005843 Zhang B., 2004, SPIE DOCUMENT RECOGN, VXI, P45 Zhang W., 2007, P 9 INT C DOC AN REC, P536 Zhang Z., 2009, P INT C DOC AN REC, P763 NR 85 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-22912-1 J9 STUD COMPUT INTELL PY 2011 VL 375 BP 181 EP 204 D2 10.1007/978-3-642-22913-8 PG 24 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BXD62 UT WOS:000295820300009 ER PT J AU Bi, B Lee, SD Kao, B Cheng, R AF Bi, Bin Lee, Sau Dan Kao, Ben Cheng, Reynold GP IEEE TI CubeLSI: An Effective and Efficient Method for Searching Resources in Social Tagging Systems SO IEEE 27TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2011) SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT IEEE 27th International Conference on Data Engineering (ICDE 2011) CY APR 11-16, 2011 CL Hannover, GERMANY SP IEEE AB In a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse audience of causal users (taggers). This leads to two issues that gravely affect the effectiveness of resource retrieval: (1) Noise: tags are picked from an uncontrolled vocabulary and are assigned by untrained taggers. The tags are thus noisy features in resource retrieval. (2) A multitude of aspects: different taggers focus on different aspects of a resource. Representing a resource using a flattened bag of tags ignores this important diversity of taggers. To improve the effectiveness of resource retrieval in social tagging systems, we propose CubeLSI - a technique that extends traditional LSI to include taggers as another dimension of feature space of resources. We compare CubeLSI against a number of other tag-based retrieval models and show that CubeLSI significantly outperforms the other models in terms of retrieval accuracy. We also prove two interesting theorems that allow CubeLSI to be very efficiently computed despite the much enlarged feature space it employs. C1 [Bi, Bin; Lee, Sau Dan; Kao, Ben; Cheng, Reynold] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. RP Bi, B (reprint author), Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. EM bbi@cs.hku.hk; sdlee@cs.hku.hk; kao@cs.hku.hk; ckcheng@cs.hku.hk CR ADRIAN B, 2007, P 1 SEM 07 Barroso L., 2003, MICRO IEEE, V23 BI B, 2009, P CIKM ACM, P1919, DOI 10.1145/1645953.1646265 BI B, 2010, TR201010 U HONG KONG BRIN S, 1998, COMPUTER NETWORK APR BUDANITSKY A, 2006, COMPUT LINGUIST, V32 Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 BYDE A, 2007, ICWSM 07 CHEN HM, 2008, P 16 MM ACM DEERWESTER S, 1990, JASIST DUMAIS ST, 1995, OV 3 TEXT RETR C TRE, P219 GARG N, 2008, P 2008 ACM C REC SYS, P67, DOI 10.1145/1454008.1454020 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Jarvelin K., 2002, ACM T INFORM SYSTEMS, V20 JASCHKE R, 2007, P 11 PKDD Jiang Jay J., 1997, P INT C RES COMP LIN, P19 KOLDA TG, 2008, P IEEE ICDM, P363 Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263 Korner C., 2010, P 19 INT C WORLD WID, P521, DOI 10.1145/1772690.1772744 LATHAUWER LD, 2000, SIAM J MATRIX ANAL A, V21 Lathauwer LD, 2000, SIAM J MATRIX ANAL A, V21, P1253 Manning C.D., 2008, INTRO INFORM RETRIEV MARINHO LB, 2007, P C GES KLASS GFKL Ng A., 2001, P NIPS, P849 Resnik P., 1995, P 14 INT JOINT C ART, P448 RISVIK KM, 2003, LA WEB, P132 Salakhutdinov R., 2007, ADV NEURAL INFORM PR, V20 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sigurbornsson B., 2008, WWW 08, P327 TAKACS G, 2009, JMLR Wu X., 2006, P 15 INT C WORLD WID Xu Z., 2006, P COLL WEB TAGG WORK NR 32 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1084-4627 BN 978-1-4244-8958-9 J9 PROC INT CONF DATA PY 2011 BP 27 EP 38 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BWX31 UT WOS:000295216600005 ER PT J AU Shu, LC Chen, AY Xiong, M Meng, WY AF Shu, Liangcai Chen, Aiyou Xiong, Ming Meng, Weiyi GP IEEE TI Efficient Spectral Neighborhood Blocking for Entity Resolution SO IEEE 27TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2011) SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT IEEE 27th International Conference on Data Engineering (ICDE 2011) CY APR 11-16, 2011 CL Hannover, GERMANY SP IEEE ID LINKAGE AB In many telecom and web applications, there is a need to identify whether data objects in the same source or different sources represent the same entity in the real-world. This problem arises for subscribers in multiple services, customers in supply chain management, and users in social networks when there lacks a unique identifier across multiple data sources to represent a real-world entity. Entity resolution is to identify and discover objects in the data sets that refer to the same entity in the real world. We investigate the entity resolution problem for large data sets where efficient and scalable solutions are needed. We propose a novel unsupervised blocking algorithm, namely SPectrAl Neighborhood (SPAN), which constructs a fast bipartition tree for the records based on spectral clustering such that real entities can be identified accurately by neighborhood records in the tree. There are two major novel aspects in our approach: 1) We develop a fast algorithm that performs spectral clustering without computing pairwise similarities explicitly, which dramatically improves the scalability of the standard spectral clustering algorithm; 2) We utilize a stopping criterion specified by Newman-Girvan modularity in the bipartition process. Our experimental results with both synthetic and real-world data demonstrate that SPAN is robust and outperforms other blocking algorithms in terms of accuracy while it is efficient and scalable to deal with large data sets. C1 [Shu, Liangcai; Meng, Weiyi] SUNY Binghamton, Dept Comp Sci, Binghamton, NY 13902 USA. RP Shu, LC (reprint author), SUNY Binghamton, Dept Comp Sci, POB 6000, Binghamton, NY 13902 USA. EM lshu@cs.binghamton.edu; aiyouchen@google.com; mxiong@google.com; meng@cs.binghamton.edu CR Aggarwal C. C., 2001, P 20 ACM SIGMOD SIGA, P256, DOI 10.1145/375551.383213 Batini C., 2006, DATA QUALITY CONCEPT Baxter R., 2003, P 9 ACM SIGKDD WORKS Bhattacharya I., 2004, ACM SIGMOD WORKSH RE Bhattacharya I., 2004, ACM SIGKDD WORKSH LI Bhattacharya I., 2006, SIAM INT C DAT MIN Bickel PJ, 2009, P NATL ACAD SCI USA, V106, P21068, DOI 10.1073/pnas.0907096106 Bilenko M., 2003, SIGKDD Chung F. R. K., 1997, CBMS REGIONAL C SERI, V92 Churches T., 2005, FEBRL FREELY EXTENSI Cohen W. W., 2002, SIGKDD DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581 Fan W., 2009, 35 INT C VER LARG DA FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061 Fowlkes C, 2004, IEEE T PATTERN ANAL, V26, P214, DOI 10.1109/TPAMI.2004.1262185 Garcia-Molina H., 2009, SIGMOD Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Goodman J. E., 2004, HDB DISCRETE COMPUTA, V2nd Gravano L., 2003, WWW, P90 Guha S., 2004, VLDB Han H, 2005, JCDL 05, P334 Hassanzadeh O., 2009, VLDB Hernandez MA, 1998, DATA MIN KNOWL DISC, V2, P9, DOI 10.1023/A:1009761603038 LEE DT, 1977, ACTA INFORM, V9, P23 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Lin F., 2010, ICML Manning C.D., 2008, INTRO INFORM RETRIEV McCallum A., 2000, KNOWLEDGE DISCOVERY, P169 McNamee P, 2004, INFORM RETRIEVAL, V7, P73, DOI 10.1023/B:INRT.0000009441.78971.be Netflix, NETFL PRIZ NEWCOMBE HB, 1959, SCIENCE, V130, P954, DOI 10.1126/science.130.3381.954 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Ng A. Y., 2002, NIPS 14 Pasula H., 2002, ADV NEURAL INFORM PR Pinheiro J. C., 1998, SIGKDD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarawagi S, 2002, SIGKDD, P269 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Shu L., 2009, ICDE UKKONEN E, 1992, THEOR COMPUT SCI, V92, P191, DOI 10.1016/0304-3975(92)90143-4 Verykios VS, 2000, INFORM SCIENCES, V126, P83, DOI 10.1016/S0020-0255(00)00013-X von Luxburg U, 2008, ANN STAT, V36, P555, DOI 10.1214/009053607000000640 Wang Y. R., 1989, IEEE INT C DAT ENG, P46 Winkler W. E., 1999, STATE RECORD LINKAGE Winkler WE, 2006, OVERVIEW RECORD LINK Yan D., 2009, SIGKDD, P907 NR 47 TC 4 Z9 4 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1084-4627 BN 978-1-4244-8958-9 J9 PROC INT CONF DATA PY 2011 BP 1067 EP 1078 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BWX31 UT WOS:000295216600092 ER PT J AU Duflou, JR Verhaegen, PA AF Duflou, J. R. Verhaegen, P. -A. TI Systematic innovation through patent based product aspect analysis SO CIRP ANNALS-MANUFACTURING TECHNOLOGY LA English DT Article DE Design method; Conceptual design; Systematic innovation AB Patent repositories are considered as a state of the art in innovation efforts for specific product categories. Using text mining techniques on selected word categories in such repositories, complemented by a dimensional reduction method, generic product aspects are identified, allowing systematic quantification of the development state of product categories. The derived product profiles are applied for systematic identification of product categories that can contribute to the further development of selected product aspects for a product of interest. The effectiveness of this methodology was experimentally validated, demonstrating the significance of the proposed design-by-analogy approach based on the described product aspect analysis. (C) 2011 CIRP. C1 [Verhaegen, P. -A.] Katholieke Univ Leuven, Dept Mech Engn, B-3001 Heverlee, Belgium. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE GOUGH HG, 1979, J PERS SOC PSYCHOL, V37, P1398, DOI 10.1037/0022-3514.37.8.1398 HERRMANN N, 1981, TRAIN DEV J, V35, P11 Jansson D., 1991, DESIGN STUDIES, V12, P3, DOI 10.1016/0142-694X(91)90003-F KAISER HF, 1958, PSYCHOMETRIKA, V23, P187, DOI 10.1007/BF02289233 LINSEY JS, 2007, THESIS U TEXAS Nelson BA, 2009, DESIGN STUD, V30, P737, DOI 10.1016/j.destud.2009.07.002 PEETERS J, 2010, P IDMME VIRT CONC 20 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHERVISH MJ, 1996, AM STAT, V50, P206 Shah J. J., 2003, Design Studies, V24, DOI 10.1016/S0142-694X(02)00034-0 Skillicorn D. B., 2007, UNDERSTANDING COMPLE VERHAEGEN PA, 2010, CIRP DES C 2010 P CI EPO WORLDWIDE PATENT NR 15 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0007-8506 J9 CIRP ANN-MANUF TECHN JI CIRP Ann-Manuf. Technol. PY 2011 VL 60 IS 1 BP 203 EP 206 DI 10.1016/j.cirp.2011.03.007 PG 4 WC Engineering, Industrial; Engineering, Manufacturing SC Engineering GA 828IP UT WOS:000295495000051 ER PT J AU Wiesner, M Rotter, S Pfeifer, D AF Wiesner, Martin Rotter, Stefan Pfeifer, Daniel BE Olive, M Solomonides, T TI Leveraging Semantic Networks for Personalized Content in Health Recommender Systems SO 2011 24TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) SE IEEE International Symposium on Computer-Based Medical Systems LA English DT Proceedings Paper CT 24th International Symposium on Computer-Based Medical Systems (CBMS) CY JUN 27-30, 2011 CL Bristol, ENGLAND SP IEEE, IEEE Engn Med & Biol Soc (EMB), British Comp Soc, Bristol Branch, ACM, IFIP HO Univ W England AB Since the emergence of the Internet in the early 90's of the last century medical knowledge is spreading around the globe increasingly fast. Though publicly available, it is a difficult task to determine individual relevance for most non professionals. Additionally, relationships between medical terms are hard to discover even for professionals. In this paper we present an approach on how semantic query expansion can be exploited to enhance classic information retrieval (IR) techniques in order to gather health information artifacts for consumers. The approach is based on health related semantic networks which are automatically generated from public resources such as Wikipedia. A scenario for integrating such networks is a so-called health recommender systems (HRS) which can be embedded into a personal health record system (PHRS). This way, relevant personalized medical content can be delivered automatically to end users and owners of health records. C1 [Wiesner, Martin; Rotter, Stefan; Pfeifer, Daniel] Heilbronn Univ, Dept Med Informat, D-74081 Heilbronn, Germany. RP Wiesner, M (reprint author), Heilbronn Univ, Dept Med Informat, Max Planck Str 39, D-74081 Heilbronn, Germany. EM wiesner@hs-heilbronn.de; srotter@stud.hs-heilbronn.de; daniel.pfeifer@hs-heilbronn.de CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 *AP SOFTW FDN, AP LUC TM TEXT SEARC Fernandez-Luque L, 2009, STUD HEALTH TECHNOL, V150, P903, DOI 10.3233/978-1-60750-044-5-903 Gavgani V.Z., 2010, P 1 ACM INT HLTH INF, P575, DOI 10.1145/1882992.1883086 Gobel G, 2001, INT J MED INFORM, V64, P241, DOI 10.1016/S1386-5056(01)00217-9 *GOOGL INC, 2011, GOOGL HLTH TAK CHARG Grossman D., 2004, INFORM RETRIEVAL SER Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 KUKICH K, 1992, COMPUT SURV, V24, P377 McCray AT, 2004, ST HEAL T, V107, P1152 *MEDP, 2010, MEDP FAQ OP PLATF CO Munir K., 2008, P 2008 INT S DB ENG, P81, DOI 10.1145/1451940.1451953 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Roitman H, 2010, P 1 ACM INT HLTH INF, P430, DOI 10.1145/1882992.1883057 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sowa J.F., 1991, PRINCIPLES SEMANTIC Spink Amanda, 2004, Health Info Libr J, V21, P44, DOI 10.1111/j.1471-1842.2004.00481.x The Pew Internet & American Life Project, 2011, HLTH TOP 80 INT US L Wiesner M, 2010, P 1 ACM INT HLTH INF, P410, DOI DOI 10.1145/1882992.1883053 Zeng QT, 2006, J AM MED INFORM ASSN, V13, P80, DOI 10.1197/jamia.M1820 Zhang Y., 2010, P 1 ACM INT HLTH INF, P210 NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1063-7125 BN 978-1-4577-1190-9 J9 COMP MED SY PY 2011 PG 6 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BWZ98 UT WOS:000295472700077 ER PT J AU Lee, C Lee, YH Peng, Y Cho, H AF Lee, Choonghyun Lee, Young Hoon Peng, Yun Cho, Hyunbo TI A SUPPLIER DISCOVERY FRAMEWORK FOR EFFECTIVE AND EFFICIENT CONFIGURATION OF A SUPPLY CHAIN SO INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE LA English DT Article DE Supplier discovery; supplier registry; classification and clustering; classification scheme; similarity measure ID CLUSTERING XML DOCUMENTS; STRUCTURAL SIMILARITY; ALGORITHM AB A supplier registry can play a central role in configuring a global supply chain for service-oriented enterprise integration by providing an open platform for publishing and discovering suppliers distributed over Internet. The availability of correct classification schemes used to organize suppliers based on their capability descriptions is the key to building an effective registry. This paper discusses the clustering-based construction of classification schemes from existing capability descriptions of suppliers and then the registration and retrieval of suppliers based on these classification schemes. This approach is based on the use of similarity measures to discriminate any two suppliers' capability descriptions. The results look promising in that small medium suppliers can expose themselves to the large companies in an efficient manner, while large companies can diversify their supply sources. C1 [Lee, Choonghyun; Lee, Young Hoon] Yonsei Univ, Dept Informat & Ind Engn, Seoul 120749, South Korea. [Peng, Yun] Univ Maryland Baltimore Cty, Baltimore, MD 21250 USA. [Cho, Hyunbo] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang 790784, South Korea. RP Lee, C (reprint author), Yonsei Univ, Dept Informat & Ind Engn, 262 Seongsanno, Seoul 120749, South Korea. RI Lee, Young /G-8071-2012; Cho, Hyunbo/F-6973-2013 CR Amer-Yahia S., 2005, P 31 INT C VER LARG, P361 Beringer J., 2007, P IEEE INT C FUZZ SY, P1 Bertino E, 2004, INFORM SYST, V29, P23, DOI 10.1016/S0306-4379(03)00031-0 BREININGER K, 2003, OASIS EBXML REGISTRY Buttler D., 2004, P INT C INT COMP LAS, P3 Costa G, 2004, LECT NOTES ARTIF INT, V3202, P137 Dalamagas T, 2006, INFORM SYST, V31, P187, DOI 10.1016/j.is.2004.11.009 Dogac A., 2004, Proceedings. 14th International Workshop on Research Issues on Data Engineering: Web Services for e-Commerce and e-Government Applications (RIDE-WS-FECEG) Duda R.O., 2001, PATTERN CLASSIFICATI Estivill-Castro V., 2000, P 9 INT S SPAT DAT H, P26 Flesca S, 2005, IEEE T KNOWL DATA EN, V17, P160, DOI 10.1109/TKDE.2005.27 Frey BJ, 2007, SCIENCE, V315, P972, DOI 10.1126/science.1136800 Gottschalk K, 2002, IBM SYST J, V41, P170 Hu X., 2004, NEURAL INFORM PROCES, V4, P1 JANG J, 2005, IFIP 5 7 ADV PROD MA JEONG B, 2007, P 20 INT C IND ENG O, P572 JEONG B, 2007, P IEEE INT C INF REU, P379 JEONG B, 2006, THESIS POHANG U SCI Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KOKASH N, 2006, P EUR START AI RES S, P220 Kulvatunyou B., 2005, International Journal of Knowledge-Based and Intelligent Engineering Systems, V9 Lian W, 2004, IEEE T KNOWL DATA EN, V16, P82 LU W, 2005, P 17 IEEE INT C TOOL, P712 Luo J., 2006, P IEEE INT C WEB SER, P153 Nierman A., 2002, P 5 INT WORKSH WEB D, P61 O'Brien Liam, 2005, CMUSEI2005TN014 PAOLUCCI M, 2002, P WEB SERV E BUS SEM, P225 Pokraev S., 2003, P ICWS, P184 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan N., 2004, P 1 INT WORKSH SEM W Wu J., 2005, P 2005 IEEE INT C SE, P287 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Yang J., 2005, P 2005 IEEE INT C E, P353 Zeng L., 2003, P 12 INT C WORLD WID, P411, DOI DOI 10.1145/775152.775211 NR 35 TC 0 Z9 0 PU UNIV CINCINNATI INDUSTRIAL ENGINEERING PI CINCINNATI PA UNIV CINCINNATI, CINCINNATI, OH 45221-0116 USA SN 1943-670X J9 INT J IND ENG-THEORY JI Int. J. Ind. Eng.-Theory Appl. Pract. PY 2011 VL 18 IS 3 BP 109 EP U14 PG 12 WC Engineering, Industrial; Engineering, Manufacturing SC Engineering GA 829KP UT WOS:000295579400001 ER PT S AU Berzak, Y Richter, M Ehrler, C Shore, T AF Berzak, Yevgeni Richter, Michal Ehrler, Carsten Shore, Todd BE Sporleder, C VandenBosch, A Zervanou, K TI Information Retrieval and Visualization for the Historical Domain SO LANGUAGE TECHNOLOGY FOR CULTURAL HERITAGE: SELECTED PAPERS FROM THE LATECH WORKSHOP SERIES SE Theory and Applications of Natural Language Processing LA English DT Article; Book Chapter DE historical collections; information retrieval; graph visualization; clustering; recommender system ID STRING KERNELS AB Working with large and unstructured collections of historical documents is a challenging task for historians. Despite the recent growth in the volume of digitized historical data, available collections are rarely accompanied by computational tools that significantly facilitate this task. We address this shortage by proposing a visualization method for document collections that focuses on the graphical representation of similarities between documents. The strength of the similarities is measured according to the overlap of historically significant information such as named entities, or the overlap of general vocabulary. Similarity strengths are then encoded in the edges of a graph. The graph provides visual structure, revealing interpretable clusters and links between documents that are otherwise difficult to establish. We implement the idea of similarity graphs within an information retrieval system supported by an interactive graphical user interface. The system allows querying the database, visualizing the results and browsing the collection in an effective and intuitive way. Our approach can be easily adapted and extended to collections of documents in other domains. C1 [Berzak, Yevgeni; Richter, Michal; Ehrler, Carsten; Shore, Todd] Univ Saarland, D-66041 Saarbrucken, Germany. RP Berzak, Y (reprint author), Univ Saarland, D-66041 Saarbrucken, Germany. EM berzak@coli.uni-sb.de; mrichter@mpi-inf.mpg.de; cehrler@mpi-inf.mpg.de; tshore@coli.uni-saarland.de CR [Anonymous], CASTRO SPEECH DATABA Biemann C., 2006, TEXTGRAPHS 06 P TEXT Blei D., 2009, TEXT MINING CLASSIFI, P71 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Dunne C., 2010, HUM COMP INT LAB 27 Eades P., 1996, LECT NOTES COMPUTER, V1115, P40 Finkel J. R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885 Fruchterman T. M. J., 1991, GRAPH DRAWING FORCE Greaves M., 2009, P EUR C MACH LEARN 1, P3 Hearst M. A., 2009, SEARCH USER INTERFAC, V1 Koren Y, 2003, LECT NOTES COMPUT SC, V2697, P496 Leslie C, 2004, J MACH LEARN RES, V5, P1435 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Luxburg U.V., 2007, STAT COMPUT, V17, P395, DOI DOI 10.1007/S11222-007-9033-Z Manning C.D., 2008, INTRO INFORM RETRIEV Nadeau D., 2009, NAMED ENTITIES RECOG Puppe T., 2008, SPECTRAL GRAPH DRAWI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanguinetti G., 2005, IEEE MACHINE LEARNIN, P28 Scholkopf B, 2001, ADV NEUR IN, V13, P301 Schvaneveldt R. W., 1990, ABLEX SERIES COMPUTA Shawe-Taylor J., 2004, KERNEL METHODS PATTE Verma D., 2003, COMPARISON SPECTRAL Versley Y, 2008, P 6 INT C LANG RES E Versley Y., 2008, P 22 INT C COMP LING NR 26 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 2192-032X BN 978-3-642-20226-1 J9 THEOR APPL NAT LANG PY 2011 BP 197 EP 212 DI 10.1007/978-3-642-20227-8_11 D2 10.1007/978-3-642-20227-8 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BWI06 UT WOS:000293925300013 ER PT S AU Petasis, G Karkaletsis, V Paliouras, G Krithara, A Zavitsanos, E AF Petasis, Georgios Karkaletsis, Vangelis Paliouras, Georgios Krithara, Anastasia Zavitsanos, Elias BE Paliouras, G Spyropoulos, CD Tsatsaronis, G TI Ontology Population and Enrichment: State of the Art SO KNOWLEDGE-DRIVEN MULTIMEDIA INFORMATION EXTRACTION AND ONTOLOGY EVOLUTION: BRIDGING THE SEMANTIC GAP SE Lecture Notes in Artificial Intelligence LA English DT Article; Book Chapter ID SEMANTIC WEB; KNOWLEDGE; EXTRACTION; FRAMEWORK; INCONSISTENCY; PRINCIPLES AB Ontology learning is the process of acquiring (constructing or integrating) an ontology (semi-) automatically. Being a knowledge acquisition task, it is a complex activity, which becomes even more complex in the context of the BOEMIE project(1), due to the management of multimedia resources and the multi-modal semantic interpretation that they require. The purpose of this chapter is to present a survey of the most relevant methods, techniques and tools used for the task of ontology learning. Adopting a practical perspective, an overview of the main activities involved in ontology learning is presented. This breakdown of the learning process is used as a basis for the comparative analysis of existing tools and approaches. The comparison is done along dimensions that emphasize the particular interests of the BOEMIE project. In this context, ontology learning in BOEMIE is treated and compared to the state of the art, explaining how BOEMIE addresses problems observed in existing systems and contributes to issues that are not frequently considered by existing approaches. C1 [Petasis, Georgios; Karkaletsis, Vangelis; Paliouras, Georgios; Krithara, Anastasia; Zavitsanos, Elias] Natl Ctr Sci Res Demokritos, Inst Informat & Telecommun, Athens 15310, Greece. RP Petasis, G (reprint author), Natl Ctr Sci Res Demokritos, Inst Informat & Telecommun, Athens 15310, Greece. EM petasis@iit.demokritos.gr; vangelis@iit.demokritos.gr; paliourg@iit.demokritos.gr; akrithara@iit.demokritos.gr; izavits@iit.demokritos.gr; paliourg@iit.demokritos.gr CR Agichtein E., 2000, P 5 ACM INT C DIG LI, P85, DOI 10.1145/336597.336644 AGIRRE E, 2000, WORKSH ONT CONSTR EU AHMAD K, 1994, WHAT IS TERM SEMIAUT ALANI H, 2003, P WORKSH KNOWL MARK Alani H, 2003, IEEE INTELL SYST, V18, P14, DOI 10.1109/MIS.2003.1179189 ALANI H, 2003, P HLT 2003 Alfonseca E., 2006, P 2 WORKSH ONT LEARN, P49 AUSSENACGILLES N, 2002, ECAI 2002 WORKSH MAC AUSSENACGILLES N, 2000, EKAW 2000 WORKSH ONT BARONI M, 2004, P 4 INT C LANG RES E, V5, P1725 Blei D., 2004, ADV NEURAL INFORM PR, V16 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BRANK J, 2006, P EON WORKSH Brewster C, 2002, LECT NOTES COMPUT SC, V2553, P203 BREWSTER C, 2007, P RECENT ADV NATURAL Brewster C., 2004, P INT C LANG RES EV BUITELAAR P, 2004, P ECAI 2004 WORKSH O BUITELAAR P, 2008, P ECAI 2008 WORKSH O Buitelaar P., 2006, P INT C LANG RES EV, P2321 Buitelaar P, 2005, ONTOLOGY LEARNING TE BUITELAAR P, 2006, WORKSH ONT LEARN POP CASTANO S, 2008, J LOGIC COMPUTAT SEP Cimiano P., 2006, ONTOLOGY LEARNING PO CIRAVEGNA F, 2002, P 25 ANN INT ACM SIG Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 Cunningham H., 2002, P ACL Cunningham H., 2002, P 40 ANN M ASS COMP DAGAN I, 2005, PASCAL RECOGNISING T DAMERAU FJ, 1993, INFORM PROCESS MANAG, V29, P433, DOI 10.1016/0306-4573(93)90039-G Downey O., 2004, P AAAI WORKSH AD TEX DROZDZYNSKI W, 2004, KUNSTLICHE INTELLIGE, V1, P17 EHRIG M, 2005, P EUR C INF SYS Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001 Etzioni O., 2004, P 13 INT C WORLD WID, P100, DOI 10.1145/988672.988687 Faatz A., 2002, SEM WEB MIN 2 WORKSH Faure D., 1998, ICSTR8816 Faure D, 2000, P ECAI 2000 WORKSH O FELLBAUM C, ONLINE LEXICAL DATAB FORTUNA B, 2005, ACAI 2005 SUMMER SCH Fox M.S., 1998, ORG ONTOLOGY ENTERPR Frantzi K., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900023 Gomez-Perez Asuncion, SURVEY ONTOLOGY LEAR Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Guarino N, 2002, COMMUN ACM, V45, P61 GUPTA KM, 2002, P 1 INT C GLOB WORDN, P207 GUREVYCH I, 2003, P HLT NAACL Haase P, 2008, LECT NOTES ARTIF INT, V5327, P366 Haase P, 2005, LECT NOTES COMPUT SC, V3729, P353 Haase P, 2005, LECT NOTES COMPUT SC, V3532, P182 Hahn U., 2002, P ECAI 2002 WORKSH M HARRIS Z, 1990, P ANN M ASS COMP LIN, P268 Hearst M.A., 1992, P 14 INT C COMP LING IRIA J, 2006, 5 INT C LANG RES EV, P24 IWANSKA LM, 2000, FULLY AUTOMATIC ACQU, P335 JONES AB, 2004, SEMIOTIC SUITE ASSES KIETZ JU, 2000, P ECAW 2000 WORKSH O KIM S, 2002, P WORKSH SEM AUTH AN Kim S. S., 2008, 3 WORKSH ONT LEARN P KOZAKOV L, 2004, IBM SYSTEM J, V43 Krauthammer M, 2004, J BIOMED INFORM, V37, P512, DOI 10.1016/j.jbi.2004.08.004 Lafferty John, 2001, P 18 INT C MACH LEAR Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LESK M, 1986, 5 INT C SYST DOC ACM Levenshtein I. V., 1966, CYBERNETICS CONTROL, V10, P707 Li W., 2007, UNCERTAINTY ARTIFICI Li W, 2006, P 23 INT C MACH LEAR, P577, DOI DOI 10.1145/1143844.1143917 Lin D., 2002, P C COMP LING, P577 Lin D., 2001, P ACM SIGKDD C KNOWL, P317, DOI 10.1145/502512.502558 Lin D., 2001, P ACM SIGKDD C KNOWL, P323, DOI [10.1145/502512.502559, DOI 10.1145/502512.502559] Lisi FA, 2005, LECT NOTES COMPUT SC, V3703, P118 LOZANOTELLO A, 2003, SELECTION ONTOLOGIES, P413 Lozano-Tello A, 2004, J DATABASE MANAGE, V15, P1, DOI 10.4018/jdm.2004040101 Maedche A, 2002, LECT NOTES ARTIF INT, V2473, P251 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 MAEDCHE A, 2000, P ECAI 2000 Maedche A., 2004, HDB ONTOLOGIES MAEDCHE A, 2001, IJCAI 2001 WORKSH ON MAEDEHE A, 2000, P 12 INT C SOFTW ENG Maynard D, 2006, P EON 2006 WORKSH Meilicke C, 2008, LECT NOTES ARTIF INT, V5268, P93, DOI 10.1007/978-3-540-87696-0_11 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 MIMA H, 2001, LECT NOTES ARTIF INT, V2166, P126 Mimmo D, 2007, P 24 INT C MACH LEAR, P633, DOI DOI 10.1145/1273496.1273576 Morin E., 1999, P 5 INT C TERM KNOWL Navigli R, 2006, LECT NOTES ARTIF INT, V4248, P126 Navigli R., 2006, P 2 WORKSH ONT LEARN, P1 PORZEL R, 2004, ECAI 2004 WORKSH ONT QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105 Sabou M., 2005, J WEB SEMANTICS, V3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANGUN P, 2007, FRAMEWORK ONTOLOGY B SCHATZE H, 1993, WORD SPACE ADV NEURA, V5 Schulte im Walde S., 2000, P 18 INT C COMP LING, P747 Schutz A, 2005, LECT NOTES COMPUT SC, V3729, P593 Sclano F., 2007, 9 C TERM ART INT TIA Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 SHAMSFAR M, 2002, P 6 C ART INT SOFT C SHAMSFAR M, 2004, INT J HUMANCOMPUTER, P17 Shamsfard M, 2003, KNOWL ENG REV, V18, P293, DOI 10.1017/S0269888903000687 SHAMSFARD M, 2003, THESIS AMIRKABIR U T Shvaiko P., 2007, ONTOLOGY MATCHING SNOW R, 2006, ACLY 2006 SNOW R, 2004, P ADV NEUR INFORM PR Specia L., 2006, P 2 WORKSH ONT LEARN, P57 STAAB S, 2000, P WORKSH ONT LEARN Suchanek F.M., 2006, P 2 WORKSH ONT LEARN, P18 Turney P.D., 2001, LNCS LNAI, V2167, P491 Velardi P, 2007, IEEE T KNOWL DATA EN, V19, P180, DOI 10.1109/TKDE.2007.21 VELARDI P, 2005, EVALUATION ONTOLEARN Volker J, 2005, LECT NOTES COMPUT SC, V3729, P716 Weber N., 2006, P WORKSH WEB CONT MI Wei W, 2010, IEEE T KNOWL DATA EN, V22, P1028, DOI 10.1109/TKDE.2009.122 YANG H, 2009, ACL 2009 YAROWSKY D, 1992, COLING 1992 ZAVITSANOS E, 2010, INT J, V8, P37 ZAVITSANOS E, 2010, T KNOWLEDGE DATA ENG ZAVITSANOS E, 2006, DEMO20063 ZAVITSANOS E, 2008, 3 ONT LEARN POP WORK, P2008 NR 118 TC 14 Z9 14 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-20794-5 J9 LECT NOTES ARTIF INT PY 2011 VL 6050 BP 134 EP 166 D2 10.1007/978-3-642-20795-2 PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BWB40 UT WOS:000293367600006 ER PT S AU O'Shea, J Bandar, Z Crockett, K AF O'Shea, James Bandar, Zuhair Crockett, Keeley BE Tolk, A Jain, LC TI Systems Engineering and Conversational Agents SO INTELLIGENCE-BASED SYSTEMS ENGINEERING SE Intelligent Systems Reference Library LA English DT Article; Book Chapter DE Conversational agent; systems engineering; dialogue; evaluation; methodology; semantic similarity; short text ID NATURAL-LANGUAGE DIALOGUE; SEMANTIC SIMILARITY; INFORMATION; DESIGN; GUIDE AB This chapter describes Conversational Agents (CAs) in the context of Systems Engineering. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service. CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in project development. CAs offer the opportunity to automate more complex applications than are feasible with conventional web interfaces. Currently such applications require a human expert in the domain to mediate between the user and the application. The CA effectively replaces the human expert. This chapter reviews the current capabilities of various CA technologies, outlines a development methodology for systems engineering practitioners interested in developing real world applications and suggests a number of directions for systems engineers who wish to participate in CA research. C1 [O'Shea, James; Bandar, Zuhair; Crockett, Keeley] Manchester Metropolitan Univ, Sch Comp Math & Digital Technol, Manchester M1 5GD, Lancs, England. RP O'Shea, J (reprint author), Manchester Metropolitan Univ, Sch Comp Math & Digital Technol, Chester St, Manchester M1 5GD, Lancs, England. EM j.d.oshea@mmu.ac.uk; z.bandar@mmu.ac.uk; k.crockett@mmu.ac.uk CR Achananuparp P, 2008, LECT NOTES COMPUT SC, V5182, P305, DOI 10.1007/978-3-540-85836-2_29 ANDERNACH T, 1995, 15 INT C LANG ENG AI, P351 Andersen V, 2003, APPL ARTIF INTELL, V17, P745, DOI 10.1080/08839510390225140 Austin J. L., 1975, THINGS WORDS W JAMES Babu S, 2006, LECT NOTES ARTIF INT, V4133, P169 *BBC, 2009, TOR CRIT FLU ADV LIN BEVACQUA E, 2007, AISB 2007 ARTIFICIAL Bickmore T, 2006, J BIOMED INFORM, V39, P556, DOI 10.1016/j.jbi.2005.12.004 BICKMORE T, 2000, AM ASS ARTIFICIAL IN, P4 BLOCK N, 1981, PHILOS REV, V90, P5, DOI 10.2307/2184371 Bohus D., 2009, SIGDIAL 2009 10 ANN, P225 BOHUS D, 2009, SIGDIAL 2009 C 10 AN Bouwman G, 1999, INT CONF ACOUST SPEE, P493 BOUWMAN G, 1998, 1 INT C LANG RES EV, P191 Capuano N., 2009, 9 1EEE INT C ADV LEA, P484 Cassell J., 1999, Autonomous Agents and Multi-Agent Systems, V2, DOI 10.1023/A:1010027123541 CASSELL J, 2000, EMBODIED CAS Cassell J, 2001, KNOWL-BASED SYST, V14, P55, DOI 10.1016/S0950-7051(00)00102-7 Cassell J, 2003, USER MODEL USER-ADAP, V13, P89, DOI 10.1023/A:1024026532471 Crockett K., 2009, KNOWLEDGE REASONING, P1 De Angeli A, 2009, PSYCHNOLOGY J, V7, P49 Deerwester S., 1989, COMPUTER INFORM RETR Dehaene S, 2001, COGNITION, V79, P1, DOI 10.1016/S0010-0277(00)00123-2 DETHLEFS N, 2010, SEMDIAL 2010 14 WORK DeVault D., 2009, SIGDIAL 2009 C 10 AN, P11 Farquhar A, 1997, INT J HUM-COMPUT ST, V46, P707, DOI 10.1006/ijhc.1996.0121 FERRI F, 2007, 19 INT C SOFTW ENG K, P664 Forbes-Riley K, 2011, COMPUT SPEECH LANG, V25, P105, DOI 10.1016/j.csl.2009.12.002 Foster ME, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P1818 Gacitua-Decar V, 2009, 2009 WORLD CONFERENCE ON SERVICES PART, P111, DOI 10.1109/SERVICES-2.2009.28 GERGLE D, 2007, SIGCHI C HUM FACT CO, P1543 GIRAUDO E, 2009, EVALUATION NLP SPEEC GLASS J, 2004, 4 INT C COMP AID DES, P347 Gorin AL, 1997, SPEECH COMMUN, V23, P113, DOI 10.1016/S0167-6393(97)00040-X Graesser AC, 2005, IEEE T EDUC, V48, P612, DOI 10.1109/TE.2005.856149 GUNDERSON K, 1964, MIND, V73, P234 HILLARD DL, 2008, ELECT ENG HOSOM JP, 2003, AUTOMATIC SPEECH REC, P20 HUANG JJ, 2008, 5 IEEE CONS COMM NET, P289 HULSTIJN H, 1996, TWENT WORKSH LANG TE, V11 HUNT A, 1997, COMP SPEECH FAQ 6 Hunt M. J., 1990, SPEECH COMMUN, V9, P239 Inkpen D., 2007, STUD U BABES BOLYAI, VLII, P11 Jackson P., 2007, NATURAL LANGUAGE PRO KATO T, 2010, SIGIR 2010 33 ANN IN, P3 KEGEL, 2010, KEGEL OLDEST HARLEY Kopp S, 2005, LECT NOTES ARTIF INT, V3661, P329 Laham D., 1998, LATENT SEMANTIC ANAL Lamel L, 2002, SPEECH COMMUN, V38, P131, DOI 10.1016/S0167-6393(01)00048-6 Lamel LF, 1997, SPEECH COMMUN, V23, P67, DOI 10.1016/S0167-6393(97)00037-X LANCE B, 2008, 7 INT C AUT AG MULT, P199 Lance B, 2008, INT C AUT AG EST POR, P199 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Le Bigot L, 2004, APPL ERGON, V35, P557, DOI 10.1016/j.apergo.2004.06.001 Lee C., 2010, J COMPUT SCI ENG, V4, P1 LEFEVRE F, 2009, SIGDIAL 2009 C 10 AN, P272 Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Litman DJ, 2002, USER MODEL USER-ADAP, V12, P111, DOI 10.1023/A:1015036910358 Liu XY, 2007, ICSC 2007: INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, PROCEEDINGS, P250, DOI 10.1109/ICSC.2007.48 MASSARO DW, 2000, EMBODIED CONVERSATIO, P286 MCGEARY Z, 2005, CUSTOMER RELATIONSHI McKevitt P, 1999, INT J HUM-COMPUT ST, V51, P947, DOI 10.1006/ijhc.1999.0271 MICHIE D, 2001, RETURN IMITATION GAM, V6, P205 MIN F, 2006, 2 INT C SEM KNOWL GR, P41, DOI 10.1109/SKG.2006.28 MINKER W, 1996, 4 INT C SPOK LANG IC, P1013 MORGE M, 2010, 9 INT C AUT AG MULT, P127 O'Shea K., 2009, LECT NOTES ELECT ENG, V39, P505 OPDENAKKER H, 2009, SIGDIAL 2009 C 10 AN, P21 Ortony A., 1990, COGNITIVE STRUCTURE OSHEA J, 2010, COMPUTING MATH MANCH OSHEA K, 2008, P WORLD C ENG WCE 20, P321 OWDA M, 2007, IEEE WIC ACM INT C W Pfleeger S. L., 1998, SOFTWARE METRICS RIG PLANTEC P, 1998, ZEN SCRIPTING VERBOT RAUT CK, 2009, EMMANUEL COLL Resnik P, 1999, J ARTIF INTELL RES, V11, P95 RIGO S, 2009, EVALUATION NLP SPEEC ROBINSON S, 2008, LANG RES EV C 2008 M, P1125 SAGAE K, 2009, NAACL HLT 2009, P53 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMMUT C, 2001, ELECT T ARTIFICIAL I, V5, P191 SANDERS GA, 2000, EMBODIED CAS Searle J., 1999, MIND LANGUAGE SOC SEARLE JR, 1980, BEHAV BRAIN SCI, V3, P417 Semeraro G, 2003, LECT NOTES COMPUT SC, V2615, P360 SKANTZE G, 2009, SIGDIAL 2009 C 10 AN, P310 SPARCKJONES K, 1972, J DOC, V28, P11 Tsatsaronis G, 2010, J ARTIF INTELL RES, V37, P1 Turing A. M., 1950, MIND, P433, DOI DOI 10.1093/MIND/LIX.236.433 Walker MA, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P271 WALKER MA, 2002, INT C NAT LANG GEN, P73 WALKER MA, 2000, LANGUAGE RESOURCES E WALKER MA, 2001, EUR C SPEECH LANG PR, P1371 Walker MA, 2001, 39TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P515 WEBB N, 2010, 7 C INT LANG RES EV Young S, 2010, COMPUT SPEECH LANG, V24, P150, DOI 10.1016/j.csl.2009.04.001 Yuan X, 2005, COMPUT ANIMAT VIRT W, V16, P109, DOI 10.1002/cav.65 Zdenek S, 2001, MIND MACH, V11, P53, DOI 10.1023/A:1011214808628 ZDRAVKOVA K, 2000, 22 INT C INF TECHN I, P189 NR 100 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1868-4394 BN 978-3-642-17930-3 J9 INTEL SYST REF LIBR PY 2011 VL 10 BP 201 EP 232 DI 10.1007/978-3-642-17931-0_8 D2 10.1007/978-3-642-17931-0 PG 32 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BWA74 UT WOS:000293318100008 ER PT B AU Law, N Yuen, J Wong, WOW Leng, J AF Law, Nancy Yuen, Johnny Wong, Wing O. W. Leng, Jing BE Puntambekar, S Erkens, G HmeloSilver, CE TI Understanding Learners' Knowledge Building Trajectory Through Visualizations of Multiple Automated Analyses SO ANALYZING INTERACTIONS IN CSCL: METHODS, APPROACHES AND ISSUES SE Computer-Supported Collaborative Learning LA English DT Article; Book Chapter AB In recent years, knowledge building as a pedagogical approach has gained in popularity, and some sizeable networks of teachers using the Knowledge Forum (R) platform to support the learning and teaching process have emerged. This provides a unique opportunity for empirical explorations that span across different classrooms to identify trends and regularities in the knowledge building developmental trajectory of a community of learners (including the teacher who is designing and facilitating the educational process) in classroom contexts. On the other hand, it poses a serious challenge to teachers and researchers on how to analyze and make sense of the large corpora of discourses generated by students. This study is an attempt. to develop a methodology that can take advantage of the increasing collection of online corpora in Knowledge Forum (R) for the incremental establishment of profiles and models of knowledge building behavior and interactions that reflect different levels of productivity in knowledge building. The focus of the present research is on the progress and development of the entire community, which aligns with the predominant focus on the construction and advancement of collective knowledge in knowledge building research. The goal of this study is to develop a methodology, including the identification of appropriate indicators and tools that can be used to provide a quick, first level assessment of the level of knowledge building reflected in a Knowledge Forum (R) corpus using machine analysis and visualization, building on previous research in the area. Such a methodology will help to (1) build an empirically grounded understanding of learners' trajectories of advancement in knowledge building; and (2) identify pedagogical and facilitation designs that are more conducive to deeper levels of knowledge building by students. We implemented automatic encoding and visualization on a number of Knowledge Forum corpora collected from primary and secondary classrooms in Hong Kong around the themes of energy crisis and global warming. The findings indicate that visualization of different indicators applied in sequence provides some useful insight on the quality and level of engagement in online asynchronous discussions. Basic participation statistics at discourse and thread levels are useful in discriminating truncated inquiries from other, more engaging ones. Linear visualization of the time sequence of discourse markers for argumentation, questions and scaffolds sheds light on whether there is evidence of cycles of progressive inquiry. Fine grained identification of subject matter content reveals whether the discussion is likely to be an extensive inquiry or an intensive one. The study also demonstrates a clear need for automated coding and visualization of discourse corpora to be augmented and validated by a review of the actual discourse data by human researchers to yield more in-depth understanding of the knowledge building processes. C1 [Law, Nancy; Yuen, Johnny; Wong, Wing O. W.; Leng, Jing] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. RP Law, N (reprint author), Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. EM nlaw@hku.hk CR Andriessen Jerry, 2003, ARGUING LEARN CONFRO Bereiter C, 2002, ED MIND KNOWLEDGE AG Biggs J. B., 1982, EVALUATING QUALITY L Biggs J. B., 1999, TEACHING QUALITY LEA BURTIS J, 1998, ONTARIO I STUDIES ED Card S. K., 1999, READINGS INFORM VISU Coirier P., 1999, FDN ARGUMENTATIVE TE Dimitracopoulou A., 2009, COMM EV P 9 INT C CO, P174 DYKE G, 2009, P 9 INT C COMP SUPP, V1, P58, DOI 10.3115/1600053.1600062 Erkens G, 2008, INT J COMP-SUPP COLL, V3, P447, DOI 10.1007/s11412-008-9052-6 Francis M. E., 2007, LINGUISTIC INQUIRY W Goodman BA, 2005, USER MODEL USER-ADAP, V15, P85, DOI 10.1007/s11257-004-5269-x GWEON G, ANAL INTERACTIONS CS, P293 Hakkarainen K., 2002, SCI EDUC, V11, P25, DOI 10.1023/A:1013076706416 Hakkarainen K, 2004, INT J SCI EDUC, V26, P979, DOI 10.1080/1468181032000354 Hakkarainen K., 1998, THESIS U TORONTO HEEMAN PA, 1998, AAAI SPR S APPL MACH HMELOSILVER CE, ANAL INTERACTIONS CS, P83 Janssen J, 2007, COMPUT HUM BEHAV, V23, P1105, DOI 10.1016/j.chb.2006.10.005 Janssen J, 2007, COMPUT EDUC, V49, P1037, DOI 10.1016/j.compedu.2006.01.004 KOSCHMANN T, 2003, ENCY ED, P468 LAFERRIERE T, 2005, TECHNOLOGY TEACHER E, V2, P939 Lai M., 2006, J EDUC COMPUT RES, V35, P121 LAMON M, 2001, MAPPING GROWTH DEEPL Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LAW N, 2007, MICE MINDS SOC, P408 Law N, 2003, COM S COLL LEARN, V2, P57 LAW N, 2009, WORKSH AN COMP SUPP Law N, 2005, CSCL 2005: COMPUTER SUPPORTED COLLABORATIVE LEARNING 2005: THE NEXT 10 YEARS, PROCEEDINGS, P373 LAW N, 2008, WORKSH COMM FRAM CSC Lee EYC, 2006, INT J COMP-SUPP COLL, V1, P277, DOI 10.1007/s11412-006-8997-6 LEE EYC, 2005, P COMP SUPP COLL LEA Popper K. R., 1972, OBJECTIVE KNOWLEDGE Rahikainen M., 2001, EUROPEAN PERSPECTIVE, P520 REIMANN P, ANAL INTERACTIONS CS, P161 Rose C, 2008, INT J COMP-SUPP COLL, V3, P237, DOI 10.1007/s11412-007-9034-0 Saab N, 2005, BRIT J EDUC PSYCHOL, V75, P603, DOI 10.1348/000709905X42905 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scardamalia M., 1994, J LEARN SCI, V3, P265, DOI DOI 10.1207/S15327809JLS0303_3 Scardamalia M., 2003, ENCY DISTRIBUTED LEA, P269 SCARDAMALIA M, 1992, COGNITION INSTRUCT, V9, P177, DOI 10.1207/s1532690xci0903_1 Scardamalia M, 1991, J LEARN SCI, V1, P37, DOI DOI 10.1207/S15327809JLS0101_3 Scardamalia M., 2002, LIBERAL ED KNOWLEDGE, P67 Scardamalia M., 1994, CLASSROOM LESSONS IN, P201 Scardamalia M, 2006, CAMB HANDB PSYCHOL, P97 Scott J., 2000, SOCIAL NETWORK ANAL, V2nd Soller A, 2004, USER MODEL USER-ADAP, V14, P351, DOI 10.1023/B:USER.0000043436.49168.3b Stolcke A, 2000, COMPUT LINGUIST, V26, P339, DOI 10.1162/089120100561737 SUTHERS D, ANAL INTERACTIONS CS, P341 Tan S. C., 2005, J COMPUTERS MATH SCI, V24, P367 TEPLOVS C, 2007, IKIT KNOWL BUILD SUM Teplovs C., 2008, COMM FRAM CSCL INT A VANAALST, 2002, S KNOWL BUILD MULT P van Boxtel C, 2000, LEARN INSTR, V10, P311, DOI 10.1016/S0959-4752(00)00002-5 Veldhuis-Diermanse A. E., 2002, THESIS WAGENINGEN U Zhang JW, 2007, ETR&D-EDUC TECH RES, V55, P117, DOI 10.1007/s11423-006-9019-0 NR 56 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS BN 978-1-4419-7709-0 J9 COMPUT-SUPP COLLAB L PY 2011 BP 47 EP 82 DI 10.1007/978-1-4419-7710-6_3 D2 10.1007/978-1-4419-7710-6 PG 36 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA BTG50 UT WOS:000286903900003 ER PT S AU Ferilli, S AF Ferilli, Stefano BA Ferilli, S BF Ferilli, S TI Documents SO AUTOMATIC DIGITAL DOCUMENT PROCESSING AND MANAGEMENT: PROBLEMS, ALGORITHMS AND TECHNIQUES SE Advances in Pattern Recognition LA English DT Article; Book Chapter ID LATENT SEMANTIC ANALYSIS; LAYOUT ANALYSIS; INFORMATION EXTRACTION; BLOCK SEGMENTATION; SPATIAL RELATIONS; DATA-COMPRESSION; ANALYSIS SYSTEM; IMAGE-ANALYSIS; CRYPTOGRAPHY; ALGORITHM C1 Univ Bari, Dipartimento Informat, I-70126 Bari, Italy. RP Ferilli, S (reprint author), Univ Bari, Dipartimento Informat, Via E Orabona 4, I-70126 Bari, Italy. EM ferilli@di.uniba.it; ferilli@di.uniba.it RI Ferilli, Stefano/E-8841-2014 OI Ferilli, Stefano/0000-0003-1118-0601 CR ABDULRAHMAN A, 1997, PGP TRUST MODEL *AD SYST INC, 2000, PDF REF AD PORT DOC *AD SYST INC, 1992, TIFF SPEC REV 6 TECH ADDIS A, 2008, P IADIS INT C INT SY Allen J., 1994, NATURAL LANGUAGE UND Altamura O., 2001, International Journal on Document Analysis and Recognition, V4, DOI 10.1007/PL00013569 ANGELICI C, 1989, ENCICLOPEDIA GIURIDI, V11 [Anonymous], 1999, MERR WEBST COLL DICT Baird H. S., 1992, STRUCTURED DOCUMENT, P546 BAIRD HS, 1992, ADV STRUCTURAL SYNTA, P17 Baird H. S., 1990, Proceedings. 10th International Conference on Pattern Recognition (Cat. No.90CH2898-5), DOI 10.1109/ICPR.1990.118223 BAIRD HS, 1987, P C SOC PHOT SCI ENG, P14 Begeja L., 2004, HLT NAACL WORKSH INT, P1 Bentivogli L., 2004, P COLING 2004 WORKSH, P101, DOI 10.3115/1706238.1706254 Berkhin P., 2002, SURVEY CLUSTERING DA Berners-Lee T., 2001, SEMANTIC WEB Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Berry-Rogghe G.L.M., 1973, COMPUTER LIT STUDIES, P103 BOULGOURIS NV, 2002, EURASIP J APPL SIG P, V1, P418 Breuel TM, 2002, LECT NOTES COMPUT SC, V2423, P188 Briet S., 1951, QUEST CE QUE DOCUMEN Brill E, 1994, P 12 NAT C ART INT, V1, P722 Brill E, 1995, NATURAL LANGUAGE PRO, P1 BRILL E, 1992, HLT 91, P112 Buckland MK, 1997, J AM SOC INFORM SCI, V48, P804, DOI 10.1002/(SICI)1097-4571(199709)48:9<804::AID-ASI5>3.0.CO;2-V Buckland M.K., 1998, DOCUMENT NUMERIQUE, V2, P221 Burger W, 2008, TEXTS COMPUT SCI, P1 Callas J., 2007, 4880 RFC IETF Calvanese D, 2007, J AUTOM REASONING, V39, P385, DOI 10.1007/s10817-007-9078-x CALZOLARI N, 2004, MONDO DIGITALE, V2, P56 CANDIAN A, 1964, ENCICLOPEDIA GIURIDI CANNY J, 1986, IEEE T PATTERN ANAL, V8, P679 CAO H, 2007, P 9 INT C DOC AN REC, V1, P392 CARNELUTTI F, 1929, RIV DIRITTO COMMERCI, P509 Carnelutti F., 1957, NOVISSIMO DIGESTO IT *CCITT, 1992, T81 CCITT ITU *CEL CO, 2005, LIZ DJVU REF VERS 3 Cesarini F., 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), DOI 10.1109/ICDAR.1999.791850 Cestnik B., 1990, P EUR C ART INT, P147 Chaudhuri BB, 2007, ADV PATTERN RECOGNIT, P1, DOI 10.1007/978-1-84628-726-8 CHEN Q, 2003, THESIS U OTTAWA CANA Chen Y., 2004, MACHINE LEARNING STA Ciardiello G., 1988, P 9 INT C PATT REC, P739 Cocks C.C, 1973, NOTE NONSECRET ENCRY *COMP INC, 1990, GRAPH INT FORM SPEC Cooper D., 2008, 5280 RFC IETF Cowie J., 2000, HDB NATURAL LANGUAGE, P241 De Mauro Tullio, 1999, GRANDE DIZIONARIO IT DEB S, 2003, MULTIMEDIA SYSTEMS C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEUTSCH P, 1996, RFC1950 DEUTSCH P, 1996, RFC1951 Dewey M, 2003, DEWEY DECIMAL CLASSI Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 DIFFIE W, 1979, P IEEE, V67, P397, DOI 10.1109/PROC.1979.11256 DIFFIE W, 1978, IEEE COMMUNICATIONS, V16, P24 DIFFIE W, 1976, IEEE T INFORM THEORY, V22, P644, DOI 10.1109/TIT.1976.1055638 DIFFIE W, 1988, P IEEE, V76, P560, DOI 10.1109/5.4442 Di Mauro N, 2005, LECT NOTES ARTIF INT, V3533, P789 EGENHOFER M, 1991, LECTURE NOTES COMPUT, V525, P143 Egenhofer M. J., 1990, Proceedings of the 4th International Symposium on Spatial Data Handling EGENHOFER MJ, 1993, P 11 INT S COMP ASS Eisenberg JD, 2005, OASIS OPENDOCUMENT E Ellis J., 1970, POSSIBILITY SECURE N Esposito F, 2003, APPL ARTIF INTELL, V17, P859, DOI 10.1080/08839510390225203 Esposito F, 2008, STUD COMPUT INTELL, V90, P105 FATEMAN RJ, 1994, SUITE LISP PROGRAMS FEISTEL H, 1973, SCI AM, V128 FERILLI S, 2010, P 9 INT WORKS DOC AN, P349, DOI 10.1145/1815330.1815375 FERILLI S, 2009, P 10 INT C DOC AN RE, P231 Frank AU, 1996, INT J GEOGR INF SYST, V10, P269, DOI 10.1080/02693799608902079 Freitag D, 2000, MACH LEARN, V39, P169, DOI 10.1023/A:1007601113994 Gale W.A., 1992, P 4 DARPA SPEECH NAT, P233, DOI 10.3115/1075527.1075579 Garfinkel Simson, 1994, PGP PRETTY GOOD PRIV Gatos B, 2007, P 9 INT C DOC AN REC, P989, DOI DOI 10.1109/ICDAR.2007.4377063 GATTUSO A, 2007, MONDO PROFESSIONISTI, V1, pR3 Gonzalez R, 2008, DIGITAL IMAGE PROCES, V3rd Grishman R., 1997, LECT NOTES ARTIF INT, V1299, P10 GRISHMAN R, 1986, STUDIES NATURAL LANG GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Gunn SR, 1997, IEEE T PATTERN ANAL, V19, P63, DOI 10.1109/34.566812 HALLIDAY MAK, 1961, WORD, V17, P241 Hamilton E., 1992, JPEG FILE INTERCHANG Hanson N. R., 1958, PATTERNS DISCOVERY I HONG FOK, 2000, WEB PREFETCHING MODE Hough P., 1962, US Patent, Patent No. 3069654 HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898 HUNYADI L, 2001, P KEYW PROJ UNL CONT Ide N, 1998, COMPUT LINGUIST, V24, P1 Impedovo S., 1991, International Journal of Pattern Recognition and Artificial Intelligence, V5, DOI 10.1142/S0218001491000041 *INT STAND ORG, 2009, 15836 INT STAND ORG IRTI N, 1969, RIV TRIM DIR P CIV ISO/IEC, 2003, 10646 ISOIEC JAIN AK, 1999, ACM COMPUT SURV, V31, P164 KAINZ W, 1993, INT J GEOGR INF SYST, V7, P215, DOI 10.1080/02693799308901953 Kakas A. C., 1990, P 1 PAC RIM INT C AR, P438 KALISKI B, 1998, 2315 RFC IETF KARYPIS G, 2000, 00016 TR U MINN DEP Karypis G., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354772 Kass M., 1988, INT J COMPUT VISION, V1, P321, DOI DOI 10.1007/BF00133570 Kasturi R., 1995, DOCUMENT IMAGE ANAL Kerckhoffs A., 1883, J SCI MILITAIRES, VIX, P5 Kerckhoffs A., 1883, J SCI MILITAIRES, VIX, P161 Kise K, 1998, COMPUT VIS IMAGE UND, V70, P370, DOI 10.1006/cviu.1998.0684 KROVETZ R, 1998, P SENSEVAL WORKSH, P1 Lafferty J. D, 1992, P AAAI FALL S PROB A Lai X., 1992, ETH SERIES INFORM PR, V1 LAMPORT L, 1994, DOCUMENT PREPARATION Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LANDAUER TK, 1997, PSYCHOL REV, V104, P111 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Lindsay P., 1977, HUMAN INFORM PROCESS Magnini Bernardo, 2000, P LREC 2000 2 INT C, P1413 MANNING CD, 1999, FOUNDATIONS STAT NAT Manning C.D., 2008, INTRO INFORM RETRIEV Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 McCarthy J, 1955, PROPOSAL DARTMOUTH S Menezes A., 1996, HDB APPL CRYPTOGRAPH *MERR WEB INC, 1999, MERR WEBST COLL DICT Michalski R. S, 1994, MACH LEARN, VIV, P3 *MICR CORP, 2007, MICR OFF WORD 1997 2 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Mitchell TM, 1997, MACHINE LEARNING MORI S, 1992, P IEEE, V80, P1029, DOI 10.1109/5.156468 Nagy G, 1984, P 7 INT C PATT REC M, P347 NAGY G, 1992, COMPUTER, V25, P10, DOI 10.1109/2.144436 NAGY G, 1988, ACM C DOC PROC SYST Nagy G, 2000, IEEE T PATTERN ANAL, V22, P38, DOI 10.1109/34.824820 *NAT I STAN TECHN, 2009, 1863 FIBS PUB NATL I *NAT I STAND TECHN, 1998, 1861 FIPS PUB NAT I *NAT I STAND TECHN, 2008, 1803 FIPS PUB NATL I *NAT I STAND TECHN, 1995, 1801 FIPS PUB NAT I *NAT I STAND TECHN, 1993, 180 FIPS PUB NAT I S National Institute of Standards and Technology, 2002, 1802 FIPS PUB NAT I *NBS, 1987, FIPS PUB, V461 Nienhuys-Cheng S.-H., 1997, LECT NOTES COMPUTER, V1228 OGORMAN L, 1993, IEEE T PATTERN ANAL, V15, P1162, DOI 10.1109/34.244677 *OASIS, 2007, OP DOC FORM OFF APPL OBRIEN GW, 1994, CS94258 U TENN OLTRAMARI A, 2008, P ONTOLEX 2008 WORKS OTSU N, 1979, IEEE T SYST MAN CYB, V9, P62 Papadias D, 1997, INT J GEOGR INF SCI, V11, P111, DOI 10.1080/136588197242428 PAPAMARKOS N, 1996, P INT C EL CIRC SYST, V2, P684, DOI 10.1109/ICECS.1996.584454 Pavlidis White T., 1991, P 1 INT C DOC AN REC, P945 Pierce John Robinson, 1961, HARPER MODERN SCI SE PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Press W. H., 2007, ART SCI COMPUTING, V3rd REID GC, 1990, THINKING POSTSCRIPT Rice S. V., 1995, 9503 U NEV INF SCI R RIVEST R, 1992, 1321 RFC NETW WORK G Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rosenfeld A, 1969, PICTURE PROCESSING C Salembier P, 1999, IEEE T CIRC SYST VID, V9, P1147, DOI 10.1109/76.809153 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shafait F, 2010, P 9 IAPR INT WORKSH, P65, DOI 10.1145/1815330.1815339 Shannon C. E., 1949, MATH THEORY COMMUNIC Shih FY, 1996, IEEE T SYST MAN CY B, V26, P797, DOI 10.1109/3477.537322 Simon A, 1997, IEEE T PATTERN ANAL, V19, P273, DOI 10.1109/34.584106 Singh Simon, 1999, CODE BOOK Skiena S., 2008, ALGORITHM DESIGN MAN, V2nd SLEATOR D, 1993, P 3 INT WORKSH PARS Smith R., 2009, P 10 INT C DOC AN RE, P241 Smith R., 2007, P 9 INT C DOC AN REC, P629 Smith R., 1995, Proceedings of the Third International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.1995.602124 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Sonka M., 2008, IMAGE PROCESSING ANA, Vthird Sorkin A., 1984, CRYPTOLOGIA, V8, P22, DOI 10.1080/0161-118491858746 Stallings W., 2002, CRYPTOGRAPHY NETWORK Stevens M, 2009, LECT NOTES COMPUT SC, V5677, P55, DOI 10.1007/978-3-642-03356-8_4 SUN HM, 2005, P 8 INT C DOC AN REC, P116 TOSI E, 2007, CODICE DIRITTO INFOR *UK GOVT, 2009, DEP CULT MED SPORT D *UN CONS, 2006, UN STAND 5 UZUN Y, 2005, KEYWORD EXTRACTION U *W3C, 2000, 10 W3C *W3C, 2006, EXT STYL LANG XSL 1 W3C, 2006, EXT MARK LANG XML 1 *W3C, 2003, PORT NETW GRAPH SPEC *W3C, 1998, RECDOMLEVEL119981001 *W3C, 1999, HTML 4 01 SPEC W3C R W3C, 1999, XML PATH LANG XPATH W3C, 2008, EXT MARK LANG XML 1 *W3C, 1999, XSL TRANSF XSLT 1 0 *W3C SVG WORK GRO, 2003, SCALABLE VECTOR GRAP WAHL FM, 1982, COMPUT VISION GRAPH, V20, P375, DOI 10.1016/0146-664X(82)90059-4 WALL K, 1984, COMPUT VISION GRAPH, V28, P220, DOI 10.1016/S0734-189X(84)80023-7 WANG D, 1989, COMPUT VISION GRAPH, V47, P327, DOI 10.1016/0734-189X(89)90116-3 WELCH TA, 1984, COMPUTER, V17, P8 WONG KY, 1982, IBM J RES DEV, V26, P647 Wood L, 1999, IEEE INTERNET COMPUT, V3, P48, DOI 10.1109/4236.747321 WOODS WA, 1997, SML TECHNICAL REPORT *WORLD BANK, 1998, KNOWL DEV TECH REP Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 Yarowsky D., 1993, P ARPA HUM LANG TECH, P266, DOI 10.3115/1075671.1075731 Yarowsky D., 1994, P 32 ANN M ASS COMP, P88, DOI 10.3115/981732.981745 YUILLE AL, 1991, J COGNITIVE NEUROSCI, V3, P59, DOI 10.1162/jocn.1991.3.1.59 Zimmermann P.R., 1995, OFFICIAL PGP USERS G ZIV J, 1977, IEEE T INFORM THEORY, V23, P337, DOI 10.1109/TIT.1977.1055714 ZIV J, 1978, IEEE T INFORM THEORY, V24, P530, DOI 10.1109/TIT.1978.1055934 Zucker J.D., 1998, P 4 INT WORKSH MULT, P157 NR 201 TC 0 Z9 0 PU SPRINGER-VERLAG LONDON LTD PI GODALMING PA SWEETAPPLE HOUSE CATTESHALL RD FARNCOMBE, GODALMING GU7 1NH, SURREY, ENGLAND SN 1617-7916 BN 978-0-85729-197-4 J9 ADV PATTERN RECOGNIT PY 2011 BP 3 EP + DI 10.1007/978-0-85729-198-1_1 D2 10.1007/978-0-85729-198-1 PG 20 WC Computer Science, Artificial Intelligence SC Computer Science GA BTK57 UT WOS:000287169500001 ER PT S AU Ferilli, S AF Ferilli, Stefano BA Ferilli, S BF Ferilli, S TI Information Management SO AUTOMATIC DIGITAL DOCUMENT PROCESSING AND MANAGEMENT: PROBLEMS, ALGORITHMS AND TECHNIQUES SE Advances in Pattern Recognition LA English DT Article; Book Chapter ID LATENT SEMANTIC ANALYSIS; EXTRACTION; TEXT C1 Univ Bari, Dipartimento Informat, I-70126 Bari, Italy. RP Ferilli, S (reprint author), Univ Bari, Dipartimento Informat, Via E Orabona 4, I-70126 Bari, Italy. EM ferilli@di.uniba.it; ferilli@di.uniba.it RI Ferilli, Stefano/E-8841-2014 OI Ferilli, Stefano/0000-0003-1118-0601 CR ADDIS A, 2008, P IADIS INT C INT SY Begeja L., 2004, HLT NAACL WORKSH INT, P1 Berners-lee T., 2001, SCI AM Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 BOULGOURIS NV, 2002, EURASIP J APPL SIG P, V1, P418 Cestnik B., 1990, P EUR C ART INT, P147 Chen Y., 2004, MACHINE LEARNING STA Cowie J., 2000, HDB NATURAL LANGUAGE, P241 DEB S, 2003, MULTIMEDIA SYSTEMS C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Freitag D, 2000, MACH LEARN, V39, P169, DOI 10.1023/A:1007601113994 Grishman R., 1997, LECT NOTES ARTIF INT, V1299, P10 HONG FOK, 2000, WEB PREFETCHING MODE HUNYADI L, 2001, P KEYW PROJ UNL CONT KARYPIS G, 2000, 00016 TR U MINN DEP Karypis G., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354772 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LANDAUER TK, 1997, PSYCHOL REV, V104, P111 Manning C.D., 2008, INTRO INFORM RETRIEV Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 Mitchell TM, 1997, MACHINE LEARNING OBRIEN GW, 1994, CS94258 U TENN Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 UZUN Y, 2005, KEYWORD EXTRACTION U WOODS WA, 1997, SML TECHNICAL REPORT NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG LONDON LTD PI GODALMING PA SWEETAPPLE HOUSE CATTESHALL RD FARNCOMBE, GODALMING GU7 1NH, SURREY, ENGLAND SN 1617-7916 BN 978-0-85729-197-4 J9 ADV PATTERN RECOGNIT PY 2011 BP 223 EP 255 DI 10.1007/978-0-85729-198-1_7 D2 10.1007/978-0-85729-198-1 PG 33 WC Computer Science, Artificial Intelligence SC Computer Science GA BTK57 UT WOS:000287169500007 ER PT J AU Bhatt, CA Kankanhalli, MS AF Bhatt, Chidansh Amitkumar Kankanhalli, Mohan S. TI Multimedia data mining: state of the art and challenges SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Survey; Multimodal data mining; Probabilistic temporal multimedia data mining; Video mining; Audio mining; Image mining; Text mining ID EVENT DETECTION; AUDIO CLASSIFICATION; SPEECH RECOGNITION; RETRIEVAL; MACHINES; MODEL AB Advances in multimedia data acquisition and storage technology have led to the growth of very large multimedia databases. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem. This challenge has opened the opportunity for research in Multimedia Data Mining (MDM). Multimedia data mining can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. The motivation for doing MDM is to use the discovered patterns to improve decision making. MDM has therefore attracted significant research efforts in developing methods and tools to organize, manage, search and perform domain specific tasks for data from domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. This paper presents a survey on the problems and solutions in Multimedia Data Mining, approached from the following angles: feature extraction, transformation and representation techniques, data mining techniques, and current multimedia data mining systems in various application domains. We discuss main aspects of feature extraction, transformation and representation techniques. These aspects are: level of feature extraction, feature fusion, features synchronization, feature correlation discovery and accurate representation of multimedia data. Comparison of MDM techniques with state of the art video processing, audio processing and image processing techniques is also provided. Similarly, we compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. We review current multimedia data mining systems in detail, grouping them according to problem formulations and approaches. The review includes supervised and unsupervised discovery of events and actions from one or more continuous sequences. We also do a detailed analysis to understand what has been achieved and what are the remaining gaps where future research efforts could be focussed. We then conclude this survey with a look at open research directions. C1 [Bhatt, Chidansh Amitkumar; Kankanhalli, Mohan S.] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore. RP Bhatt, CA (reprint author), Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore. EM chidansh@comp.nus.edu.sg; mohan@comp.nus.edu.sg RI chen, mingang/C-7691-2011 CR Agrawal R., 1995, INT C DAT ENG Agrawal R., 1993, ACM SIGMOD INT C MAN, P207 AJMERA J, 2002, IEEE INT C AC SPEECH, P1746 ARADHYE H, 2009, INT C DAT MIN WORKSH, P144 Artigan J., 1975, CLUSTERING ALGORITHM BAILLIE M, 2004, WORKSH EV MIN DET RE Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 BENITEZ AB, 2000, MULTIMEDIA INFORM NE Box G.E.P., 1994, TIME SERIES ANAL FOR Briggs F, 2009, IEEE DATA MINING, P51, DOI 10.1109/ICDM.2009.65 CHANG E, 1999, SPIE MULTIMEDIA STOR, V6 Chang E, 2003, IEEE T CIRC SYST VID, V13, P26, DOI 10.1109/TCSVT.2002.808079 Chen M, 2006, IEEE SIGNAL PROC MAG, V23, P38 CHEN M, 2007, MULTIMEDIA DATABASES CHEN SC, 2001, ACM SIGKDD CHEN SC, 2004, ICME, P265 DAI K, 2006, MULTIMEDIA MODELLING Darrell T., 1993, IEEE C COMP VIS PATT, P335 DAVIS SB, 1980, IEEE T ACOUST SPEECH, V28, P357, DOI 10.1109/TASSP.1980.1163420 Dhillon IS, 2001, ACM SIGKDD DIMITRIADIS D, 2003, EUR C SPEECH COMM TE Duda R.O., 2001, PATTERN CLASSIFICATI ELLOM BL, 1998, SPEECH COMMUN, V25, P97 ELMALEH K, 2000, ACOUST SPEECH SIG PR, P2445 Ester M., 1996, KDD-96 Proceedings. Second International Conference on Knowledge Discovery and Data Mining Faloutsos C., 1994, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V3, DOI 10.1007/BF00962238 FAN J, 2007, ACM SIGIR, P111 Fan JP, 2005, PATTERN RECOGN, V38, P865, DOI 10.1016/j.patcog.2004.07.011 Fan JP, 2008, IEEE T MULTIMEDIA, V10, P167, DOI 10.1109/TMM.2007.911775 Fersini E, 2009, LECT NOTES ARTIF INT, V5632, P594, DOI 10.1007/978-3-642-03070-3_45 Foote Jonathan, 1997, SPIE MULTIMEDIA ST 2, V3229, P138 Forsati R, 2010, STUD COMPUT INTELL, V270, P51 Frakes W. B., 1992, INFORM RETRIEVAL DAT FRIGUI H, 2007, SIAM INT C DAT MIN FU CS, 1998, IEEE T CIRCUITS SYST, V8, P602 FURUI S, 1981, IEEE T ACOUST SPEECH, V29, P254, DOI 10.1109/TASSP.1981.1163530 GAJIC B, 2001, INT C AC SPEECH SIGN, V1, P85 GAO J, 2009, INT C WEB INF SYST M, P116 GAO Y, 2006, ACM MIR GARNER PN, 2004, ICASSP 2004, V1, P597 GHITZA O, 1987, COMPUTER SPEECH LANG, V2, P109 Goh KS, 2004, P SOC PHOTO-OPT INS, V5307, P292 Gold B., 2000, SPEECH AUDIO SIGNAL GOOL LV, 2009, ACM INT C IM VID RET, P1 GORKANI MM, 1994, IEEE C PATT REC Guo GD, 2003, IEEE T NEURAL NETWOR, V14, P209, DOI 10.1109/TNN.2002.806626 GUO Z, 2007, ACM INT C KNOWL DISC Hall Mark, 2009, SIGKDD EXPLORATIONS, V11 Han J., 2006, DATA MINING CONCEPTS Han J., 2000, SIGKDD EXPLORATIONS, V2, P14 HARB H, 2001, INT C DISTR MULT SYS, P257 HE R, 2010, INT C INF FUS HE R, 2009, AS PAC C COMP INT IN Hermansky H, 1994, IEEE T SPEECH AUDI P, V2, P578, DOI 10.1109/89.326616 HERMANSKY H, 1998, INT C SPEECH LANG PR HERMANSKY H, 1991, EUR C SPEECH COMM TE, P578 HERMANSKY H, 1987, INT C AC SPEECH SIGN, P1156 HERMANSKY H, 1990, J ACOUST SOC AM, V87, P1738, DOI 10.1121/1.399423 Hipp J., 2000, SIGKDD EXPLORATIONS, V2, P1 HUANG J, 1998, ACM MULTIMEDIA HWAN OJ, 2003, PAC AS C KNWOL DISC Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jiang CT, 2010, KNOWL-BASED SYST, V23, P302, DOI 10.1016/j.knosys.2009.11.010 Jiang T, 2009, IEEE T KNOWL DATA EN, V21, P161, DOI 10.1109/TKDE.2008.150 Juang B. H., 1993, FUNDAMENTALS SPEECH KEMP T, 2000, INT C AC SPEECH SIGN Kotsiantis Sotiris, 2006, INT T COMPUTER SCI E, V32, P71 KRUSKAL JB, 1983, SIAM REV, V25, P201, DOI 10.1137/1025045 KUBIN G, 1994, IEEE INT C AC SPEECH KURABAYASHI S, 2010, DATABASE SYSTEMS ADV Leavitt N, 2002, COMPUTER, V35, P23, DOI 10.1109/MC.2002.1039511 LI D, 2003, ACM MULT C, P604 Li N, 2010, DECIS SUPPORT SYST, V48, P354, DOI 10.1016/j.dss.2009.09.003 Li SZ, 2000, IEEE T SPEECH AUDI P, V8, P619, DOI 10.1109/89.861383 LI Y, 2005, IEEE INT C COMP VIS LILT D, 2004, IEEE INT C AC SPEECH Lin L, 2009, 2009 IEEE THIRD INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2009), P137, DOI 10.1109/ICSC.2009.59 LIN L, 2007, IEEE INT C MULT EXP, P859 Lin L, 2009, IEEE INT CON MULTI, P418 LIN W, 2002, WORKSH INT SIT AW ME Lin WH, 2003, LECT NOTES ARTIF INT, V2797, P217 LIN WH, 2002, ACM MULTIMEDIA LIU J, 2010, ACM S APPL COMP LIU Q, 2009, INT C MACH LEARN DAT, P582 Mannila H, 1997, DATA MIN KNOWL DISC, V1, P259, DOI 10.1023/A:1009748302351 Maragos P, 1999, J ACOUST SOC AM, V105, P1925, DOI 10.1121/1.426738 MARAGOS P, 1991, IEEE INT C AC SPEECH MASE K, 2009, MULTIMODAL SENSOR BA, P1 MATSUO Y, 2003, INT WORKSH MULT DAT, P18 MEGALOOIKONOMOU V, 1999, ACM SIGKDD MEINEDO H, 2005, INTERSPEECH EUROSPEE MESGARANI N, 2004, INT C AC SPEECH SIGN, V1, P601 MESSINA A, 2009, INT C WORLD WID WEB, P321 MONTAGNUOLO M, 2010, S APPL COMP SAC, P1823 MORENO PJ, 2000, IEEE INT C AC SPEECH NORVAG K, 2006, INT S METH INT SYST, P745 NORVAG K, 2009, INT C COMP LING INT, P442 Oates T., 1996, ICML OH J, 2002, INT WORKSH MULT DAT, P1 ORDONEZ C, 1999, IEEE ADV DIG LIBR C PAN J, 2002, INT C AS DIG LIBR IC, P194 PAN JY, 2004, ACM SIGKDD C KNOWL D PATEL N, 2007, MULTIMEDIA DATA MINI Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 PFEIFFER S, 1996, ACM MULTIMEDIA, P21 PINQUIER J, 2002, INT C SPOK LANG PROC, V3, P2005 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QUATIERI TF, 1990, INT C AC SPEECH SIGN Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 Rajendran P., 2009, INT J COMPUTER SCI I, V6, P107 RAMACHANDRAN C, 2009, ACM INT C MULT, P721 Ribeiro MX, 2009, STUD COMPUT INTELL, V165, P113 RIJSBERGEN CJV, 1986, COMPUT J, V29, P481 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACENO C, 1997, IEEE INT C AC SPEECH, V4, P2597 Saunders J, 1996, IEEE INT C AC SPEECH, V2, P993 SCLAROFF S, 2001, INT WORKSH MULT DAT SENEFF S, 1988, J PHON, V16, P57 Seneff S., 1984, ICASSP 84. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing SHAO X, 2003, IEEE PAC RIM C MULT SHEIKHOLESLAMI G, 1998, INT C VER LARG DAT B, P428 SHIRAHAMA K, 2008, MULT DAT MIN SHIRAHAMA K, 2009, INT C DAT MIN WORKSH, P176 SHIRAHAMA K, 2004, INT C MULT EXP SHIRAHAMA K, 2005, IEEE INT S MULT Shyu ML, 2008, IEEE T MULTIMEDIA, V10, P252, DOI 10.1109/TMM.2007.911830 SMITH JR, 1996, IEEE P INT C IM P, V3, P1011 Sohn J, 1999, IEEE SIGNAL PROC LET, V6, P1 STEINBACH M, 2000, ACM SIGKDD WORLD TEX STEMBRIDGE B, 2004, INTELLECT ASSET MANA Stricker M.A., 1995, SPIE, V2420, P381 SWAIN MJ, INT J COMPUT VIS, V7, P11 TADA T, 2009, INT MULT ENG COMP SC Tong S., 2001, ACM MULTIMEDIA TOWNSHEND B, 1990, IEEE INT C AC SPEECH Trippe A. J., 2003, World Patent Information, V25, DOI 10.1016/S0172-2190(03)00079-6 VAILAYA A, 1998, SPIE, V3656 Vapnik V., 1995, NATURE STAT LEARNING Victor S.P, 2010, EUROPEAN J SCI RES E, V40, P540 WANG JZ, 2001, LECT NOTES COMPUTER, P232 Wei SK, 2010, IEEE T KNOWL DATA EN, V22, P1191, DOI 10.1109/TKDE.2009.145 Williams Gethin, 1999, EUROSPEECH WU Y, 2005, ACM INT C MULT MM, P872 Wynne Hsu, 2002, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V19 XIE L, 2004, IEEE INT C IM PROC Yamato J., 1992, IEEE C COMP VIS PATT, P379 Yan R., 2004, ACM MULTIMEDIA 2004, P548 Yang Y., 2008, WORLD PAT INF, V30, P280, DOI 10.1016/j.wpi.2008.01.007 YEUNG M, 2001, READINGS MULTIMEDIA YEUNG MM, 1996, INT C PATT REC, V3, P375 ZAIANE O, 1998, ACM SIGMOD, P581 ZHANG C, 2009, J MULTIMEDIA, V4, P321 ZHANG HJ, 1995, SPIE C STOR RETR IM ZHANG R, 2005, IEEE INT C COMP VIS Zhang T., 1996, SIGMOD 96, P103 Zhang T, 2001, IEEE T SPEECH AUDI P, V9, P441, DOI 10.1109/89.917689 ZHU R, 2009, INT C ADV DAT MIN AP, P780 Zhu XQ, 2005, IEEE T KNOWL DATA EN, V17, P665 ZIANG J, 2002, INT C AC SPEECH SIGN NR 161 TC 8 Z9 10 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JAN PY 2011 VL 51 IS 1 BP 35 EP 76 DI 10.1007/s11042-010-0645-5 PG 42 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 705BO UT WOS:000286103800003 ER PT J AU Sawant, N Li, J Wang, JZ AF Sawant, Neela Li, Jia Wang, James Z. TI Automatic image semantic interpretation using social action and tagging data SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE Web 2.0; Social media; Collaborative annotation; Image semantics; Folksonomic features; Survey ID ANNOTATION; RETRIEVAL; FOLKSONOMIES; PICTURES; SYSTEMS; RECOGNITION; STREAMS; TOOL AB The plethora of social actions and annotations (tags, comments, ratings) from online media sharing Websites and collaborative games have induced a paradigm shift in the research on image semantic interpretation. Social inputs with their added context represent a strong substitute for expert annotations. Novel algorithms have been designed to fuse visual features with noisy social labels and behavioral signals. In this survey, we review nearly 200 representative papers to identify the current trends, challenges as well as opportunities presented by social inputs for research on image semantics. Our study builds on an interdisciplinary confluence of insights from image processing, data mining, human computer interaction, and sociology to describe the folksonomic features of users, annotations and images. Applications are categorized into four types: concept semantics, person identification, location semantics and event semantics. The survey concludes with a summary of principle research directions for the present and the future. C1 [Sawant, Neela; Wang, James Z.] Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA. [Li, Jia] Penn State Univ, Dept Stat, University Pk, PA 16802 USA. RP Sawant, N (reprint author), Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA. EM nks125@psu.edu; jiali@psu.edu; jwang@psu.edu FU National Science Foundation [IIS-0949891, IIS-0347148]; Pennsylvania State University FX The material was based upon work supported in part by the National Science Foundation under Grant Nos. IIS-0949891 and IIS-0347148, and by The Pennsylvania State University. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Aesthetics, AESTH Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148177 Ahern S, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P1, DOI 10.1145/1255175.1255177 Allan J., 2002, TOPIC DETECTION TRAC Allan M, 2009, P BR MACH VIS C Ames M, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P971 Andrienko N, 2003, J VISUAL LANG COMPUT, V14, P503, DOI 10.1016/S1045-926X(03)00046-6 Anguelov D., 2007, P IEEE C COMP VIS PA, P1 Bailloeul T, 2008, P 9 ACM INT C MULT I, P75, DOI [10.1145/1460096.1460110, DOI 10.1145/1460096.1460110] Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Barnard K, 2008, INT J COMPUT VISION, V77, P199, DOI 10.1007/s11263-007-0068-6 Berg T.L., 2007, AUTOMATIC RANKING IC Bian J., 2008, P 4 INT WORKSH ADV I, P53, DOI 10.1145/1451983.1451997 Bian J, 2009, P 18 INT C WORLD WID, P51, DOI DOI 10.1145/1526709.1526717 Bottcher M., 2008, SIGKDD EXPLORATIONS, V10, P3 Boutell M, 2004, INT C PATT RECOG, P901, DOI 10.1109/ICPR.2004.1333918 BRINKER K, 2007, P 20 INT JOINT C ART, P702 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Budiu R, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P615 Cai D., 2004, P 12 ANN ACM INT C M, P952, DOI 10.1145/1027527.1027747 Cao L., 2008, P 16 ACM INT C MULT, P121, DOI 10.1145/1459359.1459376 Cao L, 2008, P COMP VIS PATT REC Cattuto C, 2007, AI COMMUN, V20, P245 Chandola V., 2007, OUTLIER DETECTION SU Chandramouli K, 2010, P ACM MULT INF RET, P507, DOI 10.1145/1743384.1743472 Chatzilari E, 2009, P DIG SIGN P, P1 Chen H.-M., 2008, P 16 ACM INT C MULT, P737, DOI 10.1145/1459359.1459473 Chen Ling, 2009, P 18 ACM C INF KNOWL, P523, DOI 10.1145/1645953.1646021 Chen W., 2009, P 17 ACM INT C MULT, P789, DOI 10.1145/1631272.1631415 Chi E. H., 2008, P 19 ACM C HYP HYP, P81, DOI 10.1145/1379092.1379110 Choi J, 2008, P INT C MULT INF RET, P44, DOI 10.1145/1460096.1460106 Chua T. S., 2009, P ACM INT C IM VID R, DOI [10.1145/1646396.1646452, DOI 10.1145/1646396.1646452] Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 Cristani M, 2008, P COMP VIS PATT REC Cristani M, 2008, EXT ABSTR HUM FACTOR, P2823 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dabbish L., 2004, P SIGCHI C HUM FACT, P319, DOI DOI 10.1145/985692.985733.ISBN Datta R., 2007, P ACM MULT, P393, DOI 10.1145/1291233.1291328 Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248 Datta R, 2006, LECT NOTES COMPUT SC, V3953, P288 DATTA R., 2010, P INT C MULT INF RET, P421, DOI 10.1145/1743384.1743457 Davis M, 2006, P S EL IM SCI TECH Davis Marc, 2005, P 13 ANN ACM INT C M, P483, DOI 10.1145/1101149.1101257 Dean J., 2004, P 6 C S OP SYST DES, P10 Deng J, 2009, CONSTRUCTION ANAL LA Dong W, 2010, P HUM FACT COMP SYST, P981, DOI 10.1145/1753326.1753472 Donmez P, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P259 Dubinko M., 2006, P 15 INT C WORLD WID, P193, DOI 10.1145/1135777.1135810 Duda R., 2000, PATTERN CLASSIFICATI, V2nd ESTER M, 1999, P 23 GERM C ART INT, P61 Fei-Fei L, 2004, COMPUTER VISION IMAG, V106, P59, DOI DOI 10.1016/J.CVIU.2005.09.012 Fei-Fei Li, 2006, IEEE Trans Pattern Anal Mach Intell, V28, P594 Fellbaum C, 1998, WORDNET ELECT LEXICA Fu WT, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P229 Furnas G, 1984, P SIGCHI C HUM FACT, P187 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Gaber MM, 2005, SIGMOD RECORD, V34, P18, DOI 10.1145/1083784.1083789 Garg N, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P67 Giannakidou E, 2008, P 2 IEEE INT C SEM C, P128 Girgensohn A, 2004, P 6 ACM SIGMM INT WO, P99, DOI 10.1145/1026711.1026728 Golder S., 2008, P 19 ACM C HYP HYP, P43, DOI 10.1145/1379092.1379104 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Goncalves D, 2008, EXT ABSTR HUM FACTOR, P2685 Griffin G, 2007, 7694 CALTECH Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Hamilton J. D., 1994, TIME SERIES ANAL Hodge VJ, 2004, ARTIF INTELL REV, V22, P85, DOI 10.1023/B:AIRE.0000045502.10941.a9 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hotho A., 2006, P C FACHGR INF RET, P111 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Huiskes M. J., 2008, P 1 ACM INT C MULT I, P39, DOI DOI 10.1145/1460096.1460104 Ihler A., 2007, ACM T KNOWL DISCOV D, V1, P13, DOI 10.1145/1297332.1297337 Ivanov I., 2010, P INT C MULT INF RET, P497, DOI 10.1145/1743384.1743471 Jaffe A, 2006, P 8 ACM INT WORKSH M, P89, DOI 10.1145/1178677.1178692 JASCHKE R., 2007, P 11 EUR C PRINC PRA, P506 Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 Jin X., 2010, P WORLD WID WEB, P1261, DOI 10.1145/1772690.1772897 Jin Y., 2005, P 13 ANN ACM INT C M, P706, DOI 10.1145/1101149.1101305 Jones W, 1986, P SIGCHI C HUM FACT, P298, DOI 10.1145/22627.22387 Joshi D., 2008, P INT C CONT BAS IM, P37, DOI 10.1145/1386352.1386361 Ke Y., 2006, P IEEE C COMP VIS PA, P419 Kennedy L., 2007, P 15 INT C MULT, P631, DOI 10.1145/1291233.1291384 Kennedy L. S., 2006, P 8 ACM INT WORKSH M, DOI 10.1145/1178677.1178712 Kennedy L.S., 2009, P 1 WORKSH WEB SCAL, P17, DOI 10.1145/1631135.1631139 Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 KOPERSKI K, 1996, WORKSH RES ISS DAT M, P1 Koutrika G., 2007, P 3 INT WORKSH ADV I, P57, DOI 10.1145/1244408.1244420 Krestel R, 2008, P C PRACT KNOWL DISC Kucuktunc O, 2008, P SEM DIG MED TECHN Kustanowitz J, 2005, MOTIVATING ANNOTATIO Lambiotte R., 2006, P COMP SCI, P1114 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Leskovec J., 2008, P 17 INT C WORLD WID, P695, DOI DOI 10.1145/1367497.1367591 Levi K., 2004, P IEEE C COMP VIS PA, V2, P53, DOI 10.1109/CVPR.2004.1315144 Li J, 2003, IEEE T PATTERN ANAL, V25, P1075 Li J, 2008, IEEE T PATTERN ANAL, V30, P985, DOI 10.1109/TPAMI.2007.70847 Li QF, 2008, IEEE MULTIMEDIA, V15, P14, DOI 10.1109/MMUL.2008.54 Li X., 2008, P 1 ACM INT C MULT I, P180, DOI 10.1145/1460096.1460126 Li Xirong, 2006, P 14 ANN ACM INT C M, P607, DOI 10.1145/1180639.1180764 Lindstaedt S, 2009, MULTIMED TOOLS APPL, V42, P97, DOI 10.1007/s11042-008-0247-7 LINDSTAEDT S, 2008, P INT WEB APPL SERV, P506 Liu D., 2009, P ACM MULT, P809, DOI 10.1145/1631272.1631420 Liu D., 2009, P 18 INT C WORLD WID, DOI 10.1145/1526709.1526757 Liu D, 2009, IEEE INT CON MULTI, P350 Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045 Lu Y, 2008, P COMP VIS PATT REC Luxburg U.V., 2007, STAT COMPUT, V17, P395, DOI DOI 10.1007/S11222-007-9033-Z Manning C, 2008, INTRO INFORM RETRIEV, P85 Marlow C., 2006, P 17 C HYP HYP, P31, DOI DOI 10.1145/1149941.1149949 Marques O, 2008, P MS 08 NEW YORK NY, P40, DOI 10.1145/1460676.1460685 Miller G. A., 1983, STUDY INFORM INTERDI, P111 Miller H. J., 2001, GEOGRAPHIC DATA MINI Moxley E., 2008, P 1 ACM INT C MULT I, P24, DOI 10.1145/1460096.1460102 Naaman M, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P178, DOI 10.1145/1065385.1065430 Naaman M, 2003, LECT NOTES COMPUT SC, V2888, P196 Naaman M, 2008, IEEE MULTIMEDIA, V15, P34, DOI 10.1109/MMUL.2008.69 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Negoescu Radu Andrei, 2008, P 2008 INT C CONT BA, P417, DOI 10.1145/1386352.1386406 Ng R. T., 1994, P 20 INT C VER LARG, P144 Nisbett RE, 2001, PSYCHOL REV, V108, P291, DOI 10.1037//0033-295X.108.2.291 Noll MG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P612, DOI 10.1145/1571941.1572046 Nov O, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1097 Obrador P., 2009, P SIGMM WORKSH SOC M, P65, DOI 10.1145/1631144.1631158 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Pedersen T., 2004, DEMONSTRATION PAPERS, P38, DOI 10.3115/1614025.1614037 Pirolli P, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P605 Pirolli P, 2007, INFORM FORAGING THEO Ramakrishnan G, 2005, P 22 INT C MACH LEAR, P681, DOI 10.1145/1102351.1102437 Rattenbury T., 2007, P 30 ANN INT ACM SIG, P103, DOI 10.1145/1277741.1277762 Rebbapragada U, 2007, P EUR C MACH LEARN, P708 Robertson S., 2009, P 27 INT C HUM FACT, P3937, DOI 10.1145/1520340.1520597 Roddick J. F., 2008, SIGKDD EXPLORATIONS, V10, P5 Roddick JF, 2002, IEEE T KNOWL DATA EN, V14, P750, DOI 10.1109/TKDE.2002.1019212 Ruger Stefan, 2010, P INT C MULT INF RET, P557, DOI 10.1145/1743384.1743478 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 Russell BC, 2008, INT J COMPUT VISION, V77, P157, DOI 10.1007/s11263-007-0090-8 Salton G., 1987, TERM WEIGHTING APPRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 2001, IEEE T KNOWL DATA EN, V13, P337, DOI 10.1109/69.929893 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Sawant N, 2010, P ACM MULT INF RET, P231, DOI 10.1145/1743384.1743424 Sen S, 2006, P 2006 20 ANN C COMP, P181, DOI 10.1145/1180875.1180904 Sen S, 2007, GROUP'07: PROCEEDINGS OF THE 2007 INTERNATIONAL ACM CONFERENCE ON SUPPORTING GROUP WORK, P361 Sen S., 2009, P 13 INT C INT US IN, P87 Seneviratne L., 2010, P ACM MULT INF RET, P517, DOI 10.1145/1743384.1743473 Serdyukov P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P484, DOI 10.1145/1571941.1572025 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 Sigurbjornsson B., 2008, P 17 INT C WORLD WID, DOI [10.1145/1367497.1367542, DOI 10.1145/1367497.1367542] Sinha P., 2008, P 2008 INT C CONT BA, P309, DOI 10.1145/1386352.1386394 Sivic J., 2006, P BRIT MACH VIS C ED, P909 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith M., 1999, COMMUNITIES CYBERSPA, P107 Snavely N, 2006, ACM T GRAPHIC, V25, P835, DOI 10.1145/1141911.1141964 Solli M., 2010, P ACM IM VID RET, P398, DOI 10.1145/1816041.1816099 Song Y., 2006, P EUR C COMP VIS, pIII Sorokin A., 2008, COMP VIS PATT REC WO Stumme G., 2008, P 3 WORKSH ONT LEARN, P39 Suchanek F. M., 2008, P 17 ACM C INF KNOWL, P223, DOI DOI 10.1145/1458082.1458114 Suh B, 2007, INTERACT COMPUT, V19, P524, DOI 10.1016/j.intcom.2007.02.002 Sun A., 2009, P SIGMM WORKSH SOC M, P19, DOI 10.1145/1631144.1631150 Surowiecki J., 2004, WISDOM CROWDS WHY MA Sylvan E, 2010, P HUM FACT COMP SYST, P1913, DOI 10.1145/1753326.1753614 Tang J., 2009, P 17 ACM INT C MULT, P223, DOI DOI 10.1145/1631272.1631305 Truran M., 2005, P 13 ACM INT C MULT, P547, DOI 10.1145/1101149.1101273 Tuytelaars T., 2008, FDN TRENDS COMPUTER, V3, P177, DOI DOI 10.1561/0600000017 Verbeek J, 2010, P 11 ACM INT C MULT, P537, DOI 10.1145/1743384.1743476 Vlachos M., 2004, P ACM SIGMOD INT C M, P131, DOI DOI 10.1145/1007568.1007586 von Ahn L., 2006, P SIGCHI C HUM FACT, P55, DOI 10.1145/1124772.1124782 von Ahn L, 2008, COMMUN ACM, V51, P58, DOI 10.1145/1378704.1378719 Wang C., 2007, P COMP VIS PATT REC, P1, DOI DOI 10.1109/FUZZY.2007.4295670 Wang C. H., 2006, P 14 ACM INT C MULT, DOI 10.1145/1180639.1180774 Wang M., 2009, PLANT GENETIC RESOUR, P1, DOI DOI 10.1017/S1479262109990177 Wang S, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P587 Wang X.-J., 2006, P IEEE C COMP VIS PA, P1483 Weinberger K. Q., 2008, P ACM MULT, P111, DOI 10.1145/1459359.1459375 Wu L., 2009, P 18 INT C WORLD WID, DOI [10.1145/1526709.1526758, DOI 10.1145/1526709.1526758] Wu L., 2008, P 16 ACM INT C MULT, P31, DOI DOI 10.1145/1459359.1459364 Xue G.-R., 2004, P 13 ACM INT C INF K, P118, DOI 10.1145/1031171.1031192 Yacoob Y, 2006, IEEE T PATTERN ANAL, V28, P1164, DOI 10.1109/TPAMI.2006.139 Yan R, 2009, IEEE MULTIMEDIA, V16, P26 Yang Q., 2008, P INT C IM VID RETR, P591, DOI 10.1145/1386352.1386438 Yang Q, 2010, P ACM MULT INF RET, P397, DOI 10.1145/1743384.1743451 Yang Yiming, 1998, P 21 ANN INT ACM SIG, P28, DOI DOI 10.1145/290941.290953 Yao B., 2007, P INT C EN MIN METH, P169 Nov O, 2010, COMMUN ACM, V53, P128, DOI 10.1145/1785414.1785450 Yuan J., 2008, P 1 ACM INT C MULT I, P2, DOI 10.1145/1460096.1460099 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 Zhang L, 2004, P ACM MULT, P716, DOI 10.1145/1027527.1027689 Zhang S., 2009, P 42 HAW INT C SYST, P1, DOI 10.1145/1531674.1531676 ZHAO M, 2006, P INT C IM VID RETR, P163 Zhao W, 2003, ACM COMPUT SURV, V35, P399, DOI 10.1145/954339.954342 Zunjarwad A, 2007, P ACM MULT, P615, DOI 10.1145/1291233.1291382 NR 196 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD JAN PY 2011 VL 51 IS 1 BP 213 EP 246 DI 10.1007/s11042-010-0650-8 PG 34 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 705BO UT WOS:000286103800009 ER PT J AU Stone, B Dennis, S Kwantes, PJ AF Stone, Benjamin Dennis, Simon Kwantes, Peter J. TI Comparing Methods for Single Paragraph Similarity Analysis SO TOPICS IN COGNITIVE SCIENCE LA English DT Article DE Semantic models; Paragraph similarity; Corpus preprocessing; Corpus construction; Wikipedia corpora ID REPRESENTATION; TOPICS AB The focus of this paper is two-fold. First, similarities generated from six semantic models were compared to human ratings of paragraph similarity on two datasets-23 World Entertainment News Network paragraphs and 50 ABC newswire paragraphs. Contrary to findings on smaller textual units such as word associations (Griffiths, Tenenbaum, & Steyvers, 2007), our results suggest that when single paragraphs are compared, simple nonreductive models (word overlap and vector space) can provide better similarity estimates than more complex models (LSA, Topic Model, SpNMF, and CSM). Second, various methods of corpus creation were explored to facilitate the semantic models' similarity estimates. Removing numeric and single characters, and also truncating document length improved performance. Automated construction of smaller Wikipedia-based corpora proved to be very effective, even improving upon the performance of corpora that had been chosen for the domain. Model performance was further improved by augmenting corpora with dataset paragraphs. C1 [Dennis, Simon] Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA. [Stone, Benjamin] Univ Adelaide, Adelaide, SA 5005, Australia. [Kwantes, Peter J.] Def Res & Dev Canada Toronto, Toronto, ON, Canada. RP Dennis, S (reprint author), Ohio State Univ, Dept Psychol, 225 Psychol Bldg,1835 Neil Ave, Columbus, OH 43210 USA. EM simon.dennis@gmail.com CR Bell N, 2008, NVR2008004 NVIDIA Berry M. W., 2005, Computational & Mathematical Organization Theory, V11, DOI 10.1007/s10588-005-5380-5 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BULLINARIA JA, 2006, P NATL ACAD SCI USA, V39, P510 CHOUEKA Y, 1985, COMPUT HUMANITIES, V19, P147, DOI 10.1007/BF02259530 Foltz P. W., 1999, INTERACTIVE MULTIMED, V1 Foltz P. W, 2007, HDB LATENT SEMANTIC, P167 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Giles J, 2005, NATURE, V438, P900, DOI 10.1038/438900a Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Griffiths TL, 2002, P 24 ANN C COGN SCI, P381 Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 KIREYEV K, 2008, P ESSLLI WORKSH DIST, P25 Kwantes PJ, 2005, PSYCHON B REV, V12, P703, DOI 10.3758/BF03196761 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lee MD, 2003, COGNITIVE SCI, V27, P159, DOI 10.1016/S0364-0213(02)00117-9 Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Martin D. I., 2007, HDB LATENT SEMANTIC, P35, DOI 10.1002/9780470690819.ch2 MARTIN MJ, 2004, HLT NAACL 2004 SHORT, P97 MASSON MEJ, 1982, J EXP PSYCHOL LEARN, V8, P400, DOI 10.1037/0278-7393.8.5.400 McNamara D. S., 2007, HDB LATENT SEMANTIC, P379 Moed H. F., 2004, HDB QUANTITATIVE SCI Nelson D. L., 1998, U S FLORIDA WORD ASS PINCOMBE BM, 2004, DSTORR0278 INT SURV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shashua A, 2005, P 22 INT C MACH LEAR, P792, DOI 10.1145/1102351.1102451 STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037//0033-2909.87.2.245 Toffler A., 1973, FUTURE SHOCK Xu W., 2003, SIGIR 03, P267 Zelikovitz S., 2006, P 19 INT FLAIRS C, P598 NR 32 TC 4 Z9 4 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1756-8757 J9 TOP COGN SCI JI Top. Cogn. Sci. PD JAN PY 2011 VL 3 IS 1 BP 92 EP 122 DI 10.1111/j.1756-8765.2010.01108.x PG 31 WC Psychology, Experimental SC Psychology GA 704LG UT WOS:000286053900006 PM 25164176 ER PT J AU Muhlestein, D Lim, S AF Muhlestein, Dennis Lim, SeungJin TI Online learning with social computing based interest sharing SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Online learning; Interest sharing; Social computing ID ASSOCIATION RULES AB Communities on the Internet are highly self-organizing, dynamic, and ubiquitous. One objective of peers in such a community is sharing common interests, even when compromising privacy. This paper presents a model for peers on the Internet that allows them to discover their common interests in terms of sets of frequently visited URLs. This model assists online learning by automatically presenting users with URLs related to what they are currently browsing, thus saving users' time searching for additional information and helping to educate them on the current topic. To implement the model and collect test data, FireShare was developed as a plugin for the popular Web browser Firefox. Data was collected and analyzed on the number of discovered frequently visited URL sets, relevancy of mined association rules, and the overhead FireShare imposes on a network. While FireShare favorably validated the proposed model, analysis of the submitted test data shows high potential for success with future versions. C1 [Muhlestein, Dennis] Utah State Univ, Dept Comp Sci, Logan, UT 84322 USA. [Lim, SeungJin] Marshall Univ, Huntington, WV USA. RP Muhlestein, D (reprint author), Utah State Univ, Dept Comp Sci, Logan, UT 84322 USA. EM djmuhlestein@gmail.com CR Agrawal R, 1996, IEEE T KNOWL DATA EN, V8, P962, DOI 10.1109/69.553164 Agrawal R., 1993, SIGMOD, P207 Agrawal R., 1999, P 5 ACM SIGKDD INT C, P145, DOI 10.1145/312129.312219 BERNERSLEE T, 2000, WHAT SEMANTIC WEB CA Blood R, 2004, COMMUN ACM, V47, P53, DOI 10.1145/1035134.1035165 Claypool M, 2001, IEEE INTERNET COMPUT, V5, P32, DOI 10.1109/4236.968829 Dang XH, 2008, KNOWL INF SYST, V16, P245, DOI 10.1007/s10115-007-0106-2 da Silva JC, 2005, ENG APPL ARTIF INTEL, V18, P791, DOI 10.1016/j.engappai.2005.06.004 Datta S, 2006, IEEE INTERNET COMPUT, V10, P18, DOI 10.1109/MIC.2006.74 *DEL, SOC BOOKM *DIGG, ALL NEWS VID IM GROSSMAN L, 2006, TIME MAGAZINE DEC, V168 Gunduz S., 2003, P 9 ACM SIGKDD INT C, P535 Halpin H., 2007, WWW 07, P211, DOI 10.1145/1242572.1242602 Herlocker J. L., 2000, CSCW 2000. ACM 2000 Conference on Computer Supported Cooperative Work Ivncsy Renta, 2006, ACTA POLYTECH HUNG, V3, P77 JOHNSON S, 2007, EXTRALIFE S JOHNSONS KORTHAUS A, 2003, P WORKSH KNOWL GRID, P207 LAM CM, 2004, WI 04, P748 Lerman K, 2006, SOCIAL NETWORKS SOCI Leung CWK, 2006, KNOWL INF SYST, V10, P357, DOI 10.1007/s10115-006-0002-1 LIM S, 2006, INT J WEB INF SYST, V2, P77 LISHA G, 2006, AICT ICIW 06, P11 Mobasher B, 2001, P 3 INT WORKSH WEB I, P9 Mobasher B, 2000, LECT NOTES COMPUT SC, V1875, P165 *MOZ FDN, EXT *MOZ FDN, FIREFOX RED WEB *MOZ FDN, JAV SCRIPT Mozilla Foundation, XML US INT LANG MUHLESTEIN D, 2007, P 2007 INT C MULT UB, P83 *ONL PUBL ASS, INT ACT IND Pierrakos D, 2003, USER MODEL USER-ADAP, V13, P311, DOI 10.1023/A:1026238916441 Porter MF, 1997, ALGORITHM SUFFIX STR, P313 *POSTGRESQL GLOB D, POSTGRESQL WORLD MOS Python Software Foundation, PYTH PROGR LANG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidt A., 2004, J UNIVERS COMPUT SCI, V10, P28 Schuster A, 2004, DATA MIN KNOWL DISC, V8, P171, DOI 10.1023/B:DAMI.0000015870.80026.6a Shyu ML, 2006, KNOWL INF SYST, V10, P515, DOI 10.1007/s10115-006-0004-z SILVESTRI C, 2005, SAC 05, P529 *SKYP, INT CALLS Sripanidkulchai K, 2003, INFOCOM 2003, V3, P2166, DOI 10.1109/INFCOM.2003.1209237 Stojanovic L., 2001, WEBNET2001, P23 Tan AH, 2004, KNOWL INF SYST, V6, P595, DOI 10.1007/s10115-003-0130-9 *TECHN, WHO SAYS WHAT RIGHT WOLFF R, 2003, ICDM 03, P363 Yanbe Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P107, DOI 10.1145/1255175.1255198 NR 47 TC 9 Z9 9 PU SPRINGER LONDON LTD PI LONDON PA 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD JAN PY 2011 VL 26 IS 1 BP 31 EP 58 DI 10.1007/s10115-009-0265-4 PG 28 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 702EZ UT WOS:000285878700002 ER PT J AU Wahlgreen, BO Hansen, LK AF Wahlgreen, Bjarne Orum Hansen, Lars Kai GP IEEE BE Tan, T Katagiri, S Tao, J Nakamura, A Larsen, J TI LARGE SCALE TOPIC MODELING MADE PRACTICAL SO 2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) SE IEEE International Workshop on Machine Learning for Signal Processing LA English DT Proceedings Paper CT 21st IEEE International Workshop on Machine Learning for Signal Processing (MLSP) CY SEP 18-21, 2011 CL Beijing, PEOPLES R CHINA SP IEEE, CASIA, Intel, Dolby, IEEE Signal Proc Soc HO Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit (NLPR) ID NONNEGATIVE MATRIX FACTORIZATION AB Topic models are of broad interest. They can be used for query expansion and result structuring in information retrieval and as an important component in services such as recommender systems and user adaptive advertising. In large scale applications both the size of the database (number of documents) and the size of the vocabulary can be significant challenges. Here we discuss two mechanisms that can make scalable solutions possible in the face of large document databases and large vocabularies. The first issue is addressed by a parallel distributed implementation, while the vocabulary problem is reduced by use of large and carefully curated term set. We demonstrate the performance of the proposed system and in the process break a previously claimed 'world record' announced April 2010 both by speed and size of problem. We show that the use of a WordNet derived vocabulary can identify topics at par with a much larger case specific vocabulary. C1 [Wahlgreen, Bjarne Orum; Hansen, Lars Kai] Tech Univ Denmark, DTU Informat, DK-2800 Lyngby, Denmark. RP Wahlgreen, BO (reprint author), Tech Univ Denmark, DTU Informat, DK-2800 Lyngby, Denmark. EM bowa@imm.dtu.dk; lkh@imm.dtu.dk CR Amdahl G. M., 1967, P APR 18 20 1967 SPR, P483, DOI DOI 10.1145/1465482.1465560 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Chang J., 2009, NEURAL INFORM PROCES Ding C., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148295 FELLBAUM C, 1998, WORDNET ELECT LEXICA, V5 Gaussier E, 2005, P 28 ANN INT ACM SIG, P601, DOI DOI 10.1145/1076034.1076148 Girolami M., 2003, P 26 ANN INT ACM SIG, P433 Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 LE DT, 2008, IEEE WIC ACM INT C W, V1, P888 Lee DD, 1999, NATURE, V401, P788 Lin CJ, 2007, NEURAL COMPUT, V19, P2756, DOI 10.1162/neco.2007.19.10.2756 Liu C., 2010, P 19 INT C WORLD WID, P681, DOI 10.1145/1772690.1772760 MADSEN RE, 2004, P INT JOINT C NEUR N MOLGAARD LL, 2007, IEEE INT C AC SPEECH, V4, P93 MORUP M, 2009, J BOOK C NIPS WORKSH, P4 MORUP M, 17 EUR SIGN PROC C Newman D, 2009, J MACH LEARN RES, V10, P1801 PAATERO P, 1994, ENVIRONMETRICS, V5, P111, DOI 10.1002/env.3170050203 PORTER MF, 1993, PROGRAM ELECT LIB IN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Witten I. H., 2005, M KAUFMANN SERIES DA Zhai C., 2008, FOUND TRENDS INF RET, V2, P137, DOI DOI 10.1561/1500000008 NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2161-0363 BN 978-1-4577-1623-2 J9 IEEE INT WORKS MACH PY 2011 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BYE72 UT WOS:000298259900083 ER PT J AU Spampinato, C Giordano, D Kavasidis, I Milardo, S AF Spampinato, Concetto Giordano, Daniela Kavasidis, Isaak Milardo, Sebastiano BE Soda, P Tortorella, F TI BioWizard: Discovering and Validating Associations Between Biological Entities by Integrated Analysis of Scientific Literature and Experimental Data SO 2012 25TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) LA English DT Proceedings Paper CT 25th International Symposium on Computer-Based Medical Systems (CBMS) CY JUN 20-22, 2012 CL Rome, ITALY SP IEEE, IEEE Comp Soc, Gruppo Italiano Ricercatori Pattern Recognit, EMB, ifip, Univ degli Studi Cassino Lazio Meridionale HO Univ Campus Bio-Medico Rome ID GENE CANDIDATES; SYSTEMS BIOLOGY; UPDATE; PRIORITIZATION; TOOL AB In this paper, we present BioWizard, a bioinformatics knowledge discovery tool for extracting and validating implicit associations between biological entities. By mining specialized scientific literature, BioWizard not only generates biological hypotheses in the form of associations between genes, proteins and diseases, but also validates the plausibility of such associations against high-throughput biological data (microarrays) and annotated databases. The main novelties of the proposed approach are that: 1) it infers associations between biological entities by mining full text papers instead of only abstracts as usually performed by the existing tools, 2) a named entity recognition that improves the precision of the derived associations by enriching the vocabularies used in the mining loop with terms extracted directly from the text and, 3) the inferred associations are filtered according to their evidence in experimental data. We tested the precision and the recall of our system in retrieving known-associations (which did not appear in the same document) from gold standards and the results shown the ability of BioWizard in retrieving valid associations, thus providing a valuable tool for the use of biomedical researchers to speed up scientific progress. C1 [Spampinato, Concetto; Giordano, Daniela; Kavasidis, Isaak; Milardo, Sebastiano] Univ Catania, Dept Elect Elect & Comp Engn, I-95125 Catania, Italy. RP Spampinato, C (reprint author), Univ Catania, Dept Elect Elect & Comp Engn, I-95125 Catania, Italy. EM cspampin@diit.unict.it; dgiordan@diit.unict.it; ikavasidis@diit.unict.it; smilardo@diit.unict.it CR Ananiadou S, 2006, TRENDS BIOTECHNOL, V24, P571, DOI 10.1016/j.tibtech.2006.10.002 Ben-Dor A, 1999, J COMPUT BIOL, V6, P281, DOI 10.1089/106652799318274 Ekins S, 2007, METH MOL B, V356, P319 Faro A., 2012, BRIEF BIOINFORM, V13, P61 Faro A., 2010, 9 INT C INF TECHN AP Fernandez-Suarez X. M., 2010, CURR PROTOC BIOINFOR Frijters R, 2008, NUCLEIC ACIDS RES, V36, pW406, DOI 10.1093/nar/gkn215 Giordano D., 2011, CBMI 2011 JUN, P181 Giordano D., 2007, EMBS 2007 AUG, P6551 Giordano D, 2010, IEEE T INSTRUM MEAS, V59, P2539, DOI 10.1109/TIM.2010.2058210 Giordano D., 2009, 9 INT C INF TECHN AP, P1 Kell DB, 2004, CURR OPIN MICROBIOL, V7, P296, DOI 10.1016/j.mib.2004.04.012 Krishnan A, 2004, NEW GENERAT COMPUT, V22, P111 Perez-Iratxeta C, 2005, BMC GENET, V6, DOI 10.1186/1471-2156-6-45 Perez-Iratxeta C, 2007, NUCLEIC ACIDS RES, V35, pW212, DOI 10.1093/nar/gkm223 Pruitt KD, 2003, NUCLEIC ACIDS RES, V31, P34, DOI 10.1093/nar/gkg111 Saeed AI, 2003, BIOTECHNIQUES, V34, P374 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tiffin N, 2005, NUCLEIC ACIDS RES, V33, P1544, DOI 10.1093/nar/gki296 Tranchevent LC, 2008, NUCLEIC ACIDS RES, V36, pW377, DOI 10.1093/nar/gkn325 Wu JM, 2006, NUCLEIC ACIDS RES, V34, pW720, DOI 10.1093/nar/gkl167 zgr A., 2008, BIOINFORMATICS, V24, pi277 NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4673-2051-1 PY 2011 PG 6 WC Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic; Radiology, Nuclear Medicine & Medical Imaging SC Computer Science; Engineering; Radiology, Nuclear Medicine & Medical Imaging GA BDA86 UT WOS:000312384400037 ER PT J AU Qiu, XP Zhou, JL Huang, XJ AF Qiu, Xipeng Zhou, Jinlong Huang, Xuanjing BE Huang, JZ Cao, L Srivastava, J TI An Effective Feature Selection Method for Text Categorization SO ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PT I: 15TH PACIFIC-ASIA CONFERENCE, PAKDD 2011 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) CY MAY 24-27, 2011 CL Shenzhen, PEOPLES R CHINA SP Shenzhen Inst Adv Technol, Chinese Acad Sci, Univ Hong Kong, Univ Technol Sydney ID SUPPORT VECTOR MACHINES AB Feature selection is an efficient strategy to reduce the dimensionality of data and removing the noise in text categorization. However, most feature selection methods aim to remove non-informative features based on corpus statistics, which do not relate to the classification accuracy directly. In this paper, we propose an effective feature selection method, which aims at the classification accuracy of KNN. Our experiments show that our method is better than the traditional methods, and it is also beneficial to other classifiers, such as Support Vector Machines (SVM). C1 [Qiu, Xipeng; Zhou, Jinlong; Huang, Xuanjing] Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China. RP Qiu, XP (reprint author), Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China. EM xpqiu@fudan.edu.cn; 10210240083@fudan.edu.cn; xjhuang@fudan.edu.cn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Chang C.-C., 2001, LIBSVM LIB SUPPORT V Chen X., 2008, P 14 ACM SIGKDD INT, P124, DOI 10.1145/1401890.1401910 Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Duda R.O., 2001, PATTERN CLASSIFICATI Dunning T., 1993, Computational Linguistics, V19 Galavotti L., 2010, RES ADV TECHNOLOGY D, P59 Howland P, 2004, IEEE T PATTERN ANAL, V26, P995, DOI 10.1109/TPAMI.2004.46 Joachims T., 1998, LNCS, V1398, P137 Kim H, 2005, J MACH LEARN RES, V6, P37 Lang K, 1995, P 12 INT C MACH LEAR, P331 Lee C., 2004, P HLT NAACL 2004 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 Nigam K., 1999, IJCAI 99 WORKSH MACH, P61 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Yan J, 2005, P 28 ANN INT ACM SIG, P122, DOI 10.1145/1076034.1076058 Yang Y., 1997, P INT C MACH LEARN I, V97 Yang Y., 1999, P SIGIR Yang Y., 2001, P 24 ANN INT ACM SIG, P137, DOI 10.1145/383952.383975 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-20840-9 J9 LECT NOTES ARTIF INT PY 2011 VL 6634 BP 50 EP 61 PN 1 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDA05 UT WOS:000312259200005 ER PT J AU Ushiama, T Eguchi, T AF Ushiama, Taketoshi Eguchi, Tomoya BE OShea, J Nguyen, NT Crockett, K Howlett, RJ Jain, LC TI An Information Recommendation Agent on Microblogging Service SO AGENT AND MULTI-AGENT SYSTEMS: TECHNOLOGIES AND APPLICATIONS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 5th KES International Conference on Agent and Multi-Agent Systems - Technologies and Applications (KES-AMSTA) CY JUN 29-JUL 01, 2011 CL Manchester, ENGLAND SP KES Int HO Manchester Metropolitan Univ DE Recommendation; microblogging service; Twitter; content-based filtering; automatic profile composition AB In this paper, we introduce an agent for recommending information to a user on Twitter, which is one of the most popular microblogging services. For recommending sufficient information for a user, it is important to extract automatically user's interest with accuracy and to collect new information which interests and attracts the user. The agent that is introduced in this paper extracts automatically user's interests from tweets on the timeline of the user and finds the web sites that would provide new information which interests the user from the tweets. The agent selects recommending information from the web sites and posts it on the user's timeline. Experimental results show that our agent is able to recommend sufficient information for users of Twitter in a natural manner. C1 [Ushiama, Taketoshi; Eguchi, Tomoya] Kyushu Univ, Fac Design, Minami Ku, Fukuoka 8158540, Japan. RP Ushiama, T (reprint author), Kyushu Univ, Fac Design, Minami Ku, 4-9-1 Shiobaru, Fukuoka 8158540, Japan. EM ushiama@design.kyushu-u.ac.jp; eguchi@gospel.aid.design.kyushu-u.ac.jp CR Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Jansen BJ, 2009, J AM SOC INF SCI TEC, V60, P2169, DOI 10.1002/asi.21149 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Su X., 2009, ADV ARTIFICIAL INTEL, V4, P2 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21999-3 J9 LECT NOTES ARTIF INT PY 2011 VL 6682 BP 573 EP 582 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BCX60 UT WOS:000311841600059 ER PT J AU Islam, MS Islam, MR AF Islam, Md Saiful Islam, Md Rafiqul BE Hui, CL TI Modeling Spammer Behavior: Artificial Neural Network vs. Naive Bayesian Classifier SO ARTIFICIAL NEURAL NETWORKS - APPLICATION LA English DT Article; Book Chapter ID CATEGORIZATION; LEVEL C1 [Islam, Md Saiful] Univ Dhaka, Dhaka, Bangladesh. [Islam, Md Rafiqul] Deakin Univ, Geelong, Vic 3217, Australia. RP Islam, MS (reprint author), Univ Dhaka, Dhaka, Bangladesh. CR Aery M., 2005, P 5 IEEE INT C DAT M, P1 Caruana R., 2006, P 23 INT C MACH LEAR, V2006, P161 Eichler K., 2005, THESIS EBERHARD KARL Guerra P. H. C., 2009, 6 C EM ANT JUL 1617 Hero A. O., 2010, 7 ANN COLL EL MESS A Holmes G, 1994, P 2 AUSTR NZ C INT I, P357 Islam M. R., 2005, P IADIS INT C WWW IN, P419 Islam M. S., 2007, J COMPUTER SCI, V1, P15 Islam MR, 2009, J NETW COMPUT APPL, V32, P357, DOI 10.1016/j.jnca.2008.02.023 Kamber M., 2001, DATA MINING CONCEPTS Makhoul J, 1999, P DARPA BROADC NEWS, P249 Paliouras G., 2006, 3 C EM ANT JUL 2728 Ramachandran A, 2006, ACM SIGCOMM COMP COM, V36, P291, DOI 10.1145/1151659.1159947 Ramachandran A, 2007, CCS'07: PROCEEDINGS OF THE 14TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P342 Ranawana R., 2006, INT J HYBRID INTELLI, V3, P35 Rojas R., 1996, NEURAL NETWORKS SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sperotto A, 2009, LECT NOTES COMPUT SC, V5733, P208 Stuart J. I., 2004, LECT NOTES COMPUTER, V3163, P442 Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Zhang X, 2006, LECT NOTES COMPUT SC, V4223, P1289 NR 22 TC 0 Z9 0 PU INTECH EUROPE PI RIJEKA PA JANEZA TRDINE9, RIJEKA, 51000, CROATIA BN 978-953-307-188-6 PY 2011 BP 241 EP 252 D2 10.5772/2052 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BC9KX UT WOS:000356532600013 ER PT S AU Sani, S Wiratunga, N Massie, S Lothian, R AF Sani, Sadiq Wiratunga, Nirmalie Massie, Stewart Lothian, Robert BE Ram, A Wiratunga, N TI Term Similarity and Weighting Framework for Text Representation SO CASE-BASED REASONING RESEARCH AND DEVELOPMENT, ICCBR 2011 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 19th International Conference on Case-Based Reasoning CY SEP 11-14, 2011 CL London, ENGLAND ID RETRIEVAL AB Expressiveness of natural language is a challenge for text representation since the same idea can be expressed in many different ways. Therefore, terms in a document should not be treated independently of one another since together they help to disambiguate and establish meaning. Term-similarity measures are often used to improve representation by capturing semantic relationships between terms. Another consideration for representation involves the importance of terms. Feature selection techniques address this by using statistical measures to quantify term usefulness for retrieval. In this paper we present a framework that combines term-similarity and weighting for text representation. This allows us to comparatively study the impact of term similarity, term weighting and any synergistic effect that may exist between them. Study of term similarity is based on approaches that exploit term co-occurrences within document and sentence contexts whilst term weighting uses the popular Chi-squared test. Our results on text classification tasks show that the combined effect of similarity and weighting is superior to each technique independently and that this synergistic effect is obtained regardless of co-occurrence context granularity. C1 [Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Lothian, Robert] Robert Gordon Univ, Sch Comp, Aberdeen AB25 1HG, Scotland. RP Sani, S (reprint author), Robert Gordon Univ, Sch Comp, Aberdeen AB25 1HG, Scotland. EM s.a.sani@rgu.ac.uk; n.wiratunga@rgu.ac.uk; s.massie@rgu.ac.uk; r.m.lothian@rgu.ac.uk CR Bollegala D, 2007, WWW 2007, P757 Boyd-Graber Jordan, 2006, P 3 INT WORDNET C Brank J., 2005, FRAMEWORK CHARACTERZ Chakraborti S, 2006, LECT NOTES ARTIF INT, V4106, P400 Chakraborti S, 2007, LECT NOTES COMPUT SC, V4626, P61 Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gabrilovich E, 2009, J ARTIF INTELL RES, V34, P443 Gracia J, 2008, LECT NOTES COMPUT SC, V5175, P136, DOI 10.1007/978-3-540-85481-4_12 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Resniks P., 1995, P 14 INT JOINT C ART, P448 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 Wiratunga N, 2004, LECT NOTES COMPUT SC, V3155, P806 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23290-9 J9 LECT NOTES ARTIF INT PY 2011 VL 6880 BP 304 EP 318 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBB51 UT WOS:000306342100023 ER PT S AU Stefanov, V Tait, JI AF Stefanov, Veronika Tait, John I. BE Lupu, M Mayer, K Tait, J Trippe, AJ TI An Introduction to Contemporary Search Technology SO CURRENT CHALLENGES IN PATENT INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Editorial Material; Book Chapter AB This chapter is the counterpart of the preceding chapter. It gives an overview of some of the most important terms and concepts used in Search Technology and Information Retrieval today. We hope it can be useful to readers who are not researchers in these areas. After a short dip into the history of the field, we start with a high level overview of the different types of search, and the gap between user requirements and how search systems can be evaluated, finally narrowing it down to the main evaluation methodology used today. This is followed by a step by step guide to the architectural components of a generic full text document search system and its design implications. We then describe how the underlying models define to a large extent what the system can and cannot do. This chapter concludes with a short introduction to semantic search and an outlook to the challenges in patent IR, the main subject of this book. C1 [Stefanov, Veronika; Tait, John I.] Informat Retrieval Facil, A-1220 Vienna, Austria. RP Stefanov, V (reprint author), Informat Retrieval Facil, Donau City Str 1, A-1220 Vienna, Austria. EM v.stefanov@ir-facility.org; john.tait@ir-facility.org CR AZZOPARDI L, 2010, P 33 INT ACM SIGIR C, P775, DOI 10.1145/1835449.1835610 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Belew R.K., 2000, FINDING OUT COGNITIV Bonino Dario, 2010, World Patent Information, V32, DOI 10.1016/j.wpi.2009.05.008 BUCKLEY C, 2008, SPECIAL PUBLICATION Buckley C., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148284 Buckley C, 2007, INFORM RETRIEVAL, V10, P491, DOI 10.1007/s10791-007-9032-x Bush V, 1945, ATLANTIC MONTHLY Buttcher S., 2010, INFORM RETRIEVAL IMP Cleverdon C. W., 1991, P 14 ANN INT ACM SIG, P3, DOI 10.1145/122860.122861 CLEVERDON CW, 1963, ASLIB PROC, V15, P106, DOI 10.1108/eb049925 Deerwester S., 1988, COMPUTER INFORM RETR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Grossman D. A., 2004, INFORM RETRIEVAL ALG Hunt D., 2007, PATENT SEARCHING TOO Joho H., 2010, P 3 INF INT CONT S I, P13, DOI 10.1145/1840784.1840789 JONES KS, 1997, READINGS INFORMATION, P167 Korfhage R., 1997, INFORM STORAGE RETRI Liu T.-Y., 2009, FOUND TRENDS INF RET, V3, P225, DOI [DOI 10.1561/1500000016, 10.1561/1500000016] Lupu Mihai, 2009, SIGIR Forum, V43 Manning C.D., 2008, INTRO INFORM RETRIEV Mooers CE, 1950, AM DOC, V1, P225, DOI 10.1002/asi.5090010409 MOOERS CE, 1961, INFORM RETRIEVAL, pR17 Morville Peter, 2006, INFORM ARCHITECTURE, V29 Robertson S., 1994, UCAMCLTR356 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Roda G, 2010, LECT NOTES COMPUT SC, V6241, P385, DOI 10.1007/978-3-642-15754-7_47 Rowley Jennifer, 2000, ORG KNOWLEDGE INTRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SCHRETTINGER M, 1803, VERSUCH VOLLSTANDIGE Singhal A., 2001, IEEE DATA ENG B, V24, P35 Turtle H., 1994, P 17 ANN INT ACM SIG, P212 ZARAGOZA H, 2004, SPECIAL PUBLICATION NR 34 TC 2 Z9 2 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-642-19230-2 J9 INFORM RETRIEVAL SER PY 2011 VL 29 BP 45 EP 65 DI 10.1007/978-3-642-19231-9_2 D2 10.1007/978-3-642-19231-9 PG 21 WC Computer Science, Information Systems SC Computer Science GA BUU97 UT WOS:000290417900002 ER PT S AU Kowald, A Schmeier, S AF Kowald, Axel Schmeier, Sebastian BE Hamacher, M Eisenacher, M Stephan, C TI Text Mining for Systems Modeling SO DATA MINING IN PROTEOMICS: FROM STANDARDS TO APPLICATIONS SE Methods in Molecular Biology LA English DT Article; Book Chapter ID GENE ONTOLOGY; PROTEINS; BIOLOGY AB The yearly output of scientific papers is constantly rising and makes it often impossible for the individual researcher to keep up. Text mining of scientific publications is, therefore, an interesting method to automate knowledge and data retrieval from the literature. In this chapter, we discuss specific tasks required for text mining, including their problems and limitations. The second half of the chapter demonstrates the various aspects of text mining using a practical example. Publications are transformed into a vector space representation and then support vector machines arc used to classify papers depending on their content of kinetic parameters, which arc required for model building in systems biology. C1 [Kowald, Axel] Protagen AG, Dortmund, Germany. [Schmeier, Sebastian] Univ Western Cape, S African Natl Bioinformat Inst, ZA-7535 Bellville, South Africa. RP Kowald, A (reprint author), Protagen AG, Dortmund, Germany. CR Ashburner M, 2000, NAT GENET, V25, P25 Chen LF, 2005, BIOINFORMATICS, V21, P248, DOI 10.1093/bioinformatics/bth496 Doms A, 2005, NUCLEIC ACIDS RES, V33, pW783, DOI 10.1093/nar/gki470 Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Glenisson P, 2003, Pac Symp Biocomput, P391 Hakenberg J, 2004, OMICS, V8, P131, DOI 10.1089/1536231041388366 Hoffmann R, 2004, NAT GENET, V36, P664, DOI 10.1038/ng0704-664 Plake C, 2006, BIOINFORMATICS, V22, P2444, DOI 10.1093/bioinformatics/btl408 Rebholz-Schuhmann D, 2007, BIOINFORMATICS, V23, pE237, DOI 10.1093/bioinformatics/btl302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346 Soldatova LN, 2005, NAT BIOTECHNOL, V23, P1095, DOI 10.1038/nbt0905-1095 Spasic I, 2005, BRIEF BIOINFORM, V6, P239, DOI 10.1093/bib/6.3.239 Strasberg HR, 2000, J AM MED INFORM ASSN, P838 Vapnik V., 1995, NATURE STAT LEARNING White J, 1999, NATURE, V402, P347, DOI 10.1038/46405 NR 16 TC 5 Z9 5 PU HUMANA PRESS INC PI TOTOWA PA 999 RIVERVIEW DR, STE 208, TOTOWA, NJ 07512-1165 USA SN 1064-3745 BN 978-1-60761-986-4 J9 METHODS MOL BIOL JI Methods Mol. Biol. PY 2011 VL 696 BP 305 EP 318 DI 10.1007/978-1-60761-987-1_19 D2 10.1007/978-1-60761-987-1 PG 14 WC Biochemical Research Methods; Biochemistry & Molecular Biology SC Biochemistry & Molecular Biology GA BSC46 UT WOS:000284120800019 PM 21063956 ER PT J AU Green, JM Harnsomburana, J Schaeffer, ML Lawrence, CJ Shyu, CR AF Green, Jason M. Harnsomburana, Jaturon Schaeffer, Mary L. Lawrence, Carolyn J. Shyu, Chi-Ren TI Multi-source and ontology-based retrieval engine for maize mutant phenotypes SO DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION LA English DT Article ID RESOURCE; BIOLOGY AB Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. C1 [Green, Jason M.; Harnsomburana, Jaturon; Shyu, Chi-Ren] Univ Missouri, Dept Comp Sci, Columbia, MO 65211 USA. [Schaeffer, Mary L.] Univ Missouri, USDA ARS Plant Genet Res Unit, Columbia, MO USA. [Schaeffer, Mary L.] Univ Missouri, Div Plant Sci, Columbia, MO USA. [Lawrence, Carolyn J.] Iowa State Univ, USDA ARS Corn Insects & Crop Genet Res Unit, Dept Genet Dev & Cell Biol, Ames, IA USA. [Shyu, Chi-Ren] Univ Missouri, Inst Informat, Columbia, MO USA. RP Shyu, CR (reprint author), Univ Missouri, Dept Comp Sci, Columbia, MO 65211 USA. EM shyuc@missouri.edu FU National Science Foundation [DBI-0447794]; National Library of Medicine (NLM) Bioinformatics and Health Informatics Training (BHIRT) [2T15LM007089-17]; Shumaker Endowment for Bioinformatics FX National Science Foundation (DBI-0447794) and the National Library of Medicine (NLM) Bioinformatics and Health Informatics Training (BHIRT) (2T15LM007089-17 to J.G.). Funding for open access charge: Shumaker Endowment for Bioinformatics. CR [Anonymous], SOYBASE SOYB BREED T Aronson A R, 2001, Proc AMIA Symp, P17 Ashburner M, 2000, NAT GENET, V25, P25 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Banerjee S., 2002, COMPUTATIONAL LINGUI, V2276, P117 Fu GH, 2005, LECT NOTES COMPUT SC, V3761, P1466 Harabagiu S., 2005, P 28 ANN INT ACM SIG, P202, DOI 10.1145/1076034.1076071 Jaiswal P, 2006, NUCLEIC ACIDS RES, V34, pD717, DOI 10.1093/nar/gkj154 Jaiswal Pankaj, 2005, Comp Funct Genomics, V6, P388, DOI 10.1002/cfg.496 Kurata N, 2006, PLANT PHYSIOL, V140, P12, DOI 10.1104/pp.105.063008 Lawrence C, 2008, INT J PLANT GENOMICS, DOI [10.1155/2008/496957, DOI 10.1155/2008/496957] Lee CS, 2005, IEEE T SYST MAN CY B, V35, P859, DOI 10.1109/TSMCB.2005.845032 Li X., 1995, P IJCAI 95, P1368 Mueller LA, 2005, PLANT PHYSIOL, V138, P1310, DOI 10.1104/pp.105.060707 Navigli R., 2003, P INT WORKSH TUT AD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shyu Chi-Ren, 2007, J Bioinform Comput Biol, V5, P1193, DOI 10.1142/S0219720007003181 Swarbreck D, 2008, NUCLEIC ACIDS RES, V36, pD1009, DOI 10.1093/nar/gkm965 Widdows D., 2003, P ACL 2003 WORKSH NL Wollersheim D., 2005, J WEB INF SYSTEMS, V1, P101 NR 20 TC 2 Z9 2 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 1758-0463 J9 DATABASE-OXFORD JI Database PY 2011 AR bar012 DI 10.1093/database/bar012 PG 15 WC Mathematical & Computational Biology SC Mathematical & Computational Biology GA 883JQ UT WOS:000299630600015 ER PT J AU Gruning, M AF Gruening, Michael TI Artificial Intelligence Measurement of Disclosure (AIMD) SO EUROPEAN ACCOUNTING REVIEW LA English DT Article ID CORPORATE DISCLOSURE; INFORMATION ASYMMETRY; INSTITUTIONAL INVESTORS; ACCOUNTING DISCLOSURES; RETURN VOLATILITY; CAPITAL-MARKETS; COST; LIQUIDITY; EARNINGS; STOCK AB Empirical research on voluntary disclosure lacks an appropriate measurement technique for quantifying the intensity of a firm's disclosure. In this paper, I introduce artificial intelligence measurement of disclosure (AIMD), a computerised technique for measuring disclosure using artificial intelligence, which derives disclosure proxies from English-language annual reports for 10 different information dimensions without human involvement. Criterion validity tests indicate that, controlling for a robust set of covariates and multiple statistical techniques, AIMD is negatively associated with information asymmetry as proxied by spreads and PIN. Furthermore, AIMD has construct validity when compared to the AIMR disclosure rating, Standard & Poor's Transparency and Disclosure Rating, several proprietary manual disclosure scorings and companies' own assessment of their level of disclosure as indicated by a survey. I also demonstrate the applicability of AIMD as a cost-effective technique for measuring disclosure using a sample of 127,895 firm-year observations of companies regulated by the SEC. C1 Frankfurt Sch Finance & Management, D-60314 Frankfurt, Germany. RP Gruning, M (reprint author), Frankfurt Sch Finance & Management, Sonnemannstr 9-11, D-60314 Frankfurt, Germany. EM m.gruening@fs.de CR Abrahamson E., 1996, J BUSINESS FINANCE A, V23, P1157, DOI DOI 10.1111/J.1468-5957.1996.TB01163.X Adams C., 1996, ACCOUNTING ACCOUNTAB Adams C. A., 1995, ENV EMPLOYEE ETHICAL Adams C. A., 1998, BRIT ACCOUNTING REV, V30, P1, DOI DOI 10.1006/BARE.1997.0060 Adamss CA, 2000, EUROPEAN ACCOUNTING, V9, P53, DOI DOI 10.1080/096381800407941 Agrawal V., 2004, Q REV EC FINANCE, V44, P58, DOI 10.1016/S1062-9769(02)00195-3 Ahmad Z, 2003, INT J BUSINESS STUDI, V11, P69 Ahmed K., 1999, BRIT ACCOUNTING REV, V31, P35, DOI 10.1006/bare.1998.0082 Akerkar R, 2005, INTRO ARTIFICIAL INT ALFORD A, 1993, J ACCOUNTING RES, V31, P183, DOI 10.2307/2491170 Alford A. W., 1998, J CORP FINANC, V4, P373, DOI 10.1016/S0929-1199(98)00010-8 Amihud Y, 2002, J FINANC MARK, V5, P31, DOI 10.1016/S1386-4181(01)00024-6 Archambault J. J., 2003, INT J ACCOUNTING, V38, P173, DOI 10.1016/S0020-7063(03)00021-9 Baiman S, 1996, J ACCOUNTING RES, V34, P1, DOI 10.2307/2491329 Baumann U., 2004, EC POLICY REV, V10, P31 Beattie V., 2002, EYES MANAGEMENT STUD Beattie V., 2004, ACCOUNTING FORUM, V28, P205, DOI DOI 10.1016/J.ACCFOR.2004.07.001 Beattie V., 2007, ACCOUNTING FORUM, V31, P129, DOI DOI 10.1016/J.ACCFOR.2007.02.001 Benbrahim H, 2009, LECT NOTES COMPUT SC, V5640, P11, DOI 10.1007/978-3-642-03226-4_2 Beretta S., 2008, J ACCOUNTING AUDITIN, V23, P333 Beretta S., 2004, INT J ACCOUNTING, V39, P265, DOI 10.1016/j.intacc.2004.06.006 Berry M.W., 2004, SURVEY TEXT MINING C Botosan CA, 1997, ACCOUNT REV, V72, P323 Botosan CA, 2002, J ACCOUNTING RES, V40, P21, DOI 10.1111/1475-679X.00037 Brockman P, 2003, J FINANC, V58, P921, DOI 10.1111/1540-6261.00551 Bushee BJ, 2000, J ACCOUNTING RES, V38, P171, DOI 10.2307/2672914 Bushee BJ, 2007, J ACCOUNTING RES, V45, P289, DOI 10.1111/j.1475-679X.2007.00234.x Cameron A., 2006, T0327 NBER Campbell D., 2005, BRIT ACCOUNTING REV, V37, P339 CARROLL JM, 1969, AM DOC, V20, P227, DOI 10.1002/asi.4630200308 Center for International Financial Analysis & Reporting (CIFAR), 1995, INT ACC AUD TRENDS Chavent M, 2006, EUR ACCOUNT REV, V15, P181, DOI 10.1080/09638180500253092 Chen WP, 2007, CORP GOV, V15, P644, DOI 10.1111/j.1467-8683.2007.00594.x CHIANG R, 1988, J FINANC, V43, P1041, DOI 10.2307/2328153 Clarkson P, 2006, ACCOUNT FINANC, V46, P771 Cook T. E., 1989, J INT FIN MANAG ACC, V1, P171, DOI DOI 10.1111/J.1467-646X.1989.TB00009.X Cooke T. E., 1993, J BUSINESS FINANCE A, V20, P521, DOI 10.1111/j.1468-5957.1993.tb00272.x Cooke T. E., 1992, INT J ACCOUNTING, V26, P174 Cooke T.E., 2007, CONTINUUM DISCLOSURE COPELAND RM, 1968, J ACCOUNTING RES, V6, P106, DOI 10.2307/2490127 Core JE, 2001, J ACCOUNT ECON, V31, P441, DOI 10.1016/S0165-4101(01)00036-2 Courtenay S. M., 2006, INT J ACCOUNTING, V41, P262, DOI 10.1016/j.intacc.2006.07.001 Courtis J. K., 1986, CANADIAN J COMMUNICA, V12, P1 Daske H, 2006, ABACUS, V42, P461, DOI 10.1111/j.1467-6281.2006.00211.x Davis F. B., 1974, STANDARDS ED PSYCHOL Deegan C, 2006, ACCOUNT ORG SOC, V31, P343, DOI 10.1016/j.aos.2005.04.001 DEFFNER G, 1986, PSYCHIAT RES, V18, P151, DOI 10.1016/0165-1781(86)90027-2 Demers E. A., 2010, SOFT INFORM EARNINGS DIAMOND DW, 1985, J FINANC, V40, P1071, DOI 10.2307/2328395 Dohle P., 2006, MANAGERMAGAZIN, V36, P118 Dohle P., 2005, MANAGERMAGAZIN, V35, P126 Dohle P., 2007, MANAGERMAGAZIN, V37, P104 Dominich S., 2001, MATH FDN INFORM RETR Easley D, 2010, J FINANC QUANT ANAL, V45, P293, DOI 10.1017/S0022109010000074 Easley D, 2004, J FINANC, V59, P1553, DOI 10.1111/j.1540-6261.2004.00672.x Easley D, 1997, REV FINANC STUD, V10, P805, DOI 10.1093/rfs/10.3.805 Easley D, 1996, J FINANC, V51, P1405, DOI 10.2307/2329399 Francis JR, 2005, ACCOUNT REV, V80, P1125, DOI 10.2308/accr.2005.80.4.1125 FRAZIER KB, 1984, J ACCOUNTING RES, V22, P318, DOI 10.2307/2490713 Gelb D., 2004, J ACCOUNTING FINANCE, V12, P41 Gruning M., 2007, MANAGEMENT RES NEWS, V30, P646, DOI 10.1108/01409170710779962 Gunther T., 2003, Z INT KAPITALMARKTOR, V3, P448 Guthrie J, 2004, J INTELLECTUAL CAPIT, V5, P282, DOI DOI 10.1108/14691930410533704 Haddad B, 2005, LECT NOTES COMPUT SC, V3492, P147, DOI 10.1007/11422532_10 Hail L, 2006, J ACCOUNTING RES, V44, P485, DOI 10.1111/j.1475-679X.2006.00209.x HARRIS TS, 1994, J ACCOUNTING RES, V32, P187, DOI 10.2307/2491281 Healy P. M., 1999, CONTEMP ACCOUNT RES, V16, P485 Healy PM, 2001, J ACCOUNT ECON, V31, P405, DOI 10.1016/S0165-4101(01)00018-0 Heflin FL, 2005, CONTEMP ACCOUNT RES, V22, P829, DOI 10.1506/EETM-FALM-4KDD-9DT9 Ho S. S. M., 2004, CORPORATE OWNERSHIP, V1, P61 Hope OK, 2003, J ACCOUNTING RES, V41, P235, DOI 10.1111/1475-679X.00102 Hussainey K, 2009, ACCOUNT BUS RES, V39, P37, DOI 10.1080/00014788.2009.9663348 HUSSAINEY K, 2003, ACCOUNTING BUSINESS, V33, P275 Hutajulu S., 2006, ADV ACCOUNTING, V22, P201, DOI 10.1016/S0882-6110(06)22009-1 INGRAM RW, 1983, J BUS RES, V11, P49, DOI 10.1016/0148-2963(83)90038-3 Jaggi B., 2000, INT J ACCOUNTING, V35, P495, DOI 10.1016/S0020-7063(00)00076-5 Jayaraman S, 2008, J ACCOUNT RES, V46, P809, DOI 10.1111/J.1475-679X.2008.00293.X Khanna T, 2004, J ACCOUNTING RES, V42, P475, DOI 10.1111/j.1475-679X.2004.00146.x Kothari SP, 2009, ACCOUNT REV, V84, P1639, DOI 10.2308/accr.2009.84.5.1639 Kowalski G., 1997, INFORM RETRIEVAL SYS Krippendorff K., 2004, CONTENT ANAL INTRO I, V2nd Krishnamurti C., 2005, J MULTINATIONAL FINA, V15, P435, DOI 10.1016/j.mulfin.2005.04.007 LANG M, 1993, J ACCOUNTING RES, V31, P246, DOI 10.2307/2491273 Lang MH, 1996, ACCOUNT REV, V71, P467 Lee YT, 2008, J FINANC QUANT ANAL, V43, P717 Leuz C, 2000, J ACCOUNTING RES, V38, P91, DOI 10.2307/2672910 Leuz C, 2003, J ACCOUNTING RES, V41, P445, DOI 10.1111/1475-679X.00112 Loughran T, 2011, J FINANC, V66, P35, DOI 10.1111/j.1540-6261.2010.01625.x Luhn H. P., 1959, MODERN TRENDS DOCUME, P45 Luhn H. P., 1959, RC101 IBM Luhn H. P., 1959, AUTOMATIC DERIVATION Lundholm R, 2002, J ACCOUNTING RES, V40, P809, DOI 10.1111/1475-679X.00072 Marston C.L., 1991, BRIT ACCOUNTING REV, V23, P195, DOI 10.1016/0890-8389(91)90080-L McConnell D., 1986, FINANCIAL ANAL J, V42, P66, DOI 10.2469/faj.v42.n5.66 MEEK GK, 1995, J INT BUS STUD, V26, P555, DOI 10.1057/palgrave.jibs.8490186 MORRIS R, 1994, J MANAGE, V20, P903, DOI 10.1177/014920639402000410 Murphy R., 2007, ENGLISH GRAMMAR USE North R.C., 1963, CONTENT ANAL HDB APP Oswald DR, 2007, EUR ACCOUNT REV, V16, P703, DOI 10.1080/09638180701706815 Papendick U., 2004, MANAGERMAGAZIN, V34, P136 Patel S. A., 2002, TRANSPARENCY DISCLOS Popping R., 1997, TEXT ANAL SOCIAL SCI, P209 Pronk M, 2006, J ACCOUNTING AUDITIN, V21, P27 Roberts C. B., 1991, ACCOUNTING AUDITING, V4, P62 Roberts C. B., 1990, INT TRENDS SOCIAL EM Rogers WH, 1993, STATA TECHNICAL B, V13, P19, DOI DOI 10.1016/J.IJFORECAST.2005.03.004 ROSENBERG SD, 1990, J PERS ASSESS, V54, P298, DOI 10.1207/s15327752jpa5401&2_28 Rutherford B. A., 2005, Journal of Business Communication, V42, DOI 10.1177/0021943605279244 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Mauldin E. G., 2004, International Journal of Accounting Information Systems, V5, DOI 10.1016/j.accinf.2004.04.005 Santema S., 2005, EUROPEAN BUSINESS RE, V17, P352, DOI 10.1108/09555340510607398 Schleicher T, 2007, BRIT ACCOUNTING REV, V39, P153, DOI 10.1016/j.bar.2007.03.005 Schnatterly K, 2003, STRATEGIC MANAGE J, V24, P587, DOI 10.1002/smj.330 Sengupta P, 1998, ACCOUNT REV, V73, P459 Smith M., 2000, ACCOUNTING AUDITING, V13, P624, DOI 10.1108/09513570010353738 Stoll HR, 2000, J FINANC, V55, P1479, DOI 10.1111/0022-1082.00259 Taylor R. L., 1984, J AM TAXATION ASS, V5, P7 Tennyson B., 1990, J BUSINESS FINANCE A, V17, P391 Van Someren MW, 1994, THINK ALOUD METHOD P Vandemaele S. N., 2005, J INTELLECTUAL CAPIT, V6, P417, DOI DOI 10.1108/14691930510611148 VERRECCHIA RE, 1982, J ACCOUNTING RES, V20, P1, DOI 10.2307/2674672 Weber R. P., 1990, BASIC CONTENT ANAL Welker M., 1995, CONTEMP ACCOUNT RES, V11, P801, DOI [10.1111/j.1911-3846.1995.tb00467.x, DOI 10.1111/J.1911-3846.1995.TB00467.X] WHITE H, 1980, ECONOMETRICA, V48, P817, DOI 10.2307/1912934 White R., 2002, SCAND J MANAG, V18, P285, DOI 10.1016/S0956-5221(01)00013-6 NR 125 TC 3 Z9 3 PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND SN 0963-8180 J9 EUR ACCOUNT REV JI Eur. Account. Rev. PY 2011 VL 20 IS 3 BP 485 EP 519 DI 10.1080/09638180.2011.585792 PG 35 WC Business, Finance SC Business & Economics GA 878UP UT WOS:000299282800003 ER PT J AU Szymanski, J AF Szymanski, Julian BE Nguyen, NT Kim, CG Janiak, A TI Self-Organizing Map Representation for Clustering Wikipedia Search Results SO INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2011, PT II SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 3rd International Asian Conference on Intelligent Information and Database Systems (ACIIDS) CY APR 20-22, 2011 CL Daegu, SOUTH KOREA SP Yeungnam Univ, Wroclaw Univ Technol, Univ Informat Technol AB The article presents an approach to automated organization of textual data. The experiments have been performed on selected sub-set of Wikipedia. The Vector Space Model representation based on terms has been used to build groups of similar articles extracted from Kohonen Self-Organizing Maps with DBSCAN clustering. To warrant efficiency of the data processing, we performed linear dimensionality reduction of raw data using Principal Component Analysis. We introduce hierarchical organization of the categorized articles changing the granularity of SOM network. The categorization method has been used in implementation of the system that clusters results of keyword-based search in Polish Wikipedia. C1 Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. RP Szymanski, J (reprint author), Gdansk Univ Technol, Dept Comp Syst Architecture, Gdansk, Poland. EM julian.szymanski@eti.pg.gda.pl CR Allen J., 1995, NATURAL LANGUAGE UND Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bennett CH, 2003, SCI AM, V288, P76 Berkhin P, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P25, DOI 10.1007/3-540-28349-8_2 Blachnik M, 2006, LECT NOTES COMPUT SC, V4029, P573 Duch W., P 2 C NEUR NETW THEI, V1, P138 Ester M., 1996, P 2 INT C KNOWL DISC, P226 Gersho A., 1992, VECTOR QUANTIZATION Hayes P., 1987, ENCY ARTIFICIAL INTE Honkela T., 1997, P WSOM, V97, P4 Jian F., 2007, SCI INFORM, V1 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Kohonen T, 1998, NEUROCOMPUTING, V21, P19, DOI 10.1016/S0925-2312(98)00031-9 Koikkalainen P., 1990, 1990 IJCNN INT JOINT, P279 Li M., 2008, INTRO KOLMOGOROV COM Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Miller G.A., 1993, INTRO WORDNET ON LIN Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 Russell S. J., 1995, ARTIFICIAL INTELLIGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Szymanski J., 2008, WYDAWNICTWO NAUKOWE, V18, P89 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 NR 24 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-20042-7 J9 LECT NOTES ARTIF INT PY 2011 VL 6592 BP 140 EP 149 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BEV02 UT WOS:000318243100015 ER PT J AU Sun, HL Huang, JB Feng, BQ AF Sun, Heli Huang, Jianbin Feng, Boqin TI QoRank: A Query-Dependent Ranking Model Using LSE-Based Weighted Multiple Hyperplanes Aggregation for Information Retrieval SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article AB Ranking is a core problem for information retrieval since the performance of the search system is directly impacted by the accuracy of ranking results. Ranking model construction has been the focus of both the fields of information retrieval and machine learning, and learning to rank in particular has attracted much interest. Many ranking models have been proposed, for example, RankSVM is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. However, most of the proposed methods do not consider about the significant differences between queries, only resort to a single function in ranking. In this paper, we present a novel ranking model named QoRank, which performs the learning task dependent on queries. We also propose a LSE (least-squares estimation) -based weighted method to aggregate the ranking lists produced by base decision functions as the final ranking. Comparison of QoRank with other ranking techniques is conducted, and several evaluation criteria are employed to evaluate its performance. Experimental results on the LETOR OHSUMED data set show that QoRank strikes a good balance of accuracy and complexity, and outperforms the baseline methods. C (C) 2010 Wiley Periodicals, Inc. C1 [Sun, Heli; Feng, Boqin] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China. [Huang, Jianbin] Xidian Univ, Sch Software, Xian, Peoples R China. RP Sun, HL (reprint author), Xi An Jiao Tong Univ, Dept Comp Sci & Technol, Xian 710049, Peoples R China. EM helisun.sunny@gmail.com; jbhuang@xidian.edu.cn FU Nation 863 High Technology Program of China [2008AA01Z131]; Natural Science Basic Research Plan in Shaanxi Province of China [SJ08-ZT14] FX We would like to thank Prof. Jiawei Han for his comments on our paper. This work was supported by the Nation 863 High Technology Program of China (2008AA01Z131) and Natural Science Basic Research Plan in Shaanxi Province of China (No. SJ08-ZT14). CR Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 BURGES C, 2007, LEARNING RANK NONSMO, P193 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Cao Y., 2006, P 29 ANN INT ACM SIG, P186, DOI 10.1145/1148170.1148205 Cao Zhe, 2007, P 24 INT C MACH LEAR, P129, DOI 10.1145/1273496.1273513 Chang C.-C., 2001, LIBSVM LIB SUPPORT V CHIRITA PA, 2005, P 14 ACM INT C INF K, P373, DOI 10.1145/1099554.1099671 COLLINS M, 2002, P 40TH ANN M ASS COM, P7 Crammer K., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226 Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Fagin R., 1997, P 6 INT C DAT THEOR, P247 FOX JA, 1994, ACM SIGIR 94 Freund Y., 2003, J MACHINE LEARNING R, V6, P933, DOI 10.1162/jmlr.2003.4.6.933 Friedman JH, 2002, COMPUT STAT DATA AN, V38, P367, DOI 10.1016/S0167-9473(01)00065-2 Friedman JH, 2001, ANN STAT, V29, P1189, DOI 10.1214/aos/1013203451 Geng X, 2008, P 31 ANN INT ACM SIG, P115, DOI 10.1145/1390334.1390356 Gyongi Z., 2004, P 30 INT C VER LARG, P576, DOI 10.1016/B978-012088469-8/50052-8 Harman D., 1992, INFORMATION RETRIEVA, P363 HARRINGTON EF, 2003, MACH LEARN INT WORKS, V20, P250 HERBRICH R, 2002, KDD 02 P 8 ACM SIGKD, P133 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lee J. H., 1997, SIGIR FORUM, V31, P267 Liu T., 2007, P SIGIR 2007 WORKSH, P3 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 Nallapati R., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009006 Page L, 1998, PAGERANK CITATION RA PANG B, 2005, ACL 2005 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Qin T., 2008, P 17 INT C WORLD WID, P407, DOI 10.1145/1367497.1367553 Qin T, 2007, P 30 ANN INT ACM SIG, P279, DOI 10.1145/1277741.1277791 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 ROBERTSON S, 2001, NIST SPECIAL PUBLICA, P25 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tsai M., 2007, P 30 ANN INT ACM SIG, P383, DOI 10.1145/1277741.1277808 Veloso A, 2008, P 31 ANN INT ACM SIG, P267, DOI 10.1145/1390334.1390381 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 VOORHEES EM, 1995, OV 3 TEXT RRET C TRE, P95 Witten I. H., 1999, MANAGING GIGABYTES C Xia F., 2008, P 25 INT C MACH LEAR, P1192, DOI 10.1145/1390156.1390306 Xu J., 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809 Yager RR, 1998, J AM SOC INFORM SCI, V49, P1177, DOI 10.1002/(SICI)1097-4571(1998)49:13<1177::AID-ASI5>3.0.CO;2-E YUE Y, 2007, P 30 ANN INT ACM SIG, P278 Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zheng Z., 2008, P 21 ANN C NEUR INF, P1697 ZOBEL J, 1998, ACM SIGIR FORUM, V32, P34 NR 55 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0884-8173 J9 INT J INTELL SYST JI Int. J. Intell. Syst. PD JAN PY 2011 VL 26 IS 1 BP 73 EP 97 DI 10.1002/int.20455 PG 25 WC Computer Science, Artificial Intelligence SC Computer Science GA 693ZB UT WOS:000285262800005 ER PT J AU Liu, FF Chi, Y Yu, J Luo, XF Xu, Z AF Liu, Fangfang Chi, Yan Yu, Jie Luo, Xiangfeng Xu, Zheng TI Discovery of Web Service Flow based on Service Context SO INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH LA English DT Article DE E-FCM; Service Combination; Service Flow; Service Relation; Web ID KNOWLEDGE; MANAGEMENT; MODEL; MAPS AB The growing number of services faces the need to combine them to create complex services and meet users' requirements. Discovery based on the similarity evaluation only focuses on those services with similar functions. Techniques based on the conventional semantic Web offer many relations to connect services, but considerable time is spent on reasoning. This paper proposes an approach to discover associated service flows for the purpose of combination. The authors firstly obtain the text descriptions with rich semantics about the service functions from the Web to build the service contexts. They then create E-FCM representation for services based on the contexts to describe the functions of services because it can keep the semantic information and be automatically created. Furthermore, instead of reasoning, the association relations among services can be quickly found based on computation. Associated web service flows are generated from the association relations to recommend services with related functions to users. Thus, discovery effectiveness can be improved and facilitate the utilization of services. C1 [Liu, Fangfang; Yu, Jie; Luo, Xiangfeng; Xu, Zheng] Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. RP Liu, FF (reprint author), Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China. FU NSFC [60803143] FX This work is supported by NSFC (grant number: 60803143). CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Bruijn J. D., 2005, WEB SERVICES MODELIN Geng LQ, 2006, ACM COMPUT SURV, V38, DOI 10.1145/1132960.1132963 Gomadam K., 2008, P IEEE INT C WEB SER, P177, DOI DOI 10.1109/ICWS.2008.105 Hanks P., 1989, P 27 ANN C ASS COMP, P76 Kardaras D., 2006, P 3 INT IEEE C INT S, P227 Kazhamiakin R., 2006, P INT WORLD WID WEB Klusch M., 2006, P 5 INT JOINT C AUT, P915, DOI DOI 10.1145/1160633.1160796 Kuster U., 2007, P 16 INT C WORLD WID, P1033, DOI [10.1145/1242572.1242711, DOI 10.1145/1242572.1242711] Lextek International, ON TEXT RETR TOOLK Liu F. F., 2008, P INT WORKSH SEM COM, P89 Liu F. F., 2009, P INT C WEB SERV, P319 Luo X. F., 2008, P 1 IEEE INT C UB ME, P119 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P839, DOI 10.1002/cpe.1271 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P1791, DOI 10.1002/cpe.1319 McGuinness D. L., 2004, OWL WEB ONTOLOGY LAN Mecella M., 2001, P 2 INT VLDB WORKSH, P44 Paolucci M, 2002, LECT NOTES COMPUT SC, V2512, P225 Perusich K, 2006, IEEE T SYST MAN CY C, V36, P810, DOI 10.1109/TSMCC.2005.855509 Plebani P, 2009, IEEE T KNOWL DATA EN, V21, P1629, DOI 10.1109/TKDE.2009.35 Qu H., 2008, P IEEE INT C SERV CO, P101 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sivashanmugam K., 2004, P IEEE 2 INT C WEB S, P270 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 Syeda-Mahmood T., 2005, Proceedings. 2005 IEEE International Conference on Web Service Zhuge H, 2005, J COMPUT SCI TECHNOL, V20, P289, DOI 10.1007/s11390-005-0289-5 Zhuge H., 2006, J SYST SOFTWARE, V79, P969, DOI DOI 10.1016/J.JSS.2005.08.022 NR 28 TC 0 Z9 0 PU IGI PUBL PI HERSHEY PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA SN 1545-7362 J9 INT J WEB SERV RES JI Int. J. Web. Serv. Res. PY 2011 VL 8 IS 4 BP 29 EP 46 DI 10.4018/jwsr.2011100102 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 860TV UT WOS:000297971500002 ER PT J AU Crasso, M Zunino, A Campo, M AF Crasso, Marco Zunino, Alejandro Campo, Marcelo TI A Survey of Approaches to Web Service Discovery in Service-Oriented Architectures SO JOURNAL OF DATABASE MANAGEMENT LA English DT Review DE Service Discovery; Service-Oriented Architecture; Service-Oriented Computing; Web Service Discovery; Web Service Technology ID TEXT CATEGORIZATION; SYSTEMS AB Discovering services acquires importance as Service-Oriented Computing (SOC) becomes an adopted paradigm. SOC's most popular materializations, namely Web Services technologies, have different challenges related to service discovery and, in turn, many approaches have been proposed. As these approaches are different, one solution may be better than another according to certain requirements. In consequence, choosing a service discovery system is a hard task. To alleviate this task, this paper proposes eight criteria, based on the requirements for discovering services within common service-oriented environments, allowing the characterization of discovery systems. These criteria cover functional and non-functional aspects of approaches to service discovery. The results of the characterization of 22 contemporary approaches and potential research directions for the area are also shown. C1 [Crasso, Marco; Zunino, Alejandro; Campo, Marcelo] ISISTAN UNICEN, Tandil, Buenos Aires, Argentina. RP Crasso, M (reprint author), ISISTAN UNICEN, Tandil, Buenos Aires, Argentina. RI Zunino, Alejandro/ OI Zunino, Alejandro/0000-0002-9537-3541 FU ANPCyT [PAE-PICT2007-02311, PAE-PICT 2007-02312] FX We thank the anonymous reviewers for their helpful comments and suggestions to improve the quality of the paper. We acknowledge the financial support provided by ANPCyT through grants PAE-PICT2007-02311 and PAE-PICT 2007-02312. CR Al-Masri Eyhab, 2007, Proceedings 16th International Conference on Computer Communications and Networks Birukou A, 2007, IEEE SOFTWARE, V24, P47, DOI 10.1109/MS.2007.169 BOUGUETTAYA A, 2004, J DATABASE MANAGE, V17, P20 Cardoso J, 2003, J INTELL INF SYST, V21, P191, DOI 10.1023/A:1025542915514 Chen Y, 2006, J DATABASE MANAGE, V17, P67, DOI 10.4018/jdm.2006010105 CRASSO M, 2009, APPROACH ASSIST DEV, P195 Crasso M, 2008, SCI COMPUT PROGRAM, V71, P144, DOI 10.1016/j.scico.2008.02.002 CRASSO M, 2009, INFORM SYSTEMS FRONT de Bruijn J, 2006, LECT NOTES COMPUT SC, V4011, P590 DONG Z, 2004, P 30 INT C VER LARG, P372 Erickson J, 2008, J DATABASE MANAGE, V19, P42, DOI 10.4018/jdm.2008070103 Evermann J, 2008, J DATABASE MANAGE, V19, P55, DOI 10.4018/jdm.2008070104 FELLBAUM C, 1989, WORDNET ELECT LEXICA Firat A, 2009, J DATABASE MANAGE, V20, P1, DOI 10.4018/jdm.2009092201 Garofalakis J, 2006, J WEB ENG, V5, P265 Gotthelf P, 2008, INT J COOP INF SYST, V17, P523, DOI 10.1142/S0218843008001920 HAUCK FJ, 2005, SEM 05, P69 Kawamura T., 2005, IT Professional, V7, DOI 10.1109/MITP.2005.45 Kokash N, 2006, LECT NOTES COMPUT SC, V4294, P522 KOKASH N, 2006, P EUR START AI RES S, P220 Korfhage R., 1997, INFORM STORAGE RETRI Kozlenkov A, 2007, INT J WEB SERV RES, V4, P82, DOI 10.4018/jwsr.2007040104 LEE KH, 2007, P INT C MULT UB ENG, P773 Lertnattee V, 2004, INFORM SCIENCES, V158, P89, DOI 10.1016/j.ins.2003.07.007 LI K, 2006, SEMANTIC EB SERVICES, P161 Li SH, 2007, J DATABASE MANAGE, V18, P1, DOI 10.4018/jdm.2007100101 Makris C, 2006, J SYST SOFTWARE, V79, P480, DOI 10.1016/j.jss.2005.06.002 Martin D, 2007, WORLD WIDE WEB, V10, P243, DOI 10.1007/s11280-007-0033-x McConnell Steve, 2006, SOFTWARE ESTIMATION McCooI R., 2006, IEEE Internet Computing, V10, DOI 10.1109/MIC.2006.18 Oh SC, 2009, INT J WEB SERV RES, V6, P1, DOI 10.4018/jwsr.2009092301 Oldham N., 2004, LNCS, V3387, P137 Overhage S, 2003, LECT NOTES COMPUT SC, V2593, P100 Paolucci M., 2002, INT SEM WEB C, P333 Paolucci M, 2003, IEEE INTERNET COMPUT, V7, P34, DOI 10.1109/MIC.2003.1232516 PAOLUCCI M, 2003, P 1 INT C WEB SERV I, P203 Patil A.A., 2004, WWW, P553 Platzer C., 2005, P 3 EUR C WEB SERV E, P62 Ran S., 2003, SIGECOM EXCH, V4, P1, DOI DOI 10.1145/844357.844360 Reed JW, 2006, ICMLA 2006: 5th International Conference on Machine Learning and Applications, Proceedings, P258 RODRIGUEZ JM, 2009, P 10 ARG S SOFTW ENG, P1 RODRIGUEZ JM, 2010, SCI COMPUTER PROGRAM Roman D, 2005, APPL ONTOL, V1, P77 Sagan H, 1994, SPACE FILLING CURVES SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAPKOTA B, 2005, P 6 INT C WEB INF SY, P418 Schmidt C, 2004, WORLD WIDE WEB, V7, P211, DOI 10.1023/B:WWWJ.0000017210.55153.3d Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shvaiko P., 2007, ONTOLOGY MATCHING Sivashanmugam K., 2003, P 1 INT C WEB SERV I, P395 Song H, 2007, Proceedings of the 26th International Conference on Offshore Mechanics and Arctic Engineering, Vol 5, P265 STEINMETZ R, 2005, PEER TO PEER SYSTEMS, DOI DOI 10.1007/11530657 Stoica I, 2003, IEEE ACM T NETWORK, V11, P17, DOI 10.1109/TNET.2002.808407 Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 TOMA I, 2005, LNI, V69, P233 Verma K., 2005, Information Technology & Management, V6, DOI 10.1007/s10799-004-7773-4 Wang SX, 2008, 2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2, P500, DOI 10.1109/CIMCA.2008.11 Wang Y., 2003, WISE 03, P147 Wang Yao, 2007, INT T SYSTEMS SCI AP, V3, P118 Wu J, 2005, P IEEE INT C SERV CO, V1, P287 Yang B., 2001, VLDB J SEP, P561 Yu Q, 2008, VLDB J, V17, P537, DOI 10.1007/s00778-006-0020-3 Zhou C., 2004, INT J WEB SERV RES, P58, DOI 10.4018/jwsr.2004040104 ZHUGE H, 2004, J SYST SOFTWARE, V70, P107, DOI DOI 10.1016/S0164-1212(03)00003-7 NR 65 TC 4 Z9 4 PU IGI PUBL PI HERSHEY PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA SN 1063-8016 J9 J DATABASE MANAGE JI J. Database Manage. PD JAN-MAR PY 2011 VL 22 IS 1 BP 102 EP 132 DI 10.4018/jdm.2011010105 PG 31 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 699WK UT WOS:000285693400005 ER PT J AU Ho, CM Chan, KY AF Ho, Chun-Min Chan, Kuei-Yuan TI Modified Reduced Gradient With Realization Sorting for Hard Equality Constraints in Reliability-Based Design Optimization SO JOURNAL OF MECHANICAL DESIGN LA English DT Article DE equality constraints; RBDO; reduced gradient; design under uncertainty AB In this work, the presence of equality constraints in reliability-based design optimization (RBDO) problems is studied. Relaxation of soft equality constraints in RBDO and its challenges are briefly discussed, while the main focus is on hard equalities that cannot be violated even under uncertainty. Direct elimination of hard equalities to reduce problem dimensions is usually suggested; however, for nonlinear or black-box functions, variable elimination requires expensive root-finding processes or inverse functions that are generally unavailable. We extend the reduced gradient methods in deterministic optimization to handle hard equalities in RBDO. The efficiency and accuracy of the first-and second-order predictions in reduced gradient methods are compared. Results show that the first-order prediction is more efficient when realizations of random variables are available. Gradient-weighted sorting with these random samples is proposed to further improve the solution efficiency of the reduced gradient method. Feasible design realizations subject to hard equality constraints are then available to be implemented with state-of-the- art sampling techniques for RBDO problems. Numerical and engineering examples show the strength and simplicity of the proposed method. [DOI: 10.1115/1.4003036] C1 [Ho, Chun-Min; Chan, Kuei-Yuan] Natl Cheng Kung Univ, Dept Mech Engn, Tainan 701, Taiwan. RP Chan, KY (reprint author), Natl Cheng Kung Univ, Dept Mech Engn, Tainan 701, Taiwan. EM hocm@solab.me.ncku.edu.tw; chanky@mail.ncku.edu.tw FU National Science Council of Taiwan [NSC 98-2221-E-006-048] FX This research was partially supported by the National Science Council of Taiwan under Grant No. NSC 98-2221-E-006-048. This support is gratefully acknowledged. CR Au SK, 1999, STRUCT SAF, V21, P135, DOI 10.1016/S0167-4730(99)00014-4 Breitung K., 1989, PROBALISTIC ENG MECH, V4, P187, DOI 10.1016/0266-8920(89)90024-6 Chan KY, 2006, J MECH DESIGN, V128, P893, DOI 10.1115/1.2202887 Dennis JE, 1987, NUMERICAL METHODS UN HOHENBICHLER M, 1983, STRUCT SAF, V1, P177, DOI 10.1016/0167-4730(82)90024-8 Hyvarinen A., 2001, INDEPENDENT COMPONEN Incropera P., 2007, INTRO HEAT TRANSFER JONES DR, 1993, J OPTIMIZ THEORY APP, V79, P157, DOI 10.1007/BF00941892 Kalagnanam JR, 1997, TECHNOMETRICS, V39, P308, DOI 10.2307/1271135 Kim KJ, 2002, IND ENG CHEM RES, V41, P1276, DOI 10.1021/ie0101689 Mattson C., 2003, COLLECTION TECHNICAL, V5, P3464 McDonald M, 2008, J MECH DESIGN, V130, DOI 10.1115/1.2813782 Messac A, 2002, STRUCT MULTIDISCIP O, V23, P357, DOI 10.1007/s00158-002-0196-0 Papalambros P.Y., 2000, PRINCIPLES OPTIMAL D RAMAKRISHNAN Balaji, 1991, ADV DESIGN AUTOMATIO, V32, P241 Rangavajhala S, 2007, STRUCT MULTIDISCIP O, V34, P381, DOI 10.1007/s00158-007-0104-8 ROSENBLATT M, 1952, ANN MATH STAT, V23, P470, DOI 10.1214/aoms/1177729394 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siddall J. N., 1983, PROBABILISTIC ENG DE, V1st Wu Y., 1993, P IUTAM S, P550 NR 20 TC 0 Z9 0 PU ASME PI NEW YORK PA TWO PARK AVE, NEW YORK, NY 10016-5990 USA SN 1050-0472 J9 J MECH DESIGN JI J. Mech. Des. PD JAN PY 2011 VL 133 IS 1 AR 011004 DI 10.1115/1.4003036 PG 9 WC Engineering, Mechanical SC Engineering GA 704NG UT WOS:000286059100005 ER PT S AU Lim, EHY Liu, JNK Lee, RST AF Lim, Edward H. Y. Liu, James N. K. Lee, Raymond S. T. TI Knowledge Seeker - Ontology Modelling for Information Search and Management: A Compendium SO KNOWLEDGE SEEKER - ONTOLOGY MODELLING FOR INFORMATION SEARCH AND MANAGEMENT: A COMPENDIUM SE Intelligent Systems Reference Library LA English DT Article; Book ID VECTOR-SPACE MODEL; SEMANTIC WEB; TEXT CATEGORIZATION; RETRIEVAL-SYSTEM; QUERY EXPANSION; FORMAL ONTOLOGY; FRAMEWORK; LANGUAGE; CLASSIFICATION; TAXONOMY C1 [Lim, Edward H. Y.; Liu, James N. K.] Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. [Lee, Raymond S. T.] IATOPIA Res Lab, Kowloon, Hong Kong, Peoples R China. RP Lim, EHY (reprint author), Hong Kong Polytech Univ, Dept Comp, Kowloon, Hong Kong, Peoples R China. EM edward@iatopia.com; csnkliu@inet.polyu.edu.hk; raymond@iatopia.com CR Abdoullaev A., 2008, REALITY UNIVERSAL ON Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Aggarwal CC, 2004, IEEE T KNOWL DATA EN, V16, P245, DOI 10.1109/TKDE.2004.1269601 AGICHTEIN Y, 2000, P 5 ACM INT C DIG LI, P85 Alani H., 2006, P 15 INT C WORLD WID, P491, DOI 10.1145/1135777.1135849 Anderberg M. R., 1973, CLUSTER ANAL APPL ANGELACCIO M, 1990, IEEE T SOFTWARE ENG, V16, P1150, DOI 10.1109/32.60295 ANICK PG, 1993, IEEE EXPERT, V8, P9, DOI 10.1109/64.248348 ARISTOTLE, 1990, GREAT BOOKS W WORLD, V1 Aristotle, 1995, COMPLETE WORKS ARIST Arpirez J. C., 2000, Knowledge and Information Systems, V2, DOI 10.1007/PL00011649 Artale A, 1996, DATA KNOWL ENG, V20, P347, DOI 10.1016/S0169-023X(96)00013-4 Avesani P, 2005, LECT NOTES COMPUT SC, V3729, P67 Barbara D., 1990, P INT C EXT DAT TECH, P60 BARR A, 1980, HDB ARTIFICIAL INTEL Berendt B, 2002, LECT NOTES COMPUT SC, V2342, P264 Berners-Lee T, 2001, SCI AM, P1 BERNERSLEE T, 2000, NOTATION 3 RDF LANGU Bhogal J, 2007, INFORM PROCESS MANAG, V43, P866, DOI 10.1016/j.ipm.2006.09.003 BLOMQVIST E, 2005, P 5 INT C ONT DAT AP Blomqvist E, 2007, LECT NOTES COMPUT SC, V4825, P911 BLONDEL VD, 2004, MEASURE SIMILARITY G Bonino D., 2005, P ACM S APPL COMP SA, P1639, DOI 10.1145/1066677.1067050 Bontas E. Paslaru, 2005, P 5 INT C KNOWL MAN, P345 Borst WN, 1997, INT J HUMAN COMPUTER, V46, P365 BOULTON M, 2005, ICONS SYMBOLS SEMIOT Brachman R.J., 1996, ADV KNOWLEDGE DISCOV, P37 Brank J., 2005, P 8 INT MULT INF SOC Brunzel M., 2008, P 2008 C ONT LEARN P, P3 Buitelaar P, 2004, LECT NOTES COMPUT SC, V3053, P31 Buitelaar P, 2005, ONTOLOGY LEARNING TE Buitelaar P, 2008, FRONT ARTIF INTEL AP, V167, pV Busagala LSP, 2008, IEICE T INF SYST, VE91D, P1101, DOI 10.1093/ietisy/e91-d.4.1101 Cao G, 2005, P 28 ANN INT ACM SIG, P298 Cao Y., 2002, P COMP LING 3, P1 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Catalano CE, 2007, AI EDAM, V21, P73, DOI 10.1017/S0890060407070151 CHAN CW, 1992, APPL MATH LETT J, V3, P7 Chandler D., 2010, SEMIOTICS BEGINNERS Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902 CHEN H, 1996, MACHINE LEARNING APP CHEN LL, 2000, P PAC KNOWL ACQ WORK Cimiano P, 2005, LECT NOTES COMPUT SC, V3513, P227 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 CIMIANO P, 2004, P EUR C ART INT ECAI, P435 CLARIANA RB, 2004, P 1 INT C CONC MAPP, P125 Clifton C, 2004, IEEE T KNOWL DATA EN, V16, P949, DOI 10.1109/TKDE.2004.32 COLACE C, 2004, P INT C INF COMM TEC CRISTIANI M, 2005, SURVEY ONTOLOGY CREA CRUSE DA, 2004, MEANING LANGUAGE INT, P125 Daft Richard L., 2004, ORG THEORY DESIGN, V8th [代六ēŽ² Dai Liuling], 2004, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V18, P26 Danesi Marcel, 2004, MESSAGES SIGNS MEANI Deng J, 2009, PROC CVPR IEEE, P248 Denny M., 2004, ONTOLOGY EDITOR SURV Ding L., 2004, P 13 ACM INT C INF K, P652, DOI 10.1145/1031171.1031289 Ding L, 2005, LECT NOTES COMPUT SC, V3729, P156 Ding L., 2004, P 13 ACM C INF KNOWL DING Z, 2005, P WORKSH CONT ONT 20 DONG Z, 2003, ONTOLOGY HOWNET Dong Zh.D., 1999, HOWNET Duch W, 2008, NEURAL NETWORKS, V21, P1500, DOI 10.1016/j.neunet.2008.05.008 Duda R.O., 2001, PATTERN CLASSIFICATI Duda R.O., 1973, PATTERN CLASSIFICATI Ehrig M, 2004, LECT NOTES COMPUT SC, V3053, P76 Ehrig M, 2004, LECT NOTES COMPUT SC, V3298, P683 El-Diraby TA, 2005, J COMPUT CIVIL ENG, V19, P394, DOI 10.1061/(ASCE)0887-3801(2005)19:4(394) ENDRES B, 2005, PLATFORM INTEGRATION Ereshefsky M., 2000, POVERTY LINNAEAN HIE Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 FARKAS C, 2003, IFIP 17 WG 11 3 WORK Fellbaum C, 1998, WORDNET ELECT LEXICA Foo S, 2004, INFORM PROCESS MANAG, V40, P161, DOI 10.1016/S0306-4573(02)00079-1 Fox C, 1992, INFORM RETRIEVAL DAT Fox Mark S., 1995, P WORKSH BAS ONT ISS Fragos K., 2003, P 1 BALK C INF THESS FU SX, 2002, J GUANGXI ACAD SCI, V18, P252 Genesereth M.R., 1987, LOGICAL FDN ARTIFICI Gernsbacher M. A., 1990, LANGUAGE COMPREHENSI GOMEZ P, 2004, ONTOLOGICAL ENG EXAM Gomez-Perez A., 2004, ADV INFORM KNOWLEDGE *GRAPHML, 2007, WHAT IS GRAPHML GRAP Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Gruber T.R., 2008, ENCY DATABASE SYSTEM GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Guan Y., 2002, P 1 INT C MACH LEARN, P234 Guarino N, 1999, IEEE INTELL SYST APP, V14, P70, DOI 10.1109/5254.769887 Guarino N, 1995, VERY LARGE KNOWLEDGE, P25 Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066 Guarino N., 1998, P 1 INT C FORM ONT I Guiraud P., 1975, SEMIOLOGY GULLA JA, 2007, P 6 INT C ONT DAT AP Haase P, 2008, LECT NOTES ARTIF INT, V5327, P366 Haase P., 2005, P WORKSH UNC REAS SE, P45 Hahn U., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Handschuh S, 2003, COMPUT NETW, V42, P579, DOI 10.1016/S1389-1286(03)00226-3 Handschuh S, 2003, ANNOTATION SEMANTIC HANDSCHUH S, 2002, P 11 INT WORLD WID W Harmelen F, 2008, FOUND ARTIF INTELL, pVII Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 HAZMAN M, 2008, P 6 INT C INF SYST I, P113 Hearst M. A., 1997, UW MS WORKSH DAT MIN Heflin J, 2001, IEEE INTELL SYST APP, V16, P54, DOI 10.1109/5254.920600 HENDLER J, 2001, IEEE INTELLIGENT MAR, P18 Hendler J, 2001, IEEE INTELL SYST APP, V16, P30, DOI 10.1109/5254.920597 Hirst G., 2004, HDB ONTOLOGIES, P209 HOOGS A, 2003, IEEE COMP SOC C COMP, V2, P327 Hotho A, 2005, J LANGUAGE TECHNOLOG, V20, P19 HOU T, 2005, COMPUT COMMUN, V23, P114 Hu H, 2006, Computational Methods, Pts 1 and 2, P1249, DOI 10.1007/978-1-4020-3953-9_36 Hua Z., 2005, P 13 ANN ACM INT C M, P467, DOI DOI 10.1145/1101149.1101253 IDE N, 1994, RES HUMAN COMP, V2, P145 *IEC SC, 2008, 34 IEC SC Jameson Frederic, 1972, PRISON HOUSE LANGUAG JENINGS NR, 1998, APPL INTELLIGENT AGE JI H, 2000, ANN M ACL, V1, P93 Joachims T, 1997, P 14 INT C MACH LEAR, P143 JONES KS, 1991, ACM SIGIR ACM SPECIA, V25, P10 Kalfoglou Y., 2003, J DATA SEMANTICS, V1, P98 KALVANS J, 2001, P HUM LANG TECHN 200 KARMAKAR S, 2007, DESIGNING DOMAIN ONT Kitamura Y, 2004, ADV ENG INFORM, V18, P115, DOI 10.1016/j.aei.2004.09.002 Klagges B., 2008, APPL ONTOLOGY INTRO KLUSCH M, 2001, J DATA KNOWLEDGE ENG, V36, P93, DOI DOI 10.1016/J.AEI.2004.09.002 Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 KOHONEN T, 1988, NEURAL NETWORKS, V1, P3, DOI 10.1016/0893-6080(88)90020-2 KOK WG, 2000, P 2 WORKSH CHIN LANG, P85 KONG J, 2007, DATA SCI J, V6, P500 Kotis K, 2006, KNOWL INF SYST, V10, P109, DOI 10.1007/s10115-005-0227-4 Kotis K, 2005, LECT NOTES COMPUT SC, V3372, P155 KRIEG P, 2007, WHAT MAKES THINKING Kumar A, 2004, P 3 INT WORKSH COMP Kwon OW, 2003, INFORM PROCESS MANAG, V39, P25, DOI 10.1016/S0306-4573(02)00022-5 Lakoff G, 1980, METAPHORS WE LIVE BY Lam S. L. Y., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765752 Lammari N, 2004, DATA KNOWL ENG, V48, P155, DOI 10.1016/S0169-023X(03)00103-4 Lancaster F. W., 1993, INFORM RETRIEVAL TOD Lawvere F. W., 1997, CONCEPTUAL MATH Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 LEE JH, 2005, IEEE T SYST MAN CYB, V35, P859 Lee MC, 2008, COMPUT EDUC, V50, P1240, DOI 10.1016/j.compedu.2006.12.007 LEE RST, 2005, FUZZY NEUROAPPROACH, DOI DOI 10.1016/J.COMPEDU.2006.12.007 LEE RST, 2001, P 5 PAC AS C KNOWL D, P28 LEENHEER PD, 2007, J DATA SEMANTICS, V8, P26 Lenat D., 1990, BUILDING LARGE KNOWL LI F, 2006, INTRO SEMANTICS LI P, 1996, P C INT SYST SEM GAI, V2, P14 LI Y, 2004, P IEEE WIC ACM INT C, P256 Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 Li YJ, 2008, IEEE T KNOWL DATA EN, V20, P641, DOI 10.1109/TKDE.2007.190740 Lim E, 2009, P 6 INT C FUZZ SYST, V7, P227 LIM EHY, 2009, P 18 INT C FUZZ SYST, P1050 LIM EHY, 2008, P IEEE INT C FUZZ SY, P1034 LIM EHY, 2007, IEEE T KNOWL DATA EN, P127, DOI DOI 10.1109/TKDE.2007.190740 LIN HF, 2001, J COMPUTER RES DEV, V38, P136 Liu K., 2000, SEMIOTICS INFORM SYS LIU T, 2003, P INT C MACH LEARN W, P415 Liu Ting, 1998, J CHINESE INFORM PRO, V12, P17 Liu Y, 2009, EXPERT SYST APPL, V36, P690, DOI 10.1016/j.eswa.2007.10.042 Louwerse M., 2002, COGN LINGUIST, V12, P291, DOI 10.1515/cogl.2002.005 LOUWERSE MM, 2002, COGN LINGUIST, P291, DOI DOI 10.1016/J.ESWA.2007.10.042 MAAREK YS, 1989, SIGIR FORUM, V23, P198, DOI 10.1145/75334.75355 Maedche A, 2002, LECT NOTES ARTIF INT, V2473, P251 Maedche A., 2002, ONTOLOGY LEARNING SE Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 MAGKANARAKI A, 2002, 308 ICS FORTH Mahinovs A, 2007, DECISION ENG REPORT, P1 Mancini C, 2006, INT J HUM-COMPUT ST, V64, P1154, DOI 10.1016/j.ijhcs.2006.07.002 MANNING C, 1999, FDN STAT NATURAL LAN, DOI DOI 10.1016/J.IJHCS.2006.07.002 Martin P, 2000, IEEE INTELL SYST APP, V15, P18, DOI 10.1109/5254.846281 Meadow C.T., 1992, TEXT INFORM RETRIEVA MESLEH AM, 2007, P 2 INT C SOFTW DAT, P235 MILLER A, 1990, J LEXICOGRAPHY, V3, P1 Miller G., 1998, WORDNET ELECT LEXICA Miller G.A., 1995, ENGLISH, V38, P39 Minick N., 1987, COLLECTED WORKS LS V, V1, P17 Mitchell TM, 1997, MACHINE LEARNING MITRA P, 1999, P INT C INF FUS Muller J., 2000, PEIRCE SEMIOTICS PSY Navigli R, 2008, FRONT ARTIF INTEL AP, V167, P71 Navigli R, 2004, COMPUT LINGUIST, V30, P151, DOI 10.1162/089120104323093276 NECHES R, 1991, AI MAG, V12, P36 Neuman Y, 2006, INFORM SCIENCES, V176, P1435, DOI 10.1016/j.ins.2005.03.006 NEWELL A, 1981, AI MAG, V1, P1 Ni X., 2007, P 16 INT C WORLD WID, P281, DOI 10.1145/1242572.1242611 Nicola A. D., 2009, INFORM SYST, V34, P258 Niles Ian, 2001, P 2 INT C FORM ONT I, P2 NILSSON N, 1991, J ARTIFICIAL INTELLI, P31 NOSEK JT, 1990, INT J MAN MACH STUD, V33, P227, DOI 10.1016/S0020-7373(05)80030-6 NYAMSUREN E, 2009, P 11 INT C ADV COMM, P298 Obrst Leo, 2003, P 12 INT C INF KNOWL, P366 Oddy RN, 1981, INFORM RETRIEVAL RES Pan R, 2005, LECT NOTES COMPUT SC, V3729, P563 Park Y., 2002, P 19 INT C COMP LING, P772 Patel C., 2003, P 5 ACM INT WORKSH W, P58 PATEL M, 2004, P EUR SEM WEB S 2004, P121 PATWARDHAN S, 2006, P C EUR CHAPT ASS CO PERLOVSKY L, 2006, KNOWLEDGE INSTINCT B PHILIPPE M, 1995, P NATL ACAD SCI USA, V99, P1742 Pinto H., 2004, P 16 EUR C ART INT E PIRR G, 2007, P 4 IT SWAP WORKSH S Polpinij Jantima, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.68 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PRETORIUS AJ, 2004, LEXON VISUALISATION, P1 Protege, 2009, PROT ONT ED KNOWL AC Prusak L, 2000, WORKING KNOWLEDGE OR QUINLAN JR, 1986, MACH LEARN, V1, P1, DOI DOI 10.1023/A:1022643204877 RANDALL D, 1993, AI MAG, V14, P17 REYNAUD C, 2007, P INT WORKSH ONT MAT Rezgui Y, 2007, KNOWL ENG REV, V22, P379, DOI 10.1017/S0269888907001130 RIFAIEH R, 2006, WEB SEMANTICS ONTOLO, P115, DOI DOI 10.1017/S0269888907001130 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd Roche C, 2006, LECT NOTES COMPUT SC, V4031, P1034 Rogati M., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 RUSSELL B. A. W., 1919, INTRO MATH PHILOS Salto G., 1983, INTRO MODERN INFORM Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1986, INTRO MODERN INFORM SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1992, INFORM PROCESS MANAG, V28, P441, DOI 10.1016/0306-4573(92)90002-H SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez D, 2004, FR ART INT, V113, P11 Saussure F.D., 1972, COURSE GEN LINGUISTI Savage L. J., 1954, FDN STAT SCHOLTES JC, 1993, NEURAL NETWORKS NATU Schreiber AT, 2001, IEEE INTELL SYST APP, V16, P66, DOI 10.1109/5254.940028 SCHREIBER Z, 2003, SEMANTIC INFORM MANA Schwarz U., 2008, APPL ONTOLOGY INTRO Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sedding J., 2004, P 3 WORKSH ROB METH SIDHU AS, 2006, KNOWLEDGE DISCOVERY Song M., 2009, HDB RES TEXT WEB MIN Soo Von-Wun, 2003, P 3 ACM IEEE CS JOIN, P61 Sowa J., 2000, KNOWLEDGE REPRESENTA Sowa J. F., 1984, CONCEPTUAL STRUCTURE SOWA JF, 1976, IBM J RES DEV, V20, P336 Staab S., 2004, HDB ONTOLOGIES STUMME G, 2009, P INT JOINT C ART IN, P225 Su XM, 2006, DATA KNOWL ENG, V58, P47, DOI 10.1016/j.datak.2005.05.012 *SUMO ONT, 2004, SUGG UPP MERG ONT SU TAGUE J, 1989, INFORM PROCESS MANAG, V25, P377, DOI 10.1016/0306-4573(89)90066-6 Tang J, 2006, J WEB SEMANT, V4, P243, DOI 10.1016/j.websem.2006.06.001 Tho QT, 2006, IEEE T KNOWL DATA EN, V18, P842, DOI 10.1109/TKDE.2006.87 Tian X, 2009, COMPUT MATH APPL, V57, P1048, DOI 10.1016/j.camwa.2008.10.059 Tvarozek M, 2007, LECT NOTES COMPUT SC, V4362, P796 UEBERALL M, 2006, P 2 INT C TOP MAPS R, DOI DOI 10.1016/J.CAMWA.2008.10.059 Uschold Mike, 1995, P WORKSH BAS ONT ISS Ushoid M., 1996, KNOWL ENG REV, V11, P93 Valerio A, 2006, P 2 INT C CONC MAPP, P296 VALLET D, 2006, P 1 INT WORKSH SEM M, P19 Velardi P, 2007, IEEE T KNOWL DATA EN, V19, P180, DOI 10.1109/TKDE.2007.21 Vogt P, 2003, ROBOT AUTON SYST, V43, P109, DOI 10.1016/S0921-8890(02)00353-6 *W3C, 2007, W3C SEM WEB *W3C, 2009, W3C SEM WEB W3C SEM WANG W, 2003, P ACM SIG 2003 WORKS Widyantoro DH, 2005, IEEE T KNOWL DATA EN, V17, P401, DOI 10.1109/TKDE.2005.48 Wilcock G., 2003, P HUM LANG TECHN SEM, P109 WISETPHANICHKIJ S, 1999, P AS C REM SENS HONG, P53 WONG SWK, 2009, P 18 INT C FUZZ SYST, P1863 WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 YAN J, 2008, P PAC AS C INF SYST Yang D, 2009, EXPERT SYST APPL, V36, P4399, DOI 10.1016/j.eswa.2008.05.026 Yang SY, 2008, EXPERT SYST APPL, V35, P2056, DOI 10.1016/j.eswa.2007.09.024 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 YU W, 2008, P 6 INT C MAN RES IC [č¢å…“å®‡ YUAN Xingyu], 2006, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V20, P63 ZHANG C, 2005, J SYSTEM SIMULATION, V1, P138 ZHANG N, 2005, COMPUTER ENG, V31, P127 Zhang X, 2006, LECT NOTES COMPUT SC, V4185, P106 ZHAO Y, 2007, E SERVICE INTELLIGEN, P75 ZHENG ZH, 2003, P 3 IEEE INT C DAT M, P705 2003, HOWNET COMPUTATION M 2007, RDF W3C RESOURCE DES 2004, OWL OWL WEB ONTOLOGY 2009, YED YED GRAPH EDITOR, DOI DOI 10.1016/J.ESWA.2007.09.024 2009, YFILE XML EXTENSION 2005, SUO 4D ONTOLOGY DEVE NR 278 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1868-4394 BN 978-3-642-17915-0 J9 INTEL SYST REF LIBR PY 2011 VL 8 BP 3 EP 209 DI 10.1007/978-3-642-17916-7 PG 207 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA BTK83 UT WOS:000287182600001 ER PT J AU Raj, RG Balakrishnan, V AF Raj, Ram Gopal Balakrishnan, Vimala TI A MODEL FOR DETERMINING THE DEGREE OF CONTRADICTIONS IN INFORMATION SO MALAYSIAN JOURNAL OF COMPUTER SCIENCE LA English DT Article DE Information; contradiction; vector space model AB Conversational systems are gaining popularity rapidly. Consequently, the believability of the conversational systems or chatterbots is becoming increasingly important. Recent research has proven that learning chatterbots tend to be rated as being more believable by users. Based on Raj's Model for Chatterbot Trust, we present a model for allowing chatterbots to determine the degree of contradictions in contradictory statements when learning thereby allowing them to potentially learn more accurately via a form of discourse. Some information that is learnt by a chatterbot may be contradicted by other information presented subsequently. Choosing correctly which information to use is critical in chatterbot believability. Our model uses sentence structures and patterns to compute contradiction degrees that can be used to overcome the limitations of Raj's Trust Model, which takes any contradictory information as being equally contradictory as opposed to some contradictions being greater than others and therefore having a greater impact on the actions that the chatterbot should take. This paper also presents the relevant proofs and tests of the contradiction degree model as well as a potential implementation method to integrate our model with Raj's Trust Model. C1 [Raj, Ram Gopal] Univ Malaya, Dept Artificial Intelligence, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia. [Balakrishnan, Vimala] Univ Malaya, Dept Informat Sci, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia. RP Raj, RG (reprint author), Univ Malaya, Dept Artificial Intelligence, Fac Comp Sci & Informat Technol, Kuala Lumpur 50603, Malaysia. EM gopalraj@tm.net.my; vimala_balakrishnan@um.edu.my RI Raj, Ram Gopal/B-3399-2010 OI Raj, Ram Gopal/0000-0002-8627-1113 CR Campbell C., 2004 Covington M.A., 2001 Danieli M., 1995 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Klein D., 2003, P 41 M ASS COMP LING, P423 Kolln M., 2002, Understanding English Grammar Lafferty J., P111 Lavrenko V., 2004, THESIS U MASSACHUSET Manning C. D., 1999, FDN STAT NATURAL LAN, P24 Mauldin M., 1994 Raj R. G., 2011, THESIS U MALAYA KUAL Raj R.G., 2010, Journal of Computer Science and Engineering, V4, P1 Raj RG, 2009, MALAYAS J COMPUT SCI, V22, P138 Ravi S., 2008, P 2008 C EMP METH NA, P887, DOI 10.3115/1613715.1613829 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, B IEEE COMPUTER SOC, V24, P35 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 NR 20 TC 12 Z9 12 PU UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH PI KUALA LUMPUR PA UNIV MALAYA, FAC COMPUTER SCIENCE & INFORMATION TECH, KUALA LUMPUR, 50603, MALAYSIA SN 0127-9084 J9 MALAYS J COMPUT SCI JI Malayas. J. Comput. Sci. PY 2011 VL 24 IS 3 BP 160 EP 167 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA 858MH UT WOS:000297801700003 ER PT J AU Liu, R Zhen, HY Zhu, M AF Liu, Rui Zhen, Haiyang Zhu, Ming BE Roberts, J Ma, J TI Face-sets based video shot retrieval using the text-search analogous approach SO MIPPR 2011: PATTERN RECOGNITION AND COMPUTER VISION SE Proceedings of SPIE LA English DT Proceedings Paper CT 7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision CY NOV 04-06, 2011 CL Guilin, PEOPLES R CHINA SP Huazhong Univ Sci & Technol, Natl Key Lab Sci & Technol Multi-Spectral Informat Proc, Guilin Univ Elect Technol, SPIE DE pattern recognition; face recognition; shot retrieval; video retrieval; text-search; face-sets; visual words; bag of words ID MODELS AB The objective of this work is to retrieve shots containing particular people in video material. Due to well known problems such as illumination, pose and expression variations, it has been a challenging task. To this end, a robust and scalable approach analogized with text search method is proposed in this paper, which mainly consists of four steps: Obtaining sets of face exemplars by tracking, Local Facial Features Extraction and Fusion, Visual words established, Document descriptor obtained and VSM established for shot retrieval. Experimental results demonstrate that the proposed approach is not only efficient but also outperforms the competing methods. C1 [Liu, Rui; Zhen, Haiyang] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei, Anhui, Peoples R China. RP Liu, R (reprint author), Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei, Anhui, Peoples R China. CR Arandjelovi'c O., 2005, P CVPR, V1, P860 Cootes TF, 2001, IEEE T PATTERN ANAL, V23, P681, DOI 10.1109/34.927467 Duda R., 2000, COMPUTER, V2, P654 Ekenel H. K., 2005, 13 EUR SIGN PROC C Fischer M., 2010, SPATIAL DATA ANAL MO, P1, DOI 10.1007/s11229-010-9833-0 Fischer M., 2010, MULTIMEDIA TOOLS APP Jegou H., 2008, P ECCV, P304 Li P., 2007, P ACM CIVR, P57 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Nister D, 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264 Philbin J., 2007, P CVPR, V3613, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sivic J., 2005, P C IM VID RETR, V5684, P226 Sivic J., 2003, ICCV, V2, P1470, DOI DOI 10.1109/1CCV.2003.1238663 Wu Z, 2010, PROC CVPR IEEE, P3469, DOI 10.1109/CVPR.2010.5539976 NR 15 TC 0 Z9 0 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 978-0-81948-578-6 J9 PROC SPIE PY 2011 VL 8004 AR 800417 DI 10.1117/12.902752 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Optics SC Computer Science; Engineering; Optics GA BYC12 UT WOS:000297914300043 ER PT J AU Kumar, NK Santosh, GSK Varma, V AF Kumar, Kiran N. Santosh, G. S. K. Varma, Vasudeva BE Hanbury, A Rauber, A DeVries, AP TI Multilingual Document Clustering Using Wikipedia as External Knowledge SO MULTIDISCIPLINARY INFORMATION RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd Information Retrieval Facility Conference (IRFC) CY JUN 06, 2011 CL Vienna, AUSTRIA SP Google, European Informat Retrieval Specialist Grp (CEPIS-EIRSG) DE Multilingual Document Clustering; Wikipedia; Document Representation AB This paper presents Multilingual Document Clustering (MDC) on comparable corpora. Wikipedia has evolved to be a major structured multilingual knowledge base. It has been highly exploited in many monolingual clustering approaches and also in comparing multilingual corpora. But there is no prior work which studied the impact of Wikipedia on MDC. Here, we have studied availing Wikipedia in enhancing MDC performance. We have leveraged Wikipedia knowledge structure (such as cross-lingual links, category, outlinks, Infobox information, etc.) to enrich the document representation for clustering multilingual documents. We have implemented Bisecting k-means clustering algorithm and experiments are conducted on a standard dataset provided by FIRE 1 for their 2010 Ad-hoc Cross-Lingual document retrieval task on Indian languages. We have considered English and Hindi datasets for our experiments. By avoiding language-specific tools, our approach provides a general framework which can be easily extendable to other languages. The system was evaluated using F-score and Purity measures and the results obtained were encouraging. C1 [Kumar, Kiran N.; Santosh, G. S. K.; Varma, Vasudeva] Int Inst Informat Technol, Hyderabad, Andhra Pradesh, India. RP Kumar, NK (reprint author), Int Inst Informat Technol, Hyderabad, Andhra Pradesh, India. EM kirankumar.n@research.iiit.ac.in; santosh.gsk@research.iiit.ac.in; vv@iiit.ac.in CR Evans D. K., 2004, HLT NAACL 2004 DEMON, P1 Ganesh S., 2008, P 3 INT JOINT C NAT Hu J., 2008, SIGIR 08, P179 Hu XH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P389 Leftin L.J, 2003, COLUMBIA COMPUTER SC Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 RAUBER A., 2001, 3 ALL RUSS C DIG LIB Romaric B.M., 2004, RIAO, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Silva J., 2004, PLISKA STUDIA MATH B, P207 Steinbach M., 2000, TEXTMINING WORKSH KD Steinberger R, 2004, P 4 SLOV LANG TECHN STEINBERGER R., 2002, LNCS, V2276, P415 Wang P., 2008, KDD 08, P713, DOI DOI 10.1145/1401890.1401976 Zhao Y., 2002, CRITERION FUNCTIONS NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-21353-3 J9 LECT NOTES COMPUT SC PY 2011 VL 6653 BP 108 EP 117 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BAX04 UT WOS:000305903200009 ER PT S AU Kumar, NK Santosh, GSK Varma, V AF Kumar, N. Kiran Santosh, G. S. K. Varma, Vasudeva BE Forner, P Gonzalo, J Kekalainen, J Lalmas, M DeRijke, M TI A Language-Independent Approach to Identify the Named Entities in Under-Resourced Languages and Clustering Multilingual Documents SO MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS EVALUATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Conference of the Cross-Language Evaluation Forum, CLEF 2011 CY SEP 19-22, 2011 CL Amsterdam, NETHERLANDS SP Univ Amsterdam, Ctr Creat, Content & Technol, City Amsterdam, ELIAS Res Network Programme, European Language Resources Assoc, Microsoft Res, Netherlands Inst Sound Vision, Netherlands Org Sci Res, PROMISE Network Excellence, Univ Amsterdam, Xerox Res Ctr Europe DE Multilingual Document Clustering; Named entities; Language-independent ID MODEL AB This paper presents a language-independent Multilingual Document Clustering (MDC) approach on comparable corpora. Named entites (NEs) such as persons, locations, organizations play a major role in measuring the document similarity. We propose a method to identify these NEs present in under-resourced Indian languages (Hindi and Marathi) using the NEs present in English, which is a high resourced language. The identified NEs are then utilized for the formation of multilingual document clusters using the Bisecting k-means clustering algorithm. We didn't make use of any non-English linguistic tools or resources such as Word Net, Part-Of-Speech tagger, bilingual dictionaries, etc., which makes the proposed approach completely language-independent. Experiments are conducted on a standard dataset provided by FIRE1 for their 2010 Ad-hoc Cross-Lingual document retrieval task on Indian languages. We have considered English, Hindi and Marathi news datasets for our experiments. The system is evaluated using F-score, Purity and Normalized Mutual Information measures and the results obtained are encouraging. C1 [Kumar, N. Kiran; Santosh, G. S. K.; Varma, Vasudeva] Int Inst Informat Technol, Hyderabad, Andhra Pradesh, India. RP Kumar, NK (reprint author), Int Inst Informat Technol, Hyderabad, Andhra Pradesh, India. EM kirankumar.n@research.iiit.ac.in; santosh.gsk@research.iiit.ac.in; vv@iiit.ac.in CR Friburger N, 2002, P WORKSH MATH FORM M, P155 Kumar N.K., 2011, P IRFC MONTALVO S, 2006, P 21 INT C COMP LING, P1145, DOI 10.3115/1220175.1220319 Negri M., 2004, P 2 GLOB WORDN C, P169 Pianta E., 2002, P 1 INT GLOB WORDNET Pirkola A, 2001, INFORM RETRIEVAL, V4, P209, DOI 10.1023/A:1011994105352 Richman A.E., 2008, P ACL 2008 HLT Romaric B.M., 2004, RIAO, P1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santosh G., P 12 INT C INT TEXT Steinbach M., 2000, TEXTMINING WORKSH KD Zhao Y., 2002, INERT1989 ROC Zhong S, 2005, KNOWL INF SYST, V8, P374, DOI 10.1007/s10115-004-0194-1 NR 13 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-23707-2 J9 LECT NOTES COMPUT SC PY 2011 VL 6941 BP 74 EP 82 PG 9 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBF21 UT WOS:000306674600009 ER PT S AU Rovetta, S Masulli, F Monville, ME Russo, G AF Rovetta, Stefano Masulli, Francesco Monville, Maura Edelweiss Russo, Giuseppe BE Apolloni, B Bassis, S Esposito, A Morabito, CF TI Context analysis in microRNA data: A Rosetta Stone approach SO NEURAL NETS WIRN10 SE Frontiers in Artificial Intelligence and Applications LA English DT Proceedings Paper CT 20th Italian Workshop on Neural Nets of the Italian-Neural-Network-Society (SIREN) CY 2010 CL Vietri sul Mare, ITALY SP Italian Neural Network Soc DE MicroRNA; Latent Semantic Analysis; Genomic Data ID IDENTIFICATION; PREDICTION; SITES AB MicroRNAs are a hot topic of research in molecular biology. Their role is however still to be elucidated in full. We present a data-oriented approach to the study of microRNA-gene interactions, building heavily on methods of document analysis. The paper aims at illustrating an approach rather than specific methods of analysis, however some sample results are also presented, which show how latent information can be exploited to suggest directions for laboratory experiments, thus avoiding unnecessary expense in time and resources. C1 [Rovetta, Stefano] Univ Genoa, DISI, I-16146 Genoa, Italy. RP Rovetta, S (reprint author), Univ Genoa, DISI, Via Dodecaneso 35, I-16146 Genoa, Italy. EM ste@disi.unige.it CR Ashburner M, 2000, NAT GENET, V25, P25 Bonauer A, 2009, CELL CYCLE, V8, P3866, DOI 10.4161/cc.8.23.9994 Bonci D, 2008, EUR UROL SUPPL, V7, P271, DOI 10.1016/S1569-9056(08)60799-X DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Enright AJ, 2003, GENOME BIOL, V5, pR1, DOI DOI 10.1186/GB-2003-5-1-R1 Galassi M., 2009, GNU SCI LIB REFERENC, V3rd Hafner M, 2010, CELL, V141, P129, DOI 10.1016/j.cell.2010.03.009 Ihaka R., 1996, J COMPUTATIONAL GRAP, V5, P299, DOI DOI 10.2307/1390807 Jackson RJ, 2007, SCI STKE, DOI [10.1126/stke.3672007re1., DOI 10.1126/STKE.3672007RE1] Krek A, 2005, NAT GENET, V37, P495, DOI 10.1038/ng1536 Krichevsky AM, 2003, RNA, V9, P1274, DOI 10.1261/rna.5980303 Lewis BP, 2003, CELL, V115, P787, DOI 10.1016/S0092-8674(03)01018-3 Masulli F., 2009, IJCNN09 NEUR NETW IE, P2021 Miranda KC, 2006, CELL, V126, P1203, DOI 10.1016/j.cell.2006.07.031 R Foundation for Statistical Computing, 2005, R LANG ENV STAT COMP Rehmsmeier M, 2004, RNA, V10, P1507, DOI 10.1021/rna.5248604 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 17 TC 0 Z9 0 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-60750-692-8; 978-1-60750-691-1 J9 FRONT ARTIF INTEL AP PY 2011 VL 226 BP 135 EP 143 DI 10.3233/978-1-60750-692-8-135 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BHF23 UT WOS:000325223000015 ER PT S AU Szymanski, J AF Szymanski, Julian BE Gen, M Barwolff, G Li, PK TI Management of Textual Data at Conceptual Level SO PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON INFORMATION AND MANAGEMENT SCIENCES SE Series of Information and Management Sciences LA English DT Proceedings Paper CT 10th International Conference on Information and Management Sciences CY AUG 06-11, 2011 CL Lhasa, PEOPLES R CHINA SP Int Assoc Informat & Management Sci, Fuzzy Logic Syst Inst, Chinese Univ Hong Kong, Tsinghua Univ, KAIST DE text representation; information retrieval; clustering AB The article presents the approach to the management of a large repository of documents at conceptual level. We describe our approach to representing Wikipedia articles using their categories. The representation has been used to construct groups of similar articles. Proposed approach has been implemented in prototype system that allows to organize articles that are search results for a given query. Constructed clusters allow to show directions in which user may continue the retrieval process to narrow search results. C1 [Szymanski, Julian] Gdansk Univ Technol, Dept Elect Telecommun & Informat, Gdansk, Poland. EM julian.szymanski@eti.pg.gda.pl CR Ceglowski M., 2003, SEMANTIC SEARCH UNST, P10 Ester M., 1996, P 2 INT C KNOWL DISC, V1996, P226 Halkidi M, 2002, SIGMOD RECORD, V31, P40 Manning C. D., 2008, INTRO INFORM RETRIEV, V1 MATYKIEWICZ P, 2009, ADV NEUROINFORMATION, V5507, P70, DOI 10.1007/978-3-642-03040-6_9 Pestov V, 2000, INFORM PROCESS LETT, V73, P47, DOI 10.1016/S0020-0190(99)00156-8 Rosell Magnus, 2008, INTRO TEXT CLUSTERIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strehl A., 2000, WORKSH ART INT WEB S, P58 Szymanski J., 2010, P 9 INT C INF MAN SC, P107 Lee JA, 2007, INFORM SCI STAT, P1 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 NR 12 TC 0 Z9 0 PU CALIFORNIA POLYTECHNIC STATE UNIV PI SAN LUIS OBISPO PA CAL POLY, SAN LUIS OBISPO, CA 93407 USA SN 1539-2023 J9 SER INF MANAGE SCI PY 2011 VL 10 BP 178 EP 183 PG 6 WC Management; Operations Research & Management Science; Mathematics, Applied; Social Sciences, Mathematical Methods SC Business & Economics; Operations Research & Management Science; Mathematics; Mathematical Methods In Social Sciences GA BFO42 UT WOS:000320736100033 ER PT S AU Bron, M Huurnink, B de Rijke, M AF Bron, Marc Huurnink, Bouke de Rijke, Maarten BE Gradmann, S Borri, F Meghini, C Schuldt, H TI Linking Archives Using Document Enrichment and Term Selection SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES, TPDL 2011 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th International Conference on Theory and Practice of Digital Libraries (TPDL 2011) CY SEP 26-28, 2011 CL Berlin, GERMANY SP Emerald Grp Publish, Ex Libris, Swets Informat Serv, IOS Press, Ashgate Publish Grp, Coalit Networked Informat ID NEWS; SEARCH AB News, multimedia and cultural heritage archives are increasingly offering opportunities to create connections between their collections. We consider the task of linking archives: connecting an item in one archive to one or more items in other, often complementary archives. We focus on a specific instance of the task: linking items with a rich textual representation in a news archive to items with sparse annotations in a multimedia archive, where items should be linked if they describe the same or a related event. We find that the difference in textual richness of annotations presents a challenge and investigate two approaches: (i) to enrich sparsely annotated items with textually rich content; and (ii) to reduce rich news archive items using term selection. We demonstrate the positive impact of both approaches on linking to same events and linking to related events. C1 [Bron, Marc; Huurnink, Bouke; de Rijke, Maarten] Univ Amsterdam, ISLA, NL-1098 XH Amsterdam, Netherlands. EM m.m.bron@uva.nl; b.huurnink@uva.nl; derijke@uva.nl CR Allan J., 1998, P DARPA BROADC NEWS, P194 Allan J., 2004, SIGIR 04, P297 Bron M., 2011, EUROHCIR 2011 Carrick C, 1997, INFORM PROCESS MANAG, V33, P615, DOI 10.1016/S0306-4573(97)00021-6 Cohn D., 2001, NEURAL INFORM PROCES, P430 Diaz F, 2006, SIGIR 2006, P154 Finkel J.R., 2005, ACL 05, P363, DOI DOI 10.3115/1219840.1219885 Franz M., 2001, SIGIR Forum Harman D.K., 2005, TREC EXPT EVALUATION Henzinger M, 2005, WORLD WIDE WEB, V8, P101, DOI 10.1007/s11280-004-4870-6 Huurnink B, 2010, J AM SOC INF SCI TEC, V61, P1180, DOI 10.1002/asi.21327 Kern R, 2010, LECT NOTES COMPUT SC, V6273, P315, DOI 10.1007/978-3-642-15464-5_32 Li Z., 2005, SIGIR, P106 Ma Q, 2006, INFORM SYST, V31, P659, DOI 10.1016/j.is.2005.12.004 Meij E, 2009, LECT NOTES COMPUT SC, V5823, P424, DOI 10.1007/978-3-642-04930-9_27 Mihalcea R., 2007, CIKM 2007, V23, P233, DOI [10.1145/1321440.1321475, DOI 10.1145/1321440.1321475] Radev D, 2005, COMMUN ACM, V48, P95, DOI 10.1145/1089107.1089111 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tao T., 2006, HLT NAACL 2006, P407 Tsagkias M., 2011, WSDM, P565 Zhang Y., 2002, SIGIR 2002, P81 NR 21 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-24468-1 J9 LECT NOTES COMPUT SC PY 2011 VL 6966 BP 360 EP 371 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BBA16 UT WOS:000306258000037 ER PT J AU Senso, JA Leiva-Mederos, AA Dominguez-Velasco, SE AF Senso, Jose A. Abel Leiva-Mederos, Amed Eduardo Dominguez-Velasco, Sandor TI A model for ontology evaluation. The case of Onto-Satcol SO REVISTA ESPANOLA DE DOCUMENTACION CIENTIFICA LA Spanish DT Article DE Ontologies; Ontology Evaluation; Domain ontologies; Ontological analysis and evaluation; port engineering; coastal engineering AB This paper analyzes the conceptual and theoretical framework for the evaluation of ontologies, in order to understand the procedures used in the evaluation of these systems and to establish new guidelines for evaluating the system employed by the ontological program, SATCOL, that specializes in Port and Coastal Engineering. This paper describes the characteristics of the Onto-SATCOL ontology and evaluates it by using several indicators (lexical, information retrieval, and syntactic structure). Through an experiment conducted by six experts aided by the tool, Protex, semantic and structural inconsistencies are identified, as are errors in the ontology's organization of knowledge. C1 [Senso, Jose A.] Univ Granada, Dept Bibliotecon & Documentac, E-18071 Granada, Spain. [Abel Leiva-Mederos, Amed; Eduardo Dominguez-Velasco, Sandor] Univ Cent Marta Abreu Las Villas, Santa Clara, Cuba. RP Senso, JA (reprint author), Univ Granada, Dept Bibliotecon & Documentac, E-18071 Granada, Spain. EM jsenso@ugr.es; amed@uclv.edu.cu RI Leiva Mederos, Amed/J-9767-2012; Dominguez-Velasco, Sandor/K-1032-2012 OI Leiva Mederos, Amed/0000-0002-9144-5018; Dominguez-Velasco, Sandor/0000-0002-1410-7445 CR Boehm B, 1981, SOFTWARE ENG EC Brank J, 2005, SIKDD 2005 MULT IS 2 Brewster C., 2004, P LANG RES EV C LREC Domiguez S., 2010, PROTEX VERSION BETA Egana Aranguren M., 2009, ICBO INT C BIOM ONT Evermann J, 2010, INFORM SYST, V35, P391, DOI 10.1016/j.is.2008.09.001 Fahad M., 2008, SUPPL P 16 INT C CON Fellbaum C, 1998, WORDNET ELECT LEXICA Fernandez-Lopez M., 1997, SPR S ONT ENG AAAI C Gangemi A., 2005, THEORETICAL FRAMEWOR Gomez Perez A., 1994, C IEEE EV Grunninger M., 1995, WORKSH BAS ONL ISS K Guarino N, 2002, COMMUN ACM, V45, P61 Hartmann J., 2005, D1 2 3 METHODS ONTOL Kent A., 1955, AM DOC, P93, DOI 10.1002/asi.5090060209 Kuhn T. S., 1979, STRUCTURE SCI REVOLU Leiva-Mederos A, 2009, 2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, P85, DOI 10.1109/ISDA.2009.120 Lozano-Tello A., 2004, J DATABASE MANAGEMEN, V15 Maedche A, 2002, P EUR C KNOWL ACQ MA Maedche A., 2003, EEE INTELLIGENT SYST, P26 Maiga G., 2008, 1 INT WORKSH ONT INT Ning H., 2006, INT C E BUS ENG Obrst L, 2007, SEMANTIC WEB REVOLUT Paslaru Bontas E., 2005, COST MODEL ONTOLOGY Qadir M.A., 2007, 2 INT C INT WEB APPL Ramos E., 2009, REV VENEZOLANA INFOR, V6, P57 Sabou M., 2006, WWW2006 ED SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spyns P., 2005, EVALEXON ASSESSING T Staab S., 2004, IEEE INTELLIGENT SYS Uschold M., 1995, WORKSH BAS ONT ISS K Yao H., 2005, J COMPUTER SCI, V1, P107 Yu Jonathan, 2009, Information Systems, V34, DOI 10.1016/j.is.2009.04.002 Zhang T, 2008, KNOWL-BASED SYST, V21, P794, DOI 10.1016/j.knosys.2008.03.060 NR 34 TC 0 Z9 0 PU CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS PI MADRID PA VITRUVIO 8, 28006 MADRID, SPAIN SN 0210-0614 J9 REV ESP DOC CIENT JI Rev. Esp. Doc. Cient. PY 2011 VL 34 IS 3 BP 334 EP 356 DI 10.3989/redc.2011.3.788 PG 23 WC Information Science & Library Science SC Information Science & Library Science GA 826PM UT WOS:000295366100002 ER PT J AU Santos, I Laorden, C Ugarte-Pedrero, X Sanz, B Bringas, PG AF Santos, Igor Laorden, Carlos Ugarte-Pedrero, Xabier Sanz, Borja Bringas, Pablo G. GP IEEE TI ANOMALY-BASED SPAM FILTERING SO SECRYPT 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SECURITY AND CRYPTOGRAPHY LA English DT Proceedings Paper CT International Conference on Security and Cryptography (SECRYPT) CY JUL 18-21, 2011 CL Seville, SPAIN SP Inst Syst & Technologies Information, Control & Commun, Inst Elect & Elect Engn, Inst Elect & Elect Engn Syst Council HO ETSII Univ Seville DE Computer security; Spam filtering; Anomaly detection; Text classification ID SELECTION; CATEGORIZATION; MODELS AB Spam has become an important problem for computer security because it is a channel for the spreading of threats such as computer viruses, worms and phishing. Currently, more than 85% of received e-mails are spam. Historical approaches to combat these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many solutions utilise machine-learning approaches trained using statistical representations of the terms that usually appear in the e-mails. However, these methods require a time-consuming training step with labelled data. Dealing with the situation where the availability of labelled training instances is limited slows down the progress of filtering systems and offers advantages to spammers. In this paper, we present the first spam filtering method based on anomaly detection that reduces the necessity of labelling spam messages and only employs the representation of legitimate e-mails. This approach represents legitimate e-mails as word frequency vectors. Thereby, an email is classified as spam or legitimate by measuring its deviation to the representation of the legitimate e-mails. We show that this method achieves high accuracy rates detecting spam while maintaining a low false positive rate and reducing the effort produced by labelling spam. C1 [Santos, Igor; Laorden, Carlos; Ugarte-Pedrero, Xabier; Sanz, Borja; Bringas, Pablo G.] Univ Deusto, Deusto Inst Technol, DeustoTech, Lab S3, Bilbao 48007, Spain. RP Santos, I (reprint author), Univ Deusto, Deusto Inst Technol, DeustoTech, Lab S3, Ave Univ 24, Bilbao 48007, Spain. EM isantos@deusto.es; claorden@deusto.es; xabier.ugarte@deusto.es; borja.sanz@deusto.es; pablo.garcia.bringas@deusto.es RI Santos Grueiro, Igor/L-3854-2013 OI Santos Grueiro, Igor/0000-0002-9511-8612 CR Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 Androutsopoulos I., 2000, P MACH LEARN TEXT IN Awad A, 2008, IEEE EDOC CONF, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bates M., 1993, CHALLENGES NATURAL L Becker J, 2003, P 6 INT C BUS INF SY, P7 Blanzieri E., 2007, P FLAIRS 20, P441 Bratko A, 2006, J MACH LEARN RES, V7, P2673 Cano JR, 2006, APPL SOFT COMPUT, V6, P323, DOI 10.1016/j.asoc.2005.02.006 Carnap Rudolf, 1955, PHILOS STUD, VVI, P33, DOI DOI 10.1007/BF02330951 Carpinter J, 2006, COMPUT SECUR, V25, P566, DOI 10.1016/j.cose.2006.06.001 Carreras X, 2001, P 4 INT C REC ADV NA, P58 Cohen D., 1974, EXPLAINING LINGUISTI Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 Cruse D., 1975, ARCH LINGUIST, V6, P26 Czarnowski I., 2006, P SCI SESS ORG 21 FA, P60 Dash M, 2003, ARTIF INTELL, V151, P155, DOI 10.1016/S0004-3702(03)00079-1 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Elkan C, 2001, INT JOINT C ART INT, V17, P973 Heron Simon, 2009, Network Security, V2009, DOI 10.1016/S1353-4858(09)70007-8 Ide N., 1998, COMPUTATIONAL LINGUI, V24, P2 Jagatic TN, 2007, COMMUN ACM, V50, P94, DOI 10.1145/1290958.1290968 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 Jung J., 2004, P 4 ACM SIGCOMM C IN, P370, DOI 10.1145/1028788.1028838 Karlberger C., 2007, P 1 USENIX WORKSH OF, P1 KENT JT, 1983, BIOMETRIKA, V70, P163 Kohavi R, 1995, INT JOINT C ART INT, V14, P1137 Kolcz A., 2004, P 1 C EM ANT CEAS 20 Kuropka D., 2004, ADV INFORM SYSTEMS M, V10 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Liu H., 2001, INSTANCE SELECTION C LOVINS JB, 1968, MECH TRANSL, V11, P22 Maron O, 1998, ADV NEUR IN, V10, P570 McGill M. J., 1983, INTRO MODERN INFORM Ming-Tzu KW, 2004, APPL LINGUIST, V25, P291, DOI 10.1093/applin/25.3.291 Mishne Gilad, 2005, P 1 INT WORKSH ADV I, P1 Liu H, 2008, CH CRC DATA MIN KNOW, P3 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 Polyvyanyy A., 2007, THESIS Pyle D., 1999, DATA PREPARATION DAT Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Radden Gunter, 1999, METONYMY LANGUAGE TH, P17 Ramachandran A., 2006, C EM ANT CIT Sahami M., 1998, LEARNING TEXT CATEGO, V62, P98 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneider K., 2003, P 10 C EUR CHAPT ASS, P307 Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Seewald AK, 2007, INTELL DATA ANAL, V11, P497 Torkkola K., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753742 Tsang ECC, 2003, IEEE T FUZZY SYST, V11, P202, DOI 10.1109/TFUZZ.2003.809895 Vapnik V. N., 2000, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wittel G., 2004, P 1 C EM ANT CEAS Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 Zhou Y., 2007, P 19 IEEE INT C TOOL, V02, P298 NR 59 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA PY 2011 BP 5 EP 14 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BB2SG UT WOS:000342217900005 ER PT J AU McNamara, DS AF McNamara, Danielle S. TI Computational Methods to Extract Meaning From Text and Advance Theories of Human Cognition SO TOPICS IN COGNITIVE SCIENCE LA English DT Article DE Sematic models; Computational techniques; Meaning extraction; Cognition; Memory; Embodiment; Latent representations; LSA ID PERCEPTUAL SYMBOL SYSTEMS; LATENT SEMANTIC ANALYSIS; FEATURE PRODUCTION NORMS; LARGE SET; KNOWLEDGE; MEMORY; MODEL; REPRESENTATION; COOCCURRENCE; COHESION AB Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. C1 Univ Memphis, Dept Psychol, Inst Intelligent Syst, Memphis, TN 38152 USA. RP McNamara, DS (reprint author), Univ Memphis, Dept Psychol, Inst Intelligent Syst, 202 Psychol Bldg, Memphis, TN 38152 USA. EM dsmcnamara1@gmail.com CR Barsalou LW, 2003, PHILOS T ROY SOC B, V358, P1177, DOI 10.1098/rstb.2003.1319 Barsalou LW, 1999, BEHAV BRAIN SCI, V22, P577 de Vega M., 2008, SYMBOLS EMBODIMENT D DENNIS S, 2008, HIGHER LEVEL LANGUAG, P105 Dennis S, 2005, COGNITIVE SCI, V29, P145, DOI 10.1207/s15516709cog0000_9 Dunning T., 1993, Computational Linguistics, V19 FENSON L, 1994, MONOGR SOC RES CHILD, V59, pR5 Firth J. R., 1957, STUDIES LINGUISTIC A, P1 Glenberg AM, 1997, BEHAV BRAIN SCI, V20, P1 GRAESSER AC, 2010, TOP COGN SCI, DOI DOI 10.1111/J.1756-8765.2010.01081.X Church K. W., 1990, Computational Linguistics, V16 Hinton G, 2011, TOP COGN SCI, V3, P74, DOI 10.1111/j.1756-8765.2010.01109.x Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527 HINTZMAN DL, 1984, BEHAV RES METH INS C, V16, P96 Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388 Howard MW, 2011, TOP COGN SCI, V3, P48, DOI 10.1111/j.1756-8765.2010.01112.x Jones M. N., 2010, TOP COGN SCI, DOI [10.1111/j.1756-8765.2010.01111.x., DOI 10.1111/J.1756-8765.2010.01111.X, 10.1111/j.1756-8765.2010.01111. x] Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Jones MN, 2006, J MEM LANG, V55, P534, DOI 10.1016/j.jml.2006.07.003 Kintsch W., 1998, COMPREHENSION PARADI Kintsch W., 2007, HDB LATENT SEMANTIC, P89 Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1 Kintsch W., 2008, SYMBOLS EMBODIMENT D, P145 KINTSCH W, 2010, TOP COGN SCI, DOI DOI 10.1111/J.1756-8765.2010.01107.X Kwantes PJ, 2005, PSYCHON B REV, V12, P703, DOI 10.3758/BF03196761 Landauer T., 2007, HDB LATENT SEMANTIC, P3 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Louwerse M. M., 2010, TOP COGN SCI, DOI [10.1111/j.1756-8765.2010.01106.x, DOI 10.1111/J.1756-8765.2010.01106.X] Lowe W., 2001, P 23 ANN C COGN SCI, P576 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 MacWhinney B., 2000, CHILDES PROJECT TOOL, V3rd McNamara D. S., 2007, HDB LATENT SEMANTIC, P379 McNamara D. S., 2009, PSYCHOL LEARN MOTIV, P298 McNamara DS, 1996, DISCOURSE PROCESS, V22, P247 McNamara D.S., 2007, HDB LATENT SEMANTIC, P227 McNamara DS, 2010, DISCOURSE PROCESS, V47, P292, DOI 10.1080/01638530902959943 McRae K, 2005, BEHAV RES METHODS, V37, P547, DOI 10.3758/BF03192726 MURDOCK BB, 1992, LEARNING THEORY CONN, V1, P201 O'Reilly T, 2007, DISCOURSE PROCESS, V43, P121, DOI 10.1207/s15326950dp4302_2 OROURKE ST, 2009, HDB RES WEB 2 0 3 0 Osgood C. E., 1957, MEASUREMENT MEANING PAIVIO A, COGNITIVE S IN PRESS RECCHIA GL, 2009, BEHAV RES METHODS, V41, P657 Rohde D. L. T., 2005, IMPROVED METHO UNPUB SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shaoul C, 2006, BEHAV RES METHODS, V38, P190, DOI 10.3758/BF03192768 Shapiro AM, 2000, J EDUC COMPUT RES, V22, P1 SMITH EE, 1974, PSYCHOL REV, V81, P214, DOI 10.1037/h0036351 Steyvers M, 2011, TOP COGN SCI, V3, P18, DOI 10.1111/j.1756-8765.2010.01097.x Steyvers M., 2007, HDB LATENT SEMANTIC, P427 Stone B, 2011, TOP COGN SCI, V3, P92, DOI 10.1111/j.1756-8765.2010.01108.x Vinson DP, 2008, BEHAV RES METHODS, V40, P183, DOI 10.3758/BRM.40.1.183 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 NR 54 TC 10 Z9 10 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1756-8757 J9 TOP COGN SCI JI Top. Cogn. Sci. PD JAN PY 2011 VL 3 IS 1 BP 3 EP 17 DI 10.1111/j.1756-8765.2010.01117.x PG 15 WC Psychology, Experimental SC Psychology GA 704LG UT WOS:000286053900002 PM 25164173 ER PT J AU Erk, K Pado, S Pado, U AF Erk, Katrin Pado, Sebastian Pado, Ulrike TI A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences SO COMPUTATIONAL LINGUISTICS LA English DT Article ID COMPREHENSION; PLAUSIBILITY; CONSTRAINTS; ROLES; NOUNS; VERBS AB We present a vector space-based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, plausibility scores for predicates given argument heads. We evaluate our model on one NLP task (pseudo-disambiguation) and one cognitive task (prediction of human plausibility judgments), gauging the influence of different parameters and comparing our model against other model classes. We obtain consistent benefits from using the disambiguation and semantic role information provided by a semantically tagged primary corpus. As for parameters, we identify settings that yield good performance across a range of experimental conditions. However, frequency remains a major influence of prediction quality, and we also identify more robust parameter settings suitable for applications with many infrequent items. C1 [Erk, Katrin] Univ Texas Austin, Dept Linguist, Austin, TX 78712 USA. [Pado, Sebastian] Univ Heidelberg, D-6900 Heidelberg, Germany. RP Erk, K (reprint author), Univ Texas Austin, Dept Linguist, Calhoun Hall 512,1 Univ Stn B 5100, Austin, TX 78712 USA. EM katrin.erk@mail.utexas.edu; pado@cl.uni-heidelberg.de; ulrike.pado@vico-research.com CR Abe N., 1996, P 13 INT C MACH LEAR, P3 Baroni M, 2009, LANG RESOUR EVAL, V43, P209, DOI 10.1007/s10579-009-9081-4 Bergsma Shane, 2008, P 2008 C EMP METH NA, P59, DOI 10.3115/1613715.1613725 BEST J, 1975, APPL STAT, V24, P377 Brew C., 2004, P 42 ANN M ASS COMP, P17, DOI 10.3115/1218955.1218958 BRISCOE T, 1989, COMPUTATIONAL LEXICO Brockmann C., 2003, P 10 C EUR CHAPT ASS, P27 Burnard L., 1995, USERS GUIDE BRIT NAT Ciaramita Massimiliano, 2000, P 18 INT C COMP LING, P187 CLARK S, 2001, P 2 M N AM CHAPT ASS, P95 COLLINS M, 1997, ACL 97 Curran James R., 2004, THESIS U EDINBURGH Daelemans W., 2005, MEMORY BASED LANGUAG Dagan Ido, 1999, MACH LEARN, V34, P34 Dligach Dmitriy, 2008, P 46 ANN M ASS COMP, P29, DOI 10.3115/1557690.1557699 Dunning T., 1993, Computational Linguistics, V19 Efron B, 1994, MONOGRAPHS STAT APPL, V57 Erk K., 2008, P C EMP METH NAT LAN, P897, DOI 10.3115/1613715.1613831 Erk Katrin, 2007, P 45 ANN M ASS COMP, P216 Fillmore CJ, 2003, INT J LEXICOGR, V16, P235, DOI 10.1093/ijl/16.3.235 Firth J. R., 1957, STUDIES LINGUISTIC A, P1 Garnsey SM, 1997, J MEM LANG, V37, P58, DOI 10.1006/jmla.1997.2512 Gildea D, 2002, COMPUT LINGUIST, V28, P245, DOI 10.1162/089120102760275983 Grefenstette G., 1994, EXPLORATIONS AUTOMAT GRISHMAN R, 1992, P 14 INT C COMP LING, P658 Harris Z., 1968, MATH STRUCTURE LANGU Hay J, 2006, LINGUIST REV, V23, P351, DOI 10.1515/TLR.2006.014 Hindle D., 1990, P 28 ANN M ASS COMP, P268, DOI 10.3115/981823.981857 Hindle D., 1993, Computational Linguistics, V19 Hying Christian, 2008, P 46 ANN M ASS COMP, P496 KATZ JJ, 1964, RES MONOGRAPH, V26 KATZ JJ, 1963, LANGUAGE, V39, P170, DOI 10.2307/411200 Konieczny L., 2003, P 4 INT C COGN SCI S, P330 Lakoff G., 1980, METAPHORS WE LIVE Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Lin Dekang, 1998, P LREC WORKSH EV PAR Lin DK, 1993, P 31 ANN M ASS COMP, P112, DOI 10.3115/981574.981590 Lowe W., 2001, P 23 ANN C COGN SCI, P576 Lowe Will, 2000, P 22 ANN C COGN SCI, P675 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 McCarthy D, 2003, COMPUT LINGUIST, V29, P639, DOI 10.1162/089120103322753365 McCarthy D., 2004, P 42 M ASS COMP LING, P279, DOI 10.3115/1218955.1218991 McCarthy D., 2000, P 1 C N AM CHAPT ASS, P256 McCarthy D, 2007, P 2007 JOINT C EMP M, P369 McRae K, 2005, MEM COGNITION, V33, P1174, DOI 10.3758/BF03193221 McRae K, 1998, J MEM LANG, V38, P283, DOI 10.1006/jmla.1997.2543 McRae K., 2001, P 23 ANN C COGN SCI, P617 McRae K, 1997, LANG COGNITIVE PROC, V12, P137 Miller G., 1990, INT J LEXICOGR, V3, P235 NAKATANI K, 2009, COGNITIVE SCI, V1, P94 Nastase V., 2008, P 3 INT JOINT C NAT, P757 NOSOFSKY RM, 1986, J EXP PSYCHOL GEN, V115, P39, DOI 10.1037/0096-3445.115.1.39 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Pado Sebastian, 2007, P 12 JOINT C EMP MET, P400 Pado U, 2009, COGNITIVE SCI, V33, P794, DOI 10.1111/j.1551-6709.2009.01033.x PADO U, 2007, THESIS SAARLAND U SA Pantel Patrick, 2007, P HLT NAACL ROCH NY, P564 Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] Pustejovsky J., 1995, GENERATIVE LEXICON Resnik P, 1996, COGNITION, V61, P127, DOI 10.1016/S0010-0277(96)00722-6 Rooth Mats, 1999, P 37 ANN M ASS COMP, P104, DOI 10.3115/1034678.1034703 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEPARD RN, 1987, SCIENCE, V237, P1317, DOI 10.1126/science.3629243 Stolcke A., 2002, P INT C SPOK LANG PR, P901 Toutanova K., 2005, J RES LANGUAGE COMPU, V3, P83 TRUESWELL, 1994, J MEM LANG, V33, P285 VANDEKERCKHOVE B, 2009, P 12 M EUR CHAPT ASS, P826, DOI 10.3115/1609067.1609159 WALDE SSI, 2010, P 7 INT C LANG RES E, P1381 Wilks Yorick, 1975, FORMAL SEMANTICS NAT, P329, DOI DOI 10.1017/CBO9780511897696.022 Yarowsky D., 1993, P ARPA HUM LANG TECH Zanzotto FM, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P849 Zapirain Benat, 2009, P JOINT C 47 ANN M A, P73, DOI 10.3115/1667583.1667607 NR 73 TC 7 Z9 8 PU MIT PRESS PI CAMBRIDGE PA 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA SN 0891-2017 J9 COMPUT LINGUIST JI Comput. Linguist. PD DEC PY 2010 VL 36 IS 4 BP 723 EP 763 DI 10.1162/coli_a_00017 PG 41 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 695OT UT WOS:000285382400007 ER PT J AU de Moura, ES Fernandes, D Ribeiro-Neto, B da Silva, AS Goncalves, MA AF de Moura, Edleno S. Fernandes, David Ribeiro-Neto, Berthier da Silva, Altigran S. Goncalves, Marcos Andre TI Using Structural Information to Improve Search in Web Collections SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article AB In this work, we investigate the problem of using the block structure of Web pages to improve ranking results. Starting with basic intuitions provided by the concepts of term frequency (TF) and inverse document frequency (IDF), we propose nine block-weight functions to distinguish the impact of term occurrences inside page blocks, instead of inside whole pages. These are then used to compute a modified BM25 ranking function. Using four distinct Web collections, we ran extensive experiments to compare our block-weight ranking formulas with two other baselines: (a) a BM25 ranking applied to full pages, and (b) a BM25 ranking that takes into account best blocks. Our methods suggest that our block-weighting ranking method is superior to all baselines across all collections we used and that average gain in precision figures from 5 to 20% are generated. C1 [de Moura, Edleno S.; da Silva, Altigran S.] Univ Fed Amazonas, Dept Comp Sci, Manaus, Amazonas, Brazil. [Fernandes, David; Ribeiro-Neto, Berthier; Goncalves, Marcos Andre] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil. RP de Moura, ES (reprint author), Univ Fed Amazonas, Dept Comp Sci, Manaus, Amazonas, Brazil. EM edleno@dcc.ufam.edu.br; davidf@dcc.ufmg.br; berthier@dcc.ufmg.br; alti@dcc.ufam.edu.br; mgoncalv@dcc.ufmg.br RI InWeb, Inct/J-9839-2013 FU CNPq [550874/2007-0, 573871/2008-6, 55.3126/2005-9, 302209/2007-7, 308528/2007-7] FX This work was partially supported by grants from projects InfoWeb (550874/2007-0 CNPq), InWeb (573871/2008-6 CNPq), SIRIAA (55.3126/2005-9 CNPq); by individual CNPq fellowship grants to Edleno S. de Moura (302209/2007-7), Altigran S. Silva (308528/2007-7); by a CNPQ Posgrad scholarship. CR AHNIZERET K, 2004, P 23 C CONC MOD, P402 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Cai D, 2004, P 27 ANN INT ACM SIG, P456, DOI 10.1145/1008992.1009070 Cai D., 2004, P 27 ANN INT ACM SIG, P440, DOI 10.1145/1008992.1009068 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Cao YJ, 2008, ALPIT 2008: SEVENTH INTERNATIONAL CONFERENCE ON ADVANCED LANGUAGE PROCESSING AND WEB INFORMATION TECHNOLOGY, PROCEEDINGS, P544, DOI 10.1109/ALPIT.2008.106 Deng Cai, 2003, VIPS VISION BASED PA Fagin R, 2003, SIAM PROC S, P28 Fernandes D, 2007, P 16 ACM C INF KNOWL, P165, DOI 10.1145/1321440.1321466 Hattori G., 2007, P 16 INT C WORLD WID, P361, DOI 10.1145/1242572.1242622 Hawking D, 1999, COMPUT NETW, V31, P1321, DOI 10.1016/S1389-1286(99)00024-9 Hawking D., 1998, P 7 TEXT RETR C TREC, P91 Kang J., 2007, P 2007 INT S INF TEC, P306 Kohlschutter C., 2008, P 17 ACM C INF KNOWL, P1173, DOI 10.1145/1458082.1458237 Li L, 2007, Proceedings of the IAMG '07: Geomathematics and GIS Analysis of Resources, Environment and Hazards, P502 Lin Shian-Hua, 2002, P 8 ACM SIGKDD INT C, P588, DOI DOI 10.1145/775047.775134 LIU Y, 2006, P 2006 IEEE WIC ACM, P761 MOURA ESD, 2008, ACM T INFORM SYSTEMS, V26, P1, DOI 10.1145/1344411.1344415 Rajagopalan Sridhar, 2002, P 11 INT C WORLD WID, P580 Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181 ROBERTSON S, 1996, 3 TEXT RETRIEVAL C T, P109 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 2000, 8 TEXT RETRIEVAL C T SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 Yi L., 2003, P 9 ACM SIGKDD INT C, P296 Yin X., 2004, P INT WORLD WID WEB, P338, DOI 10.1145/988672.988718 NR 27 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD DEC PY 2010 VL 61 IS 12 BP 2503 EP 2513 DI 10.1002/asi.21436 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 680KP UT WOS:000284231100010 ER PT J AU Fisher, M Hanrahan, P AF Fisher, Matthew Hanrahan, Pat TI Context-Based Search for 3D Models SO ACM TRANSACTIONS ON GRAPHICS LA English DT Article DE 3D model search; scene modeling; shape retrieval; spatial context AB Large corpora of 3D models, such as Google 3D Warehouse, are now becoming available on the web. It is possible to search these databases using a keyword search. This makes it possible for designers to easily include existing content into new scenes. In this paper, we describe a method for context-based search of 3D scenes. We first downloaded a large set of scene graphs from Google 3D Warehouse. These scene graphs were segmented into individual objects. We also extracted tags from the names of the models. Given the object shape, tags, and spatial relationship between pairs of objects, we can predict the strength of a relationship between a candidate model and an existing object in the scene. Using this function, we can perform context-based queries. The user specifies a region in the scene they are modeling using a 3D bounding box, and the system returns a list of related objects. We show that context-based queries perform better than keyword queries alone, and that without any keywords our algorithm still returns a relevant set of models. EM mdfisher@stanford.edu; hanrahan@cs.stanford.edu RI chen, mingang/C-7691-2011 FU Fannie and John Hertz Foundation FX Support for this research was provided by the Fannie and John Hertz Foundation. We would like to thank Ed Luong for writing the webcrawler that was used to acquire scenes from Google 3D Warehouse and the web interface used in the user study. We would also like to thank Google for allowing the images of 3D Warehouse models to be published. CR Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669 Fellbaum C, 1998, WORDNET ELECT LEXICA Funkhouser T, 2006, P 4 EUR S GEOM PROC, P142 Funkhouser T, 2003, ACM T GRAPHIC, V22, P83, DOI 10.1145/588272.588279 Funkhouser T., 2004, ACM SIGGRAPH ACM, P663 Galleguillos C., 2008, IEEE C COMP VIS PATT, P1 Goldfeder Corey, 2008, Joint Conference on Digital Libraries (JCDL 2008), DOI 10.1145/1378889.1378950 Hays J, 2007, ACM T GRAPHIC, V26, DOI 10.1145/1276377.1276382 He X., 2004, CVPR, V2, pII Malisiewicz T., 2009, NIPS Min P, 2004, LECT NOTES COMPUT SC, V3232, P209 NOVOTNI M, 2003, SOLID MODELING APPL, P225 PAPADAKIS P, 2008, COMPUT AIDED DESIGN, V5, P5 Rabinovich A., 2007, IEEE INT C COMP VIS, P1 Russell BC, 2008, INT J COMPUT VISION, V77, P157, DOI 10.1007/s11263-007-0090-8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHILANE P., 2004, SHAPE MODELING INT STRAT TM, 1991, IEEE T PATTERN ANAL, V13, P1050, DOI 10.1109/34.99238 TORRALBA A, 2010, CONTEXT CHALLENGE NR 19 TC 13 Z9 13 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0730-0301 J9 ACM T GRAPHIC JI ACM Trans. Graph. PD DEC PY 2010 VL 29 IS 6 AR 182 DI 10.1145/1866158.1866204 PG 10 WC Computer Science, Software Engineering SC Computer Science GA 689QJ UT WOS:000284943000046 ER PT J AU Huang, HH Kuo, YH AF Huang, Hsun-Hui Kuo, Yau-Hwang TI Cross-Lingual Document Representation and Semantic Similarity Measure: A Fuzzy Set and Rough Set Based Approach SO IEEE TRANSACTIONS ON FUZZY SYSTEMS LA English DT Article DE Cross-lingual; document representation; fuzzy-rough hybrid; sense association network; similarity measure ID INFORMATION-RETRIEVAL; ONTOLOGY; SEARCH AB As cross-lingual information retrieval is attracting increasing attention, tools that measure cross-lingual semantic similarity between documents are becoming desirable. In this paper, two aspects of cross-lingual semantic document similarity measures are investigated: One is document representation, and the other is the formulation of similarity measures. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. Our approach first develops a language-independent sense-level document representation based on the fuzzy set model to reduce the barrier between different languages and further explores the fuzzy-rough hybrid approach to obtain a more robust macrosense-level document representation through the partitioning of the integrated sense association network of the document collection into macrosenses. Then, Tversky's notion of similarity and the F1 measure on information retrieval are adopted to formulate, respectively, two document similarity measures with fuzzy set operations on the two proposed document representations. The effectiveness of our approach is demonstrated by its success rate in identifying the English translations to their corresponding Chinese documents in a collection of Chinese-English parallel documents. Moreover, the proposed approach can be easily extended to process documents in other languages. It is believed that the proposed representations, along with the similarity measures, will enable more effective text mining processes. C1 [Huang, Hsun-Hui; Kuo, Yau-Hwang] Natl Cheng Kung Univ, Intelligent Syst Media Proc Lab, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan. [Huang, Hsun-Hui] Tajen Univ, Dept Management Informat Syst, Pingtung 907, Taiwan. RP Huang, HH (reprint author), Natl Cheng Kung Univ, Intelligent Syst Media Proc Lab, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan. EM hhhuang@ismp.csie.ncku.edu.tw; kuoyh@ismp.csie.ncku.edu.tw CR *AC SIN, 1993, 9305 AC SIN Bader B., 2008, P 22 INT C COMP LING, P49 Bezdek J. C., 1981, PATTERN RECOGNITION BUCKLEY C, 1993, P WORKSH HUM LANG TE, P392, DOI 10.3115/1075671.1075771 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 CARDENOSA J, 2005, P 4 C EUR SOC FUZZ L, P1231 Chen H., 2002, ACM T ASIAN LANGUAGE, V1, P103, DOI 10.1145/568954.568955 CHEN KJ, 2007, E HOWNET CHEN SM, 1995, FUZZY SET SYST, V72, P79 Chim H, 2008, IEEE T KNOWL DATA EN, V20, P1217, DOI 10.1109/TKDE.2008.50 CHOI JY, 2008, CLUSTERING ANAL COLL *CKIP, 1999, VER 1 0 AUT Cross V, 2006, NAFIPS 2006 - 2006 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY, VOLS 1 AND 2, P541, DOI 10.1109/NAFIPS.2006.365467 Dave RN, 2002, IEEE T FUZZY SYST, V10, P713, DOI 10.1109/TFUZZ.2002.805899 Davenport J. W., 1995, Proceedings of Neural, Parallel and Scientific Computations. Vol.1. Proceedings of the First International Conference DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DONG Z, 2010, HOWNET Dubois D., 1992, INTELLIGENT DECISION, P203 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 HATHAWAY RJ, 1994, PATTERN RECOGN, V27, P429, DOI 10.1016/0031-3203(94)90119-8 Jensen R, 2004, IEEE T KNOWL DATA EN, V16, P1457, DOI 10.1109/TKDE.2004.96 Jin W, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P807 Johanyak Z. C., 2005, P 3 SLOV HUNG JOINT, P265 Krishnapuram R., 1993, IEEE Transactions on Fuzzy Systems, V1, DOI 10.1109/91.227387 Landauer T., 1991, P 11 INT C EXP SYST, V8, P77 Lee CS, 2007, DATA KNOWL ENG, V60, P547, DOI 10.1016/j.datak.2006.04.001 Lee CS, 2005, IEEE T SYST MAN CY B, V35, P859, DOI 10.1109/TSMCB.2005.845032 Lee-Kwang H., 1994, FUZZY SETS SYSTEMS, V62, P291, DOI 10.1016/0165-0114(94)90113-9 Lin T.Y., 1992, HDB APPL ADV ROUGH S, P287 Lingras P, 2007, IEEE INT CONF FUZZY, P125 Littman M., 1998, CROSS LANGUAGE INFOR, P51 MEI JJ, 1983, TONGYICI CILIN Montalvo S, 2007, LECT NOTES ARTIF INT, V4629, P107 Montalvo S, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P1145 MONTESYGOMEZ M, 2000, P 11 INT C DAT EXP S, P312 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Pedersen T., 2005, 200525 UMSI POULIQUEN B, 2003, P INT C REC ADV NAT, P401 PUCHER M, 2006, P 6 INT WORKSH COMP, P332 Radzikowska A. M., 2002, FUZZY SETS SYSTEMS, V126, P137, DOI 10.1016/S0165-0114(01)00032-X Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Robertson S. E., 1988, RELEVANCE WEIGHTING, P143 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracoglu R, 2008, EXPERT SYST APPL, V34, P2545, DOI 10.1016/j.eswa.2007.04.003 Schenker A., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Srinivasan P, 2001, INFORM PROCESS MANAG, V37, P15, DOI 10.1016/S0306-4573(00)00014-5 Steinberger R., 2002, P 3 INT C COMP LING, P415 Tseng VS, 2007, IEEE T FUZZY SYST, V15, P1188, DOI 10.1109/TFUZZ.2006.890673 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 UCHIDA H, 2004, 4 NTCIR WORKSH M TOK Wan XJ, 2008, INFORM PROCESS MANAG, V44, P1032, DOI 10.1016/j.ipm.2007.07.012 Wan XJ, 2007, INFORM SCIENCES, V177, P3718, DOI 10.1016/j.ins.2007.02.045 WANG GJ, 1992, FUZZY SET SYST, V47, P351 Yao Y. Y., 1997, ROUGH SETS DATA MINI, P301 Zwick R., 1987, International Journal of Approximate Reasoning, V1, DOI 10.1016/0888-613X(87)90015-6 2003, ACAD SINICA BILINGUA NR 57 TC 9 Z9 10 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1063-6706 J9 IEEE T FUZZY SYST JI IEEE Trans. Fuzzy Syst. PD DEC PY 2010 VL 18 IS 6 BP 1098 EP 1111 DI 10.1109/TFUZZ.2010.2065811 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 688NC UT WOS:000284855200007 ER PT J AU Mitchell, J Lapata, M AF Mitchell, Jeff Lapata, Mirella TI Composition in Distributional Models of Semantics SO COGNITIVE SCIENCE LA English DT Article DE Distributional models; Semantic spaces; Compositionality; Meaning representations; Connectionism; Phrase similarity ID DISCOURSE COMPREHENSION; NATURAL-LANGUAGE; EARLIER PROBLEMS; SPACE MODELS; SIMILARITY; REPRESENTATIONS; CONTEXT; KNOWLEDGE; CONSTRUCTION; COOCCURRENCE AB Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e. g., in sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task. C1 [Mitchell, Jeff; Lapata, Mirella] Univ Edinburgh, Sch Informat, Edinburgh EH10 9AB, Midlothian, Scotland. RP Mitchell, J (reprint author), Univ Edinburgh, Sch Informat, 10 Chrichton St, Edinburgh EH10 9AB, Midlothian, Scotland. EM jeff.mitchell@ed.ac.uk; mlap@inf.ed.ac.uk CR Aerts D, 2004, J PHYS A-MATH GEN, V37, pL123, DOI 10.1088/0305-4470/37/12/L01 Andrews M, 2009, PSYCHOL REV, V116, P463, DOI 10.1037/a0016261 Baldwin Timothy, 2003, P ACL 2003 WORKSH MU, P89 Bannard C., 2003, P ACL 2003 WORKSH MU, P65 Bannard Colin, 2005, P 43 ANN M ASS COMP, P597, DOI DOI 10.3115/1219840.1219914 Barzilay R., 2003, P HLT NAACL, P16 Blackburn P, 2005, REPRESENTATION INFER Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020 Carroll J. A., 2002, P 3 INT C LANG RES E, P1499 Clark S., 2008, P 2 S QUANT INT, P133 Clark S., 2007, P AAAI SPRING S QUAN, P52 Cohen J., 1983, APPL MULTIPLE REGRES, V2nd COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEESE J, 1959, J EXP PSYCHOL, V58, P17, DOI 10.1037/h0046671 DEMBERG V, 2008, COGNITION, V101, P193 Denhiere G, 2004, PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P297 Dennis Simon, 2007, HDB LATENT SEMANTIC, P449 Doumas L. A. A., 2005, CAMBRIDGE HDB THINKI, P73 Doumas LAA, 2008, PSYCHOL REV, V115, P1, DOI 10.1037/0033-295X.115.1.1 DUFFY SA, 1989, J EXP PSYCHOL LEARN, V15, P791, DOI 10.1037/0278-7393.15.5.791 Eliasmith C, 2001, COGNITIVE SCI, V25, P245, DOI 10.1207/s15516709cog2502_3 Elman Jeffrey L., 1996, RETHINKING INNATENES Estes William K., 1994, CLASSIFICATION COGNI Fellbaum Cristiane, 1998, WORDNET ELECT DATABA Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 FODOR JA, 1988, COGNITION, V28, P3, DOI 10.1016/0010-0277(88)90031-5 FOLTZ PW, 1998, DISCOURSE PROCESS, V15, P85 FOSS DJ, 1982, COGNITIVE PSYCHOL, V14, P590, DOI 10.1016/0010-0285(82)90020-2 Frank S, 2008, DISCOURSE PROCESS, V45, P429, DOI 10.1080/01638530802069926 FREGE G., 1884, GRUNDLAGEN ARITHMETI, VCentenar GENTNER D, 1989, SIMILARITY AND ANALOGICAL REASONING, P199, DOI 10.1017/CBO9780511529863.011 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Harris Z., 1968, MATH STRUCTURES LANG HEIT E, 1994, J EXP PSYCHOL LEARN, V20, P411, DOI 10.1037/0278-7393.20.2.411 HINTON J, 1991, PSYCHOL REV, V98, P74 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 HOLYOAK KJ, 1987, MEM COGNITION, V15, P332, DOI 10.3758/BF03197035 Holyoak KJ, 2000, COGNITIVE DYNAMICS, P229 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kako E, 1999, ANIM LEARN BEHAV, V27, P1, DOI 10.3758/BF03199424 Kanerva P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P1036 Kanerva P, 2009, COGN COMPUT, V1, P139, DOI 10.1007/s12559-009-9009-8 Kanerva P., 1988, SPARSE DISTRIBUTED M Keller F, 2009, BEHAV RES METHODS, V41, P1, DOI 10.3758/BRM.41.1.12 Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1 KINTSCH W, 1988, PSYCHOL REV, V95, P163, DOI 10.1037/0033-295X.95.2.163 Laham D., 2000, THESIS U COLORADO BO Lakoff George, 1977, 13 REG M CHIC LING S, P236 Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lapata Mirella, 2003, COMPUTATIONAL LINGUI, V29, P263 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 Lin D., 1998, P 17 INT C COMP LING, P768 Lin D., 2001, P ACM SIGKDD C KNOWL, P323, DOI [10.1145/502512.502559, DOI 10.1145/502512.502559] Lowe W, 2000, PERSP NEURAL COMP, P303 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Mangalath P., 2004, P 26 ANN M COGN SCI, P1623 Markman A. B., 1998, KNOWLEDGE REPRESENTA MASSON MEJ, 1986, J MEM LANG, V25, P588, DOI 10.1016/0749-596X(86)90013-6 McDonald S, 2000, THESIS U EDINBURGH McRae K, 1997, J EXP PSYCHOL GEN, V126, P99, DOI 10.1037/0096-3445.126.2.99 METCALFE EJ, 1990, PSYCHOL REV, V88, P627 Mitchell J., 2008, P 46 ANN M ASS COMP, P236 Montague Richard, 1974, FORMAL PHILOS SELECT, P188 MORRIS RK, 1994, J EXP PSYCHOL LEARN, V20, P92, DOI 10.1037/0278-7393.20.1.92 MURPHY GL, 1985, PSYCHOL REV, V92, P289, DOI 10.1037//0033-295X.92.3.289 Nelson D. L., 1999, U S FLORIDA WORD ASS NEVILLE H, 1991, J COGNITIVE NEUROSCI, V3, P151, DOI 10.1162/jocn.1991.3.2.151 NOSOFSKY RM, 1984, J EXP PSYCHOL LEARN, V10, P104, DOI 10.1037/0278-7393.10.1.104 NOSOFSKY RM, 1986, J EXP PSYCHOL GEN, V115, P39, DOI 10.1037/0096-3445.115.1.39 NUNBERG G, 1994, LANGUAGE, V70, P491, DOI 10.2307/416483 OSEAGHDHA PG, 1989, J EXP PSYCHOL LEARN, V15, P73 Osgood C. E., 1957, MEASUREMENT MEANING Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Partee B., 1995, INVITATION COGNITI 1, P311 Partee Barbara H., 2004, COMPOSITIONALITY FOR Pedersen T., 2004, HLT NAACL 2004, P38 Pinker Steven, 1994, LANGUAGE INSTINCT MI Plate T. A., 1991, P 12 INT JOINT C ART, P30 PLATE TA, 1995, IEEE T NEURAL NETWOR, V6, P623, DOI 10.1109/72.377968 Plate TA, 2000, EXPERT SYST, V17, P29, DOI 10.1111/1468-0394.00125 POLLACK JB, 1990, ARTIF INTELL, V46, P77, DOI 10.1016/0004-3702(90)90005-K Pullum GK, 2007, CROAT J PHILOS, V7, P375 Pynte J., 2008, J EYE MOVEMENT RES, V2, P1 RAAIJMAKERS JGW, 1981, PSYCHOL REV, V88, P93, DOI 10.1037//0033-295X.88.2.93 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P399 RIPS LJ, 1975, J VERB LEARN VERB BE, V14, P665, DOI 10.1016/S0022-5371(75)80055-7 ROSS BH, 1989, J EXP PSYCHOL LEARN, V15, P456, DOI 10.1037//0278-7393.15.3.456 ROSS BH, 1984, COGNITIVE PSYCHOL, V16, P371, DOI 10.1016/0010-0285(84)90014-8 ROSS BH, 1987, J EXP PSYCHOL LEARN, V13, P629, DOI 10.1037/0278-7393.13.4.629 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 Rumelhart D. E., 1986, PARALLEL DISTRIBUTED, V1 Sahlgren M., 2008, P 30 ANN C COGN SCI, P1300 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIMPSON GB, 1989, J EXP PSYCHOL LEARN, V15, P88 Sloman SA, 1998, COGNITION, V65, P87, DOI 10.1016/S0010-0277(97)00048-6 Smith E. E., 1981, CATEGORIES CONCEPTS SMOLENSKY P, 1990, ARTIF INTELL, V46, P159, DOI 10.1016/0004-3702(90)90007-M SPENADER J, 2007, COGNITIVE FDN INTERP, P163 Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 TILL RE, 1988, MEM COGNITION, V16, P283, DOI 10.3758/BF03197039 Turney PD, 2006, COMPUT LINGUIST, V32, P379, DOI 10.1162/coli.2006.32.3.379 Turney PD, 2010, J ARTIF INTELL RES, V37, P141 WALLACH HM, 2002, THESIS U CAMBRIDGE Weeds J., 2003, THESIS U SUSSEX BRIG Weisss S. M., 1991, COMPUTER SYSTEMS LEA WEST RF, 1986, MEM COGNITION, V14, P104, DOI 10.3758/BF03198370 WIDDOWS D, 2008, P 2 S QUANT INT QI 2 NR 113 TC 30 Z9 30 PU WILEY-BLACKWELL PUBLISHING, INC PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 0364-0213 J9 COGNITIVE SCI JI Cogn. Sci. PD NOV PY 2010 VL 34 IS 8 BP 1388 EP 1429 DI 10.1111/j.1551-6709.2010.01106.x PG 42 WC Psychology, Experimental SC Psychology GA 676VT UT WOS:000283947500003 PM 21564253 ER PT J AU Chen, JM Chen, MC Sun, YS AF Chen, Jun-Ming Chen, Meng-Chang Sun, Yeali S. TI A novel approach for enhancing student reading comprehension and assisting teacher assessment of literacy SO COMPUTERS & EDUCATION LA English DT Article DE Cooperative/collaborative learning; Architectures for educational technology system; Interactive learning environments ID LATENT SEMANTIC ANALYSIS; WEB-BASED ASSESSMENT; ENVIRONMENT; STRATEGIES; FEEDBACK; LESSONS; SUPPORT AB For students of English as a Foreign Language (EFL), reading exercises are critical not only for developing strong reading comprehension, but also for developing listening, speaking, and writing skills. Prior research suggests that social, collaborative learning environments are best suited for improving language ability. However, opportunities for English learners to collaboratively practice reading comprehension are minimal, and due to resource constraints and a lack of accurate evaluation methods, English instructors rarely assess student literacy effectively. In response to these problems, we propose a Tag-based Collaborative reading learning System (TACO) that makes use of Web 2.0 Internet social tagging techniques to provide a collaborative environment for reading English. We test our system's ability to both improve reading comprehension and aid teachers in accurately assessing literacy by conducting a three-month trial with 56 participating Taiwanese high school students from February to May 2009. During this period, post-testing results show a significant improvement in reading scores among participants in our tag-based system, and survey feedback from teachers suggests an improved capacity for literacy assessment. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Chen, Jun-Ming; Sun, Yeali S.] Natl Taiwan Univ, Dept Informat Management, Taipei, Taiwan. [Chen, Meng-Chang] Acad Sinica, Inst Informat Sci, Taipei, Taiwan. RP Chen, JM (reprint author), Natl Taiwan Univ, Dept Informat Management, 1 Sec 4,Roosevelt Rd, Taipei, Taiwan. EM d96725001@ntu.edu.tw FU National Science Council, Taiwan [NSC-97-2631-S-008-004] FX This study is supported in part by the National Science Council, Taiwan under contract numbers NSC-97-2631-S-008-004. The authors would like to thank Professor David Wible, Professor Chin-Hwa Kuo, Mr. Sin-Yan Lin and the IWiLL team for their helps during the study. CR Agarwal R., 2004, SIGSOFT SOFTW ENG NO, V29, P1 ARTER J, 2001, NASSP B, V85, P53, DOI 10.1177/019263650108562107 Bateman S., 2007, P WORKSH TAGG MET SO BATEMAN S, 2006, O I2LOR 2006 MONTR C BECKETT D, 2006, XTECH 2006 BUL WEB 2 Carlisle J. F., 2004, HDB LANGUAGE LITERAC Casamayor A, 2009, COMPUT EDUC, V53, P1147, DOI 10.1016/j.compedu.2009.05.025 CHEN JM, 2009, TECHN ENH LEARN C 20 Chiarella A., 2006, P WORLD C E LEARN CO, P2753 Chiou CK, 2009, COMPUT EDUC, V53, P261, DOI 10.1016/j.compedu.2009.02.006 Cochran S. W., 1983, J HUMAN SCI, V2, P111 Cole M, 2009, COMPUT EDUC, V52, P141, DOI 10.1016/j.compedu.2008.07.003 COOK LK, 1983, COGNITIVE STRATEGY R DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding N, 2009, COMPUT EDUC, V52, P509, DOI 10.1016/j.compedu.2008.10.009 Dlugosz D. W., 2000, ENGLISH LANGUAGE TEA, V54, P284, DOI 10.1093/elt/54.3.284 Duke N.K., 2004, HDB LANGUAGE LITERAC, P501 Fellbaum C, 1998, WORDNET ELECT LEXICA Glover I, 2007, COMPUT EDUC, V49, P1308, DOI 10.1016/j.compedu.2006.02.006 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 GONG Y, 2001, P 24 ANN INT ACM SIG, P24 GRAHAM R, 2008, IEEE WIC ACM INT C W, P141 Hakkarainen K., 2002, CSCL 2 CARRYING FORW, P129 Harasim L, 1999, COMPUTER, V32, P44, DOI 10.1109/2.789750 Hou HT, 2007, INTERACT LEARN ENVIR, V15, P237, DOI 10.1080/10494820701206974 HSIAO JWD, 1996, CSCL THEORIES HUANG CC, 2006, P 10 ANN GLOB CHIN C Huang HC, 2009, COMPUT EDUC, V52, P13, DOI 10.1016/j.compedu.2008.06.003 HUANG YC, 2008, AAAI Hwang GJ, 2008, COMPUT EDUC, V51, P926, DOI 10.1016/j.compedu.2007.09.011 Karacapilidis N., 2003, International Transactions in Operational Research, V10, DOI 10.1111/1475-3995.00390 Klingner JK, 2004, REM SPEC EDUC, V25, P291, DOI 10.1177/07419325040250050301 KOIVUNEN MR, 2001, P K CAP2001 WORKSH K KOSCHMANN T, 2002, CSCL 2 CANYING FORWA KUO CH, 2002, J EDUC COMPUT RES, V27, P229, DOI 10.2190/XJVE-X9JM-UL46-ER0T LAN YJ, 2006, JALT CALL J, V2, P3 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LI X, 2008, P 17 WORLD WID WEB C, V17, P675 Lohmann S, 2009, LECT NOTES COMPUT SC, V5726, P392, DOI 10.1007/978-3-642-03655-2_43 Luhn H.P., 1957, IBM J RES DEV LUHN HP, 1958, IBM J RES DEV, V2, P159 McCormick C. B., 1995, COGNITION TEACHING A Mercier J, 2008, COMPUT EDUC, V51, P17, DOI 10.1016/j.compedu.2007.03.004 Michlmayr E., 2007, P WORKSH TAGG MET SO Mokhtari K, 2002, J EDUC PSYCHOL, V94, P249, DOI 10.1037//0022-0663.94.2.249 MURRY JW, 1995, REV HIGH EDUC, V18, P423 NENKOVA A, 2006, P 29 ANN INT ACM SIG Noll Michael G, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.341 NOLL MG, 2007, WEB SEARCH PERSONALI, P367 Nunnally J. C., 1978, PSYCHOMETRIC THEORY O'Reilly T., 2005, WHAT IS WEB 20 DESIG Popham WJ, 2006, EDUC LEADERSHIP, V63, P84 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PRESSLEY M, 2004, HDB LANGUAGE LITERAC Reichard C. A., 2004, SYSTEM, V32, P379, DOI 10.1016/j.system.2004.04.005 REITSMA P, 1988, READ RES QUART, V23, P219, DOI 10.2307/747803 Sachs G. T., 2003, RELC, V34, P338, DOI 10.1177/003368820303400305 SAFRAN C, 2007, P ICI2007 SEP 26 28 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schaffert S., 2006, P 15 IEEE INT WORKSH, P388 SEN S, 2006, P 2006 20 ANN C COMP, V20, P181 Sheorey R., 2001, SYSTEM, V29, P431, DOI DOI 10.1016/S0346-251X(01)00039-2 So HJ, 2008, COMPUT EDUC, V51, P318, DOI 10.1016/j.compedu.2007.05.009 Stacey E., 1999, J DISTANCE ED, V14, P14 Stiggins R, 2004, PHI DELTA KAPPAN, V86, P22 Stone CA, 2005, HDB LANGUAGE LITERAC VANBOXTEL C, 2000, THESIS PRINT PARTNER van der Pol J, 2008, COMPUT EDUC, V51, P1804, DOI 10.1016/j.compedu.2008.06.001 Wang QY, 2009, COMPUT EDUC, V53, P1138, DOI 10.1016/j.compedu.2009.05.023 Wang TH, 2004, J COMPUT ASSIST LEAR, V20, P59, DOI 10.1111/j.1365-2729.2004.00066.x Wang TH, 2008, COMPUT EDUC, V51, P448, DOI 10.1016/j.compedu.2007.06.010 Wei FH, 2006, BRIT J EDUC TECHNOL, V37, P917, DOI 10.1111/j.1467-8535.2006.00674.x Wen JR, 2008, COMPUT EDUC, V50, P787, DOI 10.1016/j.compedu.2006.08.011 WILLIAMS JP, 2007, READING COMPREHENSIO, P397 NR 74 TC 9 Z9 9 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0360-1315 J9 COMPUT EDUC JI Comput. Educ. PD NOV PY 2010 VL 55 IS 3 BP 1367 EP 1382 DI 10.1016/j.compedu.2010.06.011 PG 16 WC Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA 639PB UT WOS:000280985600042 ER PT J AU Chenthamarakshan, V Dixit, K Gattani, M Goyal, M Gupta, P Kambhatla, N Lotlikar, RM Majumdar, D Parija, GR Roy, S Soni, S Visweswariah, K AF Chenthamarakshan, V. Dixit, K. Gattani, M. Goyal, M. Gupta, P. Kambhatla, N. Lotlikar, R. M. Majumdar, D. Parija, G. R. Roy, S. Soni, S. Visweswariah, K. TI Effective decision support systems for workforce deployment SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT LA English DT Article AB Efficient utilization of human resources is imperative for information technology (IT) service businesses that continually manage the assignment and movement of practitioners to or between projects. These organizations constantly strive to balance the many objectives of multiple stakeholders in order to minimize idle resources while aiming to increase revenue from new project opportunities, as well as to improve the quality of practitioners assigned to each project. However, these various objectives conflict, so we can only hope to achieve an aggregated form of these disparate objectives. This emphasizes the need for a well-designed decision support system. Such a system will facilitate optimal assignment of practitioners to projects while minimizing the overheads involved in a multiuser decision-making environment. Here, "optimal" refers to maximizing certain aggregated attributes of the various matches made with respect to the system. The term "user" refers to the decision makers who assign employees to various projects. The system comprises a text-matching module, a business logic module, an optimization module, and a user interface (UI) module. Key innovations include translating the business objectives into optimization criteria to be solved using integer programming, incorporating individual user preferences into the optimization, and encapsulating user experience into the matching logic. Additional innovations include those in text analytics to differentiate skill and nonskill terms and a UI design that helps users understand the relative global optimality of each recommendation in familiar terms while influencing decisions made independently by individual users in a way that collectively leads to organizationally optimal outcomes. We also explain the role of decision support in implementing job-rotation and cross-training programs for practitioners. Finally, we share our experiences in the development and deployment of this system in an IT service company and describe areas where further work is needed. C1 [Chenthamarakshan, V.; Goyal, M.; Kambhatla, N.; Lotlikar, R. M.; Majumdar, D.; Visweswariah, K.] IBM Res Div, India Res Lab, Bangalore 560071, Karnataka, India. [Dixit, K.; Gupta, P.; Parija, G. R.; Roy, S.; Soni, S.] IBM Res Div, India Res Lab, Vasant Vihar Inst Area, New Delhi 110070, DL, India. [Gattani, M.] Indian Inst Technol, Delhi, India. RP Chenthamarakshan, V (reprint author), IBM Res Div, India Res Lab, Bangalore 560071, Karnataka, India. EM vijil.e.c@in.ibm.com; kadixit1@in.ibm.com; munish.goyal@in.ibm.com; prguptan@in.ibm.com; kambhatla@in.ibm.com; rohitmlo@in.ibm.com; debapriyo@in.ibm.com; gyana.parija@in.ibm.com; sambuddha@in.ibm.com; soujanya.soni@in.ibm.com; v-karthik@in.ibm.com CR Ba SL, 2001, INFORM SYST RES, V12, P225, DOI 10.1287/isre.12.3.225.9712 BOLAND RJ, 1994, ORGAN SCI, V5, P456, DOI 10.1287/orsc.5.3.456 Datta R., 2008, APPL DATA MINING E B, P133 Dixit K., 2009, P INT IEEE SCC, P104 Dong J., 2008, P IEEE SOLI, P2496 Hu J., 2008, P KDD WORKSH DAT MIN Hu J, 2007, IBM J RES DEV, V51, P281 Lovasz L, 1986, ANN DISCRETE MATH Naveh Y, 2007, IBM J RES DEV, V51, P263 Richter Yossi, 2008, International Journal of Services Operations and Informatics, V3, DOI 10.1504/IJSOI.2008.021338 Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarawagi Sunita, 2008, Foundations and Trends in Databases, V1, DOI 10.1561/1500000003 Sharma M., 2008, P INFORMS ANN M Ursu M. F., 2005, International Journal of Knowledge-Based and Intelligent Engineering Systems, V9 Yoshimura M, 2006, INT J PROD RES, V44, P831, DOI 10.1080/00207540500272519 NR 16 TC 1 Z9 1 PU IBM CORP PI ARMONK PA 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA SN 0018-8646 J9 IBM J RES DEV JI IBM J. Res. Dev. PD NOV-DEC PY 2010 VL 54 IS 6 AR 5 DI 10.1147/JRD.2010.2074310 PG 15 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA V22EW UT WOS:000208259500004 ER PT J AU Yu, H Liu, FF Ramesh, BP AF Yu, Hong Liu, Feifan Ramesh, Balaji Polepalli TI Automatic Figure Ranking and User Interfacing for Intelligent Figure Search SO PLOS ONE LA English DT Article ID ORDINAL REGRESSION; MASS-SPECTROMETRY; RERANKING; MODELS; ENGINE; EXPRESSION; PROTEIN; IMAGES AB Background: Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of "figure ranking'': figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery. Methodology/Findings: We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation. Conclusion/Significance: The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists. C1 [Yu, Hong] Univ Wisconsin, Dept Hlth Sci, Milwaukee, WI 53201 USA. [Yu, Hong; Liu, Feifan; Ramesh, Balaji Polepalli] Univ Wisconsin, Dept Elect Engn & Comp Sci, Milwaukee, WI 53201 USA. RP Yu, H (reprint author), Univ Wisconsin, Dept Hlth Sci, Milwaukee, WI 53201 USA. EM hongyu@uwm.edu FU National Institutes of Health [5R01LM009836, 5R21RR024933]; University of Wisconsin-Milwaukee FX Funding for this study was provided by the National Institutes of Health (http://www.nih.gov/) grant 5R01LM009836 and 5R21RR024933 and the Research Growth Initiative (RGI) from University of Wisconsin-Milwaukee. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CR Agarwal S, 2009, AMIA ANN S P Agarwal Shashank, 2009, BIOINFORMATICS BARZILAY R, 1999, 37 ASS COMPUTATIONAL Bengoechea TG, 2009, P NATL ACAD SCI USA, V106, P7870, DOI 10.1073/pnas.0901533106 Braschler M, 2004, INFORM RETRIEVAL, V7, P7, DOI 10.1023/B:INRT.0000009438.69013.fa Charniak E., 2005, P 43 ANN M ASS COMP Chu W, 2007, NEURAL COMPUT, V19, P792, DOI 10.1162/neco.2007.19.3.792 Collins M, 2005, COMPUT LINGUIST, V31, P25, DOI 10.1162/0891201053630273 Collins M., 2002, P 40 ANN M ASS COMP Collobert R, 2001, J MACH LEARN RES, V1, P143, DOI 10.1162/15324430152733142 Crammer K., 2001, P C NEUR INF PROC SY Demner-Fushman D, 2009, INT J MED INFORM Erkan G, 2004, J ARTIF INTELL RES, V22, P457 Ge Y, 2009, P NATL ACAD SCI USA, V106, P12658, DOI 10.1073/pnas.0813369106 Chu W, 2005, J MACH LEARN RES, V6, P1019 Har-Peled S., 2002, ADV NEURAL INFORM PR, V15, P785 Hearst MA, 2007, BIOINFORMATICS, V23, P2196, DOI 10.1093/bioinformatics/btm301 Huang L, 2008, P ACL Kahn CE, 2007, AM J ROENTGENOL, V188, P1475, DOI 10.2214/AJR.06.1740 Kou ZZ, 2007, Pacific Symposium on Biocomputing 2007, P257 Lane AL, 2009, P NATL ACAD SCI USA, V106, P7314, DOI 10.1073/pnas.0812020106 Lane MC, 2006, J BIOL CHEM, V281, P508, DOI 10.1074/jbc.M507238200 Li L., 2007, ADV NEURAL INFORM PR, V19, P865 Li Z, 2009, FOREST RERANKING MAC LIU F, 2010, IEEE T AUDIO SPEECH Liu Q, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-433 LIU Y, 2008, P ICASSP, V8 MCCULLAGH P, 1980, J ROY STAT SOC B MET, V42, P109 Murphy RF, 2001, IEEE INT S BIOINF BI, P119 Papanayotou C, 2008, PLOS BIOL, V6, P109, DOI 10.1371/journal.pbio.0060002 RADEV D, 2002, P 11 INT C WORLD WID RAFKIND B, 2006, HLT NAACL BIONLP Rajaram S, 2003, LECT NOTES ARTIF INT, V2837, P301 ROBERTSON S, 2004, ACM CIKM Rosset S, 2007, KNOWL INF SYST, V12, P331, DOI 10.1007/s10115-006-0037-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shashua A., 2003, ADV NEURAL INFORM PR Shatkay H, 2006, BIOINFORMATICS, V22, pE446, DOI 10.1093/bioinformatics/btl235 SHEN L, 2004, P JOINT HLT NAACL C Shen L., 2003, P 7 C COMP NAT LANG, P9 Shen LB, 2005, MACH LEARN, V60, P73, DOI 10.1007/s10994-005-0918-9 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 Xu SH, 2008, BIOINFORMATICS, V24, P1968, DOI 10.1093/bioinformatics/btn340 Yan D, 2008, J BIOL CHEM, V283, P332, DOI 10.1074/jbc.M705313200 Yates R. B., 1999, MODERN INFORM RETRIE Yoneda T, 2000, J BIOL CHEM, V275, P11114, DOI 10.1074/jbc.275.15.11114 Yu H, 2006, BIOINFORMATICS, V22, pE547, DOI 10.1093/bioinformatics/btl261 Yu Hong, 2009, J Biomed Discov Collab, V4, P1, DOI 10.1186/1747-5333-4-1 YU H, 2006, HLT NAACL Yu Hong, 2006, AMIA Annu Symp Proc, P834 Zhou JX, 2009, P NATL ACAD SCI USA, V106, P7840, DOI 10.1073/pnas.0901854106 ZHOU Z, 2006, P ICASSP, V1 NR 52 TC 4 Z9 5 PU PUBLIC LIBRARY SCIENCE PI SAN FRANCISCO PA 185 BERRY ST, STE 1300, SAN FRANCISCO, CA 94107 USA SN 1932-6203 J9 PLOS ONE JI PLoS One PD OCT 7 PY 2010 VL 5 IS 10 AR e12983 DI 10.1371/journal.pone.0012983 PG 12 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 660IN UT WOS:000282633700002 PM 20949102 ER PT J AU Basili, R Pennacchiotti, M AF Basili, R. Pennacchiotti, M. TI Distributional lexical semantics: Toward uniform representation paradigms for advanced acquisition and processing tasks SO NATURAL LANGUAGE ENGINEERING LA English DT Article ID NONLINEAR DIMENSIONALITY REDUCTION; INFORMATION-RETRIEVAL; TEXT CLASSIFICATION; GEOMETRIC FRAMEWORK; COOCCURRENCE; KERNELS; LOGIC; MODELS C1 [Basili, R.] Univ Roma Tor Vergata, I-00133 Rome, Italy. [Pennacchiotti, M.] Yahoo Inc, Santa Clara, CA USA. RP Basili, R (reprint author), Univ Roma Tor Vergata, Via Ric Sci, I-00133 Rome, Italy. CR BASILI R, 2007, P REC ADV NAT LANG P Belkin M, 2006, J MACH LEARN RES, V7, P2399 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 Bloehdorn S, 2006, IEEE DATA MINING, P808 Bloehdorn S, 2007, LECT NOTES COMPUT SC, V4425, P307 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020 CHIARAMELLA Y, 1992, COMPUT J, V35, P233, DOI 10.1093/comjnl/35.3.233 Church K. W., 1993, COMPUTATIONAL LINGUI, V19, P1 Collins M., 2002, ACL02, P263 Cristianini N., 2000, INTRO SUPPORT VECTOR Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Erk K., 2008, EMNLP 08, P897 FENSTAD JE, 1999, COLLECTION PAPER 50 Firth J. R., 1957, STUDIES LINGUISTIC A, P1 FURNAS GW, 1988, P SIGIR 88 Gardenfors P, 2004, CONCEPTUAL SPACES GE Globerson A, 2007, J MACH LEARN RES, V8, P2265 Harris Z., 1968, MATH STRUCTURES LANG Haussler D., 1999, UCSCCRL9910 He X., 2003, P ADV NEUR INF PROC Hofmann T., 1999, P UNC ART INT UAI 99 Kintsch W, 2000, PSYCHON B REV, V7, P257, DOI 10.3758/BF03212981 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lin Dekang, 2001, P ACM C KNOWL DISC D Lin Dekang, 1998, P JOINT INT C COMP L LOWE W, 2000, COGSCI 2000 L ERLB A, P675 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Mitchell TM, 2008, SCIENCE, V320, P1191, DOI 10.1126/science.1152876 Moschitti A., 2008, COMPUTATIONAL LINGUI, V34 Moschitti A., 2004, P 42 ANN M ASS COMP, P335, DOI 10.3115/1218955.1218998 O Seaghdha D., 2008, P 22 INT C COMP LING, P649 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 PANTEL P, 2003, P HUM LANG TECHN N A PANTEL P, 2007, P HLT NAACL ROCH NY PENNACCHIOTTI M, 2008, P EMP METH NAT LANG Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] Pollard Carl J., 1994, HEAD DRIVEN PHRASE S Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323 Sahlgren M., 2006, THESIS STOCKHOLM U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saul L.K., 2003, J MACHINE LEARNING R, V4, P119, DOI DOI 10.1162/153244304322972667 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Sindhwani V., 2006, SEMISUPERVISED LEARN, P217 SINDHWANI V, 2008, P IEEE ICDM Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Van der Plas L., 2008, THESIS U GRONINGEN G Van Rijsbergen K., 2004, GEOMETRY INFORM RETR VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN K, 1996, J AMERICAN SOC INFOR, V47, P385 Vapnik V., 1995, NATURE STAT LEARNING Varadarajan V S, 1985, GEOMETRY QUANTUM THE, V2nd WEINBERGER KQ, 2008, NIPS, P1737 Widdows Dominic, 2004, GEOMETRY AND MEANING Wittgenstein Ludwig, 1953, PHILOS INVESTIGATION NR 56 TC 1 Z9 1 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 J9 NAT LANG ENG JI Nat. Lang. Eng. PD OCT PY 2010 VL 16 SI SI BP 347 EP 358 DI 10.1017/S1351324910000112 PN 4 PG 12 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 675NF UT WOS:000283836700001 ER PT J AU Yu, WD Yang, JB Tseng, JCR Liu, SJ Wu, JW AF Yu, Wen-der Yang, Jyh-bin Tseng, Judy C. R. Liu, Shen-jung Wu, Ji-wei TI Proactive problem-solver for construction SO AUTOMATION IN CONSTRUCTION LA English DT Article DE Knowledge management; Problem-solving; Knowledge map AB Construction is an experience-based discipline. Knowledge or experience accumulated from previous projects plays a very important role in successful performance of new works. More and more construction organizations have adopted commercial Knowledge Management Systems (KMSs) to develop their own Knowledge Management (KM) functionalities. Most of the existing KMSs adopt Communities of Practice (CoPs) for knowledge sharing and exchange. Such an approach is found on the reactive problem-solver (RPS); that is, the problem raised by the questioner in the CoP has to "wait" for the "solution knower" to respond (or reply). Previous research indicated that the RPS approach may suffer in poor time and cost effectiveness. This paper proposes a Proactive Problem-Solver (PPS) approach for the problems encountered in construction engineering and management. Unlike RPS, the PPS proactively solves the problem based on lessons learned from previous projects. Should the solution be not available; the PPS dispatches the problem to the most appropriate domain experts so that the problem can be tackled timely and efficiently. A case A/E consulting firm is selected for implementation of the proposed PPS to demonstrate its applicability. It is shown that the proposed PPS improves more than 89.5% of efficiency both for timeliness and cost-saving of problem-solving. The proposed PPS demonstrates great potentials for improvement of emergent problem solving and enhancement of market competitiveness of a construction organization. (C) 2010 Elsevier B.V. All rights reserved. C1 [Yu, Wen-der; Yang, Jyh-bin] Chung Hua Univ, Inst Const Management, Hsinchu, Taiwan. [Tseng, Judy C. R.] Chung Hua Univ, Dept Comp Sci & Informat Engn, Hsinchu, Taiwan. [Liu, Shen-jung] Natl Taiwan Univ, Dept Civil Engn, Taipei 10764, Taiwan. [Wu, Ji-wei] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu, Taiwan. RP Yu, WD (reprint author), Chung Hua Univ, Inst Const Management, Hsinchu, Taiwan. EM wenderyu@chu.edu.tw FU National Science Council, Taiwan [NSC 97-2221-E-216-039] FX The funding of this research project was partially supported by the National Science Council, Taiwan, under project No. NSC 97-2221-E-216-039. Sincere appreciations are given to the sponsor by the authors. The valuable case study information presented in this paper was provided by CECI Engineering Consultants, Inc., Taipei. The authors would like to express sincere appreciations to Mr. Chen, G. L. and other staffs of the Department of Business and Research and Department of Information Systems, CECI Engineering Consultants, Inc., Taipei, Taiwan. CR Altshuller Genrich, 2000, INNOVATION ALGORITHM Caldas CH, 2002, J COMPUT CIVIL ENG, V16, P234, DOI 10.1061/(ASCE)0887-3801(2002)16:4(234) CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 *CONSTR IND I, 1993, PUBL CONSTR IND I, V341 FISHER D, 1998, 12311 CONSTR IND I H Li, 1998, CONSTRUCTION MANAGEM, V16, P721, DOI 10. 1080/014461998372015 IOANNOU PG, 1992, 84 CONSTR IND I JOHANSSON F, 2002, MEDICI EFFECT BREAKT KARTAM N, 1994, P COMP CIV ENG NEW Y, P1139 Kartam N., 1997, J PERFORM CONSTR FAC, V11, P178, DOI 10.1061/(ASCE)0887-3828(1997)11:4(178) Kim S, 2003, J KNOWLEDGE MANAGEME, V7, P34, DOI 10.1108/13673270310477270 Loss J., 1987, J PERFORMANCE CONSTR, V1, P11 Ma W. Y., 2003, P ACL 2 SIGHAN WORKS, P168 MCCULLOUCH B, 1994, P 1 C COMP CIV ENG, P1397 Mezher T, 2005, J MANAGE ENG, V21, P138, DOI 10.1061/(ASCE)0742-597X(2005)21:3(138) Mohamed Y, 2005, J CONSTR ENG M ASCE, V131, P993, DOI 10.1061/(ASCE)0733-9364(2005)131:9(993) NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TSENG JCR, 2007, ELECTRON COMMER R A, P18 WALTZ DL, 1978, COMMUN ACM, V21, P526, DOI 10.1145/359545.359550 Wenger E., 1991, SITUATED LEARNING LE Wu J.W., 2007, P 2007 WORLD C ED MU Yu W., 2009, CONSTR MANAGE EC, V27, P733, DOI DOI 10.1080/01446190903074978 YU WD, 2006, P INT S AUT ROB CONS YU WD, 2008, P INT S AUT ROB CONS YU WD, 2008, P 6 INT C ENG COMP T YU WD, 2007, P CME 25 C U READ RE NR 28 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0926-5805 J9 AUTOMAT CONSTR JI Autom. Constr. PD OCT PY 2010 VL 19 IS 6 BP 808 EP 816 DI 10.1016/j.autcon.2010.05.003 PG 9 WC Construction & Building Technology; Engineering, Civil SC Construction & Building Technology; Engineering GA 645CG UT WOS:000281427900015 ER PT J AU Suh, JH Park, CH Jeon, SH AF Suh, Jong Hwan Park, Chung Hoon Jeon, Si Hyun TI Applying text and data mining techniques to forecasting the trend of petitions filed to e-People SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Text mining; Data mining; Petition; Keyword extracting; Document clustering; Forecasting; e-Government; Open Innovation; e-People ID BACKPROPAGATION NEURAL-NETWORK; RADIAL BASIS FUNCTION; K-MEANS ALGORITHM; MARKET-SEGMENTATION; DECISION TREE; MACHINE; INTEGRATION; PREDICTION; ENSEMBLE; MODEL AB As the Internet has been the virtual place where citizens are united and their opinions are promptly shifted into the action, two way communications between the government sector and the citizen have been more important among activities of e-Government. Hence, Anti-corruption and Civil Rights Commission (ACRC) in the Republic of Korea has constructed the online petition portal system named e-People. In addition, the nation's Open Innovation through e-People has gained increasing attention. That is because e-People can be applied for the virtual space where citizens participate in improving the national law and policy by simply filing petitions to e-People as the voice of the nation. However, currently there are problems and challenging issues to be solved until e-People can function as the virtual space for the nation's Open Innovation based on petitions collected from citizens. First, there is no objective and systematic method for analyzing a large number of petitions filed to e-People without a lot of manual works of petition inspectors. Second, e-People is required to forecast the trend of petitions filed to e-People more accurately and quickly than petition inspectors for making a better decision on the national law and policy strategy. Therefore, in this paper, we propose the framework of applying text and data mining techniques not only to analyze a large number of petitions filed to e-People but also to predict the trend of petitions. In detail, we apply text mining techniques to unstructured data of petitions to elicit keywords from petitions and identify groups of petitions with the elicited keywords. Moreover, we apply data mining techniques to structured data of the identified petition groups on purpose to forecast the trend of petitions. Our approach based on applying text and data mining techniques decreases time-consuming manual works on reading and classifying a large number of petitions, and contributes to increasing accuracy in evaluating the trend of petitions. Eventually, it helps petition inspectors to give more attention on detecting and tracking important groups of petitions that possibly grow as nationwide problems. Further, the petitions ordered by their petition groups' trend values can be used as the baseline for making a better decision on the national law and policy strategy. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Suh, Jong Hwan] SAMSUNG SDS, Informat & Commun Technol Res & Dev Ctr, Seoul 135798, South Korea. [Park, Chung Hoon] SAMSUNG SDS, Consulting Div, Seoul 135918, South Korea. [Jeon, Si Hyun] ACRC, Seoul 125705, South Korea. RP Suh, JH (reprint author), SAMSUNG SDS, Informat & Commun Technol Res & Dev Ctr, 159-1 Samseong Dong, Seoul 135798, South Korea. EM jonghwan.suh@gmail.com CR *ACRC, 2008, DEM CONT SESS E CHAL Akhlaghi Y, 2005, ANAL CHIM ACTA, V537, P331, DOI 10.1016/j.aca.2004.12.079 Alfaro E, 2008, DECIS SUPPORT SYST, V45, P110, DOI 10.1016/j.dss.2007.12.002 Aliguliyev RM, 2009, EXPERT SYST APPL, V36, P7904, DOI 10.1016/j.eswa.2008.11.017 Atkinson R. D., 2008, DIGITAL QUALITY LIFE Berry M. J. A., 2000, MASTERING DATA MININ Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Bose I, 2001, INFORM MANAGE-AMSTER, V39, P211, DOI 10.1016/S0378-7206(01)00091-X Chang CL, 2009, EXPERT SYST APPL, V36, P4035, DOI 10.1016/j.eswa.2008.03.007 Chau M, 2006, DECIS SUPPORT SYST, V42, P1216, DOI 10.1016/j.dss.2006.01.004 CHEN LD, 2000, INFORM SYSTEMS MANAG, V17, P1 Chen T, 2007, FUZZY SET SYST, V158, P2153, DOI 10.1016/j.fss.2007.04.013 Chen Y, 2008, EXPERT SYST APPL, V35, P581, DOI 10.1016/j.eswa.2007.07.015 Chesbrough H. W., 2003, OPEN INNOVATION NEW CHOU PA, 1991, IEEE T PATTERN ANAL, V13, P340, DOI 10.1109/34.88569 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Delen D, 2008, EXPERT SYST APPL, V34, P1707, DOI 10.1016/j.eswa.2007.01.035 Deng WJ, 2008, EXPERT SYST APPL, V34, P1115, DOI 10.1016/j.eswa.2006.12.016 DERKS EPPA, 1995, CHEMOMETR INTELL LAB, V28, P49, DOI 10.1016/0169-7439(94)00082-T Enke D, 2005, EXPERT SYST APPL, V29, P927, DOI 10.1016/j.eswa.2005.06.024 Evans D, 2006, GOV INFORM Q, V23, P207, DOI 10.1016/j.giq.2005.11.004 Fan W., 2006, COMMUN ACM, V49, P76, DOI DOI 10.1145/1151030.1151032 Fayyad U, 1996, AI MAG, V17, P37 Guzella TS, 2009, EXPERT SYST APPL, V36, P10206, DOI 10.1016/j.eswa.2009.02.037 Kamber M., 2001, DATA MINING CONCEPTS Kiang MY, 2009, DECIS SUPPORT SYST, V47, P51, DOI 10.1016/j.dss.2008.12.010 Kohonen T., 1995, SELF ORG MAPS Kuo RJ, 2002, COMPUT OPER RES, V29, P1475, DOI 10.1016/S0305-0548(01)00043-0 Kuo RJ, 2006, EXPERT SYST APPL, V30, P313, DOI 10.1016/j.eswa.2005.07.036 Lam M, 2004, DECIS SUPPORT SYST, V37, P567, DOI 10.1016/S0167-9236(03)00088-5 Larose D. T., 2005, DISCOVERING KNOWLEDG LEE JA, 2004, STRATEGY IMPLEMENTIN Lee TL, 2004, OCEAN ENG, V31, P225, DOI 10.1016/S0029-8018(03)00115-X Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 Marchionini G., 2003, COMMUN ACM, V46, P25 PALVIA SCJ, 2007, E GOVT E GOVERNANCE, P1 PANDA SS, 2007, APPL SOFT COMPUT, V29, P927 Roussinov DG, 1999, DECIS SUPPORT SYST, V27, P67, DOI 10.1016/S0167-9236(99)00037-8 SAHIN F, ETD6197223641 VIRG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCOTT D, 1990, J AM SOC INFORM SCI, V41, P391 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Strauch M., 2007, J INTEGRATIVE BIOINF, V4, P54 Sun ZL, 2008, DECIS SUPPORT SYST, V46, P411, DOI 10.1016/j.dss.2008.07.009 Tsaih R, 1998, DECIS SUPPORT SYST, V23, P161, DOI 10.1016/S0167-9236(98)00028-1 Ture M, 2009, EXPERT SYST APPL, V36, P2017, DOI 10.1016/j.eswa.2007.12.002 *UN, 2003, BENCHM E GOV GLOB PE VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X Walczak B, 2000, CHEMOMETR INTELL LAB, V50, P179, DOI 10.1016/S0169-7439(99)00056-8 Wang JB, 2006, LECT NOTES ARTIF INT, V3930, P633 Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Wei CP, 2006, INFORM PROCESS MANAG, V42, P350, DOI 10.1016/j.ipm.2005.06.008 Weng SS, 2003, EXPERT SYST APPL, V25, P355, DOI 10.1016/S0957-4174(03)00076-9 Wu HF, 2006, CHINESE J ANAL CHEM, V34, P21, DOI 10.1016/S1872-2040(06)60004-2 Wu X., 2006, KNOWL INF SYST, V14, P1 Yang HC, 2005, EXPERT SYST APPL, V29, P723, DOI 10.1016/j.eswa.2005.05.003 Yang Y., 1997, 14 INT C MACH LEARN, P412 Yu L, 2008, NEUROCOMPUTING, V71, P3295, DOI 10.1016/j.neucom.2008.04.029 Zhang DS, 2004, IEEE T SYST MAN CY C, V34, P513, DOI 10.1109/TSMCC.2004.829279 NR 59 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT PY 2010 VL 37 IS 10 BP 7255 EP 7268 DI 10.1016/j.eswa.2010.04.002 PG 14 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 619DA UT WOS:000279408200062 ER PT J AU Hao, YN Zhang, YC Cao, JL AF Hao, Yanan Zhang, Yanchun Cao, Jinli TI Web services discovery and rank: An information retrieval approach SO FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING-THEORY METHODS AND APPLICATIONS LA English DT Article DE Web service; Discovery; Ranking ID SEARCH AB With the rapid development of e-commerce over the Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. They can then dynamically combine individual services to provide new value-added services. A main problem that remains is how to discover desired web services. In this paper, we propose a novel IR-Style mechanism for discovering and ranking web services automatically, given a textual description of desired services. In particular, we introduce the notion of preference degree for web services and then we define service relevance and service importance as two desired properties for measuring the preference degree. Furthermore, various algorithms are given for computing the relevance and importance of services, respectively. At the same time, we also develop a new schema tree matching algorithm to measure service connectivity, which is a novel metric to evaluate the importance of services. Experimental results show the proposed IR-style search strategy is efficient and practical. (c) 2010 Elsevier B.V. All rights reserved. C1 [Hao, Yanan; Zhang, Yanchun] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia. [Cao, Jinli] La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia. RP Hao, YN (reprint author), Victoria Univ, Sch Sci & Engn, POB 14428, Melbourne, Vic 8001, Australia. EM yanan.hao1@live.vu.edu.au CR Abramson D, 2005, FUTURE GENER COMP SY, V21, P69, DOI 10.1016/j.future.2004.09.025 Agrawal S., 2002, P INT C DAT ENG ICDE Al-Masri E., 2007, IEEE 16 INT C COMP C, P529 Al-Mastri E., 2008, WWW 2008, P795 Bhiri S., 2005, P 14 INT C WORLD WID, P138, DOI 10.1145/1060745.1060769 Booth D., 2004, WEB SERVICES ARCHITE Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X CAVERLEE J, 2004, INT C SERV OR COMP, P153 Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 HE Q, 2008, IEEE SCC, P221 Hristidis V, 2003, P 29 INT C VER LARG, P850, DOI 10.1016/B978-012722442-8/50080-X Hristidis V., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases Kaufman L, 1990, FINDING GROUPS DATA Liu YF, 2005, FUTURE GENER COMP SY, V21, P583, DOI 10.1016/j.future.2001.12.001 Moormann A. Zaremski, 1997, ACM T SOFTW ENG METH, V6, P333, DOI 10.1145/261640.261641 Nierman A., 2002, WEBDB, P61 Olejnik R, 2009, FUTURE GENER COMP SY, V25, P436, DOI 10.1016/j.future.2008.09.011 Power DJ, 2006, FUTURE GENER COMP SY, V22, P547, DOI 10.1016/j.future.2005.09.003 Pu K., 2006, P INT C DAT ENG, P31 Pullen JM, 2005, FUTURE GENER COMP SY, V21, P97, DOI 10.1016/j.future.2004.09.031 Quiroz A, 2008, FUTURE GENER COMP SY, V24, P452, DOI 10.1016/j.future.2007.07.001 Reis D., 2004, P 13 INT C WORLD WID, P502, DOI 10.1145/988672.988740 ROMAN D, 2005, 10 WSMO SAJJANHAR A, 2004, P APWEB, V3007, P665, DOI DOI 10.1007/978-3-540-24655-8_72 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Z., 2005, P 2005 IEEE INT C SE, V01, P279 Sycara K, 2002, AUTON AGENT MULTI-AG, V5, P173, DOI 10.1023/A:1014897210525 WANG Y, 2003, P INT C WEB INF SYST Xie T, 2006, LECT NOTES COMPUT SC, V3841, P319 Yu C., 2006, VLDB, P319 NR 30 TC 14 Z9 16 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-739X J9 FUTURE GENER COMP SY JI Futur. Gener. Comp. Syst. PD OCT PY 2010 VL 26 IS 8 BP 1053 EP 1062 DI 10.1016/j.future.2010.04.012 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA 646BA UT WOS:000281508700001 ER PT J AU Karapanos, E Zimmerman, J Forlizzi, J Martens, JB AF Karapanos, Evangelos Zimmerman, John Forlizzi, Jodi Martens, Jean-Bernard TI Measuring the dynamics of remembered experience over time SO INTERACTING WITH COMPUTERS LA English DT Article DE User experience; Experience-centered design; Qualitative methods; Longitudinal methodology ID TECHNOLOGY; ACCEPTANCE; MODEL; LIFE AB A wealth of studies in the field of user experience have tried to conceptualize new measures of product quality and inquire into how the overall goodness of a product is formed on the basis of product quality perceptions. An interesting question relates to how the perception as well as the relative dominance of different product qualities evolve across different phases in the adoption of a product. However, temporality of experience poses substantial challenges to traditional reductive evaluation approaches. In this paper we present an alternative methodological approach for studying how users' experiences with interactive products develop over time. The approach lies in the elicitation of rich qualitative insights in the form of experience narratives, combined with content-analytical approaches for the aggregation of idiosyncratic insights into generalized knowledge. We describe a tool designed for eliciting rich experience narratives retrospectively, and illustrate this tool by means of a study that inquired into how users' experiences with mobile phones change over the first 6 months of use. We use the insights of the study to validate and extend a framework of temporality proposed by Karapanos et al. (2009b). (C) 2010 Elsevier By. All rights reserved. C1 [Karapanos, Evangelos; Martens, Jean-Bernard] Eindhoven Univ Technol, Dept Ind Design, NL-5600 MB Eindhoven, Netherlands. [Zimmerman, John; Forlizzi, Jodi] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15213 USA. [Zimmerman, John; Forlizzi, Jodi] Carnegie Mellon Univ, Sch Design, Pittsburgh, PA 15213 USA. RP Karapanos, E (reprint author), Eindhoven Univ Technol, Dept Ind Design, POB 513, NL-5600 MB Eindhoven, Netherlands. EM E.Karapanos@tue.nl RI Karapanos, Evangelos/; Zimmerman, John/ OI Karapanos, Evangelos/0000-0001-5910-4996; Zimmerman, John/0000-0001-5299-8157 CR Barsalou L. W., 1988, REMEMBERING RECONSID, P193, DOI DOI 10.1017/CBO9780511664014.009 Bolger N, 2003, ANNU REV PSYCHOL, V54, P579, DOI 10.1146/annurev.psych.54.101601.145030 Christianson S.-A., 1992, HDB EMOTION MEMORY R Corbin J. M., 1998, BASICS QUALITATIVE R, V2nd DAVIS FD, 1989, MANAGE SCI, V35, P982, DOI 10.1287/mnsc.35.8.982 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENOUDEN E, 2006, THESIS EINDHOVEN U T Fleiss JL, 2003, STAT METHODS RATES P Gefen D., 2000, J ASSOC INF SYST, V1, P1 Hassenzahl M, 2004, HUM-COMPUT INTERACT, V19, P319, DOI 10.1207/s15327051hci1904_2 Hektner J. M., 2007, EXPERIENCE SAMPLING Hornbaek K, 2008, HUM-COMPUT INT-SPRIN, P267, DOI 10.1007/978-1-84628-941-5_12 Hsieh HF, 2005, QUAL HEALTH RES, V15, P1277, DOI 10.1177/1049732305276687 Kahneman D, 2004, SCIENCE, V306, P1776, DOI 10.1126/science.1103572 Kahneman D., 1982, JUDGMENT UNCERTAINTY Karapanos E., 2008, CHI 08, P3561 Karapanos E, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P729 Karapanos E., 2009, Reconstructing Experiences through Sketching Koriat A, 2000, ANNU REV PSYCHOL, V51, P481, DOI 10.1146/annurev.psych.51.1.481 Larsen K. R., 2008, P 41 ANN HAW INT C S, DOI 10.1109/HICSS.2008.165 Lee Y, 2003, COMMUNICATIONS ASS I, V12, P752, DOI DOI 10.1016/S0318-7206(01)00143-4 Norman D. A., 2009, INTERACTIONS, V16, P24 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanford AJS, 2006, DISCOURSE PROCESS, V42, P109, DOI 10.1207/s15326950dp4202_2 Schwarz N, 2007, APPL COGNITIVE PSYCH, V21, P277, DOI 10.1002/acp.1340 Silverstone Roger, 1996, COMMUNICATION DESIGN, P44 Suri J, 2002, PLEASURE PRODUCTS US, P161 Venkatesh V, 2000, MANAGE SCI, V46, P186, DOI 10.1287/mnsc.46.2.186.11926 NR 28 TC 12 Z9 12 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0953-5438 J9 INTERACT COMPUT JI Interact. Comput. PD SEP PY 2010 VL 22 IS 5 SI SI BP 328 EP 335 DI 10.1016/j.intcom.2010.04.003 PG 8 WC Computer Science, Cybernetics; Ergonomics SC Computer Science; Engineering GA 640UB UT WOS:000281077600003 ER PT J AU Cota, RG Ferreira, AA Nascimento, C Goncalves, MA Laender, AHF AF Cota, Ricardo G. Ferreira, Anderson A. Nascimento, Cristiano Goncalves, Marcos Andre Laender, Alberto H. F. TI An Unsupervised Heuristic-Based Hierarchical Method for Name Disambiguation in Bibliographic Citations SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID MODEL AB Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. C1 [Cota, Ricardo G.; Ferreira, Anderson A.; Nascimento, Cristiano; Goncalves, Marcos Andre; Laender, Alberto H. F.] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil. RP Cota, RG (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, Av Antonio Carlos 6627, BR-31270010 Belo Horizonte, MG, Brazil. EM ricardo@dcc.ufmg.br; ferreira@dcc.ufmg.br; crist@dcc.ufmg.br; mgoncalv@dcc.ufmg.br; laender@dcc.ufmg.br RI InWeb, Inct/J-9839-2013 FU MCT/CNPq/CT-INFO [551.013/2005-2, 55.0874/2007-0]; InWeb - The National Institute of Science and Technology for the Web (MCT/CNPq/FAPEMIG) [573871/2008-6]; CAPES; CNPq; FAPEMIG FX This research is partially funded by the MCT/CNPq/CT-INFO projects 5S-VQ (grant number 551.013/2005-2) and InfoWeb (grant number 55.0874/2007-0), by InWeb - The National Institute of Science and Technology for the Web (MCT/CNPq/FAPEMIG grant number 573871/2008-6), and by the authors' individual research grants from CAPES, CNPq. and FAPEMIG. The authors also thank the anonymous reviewer for many comments that helped to improve this article. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bekkerman R, 2005, P 14 INT C WORLD WID, P463, DOI 10.1145/1060745.1060813 Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI 10.1145/1217299.1217304 BHATTACHARYA I, 2006, 6 SIAM INT C DAT MIN Bordes A, 2005, J MACH LEARN RES, V6, P1579 Chang C.-C., 2001, LIBSVM LIB SUPPORT V CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cota R., 2007, P 22 BRAZ S DAT JOAO, P20 Culotta A., 2007, 6 INT WORKSH INF INT Dhillon Inderjit, 2005, P 11 ACM SIGKDD INT, P629, DOI 10.1145/1081870.1081948 Ester M., 1996, P 2 INT C KNOWL DISC, P226 Galvez C, 2007, J AM SOC INF SCI TEC, V58, P1960, DOI 10.1002/asi.20671 Geisser S, 1993, PREDICTIVE INFERENCE Goncalves MA, 2004, ACM T INFORM SYST, V22, P270, DOI 10.1145/984321.984325 Han H, 2005, PROCEEDINGS OF THE 5TH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, PROCEEDINGS, P334, DOI 10.1145/1065385.1065462 Han H., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996419 Han H., 2005, P 2005 ACM S APPL CO, P1065, DOI 10.1145/1066677.1066920 Huang J, 2006, LECT NOTES ARTIF INT, V4213, P536 KANANI P, 2007, P 20 INT JOINT C ART, P429 Kang IS, 2009, INFORM PROCESS MANAG, V45, P84, DOI 10.1016/j.ipm.2008.06.006 Lagoze C., 2001, P 1 ACM IEEE CS JOIN, P54, DOI 10.1145/379437.379449 LAPIDOT I, 2002, SELF ORG MAPS BIC SP Lee D, 2007, COMMUN ACM, V50, P33, DOI 10.1145/1323688.1323690 Lee D., 2005, P 2 INT WORKSH INF Q, P69, DOI 10.1145/1077501.1077514 Ley M., 2002, LNCS, V2476, P1, DOI DOI 10.1007/3-540-45735-6_1 Liming L, 2005, SCIENTOMETRICS, V26, P263 Malin B., 2005, P SIAM WORKSH LINK A, P93 Mitchell TM, 1997, MACHINE LEARNING OLIVEIRA JWA, 2005, P 20 BRAZ S DAT, P205 On BW, 2007, PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, P575 On B.-W., 2006, P 6 ACM IEEE CS JOIN, P51, DOI 10.1145/1141753.1141761 On BW, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P344, DOI 10.1145/1065385.1065463 PEREIRA DA, 2009, P 9 ACM IEEE CS JOIN, P49, DOI 10.1145/1555400.1555409 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smalheiser NR, 2009, ANNU REV INFORM SCI, V43, P287 Soler JM, 2007, SCIENTOMETRICS, V72, P281, DOI 10.1007/s11192-007-1730-z Song Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P342, DOI 10.1145/1255175.1255243 Torvik VI, 2005, J AM SOC INF SCI TEC, V56, P140, DOI [10.1002/asi.20105, 10.1002/asi/20105] Torvik V.I., 2009, ACM T KNOWL DISCOV D, V3, P1 Treeratpituk P., 2009, P 9 ACM IEEE CS JOIN, P39, DOI 10.1145/1555400.1555408 Vu Q.M., 2007, P 2007 ACM S APPL CO, P839, DOI 10.1145/1244002.1244188 Yang KH, 2008, LECT NOTES COMPUT SC, V5173, P185 Zha H., 2001, NIPS, P1057 NR 45 TC 23 Z9 24 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD SEP PY 2010 VL 61 IS 9 BP 1853 EP 1870 DI 10.1002/asi.21363 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 639XW UT WOS:000281011400010 ER PT J AU Ozgur, L Gungor, T AF Oezguer, Levent Geungoer, Tunga TI Text classification with the support of pruned dependency patterns SO PATTERN RECOGNITION LETTERS LA English DT Article DE Text classification; Lexical dependency; Pruning analysis; Reuters-21578 AB We propose a novel text classification approach based on two main concepts, lexical dependency and pruning. We extend the standard bag-of-words method by including dependency patterns in the feature vector. We perform experiments with 37 lexical dependencies and the effect of each dependency type is analyzed separately in order to identify the most discriminative dependencies. We analyze the effect of pruning (filtering features with low frequencies) for both word features and dependency features. Parameter tuning is performed with eight different pruning levels to determine the optimal levels. The experiments were repeated on three datasets with different characteristics. We observed a significant improvement on the success rates as well as a reduction on the dimensionality of the feature vector. We argue that, in contrast to the works in the literature, a much higher pruning level should be used in text classification. By analyzing the results from the dataset perspective, we also show that datasets in similar formality levels have similar leading dependencies and show close behavior with varying pruning levels. (C) 2010 Elsevier B.V. All rights reserved. C1 [Oezguer, Levent; Geungoer, Tunga] Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey. RP Gungor, T (reprint author), Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey. EM ozgurlev@boun.edu.tr; gungort@boun.edu.tr FU Bogazici University [05A103D] FX This work has been supported by the Bogazici University Research Fund under the Grant No. 05A103D. CR ASUNCION A, 2007, UCI MACHING LEARNING BASILI R, 2000, P INT C AD PERS FUS, P908 CAHILL A, 2009, 14 INT LFG C CAMBR CHARNIAK E, 2003, P MT SUMM 9 INT ASS Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Furnkranz J, 1998, AAAI 98 WORKSH LEARN GHANEM MM, 2002, ACM SIGKDD, V4, P95, DOI 10.1145/772862.772876 Herrera J, 2006, LECT NOTES COMPUTER, V3944/2006 Joachims T, 1999, ADV KERNEL METHODS S Konig A.C., 2006, P 12 ACM SIGKDD C KN, P598, DOI 10.1145/1150402.1150474 Larson R, 2000, ELEMENTARY STAT PICT LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Liu T., 2004, P 4 IEEE INT C DAT M Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Manning C.D., 2008, INTRO INFORM RETRIEV Manning Christopher D., 2003, ADV NEURAL INFORM PR, V15 Marneffe M, 2006, P 5 INT C LANG RES E, P449 Montgomery D. C., 2001, DESIGN ANAL EXPT Moschitti A., 2008, P ACM 17 C INF KNOWL MOSCHITTI A, 2004, P 26 EUR C INF RETR, P181 MULLEN T., 2004, P C EMP METH NAT LAN NASTASE V, 2006, P 19 CAN C ART INT Q Ozgur A, 2005, LECT NOTES COMPUT SC, V3733, P606 OZGUR L, 2009, P 10 INT C INT TEXT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stevenson M, 2005, P 43 ANN M ACL ANN A Stevenson M., 2006, P WORKSH INF EXTR DO, P12, DOI 10.3115/1641408.1641410 WANG Z, 2006, P 3 INT S NEUR NETW, P1381 Wellner B., 2006, P 7 SIGDIAL WORKSH D, P117, DOI 10.3115/1654595.1654618 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 33 TC 7 Z9 7 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-8655 J9 PATTERN RECOGN LETT JI Pattern Recognit. Lett. PD SEP 1 PY 2010 VL 31 IS 12 SI SI BP 1598 EP 1607 DI 10.1016/j.patrec.2010.05.005 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA 644IJ UT WOS:000281368200014 ER PT J AU Pak, AN Chung, CW AF Pak, Alexander N. Chung, Chin-Wan TI A Wikipedia Matching Approach to Contextual Advertising SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE contextual advertising; wikipedia matching AB Contextual advertising is an important part of today's Web. It provides benefits to all parties: Web site owners and an advertising platform share the revenue, advertisers receive new customers, and Web site visitors get useful reference links. The relevance of selected ads for a Web page is essential for the whole system to work. Problems such as homonymy and polysemy, low intersection of keywords and context mismatch can lead to the selection of irrelevant ads. Therefore, a simple keyword matching technique gives a poor accuracy. In this paper, we propose a method for improving the relevance of contextual ads. We propose a novel "Wikipedia matching" technique that uses Wikipedia articles as "reference points" for ads selection. We show how to combine our new method with existing solutions in order to increase the overall performance. An experimental evaluation based on a set of real ads and a set of pages from news Web sites is conducted. Test results show that our proposed method performs better than existing matching strategies and using the Wikipedia matching in combination with existing approaches provides up to 50% lift in the average precision. TREC standard measure bpref-10 also confirms the positive effect of using Wikipedia matching for the effective ads selection. C1 [Pak, Alexander N.; Chung, Chin-Wan] Korea Adv Inst Sci & Technol, Dept EECS, Div Comp Sci, Taejon 305701, South Korea. RP Chung, CW (reprint author), Korea Adv Inst Sci & Technol, Dept EECS, Div Comp Sci, Taejon 305701, South Korea. EM alexpak@islab.kaist.ac.kr; chungcw@kaist.edu RI Chung, Chin-Wan/C-2029-2011 FU Ministry of Education, Science and Technology [2009-0081365] FX This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (grant number 2009-0081365). CR Anagnostopoulous A., 2007, P 16 ACM C C INF KNO, P331, DOI 10.1145/1321440.1321488 ANTHONY HJ, 2007, PROBABILITY STAT ENG Broder A., 2007, P 30 ANN INT ACM SIG Buckle JS, 2004, WA SCI TECHNOL, V4, P25 DEERWESTER S, 1988, J AM SOC INFORM SCI, V41, P491 Ding C., 2004, ICML 04, P29 Dwork C., 2001, WWW 01, P613, DOI DOI 10.1145/371920.372165 *IDC, 2009, 4Q08 IDC Jun Z., 2007, COMPREHENSIVE PERL A Manning C. D., 1999, FDN STAT NATURAL LAN Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 Porter M., 2006, PORTER STEMMING ALGO Porter M. F., 1997, READINGS INFORM RETR, P313 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sullivan D., 2003, SEARCH ENGINE WATCH Zhang Y, 2004, P 10 INT C THEOR MET, P4 2006, 15 TEXT RETR C TREC NR 18 TC 7 Z9 7 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD SEP PY 2010 VL 13 IS 3 BP 251 EP 274 DI 10.1007/s11280-010-0084-2 PG 24 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 608LL UT WOS:000278585500002 ER PT J AU Sezer, EA AF Sezer, Ebru Akcapinar TI Performance assessment of a semantic information retrieval system using stagnant and active images SO SCIENTIFIC RESEARCH AND ESSAYS LA English DT Article DE Fuzzy domain ontology; image and text retrieval; semantic querying ID ONTOLOGY; WEB; ANNOTATION AB Videos, images, graphics and text are the main multimedia data types. Generally, one document includes two or more data types because each data type enriches the meaning of document. For this reason, the documents become more complex for querying when compared with only text querying. To overcome this difficulty, some new information retrieval systems have been developed. In this study, an object action concept (OAC) based semantic information retrieval system for text and image data by using Fuzzy ontology was considered. Assessment of the performance of the retrieval systems is highly important. The purpose of this study is to assess the performance of an object action concept based semantic information retrieval system when using stagnant and active images. When considering the precision recall, precision and r(norm) parameters, it is clear that the performance of the system for the stagnant images is higher than that for the active images. C1 Hacettepe Univ, Fac Engn, Dept Comp Engn, TR-06800 Ankara, Turkey. RP Sezer, EA (reprint author), Hacettepe Univ, Fac Engn, Dept Comp Engn, TR-06800 Ankara, Turkey. EM ebru@hacettepe.edu.tr RI Sezer, Ebru Akcapinar/H-5566-2011 OI Sezer, Ebru Akcapinar/0000-0002-9287-2679 CR Allampalli-Nagaraj G, 2009, ENG APPL ARTIF INTEL, V22, P18, DOI 10.1016/j.engappai.2008.04.018 Berners-Lee T, 2001, SCI AM, V284, P34 Bhatt M, 2009, J WEB SEMANT, V7, P317, DOI 10.1016/j.websem.2009.05.004 Buitelaar P., 2006, P INT C LANG RES EV, P2321 Chang-Shing Lee, 2005, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), V35, DOI 10.1109/TSMCB.2005.845032 Diaz-Galiano MC, 2009, COMPUT BIOL MED, V39, P396, DOI 10.1016/j.compbiomed.2009.01.012 DOULAVERAKIS C, 2005, EUROCON 2005 BELGR Hernandez N, 2007, INFORM RETRIEVAL, V10, P143, DOI 10.1007/s10791-006-9018-0 Hu B, 2003, PROC INT C TOOLS ART, P77 Hudelot C, 2008, FUZZY SET SYST, V159, P1929, DOI 10.1016/j.fss.2008.02.011 HYVONEN E, 2003, INT SEPIA C FINN MUS KHAN L, 2006, MULTIMEDIA INFO SYST, V29, P56 Kim HH, 2005, J AM SOC INF SCI TEC, V56, P1167, DOI 10.1002/asi.20220 LACOSTE C, 2007, IEEE T CIRCUITS SYST, V7, P889 LAGOZE C, 2001, J DIGITAL INFORM, V2 Lee CS, 2007, DATA KNOWL ENG, V60, P547, DOI 10.1016/j.datak.2006.04.001 PARRY D, 2004, DATA MINING WEB INTE, V32, P121 Reinberger ML, 2004, LECT NOTES COMPUT SC, V3290, P600 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHOBER JP, 2004, P KI 2004 WORKSH APP Schreiber AT, 2001, IEEE INTELL SYST APP, V16, P66, DOI 10.1109/5254.940028 SEZER E, 2006, THESIS HACETTEPE U Sezer E, 2006, LECT NOTES COMPUT SC, V4243, P158 SONG JF, 2005, IEEE COMPUTER SOC, P152 SUGMARAN V, 2002, DATA KHOWL ENG, V42, P251 Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455 Wei W, 2007, Proceedings of the Fifth IASTED International Conference on Biomedical Engineering, P315 WIDYANTORO DH, 2001, IEEE INT FUZZ SYST C, P1291 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZINGER S, 2005, P MUSCLE IMAGECLEF W, P1 NR 30 TC 0 Z9 0 PU ACADEMIC JOURNALS PI VICTORIA ISLAND PA P O BOX 5170-00200 NAIROBI, VICTORIA ISLAND, LAGOS 73023, NIGERIA SN 1992-2248 J9 SCI RES ESSAYS JI Sci. Res. Essays PD AUG 18 PY 2010 VL 5 IS 16 BP 2099 EP 2106 PG 8 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 653AI UT WOS:000282053800002 ER PT J AU Alfred, R AF Alfred, Rayner TI FEATURE TRANSFORMATION: A GENETIC-BASED FEATURE CONSTRUCTION METHOD FOR DATA SUMMARIZATION SO COMPUTATIONAL INTELLIGENCE LA English DT Article DE feature construction; feature transformation; data summarization; genetic algorithm; clustering ID ATTRIBUTES AB The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic-based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features. C1 [Alfred, Rayner] Univ Malaysia Sabah, Sch Engn & Informat Technol, Kota Kinabalu 88999, Sabah, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Sch Engn & Informat Technol, Locked Bag 2073, Kota Kinabalu 88999, Sabah, Malaysia. EM ralfred@ums.edu.my RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR AHA DW, 1991, MACHINE LEARNING, P117 Alfred R, 2006, LECT NOTES ARTIF INT, V4093, P889 Amaldi E, 1998, THEOR COMPUT SCI, V209, P237, DOI 10.1016/S0304-3975(97)00115-1 BENSUSAN H, 1996, ICML 96 BLOCKEEL H, 2000, LECT NOTES ARTIF INT, V1866, P66 Cubero JC, 1999, INFORM SCIENCES, V121, P233, DOI 10.1016/S0020-0255(99)00104-8 DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DIETTERICH TG, 1981, ARTIF INTELL, V16, P257, DOI 10.1016/0004-3702(81)90002-3 Frank E., 1999, DATA MINING PRACTICA Freitas AA, 2001, ARTIF INTELL REV, V16, P177, DOI 10.1023/A:1011996210207 Holland J. H., 1975, ADAPTATION NATURAL A HU YJ, 1996, GENERATION ATTRIBUTE, V1, P806 Hu Y-J, 1998, P 3 ANN GEN PROGR C, P146 KIRSTEN M, 1998, 8 INT C IND LOG PROG, P261 Koller D, 1996, INT C MACH LEARN, P284 Koza J. R., 1994, STAT COMPUT, V4, P191 Kramer S, 2000, AI COMMUN, V13, P275 Krawiec K., 2002, Genetic Programming and Evolvable Machines, V3, DOI 10.1023/A:1020984725014 Lavrac N., 2001, ACM T COMPUT LOG, V2, P458, DOI 10.1145/383779.383781 Michalewicz Z, 1994, GENETIC ALGORITHMS P, V2nd OTERO FEB, 2003, EUROGP, P384 PAGALLO G, 1990, MACH LEARN, V5, P71, DOI 10.1023/A:1022611825350 Perlich C, 2006, MACH LEARN, V62, P65, DOI 10.1007/s10994-006-6064-1 Quinlan R., 1993, C4 5 PROGRAMS MACHIN Raschia G, 2002, FUZZY SET SYST, V129, P137, DOI 10.1016/S0165-0114(01)00197-X SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAFTI LS, 2003, IDA, P599 Shannon Claude E., BELL SYSTEM TECHNICA, V27, P379 Srinivas NR, 1996, J PHARM SCI, V85, P1, DOI 10.1021/js950347d Vafaie H, 1998, IEEE INTELL SYST APP, V13, P57, DOI 10.1109/5254.671093 Wiener N., 1965, CYBERNETICS CONTROL ZHENG Z, 1996, ICTAI, P254 Zheng ZJ, 2000, MACH LEARN, V40, P35, DOI 10.1023/A:1007626017208 NR 33 TC 3 Z9 3 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 0824-7935 J9 COMPUT INTELL-US JI Comput. Intell. PD AUG PY 2010 VL 26 IS 3 SI SI BP 337 EP 357 PG 21 WC Computer Science, Artificial Intelligence SC Computer Science GA 631PE UT WOS:000280359100007 ER PT J AU Alves, V Niu, N Alves, C Valenca, G AF Alves, Vander Niu, Nan Alves, Carina Valenca, George TI Requirements engineering for software product lines: A systematic literature review SO INFORMATION AND SOFTWARE TECHNOLOGY LA English DT Review DE Software product lines; Requirements engineering; Systematic literature review ID DOMAIN AB Context: Software product line engineering (SPLE) is a growing area showing promising results in research and practice. In order to foster its further development and acceptance in industry, it is necessary to assess the quality of the research so that proper evidence for adoption and validity are ensured. This holds in particular for requirements engineering (RE) within SPLE, where a growing number of approaches have been proposed. Objective: This paper focuses on RE within SPLE and has the following goals: assess research quality, synthesize evidence to suggest important implications for practice, and identify research trends, open problems, and areas for improvement. Method: A systematic literature review was conducted with three research questions and assessed 49 studies, dated from 1990 to 2009. Results: The evidence for adoption of the methods is not mature, given the primary focus on toy examples. The proposed approaches still have serious limitations in terms of rigor, credibility, and validity of their findings. Additionally, most approaches still lack tool support addressing the heterogeneity and mostly textual nature of requirements formats as well as address only the proactive SPLE adoption strategy. Conclusions: Further empirical studies should be performed with sufficient rigor to enhance the body of evidence in RE within SPLE. In this context, there is a clear need for conducting studies comparing alternative methods. In order to address scalability and popularization of the approaches, future research should be invested in tool support and in addressing combined SPLE adoption strategies. (C) 2010 Elsevier B.V. All rights reserved. C1 [Alves, Vander] Univ Brasilia, Dept Ciencia Comp, BR-70910900 Brasilia, DF, Brazil. [Niu, Nan] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA. [Alves, Carina; Valenca, George] Univ Fed Pernambuco, Ctr Informat, BR-50740540 Recife, PE, Brazil. RP Alves, V (reprint author), Univ Brasilia, Dept Ciencia Comp, Campus Darcy Ribeiro, BR-70910900 Brasilia, DF, Brazil. EM valves@unb.br; niu@cse.msstate.edu; cfa@cin.ufpe.br; gavs@cin.ufpe.br CR ALVES V, 2008, P 2008 12 INT SOFTW, P67 ATKINS D, 2004, BMJ, V328 Bosch J, 2000, DESIGN USE SOFTWARE Brereton P, 2007, J SYST SOFTWARE, V80, P571, DOI 10.1016/j.jss.2006.07.009 Chen Lianping, 2009, P 13 INT SOFTW PROD, P81 Clements P., 2001, SOFTWARE PRODUCT LIN Coplien J, 1998, IEEE SOFTWARE, V15, P37, DOI 10.1109/52.730836 DYBA T, 2008, P 2 INT S EMP SOFTW, P178, DOI DOI 10.1145/1414004.1414034 Dyba T, 2008, INFORM SOFTWARE TECH, V50, P833, DOI 10.1016/j.infsof.2008.01.006 Jacobson I, 1997, SOFTWARE REUSE ARCHI Jorgensen M, 2007, IEEE T SOFTWARE ENG, V33, P33, DOI 10.1109/TSE.2007.256943 KANG K, 1990, CMUSEI90TR21 CARN ME Khurum M, 2009, J SYST SOFTWARE, V82, P1982, DOI 10.1016/j.jss.2009.06.048 Kitchenham B, 2009, INFORM SOFTWARE TECH, V51, P7, DOI 10.1016/j.infsof.2008.09.009 Kitchenham B., 2004, TRSE0401 KEEL U COMP Kitchenham BA, 2004, PROC INT CONF SOFTW, P273, DOI 10.1109/ICSE.2004.1317449 Krueger C. W., 2001, P 4 INT WORKSH SOFTW, P282 Lisboa LB, 2010, INFORM SOFTWARE TECH, V52, P1, DOI 10.1016/j.infsof.2009.05.001 Luisa M., 2004, Requirements Engineering, V9, DOI 10.1007/s00766-003-0179-8 Mannion M, 1998, IEEE SOFTWARE, V15, P95, DOI 10.1109/52.646889 NEIVA DFS, 2009, THESIS U FEDERAL PER Nicolas J, 2009, INFORM SOFTWARE TECH, V51, P1291, DOI 10.1016/j.infsof.2009.04.001 Nuseibeh B., 2000, FUTURE SOFTWARE ENG, P35 Perry D. E., 2006, P 28 INT C SOFTW ENG, P1045, DOI 10.1145/1134285.1134497 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tichy WF, 2007, PROC INT CONF SOFTW, P163, DOI 10.1109/ICSECOMPANION.2007.33 Zave P, 1997, ACM COMPUT SURV, V29, P315, DOI 10.1145/267580.267581 NR 27 TC 22 Z9 22 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-5849 J9 INFORM SOFTWARE TECH JI Inf. Softw. Technol. PD AUG PY 2010 VL 52 IS 8 BP 806 EP 820 DI 10.1016/j.infsof.2010.03.014 PG 15 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 614DI UT WOS:000279035100003 ER PT J AU Schedl, M Pohle, T AF Schedl, Markus Pohle, Tim TI Enlightening the sun SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE User interface; Web mining; Multimedia information retrieval; Co-occurrence browser; Evaluation AB This article presents an approach to browse collections of web pages about music artists by means of descriptive terms and multimedia content. To this end, a user interface called Three-Dimensional Co-Occurrence Browser (3D-COB) is introduced. 3D-COB automatically extracts and weights terms from artist-related web pages. This textual information is complemented with information on the multimedia content found on the web pages. For the user interface of 3D-COB, we elaborated a three-dimensional extension of the Sunburst visualization technique. The hierarchical data to be visualized is obtained by analyzing the web pages for combinations of co-occurring terms that are highly ranked by a term weighting function. We further investigated, in a first user study, different term weighting strategies to generate the visualization. A second user study was carried out to assess ergonomic aspects of 3D-COB, especially its usefulness for gaining a quick overview of a set of web pages and for efficiently browsing within this set. C1 [Schedl, Markus; Pohle, Tim] Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. RP Schedl, M (reprint author), Johannes Kepler Univ Linz, Dept Computat Percept, A-4040 Linz, Austria. EM markus.schedl@jku.at; tim.pohle@jku.at FU Austrian Fonds zur Forderung der Wissenschaftlichen Forschung (FWF) [L511-N15] FX This research is supported by the Austrian Fonds zur Forderung der Wissenschaftlichen Forschung (FWF) under project number L511-N15. CR ANDREWS K, 1998, P IEEE INF VIS 1998 Dittenbach M, 2002, NEUROCOMPUTING, V48, P199, DOI 10.1016/S0925-2312(01)00655-5 ECK D, 2007, P 8 INT C MUS INF RE Friedman M, 1940, ANN MATH STAT, V11, P86, DOI 10.1214/aoms/1177731944 Geleijnse G., 2006, P 7 INT C MUS INF RE Goto Masataka, 2005, P 6 INT C MUS INF RE JOHNSON B, 1991, P 2 IEEE C VIS SAN D KNEES P, 2006, P 14 ACM C MULT 2006 Knees P., 2004, P 5 INT S MUS INF RE KOBSA A, 2004, P 10 IEEE S INF VIS Kohonen T., 2001, SPRINGER SERIES INFO, V30 Lamping J., 1995, CHI 95, P401 MORCHEN F, 2005, P 6 INT C MUS INF RE Pampalk E., 2007, P 8 INT C MUS INF RE Pampalk E., 2005, P 9 EUR C RES ADV TE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEDL M, 2006, P 7 INT C MUS INF RE Schedl M., 2007, P 9 EUR IEEE VGTC S SHESKIN D., 2004, HDB PARAMETRIC NONPA, V3rd STASKO J, 2000, P IEEE INF VIS 2000 Stone D., 2005, USER INTERFACE DESIG VIGNOLI F, 2004, P 5 INT S MUS INF RE Walter J. A., 2002, P ACM SIGKDD INT C K, P123 WHITMAN B, 2002, P 2002 INT COMP MUS *WIK, 2007, LIST FIL FORM WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968 YANG J, 2002, P IEEE S INF VIS 200 Zadel M., 2004, P 5 INT S MUS INF RE NR 28 TC 3 Z9 3 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD AUG PY 2010 VL 49 IS 1 SI SI BP 101 EP 118 DI 10.1007/s11042-009-0396-3 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 595WF UT WOS:000277643600006 ER PT J AU Bae, SH Juang, BH AF Bae, Soo Hyun Juang, Biing-Hwang TI IPSILON: Incremental Parsing for Semantic Indexing of Latent Concepts SO IEEE TRANSACTIONS ON IMAGE PROCESSING LA English DT Article DE Feature extraction; incremental parsing; image retrieval; latent semantic analysis; Lempel-Ziv; vector quantization ID IMAGE RETRIEVAL; VECTOR QUANTIZATION; RELEVANCE FEEDBACK; CLASSIFICATION; SEGMENTATION; COMPRESSION; ANNOTATION; MODEL; TOOL; GAP AB A new framework for content-based image retrieval, which takes advantage of the source characterization property of a universal source coding scheme, is investigated. Based upon a new class of multidimensional incremental parsing algorithm, extended from the Lempel-Ziv incremental parsing code, the proposed method captures the occurrence pattern of visual elements from a given image. A linguistic processing technique, namely the latent semantic analysis (LSA) method, is then employed to identify associative ensembles of visual elements, which lay the foundation for intelligent visual information analysis. In 2-D applications, incremental parsing decomposes an image into elementary patches that are different from the conventional fixed square-block type patches. When used in compressive representations, it is amenable in schemes that do not rely on average distortion criteria, a methodology that is a departure from the conventional vector quantization. We call this methodology a parsed representation. In this article, we present our implementations of an image retrieval system, called IPSILON, with parsed representations induced by different perceptual distortion thresholds. We evaluate the effectiveness of the use of the parsed representations by comparing their performance with that of four image retrieval systems, one using the conventional vector quantization for visual information analysis under the same LSA paradigm, another using a method called SIMPLIcity which is based upon an image segmentation and integrated region matching, and the other two based upon query-by-semantic-example and query-by-visual-example. The first two of them were tested with 20 000 images of natural scenes, and the others were tested with a portion of the images. The experimental results show that the proposed parsed representation efficiently captures the salient features in visual images and the IPSILON systems outperform other systems in terms of retrieval precision and distortion robustness. C1 [Bae, Soo Hyun; Juang, Biing-Hwang] Georgia Inst Technol, Ctr Signal & Image Proc, Atlanta, GA 30332 USA. RP Bae, SH (reprint author), Sony US Res Ctr, San Jose, CA 95112 USA. EM soohyun@ieee.org; juang@ece.gatech.edu FU Hewlett-Packard Laboratories FX Manuscript received August 05, 2008; revised February 06, 2010. First published March 08, 2010; current version published June 16, 2010. This work was supported in part by Hewlett-Packard Laboratories. The material in this paper was presented in part at IEEE International Conference on Image Processing, October 2008. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Peter C. Doerschuk. CR Bae SH, 2008, IEEE T IMAGE PROCESS, V17, P1837, DOI 10.1109/TIP.2008.2002308 BAE SH, 2007, P EUR SIGN PROC C PO, P653 BANERJEE S, 2007, P 9 INT C DOC AN REC, P640 BELLEGARDA JR, 1996, P ICASSP 96, V1, P172 Bosch A., 2006, P EUR C COMP VIS, P517 Bosch A, 2008, IEEE T PATTERN ANAL, V30, P712, DOI 10.1109/TPAMI.2007.70716 Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61 CASSTELLI V, 2002, IMAGE DATABASES, P1 DAPTARDAR AH, 2006, P DCC 2006 DAT COMPR, P342 Datta R, 2007, IEEE MULTIMEDIA, V14, P24, DOI 10.1109/MMUL.2007.67 Datta R., 2008, ACM COMPUT SURV, V40 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DESELAERS T, 2003, THESIS AACHEN U TECH Fei-Fei L., 2005, P IEEE COMP SOC C CO, V2, P524 Flickner M., 1995, IEEE COMPUT, V28, P23 FRIGUI H, 2006, P IEEE C FUZZ SYST V, P72 Gersho A., 1992, VECTOR QUANTIZATION Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Grigorova A, 2007, IEEE T MULTIMEDIA, V9, P1183, DOI 10.1109/TMM.2007.902828 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 HOHL L, 2004, P 3 INT C IM VID RET, P564 Idris F, 1997, MACH VISION APPL, V10, P43, DOI 10.1007/s001380050058 *ITU R, BT6014 ITUR Jeong S, 2004, COMPUT VIS IMAGE UND, V94, P44, DOI 10.1016/j.cviu.2003.10.015 Jin R, 2004, P 12 ANN ACM INT C M, P892, DOI DOI 10.1145/1027527.1027732 KEYS RG, 1981, IEEE T ACOUST SPEECH, V29, P1153, DOI 10.1109/TASSP.1981.1163711 Kherfi ML, 2007, IEEE T MULTIMEDIA, V9, P893, DOI 10.1109/TMM.2007.893349 Ko B, 2005, IEEE T MULTIMEDIA, V7, P105, DOI 10.1109/TMM.2004.840603 Lacoste C, 2007, IEEE T CIRC SYST VID, V17, P889, DOI 10.1109/TCSVT.2007.897114 Li J., 2000, P 8 ACM INT C MULT, P147, DOI 10.1145/354384.354452 LINDE Y, 1980, IEEE T COMMUN, V28, P1 Minka TP, 1997, PATTERN RECOGN, V30, P565, DOI 10.1016/S0031-3203(96)00113-6 Monay F., 2004, P 12 ANN ACM INT C M, P348, DOI 10.1145/1027527.1027608 Monay F, 2007, IEEE T PATTERN ANAL, V29, P1802, DOI 10.1109/TPAMI.2007.1097 Muller H., 2002, P INT C IM VID RETR, P38 Naphade MR, 2002, IEEE T NEURAL NETWOR, V13, P793, DOI 10.1109/TNN.2002.1021881 NATSEV A, 2003, IEEE T KNOWL DATA EN, V15, P1 Pappas TN, 2007, IEEE COMMUN MAG, V45, P44, DOI 10.1109/MCOM.2007.284537 Pecenovic Z, 1997, THESIS ECOLE POLYTEC Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 PRAKS P, 2003, P SIAM C APPL LIN AL Qiu GP, 2003, IEEE T IMAGE PROCESS, V12, P93, DOI 10.1109/TIP.2002.807356 Quelhas P., 2005, Proceedings. Tenth IEEE International Conference on Computer Vision Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 Rasiwasia N., 2008, P IEEE C COMP VIS PA, P1 Rui Y., 1999, J VISUAL COMMUNICATI, V10, P1 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sivic J., 2005, Proceedings. Tenth IEEE International Conference on Computer Vision Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith J. R., 1996, Proceedings ACM Multimedia 96, DOI 10.1145/244130.244151 Teng SW, 2007, PATTERN RECOGN, V40, P3299, DOI 10.1016/j.patcog.2007.01.029 THEOHARATOS C, 2005, P IEEE INT C IM PROC, V1, P537 Vailaya A, 2001, IEEE T IMAGE PROCESS, V10, P117, DOI 10.1109/83.892448 Vasconcelos N, 2004, IEEE T SIGNAL PROCES, V52, P2322, DOI 10.1109/TSP.2004.831125 Veltkamp R. C., 2004, UUCS200034 Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109 Yang XK, 2005, SIGNAL PROCESS-IMAGE, V20, P662, DOI 10.1016/j.image.2005.04.001 YEH CH, 2006, OPT ENG, V45, P43 Zhang RF, 2007, IEEE T IMAGE PROCESS, V16, P562, DOI 10.1109/TIP.2006.888350 ZHAO R, 2000, P IEEE INT C MULT EX, P679 Zhou XS, 2003, MULTIMEDIA SYST, V8, P536, DOI 10.1007/s00530-002-0070-3 ZIV J, 1977, IEEE T INFORM THEORY, V23, P337, DOI 10.1109/TIT.1977.1055714 ZIV J, 1978, IEEE T INFORM THEORY, V24, P530, DOI 10.1109/TIT.1978.1055934 2004, MERRIAMWEBSTER DICT NR 65 TC 1 Z9 1 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1057-7149 J9 IEEE T IMAGE PROCESS JI IEEE Trans. Image Process. PD JUL PY 2010 VL 19 IS 7 BP 1933 EP 1947 DI 10.1109/TIP.2010.2045019 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 611KK UT WOS:000278813800021 PM 20215073 ER PT J AU Kim, S Park, J Han, S Kim, H AF Kim, Seunghwan Park, Jinkyun Han, Sangyong Kim, Hanjeom TI Development of extended speech act coding scheme to observe communication characteristics of human operators of nuclear power plants under abnormal conditions SO JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES LA English DT Article DE Speech act coding scheme; Verbal protocol analysis; Multi-Way communication; Communication characteristic; Abnormal condition; Nuclear power plant AB Communication problems have long been regarded as one of the biggest causes of trouble in many workplaces. Consequently, as a part of human error analysis, extensive research has been conducted into communication. The existing research indicates that maintaining reliable communication is essential to securing the safety of a large and complex process or system. To observe the characteristics of communication, verbal protocol analysis using speech act coding schemes have been widely used in much of the related research. However, as existing speech act coding schemes do not provide a sufficient analysis information other than the distribution rate of each pattern, the development of extended communication categories is essential to overcoming the weaknesses of protocol analysis. The objective of this study is to suggest an extended speech act coding scheme and to elucidate the communication pattern characteristics of an operator's conversation during a simulated abnormal condition in a nuclear power plant (NPP). The expected results are that conversation between operators using Multi-Way communication can increase the accuracy of information delivery and eliminate the possibility of improper operation. Therefore, communication quality control can be regarded as an important research area to minimize the occurrence of human error during communication. From this perspective, the results of this study would be a good starting point to improving the quality of communications in the workplace. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Kim, Seunghwan; Park, Jinkyun] Korea Atom Energy Res Inst, Integrated Safety Assessment Div, Taejon 305353, South Korea. [Han, Sangyong] Chung Ang Univ, Sch Comp Sci & Engn, Seoul, South Korea. RP Kim, S (reprint author), Korea Atom Energy Res Inst, Integrated Safety Assessment Div, Taejon 305353, South Korea. EM kimsh@kaeri.re.kr FU Korean government (Ministry of Education, Science and Technology) [2010-0001029] FX This research was supported by Nuclear Research & Development Program of the National Research Foundation (NRF) grant funded by the Korean government (Ministry of Education, Science and Technology), (Grant code: 2010-0001029). CR Bales R. F., 1950, INTERACTION PROCESS Billings C. E., 1981, INFORM TRANSFER PROB Combustion Engineering Owner's Group (CEOG), 1996, CEN152 CEOG Ericsson K. A., 1993, PROTOCOL ANAL VERBAL FOUSHEE HC, 1981, INFORM TRANSFER PROB Hirotsu Y., 2001, Cognition, Technology & Work, V3, DOI 10.1007/PL00011526 Hoegl M, 2001, ORGAN SCI, V12, P435, DOI 10.1287/orsc.12.4.435.10635 KANKI BG, 1989, AVIAT SPACE ENVIR MD, V60, P402 KETTUNEN J, 2000, TAU00100 *KHNP, 1998, YOUNGGW UN 3 4 ABN O KIM S, HUMAN FACTO IN PRESS KONDO S, 1994, RELIABILITY ENG SYST Min D. H., 2004, P IFAC IFIP IFORS IE Morgan B. B., 1986, TR86014 NAV TRAIN SY Murphy P., 2001, ENG PSYCHOL COGNITIV, V5 OLIVER S, 2003, COMMUNICATION HIGH R, V12, P155 Olson G. M., 1992, Human-Computer Interaction, V7, DOI 10.1207/s15327051hci0704_1 RIFKIND LJ, 1999, M P 10 FED AV ADM M, P25 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sasou K, 1993, SAFETY SCI, V81, P143 SCHLICTER K, 1997, WORKSH COORD TECHN C SCHRAAGEN JM, 2001, P 6 INT COMM CONTR R SEARLE JR, 1969, SPEECH ACCTS ESSAY P Sexton J B, 2000, Hum Perf Extrem Environ, V5, P63 SIAN B, 1996, MAINTENANCE RESOURCE SILBERSTEIN D, 2003, COMMUNICATION HIGH R, V12, P9 SMITH HPR, 1979, TM78482 NASA VEINOTT ES, 1995, P METH METR VOIC COM VICKI A, 2000, DOTFAACTTN0014 WILSON RM, 1995, MED J AUSTR, V163 NR 30 TC 8 Z9 9 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0950-4230 J9 J LOSS PREVENT PROC JI J. Loss Prev. Process Ind. PD JUL PY 2010 VL 23 IS 4 BP 539 EP 548 DI 10.1016/j.jlp.2010.04.005 PG 10 WC Engineering, Chemical SC Engineering GA 627FV UT WOS:000280025800008 ER PT J AU Chen, CM Ibekwe-SanJuan, F Hou, JH AF Chen, Chaomei Ibekwe-SanJuan, Fidelia Hou, Jianhua TI The Structure and Dynamics of Cocitation Clusters: A Multiple-Perspective Cocitation Analysis SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-SCIENCE; RESEARCH FRONTS; COMBINING BIBLIOMETRICS; SCIENTIFIC LITERATURE; RELEVANCE THEORY; CONCEPT SYMBOLS; GOOGLE SCHOLAR; KNOWLEDGE; NETWORKS; WEB AB A multiple-perspective cocitation analysis method is introduced for characterizing and interpreting the structure and dynamics of cocitation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Cocitation networks are decomposed into cocitation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a cocitation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of information science as defined by 12 journals published between 1996 and 2008: (a) a comparative author cocitation analysis (ACA), (b) a progressive ACA of a time series of cocitation networks, and (c) a progressive document cocitation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks. C1 [Chen, Chaomei] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. [Ibekwe-SanJuan, Fidelia] Univ Lyon 3, ELICO, F-69008 Lyon, France. [Hou, Jianhua] Dalian Univ Technol, WISELAB, Dalian, Peoples R China. RP Chen, CM (reprint author), Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA. EM chaomei.chen@drexel.edu; fidelia.ibekwe-sanjuan@univ-lyon3.fr; hqzhixing@gmail.com RI Chen, Chaomei/A-1252-2007 OI Chen, Chaomei/0000-0001-8584-1041 FU National Science Foundation [IIS-0612129] FX This work is supported in part by the National Science Foundation under grant IIS-0612129. We wish to thank Eric SanJuan of the University of Avignon, France, for implementing the Enertex sentence ranking algorithm used in this study; Howard White, Drexel University, for his detailed and constructive comments on an earlier draft; Dangzhi Zhao and Andreas Strotmann of the University of Alberta, Canada, for providing detailed factor loading results of their factor analysis for the comparative ACA and for their comments on an earlier draft of the article; and anonymous reviewers for their detailed reviews. Special thanks to Zeyuan Liu and members of the WISELAB at Dalian University of Technology, China, for their valuable feedback based on their extensive use of earlier versions of CiteSpace. CR Astrom F, 2007, J AM SOC INF SCI TEC, V58, P947, DOI 10.1002/asi.20567 Bar-Ilan J, 2008, SCIENTOMETRICS, V74, P257, DOI 10.1007/s11192-008-0216-y Batagelj V., 1998, CONNECTIONS, V212, P47 Ben-Hur Asa, 2002, Pac Symp Biocomput, P6 BONACICH P, 1987, AM J SOCIOL, V92, P1170, DOI 10.1086/228631 Brandes U, 2001, J MATH SOCIOL, V25, P163 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Carmel D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P139, DOI 10.1145/1571941.1571967 Chen-Lung Chan, 2005, Proceedings. 11th International Conference on Parallel and Distributed Systems, DOI 10.1145/1040830.1040859 Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5 Chen CM, 2004, P NATL ACAD SCI USA, V101, P5303, DOI 10.1073/pnas.0307513100 Chen CM, 2009, IEEE INTELL SYST, V24, P74 Chen CM, 2008, DATA KNOWL ENG, V67, P234, DOI 10.1016/j.datak.2008.05.004 Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317 Chen Y, 2009, IET COMMUN, V3, P209, DOI 10.1049/iet-com:20080064 CRONIN B, 1981, J INFORM SCI, V3, P27, DOI 10.1177/016555158100300104 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dunning T., 1993, Computational Linguistics, V19 FERNANDEZ S, 2007, P 6 MEX INT C ART IN, P861 Fiszman M, 2009, J BIOMED INFORM, V42, P801, DOI 10.1016/j.jbi.2008.10.002 Frank E., 1999, DATA MINING PRACTICA FREEMAN LC, 1977, SOCIOMETRY, V40, P35, DOI 10.2307/3033543 Garfield E., 1979, CITATION INDEXING IT Garfield E, 2004, J INF SCI, V30, P119, DOI 10.1177/0165551504042802 IBEKWESANJUAN F, 2009, P ANN M AM SOC INF S Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Janssens F, 2006, INFORM PROCESS MANAG, V42, P1614, DOI 10.1016/j.ipm.2006.03.025 Kiss C, 2008, DECIS SUPPORT SYST, V46, P233, DOI 10.1016/j.dss.2008.06.007 Klavans R., 2009, P 12 INT C SCI INF I, P265 Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI DOI 10.1023/A:1024940629314 Kumar R., 2003, P 12 INT C WORLD WID, P568, DOI 10.1145/775152.775233 Lane J, 2009, SCIENCE, V324, P1273, DOI 10.1126/science.1175335 Leydesdorff L, 2005, J AM SOC INF SCI TEC, V56, P769, DOI 10.1002/asi.20130 Luxburg U. v., 2009, LIMITS SPECTRAL CLUS Meho LI, 2007, J AM SOC INF SCI TEC, V58, P2105, DOI 10.1002/asi.20677 MIHALCEA R, 2004, P C EMP METH NAT LAN, P8 Morris S, 2002, COMPUT IND ENG, V43, P841, DOI 10.1016/S0360-8352(02)00143-2 Morris SA, 2008, ANNU REV INFORM SCI, V42, P213 Morris SA, 2003, J AM SOC INF SCI TEC, V54, P413, DOI 10.1002/asi.10227 Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103 Ng AY, 2002, ADV NEUR IN, V14, P849 PERSSON O, 1994, J AM SOC INFORM SCI, V45, P31, DOI 10.1002/(SICI)1097-4571(199401)45:1<31::AID-ASI4>3.0.CO;2-G Radev Dragomir R., 2004, J ARTIFICIAL INTELLI, V22, P457 ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMID H, 1999, NATURAL LANGUAGE PRO, P13 Schneider JW, 2009, SCIENTOMETRICS, V80, P103, DOI 10.1007/s11192-007-2019-y Schneider JW, 2006, SCIENTOMETRICS, V68, P573, DOI 10.1007/s11192-006-0131-z Schneider J.W., 2009, P 12 INT C SCI INF I, P443 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Shibata N, 2008, TECHNOVATION, V28, P758, DOI 10.1016/j.technovation.2008.03.009 SMALL H, 1986, SCIENTOMETRICS, V10, P95, DOI 10.1007/BF02016863 SMALL H, 1980, J DOC, V36, P183, DOI 10.1108/eb026695 Small H, 2003, J AM SOC INF SCI TEC, V54, P394, DOI 10.1002/asi.10225 SMALL H, 1986, J AM SOC INFORM SCI, V37, P97, DOI 10.1002/asi.4630370302 SMALL H, 1985, SCIENTOMETRICS, V8, P321, DOI 10.1007/BF02018057 SMALL H, 1985, SCIENTOMETRICS, V7, P391, DOI 10.1007/BF02017157 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SMALL HG, 1978, SOC STUD SCI, V8, P327, DOI 10.1177/030631277800800305 Sparck Jones K., 1999, ADV AUTOMATIC TEXT S, P2 Tabah AN, 1999, ANNU REV INFORM SCI, V34, P249 Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936 von Luxburg U., 2006, TUTORIAL SPECTRAL CL WHITE HD, 1982, J DOC, V38, P255, DOI 10.1108/eb026731 White HD, 2007, J AM SOC INF SCI TEC, V58, P583, DOI 10.1002/asi.20542 White HD, 2007, J AM SOC INF SCI TEC, V58, P536, DOI 10.1002/asi.20543 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P2070, DOI 10.1002/asi.20910 Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P916, DOI 10.1002/asi.20799 Zins C, 2007, J AM SOC INF SCI TEC, V58, P526, DOI 10.1002/asi.20505 Zins C, 2007, J AM SOC INF SCI TEC, V58, P479, DOI 10.1002/asi.20508 Zins C, 2007, J AM SOC INF SCI TEC, V58, P645, DOI 10.1002/asi.20506 Zins C, 2007, J AM SOC INF SCI TEC, V58, P335, DOI 10.1002/asi.20507 Zuccala A, 2006, J AM SOC INF SCI TEC, V57, P1487, DOI 10.1002/asi.20468 NR 74 TC 57 Z9 65 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUL PY 2010 VL 61 IS 7 BP 1386 EP 1409 DI 10.1002/asi.21309 PG 24 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 615QV UT WOS:000279151600007 ER PT J AU Davis, N Giraud-Carrier, C Jensen, D AF Davis, N. Giraud-Carrier, C. Jensen, D. TI A topological embedding of the lexicon for semantic distance computation SO NATURAL LANGUAGE ENGINEERING LA English DT Article ID RELATEDNESS; SIMILARITY; KNOWLEDGE; WORDNET AB We show how a quantitative context may be established for what is essentially qualitative in nature by topologically embedding a lexicon (here, WordNet) in a complete metric space. This novel transformation establishes a natural connection between the order relation in the lexicon (e.g., hyponymy) and the notion of distance in the metric space, giving rise to effective word-level and document-level lexical semantic distance measures. We provide a formal account of the topological transformation and demonstrate the value of our metrics on several experiments involving information retrieval and document clustering tasks. C1 [Davis, N.; Giraud-Carrier, C.] Brigham Young Univ, Dept Comp Sci, Provo, UT 84602 USA. [Jensen, D.] KJ Nova Inc, Provo, UT 84601 USA. RP Davis, N (reprint author), Google Inc, Kirkland, WA 98033 USA. EM cgc@cs.byu.edu CR Agirre E., 2009, P HUM LANG TECHN 200, P19, DOI 10.3115/1620754.1620758 Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BLEI F, 2002, P 8 ACM SIGKDD INT C, P436 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Choudhary B, 2002, P 11 INT WORLD WID W Curran J. R., 2004, THESIS U EDINBURGH E Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Edmonds P., 2007, WORD SENSE DISAMBIGU Edwards R., 1965, FUNCTIONAL ANAL THEO ERNE M, 1993, ANN NY ACAD SCI, V704, P103, DOI 10.1111/j.1749-6632.1993.tb52513.x Everitt B, 1993, CLUSTER ANAL Fellbaum C, 1998, WORDNET ELECT LEXICA Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 Fung BCM, 2003, SIAM PROC S, P59 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 GENNARI JH, 1989, ARTIF INTELL, V40, P11, DOI 10.1016/0004-3702(89)90046-5 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Guha R. V., 2003, P 12 INT C WORLD WID, P700 HENSTOCK PV, 2001, P 24 ANN INT ACM SIG, P384, DOI 10.1145/383952.384029 Hirst G., 2006, P C EMP METH NAT LAN, P35, DOI 10.3115/1610075.1610081 Hocking J.G., 1961, TOPOLOGY Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 HOTHO A, 2001, P IJCAI 2001 WORKSH, P30 HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jensen D, 2008, J WEB SEMANT, V6, P99, DOI 10.1016/j.websem.2007.11.001 JENSEN D, 2007, P 7 INT WORKSH COMP, P259 Johnson SC, 1967, PSYCHOMETRIKA, V2, P241 Kaufman L, 1990, FINDING GROUPS DATA Kim S, 2004, P 27 ANN INT ACM SIG, P258, DOI 10.1145/1008992.1009038 Kogan J., 2003, P WORKSH CLUST LARG, P63 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lang K, 1995, P 12 INT C MACH LEAR, P331 Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 LEBART L, 2000, HDB NATURAL LANGUAGE, P477 Leouski A. V., 1996, IR76 U MASS DEP COMP *LUC, 2007, OP SOURC INF RETR LI Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Mann Gideon S., 2002, P COLING 2002 SEMANE, P1 MARTON Y, 2009, P 2009 C EMP METH NA, P775 Mihalcea R. F., 2001, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001, DOI 10.1109/ICTAI.2001.974475 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Navigli R, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459355 NORVIG P, 2006, 6 ANN WORKSH TECHN F Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 Pedersen T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P1024 Ponzetto SP, 2007, J ARTIF INTELL RES, V30, P181 Prince V., 2003, Proceedings the Second IEEE International Conference on Cognitive Informatics PRISS UE, 1998, WORDNET ELECT LEXICA, P179 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P. S., 1993, SELECTION INFORM CLA Richardson R., 1995, CA0395 DUBL CIT U SC Rocha C., 2004, P 13 INT C WORLD WID, P374, DOI 10.1145/988672.988723 Rosso P., 2004, P 2 INT WORDNET C RE, P299 Roy P., 2005, P 14 ACM INT C INF K, P405, DOI 10.1145/1099554.1099676 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 RUMELHART DE, 1985, COGNITIVE SCI, V9, P75, DOI 10.1207/s15516709cog0901_5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 SONG W, 2007, P 3 INT C SEM KNOWL, P539 Stairmand MA, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P140, DOI 10.1145/258525.258552 Steinbach M., 2000, P KDD WORKSH TEXT MI, P109 TERMIER A, 2001, P IJCAI WORKSH ONT L, P49 THRALL RM, 1970, VECTOR SPACES MATRIC Tsuruoka Y, 2005, P HLT EMNLP, P467, DOI 10.3115/1220575.1220634 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 Voorhees E. M., 2005, SIGIR Forum, V39 WANG Y, 2006, P 39 HAW INT C SYST Weeds Julie, 2004, P 20 INT C COMP LING, P1015, DOI 10.3115/1220355.1220501 WIEBE J, 2006, P 21 INT C COMP LING, P1065, DOI 10.3115/1220175.1220309 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhang J., 2004, P C NEUR INF PROC SY, P1617 Zhang LL, 2005, Proceedings of the 2005 International Conference on Management Science and Engineering, P453, DOI 10.1145/1060745.1060812 NR 81 TC 0 Z9 0 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 J9 NAT LANG ENG JI Nat. Lang. Eng. PD JUL PY 2010 VL 16 BP 245 EP 275 DI 10.1017/S1351324910000045 PN 3 PG 31 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 626PY UT WOS:000279980200002 ER PT J AU Honkela, T Hyvarinen, A Vayrynen, JJ AF Honkela, Timo Hyvarinen, Aapo Vayrynen, Jaakko J. TI WordICA-emergence of linguistic representations for words by independent component analysis SO NATURAL LANGUAGE ENGINEERING LA English DT Article ID LATENT SEMANTIC ANALYSIS; RECOGNITION AB We explore the use of independent component analysis (ICA) for the automatic extraction of linguistic roles or features of words. The extraction is based on the unsupervised analysis of text corpora. We contrast ICA with singular value decomposition (SVD), widely used in statistical text analysis, in general, and specifically in latent semantic analysis (LSA). However, the representations found using the SVD analysis cannot easily be interpreted by humans. In contrast, ICA applied on word context data gives distinct features which reflect linguistic categories. In this paper, we provide justification for our approach called Word ICA, present the WordICA method in detail, compare the obtained results with traditional linguistic categories and with the results achieved using an SVD-based method, and discuss the use of the method in practical natural language engineering solutions such as machine translation systems. As the WordICA method is based on unsupervised learning and thus provides a general means for efficient knowledge acquisition, we foresee that the approach has a clear potential for practical applications. C1 [Honkela, Timo; Vayrynen, Jaakko J.] Aalto Univ, Sch Sci & Technol, Adapt Informat Res Ctr, FI-00076 Aalto, Finland. [Hyvarinen, Aapo] Univ Helsinki, Dept Comp Sci, Dept Math & Stat, FI-00014 Helsinki, Finland. [Hyvarinen, Aapo] Univ Helsinki, Helsinki Inst Informat Technol, FI-00014 Helsinki, Finland. RP Honkela, T (reprint author), Aalto Univ, Sch Sci & Technol, Adapt Informat Res Ctr, POB 15400, FI-00076 Aalto, Finland. EM timo.honkela@tkk.fi RI Vayrynen, Jaakko/E-8319-2012 FU Academy of Finland through the Adaptive Informatics Research Centre; Finnish Centre of Excellence Programme; HeCSE; Helsinki Graduate School in Computer Science and Engineering FX This work was supported by the Academy of Finland through the Adaptive Informatics Research Centre that is a part of the Finnish Centre of Excellence Programme and HeCSE, Helsinki Graduate School in Computer Science and Engineering. We warmly thank the anonymous reviewers and the editors of the journal as well as our colleagues, in particular, Oskar Kohonen, Tiina Lindh-Knuutila, and Sami Virpioja, for their detailed and constructive comments on earlier versions of this paper. CR Bazzi I., 2000, P INT C SPOK LANG PR, V1, P401 Bingham E., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval BINGHAM E, 2001, P 10 INT WORLD WID W, P198 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Borschbach M, 2007, LECT NOTES COMPUT SC, V4666, P689 Brants T., 2000, P 6 C APPL NAT LANG, P224, DOI 10.3115/974147.974178 BRILL E, 1992, HLT 91, P112 Buntine W, 2004, P 20 C UNC ART INT, P59 Callison-Burch Chris, 2008, P 3 WORKSH STAT MACH, P70, DOI 10.3115/1626394.1626403 Choi FYY, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P109 Chomsky Noam, 1975, LOGICAL STRUCTURE LI Church K. W., 1988, P 2 C APPL NAT LANG, P136, DOI 10.3115/974235.974260 CLARK A, 2001, THESIS U SUSSEX FALM Clark A.S., 2000, P CONLL 2000 LLL 200, P91 COMON P, 1994, SIGNAL PROCESS, V36, P287, DOI 10.1016/0165-1684(94)90029-9 Creutz Mathis, 2007, ACM T SPEECH LANGUAG, V4, P1 Croft William, 2004, COGNITIVE LINGUISTIC Davidson D., 2001, INQUIRIES TRUTH INTE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dumais ST., 1997, AAAI S CROSS LANG TE Fillmore Charles J., 1968, UNIVERSALS LINGUIST, P1 FINCH S, 1992, ARTIFICIAL NEURAL NE, V2, P1365 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Francis W. N., 1964, BROWN CORPUS MANUAL FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Geoffrey E, 1999, UNSUPERVISED LEARNIN HAGHIGHI A, 2006, ACL 44, P881 Haghighi A., 2006, P MAIN C HUM LANG TE, P320, DOI 10.3115/1220835.1220876 Church K. W., 1990, Computational Linguistics, V16 Hansen L.K., 2005, P AKRR 05 INT INT C, P148 Haykin S., 1999, NEURAL NETWORKS COMP Hirsimaki T, 2006, COMPUT SPEECH LANG, V20, P515, DOI 10.1016/j.csl.2005.07.002 Honkela T., 2005, Proceedings of the Ninth Neural Computation and Psychology Workshop. Modeling Language, Cognition and Action Honkela T., 1995, P ICANN 95 INT C ART, V2, P3 Hopper P., 1987, BERKELEY LINGUISTICS, V13, P139 HURRI J, 2002, FASTICA SOFTWARE PAC Hyvarinen A, 1999, IEEE T NEURAL NETWOR, V10, P626, DOI 10.1109/72.761722 Hyvarinen A., 2009, NATURAL IMAGE STAT P Hyvarinen A., 2001, INDEPENDENT COMPONEN Johnson M, 2007, P 2007 JOINT C EMP M, P296 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 JUTTEN C, 1991, SIGNAL PROCESS, V24, P1, DOI 10.1016/0165-1684(91)90079-X Kanaan G., 2005, Information Technology Journal, V4 Koehn Philipp, 2007, P 2007 JOINT C EMP M, P868 Kolenda T., 2000, ADV INDEPENDENT COMP, P229 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Levy J. P., 1998, S PACIFIC J PSYCHOL, V10, P99 Lund K., 1996, P 18 ANN C COGN SCI, V18, P603 Manning C. D., 1999, FDN STAT NATURAL LAN McKeown MJ, 1998, HUM BRAIN MAPP, V6, P368, DOI 10.1002/(SICI)1097-0193(1998)6:5/6<368::AID-HBM7>3.0.CO;2-E Merialdo B., 1994, Computational Linguistics, V20 NIU C, 2004, SENSEVAL3 3 INT WORK, P187 Och F. J., 1999, P 9 C EUR CHAPT ASS, P71, DOI 10.3115/977035.977046 OJA E, 2004, P SPIE DEF SEC S ORL, P1 RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 Sahlgren M., 2006, THESIS STOCKHOLM U Sahlgren M., 2008, P 30 ANN C COGN SCI, P1300 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1992, Proceedings. Supercomputing '92. (Cat. No.92CH3216-9), DOI 10.1109/SUPERC.1992.236684 Schutze Hinrich, 1995, P 7 C EUR CHAPT ASS, P141, DOI 10.3115/976973.976994 Steinberger J., 2005, P HLT EMNLP ASS COMP, P1, DOI 10.3115/1220575.1220576 Tarski A, 1983, LOGIC SEMANTICS META, V34, P152 Ueffing N., 2003, P 10 C EUR CHAPT ASS, P347 Vayrynen Jaakko, 2005, P AKRR 05 INT INT C, P135 VAYRYNEN JJ, 2006, P SCAI 06 SCAND C AR, P101 Vayrynen J., 2004, Proceedings of the 6th Nordic Signal Processing Symposium (IEEE Cat. No.04EX856) Vicente MA, 2007, IEEE T PATTERN ANAL, V29, P896, DOI [10.1109/TPAMI.2007.1074, 10.1109/TPAMI.2007.1025] WANE QI, 2005, P IEEE INT C NAT LAN, P219 Wilks Y., 1998, J NATURAL LANGUAGE E, V4, P135 Yu C, 2003, PROCEEDINGS OF THE TWENTY-FIFTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, PTS 1 AND 2, P1293 NR 70 TC 6 Z9 6 PU CAMBRIDGE UNIV PRESS PI CAMBRIDGE PA EDINBURGH BLDG, SHAFTESBURY RD, CB2 8RU CAMBRIDGE, ENGLAND SN 1351-3249 J9 NAT LANG ENG JI Nat. Lang. Eng. PD JUL PY 2010 VL 16 BP 277 EP 308 DI 10.1017/S1351324910000057 PN 3 PG 32 WC Computer Science, Artificial Intelligence; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA 626PY UT WOS:000279980200003 ER PT J AU Zanghi, H Picard, F Miele, V Ambroise, C AF Zanghi, Hugo Picard, Franck Miele, Vincent Ambroise, Christophe TI STRATEGIES FOR ONLINE INFERENCE OF MODEL-BASED CLUSTERING IN LARGE AND GROWING NETWORKS SO ANNALS OF APPLIED STATISTICS LA English DT Article DE Graph clustering; EM Algorithms; online strategies; web graph structure analysis ID EM ALGORITHM; STOCHASTIC BLOCKMODELS; MIXED MEMBERSHIP; INCOMPLETE DATA; MIXTURE-MODELS; CONVERGENCE; ENVIRONMENT; PREDICTION AB In this paper we adapt online estimation strategies to perform model-based clustering on large networks. Our work focuses on two algorithms, the first based on the SAEM algorithm, and the second on variational methods. These two strategies are compared with existing approaches on simulated and real data. We use the method to decipher the connexion structure of the political websphere during the US political campaign in 2008. We show that our online EM-based algorithms offer a good trade-off between precision and speed, when estimating parameters for mixture distributions in the context of random graphs. C1 [Zanghi, Hugo] Exalead, F-75008 Paris, France. [Picard, Franck; Miele, Vincent] UCB Lyon 1, Lab Biometrie & Biol Evolut, F-69622 Villeurbanne, France. [Ambroise, Christophe] CNRS, INRA, Lab Stat & Genome, UEVE 1152,UMR 8071, F-91000 Evry, France. RP Zanghi, H (reprint author), Exalead, 10 Pl Madeleine, F-75008 Paris, France. EM hugo.zanghi@exalead.com; picard@biomserv.univ-lyon1.fr; miele@biomserv.univ-lyon1.fr; cambroise@genopole.cnrs.fr FU French Agence Nationale de la Recherche [NeMo ANR-08-BLAN-0304-01] FX Supported in part by French Agence Nationale de la Recherche Grant NeMo ANR-08-BLAN-0304-01. CR Adamic L. A., 2005, P 3 INT WORKSH LINK, P36, DOI DOI 10.1145/1134271.1134277 AIROLDI E, 2005, 3 INT WORKSH LINK DI, P82 Airoldi EM, 2007, LECT NOTES COMPUT SC, V4503, P57 Airoldi EM, 2008, J MACH LEARN RES, V9, P1981 Bickel PJ, 2009, P NATL ACAD SCI USA, V106, P21068, DOI 10.1073/pnas.0907096106 Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 CELEUX G, 1992, COMPUT STAT DATA AN, V14, P315, DOI 10.1016/0167-9473(92)90042-E DAUDIN J., 2008, STAT COMPUT, V18, P1 DAVISON BD, 2000, SIGIR 00, P272 Delyon B, 1999, ANN STAT, V27, P94 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DRUGEON T, 2005, 5 INT WEB ARCH WORKS FOUETILLOU G, 2007, RESEAUX, V144, P279 GHITALLA F, 2003, OUTRE LECT MANIPULER Handcock MS, 2007, J ROY STAT SOC A STA, V170, P301, DOI 10.1111/j.1467-985X.2007.00471.x HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075 Jordan MI, 1999, MACH LEARN, V37, P183, DOI 10.1023/A:1007665907178 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Latouche P, 2008, BAYESIAN METHODS GRA Liu Z, 2006, COMPUT STAT DATA AN, V50, P1052, DOI 10.1016/j.csda.2004.11.002 MacQueen J., 1967, 5TH P BERK S MATH ST, V1, P281 Newman MEJ, 2007, P NATL ACAD SCI USA, V104, P9564, DOI 10.1073/pnas.0610537104 Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103 Ng AY, 2002, ADV NEUR IN, V14, P849 Nowicki K, 2001, J AM STAT ASSOC, V96, P1077, DOI 10.1198/016214501753208735 OPPER M, 1999, ON LINE LEARNING NEU, V16, P363 Picard F., 2009, BMC BIOINFORMATICS, V10, P1, DOI DOI 10.1186/1471-2105-10-S6-S17 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sampson FS, 1968, THESIS CORNELL U Shannon P, 2003, GENOME RES, V13, P2498, DOI 10.1101/gr.1239303 Snijders TAB, 1997, J CLASSIF, V14, P75, DOI 10.1007/s003579900004 TITTERINGTON DM, 1984, J ROY STAT SOC B MET, V46, P257 Wang SJ, 2006, STAT PROBABIL LETT, V76, P2001, DOI 10.1016/j.spl.2006.05.017 Zanghi H, 2008, PATTERN RECOGN, V41, P3592, DOI 10.1016/j.patcog.2008.06.019 NR 34 TC 6 Z9 9 PU INST MATHEMATICAL STATISTICS PI CLEVELAND PA 3163 SOMERSET DR, CLEVELAND, OH 44122 USA SN 1932-6157 J9 ANN APPL STAT JI Ann. Appl. Stat. PD JUN PY 2010 VL 4 IS 2 BP 687 EP 714 DI 10.1214/10-AOAS359 PG 28 WC Statistics & Probability SC Mathematics GA 671RW UT WOS:000283528500008 ER PT J AU Du, YJ Li, HM AF Du, YaJun Li, HaiMing TI Strategy for mining association rules for web pages based on formal concept analysis SO APPLIED SOFT COMPUTING LA English DT Article DE Association rule; Formal concept analysis; Web pages; User query keywords ID BOOLEAN INFORMATION-RETRIEVAL; CONCEPT LATTICES; MODEL AB To avoid returning irrelevant web pages for search engine results, technologies that match user queries to web pages have been widely developed. In this study, web pages for search engine results are classified as low-adjacence ( each web page includes all query keywords) or high-adjacence ( each web page includes some of the query keywords) sets. To match user queries with web pages using formal concept analysis (FCA), a concept lattice of the low-adjacence set is defined and the non-redundancy association rules defined by Zaki for the concept lattice are extended. OR- and AND-RULEs between non-query and query keywords are proposed and an algorithm and mining method for these rules are proposed for the concept lattice. The time complexity of the algorithm is polynomial. An example illustrates the basic steps of the algorithm. Experimental and real application results demonstrate that the algorithm is effective. (C) 2009 Elsevier B. V. All rights reserved. C1 [Du, YaJun] Xihua Univ, Sch Math & Comp Sci, Chengdu 610039, Sichuan, Peoples R China. [Li, HaiMing] Shanghai Univ Elect Power, Sch Comp & Informat Technol, Shanghai, Peoples R China. RP Du, YJ (reprint author), Xihua Univ, Sch Math & Comp Sci, Chengdu 610039, Sichuan, Peoples R China. EM duyajun@mail.xhu.edu.cn; zjlhm@zj165.com FU National Nature Science Foundation [60872089]; Cultivating Foundation of Science & Technology Leaders of Sichuan Province FX This work is one of the projects supported by National Nature Science Foundation (Grant No. 60872089), Cultivating Foundation of Science & Technology Leaders of Sichuan Province. This work is an extension of reference [28]. CR AGRAWAL R, 1998, ADV KNOWLEDGE DISCOV, P307 Bastide Y., 2000, P 1 INT C COMP LOG, P972 BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I Burdick D, 2001, PROC INT CONF DATA, P443, DOI 10.1109/ICDE.2001.914857 Crouch D. B., 1989, P ACM HYPERTEXT 89 P, P225, DOI 10.1145/74224.74243 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Deogun J. S., 1998, P JOINT C INF SCI, P335 DU YJ, 2005, THESIS SW JIAOTONG U DU YJ, 2007, P 2007 INT C COMP SC, P434 DU YJ, 2005, P 2005 INT C MACH LE, V4, P2368 DU YJ, 2008, J XIHUA U NATURAL SC, V27, P38 GODIN R, 1995, COMPUT INTELL, V11, P246, DOI 10.1111/j.1467-8640.1995.tb00031.x Hu K., 2000, J SOFTWARE, V11, P1479 Liu H, 1998, ELEC SOC S, V98, P86 Losada DE, 2001, COMPUT J, V44, P410, DOI 10.1093/comjnl/44.5.410 Luxenburger M., 1991, MATH INFORMATIQUE SC, V113, P35 Martinez R, 2008, BIOINFORMATICS, V24, P2643, DOI 10.1093/bioinformatics/btn490 Merz C.J., UCI REPOSITORY MACHI Rajapakse R, 2005, NEURAL COMPUT, V17, P2291, DOI 10.1162/0899766054615662 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang Zhi-Hai, 1999, Chinese Journal of Computers, V22 Wille R., 1982, ORDERED SETS, P445 XIE ZP, 2000, J COMPUTER RES DEV, V37, P1415 ZAKI MJ, 1998, P 3 ACM SIGMOD WORKS, P1 Zaki MJ, 2004, DATA MIN KNOWL DISC, V9, P223, DOI 10.1023/B:DAMI.0000040429.96086.c7 Zaki MJ, 2002, SIAM PROC S, P457 NR 28 TC 5 Z9 7 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1568-4946 J9 APPL SOFT COMPUT JI Appl. Soft. Comput. PD JUN PY 2010 VL 10 IS 3 BP 772 EP 783 DI 10.1016/j.asoc.2009.09.007 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 566NG UT WOS:000275377500011 ER PT J AU Xiang, EW Cao, B Hu, DH Yang, Q AF Xiang, Evan Wei Cao, Bin Hu, Derek Hao Yang, Qiang TI Bridging Domains Using World Wide Knowledge for Transfer Learning SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Data mining; transfer learning; cross-domain; text classification; Wikipedia AB A major problem of classification learning is the lack of ground-truth labeled data. It is usually expensive to label new data instances for training a model. To solve this problem, domain adaptation in transfer learning has been proposed to classify target domain data by using some other source domain data, even when the data may have different distributions. However, domain adaptation may not work well when the differences between the source and target domains are large. In this paper, we design a novel transfer learning approach, called BIG (Bridging Information Gap), to effectively extract useful knowledge in a worldwide knowledge base, which is then used to link the source and target domains for improving the classification performance. BIG works when the source and target domains share the same feature space but different underlying data distributions. Using the auxiliary source data, we can extract a "bridge" that allows cross-domain text classification problems to be solved using standard semisupervised learning algorithms. A major contribution of our work is that with BIG, a large amount of worldwide knowledge can be easily adapted and used for learning in the target domain. We conduct experiments on several real-world cross-domain text classification tasks and demonstrate that our proposed approach can outperform several existing domain adaptation approaches significantly. C1 [Xiang, Evan Wei; Cao, Bin; Hu, Derek Hao; Yang, Qiang] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China. RP Xiang, EW (reprint author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China. EM wxiang@cse.ust.hk; caobin@cse.ust.hk; derekhh@cse.ust.hk; qyang@cse.ust.hk RI Cao, Bin/I-7535-2015 OI Cao, Bin/0000-0001-6513-1800 FU Hong Kong CERG [621307]; NEC China Lab FX The authors thank the support from Hong Kong CERG grant 621307 and a grant from NEC China Lab. CR ARGYRIOU A, 2007, P 21 ANN C NEUR INF Beitzel S.M., 2005, P 5 IEEE INT C DAT M, P42 Ben-David S., 2006, P NIPS 2006, P137 Bishop C. M., 2006, PATTERN RECOGNITION Blei D. M., 2001, P NIPS, P601 Blitzer J., 2007, P 45 ANN M ASS COMP, P440 BLITZER J, 2007, P 21 ANN C NEUR INF Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Blum PR, 2001, STUD EUROP JUDAISM, V1, P19 Broder A.Z., 2007, P 30 ANN INT ACM SIG, P231, DOI 10.1145/1277741.1277783 DAI W, 2007, P 13 ACM SIGKDD INT, P210, DOI 10.1145/1281192.1281218 Dai W., 2007, P 24 INT C MACH LEAR, P193, DOI DOI 10.1145/1273496.1273521 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Gabrilovich E, 2007, J MACH LEARN RES, V8, P2297 GABRILOVICH E, 2005, P 19 INT JOINT C ART, P1048 Gabrilovich Evgeniy, 2006, P 21 NAT C ART INT 1 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 HOFMANN T, 1999, P 15 C UNC ART INT U, P289 Jiang J., 2007, P 45 ANN M ASS COMP Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Lang K, 1995, P 12 INT C MACH LEAR, P331 Lee S, 2007, PROC MONOGR ENG WATE, P489, DOI 10.1145/1273496.1273558 Pan SJ, 2010, IEEE T KNOWL DATA EN, V22, P1345, DOI 10.1109/TKDE.2009.191 Phan Xuan-Hieu, 2008, P 17 INT C WORLD WID, P91, DOI DOI 10.1145/1367497.1367510 Raina R, 2007, P 24 INT C MACH LEAR, P759, DOI DOI 10.1145/1273496.1273592 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen D., 2006, P 29 ANN INT ACM SIG, P131, DOI 10.1145/1148170.1148196 WANG P, 2007, P 7 IEEE INT C DAT M WANG P, 2008, IEEE INTELLIGENT INF, V9, P5 Wang P, 2008, P 8 IEEE INT C DAT M, P1085 Wang Shuhe, 2008, Chinese Journal of Applied and Environmental Biology, V14, P721, DOI 10.3724/SP.J.1145.2008.00721 Xue G., 2008, P 31 ANN INT ACM SIG, P627, DOI 10.1145/1390334.1390441 YANG Q, 2009, P 47 ANN M ASS COMP Zadrozny B., 2004, P 21 INT C MACH LEAR, P114 Zelikovitz S., 2000, P 17 INT C MACH LEAR, P1183 Zelikovitz S., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Zheng V., 2008, P 23 AAAI C ART INT, P1427 Zheng V.W., 2009, P 11 INT C UB COMP U, P61, DOI 10.1145/1620545.1620554 Zheng VW, 2008, P 23 ASS ADV ART INT, P1421 2007, P 45 ANN M ASS COMP NR 40 TC 9 Z9 17 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JUN PY 2010 VL 22 IS 6 BP 770 EP 783 DI 10.1109/TKDE.2010.31 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 585CG UT WOS:000276801300003 ER PT J AU Chen, DP Xiong, Y Yan, J Xue, GR Wang, G Chen, Z AF Chen, Depin Xiong, Yan Yan, Jun Xue, Gui-Rong Wang, Gang Chen, Zheng TI Knowledge transfer for cross domain learning to rank SO INFORMATION RETRIEVAL LA English DT Article DE Information retrieval; Learning to rank; Knowledge transfer; Ranking SVM ID INFORMATION-RETRIEVAL; DISCOVERY; FRAMEWORK AB Recently, learning to rank technology is attracting increasing attention from both academia and industry in the areas of machine learning and information retrieval. A number of algorithms have been proposed to rank documents according to the user-given query using a human-labeled training dataset. A basic assumption behind general learning to rank algorithms is that the training and test data are drawn from the same data distribution. However, this assumption does not always hold true in real world applications. For example, it can be violated when the labeled training data become outdated or originally come from another domain different from its counterpart of test data. Such situations bring a new problem, which we define as cross domain learning to rank. In this paper, we aim at improving the learning of a ranking model in target domain by leveraging knowledge from the outdated or out-of-domain data (both are referred to as source domain data). We first give a formal definition of the cross domain learning to rank problem. Following this, two novel methods are proposed to conduct knowledge transfer at feature level and instance level, respectively. These two methods both utilize Ranking SVM as the basic learner. In the experiments, we evaluate these two methods using data from benchmark datasets for document retrieval. The results show that the feature-level transfer method performs better with steady improvements over baseline approaches across different datasets, while the instance-level transfer method comes out with varying performance depending on the dataset used. C1 [Yan, Jun; Wang, Gang; Chen, Zheng] Microsoft Res Asia, Beijing 100190, Peoples R China. [Chen, Depin; Xiong, Yan] Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Peoples R China. [Xue, Gui-Rong] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China. RP Yan, J (reprint author), Microsoft Res Asia, 49 Zhichun Rd, Beijing 100190, Peoples R China. EM depin.chen@gmail.com; yxiong@ustc.edu.cn; junyan@microsoft.com; grxue@apex.sjtu.edu.cn; gawa@microsoft.com; zhengc@microsoft.com CR Ando RK, 2005, J MACH LEARN RES, V6, P1817 Argyriou A, 2007, NIPS Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BURGES C, 2006, ADV NEURAL INFORM PR, P395 BURGES C, 2006, ICML 2006, P89 Cao G., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Cao Z, 2007, LECT NOTES COMPUT SC, V4464, P129 Caruana R, 1997, MACH LEARN, V28, P41, DOI 10.1023/A:1007379606734 CHAN YS, 2005, P 19 INT JOINT C ART, P1010 Chen D., 2008, ICDM WORKSH 2008, P106 Dai WY, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P210 DO C, 2006, ADV NEURAL INFORM PR, V18, P299 Fan WG, 2004, IEEE T KNOWL DATA EN, V16, P523, DOI 10.1109/TKDE.2004.1269663 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 Freund Y., 2003, J MACHINE LEARNING R, V6, P933, DOI 10.1162/jmlr.2003.4.6.933 Herbrich R, 2000, ADV NEUR IN, P115 Huang YT, 2007, J INTEL MAT SYST STR, V18, P601, DOI 10.1177/1045389X06067929 Jiang J., 2007, ACL 2007, P264 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LI P, 2007, ADV NEURAL INFORM PR Lin Y, 2002, MACH LEARN, V46, P191, DOI 10.1023/A:1012406528296 Liu T-Y, 2007, LR4IR 2007 CONJUNCTI Robertson S. E., 1994, P TREC 3 Ruckert U, 2008, LECT NOTES COMPUTER, P220, DOI [10.1007/978-3-540-87481-2_15, DOI 10.1007/978-3-540-87481-2_15] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sugiyama M., 2005, STAT DECISIONS, V23, P249, DOI 10.1524/stnd.2005.23.4.249 Trotman A, 2005, INFORM RETRIEVAL, V8, P359, DOI 10.1007/s10791-005-6991-7 Wu Q., 2008, MSRTR2008109 Xu J., 2007, SIGIR 07, P391 NR 29 TC 3 Z9 4 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2010 VL 13 IS 3 SI SI BP 236 EP 253 DI 10.1007/s10791-009-9111-2 PG 18 WC Computer Science, Information Systems SC Computer Science GA 608YH UT WOS:000278621400004 ER PT J AU Tseng, SP Chiang, MC Yang, CS Tsai, CW AF Tseng, Shih-Pang Chiang, Ming-Chao Yang, Chu-Sing Tsai, Chun-Wei TI An Ontology-based Framework for Itembank Integration and Knowledge Sharing SO INTERNATIONAL JOURNAL OF FUZZY SYSTEMS LA English DT Article DE Information Retrieval; Ontology; Itembank ID SPACE AB The Internet is abounding with information, but there is simply no easy way to find all the information we need in a single place, well organized and easily accessible, let alone share. For this reason, we present a novel framework for resolving these issues. The proposed framework incorporates and unifies techniques from a variety of disciplines such as information extraction, information retrieval, fuzzy clustering, and ontology. To evaluate the performance of the proposed framework, we use it to solve the itembank integration and knowledge sharing problem faced by many e-Learning systems. Our experimental results show that the proposed framework can not only effectively integrate information available in the Internet, but it can also properly organize the information into a unified itembank of an e-Learning system, thus making it possible for the users of such a system to not just find but share all the information. C1 [Chiang, Ming-Chao; Yang, Chu-Sing] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan. [Tseng, Shih-Pang] Tajen Univ, Dept Comp Sci & Informat Engn, Pingtung, Taiwan. [Yang, Chu-Sing] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 80424, Taiwan. [Yang, Chu-Sing] Natl Sun Yat Sen Univ, Ctr Comp, Kaohsiung 80424, Taiwan. RP Chiang, MC (reprint author), Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, 70 Lienhai Rd, Kaohsiung 80424, Taiwan. EM mcchiang@cse.nsysu.edu.tw FU Chinese Knowledge and Information Processing (CKIP) group, Institute of Information Science, Academia Sinica for providing their Chinese Word Segmentation System; National Science Council, Taiwan, ROC [95-2221-E-006-509-MY2, 98-2218-E-006-003] FX The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions on the paper. The authors would also like to thank Kang Hsuan Publishing and Han Lin Publishing for providing their itembanks and the Chinese Knowledge and Information Processing (CKIP) group, Institute of Information Science, Academia Sinica for providing their Chinese Word Segmentation System to support this research. This work was supported in part by National Science Council, Taiwan, ROC, under Contract Nos. 95-2221-E-006-509-MY2 and 98-2218-E-006-003. CR [Anonymous], 2003, INT REV RES OPEN DIS, V4 BERNERSLEE T, 1999, WHY RDF MODEL IS DIF Bezdek J. C., 1981, PATTERN RECOGNITION Borst W., 1997, THESIS U TWENTE ENSC Clara N, 2007, INT J FUZZY SYST, V9, P220 Friesen N., 2004, ONLINE ED USING LEAR, P59 Graham S, 1998, PROG HUM GEOG, V22, P165, DOI 10.1191/030913298671334137 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Ismail J., 2002, INTERNET HIGH EDUC, V4, P329 JENG WJ, 2009, P NAT COMP S NCS 200 *K CONS INC, 2005, EL DISC COST MAN SYS, P1 LEHTI P, 2004, INT S APPL INT, P160 Li CH, 2008, INT J FUZZY SYST, V10, P168 Liu Bing, 2007, WEB DATA MINING EXPL MENDES M, 2002, P LCS 2002 Noy NF, 2004, SIGMOD RECORD, V33, P65 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 *PROT TEAM, PROT ONT ED KNOWL AC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RONALD HS, 1983, ITEM RESPONSE THEORY SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STOJANOVIC L, 2001, WORLD C WWW INT Swick R., 1999, RESOURCE DESCRIPTION Tanenbaum AS, 2002, COMPUTER NETWORKS THOMPSON M, 1996, J CONTINUING HIGHER, V44, P29 TSAI CW, 2009, P INT C INT SYST DES, P919 *W3C, 2003, OWL WEB ONT LANG Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 CHINESE DOCUMENT SEG NR 30 TC 1 Z9 1 PU TAIWAN FUZZY SYSTEMS ASSOC PI TAIPEI PA NATL TAIPEI UNIV TECHNOL, 1 SEC 3 CHUNG-HSIAO E RD, TAIPEI, ROC 00000, TAIWAN SN 1562-2479 J9 INT J FUZZY SYST JI Int. J. Fuzzy Syst. PD JUN PY 2010 VL 12 IS 2 SI SI BP 116 EP 124 PG 9 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 634WL UT WOS:000280616500004 ER PT J AU Tan, GX Viard-Gaudin, C Kot, AC AF Tan, Guo Xian Viard-Gaudin, Christian Kot, Alex C. TI Individuality of alphabet knowledge in online writer identification SO INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION LA English DT Article; Proceedings Paper CT 16th Document Recognition and Retrieval Conference CY JAN, 2009 CL San Jose, CA SP IS&T, SPIE Elect Imaging DE Writer identification; Allograph; Alphabet; Online handwriting; Information retrieval ID CONNECTED-COMPONENT CONTOURS; HANDWRITTEN DOCUMENTS; VERIFICATION SYSTEM; FEATURES; RECOGNITION; CHARACTER; MODEL AB Allograph prototype approaches for writer identification have been gaining popularity recently due to its simplicity and promising identification rates. Character prototypes that are used as allographs produce a consistent set of templates that models the handwriting styles of writers, thereby allowing high accuracies to be attained. We hypothesize that the alphabet knowledge inherent in such character prototypes can provide additional writer information pertaining to their styles of writing and their identities. This paper utilizes a character prototype approach to establish evidence that knowledge of the alphabet offers additional clues which help in the writer identification process. This paper then introduces an alphabet information coefficient (AIC) to better exploit such alphabet knowledge for writer identification. Our experiments showed an increase in writer identification accuracy from 66.0 to 87.0% on a database of 200 reference writers when alphabet knowledge was used. Experiments related to the reduction in dimensionality of the writer identification system are also reported. Our results show that the discriminative power of the alphabet can be used to reduce the complexity while maintaining the same level of performance for the writer identification system. C1 [Tan, Guo Xian; Kot, Alex C.] Nanyang Technol Univ, Ctr Informat Secur, Singapore, Singapore. [Tan, Guo Xian; Viard-Gaudin, Christian] Univ Nantes, IRCCyN UMR CNRS 6597, Ecole Polytech, Nantes 3, France. RP Tan, GX (reprint author), Nanyang Technol Univ, Ctr Informat Secur, Singapore, Singapore. EM tanguoxian@pmail.ntu.edu.sg; christian.viard-gaudin@univ-nantes.fr; eackot@ntu.edu.sg CR BENSEFIA A, 2003, DOC AN REC 2003 P 7, P946 Bensefia A., 2005, ELECT LETT COMPUTER, V5, P72 Bensefia A, 2005, PATTERN RECOGN LETT, V26, P2080, DOI 10.1016/j.patrec.2005.03.024 Bulacu M, 2007, IEEE T PATTERN ANAL, V29, P701, DOI 10.1109/TPAMI.2007.1009 CHA SH, 2006, J FORENSIC DATA EXAM, V17, P1 CHAN SK, 2008, SPIE IS T ELECT IMAG, V6815 Chellapilla K., 2006, 10 INT WORKSH FRONT, P423 Connell SD, 2002, IEEE T PATTERN ANAL, V24, P329, DOI 10.1109/34.990135 Fornes A., 2008, DAS 2008, P347 FOSTER C, 1982, CRYPTANALYSIS FORMIC Fujisawa H, 2008, PATTERN RECOGN, V41, P2435, DOI 10.1016/j.patcog.2008.03.015 Gupta S, 2007, LECT NOTES COMPUT SC, V4642, P356 Han Jiawei, 2006, DATA MINING CONCEPTS, VSecond, P383 He ZY, 2008, PATTERN RECOGN, V41, P1295, DOI 10.1016/j.patcog.2007.08.017 HUBER R, 1999, HANDWRITING IDENTIFI, P175 KELLY JS, 2006, SCI EXAMINATION QUES, P47 MORRIS RN, 2000, FORENSIC HANDWRITING, P1 Niels R., 2008, INT C FRONT HANDWR R, P481 Niels R, 2007, INT J PATTERN RECOGN, V21, P61, DOI 10.1142/S0218001407005302 Pervouchine V, 2007, PATTERN RECOGN, V40, P1004, DOI 10.1016/j.patcog.2006.08.008 PITAK T, 2003, IEICE T FUND ELECTR, V86, P2535 Pitak T., 2004, INT C IM PROC ICIP, V2, P889 SALDARRIAGA SP, 2009, SPIE IS T ELECT IMAG, V7247 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlapbach A, 2007, PATTERN ANAL APPL, V10, P33, DOI 10.1007/s10044-006-0047-5 Schlapbach A, 2008, PATTERN RECOGN, V41, P2381, DOI 10.1016/j.patcog.2008.01.006 Schomaker L, 2007, PATTERN RECOGN LETT, V28, P719, DOI 10.1016/j.patrec.2006.08.005 Schomaker L, 2004, IEEE T PATTERN ANAL, V26, P787, DOI 10.1109/TPAMI.2004.18 Srihari S., 2003, P 11 C INT GRAPH SOC, P278 Srihari S. N., 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.2001.953974 Srihari SN, 2002, J FORENSIC SCI, V47, P856 Tan G., 2008, 19 INT C PATT REC, P1 TAN GX, 2008, 11 INT C FRONT HANDW, P475 Tan GX, 2009, PATTERN RECOGN, V42, P3313, DOI 10.1016/j.patcog.2008.12.019 Viard-Gaudin Christian, 1999, INT C DOC AN REC ICD, P455 ZHANG B, 2003, ICDAR 03, P1142 Zois EN, 2000, PATTERN RECOGN, V33, P385, DOI 10.1016/S0031-3203(99)00063-1 2009, VISION OBJECTS IND T NR 38 TC 0 Z9 0 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1433-2833 J9 INT J DOC ANAL RECOG JI Int. J. Doc. Anal. Recognit. PD JUN PY 2010 VL 13 IS 2 SI SI BP 147 EP 157 DI 10.1007/s10032-009-0110-z PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA 613UJ UT WOS:000279007000007 ER PT J AU Saldarriaga, SP Viard-Gaudin, C Morin, E AF Saldarriaga, Sebastian Pena Viard-Gaudin, Christian Morin, Emmanuel TI Impact of online handwriting recognition performance on text categorization SO INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION LA English DT Article; Proceedings Paper CT 16th Document Recognition and Retrieval Conference CY JAN, 2009 CL San Jose, CA SP IS&T, SPIE Elect Imaging DE Text categorization; Noisy text; Document categorization; Online handwriting recognition ID ALGORITHM AB Today, there is an increasing demand of efficient archival and retrieval methods for online handwritten data. For such tasks, text categorization is of particular interest. The textual data available in online documents can be extracted through online handwriting recognition; however, this process produces errors in the resulting text. This work reports experiments on the categorization of online handwritten documents based on their textual contents. We analyze the effect of word recognition errors on the categorization performances, by comparing the performances of a categorization system with the texts obtained through online handwriting recognition and the same texts available as ground truth. Two well-known categorization algorithms (kNN and SVM) are compared in this work. A subset of the Reuters-21578 corpus consisting of more than 2,000 handwritten documents has been collected for this study. Results show that classification rate loss is not significant, and precision loss is only significant for recall values of 60-80% depending on the noise levels. C1 [Saldarriaga, Sebastian Pena; Morin, Emmanuel] Univ Nantes, LINA UMR CNRS 6241, Nantes, France. [Viard-Gaudin, Christian] Univ Nantes, IRCCyN UMR CNRS 6597, Nantes, France. RP Saldarriaga, SP (reprint author), Univ Nantes, LINA UMR CNRS 6241, Nantes, France. EM sebastian.pena-saldarriaga@univ-nantes.fr; christian.viard-gaudin@univ-nantes.fr; emmanuel.morin@univ-nantes.fr CR Adai AT, 2004, J MOL BIOL, V340, P179, DOI 10.1016/j.jmb.2004.04.047 Apte C., 1994, P 17 ANN INT ACM SIG, P23 BAEZA-YATES R, 1999, MODERN INFORM RETRIE, P73 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 Chen N, 2007, INT J DOC ANAL RECOG, V10, P1, DOI 10.1007/s10032-006-0020-2 Conover W. J., 1998, PRACTICAL NONPARAMET, V3rd CROFT B, 1994, P S DOC AN INF RETR, P115 Dasarathy B. V., 1991, NEAREST NEIGHBOR NN Debole F, 2005, J AM SOC INF SCI TEC, V56, P584, DOI 10.1002/asi.20147 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DOULGERI N, 2009, P DOC REC RETR 16 SP, V7247 Forman G, 2004, P 21 INT C MACH LEAR Ittner David J., 1995, P SDAIR 95 4 ANN S D, P301 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Junker M., 1998, International Journal on Document Analysis and Recognition, V1, DOI 10.1007/s100320050012 KOCH G, 2006, THESIS U ROUEN Kolcz A, 2000, PATTERN ANAL APPL, V3, P153, DOI 10.1007/s100440070020 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Marinai S., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition Milewski RJ, 2009, INT J DOC ANAL RECOG, V11, P203, DOI 10.1007/s10032-008-0077-1 Mitchell T, 1997, MACH LEARN, P239 MURATA M, 2006, P 7 IAPR WORKSH DOC, P506 Okamoto S, 2003, THEOR COMPUT SCI, V298, P207, DOI 10.1016/S0304-3975(02)00424-3 Perraud F, 2005, IEICE T INF SYST, VE88D, P1807, DOI 10.1093/ietisy/e88-d.8.1807 PFEIFER U, 1995, P 3 INT WORLD WID WE, P1027 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rath T. M., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009056 Rath T. M., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Russell G., 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, DOI 10.1109/IWFHR.2002.1030915 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 TAGHVA K, 2000, P DOC REC RETR 8 SPI, V4307, P68, DOI 10.1117/12.410861 Tan CL, 2002, IEEE T PATTERN ANAL, V24, P838 TOMAI C, 2002, P 8 INT WORKSH FRONT, P3453 Vapnik V., 1995, NATURE STAT LEARNING Vinciarelli A, 2005, IEEE T PATTERN ANAL, V27, P1882, DOI 10.1109/TPAMI.2005.248 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 Weston J, 2001, ADV NEUR IN, V13, P668 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 2001, P 24 ANN INT ACM SIG, P137, DOI 10.1145/383952.383975 NR 42 TC 0 Z9 0 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1433-2833 J9 INT J DOC ANAL RECOG JI Int. J. Doc. Anal. Recognit. PD JUN PY 2010 VL 13 IS 2 SI SI BP 159 EP 171 DI 10.1007/s10032-009-0108-6 PG 13 WC Computer Science, Artificial Intelligence SC Computer Science GA 613UJ UT WOS:000279007000008 ER PT J AU Garcia-Crespo, A Colomo-Palacios, R Gomez-Berbis, JM Ruiz-Mezcua, B AF Garcia-Crespo, Angel Colomo-Palacios, Ricardo Miguel Gomez-Berbis, Juan Ruiz-Mezcua, Belen TI SEMO: a framework for customer social networks analysis based on semantics SO JOURNAL OF INFORMATION TECHNOLOGY LA English DT Article DE social networks; customer relationship management; semantics; emotions; natural language processing ID RELATIONSHIP MANAGEMENT; EMOTIONS; WEB; SATISFACTION; DOCUMENTS AB The increasing importance of the Internet in most domains has brought about a paradigm change in consumer relations. The influence of Social Networks has entered the Customer Relationship Management domain under the coined term CRM 2.0. In this context, the need to understand and classify the interactions of customers by means of new platforms has emerged as a challenge for both researchers and professionals worldwide. This is the perfect scenario for the use of SEMO, a platform for Customer Social Networks Analysis based on Semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the Natural Language Processing domain. The results of the evaluation of the experimental implementation of SEMO reveal a promising and viable platform from a technical perspective. Journal of Information Technology (2010) 25, 178-188. doi: 10.1057/jit.2010.1 Published online 18 May 2010 C1 [Garcia-Crespo, Angel; Colomo-Palacios, Ricardo; Miguel Gomez-Berbis, Juan; Ruiz-Mezcua, Belen] Univ Carlos III Madrid, Dept Comp Sci, Madrid, Spain. RP Colomo-Palacios, R (reprint author), Univ Carlos III Madrid, Dept Comp Sci, Avda Univ 30, Madrid, Spain. EM ricardo.colomo@uc3m.es RI Colomo-Palacios, Ricardo/E-5139-2010; Garcia Crespo, Angel/ OI Colomo-Palacios, Ricardo/0000-0002-1555-9726; Garcia Crespo, Angel/0000-0003-4206-7601 FU Spanish Ministry of Industry, Tourism, and Commerce [TSI-020400-2009-148, TSI-020100-2008-665, TSI-020400-2009-127] FX This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665) and GO2 (TSI-020400-2009-127). CR Baader F., 2003, DESCRIPTION LOGIC HD Bagozzi RP, 1999, J ACAD MARKET SCI, V27, P184, DOI 10.1177/0092070399272005 BAND W, 2008, FORRESTER WAVET ENTE Berners-Lee T., 2001, SCI AM, V284, P35, DOI DOI 10.1038/SCIENTIFICAMERICAN0501-34 Bueren A., 2005, Business Process Management Journal, V11, DOI 10.1108/14637150510619894 Chang CW, 2009, EXPERT SYST APPL, V36, P1433, DOI 10.1016/j.eswa.2007.11.027 Chen I. J., 2003, Business Process Management Journal, V9, DOI 10.1108/14637150310496758 Cleverdon C. W., 1966, FACTORS DETERMINING Colomo-Palacios R, 2008, INT J KNOWL LEARN, V4, P452 DANISMAN T, 2008, P AISB 2008 CONV COM, P53 Davenport T., 2001, SLOAN MANAGE REV, V42, P63 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eckerson W. W., 1995, OPEN INFORMATION SYS, V10, P1 EIKELMANN S, 2008, J DIRECT DATA DIGITA, V9, P293, DOI 10.1057/palgrave.dddmp.4350094 Fensel D., 2002, ONTOLOGIES SILVER BU FRANCISCO V, 2007, P 1 INT C WEB REAS R, P88 GARCIACRESPO A, 2008, SOCIAL WEB EVOLUTION Giunchiglia F., 2007, J DATA SEMANTICS, VVIII, P57 Gountas J, 2007, J BUS RES, V60, P72, DOI 10.1016/j.jbusres.2006.08.007 Heyman P., 2006, COLLABORATIVE CREATI Huang MH, 2001, J BUS PSYCHOL, V16, P239, DOI 10.1023/A:1011109200392 Izard CD, 1977, HUMAN EMOTIONS JARRAR M, 2008, SEMANTIC WEB BUSINES Kazmer MM, 2007, LIBR INFORM SCI RES, V29, P5, DOI 10.1016/j.lisr.2006.10.006 King SF, 2008, IND MARKET MANAG, V37, P421, DOI 10.1016/j.indmarman.2007.02.005 Laros FJM, 2005, J BUS RES, V58, P1437, DOI 10.1016/j.jbusres.2003.09.013 LEVITT T, 1983, HARVARD BUS REV, V61, P87 Linoff G. S., 2002, MINING WEB TRANSFORM LOPEZ JM, 2008, P 1 WORLD SUMM KNOWL, P96 MAGRO D, 2008, P 1 INT WORKSH ONT S Mahdavi I, 2008, DATA KNOWL ENG, V65, P355, DOI 10.1016/j.datak.2007.12.003 MAOZ M, 2008, MAGIC QUADRANT CRM C MARSTON P, 2008, FORRESTER WAVET MIDM MATHIEU Y, 2005, P 1 INT C ASCII 2005, P350 McKinsey Quarterly, 2007, MCKINSEY Q MAR Miao QL, 2009, EXPERT SYST APPL, V36, P7192, DOI 10.1016/j.eswa.2008.09.035 Morrison PJ, 2008, INFORM PROCESS MANAG, V44, P1562, DOI 10.1016/j.ipm.2007.12.010 O'reilly T., 2007, COMMUNICATIONS STRAT, V1, P17 O'Reilly T., 2005, WHAT WEB 2 0 DESIGN *OPENSOCIAL, 2008, GOOGL COD OFF WEB SI RIVERA I, 2008, P 3 IEEE INT C SYST, P13 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMITZ P, 2006, P 15 WWW C ED UK, P63 Schoefer K, 2008, SERV BUS, V2, P65, DOI 10.1007/s11628-007-0024-0 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shani D., 1992, J CONSUMER MARKETING, V9, P33, DOI 10.1108/07363769210035215 Stone M., 2009, J DATABASE MARKETING, V16, P101 STRAPPARAVA C, 2008, P 2008 ACM S APPL CO, P1556, DOI 10.1145/1363686.1364052 TAKASHI, 2006, NATURAL LANGUAGE PRO, V13, P201 van Atteveldt Wouter, 2008, J INF TECHNOL, V5, P73 van Rijsbergen C. J., 1979, INFORM RETRIEVAL VANDAMME C, 2007, P IADIS MULT C COMP, P330 van Dolen W, 2004, J BUS RES, V57, P437, DOI 10.1016/S0148-2963(02)00277-1 Zeelenberg M, 2004, J BUS RES, V57, P445, DOI 10.1016/S0148-2963(02)00278-3 Tan SB, 2008, EXPERT SYST APPL, V34, P2622, DOI 10.1016/j.eswa.2007.05.028 NR 55 TC 36 Z9 36 PU PALGRAVE MACMILLAN LTD PI BASINGSTOKE PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND SN 0268-3962 J9 J INF TECHNOL JI J. Inf. Technol. PD JUN PY 2010 VL 25 IS 2 BP 178 EP 188 DI 10.1057/jit.2010.1 PG 11 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 612TT UT WOS:000278927100007 ER PT J AU Merabti, M Liu, Z Yu, H Kundur, D AF Merabti, Madjid Liu, Zhu Yu, Heather Kundur, Deepa TI Advances In Peer-To-Peer Content Search SO JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY LA English DT Article DE Peer-to-peer; Content search AB Peer-to-peer (P2P) computer networks have recently received tremendous attention due to their inherent scalability and flexibility, which facilitates a broad spectrum of innovative multimedia applications. Such networks rely on the power of participant nodes of the network (called peers) for communications and computation. Traditional applications of P2P multimedia include decentralized file sharing and content distribution. Yet, the value of the virtually unlimited amount of data distributed in the P2P network will be sacrificed if effective and efficient ways to locate the content are missing. This challenge has stimulated extensive research in recent years, and many new P2P content search methods have been proposed. This paper provides a timely review of influential work in the area of peer-to-peer (P2P) content search. We begin with a survey of text-based P2P search mechanisms and continue with an exposition of content-based and semantic-based approaches followed by a discussion of future directions. C1 [Liu, Zhu] AT&T Labs Res, Middletown, NJ USA. [Merabti, Madjid] Liverpool John Moores Univ, Sch Comp & Math Sci, Liverpool L3 5UX, Merseyside, England. [Yu, Heather] Huawei Technol USA, Bridgewater, NJ USA. [Kundur, Deepa] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX USA. RP Liu, Z (reprint author), AT&T Labs Res, Middletown, NJ USA. EM zliu@research.att.com CR Ahmed R, 2007, IEEE J SEL AREA COMM, V25, P73, DOI 10.1109/JSAC.2007.070108 ARDIZZONE E, 2006, LECT NOTES COMPUTER, V4351 *BEARSHARE, 2005, FREE PEERS Cai HL, 2006, IEEE T PARALL DISTR, V17, P1189 CHEN H, 2008, EFFICIENT MULTIKEYWO CHRISTEL M, 2005, ADDRESSING CHALLENGE DABEK F, 2001, BUILDING PEER TO PEE, P81 Datta S, 2006, IEEE INTERNET COMPUT, V10, P18, DOI 10.1109/MIC.2006.74 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 EBERSPACHER J, 2004, STRUCTURED P2P NETWO GIBBON D, 2006, MIRACLE VIDEO SEARCH *GNUT, V04 GNUT Gong L, 2001, IEEE INTERNET COMPUT, V5, P88 Gradecki J., 2002, MASTERING JXTA BUILD *IBM TJ WATS RES C, MARVEL MULT AN RETR *IMESH INC, 2005, IMESH PROF 5 0 KUNDUR D, 2006, ENCY MULTIMEDIA Latent Semantic Analysis @ CU Boulder, LATENT SEMANTIC ANAL LEE I, 2004, ISCAS 2004 Lu J., 2003, P ACM CIKM 03 NEW OR LUH W, 2007, EURASIP J APPL SIGNA LUH W, 2006, P 2 DSSNS COL MD APR Minar N., 2001, PEER TO PEER HARNESS RATNASAMY S, 2003, IRBTR03009 Ratnasamy Sylvia, 2001, SIGCOMM 01, P161, DOI DOI 10.1145/383059.383072 Rowstron A., 2001, IFIP ACM INT C DISTR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tang C., 2002, PEERSEARCH EFFICIENT Tang C., 2003, P 2003 C APPL TECHN, P175 Thuraisingham B, 2004, SENS LETT, V2, P37, DOI 10.1166/sl.2004.022 Traversat B., PROJECT JXTA LOOSELY Wang MF, 2007, PHYTOCHEM ANALYSIS, V18, P157, DOI 10.1002/pca.965 YANG C, 2003, WWW 2003 Yang K.-H., 2006, WI 2006, P702, DOI [10.1109/WI.2006.137, DOI 10.1109/WI.2006.137] ZHANG R, 2005, ASSISTED PEER TO PEE Zhu Y., 2004, ESS EFFICIENT SEMANT NR 36 TC 1 Z9 2 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1939-8018 J9 J SIGNAL PROCESS SYS JI J. Signal Process. Syst. Signal Image Video Technol. PD JUN PY 2010 VL 59 IS 3 BP 309 EP 318 DI 10.1007/s11265-009-0343-6 PG 10 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 576YC UT WOS:000276185000007 ER PT J AU Kashef, R Kamel, MS AF Kashef, Rasha Kamel, Mohamed S. TI Cooperative clustering SO PATTERN RECOGNITION LA English DT Article DE Cooperative clustering; Similarity histogram; Cooperative contingency graph ID GENE-EXPRESSION; ALGORITHMS; PERFORMANCE; PATTERNS; NETWORK; CANCER; ROBUST AB Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution for all datasets of different properties, sizes, structures, and distributions. In this paper, a novel cooperative clustering (CC) model is presented. It involves cooperation among multiple clustering techniques for the goal of increasing the homogeneity of objects within the clusters. The CC model is capable of handling datasets with different properties by developing two data structures, a histogram representation of the pair-wise similarities and a cooperative contingency graph. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of clusters through a coherent merging process. The cooperative model is consistent and scalable in terms of the number of adopted clustering approaches. Experimental results show that the cooperative clustering model outperforms the individual clustering algorithms over a number of gene expression and text documents datasets. (C) 2010 Elsevier Ltd. All rights reserved. C1 [Kamel, Mohamed S.] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. EM rkashef@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR AYAD H, 2007, IEEE T PATTERN ANAL BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 Duda R.O., 1973, PATTERN CLASSIFICATI Eisen MB, 1998, P NATL ACAD SCI USA, V95, P14863, DOI 10.1073/pnas.95.25.14863 ENG Y, 2004, INT C ART INT SCI TE, P138 Fan JC, 2009, PATTERN RECOGN, V42, P2527, DOI 10.1016/j.patcog.2009.04.013 Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 GREENE D, 2006, EFFICIENT ENSEMBLE M HAMMOUDA K, 2006, 2006 SIAM C DAT MIN, P453 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 ISMAIL MA, 1989, PATTERN RECOGN, V22, P75, DOI 10.1016/0031-3203(89)90040-X Jain MN, 1999, ACM COMPUT SURV, V31, P264 Kashef R, 2009, PATTERN RECOGN, V42, P2557, DOI 10.1016/j.patcog.2009.03.011 KASHEF R, 2007, IEEE 7 INT C BIOINF, P116 Kaufmann Leonard, 1990, FINDING GROUPS DATA Kim JK, 2009, PATTERN RECOGN, V42, P2020, DOI 10.1016/j.patcog.2008.11.022 Lai JZC, 2009, PATTERN RECOGN, V42, P2551, DOI 10.1016/j.patcog.2009.02.014 Lin CR, 2005, IEEE T KNOWL DATA EN, V17, P145 Maulik U, 2002, IEEE T PATTERN ANAL, V24, P1650, DOI 10.1109/TPAMI.2002.1114856 Monti S., 2003, CONSENSUS CLUSTERING Ng R. T., 1994, VLDB, P144 Ning HZ, 2010, PATTERN RECOGN, V43, P113, DOI 10.1016/j.patcog.2009.06.001 QIAN Y, 2000, INT C PATT REC ICPR, V2, P732 Qin AK, 2004, NEURAL NETWORKS, V17, P1135, DOI 10.1016/j.neunet.2004.06.013 Qin AK, 2005, PATTERN RECOGN, V38, P1275, DOI 10.1016/j.patcog.2004.12.007 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savares S.M., 2001, P 1 SIAM INT C DAT M, P1 Steinbach M., 2000, P KDD WORKSH TEXT MI, P109 Strehl A., 2002, C ART INT AAAI 2002, P93 Tamayo P, 1999, P NATL ACAD SCI USA, V96, P2907, DOI 10.1073/pnas.96.6.2907 Tavazoie S, 1999, NAT GENET, V22, P281 West M, 2001, P NATL ACAD SCI USA, V98, P11462, DOI 10.1073/pnas.201162998 Wu KL, 2009, PATTERN RECOGN, V42, P2541, DOI 10.1016/j.patcog.2009.02.010 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Xu ST, 2004, J SUPERCOMPUT, V30, P117, DOI 10.1023/B:SUPE.0000040611.25862.d9 Zhao Y., 2002, CRITERION FUNCTIONS NR 40 TC 8 Z9 12 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD JUN PY 2010 VL 43 IS 6 BP 2315 EP 2329 DI 10.1016/j.patcog.2009.12.018 PG 15 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 574KJ UT WOS:000275987700025 ER PT J AU Rodriguez, A Chaovalitwongse, WA Zhe, L Singhal, H Pham, H AF Rodriguez, Andrew Chaovalitwongse, W. Art Zhe, Liang Singhal, Harsh Pham, Hoang TI Master Defect Record Retrieval Using Network-Based Feature Association SO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS LA English DT Article DE Information retrieval (IR); keyword library; keyword weighting; query; text mining; vector space model (VSM) AB As electronic records (e.g., medical records and technical defect records) accumulate, the retrieval of a record from a past instance with the same or similar circumstances, has become extremely valuable. This is because a past record may contain the correct diagnosis or correct solution to the current circumstance. We refer to the two records of the same or similar circumstances as master and duplicate records. Current record retrieval techniques are lacking when applied to this special master defect record retrieval problem. In this study, we propose a new paradigm for master defect record retrieval using network-based feature association (NBFA). We train the master record retrieval process by constructing feature associations to limit the search space. The retrieval paradigm was employed and tested on a real-world large-scale defect record database from a telecommunications company. The empirical results suggest that the NBFA was able to significantly improve the performance of master record retrieval, and should be implemented in practice. This paper presents an overview of technical aspects of the master defect record retrieval problem, describes general methodologies for retrieval of master defect records, proposes a new feature association paradigm, provides performance assessments on real data from a telecommunications company, and highlights difficulties and challenges in this line of research that should be addressed in the future. C1 [Rodriguez, Andrew; Chaovalitwongse, W. Art; Zhe, Liang; Pham, Hoang] Rutgers State Univ, Dept Ind & Syst Engn, Piscataway, NJ 08854 USA. RP Rodriguez, A (reprint author), Rutgers State Univ, Dept Ind & Syst Engn, Piscataway, NJ 08854 USA. EM wchaoval@rci.rutgers.edu; singhalblr@gmail.com RI Liang, Zhe/A-9886-2012 FU National Science Foundation [0546574]; Cisco-Academic Research and Technology Initiative FX Manuscript received December 30, 2008; revised May 14, 2009 and September 30, 2009. First published February 5, 2010; current version published April 14, 2010. This work was supported by the National Science Foundation under CAREER Grant 0546574 (Chaovalitwongse) and the Cisco-Academic Research and Technology Initiative grant. This paper was recommended by Associate Editor Z. Zdrahal. CR AAMODT A, 1994, AI COMMUN, V7, P39 CERRITO P, 2006, P SAS SUGI, P1 CLEVERDON C, 1997, CRANFIELD TESTS INDE DAGLI C, 1997, IMPACTS DATA MINING Duda R.O., 2001, PATTERN CLASSIFICATI GROSSMAN DA, 1998, KLUWER INT SERIES EN JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S KLOPTCHENKO A, 2002, P 8 AM C INF SYST DA, P20 KREYSS J, 2003, P 3 IEEE INT C DAT M, P727 Lancaster F. W., 1968, INFORM RETRIEVAL SYS Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Lewis DD, 2004, J MACH LEARN RES, V5, P361 Liu B., 2003, P 9 ACM SIGKDD INT C, P601 Menon R, 2004, QUAL RELIAB ENG INT, V20, P1, DOI 10.1002/qre.536 Myllymaki P., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989584 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zobel J., 1998, SIGIR Forum, V32 NR 17 TC 2 Z9 2 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1094-6977 J9 IEEE T SYST MAN CY C JI IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev. PD MAY PY 2010 VL 40 IS 3 BP 319 EP 329 DI 10.1109/TSMCC.2010.2040079 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications SC Computer Science GA 583NI UT WOS:000276683400008 ER PT J AU Zou, XC Settimi, R Cleland-Huang, J AF Zou, Xuchang Settimi, Raffaella Cleland-Huang, Jane TI Improving automated requirements trace retrieval: a study of term-based enhancement methods SO EMPIRICAL SOFTWARE ENGINEERING LA English DT Article DE Requirements traceability; Requirements management; Information retrieval models ID INFORMATION-RETRIEVAL; TRACEABILITY; MODEL AB Automated requirements traceability methods that utilize Information Retrieval (IR) methods to generate and maintain traceability links are often more efficient than traditional manual approaches, however the traces they generate are imprecise and significant human effort is needed to evaluate and filter the results. This paper investigates and compares three term-based enhancement methods that are designed to improve the performance of a probabilistic automated tracing tool. Empirical studies show that the enhancement methods can be effective in increasing the accuracy of the retrieved traces; however the effectiveness of each method varies according to specific project characteristics. The analysis of such characteristics has lead to the development of two new project-level metrics which can be used to predict the effectiveness of each enhancement method for a given data set. A procedure to automatically extract critical keywords and phrases from a set of traceable artifacts is also presented to enhance the automated trace retrieval algorithm. The procedure is tested on two new datasets. C1 [Settimi, Raffaella; Cleland-Huang, Jane] Depaul Univ, Sch Comp, Syst & Requirements Engn Ctr, Chicago, IL 60604 USA. RP Settimi, R (reprint author), Depaul Univ, Sch Comp, Syst & Requirements Engn Ctr, Chicago, IL 60604 USA. EM xzou@cdm.depaul.edu; rsettimi@cdm.depaul.edu; jhuang@cdm.depaul.edu FU NSF [CCR-0306303, CCF0810924] FX The work described in this paper was partially funded by NSF grants CCR-0306303 and CCF0810924. CR Antoniol G, 2000, PROC IEEE INT CONF S, P40, DOI 10.1109/ICSM.2000.883003 BORGER E, 2000, J UNIVERS COMPUT SCI, V6, P580 BURKE R, 1997, AAAI SPRING S NAT LA, P17 Cleland-Huang J., 2005, Proceedings. 13th IEEE International Conference on Requirements Engineering Cleland-Huang J, 2005, PROC INT CONF SOFTW, P362 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Davis A. M., 1990, SOFTWARE REQUIREMENT De Lucia A, 2007, ACM T SOFTW ENG METH, V16, DOI 10.1145/1276933.1276934 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEKHTYAR A, 2007, P 15 INT REQ ENG C, P141 De Lucia A, 2009, EMPIR SOFTW ENG, V14, P57, DOI 10.1007/s10664-008-9090-8 EVANS MW, 1989, SOFTWARE FACTORY FAGAN J, 1987, THESIS CORNELL U, P87 Fellbaum C, 1998, WORDNET ELECT LEXICA FORSYTHE G, 1977, COMPUTER METHODS MAT, pCH9 Frakes W. B., 1992, INFORM RETRIEVAL DAT Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 GAY LS, 1990, INFORM PROCESS MANAG, V26, P21, DOI 10.1016/0306-4573(90)90007-O Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), DOI 10.1109/ICRE.1994.292398 Church K. W., 1990, Computational Linguistics, V16 Hayes J. Huffman, 2003, P INT C REQ ENG RE S Hayes JH, 2006, IEEE T SOFTWARE ENG, V32, P4, DOI 10.1109/TSE.2006.3 *INT DEV ENV, 1991, SOFTW PICT PROD SERV JOHO H, 2007, P 8 RECH INF ASS ORD Kaindl H., 1993, ACM SOFTWARE ENG NOT, V18, P30, DOI 10.1145/159420.155836 LIN J, 2006, P 14 IEEE INT C REQ, P11 Maletic J., 2003, P 2 INT WORKSH TRAC, P47 Marchal D, 2003, J STRUCT GEOL, V25, P135, DOI 10.1016/S0191-8141(02)00011-1 Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 *PROMISE, 2008, SOFTW ENG REP Robertson S., 1999, MASTERING REQUIREMEN Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Settimi R., 2004, Proceedings. 7th International Workshop on Principles of Software Evolution, DOI 10.1109/IWPSE.2004.1334768 Singh A.P., 1999, P INT C INTELL SYST, V7, P252 Sparck Jones K., 1976, J DOC, V32, P59, DOI 10.1108/eb026616 Tufis D., 1998, P 1 INT C LANG RES E, P589 WONG SKM, 1991, INFORM SYST, V16, P301, DOI 10.1016/0306-4379(91)90003-R Zou X, 2008, P INT C SOFTW ENG RE, P157 Zou X., 2007, P INT S GRAND CHALL, P40 ZOU X, 2009, THESIS DEPAUL U CHIC Zou XC, 2006, P INT COMP SOFTW APP, P265 NR 45 TC 12 Z9 12 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1382-3256 J9 EMPIR SOFTW ENG JI Empir. Softw. Eng. PD APR PY 2010 VL 15 IS 2 BP 119 EP 146 DI 10.1007/s10664-009-9114-z PG 28 WC Computer Science, Software Engineering SC Computer Science GA 570VH UT WOS:000275705900001 ER PT J AU Kim, YB Kim, YS AF Kim, Young-Bum Kim, Yu-Seop TI An autonomous assessment system based on combined latent semantic kernels SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Autonomous assessment system; Latent semantic kernel; WordNet; Singular value decomposition; Corpus; Combined kernel; BLEU ID WORDNET AB In this paper, we develop an autonomous assessment system based on the kernel combinations which are mixed by two kernel matrices from the WordNet and corpus. Many researchers have tried to integrate these two resources in many applications, to utilize diverse information extracted from each resource. However, since two resources have been represented in quite different ways, one resource has been secondary to another. To fully integrate two resources at the same level, we first transform the WordNet, which has a hierarchical structure, into a matrix structure. Concurrently, another matrix, which represents a co-occurrence of words in the collection of text documents, is constructed. We then build two initial latent semantic kernels from both matrices and merge them into a new single kernel matrix. When we merge two matrices, we split each initial matrix into independent columns and mix the columns with various methods. We acquire a few combined kernel matrices which show various performances in experiments. Compared to the basic vector space model, original kernel matrices, and the BLEU based method, the combined matrices improve the accuracy of assessment. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Kim, Young-Bum; Kim, Yu-Seop] Hallym Univ, Dept Comp Engn, Chunchon 200702, Gangwon Do, South Korea. RP Kim, YS (reprint author), Hallym Univ, Dept Comp Engn, 39 Hallymdaehak Gil, Chunchon 200702, Gangwon Do, South Korea. EM stylemove@hallym.ac.kr; yskim01@hallym.ac.kr FU Korean Government (MOEHRD, Basic Research Promotion Fund) [KRF-2006-331-D00534] FX This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2006-331-D00534). CR AAMODT A, 1994, AI COMMUN, V7, P39 BAIN L, 1987, THOMSON LEARNING BANG H, 2001, P KIPS BRISCOESMITH A, 2002, P ISECON 2002, P19 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Budanitsky A., 2001, WORKSH WORDNET OTH L BURGUN A, 2001, WORKSH WORDNET OTH L Burstein J., 1998, P 36 ANN M ASS COMP, P206 CHEONG D, 2001, THESIS DONGGUK U Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Fellbaum C, 1998, WORDNET ELECT LEXICA GEIGER M, 1996, EDUCATION, P1 Gonzalo J, 1998, P COLING ACL 98 WORK, P38 KANG SS, 2004, GERNERAL PURPOSED MO Kim SH, 2005, FIBER POLYM, V6, P108, DOI 10.1007/BF02875600 KIM YB, 2008, P 7 INT C ADV LANG P Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 2000, IEEE INTELL SYST APP, V15, P27 LEE ER, 2004, KOREAN WORDNET VERSI Martinez ME, 1999, EDUC PSYCHOL, V34, P207, DOI 10.1207/s15326985ep3404_2 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 PAGE EB, 1995, PHI DELTA KAPPAN, V76, P561 Papineni Kishore, 2002, P 40 ANN M ASS COMP, P311, DOI DOI 10.3115/1073083.1073135 Perez D., 2004, P LANG RES EV C LREC Pulman Stephen G., 2005, P 2 WORKSH BUILD ED, P9, DOI 10.3115/1609829.1609831 RICHARDSON R, 1994, CA1294 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEELE CW, 1997, COLL TEACHING, V45, P371 VOORHEES EM, 1993, P 16 ANN INT ACM SIG Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 NR 31 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD APR PY 2010 VL 37 IS 4 BP 3219 EP 3228 DI 10.1016/j.eswa.2009.09.058 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 551JB UT WOS:000274202900059 ER PT J AU Casamayor, A Godoy, D Campo, M AF Casamayor, Agustin Godoy, Daniela Campo, Marcelo TI Identification of non-functional requirements in textual specifications: A semi-supervised learning approach SO INFORMATION AND SOFTWARE TECHNOLOGY LA English DT Article DE Non-functional requirements; Requirement classification; Semi-supervised text learning ID NATURAL-LANGUAGE REQUIREMENTS; ANALYSIS-SUPPORTING SYSTEM; CLASSIFICATION; ELICITATION; ALGORITHM; EM AB Context: Early detection of non-functional requirements (NFRs) is crucial in the evaluation of architectural alternatives starting from initial design decisions. The application of supervised text categorization strategies for requirements expressed in natural language has been proposed in several works as a method to help analysts in the detection and classification of NFRs concerning different aspects of software. However, a significant number of pre-categorized requirements are needed to train supervised text classifiers, which implies that analysts have to manually assign categories to numerous requirements before being able of accurately classifying the remaining ones. Objective: We propose a semi-supervised text categorization approach for the automatic identification and classification of non-functional requirements. Therefore, a small number of requirements, possibly identified by the requirement team during the elicitation process, enable learning an initial classifier for NFRs, which could successively identify the type of further requirements in an iterative process. The goal of the approach is the integration into a recommender system to assist requirement analysts and software designers in the architectural design process. Method: Detection and classification of NFRs is performed using semi-supervised learning techniques. Classification is based on a reduced number of categorized requirements by taking advantage of the knowledge provided by uncategorized ones, as well as certain properties of text. The learning method also exploits feedback from users to enhance classification performance. Results: The semi-supervised approach resulted in accuracy rates above 70%, considerably higher than the results obtained with supervised methods using standard collections of documents. Conclusion: Empirical evidence showed that semi-supervision requires less human effort in labeling requirements than fully supervised methods, and can be further improved based on feedback provided by analysts. Our approach outperforms previous supervised classification proposals and can be further enhanced by exploiting feedback provided by analysts. (C) 2009 Elsevier B.V. All rights reserved. C1 [Casamayor, Agustin] UNICEN Univ, ISISTAN Res Inst, Tandil, Bs As, Argentina. Natl Council Sci & Tech Res, CONICET, Bs As, Argentina. RP Casamayor, A (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,Paraje Arroyo Seco,B7001BBO, Tandil, Bs As, Argentina. EM acasamay@exa.unicen.edu.ar CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Al Balushi TH, 2007, LECT NOTES COMPUT SC, V4542, P306 CASAMAYOR A, 2009, P 10 ARG S ART INT A CASTROHERRERA C, 2009, ACM S APPL COMP Chapelle O., 2006, SEMISUPERVISED LEARN Cleland-Huang J, 2006, P 14 IEEE INT REQ EN, P36 Cleland-Huang J., 2008, P 2 INT WORKSH ULTR, P3, DOI DOI 10.1145/1370700.1370702 Cleland-Huang J, 2007, REQUIR ENG, V12, P103, DOI 10.1007/s00766-007-0045-1 Cybulski J. L., 1998, Proceedings 1998 Asia Pacific Software Engineering Conference (Cat. No.98EX240), DOI 10.1109/APSEC.1998.733606 Cysneiros LM, 2004, IEEE T SOFTWARE ENG, V30, P328, DOI 10.1109/TSE.2004.10 Dag JNO, 2005, IEEE SOFTWARE, V22, P32, DOI 10.1109/MS.2005.1 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DORR J, 2003, P INT WORKSH REQ ENG, P23 Eyheramendy S., 2002, P 9 INT WORKSH ART I Fantechi A., 2002, Proceedings IEEE Joint International Conference on Requirements Engineering, DOI 10.1109/ICRE.2002.1048518 Fernandez-Carriba S, 2004, LATERALITY, V9, P1, DOI 10.1080/13576500342000095 Gervasi V, 2002, SOFTWARE PRACT EXPER, V32, P113, DOI 10.1002/spc.430 Gervasi V, 2005, ACM T SOFTW ENG METH, V14, P277, DOI 10.1145/1072997.1072999 Happel H., 2008, RSSE 08, P11 Jalaji A., 2006, MAKING SENSE TEXT ID JINGBAI T, 2008, P IEEE INT WORKSH SE, P1 Joachims T., 1997, 14 INT C MACH LEARN, P143 Kaiya H., 2004, Proceedings. 12th IEEE International Requirements Engineering Conference Kitapci H, 2006, P 4 INT WORKSH COMP, P48, DOI [10.1109/CERE.2006.8, DOI 10.1109/CERE.2006.8] Ko Y, 2000, P 12 INT C SOFTW ENG, P334 Ko Y, 2007, INFORM SOFTWARE TECH, V49, P1128, DOI 10.1016/j.infsof.2006.11.007 Kowalski G. J., 2000, INFORM STORAGE RETRI Lieberman H, 2001, COMMUN ACM, V44, P69, DOI 10.1145/381641.381661 Liu Bing, 2006, WEB DATA MINING EXPL McCallum A, 1998, AAAI 98 WORKSH LEARN, V752, P41 Mich L, 2004, REQUIR ENG, V9, P40, DOI 10.1007/s00766-003-0179-8 Miller C., 2003, HUMAN DIMENSIONS WIL, V8, P263, DOI 10.1080/716100421 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Ormandjieva O., 2007, P 4 INT WORKSH SOFTW, P39 PALMER JD, 1992, INFORM DECIS TECHNOL, V18, P283 Park S, 2000, INFORM SOFTWARE TECH, V42, P429, DOI 10.1016/S0950-5849(99)00102-0 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Resnick P., 1994, CSCW 94, P175 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer J. B., 2001, Data Mining and Knowledge Discovery, V5, DOI 10.1023/A:1009804230409 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Steele A, 2006, P 1 INT WORKSH MULT, P3 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 44 TC 8 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-5849 J9 INFORM SOFTWARE TECH JI Inf. Softw. Technol. PD APR PY 2010 VL 52 IS 4 BP 436 EP 445 DI 10.1016/j.infsof.2009.10.010 PG 10 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 576CL UT WOS:000276121400006 ER PT J AU Kohn, A Bry, F Manta, A AF Kohn, Alex Bry, Francois Manta, Alexander TI Semantic Search on Unstructured Data: Explicit Knowledge through Data Recycling SO INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS LA English DT Article DE Empirical System Evaluation; Enterprise Information Systems; Information Extraction; Information Search and Retrieval; Semantic Technologies AB Studies agree that searchers are often not satisfied with the performance of current enterprise search engines. As a consequence, more scientists worldwide are actively investigating new avenues for searching to improve retrieval performance. This paper contributes to YASA (Your Adaptive Search Agent), a fully implemented and thoroughly evaluated ontology-based information retrieval system for the enterprise. A salient particularity of YASA is that large parts of the ontology are automatically filled with facts by recycling and transforming existing data. YASA offers context-based personalization, faceted navigation, as well as semantic search capabilities. YASA has been deployed and evaluated in the pharmaceutical research department of Roche, Penzberg, and results show that already semantically simple ontologies suffice to considerably improve search performance. C1 [Bry, Francois] Univ Munich, D-80539 Munich, Germany. CR Basili V., 1994, ENCY SOFTWARE ENG, P528 Brin S., 1998, P 7 INT WORLD WID WE, P107 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Dabbish L., 2004, P SIGCHI C HUM FACT, P319, DOI DOI 10.1145/985692.985733.ISBN Fagin Ronald, 2003, P 12 INT C WORLD WID, P366 Feldman S., 2004, KM WORLD, V13 Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 GEDIGA G, 1999, P 8 INT C HUM COMP I, P1018 HAWKING D, 2004, P ACM INT C P SER, V52, P15 Joachims T., 1998, P 10 EUR C MACH LEAR Joachims T, 2003, ADV SOFT COMP, P79 KIFER M, 1995, J ASSOC COMPUT MACH, V42, P741, DOI 10.1145/210332.210335 Levesque H. J., 2001, LOGIC KNOWLEDGE BASE MASSUN M, 2008, COLLABORATIVE INFORM McNemar Q, 1947, PSYCHOMETRIKA, V12, P153, DOI 10.1007/BF02295996 Micarelli A., 2007, The Adaptive Web. Methods and Strategies of Web Personalization Mitchell TM, 1997, MACHINE LEARNING MTIHLBACHER S, 2008, SCI INFORM LITERACY Nadeau D., 2007, LINGVISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Reeve L., 2005, P 2005 ACM S APPL CO, P1634, DOI DOI 10.1145/1066677.1067049 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Wilcoxon F., 1945, BIOMETRICS B, P80, DOI DOI 10.2307/3001968 Xue G., 2003, P 26 INT ACM SIGIR C, P56 NR 26 TC 0 Z9 0 PU IGI PUBL PI HERSHEY PA 701 E CHOCOLATE AVE, STE 200, HERSHEY, PA 17033-1240 USA SN 1552-6283 J9 INT J SEMANT WEB INF JI Int. J. Semant. Web Inf. Syst. PD APR-JUN PY 2010 VL 6 IS 2 BP 17 EP 35 DI 10.4018/jswis.2010040102 PG 19 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 636AW UT WOS:000280701100002 ER PT J AU Doxas, I Dennis, S Oliver, WL AF Doxas, Isidoros Dennis, Simon Oliver, William L. TI The dimensionality of discourse SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA LA English DT Article DE correlation dimension; language; latent semantic analysis ID FRACTAL DIMENSION; ATTRACTORS AB The paragraph spaces of five text corpora, of different genres and intended audiences, in four different languages, all show the same two-scale structure, with the dimension at short distances being lower than at long distances. In all five cases the short-distance dimension is approximately eight. Control simulations with randomly permuted word instances do not exhibit a low dimensional structure. The observed topology places important constraints on the way in which authors construct prose, which may be universal. C1 [Dennis, Simon] Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA. [Doxas, Isidoros] Univ Colorado, Ctr Integrated Plasma Studies, Boulder, CO 80309 USA. [Oliver, William L.] Univ Colorado, Inst Cognit Sci, Boulder, CO 80309 USA. RP Dennis, S (reprint author), Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA. EM simon.dennis@gmail.com FU National Science Foundation; US Department of Education FX We thank the late Stephen Ivens and Touchstone Applied Science Associates (TASA) of Brewster, NY for providing the TASA corpus; Wolfgang Lenhard of the University of Wuzburg for permission to use the German corpus; and the Perseus Digital Library of Tufts University for making the complete Homer available on the web. This work was partly supported by grants from the National Science Foundation and the US Department of Education. CR ABRAHAM NB, 1986, PHYS LETT A, V114, P217, DOI 10.1016/0375-9601(86)90210-0 Blei DM, 2004, ADV NEUR IN, V16, P17 Chiu G, 2006, J AM STAT ASSOC, V101, P542, DOI 10.1198/016214505000001177 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 ELLNER S, 1988, PHYS LETT A, V133, P128, DOI 10.1016/0375-9601(88)90772-4 Elvevag B, 2007, SCHIZOPHR RES, V93, P304, DOI 10.1016/j.schres.2007.03.001 Endres DM, 2003, IEEE T INFORM THEORY, V49, P1858, DOI 10.1109/TIT.2003.813506 Foltz P.W., 1999, INTELLIGENT ESSAY AS GRASSBERGER P, 1983, PHYSICA D, V9, P189, DOI 10.1016/0167-2789(83)90298-1 Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211 Hastie T., 2008, ELEMENTS STAT LEARNI Kintsch W, 2006, HDB LATENT SEMANTIC Kwantes PJ, 2005, PSYCHON B REV, V12, P703, DOI 10.3758/BF03196761 Lamberti PW, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.052311 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lee DD, 2001, ADV NEUR IN, V13, P556 Levina E., 2005, ADV NEURAL INFORM PR, V15, P777 LICHTENBERG AJ, 1992, APPL MATH SERIES, V38 Press W. H., 1986, NUMERICAL RECIPES PRICHARD D, 1993, GEOPHYS RES LETT, V20, P2817, DOI 10.1029/93GL03012 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sprott JC, 2001, INT J BIFURCAT CHAOS, V11, P1865, DOI 10.1142/S021812740100305X STEYVERS M, 2006, HDB LATENT SEMANTIC, P427 TAKENS F, 1985, LECT NOTES MATH, V1125, P99 Takens F., 1981, LECT NOTES MATH, V898, P366, DOI DOI 10.1007/BFB0091924 THEILER J, 1988, PHYS LETT A, V133, P195, DOI 10.1016/0375-9601(88)91016-X THEILER J, 1990, J OPT SOC AM A, V7, P1055, DOI 10.1364/JOSAA.7.001055 NR 27 TC 3 Z9 3 PU NATL ACAD SCIENCES PI WASHINGTON PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA SN 0027-8424 J9 P NATL ACAD SCI USA JI Proc. Natl. Acad. Sci. U. S. A. PD MAR 16 PY 2010 VL 107 IS 11 BP 4866 EP 4871 DI 10.1073/pnas.0908315107 PG 6 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 570YI UT WOS:000275714300015 PM 20194761 ER PT J AU Fersini, E Messina, E Archetti, F AF Fersini, E. Messina, E. Archetti, F. TI A probabilistic relational approach for web document clustering SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Relational document clustering; Relational web structure estimation AB The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Fersini, E.; Messina, E.; Archetti, F.] Univ Milano Bicocca, Dipartimento Informat Sistemist & Comunicaz, Milan, Italy. [Archetti, F.] Consorzio Milano Ric, I-20126 Milan, Italy. RP Fersini, E (reprint author), Univ Milano Bicocca, Dipartimento Informat Sistemist & Comunicaz, Milan, Italy. EM fersini@disco.unimib.it CR ARCHETTI F, 2006, P 7 INT C FLEX QUER, P257 Cai D., 2003, P 5 AS PAC WEB C, P406 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dittenbach M., 2001, P INT C ART NEUR NET Fersini E, 2008, INFORM PROCESS MANAG, V44, P1431, DOI 10.1016/j.ipm.2007.11.003 FRIEDMAN N, 2001, P 16 INT JOINT C ART, P1300 Fung BC, 2003, P 3 SIAM INT C DAT M Hubert L., 1985, J CLASSIF, V2 JOO KH, 2005, P DISTR COMP INT TEC, P321 Lin CR, 2005, IEEE T KNOWL DATA EN, V17, P145 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Neville J., 2003, P TEXT MIN LINK AN W NICHOLAS C, 2003, COMPREHENSIVE SURVEY PENG J, 2006, P IEEE INT JOINT C N, P4146 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, P 6 ACM SIGKDD INT C Strehl A, 2000, P AAAI WORKSH AI WEB, P58 TAKAHASHI K, 2005, P 21 INT C DAT ENG I, P1225 Taskar B, 2001, P 17 INT JOINT C ART, P870 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wagsaff K., 2001, P 18 INT C MACH LEAR, P577 Wang J, 2003, P ACM SIGIR C RES DE, P274 ZENG HJ, 2002, P 3 INT C WEB INF SY, P161 Zhong S, 2003, J MACHINE LEARNING R, V4, P1001, DOI 10.1162/jmlr.2003.4.6.1001 Zhong S, 2005, IEEE IJCNN, P3180 NR 28 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2010 VL 46 IS 2 BP 117 EP 130 DI 10.1016/j.ipm.2009.08.003 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 569PX UT WOS:000275611200001 ER PT J AU Li, DD Kwong, CP AF Li, Dandan Kwong, Chung-Ping TI Understanding Latent Semantic Indexing: A Topological Structure Analysis Using Q-Analysis SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; SPACE; MODEL AB The method of latent semantic indexing (LSI) is well-known for tackling the synonymy and polysemy problems in information retrieval; however, its performance can be very different for various datasets, and the questions of what characteristics of a dataset and why these characteristics contribute to this difference have not been fully understood. In this article, we propose that the mathematical structure of simplexes can be attached to a term-document matrix in the vector space model (VSM) for information retrieval. The Q-analysis devised by R.H. Atkin (1974) may then be applied to effect an analysis of the topological structure of the simplexes and their corresponding dataset. Experimental results of this analysis reveal that there is a correlation between the effectiveness of LSI and the topological structure of the dataset. By using the information obtained from the topological analysis, we develop a new method to explore the semantic information in a dataset. Experimental results show that our method can enhance the performance of VSM for datasets over which LSI is not effective. C1 [Li, Dandan] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. [Kwong, Chung-Ping] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China. RP Li, DD (reprint author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. EM ddli@cse.ust.hk; cpkwong@mae.cuhk.edu.hk CR Atkin R. H., 1974, MATH STRUCTURE HUMAN BAST H., 2005, P 28 INT C RES DEV I, P11, DOI 10.1145/1076034.1076040 BEAUMONT JR, 1982, INTRO Q ANAL Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding CHQ, 2005, J AM SOC INF SCI TEC, V56, P597, DOI 10.1002/asi.20148 Ding C. H. Q., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312652 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 DUPRET G, 2003, P ACM C RES DEV INF, P221 Edmonds P., 1997, P 35 ANN M ASS COMP, P507 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 GOULD P, 1981, ANN ASSOC AM GEOGR, V71, P166, DOI 10.1111/j.1467-8306.1981.tb01346.x Hoenkamp E, 2003, J AM SOC INF SCI TEC, V54, P314, DOI 10.1002/asi.10211 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Husbands P, 2005, INFORM PROCESS MANAG, V41, P777, DOI 10.1016/j.ipm.2004.03.006 Jessup ER, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P121 JOHNSON JH, 1981, ENVIRON PLANN B, V8, P419, DOI 10.1068/b080419 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 KWONG CP, 2003, CUHKACE0303 Li D., 2008, P 16 ACM SIGSPATIAL, P6 LI DD, 2007, P INT C INF KNOWL EN, P110 Li DD, 2010, J AM SOC INF SCI TEC, V61, P175, DOI 10.1002/asi.21219 LI DD, 2007, THESIS CHINESE U HON Manning C.D., 2008, INTRO INFORM RETRIEV Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 PEAT H, 1991, J AM SOC INFORM SCI, V45, P378 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shannon E. C., 1948, BELL SYST TECH J, V27, P623 van Rijsbergen C. J., 1979, INFORM RETRIEVAL YAN J, 2009, P SIAM INT C DAT MIN, P1159 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 34 TC 0 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD MAR PY 2010 VL 61 IS 3 BP 592 EP 608 DI 10.1002/asi.21270 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 560TO UT WOS:000274926000012 ER PT J AU Hai, ZG AF Hai Zhuge TI Interactive semantics SO ARTIFICIAL INTELLIGENCE LA English DT Article DE Classification; Open system; Semantics; Semantic link network; Social semantics; Interaction; Interactive semantics ID NETWORKS; MODEL; REPRESENTATION; DISCOVERY; RICHNESS; LEVEL AB Much research pursues machine intelligence through better representation of semantics. What is semantics? People in different areas view semantics from different facets although it accompanies interaction through civilization. Some researchers believe that humans have some innate structure in mind for processing semantics. Then, what the structure is like? Some argue that humans evolve a structure for processing semantics through constant learning. Then, how the process is like? Humans have invented various symbol systems to represent semantics. Can semantics be accurately represented? Turing machines are good at processing symbols according to algorithms designed by humans, but they are limited in ability to process semantics and to do active interaction. Super computers and highspeed networks do not help solve this issue as they do not have any semantic worldview and cannot reflect themselves. Can future cyber-society have some semantic images that enable machines and individuals (humans and agents) to reflect themselves and interact with each other with knowing social situation through time? This paper concerns these issues in the context of studying an interactive semantics for the future cyber-society. It firstly distinguishes social semantics from natural semantics, and then explores the interactive semantics in the category of social semantics. Interactive semantics consists of an interactive system and its semantic image, which co-evolve and influence each other. The semantic worldview and interactive semantic base are proposed as the semantic basis of interaction. The process of building and explaining semantic image can be based on an evolving structure incorporating adaptive multi-dimensional classification space and self-organized semantic link network. A semantic lens is proposed to enhance the potential of the structure and help individuals build and retrieve semantic images from different facets, abstraction levels and scales through time. (C) 2009 Elsevier B.V. All rights reserved. C1 Chinese Acad Sci, Knowledge Grid Res Grp, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China. RP Hai, ZG (reprint author), Chinese Acad Sci, Knowledge Grid Res Grp, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China. EM zhuge@ict.ac.cn FU National Basic Research Program of China [2003CB317001] FX This research was supported by National Basic Research Program of China (973 Project No. 2003CB317001) from August 2003 to August 2009. CR Adamic LA, 2000, SCIENCE, V287, DOI 10.1126/science.287.5461.2115a Aiello M., 2002, International Journal on Document Analysis and Recognition, V5, DOI 10.1007/s10032-002-0080-x Ainsworth M. S., 1979, PATTERNS ATTACHMENT Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 BARWISE J., 1989, SITUATION LOGIC Berners-Lee T, 2001, SCI AM, V284, P34 Booch G., 1999, UNIFIED MODELING LAN Cattuto C, 2007, P NATL ACAD SCI USA, V104, P1461, DOI 10.1073/pnas.0610487104 Chen P. P., 1976, ACM T DATABASE SYST, P9, DOI 10.1145/320434.320440 Codd E. F., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320107.320109 Cooper MC, 2005, COMPUT LINGUIST, V31, P227, DOI 10.1162/0891201054223995 CREARY LG, 1985, P 23 ANN M ASS COMP, P172, DOI 10.3115/981210.981232 Croft William, 2004, COGNITIVE LINGUISTIC DAFT RL, 1986, MANAGE SCI, V32, P554, DOI 10.1287/mnsc.32.5.554 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FIKES R, 1985, COMMUN ACM, V28, P904, DOI 10.1145/4284.4285 Finin T., 1994, P 3 INT C INF KNOWL, P456, DOI 10.1145/191246.191322 Fortunato S, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.056104 FRAWLEY WJ, 1992, AI MAG, V13, P57 Girvan M., 2002, P NATL ACAD SCI USA, V99, P8271 GOLDER S, 2005, ARXIVCS0508082V1CSDL Wegner P, 2003, COMMUN ACM, V46, P100, DOI 10.1145/641205.641235 Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066 HAREL D, 2004, IEEE COMPUT, V37, P64 Kamber M., 2001, DATA MINING CONCEPTS Kleinberg JM, 2000, NATURE, V406, P845, DOI 10.1038/35022643 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Knuth D. E., 1968, Mathematical Systems Theory, V2, DOI 10.1007/BF01692511 KUMAR S, P AAAI 2000, P42 Lakoff George, 1999, PHILOS FLESH Langacker Ronald W., 1987, FDN COGNITIVE GRAMMA, V1 Manning C. D., 1999, FDN STAT NATURAL LAN McCarthy J, 2007, ARTIF INTELL, V171, P1174, DOI 10.1016/j.artint.2007.10.009 McCarthy J, 2008, ARTIF INTELL, V172, P2003, DOI 10.1016/j.artint.2008.10.001 MILNER R, 1993, COMMUN ACM, V36, P78, DOI 10.1145/151233.151240 Minsky M., 2006, EMOTION MACHINE COMM Newell A., 1972, HUMAN PROBLEM SOLVIN NEWELL A, 1982, ARTIF INTELL, V18, P87, DOI 10.1016/0004-3702(82)90012-1 Ngwenyama OK, 1997, MIS QUART, V21, P145, DOI 10.2307/249417 POWERS WT, 1973, SCIENCE, V179, P351, DOI 10.1126/science.179.4071.351 Quillian M. R., 1968, SEMANTIC INFORM PROC Rumbaugh J, 1991, OBJECT ORIENTED MODE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schweitzer F, 2009, SCIENCE, V325, P422, DOI 10.1126/science.1173644 TURING A, 1950, MIND, V21 Wegner P, 1997, COMMUN ACM, V40, P80, DOI 10.1145/253769.253801 Winskel G, 1993, FORMAL SEMANTICS PRO Zhuge H., 2007, IEEE T KNOWL DATA EN, V19, P759 Zhuge H, 2009, IEEE T KNOWL DATA EN, V21, P785, DOI 10.1109/TKDE.2008.141 Zhuge H, 2008, ACM T INTERNET TECHN, V8, DOI 10.1145/1391949.1391954 Zhuge H, 2008, WEB INF SYST ENG INT, V4, P1, DOI 10.1007/978-0-387-72772-1 Zhuge H., 2010, KNOWLEDGE GRID NR 52 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0004-3702 J9 ARTIF INTELL JI Artif. Intell. PD FEB PY 2010 VL 174 IS 2 SI SI BP 190 EP 204 DI 10.1016/j.artint.2009.11.014 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA 559YB UT WOS:000274867000012 ER PT J AU Chen, ZX Fu, B Abraham, J AF Chen, Zhixiang Fu, Bin Abraham, John TI A quadratic lower bound for Rocchio's similarity-based relevance feedback algorithm with a fixed query updating factor SO JOURNAL OF COMBINATORIAL OPTIMIZATION LA English DT Article DE Information retrieval; Relevance feedback; Vector space models; Similarity; Lower bounds ID VECTOR-SPACE MODEL; RETRIEVAL AB Rocchio's similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In practice, Rocchio's algorithm often uses a fixed query updating factor. When this is the case, we strengthen the linear Omega(n) lower bound obtained by Chen and Zhu (Inf. Retr. 5:61-86, 2002) and prove that Rocchio's algorithm makes Omega(k(n-k)) mistakes in searching for a collection of documents represented by a monotone disjunction of k relevant features over the n-dimensional binary vector space {0,1} (n) , when the inner product similarity measure is used. A quadratic lower bound is obtained when k is linearly proportional to n. We also prove an O(k(n-k)(3)) upper bound for Rocchio's algorithm with the inner product similarity measure in searching for such a collection of documents with a constant query updating factor and a zero classification threshold. C1 [Chen, Zhixiang; Fu, Bin; Abraham, John] Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78541 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, 1201 W Univ Dr, Edinburg, TX 78541 USA. EM chen@cs.panam.edu; binfu@cs.panam.edu; jabraham@cs.panam.edu FU NSF [CNS-0521585]; UTPA FX We thank three anonymous referees for their valuable comments and suggestions to help us revise the paper. An extended abstract of this paper was reported in Chen and Fu (2005). Theorems 1 and 2 were proved for all the four similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient). Theorems 3 to 5 were proved for the inner product similarity. Research in this paper is supported in part by NSF CNS-0521585 and UTPA Faculty Start-up Research Fund. CR Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN Z, 2004, COMPUTATIONAL WEB IN, P303 CHEN Z, 2000, P AAAI 2000 WORKSH A, P12 CHEN Z, 2002, KNOWL INF SYST, V4, P219, DOI 10.1007/s101150200005 Chen ZX, 2001, J AM SOC INF SCI TEC, V52, P655 Chen ZX, 2005, LECT NOTES COMPUT SC, V3595, P955 Chen ZX, 2001, LECT NOTES COMPUT SC, V2108, P540 Chen ZX, 2007, J AM SOC INF SCI TEC, V58, P1392, DOI 10.1002/asi.20612 Chen ZX, 2002, INFORM RETRIEVAL, V5, P61, DOI 10.1023/A:1012730924277 Frakes W. B., 1992, INFORM RETRIEVAL DAT Ide E., 1971, SMART RETRIEVAL SYST, P373 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Van Rijsbergen K., 1979, INFORM RETRIEVAL WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 21 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1382-6905 J9 J COMB OPTIM JI J. Comb. Optim. PD FEB PY 2010 VL 19 IS 2 BP 134 EP 157 DI 10.1007/s10878-008-9169-6 PG 24 WC Computer Science, Interdisciplinary Applications; Mathematics, Applied SC Computer Science; Mathematics GA 552UB UT WOS:000274318000002 ER PT J AU Magerman, T Van Looy, B Song, XY AF Magerman, Tom Van Looy, Bart Song, Xiaoyan TI Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications SO SCIENTOMETRICS LA English DT Article DE Text mining; Latent Semantic Analysis; Science-technology linkages; Patent-publication pairs; Author-inventor relationships ID COMBINING FULL-TEXT; INFORMATION-RETRIEVAL; SCIENCE; TECHNOLOGY; KNOWLEDGE; CLASSIFICATION; RESEARCHERS; PERFORMANCE; SPACE; RANK AB In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected-and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents-in this case, patents and publications-might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n = 6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed. C1 [Magerman, Tom; Van Looy, Bart; Song, Xiaoyan] Ctr R&D Monitoring, ECOOM, B-3000 Leuven, Belgium. [Magerman, Tom; Van Looy, Bart; Song, Xiaoyan] Katholieke Univ Leuven, Fac Business & Econ, Dept Managerial Econ Strategy & Innovat, B-3000 Leuven, Belgium. [Van Looy, Bart] Leuven Res & Dev, INCENTIM, B-3000 Leuven, Belgium. RP Magerman, T (reprint author), Ctr R&D Monitoring, ECOOM, Dekenstr 2, B-3000 Leuven, Belgium. EM tom.magerman@econ.kuleuven.be; bart.vanlooy@econ.kuleuven.be; xiaoyan.song@econ.kuleuven.be CR ATHERTON P, 1965, 651 AIPDRP Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bassecoulard E, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P665 Berry M., 1999, UNDERSTANDING SEARCH Berry M.W., 2003, SURVEY TEXT MINING Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 CALDERINI MC, 2005, 169 CESPRI CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003 CARROLL JD, 1980, ANNU REV PSYCHOL, V31, P607, DOI 10.1146/annurev.ps.31.020180.003135 COURTIAL JP, 1994, SCIENTOMETRICS, V31, P251, DOI 10.1007/BF02016875 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding W., 2006, 11917 NBER Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367 ENGELSMAN EC, 1994, RES POLICY, V23, P1, DOI 10.1016/0048-7333(94)90024-8 European Commission, 2003, 3 EUR REP S T IND FABRIZIO KR, 2005, COMMERCIALIZING LAB GLANZEL W, 2004, BIOTECHNOLOGY ANAL P Glenisson P, 2005, SCIENTOMETRICS, V63, P163, DOI 10.1007/s11192-005-0208-0 Glenisson P, 2005, INFORM PROCESS MANAG, V41, P1548, DOI 10.1016/j.ipm.2005.03.021 GRZYBEK P, 2004, GLOTTOMETRICS, V7, P4 HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P HARMAN D, 1986, ASS COMPUTING MACHIN HICKS D, 1986, R&D MANAGE, V16, P211, DOI 10.1111/j.1467-9310.1986.tb01305.x Hinze S, 1996, SCIENTOMETRICS, V37, P313, DOI 10.1007/BF02093627 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Janssens F, 2006, INFORM PROCESS MANAG, V42, P1614, DOI 10.1016/j.ipm.2006.03.025 Jones K., 1971, AUTOMATIC KEYWORD CL KROVETS B, 1995, THESIS U MASSACHUSET Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LENNON M, 1981, J INFORM SCI, V3, P177, DOI 10.1177/016555158100300403 Leopold E, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P187 Leydesdorff L, 2004, J AM SOC INF SCI TEC, V55, P991, DOI 10.1002/asi.20045 Manning CD, 2000, FDN STAT NATURAL LAN, V2nd Meyer M, 2006, SCIENTOMETRICS, V68, P545, DOI 10.1007/s11192-006-0129-6 Meyer M, 2000, SCIENTOMETRICS, V48, P151, DOI 10.1023/A:1005692621105 MOENS MF, 2006, INFORM RETRIEVAL SER, V21 Murray Fiona, 2005, 11465 NBER National Science Foundation (NSF), 2006, SCI ENG IND NOYONS ECM, 1994, RES POLICY, V23, P443, DOI 10.1016/0048-7333(94)90007-8 OSSORIO PG, 1966, MULTIVAR BEHAV RES, V1, P479, DOI 10.1207/s15327906mbr0104_6 Porter AL, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P587 Porter Martin, 2001, SNOWBALL LANGUAGE ST PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RABEHARISOA V, 1992, DYNAMICS SCI BASED I, P45 SALTON G, 1981, INFORMATION RETRIEVA, P9 Salton G., 1968, AUTOMATIC INFORM ORG Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmoch U, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P717 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANDROMME D, 2006, 9 STI IND C LEUV 200 Van Looy B, 2006, RES POLICY, V35, P596, DOI 10.1016/j.respol.2006.02.003 Van Looy B, 2004, RES POLICY, V33, P425, DOI 10.1016/j.respol.2003.09.004 VANRIJSBERGEN CJ, 1980, 5587 BRIT LIB RES DE WONG SKM, 1995, ACM T INFORM SYST, V13, P69 WYLLYS RE, 1975, P AM SOC INFORM SCI, V12, P30 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 58 TC 13 Z9 15 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD FEB PY 2010 VL 82 IS 2 BP 289 EP 306 DI 10.1007/s11192-009-0046-6 PG 18 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 550ER UT WOS:000274111600006 ER PT J AU Blanco, R Barreiro, A AF Blanco, Roi Barreiro, Alvaro TI Probabilistic Static Pruning of Inverted Files SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Experimentation; Performance; Pruning; inverted files; probabilistic models; compression; efficiency ID INFORMATION-RETRIEVAL; MODELS; TERMS AB Information retrieval (IR) systems typically compress their indexes in order to increase their efficiency. Static pruning is a form of lossy data compression: it removes from the index, data that is estimated to be the least important to retrieval performance, according to some criterion. Generally, pruning criteria are derived from term weighting functions, which assign weights to terms according to their contribution to a document's contents. Usually, document-term occurrences that are assigned a low weight are ruled out from the index. The main assumption is that those entries contribute little to the document content. We present a novel pruning technique that is based on a probabilistic model of IR. We employ the Probability Ranking Principle as a decision criterion over which posting list entries are to be pruned. The proposed approach requires the estimation of three probabilities, combining them in such a way that we gather all the necessary information to apply the aforementioned criterion. We evaluate our proposed pruning technique on five TREC collections and various retrieval tasks, and show that in almost every situation it outperforms the state of the art in index pruning. The main contribution of this work is proposing a pruning technique that stems directly from the same source as probabilistic retrieval models, and hence is independent of the final model used for retrieval. C1 [Blanco, Roi; Barreiro, Alvaro] Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. RP Blanco, R (reprint author), Univ A Coruna, Dept Comp Sci, IRLab, La Coruna, Spain. EM rblanco@udc.es; barreiro@udc.es CR Amati G, 2006, LECT NOTES COMPUT SC, V3936, P13 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Anh V. N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148235 Anh V.N., 2002, P 25 ANN INT ACM SIG, P3 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baeza-Yates Ricardo, 2007, P 30 ANN INT ACM SIG, P183, DOI DOI 10.1145/1277741.1277775 Blanco R, 2007, LECT NOTES COMPUT SC, V4425, P64 Blanco R., 2007, P 30 ANN INT ACM SIG, P777, DOI 10.1145/1277741.1277904 BRANDOW R, 1995, INFORM PROCESS MANAG, V31, P675, DOI 10.1016/0306-4573(95)00052-I Brin S., 1998, P 7 INT WORLD WID WE, P107 Buckley C., 1995, P 4 TEXT RETR C TREC, P25 Buttcher Stefan, 2006, P 15 ACM INT C INF K, P182, DOI 10.1145/1183614.1183644 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 CARMEL D, 2001, P 10 TEXT RETR C, P228 Chowdhury A., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CRASWELL N, 2004, P 13 TEXT RETRIEVAL CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 Fagin R, 2003, SIAM J DISCRETE MATH, V17, P134, DOI 10.1137/S0895480102412856 He Ben, 2003, P 12 INT C INF KNOWL, P10 Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval LAFFERTY J, 2003, LANGUAGE MODELING IN, P11 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Long X., 2003, P 29 INT C VER LARG, P129, DOI 10.1016/B978-012722442-8/50020-3 Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005 Ntoulas A., 2007, P 30 ANN INT ACM SIG, P191, DOI 10.1145/1277741.1277776 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S., 1981, P 3 ANN ACM C RES DE, P35 Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sakai T., 2001, P 24 ANN INT ACM SIG, P190, DOI 10.1145/383952.383987 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, P 28 ANN INT ACM SIG, P162, DOI 10.1145/1076034.1076064 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Strohman T., 2005, P 28 ANN INT ACM SIG, P219, DOI 10.1145/1076034.1076074 Theobald M., 2004, P 30 INT C VER LARG, P648, DOI 10.1016/B978-012088469-8/50058-9 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H Upstill T, 2003, ACM T INFORM SYST, V21, P286, DOI 10.1145/858476.858479 van Rijsbergen C. J., 1979, INFORM RETRIEVAL VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Voorhees Ellen M., 2005, TREC EXPT EVALUATION WESTERVELD T, 2002, P 10 TEXT RETR C TRE, P663 Witten I. H., 1999, MANAGING GIGABYTES C Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zobel J., 2006, ACM COMPUT SURV, V38, P1 NR 46 TC 1 Z9 2 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD JAN 10 PY 2010 VL 28 IS 1 AR 1 DI 10.1145/1658377.1658378 PG 33 WC Computer Science, Information Systems SC Computer Science GA 549EN UT WOS:000274028700001 ER PT J AU Huang, YH Li, GY Wang, DY AF Huang, Yinghui Li, Guanyu Wang, Dongyan BE Yu, W Zhang, M Wang, L Song, Y TI A Method of Chinese Document Expression for Extracting Formal Context SO 2010 3RD INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2010), VOLS 1-7 SE International Conference on Biomedical Engineering and Informatics LA English DT Proceedings Paper CT 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010) CY OCT 16-18, 2010 CL Yantai, PEOPLES R CHINA SP IEEE Engn Med & Biol Soc HO Yantai Univ DE set of similar word set; formal context; VSM; document expression AB As a kind of data model, a formal context must be extracted from some actual data sources such as documents. For case of unstructured Chinese document, it is the first question to decide how to express the document. Vector space model (VSM) which is the dominant model of document expression now takes a single word as a feature item, so that neglects the lexical semantic relationship between words in a natural language, thereby it cannot show the semantic information implied in the documents. This paper discusses an improved method which is to take Hownet as knowledge base, to establish the concept vector space of Chinese document by using the set of similar word set to replace the single feature word in VSM. This method is convenient to extract a formal context according to the threshold made by users, and how to apply it is illustrated by an example. C1 [Huang, Yinghui; Li, Guanyu; Wang, Dongyan] Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian, Peoples R China. RP Huang, YH (reprint author), Dalian Maritime Univ, Informat Sci & Technol Coll, Dalian, Peoples R China. CR CAI XY, 2008, J ORDNANCE ENG COLL, V20, P62 CROSS V, 2008, FUZZY SYSTEMS, V3, P2014 DONG ZD, 2004, HOWNET Ide N, 1998, COMPUT LINGUIST, V24, P1 JUDITA P, 2004, COMPUTER SPEECH LANG, V18, P319 Liu Q., 2002, COMPUTATIONAL LINGUI, V7, P59 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAO QX, 2009, J NANCHANG AVIATION, V23, P66 VAJRABHAYA M, 1989, PLANT CELL REP, V8, P411, DOI 10.1007/BF00270081 NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1948-2914 BN 978-1-4244-6496-8 J9 INT CONF BIOMED PY 2010 BP 3020 EP 3023 DI 10.1109/BMEI.2010.5639893 PG 4 WC Engineering, Biomedical; Medical Informatics SC Engineering; Medical Informatics GA BVY38 UT WOS:000293143503123 ER PT J AU Prados-Suarez, B Molina, C Yanez, CP de Reyes, MP AF Prados-Suarez, B. Molina, C. Pena Yanez, C. Prados de Reyes, M. GP IEEE TI Contextualized Access to Digital Medical Records SO 2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) SE IEEE International Conference on Fuzzy Systems LA English DT Proceedings Paper CT 2010 IEEE World Congress on Computational Intelligence CY JUL 18-23, 2010 CL Barcelona, SPAIN SP IEEE, IEEE Computat Intelligence Soc, Int Neural Network Soc, Evolut Program Soc, IET AB Our purpose is to contextualize/ put in context the access to digital medical records (DMR), joint to its integration with tools like Tablet PC, so that we can get an autonomous and independent prototype Medical Workstation (MW). In this way, the medical workstation offers all the necessary hardware and software to perform the tasks on the target context. As a consequence of the use of these MW we can get Mobility in the use and access to DMR, as well as exploit the use of mobile devices like PDAs and Tablets. To achieve this purpose the use of DMR must be contextualized, adapting the information available from it to the concrete medical assistance and the context of demand, avoiding superfluous and impertinent information as well as access through complex DMR navigation systems. This contextualization is made through pertinence relations, that relate each data in the DMR with the context in which the data is used, assigning pertinence values in [0,1] interval. With these values we can give more priority to the visualization of the DMR to the most relevant information, according to the assistance in process. C1 [Prados-Suarez, B.] Univ Granada, Dept Software Engn Dept, Granada, Spain. RP Prados-Suarez, B (reprint author), Univ Granada, Dept Software Engn Dept, Granada, Spain. EM belenps@ugr.es; carlosmo@ujaen.es; car-menpy@decsai.ugr.es; prados@decsai.ugr.es RI Prados Suarez, Belen/E-1020-2012 CR [Anonymous], 2006, SIGIR 06 DELGADO M, 2001, FUZZY LOGIC MED, P286 Gil-Leyva I., 1966, REV ESP DOC CIENT, V19, P273 Jarvelin K., 2000, SIGIR 00, P41 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560573 Kanoulas E., 2010, ADV INFORM RETRIEVAL, P152 Prados M., 2003, SISTEMAS INFORM HOSP Salton G., 1994, P SIGIR 94, P292 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zadeh L.A., 1965, INFORM CONTR, V8 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7584 BN 978-1-4244-6920-8 J9 IEEE INT CONF FUZZY PY 2010 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BTO07 UT WOS:000287453603053 ER PT B AU Yu, JA Wang, JR Liu, XA Yu, M Zhao, Z Zhang, Y AF Yu, Jian Wang, Jianrong Liu, Xiao Yu, Mei Zhao, Zheng Zhang, Yan BE Chen, W Li, S TI Community-based Trust Mechanism in P2P Social Networks SO 2010 IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND INFORMATION SECURITY (WCNIS), VOL 2 LA English DT Proceedings Paper CT IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS) CY JUN 25-27, 2010 CL Beijing, PEOPLES R CHINA SP IEEE Beijing Sect, N China Elect Power Univ, Shanghai Jiaotong Univ, Xiamen Univ, Beijing Univ Posts & Telecommunications, Beijing UnivTechnol HO N China Elect Power Univ DE Community-based; Trust Mechanism; P2P Social Networks ID MODEL AB Malicious behaviors is always the problem to solve in P2P social networks. Building the trust model is effective method. In this paper a community-based trust mechanism is presented in the semantic-based P2P Social Networks. The ordinary peers form a community based on similar semantic and intra-community reputation of peers is calculated. The super peers constitute the structured P2P network and inter-community reputation is evaluated. This mechanism combines the local trust and the global trust. The experiments show that this trust mechanism can be effective in reducing effects caused by a variety of malicious behavior. C1 [Yu, Jian] Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China. RP Yu, JA (reprint author), Tianjin Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China. CR Aberer K., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management, DOI 10.1145/502585.502638 AHMAN J, 2003, TR120306 SUN MICR Berket K., 2004, Proceedings. Fourth International Conference on Peer-to-Peer Computing, DOI 10.1109/PTP.2004.1334930 BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BUCHEGGER S, P SOCIALNETS 2009 Buchmann E, 2004, LECT NOTES COMPUT SC, V3290, P337 Buragohain C., P 3 INT C PEER TO PE Dou W, 2003, THESIS NATL DEFENSE FAN L, 1998, ACM SIGCOMM, P254 Kamvar S. D., 2003, P 12 INT C WORLD WID, P640, DOI DOI 10.1145/775152.775242 Li JT, 2006, THESIS FUDAN U CHINA LI X, 2003, P 4 ACM C EL COMM SA, P228 MULLIN JK, 1990, IEEE T SOFTWARE ENG, V16, P558, DOI 10.1109/32.52778 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton RG, 1983, INTRO MODERN INFORM Ellison C., 2000, Computer Security Journal, V16 Wang Y, 2004, LECT NOTES COMPUT SC, V2872, P23 XIONG L, IEEE T KNOWLEDGE DAT XIONG L, 2002, GITCC0229 Yu YQ, 2008, JOUNAL COMPUTERS, V31, P1 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5851-6 PY 2010 BP 669 EP 673 PG 5 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BTQ45 UT WOS:000287768500136 ER PT B AU Yu, M Wang, JR Wang, QJ Liu, XA Zhao, Z Zhang, Y AF Yu, Mei Wang, Jianrong Wang, Qingjie Liu, Xuan Zhao, Zheng Zhang, Yan BE Chen, W Li, S TI Semantic-based Query Routing in P2P Social Networks SO 2010 IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND INFORMATION SECURITY (WCNIS), VOL 2 LA English DT Proceedings Paper CT IEEE International Conference on Wireless Communications, Networking and Information Security (WCNIS) CY JUN 25-27, 2010 CL Beijing, PEOPLES R CHINA SP IEEE Beijing Sect, N China Elect Power Univ, Shanghai Jiaotong Univ, Xiamen Univ, Beijing Univ Posts & Telecommunications, Beijing UnivTechnol HO N China Elect Power Univ DE Semantic-based Community; P2P Social Network; Vector Space Model (VSM); Inverted Index; Bloom Filter AB In P2P social networks, query efficiency is always a significant challenge. In this paper we build a semantic-based query routing strategy to improve the searching efficiency. Based on Vector Space Model (VSM), similar semantic ordinary peers set up an unstructured semantic community. Super peers in each community constitute a structured P2P network and store the keywords information in a global inverted file. We exploit Bloom Filters to complete multi-keyword query. Simulation results show that the proposed scheme is able to increase query efficiency and also reduce query cost. C1 [Yu, Mei; Wang, Jianrong; Wang, Qingjie; Zhao, Zheng] Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. RP Yu, M (reprint author), Tianjin Univ, Sch Comp Sci & Technol, Tianjin, Peoples R China. CR American National Standards Institute, X93021997 AM NAT STA, P2 [Anonymous], 1995, 1801 FIPS US DEP COM Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BUCHEGGER S, P SOCIALNETS 2009 Chen Han-Hua, 2006, Journal of Software, V17, DOI 10.1360/jos171170 CRESPO A, 2003, GARCIAMOLINA SEMANTI Cuenca-Acuna F. M., 2003, Proceedings 12th IEEE International Symposium on High Performance Distributed Computing Li F, 1998, P ACM SIGCOMM 98, P254 Milgram S., 1967, SMALL WORLD PROBLEM MULLIN JK, 1990, IEEE T SOFTWARE ENG, V16, P558, DOI 10.1109/32.52778 REYNOLDS P, 2003, LECT NOTES COMPUTER Ribeiro-Neto B, 1999, P 22 ACM C RES DEV I SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton RG, 1983, INTRO MODERN INFORM TANG C, P ACM SIGCOMM 03 TRIANTALLOU P, 2003, P C INN DAT SYST RES Witten IH, 1999, DATA MINING XU LB, 2006, J SOFTWARE, V7, P2106 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5851-6 PY 2010 BP 674 EP 678 PG 5 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BTQ45 UT WOS:000287768500137 ER PT J AU De Silva, D Alahakoon, D AF De Silva, Daswin Alahakoon, Damminda GP IEEE TI Incremental Knowledge Acquisition and Self Learning from Text SO 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT World Congress on Computational Intelligence (WCCI 2010) CY 2010 CL Barcelona, SPAIN SP IEEE ID MAPS AB Incremental learning is a core necessity in developments towards intelligent machines. Artificial learning as implemented in contemporary neural network algorithms does not fully encompass an incremental, autonomous learning capacity. In this paper we present a self learning algorithm capable of incrementally acquiring knowledge across learning periods. A dynamic unsupervised learning algorithm, the GSOM algorithm, forms the basis of the presented incrementally knowledge acquiring self learning (IKASL) algorithm, to which we have introduced a layer of aggregation for continuous learning, knowledge acquisition and retention. We also present a novel application of the IKASL algorithm for continuous learning of hidden patterns from semantics of text. C1 [De Silva, Daswin; Alahakoon, Damminda] Monash Univ, Fac Informat Technol, Cognit & Connectionist Syst Lab, Clayton, Vic 3800, Australia. RP De Silva, D (reprint author), Monash Univ, Fac Informat Technol, Cognit & Connectionist Syst Lab, Clayton, Vic 3800, Australia. EM daswin.desilva@infotech.monash.edu.au; damminda.alahakoon@infotech.monash.edu.au CR Alahakoon D, 2000, IEEE T NEURAL NETWOR, V11, P601, DOI 10.1109/72.846732 CHEN Z, 2007, INT JOINT C NEUR NET, V12, P2592 Crooks R. L., 1991, PSYCHOL SCI BEHAV LI Fritzke B., 1995, ADV NEURAL INFORMATI, V7, P625 Furao Shen, 2006, Neural Netw, V19, P90, DOI 10.1016/j.neunet.2005.04.006 Gabrys B, 2000, IEEE T NEURAL NETWOR, V11, P769, DOI 10.1109/72.846747 Gilbert CD, 2001, NEURON, V31, P681, DOI 10.1016/S0896-6273(01)00424-X GROSSBERG S, 1987, COGNITIVE SCI, V11, P23 Hamker FH, 2001, NEURAL NETWORKS, V14, P551, DOI 10.1016/S0893-6080(01)00018-1 Hearst M. A, 1999, P 37 ANN M ASS COMP, P3, DOI DOI 10.3115/1034678.1034679 KNUDSEN EI, 1987, ANNU REV NEUROSCI, V10, P41 Kohonen T., 1995, SELF ORG MAPS LEE KM, 1995, FUZZY SET SYST, V75, P301, DOI 10.1016/0165-0114(94)00387-M McCloskey M, 1989, PSYCHOL LEARNING MOT Mountcastle VB, 1997, BRAIN, V120, P701, DOI 10.1093/brain/120.4.701 OBERRNAYER K, 2001, SELF ORG MAP FORMATI Polikar R, 2001, IEEE T SYST MAN CY C, V31, P497, DOI 10.1109/5326.983933 Prudent Y, 2005, IEEE IJCNN, P1211 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZ D, 2008, P 2008 IEEE INT C DA, P664 YAGER RR, 1993, IEEE T SYST MAN CYB, V23, P467, DOI 10.1109/21.229459 YIN H, 2007, PRINCIPAL MANIFOLDS, P68 NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4244-6917-8 J9 IEEE IJCNN PY 2010 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BTN74 UT WOS:000287421403080 ER PT S AU Skabar, A Memon, S AF Skabar, Andrew Memon, Shahmeer GP IEEE TI Density Estimation-Based Document Categorization using von Mises-Fisher Kernels SO 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT World Congress on Computational Intelligence (WCCI 2010) CY 2010 CL Barcelona, SPAIN SP IEEE AB Although classifiers such as Support Vector Machines (SVMs) and k Nearest Neighbors (kNN) are able to achieve excellent document classification performance as measured by common information retrieval measures such as F1 score and breakeven point, they do not produce output values that can reliably be interpreted as probabilities. This means that without additional post-processing of their outputs, these classifiers are not capable of answering a question as simple as "To which classes does a document belong with a minimum probability of 80%?" This paper presents a density estimation-based classification technique which outputs probabilities directly. The technique is based on estimating densities using von Mises-Fisher kernels, and combining these under Bayes' Theorem to arrive at posterior probabilities of class membership. Results of applying the technique to the Reuters-21578 dataset show that the technique is computationally feasible, that its classification performance as measured by F1 score is comparable to that of SVMs and better than that of kNN classifiers, and that the output values can be interpreted as well-calibrated probabilities. C1 [Skabar, Andrew; Memon, Shahmeer] La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia. RP Skabar, A (reprint author), La Trobe Univ, Dept Comp Sci & Comp Engn, Bundoora, Vic 3086, Australia. EM a.skabar@latrobe.edu.auauthor CR Bennett P.N., 2000, CMUCS00155 SCH COMP Bishop C. M., 2006, PATTERN RECOGNITION Bishop C. M., 1995, NEURAL NETWORKS PATT DEGROOT MH, 1983, J ROY STAT SOC D-STA, V32, P12 Domingos P., 1996, P 13 INT C MACH LEAR, P105 FISHER R, 1953, PROC R SOC LON SER-A, V217, P295, DOI 10.1098/rspa.1953.0064 Han E.H., 2001, P 5 PAC AS C KNOWL D, P53 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 LAWRENCE S, 1996, NEURAL NETWORKS TRIC, P299 PARZEN E, 1962, ANN MATH STAT, V33, P1065, DOI 10.1214/aoms/1177704472 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHNEIDER KM, 2005, P 6 INT C INT TEXT P, P682 Specht D., 1990, NEURAL NETWORKS, V3, P110 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4244-6917-8 J9 IEEE IJCNN PY 2010 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BTN74 UT WOS:000287421400111 ER PT J AU Wiedau, M Alsmeyer, F Harwardt, A Marquardt, W AF Wiedau, Michael Alsmeyer, Frank Harwardt, Andreas Marquardt, Wolfgang BE Pierucci, S Ferraris, BG TI Personal micro-blogging for workflow support in conceptual design SO 20TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING SE Computer-Aided Chemical Engineering LA English DT Proceedings Paper CT 20th European Symposium on Computer Aided Process Engineering (ESCAPE) CY JUN 06-09, 2010 CL Ischia, ITALY DE workflow support; conceptual design; micro-blogging; shortcut-tools AB This contribution reports on how the conceptual chemical process design can benefit from workflow support using tracing and micro-blogging as part of a process design software environment. Micro-blogging allows users to write short messages on what they are doing, feeling or thinking. Micro-blogging is suggested to be integrated into the user interface of a conceptual process design software environment to support the engineer during his/her work. By adding features to the user interface, micro-blogging can informally extend the documentation of the design process beyond what the machine can log by itself. During the development of a new chemical process in the conceptual design phase, the engineer will not only be informed on what the machine logged in earlier design steps but will also be provided with former corresponding messages other engineers manually added to the system during their design activity. The important difference between the commonly known micro-blogging systems and the process design software concept proposed here is that the messages are stored locally on the client and are hence only accessible from within the company but not by the public. Another difference is the analysis, filtering and reuse of former messages. This way, methods and tools used during the conceptual design of a chemical plant are acquired from the engineers who use micro-blogging as an extension of a design software system. An exploratory case study has been carried out to show how corresponding blog-entries can be retrieved and what benefits can be achieved for the design engineering team using such a system. A larger case study with industrial users is in progress to help assess practical benefits and the still existing potential for improvement of the concept and its implementation. C1 [Wiedau, Michael; Alsmeyer, Frank] AixCAPE eV, D-52072 Aachen, Germany. RP Wiedau, M (reprint author), AixCAPE eV, Intzestr 1, D-52072 Aachen, Germany. EM wiedau@aixcape.org; alsmeyer@aixcape.org; andreas.harwardt@avt.rwth-aachen.de; wolfgang.marquardt@avt.rwth-aachen.de CR BANARESALCANTARA R, 1995, COMPUT CHEM ENG, V19, P279, DOI 10.1016/0098-1354(94)00051-O Bausa J, 1998, AICHE J, V44, P2181, DOI 10.1002/aic.690441008 Deerwester S.C., 1990, J AM SOC INFORM SCI Fellbaum C, 1998, WORDNET ELECT LEXICA Kuropka D., 2004, MODELLE REPRASENTATI LEAKE D, 2001, P 6 INT C INT US INT LIM WC, 1994, IEEE SOFTWARE, V11, P23, DOI 10.1109/52.311048 MIATIDIS M, 2008, USING DEV EXPERIENCE MILLE A, 2006, 9 IFAC S NANC FRANC MORBACH J, 2009, COMPUTERS CHEM ENG PFENNIG A, 2004, THERMODYNAMIK GEMISC POHL K, 1997, P 9 INT C ADV INF SY Ryll O, 2008, CHEM-ING-TECH, V80, P207, DOI 10.1002/cite.200700113 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOEMERS M, 2009, 15 IFAC S SYST ID JU SUBRAHMANIAN E, 1997, P ASME C van der Aalst W., 2002, WORKFLOW MANAGEMENT NR 17 TC 0 Z9 0 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1570-7946 BN 978-0-444-53569-6 J9 COMPUT-AIDED CHEM EN PY 2010 VL 28 BP 1321 EP 1326 PG 6 WC Engineering, Chemical; Operations Research & Management Science SC Engineering; Operations Research & Management Science GA BTO91 UT WOS:000287659500221 ER PT B AU Shi, QW Wu, RT AF Shi, Qingwei Wu, Rongteng BE Ma, P Qaiser, M TI A String Kernel Based Method for Document Clustering SO 2ND INTERNATIONAL SYMPOSIUM ON COMPUTER NETWORK AND MULTIMEDIA TECHNOLOGY (CNMT 2010), VOLS 1 AND 2 LA English DT Proceedings Paper CT 2nd International Symposium on Computer Network and Multimedia Technology (CNMT) CY DEC 24-26, 2010 CL Wuhan, PEOPLES R CHINA SP Huazhong Normal Univ, Huazhong Univ Sci & Technol, Wuhan Univ, Natl Technol Univ Ukraine, Royal Inst Technol, Columbia Univ, Harbin Univ Technol, Res Assoc Modern Educ & Comp Sci, IEEE Educ Soc, IEEE Wuhan Sect E Chapter, IEEE DE exploratory data analysis; document clustering; string kernel; spectral clustering; support vector machine AB This paper presents a string kernel based method for documents clustering. Documents are viewed as sequences of strings, and documents similarity is calculated by the kernel function. According to the documents similarity, spectral clustering algorithm is used to group documents. Experimental results shows that string kernel method outperform the standard k-means algorithm on the Reuters-21578 dataset. C1 [Shi, Qingwei] Liaoning Tech Univ, Sch Software, Huludao, Peoples R China. RP Shi, QW (reprint author), Liaoning Tech Univ, Sch Software, Huludao, Peoples R China. EM fxsteve@163.com CR Amayri O, 2009, LECT NOTES COMPUT SC, V5856, P621, DOI 10.1007/978-3-642-10268-4_73 Andrews NO, 2007, TR0735 VIRG TECH Christoper K. I., 2001, ADV NEURAL INFORMATI, V13, P682 Cristianini N., 2000, INTRO SUPPORT VECTOR Elkan C, 2003, P 20 INT C MACH LEAR, P147 Filippone M, 2008, PATTERN RECOGN, V41, P176, DOI 10.1016/j.patcog.2007.05.018 Fowlkes C, 2004, IEEE T PATTERN ANAL, V26, P214, DOI 10.1109/TPAMI.2004.1262185 HERBRICH R., 2002, LEARNING KERNEL CLAS Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Karatzoglou A., COMPUTATIONAL STAT D, V54, P290 Lewis D., 1997, REUTERS 21578 TEXT C Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Moschitti A., 2009, P 12 C EUR CHAPT ASS, P576, DOI 10.3115/1609067.1609131 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Vishwanathan S.V.N., 2004, FAST KERNELS STRING von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z YAN DH, 2009, P 15 ACM SIGKDD INT, P907, DOI 10.1145/1557019.1557118 Zheng S., 2009, 5 INT C SEM KNOWL GR, P113 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-8069-2 PY 2010 BP 526 EP 529 PG 4 WC Computer Science, Hardware & Architecture SC Computer Science GA BXE84 UT WOS:000295921100126 ER PT J AU Chen, HW Li, WH Yu, SS AF Chen, Hongwei Li, Wenhao Yu, Shengsheng BE Ma, P Qaiser, M TI Self-adaptive Optimization Algorithm for P2P Network Topology based on Vector Space Model SO 2ND INTERNATIONAL SYMPOSIUM ON COMPUTER NETWORK AND MULTIMEDIA TECHNOLOGY (CNMT 2010), VOLS 1 AND 2 LA English DT Proceedings Paper CT 2nd International Symposium on Computer Network and Multimedia Technology (CNMT) CY DEC 24-26, 2010 CL Wuhan, PEOPLES R CHINA SP Huazhong Normal Univ, Huazhong Univ Sci & Technol, Wuhan Univ, Natl Technol Univ Ukraine, Royal Inst Technol, Columbia Univ, Harbin Univ Technol, Res Assoc Modern Educ & Comp Sci, IEEE Educ Soc, IEEE Wuhan Sect E Chapter, IEEE DE Vector Space Model; Traffic Optimization; Similarity; Clustering; Peer-to-Peer ID SEARCH AB Peer-to-Peer (P2P) services have emerged as a popular way to share large multimedia contents, giving rise to large traffic volume and new traffic patterns different from conventional Internet applications. In this paper, we present a new self-adaptive optimization approach for P2P network topology based on Vector Space Model(VSM) to reduce traffic in P2P network. The topological adjustment algorithm for P2P network traffic optimization is combined with Vector Space Model method based on similarity inquiry thought and K-means algorithm, and reach the purpose of implementing optimization to entire P2P network by letting peers have targeted connection and optimizing links of network. C1 [Chen, Hongwei; Yu, Shengsheng] Huazhong Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430074, Peoples R China. RP Chen, HW (reprint author), Huazhong Univ Sci & Technol, Coll Comp Sci & Technol, Wuhan 430074, Peoples R China. EM chw75@sohu.com CR Aggarwal V., 2008, 11 IEEE GLOB INT S Aggarwal V, 2007, ACM SIGCOMM COMP COM, V37, P29, DOI 10.1145/1273445.1273449 Aggarwal V, 2008, NETW HETEROG MEDIA, V3, P251, DOI 10.3934/nhm.2008.3.251 Aggarwal Vinay, 2007, 11 INT C INT SERV DE Choffnes DR, 2008, ACM SIGCOMM COMP COM, V38, P363, DOI 10.1145/1402946.1403000 Horovitz S., 2008, P 5 INT WORKSH HOT T Huang CK, 2003, J AM SOC INF SCI TEC, V54, P638, DOI 10.1002/asi.10256 Ren Shansi, 2010, P INFOCOM 10 SAN DIE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seedorf Jan, 2009, 9 INT C PEER TO PEER Wang Jiajun, 2009, ACM INT C MULT BEIJ, P869 Xie H., 2007, P4P EXPLICIT COMMUNI Xie Haiyong, 2007, P 6 INT WORKSH PEER Xie Haiyong, 2007, YALEDCSTR1377 Xie Haiyong, P SIGCOMM 2008 Xie HY, 2008, LECT NOTES COMPUT SC, V4982, P375 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-8069-2 PY 2010 BP 648 EP 650 PG 3 WC Computer Science, Hardware & Architecture SC Computer Science GA BXE84 UT WOS:000295921100158 ER PT S AU Schockaert, S Makarytska, N De Cock, M AF Schockaert, Steven Makarytska, Nataliya De Cock, Martine BE Cornelis, C Deschrijver, G Nachtegael, M Schockaert, S Shi, Y TI Fuzzy Methods on the Web: A Critical Discussion SO 35 YEARS OF FUZZY SET THEORY: CELEBRATORY VOLUME DEDICATED TO THE RETIREMENT OF ETIENNE E. KERRE SE Studies in Fuzziness and Soft Computing LA English DT Article; Book Chapter ID DESCRIPTION LOGIC PROGRAMS; INFORMATION-RETRIEVAL; POSSIBILISTIC LOGIC; RECOMMENDER SYSTEMS; IMAGE RETRIEVAL; SEMANTIC WEB; MODEL; UNCERTAINTY; VAGUENESS; SETS AB Gradual concepts abound in many web-related domains, ranging from the notion of relevance in information retrieval, to the strength of connection in online social networks. As a result, fuzzy set theory is often a natural choice in implementing web systems. In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems. In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial. C1 [Schockaert, Steven; De Cock, Martine] Univ Ghent, Dept Appl Math & Comp Sci, Ghent, Belgium. [Makarytska, Nataliya] Univ Washington, Inst Technol, Tacoma, WA USA. RP Schockaert, S (reprint author), Univ Ghent, Dept Appl Math & Comp Sci, Ghent, Belgium. EM steven.schockaert@ugent.be; natashka@u.washington.edu; martine.decock@ugent.be; steven.schockaert@ugent.be CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Arampatzis A, 2006, COMPUT ENVIRON URBAN, V30, P436, DOI 10.1016/j.compenvurbsys.2005.08.001 Arotaritei D, 2004, FUZZY SET SYST, V148, P5, DOI 10.1016/j.fss.2004.03.003 Baader F., 2007, DESCRIPTION LOGIC HD BAUTERS K, P 26 C UNC IN PRESS Baziz M, 2007, STUD FUZZ SOFT COMP, V215, P193 Baziz M., 2006, FUZZY LOGIC SEMANTIC, P363 Benferhat S, 2004, FUZZY SET SYST, V144, P25, DOI 10.1016/j.fss.2003.10.012 Benferhat S, 2002, ANN MATH ARTIF INTEL, V34, P217, DOI 10.1023/A:1014446411602 Benferhat S., 2006, Information Fusion, V7, DOI 10.1016/j.inffus.2005.01.006 Berners-Lee T., 2001, SCI AM, V284, P28 Binaghi E., 1994, International Journal of Pattern Recognition and Artificial Intelligence, V8, DOI 10.1142/S0218001494000486 Bobillo F, 2008, LECT NOTES ARTIF INT, V5327, P189 BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I BOSTEELS K, 2007, P 2007 INT C MULT UB, P361 Bosteels K, 2009, PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, P25 Boughanem M, 2009, INT J APPROX REASON, V50, P957, DOI 10.1016/j.ijar.2008.10.005 Cao YK, 2007, EXPERT SYST APPL, V33, P230, DOI 10.1016/j.eswa.2006.04.012 Chen YX, 2002, IEEE T PATTERN ANAL, V24, P1252 CONFALONIERI R, 2009, P C IT ASS ART INT, P52 Cordon O., 2002, Soft Computing, V6, DOI 10.1007/s00500-002-0184-8 Cornelis C, 2007, INFORM SCIENCES, V177, P4906, DOI [10.1016/j.ins.2007.07.001, 10.1016/i.ins.2007.07.001] Crestani F, 2003, INFORM PROCESS MANAG, V39, P161, DOI 10.1016/S0306-4573(02)00086-9 Damasio CV, 2000, LECT NOTES ARTIF INT, V1919, P57, DOI 10.1007/3-540-40006-0_5 Dubois D., 2000, HDB FUZZY SETS SERIE, P21 DUBOIS D, 1989, EUR J OPER RES, V40, P135, DOI 10.1016/0377-2217(89)90326-3 DUBOIS D, 2006, POSSIBILISTIC UNCERT, P101 Dubois D, 1988, POSSIBILITY THEORY DUBOIS D, 1991, LOGIC PROGRAMM, P581 Dubois D., 1997, P 13 C UNC ART INT P, P157 Dubois D, 1997, FUZZY SET SYST, V90, P141, DOI 10.1016/S0165-0114(97)00080-8 Eiter T, 2008, ARTIF INTELL, V172, P1495, DOI 10.1016/j.artint.2008.04.002 GABRILOVICH E, 2007, P 20 INT JOINT C ART, P1606 Gelfond M., 1988, P 5 INT C S LOG PROG, P1081 Golbeck J, 2006, P 3 IEEE CONS COMM N, P282 Hajek P, 2005, FUZZY SET SYST, V154, P1, DOI 10.1016/j.fss.2005.03.005 Hajek P., 1998, METAMATHEMATICS FUZZ Hedlund T, 2001, INFORM PROCESS MANAG, V37, P147, DOI 10.1016/S0306-4573(00)00024-8 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 HEYMANS S, 2008, P 2 INT C WEB REAS R, P181 HISDAL E, 1988, FUZZY SET SYST, V25, P325, DOI 10.1016/0165-0114(88)90018-8 HOLLUNDER B, 1995, INT J APPROX REASON, V12, P85, DOI 10.1016/0888-613X(94)00015-U Hoppner F., 1999, FUZZY CLUSTER ANAL M Janssen J., 2009, P 8 INT WORKSH FUZZ, P352 Jing Y., 1994, P RIAO 94, P146 KERRE EE, 1986, J AM SOC INFORM SCI, V37, P341, DOI 10.1002/(SICI)1097-4571(198609)37:5<341::AID-ASI9>3.0.CO;2-A KRAFT D, 2003, STUDIES FUZZINESS SO, P152 Krishnapuram R., 1999, IEEE INT FUZZ SYST C, V3, P1281 Krishnapuram R, 2004, IEEE T KNOWL DATA EN, V16, P1185, DOI 10.1109/TKDE.2004.53 La Cascia M, 1998, IEEE WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO LIBRARIES - PROCEEDINGS, P24 Lamere P., 2007, MUSIC RECOMMENDATION Lukasiewicz T, 2009, INT J APPROX REASON, V50, P837, DOI 10.1016/j.ijar.2009.03.004 Lukasiewicz T, 2007, LECT NOTES ARTIF INT, V4772, P16 LUKASIEWICZ T, 2007, P 1 INT C WEB REAS R, P289 Lukasiewicz T, 2008, FUND INFORM, V82, P289 MAMDANI EH, 1975, INT J MAN MACH STUD, V7, P1, DOI 10.1016/S0020-7373(75)80002-2 Martin-Bautista M. J., 2002, Soft Computing, V6, DOI 10.1007/S00500-002-0190-X MARTINBAUTISTA MJ, 2009, SOFT COMPUT, V14, P847 Martin-Bautista MJ, 1999, J AM SOC INFORM SCI, V50, P760, DOI 10.1002/(SICI)1097-4571(1999)50:9<760::AID-ASI4>3.3.CO;2-F Martin-Bautista MJ, 2004, FUZZY SET SYST, V148, P85, DOI 10.1016/j.fss.2004.03.007 Massa P, 2007, INT J SEMANT WEB INF, V3, P39, DOI 10.4018/jswis.2007010103 MCCLOSKEY M, 1979, COGNITIVE PSYCHOL, V11, P1, DOI 10.1016/0010-0285(79)90002-1 MEDINA J., 2001, LECT NOTES ARTIF INT, V2173, P351 Medina J, 2004, FUZZY SET SYST, V146, P43, DOI 10.1016/j.fss.2003.11.005 Meghini C, 2001, J ACM, V48, P909, DOI 10.1145/502102.502103 MIYAMOTO S, 1990, FUZZY SET SYST, V38, P191, DOI 10.1016/0165-0114(90)90149-Z Montello D., 2003, SPAT COGN COMPUT, V2, P185, DOI 10.1207/S15427633SCC032&3_06 Nachtegael M., 2007, IEEE INT C IM PROC, P13 Nauck D, 1997, FUZZY SET SYST, V89, P277, DOI 10.1016/S0165-0114(97)00009-2 Nicolas P, 2006, ANN MATH ARTIF INTEL, V47, P139, DOI 10.1007/s10472-006-9029-y Nie JY, 1999, INFORM PROCESS MANAG, V35, P443, DOI 10.1016/S0306-4573(98)00051-X Nie Z., 2007, P 16 INT C WORLD WID, P81, DOI 10.1145/1242572.1242584 NIEVES JC, 2007, P INT C LOG PROGR NO, P315 Pan Jeff Z., 2008, P WWW 2008, P575, DOI 10.1145/1367497.1367575 PAVELKA J, 1979, Z MATH LOGIK, V25, P45, DOI 10.1002/malq.19790250304 PAVELKA J, 1979, Z MATH LOGIK, V25, P119, DOI 10.1002/malq.19790250706 PAVELKA J, 1979, FUZZY LOGIC Z MATH L, V25, P447 Pazzani M.J., 2007, LNCS, V4321, P325, DOI DOI 10.1007/978-3-540-72079-9_10 PEREZIGLESIAS J, 2008, P 2008 IEEE WIC ACM, P755 PETENES C, 2003, P WEBKDD, P37 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Porcel C, 2009, EXPERT SYST APPL, V36, P5173, DOI 10.1016/j.eswa.2008.06.038 Qi GL, 2007, LECT NOTES ARTIF INT, V4724, P828 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RUSPINI EH, 1970, INFORM SCIENCES, V2, P319, DOI 10.1016/S0020-0255(70)80056-1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez E., 2006, FUZZY LOGIC SEMANTIC Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Schockaert S, 2008, ARTIF INTELL, V172, P1158, DOI 10.1016/j.artint.2008.01.001 Schockaert S, 2007, INT J INTELL SYST, V22, P455, DOI 10.1002/int.20209 Schockaert S, 2010, SOFT COMPUT, V14, P869, DOI 10.1007/s00500-009-0471-8 Schockaert S., 2007, P 30 ANN INT ACM SIG, P167, DOI 10.1145/1277741.1277772 Schockaert S, 2005, LECT NOTES COMPUT SC, V3762, P1077 Shackle G. L. S., 1961, DECISION ORDER TIME Sinclair J, 2008, J INF SCI, V34, P15, DOI 10.1177/0165551506078083 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 SOTO A, 2008, FUZZY APPROACH SYNON, P179 Stoilos G, 2007, J ARTIF INTELL RES, V30, P273 STOILOS G, 2006, P 17 EUR C ART INT E, P457 Stoilos G, 2008, LECT NOTES ARTIF INT, V5327, P262 Straccia U., 2005, 21 C UNC ART INT UAI, P559 Straccia U., 2006, P 11 INT C INF PROC Straccia U, 2001, J ARTIF INTELL RES, V14, P137 Straccia U., 2006, P IPMU 2006, P1818 Straccia U, 2009, SIAM J COMPUT, V38, P1881, DOI 10.1137/070695976 Victor P, 2009, FUZZY SET SYST, V160, P1367, DOI 10.1016/j.fss.2008.11.014 VICTOR P, 2010, THESIS GHENT U Vojtas P, 2001, FUZZY SET SYST, V124, P361, DOI 10.1016/S0165-0114(01)00106-3 VRETTOS S, 1990, FAULT TOLERANT DISTR, V448, P453 WATERS T, 2003, P 7 INT C GEOC Yager RR, 2003, FUZZY SET SYST, V136, P133, DOI 10.1016/S0165-0114(02)00223-3 Zadeh LA, 2004, DATA KNOWL ENG, V50, P291, DOI 10.1016/j.datak.2004.04.001 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X Zadeh L.A., 1978, FUZZY SETS SYSTEMS, V1, P1, DOI DOI 10.1016/0165-0114(78)90029-5 Zhao Y, 2004, P 13 ACM C INF KNOWL, P246, DOI 10.1145/1031171.1031225 Zhou X., 2006, P ACM SIGIR 2006, P170, DOI 10.1145/1148170.1148203 Zobel J., 1998, SIGIR Forum, V32 ZUCKER JD, 1999, P EUROFUSE SIC, P279 NR 118 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1434-9922 BN 978-3-642-16628-0 J9 STUD FUZZ SOFT COMP PY 2010 VL 261 BP 237 EP 266 D2 10.1007/978-3-642-16629-7 PG 30 WC Computer Science, Artificial Intelligence; Mathematics, Applied SC Computer Science; Mathematics GA BRS22 UT WOS:000283523800012 ER PT S AU Lee, S Baker, J Song, J Wetherbe, JC AF Lee, Sangno Baker, Jeff Song, Jaeki Wetherbe, James C. GP IEEE TI An Empirical Comparison of Four Text Mining Methods SO 43RD HAWAII INTERNATIONAL CONFERENCE ON SYSTEMS SCIENCES VOLS 1-5 (HICSS 2010) SE Proceedings of the Annual Hawaii International Conference on System Sciences LA English DT Proceedings Paper CT 43rd Hawaii International Conference on Systems Sciences (HICSS 2010) CY JAN 05-08, 2010 CL Honolulu, HI SP Univ Hawaii, Shidler Coll Business ID LATENT SEMANTIC ANALYSIS; INFORMATION-SYSTEMS; MODEL; RETRIEVAL; LANGUAGE AB The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their advantages and limitations The four methods that we examine are (I) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) the correlated topic model We compare these four methods and highlight the optimal conditions under which to apply the various methods Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods. C1 [Lee, Sangno; Song, Jaeki; Wetherbe, James C.] Texas Tech Univ, Lubbock, TX 79409 USA. RP Lee, S (reprint author), Texas Tech Univ, Lubbock, TX 79409 USA. EM JCWetherbe@aol.com CR Aldous D., 1985, SPRINGER LECT NOTES, V1117, P1 Berry M., 2003, SURVEY TEXT MINING C Blei DM, 2007, ANN APPL STAT, V1, P17, DOI 10.1214/07-AOAS114 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Cheung KW, 2003, DECIS SUPPORT SYST, V35, P231, DOI 10.1016/S0167-9236(02)00108-2 CONNOR RJ, 1969, J AM STAT ASSOC, V64, P194, DOI 10.2307/2283728 Das SR, 2007, MANAGE SCI, V53, P1375, DOI 10.1287/mnsc.1070.0704 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dempster A., 1977, J ROYAL STAT SOC B, P1 Ding CHQ, 2005, J AM SOC INF SCI TEC, V56, P597, DOI 10.1002/asi.20148 Fortuna B, 2006, LECT NOTES COMPUT SC, V4289, P121 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GIROLAMI M, 2003, EQUIVALENCE PLSI LDA, P433 Griffiths T. L., 2004, FINDING SCI TOPICS, P5228 Haight F. A., 1967, HDB POISSON DISTRIBU Harman D., 1993, 1 TEXT RETR C TREC 1, P1 Hofmann T., 1999, ADV NEURAL INFORM PR, V11 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 IDE M, 1998, COMPUT LINGUIST, V24, P2 JOHSON R, 2001, APPL MULTIVARIATE ST Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Larsen KR, 2008, DECIS SUPPORT SYST, V45, P884, DOI 10.1016/j.dss.2008.02.009 LENK P, 1988, J AM STAT ASSOC, P509 McLachlan G. J., 1997, EM ALGORITHM EXTENSI Miller T.W., 2004, DATA TEXT MINING BUS Pacheco P., 1997, PARALLEL PROGRAMMING Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 Pons-Porrata A, 2007, INFORM PROCESS MANAG, V43, P752, DOI 10.1016/j.ipm.2006.06.001 Rohde D., SVDLIBC ROSENFELD R, 2000, PHILOS T MATH PHYS E, P1311 Rosenfeld R, 2000, P IEEE, V88, P1270, DOI 10.1109/5.880083 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sidorova A, 2008, MIS QUART, V32, P467 Sivic J., 2005, INT C COMP VIS ICCV Strunk W, 2007, ELEMENTS STYLE Tetlock PC, 2008, J FINANC, V63, P1437, DOI 10.1111/j.1540-6261.2008.01362.x Yang WS, 2008, EXPERT SYST APPL, V34, P2029, DOI 10.1016/j.eswa.2007.02.028 NR 39 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1060-3425 BN 978-1-4244-5509-6 J9 P ANN HICSS PY 2010 BP 1142 EP 1151 PG 10 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BRD18 UT WOS:000282391801026 ER PT S AU Kettani, H Newby, GB AF Kettani, Houssain Newby, Gregory B. GP IEEE TI Instability of Relevance-Ranked Results Using Latent Semantic Indexing for Web Search SO 43RD HAWAII INTERNATIONAL CONFERENCE ON SYSTEMS SCIENCES VOLS 1-5 (HICSS 2010) SE Proceedings of the Annual Hawaii International Conference on System Sciences LA English DT Proceedings Paper CT 43rd Hawaii International Conference on Systems Sciences (HICSS 2010) CY JAN 05-08, 2010 CL Honolulu, HI SP Univ Hawaii, Shidler Coll Business ID MODEL AB The latent semantic indexing (LSI) methodology for information retrieval applies the singular value decomposition to identify an eigensystem for a large matrix, in which cells represent the occurrence of terms (words) within documents. This methodology is used to rank text documents, such as Web pages or abstracts, based on their relevance to a topic The LSI was introduced to address the issues of synonymy (different words with the same meaning) and polysemy (the same words with multiple meanings), thus addressing the ambiguity in human language by utilizing the statistical context of words Rather than keeping all k possible eigenvectors and eigenvalues from the singular value decomposition Ouch approximates the original term by document matrix, a smaller number is used - essentially allowing a fuzzy match of a topic to the original term by document matrix In this paper, we show that the choice k impacts the resultant ranking and there is no value of k that results in stability of ranked results for similarity of the topic to documents. This is a surprising result, because prior literature indicates that eigensystems based on successively large values of k should approximate the complete (max k) eigensystems. The finding that document-query similarity rankings with larger values of k do not, in fact, maintain consistency, makes it difficult to assert that any particular value of k is optimal. This in turn renders LSI potentially untrustworthy for use in ranking text documents, even for values that differ by only 10% of the max k. C1 [Kettani, Houssain] Polytech Univ Puerto Rico, ECECS Dept, San Juan, PR 00919 USA. RP Kettani, H (reprint author), Polytech Univ Puerto Rico, ECECS Dept, San Juan, PR 00919 USA. EM hkettani@pupr.edu CR CALVE L, 2000, INFOM PROCESSING MAN, V36, P341 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 FALLEN C, 2008, DISTRIBUTED MULTISEA GAMIEL K, 2003, GRID INFORM RETRIEVA Garzon E. M., 2004, International Journal of Computers & Applications, V26 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Guarracino MR, 2006, INT J AP MAT COM-POL, V16, P241 KETTANI H, 2009, P 2009 INT C DAT MIN RAGHAVAN S, 2001, P VER LARG DAT VLDB SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C VOORHEES E, 2007, P 16 ANN TEXT RETR C XU J, 2006, P IEEE INT C INT SEC NR 14 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1060-3425 BN 978-1-4244-5509-6 J9 P ANN HICSS PY 2010 BP 1553 EP 1558 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BRD18 UT WOS:000282391801069 ER PT J AU Tu, SG Lu, CJ AF Tu, Songgao Lu, Chaojun BE Cao, L Feng, Y Zhong, J TI Topic-Based User Segmentation for Online Advertising with Latent Dirichlet Allocation SO ADVANCED DATA MINING AND APPLICATIONS (ADMA 2010), PT II SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 6th International Conference on Advanced Data Mining and Applications (ADMA) CY NOV 19-21, 2010 CL Chongqing, PEOPLES R CHINA SP Chongqing Univ, Univ Queensland, Sch Informat Technol & Elect Engn, Natl Nat Sci Fdn China, Chongqing Acad Sci & Technol, Chongqing Sci & Technol Commiss, IEEE Queensland Sect DE Latent Dirichlet Allocation; Behavioral targeting; User segmentation AB Behavioral Targeting (BT), as a useful technique to deliver the most appropriate advertisements to the most interested users by analyzing the user behaviors pattern, has gained considerable attention in online advertising market in recent year. A main task of BT is how to automatically segment web users for ads delivery, and good user segmentation may greatly improve the effectiveness of their campaigns and increase the ad click-through rate (CTR). Classical user segmentation methods, however, rarely take the semantics of user behaviors into consideration and can not mine the user behavioral pattern as properly as should be expected. In this paper, we propose an innovative approach based on the effective semantic analysis algorithm Latent Dirichlet Allocation (LDA) to attack this problem. Comparisons with other three baseline algorithms through experiments have confirmed that the proposed approach can increase effectiveness of user segmentation significantly. C1 [Tu, Songgao; Lu, Chaojun] Shanghai Jiao Tong Univ, Deptartment Comp Sci & Engn, Shanghai 200240, Peoples R China. RP Tu, SG (reprint author), Shanghai Jiao Tong Univ, Deptartment Comp Sci & Engn, 800 Dongchuan Rd, Shanghai 200240, Peoples R China. EM tusonggao@sjtu.edu.cn; lu-cj@cs.sjtu.edu.cn CR Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Broder A., 2007, P 30 ANN INT ACM SIG, P566 Fain D. C., 2006, Bulletin of the American Society for Information Science and Technology, V32, DOI 10.1002/bult.1720320206 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Hu J., 2007, P 16 INT C WORLD WID, P160 Minka T, 2002, P 18 C UNC ART INT, P352 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wu Xiaohui, 2009, P 3 INT WORKSH DAT M, P10, DOI 10.1145/1592748.1592751 Yan J, 2009, P 18 INT C WORLD WID, P261, DOI 10.1145/1526709.1526745 Zhou YK, 2006, LECT NOTES COMPUT SC, V4082, P11 NR 10 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-17312-7 J9 LECT NOTES ARTIF INT PY 2010 VL 6441 BP 259 EP 269 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BFG92 UT WOS:000319827000025 ER PT S AU Rebolledo, VL L'Huillier, G Velasquez, JD AF Rebolledo, Victor L. L'Huillier, Gaston Velasquez, Juan D. BE Velasquez, JD Jain, LC TI Web Pattern Extraction and Storage SO ADVANCED TECHNIQUES IN WEB INTELLIGENCE - 1 SE Studies in Computational Intelligence LA English DT Article; Book Chapter ID FEATURE-SELECTION; KNOWLEDGE-BASE; LEARNABILITY; SYSTEM AB Web data provides information and knowledge to improve the web site content and structure. Indeed, it eventually contains knowledge which suggests changes that makes a web site more efficient and effective to attract and retain visitors. Making use of a Data Webhouse or a web analytics solution, it is possible to store statistical information concerning the behaviour of users in a website. Likewise, through applying web mining algorithms, interesting patterns can be discovered, interpreted and transformed into useful knowledge. On the other hand, web data include quantities of irrelevant but complex data preprocessing that must be applied in order to model and understand visitor browsing behaviour. Nevertheless, there are many ways to pre-process web data and model the browsing behaviour, hence different patterns can be obtained depending on which model is used. In this sense, a knowledge representation is necessary to store and manipulate web patterns. Generally, different patterns are discovered by using distinct web mining techniques on web data with dissimilar treatments. Consequently, patterns meta-data are relevant to manipulate the discovered knowledge. In this chapter, topics like feature selection, web mining techniques, models characterisation and pattern management will be covered in order to build a repository that stores patterns' meta-data. Specifically, a Pattern Webhouse that facilitates knowledge management in the web environment. C1 [Rebolledo, Victor L.; L'Huillier, Gaston; Velasquez, Juan D.] Univ Chile, Sch Sci & Engn, Dept Ind Engn, Web Intelligence Res Grp, Santiago, Chile. RP Rebolledo, VL (reprint author), Univ Chile, Sch Sci & Engn, Dept Ind Engn, Web Intelligence Res Grp, Republ 701, Santiago, Chile. EM vireboll@ing.uchile.cl; glhuilli@dii.uchile.cl; jvelasqu@dii.uchile.cl CR Agrawal R., 1993, P ACM SIGMOD C MAN D, V22, P207, DOI DOI 10.1145/170035.170072 Agrawal R., 1994, VLDB, P487 Arnold W., 1985, ARTIFICIAL INTELLIGE Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 BLUMER A, 1989, J ACM, V36, P929, DOI 10.1145/76359.76371 Boser B. E., 1992, COMPUTATIONAL LEARNI, P144 CATANIA B, 2005, VLDB, P1346 CHIMPHLEE S, 2006, AIA 2006, P422 Cooley R., 1999, J KNOWLEDGE INFORM S, V1, P5 Davenport T. H., 1997, WORKING KNOWLEDGE OR DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 DAVIS R, 1993, AI MAG, V14, P17 Dell Robert F, 2008, Wl 2008. 2008 IEEE/WIC/ACM International Conference on Web Intelligence. IAT 2008. 2008 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. Wl-IAT Workshop 2008 2008 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, DOI 10.1109/WIIAT.2008.181 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Dujovne LE, 2009, LECT NOTES ARTIF INT, V5711, P301 Elder JF, 2009, P 15 ACM SIGKDD INT Fleuret F, 2004, J MACH LEARN RES, V5, P1531 Freund Y., 1995, LNCS, V904, P23, DOI DOI 10.1007/3-540-59119-2_ Freund Y., 1996, INT C MACH LEARN, P148 Grossman RL, 2002, COMMUN ACM, V45, P59 GROSSMAN RL, 2009, EXPLOR NEWSL, V11, P5 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 HAH J, 1996, DMQL DATA MINING QUE Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Hastie Trevor, 2009, SPRINGER SERIES STAT Imielinski T, 1999, DATA MIN KNOWL DISC, V3, P373, DOI 10.1023/A:1009816913055 Inmon W. H., 2005, BUILDING DATA WAREHO, V4th Kimball R., 2002, DATA WAREHOUSE TOOLK Kimball Ralph, 2000, DATA WEBHOUSE TOOLKI KLOPOTEK MA, 2006, INT INF PROC WEB MIN Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X Kohonen T., 2001, SELF ORG MAPS Larsen J, 2002, COMPUT STAT DATA AN, V38, P517, DOI 10.1016/S0167-9473(01)00076-7 Liu Bing, 2007, WEB DATA MINING EXPL Luo P, 2009, P 15 ACM SIGKDD INT, P1235, DOI 10.1145/1557019.1557152 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Maldonado S, 2009, INFORM SCIENCES, V179, P2208, DOI 10.1016/j.ins.2009.02.014 Markov Z., 2007, DATA MINING WEB UNCO Meo R, 1998, DATA MIN KNOWL DISC, V2, P195, DOI 10.1023/A:1009774406717 Mitchell TM, 1997, MACHINE LEARNING Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 PECHTER R, 2009, SIGKDD EXPLOR NEWSL, V11, P19 Rosenblatt F., 1962, PRINCIPLES NEURODYNA ROUSSEEUW PJ, 1987, J COMPUT APPL MATH, V20, P53, DOI 10.1016/0377-0427(87)90125-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAPIRE RE, 1990, MACH LEARN, V5, P197, DOI 10.1023/A:1022648800760 Scholkopf B., 2001, LEARNING KERNELS SUP Sebastiani Fabrizio, 2005, TEXT MINING ITS APPL, P109 TERROVITIS M, 2004, INT C SCI STAT DAT M, P265 Torkkola K., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753742 Vapnik V., 1999, NATURE STAT LEARNING VAPNIK VN, 1971, THEOR PROBAB APPL+, V16, P264, DOI 10.1137/1116025 VELASQUEZ JD, 2005, INT J WEB INFORM SYS, V1, P53 Velasquez JD, 2007, INT J ARTIF INTELL T, V16, P793, DOI 10.1142/S0218213007003552 Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 Velasquez JD, 2008, FRONT ARTIF INTEL AP, V170, P1 Velasquez JD, 2007, KNOWL-BASED SYST, V20, P238, DOI 10.1016/j.knosys.2006.05.015 WANG Y, 2003, INT C TOOLS ART INT, P560 Wen CW, 2001, LECT NOTES COMPUT SC, V2118, P103 Werbos P. J., 1994, ROOTS BACKPROPAGATIO Wolf L, 2005, J MACH LEARN RES, V6, P1855 Wu J, 2009, P 15 ACM SIGKDD INT, P877, DOI 10.1145/1557019.1557115 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 YIN ZJ, 2009, P 15 ACM SIGKDD INT, P957, DOI 10.1145/1557019.1557123 YOUNG TY, 1971, IEEE T COMPUT, VC 20, P967, DOI 10.1109/T-C.1971.223390 Zeller M., 2009, KDD 2009, P11 Zhao Z., 2007, ICML, P1151 NR 67 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-14460-8 J9 STUD COMPUT INTELL PY 2010 VL 311 BP 49 EP 77 D2 10.1007/978-3-642-14461-5 PG 29 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BRA34 UT WOS:000282237500003 ER PT S AU Shah, AR Adkins, JN Baxter, DJ Cannon, WR Chavarria-Miranda, DG Choudhury, S Gorton, I Gracio, DK Halter, TD Jaitly, ND Johnson, JR Kouzes, RT Macduff, MC Marquez, A Monroe, ME Oehmen, CS Pike, WA Scherrer, C Villa, O Webb-Robertson, BJ Whitney, PD Zuljevic, N AF Shah, Anuj R. Adkins, Joshua N. Baxter, Douglas J. Cannon, William R. Chavarria-Miranda, Daniel G. Choudhury, Sutanay Gorton, Ian Gracio, Deborah K. Halter, Todd D. Jaitly, Navdeep D. Johnson, John R. Kouzes, Richard T. Macduff, Matthew C. Marquez, Andres Monroe, Matthew E. Oehmen, Christopher S. Pike, William A. Scherrer, Chad Villa, Oreste Webb-Robertson, Bobbie-Jo Whitney, Paul D. Zuljevic, Nino BE Zelkowitz, MV TI Applications in Data-Intensive Computing SO ADVANCES IN COMPUTERS, VOL 79 SE Advances In Computers LA English DT Article; Book Chapter ID TANDEM MASS-SPECTRA; PROTEOMIC BIOMARKER DISCOVERY; OCEAN SAMPLING EXPEDITION; ACCURATE MASS; PROTEIN IDENTIFICATION; PEPTIDE IDENTIFICATION; SHOTGUN PROTEOMICS; SPECTROMETRY DATA; OVARIAN-CANCER; SYSTEM AB The total quantity of digital information in the world is growing at an alarming rate. Scientists and engineers are contributing heavily to this data "tsunami" by gathering data using computing and instrumentation at incredible rates. As data volumes and complexity grow, it is increasingly arduous to extract valuable information from the data and derive knowledge from that data. Addressing these demands of ever-growing data volumes and complexity requires game-changing advances in software, hardware, and algorithms. Solution technologies also must scale to handle the increased data collection and processing rates and simultaneously accelerate timely and effective analysis results. This need for ever faster data processing and manipulation as well as algorithms that scale to high-volume data sets have given birth to a new paradigm or discipline known as "data-intensive computing." In this chapter, we define data-intensive computing, identify the challenges of massive data, outline solutions for hardware, software, and analytics, and discuss a number of applications in the areas of biology, cyber security, and atmospheric research. C1 [Shah, Anuj R.] Pacific NW Natl Lab, Sci Data Management Grp, Richland, WA 99352 USA. [Oehmen, Christopher S.; Webb-Robertson, Bobbie-Jo] Pacific NW Natl Lab, Dept Energy, Computat Biol & Bioinformat Grp, Richland, WA 99352 USA. [Chavarria-Miranda, Daniel G.] Pacific NW Natl Lab, DOE ASCR Ctr Scalable Parallel Programming Models, Richland, WA 99352 USA. [Shah, Anuj R.] Pacific NW Natl Lab, Data Intens Comp Initiat, Directed Res & Dev Project, Richland, WA 99352 USA. [Jaitly, Navdeep D.] IBM Toronto Labs, Toronto, ON, Canada. [Jaitly, Navdeep D.] Caprion Pharmaceut, Montreal, PQ, Canada. [Johnson, John R.] Pacific NW Natl Lab, Def & Special Programs, Richland, WA 99352 USA. [Johnson, John R.] Lawrence Livermore Natl Lab, Livermore, CA USA. [Kouzes, Richard T.; Webb-Robertson, Bobbie-Jo] Washington State Univ, Pullman, WA 99164 USA. [Villa, Oreste] Pacific NW Natl Lab, HPC Grp, Richland, WA 99352 USA. RP Shah, AR (reprint author), Pacific NW Natl Lab, Sci Data Management Grp, Richland, WA 99352 USA. EM ian.gorton@pnl.gov; Debbie.gracio@pnl.gov; rkouzes@pnl.gov; andres.marquez@pnl.gov RI Gorton, Ian/A-8247-2009; Adkins, Joshua/B-9881-2013; Cannon, William/K-8411-2014 OI Adkins, Joshua/0000-0003-0399-0700; Cannon, William/0000-0003-3789-7889 CR ACKERMAN TP, 2005, DOESCARMTR067 PAC NW Aebersold R, 2003, NATURE, V422, P198, DOI 10.1038/nature01511 Aebersold R, 2005, J PROTEOME RES, V4, P1104, DOI 10.1021/pr050027n AJWANI D, 2008, P 7 INT WORKSH EXP A, P208 Alaiya A, 2005, J PROTEOME RES, V4, P1213, DOI 10.1021/pr050149f ALTINTAS I, 2008, KEPLER PROJECT Altintas I, 2004, P 16 INT C SCI STAT Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 Anderson KS, 2005, J PROTEOME RES, V4, P1123, DOI 10.1021/pr0500814 ANDERSON W, 2003, P 2003 ACM IEEE C SU, P46, DOI 10.1145/1048935.1050196 ANTHES G, 2006, SIZE IS OPPORTUNITY, P22 Barabasi AL, 2004, NAT REV GENET, V5, P101, DOI 10.1038/nrg1272 Beer I, 2004, PROTEOMICS, V4, P950, DOI 10.1002/pmic.200300652 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bigham J, 2003, LECT NOTES COMPUT SC, V2776, P171 BLAGOJEVIC F, 2007, PRINCIPLES PRACTICE Bogdan I, 2007, BIOINFORMATICS, V23, P724, DOI 10.1093/bioinformatics/btl656 Bogdanov B, 2005, MASS SPECTROM REV, V24, P168, DOI 10.1002/mas.20015 Buhler J. D., 2007, P 3 ANN REC SYST SUM Cannon WR, 2005, J PROTEOME RES, V4, P1687, DOI 10.1021/pr050147v CHAVARRIAMIRAND.D, 2008, IEEE INT S PAR DISTR CHAVARRIAMIRAND.D, 2008, P MTAAP 08 WORKSH MU Chen Y, 2006, INT FED INFO PROC, V207, P311 CHENEY J, 2009, PRINCIPLES PROVENANC CHONG J, 2008, P 1 ANN WORKSH EM AP Clowers BH, 2006, ANAL CHEM, V78, P44, DOI 10.1021/ac050615k Conti G, 2006, IEEE COMPUT GRAPH, V26, P60, DOI 10.1109/MCG.2006.30 Craig R, 2006, J PROTEOME RES, V5, P1843, DOI 10.1021/pr0602085 Craig R, 2004, BIOINFORMATICS, V20, P1466, DOI 10.1093/bioinformatics/bth092 Craig R, 2003, RAPID COMMUN MASS SP, V17, P2310, DOI 10.1002/rcm.1198 Crandall R.W., 2006, VIDEO GAMES SERIOUS *CRAY, 2009, CRAY XD 1 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 D'Amico A., 2007, P WORKSH VIS COMP SE, P19 DAVIDSON GS, 2006, DATA CENTRIC COMPUTI Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Diamandis EP, 2002, LANCET, V360, P170, DOI 10.1016/S0140-6736(02)09390-X Elwood M, 2002, LANCET, V360, P170, DOI 10.1016/S0140-6736(02)09389-3 ENG JK, 1994, J AM SOC MASS SPECTR, V5, P976, DOI 10.1016/1044-0305(94)80016-2 Feng L, 2004, COMPUT SECUR, V23, P241, DOI 10.1016/j.cose.2004.01.016 Feo J., 2005, P 2 C COMP FRONT, P28, DOI DOI 10.1145/1062261.1062268 FERNANDO R, 2004, GPU GEMS Flachs B., 2005, IEEE INT SOL STAT CI FLATH L, 2004, GP2 WORKSH CHAP HILL Gantz J., 2008, DIVERSE EXPLODING DI GANTZ JF, 2007, IEC EXPANDING DIGITA Gillette MA, 2005, J PROTEOME RES, V4, P1143, DOI 10.1021/pr0500962 Gokhale M, 2008, COMPUTER, V41, P60, DOI 10.1109/MC.2008.125 GORTON I, 2009, P INT S COMP BAS SOF GORTON I, 2008, WICSA 2008, P95 GOVINDARAJU NK, 2005, P 9 ANN WORKSH HIGH HIGHFIELD R, 2008, LARGE HADRON COLLIDE HILDEBRA.PH, 1974, J APPL METEOROL, V13, P808, DOI 10.1175/1520-0450(1974)013<0808:ODOTNL>2.0.CO;2 HOFSTEE HP, 2005, HPCA Horn DM, 2000, J AM SOC MASS SPECTR, V11, P320, DOI 10.1016/S1044-0305(99)00157-9 Jacobs JM, 2005, J PROTEOME RES, V4, P1073, DOI 10.1021/pr0500657 Jaitly N, 2006, ANAL CHEM, V78, P7397, DOI 10.1021/ac052197p JARED C, 2009, P 2009 C SERV IEEE C, V1 JOHNSON J, 2004, EVALUATION GPU BASED Kafadar K, 2006, COMPUT STAT DATA AN, V50, P3721, DOI 10.1016/j.csda.2005.06.017 Kastner C.M., 2005, INT C FIELD PROGR LO, P499, DOI 10.1109/FPL.2005.1515771 KIMMEL JR, 2004, THESIS STANFORD U, P18 Kollias P, 2007, J ATMOS OCEAN TECH, V24, P1199, DOI 10.1175/JTECH2033.1 Kongetira P, 2005, IEEE MICRO, V25, P21, DOI 10.1109/MM.2005.35 Kouzes RT, 2009, COMPUTER, V42, P26, DOI 10.1109/MC.2009.26 KRASSER S, 2005, IEEE WORKSH INF ASS LIU LK, 2007, P 2007 IEEE INT C MU, P1866 Liu Y, 2007, IBM J RES DEV, V51, P583 LIU Y, 2006, INT C COMP SCI, P188 Long C. N., 2008, Open Atmosphere Science Journal, V2, P23, DOI 10.2174/1874282300802010023 Ludascher B, 2006, CONCURR COMP-PRACT E, V18, P1039, DOI 10.1002/cpe.994 LUEBKE D, 2004, SI00RAPH04 ACM SIGGR MACDUFF MC, 2004, DOESCARMTR046 PAC NW Markowitz V. M., 2008, NUCLEIC ACIDS RES, V36, P534 Markowitz V. M., 2006, NUCLEIC ACIDS RES, V34, pD344, DOI 10.1093/nar/gkj024 McDonald WH, 2003, CURR OPIN MOL THER, V5, P302 Monroe ME, 2007, BIOINFORMATICS, V23, P2021, DOI 10.1093/bioinformatics/btm281 Moore A, 1998, J ARTIF INTELL RES, V8, P67 Muggleton SH, 2006, NATURE, V440, P409, DOI 10.1038/440409a NAWATHE UG, 2007, P 2007 INT S PHYS DE, P2 Nickolls John, 2008, ACM Queue, V6, DOI 10.1145/1365490.1365500 NOEL S, 2005, IEEE WORKSH VIS COMP Oehmen C, 2006, IEEE T PARALL DISTR, V17, P740, DOI 10.1109/TPDS.2006.112 Oehmen CS, 2008, J PHYS CONF SER, V125, P12052, DOI 10.1088/1742-6596/125/1/012052 Owens JD, 2007, COMPUT GRAPH FORUM, V26, P80, DOI 10.1111/j.1467-8659.2007.01012.x PARSONS S, 2008, Patent No. 20070243675 Pasa-Tolic L, 2004, BIOTECHNIQUES, V37, P621 PASATOLIC L, 2004, BIOTECHNIQUES, V37, P626 Paulson LD, 2004, COMPUTER, V37, P17, DOI 10.1109/MC.2004.1266288 PEPPLER RA, 2008, DOESCARMTR082 PAC NW Petricoin EF, 2002, LANCET, V359, P572, DOI 10.1016/S0140-6736(02)07746-2 Petritis K, 2006, ANAL CHEM, V78, P5026, DOI 10.1021/ac060143p PIKE W, 2007, VIZSEC 2007, P203 *PRIC WAT COOP, 2007, GLOB ENT MED OUTL 20 Riedel E., 1998, P 24 INT C VER LARG Rockhill B, 2002, LANCET, V360, P169, DOI 10.1016/S0140-6736(02)09387-X ROCKWOOD AL, 1995, ANAL CHEM, V67, P2699, DOI 10.1021/ac00111a031 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saraiya P., 2005, Information Visualization, V4, DOI 10.1057/palgrave.ivs.9500102 SCARPAZZA DP, 2007, IEEE INT PAR DISTR P SCHERRER C, 2007, IEEE INT PAR DISTR P, P1 Schmidt G., 1993, RELATIONS GRAPHS Sechi S, 2003, CURR OPIN CHEM BIOL, V7, P70, DOI 10.1016/S1367-5931(02)00010-8 SENKO MW, 1995, J AM SOC MASS SPECTR, V6, P229, DOI 10.1016/1044-0305(95)00017-8 SENKO MW, 1995, J AM SOC MASS SPECTR, V6, P52, DOI 10.1016/1044-0305(94)00091-D SHAH AR, 2008, P 4 IEEE INT C ESCIE, P190 Shah AR, 2008, BIOINFORMATICS, V24, P783, DOI 10.1093/bioinformatics/btn028 SHAH AR, 2007, IEEE NIH LIF SCI SYS, P89 SIOZIOS K, 2009, 3 HIPEC WORKSH REC C SOURDIS I, 2003, P 13 INT C FIELD PRO Srivastava S, 2005, J PROTEOME RES, V4, P1098, DOI 10.1021/pr050016u Srivastava S, 2005, J PROTEOME RES, V4, P1171, DOI 10.1021/pr050017m STEINKRAUS D, 2005, P 5 INT C DOC AN REC STRAND W, 2006, GEOPH RES ABS, V8 SZALAY A, 2003, SPIE ASTRONOMY TELES Szalay A, 2006, NATURE, V440, P413, DOI 10.1038/440413a Tanner S, 2005, ANAL CHEM, V77, P4626, DOI 10.1021/ac050102d Thomas J.J., 2005, ILLUMINATING PATH RE ULMER C, 2008, HIGH PERF EMB COMP H *US DOE, DOESCARM0706 *US DOE, 2007, DOESCARM0707 PAC NW VILLA O, 2007, P 21 IEEE INT PAR DI Villanueva J, 2005, J PROTEOME RES, V4, P1060, DOI 10.1021/pr050034b Vitzthum F, 2005, J PROTEOME RES, V4, P1086, DOI 10.1021/pr050080b Waterston RH, 2002, P NATL ACAD SCI USA, V99, P3712, DOI 10.1073/pnas.042692499 Webb-Robertson BJM, 2008, COMPUT BIOL CHEM, V32, P458, DOI 10.1016/j.compbiolchem.2008.07.017 Wegman E.J., 1995, J COMPUTATIONAL GRAP, V4, P281, DOI 10.2307/1390857 Wegman EJ, 2003, J COMPUT GRAPH STAT, V12, P893, DOI 10.1198/1061860032625 Whitney P, 2003, J COMPUT GRAPH STAT, V12, P915, DOI 10.1198/1061860032535 WIDENER K, 2009, P 17 ARM SCI TEAM M WILLIAMS S, 2005, C COMP FRONT ISCH IT Williamson SJ, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001456 Wolters DA, 2001, ANAL CHEM, V73, P5683, DOI 10.1021/ac010617e Wu CC, 2003, NAT BIOTECHNOL, V21, P532, DOI 10.1038/nbt819 Yates JR, 1998, ANAL CHEM, V70, P3557, DOI 10.1021/ac980122y YOO A, 2006, C SUP TAMP FL Yooseph S, 2007, PLOS BIOL, V5, P432, DOI 10.1371/journal.pbio.0050016 ZHUGE H, 2003, IEEE COMPUT, V36, P114 Zimmer JSD, 2006, MASS SPECTROM REV, V25, P450, DOI 10.1002/mas.20071 2007, INFORM WEEK 0806 NR 141 TC 1 Z9 1 PU ELSEVIER ACADEMIC PRESS INC PI SAN DIEGO PA 525 B STREET, SUITE 1900, SAN DIEGO, CA 92101-4495 USA SN 0065-2458 BN 978-0-12-381027-4 J9 ADV COMPUT JI Adv. Comput. PY 2010 VL 79 BP 1 EP 70 DI 10.1016/S0065-2458(10)79001-X PG 70 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science GA BOE96 UT WOS:000276405000001 ER PT S AU Wang, YBJ Li, F Coenen, F Sanderson, R Xin, Q AF Wang, Yanbo J. Li, Fan Coenen, Frans Sanderson, Robert Xin, Qin BE Perner, P TI Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification SO ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 10th Industrial Conference on Data Mining CY JUL 12-14, 2010 CL Berlin, GERMANY DE Associative Classification; (Language-independent) Text Classification; Text Mining; Textual Feature Selection ID CATEGORIZATION; RETRIEVAL AB Textual Feature Selection (TFS) is an important phase in the process of text classification. It aims to identify the most significant textual features (i.e. key words and/or phrases), in a textual dataset, that serve to distinguish between text categories. In TFS, basic techniques can be divided into two groups: linguistic vs. statistical. For the purpose of building a language-independent text classifier, the study reported here is concerned with statistical TFS only. In this paper, we propose a novel statistical TFS approach that hybridizes the ideas of two existing techniques, DIAAF (Darmstadt Indexing Approach Association Factor) and RS (Relevancy Score). With respect to associative (text) classification, the experimental results demonstrate that the proposed approach can produce greater classification accuracy than other alternative approaches. C1 [Wang, Yanbo J.; Li, Fan] China Minsheng Banking Corp Ltd, Informat Management Ctr, Beijing, Peoples R China. RP Wang, YBJ (reprint author), China Minsheng Banking Corp Ltd, Informat Management Ctr, Beijing, Peoples R China. EM wangyanbo@cmbc.com.cn; lifan@cmbc.com.cn; coenen@liverpool.ac.uk; rsanderson@lanl.gov; xin@simula.no RI Sanderson, Robert/A-3613-2013 OI Sanderson, Robert/0000-0003-4441-6852 CR Agrawal R., 1993, 1993 ACM SIGMOD INT, P207 Ali K., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Antonie M.-L., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183881 Church KW, 1989, P 27 ANN M ASS COMP, P76, DOI 10.3115/981623.981633 Coenen F, 2004, FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P359, DOI 10.1109/ICDM.2004.10012 COENEN F, 2007, P 5 INT C MACH LEARN, P838 Coenen F, 2007, DATA KNOWL ENG, V60, P345, DOI 10.1016/j.datak.2006.02.005 Coenen F, 2005, LECT NOTES ARTIF INT, V3518, P216 Cohen W., 1995, 12 INT C MACH LEARN, P115 DENG ZH, 2002, P 3 INT C WEB INF SY, P223 FANO RM, 1961, STAT THEORY COMMUNIC Fragoudis D, 2005, KNOWL INF SYST, V8, P16, DOI 10.1007/s10115-004-0177-2 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 KOBAYASHI M, 2004, SURVEY TEXT MINING C, V103, P122 Lang K., 1995, Machine Learning. Proceedings of the Twelfth International Conference on Machine Learning Li W., 2001, P IEEE INT C DAT MIN, P369 Li X. L., 2003, P 18 INT JOINT C ART, P587 Liu B., 1998, KNOWLEDGE DISCOVERY, P80 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 Moschitti A, 2004, LECT NOTES COMPUT SC, V2997, P181 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEFFER T, 2002, P WORKSH TEXT LEARN Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHIDARA Y, 2007, P 5 INT C MACH LEARN, P490 WANG YJ, 2008, P 10 INT C DAT WAR K, P357, DOI DOI 10.1007/978-3-540-85836-2_34 WANG YJ, 2006, P 26 SGAI INT C INN, P413 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Yin XX, 2003, SIAM PROC S, P331 YOON Y, 2005, P 2 AS INF RETR S JE, P467 ZAIANE O, 2002, C RES PRACT INF TECH, V5, P215 NR 34 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14399-1 J9 LECT NOTES ARTIF INT PY 2010 VL 6171 BP 222 EP 236 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BTG35 UT WOS:000286902300018 ER PT S AU Carrillo, M Villatoro-Tello, E Lopez-Lopez, A Eliasmith, C Villasenor-Pineda, L Montes-y-Gomez, M AF Carrillo, Maya Villatoro-Tello, Esau Lopez-Lopez, Aurelio Eliasmith, Chris Villasenor-Pineda, Luis Montes-y-Gomez, Manuel BE Loftsson, H Rognvaldsson, E Helgadottir, S TI Concept Based Representations for Ranking in Geographic Information Retrieval SO ADVANCES IN NATURAL LANGUAGE PROCESSING SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 7th International Conference on Natural Language Processing CY AUG 16-18, 2010 CL Reykjavik, ICELAND SP IZETeam, Microsoft Island, Post & Telecom Adm Iceland DE Geographic Information Retrieval; Vector Model; Random Indexing; Context Vectors; Holographic Reduced Representation AB Geographic Information Retrieval (GIR) is a specialized Information Retrieval (I R.) branch that deals with information related to geographical locations. Traditional IR, engines are perfectly able to retrieve the majority of the relevant documents for most geographical queries, but they have severe difficulties generating a pertinent ranking of the retrieved results, which leads to poor performance. A key reason for this ranking problem has been a lack of information. Therefore, previous GIR. research has tried to fill this gap using robust geographical resources (i.e. a geographical ontology), while other research with the same aim has used relevant feedback techniques instead. This paper explores the use of Bag of Concepts (BoC; a representation where documents are considered as the union of the meanings of its terms) and Holographic Reduced Representation (HRR; a novel representation for textual structure) as re-ranking mechanisms for GIR. Our results reveal an improvement in mean average precision (MAP) when compared to the traditional vector space model, even if Pseudo Relevance Feedback is employed. C1 [Carrillo, Maya; Villatoro-Tello, Esau; Lopez-Lopez, Aurelio; Villasenor-Pineda, Luis; Montes-y-Gomez, Manuel] INAOE, Coordinac Ciencias Computac, Puebla 72840, Mexico. RP Carrillo, M (reprint author), INAOE, Coordinac Ciencias Computac, Luis Enrique Erro 1, Puebla 72840, Mexico. EM cmaya@inaoep.mx; villatoroe@inaoep.mx; allopez@inaoep.mx; celiasmith@uwaterloo.ca; villasen@inaoep.mx; mmontesg@inaoep.mx CR ANDRADE L, 2006, P 3 WORKSH GEOGR INF DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 Ferres D, 2008, LECT NOTES COMPUT SC, V5152, P830, DOI 10.1007/978-3-540-85760-0_105 Fishbein JM, 2008, LECT NOTES COMPUT SC, V4956, P514 HENRICH A, 2007, P WORKSH GEOGR INF R KANERVA P, 2000, P 22 ANN C COGN SC S LARSON RR, 2008, CLEF 2008 WORKSH AAR LARSON RR, 2005, CLEF 2005 WORKSH WIE LAVELLI A, 2004, CIKM 2004, P615 MANDL T, 2008, CLEF WORKSH AARH DEN MARTINIS B, 2006, CLEF WORKSH SPAIN Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Plate T.A., 2003, HOLOGRAPHIC REDUCED SAHLGREN M, 2005, J NATURAL LANGUAGE E, V11, P327 Sahlgren M., 2004, P 20 INT C COMP LING, P487, DOI 10.3115/1220355.1220425 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VILLATOROTELLO E, 2008, CLEF 2008 WORKSH AAR WANG R, 2008, CLEF WORKSH AARH DEN NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14769-2 J9 LECT NOTES ARTIF INT PY 2010 VL 6233 BP 85 EP 96 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BUF75 UT WOS:000289187000011 ER PT S AU O'Shea, J Bandar, Z Crockett, K AF O'Shea, James Bandar, Zuhair Crockett, Keeley BE Jedrzejowicz, P Nguyen, NT Howlett, RJ Jain, LC TI A Machine Learning Approach to Speech Act Classification Using Function Words SO AGENT AND MULTI-AGENT SYSTEMS: TECHNOLOGIES AND APPLICATIONS, PT II, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 4th KES International Symposium on Agent and Multi-Agent Systems CY JUN 23-25, 2010 CL Gdynia, POLAND SP KES Int, Focus Grp Agent & Multi Agent Syst HO Gdynia Maritime Univ DE Dialogue Act; Speech Act; Classification; Semantic Similarity; Decision Tree AB This paper presents a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. It focuses on classifying questions or non-questions as a generally useful task in agent-based systems. The proposed technique extracts salient features by replacing function words with numeric tokens and replacing each content word with a standard numeric wildcard token. The Decision Tree, which is a well-established classification technique, has been chosen for this work. Experiments provide evidence of potential for highly effective classification, with a significant achievement on a challenging dataset, before any optimisation of feature extraction has taken place. C1 [O'Shea, James; Bandar, Zuhair; Crockett, Keeley] Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England. RP O'Shea, J (reprint author), Manchester Metropolitan Univ, Dept Comp & Math, Manchester M15 6BH, Lancs, England. EM j.d.oshea@mmu.ac.uk; z.bandar@mmu.ac.uk; k.crockett@mmu.ac.uk CR Aleksander I., 1995, INTRO NEURAL COMPUTI Bickmore T, 2006, J BIOMED INFORM, V39, P556, DOI 10.1016/j.jbi.2005.12.004 Bollacker K., 1998, 2 INT ACM C AUT AG, P116 CROCKETT, 2009, KNOWLEDGE REASONING Deerwester S., 1989, COMPUTER INFORM RETR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fong T., 2001, IEEE INT WORKSH ROB Frank E, 2005, DATA MINING PRACTICA Islam A., 2008, ACM T KNOWL DISCOV D, V2, P1, DOI DOI 10.1145/1376815.1376819 KEIZER S, 2001, BI DIALOG 2001 5 WOR, P88 Keizer S. A. R., 2002, 3 ACL SIGDIAL WORKSH, P88 LANG LET, 1995, INTELLIGENT AUTONOMO, P357 LI Y, 2004, 17 INT FLOR ART INT, P820 Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 Quinlan J. R., 1993, C4 5 PROGRAMS MACH L Quinlan JR, 1986, MACH LEARN, V1, P106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Serafin R., 2003, 2003 C N AM CHAPT AS SPARCKJONES K, 1972, J DOC, V28, P11 van Rijsbergen C.J., 1980, INFORM RETRIEVAL Venkataraman A., 2002, 9 AUSTR INT C SPEECH Verbree D., 2006, IEEE SPOK LANG TECHN, P70 Webb N., 2005, AAAI 2005 WERMTER S, 1996, COLING 1996 16 INT C, V16, P996 NR 24 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13540-8 J9 LECT NOTES ARTIF INT PY 2010 VL 6071 BP 82 EP 91 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQP75 UT WOS:000281485700009 ER PT S AU Culpepper, JS Navarro, G Puglisi, SJ Turpin, A AF Culpepper, J. Shane Navarro, Gonzalo Puglisi, Simon J. Turpin, Andrew BE DeBerg, M Meyer, U TI Top-k Ranked Document Search in General Text Databases SO ALGORITHMS-ESA 2010, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 16th Annual European Symposium on Algorithms (ESA 2010) CY SEP 06-08, 2010 CL Liverpool, ENGLAND SP European Assoc Theoret Comp Sci, Int Soc Comp Geometry, London Math Soc, Springer, Univ Liverpool ID EFFICIENT ALGORITHMS; RETRIEVAL PROBLEMS; SUFFIX ARRAYS; INDEXES; TREES AB Text search engines return a set of k documents ranked by similarity to a query. Typically, documents and queries are drawn from natural language text, which can readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques, which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for English text when word queries are issued. C1 [Culpepper, J. Shane; Puglisi, Simon J.; Turpin, Andrew] RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. RP Culpepper, JS (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. EM shane.culpepper@rmit.edu.au; gnavarro@dcc.uchile.cl; simon.puglisi@rmit.edu.au; andrew.turpin@rmit.edu.au CR Anh V. N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148235 Apostolico A., 1985, COMBINATORIAL ALGORI, P85 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bender MA, 2000, LECT NOTES COMPUT SC, V1776, P88, DOI 10.1007/10719839_9 Ferragina P., 2007, ACM T ALGORITHMS, V3, P20, DOI 10.1145/1240233.1240243 Fischer J, 2007, LECT NOTES COMPUT SC, V4614, P459 Gagie T, 2009, LECT NOTES COMPUT SC, V5721, P1, DOI 10.1007/978-3-642-03784-9_1 Grossi R, 2003, SIAM PROC S, P841 Hon WK, 2009, ANN IEEE SYMP FOUND, P713, DOI 10.1109/FOCS.2009.19 MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 Manzini G, 2001, J ACM, V48, P407, DOI 10.1145/382780.382782 Muthukrishnan S, 2002, SIAM PROC S, P657 Navarro G, 2007, ACM COMPUT SURV, V39, DOI 10.1145/1216370.1216372 Persin M, 1996, J AM SOC INFORM SCI, V47, P749, DOI 10.1002/(SICI)1097-4571(199610)47:10<749::AID-ASI3>3.0.CO;2-2 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Puglisi SJ, 2006, LECT NOTES COMPUT SC, V4209, P122 Raman R, 2002, SIAM PROC S, P233 ROBERTSON SE, 1994, P 3 TREC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Sadakane K., 2007, J DISCRETE ALGORITHM, V5, P12, DOI 10.1016/j.jda.2006.03.011 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Valimaki N, 2007, LECT NOTES COMPUT SC, V4580, P205 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd ZOBEL J, 2006, ACM COMPUT SURV, V38, pNI236, DOI DOI 10.1145/1132956.1132959 NR 24 TC 16 Z9 16 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15780-6 J9 LECT NOTES COMPUT SC PY 2010 VL 6347 BP 194 EP 205 PG 12 WC Computer Science, Theory & Methods; Mathematics SC Computer Science; Mathematics GA BSZ41 UT WOS:000286165200017 ER PT S AU Cummins, R O'Riordan, C AF Cummins, Ronan O'Riordan, Colm BE Coyle, L Freyne, J TI The Effect of Query Length on Normalisation in Information Retrieval SO ARTIFICIAL INTELLIGENCE AND COGNITIVE SCIENCE SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 20th Irish Conference on Artificial Intelligence and Cognitive Science CY AUG 19-21, 2009 CL Dublin, IRELAND SP Sch Comp Sci & Informat, CLARITY, Sci Fdn Ireland Ctr Sci, Engn & Technol, Tyndall Natl Inst, CLIQUE, Strateg Res Cluster, Sci Fdn Ireland (SFI), IBM, Idiro Technol, Norkom Technol HO Univ Coll Dublin AB Document length normalisation is known to be a difficult problem in IR, as tuning is often needed to overcome the collection dependence problem known to affect many normalisation schemes. Furthermore, it has been shown in various studies that the most optimal level of normalisation to apply is correlated with query length. In this paper, we confirm this correlation and present experiments which investigates and explains the effect of query length on normalisation. C1 [Cummins, Ronan] Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Cummins, R (reprint author), Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. EM ronanc@dcs.gla.ac.uk; colm.oriordan@nuigalway.ie CR CHOWDHURY A, 2002, SIGIR 02, P381 CHUNG TL, 2006, ACM T ASIAN LANGUAGE, V5, P245, DOI 10.1145/1194936.1194941 Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6 CUMMINS R, 2007, ARTIF INTELL, P51 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1996, RES DEV INFORM RETRI, P21 Trotman A, 2005, INFORM RETRIEVAL, V8, P359, DOI 10.1007/s10791-005-6991-7 TURTLE HR, 1990, SIGIR 90 13 INT C RE, P1 NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-17079-9 J9 LECT NOTES ARTIF INT PY 2010 VL 6206 BP 26 EP 32 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA BTF44 UT WOS:000286799900005 ER PT S AU Carrillo, M Lopez-Lopez, A AF Carrillo, Maya Lopez-Lopez, Aurelio BE Papadopoulos, H Andreou, AS Bramer, M TI Concept Based Representations as Complement of Bag of Words in Information Retrieval SO ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS SE IFIP Advances in Information and Communication Technology LA English DT Proceedings Paper CT 6th IFIP Conference on Artificial Intelligence Applications and Innovations CY OCT 06-07, 2010 CL Larnaca, CYPRUS SP Univ Cyprus, Cyprus Univ Technol, Frederick Univ, Cyprus Tourism Org DE Information Retrieval; Concept Based Representation; Vector Model; Random Indexing; Holographic Reduced Representation ID MODEL AB Information Retrieval models, which do not represent texts merely as collections of the words they contain, but rather as collections of the concepts they contain through synonym sets or latent dimensions, are known as Bag-of-Concepts (BoC) representations. In this paper we use random indexing, which uses co-occurrence information among words to generate semantic context vectors and then represent the documents and queries as BoC. In addition, we use a novel representation, Holographic Reduced Representation, previously proposed in cognitive models, which can encode relations between words. We show that these representations can be successfully used in information retrieval, can associate terms, and when they are combined with the traditional vector space model, they improve effectiveness, in terms of mean average precision. C1 [Carrillo, Maya; Lopez-Lopez, Aurelio] INAOE, Puebla 72840, Mexico. RP Carrillo, M (reprint author), INAOE, Luis Enrique Erro 1, Puebla 72840, Mexico. EM cmaya@inaoep.mx; allopez@inaoep.mx CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Carrillo M, 2009, LECT NOTES ARTIF INT, V5729, P24, DOI 10.1007/978-3-642-04208-9_7 CARRILLO M, 2009, P FLEX QU ANSW SYST, P239 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 Fishbein JM, 2008, LECT NOTES COMPUT SC, V4956, P514 GABOR D, 1968, NATURE, V217, P1288, DOI 10.1038/2171288a0 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Kanerva P., 2000, P 22 ANN C COGN SCI, P103 KVASNICKA V, 2004, NEURAL NETWORK WOR 6, V14, P521 LIU HG, 2004, MONTY LINGUA END TO METCALFE EJ, 1985, PSYCHOL REV, V92, P1 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 MURDOCK BB, 1982, PSYCHOL REV, P316 Plate T.A., 2003, HOLOGRAPHIC REDUCED SAHLGREN M, 2005, J NATURAL LANGUAGE E, V11, P327 Sahlgren M., 2004, P 20 INT C COMP LING, P487, DOI 10.3115/1220355.1220425 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SLACK JN, 1984, ADV ARTIFICIAL INTEL VILARES J, 2005, P 2005 ACM S DOC ENG, P162, DOI 10.1145/1096601.1096643 WILLSHAW DJ, 1969, NATURE, V222, P960, DOI 10.1038/222960a0 NR 25 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1868-4238 BN 978-3-642-16238-1 J9 IFIP ADV INF COMM TE PY 2010 VL 339 BP 154 EP 161 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BWD73 UT WOS:000293683700022 ER PT S AU Jehng, JC Chou, S Cheng, CY AF Jehng, Jihn-Chang Chou, Shihchieh Cheng, Chin-Yi BE Taniar, D Gervasi, O Murgante, B Pardede, E Apduhan, BO TI A Formal Concept Analysis-Based Domain-Specific Thesaurus and Its Application in Document Representation SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2010, PT 3, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT International Conference on Computational Science and Its Applications CY MAR 20-28, 2010 CL Fukuoka, JAPAN SP Univ Perugia, Monash Univ, La Trobe Univ, Univ Basilicata, Kyushu Sangyo Univ DE Information Retrieval; Formal Concept Analysis; Concept Lattice; Vector Space Model ID INFORMATION-RETRIEVAL; WORDNET; SYSTEM AB Many techniques in the process of document retrieval and clustering, based on the vector space model, represent documents by vectors. They ignore the conceptual relationships of terms such as synonyms, hypernyms and hyponyms and, especially, treat terms as a bag of terms. The application of conceptual relationships of terms has been proved by generating improved results for document clustering in previous studies. For those studies, thesauri like WordNet were used to provide the information of relationships between terms. However, some domain-specific terms like "query expansion" and "document clustering" cannot be found in these thesauri. These terms are thought of as important features in domain-specific documents. In this paper, we propose an automatic domain-specific thesaurus building approach based on Formal Concept Analysis (FCA) dealing with the problem with general thesauri. We also apply the domain-specific thesaurus as background knowledge to represent documents by concept dimension vectors. In the evaluation, an improved result by our method compared to traditional approaches is shown. C1 [Jehng, Jihn-Chang] Natl Cent Univ, Inst Human Resource Management, Jhongli 32001, Taoyuan County, Taiwan. RP Jehng, JC (reprint author), Natl Cent Univ, Inst Human Resource Management, 300 Jhongda Rd, Jhongli 32001, Taoyuan County, Taiwan. EM jehng@mgt.ncu.edu.tw; scchou@mgt.ncu.edu.tw; yoda0612@seed.net.tw CR BRIKOFF G, 1967, LATTICE THEORY Carpineto C, 1996, MACH LEARN, V24, P95 Carpineto C, 2004, J UNIVERS COMPUT SCI, V10, P985 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 Ganter B., 1999, FORMAL CONCEPT ANAL Grootjen FA, 2006, DATA KNOWL ENG, V56, P174, DOI 10.1016/j.datak.2005.03.006 Hotho A, 2003, P SIGIR 2003 SEM WEB HOTHO A, 2001, IJCAI 2001 WORKSH ON Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Marcus M. P., 1994, COMPUTATIONAL LINGUI, V19, P313 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Ou S, 2008, J INF SCI, V34, P308, DOI 10.1177/0165551507084630 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rajapakse RK, 2006, INFORM PROCESS MANAG, V42, P1260, DOI 10.1016/j.ipm.2005.12.005 Recupero DR, 2007, INFORM RETRIEVAL, V10, P563, DOI 10.1007/s10791-007-9035-7 Romano G., 2004, CONCEPT DATA ANAL TH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wille R., 1982, ORDERED SETS, P445 Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12178-4 J9 LECT NOTES COMPUT SC PY 2010 VL 6018 BP 431 EP 442 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BPN27 UT WOS:000279384400036 ER PT B AU Furnkranz, J AF Fuernkranz, Johannes BE Maimon, O Rokach, L TI Web Mining SO DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION LA English DT Article; Book Chapter DE web mining; content mining; structure mining; usage mining; text classification; hypertext classification; information extraction; wrapper induction; collaborative filtering; recommender systems; Semantic Web ID WORLD-WIDE-WEB; INFORMATION EXTRACTION; RECOMMENDER SYSTEMS; TEXT; PERSONALIZATION; CATEGORIZATION; ONTOLOGIES; RETRIEVAL; INTERFACE; HYPERTEXT AB The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. This chapter provides a brief overview of web mining techniques and research areas, most notably hypertext classification, wrapper induction, recommender systems and web usage mining. C1 Tech Univ Darmstadt, Knowledge Engn Grp, Darmstadt, Germany. RP Furnkranz, J (reprint author), Tech Univ Darmstadt, Knowledge Engn Grp, Darmstadt, Germany. CR Albert R, 1999, NATURE, V401, P130 ANDROUTSOPOULOS I, 2004, 20042 NCSR ARMSTRONG R, 2006, P AAAI SPRING S INFO, P6 BALABANOVI M, 1913, P AAAI SPRING S INFO, P13 Basu C, 2001, J ARTIF INTELL RES, V14, P231 Berendt B., 2002, P 1 INT SEM WEB C IS, P264 Berendt B, 2002, DATA MIN KNOWL DISC, V6, P37, DOI 10.1023/A:1013280719795 Berners-lee T., 2001, SCI AM BERNERSLEE T, 1994, COMMUN ACM, V37, P76, DOI 10.1145/179606.179671 Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 BHARAT K, 1998, COMPUTER NETWORKS, V30, P107 Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P469, DOI 10.1016/S0169-7552(98)80047-0 Breese J S, 1998, P 14 C UNC ART INT, P43 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 Burke RD, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P462 Burke RD, 1997, AI MAG, V18, P57 Califf M. E., 2003, J MACHINE LEARNING R, V4, P177 CALIFF ME, 1999, P AAAI 99 WORKSH Chakrabart S., 2000, SIGKDD EXPLORATIONS, V1, P1 Chakrabarti S, 1998, COMPUT NETWORKS ISDN, V30, P65, DOI 10.1016/S0169-7552(98)00087-7 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S., 2002, MINING WEB ANAL HYPE CHANG G, 2001, MINING WORLD WIDE WE Cohen W., 1996, P AAAI SPRING S MACH COHEN WW, 2000, P 9 INT WORLD WID C Cooley R., 1999, Knowledge and Information Systems, V1 Craven M, 2001, MACH LEARN, V43, P97, DOI 10.1023/A:1007676901476 Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 CRAVEN M, 1998, P 10 EUR C MACH LEAR, P250 CRAWFORD E, 2002, P 19 INT C MACH LEAR, P263 Dean J, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P389 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich T., 2000, 1 INT WORKSH MULT CL, P1 Doan A, 2003, VLDB J, V12, P303, DOI 10.1007/S00778-003-0104-2 Doorenbos R. B., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267666 Dzeroski S., 2001, RELATIONAL DATA MINI EIKVIL L, 1999, 945 NORWEGIAN COMPUT ETZIONI O, 1994, COMMUN ACM, V37, P72, DOI 10.1145/176789.176797 Etzioni O, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P1322 Faloutsoss M, 1999, P ACM SIGCOMM, P251, DOI 10.1145/316188.316229 FAWCETT T, 2003, SIGKDD EXPLORATIONS, V5 Fensel Dieter, 2001, SILVER BULLET KNOWLE FREITAG D, 1998, P 15 NAT C ART INT A Furnkranz J, 2002, APPL ARTIF INTELL, V16, P243, DOI 10.1080/08839510252906453 Furnkranz J., 1998, OEFAITR9830 AUSTR RE Furnkranz J., 2002, Information Fusion, V3, DOI 10.1016/S1566-2535(02)00090-8 FURNKRANZ J, 1998, LEARNING TEXT CATEGO, P5 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Gottlob G, 2004, P S PRINC DAT SYST P GRAHAM P, 2003, P 2003 SPAM C CAMBR Grieser G., 2000, P 3 INT C DISC SCI, P50 HOFMANN T, 1999, P 16 INT JOINT C ART, P688 Hsu CN, 1998, INFORM SYST, V23, P521, DOI 10.1016/S0306-4379(98)00027-1 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 KOZIEROK R, 1993, P 11 NAT C ART INT A, P459 Kushmerick N, 2000, ARTIF INTELL, V118, P15, DOI 10.1016/S0004-3702(99)00100-9 Lang K, 1995, P 12 INT C MACH LEAR, P331 LASHKARI Y, 1994, PROCEEDINGS OF THE TWELFTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P444 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 Lerman K, 2003, J ARTIF INTELL RES, V18, P149 Levene M., 2001, Knowledge and Information Systems, V3, DOI 10.1007/PL00011657 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lin WY, 2002, DATA MIN KNOWL DISC, V6, P83, DOI 10.1023/A:1013284820704 MAEDCHE A, 2001, P 2 WORKSH ONT LEARN, V38 Maedche A, 2003, WEB INTELLIGENCE, P301 Maedche A., 2001, IEEE INTELLIGENT SYS, V16 Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 MCBRYAN O, 1994, P 1 INT WWW C, P58 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Melville P., 2002, P 18 NAT C ART INT, P187 Mladenic D, 1999, IEEE INTELL SYST APP, V14, P44, DOI 10.1109/5254.784084 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Mladenic D., 1998, P 13 EUR C ART INT E, P473 MLADENIC D, 1996, IJSDP7472 DEP INT SY MLADENIC D, 1998, P 17 EL COMP SCI C E Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Mobasher B, 2002, DATA MIN KNOWL DISC, V6, P61, DOI 10.1023/A:1013232803866 Mock KJ, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P48 MYIIYMAKI J, 2001, P 10 INT WORLD WID W Oh H J, 2000, P 23 ANN INT ACM SIG, P264, DOI 10.1145/345508.345594 Payne TR, 1997, APPL ARTIF INTELL, V11, P1, DOI 10.1080/088395197118325 Pazienza MT, 2003, INFORM EXTRACTION WE Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 Pierrakos D, 2003, USER MODEL USER-ADAP, V13, P311, DOI 10.1023/A:1026238916441 Popescul A., 2001, P 17 C UNC ART INT U, P437 QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105 QUINLAN JR, 1991, P 8 INT WORKSH MACH, P442 Resnick P., 1997, COMMUNICATIONS ACM, V40 Richards B. L., 1992, P AAAI C SAN JOS, P50 Riloff E, 1996, ARTIF INTELL, V85, P101, DOI 10.1016/0004-3702(95)00123-9 Riloff E, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P1044 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer J. B., 2001, Data Mining and Knowledge Discovery, V5, DOI 10.1023/A:1009804230409 SCHEFFER T, 2004, INTELLIGENT DATA ANA, V8 Scott S., 1999, P 16 INT C MACH LEAR, P379 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SERWAR BM, 2001, P 10 INT WORLD WID W Sheth B., 1993, Proceedings. The Ninth Conference on Artificial Intelligence for Applications (Cat. No.93CH3254-0), DOI 10.1109/CAIA.1993.366590 Slattery S, 2000, P 17 INT C MACH LEAR, P895 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Spertus E, 1997, COMPUT NETWORKS ISDN, V29, P1205, DOI 10.1016/S0169-7552(97)00033-0 Spiliopoulou M, 1999, COMPUT SYST SCI ENG, V14, P113 Srivastava J., 2000, SIGKDD EXPLORATIONS, V1, P12 Staab S., 2004, HDB ONTOLOGIES INT H Staab S, 2001, AI MAG, V22, P63 STAAB S, 2000, P 1 WORKSH ONT LEARN, V31 STUMME G, 2001, P ECML PKDD 2001 WOR STUMME G, 2002, P ECML PKDD 2001 WOR Tan PN, 2002, DATA MIN KNOWL DISC, V6, P9, DOI 10.1023/A:1013228602957 Ungar LH, 1998, P AAAI 98 WORKSH REC, P112 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 NR 119 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES BN 978-0-387-09822-7 PY 2010 BP 913 EP 929 DI 10.1007/978-0-387-09823-4_47 D2 10.1007/978-0-387-09823-4 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA BVX97 UT WOS:000293102200047 ER PT S AU Sheng, C Zheng, Y Hsu, W Lee, ML Xie, X AF Sheng, Chang Zheng, Yu Hsu, Wynne Lee, Mong Li Xie, Xing BE Kitagawa, H Ishikawa, Y Li, Q Watanabe, C TI Answering Top-k Similar Region Queries SO DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PT I, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 15th International Conference on Database Systems for Advanced Applications CY APR 01-04, 2010 CL Tsukuba, JAPAN ID PATTERNS AB Advances in web technology have given rise to new information retrieval applications. In this paper, we present a model for geographical region search and call this class of query similar region query. Given a spatial map and a query region, a similar region search aims to find the top-h; most similar regions to the query region on the spatial map. We design a quadtree based algorithm to access the spatial map at different resolution levels. The proposed search technique utilizes a filter-and-refine manner to prune regions that are not likely to be part of the top-h; results. and refine the remaining regions. Experimental study based on a real world dataset verifies the effectiveness of the proposed region similarity measure and the efficiency of the algorithm. C1 [Sheng, Chang; Hsu, Wynne; Lee, Mong Li] Natl Univ Singapore, Sch Comp, Singapore 117548, Singapore. RP Sheng, C (reprint author), Natl Univ Singapore, Sch Comp, Singapore 117548, Singapore. EM shengcha@comp.nus.edu.sg; yuzheng@microsoft.com; whsu@comp.nus.edu.sg; leeml@comp.nus.edu.sg; Xing.Xie@microsoft.com RI Cong , Gao/A-3726-2011 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE CASSIE NA, 1993, STAT SPATIAL DATA Cong G., 2009, PVLDB, V2, P337 Datta AK, 2008, OPT LASER TECHNOL, V40, P1, DOI 10.1016/j.optlastec.2007.04.006 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ebdon D., 1985, STAT GEOGRAPHY Felipe I. D., 2008, ICDE, P656 Huang Y, 2004, IEEE T KNOWL DATA EN, V16, P1472, DOI 10.1109/TKDE.2004.90 Jarvelin K., 2002, ACM T INFORM SYSTEMS, V20 Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samet H., 1990, DESIGN ANAL SPATIAL Sheng C, 2008, LECT NOTES COMPUT SC, V4947, P95 Zhou Y., 2005, CIKM, P155 NR 14 TC 4 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12025-1 J9 LECT NOTES COMPUT SC PY 2010 VL 5981 BP 186 EP 201 PN I PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPO85 UT WOS:000279562400014 ER PT S AU Ferilli, S Biba, M Basile, TMA Esposito, F AF Ferilli, Stefano Biba, Marenglen Basile, Teresa M. A. Esposito, Floriana BE Agosti, M Esposito, F Thanos, C TI Using Explicit Word Co-occurrences to Improve Term-Based Text Retrieval SO DIGITAL LIBRARIES SE Communications in Computer and Information Science LA English DT Proceedings Paper CT 6th Italian Research Conference on Digital Libraries CY JAN 28-29, 2010 CL Padua, ITALY SP DELOS Assoc, Inst Inform Sci & Technol Italian, Natl Res Council, Univ Padua, Dept Informat Engn HO Univ Padua, Dept Informat Engn AB Reaching high precision and recall rates in the results of term-based queries on text collections is becoming more and more crucial, as long as the amount of available documents increases and their quality tends to decrease. In particular, retrieval techniques based on the strict correspondence between terms in the query and terms in the documents miss important and relevant documents where it just happens that the terms selected by their authors are slightly different than those used by the final user that issues the query. Our proposal is to explicitly consider term co-occurrences when building the vector space. Indeed, the presence in a document of different but related terms to those in the query should strengthen the confidence that the document is relevant as well. Missing a query term in a document, but finding several terms strictly related to it, should equally support the hypothesis that the document is actually relevant. The computational perspective that embeds such a relatedness consists in matrix operations that capture direct or indirect term co-occurrence in the collection. We propose two different approaches to enforce such a perspective, and run preliminary experiments on a prototypical implementation, suggesting that tins technique is potentially profitable. C1 [Ferilli, Stefano; Basile, Teresa M. A.; Esposito, Floriana] Univ Bari, Dipartimento Informat, I-70125 Bari, Italy. EM ferilli@di.uniba.it; marenglenbiba@unyt.edu.al; basile@di.uniba.it; esposito@di.uniba.it; esposito@di.uniba.it RI Ferilli, Stefano/E-8841-2014 OI Ferilli, Stefano/0000-0003-1118-0601 CR BUZYDLOWSKI JW, 2001, P JOINT C DIG LIB Ide N, 1998, COMPUT LINGUIST, V24, P1 KARPYS G, 2000, 00016 MINN U MINN KONTOSTATHIS A, 2002, P ICDM 2002 WORKSH F Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 KROVETZ R, 1998, NEC Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LANDAUER TK, 1997, PSYCHOL REV, V104, P111 Magnini Bernardo, 2000, P LREC 2000 2 INT C, P1413 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 OBRIEN GW, 1994, CS94258 U TENN PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 RAPP R, 2002, P 29 INT C COMP LING Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-15849-0 J9 COMM COM INF SC PY 2010 VL 91 BP 125 EP 136 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BTY00 UT WOS:000288402200013 ER PT S AU Gemmell, J Schimoler, T Mobasher, B Burke, R AF Gemmell, Jonathan Schimoler, Thomas Mobasher, Bamshad Burke, Robin BE Buccafurri, F Semeraro, G TI Resource Recommendation in Collaborative Tagging Applications SO E-COMMERCE AND WEB TECHNOLOGIES SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 11th International Conference on E-Commerce and Web Technologies CY SEP 01-03, 2010 CL Bilbao, SPAIN DE Collaborative Tagging; Information Channel; Hybrid Recommender ID MODEL AB Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender. C1 [Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad; Burke, Robin] Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA. RP Gemmell, J (reprint author), Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA. EM jgemmell@cdm.depaul.edu; tschimoler@cdm.depaul.edu; mobasher@cdm.depaul.edu; rburke@cdm.depaul.edu CR Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Gemmell J., 2008, LNCS, V5182, P196 GEMMELL J, 2009, IMPROVING FOLKRANK I GEMMELL J, 2008, PERSONALIZATION FOLK Gemmell J., 2009, ECML PKDD 2009 DISC, P59 HERLOCKER J, 1999, 22 ANN INT ACM SIGIR, P237 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Konstan J., 1997, COMMUN ACM, V40, P87 Mathes A., 2004, COMPUTER MEDIATED CO Mika P, 2007, J WEB SEMANT, V5, P5, DOI 10.1016/j.websem.2006.11.002 Rendle S., 2009, ECML PKDD 2008 DISC, P235 Rendle Steffen, 2010, P 3 ACM INT C WEB SE, P81, DOI 10.1145/1718487.1718498 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARWAR B, 2001, 10 INT C WORLD WID W, P295 Shardanand U., 1995, SOCIAL INFORM FILTER, V1, P210 Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259 SYMEONIDIS P, 2009, IEEE T KNOWLEDGE DAT Symeonidis P, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P43 NR 19 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-15207-8 J9 LECT NOTES BUS INF PY 2010 VL 61 BP 1 EP 12 PG 12 WC Computer Science, Information Systems SC Computer Science GA BTS23 UT WOS:000287951400001 ER PT J AU Wang, J Wu, YP Liu, XN Gao, XY AF Wang, Jun Wu, Yunpeng Liu, Xuening Gao, Xiaoying TI Knowledge acquisition method from domain text based on theme logic model and artificial neural network SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Knowledge acquisition; Domain text; Theme logic model; Artificial neural network; Failure analysis report ID VECTOR-SPACE MODEL; FAILURE-MECHANISM IDENTIFICATION; EXPERT-SYSTEM; INFORMATION-RETRIEVAL; INTEGRATED DATABASE; MANAGEMENT; DOCUMENTS; DESIGN AB In order to acquire knowledge from domain text such as failure analysis text of aviation product, a framework is proposed to enhance the efficiency and accuracy of knowledge acquisition. In this framework, sentence templates are defined to extract the meta-knowledge and RDF is used to describe the extracted knowledge. After the preprocessing steps, the authors propose a new model: theme logic model (TLM) to present all the themes of a piece of text and the logical relations among different themes. In this model, the text of each theme can be represented as an attribute-value vector based on domain ontology. Meanwhile, the logical relations are the domain knowledge to be acquired. The theme logic model then will be transformed to the training set of the artificial neural network to acquire the failure analysis knowledge. After training process, acquired knowledge will be extracted by SD method from the artificial neural network and represented by rules. Therefore. a prototype is developed to acquire knowledge from failure analysis reports of aviation product. Empirical results show that the framework can acquire knowledge from domain text efficiently. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Wang, Jun; Wu, Yunpeng; Liu, Xuening; Gao, Xiaoying] Beihang Univ, Sch Econ & Management, Beijing 100083, Peoples R China. RP Wang, J (reprint author), Beihang Univ, Sch Econ & Management, Beijing 100083, Peoples R China. EM king.wang@buaa.edu.cn; yunpeng.wu@sem.buaa.edu.cn; lxnyoyo@gmail.com; cynsier@gmail.com FU National Natural Science Foundation of China [70871006] FX The work described in this paper was fully supported by the National Natural Science Foundation of China under Grant No. 70871006. CR Buckley C., 1995, SIGIR Forum Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902 CHEN J, 2006, FAULT DETECTION SUPE, P1366 de Campos LM, 2004, INFORM PROCESS MANAG, V40, P829, DOI 10.1016/j.ipm.2004.04.014 de Castro PMST, 2004, MATER DESIGN, V25, P117, DOI 10.1016/j.matdes.2003.09.020 Graupe D., 2007, PRINCIPLES ARTIFICIA Gunn A, 1999, COMPUT ELECTRON AGR, V23, P71, DOI 10.1016/S0168-1699(99)00022-8 Hahn U, 2000, DATA KNOWL ENG, V35, P137, DOI 10.1016/S0169-023X(00)00031-8 Hassoun M.H., 1995, FUNDAMENTALS ARTIFIC Huang SH, 2002, FUZZY SET SYST, V132, P233, DOI 10.1016/S0165-0114(01)00239-1 KHAW JFC, 1995, NEUROCOMPUTING, V7, P225, DOI 10.1016/0925-2312(94)00013-I Kim Y, 2004, DECIS SUPPORT SYST, V37, P215, DOI 10.1016/S0167-9236(03)00008-3 Liao TW, 1999, ENG FAIL ANAL, V6, P387, DOI 10.1016/S1350-6307(98)00055-7 Liao TW, 1999, ENG FAIL ANAL, V6, P407, DOI 10.1016/S1350-6307(98)00056-9 Mao WL, 2007, DATA KNOWL ENG, V61, P76, DOI 10.1016/j.datak.2006.02.008 Maren A., 1990, HDB NEURAL COMPUTING Massey L, 2003, NEURAL NETWORKS, V16, P771, DOI 10.1016/S0893-6080(03)00088-1 MATWIN S, 1993, P 1 C PAC ASS COMP L, P33 MAYER P, 1990, CIM BULL, V83, P92 *MECH DES MAN ED B, 2007, MECH DES MAN, V5 Mitchell T. M., 2003, MACHINE LEARNING RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RICHARDSON SD, 1997, THESIS CITY U NEW YO ROBERTS R, 1980, CRITICAL ISSUES MAT, V47, P171 Rumelhart D. E., 1986, LEARNING INTERNAL RE, V1 Salton G., 1996, HYPERTEXT 96, P53 Salton G., 1975, J AM SOC INFORM SCI, V18, P613 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANTIAGO IB, 2006, FAULT DETECTION SUPE, P765 SESTITO S, 1990, PROCEEDINGS OF THE 2ND INTERNATIONAL IEEE CONFERENCE ON TOOLS FOR ARTIFICIAL INTELLIGENCE, P269, DOI 10.1109/TAI.1990.130346 SESTITO S, 1991, J APPL INTELL, V1, P157 Swick R., 1999, RESOURCE DESCRIPTION Tai XY, 2002, INFORM PROCESS MANAG, V38, P749, DOI 10.1016/S0306-4573(01)00053-X Turban E., 1995, DECISION SUPPORT EXP Turban E., 2001, DECISION SUPPORT SYS, V6th WANG J, 2008, P 1 INT C MOD SIM, V4, P259 Wei CP, 2008, DECIS SUPPORT SYST, V45, P606, DOI 10.1016/j.dss.2007.07.008 Zakarian VL, 1999, EXPERT SYST APPL, V16, P233, DOI 10.1016/S0957-4174(98)00073-6 NR 38 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JAN PY 2010 VL 37 IS 1 BP 267 EP 275 DI 10.1016/j.eswa.2009.05.009 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 516VI UT WOS:000271571000031 ER PT S AU Crouch, CJ Crouch, DB Bhirud, D Poluri, P Polumetla, C Sudhakar, V AF Crouch, Carolyn J. Crouch, Donald B. Bhirud, Dinesh Poluri, Pavan Polumetla, Chaitanya Sudhakar, Varun BE Geva, S Kamps, J Trotman, A TI A Methodology for Producing Improved Focused Elements SO FOCUSED RETRIEVAL AND EVALUATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 07-09, 2009 CL Brisbane, AUSTRALIA SP Australian Res Council Res Network Human Comm Sci ID INEX 2008; RETRIEVAL AB This paper reports the results of our experiments to consistently produce highly ranked focused elements in response to the Focused Task of the INEX Ad Hoc Track The results of these experiments performed using the 2008 INEX collection confirm that our current methodology (described herein) produces such elements for this collection Our goal for 2009 is to apply this methodology to the new extended 2009 INEX collection to determine its viability in this environment (These experiments are currently underway) Our system uses our method for dynamic element retrieval [4] working with the semi-structured text of Wikipedia [5], to produce a rank-ordered list of elements in the context of focused retrieval It is based on the Vector Space Model [15] basic functions are performed using the Smart experimental retrieval system [14] Experimental results are reported for the Focused Task of both the 2008 and 2009 INEX Ad Hoc Tracks C1 [Crouch, Carolyn J.; Crouch, Donald B.; Bhirud, Dinesh; Poluri, Pavan; Polumetla, Chaitanya; Sudhakar, Varun] Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. CR Bapat S, 2008, THESIS U MINNESOTA D BHIRUD D, 2009, THESIS U MINNESOTA D Broschart A, 2009, LECT NOTES COMPUT SC, V5631, P29 Crouch CJ, 2008, LECT NOTES COMPUT SC, V4862, P70, DOI 10.1007/978-3-540-85902-4_6 Crouch CJ, 2006, ACM T INFORM SYST, V24, P437, DOI 10.1145/1185877.1185880 Crouch CJ, 2009, LECT NOTES COMPUT SC, V5631, P33 Gery M, 2009, LECT NOTES COMPUT SC, V5631, P46 Itakura KY, 2009, LECT NOTES COMPUT SC, V5631, P132 Khanna S, 2005, THESIS U MINNESOTA D Lehtonen M, 2009, LECT NOTES COMPUT SC, V5631, P65 MONE A, 2007, THESIS U MINNESOTA D POLUMETLA C, 2009, THESIS U MINNESOTA D POLURI P, 2009, THESIS U MINNESOTA D Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 NR 16 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14555-1 J9 LECT NOTES COMPUT SC PY 2010 VL 6203 BP 70 EP 80 PG 11 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BSA64 UT WOS:000284034800008 ER PT S AU Pinto, D Tovar, M Vilarino, D Beltran, B Jimenez-Salazar, H Campos, B AF Pinto, David Tovar, Mireya Vilarino, Darnes Beltran, Beatriz Jimenez-Salazar, Hector Campos, Basilia BE Geva, S Kamps, J Trotman, A TI BUAP Performance of K-Star at the INEX'09 Clustering Task SO FOCUSED RETRIEVAL AND EVALUATION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 07-09, 2009 CL Brisbane, AUSTRALIA SP Australian Res Council Res Network Human Comm Sci AB The aim of this paper is to use unsupervised classification techniques in order to group the documents of a given huge collection into clusters We approached this challenge by using a simple clustering algorithm (K-Star) in a recursive clustering process over subsets of the complete collection The presented approach is a scalable algorithm which may automatically discover the number of clusters The obtained results outperformed different baselines presented in the INEX 2009 clustering task C1 [Pinto, David; Tovar, Mireya; Vilarino, Darnes; Beltran, Beatriz; Campos, Basilia] B Autonomous Univ Puebla, Fac Comp Sci, Puebla, Mexico. RP Pinto, D (reprint author), B Autonomous Univ Puebla, Fac Comp Sci, Puebla, Mexico. CR EISSEN SMZ, 2007, THESIS U PADERBORN G Kernighan B. W., 1970, Bell System Technical Journal, V49 MACKAY D, 2003, INFORM THEORY INFER Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Mirkin B., 1996, MATH CLASSIFICATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shin K, 2003, LECT NOTES COMPUT SC, V2588, P619 NR 7 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14555-1 J9 LECT NOTES COMPUT SC PY 2010 VL 6203 BP 434 EP 440 PG 7 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BSA64 UT WOS:000284034800043 ER PT S AU Weiss, SM Indurkhya, N Zhang, T AF Weiss, SM Indurkhya, N Zhang, T TI Fundamentals of Predictive Text Mining SO FUNDAMENTALS OF PREDICTIVE TEXT MINING SE Texts in Computer Science LA English DT Book ID NATURAL-LANGUAGE; CLASSIFICATION METHODS; INFORMATION RETRIEVAL; CATEGORIZATION; ALGORITHM; MODELS; EXTRACTION; REGRESSION; BENCHMARK; SYSTEM CR APTE F, 1994, ACM T INFORM SYSTEMS, V12, P233 BANERJEE A, 2004, P KDD 2004 BAO X, 2009, RECSYS 09, P109 Bekkerman R., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753625 BELL J, 2009, IEEE SPECTRUM, P28 Bikel D. M., 1997, 5 C APPL NAT LANG PR, P194 Bikel DM, 1999, MACH LEARN, V34, P211, DOI 10.1023/A:1007558221122 Blum A., 1998, Proceedings of the Eleventh Annual Conference on Computational Learning Theory, DOI 10.1145/279943.279962 Borthwick A., 1999, THESIS NEW YORK U Breiman L, 1999, NEURAL COMPUT, V11, P1493, DOI 10.1162/089976699300016106 Brill E, 1995, COMPUT LINGUIST, V21, P543 BURGES C, 2005, ICML 05 Califf M. E., 1998, Applying Machine Learning to Discourse Processing. Papers from the 1998 AAAI Symposium Cardie C., 1999, P 1999 JOINT SIGDAT, P82 Charniak E, 1997, AI MAG, V18, P33 Chiang David, 2000, P 38 ANN M ASS COMP, P456, DOI 10.3115/1075218.1075276 Cohen W., 1996, P AAAI SPRING S MACH Collins M., 2002, P EMNLP 02 Cossock D, 2008, IEEE T INFORM THEORY, V54, P5140, DOI 10.1109/TIT.2008.929939 Craven M, 2001, MACH LEARN, V43, P97, DOI 10.1023/A:1007676901476 CUTTING D, 1992, P SIGIR 92, P1 DAGOSTINO R, 2008, CIRCULATION, P743 Indurkhya N, 2010, CH CRC MACH LEARN PA, pXXI DAMERAU FJ, 1985, ACM T OFF INF SYST, V3, P165, DOI 10.1145/3914.3915 Damerau FJ, 2004, INFORM PROCESS MANAG, V40, P209, DOI 10.1016/S0306-4573(03)00006-2 DARROCH JN, 1972, ANN MATH STAT, V43, P1470, DOI 10.1214/aoms/1177692379 Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492 Dempster A, 1977, J ROYAL STAT SOC B, V39, P38, DOI DOI 10.2307/2984875 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Dzeroski S, 2004, MACH LEARN, V54, P255, DOI 10.1023/B.MAC.0000015881.36452.6e EARLEY J, 1970, COMMUN ACM, V13, P94, DOI 10.1145/362007.362035 EASTMAN C, 1978, P ACM SIGIR INT C IN, P131, DOI 10.1145/800096.803139 Elkan C, 2003, P 20 INT C MACH LEAR, P147 Feldbaum C., 1998, WORDNET ELECT LEXICA FELDMAN R, 2009, P KDD 2009 Florian R., 2003, P CONLL 2003, P168 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Freitag D., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 Freund Y., 2003, J MACHINE LEARNING R, V6, P933, DOI 10.1162/jmlr.2003.4.6.933 Friedman J, 2000, ANN STAT, V28, P337, DOI 10.1214/aos/1016218223 GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471 Ghemawat S, 2003, SIGOPS OPER SYST REV, V37, P29, DOI DOI 10.1145/1165389.945450 Graham P., 2003, P 2003 SPAM C Hastie T., 2001, SPRINGER SERIES STAT Hayes Philip J., 1990, P 2 C INN APPL ART I, P49 Herbrich R, 2000, ADV NEUR IN, P115 Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 HUFFMAN S, 1995, IJCAI 95 WORKSH, P246 Ide N, 1998, COMPUT LINGUIST, V24, P1 Iyengar V.S., 2000, 6 ACM SIGKDD INT C K, P91 Jarvelin K., 2000, SIGIR 00, P41 JAYNES ET, 1957, PHYS REV, V106, P620, DOI 10.1103/PhysRev.106.620 Joachims T., 1998, P 10 EUR C MACH LEAR Jurafsky D., 2008, SPEECH LANGUAGE PROC Kearns M., 1997, P 13 C UNC ART INT, P282 KIM J, 2007, IEEE T KNOWL DATA EN, V7, P713 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kolcz A., 2001, P WORKSH TEXT MIN IE KRUPKA G. R, 1998, P 7 MESS UND C MUC 7 Kudoh T., 2000, P CONLL 2000 LLL 200, P142 LAFFERTY J, 2001, P 18 INT C MACH LEAR, P282 Lappin S., 1994, Computational Linguistics, V20 LESKOVEC J, 2009, P KDD 2009, P297 LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Lewis D.D., 1994, P 11 INT C MACH LEAR, P148 Lewis DD, 2004, J MACH LEARN RES, V5, P361 LI F, 2003, P 20 INT C MACH LEAR, P472 Liere R., 1997, P 14 C AM ASS ART IN, P591 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 Luhn H. P., 1959, MODERN TRENDS DOCUME, P45 LUHN HP, 1958, IBM J RES DEV, V2, P159 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Maloof MA, 2003, IEEE IJCNN, P2764 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 McCarthy Joseph F., 1995, P 14 INT JOINT C ART, P1 McCord M., 1989, P INT S NAT LANG LOG, P118 MCKEOWN K, 2002, P HUM LANG TECH C MELVILLE P, 2009, P 15 ACM SIGKDD INT, P1275, DOI DOI 10.1145/1557019.1557156 Mikheev Andrei, 1998, P 7 MESS UND C MUC 7 MILLER S, 1998, P 7 MESS UND C MUC 7 Nigam K., 2000, MACH LEARN, V39, P1 Nigam K., 2001, THESIS CARNEGIE MELL Page L, 1998, PAGERANK CITATION RA Page L., 1998, P 7 INT WWW C WWW 98 Pang B., 2008, FOUND TRENDS INF RET, V2, P1, DOI DOI 10.1561/1500000011 Pietra S. D., 1997, IEEE T PATTERN ANAL, V19, P380 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RADEV D, 2002, P ACL 02 DEM SESS, P112 RADEV D, 2003, P HLT NAACL 2003 WOR Ramshaw L.A., 1995, P 3 WORKSH VER LARG, P82 Ratnaparkhi A, 1999, MACH LEARN, V34, P151, DOI 10.1023/A:1007502103375 RATNAPARKHI A, 1995, COMPUTATIONAL LINGUI, V21, P543 Ray E., 2001, LEARNING XML RILOFF E, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P811 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 Rosenblatt F., 1962, PRINCIPLES NEURODYNA Roth D., 2001, P 17 INT JOINT C ART, P1257 RUI H, 2009, WALL STREET J T 1130 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1980, P SIGIR, P9 SALTON G, 1964, P 19 ANN INT ACM NAT Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sang E.F.T.K., 2003, P 7 C NAT LANG LEARN, P142 Sang E.F.T.K., 2000, P CONLL 2000 LLL 200, P127 Schapire RE, 1999, MACH LEARN, V37, P297, DOI 10.1023/A:1007614523901 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 SESHASAI S, 2000, AL LAB RES ATTEMPT E Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Soderland Stephen, 1995, P 14 INT JOINT C ART, P1314 Soon WM, 2001, COMPUT LINGUIST, V27, P521, DOI 10.1162/089120101753342653 Stein G. C., 2000, P 7 C AUT NAT LANG P, P337 Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0 TAN PN, 2000, P 6 INT C KNOWL DISC, P417, DOI 10.1145/347090.347177 Taskar B., 2004, ADV NEURAL INFORM PR, V16 Tillmann C, 2008, IEEE T AUDIO SPEECH, V16, P1274, DOI 10.1109/TASL.2008.921760 Tomita M, 1985, EFFICIENT PARSING NA Tsochantaridis I, 2005, J MACH LEARN RES, V6, P1453 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 Vapnik V., 1998, STAT LEARNING THEORY Voorhees E., 2002, NIST SPECIAL PUBLICA Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 WALKER D, 2001, P 8 MACH TRANSL SUMM WEISS S, 2002, P SIGKDD 2000 Weiss S. M., 2000, Principles of Data Mining and Knowledge Discovery. 4th European Conference, PKDD 2000. Proceedings (Lecture Notes in Artificial Intelligence Vol.1910) Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Weiss SM, 2000, IEEE INTELL SYST APP, V15, P57, DOI 10.1109/5254.850828 White T., 2009, HADOOP DEFINITIVE GU WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205 ZHANG T, 2003, P CONLL 2003, P204 Zhang T, 2001, INFORM RETRIEVAL, V4, P5, DOI 10.1023/A:1011441423217 Zhang T, 2002, J MACH LEARN RES, V2, P615, DOI 10.1162/153244302320884560 Zhang T, 2003, P 7 C NAT LANG CONLL, P56 Zhang T, 2004, ANN STAT, V32, P56 Zhang T, 2002, MACH LEARN, V46, P91, DOI 10.1023/A:1012498226479 Zhang T, 2000, P 17 INT C MACH LEAR, P1191 Zhong S, 2003, J MACHINE LEARNING R, V4, P1001, DOI 10.1162/jmlr.2003.4.6.1001 NR 145 TC 15 Z9 15 PU SPRINGER-VERLAG LONDON LTD PI GODALMING PA SWEETAPPLE HOUSE CATTESHALL RD FARNCOMBE, GODALMING GU7 1NH, SURREY, ENGLAND SN 1868-0941 BN 978-1-84996-225-4 J9 TEXTS COMPUT SCI PY 2010 BP 1 EP 226 DI 10.1007/978-1-84996-226-1 PG 226 WC Computer Science, Artificial Intelligence SC Computer Science GA BPK66 UT WOS:000279068000010 ER PT B AU Jung, IY Yeom, HY AF Jung, Im Y. Yeom, Heon Y. BE Byeon, OH Kwon, JH Dunning, T Cho, KW SavoyNavarro, A TI A Dynamic Bridge for Data Sharing on e-Science Grid Implementing Web 2.0 Service SO FUTURE APPLICATION AND MIDDLEWARE TECHNOLOGY ON E-SCIENCE LA English DT Proceedings Paper CT Korea e-Science All Hands Meeting 2008 CY SEP 08-09, 2008 CL Daejeon, SOUTH KOREA HO Korea Inst Sci & Technol Informat DE e-Science Grid; science data sharing; Web 2.0 service; HVEM Data-Grid; a dynamic bridge AB This paper proposes a dynamic bridge for e-Science Grid, implementing Web 2.0 service in order to share experimental data effectively. An e-Science Grid has been established as a cyber laboratory for the users with a special research purpose on science. As an open space, e-Science Grid is expected to stimulate the collaborative researches and the cross domain ones. These research trends need a more efficient and convenient data service satisfying the science researchers. A dynamic bridge designed based on HVEM DataGrid, satisfies the users' requirements for the data sharing on e-Science Grid effectively. It supports a data tagging service in order for HVEM DataGrid to be utilized more extensively Without any modification of the existing Grid architecture or services. Moreover, it can be adopted and deleted easily without any effect to the legacy Grid. With the legacy interface to access data in e-Science Grid, the data tags endow the Grid with the flexibility for data access. This paper evaluates the usefulness of the dynamic bridge by analyzing its overhead and performance. C1 [Jung, Im Y.; Yeom, Heon Y.] Seoul Natl Univ, Sch CSE, Seoul 151742, South Korea. RP Jung, IY (reprint author), Seoul Natl Univ, Sch CSE, San 56-1,Shinlim Dong, Seoul 151742, South Korea. EM iyjung@dcslab.snu.ac.kr; yeom@snu.ac.kr RI Jung, Im/ OI Jung, Im/0000-0002-9713-1757 CR DING C, 2004, ACM INT C P SERIES FOX GC, 2007, INSTR GRID 2 INT WOR FOX GC, 2007, 3 INT C SEM KNOWL GR Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 Hartuv E, 2000, INFORM PROCESS LETT, V76, P175, DOI 10.1016/S0020-0190(00)00142-3 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 IM YJ, 2007, HVEM DATAGRID IMPLEM Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KVAK SJ, 2007, J KOREAN LIB INFORM, V38, P117 Monden A, 2005, IEICE T FUND ELECTR, VE88A, P112, DOI 10.1093/ietfec/E88-A.1.112 MUSTACOGLU AF, 2007, P 2007 INT S COLL TE Pierce ME., 2008, INT S COLL TECHN SYS Rose K, 1998, P IEEE, V86, P2210, DOI 10.1109/5.726788 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 NR 15 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES BN 978-1-4419-1718-8 PY 2010 BP 73 EP 83 DI 10.1007/978-1-4419-1719-5_8 PG 11 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Telecommunications SC Computer Science; Telecommunications GA BMR90 UT WOS:000273429400008 ER PT S AU Stein, B Eissen, SMZ Lipka, N AF Stein, Benno Eissen, Sven Meyer Zu Lipka, Nedim BE Mehler, A Sharoff, S Santini, M TI Web Genre Analysis: Use Cases, Retrieval Models, and Implementation Issues SO GENRES ON THE WEB: COMPUTATIONAL MODELS AND EMPIRICAL STUDIES SE Text Speech and Language Technology LA English DT Article; Book Chapter C1 [Stein, Benno; Eissen, Sven Meyer Zu; Lipka, Nedim] Bauhaus Univ Weimar, Fac Media Media Syst, D-99423 Weimar, Germany. RP Stein, B (reprint author), Bauhaus Univ Weimar, Fac Media Media Syst, D-99423 Weimar, Germany. EM benno.stein@uni-weimar.de; sven@meyer-zu-eissen.de; nedim.lipka@uni-weimar.de CR Antunes P., 2001, Proceedings Seventh International Workshop on Groupware. CRIWG 2001, DOI 10.1109/CRIWG.2001.951773 Bay S. D., 2000, KDD 00, P315 BOESE ES, 2005, P CIKM 05 NOV 2005 BRETAN I, 1998, P WEBN WORLD C WWW I Broder A., 2002, SIGIR Forum, V36 Crowston K, 2000, INFORM SOC, V16, P201 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEWDNEY N, 2001, P ACL WORKSH HUMANLA DIMITROVA M, 2002, P C HUM FACT COMP SY Dougherty J., 1995, P 12 INT C MACH LEAR, P194 Fayyad U., 1993, P 13 INT JOINT C ART, P1022 FINN A, 2003, IJCAI 03 WORKSH COMP Freund L., 2006, P 1 INT C INF INT CO, P30, DOI 10.1145/1164820.1164829 KARLGREN J, 1994, P 15 INT C COMP LING, V2, P1071, DOI 10.3115/991250.991324 KENNEDY Alistair, 2005, P 38 ANN HAW INT C S Kessler B., 1997, P 35 ANN M ASS COMP, P32 Lawrie D., 2001, SIGIR Forum LAWRIE DJ, 2003, SIGIR 03 P 26 ANN IN, P457 Lee Y.B., 2002, SIGIR 2002, P145 LIM CS, 2005, P NAT LANG PROC IJCN, P310 Meyer zu Eissen S., 2004, LECT NOTES ARTIF INT, V3228, P256 Mitchell TM, 1997, MACHINE LEARNING Popescul A., 2000, AUTOMATIC LABELING D Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAUBER A, 2001, ACM IEEE JOINT C DIG, P1 Rehm G., 2002, P 35 HAW INT C SYST Rendell L., 1986, Machine Learning, V1, DOI 10.1007/BF00114117 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roussinov D., 2001, P 34 HAW INT C SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini M., 2007, THESIS U BRIGHTON SANTINI M, 2006, P ECAI WORKSH TIR 06 SELLENTIN J, 1999, THESIS U STUTTGART S Stamatatos E., 2000, P 18 INT C COMP LING STEIN B, 2006, J UNIVERS COMPUT SCI, P449 Stein B, 2005, P 2 INT WORKSH TEXT, P45 Stein B., 2008, SCANDINAVIAN J INFOR, V20, P91 STEIN B, 2004, P I KNOW 04, P353 TURNEY P, 1995, MACH LEARN, V20, P23, DOI 10.1007/BF00993473 Utgoff P., 1986, MACHINE LEARNING ART, VII, P107 YOSHIOKA T, 2000, 214 CCS WP MIT SLOAN NR 41 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-291X BN 978-90-481-9177-2 J9 TEXT SPEECH LANG TEC PY 2010 VL 42 BP 167 EP 189 DI 10.1007/978-90-481-9178-9_8 PG 23 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BRQ01 UT WOS:000283391600008 ER PT S AU Chouaib, H Cloppet, F Vincent, N AF Chouaib, Hassan Cloppet, Florence Vincent, Nicole BA Llados, J BF Llados, J BE Ogier, JM Liu, W TI Graphical Drop Caps Indexing SO GRAPHICS RECOGNITION: ACHIEVEMENTS, CHALLENGES, AND EVOLUTION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT IAPR 8th International Workshop on Graphics Recognition CY JUL 22-23, 2009 CL La Rochelle, FRANCE SP Univ La Rochelle, Town La Rochelle, French Dept Charente-Maritime, Reg Poitou-Charentes, European Union, Int Assoc Pattern Recognit, TC10, CNRS, CNRS, French Res Grp, Jouve Co, Sood Co, Aproged DE Graphics Recognition; Graphics indexing; Zipf law; tf-idf; Drop caps; law mixture AB This paper presents a method for graphical drop caps indexing. Drop caps are extracted from old books. Finding a method classifying them according to styles defined by the historian is of considerable interest. The developed method is a statistical approach, where all possible patterns included in a pixel mask are processed in order to extract indexes that characterize the image. Then these indexes are used to classify a query drop cap by searching its most similar drop caps in the indexed base. C1 [Chouaib, Hassan; Cloppet, Florence; Vincent, Nicole] Univ Paris 05, Lab CRIP5, F-75006 Paris, France. RP Chouaib, H (reprint author), Univ Paris 05, Lab CRIP5, 45 Rue St Peres, F-75006 Paris, France. EM hassan.chouaib@mi.parisdescartes.fr; florence.cloppet@mi.parisdescartes.fr; nicole.vincent@mi.parisdescartes.fr CR Caron Y, 2007, PATTERN RECOGN, V40, P2521, DOI 10.1016/j.patcog.2007.01.004 COUSTATY M, 2009, ICDAR 2009 Gadat S, 2007, J MACH LEARN RES, V8, P509 Jiang Y. G., 2007, INT C IM VID RETR CI Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 PARETI R, 2006, INT C PATT REC ICPR SALTON G, 1994, P RIAO C INT TEXT IM, P6 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13727-3 J9 LECT NOTES COMPUT SC PY 2010 VL 6020 BP 212 EP 219 PG 8 WC Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BQT31 UT WOS:000281783200019 ER PT B AU Koschmider, A Oberweis, A AF Koschmider, Agnes Oberweis, Andreas BE VomBrocke, J Rosemann, M TI Designing Business Processes with a Recommendation-Based Editor SO HANDBOOK ON BUSINESS PROCESS MANAGEMENT 1: INTRODUCTION, METHODS, AND INFORMATION SYSTEMS SE International Handbooks on Information Systems LA English DT Article; Book Chapter ID SYSTEMS AB Knowledge of modeling language syntax is usually not sufficient for building "good" process models. Profound modeling experience is required to apply a modeling language in practice. The productivity of users without any modeling experience is low and thus the quality of the modeling result may be unsatisfying if respective modeling tool support is missing. In this chapter, we present a recommendation-based editor for process modeling, which can help overcome this problem by reducing the need for the user to study the modeling notation and consequently direct her to focus on the model content. Early evaluations indicate the effectiveness of our approach, which goes beyond conventional modeling support for business processes. C1 [Koschmider, Agnes] Tech Univ Karlsruhe, Karlsruhe Inst Technol, Inst Appl Informat & Formal Descript Methods AIFB, Karlsruhe, Germany. RP Koschmider, A (reprint author), Tech Univ Karlsruhe, Karlsruhe Inst Technol, Inst Appl Informat & Formal Descript Methods AIFB, Karlsruhe, Germany. EM agnes.koschmider@kit.edu; andreas.oberweis@kit.edu CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M., 1997, P 1 INT C AUT AG MAR Basu C., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Becker J, 2000, LECT NOTES COMPUT SC, V1806, P30 BREU H, 1995, IEEE T PATTERN ANAL, V17, P529, DOI 10.1109/34.391389 Brooks CH, 2006, P 15 INT C WORLD WID, P625, DOI 10.1145/1135777.1135869 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Claypool M., 1999, P ACM SIGIR 99 WORKS DAVIES I, 2010, HDB BUSINESS PROCESS, V1 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558 Hornung T, 2008, LECT NOTES COMPUT SC, V5231, P265, DOI 10.1007/978-3-540-87877-3_20 Klink S, 2004, LECT NOTES COMPUT SC, V2956, P281 KOSCHMIDER A, 2008, LNBIP, V17, P30 Massa P, 2004, P 2 INT C TRUST MAN, P221 Oberweis A, 1996, ACM T INFORM SYST, V14, P380, DOI 10.1145/237496.237498 Pyzdek T., 2003, 6 SIGMA HDB COMPLETE REIJERS HA, 2010, HDB BUSINESS PROCESS, V1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen MX, 2004, EXPERT SYST APPL, V26, P301, DOI 10.1016/j.eswa.2003.09.002 VANDERAALST WM, 1998, J CIRCUIT SYST COMP, V1, P21 NR 22 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-3-642-00415-5 J9 INT HANDB INFORM SYS PY 2010 BP 299 EP 312 DI 10.1007/978-3-642-00416-2_14 PG 14 WC Computer Science, Information Systems SC Computer Science GA BQW57 UT WOS:000282018500014 ER PT S AU Yamaguchi, H Ohsawa, Y Nishihara, Y AF Yamaguchi, Hiroki Ohsawa, Yukio Nishihara, Yoko GP IEEE TI Discourse Analysis for Considering Emotional Effects on Others SO IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010) SE IEEE International Conference on Systems Man and Cybernetics Conference Proceedings LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics CY OCT 10-13, 2010 CL Istanbul, TURKEY SP IEEE AB We developed a system for extracting Discounted Utterances(DUs) and evaluating them. We defined the DU as "Utterance which was ignored by others in a conversation in spite of its potential." We aim to mine participants' unique stories and specialized knowledge based on their experiences from DUs. The proposed system is designed for considering emotional effects on others in conversations. We analyzed a meeting of toilet design as a case study. Each participant here has his/her own specialty. The system extracted DUs as triggers for mining the unique stories. We employed double helical model in Chance Discovery to the system. The study shows the significance of the system, and makes it clear that the DU in this paper is not just ignored in the conversation and includes speakers' unspoken stories based on speakers' own experiences. These stories are useful for the development of systems. Besides, some of DUs indicated the sense of existing values. By utilizing DUs, the system can also provide opportunities to improve existing systems. C1 [Yamaguchi, Hiroki; Ohsawa, Yukio; Nishihara, Yoko] Univ Tokyo, Sch Engn, Tokyo, Japan. RP Yamaguchi, H (reprint author), Univ Tokyo, Sch Engn, Tokyo, Japan. EM gutsu@panda.sys.t.u-tokyo.ac.jp; ohsawa@sys.t.u-tokyo.ac.jp; nishihara@sys.t.u-tokyo.ac.jp CR GOODWIN C, 1990, ANNU REV ANTHROPOL, V19, P283, DOI 10.1146/annurev.an.19.100190.001435 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hori C, 2004, IEICE T INF SYST, VE87D, P15 Leuski A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management NAOKI WS, 2007, IEICE T INF SYST, V90, P427 OHSAWA Y, 2002, NEW GENERAT COMPUT, V20, P2 OHSAWA Y, 2010, P 34 SIG LSE MOR JAP, P29 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEGLOFF EA, STRUCTURES SOCIAL AC NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-6588-0 J9 IEEE SYS MAN CYBERN PY 2010 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BWU85 UT WOS:000295015300026 ER PT J AU Yamakawa, H Peng, J Feldman, A AF Yamakawa, Hiroki Peng, Jing Feldman, Anna GP IEEE TI Semantic Enrichment of Text Representation with Wikipedia for Text Classification SO IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010) SE IEEE International Conference on Systems Man and Cybernetics Conference Proceedings LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics CY OCT 10-13, 2010 CL Istanbul, TURKEY SP IEEE DE text classification; text representation; semantics; ensemble; voting; Wikipedia AB Text classification is a widely studied topic in the area of machine learning. A number of techniques have been developed to represent and classify text documents. Most of the techniques try to achieve good classification performance while taking a document only by its words (e. g. statistical analysis on word frequency and distribution patterns). One of the recent trends in text classification research is to incorporate more semantic interpretation in text classification, especially by using Wikipedia. This paper introduces a technique for incorporating the vast amount of human knowledge accumulated in Wikipedia into text representation and classification. The aim is to improve classification performance by transforming general terms into a set of related concepts grouped around semantic themes. In order to achieve this goal, this paper proposes a unique method for breaking the enormous amount of extracted Wikipedia knowledge (concepts) into smaller pieces (subsets of concepts). The subsets of concepts are separately used to represent the same set of documents in a number of different ways, from which an ensemble of classifiers is built. Experimental results show that an ensemble of classifiers individually trained on a different representation of the document set performs better with increased accuracy and stability than that of a classifier trained only on the original document set. C1 [Yamakawa, Hiroki; Peng, Jing; Feldman, Anna] Montclair State Univ, Dept Comp Sci, Montclair, NJ 07043 USA. RP Yamakawa, H (reprint author), Montclair State Univ, Dept Comp Sci, Montclair, NJ 07043 USA. EM yamakawah1@mail.montclair.edu; pengj@mail.montclair.edu; feldmana@mail.montclair.edu CR Chu Heting, 2003, INFORM REPRESENTATIO GABRILOVICH E, J ARTIFICIAL INTELLI, V10, P1 IFRIM G, 2005, P 22 ICML LWS, P18 Ito M., 2008, P 17 ACM C INF KNOWL, P817, DOI 10.1145/1458082.1458191 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LEWIS DD, RCV1 Lin XF, 2003, PATTERN RECOGN LETT, V24, P1959, DOI 10.1016/S0167-8655(03)00035-7 Polikar R., 2006, CIRCUITS SYSTEMS MAG, V6, P21, DOI DOI 10.1109/MCAS.2006.1688199 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANERP M, 2002, HOB NJ IEEE P 8 INT, P195 ZHOU X, 2007, ICTAI 07 P 19 IEEE I, V2, P201 REUTERS 21578 TEXT C 20 NEWSGROUPS DATA S NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-6588-0 J9 IEEE SYS MAN CYBERN PY 2010 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BWU85 UT WOS:000295015304090 ER PT S AU Alpkocak, A Kilinc, D Berber, T AF Alpkocak, Adil Kilinc, Deniz Berber, Tolga BE Muller, H Clough, P Deselaers, T Caputo, B TI Expansion and Re-ranking Approaches for Multimodal Image Retrieval using Text-based Methods SO IMAGECLEF: EXPERIMENTAL EVALUATION IN VISUAL INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Article; Book Chapter ID INFORMATION-RETRIEVAL; DATABASES; SIMILARITY; QUERY; MODEL AB In this chapter, we present an approach to handle multi-modality in image retrieval using a Vector Space Model (VSM), which is extensively used in text retrieval. We simply extended the model with visual terms aiming to close the semantic gap by helping to map low-level features into high level textual semantic concepts. Moreover, this combination of textual and visual modality into one space also helps to query a textual database with visual content, or a visual database with textual content. Alongside this, in order to improve the performance of text retrieval we propose a novel expansion and re-ranking method, applied both to the documents and the query. When textual annotations of images are acquired automatically, they may contain too much information, and document expansion adds more noise to retrieval results. We propose a re-ranking phase to discard such noisy terms. The approaches introduced in this chapter were evaluated in two sub-tasks of ImageCLEF2009. First, we tested the multi-modality part in ImageCLEFmed and obtained the best rank in mixed retrieval, which includes textual and visual modalities. Secondly, we tested expansion and re-ranking methods in ImageCLE-FWiki and the results were superior to others and obtained the best four positions in text-only retrieval. The results showed that the handling of multi-modality in text retrieval using a VSM is promising, and document expansion and re-ranking plays an important role in text based image retrieval. C1 [Alpkocak, Adil; Kilinc, Deniz; Berber, Tolga] Dokuz Eylul Univ, Dept Comp Engn, TR-35160 Izmir, Turkey. RP Alpkocak, A (reprint author), Dokuz Eylul Univ, Dept Comp Engn, Tinaztepe Kampusu, TR-35160 Izmir, Turkey. EM alpkocak@cs.deu.edu.tr; dkilinc@cs.deu.edu.tr; tberber@cs.deu.edu.tr CR Allan J, 2001, INFORM PROCESS MANAG, V37, P435, DOI 10.1016/S0306-4573(00)00056-X Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Balinski J, 2005, INFORM PROCESS MANAG, V41, P759, DOI 10.1016/j.ipm.2004.01.006 Beaulieu M, 1997, P TREC 5, P143 Billerbeck B., 2005, P 10 AUSTR DOC COMP, P34 Buckley C, 1993, P DARPA WORKSH HUM L, P349, DOI 10.3115/1075671.1075753 Callan James, 1992, P 3 INT C DAT EXP SY, P78 CAN F, 1990, ACM T DATABASE SYST, V15, P483, DOI 10.1145/99935.99938 Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 CHANG NS, 1980, IEEE T SOFTWARE ENG, V6, P519 CHANG SK, 1981, COMPUTER, V14, P13 Chisholm E., 1999, NEW TERM WEIGHTING F El-Kwae EA, 2000, ACM T INFORM SYST, V18, P171, DOI 10.1145/348751.348762 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 Frankel C., 1996, WEBSEER IMAGE SEARCH JAIN R, 1997, WEBCRAWLING USING SK Lee KS, 2001, INFORM PROCESS MANAG, V37, P1, DOI 10.1016/S0306-4573(00)00017-0 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 LI Y, 2003, IEEE T KNOWLEDGE DAT LINGPENG Y, 2005, ADV INFORM RETRIEVAL, P169 Manning C.D., 2008, INTRO INFORM RETRIEV Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MULLER H, 2009, CLEF 2009 OGLE VE, 1995, COMPUTER, V28, P40, DOI 10.1109/2.410150 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 RICHARDSON R, 1994, P IR C ART INT COGN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 2000, IEEE MULTIMEDIA, V7, P26, DOI 10.1109/93.879766 SINGHAL A, 1996, P 19 ANN INT ACM SIG Singhal A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312645 TSIKRIKA T, 2009, CLEF 2009 CORF GREEC TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 Wu JK, 1997, IEEE T KNOWL DATA EN, V9, P978 Wu QS, 2001, J AM SOC INF SCI TEC, V52, P868, DOI 10.1002/asi.1134.abs Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Yang L., 2006, P 15 ACM INT C INF K, P690, DOI 10.1145/1183614.1183713 ZHANG R, 2009, COMPANION VOLUME SHO, P165 NR 38 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-642-15180-4 J9 INFORM RETRIEVAL SER PY 2010 VL 32 BP 261 EP 275 DI 10.1007/978-3-642-15181-1_14 D2 10.1007/978-3-642-15181-1 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BQV45 UT WOS:000281964600014 ER PT S AU Maisonasse, L Chevallet, JP Gaussier, E AF Maisonasse, Loic Chevallet, Jean-Pierre Gaussier, Eric BE Muller, H Clough, P Deselaers, T Caputo, B TI Conceptual Indexing Contribution to ImageCLEF Medical Retrieval Tasks SO IMAGECLEF: EXPERIMENTAL EVALUATION IN VISUAL INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Article; Book Chapter AB In this chapter, we study conceptual indexing using a language modeling approach to information retrieval. In particular, we propose a conceptual representation of documents that allows the use of both concepts and labelled relations when matching documents and queries. Such semantic indexing gives effective results when large ontologies are used. We first present a model derived from the language modeling approach to information retrieval based on a conceptual representation of documents. We then introduce an extension to take into account relations between concepts. Concept and relation detection methods are, however, error-prone. We thus develop an approach to limit such errors by combining different methods. In order to illustrate various aspects of the model proposed, we conducted a series of experiments on various medical ImageCLEF collections. Our experiments in Image-CLEFmed show that the conceptual model proposed here provides good results in medical information retrieval. Experiments furthermore show that combining concept extraction methods through fusion improves the standard language model by up to 17% MAP on the medical ImageCLEF collections. C1 [Maisonasse, Loic] TecKnowMetrix, ZAC Champfeuillet, F-38500 Voiron, France. [Chevallet, Jean-Pierre; Gaussier, Eric] Univ Grenoble, Lab Informat Grenoble, F-38041 Grenoble, France. RP Maisonasse, L (reprint author), TecKnowMetrix, ZAC Champfeuillet, F-38500 Voiron, France. EM loic.maisonnasse@laposte.net; Jean-Pierre.Chevallet@imag.fr; eric.gaussier@imag.fr CR Aronson A R, 2001, Proc AMIA Symp, P17 BENIGNO MKD, 1986, P 9 ANN INT ACM SIGR, P144 BERRUT C, 1989, SIGIR FORUM, V23, P187, DOI 10.1145/75334.75354 CHEVALLET JP, 2007, ACM 16 C INF KNOWL M Huang Y, 2003, J AM MED INFORM ASSN, V10, P580, DOI 10.1197/jamia.M1369 LACOSTE C, 2006, CLEF 2006 Lin D., 1998, WORKSH EV PARS SYST Maisonnasse L, 2008, LECT NOTES COMPUT SC, V5152, P585, DOI 10.1007/978-3-540-85760-0_73 MAISONNASSE L, 2009, 31 EUR C INF RETR MEGHINI C, 1993, P 16 ANN INT ACM SIG, P298, DOI 10.1145/160688.160753 MULLER H, 2009, 2009 CLEF WORKSH COR Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHANK RC, 1980, P 3 ANN ACM C RES DE, P94 VINTAR S, 2003, P ECML PKDD WORKSH A Zhou WN, 2007, PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P655, DOI 10.1145/1277741.1277853 NR 16 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-642-15180-4 J9 INFORM RETRIEVAL SER PY 2010 VL 32 BP 381 EP 396 DI 10.1007/978-3-642-15181-1_20 D2 10.1007/978-3-642-15181-1 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BQV45 UT WOS:000281964600020 ER PT S AU Huang, CH Yin, JA Han, D AF Huang, Cheng-hui Yin, Jian Han, Dong BE Zhu, RB Zhang, YH Liu, BX Liu, CF TI An Improved Text Retrieval Algorithm Based on Suffix Tree Similarity Measure SO INFORMATION COMPUTING AND APPLICATIONS, PT 2 SE Communications in Computer and Information Science LA English DT Proceedings Paper CT International Conference on Information Computing and Applications CY OCT 15-18, 2010 CL Tangshan, PEOPLES R CHINA SP Hebei Polytechn Univ, Hebei Scene Stat Soc, Natl Sci Fdn China, Hunan Inst Engn DE Retrieval algorithm; suffix tree; document model; similarity measure AB In information retrieval area, popular methods considered word frequency of retrieval terms and text corpus. These methods ignored the word sequence information between retrieval terms and text corpus, and then the good result limited to some special domains. This paper analyzes the word sequence information, and then computes the similarity between the query and text documents of corpus by applying a suffix tree similarity that combines with TF-IDF weighting method. Experimental results on standard document benchmark corpus RUTERS indicate that the new retrieval algorithm is an effective text retrieval algorithm. Comparing with the results of traditional word term weight TF-IDF similarity measure in the same retrieval algorithm, proposed method achieves an improvement of about 20% on the average of precision score. C1 [Huang, Cheng-hui; Yin, Jian] Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510275, Guangdong, Peoples R China. RP Yin, JA (reprint author), Sun Yat Sen Univ, Sch Informat Sci & Technol, Guangzhou 510275, Guangdong, Peoples R China. EM issjyin@mail.sysu.edu.cn CR CHIM H, 2007, 16 INT C WORLD WID W, P121 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001 Maguitman A G., 2005, INT WORLD WID WEB C, P107 Meadow C., 2000, TEXT INFORM RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shehata S, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P629 Theobald M, 2008, P 31 ANN INT ACM SIG, P563, DOI 10.1145/1390334.1390431 Wang D., 2008, 31 ANN INT ACM SIGIR, P307, DOI 10.1145/1390334.1390387 ZAMIR O, 1997, 3 INT C KNOWL DISC D, P287 Zamir O., 1998, RES DEV INFORM RETRI, P46 1997, REUTERS 21578 TEXT C NR 13 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-642-16338-8 J9 COMM COM INF SC PY 2010 VL 106 BP 150 EP 157 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUI41 UT WOS:000289451600020 ER PT B AU Varadarajan, R Hristidis, V Farfan, F Burke, R AF Varadarajan, Ramakrishna Hristidis, Vagelis Farfan, Fernando Burke, Redmond BE Hristidis, V TI Searching Electronic Health Records SO INFORMATION DISCOVERY ON ELECTRONIC HEALTH RECORDS SE Chapman & Hall-CRC Data Mining and Knowledge Discovery Series LA English DT Article; Book Chapter ID XML QUERY; KEYWORD SEARCH; ENGINE; RETRIEVAL; RELEVANCE; TEXT; DATABASES; RANKING; SYSTEMS C1 [Varadarajan, Ramakrishna] Univ Wisconsin Madison, Dept Comp Sci, Madison, WI 53706 USA. [Hristidis, Vagelis; Farfan, Fernando] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA. [Burke, Redmond] Miami Childrens Hosp, Miami, FL USA. RP Varadarajan, R (reprint author), Univ Wisconsin Madison, Dept Comp Sci, Madison, WI 53706 USA. CR Agrawal S., 2002, P 18 INT C DAT ENG I Aguilera V., 2001, P 10 WORLD WID WEB C ALTMAN DG, 1994, BRIT MED J, V308, P1552 Amer-Yahia S., 2004, P 13 INT C WORLE WID [Anonymous], 2007, REF INF MOD [Anonymous], 2007, CLIN DOC ARCH REL 2 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balmin A., 2004, P 30 INT C VER LARG Barg M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Bhalotia G., 2002, P 18 INT C DAT ENG I Bhamidipaty A., 2002, P 8 ACM SIGKDD INT C Bohm K., 1997, VLDB Journal, V6, DOI 10.1007/s007780050047 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brown LJ, 1998, THEOR PRACT OBJ SYST, V4, P227, DOI 10.1002/(SICI)1096-9942(1998)4:4<227::AID-TAPO3>3.3.CO;2-L Buckley C., 1994, P 17 INT C RES DEV I SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Buckley C., 1993, NIST SPECIAL PUBLICA, V500-207, P59 Cardie C., 2002, P 40 ANN M ASS COMP Carmel D., 2003, P 26 ANN INT ACM SIG, P151 Chakrabarti S., 2001, P 24 ANN INT ACM SIG, P208, DOI 10.1145/383952.383990 Christophides V., 1994, P 1994 ACM SIGMOD C Cimino JJ, 1996, METHOD INFORM MED, V35, P122 Cohen S., 2005, P INT C INF KNOWL MA, P389, DOI 10.1145/1099554.1099674 Cohen S, 2002, J AM SOC INF SCI TEC, V53, P454, DOI 10.1002/asi.10058 Cohen S., 2003, P 29 INT C VER LARG Croft W. B., 1996, P ANN ACM SIGIR INT Dao T., 1997, C DAT SYST ADV APPL Dar S., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Dong Xin, 2005, P 2005 ACM SIGMOD IN Elkin Peter L, 2005, BMC Med Inform Decis Mak, V5, P13, DOI 10.1186/1472-6947-5-13 Fagin R., 2004, ACM PRINCIPLES DATAB, P47 Fagin R, 2006, SIAM J DISCRETE MATH, V20, P628, DOI 10.1137/05063088X Fagin R, 2003, SIAM J DISCRETE MATH, V17, P134, DOI 10.1137/S0895480102412856 Farfan F., 2009, P IEEE INT C DAT ENG Farfan F., 2008, P IEEE INT C DAT ENG Fikes R., 2004, J WEB SEMANT, V2, P19 Florescu D, 2000, COMPUT NETW, V33, P119, DOI 10.1016/S1389-1286(00)00069-4 Fuhr N., 2001, P 24 ANN INT ACM SIG Goldman R., 1998, Proceedings of the Twenty-Fourth International Conference on Very-Large Databases Guo L., 2003, P ACM SIGMOD INT C M Harman D. K., 1993, NIST SPECIAL PUBLICA, V500-207, P1 Haveliwala T., 2002, P 11 WORLD WID WEB C Hernandez M. A., 1995, P 1995 ACM SIGMOD IN Hristidis V, 2008, ACM T DATABASE SYST, V33, DOI 10.1145/1331904.1331905 Hristidis V., 2002, P 28 INT C VER LARG Hristidis V, 2009, CH CRC DATA MIN KNOW, P471 Hristidis V., 2003, P 19 INT C DAT ENG I Hristidis V, 2006, IEEE T KNOWL DATA EN, V18, P525, DOI 10.1109/TKDE.2006.1599390 Hristidis V., 2003, P 29 INT C VER LARG Huang A., 2003, P INT C WEB AG INF M, P445 Ide NC, 2007, J AM MED INFORM ASSN, V14, P253, DOI 10.1197/jamia.M2233 Inokuchi A, 2007, IBM SYST J, V46, P115 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kalpakis K., 2001, P IEEE INT C DAT ENG Kanza Y., 2001, P 20 ACM S PRINC DAT, P40, DOI 10.1145/375551.375558 Kazai G., 2004, P 27 ANN INT ACM SIG Kim MS, 2005, LECT NOTES ARTIF INT, V3614, P1093 Kim MS, 2006, LECT NOTES COMPUT SC, V4055, P143 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lalmas M., 2003, KNOWL ENG REV, V18, P94 Lee Y. K., 1996, P 1 ACM INT C DIG LI, P91, DOI 10.1145/226931.226950 Li G., 2008, P 35 SIGMOD INT C MA, P681 Li Y., 2004, P 30 INT C VER LARG Li Y., 2004, P C VER LARG DAT BAS Luk R., 2000, P SIGIR WORKSH XML I MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McCallum A. K., 2000, P 6 ACM SIGKDD INT C McCarthy J. F., 1995, P INT JOINT C ART IN Mitchell TM, 1997, MACHINE LEARNING Motwani R., 1995, RANDOMIZED ALGORITHM Myaeng S. H., 1998, P SIGIR C Papakonstantinou Y., 2005, P SIGMOD 2005 Pfeiffer KP, 2003, LECT NOTES COMPUT SC, V2818, P5 Price Susan L, 2002, Proc AMIA Symp, P627 Rahayu W. J., 2005, ICDEW 05, P1160 Reichert J. C., 2002, P AMIA S Richardson M., 2002, ADV NEURAL INFORM PR, V14 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 RxNorm, 2007, RXNORM SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1995, P ACM SPEC INT GROUP Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1992, INFORM PROCESS MANAG, V28, P389, DOI 10.1016/0306-4573(92)90082-B Schenkel R, 2005, INFORM RETRIEVAL, V8, P521, DOI 10.1007/s10791-005-0746-3 Schenkel R, 2003, LECT NOTES COMPUT SC, V2818, P119 Schmidt A., 2001, P ICDE C Seaborne A., 2006, SPARQL QUER IN PRESS Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Smith B., 2005, P 19 INT C EUR FED M Taylor J. R., 1982, INTRO ERROR ANAL STU, P128 Tejada S., 2002, P 8 ACM SIGKDD INT C Theobald A., 2003, LNI, V26, P217 Theobald A, 2002, LECT NOTES COMPUT SC, V2287, P477 U. S. National Library of Medicine, 2004, SNOMED CLIN TERMS Varadarajan R, 2008, PROC INT CONF DATA, P883, DOI 10.1109/ICDE.2008.4497497 Wellner B., 2003, IJCAI WORKSH INF INT Winkler W. E., 1999, STATE RECORD LINKAGE World Wide Web Consortium, 2006, XQUERY XPATH FULL TE Xu J., 2006, P 11 INT C DAT SYST Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205 NR 105 TC 0 Z9 0 PU CRC PRESS-TAYLOR & FRANCIS GROUP PI BOCA RATON PA 6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA BN 978-1-4200-9041-3; 978-1-4200-9038-3 J9 CH CRC DATA MIN KNOW PY 2010 BP 127 EP 163 PG 37 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Health Care Sciences & Services SC Computer Science; Health Care Sciences & Services GA BC8CO UT WOS:000355548500007 ER PT S AU Xu, J Li, H Zhong, CL AF Xu, Jun Li, Hang Zhong, Chaoliang BE Cheng, PJ Kan, MY Lam, W Nakov, P TI Relevance Ranking Using Kernels SO INFORMATION RETRIEVAL TECHNOLOGY SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th Asia Information Retrieval Societies Conference CY DEC 01-03, 2010 CL Taipei, TAIWAN SP Natl Taiwan Univ, Natl Sci Council, Republ China, Minist Educ, Republ China DE Information Retrieval; Relevance Ranking; Kernel ID INFORMATION-RETRIEVAL AB This paper is concerned with relevance ranking in search, particularly that using term dependency information. It proposes a novel and unified approach to relevance ranking using the kernel technique in statistical learning. In the approach, the general ranking model is defined as a kernel function of query and document representations. A number of kernel functions are proposed as specific ranking models in the paper, including BM25 Kernel, LMIR Kernel, and KL Divergence Kernel. The approach has the following advantages. (I) The (general) model can effectively represent different types of term dependency information and thus can achieve high performance. (2) The model has strong connections with existing models such as BM25 and LMIR. (3) It has solid theoretical background. (4) The model can be efficiently computed. Experimental results on web search dataset and TREC datasets show that the proposed kernel approach outperforms MRF and other baseline methods for relevance ranking. C1 [Xu, Jun; Li, Hang] Microsoft Res Asia, Sigma Ctr 4F, Beijing 100190, Peoples R China. RP Xu, J (reprint author), Microsoft Res Asia, Sigma Ctr 4F, 49 Zhichun Rd, Beijing 100190, Peoples R China. EM junxu@microsoft.com; hangli@microsoft.com; clzhong@cn.fujitsu.com CR Bai J., 2008, P 17 INT C WORLD WID, P1183, DOI 10.1145/1367497.1367717 BUTCHER S, 2006, P 29 SIGIR, P621 Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024 Haussler D., 1999, CONVOLUTION KERNELS HAWKING D, 1995, P 4 TEXT RETR C Hersh William, 1994, P 17 ANN INT ACM SIG, P192 KEEN EM, 1992, J INFORM SCI, V18, P89, DOI 10.1177/016555159201800202 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 MARTINS A, 2006, STRING KERNELS SIMIL MORENO PJ, 2003, NIPS 16 NA SH, 2008, P 31 SIGIR, P821, DOI 10.1145/1390334.1390522 Ong CS, 2005, J MACH LEARN RES, V6, P1043 Robertson S.E., 1994, P 3 TEXT RETRIEVAL C Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sonnenburg S, 2006, J MACH LEARN RES, V7, P1531 Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794 TSUDA K, 1999, EUR S ART NEUR NETW, P183 Xie Y, 2007, J AM SOC INF SCI TEC, V58, P2353, DOI 10.1002/asi.20711 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 NR 22 TC 12 Z9 15 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-17186-4 J9 LECT NOTES COMPUT SC PY 2010 VL 6458 BP 1 EP 12 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BUF65 UT WOS:000289174800001 ER PT J AU Velasquez, JD Gonzalez, P AF Velasquez, Juan D. Gonzalez, Pablo TI Expanding the Possibilities of Deliberation: The Use of Data Mining for Strengthening Democracy with an Application to Education Reform SO INFORMATION SOCIETY LA English DT Article DE data mining; deliberation; education reform; participation; technological tools ID INTERNET USE; PARTICIPATION; INVOLVEMENT; TECHNOLOGY; NETWORKS; ELECTION; AGE AB Deliberation is important for strengthening democracies and enhancing the legitimacy of public policy. However, deliberation has been constrained by limits of time, space, and human capacities for listening and processing information. In this article, the authors discuss a new technology-based tool and show how it can help to partially remove these constraints. Although the Internet already provides the means to deliberate without the need to meet at the same place and time, its conjunction with data mining solves the "large numbers deliberation dilemma" that arises when large amounts of data have to be processed. The author's proposal adapts particular data-mining techniques, which simulate the learning process of a human brain with almost infinite relational capacities. The methodology was applied in a real-world case of Chilean education reform and demonstrated its potential effectiveness. C1 [Velasquez, Juan D.; Gonzalez, Pablo] Univ Chile, Sch Engn & Sci, Dept Ind Engn, Ctr Appl Econ, Santiago 8370720, Reg Metropolita, Chile. [Gonzalez, Pablo] Univ Chile, Ctr Adv Res Educ, Santiago 8370720, Reg Metropolita, Chile. RP Velasquez, JD (reprint author), Univ Chile, Sch Engn & Sci, Dept Ind Engn, Ctr Appl Econ, Av Republ 701,Off 301, Santiago 8370720, Reg Metropolita, Chile. EM jvelasqu@dii.uchile.cl RI Velasquez, Juan/H-3859-2013; Gonzalez, Pablo/I-1035-2013 OI Velasquez, Juan/0000-0003-4819-9680; Gonzalez, Pablo/0000-0001-6480-7223 CR Aas K., 1999, TEXT CATEGORISATION Ackerman B, 2002, J POLIT PHILOS, V10, P129, DOI 10.1111/1467-9760.00146 ADKENIZ Y, 2000, REINVIGORATING DEMOC, P169 Alvarez R. M., 2001, LOYOLA LOS ANGELES L, V34, P1115 Barber BR, 1998, POLIT SCI QUART, V113, P573, DOI 10.2307/2658245 Barthe Y., 2001, AGIR MONDE INCERTAIN Becker T, 2001, COMMUN ACM, V44, P39, DOI 10.1145/357489.357503 Bernard Manin, 1997, PRINCIPLES REPRESENT Berry M. J. A., 1997, DATA MINING TECHNIQU Bimber B, 1998, SOC SCI COMPUT REV, V16, P391, DOI 10.1177/089443939801600404 Cleveland H., 1975, PUBLIC MANAGEMENT, V57, P3 COLEMAN S, 2001, DEMOCRACY ONLINE WHA Dahl R. A., 1999, DEMOCRACY DELGADO M, 2002, LECT NOTES ARTIF INT, V2447, P140 DUHART JJ, 2002, MEMOIRE RECHERCHE Elmer G, 1997, CRIT STUD MASS COMM, V14, P182 Etzioni A, 1999, INFORM SOC, V15, P241, DOI 10.1080/019722499128402 Fayyad U, 1996, AI MAG, V17, P37 Fishkin James S, 1991, DEMOCRACY DELIBERATI Fishkin J.S., 2004, NATL CIVIC REV, V93, P55, DOI 10.1002/ncr.71 FONT J, 2003, PUBLIC PARTICIPATION Freeman James A., 1991, NEURAL NETWORKS ALGO Fung G, 2001, COMPREHENSIVE OVERVI GALSTON W, 2003, GOVERNANCE COM DEMOC, P40 Geoffrey E, 1999, UNSUPERVISED LEARNIN Gibson R., 2005, BRIT J POLIT INT REL, V7, P561, DOI 10.1111/j.1467-856X.2005.00209.x GIBSON RK, 2001, POLITICAL SCI Q, V114, P561 Goebel M., 1999, ACM SIGKDD EXPLORATI, V1, P20, DOI 10.1145/846170.846172 Gonzalez P., 2008, GOVERNANCE MANAGEMEN Graham S, 1996, TELECOMMUNICATIONS C Habermas J, 1996, FACTS NORMS Hampton KN, 2003, INFORM SOC, V19, P417, DOI 10.1080/01972240390241547 Hand D. J., 2001, PRINCIPLES DATA MINI HEINE J, 2002, PERSPECTIVAS POLITIC, V6, P165 HIRSCHMAN A, 1973, EXIT VOICE LOYALTY Katz JE, 2001, AM BEHAV SCI, V45, P405, DOI 10.1177/0002764201045003004 Kohonen T., 1987, SELF ORG ASSOCIATIVE Kraut R, 1998, AM PSYCHOL, V53, P1017, DOI 10.1037//0003-066X.53.9.1017 Laver M, 2003, AM POLIT SCI REV, V97, P311 Lessig L., 1999, CODE OTHER LAWS CYBE LIN X, 1999, P 14 INT ACM SIGIR C, P262 Lipow A, 1996, PARLIAMENT AFF, V49, P273 MACEY JR, 1994, U CHICAGO LAW REV, V61, P1443, DOI 10.2307/1600123 MACULLOCH WS, 1943, B MATH BIOPHYS, V5, P113 MANSBRIDGE J, 2003, AM POLIT SCI REV, V97, P518 Mossberger K., 2003, VIRTUAL INEQUALITY D Negroponte N., 1995, BEING DIGITAL Nie N. H., 2000, INTERNET SOC PRELIMI Nie NH, 2001, AM BEHAV SCI, V45, P420, DOI 10.1177/00027640121957277 Norris P., 2001, DIGITAL DIVIDE Poster Mark, 1997, VIRTUAL POLITICS IDE, P212 Putnam R. D., 2000, BOWLING ALONE Rautenfeld H., 2005, AM J POLIT SCI, V49, P184 Rios SA, 2006, LECT NOTES COMPUT SC, V4224, P869 Roberts N, 2004, AM REV PUBLIC ADM, V34, P315, DOI 10.1177/0275074004269288 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuefele D., 2002, HARVARD J PRESS POLI, V7, P55 Shah D, 2002, JOURNALISM MASS COMM, V79, P964 Shah DV, 2001, POLIT COMMUN, V18, P141, DOI 10.1080/105846001750322952 Solop FI, 2001, PS, V34, P289 STRECK J, 1999, POLITICS CYBERSPACE, P18 Sunstein C. R., 2001, REPUBLIC COM Theodoridis S., 1999, PATTERN RECOGNITION Tickle AB, 1998, IEEE T NEURAL NETWOR, V9, P1057, DOI 10.1109/72.728352 Toffler A., 1995, CREATING NEW CIVILIZ Tolbert CJ, 2003, POLIT RES QUART, V56, P175, DOI 10.2307/3219896 *UNDP, 2005, DES HUM CHIL 2004 PO *UNDP, 2002, DES HUM CHIL 2002 NO Vedel T, 2006, PARLIAMENT AFF, V59, P226, DOI 10.1093/pa/gsl005 VELASQUEZ JD, 2005, MULTIDISCIPLINARY PR, P141 Velasquez JD, 2008, FRONT ARTIF INTEL AP, V170, P1 Velasquez JD, 2007, KNOWL-BASED SYST, V20, P238, DOI 10.1016/j.knosys.2006.05.015 Veliz Claudio, 1980, CENTRALIST TRADITION WEBER L, 2001, SOCIAL SCI COMPUTER, V21, P26 Wellman B, 2001, AM BEHAV SCI, V45, P436, DOI 10.1177/00027640121957286 Wilhelm A.G., 2000, DEMOCRACY DIGITAL AG *WORLD BANK, 2004, INF COMM DEV GLOB TR NR 77 TC 2 Z9 2 PU TAYLOR & FRANCIS INC PI PHILADELPHIA PA 325 CHESTNUT ST, SUITE 800, PHILADELPHIA, PA 19106 USA SN 0197-2243 J9 INFORM SOC JI Inf. Soc. PY 2010 VL 26 IS 1 BP 1 EP 16 DI 10.1080/01972240903423329 PG 16 WC Information Science & Library Science SC Information Science & Library Science GA 552OE UT WOS:000274301300001 ER PT B AU Li, XM Zuo, C Zhang, JP Xing, ML AF Li, Xueming Zuo, Chuan Zhang, Jiapei Xing, Minling BE Vanhoof, K Ruan, D Li, T Wets, G TI FCSNV: A NEW VECTOR MODEL TO REPRESENT CHINESE DOCUMENTS SO INTELLIGENT DECISION MAKING SYSTEMS SE World Scientific Proceedings Series on Computer Engineering and Information Science LA English DT Proceedings Paper CT 4th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2009) CY NOV 27-28, 2009 CL Hasselt, BELGIUM SP SCK-CEN, FWO, UGent, SW Jiaotong Univ HO Hasselt Univ AB Similarity calculation of documents is a vital and basic step in text mining, such as document clustering and classification Traditionally, the first step of constructing feature vector of a Chinese document is to segment Chinese text Into words, as there is no apparent word boundary, like space in English The segmentation result weighs heavily on the quality of dictionary A new vector model, FCSNV, is proposed to represent Chinese document without segmentation FCSNV is built in two main steps (1) Use the nodes of Suffix Tree to represent the similarity parts of documents, (2) Use frequent closed nodesets of these nodes to represent dimensions of feature vector Our experiments show that precision is improved effectively when FCSNV is used to measure the most similar documents C1 [Li, Xueming; Zuo, Chuan; Zhang, Jiapei; Xing, Minling] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China. RP Li, XM (reprint author), Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China. CR Agrawal R., 1996, ADV KNOWLEDGE DISCOV, P307 CHEN A, 2003, P 2 SIGHAN WORKSH CH, P148 Chim H., 2007, P 16 INT C WORLD WID, P121, DOI 10.1145/1242572.1242590 Gao JF, 2005, COMPUT LINGUIST, V31, P531, DOI 10.1162/089120105775299177 Guo J, 1997, COMPUT LINGUIST, V23, P569 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tan S, 2005, P 14 ACM INT C INF K, P469, DOI DOI 10.1145/1099554.1099687 UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331 Weiner P., 1973, P 14 IEEE S SWITCH A, P1, DOI DOI 10.1109/SWAT.1973.13 Zaki MJ, 2002, SIAM PROC S, P457 ZAMIR O, 1998, P SIGIR 98 Zhang H.-P., 2003, P 2 SIGHAN WORKSH, P184 NR 13 TC 0 Z9 0 PU WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC PI ATHENS PA AG LOANNOU THEOLOGOU 17-23, 15773 ZOGRAPHOU, ATHENS, GREECE BN 978-981-4295-05-5 J9 WD SCI P COMP ENG PY 2010 VL 2 BP 486 EP 491 DI 10.1142/9789814295062_0075 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA BON07 UT WOS:000277046000075 ER PT B AU Mahmud, J Lau, T AF Mahmud, Jalal Lau, Tessa GP ACM TI Lowering the Barriers to Website Testing with CoTester SO IUI 2010 LA English DT Proceedings Paper CT Proceedings of the 14th ACM International Conference on Intelligent User Interfaces CY FEB 07-10, 2010 CL Hong Kong, PEOPLES R CHINA SP ACM SIGCHI, ACM SIGART DE Website Testing; Test Script; Instruction; Subroutine AB In this paper, we present CoTester, a system designed to decrease the difficulty of testing web applications. CoTester allows testers to create test scripts that are represented in an easy-to-understand scripting language rather than a complex programming language, which allows tests to be created rapidly and by non-developers. CoTester improves the management of test scripts by grouping sequences of low-level actions into subroutines, such as "log in" or "check out shopping cart", which help testers visualize test structure and make bulk modifications. A key innovation in CoTester is its ability to automatically identify these subroutines using a machine learning algorithm. Our algorithm is able to achieve 91% accuracy at recognizing a set of 7 representative subroutines commonly found in test scripts. C1 [Mahmud, Jalal; Lau, Tessa] IBM Almaden Res Ctr, San Jose, CA 95120 USA. RP Mahmud, J (reprint author), IBM Almaden Res Ctr, 650 Harry Rd, San Jose, CA 95120 USA. EM jumahmud@us.ibm.com; tessalau@us.ibm.com CR Allen J., 2007, P AAAI Blythe J., 2005, P 10 C INT US INT, P191, DOI 10.1145/1040830.1040874 *BORL, BORL SILKT BURSTEIN M. H., 2008, AAAI, P1274 Cypher A., 1993, WATCH WHAT DO PROGRA Gervasio M., 2008, AAAI 2008 WORKSH ENH Gusfield D., 1997, ALGORITHMS STRINGS T *HP, HP QUICKT PROFF HUFFMAN SB, 1995, FLEXIBLY INSTRUCTABL IBM, IBM RAT FUNCT TEST LAU T, 2009, P INT JOINT C ART IN LAU T, 1999, P INT US INT 1999 RE, P145, DOI 10.1145/291080.291104 Lau T., 2004, P 9 C INT US INT, P109 Leshed G, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1719 Little G, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P943 ROSENBLUM DS, 1995, IEEE T SOFTWARE ENG, V21, P19, DOI 10.1109/32.341844 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SPAULDING A, 2009, IUI 09, P389 NR 18 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-515-4 PY 2010 BP 169 EP 178 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BRV57 UT WOS:000283748700018 ER PT J AU Li, DD Kwong, CP Lee, DL AF Li, Dandan Kwong, Chung-Ping Lee, Dik Lun TI Unified Linear Subspace Approach to Semantic Analysis SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID INFORMATION-RETRIEVAL; MODEL AB The Basic Vector Space Model (BVSM) is well known in information retrieval. Unfortunately, its retrieval effectiveness is limited because it is based on literal term matching. The Generalizod Vector Space Model (GVSM) and Latent Semantic Indexing (LSI) are two prominent semantic retrieval methods, both of which assume there is some underlying latent semantic structure in a dataset that can be used to improve retrieval performance. However, while this structure may be derived from both the term space and the document space, GVSM exploits only the former and LSI the latter. In this article, the latent semantic structure of a dataset is examined from a dual perspective; namely, we consider the term space and the document space simultaneously. This new viewpoint has a natural connection to the notion of kernels. Specifically, a unified kernel function can be derived for a class of vector space models. The dual perspective provides a deeper understanding of the semantic space and makes transparent the geometrical meaning of the unified kernel function. New semantic analysis methods based on the unified kernel function are developed, which combine the advantages of I-SI and GVSM. We also prove that the new methods are stable because although the selected rank of the truncated Singular Value Decomposition (SVD) is far from the optimum, the retrieval performance will not be degraded significantly. Experiments performed on standard test collections show that our methods are promising. C1 [Li, Dandan; Lee, Dik Lun] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. [Kwong, Chung-Ping] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China. RP Li, DD (reprint author), Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China. EM ddli@cse.ust.hk; cpkwong@mae.cuhk.edu.hk; dlee@cse.ust.hk FU Hong Kong RGC [615806] FX The authors would like to thank the editors and reviewers, who gave very insightful and encouraging comments. This work was supported in part by Grant 615806 from Hong Kong RGC. CR AKAIKE H, 1974, IEEE T AUTOMAT CONTR, VAC19, P716, DOI 10.1109/TAC.1974.1100705 Atkin R. H., 1974, MATH STRUCTURE HUMAN BAST H., 2005, P 28 INT C RES DEV I, P11, DOI 10.1145/1076034.1076040 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Cichocki A., 2002, ADAPTIVE BLIND SIGNA CRISTIANINI N, 2002, J INTELL INF SYST, V18, P27 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding CHQ, 2005, J AM SOC INF SCI TEC, V56, P597, DOI 10.1002/asi.20148 Ding C. H. Q., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312652 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 DUPRET G, 2003, P ACM C RES DEV INF, P221 Edmonds P., 1997, P 35 ANN M ASS COMP, P507 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jessup ER, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P121 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Letsche TA, 1997, INFORM SCIENCES, V100, P105, DOI 10.1016/S0020-0255(97)00044-3 LI DD, 2007, P INT C INF KNOWL EN, P110 LI DD, 2007, P INT C INF KNOWL EN, P90 LI DD, 2007, THESIS CHINESE U HON Manning C.D., 2008, INTRO INFORM RETRIEV Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 Rissanen J., 1978, AUTOMATICA, V14, P445 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B., 2001, LEARNING KERNELS SUP Shawe-Taylor J., 2004, KERNEL METHODS PATTE Voorhees E., 1998, NIST SPECIAL PUBLICA, V500-242, P1 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 YAN J, 2009, P SIAM INT C DAT MIN, P1159 NR 34 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JAN PY 2010 VL 61 IS 1 BP 175 EP 189 DI 10.1002/asi.21219 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 538AD UT WOS:000273155100014 ER PT S AU Bravo-Marquez, F L'Huillier, G Rios, SA Velasquez, JD Guerrero, LA AF Bravo-Marquez, Felipe L'Huillier, Gaston Rios, Sebastian A. Velasquez, Juan D. Guerrero, Luis A. BE Setchi, R Jordanov, I Howlett, RJ Jain, LC TI DOCODE-Lite: A Meta-Search Engine for Document Similarity Retrieval SO KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems CY SEP 08-10, 2010 CL Cardiff, WALES SP Cardiff Univ, Sch Engn, KES Int DE Meta-Search architecture; Plagiarism Detection; Random Query Generation; Document Similarity Retrieval; SOA ID WEB AB The retrieval of similar documents from large scale datasets has been the one of the main concerns in knowledge management environments, such as plagiarism detection, news impact analysis, and the matching of ideas within sets of documents. In all of these applications, a light-weight architecture can be considered as fundamental for the large scale of information needed to be analyzed. Furthermore, the relevance score for documents retrieval can be significantly improved using several previously built search engines and taking into account the relevance feedback from users. In this work, we propose a. web-services architecture for the retrieval of similar documents from the web. We focus on software engineering to support the manipulation of users' knowledge into the retrieval algorithm. An human evaluation for the relevance feedback of the system over a built set of documents is presented, showing that the proposed architecture can retrieve similar documents by using the main search engines. In particular, the document plagiarism detection task was evaluated, for which its main results are shown. C1 [Bravo-Marquez, Felipe; L'Huillier, Gaston; Rios, Sebastian A.; Velasquez, Juan D.] Univ Chile, Dept Ind Engn, Santiago, Chile. RP Bravo-Marquez, F (reprint author), Univ Chile, Dept Ind Engn, Santiago, Chile. EM fbravo@dcc.uchile.cl; glhuilli@dcc.uchile.cl; srios@dii.uchile.cl; jvelasqu@dii.uchile.cl; luguerre@dcc.uchile.cl RI Bravo-Marquez, Felipe/ OI Bravo-Marquez, Felipe/0000-0002-2153-4306 CR Aslam J. A., 2001, SIGIR Forum Baeza-Yates R. A., 1999, MODERN INFORM RETRIE CORMACK GV, 1998, DERIVING VERY SHORT Lewis J, 2006, BIOINFORMATICS, V22, P2298, DOI 10.1093/bioinformatics/btl388 Pereira Jr A.R., 2004, J WEB ENG, V2, P247 RASOLOFO Y, 2001, CIKM 2001, P191 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Selberg E, 1997, IEEE EXPERT, V12, P11, DOI 10.1109/64.577468 Velasquez JD, 2008, FRONT ARTIF INTEL AP, V170, P1 WU Z, 2001, WORLD WIDE WEB, P386 WU Z, 2003, WI 2003, P658 Zaka B, 2009, J INF SCI ENG, V25, P1391 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15389-1 J9 LECT NOTES ARTIF INT PY 2010 VL 6277 BP 93 EP 102 PN II PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUI37 UT WOS:000289447900010 ER PT S AU Bouras, C Tsogkas, V AF Bouras, Christos Tsogkas, Vassilis BE Setchi, R Jordanov, I Howlett, RJ Jain, LC TI W-kmeans: Clustering News Articles Using WordNet SO KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT III SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems CY SEP 08-10, 2010 CL Cardiff, WALES SP Cardiff Univ, Sch Engn, KES Int DE News clustering; k-means; Cluster Labeling; Partitional Clustering AB Document clustering is a powerful technique that has been widely used for organizing data into smaller and manageable information kernels. Several approaches have been proposed suffering however from problems like synonymy, ambiguity and lack of a descriptive content marking of the generated clusters. We are proposing the enhancement of standard kmeans algorithm using the external knowledge from Word Net hypernyms in a twofold manner: enriching the "bag of words" used prior to the clustering process and assisting the label generation procedure following it. Our experimentation revealed a significant improvement over standard kmeans for a corpus of news articles derived from major news portals. Moreover, the cluster labeling process generates useful and of high quality cluster tags. C1 [Bouras, Christos; Tsogkas, Vassilis] Univ Patras, Comp Engn & Informat Dept, GR-26110 Patras, Greece. RP Bouras, C (reprint author), Univ Patras, Comp Engn & Informat Dept, GR-26110 Patras, Greece. EM bouras@cti.gr; tsogkas@ceid.upatras.gr CR Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027 Bouras C, 2008, LECT NOTES ARTIF INT, V5178, P593, DOI 10.1007/978-3-540-85565-1_73 Bouras C, 2008, DATA KNOWL ENG, V64, P330, DOI 10.1016/j.datak.2007.07.007 Carmel D, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P139, DOI 10.1145/1571941.1571967 Chen CL, 2009, LECT NOTES ARTIF INT, V5476, P147 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sedding J., 2004, P COLING WORKSH ROB TAEHO J, 2007, LNCS, V4492, P871 Treeratpituk P., 2006, P 2006 INT C DIG GOV Tseng Y.H., 2009, EXPERT SYSTEMS APPL, V37, P2247 Varelas G., 2005, WIDM, P10 Yanjun Li, 2007, Journal of Supercomputing, V39, DOI 10.1007/s11227-006-0002-7 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 13 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15392-1 J9 LECT NOTES ARTIF INT PY 2010 VL 6278 BP 379 EP 388 PN III PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUH91 UT WOS:000289402900043 ER PT J AU Barat, C Ducottet, C Fromont, E Legrand, AC Sebban, M AF Barat, Cecile Ducottet, Christophe Fromont, Elisa Legrand, Anne-Claire Sebban, Marc BE Balcazar, JL Bonchi, F Gionis, A Sebag, M TI Weighted Symbols-Based Edit Distance for String-Structured Image Classification SO MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I: EUROPEAN CONFERENCE, ECML PKDD 2010 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) CY SEP 20-24, 2010 CL Barcelona, SPAIN SP French Natl Inst Res Comp Sci & Control (INRIA), Pascal2 European Network Excellence, Nokia, Yahoo Labs, Google, KNIME, Aster data, Microsoft Res, HP, MODAP (Mobil, Data Min, & Privacy) ID RECOGNITION; RETRIEVAL; FEATURES; SCALE AB As an alternative to vector representations, a recent trend in image classification suggests to integrate additional structural information in the description of images in order to enhance classification accuracy. Rather than being represented in a p-dimensional space, images can typically be encoded in the form of strings, trees or graphs and are usually compared either by computing suited metrics such as the (string or tree)-edit distance, or by testing subgraph isomorphism. In this paper, we propose a new way for representing images in the form of strings whose symbols are weighted according to a TF-IDF-based weighting scheme, inspired from information retrieval. To be able to handle such real-valued weights, we first introduce a new weighted string edit distance that keeps the properties of a distance. In particular, we prove that the triangle inequality is preserved which allows the computation of the edit distance in quadratic time by dynamic programming. We show on an image classification task that our new weighted edit distance not only significantly outperforms the standard edit distance but also seems very competitive in comparison with standard histogram distances-based approaches. C1 [Barat, Cecile] Univ Lyon, F-42023 St Etienne, France. RP Barat, C (reprint author), Univ Lyon, F-42023 St Etienne, France. CR Bellet A, 2010, PATTERN RECOGN, V43, P2330, DOI 10.1016/j.patcog.2009.12.008 Bisgin H., 2008, TECHNICAL REPORT Boyer L, 2008, LECT NOTES ARTIF INT, V5212, P672, DOI 10.1007/978-3-540-87481-2_45 CHANG SK, 1987, IEEE T PATTERN ANAL, V9, P413 Chapelle O, 1999, IEEE T NEURAL NETWOR, V10, P1055, DOI 10.1109/72.788646 Daliri MR, 2008, PATTERN RECOGN, V41, P1782, DOI 10.1016/j.patcog.2007.10.020 Durbin R., 1998, BIOL SEQUENCE ANAL Freeman H., 1974, ACM COMPUT SURV, V6, P57, DOI 10.1145/356625.356627 Hsieh SM, 2008, INFORM PROCESS MANAG, V44, P1214, DOI 10.1016/j.ipm.2007.09.008 Jurie F, 2005, IEEE I CONF COMP VIS, P604 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 McCallum A., 2005, P 21 C UNC ART INT U, P388 Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2 Moulin C, 2009, LECT NOTES COMPUT SC, V5706, P779 Nowak E, 2006, LECT NOTES COMPUT SC, V3954, P490 Oncina J, 2006, PATTERN RECOGN, V39, P1575, DOI 10.1016/j.patcog.2006.03.011 Punitha P, 2008, PATTERN RECOGN, V41, P2068, DOI 10.1016/j.patcog.2007.09.012 Ristad ES, 1998, IEEE T PATTERN ANAL, V20, P522, DOI 10.1109/34.682181 Robertson S., 1994, TEXT RETRIEVAL C, P21 Robertson S. E., 1997, SIGIR Forum SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vailaya A, 2001, IEEE T IMAGE PROCESS, V10, P117, DOI 10.1109/83.892448 Vidal-Naquet M, 2003, ICCV, P281 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 Wang JZ, 2000, LECT NOTES COMPUT SC, V1929, P360 Yang J., 2007, MIR 2007, P197 Zhai C., 2001, TECHNICAL REPORT NR 27 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15879-7 J9 LECT NOTES ARTIF INT PY 2010 VL 6321 BP 72 EP 86 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA BCV25 UT WOS:000311562600011 ER PT B AU El-Alfy, EM AF El-Alfy, El-Sayed M. BE Peters, H Vogel, M TI LEARNING METHODS FOR SPAM FILTERING SO MACHINE LEARNING RESEARCH PROGRESS LA English DT Article; Book Chapter DE Machine Learning; Text Categorization; Classification; Spam Filtering; Unsolicited Commercial E-mail; Bayesian Filter; Memory-Based Learning; Boosting; Neural Networks; Support Vector Machines ID ROUGH SET APPROACH; NEURAL-NETWORK; E-MAIL; ALGORITHMS; MODEL; PERFORMANCE AB With the increasing popularity of electronic mail, several persons and companies have found it an easy way to quickly disseminate unsolicited messages to a large number of users at very low costs for the senders. Consequently, unsolicited or spam e-mails have dramatically become a major threat that can negatively impact the usability of the electronic mail as a reliable communication means. Besides wasting considerable time and money for business users and network administrators, spam consumes network bandwidth and server storage space, slows down e-mail servers, and provides a medium to distribute harmful and/or offensive content. Hence, it has become an important and indispensable aspect of any recent e-mail system to incorporate a spam filtering subsystem. In this chapter, we present an overview of the spam filtering problem and survey the state-of-the-art of the proposed and deployed machine learning based methods. We begin with a brief review of potential spam threats for network users and resources, and some market analysis indicators of the spam growth rate. After that, we formally describe the machine learning spam filtering problem and discuss various approaches for representing e-mail messages and selecting relevant features. Then, we describe some common metrics and benchmark corpora for evaluating and comparing the performance of different learning methods for spam filtering. Next, we discuss various learning algorithms that have been applied to this problem and survey the related work. Finally, we present a case study to compare the performance of a number of these learning methods on one of the publicly available datasets. C1 King Fahd Univ Petr & Minerals, Coll Comp Sci & Engn, Dhahran, Saudi Arabia. RP El-Alfy, EM (reprint author), King Fahd Univ Petr & Minerals, Coll Comp Sci & Engn, Dhahran, Saudi Arabia. CR AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470 Ahmed S, 2004, P 1 C EM ANT CEAS Androutsopoulos I., 2000, P WORKSH MACH LEARN, P9 ANDROUTSOPOULOS I, 2005, P 2 C EM ANT CEAS 05 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG ANDROUTSOPOULOS I, 2000, WORKSH MACH LEARN TE, P1 Androutsopoulos I., 2004, LEARNING FILTER UNSO ANDROUTSOPOULOS I, 2000, 20005 DEMO NCSR I IN [Anonymous], UCI MACH LEARN REP BEKKERMAN R, 2004, IR418 CIIR Bishop CM, 2007, PATTERN RECOGNITION, V2nd BLANZIERI E, 2007, INSTANCE BASED SPAM BLANZIERI E, 2006, DIT06056 Burges C. J. C., 1998, DATA MIN KNOWL DISC, V2, DOI DOI 10.1023/A:1009715923555 BURTON B, 2003, P MIT SPAM C Carpinteiro OAS, 2006, LECT NOTES COMPUT SC, V4132, P847 Carpinter J, 2006, COMPUT SECUR, V25, P566, DOI 10.1016/j.cose.2006.06.001 CARPINTER JM, 2005, 0405 HONS U CANT COM Carreras X., 2001, P 4 INT C REC ADV NA CHEN T, 2003, SPAM E MAIL FILTER U CHHABRA S, 2004, P 4 IEEE INT C DAT M CHOUCHOULAS A, 1999, THESIS U EDINBURGH CHUAN Z, 2004, P 5 INT C PAR DISTR CHUAN Z, 2005, ACM SIGOPS OPERATING, V39, P34, DOI DOI 10.1145/1044552.1044555 Clark J, 2003, P IEEE WIC INT C WEB Cohen W. W., 1995, P 12 INT C MACH LEAR COHEN WW, 1996, P AAAI 96 SPRING S M CORMACK G, 2005, 2 C EM ANT SPAM CORMACK G, 2006, 15 TEXT RETR C P CORMACK GV, 2006, 3 C EM ANT CEAS 06 M CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Cranor LF, 1998, COMMUN ACM, V41, P74, DOI 10.1145/280324.280336 Cristianini N., 2000, INTRO SUPPORT VECTOR DAELEMANS W, 0703 ILK TILB U DEP DEEPAK P, 2005, S APPL INT SAINT 05 Dietterich T, 2002, HDB BRAIN THEORY NEU DRUCKER H, 1995, NIPS 1995, P479 *ECML PKDD, 2006, DISC CHALL ELALFY ESM, 2008, P IEEE INT C COMP SY ELALFY ESM, 2008, P IEEE INT JOINT C N ERYIGIT G, 2005, P INT C ART INT APPL, P457 Fallows D., 2003, SPAM IT IS HURTING E FARRUGIA A, 2004, THESIS MONASH U Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010 Fdez-Riverola F, 2007, EXPERT SYST APPL, V33, P36, DOI 10.1016/j.eswa.2006.04.011 Fdez-Riverola F, 2007, DECIS SUPPORT SYST, V43, P722, DOI 10.1016/j.dss.2006.11.012 Freund Y., 1996, P 13 INT C MACH LEAR, P148 Fuad M.M., 2004, P 7 INT C COMP INF T Furnkranz J., 1994, P 11 INT C MACH LEAR Gansterer W.N., 2005, ANTISPAM METHODS STA GARCIA F, 2004, P 19 IFIP INT INF SE GAVRILIS D, 2006, LECT NOTES COMPUTER, V3955 GEORGIOUA E, 2007, J NETWORK C IN PRESS *GFI SOFT, 2007, WHY BAY FILT MOST EF *GFI SOFT, 2007, KEEP SPAM YOUR NETW Glymin M, 2007, LECT NOTES ARTIF INT, V4585, P350, DOI 10.1007/978-3-540-73451-2_37 Gomes LH, 2007, PERFORM EVALUATION, V64, P690, DOI 10.1016/j.peva.2006.11.001 Gordillo J, 2007, EXPERT SYST APPL, V33, P667, DOI 10.1016/j.eswa.2006.06.016 GRAHAM P, 2003, P 1 ANN SPAM C HANLEY JA, 1983, RADIOLOGY, V148, P839 Hershkop S, 2005, P 11 ACM SIGKDD INT HIDALGO JMG, 2000, P 2 WORKSH LEARN LAN, V7, P99 Hoanca B, 2006, IEEE TECHNOL SOC MAG, V25, P22, DOI 10.1109/MTAS.2006.1607720 Hsiao WF, 2008, EXPERT SYST APPL, V34, P1599, DOI 10.1016/j.eswa.2007.01.018 HUAIBIN W, 2005, LECT NOTES COMPUTER, P1147 Huang J, 2005, IEEE T KNOWL DATA EN, V17, P299 Huang Te-Ming, 2006, KERNEL BASED ALGORIT ISLAM MR, 2007, 6 IEEE ACIS INT C CO Jensen F. V., 2001, BAYESIAN NETWORKS DE Jimenez Daniel, 1998, P 1998 INT JOINT C N, P753 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims T., SVM LIGHT John G.H., 1995, P 11 C UNC ART INT, P338 Johnson PRE, 1997, BRIT J HAEMATOL, V97, P1, DOI 10.1046/j.1365-2141.1997.00984.x JUNEJO KN, 2006, P 17 EUR C MACH LEAR JUNG J, 2004, P 4 ACM SIGCOMM C IN Khorsi Ahmed, 2007, Informatica, V31 KLIMT B, 2004, P 1 C EM ANT CEAS Kohonen T., 2001, SELF ORG MAPS Kolcz A., 2001, P TEXTDM 01 WORKSH T Komorowski J., 1999, ROUGH FUZZY HYBRIDIZ Koprinska I, 2007, INFORM SCIENCES, V177, P2167, DOI 10.1016/j.ins.2006.12.005 Lai CC, 2007, KNOWL-BASED SYST, V20, P249, DOI 10.1016/j.knosys.2006.05.016 LAI CC, 2004, P 4 INT C HYBR INT S LEIBA B, 2004, P 1 C EM ANT MOUNT V Lohninger H., 1999, TEACH ME DATA ANAL LUO X, 2005, P IEEE INT JOINT C N, V4, P2571 Lyon J., 2006, 4406 RFC Manning C. D., 2007, INTRO INFORM RETRIEV *MESSAGELABS, 2006, MESSAGELABS INT 2006 METSIS V, 2006, P 2 C EM ANT CEAS MO MEYER TA, 2004, P 1 C EM ANT CEAS Mitchell TM, 1997, MACHINE LEARNING Neapolitan R. E., 2004, LEARNING BAYESIAN NE Nelson M., 2003, ANTISPAM BUSINESS IS Nicholas T., 2003, USING ADABOOST DECIS O'Brien C., 2003, P 1 INT S INF COMM T, P291 OSUMA E, 1997, 1602 MIT AI LAB Ozgur L, 2004, LECT NOTES COMPUT SC, V3177, P505 Ozgur L, 2004, PATTERN RECOGN LETT, V25, P1819, DOI 10.1016/j.patrec.2004.07.004 PANG XL, 2007, P 3 INT C NAT COMP, V2, P484 Pantel P., 1998, P AAAI WORKSH LEARN PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 Platt J. C., 1998, ADV KERNEL METHODS S Provost J., 1999, AITR99284 U TEX AUST Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAINIE L, 2004, CAN SPAM ACT HAS NOT RENNIE J, 2000, KDD 2000 TEXT MIN WO Rios G., 2004, P 1 C EM ANT MOUNT V Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Russell S. J., 2003, ARTIFICIAL INTELLIGE, V2nd Sahami M., 1998, P AAAI WORKSH LEARN, P55 Sakkis G, 2003, INFORM RETRIEVAL, V6, P49, DOI 10.1023/A:1022948414856 Sakkis G, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P44 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire R. E., 2003, NONLINEAR ESTIMATION Schneider K-M, 2003, P 10 C EUR CHAPT ASS Sculley D., 2007, P 30 ANN INT ACM SIG, P415, DOI 10.1145/1277741.1277813 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 SEBASTIANI FA, 1999, P 1 ARG S ART INT AS SEBASTIANI FA, 2001, ERCIM NEWS Sebban M, 2002, PATTERN RECOGN, V35, P835, DOI 10.1016/S0031-3203(01)00084-X SEEWALD AK, 2004, TR200411 OST FORSCH SIEFKES C, 2004, P EUR C PRINC PRACT Sinclair S, 2004, J COMPUTING SCI COLL, V19, P344 STUART I, 2004, LECT NOTES COMPUTER, P442 Su M, 2001, IEEE IJCNN, P2159 Swann A, 1998, ELECTRON LETT, V34, P1408, DOI 10.1049/el:19981000 Tresp V., 2001, HDB NEURAL NETWORK S Tretyakov K, 2004, I COMP SCI U TART DA, P60 TRUDGIAN DC, 2004, P 5 INT C INT DAT EN, P578 TUTTLE A, 2004, CS200403 DALH U Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Wang CC, 2007, COMPUT SECUR, V26, P381, DOI 10.1016/j.cose.2006.12.012 Wang CC, 2004, J RES PRACT INF TECH, V36, P3 WEBB S, 2005, INT C COLL COMP NETW WILSON DR, 1997, THESIS B YOUNG U WOITASZEK M, 2003, P 2003 S APPL INT OR Wong M., 2006, 4408 RFC Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yang Y., 2007, P 2007 IEEE INT S CO Yang Y., 1997, P 14 INT C MACH LEAR YANG Z, 2006, 6 INT C INT SYST DES YAO YY, 1992, INT J MAN MACH STUD, V37, P793, DOI 10.1016/0020-7373(92)90069-W YERAZUNIS B, 2003, P SPAM C YERAZUNIS W, 2005, MIT SPAM C Zhang HW, 2003, CHINESE J ASTRON AST, V3, P453 Zhang L., 2004, ACM T ASIAN LANGUAGE, V3, P243, DOI 10.1145/1039621.1039625 ZHAO W, 2005, IEEE TENCON 05 HONG, P2246 Zhao WQ, 2005, Proceedings of the 2005 International Conference on Active Media Technology (AMT 2005), P403 Zhao WQ, 2006, LECT NOTES ARTIF INT, V4062, P766 Zhou Y., 2005, P 17 IEEE INT C TOOL, P302 ZIARKO W, 1993, J COMPUT SYST SCI, V46, P39, DOI 10.1016/0022-0000(93)90048-2 Zorkadis V, 2005, NEURAL NETWORKS, V18, P799, DOI 10.1016/j.neunet.2005.06.045 ZORKADIS V, 2005, POC IEEE INT JOINT C, V1, P179 NR 156 TC 0 Z9 0 PU NOVA SCIENCE PUBLISHERS, INC PI HAUPPAUGE PA 400 OSER AVE, STE 1600, HAUPPAUGE, NY 11788-3635 USA BN 978-1-60456-646-8 PY 2010 BP 383 EP 425 PG 43 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BPE16 UT WOS:000278665800015 ER PT B AU Watt, E Arnold, C Sayre, J AF Watt, Emily Arnold, Corey Sayre, James BA Bui, AA Taira, RK BF Bui, AA Taira, RK TI Evaluation SO MEDICAL IMAGING INFORMATICS LA English DT Article; Book Chapter ID OPERATING CHARACTERISTIC CURVES; CLINICAL INFORMATION-SYSTEMS; IMAGE RETRIEVAL-SYSTEMS; HEALTH-CARE; HEURISTIC EVALUATION; MEDICAL APPLICATIONS; CONTROLLED-TRIALS; INTERNAL PILOT; SAMPLE-SIZE; USABILITY C1 [Watt, Emily] Univ Calif Los Angeles, Biomed Engn IDP, Los Angeles, CA 90024 USA. [Arnold, Corey] Univ Calif Los Angeles, Dept Informat Studies, Los Angeles, CA 90024 USA. [Sayre, James] Univ Calif Los Angeles, David Geffen Sch Med, Dept Biostat, Los Angeles, CA 90024 USA. [Sayre, James] Univ Calif Los Angeles, David Geffen Sch Med, Dept Radiol Sci, Los Angeles, CA 90024 USA. RP Watt, E (reprint author), Univ Calif Los Angeles, Biomed Engn IDP, Los Angeles, CA 90024 USA. CR Aisen AM, 2003, RADIOLOGY, V228, P265, DOI 10.1148/radiol.2281020126 Ammenwerth E, 2004, INT J MED INFORM, V73, P479, DOI 10.1016/j.ijmedinf.2004.04.004 Ammenwerth E, 2007, J AM MED INFORM ASSN, V14, P368, DOI 10.1197/jamia.M2276 Ammenwerth E, 2005, METHOD INFORM MED, V44, P44 ANDERSON JG, 2005, EVALUATING ORG IMPAC, P5, DOI 10.1007/0-387-30329-4_1 Benson K, 2000, NEW ENGL J MED, V342, P1878, DOI 10.1056/NEJM200006223422506 Beuscart-Zephir MC, 2001, INT J MED INFORM, V64, P157, DOI 10.1016/S1386-5056(01)00210-6 Breiman L., 1984, CLASSIFICATION REGRE Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 Card S.K., 1983, PSYCHOL HUMAN COMPUT Chin J. P., 1988, P SIGCHI C HUM FACT, P213, DOI 10.1145/57167.57203 Cleverdon C. W., 1966, FACTORS DETERMINING Concato J, 2000, NEW ENGL J MED, V342, P1887, DOI 10.1056/NEJM200006223422507 Daniels Jeremy, 2007, J Clin Monit Comput, V21, P323, DOI 10.1007/s10877-007-9091-y Dawson B, 2004, BASIC CLIN BIOSTATIS Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63 Denne JS, 1999, STAT MED, V18, P1575, DOI 10.1002/(SICI)1097-0258(19990715)18:13<1575::AID-SIM153>3.3.CO;2-Q Despont-Gros C, 2005, J BIOMED INFORM, V38, P244, DOI 10.1016/j.jbi.2004.12.004 Effken JA, 2002, INT J MED INFORM, V65, P59, DOI 10.1016/S1386-5056(02)00003-5 FLACK VF, 1988, PSYCHOMETRIKA, V53, P321, DOI 10.1007/BF02294215 Fletcher R., 2005, CLIN EPIDEMIOLOGY ES, V4th Friedman C, 2006, BIOMEDICAL INFORM CO Graham MJ, 2004, INT J MED INFORM, V73, P771, DOI 10.1016/j.ijmedinf.2004.08.002 Hajdukiewicz JR, 1998, HUM FAC ERG SOC P, P1038 Hersh W., 2003, INFORM RETRIEVAL HLT, Vsecond Hersh WR, 1998, JAMA-J AM MED ASSOC, V280, P1347, DOI 10.1001/jama.280.15.1347 Hornbaek K, 2006, INT J HUM-COMPUT ST, V64, P79, DOI 10.1016/j.ijhcs.2005.06.002 HORSTHEMKE WH, 2008, INT J HEALTHCARE INF, V4, P17 Huang X, 2006, P 2006 ANN S AM MED, P359 Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418 Kaplan B, 1997, J AM MED INFORM ASSN, V4, P94 Kernan WN, 1999, J CLIN EPIDEMIOL, V52, P19, DOI 10.1016/S0895-4356(98)00138-3 Kjeldskov J., 2008, INT J MED INFORM Kurosu M., 1995, P CHI 1995 C COMP HU, P292 Kushniruk AW, 2004, J BIOMED INFORM, V37, P56, DOI 10.1016/j.jbi.2004.01.003 Laerum H, 2001, BRIT MED J, V323, P1344 Lasko TA, 2005, J BIOMED INFORM, V38, P404, DOI 10.1016/j.jbi.2005.02.008 Lee F, 1996, J AM MED INFORM ASSN, V3, P42 Lehmann TM, 2004, METHOD INFORM MED, V43, P354 LEWIS JR, 1995, INT J HUM-COMPUT INT, V7, P57 Limbourg Q, 2003, HDB TASK ANAL HUMAN, P135 LINDGAARD G, 2007, P SIGCHI C HUM FACT, P1415, DOI 10.1145/1240624.1240839 Loh WY, 1997, STAT SINICA, V7, P815 Long L Rodney, 2009, International Journal of Healthcare Information Systems and Informatics, V4, DOI 10.4018/jhisi.2009010101 MACLURE M, 1991, AM J EPIDEMIOL, V133, P144 Maxwell J. A., 2005, EVALUATING ORG IMPAC, P30, DOI [10.1007/0-387-30329-4_2, DOI 10.1007/0-387-30329-4_] Mayhew D. J., 1999, USABILITY ENG LIFECY Metz Charles E, 2006, J Am Coll Radiol, V3, P413, DOI 10.1016/j.jacr.2006.02.021 Militello LG, 1998, ERGONOMICS, V41, P1618, DOI 10.1080/001401398186108 MORTON SC, 2004, 040033 AG HEALTHC RE MULLER H, 2007, ADV MULTILINGUAL MUL, P472 Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024 MULLER H, 2007, LNCS, P232 Muller H, 2004, COMPUT MED IMAG GRAP, V28, P295, DOI 10.1016/j.compmedimag.2004.04.005 Murff HJ, 2001, J AM MED INFORM ASSN, V8, P499 Nielsen J, 1994, USABILITY INSPECTION Nielsen J., 1993, USABILITY ENG Obuchowski NA, 2005, AM J ROENTGENOL, V184, P364 Obuchowski NA, 2003, RADIOLOGY, V229, P3, DOI 10.1148/radiol.2291010898 Pampel FC, 2000, LOGISTIC REGRESSION Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Quinlan JR, 1996, J ARTIF INTELL RES, V4, P77 Rose AF, 2005, J BIOMED INFORM, V38, P51, DOI 10.1016/j.jbi.2004.11.006 Rosenberger WF, 2002, RANDOMIZATION CLIN T SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C Shneiderman B., 2004, DESIGNING USER INTER, V4th Shyu CR, 1999, COMPUT VIS IMAGE UND, V75, P111, DOI 10.1006/cviu.1999.0768 Sittig D F, 1999, Proc AMIA Symp, P400 SNYDER C, 2006, BIAS USABILITY TESTI STEIN C, 1945, ANN MATH STAT, V16, P243, DOI 10.1214/aoms/1177731088 Stoicu-Tivadar L, 2006, INT J MED INFORM, V75, P335, DOI 10.1016/j.ijmedinf.2005.08.005 Tagare HD, 1997, J AM MED INFORM ASSN, V4, P184 Talmon J, 1999, INT J MED INFORM, V56, P107, DOI 10.1016/S1386-5056(99)00035-0 Tang ZH, 2006, TELEMED J E-HEALTH, V12, P24, DOI 10.1089/tmj.2006.12.24 TAYLOR RS, 1962, AM DOC, V13, P391, DOI 10.1002/asi.5090130405 Tractinsky N, 2000, INTERACT COMPUT, V13, P127, DOI 10.1016/S0953-5438(00)00031-X Vicente K. J., 1999, COGNITIVE WORK ANAL VIRZI RA, 1992, HUM FACTORS, V34, P457 WITTES J, 1990, STAT MED, V9, P65, DOI 10.1002/sim.4780090113 NR 81 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-1-4419-0384-6 PY 2010 BP 403 EP 438 DI 10.1007/978-1-4419-0385-3_10 D2 10.1007/978-1-4419-0385-3 PG 36 WC Engineering, Biomedical; Medical Informatics; Radiology, Nuclear Medicine & Medical Imaging SC Engineering; Medical Informatics; Radiology, Nuclear Medicine & Medical Imaging GA BNG81 UT WOS:000274524500010 ER PT J AU Garcia-Remesal, M Maojo, V Billhardt, H Crespo, J AF Garcia-Remesal, M. Maojo, V. Billhardt, H. Crespo, J. TI Integration of Relational and Textual Biomedical Sources A Pilot Experiment Using a Semi-automated Method for Logical Schema Acquisition SO METHODS OF INFORMATION IN MEDICINE LA English DT Article DE Database integration; genetic diseases; text mining; natural language processing ID INFORMATION-SYSTEMS; GENOMIC MEDICINE; ONTOLOGY; BIOINFORMATICS; DATABASES; BIOLOGY; FUTURE; ACCESS; MODEL; GENE AB Objectives: Bringing together structured and text-based sources is an exciting challenge for biomedical informaticians, since most relevant biomedical sources belong to one of these categories. In this paper we evaluate the feasibility of integrating relational and text-based biomedical sources using: i) an original logical schema acquisition method for textual databases developed by the authors, and ii) OntoFusion, a system originally designed by the authors for the integration of relational sources. Methods: We conducted an integration experiment involving a test set of seven differently structured sources covering the domain of genetic diseases. We used our logical schema acquisition method to generate schemas for all textual sources. The sources were integrated using the methods and tools provided by OntoFusion. The integration was validated using a test set of 500 queries. Results: A panel of experts answered a questionnaire to evaluate i) the quality of the extracted schemas, ii) the query processing performance of the integrated set of sources, and iii) the relevance of the retrieved results. The results of the survey show that our method extracts coherent and representative logical schemas. Experts' feedback on the performance of the integrated system and the relevance of the retrieved results was also positive. Regarding the validation of the integration, the system successfully provided correct results for all queries in the test set. Conclusions: The results of the experiment suggest that text-based sources including a logical schema can be regarded as equivalent to structured databases. Using our method, previous research and existing tools designed for the integration of structured databases can be reused possibly subject to minor modifications to integrate differently structured sources. C1 [Garcia-Remesal, M.; Maojo, V.; Crespo, J.] Univ Politecn Madrid, Fac Informat, Dep Inteligencia Artificial, Biomed Informat Grp, E-28660 Madrid, Spain. [Billhardt, H.] Univ Rey Juan Carlos, Artificial Intelligence Grp, Madrid, Spain. RP Garcia-Remesal, M (reprint author), Univ Politecn Madrid, Fac Informat, Dep Inteligencia Artificial, Biomed Informat Grp, Campus Montegancedo S-N, E-28660 Madrid, Spain. EM mgremesal@fi.upm.es RI Garcia-Remesal, Miguel/G-3147-2013; Billhardt, Holger/F-7873-2015 OI Garcia-Remesal, Miguel/0000-0002-7532-9471; Billhardt, Holger/0000-0001-8298-4178 FU European Commission through the Advancing Clinico-Genomic Trials on Cancer [IST-2005-026996]; ACTION-Grid; Spanish Ministry of Innovation and Science (RETICS); Spanish Ministry of Education [TSI2006-13021-C02-01]; Comunidad de Madrid, Spain FX This research has been supported by the European Commission through the Advancing Clinico-Genomic Trials on Cancer (IST-2005-026996) and ACTION-Grid projects, the Spanish Ministry of Innovation and Science (RETICS), the Spanish Ministry of Education (OntoMineBase project, referenceTSI2006-13021-C02-01), and the Comunidad de Madrid, Spain. We also thank the anonymous reviewers for their valuable comments and suggestions. CR Alonso-Calvo R, 2007, J BIOMED INFORM, V40, P17, DOI 10.1016/j.jbi.2006.02.014 Aronson A, 2001, P AMIA S 2001, P17 Ashburner M, 2000, NAT GENET, V25, P25 Ben Miled Z, 2005, J DATABASE MANAGE, V16, P72 Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061 BRUFORD EA, 2007, NUCL ACIDS RES FORGY CL, 1982, ARTIF INTELL, V19, P17, DOI 10.1016/0004-3702(82)90020-0 FRIEDMAN E, 2003, JESS ACTION JAVA RUL GARCIAMOLINA H, P AAAI S INF GATH 19, P61 GARCIAREMESAL M, P AMIA S 2007, P259 Gosset William Sealy, 1908, BIOMETRIKA, V6, P1, DOI [DOI 10.1093/BIOMET/6.1.1, 10.1093/biomet/6.1.1] Haas LM, 2001, IBM SYST J, V40, P489 HEARST M, P 14 C COMP LING 199, P539 HERSH WR, 1993, MED DECIS MAKING, V13, P220, DOI 10.1177/0272989X9301300308 Kawazoe Y, 2008, METHOD INFORM MED, V47, P549, DOI 10.3414/ME9126 Kersey PJ, 2003, METHOD INFORM MED, V42, P154 Knaup P, 2004, METHOD INFORM MED, V43, P302 LUSSIER YA, P S BIO 2006, P64 Maojo V, 2007, METHOD INFORM MED, V46, P484, DOI 10.1160/ME9056 Maojo V, 2006, METHOD INFORM MED, V45, P180 Maojo V, 2003, J AM MED INFORM ASSN, V10, P515, DOI 10.1197/jamia.M1305 Martin-Sanchez F, 2002, METHOD INFORM MED, V41, P25 MASON O, P 1 INT C LANG RES E, P589 Mena E, 2000, DISTRIB PARALLEL DAT, V8, P223, DOI 10.1023/A:1008741824956 Perez-Rey D, 2006, COMPUT BIOL MED, V36, P712, DOI 10.1016/j.compbiomed.2005.02.004 PYLE D, 2003, BUSINESS MODELING DA Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003 Rosse C, 2003, J BIOMED INFORM, V36, P478, DOI 10.1016/j.jbi.2003.11.007 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sander C, 2000, SCIENCE, V287, P1977, DOI 10.1126/science.287.5460.1977 Sax U, 2005, METHOD INFORM MED, V44, P546 SINCLAIR J, 2000, CORPUS CONCORDANCE C Stevens R, 2000, BIOINFORMATICS, V16, P184, DOI 10.1093/bioinformatics/16.2.184 Sujansky W, 2001, J BIOMED INFORM, V34, P285, DOI 10.1006/jbin.2001.1024 WACHE H, 1999, P INT S DAT APPL NON WIEDERHOLD G, 1992, COMPUTER, V25, P38, DOI 10.1109/2.121508 WOODS WA, 1970, COMMUN ACM, V13, P591, DOI 10.1145/355598.362773 INFOGENMED VIRTUAL L ACGT ADV CLIN GENOMI NR 39 TC 6 Z9 6 PU SCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN PI STUTTGART PA HOLDERLINSTRASSE 3, D-70174 STUTTGART, GERMANY SN 0026-1270 J9 METHOD INFORM MED JI Methods Inf. Med. PY 2010 VL 49 IS 4 BP 337 EP 348 DI 10.3414/ME0614 PG 12 WC Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics SC Computer Science; Health Care Sciences & Services; Medical Informatics GA 648MB UT WOS:000281697000003 PM 19936436 ER PT S AU Perez-Iglesias, J Garrido, G Rodrigo, A Araujo, L Penas, A AF Perez-Iglesias, Joaquin Garrido, Guillermo Rodrigo, Alvaro Araujo, Lourdes Penas, Anselmo BE Peters, C DiNunzio, GM Kurimo, M Mandl, T Mostefa, D Penas, A Roda, G TI Information Retrieval Baselines for the ResPubliQA Task SO MULTILINGUAL INFORMATION ACCESS EVALUATION I: TEXT RETRIEVAL EXPERIMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Workshop of the Cross-Language Evaluation Forum CY SEP 30-OCT 02, 2009 CL Corfu, GREECE AB The baselines proposed for the ResPubliQA 2009 task are described in this paper. The main aim for designing these baselines was to test the performance of a pure Information Retrieval approach on this task. Two baselines were run for each of the eight languages of the task. Both baselines used the Okapi-BM25 ranking function, with and without a stemming. In this paper we extend the previous baselines comparing the BM25 model with Vector Space Model performance on this task. The results prove that BM25 outperforms VSM for all cases. C1 [Perez-Iglesias, Joaquin; Garrido, Guillermo; Rodrigo, Alvaro; Araujo, Lourdes; Penas, Anselmo] Univ Nacl Educ Distancia, NLP & IR Grp, Madrid, Spain. RP Perez-Iglesias, J (reprint author), Univ Nacl Educ Distancia, NLP & IR Grp, Madrid, Spain. EM joaquin.perez@lsi.uned.es; ggarrido@lsi.uned.es; alvarory@lsi.uned.es; lurdes@lsi.uned.es; anselmo@lsi.uned.es; anselmo@lsi.uned.es RI Araujo, Lourdes/G-6272-2015; Penas, Anselmo/H-4308-2015 OI Araujo, Lourdes/0000-0002-7657-4794; Penas, Anselmo/0000-0002-7867-0149 CR HERSH WR, 2006, VOLUME SPECIAL PUBLI PEREZ J, 2009, CLEF 2009 WORKSH COR Perez-Iglesias J, 2009, CORR SAKAI T, 2008, OVERVIEW NTCIR 7 ACL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval NR 6 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15753-0 J9 LECT NOTES COMPUT SC PY 2010 VL 6241 BP 253 EP 256 PN I PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BTF32 UT WOS:000286783100028 ER PT S AU Ferro, N Harman, D AF Ferro, Nicola Harman, Donna BE Peters, C DiNunzio, GM Kurimo, M Mandl, T Mostefa, D Penas, A Roda, G TI CLEF 2009: Grid@CLEF Pilot Track Overview SO MULTILINGUAL INFORMATION ACCESS EVALUATION I: TEXT RETRIEVAL EXPERIMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Workshop of the Cross-Language Evaluation Forum CY SEP 30-OCT 02, 2009 CL Corfu, GREECE AB The Grid@CLEF track is a long term activity with the aim of running a series of systematic experiments in order to improve the comprehension of MLIA systems and gain an exhaustive picture of their behaviour with respect to languages. In particular, Grid@CLEF 2009 is a pilot track that has started to move the first steps in this direction by giving the participants the possibility of getting experienced with the new way of carrying out experimentation that is needed in Grid@CLEF to test all the different combinations of IR components and languages. Grid@CLEF 2009 offered traditional monolingual ad-hoc tasks in 5 different languages (Dutch, English, French, German, and Italian) which make use of consolidated and very well known collections from CLEF 2001 and 2002 and used a set of 84 topics. Participants had to conduct experiments according to the CIRCO framework, an XML-based protocol which allows for a distributed, loosely-coupled, and asynchronous experimental evaluation of IR systems. We provided a Java library which can be exploited to implement CIRCO and an example implementation with the Lucene IR system. The participation has been especially challenging also for the size of the XML files generated by CIRCO, which can become 50-60 times the size of the collection. Of the 9 initially subscribed participants, only 2 were able to submit runs in time and we received a total of 18 runs in 3 languages (English, French, and German) out of the 5 offered. The two participants used different IR systems or combination of them, namely Lucene, Terrier, and Cheshire II. C1 [Ferro, Nicola; Harman, Donna] Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. RP Ferro, N (reprint author), Univ Padua, Dept Informat Engn, I-35100 Padua, Italy. EM ferro@dei.unipd.it; donna.harman@nist.gov CR Agirre E, 2009, LECT NOTES COMPUT SC, V5706, P15 Braschler M, 2002, LECT NOTES COMPUT SC, V2406, P9 Braschler M, 2003, LECT NOTES COMPUT SC, V2785, P9 BRASCHLER M, 2004, LNCS, V3237 Cleverdon C. W., 1997, READINGS INFORMATION, P47 Cooper W.S., 1992, 15 ANN INT ACM SIGIR, P198 DINUZIO GM, 2009, CLEF 2009 WORKSH EIBL M, 2009, LNCS, V1 FERRO N, 2008, P 7 NTCIR WORKSH M E, P577 FERRO N, 2009, CIRCO010 IMS U PAD D FERRO N, 2009, POSTPR 5 IT RES C DI, P72 FERRO N, 2008, POSTPR 4 IT RES C DI, P29 FERRO N, 2009, LNCS, V1 LARSON RR, 2010, LNCS, V1 PETERS C, 2010, LNCS, V1 Robertson S.E., 1981, INFORMATION RETRIEVA, P9 Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J, 2002, LECT NOTES COMPUT SC, V2406, P27 Savoy J, 1999, J AM SOC INFORM SCI, V50, P944, DOI 10.1002/(SICI)1097-4571(1999)50:10<944::AID-ASI9>3.3.CO;2-H W3C, 2004, XML SCHEM 1 W3C, 2008, EXT MARK LANG XML 1 World Wide Web Consortium (W3C), 2004, XML SCHEM 2 NR 25 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15753-0 J9 LECT NOTES COMPUT SC PY 2010 VL 6241 BP 552 EP 565 PN I PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BTF32 UT WOS:000286783100068 ER PT S AU Moulin, C Barat, C Lemaitre, C Gery, M Ducottet, C Largeron, C AF Moulin, Christophe Barat, Cecile Lemaitre, Cedric Gery, Mathias Ducottet, Christophe Largeron, Christine BE Peters, C Caputo, B Gonzalez, J Jones, GJF KalpathyCramer, J Muller, H Tsikrika, T TI Combining Text/Image in WikipediaMM Task 2009 SO MULTILINGUAL INFORMATION ACCESS EVALUATION II: MULTIMEDIA EXPERIMENTS, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th Workshop of the Cross-Language Evaluation Forum CY SEP 30-OCT 02, 2009 CL Corfu, GREECE AB This paper reports our multimedia information retrieval experiments carried out for the ImageCLEF Wikipedia task 2009. We extend our previous multimedia model defined as a vector of textual and visual information based on a bag of words approach [6]. We extract additional textual information from the original Wikipedia articles and we compute several image descriptors (local colour and texture features). We show that combining linearly textual and visual information significantly improves the results. C1 [Moulin, Christophe] Univ Lyon, F-69003 Lyon, France. RP Moulin, C (reprint author), Univ Lyon, F-69003 Lyon, France. EM christophe.moulin@univ-st-etienne.fr; cecile.barat@univ-st-etienne.fr; cedric.lemaitre@univ-st-etienne.fr; mathias.gery@univ-st-etienne.fr; ducottet@univ-st-etienne.fr; largeron@univ-st-etienne.fr CR BISGIN H, 2008, PARALLEL CLUSTERING Goodrum A. A., 2000, Informing Science, V3 Jurie F, 2005, IEEE I CONF COMP VIS, P604 Lowe D., 1999, INT C COMP VIS CORF, P1150 Mikolajczyk K, 2005, INT J COMPUT VISION, V65, P43, DOI 10.1007/s11263-005-3848-x Moulin C, 2009, LECT NOTES COMPUT SC, V5706, P779 Robertson S., 1994, TEXT RETRIEVAL C, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TOLLARI S, 2006, IMAG EVAL TORJMEN M, 2009, CLEF, P756 TSIKRIKA T, 2010, LNCS, V6242 Zhai C., 2001, NOTES LEMUR TFIDF MO NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15750-9 J9 LECT NOTES COMPUT SC PY 2010 VL 6242 BP 164 EP 171 PN II PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BTC06 UT WOS:000286416400018 ER PT S AU Pan, SH Wang, L Xu, YC Xia, GP AF Pan, Shouhui Wang, Li Xu, Yingcheng Xia, Guoping BE Wunsch, DC Tan, HH Zeng, DH Luo, Q TI Improved Web Text Classification Method for Classifying Quality Safety Accidents SO NANOTECHNOLOGY AND COMPUTER ENGINEERING SE Advanced Materials Research LA English DT Proceedings Paper CT International Conference on Advances in Computer Science and Engineering CY JUL 20-21, 2010 CL Qingdao, PEOPLES R CHINA SP Intelligent Informat Technol Applicat Res Assoc, Int Ind Elect Ctr, Wuhan Inst Technol DE text classification; web mining; vector space model; quality safety AB Web text classification, as one of the fundamental techniques of web mining, plays an important role in the web mining system. An improved term weighting method is proposed in this paper. Besides term frequency, the location of the term is also considered when calculating the weight of a term. Web pages were divided into 4 text blocks and each text block has its location weight. Experimental result shows that the precision of improved term weighting method is higher than traditional term weighting method. C1 [Pan, Shouhui; Wang, Li; Xu, Yingcheng; Xia, Guoping] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China. RP Pan, SH (reprint author), Beihang Univ, Sch Econ & Management, Beijing, Peoples R China. EM pan1998@163.com CR Apte C, 1998, P C AUT LEARN DISC W Chen JN, 2009, EXPERT SYST APPL, V36, P5432, DOI 10.1016/j.eswa.2008.06.054 de Campos LM, 2009, INT J APPROX REASON, V50, P932, DOI 10.1016/j.ijar.2008.10.006 Hamilton SF, 2003, J PUBLIC ECON, V87, P799, DOI 10.1016/S0047-2727(01)00103-7 Huang YF, 2008, KNOWL-BASED SYST, V21, P62, DOI 10.1016/j.knosys.2006.11.004 Joachims T, 1998, EUR C MACH LEARN ECM, P137 KWON, 2003, INFORM PROCESSING MA, V39, P25 Miao DQ, 2009, EXPERT SYST APPL, V36, P9168, DOI 10.1016/j.eswa.2008.12.026 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar M, 2007, FUZZY SET SYST, V158, P2134, DOI 10.1016/j.fss.2007.04.023 Song QB, 2006, COMPUT IND, V57, P622, DOI 10.1016/j.compind.2005.11.006 TANG YH, IEICE T INFORM SYS D, V90, P1787 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yeh IC, 2009, EXPERT SYST APPL, V36, P11249, DOI 10.1016/j.eswa.2009.02.068 Zhang W, 2008, KNOWL-BASED SYST, V21, P879, DOI 10.1016/j.knosys.2008.03.044 NR 15 TC 0 Z9 0 PU TRANS TECH PUBLICATIONS LTD PI STAFA-ZURICH PA LAUBLSRUTISTR 24, CH-8717 STAFA-ZURICH, SWITZERLAND SN 1022-6680 BN 978-0-87849-251-0 J9 ADV MATER RES-SWITZ PY 2010 VL 121-122 BP 996 EP 1001 DI 10.4028/www.scientific.net/AMR.121-122.996 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Nanoscience & Nanotechnology; Materials Science, Multidisciplinary SC Computer Science; Engineering; Science & Technology - Other Topics; Materials Science GA BVA98 UT WOS:000290925400180 ER PT S AU Keim, DA Oelke, D Rohrdantz, C AF Keim, Daniel A. Oelke, Daniela Rohrdantz, Christian BE Horacek, H Metais, E Munoz, R Wolska, M TI Analyzing Document Collections via Context-Aware Term Extraction SO NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th International Conference on Applications of Natural Language to Information Systems CY JUN 24-26, 2009 CL Saarbrucken, GERMANY ID WEBSOM AB In large collections of documents that are divided into predefined classes, the differences and similarities of those classes are of special interest. This paper presents an approach that is able to automatically extract terms from such document collections which describe what topics discriminate a single class from the others (discriminating terms) and which topics discriminate a subset of the classes against the remaining ones (overlap terms). The importance for real world applications and the effectiveness of our approach are demonstrated by two out of practice examples. In a first application our predefined classes correspond to different scientific conferences. By extracting terms from collections of papers published on these conferences, we determine automatically the topical differences and similarities of the conferences. In our second application task we extract terms out of a collection of product reviews which show what features reviewers commented on. We get these terms by discriminating the product review class against a suitable counter-balance class. Finally, our method is evaluated comparing it to alternative approaches. C1 [Keim, Daniel A.; Oelke, Daniela; Rohrdantz, Christian] Univ Konstanz, D-7750 Constance, Germany. RP Keim, DA (reprint author), Univ Konstanz, D-7750 Constance, Germany. EM daniel.keim@uni-konstanz.de; daniela.oelke@uni-konstanz.de; christian.rohrdantz@uni-konstanz.de CR Azcarraga AP, 2004, IEEE T KNOWL DATA EN, V16, P380 BRUNZEL M, 2007, 12 INT C APPL NAT LA, P427 Collins M., 1999, P JOINT SIGDAT C EMP Drouin P., 2004, P 4 INT C LANG RES E, P79 Feldman R, 1998, LECT NOTES ARTIF INT, V1510, P65 GREENWOOD M, NOUN PHRASE CHUNKER Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 KUHLEN R, 1977, EXPT MORPHOLOGIE INF Lagus K., 1999, P 9 INT C ART NEUR N, P371 LERMAN K, 2009, P N AM ASS COMP LING Matsuo Y, 2003, P 16 INT FLOR AI RES, P392 Mei Q., 2006, P 12 ACM SIGKDD INT, P649, DOI 10.1145/1150402.1150482 Ramshaw L.A., 1995, P 3 ACL WORKSH VER L Riloff E., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEKI Y, 2005, INFORM RETRIEVAL SER, P317 THIEL K, 2007, IEEE INT C SYST MAN, P522 Toutanova K, 2000, PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA, P63 Toutanova K., 2003, P 2003 C N AM CHAPT, P173, DOI DOI 10.3115/1073445.1073478 Velardi Paola, 2001, P WORKSH HUM LANG TE, P1 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 WITSCHEL HF, 2004, CONTENT COMMUNICATIO Zhai C., 2004, P 10 ACM SIGKDD INT, P743, DOI 10.1145/1014052.1014150 ONLINE TOOL TERMINOL STANFORD LOG LINEAR NR 28 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12549-2 J9 LECT NOTES COMPUT SC PY 2010 VL 5723 BP 154 EP 168 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPN95 UT WOS:000279484800013 ER PT J AU Crasso, M Zunino, A Campo, M AF Crasso, Marco Zunino, Alejandro Campo, Marcelo TI Combining Document Classification and Ontology Alignment for Semantically Enriching Web Services SO NEW GENERATION COMPUTING LA English DT Article DE Semantic Web Services; Document Classification; Ontology Alignment ID VECTOR-SPACE MODEL; NATURAL-LANGUAGE; ARCHITECTURE; DISCOVERY AB Semantic Web Services represent the basic blocks for building a network of distributed and heterogeneous applications, without human intervention. Despite the high level of automatism that can be achieved with Semantic Web Services technology, this is not broadly adopted. One factor that hinders the widespread usage of this technology is the effort required to annotate semantically ordinary services. This paper presents AWSA (Automatic Web Service Annotator), an approach for easing the conversion of Web Services into Semantic Web enabled services. The main idea behind AWSA is to annotate Web Services with concepts defined by existing ontologies, which have been used for annotating similar services in the past. This approach combines text preprocessing, document classification and ontology alignment techniques to extract valuable information conveyed in standard service descriptions, reduce the search space and find proper concepts for the service being annotated, respectively. Experimental evaluations show the feasibility of the proposed approach. C1 [Crasso, Marco] UNICEN Univ, ISISTAN Res Inst, Buenos Aires, DF, Argentina. Consejo Nacl Invest Cient & Tecn CONICET, Buenos Aires, DF, Argentina. RP Crasso, M (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,Tandil B7001BBO, Buenos Aires, DF, Argentina. EM mcrasso@conicet.gov.ar RI Zunino, Alejandro/ OI Zunino, Alejandro/0000-0002-9537-3541 FU ANPCyT [PAE-PICT 2007-02311, PAE-PICT 2007-02312] FX We thank Paula Rudenick for helping us manually annotating the corpus of semantic Web Services used for evaluating AWSA. We thank the anonymous reviewers for their helpful comments and suggestions to improve the quality of the paper. We acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311 and PAE-PICT 2007-02312. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bechhofer S., 2004, OWL WEB ONTOLOGY LAN BELHAJJAME K, 2008, ACM TRANS WEB, V2, pNIL6 Bell D, 2007, INFORM SYST FRONT, V9, P69, DOI 10.1007/s10796-006-9018-z Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112 Brickley D., 2004, RDF VOCABULARY DESCR Burstein M, 2005, IEEE INTERNET COMPUT, V9, P72, DOI 10.1109/MIC.2005.96 Crasso M, 2008, INTELIGENCIA ARTIFIC, V37, P25 CRASSO M, 2008, SAC 08 Crasso M, 2008, SCI COMPUT PROGRAM, V71, P144, DOI 10.1016/j.scico.2008.02.002 Cristianini N., 2000, INTRO SUPPORT VECTOR DAGA A, 2005, HICSS 05 Doan A, 2003, VLDB J, V12, P303, DOI 10.1007/S00778-003-0104-2 DONG X, 2004, P 13 INT C VER LARG DUO Z, 2005, SERVICE ORIENTED SYS Ehrig M., 2005, INTEGRATING ONTOLOGI, V156 EHRIG M, 2004, LNCS, V3298 Ehrig M., 2006, ONTOLOGY ALIGNMENT B Erickson J, 2008, J DATABASE MANAGE, V19, P42, DOI 10.4018/jdm.2008070103 Fallside D. C., 2004, XML SCHEMA PART 0 PR FERDINAND M, 2004, LNCS, V3140 Foster Ian, 2004, ELSEVIER SERIES GRID Garofalakis J, 2006, J WEB ENG, V5, P265 Gomez-Perez A., 2003, ONTOLOGICAL ENG Hepp M, 2006, IEEE INTERNET COMPUT, V10, P85, DOI 10.1109/MIC.2006.42 HESS A, 2003, LNCS, V2870 HESS A, 2004, LNCS, V3298 Iannone L, 2007, APPL INTELL, V26, P139, DOI 10.1007/s10489-006-0011-5 Kay M., 2007, XSL TRANSFORMATIONS Korfhage R., 1997, INFORM STORAGE RETRI Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 LERMAN K, 2006, AAAI LEWIS D, 1998, LNCS, V1398 Manola F., 2004, RDF PRIMER Martin D, 2007, WORLD WIDE WEB, V10, P243, DOI 10.1007/s11280-007-0033-x Maruyama H, 2002, NEW GENERAT COMPUT, V20, P125 MATEOS C, 2006, LNCS, V4140 McCooI R., 2006, IEEE Internet Computing, V10, DOI 10.1109/MIC.2006.18 McCool R., 2005, IEEE INTERNET COMPUT, V9, P86 Mizoguchi R, 2004, NEW GENERAT COMPUT, V22, P61 NOY NF, 2000, 12 C INN APPL ART IN OLDHAM N, 2004, LNCS, V3387 Paar A, 2009, APPL INTELL, V30, P37, DOI 10.1007/s10489-007-0075-x Pan JZ, 2007, IEEE T KNOWL DATA EN, V19, P192, DOI 10.1109/TKDE.2007.37 Patil A., 2004, WWW 04 REIF G, 2005, 14 INT WORLD WID WEB Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Rocchio J., 1971, SMART RETRIEVAL SYST Sabou M, 2005, J WEB SEMANT, V3, P340, DOI 10.1016/j.websem.2005.09.008 Sabou M, 2007, J WEB SEMANT, V5, P142, DOI 10.1016/j.websem.2006.11.004 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] Sivashanmugam K., 2003, 2003 INT C WEB SERV van Rijsbergen C. J., 1979, INFORM RETRIEVAL WANG JZ, 2005, WEB INTELLIGENCE Witte R, 2008, IET SOFTW, V2, P3, DOI 10.1049/iet-sen:20070110 Witten I. H., 2005, M KAUFMANN SERIES DA NR 57 TC 4 Z9 4 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 0288-3635 J9 NEW GENERAT COMPUT JI New Gener. Comput. PY 2010 VL 28 IS 4 BP 371 EP 403 DI 10.1007/s00354-009-0094-8 PG 33 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA 681DE UT WOS:000284289400003 ER PT S AU Fuehres, H Fischbach, K Gloor, PA Krauss, J Nann, S AF Fuehres, Hauke Fischbach, Kai Gloor, Peter A. Krauss, Jonas Nann, Stefan BE Bastiaens, TJ Baumol, U Kramer, BJ TI Adding Taxonomies Obtained by Content Clustering to Semantic Social Network Analysis SO ON COLLECTIVE INTELLIGENCE SE Advances in Intelligent and Soft Computing LA English DT Proceedings Paper CT 1st Symposium on Collective Intelligence CY APR 15-16, 2010 CL Hagen, GERMANY SP Fern Univ, Fac Cultural & Social Sci, Fern Univ, Fac Econom, Fern Univ, Fac Math & Comp Sci ID SIMILARITY AB This paper introduces a novel method to analyze the content of communication in social networks. Content clustering methods are used to extract a taxonomy of concepts from each analyzed communication archive. Those taxonomies are hierarchical categorizations of the concepts discussed in the analyzed communication archives. Concepts are based on terms extracted from the communication's content. The resulting taxonomy provides insights into the communication not possible through conventional social network analysis. C1 [Fuehres, Hauke; Gloor, Peter A.; Krauss, Jonas; Nann, Stefan] MIT, Ctr Collect Intelligence, Cambridge, MA 02139 USA. RP Fuehres, H (reprint author), MIT, Ctr Collect Intelligence, 77 Massachusetts Ave, Cambridge, MA 02139 USA. CR Borgatti SP, 2003, J MANAGE, V29, P991, DOI 10.1016/S0149-2063(03)00087-4 boyd danah, 2007, J COMPUT-MEDIAT COMM, V13 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Cai L., 2004, CIKM 04, P78 CARLEY KM, 2003, DYNAMIC SOCIAL NETWO, P361 CUTTING DR, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P318 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eades P., 1984, C NUMERANTIUM, V42, P149 FREEMAN L, 2000, ENCY PSYCHOL, V6, P350 FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102 Gloor P., 2007, COOLHUNTING CHASING Gloor P., 2007, P AMCIS AM C INF SYS Gloor PA, 2006, Information Visualization, P130 GLOOR PA, 2004, CSCW 2004 WORKSH SOC GLOOR PA, 2005, IV 2005, P939 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Klovdahl AS, 2005, SOC NETWORKS, V27, P119, DOI 10.1016/j.socnet.2005.01.006 KORFIATIS N, 2007, SOCIAL NETWORK MODEL, P109 Kowalski G. J., 2000, INFORM STORAGE RETRI KRACKHARDT D, 1994, INTRAORGANIZATIONAL, P209 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Lin D., 1998, ICML 98, P296 Manning C.D., 2008, INTRO INFORM RETRIEV RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHONHOFEN P., 2006, WI 2006, P456 Scott J., 2000, SOCIAL NETWORK ANAL, V2nd Sokal R. R., 1973, NUMERICAL TAXONOMY P STEIN B, 2003, P ART INT APPL C Vapnik V. N., 2000, NATURE STAT LEARNING NR 31 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1867-5662 BN 978-3-642-14480-6 J9 ADV INTEL SOFT COMPU PY 2010 VL 76 BP 135 EP 146 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BUH10 UT WOS:000289266400011 ER PT S AU Chan, NN Gaaloul, W Tata, S AF Nguyen Ngoc Chan Gaaloul, Walid Tata, Samir BE Meersman, R Dillon, T Herrero, P TI Collaborative Filtering Technique for Web Service Recommendation Based on User-Operation Combination SO ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2010, PT I SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT On the Move Confederated International Conference CY OCT 25-29, 2010 CL Hersonissos, GREECE SP Vrije Univ Brussel, Curtin Univ Technol, Universidad Politecnica Madrid, Object Management Grp, Collibra DE Web Service; Recommender System; Vector Space Model ID LATENT SEMANTIC ANALYSIS; DISCOVERY; SYSTEMS AB The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we present our approach in which we take into account historical usage data instead of the text based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement three algorithms based on Vector Space Model to validate our approach. We also provide evaluation methods based on the precision, recall and root mean square error in order to compare and assert the efficiency of our algorithms. C1 [Nguyen Ngoc Chan; Gaaloul, Walid; Tata, Samir] Inst TELECOM, CNRS Samovar, UMR 5157, TELECOM SudParis, Paris, France. RP Chan, NN (reprint author), Inst TELECOM, CNRS Samovar, UMR 5157, TELECOM SudParis, Paris, France. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Birukou A, 2007, IEEE SOFTWARE, V24, P47, DOI 10.1109/MS.2007.169 Blake MB, 2007, ICWS, P575, DOI 10.1109/ICWS.2007.28 DANIEL B, 2006, LNCS, V4011 Dong X., 2004, VLDB, P372 Ferris C, 2003, COMMUN ACM, V46, P31, DOI 10.1145/777313.777335 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 JAMES B, 2007, KDDCUP 2007 Kokash N, 2007, LECT NOTES COMPUT SC, V4439, P95 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Ma J., 2008, IEEE INT C WEB SERV, P740 Manikrao US, 2005, NWESP 2005, P117 Manning C.D., 2008, INTRO INFORM RETRIEV Paliwal A. V., 2007, IEEE INT C SERV COMP, P106, DOI 10.1109/SCC.2007.131 Paolucci M, 2002, LECT NOTES COMPUT SC, V2342, P333 PATEREK A, 2007, KDDCUP 2007 PLATZER C, 2005, 3 IEEE EUR C WEB SER, P9 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang ZQ, 2007, ALPIT 2007: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON ADVANCED LANGUAGE PROCESSING AND WEB INFORMATION TECHNOLOGY, P429 Wu C, 2008, LECT NOTES COMPUT SC, V4891, P346 Wu C.-T., 2007, IEEE INT C IND ENG E, P228 Wu SF, 2009, 2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, P414, DOI 10.1109/IUCE.2009.74 ZHENG ZB, 2009, 2009 IEEE INT C WEB, P437 NR 26 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-16933-5 J9 LECT NOTES COMPUT SC PY 2010 VL 6426 BP 222 EP 239 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BUI45 UT WOS:000289454800017 ER PT J AU Liu, CC Chiang, SHF Chou, CY Chen, SY AF Liu, Chen-Chung Chiang, Shih-Hsun Fan Chou, Chih-Yueh Chen, Sherry Y. TI Knowledge exploration with concept association techniques SO ONLINE INFORMATION REVIEW LA English DT Article DE Worldwide web; Learning; Knowledge mining ID WORLD-WIDE-WEB; RECOMMENDATION; PATTERNS; INFORMATION; HYPERTEXT; SYSTEM; ORDER AB Purpose - Exploratory learning is regarded as an important ability for developing knowledge from open environments. During the exploration, learners not only need to acquire new information based on their current interests, but also they need to form new perspectives by incorporating new knowledge into their previous knowledge. This paper seeks to address these issues. Design/methodology/approach - To this end, this paper proposes an approach that includes a concept association bank to recommend related concepts in a domain based on the goal of an exploration. By doing so, learners' knowledge can be expanded beyond their current understanding. An experiment was conducted to investigate how the proposed approach facilitated the learners' exploration. Findings - The results indicated that the concept association bank is a useful mechanism to help learners gain new understanding, including providing exploration directions, reducing complexity and cognitive load, facilitating data- and goal-driven exploration strategies, and commenting on new understanding. The implications of these results are discussed. Originality/value - Current recommendation systems emphasise a data-driven strategy, which seeks isolated pieces of information, instead of suggesting directions related to their exploration goal. The problem with such an approach is that learners' exploration will be limited by their existing knowledge. Thus, this paper presents an approach to support both data- and goal-driven strategies. C1 [Liu, Chen-Chung; Chiang, Shih-Hsun Fan; Chen, Sherry Y.] Natl Cent Univ, Grad Inst Network Learning Technol, Jhongli, Taiwan. [Chou, Chih-Yueh] Yuan Ze Univ, Dept Comp Sci & Engn, Jhongli, Taiwan. RP Liu, CC (reprint author), Natl Cent Univ, Grad Inst Network Learning Technol, Jhongli, Taiwan. EM ccliu@cl.ncu.edu.tw RI Liu, Chen-Chung/B-2793-2012 FU National Science Foundation [98-2511-S-008-004-MY3, 98-2631-S-008-001, 96-2524-S-008-002] FX The authors appreciate the work of their anonymous reviewers in suggesting revisions for this paper. This research was partially funded by the National Science Foundation under 98-2511-S-008-004-MY3, 98-2631-S-008-001, and 96-2524-S-008-002. CR Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714 Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 Bigus J.P., 1996, DATA MINING NEURAL N BILLSUS D, 2000, P C INT US INT, P33, DOI 10.1145/325737.325768 Buzan T, 1994, MIND MAP BOOK USE RA Chen H. C., 2001, P ACM C INF KNOWL MA, P231 Conklin Jeff, 1987, IEEE COMPUT, V20, P17 Ezeife CI, 2005, DATA MIN KNOWL DISC, V10, P5, DOI 10.1007/s10618-005-0248-3 Good N., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Hill JR, 1997, ETR&D-EDUC TECH RES, V45, P37, DOI 10.1007/BF02299682 Hohl H, 1996, USER MODEL USER-ADAP, V6, P131, DOI 10.1007/BF00143965 Hsu MH, 2008, EXPERT SYST APPL, V34, P683, DOI 10.1016/j.eswa.2006.10.004 HUBSCHER R, 2001, P 10 C ART INT ED SA, P13 Joachims T, 1997, INT JOINT CONF ARTIF, P770 Jonassen D. H., 1990, DESIGNING HYPERMEDIA Konstan J. A., 2006, ACM C HUM FACT COMP, P1097 Land SM, 2000, ETR&D-EDUC TECH RES, V48, P45, DOI 10.1007/BF02313485 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 Liu CC, 2005, J INF SCI ENG, V21, P873 Madrid RI, 2009, COMPUT HUM BEHAV, V25, P66, DOI 10.1016/j.chb.2008.06.005 Miller B. N., 2003, P 8 INT C INT US INT, P263, DOI DOI 10.1145/604045.604094 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Ouyang Y., 2008, ONLINE INFORM REV, V32, P254, DOI 10.1108/14684520810879863 Rieman J., 1996, ACM T COMPUT-HUM INT, V3, P189, DOI 10.1145/234526.234527 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shneiderman B., 2000, ACM Transactions on Computer-Human Interaction, V7, DOI 10.1145/344949.345077 Siemens G., 2005, INT J INSTRUCTIONAL, V2, P3 Song CR, 2003, ONLINE INFORM REV, V27, P196, DOI 10.1108/14684520310481391 Svensson M, 2000, P IUI 2000, P260, DOI 10.1145/325737.325866 Tang T. Y., 2008, THESIS U SASKATCHEWA Torres R., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996402 Woodruff A., 2000, P SIGCHI C HUM FACT, P153, DOI 10.1145/332040.332419 Yamauchi Y., 2000, P 2000 ACM C COMP SU, P329, DOI 10.1145/358916.359004 NR 37 TC 7 Z9 7 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 1468-4527 J9 ONLINE INFORM REV JI Online Inf. Rev. PY 2010 VL 34 IS 5 BP 786 EP 805 DI 10.1108/14684521011084627 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 687AN UT WOS:000284746100007 ER PT B AU Vu, QH Lupu, M Ooi, BC AF Vu, QH Lupu, M Ooi, BC TI Peer-to-Peer Computing: Principles and Applications SO PEER-TO-PEER COMPUTING: PRINCIPLES AND APPLICATIONS LA English DT Book ID OF-SERVICE ATTACKS; TO-PEER SYSTEMS; INFORMATION-RETRIEVAL; CONTEXT INTERCHANGE; VECTOR-SPACES; SEARCH; TRUST; TREES CR A Vlachou, 2007, P 23 IEEE INT C DAT ABERER K, 2001, P 10 ACM INT C INF K ABERER K., 2006, P P2PIR, P41, DOI 10.1145/1183579.1183588 ABERER K, 2001, P 9 INT C COOP INF S ABERER K, 2003, P 12 WORLD WID WEB C Adamic L. A., 2001, PHYS REV, V64 Adar E., 2000, 1 MONDAY, V5 Agarwal DA, 2001, IEEE SYMP COMP COMMU, P22, DOI 10.1109/ISCC.2001.935350 Albert R, 2000, NATURE, V406, P378, DOI 10.1038/35019019 ALTINEL M, 2000, P 26INT C VER LARG D Anagnostakis K. G., 2004, Proceedings. The 2nd IEEE International Conference on Distributed Computing Systems ANDERSON D, 2001, PEER TO PEER HARNESS, P67 Andrzejak A., 2002, Proceedings Second International Conference on Peer-to-Peer Computing (P2P 2002), DOI 10.1109/PTP.2002.1046310 [Anonymous], NETWORK SIMULATOR NS [Anonymous], GNUTELLA PROTOCOL SP ARENAS M, 2003, SIGMOD REC, V32 Aspnes J, 2003, SIAM PROC S, P384 Axelrod R., 1984, EVOLUTION COOPERATIO Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAKOS B, 2006, P IWUC Balakrishnan H., 2003, Communications of the ACM, V46, DOI 10.1145/606272.606299 BALBIN I, 1987, J LOG PROGRAM, V4 Barber B., 1983, LOGIC LIMITS TRUST BATTEN C, 2001, MITLCSTM632 Bawa M., 2005, P 14 INT C WORLD WID, P651, DOI 10.1145/1060745.1060840 BEDELL D, 1999, DALLAS MORNING N DEC BENDER M, 2005, P GI FACHT DAT BUS T BENTLEY JL, 1975, COMMUN ACM, V18, P509, DOI 10.1145/361002.361007 BERCHTOLD S, 1996, P 22 INT C VER LARG Berchtold S., 1998, P ACM SIGMOD INT C M, P142, DOI 10.1145/276304.276318 BERKET K, 2004, P 4 IEEE INT C PEER Bernstein P. A., 2002, P 5 INT WORKSH WEB D Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Bertino E, 2004, IEEE T KNOWL DATA EN, V16, P827, DOI 10.1109/TKDE.2004.1318565 BHARAMBE AR, 2004, P ACM SIGCOMM C BLAZE M, 1996, P IEEE S SEC PRIV S Blaze M., 1999, RFC2704 IETF BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Boag S, 2002, XQUERY 1 0 XML QUERY Bolosky WJ, 2000, PERF E R SI, V28, P34, DOI 10.1145/339331.339345 BRODER A, 2002, ALLERTON BURLEIGH S, 2002, P INT ASTR C BYERS J, 2003, P 2 INT WORKSH PEER Byers J. W., 1998, P ACM SIGCOMM 98 VAN, P56, DOI 10.1145/285237.285258 CACERES R, 1999, P 2 WORKSH INT SERV CAI M, 2003, P 2 INT WORKSH PEER CARNEY D, 2002, P 28 INT C VER LARG CARROLL D, 2005, DISTRIBUTING SCI CASTRO M, 2002, P 5 USENIX S OP SYST Cates J., 2003, THESIS MIT *CCITT, 1993, DIRECTORY AUTH FRAM Cerf V., 2007, 4838 IETF RFC CERI S, 1990, SURVEYS COMPUTER SCI CHAN C, 2002, P 18 IEEE INT C DAT CHANDRASEKARAN S, 2003, P 1 C INN DAT SYST R CHAUM D, 1982, COMMUN ACM, V4 Chen J., 2000, P ACM SIGMOD INT C M Chen Y, 2002, LECT NOTES COMPUT SC, V2429, P306 CHERNIACK M, 2003, P 1 C INN DAT SYST R CHESHIRE S, DNS BASED SERVICE DI Chu YH, 1997, COMPUT NETWORKS ISDN, V29, P953, DOI 10.1016/S0169-7552(97)00009-3 CHUN B, 2003, ACM SIGCOMM COMPUT C, V33 Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426 CLARK I, 2000, P WORKSH DES ISS AN Clarke I, 2002, IEEE INTERNET COMPUT, V6, P40, DOI 10.1109/4236.978368 Cohen E., 2002, P 2002 C APPL TECHN, P177 CONDIE T, 2004, P 4 IEEE INT C PEER COOPER B, 2002, PROTECTING PIPE MALI Cornelli F., 2002, P 11 WORLD WID WEB C COX L. P., 2003, P 19 ACM S OP SYST P Cox L.P., 2002, P 5 USENIX S OP SYST Crainiceanu A, 2004, P 7 INT WORKSH WEB D, P25, DOI 10.1145/1017074.1017082 Crainiceanu Adina, 2007, P ACM SIGMOD INT C M Crespo A, 2002, P 22 INT C DISTR COM Crespo A., 2003, SEMANTIC OVERLAY NET Cuenca-acuna F., 2003, P 12 IEEE INT S HIGH Damiani E., 2002, P 9 ACM C COMP COMM Daswani N., 2002, P 9 ACM C COMP COMM, P181 DATTA A, 2005, P 3 INT WORKSH DAT I Datta A.M.H., 2003, P 23 INT C DISTR COM Daubechies I, 1992, 10 LECT WAVELETS DAWEI J, 2009, P INT C VER LARG DAT Dean M., 2002, OWL WEB ONTOLOGY LAN DeCandia G, 2007, P 21 ACM SIGOPS S OP, P205, DOI 10.1145/1294261.1294281 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Deutsch M, 1962, NEBR S MOT Deutsch M., 1973, RESOLUTION CONFLICT Devanbu P., 2003, Journal of Computer Security, V11 DIAO Y, 2002, P 18 IEEE INT C DAT DIGITAL P, 2003, MAGIC MIRROR BACKUP DINGLEDLINE R, 2001, PEER TO PEER HARNESS, P159 DJILALI S, 2003, P 3 IEEE ACM INT S C, P406 Djilali Samir, 2004, P 2004 ACM IEEE C SU, P39, DOI 10.1109/SC.2004.51 Douceur JR, 2002, LECT NOTES COMPUT SC, V2429, P251 Doulkeridis Christos, 2007, P 33 INT C VER LARG DRAGOVIC B, 2003, P 2 INT WORKSH TRUST Druschel P., 2001, Proceedings Eighth Workshop on Hot Topics in Operating Systems, DOI 10.1109/HOTOS.2001.990064 Druschel P., 2001, P IFIP ACM INT C DIS, P329 Du W., 2004, Proceedings. The 2nd IEEE International Conference on Distributed Computing Systems EVANS H, 2002, P WORKSH WEB ENG PEE, P289 Feigenbaum J, 2001, J COMPUT SYST SCI, V63, P21, DOI 10.1006/jcss.2001.1754 Feigenbaum J., 2002, P 21 S PRINC DISTR C, P173 Feldman M., 2003, P 1 WORKSH EC PEER T Foster I., 2003, P 2 INT WORKSH PEER Foster I, 1998, GRID BLUEPRINT NEW C Frank Dabek M, 2001, P 18 ACM S OP SYST P Freedman M., 2002, P 9 ACM C COMP COMM, P193 Gambetta D., 1990, TRUST Ganesan P., 2004, P 30 INT C VER LARG Ganesan P., 2004, P 24 INT C DISTR COM, P263 Garber L, 2000, COMPUTER, V33, P12, DOI 10.1109/MC.2000.839316 GE L, 2006, P 17 INT C DAT EXP S GEDIK B, 2003, P 23 INT C DISTR COM Ghodsi A., 2005, P 3 INT WORKSH DAT I Giakkoupis G., 2005, P 24 ACM S PRINC DIS GODFREY PB, 2005, P 24 ANN JOINT C IEE Goh CH, 1999, ACM T INFORM SYST, V17, P270, DOI 10.1145/314516.314520 GREEN TJ, 2003, P ICDT 2003 GRIBBLE S, 2001, P 4 INT WORKSH WEB D Guha S., 2006, P 5 INT WORKSH PEER GUPTA A, 2003, P 1 C INN DAT SYST R GUPTA AK, 2003, P 22 ACM S PRINC DAT GUPTA AK, 2002, P 5 INT WORKSH WEB D GUPTA AK, 2003, P ACM SIGMOD INT C M Guttman A, 1984, P ACM SIGMOD INT C M, P47 HALEVY A, 2003, P 1 C INN DAT SYST R HALEVY A, 2003, P 19 IEEE INT C DAT HALEVY A, 2003, P 12 WORLD WID WEB C HAND S, 2003, P 6 IEEE C OP ARCH N HANSON EN, 1999, P 15 IEEE INT C DAT Hardin Garrett, 1968, SCIENCE, V162, p[1243, 1248] Harren M., 2002, P 1 INT WORKSH PEER Harvey N. J. A., 2003, P USENIX S INT TECHN HINRICHS K, 1983, P 9 INT WORKSH GRAPH *HIVECACHE, 2003, SWARMB PEER TO PEER Housley R., 1999, 2459 RFC HUEBSCH R, 2003, P 29 INT C VER LARG *IETF RFC, 2246 IETF RFC Indyk P., 1998, P 30 ANN ACM S THEOR INGRAM M, 2000, ONLINE MUSIC SWAP FI IYER S, 2002, P 2U ACM S PRINC DIS Jagadish H., 2005, P 31 INT C VER LARG, P661 Jagadish HV, 2005, ACM T DATABASE SYST, V30, P364, DOI 10.1145/1071610.1071612 JAGADISH HV, 2006, P 22 IEEE INT C DAT *JOOST, JOOST P2P TV KALNIS P, 2002, P ACM SIGMOD INT C M KALOGERAKI V, 2002, P 11 ACM INT C INF K Kamvar S., 2003, P 12 WORLD WID WEB C KAN G, 2001, PEER PEER HARNESSING, P94 KARGER D, 2004, P 3 INT WORKSH PEER Karger D., 2001, P 2001 ACM SIGCOMM C, P149 KARGER DR, 2003, P 2 INT WORKSH PEER KATCHAOUNOV T, 2003, P 8 INT C DAT SYST A KATO D, 2002, P 2 IEEE INT C PEER KEMENTSIETSIDIS A, 2004, P 30 INT C VER LARG KEMENTSIETSIDIS A., 2003, P ACM SIGMOD INT C M KEMENTSIETSIDIS A, 2003, P 19 IEEE INT C DAT KENTHAPADI K, 2004, P ACM S PAR ALG ARCH Keyani P, 2002, LECT NOTES COMPUT SC, V2376, P306 KILLIAN C, 2007, P PLDI C KOWALSKI RA, 1988, COMMUN ACM, V31, P38, DOI 10.1145/35043.35046 Kubiatowicz J., 2000, P 9 INT C ARCH SUPP, P190, DOI 10.1145/378993.379239 KUMAR V, 2006, P 17 INT C DAT EXP S, P703 Landers M., 2004, Proceedings. Fourth International Conference on Peer-to-Peer Computing, DOI 10.1109/PTP.2004.1334933 LANGLEY A, 2001, PEER TO PEER HARNESS, P123 Lee J, 2004, P 2 INT WORKSH DAT I, P215 Lee S., 2003, P 22 ANN JOINT C IEE LI M, 2006, P 14 INT C NETW PROT Li X., 2006, HDB THEORETICAL ALGO, P613 LIAU CY, 2004, P 2 INT WORKSH DAT I, P78 Lillibridge M., 2003, P USENIX ANN TECHN C, P29 LING B, 2002, P IEEE C WEB INF SYS, P279 Liu L., 1999, IEEE T KNOWL DATA EN Loo B. T., 2004, P 3 INT WORKSH PEER LOO BT, 2006, THESIS U CALIFORNIA Lu Jie, 2003, P 12 INT C INF KNOWL, P199 LUDWIG S, JINGLE XMPP EXTENSIO Luhmann N., 1979, TRUST POWER Lupu M, 2008, LECT NOTES COMPUT SC, V5005, P77 LUPU M, 2007, P 23 IEEE INT C DAT LUPU M, 2006, P ICDE, P133 LUPU M, 2008, P ACM SIGMOD INT C M Lv Q., 2002, P 16 INT C SUP, P84, DOI 10.1145/514191.514206 Madden S., 2002, P ACM SIGMOD INT C M Madhavan J., 2003, P 29 INT C VER LARG Malkin D., 2002, P 21 ACM S PRINC DIS MANBER U, 1994, PROCEEDINGS OF THE WINTER 1994 USENIX CONFERENCE, P1 MANKU GS, 2004, P 23 ACM S PRINC DIS Marsh S. P., 1994, THESIS U STIRLING ST MARTINIAN E, 2003, DISTRIBUTED INTERNET Maymounkov P., 2002, P 1 INT WORKSH PEER *MCAFEE, MCAFEE RUM TECHN Merkle R. C., 1989, P CRYPTO, P218 *MICR, 2008, PEER NAM RES PROT PN MILLER J, 2001, PEER TO PEER HARNESS, P77 Mitzenmacher M, 2001, IEEE T PARALL DISTR, V12, P1094, DOI 10.1109/71.963420 MIZRAK AT, 2003, P 3 IEEE WORKSH INT MONDAL A, 2004, P EDBT WORKSH P2P DA *NAPST, 2000, NAPST FIL SYST *NAT I STAND TECHN, 1995, FIPS PUBL, V1801 NEEDHAM RM, 1994, COMMUN ACM, V37, P42, DOI 10.1145/188280.188294 Nejdl W, 2003, SIGMOD RECORD, V32, P41 NEJDL W, 2002, P 11 WORLD WID WEB C Neumann PG, 2000, COMMUN ACM, V43, P136, DOI 10.1145/332051.332797 NG CH, 2002, P 11 WORLD WID WEB C NG WS, 2003, P 12 WORLD WID WEB C NG WS, 2002, EFFICIENT PERVASIVE NG WS, 2003, P 19 IEEE INT C DAT Ng WS, 2002, PROC INT CONF DATA, P272 Ngan T., 2003, P 2 INT WORKSH PEER NOTTINGHAM M, 2003, CACHING TUTORIAL WEB Novak D., 2006, P 1 INT C SCAL INF S Nowak MA, 1998, NATURE, V393, P573, DOI 10.1038/31225 ODONNELL C, 2004, P 4 IEEE INT C PEER OLIVEIRA L, 2002, P WORKSH WEB ENG PEE, P274 Ooi B. C., 2000, PODS, P166, DOI 10.1145/335168.335219 ORAM A, 2003, GNUTELLA FREENET REP Oram A., 2001, PEER TO PEER HARNESS OTT J, 2005, TRTZI050701 HELS U T PANG H, 2004, P 20 IEEE C DAT ENG, P572 PANG X, 2003, P 8 INT C DAT SYST A Papadimos V, 2002, INFORM SOFTWARE TECH, V44, P197, DOI 10.1016/S0950-5849(02)00009-5 PAPADIMOS V, 2003, P 1 C INN DAT SYST R Papazoglou M, 2007, WEB SERVICES PRINCIP Pfitzmann A., 2001, ANONYMITY UNOBSERVAB PITKANEN M, 2008, PERVASIVE MOBILE COM Plaxton C. G., 1997, P 9 ANN ACM S PAR AL, P311, DOI DOI 10.1145/258492.258523 Pless Vera, 1998, INTRO THEORY ERROR C PUGH W, 1990, COMMUN ACM, V33, P668, DOI 10.1145/78973.78977 Pujol J.M., 2002, P 1 INT JOINT C AUT Qu C., 2004, P 2 INT S PAR DISTR Ramasubramanian V., 2004, P SIGCOMM, P331, DOI 10.1145/1015467.1015504 RAO A, 2003, P 2 INT WORKSH PEER RATAJCZAK D, 2003, IRBTR04042 Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] REED D, 1999, P 7 WORKSH HOT TOP O RENADA ME, 2004, P CIKM, P562 REYNOLDS P, 2003, P INT MIDDL C JUN, P21 RHEA SC, 2002, P 21 ANN JOINT C IEE, P21 Rivest R., 1992, 1321 RFC RODRIGUEZ A, 2004, P NDSI S RODRIGUEZGIANOL.P, 2005, P 31 INT C VER LARG ROUSSOPOULOS M, 2001, P 2003 USENIX ANN TE Rowstron A., 2001, P 18 ACM S OP SYST P, P188 SABATER J, 2001, P 4 WORKSH DEC FRAUD Sagan H, 1994, SPACE FILLING CURVES Sahin O. D., 2004, P DBISP2P, P61 Sahin O.D., 2005, P 13 ANN ACM INT C M, P946, DOI 10.1145/1101149.1101349 SAHIN OD, 2004, P 20 IEEE INT C DAT SAINTANDRE P, SERVERLESS MESSAGING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saroiu S., 2002, P 9 MULT COMP NETW M Schlosser M., 2002, P INT WORKSH AG PEER, P112 SCHLOSSER M, 2002, P 2 IEEE INT C PEER Schmidt C., 2003, P 12 INT S HIGH PERF *SHARM NETW LTD, 2001, KAZ MED DESKT Shen HT, 2004, IEEE T KNOWL DATA EN, V16, P813 SHIRKY C, 2001, PEER TO PEER HARNESS SHIRKY C, WHAT IS P2P WHAT ISN SHU Y, 2004, P DBISP2P WORKSH, P122 SHUDO K, 2008, COMPUT COMMUN, V31 SINGLA A, 2002, ULTRAPEERS STEP GNUT SIT E, 2003, P 1 IRIS STUD WORKSH Sit E., 2002, P 1 INT WORKSH PEER SIT E, 2006, P 3 INT WORKSH PEER Slee M., 2007, THRIFT SCALABLE CROS Snoeren A. C., 2001, P 18 ACM S OP SYST P Sripanidkulchai K, 2003, P 22 ANN JOINT C IEE SRIVATSA M, 2005, P 1J WORLD WID WEB C STAKHANOVA N, 2005, P 25 INT C DISTR COM STEVENS WR, 1998, UNIX NETWORK POGRAMM Stribling J, 2005, LECT NOTES COMPUT SC, V3640, P69 Suel T, 2003, P 6 INT WORKSH WEB D SUN Q, 2004, P 24 INT C DISTR COM SURYANARAYANA G, 2004, SURVEY TRUST MANAGEM Tanenbaum A.S., 2001, MODERN OPERATING SYS Tang C., 2004, P 1 S NETW SYST DES, P211 Tang C., 2003, P 2003 C APPL TECHN, P175 TANG C, 2002, P 1 WORKSH HOT TOP N TATARINOV I., 2004, P ACM SIGMOD INT C M Thompson M. R., 2003, ACM Transactions on Information and Systems Security, V6, DOI 10.1145/950191.950196 TOK WH, 2002, P 8 INT C EXT DAT TE Traversat B., 2003, PROJECT JXTA LOOSELY TRIANTAFILLOW P, 2003, P 1 C INN DAT SYST R Tridgell A., 1999, THESIS AUSTR NAT U TRYFONOPOULOS C, 2005, P 9 EUR C RES ADV TE, P25 Tsoumakos D., 2003, P 2 INT WORKSH PEER *VERISIGN, 2007, ADDR EFF ENT COMM CH Vu QH, 2008, P ACM SIGMOD INT C M *W3C, 2007, SIMPL OBJ ACC PROT R WALDMAN M, 2001, PEER TO PEER HARNESS, P145 Wallach D. S., 2002, P INT S SOFTW SEC TO WANG J, 1999, COMPUT COMMUN, V29, P36 WANG S, 2007, P 23 IEEE INT C DAT WANG X, 2002, P PEER PEER COMP WOR, P247 Wang Y., 2005, P 2005 IEEE INT C SE, P24 Wang YZ, 2006, P IEEE INT C SENS NE, P302 Weatherspoon H., 2002, P 1 INT WORKSH PEER WEATHERSPOON H, 2003, P INT WORKSH FUT DIR, P142 WIERZBICKI A, 2004, P 4 INT WORKSH GLOB WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 WU S, 2009, P INT C VER LARG DAT WU S, 2008, P ACM SIGMOD INT C M Xiong L, 2004, IEEE T KNOWL DATA EN, V16, P843 Yang B., 2002, P 22 INT C DISTR COM YANG B, 2001, P 21 INT C DISTR COM YANG B, 2003, P 19 IEEE INT C DAT Yang Beverly, 2001, P 27 INT C VER LARG YIANILOS PN, 1993, P 4 ANN ACM SIAM S D YU B, 2007, P ACM SIGMOD INT C M YUAN MJ, 2002, MOBILE P2P MESSAGING ZHANG C., 2006, P EDBT, P112 Zhang C., 2004, TR70304 PRINC U, VTR-703-04 Zhao Ben Y., 2001, UCBCSD011141 ZHOU R, 2006, IEEE T PARALL DISTR, V29, P953 ZHOU S, 2006, P ICDE, P161 Zimmermann P., 1994, PGP USERS GUIDE NR 317 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-3-642-03513-5 PY 2010 BP 1 EP 317 DI 10.1007/978-3-642-03514-2 PG 317 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BML26 UT WOS:000272717700011 ER PT B AU de Gemmis, M Iaquinta, L Lops, P Musto, C Narducci, F Semeraro, G AF de Gemmis, Marco Iaquinta, Leo Lops, Pasquale Musto, Cataldo Narducci, Fedelucio Semeraro, Giovanni BE Furnkranz, J Hullermeier, E TI Learning Preference Models in Recommender Systems SO PREFERENCE LEARNING LA English DT Article; Book Chapter ID USER PROFILES; SEARCH AB As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of nonrelevant content according to the information stored in a user profile, recommendation algorithms process information about user interests acquired in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way and exploit these data to generate a list of recommended items. Although each type of filtering method has its own weaknesses and strengths, preference handling is one of the core issues in the design of every recommender system: since these systems aim to guide users in a personalized way to interesting or useful objects in a large space of possible options, it is important for them to accurately capture and model user preferences. The goal of this chapter is to provide a general overview of the approaches to learning preference models in the context of recommender systems. In the first part, we introduce general concepts and terminology of recommender systems, giving a brief analysis of advantages and drawbacks for each filtering approach. Then we will deal with the issue of learning preference models, show the most popular techniques for profile learning and preference elicitation, and analyze methods for feedback gathering in recommender systems. C1 [de Gemmis, Marco; Iaquinta, Leo; Lops, Pasquale; Musto, Cataldo; Narducci, Fedelucio; Semeraro, Giovanni] Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. RP de Gemmis, M (reprint author), Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy. EM degemmis@di.uniba.it; iaquinta@di.uniba.it; lops@di.uniba.it; cataldomusto@di.uniba.it; narducci@di.uniba.it; semeraro@di.uniba.it RI Semeraro, Giovanni /A-2180-2012; Lops, Pasquale/E-7509-2014; de Gemmis, Marco/E-9490-2014 OI Semeraro, Giovanni /0000-0001-6883-1853; CR Ahn J., 2007, P 16 INT C WORLD WID, P11, DOI 10.1145/1242572.1242575 Alton-Scheidl R., 1997, VOTING RATING WEB4GR, P13 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BILLSUS D, 1997, P WORKSH MACH LEARN Billsus D., 1999, P 7 INT C US MOD BAN Billsus D, 2000, USER MODEL USER-ADAP, V10, P147, DOI 10.1023/A:1026501525781 Brafman RL, 2009, AI MAG, V30, P58 Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Burke R., 2000, ENCY LIB INFORM SYST, V69 Clark P., 1989, Machine Learning, V3, DOI 10.1007/BF00116835 Claypool M., 1999, P ACM SIGIR WORKSH R Cohen W.W., 1999, AAAI IAAI, P335 Cohens W, 1995, P 12 INT C MACH LEAR, P115 Degemmis M, 2007, USER MODEL USER-ADAP, V17, P217, DOI 10.1007/s11257-006-9023-4 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 GRUDIN J, 1994, COMMUN ACM, V37, P92, DOI 10.1145/175222.175230 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Jennings A., 1993, User Modeling and User-Adapted Interaction, V3, DOI 10.1007/BF01099423 JOACHIMS T, 1997, 15 INT JOINT C ART I, P770 Keenoy K, 2005, LECT NOTES ARTIF INT, V3169, P201 KIM J, 2000, USING IMPLICIT FEEDB Krulwich B, 1997, AI MAG, V18, P37 Lang K, 1995, P 12 INT C MACH LEAR, P331 LEE WS, 2001, P INT C MACH LEARN Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 LORENZI F, 2003, LECT NOTES ARTIF INT, V3169, P89 Melville P., 2002, P 18 NAT C ART INT, P187 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Miller BM, 2003, IFAC WORK S, P263 Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Morita M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Nichols D.M, 1997, P 5 DELOS WORKSH FIL, P31 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159 Pu P, 2008, AI MAG, V29, P93 Pu P., 2000, CHI 2000 Conference Proceedings. Conference on Human Factors in Computing Systems. CHI 2000. The Future is Here Quinlan J. R., 1983, MACHINE LEARNING ART, P463 QUINLAN JR, 1994, ICML, P233 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Ricci F, 2007, IEEE INTELL SYST, V22, P22, DOI 10.1109/MIS.2007.43 Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 Rocchio J.J., 1971, RELEVANCE FEEDBACK I Salter J, 2006, IEEE INTELL SYST, V21, P35, DOI 10.1109/MIS.2006.4 Salton G, 1988, TERM WEIGHTING APPRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHIAFFINO S, 2000, INT JOINT C IBERAMIA, P12 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Semeraro G, 2009, HDB RES DIGITAL LIB, P149 Sheth B., 1993, Proceedings. The Ninth Conference on Artificial Intelligence for Applications (Cat. No.93CH3254-0), DOI 10.1109/CAIA.1993.366590 SMITH B, 2000, KNOWL-BASED SYST, V13, P53 STEVENS FC, 1993, THESIS Kelly D., 2003, SIGIR Forum, V37 Terveen L., 2001, HCI NEW MILLENNIUM Towle B., 2000, PAP AAI WORKSH, P74 Wasfi A.M. Ahmad, 1999, P 1999 INT C INT US, P57 WHITE RW, 2002, P 24 BCS IRSG EUR C, P93 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 62 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY BN 978-3-642-14124-9 PY 2010 BP 387 EP 407 DI 10.1007/978-3-642-14125-6_18 PG 21 WC Computer Science, Artificial Intelligence SC Computer Science GA BSO46 UT WOS:000285117600018 ER PT J AU Chung, JY Park, B Won, YJ Strassner, J Hong, JW AF Chung, Jae Yoon Park, Byungchul Won, Young J. Strassner, John Hong, James W. BE Kiriha, Y Granville, LZ Medhi, D Tonouchi, T Kim, MS TI An Effective Similarity Metric for Application Traffic Classification SO PROCEEDINGS OF THE 2010 IEEE-IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM SE IEEE IFIP Network Operations and Management Symposium LA English DT Proceedings Paper CT 2010 IEEE-IFIP Network Operations and Management Symposium CY APR 19-23, 2010 CL Osaka, JAPAN SP IEEE, IFIP, IEEE Commun Soc DE Application level traffic classification; traffic monitoring; similarity function AB Application level traffic classification is one of the major issues in network monitoring and traffic engineering. In our previous study, we proposed a new traffic classification method that utilizes a flow similarity function based on Cosine Similarity. This paper compares the classification accuracy of three similarity metrics, Jaccard Similarity, Cosine Similarity, and Gaussian Radius Based Function, to select appropriate similarity metrics for application traffic classification. This paper also defines a new two-stage traffic classification algorithm that can guarantee high classification accuracy even under an asymmetric routing environment, with reasonable complexity. C1 [Chung, Jae Yoon; Park, Byungchul; Won, Young J.; Hong, James W.] POSTECH, Dept Comp Sci & Engn, Pohang, South Korea. RP Chung, JY (reprint author), POSTECH, Dept Comp Sci & Engn, Pohang, South Korea. EM dejavu94@postech.ac.kr; fates@postech.ac.kr; yjwon@postech.ac.kr; johns@postech.ac.kr; jwkhong@postech.ac.kr CR BOYER RS, 1977, COMMUN ACM, V20, P762, DOI 10.1145/359842.359859 Choi T., 2004, IEEE IFIP NETW OP MA, V1, P511 CHUNG JY, 2009, 8 IEEE INT WORKSH IP Endace, DAG 4 3GE ERMAN J, 2006, SIGCOMM WORKSH MIN N, P281 GUMMADI KP, 2003, ACM S OP SYST REV DE, V27, P314 HAFFNER P, 2005, ACM SIGCOMM 2005 PHI KARAGIANNIS T, 2005, ACM SIGCOMM 2005 PHI Karagiannis T, 2004, IMC 04, P121 Moore A. W., 2005, INT C MEAS MOD COMP, P50 MOORE AW, 2005, PASS ACT MEAS C BOST Park BC, 2008, IEEE IFIP NETW OPER, P160 RISSO F, 2008, INT C COMM BEIJ CHIN SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEN S, 2004, INT WORLD WID WEB C, P512 NR 16 TC 2 Z9 3 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1542-1201 BN 978-1-4244-5367-2 J9 IEEE IFIP NETW OPER PY 2010 BP 286 EP 292 PG 7 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BTM81 UT WOS:000287357500036 ER PT B AU Ho, CM Chan, KY AF Ho, Chun-Min Chan, Kuei-Yuan GP ASME TI MODIFIED REDUCED GRADIENT WITH REALIZATIONS SORTING FOR HARD EQUALITY CONSTRAINTS IN RELIABILITY-BASED DESIGN OPTIMIZATION SO PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2010, VOL 1, PTS A AND B LA English DT Proceedings Paper CT ASME Internationl Design Engineering Technical Conferences / Computers and Information in Engineering Conference CY AUG 15-18, 2010 CL Montreal, CANADA SP Amer Soc Mech Engineers, Design Engn Div, Amer Soc Mech Engineers, Comp & Informat Engn Div DE Equality Constraints; RBDO; Reduced Gradient; Design under Uncertainty AB In this work, the presence of equality constraints in reliability-based design optimization (REDO) problems is studied. Relaxation of soft equality constraints in REDO and its challenges aft. briefly discussed while the main focus is on hard equalities that can not be, violated even under uncertainty. Direct elimination of hard equalities to reduce problem dimensions is usually suggested; however, for nonlinear or black-box functions, variable elimination requires expensive root-finding processes of inverse functions that are generally unavailable. We extend the reduced gradient methods in deterministic optimization to handle hard equalities in RBDO. The efficiency and accuracy of the first and the second order predictions in reduced gradient methods are compared. Results show the first Order prediction being more efficient when realizations of random variables are available. A gradient-weighted sorting with these-random samples is proposed to further improve the solution efficiency of the reduced gradient method. Feasible design realizations subject to hard equality constraints are then available to be implemented with the state-of-the-art sampling techniques for REDO problems. Numerical and engineering examples show the strength and simplicity of the proposed method. C1 [Ho, Chun-Min; Chan, Kuei-Yuan] Natl Cheng Kung Univ, Dept Mech Engn, Tainan 70101, Taiwan. RP Chan, KY (reprint author), Natl Cheng Kung Univ, Dept Mech Engn, Tainan 70101, Taiwan. EM chanky@mail.ncku.edu.tw CR Au SK, 1999, STRUCT SAF, V21, P135, DOI 10.1016/S0167-4730(99)00014-4 Breitung K., 1989, PROBALISTIC ENG MECH, V4, P187, DOI 10.1016/0266-8920(89)90024-6 Choi KK, 2001, STRUCT MULTIDISCIP O, V22, P335 Dennis JE, 1987, NUMERICAL METHODS UN HOHENBICHLER M, 1983, STRUCT SAF, V1, P177, DOI 10.1016/0167-4730(82)90024-8 Hyvarinen A., 2001, INDEPENDENT COMPONEN Incropera P., 2007, INTRO HEAT TRANSFER JONES DR, 1993, J OPTIMIZ THEORY APP, V79, P157, DOI 10.1007/BF00941892 Kalagnanam JR, 1997, TECHNOMETRICS, V39, P308, DOI 10.2307/1271135 Kim KJ, 2002, IND ENG CHEM RES, V41, P1276, DOI 10.1021/ie0101689 Mattson C., 2003, AIAA ASME ASCE AHS A, V5, P3464 Messac A, 2002, STRUCT MULTIDISCIP O, V23, P357, DOI 10.1007/s00158-002-0196-0 Papalambros P.Y., 2000, PRINCIPLES OPTIMAL D RAMAKRISHNAN Balaji, 1991, ADV DESIGN AUTOMATIO, V32, P241 Rangavajhala S, 2007, STRUCT MULTIDISCIP O, V34, P381, DOI 10.1007/s00158-007-0104-8 ROSENBLATT M, 1952, ANN MATH STAT, V23, P470, DOI 10.1214/aoms/1177729394 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Siddall J. N., 1983, PROBABILISTIC ENG DE, V1st Wu Y., 1993, P IUTAM S, P550 NR 19 TC 0 Z9 0 PU AMER SOC MECHANICAL ENGINEERS PI NEW YORK PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA BN 978-0-7918-4409-0 PY 2010 BP 953 EP 962 PG 10 WC Automation & Control Systems; Engineering, Manufacturing; Engineering, Mechanical SC Automation & Control Systems; Engineering GA BFJ25 UT WOS:000320093200086 ER PT B AU Oberoi, S Finger, S AF Oberoi, Sharad Finger, Susan GP ASME TI INTERACTIVE DESIGN NAVIGATIONAL TOOLS SO PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, VOL 8, PTS A AND B LA English DT Proceedings Paper CT ASME International Design Engineering Technical Conferences/Computers and Information in Engineering Conference CY AUG 30-SEP 02, 2009 CL San Diego, CA SP ASME, Design Engn Div, ASME, Computers & Info Engn Div ID LATENT SEMANTIC ANALYSIS; CO-WORD ANALYSIS; SCIENTOMETRICS; KNOWLEDGE; INFORMATION; SPACE; MODEL AB For both student and professional design teams, the design and development process requires that collaborators build and retain knowledge through discussions, creating documents and sharing artifacts. Key to supporting these knowledge building activities is the development of an infrastructure that supports effective knowledge management. This paper presents the framework for an information management technology called DesignWebs, which assimilates the product structures from the evolving set of documents and discussions about an engineering artifact. A Design Web enables users to see evolving connections between concepts by using a navigable web-based interface that synthesizes the design knowledge from multiple sources of information. C1 [Oberoi, Sharad; Finger, Susan] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA. RP Finger, S (reprint author), Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA. EM Susan.Finger@cmu.edu CR Alani H, 2003, KNOWL CAPT 03 WORKSH, P2 Arguello J., 2006, P HUM LANG TECHN C N, P9, DOI 10.3115/1614049.1614052 AVERSA E, 1992, ESSAYS INFORM SCI NO, V15, P77 Barzilay R., 2004, P HLT NAACL 2004 Berlin Lucy M., 1993, P INTERCHI 93 C HUM, P23, DOI 10.1145/169059.169063 Bhattacharya S, 1998, SCIENTOMETRICS, V43, P359, DOI 10.1007/BF02457404 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Potts C., 1988, Proceedings of the 10th International Conference on Software Engineering (Cat. No.88CH2554-4), DOI 10.1109/ICSE.1988.93722 Callon M., 1991, SCIENTOMETRICS, V22, P153 Card SK, 1996, IEEE COMPUT GRAPH, V16, P63, DOI 10.1109/38.486683 Carley KM, 1997, J ORGAN BEHAV, V18, P533, DOI 10.1002/(SICI)1099-1379(199711)18:1+<533::AID-JOB906>3.3.CO;2-V CONKLIN J, 1989, J AM SOC INFORM SCI, V40, P200, DOI 10.1002/(SICI)1097-4571(198905)40:3<200::AID-ASI11>3.0.CO;2-U Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.3.CO;2-6 COURTIAL JP, 1994, SCIENTOMETRICS, V31, P251, DOI 10.1007/BF02016875 Finger S, 2006, EXPERT SYST APPL, V31, P734, DOI 10.1016/j.eswa.2006.01.012 Finger S., 2006, INT J ENG EDUC, V22, P636 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Fruchter R, 2002, AI EDAM, V16, P127, DOI 10.1017/S0890060402163025 Garfield E, 1994, CURRENT CONTENTS SOC, V7, P5 Goodwin R, 1997, APPL INTELL, V7, P167, DOI 10.1023/A:1008206214208 Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Herman I, 2000, IEEE T VIS COMPUT GR, V6, P24, DOI 10.1109/2945.841119 Kando N., 1997, BRIT COMP SOC IR SG Krsul I. V., 1998, THESIS PURDUE U Kunz W., 1970, 131 U CAL CTR PLANN Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LEE RCT, 1977, IEEE T COMPUT, V26, P288 Leifer L.J., 1991, P ICED 91 ZUR, P301 Mostafa J, 1998, J AM SOC INFORM SCI, V49, P1304, DOI 10.1002/(SICI)1097-4571(1998)49:14<1304::AID-ASI8>3.0.CO;2-E Nanba H., 1999, P 16 INT JOINT C ART, P926 Olney A., 2005, P HLT EMNLP 2005 RADEV D, 2002, EVALUATION TEXT SUMM RIP A, 1984, SCIENTOMETRICS, V6, P381, DOI 10.1007/BF02025827 RITTEL HWJ, 1973, POLICY SCI, V4, P155, DOI 10.1007/BF01405730 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shapiro A., 2005, US Patent, Patent No. 20050180330 Sim SK, 2004, RES ENG DES, V15, P40, DOI 10.1007/s00163-003-0044-2 SONG S, 2003, INT C ENG DES ICED 0 Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936 VanRaan AFJ, 1997, SCIENTOMETRICS, V38, P205 VANRAAN AJF, 1992, J AGSI, V78, P77 Wills G.J., 1997, P GRAPH DRAW 97, P403 NR 42 TC 0 Z9 0 PU AMER SOC MECHANICAL ENGINEERS PI NEW YORK PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA BN 978-0-7918-4905-7 PY 2010 BP 585 EP 591 PG 7 WC Computer Science, Interdisciplinary Applications; Engineering, Manufacturing; Engineering, Mechanical SC Computer Science; Engineering GA BUU86 UT WOS:000290416800057 ER PT J AU Xia, HS Liu, J Wang, Y AF Xia Huosong Liu Jian Wang Yi BE Guo, Q Guo, Y TI CREDIBILITY ANALYSIS OF COMMENTS OF VIRTUAL COMMUNITY BASED ON TEXT SIMILARITY COMPUTING SO PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES 2010) LA English DT Proceedings Paper CT 9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2010) CY AUG 10-12, 2010 CL Hong Kong, PEOPLES R CHINA HO Lingnan Univ DE Virtual Community; Text Similarity; Credibility; VSM; Cosine Coefficient AB With the maturity of Web2.0, information exchange and information sharing have reached an unprecedented breadth and depth, which also provide a good condition for the spreading of false information in virtual communities. Through the collection and analysis of a large number of comments of virtual communities, this paper presents a solution of credibility analysis of comments of virtual community based on text similarity computing in order to quickly find a virtual community that false information may exist, and which can be the basis that helps organizations and individuals making decisions correctly; at the same time it also gives some advice for virtual community supervisors to manage their virtual communities. C1 [Xia Huosong; Liu Jian; Wang Yi] Wuhan Univ Sci & Engn, Dept Coll Econ & Management, Wuhan 430073, Hubei, Peoples R China. RP Xia, HS (reprint author), Wuhan Univ Sci & Engn, Dept Coll Econ & Management, Wuhan 430073, Hubei, Peoples R China. EM xiahuosong8@gmail.com; liujian0906018@163.com; castives@gmail.com CR GUO QL, 2007, APPL RES COMPUTERS, V10, P24 Hilligoss B, 2008, INFORM PROCESS MANAG, V44, P1467, DOI 10.1016/j.ipm.2007.10.001 Howard Rheingold, 1993, VIRTUAL COMMUNITY HO, P5 Pan W, 2009, ELECTRON LIBR, V27, P271, DOI 10.1108/02640470910947610 Pandelaere M, 2006, ADV CONSUM RES, V33, P565 PENG ZH, 2010, CONT SOCIAL SCI PERS, V1, P8 QI XJ, 2008, INFORM THEORY EXPLOR, V32, P98 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SONG L, 2006, COMPUTER ENG APPL, V30, P160 ZHANG QY, 2008, COMPUTER KNOWLEDGE T, V4, P1677 [čµµēŽ² ZHAO Ling], 2009, [ē®”ē†å­¦ęŠ„, Chinese Journal of Management], V6, P1169 ZHENG FY, 2009, LIB INFORM WORK, V53, P63 ZHENG ZP, 2008, INFORM THEORY EXPLOR, V6, P857 ZHU N, 2009, RES LIB SCI, V11, P55 ZHU YC, 2007, J MANAGEMENT ENG, V21, P151 NR 15 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-4110-5 PY 2010 BP 234 EP 237 DI 10.1109/DCABES.2010.53 PG 4 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BWU92 UT WOS:000295029400052 ER PT J AU Wang, Y Liu, WR Bell, DA AF Wang, Ying Liu, Weiru Bell, David A. BE Deshpande, A Hunter, A TI A Structure-Based Similarity Spreading Approach for Ontology Matching SO SCALABLE UNCERTAINTY MANAGEMENT, SUM 2010 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 4th Annual International Conference on Scalable Uncertainty Management (SUM) CY SEP 27-29, 2010 CL Toulouse, FRANCE AB Most of the frequently used ontology mapping methods to date are based on linguistic information implied in ontologies. However, same concepts in different ontologies can represent different semantics under the context of different ontologies, so relationships on mapping cannot be solely recognized by applying linguistic information. Discovering and utilizing structural information in ontology is also very important. In this paper, we propose a structure-based similarity spreading method for ontology matching which consists of three steps. We first select centroid concepts from both ontologies using similarities between entities based on their linguistic information. Second, we partition each ontology based on the set of centroid concepts recognized in it using clustering method. Third, we utilize a similarity spreading method to update the similarities between entities from two ontologies and apply a greedy matching method to establish the final mapping results. The experimental results demonstrate that our approach is very effective and can obtain much better results comparing to other similarity based and similarity flooding based algorithms. C1 [Wang, Ying; Liu, Weiru; Bell, David A.] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland. RP Wang, Y (reprint author), Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT7 1NN, Antrim, North Ireland. EM ywang14@qub.ac.uk; w.liu@qub.ac.uk; da.bell@qub.ac.uk CR Bouquet P, 2003, LECT NOTES COMPUT SC, V2870, P130 Do H.H, 2001, P VLDB, P610 Doan A., 2004, HDB ONTOLOGIES INFOR, P397 Euzenat J., 2005, P K CAP 2005 WORKSH Hanif M.S., 2009, J WEB SEMANT, V7, P344 Hu W., 2005, P K CAP 2005 WORKSH Jeh G., 2002, P 8 ACM SIGKDD INT C, P538, DOI DOI 10.1145/775047.775126 Li JZ, 2009, IEEE T KNOWL DATA EN, V21, P1218, DOI 10.1109/TKDE.2008.202 Madhavan J., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Melnik S, 2002, PROC INT CONF DATA, P117, DOI 10.1109/ICDE.2002.994702 Noy N. F., 2001, WORKSH ONT INF SHAR Qu Y., 2006, P 15 INT C WORLD WID, P23, DOI DOI 10.1145/1135777.1135786 Resniks P., 1995, P 14 INT JOINT C ART, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shvaiko P., 2008, P 7 INT C ONT DATABA, P300 Shvaiko P., 2007, ONTOLOGY MATCHING Shvaiko P, 2005, LECT NOTES COMPUT SC, V3730, P146 Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624 Tang J., 2005, P 14 INT C WORLD WID, P1040 Wang P, 2009, LECT NOTES COMPUT SC, V5926, P105 Winkler W., 1999, TECHNICAL REPORT Wu W., 2004, P 2004 ACM SIGMOD IN, P95, DOI 10.1145/1007568.1007582 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zhong Q, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P669 NR 24 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15950-3 J9 LECT NOTES ARTIF INT PY 2010 VL 6379 BP 361 EP 374 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA BDB30 UT WOS:000312447800033 ER PT S AU Bozzon, A Fraternali, P AF Bozzon, Alessandro Fraternali, Piero BE Ceri, S Brambilla, M TI Chapter 8: Multimedia and Multimodal Information Retrieval SO SEARCH COMPUTING: CHALLENGES AND DIRECTIONS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 1st SeCo Workshop on Search Computing Challenges and Directions CY JUN 17-19, 2009 CL Como, ITALY SP European Res Council, Search Comp Project DE multimedia information retrieval; digital signal processing; video search engines; multi-modal query interfaces ID OF-THE-ART; RECOGNITION; CHALLENGES AB The Web is progressively becoming a multimedia content delivery platform. This trend poses severe challenges to the information retrieval theories, techniques and tools. This chapter defines the problem of multimedia information retrieval with its challenges and application areas, overviews its major technical issues, proposes a reference architecture unifying the aspects of content processing and querying, exemplifies a next-generation platform for multimedia search, and concludes by showing the close ties between multi-domain search investigated in Search Computing and multimodal/multimedia search. C1 [Bozzon, Alessandro; Fraternali, Piero] Politecn Milan, Dipartimento Elettron & Informat, I-20133 Milan, Italy. RP Bozzon, A (reprint author), Politecn Milan, Dipartimento Elettron & Informat, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy. EM alessandro.bozzon@polimi.it; piero.fraternali@polimi.it CR *3GP, 2009, 3GP VID STAND *AD, 2009, AD PREM ADISTAMBHA K, 2007, P 7 INT IEEE S COMM, P479 [Anonymous], 2002, EXCH IM FIL FORM DIG Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BALDI A, 2009, BIOMED ENG ONLINE Barbieri M, 2003, PROC SPIE, V5242, P1, DOI 10.1117/12.515733 BEITZEL SM, 2003, S DOC IM UND TECHN S *BLINKX, 2009, BLINKX VID SEARCH EN *BMAT, BMAT 2009 BOZZON A, 2009, P 32 INT ACM SIGIR C, P841, DOI 10.1145/1571941.1572161 BOZZON A, 2009, 7 IEEE INT WORKSH CO, P120 CARINGELLA N, 2006, IEEE SIGNAL PROCESSI, V23, P133 CARRATO KS, 2001, COMMUNICATION, V16, P477 CEES GM, 2009, RETRIEVAL, V4, P215 Petrovska-Delacretaz D, 2007, LECT NOTES COMPUT SC, V4391, P135 COTSACES C, 2006, IEEE SIGNAL PROCESSI *DAML, 2009, DAML ONT LIB *DELV NETW, 2009, DELV NETW ONL VID PL DEVLIN B, 2004, FILE INTERCHANGE HDB, P123 DIOU C, 2008, P 6 TREC VID RETR EV *EMP, 2009, EMP ONL SHOP EU H, 1999, SURVEY CONTINUOUS SP *EYEAL, 2009, EYEAL PLATF FAC SIM Geurts J., 2005, WORKSH MULT SEM WEB, P4 *GOOGL, 2009, GOOGL EL VID SEARCH *GOOGL, 2009, GOOGL PIC Google, 2009, GOOGL IM Hanbury A, 2008, J VISUAL LANG COMPUT, V19, P617, DOI 10.1016/j.jvlc.2008.01.002 HENRICH A, 2000, P 1 DELOS WORKSH INF HENRICH A, 2001, P 1 INT WORKSH MULT, P17 *IST SAPIR, 2009, IST SAPIR LARG SCAL LE TH, 2008, P ADV MULT MOD 14 MM, P307 Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045 *LSCOM, 2009, LSCOM LEX DEF ANN Manjunath B. S., 2002, INTRO MPEG 7 MULTIME Maragos P, 2008, MULTIMED SYST APPL, P1, DOI 10.1007/978-0-387-76316-3 Marsden Alan, 2007, Literary & Linguistic Computing, V22, DOI 10.1093/llc/fqm021 Martinez MG, 2004, FOOD POLICY, V29, P1, DOI 10.1016/j.foodpol.2004.01.001 MEYERS OC, 2007, THESIS MIT US NGO C, 2004, MULTIMEDIA SYSTEMS, V10, P261 *PICTR, 2009, PICTR SOL *PIXT, 2009, PIXT VIS SEARCH TECH *PLUGGD, 2009, PLUGGD PODC SEARCH E *PODSC, 2009, PODSC PODC SEARCH EN Potamitis I, 2008, STUD COMPUT INTELL, V120, P41 *QUAER, 2009, QUAER PROGR Radke RJ, 2005, IEEE T IMAGE PROCESS, V14, P294, DOI 10.1109/TIP.2004.838698 RASHEED Z, 2003, P IEEE COMP VIS PATT SACCO SM, 2009, INFORM RETRIEVAL SER, V25, P340 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *TIN, 2009, TIN IM SEARCH ENG Turaga P, 2008, IEEE T CIRC SYST VID, V18, P1473, DOI 10.1109/TCSVT.2008.2005594 Typke R., 2005, ISMIR P LOND, P153 *U TWENT, 2009, BUCH DEM *U TWENT, 2009, RAD OR SPEECH SEARCH Wang CK, 2007, LECT NOTES COMPUT SC, V4352, P216 Wattamwar S.S., 2008, P 2 ACM WORKSH MULT, P48, DOI 10.1145/1460676.1460686 Yilmaz A., 2006, ACM COMPUT SURVEY, V38 YU G, 2004, INT C ADV INF NETW A, V2, P112 Zhao W, 2003, ACM COMPUT SURV, V35, P399, DOI 10.1145/954339.954342 2009, SMEF 2009, LEARNING OBJECT META 2009, RSS 2009, SMILA 2009, PODCAST 2009, MEDIA RSS NR 68 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-12309-2 J9 LECT NOTES COMPUT SC PY 2010 VL 5950 BP 135 EP 155 PG 21 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BPK76 UT WOS:000279101700008 ER PT B AU Radovanovic, M Nanopoulos, A Ivanovic, M AF Radovanovic, Milos Nanopoulos, Alexandros Ivanovic, Mirjana BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI On the Existence of Obstinate Results in Vector Space Models SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE Text retrieval; vector space model; nearest neighbors; curse of dimensionality; hubs; similarity concentration; cosine similarity AB The vector space model (VSM) is a popular and widely applied model in information retrieval (IR). VSM creates vector spaces whose dimensionality is usually high (e.g., tens of thousands of terms). This may cause various problems, such as susceptibility to noise and difficulty in capturing the underlying semantic structure, which are commonly recognized as different aspects of the "curse of dimensionality." In this paper, we investigate a novel aspect of the dimensionality curse, which is referred to as hubness and manifested by the tendency of some documents (called hubs) to be included in unexpectedly many search result lists. Hubness may impact VSM considerably since hubs can become obstinate results, irrelevant to a large number of queries, thus harming the performance of an IR system and the experience of its users. We analyze the origins of hubness, showing it is primarily a consequence of high (intrinsic) dimensionality of data, and not a result of other factors such as sparsity and skewness of the distribution of term frequencies. We describe the mechanisms through which hubness emerges by exploring the behavior of similarity measures in high-dimensional vector spaces. Our consideration begins with the classical VSM (tf-idf term weighting and cosine similarity), but the conclusions generalize to more advanced variations, such as Okapi BM25. Moreover, we explain why hubness may not be easily mitigated by dimensionality reduction, and propose a similarity adjustment scheme that takes into account the existence of hubs. Experimental results over real data indicate that significant improvement can be obtained through consideration of hubness. C1 [Radovanovic, Milos; Ivanovic, Mirjana] Univ Novi Sad, Dept Math & Informat, Novi Sad, Serbia. RP Radovanovic, M (reprint author), Univ Novi Sad, Dept Math & Informat, Novi Sad, Serbia. EM radacha@dmi.uns.ac.rs; nanopoulos@ismll.de; mira@dmi.uns.ac.rs CR Aucouturier J.-J., 2007, PATTERN RECOGN, V41, P272 BERENZWEIG A, 2007, THESIS COLUMBIA U NE Casella G., 2002, STAT INFERENCE, V2nd Chapelle O., 2006, SEMISUPERVISED LEARN Erdos P, 1959, PUBL MATH-DEBRECEN, V6, P290 Forman G., 2008, P 17 ACM C INF KNOWL, P263, DOI 10.1145/1458082.1458119 Francois D, 2007, IEEE T KNOWL DATA EN, V19, P873, DOI 10.1109/TKDE.2007.1037 GOODMAN LA, 1960, J AM STAT ASSOC, V55, P708, DOI 10.2307/2281592 HICKLIN A, 2005, 7271 NAT I STAND TEC Nanopoulos A., 2009, P 3 ACM C REC SYST R, P293, DOI 10.1145/1639714.1639771 Radovanovic M., 2009, P 26 INT C MACH LEAR, P865 Robertson S, 2002, INFORM RETRIEVAL, V5, P239, DOI 10.1023/A:1015702129514 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1997, THESIS CORNELL U ITH van Rijsbergen C. J., 1979, INFORM RETRIEVAL NR 15 TC 4 Z9 4 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 186 EP 193 PG 8 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100025 ER PT B AU Woo, HW Lee, JT Lee, SW Song, YI Rim, HC AF Woo, Hyun-Wook Lee, Jung-Tae Lee, Seung-Wook Song, Young-In Rim, Hae-Chang BE Chen, HH Efthimiadis, EN Savoy, J Crestani, F MarchandMaillet, S TI Achieving High Accuracy Retrieval using Intra-Document Term Ranking SO SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2010 CL Geneva, SWITZERLAND SP Special Interest Grp Informat Retrieval, Assoc Comp Machinery, Baidu, Google, Microsoft Res, Yahoo, Informat Retrieval Fac, IBM Res, FNSNF, Swiss Natl Sci Fdn DE inter-document term ranking; precision at top ranks AB Most traditional ranking models roughly score the relevance of a given document by observing simple term statistics, such as the occurrence of query terms within the document or within the collection. Intuitively, the relative importance of query terms with regard to other individual non-query terms in a document can also be exploited to promote the ranks of documents in which the query is dedicated as the main topic. In this paper, we introduce a simple technique named intro-document term ranking, which involves ranking all the terms in a document according to their relative importance within that particular document. We demonstrate that the information regarding the rank positions of given query terms within the intra-document term ranking can be useful for enhancing the precision of top-retrieved results by traditional ranking models. Experiments are conducted on three standard TREC test collections. C1 [Woo, Hyun-Wook; Lee, Jung-Tae; Lee, Seung-Wook; Rim, Hae-Chang] Korea Univ, Dept Comp & Radio Comms Engn, Seoul, South Korea. RP Woo, HW (reprint author), Korea Univ, Dept Comp & Radio Comms Engn, Seoul, South Korea. EM hwwoo@nlp.korea.ac.kr; jtlee@nlp.korea.ac.kr; swlee@nlp.korea.ac.kr; yosong@microsoft.com; rim@nlp.korea.ac.kr CR MATVEEVA I, 2006, SIGIR 06 Ponte JM, 1998, SIGIR 98 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 3 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-896-4 PY 2010 BP 885 EP 886 PG 2 WC Computer Science, Information Systems SC Computer Science GA BTG52 UT WOS:000286904100188 ER PT J AU Roth, C Cointet, JP AF Roth, Camille Cointet, Jean-Philippe TI Social and semantic coevolution in knowledge networks SO SOCIAL NETWORKS LA English DT Article DE Knowledge networks; Socio-semantic networks; Hierarchies; Dynamics; Cohesiveness; Epistemic communities; Bicliques; Galois lattices; Blogs; Scientific networks ID SCIENTIFIC COLLABORATION; STATISTICAL EVALUATION; EPISTEMIC COMMUNITIES; GALOIS LATTICES; MODELS; DISTRIBUTIONS; BLOCKMODELS; EVOLUTION; DYNAMICS AB Socio-semantic networks involve agents creating and processing information: communities of scientists, software developers, wild Contributors and webloggers are, among others, examples Of Such knowledge networks. We aim at demonstrating that the dynamics of these communi ties can be adequately described as the coevolution of a social and a socio-semantic network. More precisely, we will first introduce a theoretical framework based on a social network and a socio-semantic network, i.e. an epistemic network featuring agents. concepts and links between agents and between agents and concepts. Adopting a relevant empirical protocol. we will then describe the joint dynamics of social and socio-semantic structures, at both macroscopic and microscopic scales, emphasizing the remarkable stability of these macroscopic properties in spite of a vivid local, agent-based network dynamics. (C) 2009 Elsevier B.V. All rights reserved. C1 [Roth, Camille] EHESS, CNRS, CAMS, F-75006 Paris, France. [Roth, Camille] U Surrey, CRESS, Guildford GU2 7XH, Surrey, England. [Roth, Camille; Cointet, Jean-Philippe] ISC PIF, F-75005 Paris, France. [Cointet, Jean-Philippe] Ecole Polytech, CNRS, CREA, F-75005 Paris, France. [Cointet, Jean-Philippe] INRA, TSV, Paris, France. RP Roth, C (reprint author), EHESS, CNRS, CAMS, 54 Bd Raspail, F-75006 Paris, France. EM roth@ehess.fr; jean-philippe.cointet@polytechnique.edu CR ALIHASAN NF, 2007, INT C WEBL SOC MED ANDERSON CJ, 1992, SOC NETWORKS, V14, P137, DOI 10.1016/0378-8733(92)90017-2 Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 Barabasi AL, 2002, PHYSICA A, V311, P590, DOI 10.1016/S0378-4371(02)00736-7 BATAGELJ V, 1995, J CLASSIF, V12, P73, DOI 10.1007/BF01202268 BOURDIEU P, 1991, SOCIOL FORUM, V6, P3, DOI 10.1007/BF01112725 Bradbury J, 2004, PLOS BIOL, V2, P568, DOI 10.1371/journal.pbio.0020148 BREIGER RL, 1974, SOC FORCES, V53, P181, DOI 10.2307/2576011 Callon M., 2001, SOCIOLOGIES TRAVAIL, P335 Callon M., 1986, MAPPING DYNAMICS SCI CARDON D, 2006, PRODUCTION SOI COMME COHENDET P., 2003, REV EC IND, V103, P15, DOI 10.3406/rei.2003.3105 COLE JR, 1973, STRATIFICATION SCI Coleman J. S., 1988, AM J SOCIOL, V94, P95, DOI DOI 10.1086/228943 Crandall D., 2008, KDD 08 PRICE DJD, 1976, J AM SOC INFORM SCI, V27, P292, DOI 10.1002/asi.4630270505 DEGENNE A, 2004, MATH SCI HUMAINES, V168, P5 DOREIAN P, 1994, SOC NETWORKS, V16, P267, DOI 10.1016/0378-8733(94)90013-2 Dorogovtsev S. N., 2003, EVOLUTION NETWORKS B EMIRBAYER M, 1994, AM J SOCIOL, V99, P1411, DOI 10.1086/230450 Erdos P, 1959, PUBL MATH-DEBRECEN, V6, P290 Falzon L, 2000, SOC NETWORKS, V22, P159, DOI 10.1016/S0378-8733(00)00021-6 FAUST K, 1992, SOC NETWORKS, V14, P5, DOI 10.1016/0378-8733(92)90013-W Fienberg S E, 1981, SOCIOL METHODOL, P156, DOI DOI 10.2307/270741 Freeman L., 1989, RES METHODS SOCIAL N, P11 FREEMAN LC, 1979, SOC NETWORKS, V1, P215, DOI 10.1016/0378-8733(78)90021-7 Freeman LC, 1996, SOC NETWORKS, V18, P173, DOI 10.1016/0378-8733(95)00271-5 FREEMAN LC, 1993, SOCIOL METHODOL, V23, P127, DOI 10.2307/271008 Glance N., 2005, LINKKDD 05, P36 GORDON MD, 1980, SCIENTOMETRICS, V2, P193, DOI 10.1007/BF02016697 HAAS PM, 1992, INT ORGAN, V46, P1 Holland P. W., 1976, SOCIOL METHODOL, V7, P1, DOI 10.2307/270703 HOLLAND PW, 1981, J AM STAT ASSOC, V76, P33, DOI 10.2307/2287037 Jeong H, 2003, EUROPHYS LETT, V61, P567, DOI 10.1209/epl/i2003-00166-9 Jin EM, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.046132 Katz JS, 1997, RES POLICY, V26, P1, DOI 10.1016/S0048-7333(96)00917-1 KITCHER R, 1995, SOCIALIZING EPISTEMO, P111 Latour B., 1988, VIE LAB PRODUCTION F Laumann E. O., 1983, APPL NETWORK ANAL, P18 Lazega E, 2006, EUR MANAG REV, V3, P113, DOI 10.1057/palgrave.emr.1500058 Lazega E, 1997, SOC NETWORKS, V19, P375, DOI 10.1016/S0378-8733(97)00006-3 Lazega E, 1992, MICROPOLITICS KNOWLE Leenders R.T.A.J., 1997, EVOLUTION SOCIAL NET, P165 Lehmann S., 2008, PHYS REV E, V78 Liben-Nowell D, 2003, CIKM 03, P556 Lind PG, 2005, PHYS REV E, V72, DOI 10.1103/PhysRevE.72.056127 Lorrain F., 1971, J MATH SOCIOL, V1, P49, DOI [DOI 10.1080/0022250X.1971.9989788), 10.1080/0022250X.1971.9989788] Lotka A. J., 1926, J WASHINGTON ACAD SC, V16, P317 McGlohon M., 2007, INT C WEBL SOC MED McPherson M, 2001, ANNU REV SOCIOL, V27, P415, DOI 10.1146/annurev.soc.27.1.415 Milo R, 2004, SCIENCE, V303, P1538, DOI 10.1126/science.1089167 Newman MEJ, 2004, P NATL ACAD SCI USA, V101, P5200, DOI 10.1073/pnas.0307545100 Newman MEJ, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.208701 Pattison P., 1993, ALGEBRAIC MODELS SOC Pattison P, 2000, J MATH PSYCHOL, V44, P536, DOI 10.1006/jmps.1999.1261 Pattison P., 1994, ADV SOCIAL NETWORK A, P79 PATTISON P, 1995, J MATH PSYCHOL, V39, P57, DOI 10.1006/jmps.1995.1005 Powell WW, 2005, AM J SOCIOL, V110, P1132, DOI 10.1086/421508 PRICE DJD, 1965, SCIENCE, V149, P510 Redner S, 2005, PHYS TODAY, V58, P49, DOI 10.1063/1.1996475 Robins G, 2001, SOC NETWORKS, V23, P1, DOI 10.1016/S0378-8733(01)00029-6 Robins G, 2001, PSYCHOMETRIKA, V66, P161, DOI 10.1007/BF02294834 Robins G., 2004, Computational & Mathematical Organization Theory, V10, DOI 10.1023/B:CMOT.0000032580.12184.c0 Roth C, 2006, SCIENTOMETRICS, V69, P429, DOI 10.1007/s11192-006-0161-6 Roth C., 2005, MATH POPUL STUD, V12, P107, DOI 10.1080/08898480590931404 Roth C., 2005, ISWC 4 INT SEM WEB C, V171, P29 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Snijders TAB, 2001, SOCIOL METHODOL, V31, P361, DOI 10.1111/0081-1750.00099 SNIJDERS TAB, 1981, SOCIAL NETWORKS, V3 SNIJDERS TAB, 1987, SOC NETWORKS, V9, P249, DOI 10.1016/0378-8733(87)90022-0 SOFFER SN, 2005, PHYS REV E, V71, P4 Sparck Jones K., 1973, LINGUISTICS INFORM S Sperber D., 1996, EXPLAINING CULTURE N SUBRAMANYAM K, 1983, J INFORM SCI, V6, P33, DOI 10.1177/016555158300600105 Tsioutsiouliklis J., 2003, ANN M MIDW POL SCI A Valente T.W., 1995, NETWORK MODELS DIFFU WASSERMAN S, 1980, J AM STAT ASSOC, V75, P280, DOI 10.2307/2287447 WASSERMAN S, 1985, J MATH PSYCHOL, V29, P406, DOI 10.1016/0022-2496(85)90003-3 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 White D, 1996, SOC NETWORKS, V18, P169, DOI 10.1016/0378-8733(96)90001-5 WILLE R, 1992, COMPUT MATH APPL, V23, P493, DOI 10.1016/0898-1221(92)90120-7 ZHANG P, 2007, ARXIV07100117 NR 82 TC 24 Z9 25 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0378-8733 J9 SOC NETWORKS JI Soc. Networks PD JAN PY 2010 VL 32 IS 1 SI SI BP 16 EP 29 DI 10.1016/j.socnet.2009.04.005 PG 14 WC Anthropology; Sociology SC Anthropology; Sociology GA 561BA UT WOS:000274948700003 ER PT S AU Moulin, C Largeron, C Gery, M AF Moulin, Christophe Largeron, Christine Gery, Mathias BE Hancock, ER Wilson, RC Windeatt, T Ulusoy, I Escolano, F TI Impact of Visual Information on Text and Content Based Image Retrieval SO STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT Joint IAPR International Workshop on SSPR & SPR CY AUG 18-20, 2010 CL Izmir, TURKEY SP IAPR, Pattern Anal Stat Modelling & Comp Learning ID SYSTEM AB Nowadays, multimedia documents composed of text and images are increasingly used, thanks to the Internet and the increasing capacity of data storage. it is more and more important to be able to retrieve needles in this huge haystack. In this paper, we present a multimedia document model which combines textual and visual information. Using a bag-of-words approach, it represents a textual and visual document using a vector for each modality. Given a multimedia query, our model combines scores obtained for each modality and returns a list of relevant retrieved documents. This paper aims at studying the influence of the weight given to the visual information relative to the textual information. Experiments on the multimedia ImageCLEF collection show that results can be Unproved by learning this weight parameter. C1 [Moulin, Christophe] Univ Lyon, F-42023 St Etienne, France. RP Moulin, C (reprint author), Univ Lyon, F-42023 St Etienne, France. EM christophe.moulin@univ-st-etienne.fr; christine.largeron@univ-st-etienne.fr; mathias.gery@univ-st-etienne.fr CR Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 Csurka G., 2004, ECCV 04 WORKSH STAT, P59 Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248 FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146 LEW MS, 2006, ACM T MULTIM COMPUT, V2, P11 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Moulin C, 2009, LECT NOTES COMPUT SC, V5706, P779 Robertson S., 1994, TEXT RETRIEVAL C, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMOULDERS AWM, 2000, IEEE T PATTERN ANAL, V22, P1349 Snoek C.G.M., 2006, ACM MULT C, P421 Tollari Sabrina, 2009, EUR C INF RETR ECIR Tsikrika T, 2009, 10 WORKSH CROSS LANG ZHAI C, 2001, NOTES LEMUR TFIDP MO NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-14979-5 J9 LECT NOTES COMPUT SC PY 2010 VL 6218 BP 159 EP 169 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BTB95 UT WOS:000286412900015 ER PT S AU Ahmed, MU Begum, S Olsson, E Xiong, N Funk, P AF Ahmed, Mobyen Uddin Begum, Shahina Olsson, Erik Xiong, Ning Funk, Peter BE Montani, S Jain, LC TI Case-Based Reasoning for Medical and Industrial Decision Support Systems SO SUCCESSFUL CASE-BASED REASONING APPLICATIONS - 1 SE Studies in Computational Intelligence LA English DT Article; Book Chapter ID RESPIRATORY SINUS ARRHYTHMIA; TIME-SERIES; FUZZY-SETS; STRESS; DISCOVERY; DIAGNOSIS; MEMORY; AGENTS; MODEL AB The amount of medical and industrial experience and knowledge is rapidly glowing and it is increasingly difficult to be up to date leading to less informed decisions. mistakes and serious accidents The demand for decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly However. traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory This chapter explores the functionalities of Case-Based Reasoning (CBR) to Facilitate experience reuse both in clinical and in industrial decision making tasks Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both clinical applications as well as decision support for engineers In both domains. DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance performance in a number of different aspects Textual in retrieval. Rule-Based Reasoning (RBR). and fuzzy logic are combined together with CUR to offer decision support to clinicians for a more reliable and efficient management of stress Agent technology and wavelet transformations ale applied with CUR to diagnose audible faults on industrial robots and to package such a system The performance of the CBR systems have been validated and have shown to be use in solving such problems in both of these domains C1 [Ahmed, Mobyen Uddin; Begum, Shahina; Olsson, Erik; Xiong, Ning; Funk, Peter] Malardalen Univ, Sch Innovat Design & Engn, SE-72123 Vasteras, Sweden. RP Ahmed, MU (reprint author), Malardalen Univ, Sch Innovat Design & Engn, POB 883, SE-72123 Vasteras, Sweden. RI Funk, Peter/ OI Funk, Peter/0000-0002-5562-1424 CR AAMODT A, 1994, AI COMMUN, V7, P39 Ahmed M. U., 2008, T CASE BASED REASONI, V1 AHMED MU, 2007, 20 INT C EXH COND MO, P305 AHMED MU, 2007, P 24 ANN WORKSH SWED, P70 AHMED MU, 2009, 7 WORKSH CAS BAS REA, P215 AHMED MU, 2009, 9 IASTED INT C ART I AHMED MU, 2008, 9 EUR C CAS BAS REAS, P57 AHMED MU, 2006, SAIS 2006, P45 Ahmed M.U., 2010, ARTIFICIAL IN PRESS Bachman G., 2000, FOURIER WAVELET ANAL BAREISS E, 1989, THESIS ANN ARBOR Bareiss E.R., 1988, THESIS U TEXAS BEGUM S, 2009, INT J COMPU IN PRESS Begum Shahina, 2009, WSEAS Transactions on Systems, V8 Begum S., 2006, P 8 EUR WORKSH CAS B, P113 BEGUM S, 2007, LNCS, P263 Begum S, 2007, LECT NOTES COMPUT SC, V4626, P478 Bemmel J., 1997, HDB MED INFORM Bergmann R, 2005, KNOWL ENG REV, V20, P209, DOI 10.1017/S0269888906000555 Bichindaritz I, 2006, ARTIF INTELL MED, V36, P127, DOI 10.1016/j.artmed.2005.10.008 BONISSONE P, 1998, HDB FUZZY COMPUTING Cannon WB, 1929, BODILY CHANGES PAIN CAROL CH, 2002, GOODNESS OF FIT MODE, P507 Daubechies I., 1988, COMMUN PURE APPL MAT, V42, P906 DVIR G, 1999, FUZZY INFORMATION PR, P33 Franklin S., 1997, LECT NOTES ARTIF INT, V1193, P21 Funk P, 2006, COMPUT INTELL-US, V22, P238, DOI 10.1111/j.1467-8640.2006.00286.x FUNK P, 2006, P 19 INT C COND COMA, P445 GIERL L, 1998, LNCS LNAI, V1400, P273 Healey JA, 2005, IEEE T INTELL TRANSP, V6, P156, DOI 10.1109/TITS.2005.848368 HINKLE D, 1995, AI MAG, V16, P65 Jang J-SR, 1997, NEUROFUZZY SOFT COMP Kappes B., 2008, APPL PSYCHOPHYS BIOF, V33, P173 KOLODNER JL, 1983, COGNITIVE SCI, V7, P281, DOI 10.1207/s15516709cog0704_2 Kolodner J., 1993, CASE BASED REASONING KOLODNER JL, 1983, COGNITIVE SCI, V7, P243, DOI 10.1207/s15516709cog0704_1 KOTON P, 1989, THESIS Lehrer P, 2000, APPL PSYCHOPHYS BIOF, V25, P193, DOI 10.1023/A:1009506909815 LUKASIEWICZ J, 1930, J PHILOS BEMERKUNGEN, P51 Lukasiewicz J., 1953, J COMPUTING SYSTEMS, V1, P111 MASON LJ, 2008, CONTROL SYMPTOMS STR Mitchell T. M., 1997, MACH LEARN, P230 MONTANI S, 2007, APPL INTELL, P275 Nilsson M, 2004, LECT NOTES COMPUT SC, V3155, P673 Noble RE, 2002, METABOLISM, V51, P37, DOI 10.1053/meta.2002.33190 Olsson E, 2004, LECT NOTES COMPUT SC, V3155, P686 OLSSON E, 2004, MARCON MAINT REL C K, P12 OLSSON E, 2004, J INTELLIGENT FUZZY Ontanon S, 2003, LECT NOTES ARTIF INT, V2689, P392 RAMON LD, 2005, KNOWL ENG REV, V20, P215 Reisbeck CK, 1989, INSIDE CASE BASED RE RICHTER MM, 2006, P 8 EUR WORKSH UNC F, P191 Russell S, 1995, ARTIF INTELL, P31 SALTON C, 1987, TR87881 CORN U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Selye H., 1956, STRESS LIFE SEUNG HK, INTELLIGENT KNOWLEDG Shortliffe EH, 1976, COMPUTER BASED MED C Simpson R.L., 1985, GITICS8518 THURSTON MG, 2001, AUTOTESTCON P, P401 Turban E., 2001, DECISION SUPPORT SYS, V6th Wang WJ, 1997, FUZZY SET SYST, V85, P305 Watson I., 1997, APPL CASE BASED REAS WATSON I, CASE BASED REASONING WEBER R, 2005, KNOWLEDGE ENG REV WILSON MDL, AUTONOMIC NERVOUS SY WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 Xiong N, 2008, APPL INTELL, V28, P247, DOI 10.1007/s10489-007-0059-x ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZADEH LA, 1973, IEEE T SYST MAN CYB, VSMC3, P28, DOI 10.1109/TSMC.1973.5408575 NR 70 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-14077-8 J9 STUD COMPUT INTELL PY 2010 VL 305 BP 7 EP 52 DI 10.1007/978-3-642-14078-5_2 D2 10.1007/978-3-642-14078-5 PG 46 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BQJ85 UT WOS:000281193600002 ER PT S AU White, T Salehi-Abari, A Box, B AF White, Tony Salehi-Abari, Amirali Box, Braden BE GarciaPedrajas, N Herrera, F Fyfe, C Benitez, JM Ali, M TI On How Ants Put Advertisements on the Web SO TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT II, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 01-04, 2010 CL Cordoba, SPAIN AB Advertising is an important aspect of the Web as many services rely on it for continued viability This paper provides insight into the effectiveness of using ant-inspired algorithms to solve the problem of Internet advertising he paper is motivated by the success of collaborative filtering systems and lie success of ant-inspired systems in solving data mining and complex classification problems Using the vector space formalism, a model is proposed that learns to associate ads with pages with no prior knowledge of users interests T model uses historical data from users' click-through pawn its in order to improve associations A test. bed and expermient.methodology is described, and the proposed model evaluated using simulation The ported results clearly show that, significant improvements in ad association performance are achievable C1 [White, Tony; Salehi-Abari, Amirali; Box, Braden] Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada. RP White, T (reprint author), Carleton Univ, Sch Comp Sci, 1125 Colonel Dr, Ottawa, ON K1S 5B6, Canada. CR Dorigo M, 2000, FUTURE GENER COMP SY, V16, P851, DOI 10.1016/S0167-739X(00)00042-X Dorigo M, 2004, ANT COLONY OPTIMIZATION, P1, DOI 10.1007/b99492 Joachims T, 2007, COMPUTER, V40, P34, DOI 10.1109/MC.2007.289 JOACHIMS T, 2002, SIGIR WORKSH MATH FO Karuga GG, 2001, DECIS SUPPORT SYST, V32, P85, DOI 10.1016/S0167-9236(01)00104-X Kim JW, 2001, INT J ELECTRON COMM, V5, P45 Kristol D., 2001, ACM T INTERNET TECHN, V1, P151, DOI 10.1145/502152.502153 Ramos V., 2003, 2003 Congress on Evolutionary Computation (IEEE Cat. No.03TH8674) SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 9 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13024-3 J9 LECT NOTES ARTIF INT PY 2010 VL 6097 BP 494 EP 503 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQR32 UT WOS:000281604400051 ER PT S AU Diagoni, M Pereira, CDC Tettamanzi, AGB AF Diagoni, Mauro Pereira, Celia Da Costa Tettamanzi, Andrea G. B. BE GarciaPedrajas, N Herrera, F Fyfe, C Benitez, JM Ali, M TI An Ontological Representation of Documents and Queries for Information Retrieval Systems SO TRENDS IN APPLIED INTELLIGENT SYSTEMS, PT II, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 01-04, 2010 CL Cordoba, SPAIN ID VECTOR-SPACE MODEL; EXPANSION AB This paper presents a vector space model, approach, for representing documents;Old queries, using concepts Instead of terms and WorldNet as a light ontology. This way. information ovedap Is reduced with respect to the classic semantic expansion technique:, Experiments carried out on the MuchMore benchinm k showed the effectiveness of the approach C1 [Diagoni, Mauro; Pereira, Celia Da Costa; Tettamanzi, Andrea G. B.] Univ Milan, Dipartimento Tecnol Informaz, I-26013 Creina, CR, Italy. RP Diagoni, M (reprint author), Univ Milan, Dipartimento Tecnol Informaz, Via Biamante 65, I-26013 Creina, CR, Italy. EM mauro.dragoni@unimi.it; celia.pereira@unimi.it RI Tettamanzi, Andrea G. B./ OI Tettamanzi, Andrea G. B./0000-0002-8877-4654 CR Azzini A., 2008, P HIS 2008 BARC SPAI, P332 BAZIZ M, 2007, RIAO CID Billerbeck B, 2004, LECT NOTES COMPUT SC, V3246, P30 BOUGHANEM M, 1998, LNCS, V1402, P355 Cai D., 2001, CIKM, P419 Calegari S., 2007, URSW CEUR WORKSH P, V327 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Cronen-Townsend S., 2004, CIKM 2004, P236 Diaz-Galiano MC, 2008, LECT NOTES COMPUT SC, V5152, P601, DOI 10.1007/978-3-540-85760-0_76 DUDI O, 2008, RCIS, P421 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Gonzalo J., 1998, CMPLG9808002 CORR Hattori T, 2006, LECT NOTES COMPUT SC, V3841, P1197 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tettamanzi A. G. B., 2006, SOFT COMPUTING ONTOL, P211 Tomassen SL, 2006, LECT NOTES COMPUT SC, V4278, P1460 VOOIHEES E, 1997, TREC, P1 WU F, 2007, CONFENIS, V254, P293 Xu J., 1996, SIGIR 96, P4 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-13024-3 J9 LECT NOTES ARTIF INT PY 2010 VL 6097 BP 555 EP 564 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQR32 UT WOS:000281604400057 ER PT S AU Parra-Arnau, J Rebollo-Monedero, D Forne, J AF Parra-Arnau, Javier Rebollo-Monedero, David Forne, Jordi BE Katsikas, S Lopez, J Soriano, M TI A Privacy-Preserving Architecture for the Semantic Web Based on Tag Suppression SO TRUST, PRIVACY AND SECURITY IN DIGITAL BUSINESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th International Conference on Trust, Privacy and Security in Digital Business CY AUG 30-31, 2010 CL Bilbao, SPAIN ID MODEL AB We propose an architecture that preserves user privacy in the semantic Web via tag suppression. In tag suppression, users may wish to tag some resources and refrain from tagging some others in order to hinder privacy attackers in their efforts to profile users' interests. Following this strategy, our architecture helps users decide which tags should be suppressed. We describe the implementation details of the proposed architecture and provide further insight into the modeling of profiles. In addition, we present a mathematical formulation of the optimal trade-off between privacy and tag suppression rate. C1 [Parra-Arnau, Javier; Rebollo-Monedero, David; Forne, Jordi] Tech Univ Catalonia UPC, Dept Telemat Engn, E-08034 Barcelona, Spain. RP Parra-Arnau, J (reprint author), Tech Univ Catalonia UPC, Dept Telemat Engn, E-08034 Barcelona, Spain. EM javier.parra@entel.upc.edu; david.rebollo@entel.upc.edu; jforne@entel.upc.edu RI Forne, Jordi/L-1557-2014 OI Forne, Jordi/0000-0002-8401-3292 CR Brickley D., 2004, RDF VOCABULARY DESCR Cover T.M., 2006, ELEMENTS INFORM THEO Elovici Y., 2002, P ACM WORKSH PRIV EL, P63, DOI 10.1145/644527.644534 Frakes W. B., 1992, INFORM RETRIEVAL DAT GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Howe Daniel C., 2006, TRACKMENOT JAYNES ET, 1982, P IEEE, V70, P939, DOI 10.1109/PROC.1982.12425 Jensen C, 2005, INT J HUM-COMPUT ST, V63, P203, DOI 10.1016/j.ijhcs.2005.04.019 John A., 2006, P COL WEB TAGG WORKS Kagal L., 2003, P INT SEM WEB C, P402 Kagal L, 2004, IEEE INTELL SYST, V19, P50, DOI 10.1109/MIS.2004.23 Kuflik T, 2003, LECT NOTES ARTIF INT, V2702, P168 Lassila O., 2001, SCI AM MAY McDonald AM, 2009, LECT NOTES COMPUT SC, V5672, P37, DOI 10.1007/978-3-642-03168-7_3 Michlmayr E., 2007, P WORKSH TAGG MET SO OWL Working Group W, 2009, OWL 2 WEB ONT LANG D REBOLLOMONEDERO D, 2009, IEEE T KNOWL DAT OCT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shapira B, 2005, J AM SOC INF SCI TEC, V56, P159, DOI 10.1002/asi.20107 Toubiana V., 2007, SQUIGGLESR NR 20 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-15151-4 J9 LECT NOTES COMPUT SC PY 2010 VL 6264 BP 58 EP 68 PG 11 WC Computer Science, Theory & Methods SC Computer Science GA BSR04 UT WOS:000285524700006 ER PT S AU Qu, SN Xu-Simon AF Qu, Shouning Xu-Simon BE Ao, SI Douglas, C Grundfest, WS Burgstone, J TI Research on the Building Method of Domain Lexicon Combining Association Rules and Improved TF*IDF SO WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, VOLS 1 AND 2 SE Lecture Notes in Engineering and Computer Science LA English DT Proceedings Paper CT World Congress on Engineering and Computer Science (WCECS)/International Conference on Advances in Engineering Technologies CY OCT 20-22, 2010 CL San Francisco, CA SP Int Assoc Engineers (IAENG), IAENG Soc Artificial Intelligence, IAENG Soc Bioinformat, IAENG Soc Chem Engn, IAENG Soc Comp Sci, IAENG Soc Data Min, IAENG Soc Elect Engn, IAENG Soc Imaging Engn, IAENG Soc Ind Engn, IAENG Soc Internet Comp & Web Serv, IAENG Soc Operat Res, IAENG Soc Sci Comp, IAENG Soc Software Engn, IAENG Soc Wireless Networks, IAENG Soc HIV/AIDS DE domain lexicon; word segmentation; statistic the frequency of the word; weight; association rule AB To improve the efficiency and accuracy of topic words extraction in information extraction and topic words classification, a new topic lexicon building method is gradually updated and finally formed by combining association rules and improved TF*IDF algorithm. The improved TF*IDF algorithm considers the affection of text length, feature item length, feature item location and identification of compound words on the topic extraction. Experiments show that the proposed method can greatly improve the efficiency and accuracy of topic words extraction, and make information extraction and text classification more effectively. C1 [Qu, Shouning] Univ Jinan, Informat Network Ctr, Jinan 250022, Peoples R China. RP Qu, SN (reprint author), Univ Jinan, Informat Network Ctr, Jinan 250022, Peoples R China. EM qsn@ujn.edu.cn; xu-simon@hotmail.com CR Agrawal R., 1994, P 20 INT C VER LARG, P487 Auen James, 1991, NATURAL LANGUAGE UND Dai Wenhua, 2008, RES TEXT CLASSIFICAT DU Jing, 2010, COMPUTER ENG DESIGN, V31, P630 Feldman Ronen, 2009, TEXT MINING HDB Holt JD, 2002, INFORM PROCESS LETT, V83, P211, DOI 10.1016/S0020-0190(01)00330-1 Hu Xiheng, 2008, J ANSHAN NORMAL U, V10, P42 Huang Wei, 2010, SCI TECHNOLOGY ENG, V10, P85 [黄ēŽ‰å…° HUANG Yulan], 2008, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V22, P111 Kodratoff Y, 1999, P ISMIS 99 WARS JUN Li Juanzi, 2007, Wuhan University Journal of Natural Sciences, V12, DOI 10.1007/s11859-007-0038-4 Liu Chunhui, 2009, RES CHINESE SEGMENTA Liu Hongzhi, 2010, COMPUTER DEV APPL, V23, P1 Liu Yao-feng, 2010, Computer Engineering, V36 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Su Fei, 2004, COMPUTER ENG APPL, V15, P91 Su Fei, 2004, COMPUTER ENG APPL, V15, P43 Sun B, MODERN CHINESE TEXT Xiong Zhong-yang, 2008, Computer Engineering and Applications, V44 NR 21 TC 0 Z9 0 PU INT ASSOC ENGINEERS-IAENG PI HONG KONG PA UNIT1, 1-F, 37-39 HUNG TO ROAD, KWUN TONG, HONG KONG, 00000, PEOPLES R CHINA SN 2078-0958 BN 978-988-17012-0-6 J9 LECT NOTES ENG COMP PY 2010 BP 473 EP 479 PG 7 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BVV37 UT WOS:000292889100089 ER PT J AU Tu, YN Seng, JL AF Tu, Yi-Ning Seng, Jia-Lang TI Research intelligence involving information retrieval - An example of conferences and journals SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Citation analysis; Scientific web intelligence; Topic discovery and tracking; Data mining; Information retrieval; Academic intelligence ID TEXT MINING APPROACH; ONLINE IMPACT; MODEL AB This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals. The goal of this research is fourfold: First, the research investigates if the conference papers' themes lead the journal papers'. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers. This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence, This Study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers Submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991-2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Seng, Jia-Lang] Natl Chengchi Univ, Coll Commerce, Dept & Grad Sch Accountancy, Taipei 115, Taiwan. [Tu, Yi-Ning] Natl Chengchi Univ, Coll Commerce, Grad Sch MIS, Taipei 115, Taiwan. RP Seng, JL (reprint author), Natl Chengchi Univ, Coll Commerce, Dept & Grad Sch Accountancy, POB 5-80, Taipei 115, Taiwan. EM 94356509@nccu.edu.tw; seng@nccu.edu.tw FU National Science Council [NSC95-2416-H-004-006-] FX This research is partially sponsored by National Science Council research grant no. NSC95-2416-H-004-006-. CR Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954 Allan J., 1998, P DARPA BROADC NEWS Cui C., 2005, P 2005 ACM S APPL CO, P1089, DOI 10.1145/1066677.1066924 FELIX MA, 2005, INFORM PROCESSING MA, V41, P1521 Joachims T., 1998, P EMNLP C Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI DOI 10.1023/A:1024940629314 Lee C, 2007, INFORM PROCESS MANAG, V43, P1249, DOI 10.1016/j.ipm.2006.02.007 Malone J, 2006, EXPERT SYST APPL, V30, P24, DOI 10.1016/j.eswa.2005.09.047 MARKKONEN J, 2004, INFORM RETRIEVAL, V7, P347 MOULINIER L, 1996, ANN S DOC AN INF RET PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUBERT H, 1995, FEMS MICROBIOL ECOL, V18, P237, DOI 10.1111/j.1574-6941.1995.tb00180.x Schultz J. M., 1999, P DARPA BROADC NEWS Tan P., 2006, INTRO DATA MINING Thelwall M, 2004, J AM SOC INF SCI TEC, V55, P149, DOI 10.1002/asi.10362 Thelwall M, 2003, ONLINE INFORM REV, V27, P333, DOI 10.1108/14684520310502298 Thelwall M, 2005, COMMUN ACM, V48, P93, DOI 10.1145/1070838.1070843 Tho QT, 2007, INFORM PROCESS MANAG, V43, P248, DOI 10.1016/j.ipm.2006.05.015 Walls F., 1999, P DARPA BROADC NEWS Yang HC, 2004, EXPERT SYST APPL, V27, P645, DOI 10.1016/j.eswa.2004.06.009 Yang HC, 2005, EXPERT SYST APPL, V29, P723, DOI 10.1016/j.eswa.2005.05.003 Yang Y, 2000, P 23 ANN INT ACM SIG, P65, DOI 10.1145/345508.345550 Yang Y, 1997, INT C MACH LEARN Yang YM, 1996, J AM SOC INFORM SCI, V47, P357 NR 28 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD DEC PY 2009 VL 36 IS 10 BP 12151 EP 12166 DI 10.1016/j.eswa.2009.03.015 PG 16 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 504VP UT WOS:000270646200019 ER PT J AU Samatsu, T Tachikawa, K Shi, Y AF Samatsu, Takashi Tachikawa, Kie Shi, Yan TI USABILITY IMPROVEMENT FOR A CAR RETRIEVAL SYSTEM EMPLOYING THE IMPORTANT DEGREES OF FUZZY GRADES SO INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL LA English DT Article; Proceedings Paper CT 1st International Symposium on Intelligent Informatics (ISII 2008) CY DEC 12-13, 2008 CL Kumamoto, JAPAN DE Fuzzy set; Car retrieval system; Purchase support AB This paper proposes a fuzzy information retrieval system for purchasing cars which employs the important degrees. This system aims to support such persons who are not good with machines or cars, When they try to purchase a car, they can use this system easily as if they ask casually someone else who know more about car. Unspecific conditions are expressed by fuzzy sets, and the level matching conditions are expressed by grade values. To use this more practically, a GUI form with selection menus has been developed. In conclusion, reviews and observation of this study show its effectiveness, remaining issues, and measures for the issues. C1 [Samatsu, Takashi; Shi, Yan] Tokai Univ, Sch Ind Engn, Kumamoto 8628652, Japan. [Tachikawa, Kie] Tokai Univ, Grad Sch Ind Engn, Kumamoto 8628652, Japan. RP Samatsu, T (reprint author), Tokai Univ, Sch Ind Engn, 9-1-1 Toroku, Kumamoto 8628652, Japan. EM samatsu@tokai.ac.jp; tachikawa@star.tokai-u.jp; yshi@ktmail.tokai-u.jp CR Brooke J, 1996, USABILITY EVALUATION Dubois D, 1980, FUZZY SETS SYSTEMS T Jin H, 2008, INT J INNOV COMPUT I, V4, P1315 Liang T, 2008, INT J INNOV COMPUT I, V4, P2011 Onut S, 2008, INT J INNOV COMPUT I, V4, P1835 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 7 TC 2 Z9 2 PU ICIC INT PI KUMAMOTO PA TOKAI UNIV, 9-1-1, TOROKU, KUMAMOTO, 862-8652, JAPAN SN 1349-4198 J9 INT J INNOV COMPUT I JI Int. J. Innov. Comp. Inf. Control PD DEC PY 2009 VL 5 IS 12B BP 5061 EP 5068 PG 8 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 530CR UT WOS:000272567000033 ER PT J AU Yue, K Liu, WY AF Yue, Kun Liu, Wei-Yi TI SEMANTIC FIELD: A THEORETICAL PERSPECTIVE OF MODELING INFORMATION RETRIEVAL SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS LA English DT Article DE Information retrieval; semantic field; semantic affinity; coacervation degree; superposition; radiation ID PROBABILISTIC MODELS AB Iormation retrieval has been paid much attention and it is widely studied and applied in real world paradigms. For various aspects of information retrieval, various approaches have been proposed from various perspectives. It is necessary to provide a formally unified and physically-interpretable model for classical problems in information retrieval (e.g., document classification, authority-page selection, and keyword extraction, etc.). In this paper we propose a theoretical model, called semantic field, inspired by the theories of lexical semantics and electrostatic field. Based on this physical model, information retrieval can be viewed from a theoretical perspective and interpreted by people's physical intuitions and natural heuristics. Centered on the concept of semantic field, we give some relevant properties, including semantic affinity, semantic coacervation degree and radiation of a semantic source. As the representative application of the proposed semantic field model, a novel method for automatic keyword extraction is discussed, and the feasibility is verified by corresponding experiments. C1 [Yue, Kun; Liu, Wei-Yi] Yunnan Univ, Sch Informat Sci & Engn, Dept Comp Sci & Engn, Kunming 650091, Peoples R China. RP Yue, K (reprint author), Yunnan Univ, Sch Informat Sci & Engn, Dept Comp Sci & Engn, 2 N Cuihu Rd, Kunming 650091, Peoples R China. EM yue.kun@gmail.com; liuweiyi2000@yahoo.com.cn FU National Natural Science Foundation of China [60763007, 60933001]; Natural Science Foundation of Yunnan Province [2008CD083]; Research Foundation of the Educational Department of Yunnan Province [08Y0023]; Cultivation Foundation of Young Teachers in Yunnan University; Research Foundation of Yunnan University [2009F32Q] FX This work was supported by the National Natural Science Foundation of China (No. 60763007, 60933001), the Natural Science Foundation of Yunnan Province (No. 2008CD083), the Research Foundation of the Educational Department of Yunnan Province (No. 08Y0023), the Cultivation Foundation of Young Teachers in Yunnan University, and the Research Foundation of Yunnan University (No. 2009F32Q). CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Chan SWK, 2006, DECIS SUPPORT SYST, V42, P759, DOI 10.1016/j.dss.2004.11.017 Cormen T.H., 2001, INTRO ALGORITHMS FALOUTSOS C, 1995, 3514 U MAR DEP COMP Fang H., 2004, P 27 ANN INT ACM SIG, P49, DOI 10.1145/1008992.1009004 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GLIOZZO A, 2006, P LREC 2006 WORKSH C Gliozzo A., 2005, THESIS U TRENTO GLIOZZO A, 2006, P 11 C EUR CHAPT ASS, P147, DOI 10.3115/1608974.1608994 Gliozzo A. M., 2007, P NAACL HLT 07, P131 Goetz B., 2000, LUCENE SEARCH ENGINE Hall M., 1967, COMBINATORIAL THEORY KLEIBERG JM, 1996, J ACM, V46, P604 KOLDA TG, 1991, ACM T INFORM SYST, V9, P223 LARKEY S, 1996, P SIGIR, P81 Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 Page L, 1998, PAGERANK CITATION RA Ristad E.S, 1995, CSTR49595 PRINC U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi S., 2005, P 28 ANN INT ACM SIG, P488, DOI 10.1145/1076034.1076117 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 Trier Jost, 1931, DTSCH WORTSCHATZ SIN TUCKER T, 2006, APPL COMBINATORICS ULLMAN S, 1957, PRINCIPLE SEMANTICS Vassilyev L M, 1974, LINGUISTICS, V137, P79 Wang QA, 2005, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, P3781 WONG SKM, 1995, ACM T INFORM SYST, V13, P69 YU C, 2001, P ACM SIGMOD C SANT, P187, DOI 10.1145/375663.375684 NR 29 TC 3 Z9 4 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-2130 J9 INT J ARTIF INTELL T JI Int. J. Artif. Intell. Tools PD DEC PY 2009 VL 18 IS 6 BP 825 EP 851 DI 10.1142/S0218213009000421 PG 27 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 541OM UT WOS:000273425700002 ER PT J AU Zhang, CL Zeng, D Li, JX Wang, FY Zuo, WL AF Zhang, Changli Zeng, Daniel Li, Jiexun Wang, Fei-Yue Zuo, Wanli TI Sentiment Analysis of Chinese Documents: From Sentence to Document Level SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID CLASSIFIERS; TEXTS AB User-generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule-based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning-based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches. C1 [Zhang, Changli; Zuo, Wanli] Jilin Univ, Coll Comp Sci & Technol, Jilin, Peoples R China. [Zhang, Changli] Artillery Command Coll Shenyang, Shenyang, Peoples R China. [Zeng, Daniel] Univ Arizona, MS Dept, Tucson, AZ 85721 USA. [Zeng, Daniel; Wang, Fei-Yue] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China. [Li, Jiexun] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. RP Zhang, CL (reprint author), Jilin Univ, Coll Comp Sci & Technol, Jilin, Peoples R China. EM shenyangzcl@163.com; zeng@email.arizona.edu; jiexun.li@ischool.drexel.edu; feiyue@gmail.com; wanli@jlu.edu.cn RI Zeng, Daniel/A-8072-2012; Zeng, Daniel/B-5888-2013 FU NNSFC [70890084, 60621001]; MOST [2006AA010106]; CAS [2F05N01, 2F07C01, 2F08N03]; NSF [IIS-0839990, IIS-0428241] FX This work is supported in part by NNSFC #70890084 and #60621001; MOST 2006AA010106; CAS #2F05N01, #2F07C01, and #2F08N03; and NSF #IIS-0839990 and #IIS-0428241. We thank Hengmin Zhou and Chao Chang from the Institute of Automation for collecting and preprocessing the AmazonCN dataset. CR Abbasi A., 2008, ACM T INFORM SYSTEMS, V26, P1 Bansal M., 2008, P INT C COMP LING CO, P15 CHE ZLW, 2008, P 12 C COMP NAT LANG, P238 Dai W., 2007, P 22 AAAI C ART INT, P540 Dong Z., 2003, P 2003 INT C NAT LAN, P820 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Efron M., 2004, P AAAI FALL S STYL M, P41 Hatzivassiloglou Vasileios, 1997, P 35 ANN M ASS COMP, P174, DOI DOI 10.3115/976909.979640 HU M, 2006, AAAI CAAW 06 SPRING Joachims T., 1998, LNCS, V1398, P137 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Kamps J., 2004, P 4 INT C LANG RES E, V4, P1115 Kim S., 2004, P 20 INT C COMP LING, P1367, DOI DOI 10.3115/1220355.1220555 Laver M, 2003, AM POLIT SCI REV, V97, P311 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Li Juanzi, 2007, Wuhan University Journal of Natural Sciences, V12, DOI 10.1007/s11859-007-0038-4 Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797 Mao Y., 2006, P 2000 NEUR INF PROC, P961 MASUYAMA T, 2002, LECT NOTES COMPUTER, V2453, P241 Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 McCallum A., 1998, COMP EVENT MODELS NA McDonald R., 2007, P 45 ANN M ASS COMP, P432 Meena A, 2007, LECT NOTES COMPUT SC, V4425, P573 Mishne G., 2006, AAAI 2006 SPRING S C Mitchell Tom, 1997, DECISION TREE LEARNI Mullen T., 2006, P AAAI S COMP APPR A, P159 Nasukawa T., 2003, P 2 INT C KNOWL CAPT, P70, DOI DOI 10.1145/945645.945658 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 Pang B., 2004, P 42 ANN M ASS COMP, P271, DOI DOI 10.3115/1218955.1218990 Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 Quinlan JR, 1996, ACM COMPUT SURV, V28, P71, DOI 10.1145/234313.234346 Riloff E., 2006, P C EMP METH NAT LAN, P440, DOI 10.3115/1610075.1610137 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHARMA A, 2007, P 3 IND INT C ART IN, P1525 Su Q., 2008, P 17 INT C WORLD WID, P959, DOI 10.1145/1367497.1367627 Thomas M., 2006, P 2006 C EMP METH NA, P327, DOI 10.3115/1610075.1610122 TSOU BKY, 2005, INT C INT AN VIENN V Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 Turney PD, 2003, ACM T INFORM SYST, V21, P315, DOI 10.1145/944012.944013 Veale T, 2005, LECT NOTES ARTIF INT, V3651, P326 Wang Y, 2003, PROC INT C TOOLS ART, P560 Wiebe J, 2004, COMPUT LINGUIST, V30, P277, DOI 10.1162/0891201041850885 Wiebe J, 2005, LECT NOTES COMPUT SC, V3406, P486 Wiebe J. M., 1999, P 37 ANN M ASS COMP, P246, DOI 10.3115/1034678.1034721 Wilson T., 2005, P HUM LANG TECHN C C YAN J, 2007, ARTIF INTELL, P17 YANG Y, REEXAMINATION TEXT C, P42 YE Q, 2006, HICSS 06 YU H, 2003, C EMP METH NAT LANG Yu H., 2002, P 8 ACM SIGKDD INT C, P239 Yuen R. W., 2004, P 20 INT C COMP LING, P1008, DOI 10.3115/1220355.1220500 Zhu HL, 2008, PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE FOR YOUNG COMPUTER SCIENTISTS, VOLS 1-5, P1677 NR 52 TC 39 Z9 39 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD DEC PY 2009 VL 60 IS 12 BP 2474 EP 2487 DI 10.1002/asi.21206 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 525ZT UT WOS:000272257800008 ER PT J AU Perez-Marin, D Pascual-Nieto, I Rodriguez, P AF Perez-Marin, Diana Pascual-Nieto, Ismael Rodriguez, Pilar TI Computer-assisted assessment of free-text answers SO KNOWLEDGE ENGINEERING REVIEW LA English DT Review ID LATENT SEMANTIC ANALYSIS AB The automatic assessment of students' free-text answers has recently received much attention, due to the necessity of exploring and taking advantage of new and more complex computer-based assessment methods. In this paper, a review of the state-of-art of the field is presented, focusing on the techniques that underpin these systems and their evaluation metrics. Although there is still a long way to go so as to reach the ideal system, the fact that the existing systems are already being used commercially and as a second opinion in exams Such as GMAT proves the uptake of this field. C1 [Perez-Marin, Diana] Univ Rey Juan Carlos, Language & Comp Syst Dept 1, Fac Comp Sci, Off 2025, Madrid 28933, Spain. [Pascual-Nieto, Ismael; Rodriguez, Pilar] Univ Autonoma Madrid, Dept Comp Sci, E-28049 Madrid, Spain. RP Perez-Marin, D (reprint author), Univ Rey Juan Carlos, Language & Comp Syst Dept 1, Fac Comp Sci, Off 2025, Ampliac Rectorado Bldg,Tulipan St, Madrid 28933, Spain. EM diana.perez@urjc.es; ismael.pascual@uam.es; pilar.rodriguez@uam.es RI Perez-Marin, Diana/L-4100-2014 OI Perez-Marin, Diana/0000-0003-3390-0251 FU Spanish Ministry of Science and Technology [TIN2007-64718] FX This work has been sponsored by Spanish Ministry of Science and Technology, project number TIN2007-64718. CR ALFONSECA E, 2004, P INT WORKSH ED AD H BIRENBAUM M, 1992, APPL PSYCH MEAS, V14, P353 BLAYNEY P, 2003, P 7 COMP ASS ASS C L Bloom B. S., 1956, HDB COGNITIVE DOMAIN Burstein J., 2001, P 5 INT COMP ASS ASS BURSTEIN J, 1998, P ANN M ASS COMP LIN Callear D., 2001, P 5 INT COMP ASS ASS CHRISTIE J, 1999, P 3 COMP ASS ASS INT CHRISTIE JR, 2003, P 7 INT COMP ASS ASS CHUNG G, 1997, 461 UCLA NAT CTR RES CUCCHIARELLI A, 2000, P 2 C LANG RES EV GR DARUS S, 2001, J MALAYSIAN ENGLISH, V30, P47 Darus S., 2001, REFLECTIONS VISIONS, P197 DATAR A, 2004, EGAL ESSAY GRADING A DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENTON P, 2003, P 7 COMP ASS ASS C L Dessus P., 2000, P 3 INT C HUM SYST L, P61 Foltz P. W., 1999, INTERACTIVE MULTIMED, V1 Wiemer-Hastings P., 2000, Interactive Learning Environments, V8 Ishioka T., 2004, Proceedings. 15th International Workshop on Database and Expert Systems Applications, DOI 10.1109/DEXA.2004.1333440 Kakkonen Tuomo, 2005, P 2 WORKSH BUILD ED, P29, DOI 10.3115/1609829.1609835 Kintsch E., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT087 Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 LEACOCK C, 2004, EXAMENS J, V1 LUTTICKE R, 2005, P ART INT ED C MALATESTA K, 2002, LECT NOTES COMPUTER, V2363 Manning Christopher D., 2001, FDN STAT NATURAL LAN Marcu D., 2000, THEORY PRACTICE DISC MARSHALL S, 1987, SYSTEM, V15, P161, DOI 10.1016/0346-251X(87)90063-7 Mason O., 2002, P 6 INT COMP ASS ASS MCGRATH P, 2003, P 7 COMP ASS ASS C L MIKHAILOV A, 1998, INTELLIGENT ENG SYST, V8, P57 MING Y, 2000, LEARNERS TOGETHER MITCHELL T, 2003, P 7 COMP ASS ASS C L MITCHELL T, 2002, P 6 COMP ASS ASS C L *MUC7, 1998, P 7 MESS UND C MUC 7 PAGE EB, 1966, PHI DELTA KAPPAN, V47, P238 PAGE E, 1994, J EXPT ED, V2, P127 PALMER K, 2003, P 7 COMP ASS ASS C L PARSONS H, 2003, P 7 COMP ASS ASS C L PEREZ D, 2005, P 18 INT C FLOR ART PEREZMARIN D, 2006, P 22 INT C SPAN SOC Perez-Marin D, 2007, LECT NOTES ARTIF INT, V4511, P329 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN ROSE C, 2003, P HLT NAACL WORKSH E RUDNER L, 2001, ED RESOURCES INF CTR RUDNER LM, 2002, P ANN M NATL COUNC M SALTON G, 1989, AUTOMATIC TEST PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEALEY C, 2003, P 7 COMP ASS ASS C L Shermis MD, 2002, EDUC PSYCHOL MEAS, V62, P5, DOI 10.1177/0013164402062001001 Streeter L., 2003, P INT IND SIM ED C I SUKKARIEH J, 2003, P 29 IAEA C THEM SOC Valenti S., 2003, J INF TECHNOL, V2, P319 van Rijsbergen C. J., 1979, INFORM RETRIEVAL *VANT LEARN TECHN, 2000, RB397 VANT LEARN TEC *VANT LEARN TECHN, 2001, RB561 VANT LEARN TEC WHITTINGDON D, 1999, P 3 INT COMP ASS ASS WIEMERHASTINGS P, 2004, P 7 INT C INT TUT SY WIEMERHASTINGS P, 1998, P 4 INT C INT TUT SY, P334 Williams R., 2001, P 10 ANN TEACH LEARN Williams R., 2004, P INF SCI C ROCKH QU NR 64 TC 9 Z9 9 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA SN 0269-8889 J9 KNOWL ENG REV JI Knowl. Eng. Rev. PD DEC PY 2009 VL 24 IS 4 BP 353 EP 374 DI 10.1017/S026988890999018X PG 22 WC Computer Science, Artificial Intelligence SC Computer Science GA 538MJ UT WOS:000273188300002 ER PT J AU Tan, GX Viard-Gaudin, C Kot, AC AF Tan, Guo Xian Viard-Gaudin, Christian Kot, Alex C. TI Automatic writer identification framework for online handwritten documents using character prototypes SO PATTERN RECOGNITION LA English DT Article; Proceedings Paper CT International Conference on Frontiers in Handwriting Recognition (ICFHR 2008) CY AUG 18-21, 2008 CL Montreal, CANADA DE Writer identification; Information retrieval; Online handwriting; Fuzzy c-means; Allographs ID SCRIPT IDENTIFICATION; RECOGNITION; FEATURES; RETRIEVAL AB This paper proposes an automatic text-independent writer identification framework that integrates an industrial handwriting recognition system, which is used to perform an automatic segmentation of an online handwritten document at the character level. Subsequently, a fuzzy c-means approach is adopted to estimate statistical distributions of character prototypes on an alphabet basis. These distributions model the unique handwriting styles of the writers. The proposed system attained an accuracy of 99.2% when retrieved from a database of 120 writers. The only limitation is that a minimum length of text needs to be present in the document in order for sufficient accuracy to be achieved. We have found that this minimum length of text is about 160 characters or approximately equivalent to 3 lines of text. In addition, the discriminative power of different alphabets on the accuracy is also reported. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Tan, Guo Xian; Kot, Alex C.] Nanyang Technol Univ, Coll Engn, Singapore, Singapore. [Tan, Guo Xian; Viard-Gaudin, Christian] Univ Nantes, Ecole Polytech, CNRS, IRCCyN,UMR 6597, F-44035 Nantes, France. RP Tan, GX (reprint author), Nanyang Technol Univ, Coll Engn, Singapore, Singapore. EM tanguoxian@pmail.ntu.edu.sg; christian.viard-gaudin@univ-nantes.fr; eackot@ntu.edu.sg CR Bensefia A., 2005, ELECT LETT COMPUTER, V5, P72 BENSEFIA A, 2003, P 7 INT C DOC AN REC, P946 Bensefia A, 2005, PATTERN RECOGN LETT, V26, P2080, DOI 10.1016/j.patrec.2005.03.024 Bulacu M, 2007, IEEE T PATTERN ANAL, V29, P701, DOI 10.1109/TPAMI.2007.1009 Busch A, 2005, IEEE T PATTERN ANAL, V27, P1720, DOI 10.1109/TPAMI.2005.227 CHA S, 2007, J FORENSIC DOCUMENT, P1 Chan S.K., 2008, P SPIE INT SOC OPTIC Chellapilla K., 2006, 10 INT WORKSH FRONT, P423 Chen SM, 1999, IEEE T SYST MAN CY B, V29, P96, DOI 10.1109/3477.740169 Connell SD, 2002, IEEE T PATTERN ANAL, V24, P329, DOI 10.1109/34.990135 Cover T. M., 1991, ELEMENTS INFORM THEO Fujisawa H, 2008, PATTERN RECOGN, V41, P2435, DOI 10.1016/j.patcog.2008.03.015 GUYON I, 1991, PATTERN RECOGN, V24, P105, DOI 10.1016/0031-3203(91)90081-F Han J., 2006, DATA MINING CONCEPTS Hochberg J, 1997, IEEE T PATTERN ANAL, V19, P176, DOI 10.1109/34.574802 Hoppner F., 1999, FUZZY CLUSTER ANAL M Huber R. A., 1999, HANDWRITING IDENTIFI Lyne A. A., 1985, VOCABULARY FRENCH BU Morris R, 2000, FORENSIC HANDWRITING Niels R., 2008, INT C FRONT HANDWR R, P481 Niels R, 2007, INT J PATTERN RECOGN, V21, P61, DOI 10.1142/S0218001407005302 Pervouchine V, 2007, PATTERN RECOGN, V40, P1004, DOI 10.1016/j.patcog.2006.08.008 Pitak T., 2004, INT C IM PROC ICIP, V2, P889 Plamondon R, 2000, IEEE T PATTERN ANAL, V22, P63, DOI 10.1109/34.824821 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 ROSENBAUM R, 2003, J QUANTITATIVE LINGU, V10, P1, DOI 10.1076/jqul.10.1.1.16550 Said HES, 2000, PATTERN RECOGN, V33, P149, DOI 10.1016/S0031-3203(99)00006-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlapbach A., 2004, Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition Schomaker L., 1999, International Journal on Document Analysis and Recognition, V2, DOI 10.1007/s100320050031 Schomaker L, 2004, IEEE T PATTERN ANAL, V26, P787, DOI 10.1109/TPAMI.2004.18 Schomaker L., 2007, 9 INT C DOC AN REC I, DOI 10.1109/ICDAR.2007.4377119 Srihari S. N., 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.2001.953974 Srihari SN, 2002, J FORENSIC SCI, V47, P856 Srinivasan H., 2007, 9 INT C DOC AN REC I, P844, DOI 10.1109/ICDAR.2007.4377034 TAN GX, 2008, 11 INT C FRONT HANDW, P475 TAN GX, 2008, 19 INT C PATT REC Viard-Gaudin Christian, 1999, INT C DOC AN REC ICD, P455 *VIS OBJ, 2008, VIS OBJ IND TEXT REC VUURPIJL L, 1997, P 4 INT C DOC AN REC, V1, P387, DOI 10.1109/ICDAR.1997.619876 Zois EN, 2000, PATTERN RECOGN, V33, P385, DOI 10.1016/S0031-3203(99)00063-1 NR 41 TC 14 Z9 15 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD DEC PY 2009 VL 42 IS 12 BP 3313 EP 3323 DI 10.1016/j.patcog.2008.12.019 PG 11 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 493HQ UT WOS:000269727800021 ER PT J AU Cao, HG Bhardwaj, A Govindaraju, V AF Cao, Huaigu Bhardwaj, Anurag Govindaraju, Venu TI A probabilistic method for keyword retrieval in handwritten document images SO PATTERN RECOGNITION LA English DT Article; Proceedings Paper CT International Conference on Frontiers in Handwriting Recognition (ICFHR 2008) CY AUG 18-21, 2008 CL Montreal, CANADA DE Word spotting; Information retrieval; Handwriting recognition ID RECOGNITION AB Keyword retrieval in handwritten document images is a challenging task because handwriting recognition does not perform adequately to produce the transcriptions, specially when using large lexicons. Existing methods build indices using OCR distances or image features for the purpose of retrieval. These alternative methods are complimentary to the traditional approaches that build indices on OCR'ed text. In this paper, we describe an improvement to the existing keyword retrieval (word spotting) methods by modeling imperfect word segmentation as probabilities and integrating these probabilities into the word spotting algorithm. The scores returned by the word recognizer are also converted into probabilities and integrated into the probabilistic word spotting model. (C) 2009 Elsevier Ltd. All rights reserved C1 [Cao, Huaigu; Bhardwaj, Anurag; Govindaraju, Venu] SUNY Buffalo, Dept Comp Sci & Engn, CUBS, Amherst, NY 14260 USA. RP Cao, HG (reprint author), SUNY Buffalo, Dept Comp Sci & Engn, CUBS, Amherst, NY 14260 USA. EM hcao@bbn.com; ab94@buffalo.edu; govind@buffalo.edu CR BAEZAYATES RA, 1999, MODERN INFORM RETRIE, P79 Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 CAO H, 2007, 6 INT C ADV PATT REC, V5296, P45 Cao H, 2008, PROCEEDINGS OF THE 40TH SOUTHEASTERN SYMPOSIUM ON SYSTEM THEORY, P371 Cao H., 2007, P CVPR, P1 Chang C.-C., 2001, LIBSVM LIB SUPPORT V CHOI SC, 1969, TECHNOMETRICS, V11, P683, DOI 10.2307/1266892 EDWARDS J, 2004, P 18 ANN C NEUR INF, P385 Howe N., 2005, P SIGIR, P377, DOI 10.1145/1076034.1076099 Jain A. K., 2003, P 7 INT C DOC AN REC, P655 KANE S, 2001, MM34 CIIR U MASS AMH, P1 Kim G, 1997, IEEE T PATTERN ANAL, V19, P366 KWOK T, 2000, P DAT MIN FIN ENG IN, P461 Manmatha R., 2003, S DOC IM UND TECHN S, P77 Marinai S., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition Milewski R, 2006, LECT NOTES COMPUT SC, V3872, P106 Rath T. M., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009056 Rath TM, 2007, INT J DOC ANAL RECOG, V9, P139, DOI [10.1007/s10032-006-0027-8, 10.1007/s10032-007-0027-8] Reynolds DA, 2000, DIGIT SIGNAL PROCESS, V10, P19, DOI 10.1006/dspr.1999.0361 ROTHFEDER JL, 2003, DOC IM AN RETR WORKS, P30 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRIHARI SN, 2008, INT C AS DIG LIB, P134 UCHIASHI S, 1999, P INT C AC SPEECH SI, P3453 WANG XW, 2002, P ICPR, P223 Zhang B, 2004, P SOC PHOTO-OPT INS, V5296, P45 NR 25 TC 14 Z9 14 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD DEC PY 2009 VL 42 IS 12 BP 3374 EP 3382 DI 10.1016/j.patcog.2009.02.003 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 493HQ UT WOS:000269727800027 ER PT J AU Eda, T Yoshikawa, M Uchiyama, T Uchiyama, T AF Eda, Takeharu Yoshikawa, Masatoshi Uchiyama, Toshio Uchiyama, Tadasu TI The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article DE folksonomy; tag; LSI; bottom-up taxonomy; classification ID WEB SEARCH; PATTERNS AB In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tags like "cool" and "fun" from folksonomy tags without any extra knowledge other than folksonomy triples and use the level of tag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effective measure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of children nodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomy tags. C1 [Eda, Takeharu; Uchiyama, Toshio; Uchiyama, Tadasu] NTT Corp, NTT Cyber Solut Labs, Kanagawa, Japan. [Yoshikawa, Masatoshi] Kyoto Univ, Kyoto, Japan. RP Eda, T (reprint author), NTT Corp, NTT Cyber Solut Labs, Kanagawa, Japan. EM eda.takeharu@lab.ntt.co.jp; yoshikawa@i.kyoto-u.ac.jp; uchiyama.toshio@lab.ntt.co.jp; uchiyama.tadasu@lab.ntt.co.jp CR Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brooks CH, 2006, P 15 INT C WORLD WID, P625, DOI 10.1145/1135777.1135869 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dubinko M., 2006, P 15 INT C WORLD WID, P193, DOI 10.1145/1135777.1135810 EDA T, 2008, P WEB INF SYST ENG W, P151 EDA T, 2009, P INT WORLD WID WEB, P1199, DOI 10.1145/1526709.1526927 Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558 Heymann P., 2006, COLLABORATIVE CREATI HOFMANN T, 1999, P 15 C UNC ART INT U, P289 Kern R., 2007, P 18 INT C DAT EXP S, P283 Khy S, 2008, WORLD WIDE WEB, V11, P1, DOI 10.1007/s11280-007-0018-9 KOLARI P, 2006, P WORKSH WEBL EC ED Lee L., 2001, P 8 INT WORKSH ART I, P65 Lin CX, 2005, LECT NOTES COMPUT SC, V3399, P707 MARTIN H, 2007, P INT WORLD WID WEB, P1313 Mathes Adam, 2004, FOLKSONOMIES COOPERA Mika P., 2005, P 4 INT SEM WEB C IS, P522 Niwa S., 2006, P 3 INT C INF TECHN, P388 Ramamohanarao K, 2007, WORLD WIDE WEB, V10, P71, DOI 10.1007/s11280-006-0012-7 RIVADENEIRA AW, 2007, P SIGCHI C HUM FACT, P995, DOI 10.1145/1240624.1240775 Sabou M, 2007, LECT NOTES COMPUT SC, V4825, P423 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sen S, 2006, P 2006 20 ANN C COMP, P181, DOI 10.1145/1180875.1180904 Sinclair J., 2007, J INF SCI, V34, P15, DOI DOI 10.1177/0165551506078083 Voss Jakob, 2007, TAGGING FOLKSONOMY C Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 Yanbe Y, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P107, DOI 10.1145/1255175.1255198 Yee Ka-Ping, 2003, CHI 03, P401, DOI DOI 10.1145/642611.642681 Yeung CMA, 2007, PROCEEDING OF THE 2007 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY, WORKSHOPS, P3 Zhou D, 2008, P 17 INT C WORLD WID, P715, DOI DOI 10.1145/1367497.1367594 NR 31 TC 7 Z9 8 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD DEC PY 2009 VL 12 IS 4 BP 421 EP 440 DI 10.1007/s11280-009-0069-1 PG 20 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 510WS UT WOS:000271124800004 ER PT J AU Lifchitz, A Jhean-Larose, S Denhiere, G AF Lifchitz, Alain Jhean-Larose, Sandra Denhiere, Guy TI Effect of tuned parameters on an LSA multiple choice questions answering model SO BEHAVIOR RESEARCH METHODS LA English DT Article ID RETRIEVAL AB This article presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of latent semantic analysis (LSA). A difficult task, which consisted of answering (French) biology multiple choice questions, was used to test the semantic properties of the truncated singular space and to study the relative influence of the main parameters. A dedicated software was designed to fine-tune the LSA semantic space for the multiple choice questions task. With optimal parameters, the performances of our simple model were quite surprisingly equal or superior to those of seventh- and eighth-grade students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of the training data sets. In addition, we present an original entropy global weighting of the answers' terms for each of the multiple choice questions, which was necessary to achieve the model's success. C1 [Lifchitz, Alain] Univ Paris 06, LIP6, DAPA, CNRS, F-75016 Paris, France. [Jhean-Larose, Sandra; Denhiere, Guy] CNRS, EPHE, Equipe CHArt, Paris, France. RP Lifchitz, A (reprint author), Univ Paris 06, LIP6, DAPA, CNRS, 104 Ave President Kennedy, F-75016 Paris, France. EM alain.lifchitz@lip6.fr CR BAIER H, 2008, SUMMA LSA INTEGRATED BERRY MW, 2005, UNDERSTANDING SEARCH, P34 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENHIERE G, 2004, P 14 ANN M SOC TEXT, P10 DENHIERE G, 2007, P 1 INT C LAT SEM AN, P15 DIAZ J, 2008, THESIS U PM CURIE PA DIAZ J, 2008, P 3 EUR C TECHN ENH, P144 Ding C. H. Q., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312652 Dumais S., 2007, HDB LATENT SEMANTIC, P293 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 Graesser A. C., 1999, Cognitive Systems Research, V1, DOI 10.1016/S1389-0417(99)00005-4 Harman D., 1986, P 9 ANN INT ACM SIGI, P186, DOI 10.1145/253168.253206 JHEANLAROSE S, 2008, KNOWLEDGE EVAL UNPUB Kantrowitz M., 2000, P 23 ANN INT ACM SIG, P357, DOI 10.1145/345508.345650 Kintsch W., 2007, HDB LATENT SEMANTIC, P89 Martin D. I., 2007, HDB LATENT SEMANTIC, P35, DOI 10.1002/9780470690819.ch2 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PORTER MF, 2001, SNOWBALL FRENCH STEM Python Software Foundation, 2009, PYTH PROGR LANG Quesada J, 2007, HDB LATENT SEMANTIC, P71 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TISSERAND D, 2007, P 1 INT C LAT SEM AN, P17 WILD F, 2005, P 9 CAA C LOUGHB UK Wilderer PA, 2007, WA SCI TECHNOL, V7, P11, DOI 10.2166/ws.2007.036 NR 26 TC 6 Z9 6 PU PSYCHONOMIC SOC INC PI AUSTIN PA 1710 FORTVIEW RD, AUSTIN, TX 78704 USA SN 1554-351X J9 BEHAV RES METHODS JI Behav. Res. Methods PD NOV PY 2009 VL 41 IS 4 BP 1201 EP 1209 DI 10.3758/BRM.41.4.1201 PG 9 WC Psychology, Mathematical; Psychology, Experimental SC Psychology GA 521OA UT WOS:000271930000024 PM 19897829 ER PT J AU Luo, XF Xu, Z Li, Q Hu, QL Yu, J Tang, XH AF Luo, Xiangfeng Xu, Zheng Li, Qing Hu, Qingliang Yu, Jie Tang, Xinhuai TI Generation of similarity knowledge flow for intelligent browsing based on semantic link networks SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE LA English DT Article CT 3rd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2008) CY MAY 25, 2008 CL Kunming, PEOPLES R CHINA DE similar knowledge flow; intelligent browsing; element fuzzy cognitive map; semantic link network; semantic web ID INFORMATION-RETRIEVAL; SCIENCE; MANAGEMENT; DISCOVERY AB Similarity Knowledge Flow (SKF) is a kind of scientific workflow, providing an effective technique and theoretical support for intelligent browsing in the Web and e-Science environment. In this paper, a Semantic Link Networks (SLN) based SKF generation method is proposed. First, the topics are represented by Element Fuzzy Cognitive Maps then the semantic values of concepts/keywords and relations are calculated. Third, semantic similarity degrees between topics are calculated to build SLN-based semantic values of concepts and their relations in Element Fuzzy Cognitive Maps. In this way, similar relations at the keyword level are extended to the topic level. With the help of SLN and based on user's demand, SKF is generated as the browsing path of topics to guide user browsing behaviors. Finally, the semantic value of SKF is defined as a criterion to evaluate the browsing path of topics. Experimental results show that the browsing path of topics is easy to be activated by SKF which is generated by SLN. The proposed method has been proved to have a very good prospect in the fields of Web services and e-Science applications. Copyright (C) 2009 John Wiley & Sons, Ltd. C1 [Luo, Xiangfeng; Xu, Zheng; Hu, Qingliang; Yu, Jie] Shanghai Univ, Digital Content Comp & Semant Grid Grp, Joint Lab Next Generat Internet Interact Comp, Shanghai 200072, Peoples R China. [Li, Qing] City Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China. [Tang, Xinhuai] Shanghai Jiao Tong Univ, Shanghai, Peoples R China. RP Luo, XF (reprint author), Shanghai Univ, Digital Content Comp & Semant Grid Grp, Joint Lab Next Generat Internet Interact Comp, 149 Yanchang Rd, Shanghai 200072, Peoples R China. EM xiangfeng.luo@gmail.com CR ADAMIC LA, 2000, POWER LAWS PARETOA R Agosto DE, 2002, J AM SOC INF SCI TEC, V53, P16, DOI 10.1002/asi.10024 Agrawal R, 1993, MINING ASS RULES SET, P207 Allan J, 1997, INFORM PROCESS MANAG, V33, P145, DOI 10.1016/S0306-4573(96)00059-3 BORGES J, 1999, P WEBKDD, P92 Chen JB, 2007, EARTHQ ENG ENG VIB, V6, P35, DOI 10.1007/s11803-007-0672-z Chen JJ, 2008, CONCURR COMP-PRACT E, V20, P347, DOI 10.1002/cpe.1220 FREI HP, 1995, INFORM PROCESS MANAG, V31, P1, DOI 10.1016/0306-4573(94)E0005-M GREEN J, 1997, P 3 WORKSH APPL NAT Green SJ, 1999, IEEE T KNOWL DATA EN, V11, P713, DOI 10.1109/69.806932 Huberman BA, 1998, NOVELTY SOCIAL SEARC Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 LUO X, 2008, 1 IEEE INT C UB MED LUO X, 2008, 3 INT C GRID PERV CO, P57 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P839, DOI 10.1002/cpe.1271 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P1791, DOI 10.1002/cpe.1319 MARK E, 2002, COMMUNICATIONS ASS I, V8, P251 MEGHAB G, 2003, INT J COMPUTATIONAL, V1, P51 Morris J., 1991, Computational Linguistics, V17 Nissen M., 2002, COMMUNICATIONS ASS I, V8, P251 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 PERKOWITZ M, 1997, ADAPTIVE WEB SITES A Rogers E.M., 1995, DIFFUSION INNOVATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sorenson O, 2006, RES POLICY, V35, P994, DOI 10.1016/j.respol.2006.05.002 Yager R.R., 1992, INTRO FUZZY LOGIC AP Zhuge H, 2007, CONCURR COMP-PRACT E, V7, P1065 ZHUGE H, 2005, IEEE COMPUT, V38, P27 Zhuge H, 2006, COMMUN ACM, V49, P101, DOI 10.1145/1125944.1125948 Zhuge H., 2006, J SYST SOFTWARE, V79, P969, DOI DOI 10.1016/J.JSS.2005.08.022 Zhuge H, 2002, KNOWL-BASED SYST, V15, P465, DOI 10.1016/S0950-7051(02)00031-X Zhuge H, 2004, IEEE INTELL SYST, V19, P13 Zipf G. K., 1932, SELECTED STUDIES PRI Zukerman I., 1999, P 7 INT C US MOD UM, P275 NR 34 TC 8 Z9 9 PU JOHN WILEY & SONS LTD PI CHICHESTER PA THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND SN 1532-0626 J9 CONCURR COMP-PRACT E JI Concurr. Comput.-Pract. Exp. PD NOV PY 2009 VL 21 IS 16 BP 2018 EP 2032 DI 10.1002/cpe.1460 PG 15 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 514NJ UT WOS:000271401300005 ER PT J AU Aerts, D AF Aerts, Diederik TI Quantum Particles as Conceptual Entities: A Possible Explanatory Framework for Quantum Theory SO FOUNDATIONS OF SCIENCE LA English DT Review DE Quantum physics; Meaning; Coherence; Conceptual; Explanatory framework ID LATENT SEMANTIC ANALYSIS; BOSE-EINSTEIN CONDENSATION; SPIN-STATISTICS THEOREM; BELL INEQUALITIES; DECISION-THEORY; DELAYED CHOICE; MECHANICS; MATTER; REPRESENTATION; COMBINATIONS AB We put forward a possible new interpretation and explanatory framework for quantum theory. The basic hypothesis underlying this new framework is that quantum particles are conceptual entities. More concretely, we propose that quantum particles interact with ordinary matter, nuclei, atoms, molecules, macroscopic material entities, measuring apparatuses,., in a similar way to how human concepts interact with memory structures, human minds or artificial memories. We analyze the most characteristic aspects of quantum theory, i.e. entanglement and non-locality, interference and superposition, identity and individuality in the light of this new interpretation, and we put forward a specific explanation and understanding of these aspects. The basic hypothesis of our framework gives rise in a natural way to a Heisenberg uncertainty principle which introduces an understanding of the general situation of 'the one and the many' in quantum physics. A specific view on macro and micro different from the common one follows from the basic hypothesis and leads to an analysis of Schrodinger's Cat paradox and the measurement problem different from the existing ones. We reflect about the influence of this new quantum interpretation and explanatory framework on the global nature and evolutionary aspects of the world and human worldviews, and point out potential explanations for specific situations, such as the generation problem in particle physics, the confinement of quarks and the existence of dark matter. C1 [Aerts, Diederik] Vrije Univ Brussels, Ctr Leo Apostel Interdisciplinary Studies, B-1160 Brussels, Belgium. [Aerts, Diederik] Vrije Univ Brussels, Dept Math, B-1160 Brussels, Belgium. [Aerts, Diederik] Vrije Univ Brussels, Dept Psychol, B-1160 Brussels, Belgium. RP Aerts, D (reprint author), Vrije Univ Brussels, Ctr Leo Apostel Interdisciplinary Studies, B-1160 Brussels, Belgium. EM diraerts@vub.ac.be CR Achinstein P., 1991, PARTICLES WAVES HIST Aerts D, 1998, INT J THEOR PHYS, V37, P291, DOI 10.1023/A:1026670802579 Aerts D, 2005, INT J THEOR PHYS, V44, P897, DOI 10.1007/s10773-005-7067-6 Aerts D, 2009, LECT NOTES ARTIF INT, V5494, P128 AERTS D, 2005, WORLDVIEWS SCI US RE, P189, DOI 10.1142/9789812702043_0011 AERTS D, 1992, INT J THEOR PHYS, V31, P1815, DOI 10.1007/BF00678294 Aerts D., 2007, GEN QUANTUM MODELING Aerts D, 2004, J PHYS A-MATH GEN, V37, pL123, DOI 10.1088/0305-4470/37/12/L01 AERTS D, 2006, THEORY DECISION LI A Aerts D., 1995, FOUND SCI, V1, P85 AERTS D, 2010, INT J THEOR IN PRESS Aerts D, 2009, J MATH PSYCHOL, V53, P314, DOI 10.1016/j.jmp.2009.04.005 AERTS D, 1986, J MATH PHYS, V27, P202, DOI 10.1063/1.527362 AERTS D, 1982, LETT NUOVO CIMENTO, V34, P107, DOI 10.1007/BF02817207 Aerts D, 2005, KYBERNETES, V34, P192, DOI 10.1108/03684920510575807 Aerts D, 2009, LECT NOTES ARTIF INT, V5494, P59 Aerts D., 2007, QUANTUM INTERFERENCE Aerts D., 1985, S FDN MOD PHYS 50 YE, P305 Aerts D, 2000, FOUND PHYS, V30, P1387, DOI 10.1023/A:1026449716544 AERTS D, 1994, FOUND PHYS, V24, P1227, DOI 10.1007/BF02148566 AERTS D, 1985, GRUNDLAGEN EXACTEN N, V6, P235 Aerts D, 2005, KYBERNETES, V34, P167, DOI 10.1108/03684920510575799 Aerts D., 1999, EINSTEIN MEETS MAGRI, P129 AERTS D, 1991, HELV PHYS ACTA, V64, P1 Aerts D., 1998, INTERPRETING BODIES, P223 AERTS D, 1995, INT J THEOR PHYS, V34, P1165, DOI 10.1007/BF00676227 ALBRECHT A, 1994, J MOD OPTIC, V41, P2467, DOI 10.1080/09500349414552311 ANDERSON MH, 1995, SCIENCE, V269, P198, DOI 10.1126/science.269.5221.198 ARAFAT S, 2007, P 1 AAI QUANT INT S, P114 ASPECT A, 1981, PHYS REV LETT, V47, P460, DOI 10.1103/PhysRevLett.47.460 BAEZ J, 1995, J MATH PHYS, V36, P108 Bell J.S., 1964, Physics, V1 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOHM D, 1952, PHYS REV, V85, P180, DOI 10.1103/PhysRev.85.180 BOHM D, 1952, PHYS REV, V85, P166, DOI 10.1103/PhysRev.85.166 Bohr N, 1928, NATURE, V121, P580, DOI 10.1038/121580a0 BROMLY DA, 1961, PHYS REV, V123, P878, DOI 10.1103/PhysRev.123.878 Busemeyer J.R., 2006, P 28 ANN C COGN SCI, P131 Busemeyer JR, 2006, J MATH PSYCHOL, V50, P220, DOI 10.1016/j.jmp.2006.01.003 Camino FE, 2005, PHYS REV B, V72, DOI 10.1103/PhysRevB.72.075342 Castellani E., 1998, INTERPRETING BODIES Chandler D, 2002, SEMIOTICS BASICS CLAUSER JF, 1969, PHYS REV LETT, V23, P880, DOI 10.1103/PhysRevLett.23.880 DAVIS KB, 1995, PHYS REV LETT, V75, P3969, DOI 10.1103/PhysRevLett.75.3969 de Broglie L, 1925, CR HEBD ACAD SCI, V177, P507 de Broglie L., 1928, P SOLV C 1928 EL PHO, P105 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEKUNDER M, 2009, SIZE WORLD WIDE WEB Dieks D, 2008, FOUND PHYS, V38, P923, DOI 10.1007/s10701-008-9243-z Duck I, 1998, AM J PHYS, V66, P284, DOI 10.1119/1.18860 LENARD A, 1968, J MATH PHYS, V9, P698, DOI 10.1063/1.1664631 DYSON FJ, 1967, J MATH PHYS, V8, P423, DOI 10.1063/1.1705209 Einstein A, 1935, PHYS REV, V47, P0777, DOI 10.1103/PhysRev.47.777 Einstein A, 1905, ANN PHYS-BERLIN, V17, P132 FEYNMAN R. P., 1961, QUANTUM ELECTRODYNAM Feynman R. P., 1987, ELEMENTARY PARTICLES Feynman R. P., 1963, FEYNMAN LECT PHYS, V3 FEYNMAN RP, 1967, CHARACTER PHYS LAW, pCH6 Fierz M., 1939, Helvetica Physica Acta, V12 Franco R, 2009, J MATH PSYCHOL, V53, P415, DOI 10.1016/j.jmp.2009.02.002 French S., 2006, IDENTITY PHYS HIST P Fresnel A.-J., 1819, ANN CHIM PHYS, V11, P246 Fresnel A.-J., 1819, ANN CHIM PHYS, V11, P337 Gabora L, 2002, J EXP THEOR ARTIF IN, V14, P327, DOI 10.1080/09528130210162253 GRIFFITHS TL, 2003, ADV NEURAL INFORM PR, V15, P11 HAMPTON JA, 1988, MEM COGNITION, V16, P579, DOI 10.3758/BF03197059 Heisenberg W, 1925, Z PHYS, V33, P879, DOI 10.1007/BF01328377 Heisenberg W., 1927, Zeitschrift fur Physik, V43, DOI 10.1007/BF01397280 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 HUYGENS C, 1990, WAVE THEORY LIGHT Khrennikov AY, 2009, J MATH PSYCHOL, V53, P378, DOI 10.1016/j.jmp.2009.01.007 Kim YH, 2000, PHYS REV LETT, V84, P1, DOI 10.1103/PhysRevLett.84.1 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LEINAAS JM, 1977, NUOVO CIMENTO B, V37, P1, DOI 10.1007/BF02727953 Lieb E. H., 1979, Chinese Journal of Physics, V17 LIEB EH, 1976, REV MOD PHYS, V48, P553, DOI 10.1103/RevModPhys.48.553 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Maxwell J. C., 1865, PHILOS T ROY SOC LON, V155, P459, DOI DOI 10.1098/RSTL.1865.0008 Muthaporn C, 2004, PHYS LETT A, V321, P152, DOI 10.1016/j.physleta.2003.12.028 Newton I., 1704, OPTICKS TREATISE REF O'Hara P, 2003, FOUND PHYS, V33, P1349, DOI 10.1023/A:1025697412645 Pauli W, 1940, PHYS REV, V58, P716, DOI 10.1103/PhysRev.58.716 PAULI W, 1950, PROG THEOR PHYS, V5, P526 Planck M, 1901, ANN PHYS-BERLIN, V4, P553 Pothos EM, 2009, P R SOC B, V276, P2171, DOI 10.1098/rspb.2009.0121 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schrodinger E, 1926, ANN PHYS-BERLIN, V79, P734 Schrodinger E, 1926, ANN PHYS-BERLIN, V79, P361 SCULLY MO, 1982, PHYS REV A, V25, P2208, DOI 10.1103/PhysRevA.25.2208 Streater R., 1989, PCT SPIN STAT ALL Tittel W, 1998, PHYS REV A, V57, P3229, DOI 10.1103/PhysRevA.57.3229 Van Rijsbergen K., 2004, GEOMETRY INFORM RETR VANFRAASSEN BC, 1995, QUANTUM MECH EMPIRIC von Neumann J., 1932, MATH FDN QUANTUM MEC Walborn SP, 2002, PHYS REV A, V65, DOI 10.1103/PhysRevA.65.033818 Weihs G, 1998, PHYS REV LETT, V81, P5039, DOI 10.1103/PhysRevLett.81.5039 Weinberg S, 1995, QUANTUM THEORY FIELD Widdows D., 2003, MATH LANGUAGE, V8, P141 Widdows D., 2008, P 2 AAAI S QUANT INT Widdows D, 2009, LECT NOTES ARTIF INT, V5494, P251 Widdows D., 2006, GEOMETRY AND MEANING Widdows D., 2003, P 41 ANN M ASS COMP, P136 WILCZEK F, 1982, PHYS REV LETT, V49, P957, DOI 10.1103/PhysRevLett.49.957 Young T., 1802, PHILOS T R SOC LOND, V92, P12, DOI DOI 10.1098/RSTL.1802.0004 NR 105 TC 14 Z9 15 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1233-1821 J9 FOUND SCI JI Found. Sci. PD NOV PY 2009 VL 14 IS 4 BP 361 EP 411 DI 10.1007/s10699-009-9166-y PG 51 WC History & Philosophy Of Science SC History & Philosophy of Science GA 511VT UT WOS:000271200000008 ER PT J AU Strobelt, H Oelke, D Rohrdantz, C Stoffel, A Keim, DA Deussen, O AF Strobelt, Hendrik Oelke, Daniela Rohrdantz, Christian Stoffel, Andreas Keim, Daniel A. Deussen, Oliver TI Document Cards: A Top Trumps Visualization for Documents SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article; Proceedings Paper CT IEEE Information Visualization Conference/IEEE Visualization Conference CY OCT 11, 2009 CL Atlantic City, NJ SP IEEE, IEEE Comp Soc, IEEE vgtc, Natl Informat Assurance Res Lab, Kitware, NVAC, NVIDIA, VisMaster, IBM Res, HP, Microsoft Res, Natl Lib Med, NSI, CYVIZ, Appl Vis Secure Decis, SCI, Morgan & Claypool Publ, Palgrave Macmillan, A K Peters Ltd DE document visualization; visual summary; content extraction; document collection browsing ID ICONS AB Finding suitable, less space consuming views for a document's main content is crucial to provide convenient access to large document collections on display devices of different size. We present a novel compact visualization which represents the document's key semantic as a mixture of images and important key terms, similar to cards in a top trumps game. The key terms are extracted using an advanced text mining approach based on a fully automatic document structure extraction. The images and their captions are extracted using a graphical heuristic and the captions are used for a semi-semantic image weighting. Furthermore, we use the image color histogram for classification and show at least one representative from each non-empty image class. The approach is demonstrated for the IEEE InfoVis publications of a complete year. The method can easily be applied to other publication collections and sets of documents which contain images. C1 [Strobelt, Hendrik; Oelke, Daniela; Rohrdantz, Christian; Keim, Daniel A.; Deussen, Oliver] Univ Konstanz, D-7750 Constance, Germany. RP Strobelt, H (reprint author), Univ Konstanz, D-7750 Constance, Germany. EM hendrik.strobelt@uni-konstanz.de; daniela.oelke@uni-konstanz.de; christian.rohrdantz@uni-konstanz.de; andreas.stoffel.ext@siemens.com; daniel.keim@uni-konstanz.de; oliver.deussen@uni-konstanz.de CR Andrade Miguel A., 2003, BMC BIOINFORMATICS, V4 BALDRIDGE J, 2009, OPENNLP PROJECT BAUER D, 2005, P HAW INT C SYST SCI, V4, pB104 Berkner K., 2003, Proceedings of the SPIE - The International Society for Optical Engineering, V5296, DOI 10.1117/12.523666 BERKNER K, 2006, P SOC PHOTO-OPT INS, V6076, P127 BREUEL TM, 2002, P IAPR 16 ICPR QUEB, V4, P476 Chang C., 2009, LIBSVM LIB SUPPORT V CHAO H, 2004, DOCUMENT ANAL SYSTEM, V6, P213 Chapelle O, 1999, IEEE T NEURAL NETWOR, V10, P1055, DOI 10.1109/72.788646 Cockburn A., 2006, P CHI 06, P1, DOI 10.1145/1124772.1124774 Erol B, 2006, P ACM C MULT SANT BA, P231, DOI 10.1145/1180639.1180701 FEINBERG J, 2009, WORDLE BEAUTIFUL WOR Feldman R, 1998, LECT NOTES ARTIF INT, V1510, P65 HENRY TR, 1990, ACM T GRAPHIC, V9, P133, DOI 10.1145/77635.77641 Itoh T, 2004, IEEE T VIS COMPUT GR, V10, P302, DOI 10.1109/TVCG.2004.1272729 Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag Korf R. E., 2003, Proceedings, Thirteenth International Conference on Automated Planning and Scheduling KOU Z, 2003, P 3 ACM SIGKDD WORKS, P2 KRUK M, 2009, PDF HTML CONVERSION KUHLEN R, 1977, EXPT MORPHOLOGIE INF Lam Heidi, 2005, P SIGCHI C HUM FACT, P681, DOI 10.1145/1054972.1055066 LEWIS J, 2004, ACM T GRAPHICS TOG, P416, DOI 10.1145/1186562.1015739 Maderlechner G, 2006, LECT NOTES COMPUT SC, V4109, P422 Matsuo Y., 2004, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V13, DOI 10.1142/S0218213004001466 Moreno Pedro J., 2004, ADV NEURAL INFORM PR Murata H., 1995, P ICCAD, P472, DOI 10.1109/ICCAD.1995.480159 RUSSELL D, 2003, P HAW INT C SYST SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuemie MJ, 2004, BIOINFORMATICS, V20, P2597, DOI 10.1093/bioinformatics/bth291 SCOTT J, 2009, PACKING LIGHTMAPS Seifert C., 2008, P 12 INT C INF VIS, P17 Setlur V, 2005, COMPUT GRAPH FORUM, V24, P647, DOI 10.1111/j.1467-8659.2005.00889.x SPAEREKJONES K, 1972, J DOC, V28, P11 Suh B., 2003, P 16 ANN ACM S US IN, P95, DOI 10.1145/964696.964707 Suh B., 2002, P CHI 2002, P251 Vasconcelos N, 2004, IEEE T INFORM THEORY, V50, P1482, DOI 10.1109/TIT.2004.830760 NR 36 TC 15 Z9 16 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1077-2626 J9 IEEE T VIS COMPUT GR JI IEEE Trans. Vis. Comput. Graph. PD NOV-DEC PY 2009 VL 15 IS 6 BP 1145 EP 1152 PG 8 WC Computer Science, Software Engineering SC Computer Science GA 506MM UT WOS:000270778900039 PM 19834183 ER PT J AU Shapira, B Shoval, P Tractinsky, N Meyer, J AF Shapira, Bracha Shoval, Peretz Tractinsky, Noam Meyer, Joachim TI ePaper: A Personalized Mobile Newspaper SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID NEWS AB This paper describes ePaper, a research prototype system of a personalized newspaper on a mobile reading device. The ePaper aggregates content (i.e., news items) from various news providers, classifies the news items according to concepts from a news domain ontology, and delivers an electronic newspaper to each subscribed user (reader). The system personalizes the content of the newspaper according to the user's profiles and preferences by applying ontological content-based and collaborative filtering algorithms. The user's profile is updated implicitly and dynamically, based on the tracking of their reading. Beyond personalization, the ePaper can also provide the user with a "standard edition" of a selected newspaper, as well as browsing capabilities in a repository of news items. The layout of the newspaper is adapted to the specifications of the reading device and to the user's preferences. In this overview paper, we highlight the main research challenges involved in the development of ePaper and describe how we addressed them. C1 [Shapira, Bracha; Shoval, Peretz; Tractinsky, Noam] Ben Gurion Univ Negev, Dept Informat Sci Engn, IL-84105 Beer Sheva, Israel. [Shapira, Bracha; Shoval, Peretz; Tractinsky, Noam; Meyer, Joachim] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. [Meyer, Joachim] Ben Gurion Univ Negev, Dept Ind Engn & Management, IL-84105 Beer Sheva, Israel. RP Shapira, B (reprint author), Ben Gurion Univ Negev, Dept Informat Sci Engn, IL-84105 Beer Sheva, Israel. EM bshapira@bgu.ac.il; shoval@bgu.ac.il; noamt@bgu.ac.il; joachim@bgu.ac.il RI Meyer, Joachim/A-5073-2010; Shoval, Peretz/F-1229-2012 OI Meyer, Joachim/0000-0002-1801-9987; Shoval, Peretz/0000-0002-0183-2832 CR ADONI H, 1995, J COMMUN, V45, P152, DOI 10.1111/j.1460-2466.1995.tb00734.x Ahn HJ, 2008, INFORM SCIENCES, V178, P37, DOI 10.1016/j.ins.2007.07.024 BLUM J, 2006, 633 IFRA CASADEMONT J, 2005, IEEE INT C WEB SERV, P841, DOI 10.1109/ICWS.2005.52 D'Haenens L, 2004, NEW MEDIA SOC, V6, P363, DOI 10.1177/1461444804042520 Ding Y., 2005, P 14 ACM INT C INF K, P485, DOI 10.1145/1099554.1099689 *ELIN, 2005, D62 ELIN INF SOC TEC ELMAGHRABY AS, 2000, INTELLIGENT APPROACH Flavian C, 2006, INTERNET RES, V16, P231, DOI 10.1108/10662240610673673 GENUTH I, 2007, FUTURE ELECT PAPER IHLSTROM C, 2005, TAGA 2005 INBAR O, 2008, 15 IND ENG MAN C IE KAMBA T, 1995, 4 INT WORLD WID WEB Kamba T, 1997, INT J HUM-COMPUT ST, V46, P789, DOI 10.1006/ijhc.1996.0113 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Lam X N, 2008, P 2 INT C UB INF MAN, P208, DOI 10.1145/1352793.1352837 MAIDEL V, 2008, P RECSYS 08 ACM C RE MIDDLETON SE, 2002, SEM WEB WORKSH 2002 Nielsen J., 1993, USABILITY ENG Peng F., 2003, P 2003 C N AM CHAPT, V1, P110, DOI 10.3115/1073445.1073470 PUUSTJARVI J, 2001, USING METADATA ELECT Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAPIRA B, 2008, P 12 INT C INT US IN SHOVAL P, 2008, INT J INFORM THEORIE, V15, P51 Tenenbaum L., 2008, P INFOS 2008 JUL 200, P89 TONGQUEUE L, 2006, P 2006 INT C E LEARN, P309 VILAR D, 2004, P ESP NAT LANG PROC, P220 YLIKOIVISTO J, 2001, P ONTO 2001 WORKSH O, P1 NR 29 TC 6 Z9 6 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD NOV PY 2009 VL 60 IS 11 BP 2333 EP 2346 DI 10.1002/asi.21172 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 511VP UT WOS:000271199600012 ER PT J AU Yalniz, IZ Altingovde, IS Gudukbay, U Ulusoy, O AF Yalniz, Ismet Zeki Altingovde, Ismail Sengor Gudukbay, Ugur Ulusoy, Ozgur TI Integrated segmentation and recognition of connected Ottoman script SO OPTICAL ENGINEERING LA English DT Article DE optical character recognition; historical document analysis; connected scripts; information retrieval ID ARABIC CHARACTER-RECOGNITION; RETRIEVAL; DOCUMENTS; IMAGES AB We propose a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides >90% precision and recall figures in terms of character recognition. In a further set of experiments, we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives. (C) 2009 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3262346] C1 [Yalniz, Ismet Zeki; Altingovde, Ismail Sengor; Gudukbay, Ugur; Ulusoy, Ozgur] Bilkent Univ, Dept Comp Engn, TR-06800 Ankara, Turkey. RP Yalniz, IZ (reprint author), Bilkent Univ, Dept Comp Engn, TR-06800 Ankara, Turkey. EM gudukbay@cs.bilkent.edu.tr RI Gudukbay, Ugur/F-1012-2011 OI Gudukbay, Ugur/0000-0003-2462-6959 FU European Union [FP6-507752] FX This work is supported by European Union 6th Framework Program under Grant No. FP6-507752 (MUSCLE Network of Excellence Project). We are grateful to Rana Nelson for proofreading and suggestions. CR ALQAHTANI SA, 2004, P 8 IASTED INT C ART Altingovde IS, 2006, 2006 IEEE 14th Signal Processing and Communications Applications, Vols 1 and 2, P968 Amin A, 1998, PATTERN RECOGN, V31, P517, DOI 10.1016/S0031-3203(97)00084-8 Ataer E., 2007, P 6 ACM INT C IM VID, P341 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 Chan J., 2006, P IEEE C COMP VIS PA, P1455 DEVELI H, 2006, OSMANLI TURKCESI KIL, V1 DHINGRA KD, 2008, LECT NOTES ELECT ENG, V4, P497 Doermann D, 1998, COMPUT VIS IMAGE UND, V70, P287, DOI 10.1006/cviu.1998.0692 Duygulu Pinar, 2006, P 8 ACM INT WORKSH M, P155, DOI 10.1145/1178677.1178700 Fataicha Y, 2006, INT J DOC ANAL RECOG, V8, P15, DOI 10.1007/s10032-005-0147-6 *GOOGL INC, 2009, GOOGL PRINT PROJ Hamami L, 2002, ARAB J SCI ENG, V27, P57 Haraty R., 2004, INT ARAB J INF TECHN, V1, P156 Hassin AH, 2004, J COMPUT SCI TECH-CH, V19, P538, DOI 10.1007/BF02944755 Hawking D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009096 Jones K. S., 2000, Information Processing & Management, V36 Kantor P. B., 2000, Information Retrieval, V2, DOI 10.1023/A:1009902609570 Kantrowitz M., 2000, P 23 ANN INT ACM SIG, P357, DOI 10.1145/345508.345650 Khorsheed MS, 2002, PATTERN ANAL APPL, V5, P31, DOI 10.1007/s100440200004 KURT Y, 2006, OSMANLICA DERSLERI, V1 Likforman-Sulem L, 2007, INT J DOC ANAL RECOG, V9, P123, DOI [10.1007/s10032-006-0023-z, 10.1007/s10032-007-0023-z] Lorigo LM, 2006, IEEE T PATTERN ANAL, V28, P712, DOI 10.1109/TPAMI.2006.102 Magdy W, 2008, INFORM RETRIEVAL, V11, P405, DOI 10.1007/s10791-008-9055-y OZTURK A, 2007, P 7 IEEE INT C EL CI, P945 SADRI J, 2007, P 9 INT S SIGN PROC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saykol E, 2004, IEEE T IMAGE PROCESS, V13, P314, DOI 10.1109/TIP.2003.821114 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 SOLEYMANI M, 2003, INT J COMPUTATIONAL, V1, P193 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P1 Witten I.H., 1994, MANAGING GIGABYTES C YALNIZ IZ, ACM J COMPU IN PRESS Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 Zobel J., 2006, ACM COMPUT SURV, V38, P1 2006, ZETTAIR ZETTAIR SEAR NR 37 TC 1 Z9 1 PU SPIE-SOC PHOTOPTICAL INSTRUMENTATION ENGINEERS PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA SN 0091-3286 J9 OPT ENG JI Opt. Eng. PD NOV PY 2009 VL 48 IS 11 AR 117205 DI 10.1117/1.3262346 PG 12 WC Optics SC Optics GA 538UW UT WOS:000273210600030 ER PT J AU Kashef, R Kamel, MS AF Kashef, R. Kamel, M. S. TI Enhanced bisecting k-means clustering using intermediate cooperation SO PATTERN RECOGNITION LA English DT Article DE Bisecting clustering; Cooperative clustering; Quality measures ID GENE-EXPRESSION; ALGORITHMS; PERFORMANCE; PATTERNS; CANCER AB Bisecting k-means (BKM) is very attractive in many applications as document-retrieval/indexing and gene expression analysis problems. However, in some scenarios when a fraction of the dataset is left behind with no other way to re-cluster it again at each level of the binary tree, a "refinement" is needed to re-cluster the resulting solutions. Current approaches to refine the Clustering Solutions produced by the BKM employ end-result enhancement using k-means (KM) Clustering. In this hybrid model, KM waits for the former BKM to finish its clustering and then it takes the final set of centroids as initial seeds for a better refinement. In this paper, a cooperative bisecting k-means (CBKM) clustering algorithm is presented. The CBKM concurrently combines the results of the BKM and KM at each level of the binary hierarchical tree using cooperative and merging matrices. Undertaken experimental results show that the CBKM achieves better clustering quality than that of KM, BKM, and single linkage (SL) algorithms with comparable time performance over a number of artificial, text documents, and gene expression datasets. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Kashef, R.; Kamel, M. S.] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. RP Kashef, R (reprint author), Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. EM rkashef@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR AYAD H, 2007, IEEE T PATTERN ANAL BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 Cover T. M., 1991, ELEMENTS INFORM THEO Duda R.O., 1973, PATTERN CLASSIFICATI Eisen MB, 1998, P NATL ACAD SCI USA, V95, P14863, DOI 10.1073/pnas.95.25.14863 ENG Y, 2004, AISAT 04 INT C ART I, P138 Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 GREENE D, 2006, EFFICIENT ENSEMBLE M Gropp W., 1996, USING MPI PORTABLE P HAMMOUDA K, 2006, 2006 SIAM C DAT MIN, P453 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 ISMAIL MA, 1989, PATTERN RECOGN, V22, P75, DOI 10.1016/0031-3203(89)90040-X Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 KASHEF R, 2007, IEEE 7 INT C BIOINF, P116 Lin CR, 2005, IEEE T KNOWL DATA EN, V17, P145 Maulik U, 2002, IEEE T PATTERN ANAL, V24, P1650, DOI 10.1109/TPAMI.2002.1114856 Monti S., 2003, CONSENSUS CLUSTERING PLUIM J, 2003, IEEE T OIL MED IMAGI QIAN Y, 2000, INT C PATT REC ICPR, V2, P732 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savares S.M., 2001, P 1 SIAM INT C DAT M, P1 Steinbach M., 2000, P KDD WORKSH TEXT MI, P109 Strehl A., 2002, C ART INT AAAI 2002, P93 Tamayo P, 1999, P NATL ACAD SCI USA, V96, P2907, DOI 10.1073/pnas.96.6.2907 Tavazoie S, 1999, NAT GENET, V22, P281 West M, 2001, P NATL ACAD SCI USA, V98, P11462, DOI 10.1073/pnas.201162998 Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Xu ST, 2004, J SUPERCOMPUT, V30, P117, DOI 10.1023/B:SUPE.0000040611.25862.d9 Zhao Y., 2002, CRITERION FUNCTIONS NR 33 TC 14 Z9 14 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 J9 PATTERN RECOGN JI Pattern Recognit. PD NOV PY 2009 VL 42 IS 11 BP 2557 EP 2569 DI 10.1016/j.patcog.2009.03.011 PG 13 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 476PF UT WOS:000268453800020 ER PT J AU Karamolegkos, PN Patrikakis, CZ Doulamis, ND Vlacheas, PT Nikolakopoulos, IG AF Karamolegkos, Pantelis N. Patrikakis, Charalampos Z. Doulamis, Nikolaos D. Vlacheas, Panagiotis T. Nikolakopoulos, Ioannis G. TI An evaluation study of clustering algorithms in the scope of user communities assessment SO COMPUTERS & MATHEMATICS WITH APPLICATIONS LA English DT Article DE Spectral clustering; Modeling; Social networking; User profile; Performance evaluation ID VECTOR-SPACE; NETWORKS; SERVICE AB In this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users' preferences. Such an evaluation study is performed in the context of our service requirements (i.e. on the basis of equal-sized group formation and of maximization of interests' commonalities between users within each social group). The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. Regarding profile's cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. The results clearly show that our approach is able to adhere to the service requirements as new users join the system, without the need of an iterative spectral clustering application that is computationally demanding. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Karamolegkos, Pantelis N.; Patrikakis, Charalampos Z.; Doulamis, Nikolaos D.; Vlacheas, Panagiotis T.; Nikolakopoulos, Ioannis G.] Natl Tech Univ Athens, Telecommun Lab, GR-15773 Athens, Greece. RP Nikolakopoulos, IG (reprint author), Natl Tech Univ Athens, Telecommun Lab, 9 Heroon Polytech Str, GR-15773 Athens, Greece. EM pkaramol@telecom.ntua.gr; bpatr@telecom.ntua.gr; ndoulam@cs.ntua.gr; panvlah@telecom.ntua.gr; gnikolakopoulos@telecom.ntua.gr CR Metwally A, 2005, LECT NOTES COMPUT SC, V3363, P398 Bach FR, 2004, ADV NEURAL INFORM PR, V16 Bollen J., 2002, D LIB MAGAZINE, V8 BREIGER RL, 1975, J MATH PSYCHOL, V12, P328, DOI 10.1016/0022-2496(75)90028-0 Breslau L., 1999, P IEEE INFOCOM 1999 Burago D., 2001, COURSE METRIC GEOMET Chen SD, 2007, LECT NOTES COMPUT SC, V4577, P391 Danon L, 2005, J STAT MECH-THEORY E, DOI 10.1088/1742-5468/2005/09/P09008 Doulamis AD, 2004, IEEE T CIRC SYST VID, V14, P656, DOI 10.1109/TCSVT.2004.826752 Doulamis ND, 2003, IEEE MULTIMEDIA, V10, P38, DOI 10.1109/MMUL.2003.1237549 *ETSI, 2002, 202325 ETSI EG HANDURUKANDE S, 2004, SIGOPS EUR WORKSH SE, P109 HENDRICKSON B, 1995, SIAM J SCI COMPUT, V16, P452, DOI 10.1137/0916028 HUANG JL, 2004, P IEEE INF HONG KONG, V2, P2050 Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Kobsa A, 2006, USER MODEL USER-ADAP, V16, P129, DOI 10.1007/s11257-006-9006-5 LANCIERI L, 2003, P IEEE INT S COMP IN, P130 LI X, 2005, TECHNOVATION, V25 Liu B, 2001, ADV ENG SOFTW, V32, P519, DOI 10.1016/S0965-9978(01)00004-7 Meila M, 2001, P 8 INT WORKSH ART I, DOI 10. 1. 1. 33. 1501 Minetou C. G., 2005, Proceedings. 5th IEEE International Conference on Advanced Learning Technologies Newman MEJ, 2006, P NATL ACAD SCI USA, V103, P8577, DOI 10.1073/pnas.0601602103 Newman MEJ, 2004, EUR PHYS J B, V38, P321, DOI 10.1140/epjb/e2004-00124-y Nouali O, 2004, EXPERT SYST APPL, V26, P171, DOI 10.1016/S0957-4174(03)00118-0 NTALIANIS K, 2001, P IEEE INT C MULT EX, P1001 Palla G, 2007, NATURE, V446, P664, DOI 10.1038/nature05670 PAPAGELIS M, 2003, P CAISE FOR 2003 KLA, P197 Phatak D., 2002, P 2002 IEEE INT C FU, V1, P705 PIERRAKOS D, 2003, P 1 EUR WEB MIN FOR RENCHER A. C., 2002, WILEY SERIES PROBABI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiaffino S, 2006, IEEE INTELL SYST, V21, P12, DOI 10.1109/MIS.2006.15 SEITZ C, 2004, P 2 IEEE ANN C PERV Semeraro G, 2007, LECT NOTES ARTIF INT, V4737, P61 SHORTREED S, P UAI 2005 Smith M, 2002, COMMUN ACM, V45, P51, DOI 10.1145/505248.505272 Sotiropoulos D. N., 2006, P 2 INT WORKSH WEB P TAKAYAMA Y, 1999, P PACLING 99 WAT ONT TONKIN E, 2006, 17 SIG CR CLASS RES ULMSCHNEIDER JE, 1983, ONLINE REV, V7, P301, DOI 10.1108/eb024132 VONLUXBURG U, 2006, TR146 M PLANCK I BIO Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 ZHANG Y, 2008, 2008 IEEE WIC ACM IN NR 44 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0898-1221 J9 COMPUT MATH APPL JI Comput. Math. Appl. PD OCT PY 2009 VL 58 IS 8 BP 1498 EP 1519 DI 10.1016/j.camwa.2009.05.023 PG 22 WC Mathematics, Applied SC Mathematics GA 504QA UT WOS:000270630800002 ER PT J AU Batsakis, S Petrakis, EGM Milios, E AF Batsakis, Sotiris Petrakis, Euripides G. M. Milios, Evangelos TI Improving the performance of focused web crawlers SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE Focused crawler; Learning crawler; Hidden Markov Model (HMM) crawler; World Wide Web ID CLASSIFICATION; ALGORITHM AB This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages (as classic crawlers do) but also paths leading to relevant pages. A novel learning crawler inspired by a previously proposed Hidden Markov Model (HMM) crawler is described as well. The crawlers have been implemented using the same baseline implementation (only the priority assignment function differs in each crawler) providing an unbiased evaluation framework for a comparative analysis of their performance. All crawlers achieve their maximum performance when a combination of web page content and (link) anchor text is used for assigning download priorities to web pages. Furthermore, the new HMM crawler improved the performance of the original HMM crawler and also outperforms classic focused crawlers in searching for specialized topics. (C) 2009 Elsevier B.V. All rights reserved. C1 [Batsakis, Sotiris; Petrakis, Euripides G. M.] TUC, Dept Elect & Comp Engn, GR-73100 Khania, Crete, Greece. [Milios, Evangelos] Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 1W5, Canada. RP Batsakis, S (reprint author), TUC, Dept Elect & Comp Engn, GR-73100 Khania, Crete, Greece. EM batsakis@softnet.tuc.gr; petrakis@intelligence.tuc.gr; eem@cs.dal.ca CR Aggarwal C.C., 2001, P 10 INT WORLD WID W, P96, DOI DOI 10.1145/371920.371955 Badia A., 2006, P 15 INT C WORLD WID, P1043, DOI 10.1145/1135777.1136006 Bao SH, 2008, IEEE T KNOWL DATA EN, V20, P1297, DOI 10.1109/TKDE.2008.98 BERGMARK D, 2002, 6 EUR C DIG LIB ROM BERGMARK D, 2002, P 2 ACM IEEE CS JOIN Brin S., 1998, P 7 INT WORLD WID WE, P107 Chakrabarti S, 1999, P 8 INT WORLD WID WE Chakrabarti S., 2002, P 11 INT WORLD WID W, P148 Chang C.-H., 2006, IEEE T KNOWLEDGE DAT CHEN Y, 2007, THESIS VIRGINIA POLY Cho J, 2001, THESIS STANFORD U Corley Courtney, 2005, P ACL WORKSH EMP MOD, P13, DOI 10.3115/1631862.1631865 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 De Bra P., 1994, P 4 RIAO C NEW YORK, P481 Diligenti M., 2000, P 26 INT C VER LARG, P527 Ehrig M., 2003, P S APPL COMP SAC 20 Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 HLIAOUTAKIS A, 2006, INT J SEMANT WEB INF, V3, P55 Kraft R, 2003, FIRST LATIN AMERICAN WEB CONGRESS, PROCEEDINGS, P84 Li J., 2005, P 14 INT WORLD WID W, P1190 Liu HY, 2006, DATA KNOWL ENG, V59, P270, DOI 10.1016/j.datak.2006.01.012 Liu H., 2004, P 6 ANN ACM INT WORK, P16, DOI 10.1145/1031453.1031458 McCown F., 2007, ACM IEEE JOINT C DIG, P309 Menczer F., 2004, ACM T INTERNET TECHN, V4, P378, DOI 10.1145/1031114.1031117 Pant G, 2005, ACM T INFORM SYST, V23, P430, DOI 10.1145/1095872.1095875 Pant G, 2006, IEEE T KNOWL DATA EN, V18, P107 Pant G., 2004, P 4 ACM IEEE CS JOIN, P142, DOI 10.1145/996350.996384 Pivk A, 2007, DATA KNOWL ENG, V60, P567, DOI 10.1016/j.datak.2006.04.002 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rabiner L. R., 1986, IEEE ASSP Magazine, V3, DOI 10.1109/MASSP.1986.1165342 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, 6 ACM SIGKDD WORLD T Varelas G., 2005, 7 ACM INT WORKSH WEB Xu Q., 2007, P 16 INT C WORLD WID, P1159, DOI 10.1145/1242572.1242744 NR 34 TC 19 Z9 20 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X J9 DATA KNOWL ENG JI Data Knowl. Eng. PD OCT PY 2009 VL 68 IS 10 BP 1001 EP 1013 DI 10.1016/j.datak.2009.04.002 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 504GK UT WOS:000270603900007 ER PT J AU Rajan, K Ramalingam, V Ganesan, M Palanivel, S Palaniappan, B AF Rajan, K. Ramalingam, V. Ganesan, M. Palanivel, S. Palaniappan, B. TI Automatic classification of Tamil documents using vector space model and artificial neural network SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Tamil text classification; Vector space model; Artificial neural network model; Corpus building ID RETRIEVAL AB Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Rajan, K.; Ramalingam, V.; Palanivel, S.; Palaniappan, B.] Annamalai Univ, Dept Comp Sci & Engn, Annamalainagar, Chidambaram, India. [Ganesan, M.] Annamalai Univ, Ctr Adv Studies Linguist, Annamalainagar, Chidambaram, India. RP Rajan, K (reprint author), Annamalai Univ, Dept Comp Sci & Engn, Annamalainagar, Chidambaram, India. EM kaliyaperumalrajan@yahoo.com CR ANNAMALAI E, 1999, MODERN TAMIL DRAVIDI APTE C, 1994, P 17 ANN INT ACM SIG, P21 Baum E. B., 1989, Neural Computation, V1, DOI 10.1162/neco.1989.1.1.151 BELEW RK, 1989, SIGIR FORUM, V23, P11, DOI 10.1145/75334.75337 CHANUNYA L, 2007, P 1 AS INT C MOD SIM CHIANG JH, 2001, P 10 IEEE INT C FUZZ, P720 Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 HUA LC, 2006, LNCS, V4234, P302 HUA LC, 2007, 7 INT C COMP INF TEC, P47 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 LANDAUER TK, 1972, COMP 19 ANN C COGN S, P412 Lehmann Thomas, 1993, GRAMMAR MODERN TAMIL LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Lin CH, 1996, IEEE T SYST MAN CY B, V26, P75 Marvin S., 1999, P INT C MACH LEARN MIGUEL ER, 1998, ADV CLASS RES, V8, P59 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RAJAN K, 2003, INT C TAM INT, P37 RAJAN K, 2002, P INT C TAM COMP RAJAN K, 2002, S TRANSL SUPP SYST I RAM DG, 2007, IEEE INT C GRAN COMP, P542 Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 SUSHMITA M, 2003, DATA MINING MULTIMED TAKENOBU T, 1994, TEXT CATEGORIZATION Tan SB, 2005, EXPERT SYST APPL, V28, P667, DOI 10.1016/j.eswa.2004.12.023 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 NR 29 TC 6 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD OCT PY 2009 VL 36 IS 8 BP 10914 EP 10918 DI 10.1016/j.eswa.2009.02.010 PG 5 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 460HT UT WOS:000267179500015 ER PT J AU Liu, SH AF Liu, Shuhua TI EXPERIENCES WITH AND REFLECTIONS ON TEXT SUMMARIZATION TOOLS SO INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS LA English DT Article DE Text summarization; sentence extraction methods; summarization evaluation; computing with words ID MODEL AB Text summarization is a process of distilling the most important content from text documents. While human beings have proven to be extremely capable summarizers, computer based automatic abstracting and summarizing has proven to be extremely challenging tasks. In this paper we report our experience with applying extractive summarization techniques to process news articles, economic reports and nursing narratives. We present analysis of the effect of different summarization methods and parameters on the summarization results. We also compare the performance of the summarizers across the three different document genres. The learned lessons are discussed and the possibilities for applying the theory of Computing with Words in text summarization are elaborated. C1 [Liu, Shuhua] Abo Akad Univ, IAMSR, FIN-20520 Turku, Finland. [Liu, Shuhua] Acad Finland, FIN-20520 Turku, Finland. RP Liu, SH (reprint author), Abo Akad Univ, IAMSR, Joukahaistenkatu 3-5A,4th Floor, FIN-20520 Turku, Finland. EM sliu@abo.fi FU Academy of Finland [111692] FX Financial support from Academy of Finland is gratefully acknowledged (Grant Number 111692). The author would also like to thank Johnny Lindroos, Fredrick Sundell and Marketta Hiisa for their contribution to the project and their assistance in carrying out some of the experiments. CR Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025 CLIMENSON WD, 1961, AUTOMATIC SYNTAX ANA EDMUNDSON, 1999, ADV AUTOMATIC TEXT S Endres-Niggemeyer B., 1998, SUMMARIZING INFORM GALLEY M, 2006, P AAAI SIGART DOCT C Harper Richard, 1998, INSIDE IMF HOVY E, 2005, AUTOMATED SUMMARIZAT Hovy E., 1999, ADV AUTOMATIC TEXT S Jurafsky D., SPEECH LANGUAGE PROC KINTSCH W, 1978, PSYCHOL REV, V85, P363, DOI 10.1037//0033-295X.85.5.363 Lacson RC, 2006, J BIOMED INFORM, V39, P541, DOI 10.1016/j.jbi.2005.12.009 LACSON RR, 2006, J BIOMEDICAL INFORM Lin Chin-Yew, 2004, TEXT SUMMARIZATION B, P74 LINDROOS J, 2006, THESIS ABO AKAD U LIU S, 2006, P 2006 IEEE WIC ACM LIU S, 2007, AUTOMATIC SUMM UNPUB LUHN HP, 1999, ADV AUTOMATIC TEXT S, V1 Mani I., 1999, ADV AUTOMATIC TEXT S MARCU D, 1999, P SIGIR 99 U BERK CA, V1 Marcu D., 2001, P NAACL 2001 WORKSH McKeown K, 2007, ACM T SPEECH LANGUAG, V4 MCKEOWN K, 2005, 28 ANN INT ACM SIGIR McKeown K, 2007, LECT NOTES COMPUT SC, V4394, P542 PAICE C, 1990, INFORM PROCESSING MA, V26 Papineni Kishore, 2002, P 40 ANN M ASS COMP, P311, DOI DOI 10.3115/1073083.1073135 Nenkova A, 2004, HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P145 RADEV D, 2003, V308 MEAD Radev DR, 2004, INFORM PROCESS MANAG, V40, P919, DOI 10.1016/j.ipm.2003.10.006 SALTON G, 1994, SCIENCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Dijk T. A., 1988, NEWS DISCOURSE van Dijk T. A., 1983, STRATEGIES DISCOURSE Zadeh L. A., 1996, IEEE T FUZZY SYST, V4, P103, DOI DOI 10.1109/91.493904 Zadeh L A, 1999, IEEE T CIRCUITS SYST, V45, P105 NR 34 TC 1 Z9 1 PU ATLANTIS PRESS PI PARIS PA 29 AVENUE LAUMIERE, PARIS, 75019, FRANCE SN 1875-6883 J9 INT J COMPUT INT SYS JI Int. J. Comput. Intell. Syst. PD OCT PY 2009 VL 2 IS 3 BP 202 EP 218 PG 17 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 525ZO UT WOS:000272257300003 ER PT J AU Villaverde, J Persson, A Godoy, D Amandi, A AF Villaverde, J. Persson, A. Godoy, D. Amandi, A. TI Supporting the discovery and labeling of non-taxonomic relationships in ontology learning SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Ontology learning; Text mining; Knowledge engineering ID TEXT AB Ontology learning (OL) from texts has been suggested as a technology that helps to reduce the bottleneck of knowledge acquisition in the construction of domain ontologies. In this learning process, the discovery, and possibly also labeling, of non-taxonomic relationships has been identified as one of the most difficult and often neglected problems. In this paper, we propose a technique that addresses this issue by analyzing a domain text corpus to extract verbs frequently applied for linking certain pairs of concepts. Integrated in an ontology building process, this technique aims to reduce the work-load of knowledge engineers and domain experts by suggesting candidate relationships that might become part of the ontology as well as prospective labels for them. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Villaverde, J.] UNICEN Univ, ISISTAN Res Inst, Tandil, Buenos Aires, Argentina. Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Villaverde, J (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,Paraje Arroyo Seco,CP 7000, Tandil, Buenos Aires, Argentina. EM jvillave@exa.unicen.edu.ar CR Agrawal R., 1994, P 20 INT C VER LARG, P487 Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Cimiano P., 2004, P 13 INT C WORLD WID, P462, DOI 10.1145/988672.988735 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Girju R., 2002, P FLAIRS C, P360 HATCHER E, 2004, ACTION Hearst M. A., 1992, P 14 INT C COMP LING, P539 Jurafsky D, 2000, SPEECH LANGUAGE PROC Kavalec M, 2004, LECT NOTES COMPUT SC, V2932, P249 Kavalec M, 2005, ONTOLOGY LEARNING TE, P44 KIETZ JU, 2000, LECT NOTES ARTIFICIA, V1937 Kim J-D, 2003, BIOINFORMATICS S1, V19, pi180, DOI DOI 10.1093/BIOINFORMATICS/BTG1023 Kowalski G. J., 2000, INFORM STORAGE RETRI MAEDCHE A., 2001, P IEEE INT C DAT MIN Maedche A., 2002, ONTOLOGY LEARNING SE Maedche A., 2000, P 12 INT C SOFTW KNO Maedche A., 2000, P 14 EUR C ART INT, P321 Menzies T, 2003, COMPUTER, V36, P22, DOI 10.1109/MC.2003.1244531 MORIN E, 1999, P 5 TERM KNOWL ENG T Navigli R, 2003, IEEE INTELL SYST, V18, P22, DOI 10.1109/MIS.2003.1179190 Navigli R, 2004, COMPUT LINGUIST, V30, P151, DOI 10.1162/089120104323093276 Pustejovsky J., 2002, PAC S BIOCOMPUT, V7, P362 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez D, 2008, DATA KNOWL ENG, V64, P600, DOI 10.1016/j.datak.2007.10.001 Schutz A, 2005, LECT NOTES COMPUT SC, V3729, P593 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] Shamsfard M, 2003, KNOWL ENG REV, V18, P293, DOI 10.1017/S0269888903000687 Toutanova K, 2000, PROCEEDINGS OF THE 2000 JOINT SIGDAT CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING AND VERY LARGE CORPORA, P63 Toutanova K., 2003, P 2003 C N AM CHAPT, P173, DOI DOI 10.3115/1073445.1073478 Voorhees E. M., 1993, SIGIR Forum NR 31 TC 14 Z9 16 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 EI 1873-6793 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD SEP PY 2009 VL 36 IS 7 BP 10288 EP 10294 DI 10.1016/j.eswa.2009.01.048 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 456MO UT WOS:000266851000010 ER PT J AU Ahn, JW Brusilovsky, P AF Ahn, Jae-Wook Brusilovsky, Peter TI Adaptive visualization of search results: Bringing user models to visual analytics SO INFORMATION VISUALIZATION LA English DT Article DE visualization; VIBE; user model; query; information filtering ID INFORMATION-RETRIEVAL; LIBRARY; SYSTEM; WEB AB Adaptive visualization is a new approach at the crossroads of user modeling and information visualization. Taking into account information about a user, adaptive visualization attempts to provide user-adapted visual presentation of information. This paper proposes Adaptive VIBE, an approach for adaptive visualization of search results in an intelligence analysis context. Adaptive VIBE extends the popular VIBE visualization framework by infusing user model terms as reference points for spatial document arrangement and manipulation. We explored the value of the proposed approach using data obtained from a user study. The result demonstrated that user modeling and spatial visualization technologies are able to reinforce each other, creating an enhanced level of user support. Spatial visualization amplifies the user model's ability to separate relevant and non-relevant documents, whereas user modeling adds valuable reference points to relevance-based spatial visualization. Information Visualization (2009) 8, 167 - 179. doi: 10.1057/ivs.2009.12; C1 [Ahn, Jae-Wook; Brusilovsky, Peter] Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15260 USA. RP Ahn, JW (reprint author), Univ Pittsburgh, Sch Informat Sci, 135 N Bellefield Ave, Pittsburgh, PA 15260 USA. EM jahn@mail.sis.pitt.edu; peterb@mail.sis.pitt.edu RI Brusilovsky, Peter/; Brusilovsky, Peter/ OI Brusilovsky, Peter/0000-0002-1902-1464; Brusilovsky, Peter/0000-0002-1902-1464 CR Ahlberg C., 1994, CHI '94 Conference Proceedings. Human Factors in Computing Systems `Celebrating Interdependence' AHN JW, 2006, WORLD C E LEARN E LE, P2707 AHN JW, 2006, WORKSH SOC NAV COMM, P497 AHN JW, 2007, 2007 INT C WEB INT W, P706 AHN JW, 2008, 17 INT C WORLD WID W, P1 AREZKI R, 2004, 3 INT C AD HYP AD WE, P275 BENFORD S, 1995, P EUROGRAPHICS 95, V14, P349 BRAUN M, 2008, 17 INT C WORLD WID W, P1031 Brusilovsky P, 2006, Information Visualization, P142 CHIRITA PA, 2006, ACM 15 C INF KNOWL M, P287 Christel M, 1998, J INTELL INF SYST, V11, P235, DOI 10.1023/A:1008638024786 Cutrell E, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P407 FOX EA, 1993, J AM SOC INFORM SCI, V44, P480, DOI 10.1002/(SICI)1097-4571(199309)44:8<480::AID-ASI7>3.0.CO;2-B Gauch S., 2007, The Adaptive Web. Methods and Strategies of Web Personalization He DQ, 2008, INFORM PROCESS MANAG, V44, P511, DOI 10.1016/j.ipm.2007.07.009 Hemmje M., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval JANSEN BJ, 2007, 16 INT WORLD WID WEB, P1139 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Keane MT, 2008, COMMUN ACM, V51, P49, DOI [10.1145/1314215.1340914, 10.1145/1314215.1314224] KOUTRIKA G, 2005, WORKSH NEW TECHN PER, P44 Leuski A, 2004, USER MODEL USER-ADAP, V14, P259, DOI 10.1023/B:USER.0000028978.09823.47 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Micarelli A., 2007, The Adaptive Web. Methods and Strategies of Web Personalization Micarelli A, 2004, USER MODEL USER-ADAP, V14, P159, DOI 10.1023/B:USER.0000028981.43614.94 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 Pazzani M. J., 2007, The Adaptive Web. Methods and Strategies of Web Personalization SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sieg A., 2007, ACM 16 C INF KNOWL M, P525 Spoerri A., 1993, CIKM 93. Proceedings of the Second International Conference on Information and Knowledge Management Teevan J, 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 NR 30 TC 8 Z9 8 PU PALGRAVE MACMILLAN LTD PI BASINGSTOKE PA BRUNEL RD BLDG, HOUNDMILLS, BASINGSTOKE RG21 6XS, HANTS, ENGLAND SN 1473-8716 J9 INFORM VISUAL JI Inf. Vis. PD FAL PY 2009 VL 8 IS 3 BP 167 EP 179 DI 10.1057/ivs.2009.12 PG 13 WC Computer Science, Software Engineering SC Computer Science GA 497WC UT WOS:000270093300004 ER PT J AU Aseervatham, S Bennani, Y AF Aseervatham, Sujeevan Bennani, Younes TI Semi-structured document categorization with a semantic kernel SO PATTERN RECOGNITION LA English DT Article DE Mercer kernel; Support vector machine; Text categorization; Semantic similarity; Semi-structured data AB Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. Furthermore, research in text categorization has mainly focused on "flat texts" whereas many documents are now semi-structured and especially under the XML format. In this paper, we propose a semantic kernel for semi-structured biomedical documents. The semantic meanings of words are extracted using the unified medical language system (UMLS) framework. The kernel, with a SVM classifier, has been applied to a text categorization task on a medical corpus of free text documents. The results have shown that the semantic kernel outperforms the linear kernel and the naive Bayes classifier. Moreover, this kernel was ranked in the top 10 of the best algorithms among 44 classification methods at the 2007 Computational Medicine Center (CMC) Medical NLP International Challenge. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Aseervatham, Sujeevan; Bennani, Younes] Univ Paris 13, LIPN, UMR 7030, CNRS, F-93430 Villetaneuse, France. RP Aseervatham, S (reprint author), Univ Paris 13, LIPN, UMR 7030, CNRS, 99 Av JB Clement, F-93430 Villetaneuse, France. EM sujeevan@lipn.univ-paris13.fr; younes@lipn.univ-paris13.fr CR Basili R., 2006, Informatica, V30 Berg C, 1984, HARMONIC ANAL SEMIGR BLOEHDORN S, 2006, P IEEE ICDM, P808 Boser B. E., 1992, COMPUTATIONAL LEARNI, P144 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DIVITA G, 2006, P AMIA FALL S, P201 Gliozzo A., 2005, P 9 C COMP NAT LANG, P56, DOI 10.3115/1706543.1706553 Haussler D., 1999, UCSCRL9910 Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE LEACOCK C, 1998, WORDNET ELECT LEXICA, V12, P265 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 PESTIAN J, 2007, P 2007 ACL BIONLP AS Resnik P., 1995, P 14 INT JOINT C ART, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Sebastiani Fabrizio, 2005, TEXT MINING ITS APPL, P109 Shawe-Taylor J., 2004, KERNEL METHODS PATTE SIOLAS G, 2000, P IJCNN 00 INT JOINT Vapnik V., 1995, NATURE STAT LEARNING Yang ZW, 2001, ALCOHOL, V24, P145, DOI 10.1016/S0741-8329(01)00145-8 NR 25 TC 8 Z9 9 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0031-3203 EI 1873-5142 J9 PATTERN RECOGN JI Pattern Recognit. PD SEP PY 2009 VL 42 IS 9 BP 2067 EP 2076 DI 10.1016/j.patcog.2008.10.024 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 459GI UT WOS:000267089000035 ER PT J AU Hawes, T Lin, J Resnik, P AF Hawes, Timothy Lin, Jimmy Resnik, Philip TI Elements of a Computational Model for Multi-Party Discourse: The Turn-Taking Behavior of Supreme Court Justices SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID ORAL ARGUMENTS; WORDS AB This work explores computational models of multi-party discourse, using transcripts from U.S. Supreme Court oral arguments. The turn-taking behavior of participants is treated as a supervised sequence-labeling problem and modeled using first- and second-order conditional random fields (CRFs). We specifically explore the hypothesis that discourse markers and personal references provide important features in such models. Results from a sequence prediction experiment demonstrate that incorporating these two types of features yields significant improvements in accuracy. Our experiments are couched in the broader context of developing tools to support legal scholarship, although we see other natural language processing applications as well. C1 [Hawes, Timothy; Resnik, Philip] Univ Maryland, Dept Linguist, College Pk, MD 20742 USA. [Hawes, Timothy; Resnik, Philip] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA. [Lin, Jimmy] Univ Maryland, iSch, College Pk, MD 20742 USA. RP Hawes, T (reprint author), Univ Maryland, Dept Linguist, Marie Mt Hall, College Pk, MD 20742 USA. EM twhawes@umd.edu; jimmylin@umd.edu; resnik@umd.edu FU National Science Foundation [BSC-062467] FX This research was conducted with support from the National Science Foundation, under award BSC-062467. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of the sponsoring institution. We would like to thank our collaborators, Wayne McIntosh and Michael Evans, for valuable discussions arid feedback. The second author would like to thank Esther and Kiri for their kind support. CR Clopper CJ, 1934, BIOMETRIKA, V26, P404, DOI 10.2307/2331986 Coen M. H., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Corston-Oliver S.H., 1998, P ACL COLING 98 WORK, P8 Craig H., 2004, COMPANION DIGITAL HU, P273 Gamon M., 2008, P INT C WEBL SOC MED GREENE S, 2007, THESIS U MARYLAND CO GREENE S, 2009, N AM CHAPT ASS COMP Halliday M. A. K., 1976, COHESION ENGLISH Johnson TR, 2001, AM POLIT RES, V29, P331, DOI 10.1177/1532673X01029004001 Johnson TR, 2006, AM POLIT SCI REV, V100, P99 Jovanovic N., 2004, P 5 SIGDIAL WORKSH D, P89 KURLAND PB, 1983, U CHICAGO LAW REV, V50, P628, DOI 10.2307/1599505 LAFFERTY J, 2001, P 18 INT C MACH LEAR, P282 Lancashire I., 1997, SHAKESPEARE STUD, V25, P171 Laver M, 2003, AM POLIT SCI REV, V97, P311 Lewis David D., 1992, THESIS U MASSACHUSET Lin J., 2007, J EMPIR LEGAL STUD, V4, P1007 MACWHINNEY B, 2004, P LANG RES EV C 2004, P525 Malouf R, 2008, INTERNET RES, V18, P177, DOI 10.1108/10662240810862239 Marcu D., 1997, P 35 ANN M ASS COMP, P96 MOSTELLER F, 1964, INFERENCE DISPUTED A OATES S, 2001, ITRI0126 U BRIGHT IN PRZYBOCKI MA, 1998, P LREC GRAN SPAIN, P331 Rehnquist W. H, 2002, SUPREME COURT Rohde DavidW, 1976, SUPREME COURT DECISI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiffrin D., 1987, DISCOURSE MARKERS Settles B., 2004, P COLING 2004 NLPBA, P107 Sha F., 2003, P 2003 C N AM CHAPT, VI, P134, DOI 10.3115/1073445.1073473 Shullman Sarah Levien, 2004, J APPELLATE PRACTICE, V6, P271 Slatcher RB, 2007, J RES PERS, V41, P63, DOI 10.1016/j.jrp.2006.01.006 Stanford V., 2003, P INT C AC SPEECH SI, V4, P736 Stolcke A, 2000, COMPUT LINGUIST, V26, P339, DOI 10.1162/089120100561737 Thomas M., 2006, P 2006 C EMP METH NA, P327, DOI 10.3115/1610075.1610122 Williams J.D., 2007, COMPUTER SPEECH LANG, V21, P231 NR 35 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD AUG PY 2009 VL 60 IS 8 BP 1607 EP 1615 DI 10.1002/asi.21087 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 475ID UT WOS:000268350900006 ER PT J AU Hubert, G Mothe, J AF Hubert, Gilles Mothe, Josiane TI An Adaptable Search Engine for Multimodal Information Retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID CONTROLLED VOCABULARY; XML RETRIEVAL; MODEL AB This article describes an information retrieval approach according to the two different search modes that exist: browsing an ontology (via categories) or defining a query in free language (via keywords). Various proposals offer approaches adapted to one of these two modes. We present a proposal leading to a system allowing the integration of both modes using the same search engine. This engine is adapted according to each possible search mode. C1 [Hubert, Gilles; Mothe, Josiane] Univ Toulouse 3, IRIT, SIG EVI, F-31062 Toulouse 9, France. [Mothe, Josiane] Univ Format Maitres, F-31400 Toulouse, France. RP Hubert, G (reprint author), Univ Toulouse 3, IRIT, SIG EVI, 118 Route Narbonne, F-31062 Toulouse 9, France. EM Gilles.Hubert@irit.fr; Josiane.Mothe@irit.fr CR AIRIO E, 2004, WEB INT S HELS FINL AUGE J, 2003, 19 JOURN BAS DONN AV, P69 AUSSENACGILLES N, 2004, P RIAO AV FRANC 26 2, P129 CHIN KK, 1998, THESIS CAMBRIDGE U C Denoyer L, 2004, INFORM PROCESS MANAG, V40, P807, DOI 10.1016/j.ipm.2004.04.009 Duke A, 2007, LECT NOTES COMPUT SC, V4519, P341 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 FIDEL R, 1991, J AM SOC INFORM SCI, V42, P501, DOI 10.1002/(SICI)1097-4571(199108)42:7<501::AID-ASI5>3.0.CO;2-V FIDEL R, 1992, SPEC LIBR, V83, P1 Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 Fugmann R, 2002, KNOWL ORGAN, V29, P217 Hearst MA, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P246, DOI 10.1145/258525.258582 HERNANDEZ N, 2005, 28 INT C RES DEV INF, P607 Hubert G, 2005, LECT NOTES COMPUT SC, V3493, P183 HUBERT G, 2007, P 9 INT C ENT INF SY, P184 Hubert G, 2006, LECT NOTES COMPUT SC, V3977, P172 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Koller D., 1997, P 14 INT C MACH LEAR, P170 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Montague M., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 ROCCHIO JJ, 1971, EXPT AUTOMATIC DOCUM, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIRI A, 2007, P 35 ANN C CAN ASS I Singhal A., 2001, IEEE DATA ENG B, V24, P35 Smucker MD, 2007, P 16 ACM C INF KNOWL, P623, DOI DOI 10.1145/1321440.1321528 Spyns P, 2002, LECT NOTES ARTIF INT, V2569, P189 Vapnik V., 1995, NATURE STAT LEARNING Vinot R, 2003, LECT NOTES ARTIF INT, V2837, P456 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 31 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD AUG PY 2009 VL 60 IS 8 BP 1625 EP 1634 DI 10.1002/asi.21091 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 475ID UT WOS:000268350900008 ER PT J AU Chiu, DY Pan, YC Yu, SY AF Chiu, Deng-Yiv Pan, Ya-Chen Yu, Shih-Yi TI An automated knowledge structure construction approach: Applying information retrieval and genetic algorithm to journal of Expert Systems with Applications SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Knowledge structure; Information retrieval; Genetic algorithm; Chi-square rest; Topic selection ID FUZZY COGNITIVE MAPS AB In this study, information retrieval and genetic algorithm are integrated to propose a new knowledge structure Construction method and this method is further applied to the papers published oil the journal of Expert Systems with Applications (ESWA). The purpose of this method is to explore the major topics as well as the related techniques and methods of the papers published in various periods of time and help understand the research tendencies among these published papers. We use vector space model to present the published papers and feature and adopt chi-square test to examine the independence of topics. Then we apply genetic algorithm to facilitate automatic topic selections used to Construct the knowledge Structures for the journal in study. We select ESWA as the source of samples mainly because the papers published oil this journal feature an extensive use of methods and techniques and have been widely applied in many domains. Moreover, it is also a SCI listed journal. In recent years, more and more outstanding scholars have published their papers oil this journal. so citation of the papers on this journal by other papers is frequent. It can be viewed as an internationally prominent journal. In our experiment, knowledge structures are constructed and analyzed. The representativeness of the selected topics and whether published papers (have been classified into appropriate topics are also evaluated. From the experimental results, we discover that the constructed knowledge structures could not only effectively present representative topics, related techniques and issues, but A SO help understand the research tendencies. (C) 2008 Flsevier Ltd. All rights reserved. C1 [Chiu, Deng-Yiv; Pan, Ya-Chen; Yu, Shih-Yi] ChungHua Univ, Dept Informat Management, Hsinchu 300, Taiwan. RP Chiu, DY (reprint author), ChungHua Univ, Dept Informat Management, 707 Sec 2,Wu Fu Rd, Hsinchu 300, Taiwan. EM chiuden@chu.edu.tw CR CHOU C, 1998, J ED MULTIMEDIA HYPE, V7, P151 Chou HC, 2007, EXPERT SYST APPL, V33, P499, DOI 10.1016/j.eswa.2006.05.020 Hong TH, 2002, EXPERT SYST APPL, V23, P1, DOI 10.1016/S0957-4174(02)00022-2 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kohonen T., 1997, SELF ORG MAPS Lin FR, 2006, INFORM PROCESS MANAG, V42, P551, DOI 10.1016/j.ipm.2005.03.026 Paolucci R., 1998, Journal of Educational Multimedia and Hypermedia, V7 PARK KS, 1995, INT J HUM-COMPUT ST, V42, P157, DOI 10.1006/ijhc.1995.1007 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shih JY, 2008, EXPERT SYST APPL, V34, P850, DOI 10.1016/j.eswa.2006.10.031 Steinbach M., 2000, KDD 2000 WORKSH TEXT TABER R, 1991, EXPERT SYST APPL, V2, P83, DOI 10.1016/0957-4174(91)90136-3 Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011 ZHANG WR, 1992, IEEE T SYST MAN CYB, V22, P103, DOI 10.1109/21.141315 NR 16 TC 1 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD JUL PY 2009 VL 36 IS 5 BP 9438 EP 9447 DI 10.1016/j.eswa.2008.12.032 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 427MG UT WOS:000264782800077 ER PT J AU Borner, K Scharnhorst, A AF Borner, Katy Scharnhorst, Andrea TI Visual conceptualizations and models of science SO JOURNAL OF INFORMETRICS LA English DT Editorial Material DE Conceptualization; Models; Scientometrics; Science of Science; Sociology of science; Webometrics; Informetrics; History of science; Cultural studies of science; Statistical analysis; Mathematical modeling; Science policy; Evolution ID SCIENTOMETRICS; LITERATURES; AUTHORSHIP; CITATION AB This is the Guest Editor's introduction to the Special Issue on "Science of Science: Conceptualizations and Models of Science", Journal of Informetrics. The introduction discusses challenges towards a theoretically grounded and practically useful science of science. It provides a brief chronological review of relevant work and argues for (1) the development of common frameworks for the comparison and combination of existing approaches, theories, laws, and measurements, (2) the combination of quantitative and qualitative studies of science, and (3) the operationalization of theoretical concepts in terms of measurement and empirical evidence. Next, three visual conceptualizations of science are discussed and compared. Each of them provides a framework for the comparison and combination of existing works, means to combine quantitative and qualitative data, and helps to operationalize and communicate theoretical concepts using empirical data. Last but not least, the contributions of and interlinkages among the papers included in this issue are discussed. (C) 2009 Elsevier Ltd. All rights reserved. C1 [Borner, Katy] Indiana Univ, Sch Lib & Informat Sci, Cyberinfrastruct Network Sci Ctr, Wells Lib 021, Bloomington, IN 47405 USA. [Scharnhorst, Andrea] Royal Netherlands Acad Arts & Sci, Virtual Knowledge Studio Humanities & Social Sci, NL-1019 AT Amsterdam, Netherlands. RP Borner, K (reprint author), Indiana Univ, Sch Lib & Informat Sci, Cyberinfrastruct Network Sci Ctr, Wells Lib 021, 10th St & Jordan Ave, Bloomington, IN 47405 USA. EM katy@indiana.edu; andrea.scharnhorst@vks.knaw.nl RI Scharnhorst, Andrea/A-7802-2010; Scharnhorst, Andrea/ OI Scharnhorst, Andrea/0000-0001-8879-8798 CR Barabasi Albert-Laszlo, 2002, LINKED NEW SCI NETWO Bernal J, 1939, SOCIAL FUNCTION SCI Bettencourt LMA, 2009, J INFORMETR, V3, P210, DOI 10.1016/j.joi.2009.03.001 Borner K, 2005, COMPLEXITY, V10, P57, DOI 10.1002/cplx.20078 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Borner K, 2007, ANNU REV INFORM SCI, V41, P537, DOI 10.1002/aris.2007.1440410119 Bowker G. C., 1999, SORTING THINGS OUT C Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6 Bradford SC, 1934, ENGINEERING-LONDON, V137, P85 BRUCKNER E, 1986, WISSENSCHAFTLICHE Z, V35, P481 BRUCKNER E, 1990, SCIENTOMETRICS, V18, P21, DOI 10.1007/BF02019160 CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003 Card S. K., 1999, READINGS INFORM VISU Chen C., 2003, MAPPING SCI FRONTIER Chen CM, 2009, J INFORMETR, V3, P191, DOI 10.1016/j.joi.2009.03.004 Chen CM, 2006, J AM SOC INF SCI TEC, V57, P359, DOI 10.1002/asi.20317 COHEN MR, 1933, ENCY SOCIAL SCI Collins R., 1998, SOCIOLOGY PHILOS GLO De Solla Price D. J., 1963, LITTLE SCI BIG SCI Ebeling W, 2000, TRAFFIC AND GRANULAR FLOW'99, P43 Elkana Y., 1978, METRIC SCI ADVENT SC Frenken K, 2009, J INFORMETR, V3, P222, DOI 10.1016/j.joi.2009.03.005 Galison Peter, 1997, IMAGE LOGIC MAT CULT GARFIELD E, 2003, ESSAYS INFORM SCI, V1 Garfield E, 2009, J INFORMETR, V3, P173, DOI 10.1016/j.joi.2009.03.009 Garfield E., 1979, CITATION INDEXING IT Gaughan M, 2008, RES EVALUAT, V17, P103, DOI 10.3152/095820208X287180 Godin B, 2005, MEASUREMENT STAT SCI GOFFMAN W, 1969, NATURE, V221, P1205, DOI 10.1038/2211205a0 HAPKE T, 2008, W OSTWALD ORG MEDIUM Hine C., 2000, VIRTUAL ETHNOGRAPHY Huberman B. A., 2001, LAWS WEB PATTERNS EC Ingwersen P, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P339 Klavans R, 2006, SCIENTOMETRICS, V68, P475, DOI 10.1007/s11192-006-0125-x Klavans R, 2006, J AM SOC INF SCI TEC, V57, P251, DOI 10.1002/asi.20274 Klavans R, 2007, Proceedings of ISSI 2007: 11th International Conference of the International Society for Scientometrics and Informetrics, Vols I and II, P437 Knorr-Cetina K., 1995, HDB SCI TECHNOLOGY S Kuhn T. S., 1962, STRUCTURE SCI REVOLU Lambiotte R, 2009, J INFORMETR, V3, P180, DOI 10.1016/j.joi.2009.03.007 LEM S, 1997, FILOZOFIA PRZYPADKU Leydesdorff L, 2007, SCIENTOMETRICS, V71, P391, DOI 10.1007/s11192-007-1694-z LEYDESDORFF L, 2008, SEMIOTIX GLOBAL INFO Leydesdorff L, 2009, J AM SOC INF SCI TEC, V60, P348, DOI 10.1002/asi.20967 Lotka A. J., 1926, J WASHINGTON ACAD SC, V16, P317 Lucio-Arias D, 2009, J INFORMETR, V3, P261, DOI 10.1016/j.joi.2009.03.003 Luhmann N., 1990, WISSENSCHAFT GESELLS Mane KK, 2004, P NATL ACAD SCI USA, V101, P5287, DOI 10.1073/pnas.0307626100 MARSHAKOVA IV, 1973, AUTOMATIC DOCUMENTAT, V7, P49 Merton R. K., 1973, SOCIOLOGY SCI THEORE, P267 Meyer ET, 2009, J INFORMETR, V3, P246, DOI 10.1016/j.joi.2009.03.006 Morris SA, 2004, P NATL ACAD SCI USA, V101, P5291, DOI 10.1073/pnas.0307604100 Nalimov V.V., 1969, NAUKOMETRIYA IZUCHEN NARRIN F, 1976, EVALUATIVE BIBLIOMET National Research Council of the National Academies, 2005, NETW SCI OTLET P., 1934, TRAITE DOCUMENTATION Pigliucci M, 2006, MAKING SENSE EVOLUTI POPPER K, 1979, TRUTH RATIONALITY GR PRICE D, 1976, INT FORUM INFORM DOC, V1, P17 PRICE DJS, 1962, SCI SINCE BABYION Rayward W. B., 2005, ENVISIONING PATH FUT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scharnhorst A, 2001, INTEGRATIVE SYSTEMS APPROACHES TO NATURAL AND SOCIAL DYNAMICS, P505 Scharnhorst A., 2003, J COMPUTER MEDIATED, V8 Schroeder R., 2007, RETHINKING SCI TECHN SHIFFRIN RM, 2004, P NATL ACAD SCI U S1, V101, P5287 Shiffrin RM, 2004, MATEMATICHESKIE MODE, V101, P5183, DOI 10.1073/pnas.0307852100 Simon H. A., 1996, SCI ARTIFICIAL Skupin A, 2009, J INFORMETR, V3, P233, DOI 10.1016/j.joi.2009.03.002 SMALL H, 1974, SCI STUD, V4, P17, DOI 10.1177/030631277400400102 Small H, 1998, SCIENTOMETRICS, V43, P143, DOI 10.1007/BF02458403 SMALL HG, 1978, SOC STUD SCI, V8, P327, DOI 10.1177/030631277800800305 STEINER H, 1989, JD BERNALS SOCIAL FU Thelwall M., 2004, LINK ANAL INFORM SCI Thelwall M, 2008, J AM SOC INF SCI TEC, V59, P1523, DOI 10.1002/asi.20829 van Leeuwen T, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P373 VONDECHEND H, 1953, JUSTUSVANLIEBIG EIGE Watts D. J., 2003, SMALL WORLDS DYNAMIC Watts D. J., 1998, NATURE, V393, P6684 Wells H. G., 1938, WORLD BRAIN Whitley R., 1984, INTELLECTUAL SOCIAL Wilford John Noble, 1981, MAPMAKERS STORY GREA Wilson E. O., 1998, CONSILIENCE UNITY KN Wouters P., 1999, THE CITATION CULTURE Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 84 TC 17 Z9 17 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1751-1577 J9 J INFORMETR JI J. Informetr. PD JUL PY 2009 VL 3 IS 3 BP 161 EP 172 DI 10.1016/j.joi.2009.03.008 PG 12 WC Information Science & Library Science SC Information Science & Library Science GA 459WT UT WOS:000267144200001 ER PT J AU Vazquez, M Carmona-Saez, P Nogales-Cadenas, R Chagoyen, M Tirado, F Carazo, JM Pascual-Montano, A AF Vazquez, Miguel Carmona-Saez, Pedro Nogales-Cadenas, Ruben Chagoyen, Monica Tirado, Francisco Maria Carazo, Jose Pascual-Montano, Alberto TI SENT: semantic features in text SO NUCLEIC ACIDS RESEARCH LA English DT Article ID NONNEGATIVE MATRIX FACTORIZATION; BIOMEDICAL LITERATURE; MICROARRAY DATA; GENE LISTS; INITIALIZATION; NETWORK; TOOL AB We present SENT (semantic features in text), a functional interpretation tool based on literature analysis. SENT uses Non-negative Matrix Factorization to identify topics in the scientific articles related to a collection of genes or their products, and use them to group and summarize these genes. In addition, the application allows users to rank and explore the articles that best relate to the topics found, helping put the analysis results into context. This approach is useful as an exploratory step in the workflow of interpreting and understanding experimental data, shedding some light into the complex underlying biological mechanisms. This tool provides a user-friendly interface via a web site, and a programmatic access via a SOAP web server. SENT is freely accessible at http:\\sent.dacya.ucm.es. C1 [Carmona-Saez, Pedro; Nogales-Cadenas, Ruben; Tirado, Francisco; Pascual-Montano, Alberto] Univ Complutense, Comp Architecture Dept, E-28040 Madrid, Spain. [Vazquez, Miguel] Univ Complutense, Software Engn Dept, E-28040 Madrid, Spain. [Chagoyen, Monica; Maria Carazo, Jose] CSIC, CNB, Natl Biotechnol Ctr, Biocomp Unit, Madrid, Spain. RP Pascual-Montano, A (reprint author), Univ Complutense, Comp Architecture Dept, E-28040 Madrid, Spain. EM pascual@fis.ucm.es RI Chagoyen, Monica/J-4391-2013 OI Chagoyen, Monica/0000-0001-6911-1591 FU Integromics; Spanish Ramon y Cajal program; Spanish grants [BIO2007-67150-C03-02, S-Gen-0166/2006, TIN2005-5619, PS-010000-2008-1]; European Union [FP7-HEALTH-F4-2008-202047] FX Spanish grants [BIO2007-67150-C03-02, S-Gen-0166/2006, TIN2005-5619, PS-010000-2008-1]; European Union Grant [FP7-HEALTH-F4-2008-202047]. Funding for open access charge: Spanish Grant number BIO2007-67150-C03-02. CR ASHBURNER M, 2000, GENE ONTOLOGY CONSOR, V25, P25, DOI DOI 10.1038/75556 Blaschke C, 1999, Proc Int Conf Intell Syst Mol Biol, P60 Boutsidis C, 2008, PATTERN RECOGN, V41, P1350, DOI 10.1016/j.patcog.2007.09.010 Carmona-Saez P, 2007, GENOME BIOL, V8, DOI 10.1186/gb-2007-8-1-r3 Chagoyen M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-41 Chaussabel D, 2002, GENOME BIOL, V3 D'Arcangelo G, 1999, NEURON, V24, P471, DOI 10.1016/S0896-6273(00)80860-0 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Frijters R, 2008, NUCLEIC ACIDS RES, V36, pW406, DOI 10.1093/nar/gkn215 HEINRICH KE, 2008, COMPUT INTELL, P12 Hoffmann R, 2005, BIOINFORMATICS, V21, P252, DOI 10.1093/bioinformatics/bti1142 Homayouni R, 2005, BIOINFORMATICS, V21, P104, DOI 10.1093/bioinformatics/bth464 Huang DW, 2009, NUCLEIC ACIDS RES, V37, P1, DOI 10.1093/nar/gkn923 Huang ZX, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-308 Jelier R, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-14 Jenssen TK, 2001, NAT GENET, V28, P21, DOI 10.1038/ng0501-21 Lee DD, 1999, NATURE, V401, P788 Mejia-Roa E, 2008, NUCLEIC ACIDS RES, V36, pW523, DOI 10.1093/nar/gkn335 Mitchell Joyce A, 2003, AMIA Annu Symp Proc, P460 PEHKONEN P, 2005, BMC BIOINFORMATICS, V6, P16 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Raychaudhuri S, 2002, GENOME RES, V12, P1582, DOI 10.1101/gr.116402 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shatkay H, 2003, J COMPUT BIOL, V10, P821, DOI 10.1089/106652703322756104 SPARCK JK, 1988, T GRAHAM SERIES FDN, V3, P132 TJIOE E, 2008, 1 WORKSH DAT MIN FUN, P185 WILBUR J, 2007, P 2 BIOCREATIVE CHAL, V1, P7 Wild S, 2004, PATTERN RECOGN, V37, P2217, DOI 10.1016/j.patcog.2004.02.013 Wren JD, 2004, BIOINFORMATICS, V20, P191, DOI 10.1093/bioinformatics/btg390 YEH A, 2005, BMC BIOINFORMATICS, V6, P1 NR 30 TC 4 Z9 4 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0305-1048 J9 NUCLEIC ACIDS RES JI Nucleic Acids Res. PD JUL 1 PY 2009 VL 37 BP W153 EP W159 DI 10.1093/nar/gkp392 PG 7 WC Biochemistry & Molecular Biology SC Biochemistry & Molecular Biology GA 469IG UT WOS:000267889100028 PM 19458159 ER PT J AU Zheng, HT Kang, BY Kim, HG AF Zheng, Hai-Tao Kang, Bo-Yeong Kim, Hong-Gee TI Exploiting noun phrases and semantic relationships for text document clustering SO INFORMATION SCIENCES LA English DT Article DE Ontology; WordNet; Text document clustering; Noun phrase; Hypernymy; Hyponymy; Holonymy; Meronymy ID WORD SENSE DISAMBIGUATION; INFORMATION-RETRIEVAL; GENE-ONTOLOGY; SIMILARITY; ALGORITHM; MODEL AB Text document clustering plays an important role in providing better document retrieval, document browsing, and text mining. Traditionally, clustering techniques do not consider the semantic relationships between words, such as synonymy and hypernymy. To exploit semantic relationships, ontologies such as WordNet have been used to improve clustering results. However, WordNet-based clustering methods mostly rely on single-term analysis of text; they do not perform any phrase-based analysis. In addition, these methods utilize synonymy to identify concepts and only explore hypernymy to calculate concept frequencies, without considering other semantic relationships such as hyponymy. To address these issues, we combine detection of noun phrases with the use of WordNet as background knowledge to explore better ways of representing documents semantically for clustering. First, based on noun phrases as well as single-term analysis, we exploit different document representation methods to analyze the effectiveness of hypernymy, hyponymy, holonymy, and metonymy. Second, we choose the most effective method and compare it with the WordNet-based clustering method proposed by others. The experimental results show the effectiveness of semantic relationships for clustering are (from highest to lowest): hypernymy, hyponymy, metonymy, and holonymy. Moreover, we found that noun phrase analysis improves the WordNet-based clustering method. (C) 2009 Elsevier Inc. All rights reserved. C1 [Zheng, Hai-Tao; Kang, Bo-Yeong; Kim, Hong-Gee] Seoul Natl Univ, Biomed Knowledge Engn Lab, Coll Dent BK21, Seoul 110810, South Korea. RP Kim, HG (reprint author), Seoul Natl Univ, Biomed Knowledge Engn Lab, Coll Dent BK21, 28 Yeongeon Dong, Seoul 110810, South Korea. EM hgkim@snu.ac.kr FU ministry for health, welfare and family affairs, South Korea [A05-0909-A80405-06A2-15010A] FX This work was supported by ministry for health, welfare and family affairs, South Korea [A05-0909-A80405-06A2-15010A, Ontology-based EHR (Electronic Health Record) Interoperability Technology Project]. We thank Charles Borchert for his helpful English revision. We also thank the anonymous reviewers whose comments and suggestions led us improve significantly the paper. CR Aas K., 1999, TEXT CATEGORISATION Abney Steven, 1996, J NATURAL LANGUAGE E, V2, P337 Adryan B, 2004, BIOINFORMATICS, V20, P2851, DOI 10.1093/bioinformatics/bth289 [Anonymous], STANFORD PARSER [Anonymous], STOPWORD LIST Ashburner M, 2000, NAT GENET, V25, P25 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 BORGELT C, 2008, INFORM SCI BREAUX TD, 2005, P 38 ANN HAW INT C S, V4, P111 Bussmann H., 1996, ROUTLEDGE DICT LANGU Cheng Jill, 2004, J Biopharm Stat, V14, P687, DOI 10.1081/BIP-200025659 Chomsky N., 1956, IEEE T INFORM THEORY, V2 CORSI C, 2007, BMC BIOINFORM S S1, V8, P8 DEBUENAGA M, 2000, RECENT ADV NATURAL L, V189, P353 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELUCA EW, 2004, P EUR S INT TECHN EU FOLTZ PW, 1990, SIGOIS B, V11, P2 Green SJ, 1999, IEEE T KNOWL DATA EN, V11, P713, DOI 10.1109/69.806932 GREEN SJ, 1997, 3 WORKSH APPL NAT LA GRISHMAN R, 1997, LNCS LNAI Hammouda KM, 2004, KNOWL INF SYST, V6, P710, DOI 10.1007/s10115-003-0118-5 Hirst G., 1998, WORDNET ELECT LEXICA Hofmann T., 1999, P 22 ANN ACM C RES D Hotho A, 2003, THIRD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P541 Hotho A., 2001, P IJCAI 2001 WORKSH Hotho A., 2003, P SEM WEB WORKSH 26 Hung C., 2004, INT J HYBRID INTELLI, V1, P127 Hung CL, 2004, IEEE INTELL SYST, V19, P68 Jackson J., 1991, USERS GUIDE PRINCIPA Jain A. K., 1988, ALGORITHMS CLUSTERIN Kang BY, 2005, INFORM SCIENCES, V170, P443, DOI 10.1016/j.ins.2004.03.013 Karlsson F., 1990, P 13 INT C COMP LING, V3, P168, DOI 10.3115/991146.991176 Kaufman L, 1990, FINDING GROUPS DATA Klein D., 2003, ADV NEURAL INFORM PR, V15, P3 Klein D., 2003, P 41 ANN M ASS COMP, V1, P423, DOI DOI 10.3115/1075096.1075150 Kohonen T, 1988, SELF ORG ASS MEMORY Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lee CH, 2008, INFORM SCIENCES, V178, P4501, DOI 10.1016/j.ins.2008.05.014 Lewis D., 1997, REUTERS 21578 TEXT C LEWIS DD, 1997, README FILE REUTERS MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Moldovan D, 2004, COMPUT SPEECH LANG, V18, P301, DOI 10.1016/j.csl.2004.05.007 MORRIS J, 1991, COMPUT LINGUIST, V17, pC21 MORRIS J, 1988, THESIS U TORONTO Nguyen CD, 2008, INFORM SCIENCES, V178, P4205, DOI 10.1016/j.ins.2008.07.016 Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38 Pantel P., 2002, 25 ANN INT ACM SIGIR Porter M. F., 1997, READINGS INFORM RETR, P313 ROSACI D, 2005, ONTOLOGY BASED 2 LEV, P31 Rosse C, 2003, J BIOMED INFORM, V36, P478, DOI 10.1016/j.jbi.2003.11.007 ROSSO P, 2004, P 2 INT WORDNET C GW Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SanJuan E, 2006, LECT NOTES COMPUT SC, V3936, P496 SEDDING J, 2004, COLING 2004 3 WORKSH Seo HC, 2004, COMPUT SPEECH LANG, V18, P253, DOI 10.1016/j.csl.2004.05.004 SILBERZTEIN M, 2007, P 12 INT C APPL NAT, P1 SMIRNOV A, 2005, P INT WORKSH AUT INT Stearns M Q, 2001, Proc AMIA Symp, P662 Steinbach M., 2000, KDD WORKSH TEXT MIN Torra V, 2005, INFORM PROCESS MANAG, V41, P587, DOI 10.1016/j.ipm.2004.01.001 Urena-Lopez LA, 2001, COMPUT HUMANITIES, V35, P215, DOI 10.1023/A:1002632712378 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Voutilainen A., 1993, P WORKSH VER LARG CO, P48 Wan XJ, 2007, INFORM SCIENCES, V177, P3718, DOI 10.1016/j.ins.2007.02.045 WEN J, 2007, P 20 IEEE INT S COMP, P225 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Zamir O., 1999, P 8 INT WORLD WID WE Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 69 TC 25 Z9 29 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD JUN 13 PY 2009 VL 179 IS 13 BP 2249 EP 2262 DI 10.1016/j.ins.2009.02.019 PG 14 WC Computer Science, Information Systems SC Computer Science GA 447UA UT WOS:000266216100014 ER PT J AU Morales-Del-Castillo, JM Peis, E Moreno, JM Herrera-Viedma, E AF Manuel Morales-del-Castillo, Jose Peis, Eduardo Manuel Moreno, Juan Herrera-Viedma, Enrique TI D-Fussion: a semantic selective disssemination of information service for the research community in digital libraries SO INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL LA English DT Article ID DECISION-MAKING; SYSTEM; OPERATORS; MODEL AB Introduction. In this paper we propose a multi-agent Selective Dissemination of Information service to improve the research community's access to digital library resources. The service also provides a new recommendation approach to satisfy researchers' specific information requirements. Method. The service model is developed by jointly applying Semantic Web technologies (used to define rich descriptions of resources and a concept scheme that helps in indexing and retrieving tasks), fuzzy linguistic modelling techniques (both ordinal and 2-tuple-based approaches, that allow us to flexibly represent and handle information that is subject to a certain degree of uncertainty), as well as content-based and collaborative filtering techniques. Analysis. An experiment has been carried out to test the performance of the proposed model using a prototype and several experts have been asked to assess the recommendations provided by the system. Results. The outcomes of the experiment reveal that the proposed model is feasible and efficient in terms of precision and recall. Conclusions. Semantic Web technologies and fuzzy linguistic modelling provide the means to develop value-added services for digital libraries, which improve users' access to resources of interest to them. Furthermore, the recommendation approach here proposed allows researchers to satisfy specific information needs not covered by traditional recommender systems. C1 [Manuel Morales-del-Castillo, Jose; Peis, Eduardo] Univ Granada, Dept Lib & Information Sci, Granada, Spain. [Manuel Moreno, Juan] Univ Murcia, Dept Informat & Commun Engn, Murcia, Spain. [Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain. RP Morales-Del-Castillo, JM (reprint author), Univ Granada, Dept Lib & Information Sci, Granada, Spain. EM josemdc@ugr.es; epeis@ugr.es; jmmoreno@um.es; viedma@decsai.ugr.es RI HERRERA-VIEDMA, ENRIQUE/C-2704-2008; Peis Redondo, Eduardo/D-3133-2012; Lopez-Gijon, Javier/F-7686-2012; Morales-del-Castillo, Jose/J-9283-2012 OI Peis Redondo, Eduardo/0000-0001-7237-8676; Morales-del-Castillo, Jose/0000-0002-4342-7077 CR AKSOY D, 1998, P AMCP 98, P194 Altinel M., 2000, VLDB J, P53 [Anonymous], 2008, DUBL COR MET EL SET Beckett D., 2004, RDF XML SYNTAX SPECI BEGEDDOV G, 2001, RDF SITE SUMMARY RSS Berners- Lee T., 2000, SEMANTIC WEB XML2000 Berners-Lee T., 2001, SCI AM, V279 Brenner W., 1998, INTELLIGENT SOFTWARE Brickley D, 2005, FOAF VOCABULARY SPEC Cao YK, 2007, EXPERT SYST APPL, V33, P230, DOI 10.1016/j.eswa.2006.04.012 DECKER K, 1999, FUZZY LOGIC ARTIFICI, P578 Faensen D., 2001, P 1 ACM IEEE JOINT C, P373, DOI 10.1145/379437.379730 Fazlollahi B, 2000, INT J INTELL SYST, V15, P849, DOI 10.1002/1098-111X(200009)15:9<849::AID-INT2>3.0.CO;2-I FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 GEISLER G, 2001, P 1 ACM IEEE CS JOIN, P199, DOI 10.1145/379437.379483 Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Guarino N, 1998, FR ART INT, V46, P3 Hendler J, 2001, IEEE INTELL SYST APP, V16, P30, DOI 10.1109/5254.920597 Herrera F, 2000, IEEE T FUZZY SYST, V8, P746, DOI 10.1109/91.890332 Herrera F, 1996, FUZZY SET SYST, V79, P175, DOI 10.1016/0165-0114(95)00162-X Herrera F, 1997, IEEE T SYST MAN CY A, V27, P646, DOI 10.1109/3468.618263 Herrera-Viedma E, 2001, J AM SOC INF SCI TEC, V52, P460, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1087>3.0.CO;2-Q HERRERAVIEDMA E, 2007, E SERVICE INTELLIGEN, P647 Huang Z., 2002, P 2 ACM IEEE CS JOIN, P65 Isaac A, 2008, SKOS SIMPLE KNOWLEDG Jaccard P., 1912, NEW PHYTOL, V11, P37, DOI DOI 10.1111/J.1469-8137.1912.TB05611.X Jenning N.R., 1998, J AUTONOMOUS AGENTS, V1, P7 Keen M., 1966, FACTORS DETERMINING, V2 Krafft DB, 2008, P 8 ACM IEEE CS JOIN, P313, DOI 10.1145/1378889.1378943 Kuflik T., 2000, P 23 ANN INT ACM SIG, P313, DOI 10.1145/345508.345615 KUOKKA D, 1995, READINGS AGENTS, P672 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 Marchionini G., 2000, RES DEV DIGITAL LIB McGuinness D. L., 2004, OWL WEB ONTOLOGY LAN NOTTINGHAM M, 2005, ATOM SYNDICATION FOR OLDAKOWSKY R, 2005, 4 INT SEM WEB C ISWC Pirmann C. M., 2009, SCHOLARLY INFORM PRA Popescul A., 2001, P 17 C UNC ART INT U, P437 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Rorvig M, 1999, J AM SOC INFORM SCI, V50, P639, DOI 10.1002/(SICI)1097-4571(1999)50:8<639::AID-ASI2>3.0.CO;2-C SALTON G, 1971, AUTOMATIC DOCUMENT P SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B, 2000, P 2 ACM C EL COMM, P158, DOI DOI 10.1145/352871.352887 Schein A. I., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/564376.564421 van Rijsbergen C. J., 1979, INFORM RETRIEVAL YAGER RR, 2007, SOFT COMPUT, V11, P632 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 ZADEH LA, 1975, INFORM SCIENCES, V8, P301, DOI 10.1016/0020-0255(75)90046-8 ZADEH LA, 1975, INFORM SCIENCES, V9, P43, DOI 10.1016/0020-0255(75)90017-1 ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5 ZHONG J, 2002, P 10 INT C CONC STRU, P92 2007, RSS HIST NR 53 TC 1 Z9 1 PU UNIV SHEFFIELD DEPT INFORMATION STUDIES PI SHEFFIELD PA UNIV SHEFFIELD, WESTERN BANK, SHEFFIELD S10 2TN, S YORKS, ENGLAND SN 1368-1613 J9 INFORM RES JI Inf. Res. PD JUN PY 2009 VL 14 IS 2 AR 398 PG 25 WC Information Science & Library Science SC Information Science & Library Science GA 496LK UT WOS:000269975300004 ER PT J AU Konstantopoulos, C Mamalis, B Pantziou, G Gavalas, D AF Konstantopoulos, Charalampos Mamalis, Basilis Pantziou, Grammati Gavalas, Damianos TI Efficient parallel Text Retrieval techniques on Bulk Synchronous Parallel (BSP)/Coarse Grained Multicomputers (CGM) SO JOURNAL OF SUPERCOMPUTING LA English DT Article DE BSP model; CGM model; Parallel algorithms; Text retrieval; Document clustering; External memory ID EXTERNAL MEMORY ALGORITHMS; CLUSTER-BASED RETRIEVAL; GRAPH ALGORITHMS; MODEL; BSP AB In this paper, we present efficient, scalable, and portable parallel algorithms for the off-line clustering, the on-line retrieval and the update phases of the Text Retrieval (TR) problem based on the vector space model and using clustering to organize and handle a dynamic document collection. The algorithms are running on the Coarse-Grained Multicomputer (CGM) and/or the Bulk Synchronous Parallel (BSP) model which are two models that capture within a few parameters the characteristics of the parallel machine. To the best of our knowledge, our parallel retrieval algorithms are the first ones analyzed under these specific parallel models. For all the phases of the proposed algorithms, we analytically determine the relevant communication and computation cost thereby formally proving the efficiency of the proposed solutions. In addition, we prove that our technique for the on-line retrieval phase performs very well in comparison to other possible alternatives in the typical case of a multiuser information retrieval (IR) system where a number of user queries are concurrently submitted to an IR system. Finally, we discuss external memory issues and show how our techniques can be adapted to the case when processors have limited main memory but sufficient disk capacity for holding their local data. C1 [Konstantopoulos, Charalampos] Univ Piraeus, Dept Informat, Piraeus, Greece. [Konstantopoulos, Charalampos] Res Acad Comp Technol Inst Patras, Piraeus, Greece. [Mamalis, Basilis; Pantziou, Grammati] Technol Educ Inst Athens, Dept Informat, Athens, Greece. [Gavalas, Damianos] Univ Aegean, Dept Cultural Technol & Commun, Mitilini, Greece. RP Konstantopoulos, C (reprint author), Univ Piraeus, Dept Informat, Piraeus, Greece. EM konstant@cti.gr; vmamalis@teiath.gr; pantziou@teiath.gr; dgavalas@aegean.gr RI Gavalas, Damianos/F-1449-2010 CR Aslam J. A., 2004, J GRAPH ALGORITHMS A, V8, P95 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BILARDI G, 2001, P INT C COMP SCI ICC, P579 CAN F, 1993, ACM T INFORM SYST, V11, P143, DOI 10.1145/130226.134466 Can F, 2004, INFORM SYST, V29, P697, DOI 10.1016/S0306-4379(03)00062-0 Chan A., 1999, Parallel Processing Letters, V9, DOI 10.1142/S0129626499000499 CHAUDHRI BB, 1994, PATTERN RECOGN, V13, P27 Chung SH, 2001, J SUPERCOMPUT, V19, P251, DOI 10.1023/A:1011178530932 CRINGEAN JK, 1990, P 13 INT C RES DEV I, P429, DOI 10.1145/96749.98249 Dehne F, 2003, ALGORITHMICA, V36, P97, DOI 10.1007/s00453-002-1009-y Dehne F, 2002, ALGORITHMICA, V33, P183, DOI 10.1007/s00453-001-0109-4 Dehne F, 2002, THEOR COMPUT SYST, V35, P567, DOI 10.1007/s00224-002-1066-2 DEHNE F., 1995, P ACM S PAR ALG ARCH, P27, DOI 10.1145/215399.215410 Dehne F., 1999, Proceedings 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing. IPPS/SPDP 1999, DOI 10.1109/IPPS.1999.760428 Dehne F., 1997, P 9 ACM S PAR ALG AR, P106, DOI 10.1145/258492.258503 Dehne F., 1994, Proceedings. Sixth IEEE Symposium on Parallel and Distributed Processing (Cat. No.94TH0675-9), DOI 10.1109/SPDP.1994.346119 Dehne Frank, 1993, P ACM 9 ANN COMP GEO, P298, DOI 10.1145/160985.161154 Dobrynin V, 2005, IEEE T INF TECHNOL B, V9, P256, DOI 10.1109/TITB.2005.847184 EFRAIMIDIS P, 1995, P 18 ANN INT ACM SIG, P58, DOI 10.1145/215206.215332 GAVALAS D, 2005, P 17 IASTED INT C PA, P301 GERBESSIOTIS AV, 1996, PRGTR2396 OXF U COMP GERBESSIOTIS AV, 1994, J PARALLEL DISTR COM, V22, P251, DOI 10.1006/jpdc.1994.1085 Goodrich MT, 1999, SIAM J COMPUT, V29, P416, DOI 10.1137/S0097539795294141 HAWKING D, 1996, P 1996 INT C PAR DIS, P1354 Ishikawa Y., 2001, P 5 EUR C RES ADV TE, P325 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Juurlink BHH, 1996, INFORM PROCESS LETT, V58, P303, DOI 10.1016/0020-0190(96)00073-7 Kanellakis P.C., 1997, FAULT TOLERANT PARAL Kang JH, 2004, LECT NOTES COMPUT SC, V3019, P682 Karger D., 1997, ALGORITHMS THEORY CO Katriel I, 2003, LECT NOTES COMPUT SC, V2625, P62 Kleinberg J., 2006, ALGORITHM DESIGN Kunder M.d., 2008, SIZE WORLD WIDE WEB Kurland O., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009027 Levering R., 2006, P 2006 ACM S DOC ENG, P198, DOI 10.1145/1166160.1166213 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 LU Z, 2000, P 22 INT ACM SIGIR C, P97 MACFARLANE A, 2000, P 8 TEXT RETR C TREC, P241 MAMALIS B, 2003, INT J COMPUT APPL, V10, P115 Manning C.D., 2008, INTRO INFORM RETRIEV Martin JMR, 2004, CONCUR SYST ENGN SER, V62, P219 MCCOLL WF, 1994, P 13 IFIP WORLD COMP, P539 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 RUNGSAWANG A, 2001, P EUR PVM MPI, P419 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stanfill C., 1989, Proceedings of the 13th International Conference on Research and Development in Information Retrieval VALIANT L, 1991, HDB THEORETICAL COMP, VA, P943 VALIANT LG, 1990, COMMUN ACM, V33, P103, DOI 10.1145/79173.79181 VANRIJSBERGERN C, 1971, INFORM RETRIEVAL Vitter JS, 2001, ACM COMPUT SURV, V33, P209, DOI 10.1145/384192.384193 Williams TL, 2000, LECT NOTES COMPUT SC, V1800, P102 WILLIAMS TL, 2000, THESIS U CENTRAL FLO WITTEN T, 1999, MANAGING GIGABYTES C NR 54 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0920-8542 J9 J SUPERCOMPUT JI J. Supercomput. PD JUN PY 2009 VL 48 IS 3 BP 286 EP 318 DI 10.1007/s11227-008-0225-x PG 33 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 459OF UT WOS:000267112000004 ER PT J AU Chen, HL Chen, MS Lin, SC AF Chen, Hung-Leng Chen, Ming-Syan Lin, Su-Chen TI Catching the Trend: A Framework for Clustering Concept-Drifting Categorical Data SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Data mining; categorical clustering; data labeling ID MULTIPLE EVOLVING STREAMS AB Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named MAximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, N-Nodeset Importance Representative(abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency, and 2) MARDL can achieve high intracluster similarity and low intercluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. MARDL is empirically validated on real and synthetic data sets and is shown to be significantly more efficient than prior works while attaining results of high quality. C1 [Chen, Hung-Leng; Chen, Ming-Syan; Lin, Su-Chen] Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan. [Chen, Ming-Syan] Natl Taiwan Univ, Grad Inst Commun Engn, Taipei 106, Taiwan. RP Chen, HL (reprint author), Natl Taiwan Univ, Dept Elect Engn, 1,Sec 4,Roosevelt Rd, Taipei 106, Taiwan. EM kidd@AEA-arbor.ee.ntu.edu.tw; mschen@AEA-cc.ee.ntu.edu.tw; sclin@AEA-arbor.ee.ntu.edu.tw FU National Science Council of Taiwan, ROC [NSC95-2752-E002-006-PAE] FX The work was supported in part by the National Science Council of Taiwan, ROC., under Contract NSC95-2752-E002-006-PAE. CR Aggarwal C., 2003, P 29 INT C VER LARG Aggarwal C. C., 1999, P 1999 ACM SIGMOD IN, P61, DOI 10.1145/304182.304188 ANDRITSOS P, 2004, P 9 INT C EXT DAT TE BARBARA D, 2002, P ACM INT C INF KNOW Cao F, 2006, P 6 SIAM INT C DAT M Chakrabarti D., 2006, P 12 ACM SIGKDD INT, P554, DOI DOI 10.1145/1150402.1150467 CHEN HL, 2005, P 5 IEEE INT C DAT M CHI Y, 2007, P 13 ACM SIGKDD INT, P153, DOI DOI 10.1145/1281192.1281212 Dai BR, 2006, IEEE T KNOWL DATA EN, V18, P1166 DEMPSTER AP, 2010, J ROYAL STAT SOC Fisher D. H., 1987, MACHINE LEARNING Gaber MM, 2006, INT J INF TECH DECIS, V5, P659, DOI 10.1142/S0219622006002179 GANTI V, 1999, P ACM SIGKDD GIBSON D, 2000, VLDB J, V8, P236 Gluck M., 1985, P 7 ANN C COGN SCI S, P283 GUHA S, 1999, P 15 INT C DAT ENG I Han E., 1997, P ACM SIGMOD WORKSH HUANG Z, 1999, IEEE T FUZZY SYSTEMS HUANG ZX, 1998, DATA MINING KNOWLEDG HULTEN G, 2001, P ACM SIGKDD Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain A.K., 1999, ACM COMPUTING SURVEY Kamber M., 2001, DATA MINING CONCEPTS Nasraoui O, 2008, IEEE T KNOWL DATA EN, V20, P202, DOI 10.1109/TKDE.2007.190667 NASRAOUI O, 2006, P 6 SIAM INT C DAT M Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shannon Claude, 1948, BELL SYSTEM TECHNICA Sun Y, 2002, PATTERN RECOGNITION, V23 WANG H, 2003, P ACM SIGKDD Widmer G., 1996, MACHINE LEARNING Yeh MY, 2007, IEEE T KNOWL DATA EN, V19, P1349, DOI 10.1109/TKDE.2007.1066 ZAKI MJ, 2005, P 21 INT C DAT ENG ZHANG T, 1996, P ACM SIGMOD ZHOU A, 2007, KNOWLEDGE INFORM SYS NR 35 TC 7 Z9 9 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAY PY 2009 VL 21 IS 5 BP 652 EP 665 DI 10.1109/TKDE.2008.192 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 420PB UT WOS:000264300600004 ER PT J AU Hammouda, KM Kamel, MS AF Hammouda, Khaled M. Kamel, Mohamed S. TI Hierarchically Distributed Peer-to-Peer Document Clustering and Cluster Summarization SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Distributed data mining; distributed document clustering; hierarchical peer-to-peer networks ID CATEGORIZATION AB In distributed data mining, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility, and scalability, we propose a Hierarchically distributed Peer-to-Peer (HP2PC) architecture and clustering algorithm. The architecture is based on a multilayer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Within a certain level of the hierarchy, peers cooperate within their respective neighborhoods to perform P2P clustering. Using this model, we can partition the clustering problem in a modular way across neighborhoods, solve each part individually using a distributed K-means variant, then successively combine clusterings up the hierarchy where increasingly more global solutions are computed. In addition, for document clustering applications, we summarize the distributed document clusters using a distributed keyphrase extraction algorithm, thus providing interpretation of the clusters. Results show decent speedup, reaching 165 times faster than centralized clustering for a 250-node simulated network, with comparable clustering quality to the centralized approach. We also provide comparison to the P2P K-means algorithm and show that HP2PC accuracy is better for typical hierarchy heights. Results for distributed cluster summarization match those of their centralized counterparts with up to 88 percent accuracy. C1 [Hammouda, Khaled M.] Desire2Learn Inc, Kitchener, ON N2G 1B9, Canada. [Kamel, Mohamed S.] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. RP Hammouda, KM (reprint author), Desire2Learn Inc, 305 King St W,Suite 200, Kitchener, ON N2G 1B9, Canada. EM khaledh@gmail.com; mkamel@uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Bandyopadhyay S, 2006, INFORM SCIENCES, V176, P1952, DOI 10.1016/j.ins.2005.11.007 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X da Silva JC, 2005, ENG APPL ARTIF INTEL, V18, P791, DOI 10.1016/j.engappai.2005.06.004 Datta S., 2005, P 8 INT WORKSH HIGH Datta S, 2006, SIAM PROC S, P153 Datta S, 2006, IEEE INTERNET COMPUT, V10, P18, DOI 10.1109/MIC.2006.74 Dhillon IS, 2000, LECT NOTES ARTIF INT, V1759, P245, DOI 10.1007/3-540-46502-2_13 Dunn J. C., 1974, Journal of Cybernetics, V4 EISENHARDT M, 2003, INFORMATIK 2003 INNO HAMMOUDA K, 2006, P 6 SIAM INT C DAT M, P453 Hammouda K. M., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) HAMMOUDA K, 2005, P IAPR INT C MACH LE, P265 Hammouda KM, 2004, KNOWL INF SYST, V6, P710, DOI 10.1007/s10115-003-0118-5 He J., 2003, CLUSTERING INFORM RE, P105 Januzaj E., 2004, P 9 INT C EXT DAT TE, P88 Kargupta H., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining KLUSCH M, 2003, P AGENTLINK, P104 Kumar A, 2006, IEEE INTERNET COMPUT, V10, P15, DOI 10.1109/MIC.2006.79 Lewis DD, 2004, J MACH LEARN RES, V5, P361 LI J, 2002, P 2 MIT STUD OX WORK MERUGU S, 2003, P 3 IEEE INT C DAT M, P218 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samatova NF, 2002, DISTRIB PARALLEL DAT, V11, P157 Strehl A., 2002, THESIS U TEXAS AUSTI Strehl A., 2002, J MACHINE LEARNING R, V3, P583, DOI DOI 10.1162/153244303321897735 Tan AH, 2004, KNOWL INF SYST, V6, P595, DOI 10.1007/s10115-003-0130-9 Wolff R, 2006, SIAM PROC S, P430 WONG W, 2000, P INT C INF SOC 21 C Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 32 TC 14 Z9 14 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAY PY 2009 VL 21 IS 5 BP 681 EP 698 DI 10.1109/TKDE.2008.189 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 420PB UT WOS:000264300600006 ER PT J AU Silva, AJC Goncalves, MA Laender, AHF Modesto, MAB Cristo, M Ziviani, N AF Silva, Allan J. C. Goncalves, Marcos Andre Laender, Alberto H. F. Modesto, Marco A. B. Cristo, Marco Ziviani, Nivio TI Finding what is missing from a digital library: A case study in the Computer Science field SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Full text; Digital libraries; Search engines; DBLP; Scholar; Google ID WORLD-WIDE-WEB; SEARCH ENGINES; INFORMATION AB This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and Citeseer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Silva, Allan J. C.; Goncalves, Marcos Andre; Laender, Alberto H. F.; Modesto, Marco A. B.; Ziviani, Nivio] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil. [Cristo, Marco] FUCAPI Technol & Res Fdn, Manaus, Amazonas, Brazil. RP Goncalves, MA (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil. EM allan@dcc.ufmg.br; mgoncalv@dcc.ufmg.br; laender@dcc.ufmg.br; mabm@dcc.ufmg.br; marco.cristo@fucapi.br; nivio@dcc.ufmg.br RI InWeb, Inct/J-9839-2013 FU MCT/CNPq/CT-INFO [551013/2005-2]; Info-Web [550874/2007-0]; CAPES; CNPq FX This research is partially supported by the MCT/CNPq/CT-INFO projects 5S-VQ (Grant Number 551013/2005-2) and Info-Web (Grant Number 550874/2007-0), and by the authors' scholarships and individual research grants from CAPES and CNPq. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P379, DOI 10.1016/S0169-7552(98)00127-5 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chu H.T., 1996, P 59 ANN M AM SOC IN, P127 Gordon M, 1999, INFORM PROCESS MANAG, V35, P141, DOI 10.1016/S0306-4573(98)00041-7 HARMAN DK, 1996, P 4 TEXT RETR C TREC, P1 HARRISON TL, 2006, P HYPERTEXT 2006, P145, DOI 10.1145/1149941.1149971 HOFF G, 2001, P 19 ANN INT C COMP, P201 Jain R. K., 1991, ART COMPUTER SYSTEMS Laender A. H. F., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996357 LAENDER AHF, 2008, ACM S DOC ENG SAO PA, P257 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 ON BW, 2004, P 7 ANN INT C AS DIG, P408 Qin Jialun, 2004, P 4 ACM IEEE CS JOIN, P135, DOI 10.1145/996350.996383 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANTOS RLT, 2007, P BRAZ S DAT JOAO PE, P5 Tan P. N., 2005, INTRO DATA MINING Voorhees E. M., 1999, P TREC 8, P77 Walters WH, 2007, INFORM PROCESS MANAG, V43, P1121, DOI 10.1016/j.ipm.2006.08.006 Zhuang ZM, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P301, DOI 10.1145/1065385.1065455 NR 21 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2009 VL 45 IS 3 BP 380 EP 391 DI 10.1016/j.ipm.2008.12.006 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 449BO UT WOS:000266306000006 ER PT J AU Jeong, B Cho, H Lee, C AF Jeong, Buthwan Cho, Hyunbo Lee, Choonghyun TI On the functional quality of service (FQoS) to discover and compose interoperable web services SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Functional attribute; Information compatibility; Quality of service (QoS); Semantic similarity; Service discovery; Service-oriented architecture (SOA) ID SELECTION AB Despite its prevalence, the service-oriented architecture (SOA) still has an imperative challenge to achieve interoperability within and across enterprise applications. Minimal conditions for interoperability are: (1) to discover and plug in proper services for integration and (2) to support for seamless data exchanges between component services. A similarity-based approximate matching is a practical approach for both, in that the service discovery relies on functional matches between a query and service descriptions, and the seamless data exchange is granted by mapping information from a service to others. To these ends, this paper comprehensively investigates functional attributes of web services and their manipulation, and particularly highlights an information compatibility and mapping analysis. The functional quality of service (FQoS) allows service discovery and selection to step forward. Simulation results show that the present FQoS metrics are effective in service discovery, (C) 2008 Elsevier Ltd. All rights reserved. C1 [Jeong, Buthwan] Daum Commun Corp, Data Mining Team, Cheju 690150, South Korea. [Cho, Hyunbo] Pohang Univ Sci & Technol, POSTECH, Dept Ind & Management Eng, Pohang 790784, Kyungbuk, South Korea. [Lee, Choonghyun] Yonsei Univ, Seoul 120749, South Korea. RP Jeong, B (reprint author), Daum Commun Corp, Data Mining Team, 1730-8 Odeung, Cheju 690150, South Korea. EM bjeong@postech.ac.kr RI Cho, Hyunbo/F-6973-2013 CR Anicic N., 2007, International Journal of Manufacturing Technology and Management, V10, DOI 10.1504/IJMTM.2007.011850 Chi YL, 2008, EXPERT SYST APPL, V34, P1500, DOI 10.1016/j.eswa.2007.01.022 Dalamagas T, 2006, INFORM SYST, V31, P187, DOI 10.1016/j.is.2004.11.009 Day J., 2004, P 2004 C CTR ADV STU, P293 DO H, 2003, P 29 INT C LARG DAT, P610 Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 FLESCA S, IEEE T KNOWLEDGE DAT, V17 Jarmasz M., 2003, P INT C REC ADV NAT, P212 JEONG B, 2007, P 20 INT C IND ENG O, P572 JEONG B, 2007, P IEEE INT C INF REU, P379 JEONG B, 2006, THESIS POHANG U SCI JEONG B, 2005, P INT WORKSH CONT ON Jeong BW, 2006, INT J ADV MANUF TECH, V28, P1006, DOI 10.1007/s00170-004-2434-7 KOKASH N, 2006, P EUR START AI RES S, P220 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Menasce DA, 2004, IEEE INTERNET COMPUT, V8, P90, DOI 10.1109/MIC.2004.1260710 Menasce DA, 2002, IEEE INTERNET COMPUT, V6, P72, DOI 10.1109/MIC.2002.1067740 O'Brien Liam, 2005, CMUSEI2005TN014 Pedersen T, 2004, PROCEEDING OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE SIXTEENTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE, P1024 PEITZ C, 2003, WSJ FEATURE, P1 Resnik P., 1995, P 14 INT JOINT C ART, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAWETAYLER J, 2004, KERNEL METHODS PATTE SHVAIKO P, 2005, J DATA SEMANTICS 4, V3730, P14 Wang HC, 2007, EXPERT SYST APPL, V32, P571, DOI 10.1016/j.eswa.2006.01.034 Wu J., 2005, P 2005 IEEE INT C SE, P287 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zeng L., 2003, P 12 INT C WORLD WID, P411, DOI DOI 10.1145/775152.775211 Zhang Z., 2003, P WORKSH KNOWL EXP M NR 29 TC 12 Z9 14 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD APR PY 2009 VL 36 IS 3 BP 5411 EP 5418 DI 10.1016/j.eswa.2008.06.087 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 410NJ UT WOS:000263584100153 ER PT J AU Levy, M Sandler, M AF Levy, Mark Sandler, Mark TI Music Information Retrieval Using Social Tags and Audio SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE Audio; information retrieval; music; social tags ID MODEL; RECOGNITION AB In this paper we describe a novel approach to applying text-based information retrieval techniques to music collections. We represent tracks with a joint vocabulary consisting of both conventional words, drawn from social tags, and audio muswords, representing characteristics of automatically-identified regions of interest within the signal. We build vector space and latent aspect models indexing words and muswords for a collection of tracks, and show experimentally that retrieval with these models is extremely well-behaved. We find in particular that retrieval performance remains good for tracks by artists unseen by our models in training, and even if tags for their tracks are extremely sparse. C1 [Levy, Mark; Sandler, Mark] Univ London, Dept Elect Engn, Ctr Digital Mus, London E1 4NS, England. RP Levy, M (reprint author), Last Fm, London N1 6DL, England. EM mark@last.fm; mark.sandler@elec.qmul.ac.uk FU EPSRC [EP/E017614/1] FX Manuscript received May 28, 2008: revised November 13, 2008. Current version published March 18, 2009. This work was supported by EPSRC grant EP/E017614/1 (Online Music Recognition And Searching). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Lexing Me. CR AN GG, 2007, P ISMIR Aucouturier J.-J., 2006, THESIS U PARIS 6 PAR Aucouturier JJ, 2007, J ACOUST SOC AM, V122, P881, DOI 10.1121/1.2750160 BARNARD K, 2001, P IEEE INT C COMP VI Barrington L., 2007, P IEEE INT C AC SPEE Bell RE, 2004, J ENDOVASC THER, V11, P6 Blei D., 2003, P ACM SIGIR C Chai Wei, 2003, P 11 ACM INT C MULT, P223 DAVIES M, 2008, P EUSIPCO UNPUB ECK D, 2007, P NEUR INF PROC SYST GILLET O, 2006, P ICASSP Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337 GOTO M, 2003, P ICASSP, V5, P437 Haitsma J., 2002, P ISMIR Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hofmann T., 1999, P UNC ART INT UAI 99 JEON J, 2003, P ACM SIGIR C Knees P., 2007, P 30 ANN INT ACM SIG KNEES P, 2004, THESIS VIENNA U TECH Knees P., 2004, P ISMIR Levy M, 2008, IEEE T AUDIO SPEECH, V16, P318, DOI 10.1109/TASL.2007.910781 LEVY M, J NEW MUSIC RE UNPUB LEVY M, 2007, P ISMIR LEVY M, 2006, P EUR SIGN PROC C LEVY M, 2006, P ACM MULT LEVY M, 2006, P ICASSP LU L, 2004, P 6 ACM SIGMM INT WO MADDAGE N, 2004, P 6 ACM SIGMM INT WO MANDEL M, 2005, P ISMIR Manning C.D., 2008, INTRO INFORM RETRIEV Marlin B. M., 2007, P 23 C UNC ART INT Monay F, 2007, IEEE T PATTERN ANAL, V29, P1802, DOI 10.1109/TPAMI.2007.1097 MORI Y, 1999, P INT WORKSH MULT IN Pampalk E., 2006, THESIS VIENNA U TECH PAULUS J, 2006, P 1 ACM AUD MUS COMP Quelhas P, 2007, IEEE T PATTERN ANAL, V29, P1575, DOI 10.1109/TPAMI.2007.1155 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIU Y, 2006, P 1 ACM AUD MUS COMP TURNBULL D., 2007, P INT C MUS INF RETR Turnbull D, 2008, IEEE T AUDIO SPEECH, V16, P467, DOI 10.1109/TASL.2007.913750 Vignoli F, 2005, P ISMIR Wang A, 2006, COMMUN ACM, V49, P44, DOI 10.1145/1145287.1145312 WHITMAN B, 2005, THESIS MIT CAMBRIDGE WU X, 2006, P WORLD WID WEB C Yoshii K, 2008, IEEE T AUDIO SPEECH, V16, P435, DOI 10.1109/TASL.2007.911503 NR 45 TC 24 Z9 26 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1520-9210 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD APR PY 2009 VL 11 IS 3 BP 383 EP 395 DI 10.1109/TMM.2009.2012913 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA 425JH UT WOS:000264632300005 ER PT J AU Aseervatham, S AF Aseervatham, Sujeevan TI A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS LA English DT Article DE Kernel; support vector machine; semantic similarity; text categorization AB Kernels are widely used in Natural Language Processing as similarity measures within inner-product based learning methods like the Support Vector Machine. The Vector Space Model (VSM) is extensively used for the spatial representation of the documents. However, it is purely a statistical representation. In this paper, we present a Concept Vector Space Model (CVSM) representation which uses linguistic prior knowledge to capture the meanings of the documents. We also propose a linear kernel and a latent kernel for this space. The linear kernel takes advantage of the linguistic concepts whereas the latent kernel combines statistical and linguistic concepts. Indeed, the latter kernel uses latent concepts extracted by the Latent Semantic Analysis (LSA) in the CVSM. The kernels were evaluated on a text categorization task in the biomedical domain. The Ohsumed corpus, well known for being difficult to categorize, was used. The results have shown that the CVSM improves performance compared to the VSM. C1 Univ Paris 13, CNRS, LIPN, UMR 7030, F-93430 Villetaneuse, France. RP Aseervatham, S (reprint author), Univ Paris 13, CNRS, LIPN, UMR 7030, 99 Av JB Clement, F-93430 Villetaneuse, France. EM Sujeevan.Aseervatham@lipn.univ-paris13.fr CR ASEERVATHAM S, 2008, P IJCNN 08 IEEE INT ASEERVATHAM S, 2007, P ICDM 07 IEEE INT C Basili R., 2006, Informatica, V30 Ben-Hur A., 2001, J MACHINE LEARNING R, V2, P125 Buckley C., 1994, P 3 TEXT RETRIEVAL C BURGES CJC, 1999, ADV NEURAL INFORMATI, P223 Chang C.-C., 2001, LIBSVM LIB SUPPORT V Church K.W., 1995, J NATURAL LANGUAGE E, V1, P163 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 Debole F., 2003, SAC 03, P784 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DIVITA G, 2006, P AMIA FALL S, P201 Drucker H, 1997, ADV NEUR IN, V9, P155 Gliozzo A., 2005, P 9 C COMP NAT LANG, P56, DOI 10.3115/1706543.1706553 HARMAN D, 2005, CHARTING NEW COURSE, P69 HE Q, 2007, P 2007 IEEE INT C DA Hersh W., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval HULL DA, 1995, INFORM RETRIEVAL USI Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Jones K., 1972, J DOCUMENTATION, V28 KATZ SM, 1996, J NATURAL LANGUAGE E, V2, P15 Kleinberg J, 2003, DATA MIN KNOWL DISC, V7, P373, DOI 10.1023/A:1024940629314 LAN M, 2006, AAAI 06 LAN M, 2007, P IJCNN 07 INT JOINT Liu T, 2004, P 4 IEEE INT C DAT M, P162 PEDERSEN T, 2006, J BIOMEDICAL INF JUN PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shawe-Taylor J., 2004, KERNEL METHODS PATTE SIOLAS G, 2000, P IJCNN 00 INT JOINT SOUCY P, 2005, INT JOINT C ART INT, P1130 Sparck Jones K., 1988, STAT INTERPRETATION, P132 Vapnik V., 1995, NATURE STAT LEARNING Vapnik V. N., 1996, ADV NEURAL INFORMATI, V9, P281 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 38 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-2130 J9 INT J ARTIF INTELL T JI Int. J. Artif. Intell. Tools PD APR PY 2009 VL 18 IS 2 BP 239 EP 272 PG 34 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 438GC UT WOS:000265542900003 ER PT J AU Samir, K Habiba, D AF Samir, Kechid Habiba, Drias TI PERSONALIZING THE SOURCE SELECTION AND THE RESULT MERGING PROCESS SO INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS LA English DT Article DE Distributed information retrieval; user profile; source profile; source selection; result merging ID SEARCH AB The World Wide Web knows an incessant and very fast development. Currently, finding useful information on the Web is a time consuming process. In this paper, we present PIRS a personalized Information Retrieval System in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that has the most relevant content according to the query but ignored the user's specific needs. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and thus, the same query may cover different specific information needs according to who emitted it. However, with the ever expanding Web, users are faced with a huge number of information resources. Consequently, such query-based information access strategies lead to inaccurate query results. PIRS extends the state of the art in a Web-based information retrieval system in distributed environment. First, it develops models for representing both user and information source using feature based profiles. Second, PIRS expands a user query according to his profile. Third, it develops algorithms for source selection and results merging that personalize the computation of the relevance score of a document in response to the user's query. PIRS has been experimented with several known information source. The experimental results obtained show the effectiveness of our approach. C1 [Samir, Kechid; Habiba, Drias] USTHB, Fac Elect & Comp Sci, Dept Comp Sci, LRIA, Algiers, Algeria. RP Samir, K (reprint author), USTHB, Fac Elect & Comp Sci, Dept Comp Sci, LRIA, Alia BP 32, Algiers, Algeria. EM kechidsam@yahoo.fr; h_drias@hotmail.fr CR BERTOLINI D, 2003, P 2 INT JOINT C AUT Budzik J., 2000, P 5 INT C INT US INT, P44, DOI 10.1145/325737.325776 Callan J., 2005, P 28 ANN INT ACM SIG, P83, DOI 10.1145/1076034.1076051 Callan J., 2004, P ACM CIKM C WASH DC, P32, DOI 10.1145/1031171.1031180 Callan J. P., 1995, SIGIR Forum Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 FOX EA, 1993, COMBINATION MULTIPLE Fuhr N, 1999, ACM T INFORM SYST, V17, P229, DOI 10.1145/314516.314517 Gravano L., 1997, P ACM SIGMOD INT C M, P207, DOI 10.1145/253260.253299 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 KELLY D, 2003, IMPLICIT FEEDBACK IN, P18 KWOK KL, 1995, P TREC 3 NIST GAITH, P247 LARKEY LS, P CIK 2000, P282 LIEBERMAN H, 1995, P 14 INT JOINT C ART, P929 Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28 LU J, 2006, P 28 ANN INT ACM SIG, P83 Mc Gowan J.P., 2003, THESIS U COLL DUBLIN NOTTELMANN H, 2003, P 26 ANN INT ACM SIG, P290 NUNBERG G, 2003, NY TIMES MAY Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seo Y.-W., 2000, P 2000 INT C INT US, P248, DOI 10.1145/325737.325859 Si L, 2003, ACM T INFORM SYST, V21, P457, DOI 10.1145/944012.944017 Voorhees Ellen M., 1995, P 18 ANN INT ACM SIG, P172, DOI 10.1145/215206.215357 XU J, 1998, EFFECTIVE RETRIEVAL, P112 ZHANG H, 1964, MULTIAGENT APPROACH, P456 NR 27 TC 1 Z9 1 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-2130 J9 INT J ARTIF INTELL T JI Int. J. Artif. Intell. Tools PD APR PY 2009 VL 18 IS 2 BP 331 EP 354 PG 24 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 438GC UT WOS:000265542900006 ER PT J AU Cohen, T Widdows, D AF Cohen, Trevor Widdows, Dominic TI Empirical distributional semantics: Methods and biomedical applications SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Review DE Distributional semantics; Methodological review; Latent semantic analysis; Natural language processing; Semantic similarity; Random indexing; Context vectors ID MEDLINE ABSTRACTS; INFORMATION; RETRIEVAL; KNOWLEDGE; PROTEIN; REPRESENTATION; VISUALIZATION; COOCCURRENCE; SIMILARITY; DOMAIN AB Over the past 15 years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a Corpus Of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness front free text, as well as their evaluation in a variety of biomedical and Other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. (C) 2009 Elsevier Inc. All rights reserved. C1 [Cohen, Trevor] Arizona State Univ, Sch Comp & Informat, Dept Biomed Informat, Ctr Decis Making & Cognit, Phoenix, AZ 85004 USA. RP Cohen, T (reprint author), Arizona State Univ, Sch Comp & Informat, Dept Biomed Informat, Ctr Decis Making & Cognit, 425 N 5th St, Phoenix, AZ 85004 USA. EM trevor.cohen@asu.edu FU US National Library of Medicine [R01 LM07894]; Google, Inc. FX The writing of this manuscript was supported in part by the US National Library of Medicine Grant (R01 LM07894). The authors would like to acknowledge Google, Inc. for their support of author DW's ongoing research on the subject. We would also like to thank Tom Landauer for providing the TASA corpus and his comments on the validity of the TOEFL test, as well as two anonymous referees for their recommendations and thorough reviews. CR *ARB, ARB PROJ KNOWC Aronson A R, 2000, Proc AMIA Symp, P17 ARONSON AR, 2001, P AMIA S, P1717 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BERRY M, 1993, CS93194 U TENN COMP Berry MW, 2003, HDB PARALLEL COMPUTI, P117 Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BRUZA P, 2006, OPERATIONAL ABDUCTIO BUDANITSK A, 2001, WORKSH WORDNET OTH L Burgess C, 2000, COGNITIVE DYNAMICS, P117 CEDERBERG S, 2008, INFORMAP NLP SOFTWAR Cederberg S., 2003, P 7 C NAT LANG LEARN, V4, P111, DOI 10.3115/1119176.1119191 Charniak E., 1993, STAT LANGUAGE LEARNI CHEN G, 2008, P 2 AUSTR WORKSH HLT, P9 CHUTE CG, 1991, P ANN S COMP APPL ME, P1859 CHUTE CG, 1992, P ANN S COMP APPL ME, V639, P43 Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671 Cohen AM, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-103 Cohen T, 2008, J BIOMED INFORM, V41, P1070, DOI 10.1016/j.jbi.2008.03.008 Cohen Trevor, 2005, AMIA Annu Symp Proc, P151 Cohen Trevor, 2008, AMIA Annu Symp Proc, P126 Cole RJ, 2005, LECT NOTES COMPUT SC, V3735, P84 COOPER EI, 1988, MATER LETT, V7, P5, DOI 10.1016/0167-577X(88)90069-9 Curran J., 2005, P 43 ANN M ASS COMP, P26, DOI 10.3115/1219840.1219844 CURRAN JR, 2001, P 38 ANN M ASS COMP, P231, DOI 10.3115/1073083.1073123 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dennis Simon, 2007, HDB LATENT SEMANTIC, P449 Domingos P, 2007, DATA MIN KNOWL DISC, V15, P21, DOI 10.1007/s10618-007-0069-7 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Elvevag B, 2007, SCHIZOPHR RES, V93, P304, DOI 10.1016/j.schres.2007.03.001 Fan JW, 2007, J AM MED INFORM ASSN, V14, P467, DOI 10.1197/jamia.M2314 Friedman C, 2001, Bioinformatics, V17 Suppl 1, pS74 FRIEDMAN C, 2000, P AMIA S, P19270 Ganapathiraju MK, 2004, IEEE SIGNAL PROC MAG, V21, P78, DOI 10.1109/MSP.2004.1296545 Gilber Strang, 2003, INTRO LINEAR ALGEBRA GILES JT, 2001, GTP GEN TEXT PARSER Glenisson P, 2003, Pac Symp Biocomput, P391 Gordon MD, 1998, J AM SOC INFORM SCI, V49, P674 Gorman J., 2006, P C EMP METH NAT LAN, P457, DOI 10.3115/1610075.1610139 Grefenstette Gregory, 1994, P 6 C EUR ASS LEX EU, P279 Griffiths TL, 2002, P 24 ANN C COGN SCI, P381 Griffiths TL, 2005, ADV NEURAL INFORM PR, V17, P537 Harris Z., 1968, MATH STRUCTURES LANG Harris ZS, 2002, J BIOMED INFORM, V35, P215, DOI 10.1016/S1532-0464(03)00011-X Hearst M. A., 1992, P 14 INT C COMP LING, P539 Heer J., 2005, CHI 05, P421 Heinrich G, 2005, PARAMETER ESTIMATION Hersh W., 2003, INFORM RETRIEVAL HLT, Vsecond Hersh William, 1994, P 17 ANN INT ACM SIG, P192 HOFMANN T, 1999, P 15 C UNC ART INT U, P289 Homayouni R, 2005, BIOINFORMATICS, V21, P104, DOI 10.1093/bioinformatics/bth464 Johnson W. B., 1984, CONT MATH, V26, P189 Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1 Kanejiya D., 2003, P HLT NAACL 03 WORKS, V2, P53, DOI DOI 10.3115/1118894.1118902 Kanerva P., 2000, P 22 ANN C COGN SCI, P10 KARIGREN J, 2001, ROUND REAL WORLD INT, P294 KLEINSEETHARAMA.J, 2005, NSF REPORT SOC IMPLI, P128 Koehn P., 2005, EUROPARL PARALLEL CO LANDAUER T, 1997, P 19 ANN C COGN SCI Landauer TK, 2000, IEEE INTELL SYST APP, V15, P27 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 LANDAUER TK, COMMUNICATION LANDAUER TK, 2007, LAWRENCE HDB LATENT Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Lin D., 1998, P 17 INT C COMP LING, P768 LIN J, 2007, BMC BIOINFORMATICS, V8, P1 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 MCARTHUR R, 2006, SYST SCI 2006 HICSS, pC91 McDonald J., 1990, PATHFINDER ASS NETWO, P149 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004 Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] Pustejovsky J., 1991, Computational Linguistics, V17 QUILLIAN MR, 2004, SEMANTIC INFORM PROC, P227 RAPP R, 2003, 9 MACHINE TRANSLATIO, P315 Rijsbergen CJ, 2004, GEOMETRY INFORM RETR Riloff E., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Rindflesch TC, 2003, J BIOMED INFORM, V36, P462, DOI 10.1016/j.jbi.2003.11.003 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 SAGER N, 1994, J AM MED INFORM ASSN, V1, P142 Sahlgren M., 2006, THESIS STOCKHOLM U Sahlgren M., 2004, P 20 INT C COMP LING Sahlgren M, P 30 ANN M COGN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Schvaneveldt R. W., 1990, PATHFINDER ASS NETWO SHANNON CE, 1951, AT&T TECH J, V30, P50 Sharda P, 2006, INT J MED INFORM, V75, P346, DOI 10.1016/j.ijmedinf.2005.07.027 Steyvers M., 2007, HDB LATENT SEMANTIC Stuart GW, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-204 STUART GW, 2003, J BIOINFORM COMPUT B, P1475 Swayne DF, 2003, COMPUT STAT DATA AN, V43, P423, DOI 10.1016/S0167-9473(02)00286-4 Vanteru BC, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S1-S10 Volk M, 2002, INT J MED INFORM, V67, P97, DOI 10.1016/S1386-5056(02)00058-8 WIDDOWS D, 2003, P 2003 C N AM CHAPT, V1, P197, DOI 10.3115/1073445.1073481 WIDDOWS D, 2003, NAT LANG PROC BIOM A, P9 Widdows D, 2003, MATH LANGUAGE, V8 Widdows D., 2008, P 2 AAAI S QUANT INT WIDDOWS D, 2003, P C N AM CHAP ASS CO, V4, P31, DOI 10.3115/1073427.1073443 WIDDOWS D, 2008, 6 INT C LAN IN PRESS WIDDOWS D, 2009, P 3 QUANT INT S MARC Widdows Dominic, 2004, GEOMETRY AND MEANING Yang Y, 1996, Proc AMIA Annu Fall Symp, P358 Yang Y, 1994, Proc Annu Symp Comput Appl Med Care, P157 YANG Y, 1992, P 14 INT C COMP LING, V2, P447, DOI 10.3115/992133.992139 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 Yarowsky D, 1995, P ACL, P189 Zambrano N, 2004, J BIOL CHEM, V279, P16161, DOI 10.1074/jbc.M311027200 ZHANG JJ, 2002, J BIOMED INFORM, P3517 Zipitria I., 2001, P 23 ANN C COGN SCI, P1112 TOPIC MODELING TOOLB VOCABSYN FRAMESET 1 LATENT DIRICHLET ALL NR 119 TC 31 Z9 33 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD APR PY 2009 VL 42 IS 2 BP 390 EP 405 DI 10.1016/j.jbi.2009.02.002 PG 16 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 429ZO UT WOS:000264958800020 PM 19232399 ER PT J AU Wahl, S Rieck, K Laskov, P Domschitz, P Muller, KR AF Wahl, Stefan Rieck, Konrad Laskov, Pavel Domschitz, Peter Mueller, Klaus-Robert TI Securing IMS Against Novel Threats SO BELL LABS TECHNICAL JOURNAL LA English DT Article ID PERFORMANCE AB Fixed mobile convergence (FMC) based on the 3GPP IP Multimedia Subsystem (IMS) is considered one of the most important communication technologies of this decade. Yet this all-IP-based network technology brings about the growing danger of security vulnerabilities in communication and data services. Protecting IMS infrastructure servers against malicious exploits poses a major challenge due to the huge number of systems that may be affected. We approach this problem by proposing an architecture for an autonomous and self-sufficient monitoring and protection system for devices and infrastructure inspired by network intrusion detection techniques. The crucial feature of our system is a signature-less detection of abnormal events and zero-day attacks. These attacks may be hidden in a single message or spread across a sequence of messages. Anomalies identified at any of the network domain's ingresses can be further analyzed for discriminative patterns that can be immediately distributed to all edge nodes in the network domain. (C) 2009 Alcatel-Lucent. C1 [Wahl, Stefan] Alcatel Lucent Res Ctr Stuttgart, Stuttgart, Germany. [Rieck, Konrad] Univ Berlin, Berlin, Germany. [Rieck, Konrad; Laskov, Pavel] Fraunhofer Inst, Dept Intelligent Data Anal 1, Berlin, Germany. [Laskov, Pavel] Fraunhofer Inst, Comp Secur Res Grp 1, Berlin, Germany. [Wahl, Stefan; Domschitz, Peter] Bell Labs, Serv Infrastruct Res Dept, Stuttgart, Germany. [Mueller, Klaus-Robert] Tech Univ Berlin, Machine Learning Dept, D-1000 Berlin, Germany. RP Wahl, S (reprint author), Bell Labs, Serv Infrastruct Res Dept, Stuttgart, Germany. RI Rieck, Konrad/F-2233-2010; Muller, Klaus/C-3196-2013 CR Benedikt M., 2008, P ACM SIGM INT C MAN, P487, DOI 10.1145/1376616.1376667 Cortes M, 2004, BELL LABS TECH J, V9, P155, DOI 10.1002/bltj.20048 Duda R.O., 2001, PATTERN CLASSIFICATI Joachims Thorsten, 2002, LEARNING CLASSIFY TE Krugel Christopher, 2002, P 17 ACM S APPL COMP, P201 Laskov P, 2006, J MACH LEARN RES, V7, P1909 Lee W, 1999, P IEEE S SECUR PRIV, P120 Muller KR, 2001, IEEE T NEURAL NETWOR, V12, P181, DOI 10.1109/72.914517 OBERLE K, 2006, DB110V01 MUSE Poikselka M., 2006, IMS IP MULTIMEDIA CO, VSecond Rieck K., 2007, J COMPUTER VIROLOGY, V2, P243, DOI 10.1007/s11416-006-0030-0 RIECK K, 2006, P 3 C DET INSTR MALW, P74 Rieck K, 2008, J MACH LEARN RES, V9, P23 Rosenberg J, 2008, 5039 IETF RFC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B, 1998, NEURAL COMPUT, V10, P1299, DOI 10.1162/089976698300017467 SHER M, 2007, THESIS TECHNICAL U B Siebert M., 2006, IT-Information Technology, V48, DOI 10.1524/itit.2006.48.5.253 SISALEM D, 2005, D21 SNOCER Tax DMJ, 1999, PATTERN RECOGN LETT, V20, P1191, DOI 10.1016/S0167-8655(99)00087-2 WANG K, 2006, P 9 INT S REC ADV IN, P226 Wanke S, 2007, LECT NOTES COMPUT SC, V4606, P103 NR 22 TC 2 Z9 2 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1089-7089 J9 BELL LABS TECH J JI Bell Labs Tech. J. PD SPR PY 2009 VL 14 IS 1 BP 243 EP 257 DI 10.1002/bltj.20365 PG 15 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA 450QS UT WOS:000266416500016 ER PT J AU Jiang, YG Ngo, CW AF Jiang, Yu-Gang Ngo, Chong-Wah TI Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval SO COMPUTER VISION AND IMAGE UNDERSTANDING LA English DT Article DE Visual ontology; Linguistic similarity; Soft-weighting; CEMD matching; Near-duplicate keyframe; Semantic concept ID FEATURES; TEXTURE AB Bag-of-visual-words (BoW) has recently become a popular representation to describe video and image content. Most existing approaches, nevertheless, neglect inter-word relatedness and measure similarity by bin-to-bin comparison of visual words in histograms. In this paper, we explore the linguistic and ontological aspects of visual words for video analysis. Two approaches, soft-weighting and constraint-based earth mover's distance (CEMD), are proposed to model different aspects of visual word linguistics and proximity. In soft-weighting, visual words are cleverly weighted such that the linguistic meaning of words is taken into account for bin-to-bin histogram comparison, In CEMD, a cross-bin matching algorithm is formulated such that the ground distance measure considers the linguistic similarity of words. In particular, a BoW ontology which hierarchically specifies the hyponym relationship of words is constructed to assist the reasoning. We demonstrate soft-weighting and CEMD on two tasks: video semantic indexing and near-duplicate keyframe retrieval. Experimental results indicate that soft-weighting is superior to other popular weighting schemes such as term frequency (TF) weighting in large-scale video database. In addition, CEMD shows excellent performance compared to cosine similarity in near-duplicate retrieval. (C) 2008 Elsevier Inc. All rights reserved. C1 [Jiang, Yu-Gang; Ngo, Chong-Wah] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. RP Jiang, YG (reprint author), City Univ Hong Kong, Dept Comp Sci, 83 Tat Chee Ave, Hong Kong, Hong Kong, Peoples R China. EM yjiang@cs.cityu.edu.hk; cwngo@cs.cityu.edu.hk FU City University of Hong Kong [7002241, 7002112] FX The work described in this paper was fully supported by two grants from City University of Hong Kong (Project No. 7002241 and Project No. 7002112). The authors also thank Eric Zavesky for his help on polishing the language of this paper. CR AGARWAL A, 2006, EUR C COMP VIS [Anonymous], TREC VIDEO RETRIEVAL ASLAM JA, 2006, ACM SIGIR C Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 CAMPBELL M, 2006, TRECVID ONL P CAO J, 2006, ONL P DTO Challenge Workshop on Large Scale Concept Ontology for Multimedia, 2006, DTO CHALL WORKSH LAR Fellbaum C, 1998, WORDNET ELECT LEXICA GRAUMAN K, 2007, ADV NEURAL INFORM PR HAUPTMANN AG, 2006, TRECVID ONL P JIANG JJ, 1997, P ROCLING, V10 Jiang Y.-G., 2007, ACM INT C IM VID RET JIANG YG, 2008, IEEE INT C MULT EXP JIANG YG, 2008, ACM SIGIR C RES DEV Jurie F., 2005, INT C COMP VIS Ke Y., 2004, ACM MULTIMEDIA, P869 Lazebnik S., 2006, IEEE C COMP VIS PATT LING H, 2006, IEEE C COMP VIS PATT Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 Mikolajczyk K., 2005, IEEE T PATTERN ANAL, V27 Mikolajczyk K, 2005, INT J COMPUT VISION, V65, P43, DOI 10.1007/s11263-005-3848-x MOOSMANN F, 2006, C NEUR INF PROC SYST NGO CW, 2006, ACM INT C MULT Nister D., 2006, IEEE C COMP VIS PATT Nowak E, 2006, EUR C COMP VIS PEDERSEN T, 2004, NAT C ART INT AAAI PETROV S, 2006, TRECVID ONL P Philbin J, 2007, IEEE C COMP VIS PATT Resnik P., 1995, INT JOINT C ART INT Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Sivic J., 2003, INT C COMP VIS SNOEK CGM, 2006, TRECVID ONL P Vapnik V., 1995, NATURE STAT LEARNING WU X, 2007, ACM INT C IM VID RET Wu Z., 1994, ACL, P133 ZHANG DQ, 2004, ACM INT C MULT Zhang J, 2007, INT J COMPUT VISION, V73, P213, DOI 10.1007/s11263-006-9794-4 NR 40 TC 20 Z9 21 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1077-3142 J9 COMPUT VIS IMAGE UND JI Comput. Vis. Image Underst. PD MAR PY 2009 VL 113 IS 3 BP 405 EP 414 DI 10.1016/j.cviu.2008.10.002 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 410NK UT WOS:000263584200009 ER PT J AU Lin, FR Yu, JH AF Lin, Fu-ren Yu, Jen-Hung TI Visualized cognitive knowledge map integration for P2P networks SO DECISION SUPPORT SYSTEMS LA English DT Article CT 11th Pacific Asia Conference on Information Systems CY JUL 03-06, 2007 CL Auckland, NEW ZEALAND DE Self-organizing map (SOM); Knowledge map; Peer-to-peer (P2P); Egocentric SOM (ESOM) ID MANAGEMENT; COMMUNITIES; INFORMATION; CREATION; WEB AB This study proposes a visualized cognitive knowledge map integration system, called VisCog, to facilitate knowledge management on P2P networks. By using the SOM (self-organized map)-like model, Egocentric SOM (ESOM), VisCog can merge the other peers' knowledge artifacts (e.g., documents) under a focal peer's knowledge structure and visually present the cognitive knowledge map of the P2P network. The experimental results from evaluating VisCog performance show that VisCog can retain an individual peer's knowledge structure while articulating with those of other peers to build its cognitive knowledge map. (C) 2008 Elsevier B.V. All rights reserved. C1 [Lin, Fu-ren; Yu, Jen-Hung] Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan. RP Lin, FR (reprint author), Natl Tsing Hua Univ, Inst Technol Management, 101 Sec 2,Kuangfu Rd, Hsinchu 300, Taiwan. EM frlin@mx.nthu.edu.tw; byendyu@gmail.com CR Agrawal R., 2000, P 7 INT C EXT DAT TE, P365 Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961 Bonifacio M, 2004, LECT NOTES ARTIF INT, V2926, P31 Castano S., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications Chen HC, 1996, J VIS COMMUN IMAGE R, V7, P88, DOI 10.1006/jvci.1996.0008 Cheung C F, 2007, Journal of Knowledge Management, V11, DOI 10.1108/13673270710762774 CHUANG SL, 2005, ACM T INFORM SYSTEMS, P363 Chung W, 2005, J MANAGE INFORM SYST, V21, P57 Fahey L, 1998, CALIF MANAGE REV, V40, P265 Gordon JL, 2000, KNOWL-BASED SYST, V13, P71, DOI 10.1016/S0950-7051(00)00048-4 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Kwok J. S. H., 2004, Journal of Knowledge Management, V8, DOI 10.1108/13673270410523934 LAI JY, 2008, P 41 ANN HAW INT C S, P355, DOI 10.1109/HICSS.2008.192 Li XQ, 2006, INFORM MANAGE-AMSTER, V43, P283, DOI 10.1016/j.im.2005.07.001 Lin FR, 2006, INFORM PROCESS MANAG, V42, P551, DOI 10.1016/j.ipm.2005.03.026 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Liu DR, 2008, EXPERT SYST APPL, V34, P700, DOI 10.1016/j.eswa.2006.10.005 LUHN HP, 1957, IBM J RES DEV, V1, P309 Luo XF, 2005, LECT NOTES COMPUT SC, V3795, P579 Mangisengi O., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications Markus ML, 2001, J MANAGE INFORM SYST, V18, P57 Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shih JY, 2008, EXPERT SYST APPL, V34, P850, DOI 10.1016/j.eswa.2006.10.031 Soriano-Asensi A, 2008, EXPERT SYST APPL, V34, P2988, DOI 10.1016/j.eswa.2007.05.023 von Krogh G, 1998, CALIF MANAGE REV, V40, P133 Wang CY, 2008, DECIS SUPPORT SYST, V45, P528, DOI 10.1016/j.dss.2007.06.012 Wang S, 2002, KNOWL INF SYST, V4, P31, DOI 10.1007/s10115-002-8192-7 Wei CP, 2002, EUR J INFORM SYST, V11, P208, DOI 10.1057/palgrave.ejis.3000429 NR 31 TC 4 Z9 6 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-9236 J9 DECIS SUPPORT SYST JI Decis. Support Syst. PD MAR PY 2009 VL 46 IS 4 BP 774 EP 785 DI 10.1016/j.dss.2008.11.020 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA 426IK UT WOS:000264701000004 ER PT J AU Yang, SY AF Yang, Sheng-Yuan TI Developing of an ontological interface agent with template-based linguistic processing technique for FAQ services SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article DE Ontological interface agents; Template-based query processing; FAQ services AB This paper proposes an ontological Interface agent which works as an assistant between the users and FAQ systems. We integrated several interesting techniques including domain ontology, user modeling, and template-based linguistic processing to effectively tackle the problems associated with traditional FAQ retrieval systems. Specifically, we address the following issues. Firstly, how can an interface agent learn a user's specialty in order to build a proper user model for him/her? Secondly, how can domain ontology help in establishing user models, analyzing user query, and assisting and guiding interface usage? Finally. how can the intention and focus of a user be correctly extracted? Our work features an template-based linguistic processing technique for developing ontological interface agents; a nature language query mode, along with an improved keyword-based query mode; and an assistance and guidance for human-machine interaction. Our preliminary experimentation demonstrates that user intention and focus Of Up to eighty percent of the user queries can be correctly understood by the system, and accordingly provides the query solutions with higher user satisfaction. (C) 2008 Elsevier Ltd. All rights reserved. C1 St Johns Univ, Dept Comp & Commun Engn, Tamsui 251, Taipei County, Taiwan. RP Yang, SY (reprint author), St Johns Univ, Dept Comp & Commun Engn, 499 Sec 4 TamKing Rd, Tamsui 251, Taipei County, Taiwan. EM ysy@mail.sju.edu.tw FU National Science Council, ROC [NSC-89-2213-E-011-059, NSC-89-2218-E-011-014, NSC-952221-E-129-019] FX The author would like to thank Yai-Hui Chang and Ying-Hao Chiu for their assistance in system implementation. This work was supported by the National Science Council, ROC, under Grants NSC-89-2213-E-011-059, NSC-89-2218-E-011-014, and NSC-952221-E-129-019. CR CALASSI U, 2005, P 15 INT S FDN INT S, P47 Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902 Chiu Y.H., 2003, THESIS NATL TAIWAN U COYLE M, 2005, P 27 EUR C IR RES EC, P543 DEGEMMIS M, 2004, P 8 ERCIM WORKSH US, P133 Hsu CC, 1999, EXPERT SYST APPL, V17, P257, DOI 10.1016/S0957-4174(99)00039-1 LEE CL, 2000, THESIS NATL CHENG KU Noy NF, 2001, KSL0105 STANF KNOWL Ou-yang Y. L, 2000, THESIS NATL CHIAO TU Paraiso EC, 2006, EXPERT SYST APPL, V31, P673, DOI 10.1016/j.eswa.2006.01.018 Ravichandran D., 2002, P 2 INT C HUM LANG T, P247, DOI 10.3115/1289189.1289206 RAZMERITA L, 2003, P UM2003 US MOD P 9, P213 Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 Salton G, 1983, INTRO MODEM INFORM R SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SNEIDERS E, 1999, AAAI FALL S QUEST AN, P97 Soubbotin M.M., 2001, P TREC 10 C NIST GAI, P293 TSAI CH, 2000, MMSEG WORLD IDENTIFI Wang C, 2007, EXPERT SYST APPL, V33, P425, DOI 10.1016/j.eswa.2006.05.017 Yang S. Y., 1999, P 4 C ART INT APPL C, P248 YANG SY, 2007, P 10 INT C COMP SCI, P671 YANG SY, 2006, S APPL DEV MAN INF S, P168 YANG SY, 2004, 12 NAT C FUZZ THEOR, P181 Yang SY, 2007, PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P2964 YANG SY, 2006, WSEAS T COMPUTERS, V5, P1843 Yang SY, 2007, J INTELL INF SYST, V28, P233, DOI 10.1007/s10844-006-0009-9 YANG SY, 2006, P INT AC C 2006 SPEC, P2 NR 27 TC 12 Z9 12 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0957-4174 J9 EXPERT SYST APPL JI Expert Syst. Appl. PD MAR PY 2009 VL 36 IS 2 BP 4049 EP 4060 DI 10.1016/j.eswa.2008.03.011 PG 12 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA 390QF UT WOS:000262178100153 ER PT J AU Klein, ST AF Klein, Shmuel T. TI On the use of negation in Boolean IR queries SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE Boolean queries; Negated keywords; Distance constraints; Concordance; Query processing ID INFORMATION-RETRIEVAL; SYSTEM AB The negation operator, in various forms in which it appears in Information Retrieval queries, is investigated. The applications include negated terms in Boolean queries, more specifically in the presence of metrical constraints, but also negated characters used in the definition of extended keywords by means of regular expressions. Exact definitions are suggested and their usefulness is shown on several examples. Finally, some implementation issues are discussed, in particular as to the order in which the terms of long queries, with or without negated keywords, should be processed, and efficient heuristics for choosing a good order are suggested. (C) 2008 Elsevier Ltd. All rights reserved. C1 Bar Ilan Univ, Dept Comp Sci, IL-52900 Ramat Gan, Israel. RP Klein, ST (reprint author), Bar Ilan Univ, Dept Comp Sci, IL-52900 Ramat Gan, Israel. EM tomi@cs.biu.ac.il CR BOOKSTEIN A, 1992, INFORM PROCESS MANAG, V28, P795, DOI 10.1016/0306-4573(92)90069-C CAFARELLA MJ, 2005, INT WORLD WID WEB C, P442 Chang CCK, 1999, ACM T INFORM SYST, V17, P1, DOI 10.1145/297117.297120 Choueka Y., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42039 Choueka Y., 1989, IEEE DATA ENG B, V14, P22 Cormen TH, 1990, INTRO ALGORITHMS Dunlop MD, 1997, ACM T INFORM SYST, V15, P137, DOI 10.1145/248625.248650 FLOYD RW, 1962, COMMUN ACM, V5, P345, DOI 10.1145/367766.368168 Fraenkel A. S., 1976, Jurimetrics Journal, V16 Frakes W., 1992, INFORMATION RETRIEVA, P131 Fuhr N, 2004, ACM T INFORM SYST, V22, P313, DOI 10.1145/984321.984326 GOLDSMITH JA, 2001, LECT NOTES COMPUTER, V2069, P273 Grossi R., 1993, P 1 S AM WORKSH STRI, P57 Klein ST, 2008, J AM SOC INF SCI TEC, V59, P86, DOI 10.1002/asi.20734 Koubarakis M, 2006, IEEE T KNOWL DATA EN, V18, P1659, DOI 10.1109/TKDE.2006.193 Mason D., 2006, Legal Information Management, V6, DOI 10.1017/S1472669606000831 OKEEFE RA, 2003, P 2 INEX WORKSH, P117 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SORMUNEN E, 2000, P 23 ANN INT ACM SIG, P25, DOI 10.1145/345508.345541 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Tryfonopoulos C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009047 Widdows D., 2003, P 41 ANN M ASS COMP, P136 YOO S, 2007, P ICADL C HAN VIETN, P241 Zobel J., 2006, ACM COMPUT SURV, V38, P1 NR 24 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2009 VL 45 IS 2 BP 298 EP 311 DI 10.1016/j.ipm.2008.12.003 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 422UE UT WOS:000264452400011 ER PT J AU Masutani, O Iwasaki, H AF Masutani, Osamu Iwasaki, Hirotoshi TI BEIRA: An Area-based User Interface for Map Services SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article CT 8th International Conference on Web Information Systems Engineering (WISE 2007) CY DEC 03, 2007 CL Nancy, FRANCE SP Nancy Univ, INRIA, Loria, Lorraine Reg, I 3, Grand Nancy, EMISA DE map interface; web mining; geographic information system; co-clustering ID WEB AB This paper introduces BEIRA, an area-based map user interface for location-based contents. Recently, various web map services are widely used to search for location-based contents. However, browsing a large number of contents that are arranged on a map as points may be troublesome. We tackle this issue by using area-based representations instead of points. AOI (Area of Interest), which is core concept of BEIRA, is an arbitrary shaped area boundary with text summary information. With AOI, users can instantly grasp area characteristics without examining each point. AOI is deduced by performing geo-semantic co-clustering of location-based contents. Geo-semantic co-clustering takes both geographic and semantic features of contents into account. We confirm that the ratio of the geo-semantic blend is the key to deducing an appropriate boundary. We further propose and evaluate location-aware term weighting to obtain an informative summary. C1 [Masutani, Osamu; Iwasaki, Hirotoshi] Denso IT Lab Inc, Res & Dev Grp, Shibuya Ku, Tokyo 1500002, Japan. RP Masutani, O (reprint author), Denso IT Lab Inc, Res & Dev Grp, Shibuya Ku, 2-15-1 Shibuya, Tokyo 1500002, Japan. EM omasutani@d-itlab.co.jp; hiwasaki@d-itlab.co.jp CR Amitay E., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009040 Asadi S, 2006, LECT NOTES COMPUT SC, V4255, P277 Barnard K., 2001, INT C COMP VIS, V2, P408 BUYUKOKKTEN O, 1999, P WORKSH WEB DAT WEB Calinski T., 1974, COMMUN STAT, V3, P1, DOI DOI 10.1080/03610927408827101 Ding J., 2000, 26 INT C VER LARG DA EXPOSTO J, 2005, P 2005 WORKSH GEOGR, P55, DOI 10.1145/1096985.1096999 Hiramatsu K., 2001, Proceedings 2001 Symposium on Applications and the Internet, DOI 10.1109/SAINT.2001.905173 KANADA Y, 1999, 18 INT C INF KNOWL M, P46 LIM E, 2002, P 2 ACM IEEE CS JOIN, P350 LIU TY, 2006, 4 CHIN WORKSH MACH L Morimoto Y., 2003, Proceedings 2003 Symposium on Applications and the Internet, DOI 10.1109/SAINT.2003.1183066 Neches R., 2001, New Review of Hypermedia and Multimedia, V7, DOI 10.1080/13614560108914730 RIPLEY BD, 1976, J APPL PROBAB, V13, P255, DOI 10.2307/3212829 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sclaroff S, 1999, COMPUT VIS IMAGE UND, V75, P86, DOI 10.1006/cviu.1999.0765 SILVA MJ, 2004, AM SIGIR 2004 WORKSH Sun J. T., 2005, P 14 INT C WORLD WID, P382, DOI 10.1145/1060745.1060803 Tezuka T., 2006, P 15 INT C WORLD WID, P277, DOI [10.1145/1135777.1135821, DOI 10.1145/1135777.1135821] WANG X, 2006, SIGIR, V6, P236 XI W., 2004, P 13 INT C WORLD WID, P319, DOI 10.1145/988672.988715 YOSHIDA N, 2000, IPSJ, V41, P127 NR 23 TC 1 Z9 1 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD MAR PY 2009 VL 12 IS 1 BP 51 EP 68 DI 10.1007/s11280-008-0054-0 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 403JA UT WOS:000263077700004 ER PT J AU Diaz, NN Krause, L Goesmann, A Niehaus, K Nattkemper, TW AF Diaz, Naryttza N. Krause, Lutz Goesmann, Alexander Niehaus, Karsten Nattkemper, Tim W. TI TACOA - Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach SO BMC BIOINFORMATICS LA English DT Article ID HORIZONTAL GENE-TRANSFER; PHYLOGENETIC CLASSIFICATION; DNA FRAGMENTS; PREDICTION; SEQUENCES; BACTERIAL; DATABASE; PROKARYOTES; FREQUENCIES; ALGORITHMS AB Background: Metagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning. Results: Our novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp - 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments >= 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained. Conclusion: An accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date. C1 [Diaz, Naryttza N.; Goesmann, Alexander] Univ Bielefeld, Ctr Biotechnol CeBiTec, Bielefeld, Germany. [Diaz, Naryttza N.; Nattkemper, Tim W.] Univ Bielefeld, Biodata Min & Appl Neuroinformat Grp, Fac Technol, Bielefeld, Germany. Univ Bielefeld, Fac Biol, Proteome & Metabolome Res, Bielefeld, Germany. [Krause, Lutz] Nestle Res Ctr, BioAnalyt Sci Dept, CH-1000 Lausanne, Switzerland. RP Diaz, NN (reprint author), Univ Bielefeld, Ctr Biotechnol CeBiTec, Bielefeld, Germany. EM ndiaz@CeBiTec.Uni-Bielefeld.DE; Lutz.Krause@rdls.nestle.com; agoesman@CeBiTec.Uni-Bielefeld.DE; karsten.niehaus@CeBiTec.Uni-Bielefeld.DE; tim.nattkemper@Uni-Bielefeld.DE RI Niehaus, Karsten/A-3966-2010; Krause, Lutz/G-6283-2013 OI Krause, Lutz/0000-0003-3806-0845 FU Deutscher Akademischer Austauschdienst FX NND was supported by the Deutscher Akademischer Austauschdienst. The authors wish to thank Torsten Kasch, Achim Neumann, Ralf Nolte, Bjrn Fischer and Volker Tlle as members of the Bioinformatics Resource Facility for providing the computational and technical support to accomplish this work. We thank I. Rigoutsos from the Bioinformatics and Pattern Discovery Group, IBM Thomas J Watson Research Center for all the help in using the PhyloPythia web server. CR Abe T, 2005, DNA RES, V12, P281, DOI 10.1093/dnares/dsi015 ABE T, 2006, POLAR BIOSCI, V20, P103 Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Baldi P, 2000, BIOINFORMATICS, V16, P412, DOI 10.1093/bioinformatics/16.5.412 Berrar D, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-73 Bohlin J, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-104 Bohlin J, 2008, PLOS COMPUT BIOL, V4, DOI 10.1371/journal.pcbi.1000057 Brown JR, 2003, NAT REV GENET, V4, P121, DOI 10.1038/nrg1000 Campbell A, 1999, P NATL ACAD SCI USA, V96, P9184, DOI 10.1073/pnas.96.16.9184 Chan CKK, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-215 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Finn RD, 2008, NUCLEIC ACIDS RES, V36, pD281, DOI 10.1093/nar/gkm960 FLEISCHMANN RD, 1995, SCIENCE, V269, P496, DOI 10.1126/science.7542800 Foerstner KU, 2005, EMBO REP, V6, P1208, DOI 10.1038/sj.embor.7400538 Garcia-Vallve S, 2000, GENOME RES, V10, P1719, DOI 10.1101/gr.130000 Hastie T., 2002, ELEMENTS STAT LEARNI Huson DH, 2007, GENOME RES, V17, P377, DOI 10.1101/gr.5969107 Karlin S, 1997, J BACTERIOL, V179, P3899 Keeling PJ, 2008, NAT REV GENET, V9, P605, DOI 10.1038/nrg2386 Koonin EV, 2001, ANNU REV MICROBIOL, V55, P709, DOI 10.1146/annurev.micro.55.1.709 Krause L, 2008, J BIOTECHNOL, V136, P91, DOI 10.1016/j.jbiotec.2008.06.003 Krause L, 2008, NUCLEIC ACIDS RES, V36, P2230, DOI 10.1093/nar/gkn038 Margulies M, 2005, NATURE, V437, P376, DOI 10.1038/nature03959 McHardy AC, 2007, NAT METHODS, V4, P63, DOI 10.1038/NMETH976 Overbeek R, 2005, NUCLEIC ACIDS RES, V33, P5691, DOI 10.1093/nar/gki866 Podell S, 2007, GENOME BIOL, V8, DOI 10.1186/gb-2007-8-2-r16 Raes J, 2007, CURR OPIN MICROBIOL, V10, P490, DOI 10.1016/j.mib.2007.09.001 Ruepp A, 2000, NATURE, V407, P508 Saha S, 2006, GENET MOL RES, V5, P224 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandberg R, 2001, GENOME RES, V11, P1404, DOI 10.1101/gr.186401 SANGER F, 1977, P NATL ACAD SCI USA, V74, P5463, DOI 10.1073/pnas.74.12.5463 Stein JL, 1996, J BACTERIOL, V178, P591 Teeling H, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-163 Teeling H, 2004, ENVIRON MICROBIOL, V6, P938, DOI 10.1111/j.1462-2920.2004.00624.x Tran TN, 2006, COMPUT STAT DATA AN, V51, P513, DOI 10.1016/j.csda.2005.10.001 Tyson GW, 2004, NATURE, V428, P37, DOI 10.1038/nature02340 Venter JC, 1998, SCIENCE, V280, P1540, DOI 10.1126/science.280.5369.1540 Wheeler DL, 2002, NUCLEIC ACIDS RES, V30, P13, DOI 10.1093/nar/30.1.13 Yao ZZ, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-S1-S11 ZHANG SH, 2008, NATURE PRECEDINGS, P1 Zhu M, 2007, BMC MED INFORM DECIS, V7, DOI 10.1186/1472-6947-7-41 NR 42 TC 70 Z9 73 PU BIOMED CENTRAL LTD PI LONDON PA CURRENT SCIENCE GROUP, MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD FEB 11 PY 2009 VL 10 AR 56 DI 10.1186/1471-2105-10-56 PG 16 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 416LP UT WOS:000264007900001 PM 19210774 ER PT J AU Lin, J AF Lin, Jimmy TI Is searching full text more effective than searching abstracts? SO BMC BIOINFORMATICS LA English DT Article ID INFORMATION; ARTICLES; MODEL AB Background: With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE (R) abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine. Results: Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles. Conclusion: Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations. C1 [Lin, Jimmy] Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA. [Lin, Jimmy] Univ Maryland, iSch, College Pk, MD 20742 USA. RP Lin, J (reprint author), Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA. EM jimmylin@umd.edu FU NIH; National Library of Medicine; NSF [IIS-0705832, IIS-0836560]; DARPA/IPTO [HR0011-06-2-0001] FX This work was supported by the Intramural Research Program of the NIH, National Library of Medicine; NSF under awards IIS-0705832 and IIS-0836560; DARPA/IPTO Contract No. HR0011-06-2-0001 under the GALE program. Any opinions, findings, conclusions, or recommendations expressed in this article are the author's and do not necessarily reflect those of the sponsors. I would like to thank Yahoo! for leading the development of Hadoop and Amazon for EC2/S3 support. This article benefited from the comments of two anonymous reviewers. Finally, I am grateful to Esther and Kiri for their kind support. CR [Anonymous], 2003, P 19 ACM S OP SYST P, DOI DOI 10.1145/945445.945450 Barroso LA, 2003, IEEE MICRO, V23, P22, DOI 10.1109/MM.2003.1196112 Brin S., 1998, P 7 INT WORLD WID WE, P107 Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 CLEVERDON CW, 1968, FACTORS DETERMINING, V2 Dean J., 2004, Proceedings of the Sixth Symposium on Operating Systems Design and Implementation (OSDI'04) Demner-Fushman Dina, 2005, AMIA Annu Symp Proc, P191 Elsayed T., 2008, P 46 ANN M ASS COMP, P265, DOI 10.3115/1557690.1557767 Gay Clifford W, 2005, AMIA Annu Symp Proc, P271 Harman D.K., 2005, TREC EXPT EVALUATION, P21 Hearst M. A., 1993, P 16 ANN INT ACM SIG, P56 Hersh W, 2007, P 16 TEXT RETRIEVAL Hunter L, 2006, MOL CELL, V21, P589, DOI 10.1016/j.molcel.2006.02.012 Jones K. S., 2000, Information Processing & Management, V36 Kamps J., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009009 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KOU Z, 2007, PACIFIC S BIOCOMPUTI, V12, P257 Krallinger M., 2007, P 2 BIOCREATIVE CHAL, P41 Lalmas M, 2007, LECT NOTES COMPUT SC, V4877, P187 Larsen B., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148306 Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 OGILVIE P., 2005, PARAMETER ESTIMATION, P211 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Rafkind B, 2006, P BIONLP WORKSH LINK, P73 Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1991, P 14 ANN INT ACM SIG, P21, DOI 10.1145/122860.122863 Schuemie MJ, 2004, BIOINFORMATICS, V20, P2597, DOI 10.1093/bioinformatics/bth291 Seki K, 2007, Pacific Symposium on Biocomputing 2007, P316 Shah PK, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-20 Shatkay H, 2006, BIOINFORMATICS, V22, pE446, DOI 10.1093/bioinformatics/btl235 SIGURBJORNSSON B, 2003, P 2 WORKSH IN EV XML, P19 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Tbahriti I, 2006, INT J MED INFORM, V75, P488, DOI 10.1016/j.ijmedinf.2005.06.007 Tellex S., 2003, P 26 ANN INT ACM SIG, P41 TROTMAN A, 2005, P INEX 2005 WORKSH E, P63 Wang M., 2008, P 31 ANN INT ACM SIG, P419, DOI 10.1145/1390334.1390407 WILBUR WJ, 2007, P 2 BIOCREATIVE CHAL, P7 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 Yu H, 2006, BIOINFORMATICS, V22, pE547, DOI 10.1093/bioinformatics/btl261 Yu Hong, 2002, Proc AMIA Symp, P919 Yu Hong, 2006, AMIA Annu Symp Proc, P834 Zweigenbaum P, 2007, P PAC S BIOCOMPUT, V12, P205 Zweigenbaum P, 2007, BRIEF BIOINFORM, V8, P358, DOI 10.1093/bib/bbm045 NR 48 TC 13 Z9 13 PU BIOMED CENTRAL LTD PI LONDON PA CURRENT SCIENCE GROUP, MIDDLESEX HOUSE, 34-42 CLEVELAND ST, LONDON W1T 4LB, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD FEB 3 PY 2009 VL 10 AR 46 DI 10.1186/1471-2105-10-46 PG 15 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 427FS UT WOS:000264765800001 PM 19192280 ER PT J AU Cleary, B Exton, C Buckley, J English, M AF Cleary, Brendan Exton, Chris Buckley, Jim English, Michael TI An empirical analysis of information retrieval based concept location techniques in software comprehension SO EMPIRICAL SOFTWARE ENGINEERING LA English DT Article DE Information retrieval; Software comprehension; Empirical analysis ID LATENT SEMANTIC ANALYSIS; SOURCE CODE; FEATURES AB Concept location, the problem of associating human oriented concepts with their counterpart solution domain concepts, is a fundamental problem that lies at the heart of software comprehension. Recent research has attempted to alleviate the impact of the concept location problem through the application of methods drawn from the information retrieval (IR) community. Here we present a new approach based on a complimentary IR method which also has a sound basis in cognitive theory. We compare our approach to related work through an experiment and present our conclusions. This research adapts and expands upon existing language modelling frameworks in IR for use in concept location, in software systems. In doing so it is novel in that it leverages implicit information available in system documentation. Surprisingly, empirical evaluation of this approach showed little performance benefit overall and several possible explanations are forwarded for this finding. C1 [Cleary, Brendan; Exton, Chris; Buckley, Jim; English, Michael] Univ Limerick, Limerick, Ireland. RP Cleary, B (reprint author), Univ Limerick, Limerick, Ireland. EM brendan.cleary@ul.ie; chris.exton@ul.ie; jim.buckley@ul.ie; michael.english@ul.ie CR Anquetil N., 1997, C CTR ADV STUD COLL Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 BAI J, 2005, 14 ACM INT C INF KNO BERRY M, 2007, SVDPACK BIGGERSTAFF TJ, 1994, COMMUN ACM, V37, P72, DOI 10.1145/175290.175300 BIGGERSTAFF TJ, 1993, 15 INT C SOFTW ENG BRUZA PD, 2002, P 11 INT C INF KNOWL BUCKLEY C, 2000, 23 ANN INT ACM SIGIR CANFORA G, 2006, INT WORKSH MIN SOFTW Canfora G., 2005, 11 IEEE INT S SOFTW CHUNG W, 2005, WORKING IMPLICIT CON CLEARY B, 2007, COGNITIVE ASSIGNMENT CLEARY B, 2006, 10 INT C PROGR COMPR CLEARY B, 2006, 2 INT WORKSH SUPP KN CLEARY B, 2007, 19 ANN PSYCH PROGR W Cubranic D, 2005, IEEE T SOFTWARE ENG, V31, P446, DOI 10.1109/TSE.2005.71 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Demsar J, 2006, J MACH LEARN RES, V7, P1 DIESNER J, 2004, AUTOMAP 1 2 EXTRACT Eisenbarth T, 2003, IEEE T SOFTWARE ENG, V29, P210, DOI 10.1109/TSE.2003.1183929 Friedman M, 1937, J AM STAT ASSOC, V32, P675, DOI 10.2307/2279372 GAO J, 2004, 27 ANN INT ACM SIGIR Greenfield J., 2004, SOFTWARE FACTORIES HASSAN AE, 2004, P 12 IEEE INT WORKSH HILL E, 2007, 22 IEEE ACM INT C AU IEEE, 2000, IEEE REC PRACT ARCH Jones K. S., 2000, Information Processing & Management, V36 KAGDI H, 2007, P 1K IEEE INT C PROG KICZALES G, 1997, EUR C OBJ OR PROGR S Kishida K., 2005, PROPERTY AVERAGE PRE KNIGHT C, 2002, 10 INT WORKSH PROGR Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LEGEAR A, 2005, 13 INT WORKSH PROGR LITTMAN DC, 1986, 1 WORKSH EMP STUD PR Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Manning C. D., 2007, INTRO INFORM RETRIEV MARCUS A, 2003, 25 INT C SOFTW ENG I MARCUS A, 2003, 1 IEEE INT WORKSH PR Marcus A., 2004, 11 WORK C REV ENG WC Merlo E, 2003, J SOFTW MAINT EVOL-R, V15, P205, DOI 10.1002/smr.274 Murphy GC, 2006, IEEE SOFTWARE, V23, P76, DOI 10.1109/MS.2006.105 Nemenyi PB, 1963, THESIS PRINCETON U POSHYVANYK D, 2007, 15 IEEE INT C PROGR Poshyvanyk D., 2006, 14 IEEE INT C PROGR Rajlich V., 2002, 10 INT WORKSH PROGR ROBILLARD MP, 2003, REPRESENTING CONCERN ROHDE D, 2007, SVDLIBC Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1987, TERM WEIGHTING APPRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAYYADSHIRABAB J, 1997, 5 INT WORKSH PROGR C, P97 SCHNEIDEWIND N, 1999, IEEE INT C SOFTW MAI SHEPHERD D, 2007, INT C ASP OR SOFTW D SIMMERMANN T, 2006, 2 INT WORKSH SUPP KN SIMONYI C, 2005, INTENTIONAL PROGRAMM Song D, 2003, J AM SOC INF SCI TEC, V54, P321, DOI 10.1002/asi.10213 Song D., 2001, P 24 ANN INT C RES D, P327, DOI 10.1145/383952.384017 *TREC, 2007, TEXT RETRIEVAL C WILDE N, 1995, J SOFTW MAINT-RES PR, V7, P49, DOI 10.1002/smr.4360070105 WILDE N, 2001, 5 EUR C SOFTW MAINT ZAYOUR L, 2001, 9 INT WORKSH PROGR C Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 ZHAO W, 2004, INT C SOFTW ENG ICSE NR 63 TC 11 Z9 11 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1382-3256 J9 EMPIR SOFTW ENG JI Empir. Softw. Eng. PD FEB PY 2009 VL 14 IS 1 BP 93 EP 130 DI 10.1007/s10664-008-9095-3 PG 38 WC Computer Science, Software Engineering SC Computer Science GA 414UU UT WOS:000263891500005 ER PT S AU Lin, J Li, YA AF Lin, Jessica Li, Yuan GP IEEE TI Finding Structurally Different Medical Data SO 2009 22ND IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS SE IEEE International Symposium on Computer-Based Medical Systems LA English DT Proceedings Paper CT 22nd IEEE International Symposium on Computer-Based Medical Systems CY AUG 03-04, 2009 CL Albuquerque, NM SP IEEE AB For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. This is particularly true for medical time series, as they often are not perfectly aligned. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering and classification on medical time series obtained from PhysioBank. C1 [Lin, Jessica; Li, Yuan] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. RP Lin, J (reprint author), George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. EM jessica@cs.gmu.edu; ylif@gmu.edu CR Agrawal R., 1993, P 4 INT C FDN DAT OR, P69 Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Chan KP, 1999, PROC INT CONF DATA, P126 Deng K., 1997, IEEE INT S COMP INT, V1, P246 EKAMBARAM A, ISCIS 18 18 INT S CO Faloutsos C., 1994, SIGMOD Record, V23 Gavrilov M., 2000, P 6 ACM SIGKDD Ge X., 2000, P 6 ACM SIGKDD INT C, P81, DOI 10.1145/347090.347109 Geurts P., 2001, P 5 EUR C PRINC DAT, P115 Goldberger AL, 2000, CIRCULATION, V101, pE215 Johnson SC, 1967, PSYCHOMETRIKA, V2, P241 Keogh E., 2004, P 10 ACM SIGKDD INT Keogh E., 2002, UCR TIME SERIES DATA Keogh E., 2002, P 28 INT C VER LARG Keogh E, 2002, P 8 ACM SIGKDD INT C, P102 KEOGH E, 2004, TUTORIAL SIGKDD 2004 Keogh E. J., 2001, P ACM SIGMOD C MAN D, P151, DOI 10.1145/375663.375680 Li M., 1997, INTRO KOLMOGOROV COM Lin J., 2004, 9 C EXT DAT TECHN ED Lin J., 2007, DATA MINING KNOWLEDG McQueen J., 1967, P 5 BERK S MATH STAT, V1, P281 Nanopoulos A, 2001, INFORMATION PROCESSING AND TECHNOLOGY, P49 Ratanamahatana Chotirat Ann, 2004, P SIAM INT C DAT MIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang XZ, 2006, DATA MIN KNOWL DISC, V13, P335, DOI 10.1007/s10618-005-0039-x NR 25 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1063-7125 BN 978-1-4244-4879-1 J9 COMP MED SY PY 2009 BP 290 EP 297 PG 8 WC Computer Science, Information Systems; Health Care Sciences & Services SC Computer Science; Health Care Sciences & Services GA BPJ31 UT WOS:000278974800048 ER PT B AU Teymoorian, F Mohsenzadeh, M Seyyedi, M AF Teymoorian, Farnaz Mohsenzadeh, Mehran Seyyedi, MirAli BE Li, WH Zhou, JH TI English-Persian Text Retrieval Using Concept Graph SO 2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 4 LA English DT Proceedings Paper CT 2nd IEEE International Conference on Computer Science and Information Technology CY AUG 08-11, 2009 CL Beijing, PEOPLES R CHINA SP IEEE DE Text retrieval; Concept graph; Term Weighting; Translation disambiguation AB Cross-language information retrieval (CLIR) is the retrieval process where the user presents queries in one language to retrieve documents in another language. In this field the resolution of lexical ambiguity in translating queries is a key challenge. In this paper, we propose a technique for calculating translation probabilities based on creating query terms' concept graphs for selecting the right translation sense of query terms for English-Persian text retrieval. We present an efficient statistical method for creating this graph. We test the effectiveness of the proposed disambiguation method on Hamshahri collection(1) that is standardized according to CLEF standards. Evaluation using this data collection shows great effectiveness of the proposed method. C1 [Teymoorian, Farnaz] Islamic Azad Univ N, Dept Comp Engn, Tehran Branch, Tehran, Iran. RP Teymoorian, F (reprint author), Islamic Azad Univ N, Dept Comp Engn, Tehran Branch, Tehran, Iran. EM f_teymoorian@yahoo.com; mohsenzadeh@sr.iau.ac.ir; seyyedi@behpardaz.net CR ADRIANI M, 1999, LECT NOTES COMPUTER, V1696 Aleahmad A., 2007, INT S SIGN PROC ITS ALEAHMAD A, 2008, ACM C INF KNOWL MAN AMIRI H, 2007, COMPUTATIONAL APPROA Bina B., 2008, 13 INT CSI COMP C CS CRASWELL N, 2004, P 13 TEXT RETRIEVAL DARWISH K, 2003, P 21 ANN 26 INT ACM Frank E, 2005, DATA MINING PRACTICA JadidiNezhad A.H., 2008, 6 INT C INF SYST INF Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JOHN F, 1976, IBM J RES DEV, V20, P336 LEE M, 2006, LECT NOTES COMPUTER Nayyeri A., 2006, IEEE INT C COMP SYST, P1126, DOI 10.1109/AICCSA.2006.205229 Oroumchian F., 2004, WORLD WIDE WEB 2004, P478 Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 YAGER RR, 2002, UTEPCS0216 Zheng X., 2005, INT C NEUR NETW BRAI, V2, P767 NR 18 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4518-9 PY 2009 BP 447 EP 451 DI 10.1109/ICCSIT.2009.5234499 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPT30 UT WOS:000279849200101 ER PT S AU Blanke, T Hedges, M Palmer, R AF Blanke, Tobias Hedges, Mark Palmer, Richard GP IEEE TI Restful services for the e-Humanities - web services that work for the e-Humanities ecosystem SO 2009 3RD IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES SE IEEE International Conference on Digital Ecosystems and Technologies LA English DT Proceedings Paper CT 3rd IEEE International Conference on Digital Ecosystems and Technologies CY JUN 01-03, 2009 CL Istanbul, TURKEY SP IEEE AB This paper presents how an e-Humanities High Throughput Computing application can be embedded in an e-Humanities ecosystem using a restful services based approach. The HiTHeR application is a text mining agent to create automatically chains of readings for e-Humanities research on historical collections. We show how we developed the text mining agent and realised it as restful services, which can add value to e-Humanities online publications of resources. C1 [Blanke, Tobias; Hedges, Mark; Palmer, Richard] Kings Coll London, London, England. RP Blanke, T (reprint author), Kings Coll London, London, England. EM tobias.blanke@kcl.ac.uk; mark.hedges@kcl.ac.uk; richard.d.palmer@kcl.ac.uk CR BLANKE T, 2008, E SCIENCE 08 Brockman W. S., 2001, SCHOLARLY WORK HUMAN CHANG E, IEEE DEST 2007 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 KUSTER MW, 2008, P 2 IEEE INT C DIG E Ruby Sam, 2007, RESTFUL WEB SERVICES SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TRAUTTEUR G, 2003, WETICE 03, P377, DOI 10.1109/ENABL.2003.1231440 NR 8 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2150-4938 BN 978-1-4244-2345-3 J9 I C DIGIT ECOSYST TE PY 2009 BP 290 EP 295 PG 6 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BPK74 UT WOS:000279101500051 ER PT B AU Al-Masri, E Mahmoud, QH AF Al-Masri, Eyhab Mahmoud, Qusay H. GP IEEE TI A Broker for Universal Access to Web Services SO 2009 7TH ANNUAL COMMUNICATION NETWORKS AND SERVICES RESEARCH CONFERENCE LA English DT Proceedings Paper CT 7th Annual Conference on Communication Networks and Services Research CY MAY 11-13, 2009 CL Moncton, CANADA SP Univ Moncton, Univ New Brunswick, Dalhousie Univ, IEEE Commun Soc, ACM DE Service registries; Web service discovery; crawler; ranking; SOA; Quality of Service (QoS); UDDI; ebXML; SOA AB Web service interfaces can potentially be discovered through service registries, search engines, service portals, peer-to-peer networks, among others. To discover pertinent Web services, clients have to devote excessive amount of time to browse through accessible service resources and be able to differentiate between services that share similar characteristics. Discovering Web services throughout heterogeneous environments is becoming a challenging task and raises several concerns such as performance, reliability, and robustness. In this paper, we introduce the Web Service Broker (WSB) framework that provides a universal access point for discovering Web services. WSB uses a crawler to collect the plurality of Web services disseminated throughout the Web, continuously monitor the behaviour of Web services in delivering the expected functionality, and enable clients to articulate service queries tailored to their needs. WSB can seamlessly be integrated into the existing service-oriented architecture without any alterations to existing environments. C1 [Al-Masri, Eyhab; Mahmoud, Qusay H.] Univ Guelph, Dept Comp & Informat Sci, Guelph, ON N1G 2W1, Canada. RP Al-Masri, E (reprint author), Univ Guelph, Dept Comp & Informat Sci, Guelph, ON N1G 2W1, Canada. EM ealmasri@uoguelph.ca; qmahmoud@uoguelph.ca CR ALMASRI E, 2007, IEEE INT C WEB SERV, P1104, DOI 10.1109/ICWS.2007.197 ALMASRI E, 2007, 4 IEEE CONS COMM NET, P415 Al-Masri E., 2008, P 17 INT C WORLD WID, P795, DOI DOI 10.1145/1367497.1367605 Al-Masri Eyhab, 2007, Proceedings 16th International Conference on Computer Communications and Networks Angelaccio M., 2006, P INT WORKSH EN TECH, P95 Banerjee S., 2005, P 3 INT WORKSH MIDDL, P1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong X., 2004, 13 INT C VER LARG DA, P372 *EBXML, E BUS XML GIBELIN N, 2005, P INT C SERV OR COMP, V38, P527 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Maglaughlin KL, 2002, J AM SOC INF SCI TEC, V53, P327, DOI 10.1002/asi.10049 Maximilien EM, 2004, IEEE INTERNET COMPUT, V8, P84, DOI 10.1109/MIC.2004.27 MAXIMILIEN M, 2004, P 2 INT C SERV OR CO, P212 Merialdo B., 1994, Computational Linguistics, V20 Platzer C., 2005, P 3 EUR C WEB SERV E, P62 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VEDAMUTHU A, 2006, WEB SERVICES POLICY NR 18 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4155-6 PY 2009 BP 118 EP 125 DI 10.1109/CNSR.2009.27 PG 8 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BLZ13 UT WOS:000271480000017 ER PT S AU Apostolova, E Channin, DS Demner-Fushman, D Furst, J Lytinen, S Raicu, D AF Apostolova, Emilia Channin, David S. Demner-Fushman, Dina Furst, Jacob Lytinen, Steven Raicu, Daniela GP IEEE TI Automatic Segmentation of Clinical Texts SO 2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20 SE IEEE Engineering in Medicine and Biology Society Conference Proceedings LA English DT Proceedings Paper CT Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society CY SEP 03-06, 2009 CL Minneapolis, MN SP IEEE Engn Med & Biol Soc AB Clinical narratives, such as radiology and pathology reports, are commonly available in electronic form. However, they are also commonly entered and stored as free text. Knowledge of the structure of clinical narratives is necessary for enhancing the productivity of healthcare departments and facilitating research. This study attempts to automatically segment medical reports into semantic sections. Our goal is to develop a robust and scalable medical report segmentation system requiring minimum user input for efficient retrieval and extraction of information from free-text clinical narratives. Hand-crafted rules were used to automatically identify a high-confidence training set. This automatically created training dataset was later used to develop metrics and an algorithm that determines the semantic structure of the medical reports. A word-vector cosine similarity metric combined with several heuristics was used to classify each report sentence into one of several pre-defined semantic sections. This baseline algorithm achieved 79% accuracy. A Support Vector Machine (SVM) classifier trained on additional formatting and contextual features was able to achieve 90% accuracy. Plans for future work include developing a configurable system that could accommodate various medical report formatting and content standards. C1 [Apostolova, Emilia; Furst, Jacob; Lytinen, Steven; Raicu, Daniela] Depaul Univ, Coll Comp & Digital Media, Chicago, IL 60604 USA. RP Apostolova, E (reprint author), Depaul Univ, Coll Comp & Digital Media, Chicago, IL 60604 USA. EM emilia.aposto@gmail.com; dchannin@nmh.org; ddemner@mail.nih.gov; jfurst@cdm.depaul.edu; lytinen@cs.depaul.edu; dstan@cs.depaul.edu CR *ACR, 2005, ACR PRACT GUID COMM Cao Hui, 2005, AMIA Annu Symp Proc, P106 Chapman WW, 2005, ARTIF INTELL MED, V33, P31, DOI 10.1016/j.artmed.2004.04.001 Chen ES, 2008, J AM MED INFORM ASSN, V15, P87, DOI 10.1197/jamia.M2401 CHEUNG N, 2001, STUDIES HLTH TECHNOL, P609 Cunningham D. H, 2002, GATE FRAMEWORK GRAPH Haas JP, 2005, AM J INFECT CONTROL, V33, P439, DOI 10.1016/j.ajic.2005.06.008 *M EL, 2007, AM J MANAG CARE, V13, P281 Pakhomov SV, 2005, J BIOMED INFORM, V38, P145, DOI 10.1016/j.jbi.2004.11.016 Rosenbloom ST, 2006, J AM MED INFORM ASSN, V13, P277, DOI 10.1197/jamia.M1957 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANGINNEKEN A, 2001, STUDIES HLTH TECHNOL, P693 Vapnik V. N., 2000, NATURE STAT LEARNING 2009, OPEN HLTH NATURAL LA NR 15 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1557-170X BN 978-1-4244-3295-0 J9 IEEE ENG MED BIO PY 2009 BP 5905 EP 5908 DI 10.1109/IEMBS.2009.5334831 PG 4 WC Engineering, Biomedical SC Engineering GA BQB05 UT WOS:000280543604202 ER PT B AU Ali, R Beg, MMS AF Ali, Rashid Beg, M. M. Sufyan GP IEEE TI Modified Rough Set Based Aggregation for Effective Evaluation of Web Search Systems SO 2009 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY LA English DT Proceedings Paper CT Annual Meeting of the North-American-Fuzzy-Information-Processing-Society CY JUN 14-17, 2009 CL Cincinnati, OH DE Web Search Evaluation; Rank Aggregation; Rough Set; Ranking Rules; Confidence; User Feedback; Vector Space Model; Boolean Similarity Measures; PageRank AB Rank Aggregation is the problem of generating a single consensus ranking for a given set of rankings. Rough set based Rank aggregation is a user feedback based technique for rank aggregation, which learns ranking rules using rough set theory. In this paper, we discuss an improved version of the Rough set based Rank aggregation technique, which is more suitable for aggregation of different Web search evaluation techniques. For learning the ranking rules, we obtain the implicit user feedback to the search results returned by a search engine in response to a set of fifteen queries and mine the ranking rules using rough set theory. In the modified rough set based rank aggregation technique, we incorporate the confidence of the rules in predicting a class for a given set of data. That means, we do not say surely that the record belongs to a particular class according to a particular rule. Instead, we associate a score variable to the predicted class of the record, where the value of the variable is equal to the confidence measure of the rule. We validate the mined ranking rules by comparing the predicted user feedback based ranking with the actual user feedback based ranking. We apply the ranking rules to another set of thirty seven queries for aggregating different rankings of search results obtained on the basis of different evaluation techniques. We show our experimental results pertaining to seven public search engines. C1 [Ali, Rashid] AMU, Dept Comp Engn, Aligarh, Uttar Pradesh, India. RP Ali, R (reprint author), AMU, Dept Comp Engn, Aligarh, Uttar Pradesh, India. EM rashidaliamu@rediffmail.com; mmsbeg@hotmail.com CR Ali R, 2007, P 3 IND INT C ART IN, P683 Aslam J.A., 2001, P 24 ANN INT ACM SIG, P276, DOI DOI 10.1145/383952.384007 Beg M. M. Sufyan, 2002, THESIS IIT DELHI Beg MMS, 2005, INFORM SCIENCES, V169, P365, DOI 10.1016/j.ins.2004.07.003 Beg MMS, 2007, INFORM SCIENCES, V177, P5203, DOI 10.1016/j.ins.2006.06.011 Borda J. C., 1781, HIST ACAD ROYALE SCI COHEN WW, 1998, J ADV NEURAL INFORM, V10 CRANOR LF, 1996, THESIS U WASHINGTON Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Fagin R., 2003, P 2003 ACM SIGMOD IN, P301, DOI DOI 10.1145/872757.872795 Fagin Ronald, 2003, P 12 INT C WORLD WID, P366 Li SH, 1997, IEEE T KNOWL DATA EN, V9, P863 LI SH, 1994, BOOLEAN SIMILARITY M Montague M., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Nuray R, 2006, INFORM PROCESS MANAG, V42, P595, DOI 10.1016/j.ipm.2005.03.023 Page L, 1999, PAGERANK CITATION RA PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 Renda M. E., 2003, P 2003 ACM S APPL CO, P841 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262 WEISSTEIN EW, SPEARMAN RANK CORREL VOTING METHODS SOCIA ROSETTA ROUGH SET TO NR 23 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4575-2 PY 2009 BP 175 EP 180 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BMC16 UT WOS:000271827700029 ER PT B AU Zhou, Y AF Zhou, Ying GP IEEE TI Searching and Clustering on Social Tagging Sites SO 2009 FIFTH INTERNATIONAL CONFERENCE ON SEMANTICS, KNOWLEDGE AND GRID (SKG 2009) LA English DT Proceedings Paper CT 5th International Conference on Semantics, Knowledge and Grid CY OCT 12-14, 2009 CL Zhuhai, PEOPLES R CHINA SP Chinese Acad Sci, Natl Basic Res Program DE Social Tagging; Association Rule Mining; Clustering AB Social Tagging Site has increasingly become a main avenue for people to share resources online. Users can use simple tools to publish everything from bookmarks to video clips on those sites. However, effective querying of resources in such sites is still a challenging question in industry and academic research. This paper reports a novel algorithm of presenting the web resources query result on the social tagging sites. It adopts a two step clustering approach to organize and rank resources based on their relative similarities with each other. Initial term similarities are computed using user and tag information of the resource. The query results are then organized as a group of concepts represented by a few semantically related terms. The resources that are related with each concept are rank with respect to the concept. In addition, concepts are also ranked by representing terms and the number of resources associated. C1 Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia. RP Zhou, Y (reprint author), Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia. EM zhouy@it.usyd.edu.au CR Agrawal Rakesh, P 1993 ACM SIGMOD WA, P207 Cai D., 2004, P 12 ANN ACM INT C M, P952, DOI 10.1145/1027527.1027747 Ding C. H. Q., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989507 Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 Fonseca B., 2005, P 14 ACM INT C INF K, P696, DOI 10.1145/1099554.1099726 Nie Zaiqing, 2005, P 14 INT C WORLD WID Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679 Tibshirani R, 2001, J ROY STAT SOC B, V63, P411, DOI 10.1111/1467-9868.00293 Xu J., 1996, SIGIR 96, P4 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5278-1 PY 2009 BP 99 EP 105 DI 10.1109/SKG.2009.72 PG 7 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUJ25 UT WOS:000289501800017 ER PT B AU Wang, ZQ Sun, X AF Wang, Ziqiang Sun, Xia BE Xu, J TI Orthogonal Locality Discriminant Embedding for Document Classification SO 2009 FOURTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 4th International Conference on Bio-Inspired Computing CY OCT 16-19, 2009 CL Beijing, PEOPLES R CHINA SP Dalian Univ, Zhengzhou Univ Light Ind, IEEE Beijing Sect ID FACE RECOGNITION; LAPLACIANFACES AB Dimensionality reduction algorithms, which try to reduce the dimensionality of data and to enhance the discriminant information, are of paramount importance in document classification. In this paper, a novel dimensionality reduction algorithm called orthogonal locality discriminant embedding (OLDE) is proposed to address these problems. The OLDE algorithm effectively combines the idea of local discriminant embedding (LDE) and orthogonal basis functions, which utilizes both the local manifold structure and label information to enhance discriminative power. Extensive experiments on three document databases show the effectiveness of the proposed OLDE algorithm. C1 [Wang, Ziqiang; Sun, Xia] Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450001, Peoples R China. RP Wang, ZQ (reprint author), Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450001, Peoples R China. EM wzqagent@126.com; sunxiamail@126.com CR BELKIN M, 2001, ADV NEURAL INFORM PR, V14, P1 Cai D., 2005, P 28 ANN INT ACM SIG, P3, DOI 10.1145/1076034.1076039 Cai D, 2006, IEEE T IMAGE PROCESS, V15, P3608, DOI 10.1109/TIP.2006.881945 Cai D, 2005, IEEE T KNOWL DATA EN, V17, P1624, DOI 10.1109/TKDE.2005.198 Chen HT, 2005, PROC CVPR IEEE, P846 CHUNG FRK, 1997, REGIONAL C SERIES AP Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Duda R., 2000, PATTERN CLASSIFICATI, V2nd Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd He X., 2003, ADV NEURAL INF PROCE, V16, P1 He XF, 2005, IEEE T PATTERN ANAL, V27, P328 *REUT, REUTERS21578 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TORKKOLA K, 2001, P 2001 IEEE WORKSH T, P1 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-3865-5 PY 2009 BP 170 EP 174 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematical & Computational Biology SC Computer Science; Mathematical & Computational Biology GA BOC75 UT WOS:000276195100031 ER PT B AU Chen, MZ Song, Y AF Chen, Mingzhen Song, Yu BE Pan, YH Sun, SQ Zhou, MQ Lin, ZK Hu, ZY Hyungjae, M TI Summarization of Text Clustering based Vector Space Model SO 2009 IEEE 10TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED INDUSTRIAL DESIGN & CONCEPTUAL DESIGN, VOLS 1-3: E-BUSINESS, CREATIVE DESIGN, MANUFACTURING - CAID&CD'2009 SE International Conference on Computer Aided Industrial Design and Conceptual Design-CAID&CD LA English DT Proceedings Paper CT 10th IEEE International Conference on Computer-Aided Industrial Design and Conceptual Design CY NOV 26-29, 2009 CL Wenzhou, PEOPLES R CHINA SP IEEE DE Vector Space Model (VSM); Text clustering; Document representation; Distance function AB Text clustering is an important task of natural language processing and is widely applicable in areas such as information retrieval and web mining. The representation of document and the clustering algorithm are the key issues of text clustering. This paper discusses Vector Space Model(VSM)-based clustering algorithms. This paper reviewed the text clustering algorithm. C1 [Chen, Mingzhen; Song, Yu] Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China. RP Chen, MZ (reprint author), Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China. EM cmzboy1983@163.com; ssqq@mail.hz.zj.cn CR FANG Y, 2002, P ASME INT MECH ENG, P1 Hamerly G, 2002, ALTERNATIVES K MEANS LIKAS A, 2001, IASUVA0102 U AMST CO Meila M, 2001, MACH LEARN, V42, P9, DOI 10.1023/A:1007648401407 Pelleg D., 2000, P 17 INT C MACH LEAR, P727 Rajan K, 2009, EXPERT SYST APPL, V36, P10914, DOI 10.1016/j.eswa.2009.02.010 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 7 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5267-5 J9 INT C COMP AID IND D PY 2009 BP 2362 EP 2365 PG 4 WC Computer Science, Interdisciplinary Applications; Engineering, Manufacturing SC Computer Science; Engineering GA BNZ41 UT WOS:000275947300541 ER PT B AU Farahat, AK Kamel, MS AF Farahat, Ahmed K. Kamel, Mohamed S. BE Saygin, Y Yu, JX Kargupta, H Wang, W Ranka, S Yu, PS Wu, XD TI Document Clustering Using Semantic Kernels Based on Term-Term Correlations SO 2009 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2009) LA English DT Proceedings Paper CT 9th IEEE International Conference on Data Mining CY DEC 06-09, 2009 CL Miami Beach, FL SP Knime, Mitre, CRC Press DE Document Clustering; Semantic Similarity; Term Correlations AB Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic similarity. In this paper, we propose the use of semantic kernels that are based on term-term correlations for improving the effectiveness of document clustering algorithms. The used kernels measure proximity between documents based on how their terms are statistically correlated. We analyze semantic kernels that capture different aspects of correlations between terms, and evaluate them by conducting experiments on different benchmark data sets. Results show that the proposed method achieves significant improvement in document clustering compared to VSM. C1 [Farahat, Ahmed K.; Kamel, Mohamed S.] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. RP Farahat, AK (reprint author), Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. EM afarahat@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon I., 2003, FEATURE SELECTION DO, P73 Dhillon I., 2001, P 7 ACM SIGKDD INT C, P269, DOI DOI 10.1145/502512.502550 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Huang A., 2009, P 13 PAC AS C ADV KN, P628 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Karypis G., 2003, 02017 U MINN DEP COM Kashef R. F., 2008, THESIS U WATERLOO MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUTZE H, 1997, SIGIR FORUM, V31, P74 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 18 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5384-9 PY 2009 BP 459 EP 464 DI 10.1109/ICDMW.2009.88 PG 6 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUS95 UT WOS:000290247100074 ER PT B AU Li, YR Li, TY AF Li, Y. R. Li, T. Y. GP IEEE TI A New Classification Method on the Basis of a Patent-Science Relationship SO 2009 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1-4 SE International Conference on Industrial Engineering and Engineering Management IEEM LA English DT Proceedings Paper CT IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2009) CY DEC 08-11, 2009 CL Hong Kong, PEOPLES R CHINA SP IEEE Engn Management Soc, Singapore Chapter, IEEE Technol Management Council, Hong Kong Chapter, IEEE Singapore Sect, Natl Univ Singapore, Nanyang Technol Univ, City Univ Hong Kong, Hong Kong Univ Sci & Technol DE Patent Analysis; Patent Citations; Text Mining; Technology Classification ID PERFORMANCE; TECHNOLOGY AB International patent code (IPC) and United States patent code (UPC) are the two most common classification systems in the patent database, but they have difficulties in revealing the technological characteristics of different industries. This paper proposes a new method based on a science-technology relationship, offering three main contributions: first, provide a new classification method based on the patent scientific literature; second, assist high-technology firms to measure their competitors and have foresight on future science and technology roadmap; third, analyze the technological dynamics by tracking the patent and scientific keywords. C1 [Li, Y. R.; Li, T. Y.] Univ Aletheia, Dept Informat Management, Taipei, Taiwan. RP Li, YR (reprint author), Univ Aletheia, Dept Informat Management, Taipei, Taiwan. EM yrli@email.au.edu.tw CR Arrow K. J., 1962, RATE DIRECTION INVEN, P609 Chaves CV, 2007, RES POLICY, V36, P1204, DOI 10.1016/j.respol.2007.04.007 Ernst H, 2001, RES POLICY, V30, P143, DOI 10.1016/S0048-7333(99)00098-0 Frakes W. B., 1992, INFORM RETRIEVAL DAT Heeley MB, 2008, STRATEG MANAGE J, V29, P723, DOI 10.1002/smj.682 Narin F, 1997, RES POLICY, V26, P317, DOI 10.1016/S0048-7333(97)00013-9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sullivan D., 2001, DOCUMENT WAREHOUSING Wei CP, 2002, EUR J INFORM SYST, V11, P208, DOI 10.1057/palgrave.ejis.3000429 NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4869-2 J9 IN C IND ENG ENG MAN PY 2009 BP 2409 EP 2413 DI 10.1109/IEEM.2009.5372971 PG 5 WC Engineering, Industrial; Operations Research & Management Science SC Engineering; Operations Research & Management Science GA BPX44 UT WOS:000280236601236 ER PT B AU Jiang, H Li, P Hu, X Wang, SY AF Jiang, Hua Li, Ping Hu, Xin Wang, Shuyan BE Chen, W Li, SZ Wang, YL TI An improved method of term weighting for text classification SO 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 LA English DT Proceedings Paper CT IEEE International Conference on Intelligent Computing and Intelligent Systems CY NOV 20-22, 2009 CL Shanghai, PEOPLES R CHINA SP IEEE Beijing Sect, Shanghai Jiaotong Univ, Xiamen Univ, City Univ Hong Kong, Iwate Prefectural Univ DE Text classification; tf-idf; term weighting; kNN ID CATEGORIZATION AB In text classification, term weighting methods design appropriate weights to the given terms to improve the text classification performance Traditional algorithm of term weighting only considers about tf (term frequency), idf (Inverse document frequency) and so on, and this approach simply thinks low frequency terms are Important, high frequency terms are unimportant, so it designs higher weights to the rare terms frequently In this paper, we present an effective term weighting approach to avoid the deficiency of the traditional approach, and make use of kNN classifiers to classify over widely-used benchmark data set Reuters-21578 The experimental results prove that,the new approach can Improve the accuracy of classification C1 [Jiang, Hua; Li, Ping; Hu, Xin; Wang, Shuyan] NE Normal Univ, Sch Comp Sci, Changchun, Jilin Province, Peoples R China. RP Jiang, H (reprint author), NE Normal Univ, Sch Comp Sci, Changchun, Jilin Province, Peoples R China. CR Debole F., 2003, P SAC 03 18 ACM S AP, P784 Deng ZH, 2004, LECT NOTES COMPUT SC, V3007, P588 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Lan M., 2006, P 21 NAT C ART INT A, P763 LAN M, 2009, IEEE T PATTERN ANAL, P721 Lan M, 2005, IEEE IJCNN, P546 LIU Y, 2007, EXPERT SYSTEMS APPL Montanes E, 2005, IEEE INTELL SYST, V20, P40, DOI 10.1109/MIS.2005.49 Porter M.F., 1980, ALGORITHM SUFFIX STR, P130 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 [苏金ꠑ SU JinShu], 2006, [č½Æä»¶å­¦ęŠ„, Journal of Software], V17, P1848, DOI 10.1360/jos171848 Xue Xiao-Bing, 2009, IEEE T KNOWLEDGE DAT, V21 Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 Yang Y., 1997, ICML 97, P412 YANG YM, 1997, CMUCS97127 COMP SCI NR 18 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4754-1 PY 2009 BP 294 EP 298 PG 5 WC Computer Science, Artificial Intelligence SC Computer Science GA BSM83 UT WOS:000284970100062 ER PT B AU Zhang, L Chang, JX Xiang, XZ Feng, XS AF Zhang, Lei Chang, Jingxin Xiang, Xuezhi Feng, Xiaosen BE Chen, W Li, SZ Wang, YL TI Topic indexing of spoken documents based on optimized N-best approach SO 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 4 LA English DT Proceedings Paper CT IEEE International Conference on Intelligent Computing and Intelligent Systems CY NOV 20-22, 2009 CL Shanghai, PEOPLES R CHINA SP IEEE Beijing Sect, Shanghai Jiaotong Univ, Xiamen Univ, City Univ Hong Kong, Iwate Prefectural Univ DE Topic indexing; optimaized N-best; NMF; SVD AB For topic indexing of spoken documents, the word error rate is hopefully decreased instead of the whole sentence error rate, so the center hypothesis among the N-best results is selected as the final output in speech recognition system. Then all spoken documents can be represented as vectors with high dimensions in vector space model, which can be combined with non-negative matrix factorization or singular value decomposition to map the vector space into semantic space. Experiment results show that optimized N-best approach is more suitable to the topic indexing system than one-best method. Combined with the non-negative matrix factorization, the correct topic indexing can achieve 98.1% in optimized N-best approach, which is 0.9% higher than the one-best approach under the same condition. When the semantic space is decreased to 10, there is about 11.1% difference between these two approaches. Furthermore, compared with singular value decomposition method, non-negative matrix factorization has the advantages of better performance, faster computation speed and less storage space. C1 [Zhang, Lei; Chang, Jingxin; Xiang, Xuezhi; Feng, Xiaosen] Harbin Engn Univ, Informat & Commun Engn Coll, Harbin, Peoples R China. RP Zhang, L (reprint author), Harbin Engn Univ, Informat & Commun Engn Coll, Harbin, Peoples R China. EM zhanglei@hrbeu.edu.cn CR Berry M. W., 2005, Computational & Mathematical Organization Theory, V11, DOI 10.1007/s10588-005-5380-5 Buckley C, 1985, IMPLEMENTATION SMART KURIMO M, 2000, ACOUST SPEECH SIG PR, P2425 Lee DD, 2000, P NIPS, ppp [Diong Liang ę¢ę ‹], 2006, Chinese Optics Letters, V4, P272 Mangu L, 2000, COMPUT SPEECH LANG, V14, P373, DOI 10.1006/csla.2000.0152 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Xu W., 2003, SIGIR 03, P267 YU P, ICASSP 2008, P5248 NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4754-1 PY 2009 BP 302 EP 305 PG 4 WC Computer Science, Artificial Intelligence SC Computer Science GA BSM84 UT WOS:000284972300064 ER PT B AU Lu, ZM Shi, H Zhang, Q Yuan, CY AF Lu, Zhimao Shi, Hong Zhang, Qi Yuan, Chaoyue BE Guo, S Fukuda, T Yo, Y Yu, H TI Automatic Chinese Text Categorization System Based on Mutual Information SO 2009 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS 1-7, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Mechatronics and Automation CY AUG 09-12, 2009 CL Changchum, PEOPLES R CHINA SP IEEE, IEEE Robot & Automat Soc, Changsha Univ Scie & Technol, Kagawa Univ, Natl Nat Sci Fdn China, Chinese Assoc Automat, Robot Soc Japan, Japan Soc Mech Engn, Japan Soc Precis Engn, Soc Instrument & Control Engineers, Harbin Engn Univ, Univ Elect Commun, Univ Elect Sci & Technol China, Beijing Univ Technol, Jilin Univ, Kagoshima Univ, Harbin Inst Technol, Assoc Young Sci &Technol DE Automatic Text Categorization; Feature Selection; Mutual Information; KNN; SVM AB Feature selection is a key step in automatic text categorization system and it has a significant impact on classification result. In this paper we do research on mutual information (MI) which is one basic method of feature selection. Firstly, we found out three main problems of MI by analyzing the formula of MI theoretically and systematically - the MI loss, the information difference among categories, and the excessive emphasis on low-frequency terms. Then, to solve these three questions, we proposed an improved feature selection method by calculating the absolute values of MI and calculating the differential values between maximum and average of MI. At last, we tested our method using K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier respectively, and we also compared it with the original method on Chinese corpus. The results demonstrate the effectiveness and feasibility of the proposed method. C1 [Lu, Zhimao; Shi, Hong; Zhang, Qi; Yuan, Chaoyue] Harbin Engn Univ, Informat & Commun Engn Coll, Harbin, Heilongjiang Pr, Peoples R China. RP Lu, ZM (reprint author), Harbin Engn Univ, Informat & Commun Engn Coll, Harbin, Heilongjiang Pr, Peoples R China. EM heu_lgy@yahoo.cn; ariel_0528@yahoo.cn; qi2005guduzhang@163.com; yuanchaoy@hrbeu.edu.cn CR [代六ēŽ² Dai Liuling], 2004, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V18, P26 Ding C, 2003, PROCEEDINGS OF THE 2003 IEEE BIOINFORMATICS CONFERENCE, P523 FENG XS, 2004, STUDIES SOME ESSENTI Forman G, 2003, J MACHINE LEARNING R, V3, P1533 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 [Ꝏē²¤ Li Yue], 2004, [č®”ē®—ęœŗåŗ”ē”Ø, Computer Applications], V24, P119 Liu H, 2005, IEEE INTELL SYST, V20, P64, DOI 10.1109/MIS.2005.105 [åˆ˜ęµ·å³° LIU Haifeng], 2007, [ęƒ…ęŠ„ē§‘å­¦, Information Science], V25, P1534 [å¢ę–°å›½ Lu Xinguo], 2005, [ę¹–å—å¤§å­¦å­¦ęŠ„. č‡Ŗē„¶ē§‘å­¦ē‰ˆ, Journal of Hunan University.Natural Sciences], V32, P104 MIAO DQ, 2007, CHINESE INFORM PROCE Rijsbergen V., 1979, INFORM RETRIEVAL, V2nd Saleh SN, 2007, 2007 22ND INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, P130 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Tan Jinbo, 2006, Journal of the China Society for Scientific and Technical Information, V25 Wang W., 2008, INFORM TECHNOLOGY, V12, P29 [ēŽ‹ē§€å؟ Wang Xiujuan], 2005, [č®”ē®—ęœŗåŗ”ē”Ø, Computer Applications], V25, P661 WANG YL, 2003, J XIAOGAN U, V23, P35 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zong C., 2008, STAT NATURAL LANGUAG NR 20 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2692-8 PY 2009 BP 4986 EP 4990 PG 5 WC Automation & Control Systems; Engineering, Electrical & Electronic; Robotics SC Automation & Control Systems; Engineering; Robotics GA BPW54 UT WOS:000280158102242 ER PT B AU Dixit, K Goyal, M Gupta, P Kambhatla, N Lotlikar, RM Majumdar, D Parija, GR Roy, S Soni, S AF Dixit, Kashyap Goyal, Munish Gupta, Pranav Kambhatla, Nanda Lotlikar, Rohit M. Majumdar, Debapriyo Parija, Gyana R. Roy, Sarnbuddha Soni, Soujanya GP IEEE TI Effective Decision Support For Workforce Deployment Service Systems SO 2009 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING LA English DT Proceedings Paper CT IEEE International Conference on Services Computing CY SEP 21-25, 2009 CL Bangalore, INDIA SP IEEE DE Service Systems; Resource Allocation; Workforce Optimization; Integer Programming; Computer Human Interaction; Decision Support System AB An IT services businesses must continually manage the assignment and movement of practitioners to or between projects balancing multiple objectives (1) to minimize their idle bench, while aiming (2) to increase revenue from new project opportunities. The key to simultaneously achieving these objectives is a well managed Service System for Workforce Deployment with distributed decision making. In this paper we discuss the decision support needs for this Service System, and present a decision support tool that computes an 'optimal' assignment of practitioners to projects, and improves the effectiveness and efficiency of this assignment process. The tool comprises of a matching module that matches practitioners to project openings, an optimization module that produces globally optimal recommendations, and a user interface for displaying the recommendations. We describe the decision support tool, our experiences with developing and deploying it in an IT services company, and our efforts to promote a common shared perspective among decision-makers enabling multiple decision maker.,; to independently arrive at near-optimal decisions Using the tool recommendations. C1 [Dixit, Kashyap] IBM India Res Lab, New Delhi, India. RP Dixit, K (reprint author), IBM India Res Lab, New Delhi, India. EM kasdixit@in.ibm.com; munish.goyal@in.ibm.com; prguptan@in.ibm.com; kambhatla@in.ibm.com; rohitmlo@in.ibm.com; debapriyo@in.ibm.com; gyana.parija@in.ibm.com; sambuddha@in.ibm.com; sousoni1@in.ibm.com CR Ba SL, 2001, INFORM SYST RES, V12, P225, DOI 10.1287/isre.12.3.225.9712 DATTA R, 2007, SEQUENCE MINING BUSI DONG J, 2008, IRDM SOLUTION WORKFO Hu J, 2007, IBM J RES DEV, V51, P281 HU J, 2008, USING DATA MINING AC Naveh Y, 2007, IBM J RES DEV, V51, P263 Richter Yossi, 2008, International Journal of Services Operations and Informatics, V3, DOI 10.1504/IJSOI.2008.021338 ROBERTSON SE, 1995, OKAPI TREC 4 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 SHARMA M, 2008, INFORMS ANN M 2008 Spohrer J., 2007, IEEE COMPUT, P71, DOI DOI 10.1109/MC.2007.33 Ursu M. F., 2005, International Journal of Knowledge-Based and Intelligent Engineering Systems, V9 WARD JH, 2000, ERGOMETRIKA, V1, P45 Yoshimura M, 2006, INT J PROD RES, V44, P831, DOI 10.1080/00207540500272519 1994, ORG SCI, V5, P456 NR 16 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5183-8 PY 2009 BP 104 EP 111 DI 10.1109/SCC.2009.24 PG 8 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BNP33 UT WOS:000275172500014 ER PT S AU Fu, SK Pi, BF Desmarais, M Zhou, Y Wang, WL Han, S AF Fu, Shunkai Pi, Bingfeng Desmarais, Michel Zhou, Ying Wang, Weilei Han, Song GP IEEE TI Query Recommendation and Its Usefulness Evaluation on Mobile Search Engine SO 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9 SE IEEE International Conference on Systems Man and Cybernetics Conference Proceedings LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics CY OCT 11-14, 2009 CL San Antonio, TX SP IEEE DE Mobile search; query recommendation ID USER LOGS AB In this paper, we study the role of query recommendation on mobile search engine. We start with the discussion of the role of query recommendation in modern search engines. Secondly, a mobile search engine, Roboo (R) (http://wap.roboo.com), is introduced and we discuss the need for query recommendation over mobile search engine. Thirdly, the query recommendation solution working on Roboo (R) is introduced in detail, including the models, how they are constructed and how they operate online. Finally, we demonstrate the benefits of query recommendation brought on Roboo (R) based on the analysis of real search log data collected since its release online in August, 2008. Considering the scarcity of scientific publications about the practical values of query recommendation on commercial mobile search engine, this paper should represent an interesting and useful reference for both academic and industrial colleagues. C1 [Fu, Shunkai; Pi, Bingfeng; Zhou, Ying; Wang, Weilei; Han, Song] Roboo Inc, Suzhou, Jiangsu, Peoples R China. RP Fu, SK (reprint author), Roboo Inc, Suzhou, Jiangsu, Peoples R China. CR BAEZAYATES R, 2004, P INT WORKSH CLUST I BEEFERMAN D, 2000, P ACM SIGKDD CHIRITA PA, 2007, P SIGIR C INF RETR Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DOCUMENT L, 1998, PAGERANK CITATION RA FU S, 2009, P APWEB WAIM SUZH PR FU S, 2009, P 13 PAC AS KDD PAKD FU S, 2008, P SIGIR WORKSH MOB I LIU Z, 2009, P WWW BEIJ PR CHIN A SAHAMI M, 2006, P 19 FLAIRS C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SILFVERBERG M, 2000, P SIGCHI C HUM FACT Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 NR 13 TC 0 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-2793-2 J9 IEEE SYS MAN CYBERN PY 2009 BP 1292 EP 1297 DI 10.1109/ICSMC.2009.5345920 PG 6 WC Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BPP02 UT WOS:000279574600221 ER PT B AU Liu, FF Shi, YL Luo, XF Liang, GN Xu, Z AF Liu, Fangfang Shi, Yuliang Luo, Xiangfeng Liang, Guoning Xu, Zheng BE Damiani, E Zhang, J Chang, R TI Discovery of Semantic Web Service Flow Based on Computation SO 2009 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, VOLS 1 AND 2 LA English DT Proceedings Paper CT IEEE International Conference on Web Services (ICWS 2009) CY JUL 06-10, 2009 CL Los Angeles, CA SP IEEE Comp Soc Tech Comm Serv Comp AB Key-word based researches of service discovery focus on direct match of users requirements and often neglect relations between services. While techniques based on conventional semantic offer many kinds of relations, considerable time is spent on reasoning. In this paper, we utilizes E-FCM (Element Fuzzy Cognitive Map) to describe services for the reason that E-FCM can keep the semantic information as much as possible and E-FCMs can be automatically created for web services. Furthermore, instead of reasoning, the semantic relations among E-FCM are built based on computation, therefore semantic relations among set-vices can be found out quickly. We focus on the associated semantic relations among services because complex applications always comprise of services with associated functions. The associated link network (ALN) is constructed upon associated relations to generate associated web service flows, which can be used to create complex applications, thus to facilitate discovery efficiency and improve utilization of services. C1 [Liu, Fangfang; Luo, Xiangfeng; Liang, Guoning; Xu, Zheng] Shanghai Univ, Sch Comp Engn & Sci, Joint Lab Next Generat Internet Interact Comp, Shanghai 200041, Peoples R China. RP Liu, FF (reprint author), Shanghai Univ, Sch Comp Engn & Sci, Joint Lab Next Generat Internet Interact Comp, Shanghai 200041, Peoples R China. EM ffliu@shu.edu.cn; liangyus@sdu.edu.cn; xfluo@shu.edu.cn; lgnlgn@shu.edu.cn; xuzheng@shu.edu.cn CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Geng LQ, 2006, ACM COMPUT SURV, V38, DOI 10.1145/1132960.1132963 GOMADAM K, P ICWS 2008, P177 KRADARAS D, 2006, P IEEE C INT SYST, P227 KUSTER U, P WWW2007, P1033 LIU FF, 2002, IEEE P INT WORKSH SE, P89 Luo X. F., 2008, P 1 IEEE INT C UB ME, P119 Luo XF, 2008, CONCURR COMP-PRACT E, V20, P839, DOI 10.1002/cpe.1271 Paolucci M, 2002, LECT NOTES COMPUT SC, V2512, P225 Perusich K, 2006, IEEE T SYST MAN CY C, V36, P810, DOI 10.1109/TSMCC.2005.855509 Ran S., 2003, ACM SIGECOM EXCHANGE, V4, P1, DOI DOI 10.1145/844357.844360 SAHAMI M, P WWW 2006, P377 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIVASHANMUGAM K, P ICWS 2004, P270 Zhuge H., 2006, J SYST SOFTWARE, V79, P969, DOI DOI 10.1016/J.JSS.2005.08.022 OWL NR 16 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4929-3 PY 2009 BP 319 EP 326 DI 10.1109/ICWS.2009.81 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BMP31 UT WOS:000273268500040 ER PT B AU Zhong, CC Miao, ZJ Zhang, J Du, LY Kang, DD AF Zhong, Cencen Miao, Zhenjiang Zhang, Jie Du, Luyan Kang, Dandan GP IEEE TI A Content-based Chinese Speech Document Retrieval System Design and Implementation SO 2009 IEEE PACIFIC RIM CONFERENCE ON COMMUNICATIONS, COMPUTERS AND SIGNAL PROCESSING, VOLS 1 AND 2 LA English DT Proceedings Paper CT IEEE Pacific Rim Conference on Communications, Computers and Signal Processing CY AUG 23-26, 2009 CL Victoria, CANADA SP IEEE AB The rapid development of speech processing technology provides a potential for speech retrieval. This paper designs and implements a content-based Chinese speech document retrieval system using keyword spotting and text classification. In this system, a segment of unknown spontaneous speech will be converted into a series of keywords and then classified into a certain category, called topic, hoping to establish a retrieval model with two-level semantic information, which enables users to search for desired speech by keyword or topic query. Besides, based on the theory of mutual information, text classification is also used to react on the keywords to remove some false alarms. This paper mainly describes the structure, principle and completion situation of this retrieval system, finally gives the experimental results and discussions. C1 [Zhong, Cencen; Miao, Zhenjiang; Zhang, Jie; Du, Luyan; Kang, Dandan] Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China. RP Zhong, CC (reprint author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing, Peoples R China. EM zhongcencen@163.com; zjmiao@bjtu.edu.cn CR Brown M. G., 1996, Proceedings ACM Multimedia 96, DOI 10.1145/244130.244232 CHEN YP, 2008, COMPUTER ENG, V34 CUI CX, 2007, J TAIYUAN NORMAL U N, V6, P52 Foote J, 1999, MULTIMEDIA SYST, V7, P2, DOI 10.1007/s005300050106 JAMES DA, 1994, IEEE INT C AC SPEECH, V1, P377 MENG S, 1995, P ACM MULT, P357 MENG S, 2007, ASRU, P635 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saraclar M, 2004, HLT-NAACL 2004: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE, P129 YOUNG SJ, 1997, ICASSP, V1, P199 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4560-8 PY 2009 BP 117 EP 122 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BPR97 UT WOS:000279748100021 ER PT B AU Wang, LS AF Wang, Louis S. GP IEEE TI Relevance Weighting of Multi-Term Queries for Vector Space Model SO 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING LA English DT Proceedings Paper CT IEEE Symposium on Computational Intelligence and Data Mining CY MAR 30-APR 02, 2009 CL Nashville, TN SP IEEE AB The Vector Space Model is one of the most common information retrieval (IR) methods for text document search. The cosine of the angle or the Euclidean distance between the query vector and each document vector is commonly used to measure similarity for query matching. Even though the vector space model starts with a term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords based on the adjacency between the terms in the documents. Thus, when a document contains the adjacency terms, its vector will typically move closer to the query vector to show stronger relevancy between query and the document. C1 Int Sch Minnesota, Eden Prairie, MN 55344 USA. RP Wang, LS (reprint author), Int Sch Minnesota, Eden Prairie, MN 55344 USA. EM louis_s_wang@yahoo.com CR Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164 BERRY MW, 1995, SIAM REV, V37, P537 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gyongyi Z., 2004, P 30 INT C VER LARG, V30, P576 KLEINBERG JM, 2000, Patent No. 6112202 Manning C.D., 2008, INTRO INFORM RETRIEV Marchiori Massimo, 1997, 6 INT C WORLD WID WE, P1225 MERCIER A, 2006, P 15 TEXT RETRIEVAL RAO R, 1992, P UIST 92, P23, DOI 10.1145/142621.142625 Rasolofo Yves, 2003, P 25 EUR C IR RES, P207 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUNGSAWANG A, 1997, THESIS ENST PARIS PA SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WANG LS, 2007, Patent No. 20070179930 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2765-9 PY 2009 BP 396 EP 402 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BLZ20 UT WOS:000271487700056 ER PT B AU Olney, AM AF Olney, Andrew M. GP IEEE TI Generalizing Latent Semantic Analysis SO 2009 IEEE THIRD INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2009) LA English DT Proceedings Paper CT 3rd International Conference on Semantic Computing (ICSC 2009) CY SEP 14-16, 2009 CL Berkeley, CA SP IEEE DE latent semantic analysis; vector space; n-gram; paraphrase ID INFORMATION; RETRIEVAL; SPACE AB Latent Semantic Analysis (LSA) is a vector space technique for representing word meaning. Traditionally, LSA consists of two steps, the formation of a word by document matrix followed by singular value decomposition of that matrix. However, the formation of the matrix according to the dimensions of words and documents is somewhat arbitrary. This paper attempts to reconceptualize LSA in more general terms, by characterizing the matrix as a feature by context matrix rather than a word by document matrix. Examples of generalized LSA utilizing n-grams and local context are presented and compared with traditional LSA on paraphrase comparison tasks. C1 Univ Memphis, Inst Intelligent Syst, Memphis, TN 38152 USA. RP Olney, AM (reprint author), Univ Memphis, Inst Intelligent Syst, Memphis, TN 38152 USA. EM aolney@memphis.edu CR Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Brill E., 1992, P 3 C APPL NAT LANG Burgess C, 1998, DISCOURSE PROCESS, V25, P211 CHUNG T, 2006, 20000 LEAGUES SEA PA Clark A., 2001, P CONLL 2001, P105 Cullum J.K., 2002, LANCZOS ALGORITHMS L, VI DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dolan W., 2004, P 20 INT C COMP LING, P350 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 FINCH S, 1992, PROCEEDINGS OF THE FOURTEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P820 Foltz P. W., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT111 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Graesser A. C., 2000, Interactive Learning Environments, V8, DOI 10.1076/1049-4820(200008)8:2;1-B;FT129 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Harris ZS, 1954, WORD, V10, P140 KISS GR, 1973, ADV RES THEOR, P1 Klein D., 2002, P 40 ANN M ASS COMP, P128 Klein D, 2004, P 42 ANN M ASS COMP, P478, DOI 10.3115/1218955.1219016 KLEIN D, 2002, WSD WORKSH ACL 40 LANDAUER T, 2007, HDB LATENT SEMANTIC, P379 Landauer Thomas K., 2007, HDB LATENT SEMANTIC Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LANDAUER TK, 1998, COMPUTER BASED GRADI Lowe W., 2001, P 23 ANN C COGN SCI, P576 Manning C. D., 1999, FDN STAT NATURAL LAN Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Olde B. A., 2002, P 24 ANN C COGN SCI, P708 OLNEY A, 2005, P 18 INT FLOR ART IN, P554 OLNEY AM, 2006, THESIS U MEMPHIS OLNEY AM, 2007, P TEXTGRAPHS 2 GRAPH, P45 Olney Andrew, 2005, P HUM LANG TECHN C C, P971, DOI 10.3115/1220575.1220697 Pado S, 2007, COMPUT LINGUIST, V33, P161, DOI 10.1162/coli.2007.33.2.161 Pretze O., 1998, ERROR CORRECTING COD Redington M, 1998, COGNITIVE SCI, V22, P425, DOI 10.1207/s15516709cog2204_2 REDINGTON M, 1993, PROCEEDINGS OF THE FIFTEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P848 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1993, P 31 ANN M ASS COMP, P251, DOI 10.3115/981574.981608 Schutze Hinrich, 1995, P 7 C EUR CHAPT ASS, P141, DOI 10.3115/976973.976994 Uniprot ftp (nd), 1989, [No title captured], Patent No. [4839853, 4,839,853] Weeds J, 2005, COMPUT LINGUIST, V31, P439, DOI 10.1162/089120105775299122 Zipitria I., 2001, P 23 ANN C COGN SCI, P1112 NR 41 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4962-0 PY 2009 BP 40 EP 46 DI 10.1109/ICSC.2009.89 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BOK85 UT WOS:000276911400006 ER PT B AU Grinshpoun, T Gal-Oz, N Meisels, A Gudes, E AF Grinshpoun, Tal Gal-Oz, Nurit Meisels, Amnon Gudes, Ehud BE BaezaYates, R Berendt, B Bertino, E Lim, EP Pasi, G TI CCR : A Model for Sharing Reputation Knowledge Across Virtual Communities SO 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conferences on Web Intelligence (WI)/Intelligent Agent Technologies (IAT), CY SEP 15-18, 2009 CL Milan, ITALY SP IEEE Comp Soc, Web Intelligence Consortium, ACM, Banca Popolare Sondrio, Comune Milano, Yahoo Res, DocFlow Italia SpA, IEEE, Univ Studi Milano Bicocca, Dipartimento Informat, Sistemist & Comunicaz AB Information sharing is a key objective in the age of Internet and virtual communities. Reputation information is an important part of a user's identity and is both a sensitive and desired data for communities to share. At the same time, a reputation that a user has gained at some point in time can leverage her state in new communities. Communities use various trust and reputation models to compute the internal reputation of their members and each model may represent and quantify reputation in different manners. This paper introduces the Cross-Community Reputation (CCR) model that enables to bridge the gap between communities. The CCR model identifies the fundamental terms required for a meaningful sharing of reputation information among communities and proposes means to make them feasible. The model describes the actions taken in response to a request for CCR in three major stages evaluation of preconditions, conversion of reputation values, and the matching of reputation attributes. The CCR model inherently supports policies specified by both communities and users. C1 [Grinshpoun, Tal] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. RP Grinshpoun, T (reprint author), Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. EM grinshpo@cs.bgu.ac.il; galoz@cs.bgu.ac.il; am@cs.bgu.ac.il; ehud@cs.bgu.ac.il RI GUDES, EHUD/F-1168-2012 CR ABDULRAHMAN A, 2000, HICSS 00, V6, P403 Chakraborty S., 2006, SACMAT 2006. Proceedings of Eleventh ACM Symposium on Access Control Models and Technologies DONDIO P, 2008, P 2 JOINT ITRUST PST, P87 GALOZ N, 2008, P IFIPTM 2008 JOINT, P167 GALOZ N, 2008, IADIS INT C WEB BAS JOACHIMS J, 1998, ECML 98, P137 Josang A., 2002, 15 BLED EL COMM C E KAMVAR S, 2003, P WWW2003 KINATEDER M, 2005, ITRUST, P177 Labalme F, 2001, ENHANCING INTERNET R Pingel F, 2008, LECT NOTES COMPUT SC, V5185, P69, DOI 10.1007/978-3-540-85735-8_8 Pinyol I., 2007, P 9 WORKSH TRUST AG, P90 Rahm E., 2001, VLDB J, V10, P4 RAMCHURN S, 2004, APPL ARTIFICIAL INTE, V18 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEIGNEUR JM, 2004, P 2 INT C TRUST MAN, P93 SHANNON CE, 1948, AT&T TECH J, V27, P379 STRASUNSKAS D, 2008, P 1 WORLD SUMM KNOWL, P469 TIAN H, 2006, ATC NR 19 TC 5 Z9 5 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4244-5331-3 PY 2009 BP 34 EP 41 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BPS64 UT WOS:000279800000004 ER PT B AU Samir, K Habiba, D AF Samir, Kechid Habiba, Drias BE BaezaYates, R Berendt, B Bertino, E Lim, EP Pasi, G TI Mutli-agent system for personalizing Information Source Selection SO 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conferences on Web Intelligence (WI)/Intelligent Agent Technologies (IAT), CY SEP 15-18, 2009 CL Milan, ITALY SP IEEE Comp Soc, Web Intelligence Consortium, ACM, Banca Popolare Sondrio, Comune Milano, Yahoo Res, DocFlow Italia SpA, IEEE, Univ Studi Milano Bicocca, Dipartimento Informat, Sistemist & Comunicaz ID SEARCH AB This paper proposes a new approach using a multi agents system for personalizing the information source selection. Most prior research for information source selection focused on selecting the sources that has the most relevant content according to the query but ignored the user's specific needs. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and thus, the same query may cover different specific information needs according to who emitted it. However, with the ever expanding Web, users are faced with a huge number of information resources. Our approach extends the state of the art distributed information retrieval. First, it develops models for representing both user and information source using feature based profiles. Second, it develops an agent called user-agent for managing the user profile. Third, it develops an agent called source-agent for each information source in order to manage its information source (source profile) in parallel. Fourth, it develops an agent called agent-broker for cooperating between user-agent and each source-agent in order to select the best source to the user's query. The approach has been experimented with several known information sources. The experimental results obtained show that the approach: (I) Improve the relevance of the result. (2) Reduce the response times. (3) Improve the system extensibility. C1 [Samir, Kechid; Habiba, Drias] USTHB, Artificial Intelligence Res Lab, Dept Comp Sci, Fac Elect & Comp Sci, Algiers, Algeria. RP Samir, K (reprint author), USTHB, Artificial Intelligence Res Lab, Dept Comp Sci, Fac Elect & Comp Sci, El Alia BP 32, Algiers, Algeria. EM kechidsam@yahoo.fr; h_drias@hotmail.fr CR BERTOLINI D, 2003, P 2 INT JOINT C AUT Budzik J., 2000, P 5 INT C INT US INT, P44, DOI 10.1145/325737.325776 Callan J., 2005, P 28 ANN INT ACM SIG, P83, DOI 10.1145/1076034.1076051 Callan J., 2004, P ACM CIKM C WASH DC, P32, DOI 10.1145/1031171.1031180 CALLAN J, 1995, ACM SIGIR, P21 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 FOX EA, 1993, 2 TEXT RETR C TREC 2 Fuhr N, 1999, ACM T INFORM SYST, V17, P229, DOI 10.1145/314516.314517 Gravano L., 1997, P ACM SIGMOD INT C M, P207, DOI 10.1145/253260.253299 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 KWOK KL, 1995, P TREC 3 NIST GAITH, P247 LARKEY LS, P CIK 2000, P282 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28 LU J, 2006, P 28 ANN INT ACM SIG, P83 MCGOWAN JP, 2003, THEIS U COLL DUBLIN NOTTELMANN H, P 25 ANN INT ACM SIG, P290 NUNBERG G, 2003, NY TIMES MAY Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seo Y.-W., 2000, P 2000 INT C INT US, P248, DOI 10.1145/325737.325859 Si L, 2003, ACM T INFORM SYST, V21, P457, DOI 10.1145/944012.944017 Kelly D., 2003, SIGIR Forum, V37 Voorhees Ellen M., 1995, P 18 ANN INT ACM SIG, P172, DOI 10.1145/215206.215357 XU J, 1998, ACM SIGIR C MELB AUS, P112 ZHANG H, 2004, AAMAS 04 JUL 19 23 2, P456 NR 27 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4244-5331-3 PY 2009 BP 588 EP 595 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BPS64 UT WOS:000279800000093 ER PT B AU Samarasinghe, SH Hui, SC AF Samarasinghe, Sidath Harshanath Hui, Siu Cheung BE Zhou, JH Zhou, XX TI Mathematical Document Retrieval for Problem Solving SO 2009 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND TECHNOLOGY, VOL I, PROCEEDINGS LA English DT Proceedings Paper CT International Conference on Computer Engineering and Technology CY JAN 22-24, 2009 CL Singapore, SINGAPORE SP Int Assoc Comp Sci & Informat Technol DE Knowledge Data Engineering; Data Mining; Artificial Intelligence ID MODEL AB Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonen's Self-Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their associated solutions are retrieved in order to provide hints or solutions on solving the user problem. In this paper, we will discuss the proposed mathematical document retrieval approach. The performance of the proposed approach will also be presented in comparison with other clustering techniques. C1 [Samarasinghe, Sidath Harshanath; Hui, Siu Cheung] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. RP Samarasinghe, SH (reprint author), Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore. EM sida0001@ntu.edu.sg; asschui@ntu.edu.sg CR Hussain M, 2007, NEURAL NETWORKS, V20, P260, DOI 10.1016/j.neunet.2006.10.004 Kohonen T., 1995, SELF ORG MAPS KRUGER H, 2004, ENHANCING SEARCHING MISUTKA J, 2008, P 3 INT C INF COMM T, P1 Nanni M, 2005, LECT NOTES ARTIF INT, V3518, P378 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Yates R. B., 1999, MODERN INFORM RETRIE Youssef A, 2006, LECT NOTES ARTIF INT, V4108, P2 ZHANG H, 2004, P INT C MACH LEARN C, V2, P1282 NR 11 TC 0 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3521-0 PY 2009 BP 583 EP 587 DI 10.1109/ICCET.2009.69 PG 5 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJE17 UT WOS:000265085500119 ER PT S AU He, W Wang, Y AF He Wei Wang Yu BE Lan, H TI Text Representation and Classification Based on Multi-Instance Learning SO 2009 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (16TH), VOLS I AND II, CONFERENCE PROCEEDINGS SE International Conference on Management Science and Engineering-Annual Conference Proceedings LA English DT Proceedings Paper CT 16th International Conference on Management Science and Engineering CY SEP 14-16, 2009 CL Moscow, RUSSIA SP Natl Nat Sci Fdn China, Harbin Inst Technol, State Univ Management, IEEE Technol Management Council DE bag of sentences; multi-instance learning; text classification; text representation AB In multi-instance learning, the training set comprises labeled bags which are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a text mining problem, i.e. text representation, is investigated from a multi-instance view. In detail, each text is regarded as a bag while each of its sentences is regarded as an instance. Bag can be labeled by its class label and its similarity is defined by sentence similarity The text classification problem is translated into multi-instance learning problem. In order to solve this problem, a Chinese text classifier focusing on bag has been built by KNN algorithm and good average precision 92.12% and recall 92.01% have been achieved in the experiments. C1 [He Wei; Wang Yu] Dalian Univ Technol, Sch Management, Dalian 116024, Peoples R China. RP He, W (reprint author), Dalian Univ Technol, Sch Management, Dalian 116024, Peoples R China. CR Chen Yuzhu, 2006, COMPUTER ENG DESIGN, V18, P3444 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 FOLTZ PW, 1999, P 16 INT C ICML 99 B, P27 JUN W, 2000, SOLVING MULTIPLE INS LI RL, 2008, TEXT CLASSIFICATION Li YH, 2006, IEEE T KNOWL DATA EN, V18, P1138, DOI 10.1109/TKDE.2006.130 LIU JH, 2007, COMPUTER ENG DESIGN, V13, P3213 LIU JH, 2007, COMPUTER ENG DESIGN, V13, P3219 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 [吕学å¼ŗ Lu Xueqiang], 2003, [äøœåŒ—å¤§å­¦å­¦ęŠ„. č‡Ŗē„¶ē§‘å­¦ē‰ˆ, Journal of Northeastern University], V24, P531 Maron O., 1998, FRAMEWORK MULTIPLE I Maron O, 1998, LEARNING AMBIGUITY Meadow C., 2000, TEXT INFORM RETRIEVA Mitra M., 1997, P ACL 97 EACL 97 WOR, P39 Montes-y-Gomez M, 2002, LECT NOTES ARTIF INT, V2393, P122 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WEN YK, 2005, J CHINA SOC SCI TECH, V6, P663 WEN YK, 2004, J CHINA SOC SCI TECH, V6, P643 WEN YK, 2005, KNOWLEDGE ELEMENT MI YANG SC, 2008, J CHINA SOC SCI TECH, V1, P35 ZHANG HP, ICTCLAS3 0 API OL ZHANG XL, 2002, J CHINA SOC SCI TECH, V4, P413 Zhou ZH, 2006, J COMPUT SCI TECHNOL, V21, P800, DOI 10.1007/s11390-006-0800-7 TEXT CLASSIFICATION NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 2155-1847 BN 978-1-4244-3970-6 J9 INT C MANAGE SCI ENG PY 2009 BP 34 EP 39 DI 10.1109/ICMSE.2009.5317537 PG 6 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Engineering, Industrial; Operations Research & Management Science SC Computer Science; Engineering; Operations Research & Management Science GA BMY95 UT WOS:000273956800005 ER PT B AU Sato, H Inoue, T Iwamoto, H Takahashi, N AF Sato, Hiroshi Inoue, Takeru Iwamoto, Hideaki Takahashi, Noriyuki GP IEEE TI Virtual Scent: Finding Locations of Interest in Ambient Intelligence Environments SO 2009 INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES (PDCAT 2009) LA English DT Proceedings Paper CT 10th International Conference on Parallel and Distributed Computing, Applications and Technologies CY DEC 08-11, 2009 CL Higashi, JAPAN DE privacy; location-awareness; AmI; AR AB In this paper, we propose a novel method to map locations of interest by utilizing the scent model. Location-aware services are being actively developed as they are a key component in realizing the Ambient Intelligence (AmI) world. Our proposal provides the basics needed to create location-aware services including navigation and notification. Users' interests are loosely shared by mirroring the diffusion of a real scent, and the users can sniff out the locations that match their interests. Since our model is based on a simple physical phenomenon, data processing is tractable and its user interface is intuitive. Moreover, user privacy is protected by the random wind effect without reducing map accuracy. We conduct thorough outdoor experiments in Kanagawa Prefecture, Japan, to demonstrate our model; twelve subjects were given mobile phones with GPS devices. The results show that the scent model provides an intriguing map with which to locate points of interest, and that the developed applications are promising. C1 [Sato, Hiroshi; Inoue, Takeru; Takahashi, Noriyuki] NTT Corp, NTT Network Innovat Labs, Kanagawa 2390847, Japan. RP Sato, H (reprint author), NTT Corp, NTT Network Innovat Labs, 1-1 Hikarino Oka, Kanagawa 2390847, Japan. EM sato.hiroshi@lab.ntt.co.jp CR Azuma R., 2003, Proceedings Second IEEE and ACM International Symposium on Mixed and Augmented Reality Azuma RT, 1997, PRESENCE-TELEOP VIRT, V6, P355 Bell B, 2002, IEEE COMPUT GRAPH, V22, P6, DOI 10.1109/MCG.2002.1016691 Makita K, 2004, LECT NOTES COMPUT SC, V3333, P499 Miyajima A, 2005, INT J HUM-COMPUT INT, V19, P253, DOI 10.1207/s15327590ijhc1902_6 MIYATA H, MEMORY TREE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHUZO M, 2008, KOR JAP DES ENG WORK Takano C, 2005, IEICE T COMMUN, VE88B, P1559, DOI 10.1093/ietcom/e88-b.4.1559 Takeuchi Y, 2009, PERS UBIQUIT COMPUT, V13, P119, DOI 10.1007/s00779-007-0192-x *TONCH CORP, SOC AUGM REAL MOB LO NR 11 TC 3 Z9 3 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5291-0 PY 2009 BP 384 EP 389 DI 10.1109/PDCAT.2009.41 PG 6 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BUJ28 UT WOS:000289503500056 ER PT B AU Yan, LL Chen, HNA Ji, WT Lu, Y Li, JQ AF Yan, Lili Chen, Henian Ji, Wentian Lu, Yu Li, Junqing BE Wu, JQ Ye, ZW TI Optimal VSM Model and Multi-object Quantum-inspired Genetic Algorithm for Web Information Retrieval SO 2009 INTERNATIONAL SYMPOSIUM ON COMPUTER NETWORK AND MULTIMEDIA TECHNOLOGY (CNMT 2009), VOLUMES 1 AND 2 LA English DT Proceedings Paper CT 1st International Symposium on Computer Network and Multimedia Technology CY DEC 18-18, 2009 CL Wuhan, PEOPLES R CHINA SP Res Assoc Modern Educ & Comp Sci, Huazhong Normal Univ, Huazhong Univ Sci & Technol, Wuhan Univ, Natl Technol Univ Ukraine, Harbin Inst Technol, Columbia Univ DE information retrieval; vector space model; quantum computing; quantum-inspired genetic algorithm AB It is becoming an important research issue to search the Web rapidly and effectively from a mass of information. Information retrieval for resolved these problems provide a chance. In this paper, a new adaptive method of information retrieval Web documents is proposed. We give an algorithm QIGA which combines genetic algorithm and quantum computing based on vector space model (VSM). This algorithm avoids the disadvantage of Web documents by using pure genetic algorithm which can not be utilized accurately. Experimental results show that our method can be adopted effectively in practice and is superior to other algorithms. C1 [Yan, Lili; Chen, Henian; Ji, Wentian; Lu, Yu; Li, Junqing] Hainan Software Profess Inst, Dept Software Engn, Qionghai 571400, Peoples R China. RP Chen, HNA (reprint author), Hainan Software Profess Inst, Dept Software Engn, Qionghai 571400, Peoples R China. EM yll480@163.com CR Baker J. E., 1987, P 2 INT C GEN ALG, P14 Frigui H., SURVEY TEXT MINING, P45 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 Han KH, 2002, IEEE T EVOLUT COMPUT, V6, P580, DOI 10.1109/TEVC.2002.804320 [何鹏 He Peng], 2002, [č®”ē®—ęœŗå·„ē؋, Computer Engineering], V28, P241 HE Peng, 2002, COMPUTER ENG, V28, P260 Hong J.T., 2000, INFORM PROCESS MANAG, V36, P737 Kim Yehoon, 2006, 2006 IEEE C EV COMP, P2601 Lawrence S., 1999, INT C PAR DISTR PROC Muhlenbein, 1993, EVOLUTIONARY COMPUTA, V1, P25 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 Riyaz s., 2005, INFORM TECHNOL MANAG, V6, P315 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang J., 1993, P 2 ANN S DOC AN INF, P271 YANG J, 1993, P 1 TEXT RETR C TREC, P31 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5272-9 PY 2009 BP 161 EP 164 PG 4 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BTU43 UT WOS:000288110500037 ER PT B AU Chen, ZM Shen, J AF Chen, Zhimin Shen, Jie BE Luo, Q Yi, JU Bin, C TI Research on Query-based Automatic Summarization of Webpage SO 2009 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL I LA English DT Proceedings Paper CT 2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009) CY AUG 08-09, 2009 CL Sanya, PEOPLES R CHINA SP IEEE Technol Management Council, Intelligent Informat Technol Applicat Res Assoc, IEEE SMC TC, Yangzhou Univ, Wuhan Inst Technol, Guangdong Univ Business Studies DE automatic summarization; topic segmentation; query-based; Maximal Marginal Relevance AB In order to overcome the shortcomings of traditional automatic summarization technologies, this paper proposes a novel automatic method for webpage summarization based on user's query. This method can be divided into the following steps. First, a HTML document is segmented into different topic blocks. Second, the weight of every sentence in a topic block is calculated in view of query keywords. Finally, several important sentences are dynamically extracted to compose the digest according to expected compression ratio with the improved Maximal Marginal Relevance method, which can remove redundant information in summary. The experiment results show that the proposed method can increase the digest coverage to the original document and improve the performance of information retrieval efficiently. C1 [Chen, Zhimin; Shen, Jie] Yangzhou Univ, Inst Informat Engn, Yangzhou, Peoples R China. RP Chen, ZM (reprint author), Yangzhou Univ, Inst Informat Engn, Yangzhou, Peoples R China. EM zmchen@yzu.edu.cn CR CARBONELL J, 1998, USE MMR DIVERSITY BA, P335 Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 LUHN HP, 1958, IBM J RES DEV, V2, P159 MANI I, 2001, P NTCIR WORKSH 2 M E MARTI A, 1997, COMPUTATIONAL LINGUI, V23, P33 RAU LF, 1988, P RAIO 88 C US ORIEN, P424 Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 YI L, 2003, ELIMINATING NOISY IN NR 9 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4246-1 PY 2009 BP 173 EP 176 DI 10.1109/CCCM.2009.5270475 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BNA09 UT WOS:000274016000045 ER PT B AU Ayodele, T Zhou, SK Khusainov, R AF Ayodele, Taiwo Zhou, Shikun Khusainov, Rinat GP IEEE TI Evolving Email Clustering Method for Email Grouping: A Machine Learning Approach SO 2009 SECOND INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT 2009) LA English DT Proceedings Paper CT 2nd International Conference on the Applications of Digital Information and Web Technologies CY AUG 04-06, 2009 CL London, ENGLAND SP ICADIWT, London Metropolitan Business School, IEEE UKRI Section AB This paper presents the design and implementation of a new system to manage email messages using email evolving clustering method with unsupervised learning approach to group emails base on activities found in the email messages, namely email grouping. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organise and prioritize email better. The goal is to provide highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. C1 [Ayodele, Taiwo; Zhou, Shikun; Khusainov, Rinat] Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 2UP, Hants, England. RP Ayodele, T (reprint author), Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 2UP, Hants, England. EM taiwo.ayodele@port.ac.uk; shikun.zhou@port.ac.uk; rinat.khusainov@port.ac.uk CR BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 BRYAN K, 2004, EUR C MACH LEARN CANNON RL, 1986, IEEE T PATTERN ANAL, V8, P48 Cohen William W., 1995, INT C MACH LEARN, P115 Crawford E., 2002, P 19 INT C MACH LEAR, P83 Dabbish L., 2003, CHI 2003, P924 DREDZE M, 2005, P 2 C EM ANT CEAS 20 Dunn J. C., 1974, Journal of Cybernetics, V4 FENG JC, 1998, IEEE T FUZZY SYST, V6, P2 Gonzalez A., 1997, MATHWARE SOFT COMPUT, V4, P233 *INT DAT GROUP, 2002, WORLDW MAIL US 2002 Kasabov N., 1998, METHODOLOGIES CONCEP, P271 Kasabov NK, 2002, IEEE T FUZZY SYST, V10, P144, DOI 10.1109/91.995117 MCDONALD I, 2005, EMAIL CONTINUITY MAI Platt J., 1991, Neural Computation, V3, DOI 10.1162/neco.1991.3.2.213 RAVI V, 2007, P INT C COMP INT MUL, P347 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuff D, 2007, COMPUTER, V40, P31, DOI 10.1109/MC.2007.65 STEINBACH M, 2000, 6 ACM SIGKDD WORKSH Sure Y, 2002, LECT NOTES COMPUT SC, V2519, P1205 *TIT LAB, 2007, CLASS EM SMART ARCH, P1 TYLER J, 2003, P ECSCW 2003, P238 Whittaker S., 1996, P SIGCHI C HUM FACT, P276, DOI 10.1145/238386.238530 NR 23 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4456-4 PY 2009 BP 357 EP 362 DI 10.1109/ICADIWT.2009.5273973 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BTX32 UT WOS:000288359000058 ER PT J AU Xiong, WX AF Xiong, Wenxin BE Zhao, CL Wu, YW Wang, J Liu, QT TI Text Surface Features for Genre Request in Information Retrieval SO 2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 1 LA English DT Proceedings Paper CT 2nd International Symposium on Knowledge Acquisition and Modeling CY NOV 30-DEC 01, 2009 CL Wuhan, PEOPLES R CHINA SP Inst Elect & Electron Engn, IEEE Comp Intelligence Soc, Huazhong Normal Univ DE text surface features; genre; query; information retrieval AB Traditional information retrieval focuses on topic relevance by computing the similarity between query and texts using content-based Bag of Words (BOW) strategy. This approach can not handle the terms expressing genre request in user's query, which maynot occur in target texts with the same form or other derivative forms. We propose a post text classification on the results returned by search engines to meet the request expressed by genre vocabulary in queries. Some statistics of text surface features for genre detection, i.e. average sentence length, number of specific part of speech and punctuations are examined. An experiment on identifying narrative texts and commentaries about a news event by using the above-mentioned variables has been conducted, and yielded an encouraging result. C1 Beijing Foreign Studies Univ, Natl Res Ctr Foreign Language Educ, Beijing 100089, Peoples R China. RP Xiong, WX (reprint author), Beijing Foreign Studies Univ, Natl Res Ctr Foreign Language Educ, Beijing 100089, Peoples R China. EM xiongwenxin@bfsu.edu.cn CR BAEZAYATES R, 1999, MODEM INFORM RETRIEV FINN A, 2002, P ANN EUR C INF RETR Greiff W. R., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290948 GRZYBEK P, 2005, CLASSIFICATION UBIQU KARLGREN J, 1994, P 15 INT C COMP LING Kelih E, 2006, STUD CLASS DATA ANAL, P382, DOI 10.1007/3-540-31314-1_46 KESSLER B, 1997, P 35 ANN M ASS COMP POUDAT C, 2003, P 3 INT C WEB ENG, P399 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stamatatos E., 2000, P 18 INT C COMP LING Eissen SMZ, 2004, LECT NOTES COMPUT SC, V3238, P256 XIONG W, 2006, THESIS BEIJING LANGU NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3888-4 PY 2009 BP 287 EP 290 DI 10.1109/KAM.2009.263 PG 4 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BVE88 UT WOS:000291328800073 ER PT B AU Peyravi, F Keshavarzi, A AF Peyravi, Farzad Keshavarzi, Amin GP IEEE TI Agent Based Model for Call Centers using Knowledge Management SO 2009 THIRD ASIA INTERNATIONAL CONFERENCE ON MODELLING & SIMULATION, VOLS 1 AND 2 LA English DT Proceedings Paper CT 3rd Asia International Conference on Modelling and Simulation CY MAY 25-29, 2009 CL Bandung, INDONESIA DE Call Center; Knowledge Management; Multi-agent System; Search Graph AB A call center is an organizational unit where inbound calls are received or outbound calls placed for the purposes of sales, support, advice and other business transactions. By applying knowledge management solutions, we can meet call centers' challenges and gain benefits of reduced training costs, improved call handling and greater flexibility. This paper describes a solution to agent mediated knowledge management system in call centers and new search algorithms. We introduced two agent based search algorithms for finding the expert user in system. Searching an expert is one of the most critical features which each call center system should have. Our agent based search with agent based weighted graph works well in our prototype system. Our agent model can distribute and share knowledge in whole levels of organizations and call center. C1 [Peyravi, Farzad; Keshavarzi, Amin] Islamic Azad Univ, Marvdasht Branch, Tehran, Iran. RP Peyravi, F (reprint author), Islamic Azad Univ, Marvdasht Branch, Tehran, Iran. EM FPeyravi@miau.ac.ir; Keshavarzai@miau.ac.ir CR BERGERSON B, 2003, ESSENTIAL KNOWLEDGE *CINC ENC, CALL CTR ROL CIM Do T.T., 2003, P 15 INT C SOFTW ENG ELST LV, 2003, P INT S AMKM STANF C GREENGRASS E, 2001, INFORM RETRIEVAL SUR Hayden S., 1999, P 3 INT C AUT AG AG Henderson-Sellers B., 2005, AGENT ORIENTED METHO KOH SCL, 2005, OURNAL KNOWLEDGE MAN, V9 Kolp M., 2001, P 8 INT WORKSH INT A MITZBERG H, 1992, STRUCTURE 5 DESIGNIN NANAOKA I, 1998, EUROPEAN MANAGEMENT, V16, P673 NANOKA T, 1991, KNOWLEDGE CREATION C, V69 Radding A., 1998, KNOWLEDGE MANAGEMENT ROBERTSON J, 2003, KNOWLEDGE MANAGEMENT Robertson J., 2002, KNOWLEDGE MANAGEMENT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SURESH R, 2001, KNOWLEDGE MANAGEMENT Sycara KP, 1998, AI MAG, V19, P79 TIMBRELL G, 2004, P I KNOW 04 4 INT C WOODRIDGE M, 1995, WORKSH AG THEOR ARCH Wooldridge M, 2000, AUTON AGENT MULTI-AG, V3, P285, DOI 10.1023/A:1010071910869 Wooldridge MJ, 2002, INTRO MULTIAGENT SYS NR 22 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4154-9 PY 2009 BP 51 EP 56 DI 10.1109/AMS.2009.147 PG 6 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BLX87 UT WOS:000271349800009 ER PT B AU Li, P AF Li, Peng BE Tran, D Zhou, SM TI Literature Characterization and Similarity Retrieval Based on Hierarchical Clustering SO 2009 WRI WORLD CONGRESS ON SOFTWARE ENGINEERING, VOL 1, PROCEEDINGS LA English DT Proceedings Paper CT World Congress on Software Engineering CY MAY 19-21, 2009 CL Xiamen, PEOPLES R CHINA SP World Res Inst AB The growing number of literature in journals database raises a new and challenging search problem: locating desired literature. Traditional keyword search is insufficient: the specific literature users require is possibly not captured. We introduce a new algorithm of hierarchical clustering. With this algorithm, we cluster the keywords into a concept tree, then we turn every literature into an induced tree. We propose a new method for theses retrieval, which based on concept similarity. This method improves in recall and precision. C1 Linyi Normal Univ, Sch Informat, Linyi 276003, Peoples R China. RP Li, P (reprint author), Linyi Normal Univ, Sch Informat, Linyi 276003, Peoples R China. CR Dong X., 2004, P 30 VLDB C TOR CAN Ester M., 1996, 2 INT C KNOWL DISC D, P226 Kamber M., 2001, DATA MINING CONCEPTS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 5 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3570-8 PY 2009 BP 397 EP 400 DI 10.1109/WCSE.2009.124 PG 4 WC Computer Science, Software Engineering SC Computer Science GA BPD36 UT WOS:000278560400082 ER PT J AU Carpineto, C Osinski, S Romano, G Weiss, D AF Carpineto, Claudio Osinski, Stanislaw Romano, Giovanni Weiss, Dawid TI A Survey of Web Clustering Engines SO ACM COMPUTING SURVEYS LA English DT Review DE Algorithms; Experimentation; Measurement; Performance; Information retrieval; meta search engines; text clustring; search results clustering; user interfaces ID INFORMATION-RETRIEVAL; SEARCH AB Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented. C1 [Carpineto, Claudio; Romano, Giovanni] Fdn Ugo Bordoni, I-00142 Rome, Italy. [Weiss, Dawid] Poznan Tech Univ, PL-60965 Poznan, Poland. RP Carpineto, C (reprint author), Fdn Ugo Bordoni, Via Baldassarre Castigl 59, I-00142 Rome, Italy. EM carpinet@fub.it; dawid.weiss@cs.put.poznan.pl CR Abney S., 1991, PRINCIPLE BASED PARS, P257 Allen Robert B., 1993, P ACM C ORG COMP SYS, P166, DOI 10.1145/168555.168572 Alonso O., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148273 ANAGNOSTOPOULOS A, 2006, P 15 ACM INT C INF K, P208, DOI 10.1145/1183614.1183648 Bade K., 2006, P 2006 IEEE WIC ACM, P181 Brodaty H., 2002, BRAIN AGING, V2, P3 Carpineto C, 2006, LECT NOTES COMPUT SC, V3936, P155 Carpineto C, 2009, J AM SOC INF SCI TEC, V60, P877, DOI 10.1002/asi.21036 Carpineto C, 2004, J UNIVERS COMPUT SCI, V10, P985 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Cheng D., 2005, P 24 ACM S PRINC DAT, P196, DOI 10.1145/1065167.1065192 CIGARRAN J, 2005, P 12 INT C STRING PR, P49 Cole R, 2003, APPL ARTIF INTELL, V17, P257, DOI [10.1080/08839510390187170, 10.1080/08839540390187170] Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELUCA EW, 2005, P 7 INT C HUM COMP I, P347, DOI 10.1145/1085777.1085859 Di Giacomo E, 2007, IEEE T VIS COMPUT GR, V13, P294, DOI 10.1109/TVCG.2007.40 Ding L., 2004, P 13 ACM INT C INF K, P652, DOI 10.1145/1031171.1031289 Dom Byron E., 2001, RJ10219 IBM Dong Z, 2002, THESIS SE U NANJING EADES P, 1989, CS8990 BROWN U DEP C Estivill-Castro V., 2002, SIGKDD EXPLORATIONS, V4, P65 Everitt Brian S., 2001, CLUSTER ANAL Ferragina P., 2005, P 14 INT C WORLD WID, P801 Ferragina P, 2004, LECT NOTES ARTIF INT, V3202, P506 Fred ALN, 2005, IEEE T PATTERN ANAL, V27, P835, DOI 10.1109/TPAMI.2005.113 Gabrilovich E., 2006, THESIS TECHNION ISRA Ganter B., 1999, FORMAL CONCEPT ANAL GERACI F, 2008, INTERNET MATH, V3, P413 Giannotti F., 2003, P 11 IT S ADV DAT SY, P507 Grefenstette G., 1995, P 3 INT C STAT AN TE, P263 HAASE P, 2005, P 2 EUR SEM WEB C ES, P486 Halkidi M, 2001, J INTELL INF SYST, V17, P107, DOI 10.1023/A:1012801612483 Harabagiu S., 2005, P 28 ANN INT ACM SIG, P202, DOI 10.1145/1076034.1076071 Hartigan J, 1975, CLUSTERING ALGORITHM HEARST M, 2002, COMMUN ACM, V45, P9 Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 HERMAN I, 2000, IEEE T VISUALIZATION, V6, P1 Hotho A, 2003, LECT NOTES ARTIF INT, V2838, P217 Husek D., 2006, WEB DATA MANAGEMENT, P1 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31 Johnson B., 1991, Proceedings Visualization '91 (Cat. No.91CH3046-0), DOI 10.1109/VISUAL.1991.175815 Kaki M., 2005, P SIGCHI C HUM FACT, P131, DOI 10.1145/1054972.1054991 Kantrowitz M., 2000, P 23 ANN INT ACM SIG, P357, DOI 10.1145/345508.345650 Karlson AK, 2006, P SIGCHI C HUM FACT, P711, DOI 10.1145/1124772.1124878 Katifori A, 2007, ACM COMPUT SURV, V39, DOI 10.1145/1287620.1287621 Kittler J, 1998, IEEE T PATTERN ANAL, V20, P226, DOI 10.1109/34.667881 Kummamuru Krishna, 2004, P 13 INT C WORLD WID, P658, DOI 10.1145/988672.988762 Lawrie D, 2001, P 24 ANN INT ACM SIG, P349, DOI 10.1145/383952.384022 Lawrie DJ, 2003, P 26 ANN INT ACM SIG, P457 LEUSKI A, 1996, IR76 U MASS Leuski A., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Lin D, 2002, P 19 INT C COMP LING, P1 Liu T., 2003, P 20 INT C MACH LEAR, P488 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 Liu X., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148310 Maarek Y.S., 2000, 10186 RJ IBM RES MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 Manning C. D., 1999, FDN STAT NATURAL LAN Manning C.D., 2008, INTRO INFORM RETRIEV Maslowska I, 2003, LECT NOTES COMPUT SC, V2633, P555 Meng WY, 2002, ACM COMPUT SURV, V34, P48, DOI 10.1145/505282.505284 Ngo CL, 2004, LECT NOTES ARTIF INT, V3202, P515 OSDIN R, 2002, P 11 TEXT RETRIEVAL OSINSKI S, 2004, P INT INT INF PROC W, P359 Osinski S, 2006, LECT NOTES COMPUT SC, V3936, P167 Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38 Otterbacher J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148271 Pantel P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Perugini S., 2006, IT Professional, V8, DOI 10.1109/MITP.2006.91 PIERRAKOS D, 2005, P 10 INT C US MOD, P89 Porter M. F., 1997, READINGS INFORM RETR, P313 RIGOU M, 2006, P JOINT INT WORKSH A, P35, DOI 10.1145/1149933.1149939 RIVADENEIRA W, 2003, HCILTR200336 U MAR Roberts J. C., 1998, Proceedings. 1998 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics (Cat. No.98TB100246), DOI 10.1109/IV.1998.694193 Rodden K, 2001, P SIGCHI C HUM FACT, P190, DOI DOI 10.1145/365024.365097 Romano G., 2004, CONCEPT DATA ANAL TH Rose D.E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675 Sahoo N., 2006, P 15 ACM INT C INF K, P357, DOI 10.1145/1183614.1183667 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARKAR M, 1994, COMMUN ACM, V37, P73, DOI 10.1145/198366.198384 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 SPILIOPOULOU M, 2005, LECT NOTES COMPUTER, V4289, P132 Stasko J., 2000, P IEEE S INF VIS, P57 Stefanowski J, 2003, LECT NOTES ARTIF INT, V2663, P240 STEFANOWSKI J, 2003, P INT INT INF PROC W, P209 Stein B, 2003, P 3 IASTED INT C ART, P216 STEIN B, 2004, P I KNOW 04, P353 Tagarelli A, 2006, SIAM PROC S, P188 Teevan J., 2004, P SIGCHI C HUM FACT, P415, DOI 10.1145/985692.985745 Toda H, 2005, P 7 ACM INT WORKSH W, P81, DOI 10.1145/1097047.1097063 Tombros A, 2002, INFORM PROCESS MANAG, V38, P559, DOI 10.1016/S0306-4573(01)00048-6 Turetken O, 2005, INFORM SYST FRONT, V7, P273, DOI 10.1007/s10796-005-2770-7 UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331 Van Rijsbergen K., 1979, INFORM RETRIEVAL Wagsaff K., 2001, P 18 INT C MACH LEAR, P577 Wang Xuanhui, 2007, P 30 ANN INT ACM SIG, P87, DOI http://dx.doi.org/10.1145/1277741.1277759 Wang Y., 2002, P 13 INT C DAT EXP S, P902 Weiss Dawid, 2006, THESIS POZNAN U TECH WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Ye S., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) ZAMIR O, 1999, COMPUTER NETWORKS, V31, P11 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zeng HJ., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030 Zhang D, 2004, LECT NOTES COMPUT SC, V3007, P69 Zhang YJ, 2004, INT J INTELL SYST, V19, P191, DOI 10.1002/int.10161 Zhao H., 2005, P 14 INT C WORLD WID, P66, DOI 10.1145/1060745.1060760 NR 113 TC 53 Z9 54 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PY 2009 VL 41 IS 3 AR 17 DI 10.1145/1541880.1541884 PG 38 WC Computer Science, Theory & Methods SC Computer Science GA 476TN UT WOS:000268468500004 ER PT B AU Kim, JW Candan, KS AF Kim, Jong Wook Candan, K. Selcuk GP ACM TI Skip-and-Prune: Cosine-based Top-K Query Processing for Efficient Context-Sensitive Document Retrieval SO ACM SIGMOD/PODS 2009 CONFERENCE LA English DT Proceedings Paper CT 35th ACM SIGMOD Conference CY JUN 29-JUL 02, 2009 CL Providence, RI SP ACM SIGMOD, ACM, Microsoft, ORACLE, SAP, SYBASE DE Top-K; Ranking ID INFORMATION; SYSTEMS; SEARCH AB Keyword search and ranked retrieval together emerged as popular data access paradigms for various kinds of data, from web pages to XML and relational databases. A user can submit keywords without knowing much (sometimes nothing) about the complex structure underlying a data collection, yet the system can identify, rank, and return a set of relevant matches by exploiting statistics about the distribution and structure of the data. Keyword-based data models are also suitable for capturing user's search context in terms of weights associated to the keywords in the query. Given a search context, the data in the database can also be re-interpreted for semantically correct retrieval. This option; however, is often ignored as the cost of re-assessing the content in the database naively tends to be prohibitive. In this paper, we first argue that top-k query processing can help tackle this challenge by re-assessing only the relevant parts of the database, efficiently. A road-block in this process, however, is that most efficient implementations of top-k query processing assume that the scoring function is monotonic, whereas the cosine-based scoring function needed for re-interpretation of content based on user context is not. In this paper, we develop an efficient top-k query processing algorithm, skip-and-prune (SnP), which is able to process top-k queries under cosine-based non-monotonic scoring functions. We compare the use of proposed algorithm against the alternative implementations of the context-aware retrieval, including naive top-k, accumulator-based inverted files, and full-scan. The experiment results show that while being fast, naive top-k is not an effective solution due to the non-monotonicity of underlying scoring function. The proposed technique, SnP, however, matches the precision of accumulator-based inverted files and full-scan, yet it is orders of magnitude faster than these. C1 [Kim, Jong Wook; Candan, K. Selcuk] Arizona State Univ, Comp Sci & Eng Dept, Tempe, AZ 85287 USA. RP Kim, JW (reprint author), Arizona State Univ, Comp Sci & Eng Dept, Tempe, AZ 85287 USA. EM jong@asu.edu; candan@asu.edu CR Agrawal S., 2002, ICDE Arai B., 2007, VLDB Bansal N., 2008, SIGMOD Borzsonyi S., 2001, ICDE BRODER AJ, 1990, PATTERN RECOGN, V23, P171, DOI 10.1016/0031-3203(90)90057-R Chakrabarti K., 2006, SIGMOD Cheng Tao, 2007, VLDB Cohen S., 2003, VLDB Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Fagin R, 1999, J COMPUT SYST SCI, V58, P83, DOI 10.1006/jcss.1998.1600 Fukunaga K, 1975, IEEE T COMPUTERS Harman D, 1992, INFORMATION RETRIEVA, P28 Hjaltason GR, 1999, ACM T DATABASE SYST, V24, P265, DOI 10.1145/320248.320255 Ilyas Ihab F., 2003, VLDB Nepal S., 1999, ICDE Papadias D, 2005, ACM T DATABASE SYST, V30, P41, DOI 10.1145/1061318.1061320 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun J.-T., 2005, WWW Tan K. Lee, 2001, VLDB Theobald M., 2004, VLDB, P648 Tsaparas P., 2003, ICDE untzer U. G, 2000, VLDB Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 NR 27 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-554-3 PY 2009 BP 115 EP 126 PG 12 WC Computer Science, Information Systems SC Computer Science GA BZE10 UT WOS:000301205700011 ER PT J AU Xue, GR Han, J Yu, Y Yang, Q AF Xue, Gui-Rong Han, Jie Yu, Yong Yang, Qiang TI User Language Model for Collaborative Personalized Search SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE Algorithms; Experimentation; Human Factors; Collaborative personalized search; user language model; clustering; smoothing; data Sparseness; cold-start ID ALGORITHM AB Traditional personalized search approaches rely solely on individual profiles to construct a user model. They are often confronted by two major problems: data sparseness and cold-start for new individuals. Data sparseness refers to the fact that most users only visit a small portion of Web pages and hence a very sparse user-term relationship matrix is generated, while cold-start for new individuals means that the system cannot conduct any personalization without previous browsing history. Recently, community-based approaches were proposed to use the group's social behaviors as a supplement to personalization. However, these approaches only consider the commonality of a group of users and still cannot satisfy the diverse information needs of different users. In this article, we present a new approach, called collaborative personalized search. It considers not only the commonality factor among users for defining group user profiles and global user profiles, but also the specialties of individuals. Then, a statistical user language model is proposed to integrate the individual model, group user model and global user model together. In this way, the probability that a user will like a Web page is calculated through a two-step smoothing mechanism. First, a global user model is used to smooth the probability of unseen terms in the individual profiles and provide aggregated behavior of global users. Then, in order to precisely describe individual interests by looking at the behaviors of similar users, users are clustered into groups and group-user models are constructed. The group-user models are integrated into an overall model through a cluster-based language model. The behaviors of the group users can be utilized to enhance the performance of personalized search. This model can alleviate the two aforementioned problems and provide a more effective personalized search than previous approaches. Large-scale experimental evaluations are conducted to show that the proposed approach substantially improves the relevance of a search over several competitive methods. C1 [Xue, Gui-Rong; Han, Jie; Yu, Yong] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. [Yang, Qiang] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Hong Kong, Peoples R China. RP Xue, GR (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China. EM grrue@apex.sjtu.edu.cn; jhan@apex.sjtu.edu.cn; yyu@apex.sjtu.edu.cn; qyang@cse.ust.hk FU NSFC [60873211]; Hong Kong RGC [621307]; Hong Kong ROC [621307]; MSRA-SFTU FX This research was supported by NSFC project 60873211 and Hong Kong RGC project 621307. This work was conducted while G.-R. Xue and J. Han were doing internship at Microsoft Research Asia. Q. Yang thanks Hong Kong ROC project 621307 for generous support. G.-R. Xue wishes to thank Microsoft Research Asia for their support to the MSRA-SFTU joint lab project. CR Agichtein E., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148175 Allan J., 1998, P DARPA BROADC NEWS, P194 Almeida R. B., 2004, P 13 INT C WORLD WID, P413, DOI 10.1145/988672.988728 Anand SS, 2005, LECT NOTES ARTIF INT, V3169, P1 ANICK P, 2004, P 26 ANN INT ACM SIG, P88 Bai J., 2007, P 30 ANN INT ACM SIG, P15, DOI 10.1145/1277741.1277747 BEAULIEU M, 1996, BRIT LIB RES INNOV R, V17, P103 BHARAT K, 1995, P 4 INT C WORLD WID, P159 BILLSUS D, 1999, P 7 INT C US MOD, P99 Breese J S, 1998, P 14 C UNC ART INT, P43 BROWN PF, 1992, COMPUTATIONAL LINGUI, V18, P468 Burges C., 2005, P 22 INT C MACH LEAR, P89, DOI DOI 10.1145/1102351.1102363 Chen Stanley F., 1998, TR1098 HARV U COMP S Chirita P.A., 2005, P 28 ANN INT ACM SIG, P178, DOI 10.1145/1076034.1076067 Claypool M., 2001, P 6 INT C INT US INT, P33, DOI DOI 10.1145/359784.359836 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dumais S., 2003, P 26 ANN INT ACM SIG, P72 Fisher Danyel, 2000, P SIGIR, P366, DOI 10.1145/345508.345658 Fox S, 2005, ACM T INFORM SYST, V23, P147, DOI 10.1145/1059981.1059982 Frakes W., 1992, INFORMATION RETRIEVA, P131 Goldberg K, 2001, INFORM RETRIEVAL, V4, P133, DOI 10.1023/A:1011419012209 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Haveliwala T. H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513 HOFMANN T, 1998, 1625 AI MIT ART INT Jarvelin K., 2000, P 23 ANN INT ACM SIG, P41, DOI DOI 10.1145/345508.345545.ISBN Jin R., 2002, P 25 ANN INT ACM SIG, P42 Joachims T., 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067 KERSCHBERG L, 2001, P INT C INT COMP IC, P14 Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Kritikopoulos A., 2003, P ITWP WORKSH INT TE, P229 Krulwich B, 1997, AI MAG, V18, P37 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 Li Xia, 2003, Proceedings of 2003 International Conference on Neural Networks and Signal Processing (IEEE Cat. No.03EX641) Lieberman H., 1995, P 14 INT JOINT C ART, P924 Liu F., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Liu X., 2004, P 27 ANN INT ACM SIG, P186, DOI 10.1145/1008992.1009026 MANBER U, 2002, ACM, V43, P35 McLachlan G. J., 1988, MIXTURE MODELS INFER Mitchell TM, 1997, MACHINE LEARNING Mladenic D., 1996, IJSDP7472 CARN MELL Ng K.B., 1996, P 4 TEXT RETR C TREC, P181 NTOULAS A, 2004, P 15 INT C WORLD WID, P233 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159 PITKOW I, 2001, COMMUN ACM, V45, P50 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Robertson S. E., 1994, P 17 ANN INT ACM SIG, P345 Robertson S. E., 1980, P 3 ANN ACM C RES DE, P35 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2000, P ACM WEBKDD 2000 WE, P82 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 SHAHABI C, 2003, P DAT NETW INF SYST, P5 Shen X., 2005, P 28 ANN INT ACM SIG, P43, DOI 10.1145/1076034.1076045 SMYTH B, 2003, P 18 INT JOINT C ART SPITTERS M, 2001, TDT2001 TNO Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764 SULLIVAN D, 2004, EUREKSTER LAUNCHES P Surowiecki J., 2004, WISDOM CROWDS WHY MA Kelly D., 2003, SIGIR Forum, V37 Teevan J, 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 WANG J, 2006, P EUR C INF RETR, P37 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 Xue G. R., 2005, P 28 ANN INT ACM SIG, P114, DOI 10.1145/1076034.1076056 YAMRON JP, 1999, P DARPA BROADC NEWS Zeng C., 2003, P 12 INT C WORLD WID, P652 ZHAI C, 2004, ACM T INFORM SYSTEMS, V2, P179 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 NR 70 TC 3 Z9 4 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PY 2009 VL 27 IS 2 AR 11 DI 10.1145/1462198.1462203 PG 28 WC Computer Science, Information Systems SC Computer Science GA 426BM UT WOS:000264682400005 ER PT S AU Alfred, R AF Alfred, Rayner BE Huang, R Yang, Q Pei, J Gama, J Meng, XF Li, X TI Discovering Knowledge from Multi-relational Data Based on Information Retrieval Theory SO ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th International Conference on Advanced Data Mining and Applications CY AUG 17-19, 2009 CL Beijing, PEOPLES R CHINA SP Beijing Normal Univ DE Vector Space Model; Information Retrieval; Clustering; Data Summarization; Knowledge Discovery ID 1ST-ORDER; INDUCTION AB Although the TF-IDF weighted frequency matrix (vector space model) has been widely studied and used in document clustering or document categorisation, there has been no attempt to extend this application to relational data, that contain one-to-many associations between records. This paper explains the rationale for using TF-IDF (term frequency inverse document frequency), a technique for weighting data, attributes, borrowed from Information Retrieval theory, to summarise datasets stored in a multi-relational setting with one-to-many relationships. A novel data summarisation algorithm based on TF-IDF is introduced, which is referred to as Dynamic Aggregation, of Relational Attributes (DARA). The DARA algorithm applies clustering techniques in order to summarise these datasets. The experimental results show that using the DARA algorithm finds solutions with much greater accuracy. C1 Univ Malaysia Sabah, Ctr Artificial Intelligence, Machine Learning Res Grp, Kota Kinabalu 88999, Sabah, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Ctr Artificial Intelligence, Machine Learning Res Grp, Locked Bag 2073, Kota Kinabalu 88999, Sabah, Malaysia. EM ralfred@ums.edu.my RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR Alfred R, 2007, LECT NOTES COMPUT SC, V4690, P55 ALFRED R, 2007, LNCS, V4690 Alfred R, 2008, LECT NOTES ARTIF INT, V5139, P39, DOI 10.1007/978-3-540-88192-6_6 Blockeel H., 2003, SIGKDD EXPLORATIONS, V5, P17 Blockeel H, 1998, ARTIF INTELL, V101, P285, DOI 10.1016/S0004-3702(98)00034-4 Finn P, 1998, MACH LEARN, V30, P241, DOI 10.1023/A:1007460424845 Frank E., 1999, DATA MINING PRACTICA Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 KIRSTEN M, 1998, 8 INT C IND LOG PROG, P261 Kramer S., 2001, RELATIONAL DATA MINI Krogel M. A., 2001, LECT NOTES ARTIF INT, V2157, P142 Quinlan Ross, 1993, M KAUFMANN SERIES MA Salton G., 1984, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan A, 1996, ARTIF INTELL, V85, P277, DOI 10.1016/0004-3702(95)00122-0 van Rijsbergen C. J., 1979, INFORM RETRIEVAL NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03347-6 J9 LECT NOTES COMPUT SC PY 2009 VL 5678 BP 409 EP 416 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BLN72 UT WOS:000270597500035 ER PT S AU Nguyen-Hoang, TA Hoang, K Bui-Thi, D Nguyen, AT AF Nguyen-Hoang, Tu-Anh Hoang, Kiem Bui-Thi, Danh Nguyen, Anh-Thy BE Huang, R Yang, Q Pei, J Gama, J Meng, XF Li, X TI Incremental Document Clustering Based on Graph Model SO ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 5th International Conference on Advanced Data Mining and Applications CY AUG 17-19, 2009 CL Beijing, PEOPLES R CHINA SP Beijing Normal Univ DE Incremental document clustering; graph model; shared phrases; enhanced IncrementalDBSCAN AB In this paper, we propose a new approach based oil graph model and enhanced IncrementalDBSCAN to solve incremental document clustering problem. Instead of traditional vector-based model, a graph-based is used for document representation. By using graph model, we can easily update graph structure when a new document is added to database. Meanwhile, IncrementalDBSCAN is an effective incremental clustering algorithm Suitable for mining in dynamically changing databases. Similarity between two documents is measured by hybrid similarity of their adapting feature vectors and shared-phrase information. Our experimental results demonstrate the effectiveness of the proposed method. C1 [Nguyen-Hoang, Tu-Anh; Bui-Thi, Danh; Nguyen, Anh-Thy] Univ Sci, Fac Informat Technol, Vnu Hcm 70000, Vietnam. RP Nguyen-Hoang, TA (reprint author), Univ Sci, Fac Informat Technol, Vnu Hcm 70000, Vietnam. EM nhtanh@fit.hcmuns.edu.vn; tuanaivnn@yahoo.com; popstarsongngu@yahoo.com; kiemhv@uit.edu.vn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Ester M., 1996, P 2 INT C KNOWL DISC, P226 ESTER M, 1998, P VLDB, P1 HAMMOUDA M, 2002, P 2002 IEEE INT C DA, P203 HAMMOUDA M, 2003, P IEEE WIC INT C WEB, P597 Liu T., 2003, P 20 INT C MACH LEAR, P488 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A, 2004, INT J PATTERN RECOGN, V18, P475, DOI 10.1142/S0218001404003241 Schenker A., 2003, P 2 INT WORSKH WEB D, P71 WONG W, 2000, P 2000 INT C INF SOC Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 YANG Y, 1999, IEEE INTELLIGENT SYS, V14 Yoo I, 2006, LECT NOTES ARTIF INT, V3918, P303 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining NR 14 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03347-6 J9 LECT NOTES COMPUT SC PY 2009 VL 5678 BP 569 EP 576 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BLN72 UT WOS:000270597500054 ER PT S AU Hao, YA Cao, JL Zhang, YC AF Hao, Yanan Cao, Jinli Zhang, Yanchun BE VanEck, P Gordijn, J Wieringa, R TI Efficient IR-Style Search over Web Services SO ADVANCED INFORMATION SYSTEMS ENGINEERING, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 21st International Conference on Advanced Information Systems Engineering CY JUN 08-12, 2009 CL Amsterdam, NETHERLANDS SP Vrije Univ Amsterdam, Univ Twente Netherlands, Network Inst, SIKS, CTIT, NWO, Capgemini, ZUIDAS, Tacouard AB In service-based systems, one of the most important problems is how to discover desired web services. In this paper, we propose a novel IR.-Style mechanism for discovering and ranking web services automatically. In particular, we introduce the notion of preference degree for web services and then we define service relevance and service importance as two desired properties for measuring the preference degree. Furthermore, various algorithms are given for computing the relevance and importance of services, respectively. Experimental results show the proposed IR-style search strategy is efficient and practical. C1 [Hao, Yanan; Zhang, Yanchun] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia. RP Hao, YA (reprint author), Victoria Univ, Sch Sci & Engn, POB 14428, Melbourne, Vic 8001, Australia. EM haoyn@csm.vu.edu.au; j.cao@latrobe.edu.au; yzhang@csm.vu.edu.au CR Agrawal S., 2002, P INT C DAT ENG ICDE Al-Masri E., 2007, IEEE 16 INT C COMP C, P529 Al-Mastri E., 2008, WWW 2008, P795 Booth D., 2004, WEB SERVICES ARCHITE Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 Hao YN, 2007, LECT NOTES COMPUT SC, V4495, P173 HE Q, 2008, IEEE SCC, P221 Hristidis V, 2003, P 29 INT C VER LARG, P850, DOI 10.1016/B978-012722442-8/50080-X Hristidis V., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases Kaufman L, 1990, FINDING GROUPS DATA Nierman A., 2002, WEBDB, P61 Pu K., 2006, P INT C DAT ENG, P31 Reis D., 2004, P 13 INT C WORLD WID, P502, DOI 10.1145/988672.988740 Roman D., 2005, WEB SERVICE MODELING Sajjanhar A, 2004, LECT NOTES COMPUT SC, V3007, P665 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Z., 2005, P 2005 IEEE INT C SE, V01, P279 Sycara K, 2002, AUTON AGENT MULTI-AG, V5, P173, DOI 10.1023/A:1014897210525 WANG Y, 2003, P INT C WEB INF SYST Yu C., 2006, VLDB, P319 ZHANG KZ, 1989, SIAM J COMPUT, V18, P1245, DOI 10.1137/0218082 NR 22 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-02143-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5565 BP 305 EP 318 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BKB67 UT WOS:000267696300025 ER PT S AU Li, P Li, ZX Liu, HY He, J Du, XY AF Li, Pei Li, Zhixu Liu, Hongyan He, Jun Du, Xiaoyong BE Li, Q Feng, L Pei, J Wang, SX Zhou, XF Zhu, QM TI Using Link-Based Content Analysis to Measure Document Similarity Effectively SO ADVANCES IN DATA AND WEB MANAGEMENT, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT Joint International Conference on Asia-Pacific Web Conference (APWeb)/Web-Age Information Management (WAIM 2009) CY APR 02-04, 2008-2009 CL Suzhou, PEOPLES R CHINA DE link graph; content analysis; document similarity ID MODEL; TEXT AB Along with a massive amount of information being placed online, it is a challenge to exploit the internal and external information of documents when assessing similarity between them. A variety of approaches have been proposed to model the document similarity based on different foundations, but usually they are not applicable for combining internal and external information. In this paper, we introduce a link-based method into content analysis, which is based on random walk on graphs. By defining similarity as the meeting probability of two random surfers, we propose a computational model for content analysis, which can also be integrated with external information of documents. Empirical study shows that Our method achieves good accuracy, acceptable performance and fast convergent rate in multi-relational document similarity measuring. EM lp@ruc.edu.cn; lizx@ruc.edu.cn; hyliu@tsinghua.edu.cn; hejun@ruc.edu.cn; duyong@ruc.edu.cn CR *ACM, ACM COMP CLASS SYST Aslam JA, 2003, SIGIR03 TOR CAN JUL, P449 CALADO P, 2003, CIKM 03, P394 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fogaras D., 2005, WWW, P641, DOI DOI 10.1145/1060745.1060839 GETOOR L, SIGKDD 2005 EXPLORAT, V7, P3 HAMMOUDA KM, 2002, ICDM, P203 Jeh G., 2002, SIGKDD, P538 Jin R., 2001, SIGIR Forum Kallenberg O., 1997, FDN MODERN PROBABILI Kaufman L, 1990, FINDING GROUPS DATA Lovasz Laszlo, 1993, COMBINATORICS P ERDO, V2, P1 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Renda ME, 2005, INFORM PROCESS MANAG, V41, P5, DOI 10.1016/j.ipm.2004.04.007 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Xi W., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Yin X., 2006, VLDB, P427 Zhu S., 2007, SIGIR, P487 STOP WORDS LIST NR 22 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00671-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5446 BP 455 EP 467 PG 13 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BJE53 UT WOS:000265181200038 ER PT S AU Crouch, CJ Crouch, DB Bapat, S Mehta, S Paranjape, D AF Crouch, Carolyn J. Crouch, Donald B. Bapat, Salil Mehta, Sarika Paranjape, Darshan BE Geva, S Kamps, J Trotman, A TI Finding Good Elements for Focused Retrieval SO ADVANCES IN FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 15-18, 2008 CL Dagstul, GERMANY AB This paper describes the integration of our methodology for the dynamic retrieval of MAL elements 121 with traditional article retrieval to facilitate the Focused and the Relevant-in-Context Tasks of the INEX 2008 Ad Hoc Track. The particular problems that arise for dynamic element retrieval in working with text containing both tagged and untagged elements have been solved [3]. The current challenge involves utilizing its ability to produce a rank-ordered list of elements in the context of focused retrieval. Our system is based on the Vector Space Model [81; basic functions are performed using the Smart experimental retrieval system [7]. Experimental results are reported for the Focused, Relevant-in-Context, and Best-in-Context Tasks of both the 2007 and 2008 INEX Ad Hoc Tracks. These results indicate that the goal of our 2008 investigations namely, finding good focused elements in the context of the Wikipedia collection--has been achieved. C1 [Crouch, Carolyn J.; Crouch, Donald B.; Bapat, Salil; Mehta, Sarika; Paranjape, Darshan] Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. EM ccrouch@d.umn.edu CR Bapat S, 2008, THESIS U MINNESOTA D Crouch CJ, 2008, LECT NOTES COMPUT SC, V4862, P70, DOI 10.1007/978-3-540-85902-4_6 Crouch CJ, 2006, ACM T INFORM SYST, V24, P437, DOI 10.1145/1185877.1185880 MEHTA S, 2008, THESIS U MINNESOTA D MONE A, 2007, THESIS U MINNESOTA D PARANJAPE D, 2007, THESIS U MINNESOTA D SALTON G, 1971, SMART RRETRIEVAL SYS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 8 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03760-3 J9 LECT NOTES COMPUT SC PY 2009 VL 5631 BP 33 EP 38 PG 6 WC Computer Science, Theory & Methods SC Computer Science GA BNY62 UT WOS:000275895800003 ER PT S AU Tanioka, H AF Tanioka, Hiroki BE Geva, S Kamps, J Trotman, A TI Pseudo Relevance Feedback Using Fast XML Retrieval SO ADVANCES IN FOCUSED RETRIEVAL SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 7th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 15-18, 2008 CL Dagstul, GERMANY AB This paper reports the result of experimentation of our approach using the vector space model for retrieving large-scale XML data. The purposes of the experiments are to improve retrieval precision on the INitiative for the Evaluation of XML Retrieval (INEX) 2008 Adhoc Track, and to compare the retrieval time of our system to other systems on the INEX 2008 Efficiency Track. For the INEX 2007 Adhoc Track, we developed a system using a relative inverted-path (RIP) list and a Bottom-UP approach. The system achieved reasonable retrieval time for XML data. However the system has a room for improvement in terms of retrieval precision. So for INEX 2008, the system uses CAS titles and Pseudo Relevance Feedback (PRF) to improve retrieval precision. C1 [Tanioka, Hiroki] Just Syst Corp, Innovat Technol R&D, Tokushima, Japan. RP Tanioka, H (reprint author), Just Syst Corp, Innovat Technol R&D, 108-4 Hiraishi Wakamatsu,Kawauchi Cho, Tokushima, Japan. EM hiroki.tanioka@justsystems.com CR [Anonymous], EXTENSIBLE MARKUP LA [Anonymous], XML PATH LANGUAGE XP Kamps J, 2008, LECT NOTES COMPUT SC, V4862, P24, DOI 10.1007/978-3-540-85902-4_2 MONTGOMERY J, 2004, SIGIR 2004, P476 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tanioka H, 2008, LECT NOTES COMPUT SC, V4862, P129, DOI 10.1007/978-3-540-85902-4_12 NR 6 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03760-3 J9 LECT NOTES COMPUT SC PY 2009 VL 5631 BP 218 EP 223 PG 6 WC Computer Science, Theory & Methods SC Computer Science GA BNY62 UT WOS:000275895800022 ER PT S AU Demartini, G Gaugaz, J Nejdl, W AF Demartini, Gianluca Gaugaz, Julien Nejdl, Wolfgang BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI A Vector Space Model for Ranking Entities and Its Application to Expert Search SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees ID INFORMATION; RETRIEVAL AB Entity Ranking has recently become an important search task in Information Retrieval. The goal is not to find documents matching query terms, but, instead, finding entities. In this paper we propose a formal model to search entities as well as a complete Entity Ranking system, providing examples of its application to the enterprise context. We experimentally evaluate our system on the Expert Search task in order to show how it can be adapted to different scenarios. The results show that combining simple IR techniques we improve of 53% in terms of P@10 over our baseline. C1 [Demartini, Gianluca; Gaugaz, Julien; Nejdl, Wolfgang] Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany. RP Demartini, G (reprint author), Leibniz Univ Hannover, L3S Res Ctr, Hannover, Germany. EM demartini@L3S.de; gaugaz@L3S.de; nejdl@L3S.de RI Demartini, Gianluca/G-4635-2012 CR Anick PG, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P153 Bailey P., 2007, SIGIR FORUM, V41, P42 Balog K., 2006, SIGIR 06, P43 Bast H., 2007, SIGIR, P671 Chakrabarti S., 2007, WWW, P571 Cheng T., 2007, P CIDR, P108 Cheng T., 2007, VLDB, P387 Chirita P.A., 2006, CIKM, P287 CRASWELL N, 2001, PANOPTIC EXPERT SEAR CRASWELL N, 2004, 13 TEXT RETR C TREC de Vries A. P., 2007, INEX, P245 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMARTINI G, 2008, P LAT AM WEB C LA WE Demartini G, 2008, LECT NOTES COMPUT SC, V5175, P176, DOI 10.1007/978-3-540-85481-4_15 Denoyer L., 2006, SIGIR Forum, V40 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Macdonald C., 2006, CIKM 2006, P387, DOI DOI 10.1145/1183614.1183671 Macdonald C, 2008, LECT NOTES COMPUT SC, V4956, P283 Macdonald C, 2007, LECT NOTES COMPUT SC, V4425, P431 Manning C.D., 2008, INTRO INFORM RETRIEV MCLEAN A, 2003, P AUSTR DOC COMP S Pehcevski J, 2008, LECT NOTES COMPUT SC, V4956, P258 Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zirn C, 2008, LECT NOTES COMPUT SC, V5021, P376 NR 25 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 189 EP 201 PG 13 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800016 ER PT S AU Papadopoulos, S Menemenis, F Kompatsiaris, Y Bratu, B AF Papadopoulos, Symeon Menemenis, Fotis Kompatsiaris, Yiannis Bratu, Ben BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI Lexical Graphs for Improved Contextual Ad Recommendation SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees DE Online advertising; content-targeted advertising; vocabulary impedance; lexical graphs ID MODEL AB Contextual advertising is a form of online advertising presenting consistent revenue growth since its inception. In this work. we study the problem of recommending a small set of ads to a user based solely on the currently viewed web page, often referred to as content-targeted advertising. Matching ads with web pages is a challenging task for traditional information retrieval systems due to the brevity and sparsity of advertising text, which leads to the widely recognized vocabulary impedance problem. To this end, we propose the use of lexical graphs created from web corpora, as a means of computing improved content similarity metrics between ads and web pages. The results of our experimental study provide evidence of significant improvement ill the perceived relevance of the recommended ads. C1 [Papadopoulos, Symeon; Menemenis, Fotis; Kompatsiaris, Yiannis] Informat & Telemat Inst, Multimedia Knowledge Lab, Thessaloniki 57001, Greece. RP Papadopoulos, S (reprint author), Informat & Telemat Inst, Multimedia Knowledge Lab, 6th Km Charilaou Thermi Rd, Thessaloniki 57001, Greece. EM papadop@iti.gr; fotis@iti.gr; ikom@iti.gr; ben.bratu@motorola.com CR Broder Andrei, 2007, P 30 ANN INT ACM SIG, P559, DOI 10.1145/1277741.1277837 COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37, DOI 10.1177/001316446002000104 Lacerda A., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148265 Mishne G, 2006, LECT NOTES ARTIF INT, V4139, P435 Murdock V, 2007, P 1 INT WORKSH DAT M, P21, DOI 10.1145/1348599.1348603 Ribeiro-Neto B., 2005, P 28 ANN INT ACM SIG, P496, DOI 10.1145/1076034.1076119 Richardson M., 2007, P 16 INT C WORLD WID, P521, DOI DOI 10.1145/1242572.1242643 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Widdows D., 2002, P 19 INT C COMP LING, P1 Yih Wen-tau, 2006, P 15 INT C WORLD WID, P213, DOI 10.1145/1135777.1135813 NR 10 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 216 EP 227 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800018 ER PT S AU Klein, M Nelson, ML AF Klein, Martin Nelson, Michael L. BE Boughanem, M Berrut, C Mothe, J SouleDupuy, C TI Correlation of Term Count and Document Frequency for Google N-Grams SO ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 31st European Conference on Information Research CY APR 06-09, 2009 CL Toulouse, FRANCE SP Google, Mateixware Informat Serv, Microsoft Res, Yahoo, Exalead, GDR 13, Univ Paul Sabatier, ARIA, Inforsid & Reg, Midi Pyrennees ID WORLD-WIDE-WEB; LEXICAL SIGNATURES; PAGES AB For bounded datasets such as the TREC Web Track (WT10g) the computation of term frequency (TF) and inverse document frequency (IDF) is not difficult. However, when the corpus is the entire web, direct IDF calculation is impossible and values must instead be estimated. Most available datasets provide values for term count (TC) meaning the number of times a certain term occurs in the entire corpus. Intuitively this value is different from document frequency (DF), the number of documents (e.g., web pages) a certain term occurs in. We investigate the relationship between TC and DF values of terms occcurring in the Web as Corpus (WaC) and also the similarity between TC values obtained from the WaC and the Google N-gram dataset. A strong correlation between the two would gives us confidence in using the Google N-grams to estimate accurate IDF values which for example is the foundation to generate well performing lexical signatures based on the TF-IDF scheme. Our results show a very strong correlation between TC and DFG within the WaC with Spearman's rho >= 0.8 (p <= 2.2 X 10(-16)) and a high similarity between TC values from the WaC and the Google N-grams. C1 [Klein, Martin; Nelson, Michael L.] Old Dominion Univ, Dept Comp Sci, Norfolk, VA 23529 USA. RP Klein, M (reprint author), Old Dominion Univ, Dept Comp Sci, Norfolk, VA 23529 USA. EM mklein@cs.odu.edu; mln@cs.odu.edu CR Davison BD, 2000, P 23 ANN INT ACM SIG, P272, DOI 10.1145/345508.345597 Dean J, 1999, COMPUT NETW, V31, P1467, DOI 10.1016/S1389-1286(99)00022-5 FRANZ A, ALL OUR N GRAM BELON GILPIN AR, 1993, EDUC PSYCHOL MEAS, V53, P87, DOI 10.1177/0013164493053001007 HARRISON TL, 2006, P HYPERTEXT 2006, P145, DOI 10.1145/1149941.1149971 HAWKING D, 2001, NIST SPECIAL PUBLICA, P87 Keller F, 2003, COMPUT LINGUIST, V29, P459, DOI 10.1162/089120103322711604 KLEIN M, 2008, P WIDM 2008 Klein M, 2008, LECT NOTES COMPUT SC, V5173, P371 Leech G., 2001, WORD FREQUENCIES WRI Milly Chiang W.-T., 2005, P WWW 2005, P938 NAKOV P, 2005, P RANLP 2005 Park ST, 2004, ACM T INFORM SYST, V22, P540, DOI 10.1145/1028099.1028101 PHELPS TA, UCBCSD001091 U CAL B SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soboroff I., 2002, SIGIR Forum, V36 STADDON J, 2007, USENIX SEC S Sugiyama K, 2003, P 14 ACM C HYP HYP H, P198 ZHU X, 2001, P INT C AC SPEECH SI, P533 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00957-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5478 BP 620 EP 627 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJG81 UT WOS:000265680800055 ER PT S AU Benbrahim, H Bramer, M AF Benbrahim, Houda Bramer, Max BE Bramer, M TI Text and Hypertext Categorization SO ARTIFICIAL INTELLIGENCE: AN INTERNATIONAL PERSPECTIVE SE Lecture Notes in Computer Science LA English DT Article; Book Chapter ID FEATURE-SELECTION; INFORMATION; CLASSIFICATION; RETRIEVAL AB Automatic categorization of text documents has become an important area of research in the last two decades, with features that make it significantly more difficult than the traditional classification tasks studied in machine learning. A more recent development is the need to classify hypertext documents, most notably web pages. These have features that add further complexity to the categorization task but also offer the possibility of using information that is not available in standard text classification, such as metadata and the content of the web pages that point to and are pointed at by a web page of interest. This chapter surveys the state of the art in text categorization and hypertext categorization, focussing particularly on issues of representation that differentiate them from 'conventional' classification tasks and from each other. C1 [Benbrahim, Houda] Ernst & Young LLP, London SE1 2AF, England. [Bramer, Max] Univ Portsmouth, Sch Comp, Portsmouth PO1 3HE, Hants, England. RP Benbrahim, H (reprint author), Ernst & Young LLP, 1 More London Pl, London SE1 2AF, England. EM hbenbrahim@uk.ey.com; max.bramer@port.ac.uk; max.bramer@port.ac.uk CR Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 ANDREWS A, 1971, THESIS U CAMBRIDGE APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Attardi G., 1999, P THAI 99 EUR S TEL, P105 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 BENBRAHIM H, 2004, IEEE INT C SYST MAN, V6 BENBRAHIM H, 2008, ARTIF INTELL, V2, P97 BENBRAHIM H, 2004, P 24 SGAI INT C INN, P258 BHARAT K, 1998, WWW7 COMPUTER NETWOR, V30, P379 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Bramer M., 2007, PRINCIPLES DATA MINI BUCKLEY C, 1995, TREC, V3, P500 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 CHAKRABARTI S, 2002, ENHANCED HYPERTEXT C Chakrabarti S, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P446 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Clack C., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267716 Cohen W. W., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining COHEN WW, 1998, C RES DEV INF RETR S, P307 CREECY RH, 1991, TRADING MIPS MEMORY DATTOLA RT, 1979, J AM SOC INF SCI, V30 DEHEER T, 1982, INFORM PROCESS MANAG, V18, P229, DOI 10.1016/0306-4573(82)90001-2 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 ESCUDERO G, 2000, CSCL0007010 ARXIV FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Furnkranz J, 1999, LECT NOTES COMPUT SC, V1642, P487 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 GALE WA, 1992, COMPUT HUMANITIES, V26, P415, DOI 10.1007/BF00136984 GRAY WA, 1971, INFORM STORAGE RET, V7, P167, DOI 10.1016/0020-0271(71)90002-7 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Ido Dagan, 1997, P 2 C EMP METH NAT L, P55 Ittner D. J., 1995, S DOC AN INF RETR LA, P301 Iyer R. D., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354794 Joachims T., 1996, PROBABILISTIC ANAL R JOACHIMS T, 1998, CATEGORIZATION SUPOR Kim Yu-Hwan, 2000, P 23 ANN INT ACM SIG, P168, DOI 10.1145/345508.345572 Koller D., 1997, P 14 INT C MACH LEAR, P170 Lam S. L. Y., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765752 Lam W., 1998, P 21 ANN INT ACM SIG, P81, DOI 10.1145/290941.290961 Lang K, 1995, P 12 INT C MACH LEAR, P331 Larkey Leah S., 1999, P DL 99 4 ACM C DIG, P179, DOI 10.1145/313238.313304 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Lovins J. B., 1968, DEV STEMMING ALGORIT LUHN HP, 1958, IBM J RES DEV, V2, P159 Manning C. D., 1999, FDN STAT NATURAL LAN MASAND B, 1994, SERIES COMPLEX ADAPT, P445 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P350 McGill M. J., 1983, INTRO MODERN INFORM MILLER G, 1998, WORDNET Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 MOULINIER I, 1996, P 5 ANN S DOC AN INF MOULINIER I, 1996, LNCS, V1040, P343 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354805 Oh H J, 2000, P 23 ANN INT ACM SIG, P264, DOI 10.1145/345508.345594 PETRARCA AE, 1969, P AM SOC INFORM SCI, V6, P277 Pierre J. M., 2000, EL P ECDL 2000 WORKS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Ruiz M. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312700 Sable C. L., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007990000038 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 SEBASTIANI F, 2000, IMPROVED BOOSTING AL SINKA MP, 2002, SOFT COMPUTING SYSTE, V87, P881 SJ C, 1992, COMMUN ACM, V35, P48 SLATTERY S, 2000, P ICML Slonim N, 2001, P ECIR 01 23 EUR C I Taira H., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Tauritz DR, 2000, INFORM SCIENCES, V122, P121, DOI 10.1016/S0020-0255(99)00123-1 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 Vapnik V., 1995, NATURE STAT LEARNING WAI LAM, 1997, P 15 INT JOINT C ART, P745 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1997, P 14 INT C MACH LEAR Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 98 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-03225-7 J9 LECT NOTES COMPUT SC PY 2009 VL 5640 BP 11 EP 38 D2 10.1007/978-3-642-03226-4 PG 28 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BKQ96 UT WOS:000268976000002 ER PT S AU Chen, WQ Persen, R AF Chen, Weiqin Persen, Ricard BE Dimitrova, V Mizoguchi, R DuBoulay, B Graesser, A TI A Recommender System for Collaborative Knowledge SO ARTIFICIAL INTELLIGENCE IN EDUCATION: BUILDING LEARNING SYSTEMS THAT CARE: FROM KNOWLEDGE REPRESENTATION TO AFFECTIVE MODELLING SE Frontiers in Artificial Intelligence and Applications LA English DT Proceedings Paper CT 14th International Conference on Artificial Intelligence in Education (AIED 2009) CY JUL 06-10, 2009 CL Brighton, ENGLAND SP Int Artificial Intelligence Educ Soc, Natl Sci Fdn, Alelo, Amer Assoc Artificial Intelligence, Univ Sussex, Sch Sci & Technol, Univ Memphis, Inst Intelligent Syst, Univ Leeds, Sch Comp DE Recommender; learning objects; domain model; collaborative knowledge ID VECTOR-SPACE MODEL AB To provide recommendations of learning objects and adaptive learning support has been one of the main areas within the AIED community. Learner-generated material could potentially be valuable as learning resources. How to identify relevant learner-generated resources and provide just-in-time recommendations to the learner is a challenging task. This paper presents a recommender system that can recommend collaborative knowledge constructed and accumulated by previous learners. C1 [Chen, Weiqin; Persen, Ricard] Univ Bergen, Dept Informat Sci & Media Studies, N-5020 Bergen, Norway. CR Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 BRUNS A, 2005, 2005 INT S WIK SAN D, P25 Brushe GD, 2001, DIGIT SIGNAL PROCESS, V11, P110, DOI 10.1006/dspr.2001.0389 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chen GD, 2008, COMPUT EDUC, V50, P77, DOI 10.1016/j.compedu.2006.03.004 CHEN W, 2008, 3 EUR C TECHN ENH LE, P92 Cheng CK, 2004, LECT NOTES COMPUT SC, V3094, P120 Craven M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Garrison D. R., 2000, INTERNET HIGH EDUC, V2, P87, DOI DOI 10.1016/S1096-7516(00)00016-6 Hanauer DI, 2006, SCIENCE, V314, P1880, DOI 10.1126/science.1136796 Helic D., 2004, ADV TECHNOLOGY LEARN, V1, P8 Muukkonen H., 1999, P COMP SUPP COLL LEA, P406 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scardamalia M., 2003, ENCY ED Singhal A., 2001, B IEEE COMPUTER SOC, P24 Tang T., 2005, INT J E LEARNING, V4, P105 NR 16 TC 1 Z9 1 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-60750-028-5 J9 FR ART INT PY 2009 VL 200 BP 309 EP 316 DI 10.3233/978-1-60750-028-5-309 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA BMS76 UT WOS:000273499000037 ER PT S AU Bruno, E Faessel, N Le Maitre, J Scholl, M AF Bruno, Emmanuel Faessel, Nicolas Le Maitre, Jacques Scholl, Michel GP IEEE TI BlockWeb: an IR Model for Block Structured Web Pages SO CBMI: 2009 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING SE International Workshop on Content-Based Multimedia Indexing LA English DT Proceedings Paper CT International Workshop on Content-Based Multimedia Indexing CY JUN 03-05, 2009 CL Chania, GREECE SP IEEE, European Assoc Signal Proc, Inst Commun Comp Syst, IEEE Circuits & Syst Soc Syst, Weknowit AB BlockWeb is a model that we have developed for indexing and querying web pages according to their content as well as to their visual rendering. These pages are split up into blocks what has several advantages in terms of page indexing and querying: (i) blocks of a page most similar to a query may be returned instead of the page as a whole (ii) the importance of a block can be taken into account, as well as (iii) the permeability of the blocks to the content of neighbor blocks. In this paper, we present the BlockWeb model and show its interest for indexing images of Web pages, through an experiment performed on electronic versions of French daily newspapers. We also present the engine we have implemented for block extraction, indexing and querying according to the BlockWeb model. C1 [Bruno, Emmanuel; Le Maitre, Jacques] Univ Sud Toulon Var, CNRS, LSIS UMR 6168, F-83957 La Garde, France. RP Bruno, E (reprint author), Univ Sud Toulon Var, CNRS, LSIS UMR 6168, BP 20132, F-83957 La Garde, France. EM bruno@univ-tln.fr; nicolas.faessel@lsis.org; lemaitre@univ-tln.fr; michel.scholl@cnam.fr CR BRUNO E, 2008, LSISRR2008004 CAI D, 2003, VIPS VISION BASED SE CROUCH CJ, 2005, P 4 WORKSH INITIATIV, P268 CUI H, 2003, P 25 EUR C IR RES, P73 Debnath S, 2005, IEEE T KNOWL DATA EN, V17, P1233, DOI 10.1109/TKDE.2005.138 Lin Shian-Hua, 2002, P 8 ACM SIGKDD INT C, P588, DOI DOI 10.1145/775047.775134 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simons K, 2005, P 14 ACM INT C INF K, P381, DOI DOI 10.1145/1099554.1099672 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 TOLLARI S, 2007, P ACM INT C IM VID R Yi L., 2003, P 9 ACM SIGKDD INT C, P296 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1949-3983 BN 978-1-4244-4265-2 J9 INT WORK CONTENT MUL PY 2009 BP 219 EP 224 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BND88 UT WOS:000274243500038 ER PT S AU Niu, L Lu, J Zhang, G AF Niu, L Lu, J Zhang, G TI Cognition-Driven Decision Support for Business Intelligence: Models, Techniques, Systems and Applications SO COGNITION-DRIVEN DECISION SUPPORT FOR BUSINESS INTELLIGENCE: MODELS, TECHNIQUES, SYSTEMS AND APPLICATIONS SE Studies in Computational Intelligence LA English DT Book ID MULTIPLE-CRITERIA DECISION; EVOLUTIONARY PROGRAMMING TECHNIQUES; NATURAL-LANGUAGE INTERFACE; DATA ENVELOPMENT ANALYSIS; FUZZY COGNITIVE MAP; VECTOR-SPACE MODEL; TRENCH CAP DESIGNS; R-AND-D; SITUATION AWARENESS; EXPERT-SYSTEMS CR AAMODT A, 1994, AI COMMUN, V7, P39 ACKERMANN F, 1992, 7 YOUNG OR C U WARW Adler M, 1996, GAZING ORACLE DELPHI Agrell PJ, 1995, INT J PROD ECON, V41, P59, DOI 10.1016/0925-5273(94)00054-9 ALAM DM, 2009, IMPLEMENTATION ONTOL Alavi M., 1989, Information Society, V6 Alter S. L, 1980, DECISION SUPPORT SYS Amgoud L, 2006, P 21 NAT C ART INT A, P219 Amgoud L, 2006, LECT NOTES COMPUT SC, V4049, P264 Amgoud L, 2005, INT J INTELL SYST, V20, P1195, DOI 10.1002/int.20109 Amgoud L, 2005, LECT NOTES COMPUT SC, V3571, P269 AMOTT D, 2005, J INF TECHNOL, V20, P67 ANAMARIA P, 2003, P 8 INT C INT US INT Anandalingam G., 1992, Annals of Operations Research, V34, DOI 10.1007/BF02098169 Androutsopoulos I., 1995, J LANGUAGE ENG, V1, P29 ANGEHRN AA, 1994, DECIS SUPPORT SYST, V12, P267, DOI 10.1016/0167-9236(94)90045-0 Anthony R. N., 1965, PLANNING CONTROL SYS ANTUNES CH, 1994, DECIS SUPPORT SYST, V12, P327 Ba SL, 1997, DECIS SUPPORT SYST, V19, P171, DOI 10.1016/S0167-9236(96)00055-3 BAHRICK HP, 1975, J EXP PSYCHOL GEN, V104, P54, DOI 10.1037//0096-3445.104.1.54 Ballou R. H., 2004, BUSINESS LOGISTICS S BARCLAY S, 1988, USERS MANUAL EQUITY BARCLAY S, 1987, USERS MANUAL HIVIEW BARNES JH, 1984, STRATEGIC MANAGE J, V5, P129, DOI 10.1002/smj.4250050204 Barr SH, 1997, DECIS SUPPORT SYST, V21, P133, DOI 10.1016/S0167-9236(97)00021-3 Baum E.B., 2004, WHAT IS THOUGHT Bazerman M. H., 2005, JUDGMENT MANAGERIAL BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 Bellinger G, 2004, DATA INFORM KNOWLEDG BELTON V, 1989, VISUAL INTERACTIVE S Benayoun R, 1971, MATH PROGRAM, V1, P366, DOI 10.1007/BF01584098 Bergmann R., 2002, EXPERIENCE MANAGEMEN Berry M. J. A., 1997, DATA MINING TECHNIQU Bhargava HK, 1997, DECIS SUPPORT SYST, V19, P193, DOI 10.1016/S0167-9236(96)00056-5 BIDGOLI H, 1989, DECISION SUPPORT SYS BOIS PD, 1989, EUR J OPER RES, V39, P284 BONCZEK R H, 1981, FDN DECISION SUPPORT Borenstein D, 1998, J OPER RES SOC, V49, P734, DOI 10.1057/palgrave.jors.2600545 Bose U, 1997, OMEGA-INT J MANAGE S, V25, P691, DOI 10.1016/S0305-0483(97)00040-6 BOUCHONMEUNIER B, 2000, INFORM UNCERTAINTY F Bouchon-Meunier B., 2000, UNCERTAINTY INTELLIG BOUFADEN N, 2003, 41 ANN M ASS COMP LI BRITO MPD, 2002, 290 ERASM U ROTT EC Bubnicki Z, 2002, UNCERTAIN LOGICS VAR Budgen D., 1988, Knowledge-Based Systems, V1, DOI 10.1016/0950-7051(88)90034-2 BUI T, 1989, CO OP GROUP DECISION BUI TX, 1990, EUR J OPER RES, V46, P141, DOI 10.1016/0377-2217(90)90127-W *BUSINESSOBJECTS, 2008, BUSINESSOBJECTS POL CALPINE HC, 1976, OMEGA-INT J MANAGE S, V4, P141, DOI 10.1016/0305-0483(76)90054-2 CARLEY K, 1992, SOC FORCES, V70, P601, DOI 10.2307/2579746 Carlsson C., 1996, INT PAN C SOFT INT C CASERTA J, 2004, PRACTICAL TECHNIQUES Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Chan FTS, 2000, J MATER PROCESS TECH, V107, P330, DOI 10.1016/S0924-0136(00)00679-8 Chankong V., 1983, MULTIOBJECTIVE DECIS Charnes A., 1961, MANAGEMENT MODELS IN, VI Charnes A., 1961, MANAGEMENT MODELS IN, V2 Charnes A., 1977, EUR J OPL RES, V1, P39, DOI DOI 10.1016/S0377-2217(77)81007-2 Chaudhry SS, 1996, COMPUT OPER RES, V23, P63, DOI 10.1016/0305-0548(95)00004-6 Chen JQ, 2003, DECIS SUPPORT SYST, V36, P147, DOI 10.1016/S0167-9236(02)00139-2 Chen T., 1987, DECISION ANAL Chen Z., 2000, COMPUTATIONAL INTELL Cheng CH, 1999, EUR J OPER RES, V116, P423, DOI 10.1016/S0377-2217(98)00156-8 Chidambaram L, 1996, MIS QUART, V20, P143, DOI 10.2307/249476 Chuang TT, 1998, DECIS SUPPORT SYST, V24, P73, DOI 10.1016/S0167-9236(98)00065-7 Clarke SJ, 1997, ASLIB PROC, V49, P184, DOI 10.1108/eb051463 Codd E., 1993, PROVIDING OLAP ON LI, P32 Cohon J.L., 1978, MULTIOBJECTIVE PROGR COLSON G, 1994, DECIS SUPPORT SYST, V12, P391, DOI 10.1016/0167-9236(94)90055-8 CONNELL NAD, 1990, J OPER RES SOC, V41, P431 COOK WD, 1985, MANAGE SCI, V31, P26, DOI 10.1287/mnsc.31.1.26 Courtney JF, 2001, DECIS SUPPORT SYST, V31, P17, DOI 10.1016/S0167-9236(00)00117-2 CSAKI P, 1995, DECIS SUPPORT SYST, V14, P205, DOI 10.1016/0167-9236(94)00017-M *CSIRO, 2008, BUS INT WHAT IS IT Daily B, 1996, INFORM MANAGE, V30, P281, DOI 10.1016/S0378-7206(96)01062-2 Damousis IG, 2003, IEEE T POWER SYST, V18, P198, DOI 10.1109/TPWRS.2002.807115 Davey A, 1998, GROUP DECIS NEGOT, V7, P55, DOI 10.1023/A:1008675230233 DAWES RM, 1964, J ABNORM SOC PSYCH, V68, P104, DOI 10.1037/h0047832 Dawes RM, 1988, RATIONAL CHOICE UNCE DEGOULET P, 1995, METHOD INFORM MED, V34, P202 Dennis AR, 1996, MIS QUART, V20, P433, DOI 10.2307/249563 DESPRES S, 1992, EUR J OPER RES, V61, P145, DOI 10.1016/0377-2217(92)90276-F Ditsa G., 2003, INFORM MANAGEMENT SU, P109 Donaldson G., 1983, DECISION MAKING TOP Downing CE, 2002, DECIS SUPPORT SYST, V33, P363, DOI 10.1016/S0167-9236(02)00011-8 Dubois D, 2002, J ACM, V49, P455, DOI 10.1145/581771.581772 Dubois D, 2003, INT J INTELL SYST, V18, P751, DOI 10.1002/int.10114 DUBOIS D, 2004, PLANNING BASED DECIS, P3 Dubois D, 2006, J INTELL INF SYST, V27, P95, DOI 10.1007/s10844-006-0976-x Dubois D, 2005, EUR J OPER RES, V160, P582, DOI 10.1016/j.ejor.2003.10.002 Dubois D, 2003, ARTIF INTELL, V148, P219, DOI 10.1016/S0004-3702(03)00037-7 Dubois D, 1999, EUR J OPER RES, V118, P95, DOI 10.1016/S0377-2217(98)00307-5 Dunham M.H., 2003, DATA MINING DYER JS, 1972, MANAGE SCI, V19, P62, DOI 10.1287/mnsc.19.1.62 *ECONOMISTINTELLIG, 2006, WHAT DO CO WANT BUS EDEN C, 1988, EUR J OPER RES, V36, P1, DOI 10.1016/0377-2217(88)90002-1 Eden C, 1996, GROUP DECIS NEGOT, V5, P501 EDWARDS JS, 1992, EUR J OPER RES, V61, P114, DOI 10.1016/0377-2217(92)90273-C EMERSON TJ, 1987, 871803 SAE Endsley M. R., 2003, DESIGNING SITUATION ENDSLEY MR, 1995, HUM FACTORS, V37, P32, DOI 10.1518/001872095779049543 ENDSLEY MR, 1995, HUM FACTORS, V37, P65, DOI 10.1518/001872095779049499 Endsley MR, 2000, SITUATION AWARENESS Engemann K. J., 2004, International Journal of Technology, Policy and Management, V4, DOI 10.1504/IJTPM.2004.006617 Eom SB, 1998, EUR J OPER RES, V104, P31, DOI 10.1016/S0377-2217(96)00331-1 ER MC, 1995, DECIS SUPPORT SYST, V14, P75, DOI 10.1016/0167-9236(94)00013-I Ertay T, 2006, INFORM SCIENCES, V176, P237, DOI 10.1016/j.ins.2004.12.001 Ertay T, 2005, EUR J OPER RES, V164, P800, DOI 10.1016/j.ejor.2004.01.038 EVELSON B, 2008, FORRESTER WAVE ENTER, pQ3 *EXP CHOIC INC, 1992, EXP CHOIC WALK THROU FARMER TA, 1987, DECISION SCI, V18, P178, DOI 10.1111/j.1540-5915.1987.tb01515.x Fetzer JH, 1998, J EXP THEOR ARTIF IN, V10, P371, DOI 10.1080/095281398146653 FINLAY PN, 1990, COMPUT OPER RES, V17, P535, DOI 10.1016/0305-0548(90)90059-G Giaglis GM, 1999, INT J ELECTRON COMM, V3, P35 Giordani A, 2008, LECT NOTES COMPUT SC, V5039, P367 Giuntini R., 1995, TRANSPORTATION DISTR, V36, P93 Giuntini R., 1995, TRANSPORTATION DIS 3, V36, P97 Gnyawali DR, 2005, RES METHOD STRAT MAN, V2, P225, DOI 10.1016/S1479-8387(05)02010-2 Gomez-Perez A, 2004, ONTOLOGICAL ENG Gooley T. B., 1998, LOGISTICS MANAGEMENT, V37, P49 Gordon L. A., 1984, IMPROVING CAPITAL BU Gorry G.A., 1971, SLOAN MANAGE REV, V13, P50 GORRY GA, 1971, SLOAN MANAGE REV, V13, P55 GOTTINGER HW, 1992, DECIS SUPPORT SYST, V8, P317, DOI 10.1016/0167-9236(92)90053-R Gray P, 1999, GROUP DECIS NEGOT, V8, P77, DOI 10.1023/A:1008615812104 Gray P., 1987, Decision Support Systems, V3, DOI 10.1016/0167-9236(87)90178-3 GRUMAN G, 2007, INFOWORLD, V29, P22 Guarino N., 1998, 1 INT C FORM ONT INF GUERLAIN S, 2000, P IEEE C SYST MAN CY, P1934 Guyonnet D, 2003, J ENVIRON ENG-ASCE, V129, P68, DOI 10.1061/(ASCE)0733-9372(2003)129:1(68) HAMILTON WL, 1987, 871767 SAE Haseman WD, 2005, INFORM MANAGE-AMSTER, V42, P591, DOI 10.1016/j.im.2004.04.001 Herrera F, 2000, EUR J OPER RES, V120, P144, DOI 10.1016/S0377-2217(98)00383-X HIPEL KW, 1992, WATER RESOUR BULL, V28, P3 HOFFMAN J, 2001, INTRO STRUCTURED QUE Holsapple CW, 1996, DECISION SUPPORT SYS HOVEN JVD, 1996, J SYST MANAGE, V47, P48 Hu TL, 2002, TRANSPORT RES E-LOG, V38, P457, DOI 10.1016/S1366-5545(02)00020-0 Huang C., 1995, MULTIPLE ATTRIBUTE D Hwang CL, 1981, MULTIPLE ATTRIBUTE D Hwang C.-L., 1979, MULTIPLE OBJECTIVE D HWANG CL, 1993, COMPUT OPER RES, V20, P889, DOI 10.1016/0305-0548(93)90109-V IGNIZIO JP, 1983, COMPUT OPER RES, V10, P277, DOI 10.1016/0305-0548(83)90003-5 Ignizio J. P., 1976, GOAL PROGRAMMING EXT IGNIZIO JP, 1981, COMPUT OPER RES, V8, P9, DOI 10.1016/0305-0548(81)90027-7 Ingwersen P., 1996, INFORM RETRIEVAL INT INMON WH, 1993, PRISM NEWSLETTER, P1 Inmon W.H., 2002, BUILDING DATA WAREHO, VThird Isenberg D.J., 1984, HARVARD BUSINESS NOV, P82 IZ PH, 1992, EUR J OPER RES, V61, P245, DOI 10.1016/0377-2217(92)90285-H Iz P.H., 1993, GROUP DECIS NEGOT, V2, P61, DOI 10.1007/BF01384403 JACOB VS, 1992, INT J MAN MACH STUD, V36, P817, DOI 10.1016/0020-7373(92)90074-U Jaques E., 1976, GEN THEORY BUREAUCRA Jaques E., 1996, REQUISITE ORG Jayabarathi T, 2005, ELECTR POW SYST RES, V73, P169, DOI 10.1016/j.epsr.2004.08.001 Jelassi M. T., 1985, Journal of Management Information Systems, V1 JELASSI MT, 1987, INFORM MANAGE, V13, P143, DOI 10.1016/0378-7206(87)90022-X Jensen P. A., 2003, OPERATIONS RES MODEL Johnson-Laird P. N., 1998, SISTEMI INTELLIGENTI, V9, P68 JUHANI I, 2005, SIGMIS DATABASE, V36, P8 JUNG H, 2002, 19 INT C COMP LING T KACPRZYK J, 1992, FUZZY SET SYST, V49, P21, DOI 10.1016/0165-0114(92)90107-F Kamber M., 2001, DATA MINING CONCEPTS Kasper GM, 1996, INFORM SYST RES, V7, P215, DOI 10.1287/isre.7.2.215 KEEN PGW, 1976, SLOAN MANAGE REV, V18, P1 Keen P.G.W., 1978, DECISION SUPPORT SYS Kepner C., 1973, PROBLEM ANAL DECISIO KERSTEN GE, 1985, INFORM MANAGE, V8, P237, DOI 10.1016/0378-7206(85)90001-1 KLEIN GA, 1989, IEEE T SYST MAN CYB, V19, P462, DOI 10.1109/21.31053 Klein M. R., 1995, KNOWLEDGE BASED DECI KLIMBERG R, 1992, COMPUT OPER RES, V19, P707, DOI 10.1016/0305-0548(92)90039-8 Knoblock C.A., 2000, IEEE DATA ENG B, V23, P33 KOLODNER JL, 1991, AI MAG, V12, P52 Konar A, 2005, INFORM SCIENCES, V170, P419, DOI 10.1016/j.ins.2004.03.012 Korhonen P., 1990, Decision Support Systems, V6, DOI 10.1016/0167-9236(90)90017-L KORHONEN P, 1991, LECT NOTES EC MATH S, V356 KOSKO B, 1986, INT J MAN MACH STUD, V24, P65, DOI 10.1016/S0020-7373(86)80040-2 Kuo FY, 1998, DECIS SUPPORT SYST, V24, P89, DOI 10.1016/S0167-9236(98)00056-6 LAGREZE EJ, 1984, EUR J OPER RES, V16, P48 LAHTI RK, GROUP DECISION MAKIN LAI YJ, 1995, J OPER RES SOC, V46, P958, DOI 10.2307/3009907 LAI YJ, 1994, EUR J OPER RES, V76, P486, DOI 10.1016/0377-2217(94)90282-8 Lambert DM, 2000, IND MARKET MANAG, V29, P65, DOI 10.1016/S0019-8501(99)00113-3 LANGFIELD-SMITH K, 1992, J OPER RES SOC, V43, P1135, DOI 10.2307/2584270 Leacock C, 1998, WORDNET ELECT LEXICA, P265 Lee K. C., 1997, INTELLIGENT SYSTEMS, V6, P41, DOI 10.1002/(SICI)1099-1174(199703)6:1<41::AID-ISAF119>3.0.CO;2-J Lee M, 1999, EUR J OPER RES, V118, P375, DOI 10.1016/S0377-2217(98)00320-8 Lee S. M., 1972, GOAL PROGRAMMING DEC Li YY, 2006, LECT NOTES COMPUT SC, V3896, P737 Liberatore MJ, 2003, DECISION TECHNOLOGY Lin T. Y., 2002, DATA MINING ROUGH SE Lipshitz R, 2001, J BEHAV DECIS MAKING, V14, P331, DOI 10.1002/bdm.381 Liu RX, 2002, GENOME RES, V12, P462, DOI 10.1101/gr.198002 Lu J, 2003, INT J SERV IND MANAG, V14, P570, DOI 10.1108/09564230310500237 LU J, 2003, J INFORM SYSTEMS FRO, V5, P265 LU J, 2001, P 12 AUSTR C INF SYS, P378 Lu J., 2004, ELECTRON COMMER RES, V4, P221, DOI 10.1023/B:ELEC.0000027981.81945.2a LU J, 2002, P 9 EUR C IT EV PAR, P273 LU J, 2003, P WORKSH APPL PROD S, P7 LU J, 2006, E SERVICE INTELLIGEN Lu J., 2001, NZ J APPL COMPUTER I, V5, P53 LU J, 2000, P 16 WORLD COMP C, P744 Lu J., 2007, MULTIOBJECTIVE GROUP LU J, 2000, THESIS CURTIN U TECH Lu J., 2005, IEEE INTELLIGENT INF, V5, P35 Lu J, 2001, ELECT MARKETS INT J, V11, P107 Lu JJ, 2006, ACTA PETROL SIN, V22, P397 Lycan W. G., 2008, MIND COGNITION ANTHO LYLES MA, 1988, J MANAGE STUD, V25, P131, DOI 10.1111/j.1467-6486.1988.tb00028.x MACFADYEN H, 2007, PARTS SPEECH Mallach E. G., 2000, DECISION SUPPORT DAT MANAGEMENT AFN, GATHERING PERCEPTION March J. G., 1963, ORGANIZATIONS MATHEUS CJ, 2003, 6 INT C INF FUS CAIR Maynard D., 2005, ESWC WORKSH END US A MCBRIDE N, 1997, INFORMATION SYSTEMS, V7, P4 MCCARTHY J, 1986, ARTIF INTELL, V28, P89, DOI 10.1016/0004-3702(86)90032-9 MCGUINNESS D, 2001, IEEE INTELL SYST APP, V16, P8 *MICR, 2007, TRANS SQL REF *MICR, 2007, MULT EXPR MDX REF *MICR, 2007, ADV WORKS CYCL BUS S MINCH RP, 1986, DECISION SCI, V17, P395, DOI 10.1111/j.1540-5915.1986.tb00233.x MINTZBERG H, 1976, ADMIN SCI QUART, V21, P246, DOI 10.2307/2392045 MISSIKOFF M, 2005, METHOD ONTOLOGY MODE Mitra S., 2003, DATA MINING MULTIMED Moore C. M., 1987, GROUP TECHNIQUES IDE Moreau EMF, 2006, DECIS SUPPORT SYST, V42, P593, DOI 10.1016/j.dss.2005.02.008 MOREHOUSE B, GROUP SYSTEMS CORPOR NAZARETH DL, 1993, INFOR, V31, P1 NELDER JA, 1965, COMPUT J, V7, P308 Newby GB, 2001, J AM SOC INF SCI TEC, V52, P1026, DOI 10.1002/asi.1172 NICULAE S, 2005, DATA KNOWL ENG, V55, P4 NIU L, 2007, 1 INT C RISK AN CRIS NIU L, 2008, 2008 IEEE C EV COMP NIU L, 2008, 2008 IEE WIC ACM INT Noh JB, 2000, EXPERT SYST APPL, V19, P249, DOI 10.1016/S0957-4174(00)00037-3 OGRYCZAK W, 1989, EUR J OPER RES, V43, P53, DOI 10.1016/0377-2217(89)90409-8 O'Keefe RM, 1998, COMMUN ACM, V41, P71, DOI 10.1145/272287.272300 OWENS HD, 1995, DECIS SUPPORT SYST, V13, P167, DOI 10.1016/0167-9236(93)E0039-G Paige GB, 1996, J ENVIRON QUAL, V25, P136 Paige GB, 1996, J ENVIRON QUAL, V25, P127 Pasi G, 2006, INFORM SCIENCES, V176, P390, DOI 10.1016/j.ins.2005.07.006 Rangel RAP, 2005, LECT NOTES ARTIF INT, V3789, P833 Plous S., 1993, PSYCHOL JUDGMENT DEC POGORELEC J, 1998, FRONTLINE SOLUTIONS, V1, P68 POH KL, 1990, J OPER RES SOC, V41, P1037, DOI 10.1057/jors.1990.162 POH K, 1995, OR APPL SINGAPORE, P25 Poh KL, 1998, ARTIF INTELL ENG, V12, P315, DOI 10.1016/S0954-1810(97)10001-2 Pohl RF, 2004, COGNITIVE ILLUSIONS: A HANDBOOK ON FALLACIES AND BIASES IN THINKING, JUDGEMENT AND MEMORY, P1 PORAC JF, 1990, ACAD MANAGE REV, V15, P224, DOI 10.2307/258155 Power D. J., 2002, Studies in Informatics and Control, V11 Power D. J., 2002, DECISION SUPPORT SYS POWER DJ, 1999, DECISION SUPPORT SYS QUADDUS MA, 1998, OPTIMIZATION TECHNIQ, P471 Quaddus MA, 1997, COMPUT EDUC, V28, P103, DOI 10.1016/S0360-1315(97)00002-X QUADDUS MA, 1986, IEEE T SYST MAN CYB, V16, P462, DOI 10.1109/TSMC.1986.4308979 RADERMACHER FJ, 1994, DECIS SUPPORT SYST, V12, P257, DOI 10.1016/0167-9236(94)90044-2 CARLSON DA, 1990, COMMUN ACM, V33, P311 RAO HR, 1994, DECIS SUPPORT SYST, V12, P79, DOI 10.1016/0167-9236(94)90075-2 RAO HR, 1988, INTERFACES, V18, P80, DOI 10.1287/inte.18.6.80 Reinartz T, 2001, COMPUT INTELL-US, V17, P214, DOI 10.1111/0824-7935.00141 Reisbeck CK, 1989, INSIDE CASE BASED RE REISMAN S, 1992, DECIS SUPPORT SYST, V8, P169, DOI 10.1016/0167-9236(92)90007-C REITER R, 1980, ARTIF INTELL, V13, P81, DOI 10.1016/0004-3702(80)90014-4 RESNICK ML, 2003, HUM FACT ERG SOC 47 RICE D, 2002, CHALLENGES SERVICES, V1, P1 Richardson HL, 2001, IND WEEK, V250, P37 Rocha C., 2004, P 13 INT C WORLD WID, P374, DOI 10.1145/988672.988723 Rogers D.S., 2002, INT J LOGIST MANAG, V13, P1, DOI 10.1108/09574090210806397 ROTH D, 2004, INTRO SYNTACTIC PARS Rouse W. B., 1985, LOOKING BLACK BOX PR Rouse WB, 2002, IEEE T SYST MAN CY C, V32, P282, DOI 10.1109/TSMCC.2002.806785 Rowley J., 2000, LOGISTICS TRANSPORT, V2, P27 Roy B., 1971, MATHEMATICAL PROGRAM, V1, P239, DOI 10.1007/BF01584088 Ruan D., 2003, INT J LOGISTICS INFO, V16, P401 Rughooputh H.C.S., 2003, IND TECHNOLOGY, V1, P48 Russo E., 1990, DECISION TRAPS 10 BA Saaty T.L., 1980, ANAL HIERARCHY PROCE Sage A.P., 1991, DECISION SUPPORT SYS SAKAWA M, 1982, EUR J OPER RES, V9, P386, DOI 10.1016/0377-2217(82)90183-7 SALAS E, 1995, HUM FACTORS, V37, P123, DOI 10.1518/001872095779049525 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salukvadze M, 1974, J OPTIMIZ THEORY APP, V13, P203, DOI 10.1007/BF00935540 Sarter N.B., 1991, INT J AVIAT PSYCHOL, V1, P45, DOI 10.1207/s15327108ijap0101_4 Sauter V., 1997, DECISION SUPPORT SYS Schickel-Zuber V., 2007, INT JOINT C ART INT Schmitt N., 1997, NATURALISTIC DECISIO SCHUBERT P, 2002, P 35 HAW INT C SYS S, P175 Schwartz A. E., 1994, CPA J, V64, P60 Senge P., 1990, 5 DISCIPLINE Shafer G., 1976, MATH THEORY EVIDENCE Shan F, 1996, EXPERT SYST APPL, V10, P157, DOI 10.1016/0957-4174(95)00042-9 Shim JP, 2002, DECIS SUPPORT SYST, V33, P111, DOI 10.1016/S0167-9236(01)00139-7 Shin W. S., 1991, Computers & Operations Research, V18, DOI 10.1016/0305-0548(91)90046-T Siau K, 2005, DATA KNOWL ENG, V55, P343, DOI 10.1016/j.datak.2004.12.006 *SIGNATEXT, 2008, REP BUILD Sikder I.U., 2002, INFORM RESOURCES MAN, V15, P33, DOI 10.4018/irmj.2002100103 Simon H., 1977, NEW SCI MANAGEMENT D SIMON HA, 1979, AM ECON REV, V69, P493 Singh DT, 1998, DECIS SUPPORT SYST, V24, P145, DOI 10.1016/S0167-9236(98)00066-9 Singh SK, 2002, DECIS SUPPORT SYST, V33, P71, DOI 10.1016/S0167-9236(01)00129-4 SOL HG, 1985, EUR J OPER RES, V22, P1, DOI 10.1016/0377-2217(85)90109-2 Sol H. G., 1987, Decision Support Systems, V3, DOI 10.1016/0167-9236(87)90175-8 Sonnenwald DH, 2004, INFORM PROCESS MANAG, V40, P989, DOI 10.1016/j.ipm.2003.10.002 SPANGLER W, 1991, IEEE T KNOWL DATA EN, V3, P145 Sprague RH, 1980, MIS QUART, V4, P1, DOI 10.2307/248957 Sprague R. H. Jr., 1987, Decision Support Systems, V3, DOI 10.1016/0167-9236(87)90174-6 Sprague R.H., 1993, DECISION SUPPORT SYS Sridhar S, 1998, DECIS SUPPORT SYST, V23, P19, DOI 10.1016/S0167-9236(98)00033-5 Stanners M., 2005, EMPIRICAL STUDY RELA STERNBERG RJ, 2006, COGNITIVE PSYCHOL BE Steuer R., 1977, TIMS STUDIES MANAGEM, V6, P225 STEUER RE, 1983, MATH PROGRAM, V26, P326, DOI 10.1007/BF02591870 Steuer RE, 1986, MULTIPLE CRITERIA OP STEWART TJ, 1991, J OPER RES SOC, V42, P17, DOI 10.1057/jors.1991.3 Stylios CD, 2008, APPL SOFT COMPUT, V8, P1243, DOI 10.1016/j.asoc.2007.02.022 Sun MH, 1996, EUR J OPER RES, V89, P462, DOI 10.1016/0377-2217(94)00228-2 Sutcliffe KM, 2003, HARVARD BUS REV, V81, P74 Tecle A, 1998, GROUP DECIS NEGOT, V7, P23, DOI 10.1023/A:1008671129325 TECLE A, 1992, PROCEDURE SELECTING TEENI D, 1991, EUR J OPER RES, V50, P127, DOI 10.1016/0377-2217(91)90236-O Temperley D., 1993, 3 INT WORKSH PARS TE Teodorescu H, 2000, INTELLIGENT SYSTEMS Theodorson G. A., 1969, MODERN DICT SOCIOLOG TRAVICA B, 1995, INT J INFORM MANAGE, V15, P223, DOI 10.1016/0268-4012(95)00014-X TURBAN E, 1996, P 1996 IEEE INT C SY TURBAN E., 1998, DECISION SUPPORT SYS Turban E, 2005, DECISION SUPPORT SYS, V7th Uschold M, 1998, KNOWL ENG REV, V13, P31, DOI 10.1017/S0269888998001088 Venkatachalam AR, 1999, J FORECASTING, V18, P167, DOI 10.1002/(SICI)1099-131X(199905)18:3<167::AID-FOR715>3.0.CO;2-F Venkatesh P, 2003, IEEE T POWER SYST, V18, P688, DOI 10.1109/TPWRS.2003.811008 VETSCHERA R, 1994, DECIS SUPPORT SYST, V11, P363, DOI 10.1016/0167-9236(94)90081-7 VIDULICH MA, 1995, INT C EXP AN MEAS SI VOGEL D, 1990, INFORM MANAGE, V18, P15, DOI 10.1016/0378-7206(90)90060-U Voogd H, 1983, MULTICRITERIA EVALUA VOSS JF, 1988, NATURE EXPERTISE, P271 Walters BA, 2003, INFORM MANAGE-AMSTER, V40, P487, DOI 10.1016/S0378-7206(02)00063-0 WANG HF, 1989, IEEE T SYST MAN CYB, V19, P143, DOI 10.1109/21.24546 Wang KJ, 2003, ROBOT CIM-INT MANUF, V19, P65, DOI 10.1016/S0736-5845(02)00063-7 Watson S. R., 1987, DECISION SYNTHESIS P Weck M, 1997, EUR J OPER RES, V100, P351, DOI 10.1016/S0377-2217(96)00295-0 WEISTROFFER HR, 1983, COMPUT OPER RES, V10, P311, DOI 10.1016/0305-0548(83)90006-0 WELTER TR, 1988, IND WEEK, V237, P41 *WHO DEP COMM DIS, 2004, WHO GUID GLOB SURV S Wong Z, 2003, INFORM MANAGE-AMSTER, V41, P125, DOI 10.1016/S0378-7206(03)00042-9 WOODS WA, 1973, AFIPS C 1973 ARL YADAV SB, 1992, DECIS SUPPORT SYST, V8, P55, DOI 10.1016/0167-9236(92)90037-P Yager R. R., 1999, International Journal of Intelligent Systems in Accounting, Finance and Management, V8, DOI 10.1002/(SICI)1099-1174(199906)8:2<131::AID-ISAF161>3.0.CO;2-0 Yager RR, 2004, INFORM SCIENCES, V163, P175, DOI 10.1016/j.ins.2003.03.018 Yager RR, 2002, INT J INTELL SYST, V17, P687, DOI 10.1002/int.10044 Yager RR, 1999, INT J APPROX REASON, V21, P1, DOI 10.1016/S0888-613X(99)00002-X Yager RR, 1993, GROUP DECIS NEGOT, V2, P81, DOI 10.1007/BF01384404 YAGER RR, 1992, INT J MAN MACH STUD, V36, P553, DOI 10.1016/0020-7373(92)90096-4 Yager RR, 1999, INT J UNCERTAIN FUZZ, V7, P483, DOI 10.1142/S021848859900043X Yager RR, 2004, IEEE T SYST MAN CY B, V34, P1224, DOI 10.1109/TSMCB.2003.822280 YAGER RR, 2004, IEEE T SYST MAN CY B, V34, P2386 Yakowitz DS, 1997, APPL MATH COMPUT, V83, P97, DOI 10.1016/S0096-3003(96)00171-3 YAKOWITZ DS, 1993, WATER SCI TECHNOL, V28, P47 YAU C, 1994, DECIS SUPPORT SYST, V12, P233, DOI 10.1016/0167-9236(94)90007-8 You Y, 2001, DECIS SUPPORT SYST, V32, P71, DOI 10.1016/S0167-9236(01)00101-4 ZACHARY W., 1986, HUMAN COMPUTER INTER, V2, P25, DOI 10.1207/s15327051hci0201_2 ZACHARY W, 1987, ACM SIGCHI B, V19, P72, DOI 10.1145/28189.1044805 Zadeh L.A., 1953, Journal of the Franklin Institute, V255, DOI 10.1016/0016-0032(53)90343-3 Zadeh L. A., 1970, MANAGE SCI, V17, pB, DOI DOI 10.1287/MNSC.17.4.B141 Zadeh L. A., 1979, MACH INTELL, V9, P149 Zadeh L. A., 1963, LINEAR SYSTEM THEORY Zadeh LA, 1989, IEEE T KNOWL DATA EN, V1, P89, DOI [DOI 10.1109/69.43406, 10.1109/69.43406] Zeleny M, 1982, MULTIPLE CRITERIA DE Zhang W., 2000, P 4 INT C AUT AG SPA, P116, DOI 10.1145/336595.337074 Zimmermann HJ, 1987, FUZZY SETS DECISION ZIMMERMANN HJ, 1988, MATH MODELS DECISION, P307 ZIMMERMANN HJ, 1986, EMPIRICAL RES ORG DE ZIONT S, 1975, MANAGE SCI, V22, P652 2008, WIKIMEDIA REGULAR EX 1973, MINTZBERG NATURE MAN NR 375 TC 8 Z9 8 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-03207-3 J9 STUD COMPUT INTELL PY 2009 VL 238 BP 1 EP 237 DI 10.1007/978-3-642-03208-0 PG 237 WC Business; Computer Science, Interdisciplinary Applications; Management; Operations Research & Management Science SC Business & Economics; Computer Science; Operations Research & Management Science GA BLG40 UT WOS:000270113300012 ER PT J AU Godoy, D Amand, A AF Godoy, Daniela Amand, Analia TI Interest Drifts in User Profiling: A Relevance-Based Approach and Analysis of Scenarios SO COMPUTER JOURNAL LA English DT Article DE User profiling; personal agents; interest drifts AB For personal information agents, user profiles have to represent user interests and preferences in order to satisfy long-term information needs. An implicit assumption in user-profiling is the existence of persistent interests which, however, might suffer some changes over time. Each time the interests of a user change, his profile becomes inaccurate and the predictive quality decreases. Adaptation of user profiles is, therefore, an essential requirement for personal agents that need to be capable of adjusting their behavior quickly in order to shorten the period of reduced predictive quality. In this paper, a user-profiling technique named WebProfiler, which learns a hierarchical representation of user interests using conceptual clustering, is augmented with an adaptation strategy based on relevance feedback and time-based forgetting in order to deal with drifting interests. We empirically evaluate the performance of this strategy by analyzing its behavior on multiple scenarios of interest drifts and shifts. C1 [Godoy, Daniela; Amand, Analia] UNCPBA Campus Univ, Fac Ciencias Exactas, ISISTAN Res Inst, Buenos Aires, DF, Argentina. [Godoy, Daniela; Amand, Analia] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Godoy, D (reprint author), UNCPBA Campus Univ, Fac Ciencias Exactas, ISISTAN Res Inst, CP B7001BBO, Buenos Aires, DF, Argentina. EM dgodoy@exa.unicen.edu.ar FU ANPCyT PICT [34917] FX This research has been partially supported by ANPCyT PICT No. 34917. CR BAUDISCH P, 2001, GMD RES SERIES BILLSUS D, 1999, P 7 INT C US MOD, P99 Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 Godoy D, 2006, AI COMMUN, V19, P207 Godoy D, 2007, LECT NOTES COMPUT SC, V4715, P303 Godoy D, 2006, INFORM SYST, V31, P247, DOI 10.1016/j.is.2005.02.008 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Klinkenberg R., 1998, ADAPTIVE INFORM FILT, P33 Kolodner J., 1993, CASE BASED REASONING Koychev I, 2000, P ECML2000 WORKSH MA, P39 Lieberman H., 1995, P 14 INT JOINT C ART, P924 MALOOF MA, 1995, AAAI FALL S ACT LEAR, P70 Michalski R. S., 1983, MACHINE LEARNING ART, P331 Mitchell T., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176798 Mitchell TM, 1997, MACHINE LEARNING Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 Nakhaeizadeh G., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schwab I., 2000, P 2000 INT C INT US, P241, DOI 10.1145/325737.325858 THOMPSON K, 1991, MOR KAUF M, P127 Webb G., 1996, USER MODEL USER-ADAP, V5, P117, DOI 10.1007/BF01099758 Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 Widmer G, 1996, MACH LEARN, V23, P69, DOI 10.1007/BF00116900 Widyantoro DH, 2001, J AM SOC INF SCI TEC, V52, P212, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1615>3.0.CO;2-O WIDYANTORO DH, 2003, ACM CIKM INT C INF K, P548 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 NR 28 TC 3 Z9 3 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 0010-4620 J9 COMPUT J JI Comput. J. PY 2009 VL 52 IS 7 BP 771 EP 788 DI 10.1093/comjnl/bxm107 PG 18 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 521EU UT WOS:000271903900004 ER PT S AU Moisl, H AF Moisl, Hermann BA Ludeling, A Kyto, M BF Ludeling, A Kyto, M TI Exploratory multivariate analysis SO CORPUS LINGUISTICS, PART 2 SE Handbucher zur Sprach und Kommunikationswissenschaft LA English DT Article; Book Chapter ID NONLINEAR DIMENSIONALITY REDUCTION CR ARABIE P, 1992, CLUSTERING CLASSIFIC Baayen R. H., 2001, WORD FREQUENCY DISTR BABITCH RM, 1989, J ENGL LINGUIST, V22, P83, DOI 10.1177/007542428902200113 Belew R.K., 2000, FINDING OUT COGNITIV BERDAN R, 1978, LINGUISTIC VARIATION, P149 Bertuglia Cristoforo Sergio, 2005, NONLINEARITY CHAOS C Bishop C. M., 1995, NEURAL NETWORKS PATT Borg I., 2005, MODERN MULTIDIMENSIO BUCKLEY C, 1993, HUMAN LANGUAGE TECHN Chambers Jack, 1998, DIALECTOLOGY, V2nd Church K., 1995, P 3 WORKSH VER LARG, P121 Church K. W., 1995, NAT LANG ENG, V1, P163 CROFT A, 1992, ENG MATH Dale R., 2000, HDB NATURAL LANGUAGE Diamantaras K., 1996, PRINCIPAL COMPONENT Duda R.O., 2001, PATTERN CLASSIFICATI Everitt B. S., 2001, APPL MULTIVARIATE DA, V2nd Everitt Brian S., 2001, CLUSTER ANAL Fletcher Paul, 1986, STAT LANGUAGE STUDIE Frakes W., 1992, INFORMATION RETRIEVA, P131 Gamallo P, 2005, COMPUT LINGUIST, V31, P107, DOI 10.1162/0891201053630318 Gordon AD, 1999, CLASSIFICATION Gries Stefan. Th., 2001, J QUANT LINGUIST, V8, P33, DOI 10.1076/jqul.8.1.33.4092 GRIMES C, 2006, NONLINEAR DIMENSIONA Grimm LG, 2000, READING UNDERSTANDIN Grimm L.G., 1995, READING UNDERSTANDIN Hair J. F., 2005, MULTIVARIATE DATA AN, V6th Heeringa Wilbert, 2001, LANG VAR CHANGE, V13, P375, DOI DOI 10.1017/S09543945011 Hoover D. L., 2003, Literary & Linguistic Computing, V18, DOI 10.1093/llc/18.4.341 Horvath Barbara, 1985, VARIATION AUSTR ENGL Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 Jack K Chambers, 2003, SOCIOLINGUISTIC THEO Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JASSEM W, 1995, J QUANT LINGUIST, V2, P105, DOI 10.1080/09296179508590040 Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones-Sargent Val, 1983, TYNE BYTES COMPUTERI Kachigan S.K., 1991, MULTIVARIATE STAT AN Kaski S., 1998, NEURAL COMPUTING SUR, V1, P102 Kessler B, 1995, SEVENTH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P60 Kitajima Y, 1999, J SYNCHROTRON RADIAT, V6, P167, DOI 10.1107/S0909049598016653 Kleiweg P, 2004, LECT NOTES ARTIF INT, V2980, P392 Kohonen T., 2001, SELF ORG MAPS Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Ledger G., 1995, LIT LINGUISTIC COMPU, V10, P85, DOI 10.1093/llc/10.2.85 Linmans A. J. M., 1998, Literary & Linguistic Computing, V13, DOI 10.1093/llc/13.1.1 Manning C. D., 1999, FDN STAT NATURAL LAN Mealand D., 1997, Literary & Linguistic Computing, V12, DOI 10.1093/llc/12.4.227 MILLER GA, 1955, J ACOUST SOC AM, V27, P338, DOI 10.1121/1.1907526 Moisl Hermann, 2006, LANGUAGE VARIATION E, P127 Moisl Hermann, 2007, P 9 M ACL SPEC INT G, P93, DOI 10.3115/1626516.1626528 Moisl H., 2005, Literary & Linguistic Computing, DOI 10.1093/llc/fqi026 Munkres J., 2000, TOPOLOGY, V2nd Nerbonne John, 2001, DIALECTOL GEOLINGUIS, V9, P69 Oakes M. P., 1998, STAT CORPUS LINGUIST OAKES MP, 1994, P 12 INT C MED INF E, P523 OJA M, 2001, NEURAL COMPUTING SUR, V3, P1 Pedersen T, 2006, TEXT SPEECH LANG TEC, V33, P133, DOI 10.1007/1-4020-4809-2_6 Pyle D., 1999, DATA PREPARATION DAT ROBERTSON S, 2004, J DOC, V60, P521 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sankoff David, 1989, LANGUAGE CHANGE VARI, P107, DOI 10.1075/cilt.52.06san Shepard R., 1972, HUMAN COMMUNICATION, P67 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Stevenson M., 2003, OXFORD HDB COMPUTATI, P249 Strzalkowski T., 1999, NATURAL LANGUAGE INF Tabachnick B. G., 2006, USING MULTIVARIATE S, V5th Tan P., 2006, INTRO DATA MINING Temple J. T., 1996, Literary & Linguistic Computing, V11, DOI 10.1093/llc/11.2.67 Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Tinsley H. E, 2000, HDB APPL MULTIVARIAT van Rijsbergen C. J., 1979, INFORM RETRIEVAL Verleysen M, 2003, NATO SC S SS III C S, V186, P141 Lee JA, 2007, INFORM SCI STAT, P1 Verleysen M., 2003, INT WORK C ART NAT N, P105 Vesanto J, 2000, IEEE T NEURAL NETWOR, V11, P586, DOI 10.1109/72.846731 WATTERS P, 2002, J QUANT LINGUIST, V9, P77, DOI 10.1076/jqul.9.1.77.8479 Webb A. R., 2002, STAT PATTERN RECOGNI WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 ZERNIK U, 1991, LEXICAL ACQUISITION, P97 NR 84 TC 0 Z9 0 PU WALTER DE GRUYTER & CO PI BERLIN 30 PA GENTHINER STRASSE 13, D-10785 BERLIN 30, GERMANY SN 1861-5090 BN 978-3-11-021388-1 J9 HANDB SPRACH KOMMUN PY 2009 VL 29 BP 874 EP 899 D2 10.1515/9783110213881.2 PN 2 PG 26 WC Linguistics SC Linguistics GA BOC63 UT WOS:000276188000005 ER PT S AU Han, J Gao, DB Deng, RH AF Han, Jin Gao, Debin Deng, Robert H. BE Flegel, U Bruschi, D TI On the Effectiveness of Software Diversity: A Systematic Study on Real-World Vulnerabilities SO DETECTION OF INTRUSIONS AND MALWARE, AND VULNERABILITY ASSESSMENT, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Conference on Detection of Intrusion and Malware, and Vulnerability Assessment CY JUL 09-10, 2009 CL Como, ITALY SP German Informat Soc, SIDAR ID BEHAVIORAL DISTANCE; INTRUSION DETECTION; SERVERS AB Many systems have been introduced to detect software intrusions by comparing the outputs and behavior of diverse replicas when they are processing the same, potentially malicious, input. When these replicas are constructed using off-the-shelf software products, it is assumed that they are diverse and not compromised simultaneously tinder the same attack. In this paper, we analyze vulnerabilities published in 2007 to evaluate the extent to which this assumption is valid. We focus on vulnerabilities in application software, and show that the majority of these software products - including those providing the same service (and therefore multiple software substitutes can be used in a replicated system to detect intrusions) and those that run on multiple operating systems (and therefore the same software can be used in a replicated system with different operating systems to detect intrusions) - either do not have the same vulnerability or cannot be compromised with the same exploit. We also find evidence that indicates the use of diversity in increasing attack tolerance for other software. These results show that systems utilizing off-the-shelf software products to introduce diversity are effective in detecting intrusions. C1 [Han, Jin; Gao, Debin; Deng, Robert H.] Singapore Management Univ, Sch Informat Syst, Singapore, Singapore. RP Han, J (reprint author), Singapore Management Univ, Sch Informat Syst, Singapore, Singapore. EM jin.han.2007@smu.edu.sg; dbgao@smu.edu.sg; robertdeng@smu.edu.sg RI DENG, Robert/E-8547-2012; GAO, Debin/E-8551-2012 CR Bandhakavi S, 2007, CCS'07: PROCEEDINGS OF THE 14TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P12 Barrantes E.G., 2003, CCS 2003, P281 Bhatkar S., 2003, SSYM 2003, P8 COX B, 2006, P 15 USENIX SEC S AU DHAMANKAR R, 2007, SANS TOP 20 SECURITY EDGE J, 2006, REMOTE FILE INCLUSIO FYODOR GL, 1998, REMOTE DETECTION VIA GAO D, 2008, IEEE T DEPENDABL JUL Gao DB, 2006, LECT NOTES COMPUT SC, V4219, P19 Gao DB, 2006, LECT NOTES COMPUT SC, V3858, P63 Gashi I, 2007, IEEE T DEPEND SECURE, V4, P280, DOI 10.1109/TDSC.2007.70208 Geer D., 2003, CYBERINSECURITY COST Jovanovic N., 2006, IEEE INT C SEC PRIV Just JE, 2002, LECT NOTES COMPUT SC, V2516, P158 Kc G. S., 2003, CCS 03, P272 LINGER RC, 1999, HICSS 1999, V3, P3062 ODONNELL AJ, 2004, CCS 2004, P121 REYNOLDS J, 2002, P 2002 INT C DEP SYS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGH A, 2006, MAC OS X INTERNALS S Stamp M, 2004, COMMUN ACM, V47, P120, DOI 10.1145/971617.971650 Totel E, 2006, LECT NOTES COMPUT SC, V3858, P43 Trowbridge C., 2003, OVERVIEW REMOTE OPER Vogt P., 2007, P NETW DISTR SYST SE NR 24 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-02917-2 J9 LECT NOTES COMPUT SC PY 2009 VL 5587 BP 127 EP 146 PG 20 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BKT18 UT WOS:000269147400008 ER PT B AU Bruno, E Faessel, N Glotin, H Le Maitre, J Scholl, M AF Bruno, Emmanuel Faessel, Nicolas Glotin, Herve Le Maitre, Jacques Scholl, Michel GP ACM TI Indexing by Permeability in Block Structured Web Pages SO DOCENG'09: PROCEEDINGS OF THE 2009 ACM SYMPOSIUM ON DOCUMENT ENGINEERING LA English DT Proceedings Paper CT 9th ACM Symposium on Document Engineering CY SEP 15-18, 2009 CL Munich, GERMANY SP ACM SIGWEB, ACM SIGDOC DE Document indexing; Document retrieval; Content based image retrieval; Block importance; Block permeability AB We present in this paper a model that we have developed for indexing and querying web pages based on their visual rendering. In this model pages are split up into a set of visual blocks. The indexing of a block takes into account its content, its visual importance and, by permeability, the indexing of neighbors blocks. A page is modeled as a directed acyclic graph. Each node is associated with a block and labeled by the coefficient of importance of this block. Each edge is labeled by the coefficient of permeability of the target node content to the source node content. Importance and permeability coefficients cannot be manually quantified. In the second part of this paper, we present an experiment consisting in learning optimal permeability coefficients by gradient descent for indexing images of a web page from the text blocks of this page. The dataset is drawn from real web pages of the train and test set of the ImagEval task2 corpus. Results demonstrate an improvement of the indexing using non uniform block permeabilities. C1 [Bruno, Emmanuel; Glotin, Herve; Le Maitre, Jacques] Univ Sud Toulon Var, LSIS, F-83957 La Garde, France. RP Bruno, E (reprint author), Univ Sud Toulon Var, LSIS, BP 132, F-83957 La Garde, France. EM bruno@univ-tln.fr; nicolas.faessel@lsis.org; glotin@univ-tln.fr; lemaitre@univ-tln.fr; michel.scholl@cnam.fr CR BRUNO E, 2009, P 7 INT WORKSH CONT, P219 Cai D, 2003, LECT NOTES COMPUT SC, V2642, P406 Duda R., 2000, PATTERN CLASSIFICATI, V2nd NAGY G, 1992, COMPUTER, V25, P10, DOI 10.1109/2.144436 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 Zou J., 2006, P JOINT C DIG LIB, P119, DOI 10.1145/1141753.1141777 NR 7 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-575-8 PY 2009 BP 70 EP 73 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BMF60 UT WOS:000272193400012 ER PT B AU Zhang, CZ Xu, HJ AF Zhang, Chengzhi Xu, Hongjiao TI Using Citation-KNN for Automatic Keyword Assignment SO ECBI: 2009 INTERNATIONAL CONFERENCE ON ELECTRONIC COMMERCE AND BUSINESS INTELLIGENCE, PROCEEDINGS LA English DT Proceedings Paper CT International Conference on Electronic Commerce and Business Intelligence CY JUN 06-07, 2009 CL Beijing, PEOPLES R CHINA SP Soc Management Sci China, Acad Comm DE Keyword Extraction; Keyword Assignment; mplicit Keyword Extraction; Citation-KNN AB Currently, the automatic keywords extraction method can only extract keywords appeared in the articles and it cannot extract the implicit keyword which does not appear in the articles. It is a difficult work to extract implicit keywords in an article in the task of automatic keywords extraction. This work can also be called automatic keyword assignment. In this paper, an automatic keyword assignment method based on Citation-KNN (Citation-K Nearest Neighborhood) is proposed. Experimental results show that the proposed method can not only improve the precision and recall of keyword extraction, but also extract implicit keyword which does not appear in the articles efficiently. C1 [Zhang, Chengzhi] Nanjing Univ Sci & Technol, Dept Informat Management, Nanjing, Peoples R China. EM zhangchz@istic.ac.cn; xuhongjiao_1111@163.com CR ANJEWIERDEN A, 2001, P 13 BELG NETH C ART, P23 BAETZYATES R, 1999, MODEM INFORM RETRIEV, P27 COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 Edmundson H.P., 1959, R126 PRC, P1 Ercan G, 2007, INFORM PROCESS MANAG, V43, P1705, DOI 10.1016/j.ipm.2007.01.015 Frank E, 1999, P 16 INT JOINT C ART, P668 Hulth A, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P216 Li Su-Jian, 2004, Chinese Journal of Computers, V27 LOIS LE, 1970, INFORM STORAGE RETRI, V6, P313 LUHN HP, 1957, IBM J RES DEV, V1, P309 LUHN HP, 1958, IBM J RES DEV, V2, P159 RAUBER A, 1999, P 4 ACM C DIG LIB DL, P240, DOI 10.1145/313238.313412 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAN P, 2006, INTRO DATA MINING, P225 Tomokiyo Takashi, 2003, P ACL WORKSH MULT EX, V18, P33, DOI DOI 10.3115/1119282.1119287 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 Turney P.D., 1999, ERB1057 NRC, P1 Turney P.D, 1997, ERB1051 I INF TECHN Wang J., 2000, P 17 INT C MACH LEAR, P1119 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 ZHANG CZ, 2008, RECENT ADV CHINESE C, V3, P260 NR 22 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3661-3 PY 2009 BP 131 EP 134 DI 10.1109/ECBI.2009.25 PG 4 WC Business; Computer Science, Information Systems; Economics; Management SC Business & Economics; Computer Science GA BMJ48 UT WOS:000272586700031 ER PT S AU Tu, AHN Kiem, H AF Tu Anh Hoang Nguyen Kiem Hoang BE Filipe, J Cordeiro, J TI Frequent Subgraph-Based Approach for Classifying Vietnamese Text Documents SO ENTERPRISE INFORMATION SYSTEMS-BK SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 11th International Conference on Enterprise Information Systems CY MAY 06-10, 2009 CL Milan, ITALY SP Inst Syst & Technol Informat, Control & Commun, AAAI, ACM SIGMIS, Japanese IEICE SWIM, Workflow Management Coalit DE Text classification; Graph mining; Frequent subgraph; Vietnamese ID CLASSIFICATION; PATTERNS; MODEL AB In this paper we present a simple approach for Vietnamese text classification without word segmentation, based on frequent subgraph mining techniques. A graph-based instead of traditional vector-based model is used for document representation. The classification model employs structural patterns (subgraphs) and Dice measure of similarity to identify a class of documents. This method is evaluated on Vietnamese data set for measuring classification accuracy. Results show that it can Outperform k-NN algorithm (based on vector, hybrid document representation) in terms of accuracy and classification time. C1 [Tu Anh Hoang Nguyen] Univ Sci, VNU, Fac Informat Technol, Ho Chi Minh City, Vietnam. RP Tu, AHN (reprint author), Univ Sci, VNU, Fac Informat Technol, Ho Chi Minh City, Vietnam. EM nhtanh@fit.hcmuns.edu.vn; kiemhv@uit.edu.vn CR APTE C, 1998, WORKSH 6 LEARN TEXT Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 DIEN D, 2001, P 6 NAT LANG PROC PA, P749 Dominik A, 2007, LECT NOTES ARTIF INT, V4702, P67 Gudes E, 2006, IEEE T KNOWL DATA EN, V18, P1441, DOI 10.1109/TKDE.2006.173 HUNG N, 2005, P 3 INT C RES INN VI, P168 Joachims T., 1998, LNCS, V1398, P137 Markov A, 2005, LECT NOTES COMPUT SC, V3528, P293 MARKOV A, 2006, P WEBKDD 2006 WORKSH, P31 MASAND B, 1992, P SIGIR PHUC D, 2007, P IEEE INT C COMP SC, P247 PHUC D, 2006, P 4 INT C COMP SCI R, P33 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A, 2004, INT J PATTERN RECOGN, V18, P475, DOI 10.1142/S0218001404003241 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 THANH VN, 2006, P 4 INT C COMP SCI R, P113 TOMITA J, 2004, P 13 INT WORLD WID W, P454, DOI 10.1145/1013367.1013522 VU CDH, 2007, P IEEE INT C COMP SC, P267 Washio T., 2003, SIGKDD EXPLORATIONS, V5, P59 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Worlein M, 2005, LECT NOTES ARTIF INT, V3721, P392 Yan X., 2002, P 2002 IEEE INT C DA, P721 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-01346-1 J9 LECT NOTES BUS INF P PY 2009 VL 24 BP 299 EP 308 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BJY59 UT WOS:000267425300025 ER PT S AU Moulin, C Barat, C Gery, M Ducottet, C Largeron, C AF Moulin, Christophe Barat, Cecile Gery, Mathias Ducottet, Christophe Largeron, Christine BE Peters, C TI UJM at ImageCLEFwiki 2008 SO EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008) CY SEP 17-19, 2008 CL Aarhus, DENMARK AB This paper reports our multimedia information retrieval experiments carried out for the ImageCLEF track ImageCLEFwiki[10]). We propose a new multimedia model combining textual and/or visual information which enables to perform textual, visual, or multimedia queries. We experiment the model on ImageCLEF data and we compare the results obtained using the different modalities. Our multimedia document model is based on a vector of textual and visual terms. Textual terms correspond to textual words while the visual ones are computed using local colour features. We obtain good results using only the textual part and we show that the visual information is useful in some particular cases. C1 [Moulin, Christophe] Univ Lyon, F-69003 Lyon, France. RP Moulin, C (reprint author), Univ Lyon, F-69003 Lyon, France. EM christophe.moulin@univ-st-etienne.fr; cecile.barat@univ-st-etienne.fr; mathias.gery@univ-st-etienne.fr; ducottet@univ-st-etienne.fr; largeron@univ-st-etienne.fr CR Ferrari V, 2008, IEEE T PATTERN ANAL, V30, P36, DOI 10.1109/TPAMI.2007.1144 Goodrum A., 2000, INFORM SCI, V3, P2000 Gordon A. D., 1981, CLASSIFICATION Jurie F., 2005, INT C COMP VIS Lowe D. G., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, DOI 10.1109/ICCV.1999.790410 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S., 1994, TEXT RETRIEVAL C, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TOLLARI S, 2006, IMAGEVAL Tsikrika T, 2009, LECT NOTES COMPUT SC, V5706, P539 NR 10 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04446-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5706 BP 779 EP 786 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMQ35 UT WOS:000273344500101 ER PT S AU Pu, Q He, DQ Li, Q AF Pu, Qiang He, Daqing Li, Qi BE Peters, C TI Query Expansion for Effective Geographic Information Retrieval SO EVALUATING SYSTEMS FOR MULTILINGUAL AND MULTIMODAL INFORMATION ACCESS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008) CY SEP 17-19, 2008 CL Aarhus, DENMARK AB We developed two methods for monolingual Geo-CLEF 2008 task. The GCEC method aims to test the effectiveness of our online geographic coordinates extraction and clustering algorithm, and the WIKIGEO method wants to examine the usefulness of using the geographic coordinates information in Wikipedia for identifying geo-locations. We proposed a measure of topic distance to evaluate these two methods. The experiments results show that: 1) our online geographic coordinates extraction and clustering algorithm is useful for the type of locations that do not have clear corresponding coordinates; 2) the expansion based on the geo-locations generated by GCEC is effective in improving geographic retrieval; 3) Wikipedia can help in finding the coordinates for many geo-locations, but its usage for query expansion still needs further study; 4) query expansion based on title only obtained better results than that on the title and narrative parts, even though the latter contains more related geographic information. Further Study is needed for this part. C1 [Pu, Qiang] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu 610054, Peoples R China. RP Pu, Q (reprint author), Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Chengdu 610054, Peoples R China. EM puqiang@uestc.edu.cn; dah44@pitt.edu; qil14@pitt.edu CR Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127 Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval He D, 2003, ACM T ASIAN LANGUAGE, V2, P219, DOI 10.1145/979872.979876 LI ZS, 2007, CROSS LANG EV FOR CL Li ZS, 2007, LECT NOTES COMPUT SC, V4730, P926 PU Q, 2008, CROSS LANG EV FOR CL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 7 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04446-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5706 BP 843 EP 850 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMQ35 UT WOS:000273344500110 ER PT B AU Cheng, YZ Qiu, CR Tang, WQ Deng, WD AF Cheng, Yuzhu Qiu, Chunrong Tang, Weiqi Deng, Wenda BE Pan, JS Guo, BL Abraham, A TI Research on System of Chinese Document Copy Detection SO FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 2, PROCEEDINGS LA English DT Proceedings Paper CT 5th International Conference on Information Assurance and Security CY AUG 18-20, 2009 CL Xian, PEOPLES R CHINA SP Kaohsiung Univ Appl Sci, IEEE Signal Processing Soc, Taiwan Chapter, Taiwanese Assoc consumer Elect, Machine Intelligence Res Labs, IEEE Syst Man & Cybernet Soc, TCSC HO Xidian Univ DE copy detection; Mathematical Expression of Chinese Characters; similarity computing AB Text copying is easy to do but not easy to be detected. Copy detection is a delicate ground. It determines whether a document is copied from others or not. It helps to verify and to detect paper redistribution, thesis plagiarism or copyright violation. A novel method of Chinese text copy detection design and implementation is discussed in this paper, based on theory of Mathematical Expression of Chinese Characters. It can implement the functions include Characteristic reconstruction, registration and detection. Experimental results show that the system has the advantages on detecting accuracy and high speed. C1 [Cheng, Yuzhu; Qiu, Chunrong; Tang, Weiqi; Deng, Wenda] Changsha Social Work Coll, Software Dept, Changsha 410004, Hunan, Peoples R China. RP Cheng, YZ (reprint author), Changsha Social Work Coll, Software Dept, Changsha 410004, Hunan, Peoples R China. EM vogue21ct@163.com; qcrqcr@sohu.com; twq_hn@126.com; orange8821@sohu.com CR Bao Jun-Peng, 2003, Journal of Software, V14 Brin S., 1995, P 1995 ACM SIGMOD IN, P398, DOI 10.1145/223784.223855 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIVAKUMAR N, 1995, P 2 INT C THEOR PRAC, P9 Si A., 1997, P ACM S APPL COMP, P70, DOI 10.1145/331697.335176 Song QB, 2001, J COMPUTER RES DEV, V38, P121 Su JK, 1998, COMPUT GRAPH-UK, V22, P687, DOI 10.1016/S0097-8493(98)00089-2 Sun XM, 2002, INT J PATTERN RECOGN, V16, P735, DOI 10.1142/S0218001402001939 Wise MJ, 1996, P 27 SIGCSE TECHN S, P130, DOI 10.1145/236452.236525 Zhang Chun-xia, 2005, Journal of System Simulation, V17 NR 10 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3744-3 PY 2009 BP 185 EP 188 DI 10.1109/IAS.2009.210 PG 4 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BNX93 UT WOS:000275852400043 ER PT B AU Gu, CJ Huang, LC AF Gu Chengjian Huang Lucheng BE Chen, JX Hu, ZB TI A Systematic Study of the work that Web Mining does in the Technology Opportunities Analysis SO FIRST INTERNATIONAL WORKSHOP ON DATABASE TECHNOLOGY AND APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 1st International Workshop on Database Technology and Applications CY APR 25-26, 2009 CL Wuhan, PEOPLES R CHINA SP Wuhan Univ Sci & Technol, Huazhong Univ Sci & Technol DE Technology opportunities Analysis (TOA); Web Mining (WM); Morphology Analysis (MA); Keyword ID MATHEMATICAL-THEORY; COMMUNICATION AB we put the technology opportunity analysis (TOA) on a very important position during the commercialization of emerging technologies, and rind all necessary information from a variety of sources for the accurate analysis. However, the complex and redundancy of the data make the work extremely difficulty, thanks to the advent of web mining (WM), which gives us a window in this area to allow the management of science and technology(S&T) becoming more convenient. In this paper, we use the Keyword-based MA as the analysis vector doing the research on the role of WM on the TOA, and attempt to give the focus of future research in the final. C1 [Gu Chengjian; Huang Lucheng] Beijing Univ Technol, Econ & Management Sch, Beijing, Peoples R China. RP Gu, CJ (reprint author), Beijing Univ Technol, Econ & Management Sch, Beijing, Peoples R China. EM guchengjian@emails.bjut.edu.cn; hlch@bjut.edu.cn CR AAS K, 2003, 941 NORW COMP CTR, P27 ALAN P, 1995, TECHNOLOGY OPPORTUNI Chakrabarti Soumen, 2003, MINING WEB DISCOVERI LI XF, 2005, SCI SCI MANAGEMENT S, V8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 TAN YH, 2006, COMPUTER ENG, V9, P57 WAN L, 2005, DATA EXCAVATION RES, V7, P59 WANG JW, 2008, SCI SCI MANAGEMENT S, V4 WANG JW, 2008, SHANGHAI EC REV, V5 Yoon B, 2005, TECHNOL FORECAST SOC, V72, P145, DOI 10.1016/j.techfore.2004.08.011 YOON B, 2007, EXPERT SYSTEMS A JUN, P1 YOON BC, 2004, IEEE INT ENG MANAGEM, V2, P566 ZHU DH, 2004, J IND ENG ENG MANAGE, V4 NR 15 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3604-0 PY 2009 BP 299 EP 302 DI 10.1109/DBTA.2009.53 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BND70 UT WOS:000274239000074 ER PT J AU Carrillo, M Villatoro-Tello, E Lopez-Lopez, A Eliasmith, C Montes-Y-Gomez, M Villasenor-Pineda, L AF Carrillo, Maya Villatoro-Tello, Esau Lopez-Lopez, A. Eliasmith, Chris Montes-y-Gomez, Manuel Villasenor-Pineda, Luis BE Andreasen, T Yager, RR Bulskov, H Christiansen, H Larsen, HL TI Representing Context Information for Document Retrieval SO FLEXIBLE QUERY ANSWERING SYSTEMS: 8TH INTERNATIONAL CONFERENCE, FQAS 2009 SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 8th International Conference on Flexible Query Answering Systems CY OCT 26-28, 2009 CL Roskilde, DENMARK SP Iona Coll, Machine Intelligence Inst HO Roskilde Univ, Dept Commun, Business & Informat Technol DE Information Retrieval; Vector Model; Context Information; Random Indexing; Holographic Reduced Representation AB The bag of words representation (BoW), which is widely used in information retrieval (IR), represents documents and queries as word lists that do not express anything about context information. When we look for information, we find that not everything is explicitly stated in a document, so context information is needed to understand its content. This paper proposes the use of bag of concepts (BoC) and Holographic reduced representation (HRR) in IR. These representations go beyond BoW by incorporating context information to document representations. Both HRR and BoC are produced using a vector space methodology known as Random Indexing, and allow expressing additional knowledge from different sources. Our experiments have shown the feasibility of the representations and improved the mean average precision by up to 7% when they are compared with the traditional vector space model. RP Carrillo, M (reprint author), INAOE, Luis Enrique Erro 1, Puebla 72840, Mexico. EM cmaya@inaoep.mx; villatoroe@inaoep.mx; allopez@inaoep.mx; celiasmith@uwaterloo.ca; mmontesg@inaoep.mx; villasen@inaoep.mx RI Villasenor-Pineda, Luis/A-2932-2009 OI Villasenor-Pineda, Luis/0000-0003-1294-9128 CR ANDRADE L, 2006, P 3 WORKSH GEOGR INF DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 Fishbein JM, 2008, LECT NOTES COMPUT SC, V4956, P514 Grinberg D., 1995, CMUCS95125 HENRICH A, 2007, P WORKSH GEOGR INF R Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 LIU HG, 2004, MONTYLINGUA END TO E MANDL T, 2008, WORKSH DENM Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Plate T.A., 2003, HOLOGRAPHIC REDUCED Sahlgren M., 2004, P 20 INT C COMP LING, P487, DOI 10.3115/1220355.1220425 Sahlgren Magnus, 2005, METH APPL SEM IND WO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04956-9 J9 LECT NOTES ARTIF INT PY 2009 VL 5822 BP 239 EP 250 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BQO06 UT WOS:000281409200021 ER PT J AU Peters, I AF Peters, Isabella BA Peters, I BF Peters, I TI Basic Terms in Knowledge Representation and Information Retrieval SO FOLKSONOMIES: INDEXING AND RETRIEVAL IN WEB 2.0 SE Knowledge and Information-Studies in Information Science LA English DT Article; Book Chapter ID PROBABILISTIC MODELS; RELEVANCE; DISTRIBUTIONS; FOLKSONOMY; ALGORITHM; TAG RI Peters, Isabella/C-9891-2012 OI Peters, Isabella/0000-0001-5840-0806 CR Aitchison J., 2000, THESAURUS CONSTRUCTI ALBRECHT C, 2006, FOLKSONOMY Anderson C, 2006, LONG TAIL WHY FUTURE [Anonymous], 1986, 2788 ISO *ANSI NISO, 2003, Z3919 ANSINISO Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BARILAN J, 2006, P COLL WEB TAGG WORK BERTRAM J, 2005, EINFUHRUNG INHALTLIC Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Brin S, 1998, P 7 INT WORLD WID WE Crawford W., 2006, CITES INSIGHTS CRAWF, P6 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DICE LR, 1945, ECOLOGY, V26, P297, DOI 10.2307/1932409 *DIN, 1987, 32705 DIN *DIN, 1987, 14631 DIN Dye J, 2006, ECONTENT, V29, P38 Egghe L, 2005, LIBR INFORM SCI SER, P1 Egghe L., 1990, INTRO INFORM EVANS R, 1994, P 15 NAT ONL M MEDF, P121 Ferber Reginald, 2003, INFORM RETRIEVAL SUC FOX E, 1992, INFORMATION RETRIEVA, P393 Frakes W. B., 1992, INFORM RETRIEVAL DAT Galvez C, 2005, J DOC, V61, P520, DOI 10.1108/00220410510607507 Garfield E., 1979, CITATION INDEXING GIUNCHIGLIA F, 2005, P AAAI 05 WORKSH CON GRUBER T, 2005, ONTOLOGY FOLKSONOMY GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Halpin H., 2007, P 16 INT C WORLD WID, P211, DOI DOI 10.1145/1242572.1242602 HALPIN H, 2006, EVOLVING ONTOLOGIES Harman D., 1986, P 9 ANN INT ACM SIGI, P186, DOI 10.1145/253168.253206 Harman D., 1992, INFORMATION RETRIEVA, P363 HENRICHS N, 1970, CONCEPTUS, V4, P133 HENRICHS N, 1970, NACHR DOK, V21, P20 HUANG H, 2006, P 17 ANN ASIS T SIG Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KALBACH J, 2008, B ASIST, V34, P36, DOI 10.1002/bult.2008.1720340211 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kipp M. E. I., 2006, P 7 INF ARCH SUMM VA Kipp Margaret E.I, 2007, P N AM S KNOWL ORG T, P63 KIPP MEI, 2006, P 17 ANN M AM SOC IN KIPP MEI, 2006, CANADIAN J INFORM LI, V30 KLEINBERG J, 2009, ACM COMPUTING SURVEY, V31 Kroski E., 2005, HIVE MIND FOLKSONOMI KUHLEN R, 2004, GRUNDLAGEN PRAKTISCH KUHLEN R, 1977, EXPT MORPHOLOGIE INF Kulyukin VA, 2001, J AM SOC INF SCI TEC, V52, P1224, DOI 10.1002/asi.1190 Lancaster F. W., 2003, INDEXING ABSTRACTING Lee J.H., 1993, P 16 ANN INT ACM SIG, P291, DOI 10.1145/160688.160741 Lewandowski D., 2005, INFORM SERVICES USE, V25, P137 Lewandowski Dirk, 2005, WEB INFORM RETRIEVAL LIDDY ED, 1993, Patent No. 5873058 Lin X., 2006, P 17 ANN ASIS T SIG LOVINS JB, 1968, MECH TRANSL, V11, P22 LUHN HP, 1958, IBM J RES DEV, V2, P159 Macgregor G., 2006, Library Review, V55, DOI 10.1108/00242530610667558 Mai J. E., 2006, Bulletin of the American Society for Information Science and Technology, V33, DOI 10.1002/bult.2006.1720330107 Manning C.D., 2008, INTRO INFORM RETRIEV MARKEY K, 1984, LIBR INFORM SCI RES, V6, P155 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Mathes Adam, 2004, FOLKSONOMIES COOPERA MEDEIROS N, 2008, OCLC SYSTEMS SERVICE, V24 MILLER GA, 1998, WORDNET ELECT LEXICA, P23 Newman MEJ, 2005, CONTEMP PHYS, V46, P323, DOI 10.1080/00107510500052444 Page L, 1998, PAGERANK CITATION RA PETERS I, 2007, JOINING RES PRACTICE, P1510 PETERS I, 2008, INFORM WISSENSCHAFT, V59, P100 Peterson Elaine, 2006, D LIB MAGAZINE, V12 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QUINTARELLI E, 2005, P ISKO IT UNIMIB M M Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Redmond-Neal A., 2005, ASIS T THESAURUS INF Richardson M., 2006, P 15 INT C WORLD WID, P707, DOI DOI 10.1145/1135777.1135881 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMITZ J, 2007, P 29 ONL TAG DGI FRA, P31 SHIRKY C, 2005, SEMISTRUCTURED METAD Shirky C, 2005, ONTOLOGY IS OVERRATE Shirky C., 2003, EXPOSURE FRICTION FR, P77 Sinclair J, 2008, J INF SCI, V34, P15, DOI 10.1177/0165551506078083 SKUSA A, 2008, WEB 2 0 DIENSTE ERGA, P1 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SNEATH PHA, 1957, J GEN MICROBIOL, V17, P184 Sparck-Jones K., 1997, READINGS INFORM RETR Stock W. G., 2007, INFORM RETRIEVAL INF Stock W. G., 2008, WISSENSREPRASENTATIO Stock WG, 2006, J AM SOC INF SCI TEC, V57, P1126, DOI 10.1002/asi.20359 Stock Wolfgang G, 2007, Information Services & Use, V27 Taylor Arlene G., 1999, ORG INFORM Tennis J., 2006, P 17 ANN ASIS T SIG TUDHOPE D, 2006, NEW REV HYPERMEDIA M, V12, P9 Van Damme C, 2007, P ESWC 2007 WORKSH B, P71 Voss J., 2006, COLLABORATIVE THESAU VOSS J, 2007, P 10 INT S INF SCI C Wasserman S., 1994, SOCIAL NETWORK ANAL Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Weinberger D., 2006, TAXONOMIES TAGS TREE WELLER K, 2006, P 28 ONL TAG DGI FRA, P108 Weller K, 2007, APPL WEB 2 0 INNOVAT, P108 YALTAGHIAN B, 2002, P C CTR ADV STUD COL ZENG ML, 2000, TAXONOMY KNOWLEDGE O ZIMMERMANN HH, 1998, AUTOMATISCHE INDEXIE NR 106 TC 0 Z9 0 PU WALTER DE GRUYTER & CO PI BERLIN 30 PA GENTHINER STRASSE 13, D-10785 BERLIN 30, GERMANY BN 978-3-598-44185-1 J9 KNOWL INFO-STUD INFO PY 2009 BP 119 EP 151 D2 10.1515/9783598441851 PG 33 WC Information Science & Library Science SC Information Science & Library Science GA BOX12 UT WOS:000277899800003 ER PT B AU Zhang, XG Chen, HJ Ma, J Tao, JH AF Zhang, Xiaogang Chen, Huajun Ma, Jun Tao, Jinhuo BE Wu, S Liu, SJ Wu, YW Zheng, R TI Ontology Based Semantic Relation Verification for TCM Semantic Grid SO FOURTH CHINAGRID ANNUAL CONFERENCE, PROCEEDINGS LA English DT Proceedings Paper CT 4th ChinaGrid Annual Conference CY AUG 21-22, 2009 CL Yantai, PEOPLES R CHINA DE TCM Semantic Grid; ontology; semantic relation verification AB Traditional Chinese Medicine (TCM) Semantic Grid is an application of Semantic Grid technique in TCM domain. It comprises TCM ontology and TCM Databases as data resources and Dart Search, Dart Query etc. as TCM application. This paper reports an ontology engineering component of the TCM Semantic Grid. Although ontology engineering has gained significant progress, ontology construction still mainly depends on manual work and tends to be mistaken prone. In this paper, we introduce a semantic relation verification method based on both domain ontology and domain publications. A modified vector space model is used to extract semantic relations from domain publications, which is particularly useful when the semantic relation cannot be extracted directly. Association rule learning method is used to distinguish significant relations from trivial ones. Further verification method is used to give user recommendations of relation types. We use Traditional Chinese Medicine Language System, domain ontology for Traditional Chinese Medicine, and relevant publications to validate our approach. But our method is not limited to this field. In fact, any data source that can be extracted into relevant instance pairs is applicable. C1 [Zhang, Xiaogang; Chen, Huajun; Ma, Jun; Tao, Jinhuo] Zhejiang Univ, Dept Comp Sci, Hangzhou 310003, Zhejiang, Peoples R China. RP Zhang, XG (reprint author), Zhejiang Univ, Dept Comp Sci, Hangzhou 310003, Zhejiang, Peoples R China. EM Zhang0925@gmail.com; huajunsir@gmail.com; majun@zju.edu.cn; taojinhuo@gmail.com CR Agrawal R., 1994, FAST ALGORITHMS MINI, P487 Aronson A R, 2001, Proc AMIA Symp, P17 Chen HJ, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-S3-S1 Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Ganter B., 1999, FORMAL CONCEPT ANAL GSCHWANDTNER T, P JUN SCI C 2008 JSC Hotho A., 2005, LDV FORUM, V20, P19 JIANG T, 2007, TKDE, V19, P2 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEGEV A, 2006, P ECAI WORKSH CONT O Stumme G, 2006, J WEB SEMANT, V4, P124, DOI 10.1016/j.websem.2006.02.001 Stumme G., 2001, 7 INT C ART INT IJCA, P225 Wu ZH, 2008, ADV TOP SCI TECH CHI, P1 Zhang H.-P., 2003, P 2 SIGHAN WORKSH, P184 NR 15 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3818-1 PY 2009 BP 185 EP 191 DI 10.1109/ChinaGrid.2009.35 PG 7 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BVA45 UT WOS:000290895100029 ER PT J AU Rhee, SK Lee, J Park, MW Szymczak, M Frackowiak, G Ganzha, M Paprzycki, M AF Rhee, Sang Keun Lee, Jihye Park, Myon-Woong Szymczak, Michal Frackowiak, Grzegorz Ganzha, Maria Paprzycki, Marcin TI Measuring Semantic Closeness of Ontologically Demarcated Resources SO FUNDAMENTA INFORMATICAE LA English DT Article DE Ontological matchmaking; semantic closeness; recommender system; resource management; resource matching AB In our work, an agent-based system supporting workers in an organization is centered around utilization of ontologically demarcated data. In this system, ontological matchmaking, understood as a way of establishing closeness between resources, is one of key functionalities. Specifically, it is used to autonomously provide recommendations to the user, who is represented by her/his personal agent. These recommendations specify, which among available resources are relevant / of interest to the worker. In this paper, we discuss our approach to measuring semantic closeness between ontologically demarcated information objects, while a Duty Trip Support application is used as a case study. General description of the algorithm is followed by a recommendation example based on support for a worker who is seeking advice in planning a duty trip. C1 [Rhee, Sang Keun; Lee, Jihye; Park, Myon-Woong] Korea Inst Sci & Technol, Intelligence & Interact Res Ctr, Seoul, South Korea. [Szymczak, Michal; Frackowiak, Grzegorz; Ganzha, Maria; Paprzycki, Marcin] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland. [Ganzha, Maria] Univ Gdansk, Inst Informat, PL-80952 Gdansk, Poland. RP Ganzha, M (reprint author), IBS PAN, Ul Newelska 6, PL-01447 Warsaw, Poland. EM greyrhee@kist.re.kr; myon@kist.re.kr; maria.ganzha@ibspan.waw.pl; marcin.paprzycki@ibspan.waw.pl FU KIST-SRI PAS FX This work is partially sponsored by the KIST-SRI PAS "Agent Technology for Adaptive Information Provisioning" grant. CR BADICA C, 2008, NEW CHALLENGES APPL, P134 BRIN S, 1998, P 7 WWW C BRISB AUST CHO H, 2007, HCI INTELLIGENT MULT, P4552 Crestani F, 1997, ARTIF INTELL REV, V11, P453, DOI 10.1023/A:1006569829653 DUMAIS S, 1988, P CHI 88 C EUZENAT J, 2007, STUDIES COMPUTATIONA FRACKOWIAK G, 2008, LNCS FRACKOWIAK G, 2008, STUDIES COM IN PRESS GANGEMI A, 1998, P FORM ONT INF SYST GANZHA M, 2007, P SYNASC C, P4953 JAVA A, 2006, MODELING SPREAD INFL *JEN, 2008, SEM FRAM JAV Jiang J., 1997, P INT C RES COMP LIN Korfhage R., 1997, INFORM STORAGE RETRI LEACOCK C, 1998, ELECT LEXICAL DATABA, P265 LIN D, 15 INT C MACH LEARN *OWL, OWL WEB ONT LANG GUI *OWL, OWL WEB ONT LANG OV PINTO HS, 2001, P IJCAI 01 WORKSH ON RESNIC P, 1995, INT JOINT C ART INT RHEE S, 2008, LNCS RHEE SK, 2007, P SWW 2 0 ISWC CEUR, P294 RHEE SK, 2008, P IMCSIT 08 C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *SPARQL, SPARQL QUER LANG EDF SZYMCZAK M, 2008, P IDC C, P134 SZYMCZAK M, 2008, P IMCSIT 08 C NR 27 TC 2 Z9 2 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0169-2968 J9 FUND INFORM JI Fundam. Inform. PY 2009 VL 96 IS 4 BP 395 EP 418 DI 10.3233/FI-2009-185 PG 24 WC Computer Science, Software Engineering; Mathematics, Applied SC Computer Science; Mathematics GA 538PZ UT WOS:000273197700002 ER PT B AU Jing, YJ Li, X Zhong, SC AF Jing, Yongjun Li, Xin Zhong, Shaochun BE Pan, JS Lie, J Abraham, A TI Research on Personalized Services for Users of Education Resources Net SO HIS 2009: 2009 NINTH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS, VOL 3, PROCEEDINGS LA English DT Proceedings Paper CT 9th International Conference on Hybrid Intelligent Systems (HIS 2009) CY AUG 12-14, 2009 CL Shenyang, PEOPLES R CHINA SP Shenyang Normal Univ DE education resources; personalized service; user's interest; vector space model; time-window theory AB In order to users be able to find their own favorite resources quickly and accurately, this paper presents a personalized service which recommends resources for users at massive education resources net. On the basis of analyzing the characteristics of users and resources, this paper discusses the model of resource's description and user's interest, and the treatment of user's interest drift, then designs and describes the personalized service model and implementation. The service is applied to Basic Education Resources Net of Liaoning Province. The effectiveness of such service is proved. C1 [Jing, Yongjun; Li, Xin] Shenyang Normal Univ, Sch Educ Technol, Shenyang, Peoples R China. RP Jing, YJ (reprint author), Shenyang Normal Univ, Sch Educ Technol, Shenyang, Peoples R China. EM jing.yong.jun@hotmail.com; sczhong@yahoo.com CR CENG C, 2002, SURVEY PERSONALIZATI, V10, P1952 Koychev I, 2000, P ECAI 2000 WORKSH C, P101 KOYCHEV I, 2001, P UM2001 WORKSH MACH, P49 LI LJ, 2007, CHINA ED TECHNOLOGY, V1, P60 MA N, 2006, CHINA ED TECHNOLOGY, V2, P66 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 2008, MODERN DISTANCE TEAC NR 7 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3745-0 PY 2009 BP 195 EP 197 DI 10.1109/HIS.2009.253 PG 3 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BQW19 UT WOS:000281999600042 ER PT S AU Ayodele, T Zhou, S Khusainov, R AF Ayodele, Taiwo Zhou, Shikun Khusainov, Rinat BE Salvendy, G Smith, MJ TI Email Reply Prediction: A Machine Learning Approach SO HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION: INFORMATION AND INTERACTION, PT II SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT Symposium on Human Interface held at the HCI International 2009 CY JUL 19-24, 2009 CL San Diego, CA DE Email reply prediction; machine learning; email messages; interrogative words; need reply; do not need reply; email headers; unsupervised learning AB Email has now become the most-used communication tool in the world and has also become the primary business productivity applications for most organizations and individuals. With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize and prioritize their email messages, we propose a new framework; email reply prediction with unsupervised learning. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work. C1 [Ayodele, Taiwo; Zhou, Shikun; Khusainov, Rinat] Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 3DJ, Hants, England. RP Ayodele, T (reprint author), Univ Portsmouth, Dept Elect & Comp Engn, Anglsea Bldg,Anglsea Rd, Portsmouth PO1 3DJ, Hants, England. EM taiwo.ayodele@port.ac.uk; shikun.zhou@port.ac.uk; rinat.khusainov@port.ac.uk CR DREDZE M, 2005, CEAS 2005 Ducheneaut N., 2001, Interactions, V8, DOI 10.1145/382899.383305 Klimt B, 2004, LECT NOTES COMPUT SC, V3201, P217 Kraut R. E., 1997, CULTURE INTERNET, P323 MACKAY WE, 1988, ACM T INFORM SYST, V6, P380, DOI 10.1145/58566.58567 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sproull L., 1991, CONNECTIONS NEW WAYS TYLER J, 2003, P ECSCW 2003, P238 Whittaker S., 1996, C P HUM FACT COMP SY, P276 NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-02558-7 J9 LECT NOTES COMPUT SC PY 2009 VL 5618 BP 114 EP 123 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BMB84 UT WOS:000271799300013 ER PT B AU Oberoi, SV Finger, S AF Oberoi, Sharad V. Finger, Susan BE Bergendahl, MN Grimheden, M Leifer, L Skogstad, P Chen, W TI DESIGNWEBS: INTERACTIVE ORGANIZATIONAL MEMORY SO ICED 09 - THE 17TH INTERNATIONAL CONFERENCE ON ENGINEERING DESIGN, VOL 8: DESIGN INFORMATION AND KNOWLEDGE LA English DT Proceedings Paper CT 17th International Conference on Engineering Design CY AUG 24-27, 2009 CL Stanford, CA SP Stanford Univ, Ctr Design Res, Design Soc HO Stanford Univ DE Design knowledge; organizational memory ID LATENT SEMANTIC ANALYSIS; CO-WORD ANALYSIS; KNOWLEDGE MANAGEMENT; SUPPORT; SYSTEMS; ISSUE; MODEL AB Knowledge generated during the design process frequently goes uncaptured, and when it is captured, it is usually poorly organized and buried in obscure documents. Effective capture of both semantic knowledge and episodic knowledge can have many benefits for both student and professional design teams. In this paper, we describe Design Webs, which are dynamic, navigable networks of the documents and conversations created during the design process. C1 [Finger, Susan] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA. RP Finger, S (reprint author), Carnegie Mellon Univ, Dept Civil & Environm Engn, 5000 Forbes Ave, Pittsburgh, PA 15213 USA. EM Susan.Finger@cmu.edu CR Alani H, 2003, KNOWL CAPT 03 WORKSH, P2 Arguello J., 2006, P HUM LANG TECHN C N, P9, DOI 10.3115/1614049.1614052 Argyris C., 1978, THEORY PRACTICE INCR Barzilay R., 2004, P HLT NAACL 2004 Berlin Lucy M., 1993, P INTERCHI 93 C HUM, P23, DOI 10.1145/169059.169063 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 CALLON M, 1991, SCIENTOMETRICS, V22, P155, DOI 10.1007/BF02019280 Chen JQ, 2003, COMMUN ACM, V46, P73, DOI 10.1145/953460.953462 Chung YM, 2001, J AM SOC INF SCI TEC, V52, P283, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1073>3.3.CO;2-X Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.3.CO;2-6 Davenport TH, 1998, SLOAN MANAGE REV, V39, P43 Davis JG, 2001, INFORM SYST FRONT, V3, P377, DOI 10.1023/A:1011469727367 Day C., 1993, BRIT EDUC RES J, V9, P83, DOI 10.1080/0141192930190107 El-Diraby TE, 2006, AUTOMAT CONSTR, V15, P504, DOI 10.1016/j.autcon.2005.07.005 Elton L., 1989, STARTING RES SUPERVI Finger S, 2006, EXPERT SYST APPL, V31, P734, DOI 10.1016/j.eswa.2006.01.012 Finger S., 2006, INT J ENG EDUC, V22, P636 Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Frederiksen CH, 1999, DISCOURSE PROCESS, V27, P135 Halbwachs M., 1950, COLLECTIVE MEMORY Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Hedberg B., 1981, HDB ORG DESIGN, P1 Herman I, 2000, IEEE T VIS COMPUT GR, V6, P24, DOI 10.1109/2945.841119 Hine M. J., 1998, Journal of Management Information Systems, V15 Hult GTM, 2003, DECISION SCI, V34, P189 Kando N., 1997, P BCS IRSG C, P68 Konda S., 1992, RES ENG DES, V4, P23, DOI 10.1007/BF02032390 Krsul I. V., 1998, THESIS PURDUE U Kunz W., 1970, 131 U CAL CTR PLANN Kwan MM, 2003, DECIS SUPPORT SYST, V35, P467, DOI 10.1016/S0167-9236(02)00126-4 Lamprier S., 2007, P IJCAI, P1647 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Leifer L.J., 1991, P ICED 91 ZUR, P301 Liu SF, 2006, LECT NOTES COMPUT SC, V3981, P279 Metaxiotis K., 2005, Journal of Knowledge Management, V9, DOI 10.1108/13673270510590182 Mostafa J, 1998, J AM SOC INFORM SCI, V49, P1304, DOI 10.1002/(SICI)1097-4571(1998)49:14<1304::AID-ASI8>3.0.CO;2-E Nanba H., 1999, P 16 INT JOINT C ART, P926 Olney A., 2005, P HLT EMNLP 2005 Orange G., 1999, 4 INT C NETW ENT NET Paul S, 2004, DECIS SUPPORT SYST, V36, P261, DOI 10.1016/S0167-9236(02)00144-6 Pedler M., 1996, LEARNING CO STRATEGY Radev DR, 2001, 1 DOC UND C Radev DR, 2002, COMPUT LINGUIST, V28, P399, DOI 10.1162/089120102762671927 RITTEL HWJ, 1973, POLICY SCI, V4, P155, DOI 10.1007/BF01405730 Roth G, 1998, ORGAN DYN, V27, P43, DOI 10.1016/S0090-2616(98)90023-7 Sahoo N., 2006, ACM CIKM, P357 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shapiro A., 2005, US Patent, Patent No. 20050180330 Sim SK, 2004, RES ENG DES, V15, P40, DOI 10.1007/s00163-003-0044-2 STEIN EW, 1995, INFORM SYST RES, V6, P85, DOI 10.1287/isre.6.2.85 Subrahmanian E., 2000, WORKSH CLASS SCHEM C WALSH JP, 1991, ACAD MANAGE REV, V16, P57, DOI 10.2307/258607 Watts S., 1998, THESIS BOSTON U Weiser M., 1998, Journal of Management Information Systems, V14 Westerberg A. W., 1996, AICHE S SERIES Wills G.J., 1997, P GRAPH DRAW 97, P403 NR 56 TC 1 Z9 1 PU DESIGN SOCIETY PI GLASGOW PA UNIV STRATHCLYDE, DMEM, 75 MONTROSE ST, GLASGOW, GI 1XJ, ENGLAND BN 978-1-904670-12-4 PY 2009 BP 391 EP 401 PG 11 WC Engineering, Manufacturing; Operations Research & Management Science SC Engineering; Operations Research & Management Science GA BZL68 UT WOS:000301955200035 ER PT S AU Spanakis, G Siolas, G Stafylopatis, A AF Spanakis, Gerasimos Siolas, Georgios Stafylopatis, Andreas GP IEEE TI A hybrid Web-based measure for computing semantic relatedness between words SO ICTAI: 2009 21ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE SE Proceedings-International Conference on Tools With Artificial Intelligence LA English DT Proceedings Paper CT 21st IEEE International Conference on Tools with Artificial Intelligence CY NOV 02-04, 2009 CL Newark, NJ SP IEEE Comp Soc, Biol& Artificial Intelligent Soc ID SIMILARITY AB In this paper we build a hybrid Web-based metric for commuting semantic relatedness between words. The method exploits page counts, titles, snippets and URLs returned by a Web search engine. Our technique uses traditional information retrieval methods and is enhanced by page-count-based similarity scores which are integrated with automatically extracted lexico-synantic patterns from titles, snippets and URLs for all kinds of semantically related words provided by WordNet (synonyms, hypernyms, meronyms, antonyms). A support vector machine is used to solve the arising regression problem of word relatedness and the proposed method is evaluated on standard benchmark datasets. The method achieves an overall correlation of 0.88, which is the highest among other metrics up to date. C1 [Spanakis, Gerasimos; Siolas, Georgios; Stafylopatis, Andreas] Natl Tech Univ Athens, Intelligent Syst Lab, Athens 15780, Greece. RP Spanakis, G (reprint author), Natl Tech Univ Athens, Intelligent Syst Lab, Iroon Polytechneiou 9, Athens 15780, Greece. EM gspana@ece.ntua.gr; gsiolas@image.ntua.gr; andreas@cs.ntua.gr CR Bollegala D, 2007, WWW 2007, P757 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 CHEN HH, 2006, ACL 44, P1009 Cimiano P., 2004, WWW 04, P462 Collobert R, 2001, J MACH LEARN RES, V1, P143, DOI 10.1162/15324430152733142 Fellbaum C, 1998, WORDNET ELECT LEXICA Finkelstein L, 2002, ACM T INFORM SYST, V20, P116, DOI 10.1145/503104.503110 FORMAN G, 2004, ICML 04 P 21 INT C M, P38 Grefenstette G., 1992, P 30 ANN M ASS COMP, P324, DOI 10.3115/981967.982020 IOSIF E, 2007, WI 07, P381 JARMASZ M, 2003, ROGETS THESAURUS LEX Jiang J. J., 1997, INT C RES COMP LING, P9008 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Lin D., 1998, P 17 INT C COMP LING, P768 Manning C. D., 1999, FDN STAT NATURAL LAN MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 Sahami M., 2006, WWW 06, P377 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F., 2002, ACM COMPUT SURV, V34, P47 Tsochantaridis I., 2004, ICML, P104 Turney P.D., 2001, LNCS LNAI, V2167, P491 VELEZ B, 1997, SIGIR FORUM, V31, P6 NR 25 TC 1 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1082-3409 BN 978-1-4244-5619-2 J9 PROC INT C TOOLS ART PY 2009 BP 441 EP 448 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BPP60 UT WOS:000279598100064 ER PT B AU Gu, CJ Huang, LC AF Gu Chengjian Huang Lucheng BE Muhin, VE Ye, Z TI The study on CNT-FED for emerging technology forecasting by using patent Management Map SO IEEC 2009: FIRST INTERNATIONAL SYMPOSIUM ON INFORMATION ENGINEERING AND ELECTRONIC COMMERCE, PROCEEDINGS LA English DT Proceedings Paper CT 1st International Symposium on Information Engineering and Electronic Commerce CY MAY 16-17, 2009 CL Ternopil, UKRAINE SP Natl Tech Univ Ukraine, Huazhong Univ Sci & Technol, Huazhong Normal Univ, Wuhan Univ, Ternopil Natl Econ Univ, IEEE Ukraine Sect, I&M CI Joint Chapter DE CNT-FED; patent Management Map; emerging technology; forecaste ID MATHEMATICAL-THEORY; COMMUNICATION AB Patent roadmap is the outstanding method for technology analysis, which can clearly describe the state of development of technology based on the concrete patent information. This paper does the research on global CNT-FED technology development state and the main nations situations by using the patent management map that is one branch of technology roadmap, and hoping that the analysis and prediction of the fuzzy emerging technology can give us more inspire and guidance to the R&D strategy of our nation's relevant industry, and thus expecting that we can occupy one seat on the market of Flat Panel Display in the future. C1 [Gu Chengjian; Huang Lucheng] Beijing Univ Technol, Econ & Management Sch, Beijing, Peoples R China. RP Gu, CJ (reprint author), Beijing Univ Technol, Econ & Management Sch, Beijing, Peoples R China. EM guchengjian@emails.bjut.edu.cn; hlch@bjut.edu.cn CR Aas K., 1999, 941 NORW COMP CTR CHAKRABARTI S, 2003, MINING WEB DISCOVERI, P27 LI XF, 2005, SCI SCI MANAGEMENT S, V8 PORTER A, 1995, TECHNOLOGY FORCASTIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 TAN YH, 2006, COMPUTER ENG, V9, P57 WANG JW, 2008, SCI SCI MANAGEMENT, V4 WANG JW, 2008, SHANGHAI EC REV, V5 WANG L, 2005, DATA EXCAVATION RES, V7, P59 Yoon B, 2005, TECHNOL FORECAST SOC, V72, P145, DOI 10.1016/j.techfore.2004.08.011 Yoon B., 2004, 2004 IEEE INT ENG MA, V2, P566, DOI DOI 10.1109/IEMC.2004.1407442 YOON B, 2007, EXPERT SYSTEMS A JUN, P1 ZHU DH, 2004, J IND ENG ENG MANAGE, V4 NR 15 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3686-6 PY 2009 BP 654 EP 658 DI 10.1109/IEEC.2009.143 PG 5 WC Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BNT86 UT WOS:000275528800138 ER PT B AU Che, C Teng, HF AF Che, Chao Teng, HongFei GP IEEE TI Document Representation Combining Concepts and Words in Chinese Text Categorization SO IEEE NLP-KE 2009: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING LA English DT Proceedings Paper CT IEEE International Conference on Natural Language Processing and Knowledge Engineering CY SEP 24-27, 2009 CL Dalian, PEOPLES R CHINA SP Chinese Assoc Artificial Intelligence, IEEE Signal Proc Soc DE Text categorization; combination representation; concept-based representation AB Word-based representation is widely used in text categorization. However, performance of this approach is affected by the problems derived from language variation. In this paper, we investigate a document representation combining words and concepts to integrate the advantages of two types of representations. The approach takes the part of speech as the concept for the word which is error-prone in word sense disambiguation to reduce the disambiguation mistakes. The approach employs three ways to measure the contributions of different representation forms to classification and selects the most productive one as the feature to drop the concepts not suitable for representation while not losing the lexical semantic information. We conduct experiments to compare the performance of different types of representations on Chinese text categorization corpus of Fudan University. And the results confirm the validity of our combination representation. C1 [Che, Chao; Teng, HongFei] Dalian Univ Technol, Dalian, Liaoning, Peoples R China. RP Che, C (reprint author), Dalian Univ Technol, Dalian, Liaoning, Peoples R China. EM chechao@gmail.com; tenghf@dlut.edu.cn CR Cai L, 2003, SIGIR 03, P182 CHUA S, 2004, P 2004 IEEE WIC ACM HIDALGO JMG, 2005, NATURAL LANGUAGE PRO Ide N, 1998, COMPUT LINGUIST, V24, P1 Kehagias A, 2003, J INTELL INF SYST, V21, P227, DOI 10.1023/A:1025554732352 Liao SS, 2005, LECT NOTES COMPUT SC, V3610, P1140 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1998, P COLING ACL WORKSH, P45 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4538-7 PY 2009 BP 540 EP 544 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BVF55 UT WOS:000291373000086 ER PT J AU Bhamidipati, NL Pal, SK AF Bhamidipati, Narayan L. Pal, Sankar K. TI Comparing Scores Intended for Ranking SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Score comparison; rank comparison; Kendall distance; top k lists ID INFORMATION-RETRIEVAL; COMBINATION; SIMILARITY AB Often, ranking is performed on the basis of some scores available for each item. The existing practice for comparing scoring functions is to compare the induced rankings by one of the multitude of rank comparison methods available in the literature. We suggest that it may be better to compare the underlying scores themselves. To this end, a generalized Kendall distance is defined, which takes into consideration not only the final ordering that the two schemes produce but also at the spacing between pairs of scores. This is shown to be equivalent to comparing the scores after fusing with another set of scores, making it theoretically interesting. A top k version of the score comparison methodology is also provided. Experimental results clearly show the advantages score comparison has over rank comparison. C1 [Bhamidipati, Narayan L.] Yahoo Software Dev India Pvt Ltd, Data Min & Res Grp, Bangalore 560071, Karnataka, India. [Pal, Sankar K.] Indian Stat Inst, Kolkata 700108, India. RP Bhamidipati, NL (reprint author), Yahoo Software Dev India Pvt Ltd, Data Min & Res Grp, Embassy Golf Links Businesss Pk,Off Indiranagar K, Bangalore 560071, Karnataka, India. EM narayan_bl@yahoo.com; sankar@isical.ac.in RI Pal, Sankar /G-2243-2010 CR Bar-Ilan J, 2006, COMPUT NETW, V50, P1448, DOI 10.1016/j.comnet.2005.10.020 Berkhin P, 2005, INTERNET MATH, V2, P73, DOI 10.1080/15427951.2005.10129098 Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942 Conover WJ, 1999, PRACTICAL NONPARAMET, V3rd Crestani F, 2003, J INFORM SCI, V29, P87, DOI 10.1177/016555103763031572 Debroy B, 2005, EC FREEDOM STATES IN DOMINICH S, 2003, P 12 INT WORLD WID W, P13 DONALD KM, 2003, P INT C IM VID RETR, P61 Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Fagin R, 2003, SIAM J DISCRETE MATH, V17, P134, DOI 10.1137/S0895480102412856 Ha V, 2003, ARTIF INTELL, V146, P149, DOI 10.1016/S0004-3702(03)000134 Haveliwala T.H., 1999, EFFICIENT COMPUTATIO Hawking D, 2001, INFORM RETRIEVAL, V4, P33, DOI 10.1023/A:1011468107287 HIRAI J, 2000, P 10 INT WORLD WID W Kamvar S. D., 2003, P 12 INT WORLD WID W, P261 KNIGHT WR, 1966, J AM STAT ASSOC, V61, P436, DOI 10.2307/2282833 Langville AN, 2005, SIAM REV, V47, P135, DOI 10.1137/S0036144503424786 LEE JH, 1995, P 20 ANN ACM SIGIR, P267 Lee J.H., 1995, P 18 ANN INT ACM SIG, P180, DOI 10.1145/215206.215358 Montague M., 2002, THESIS DARTMOUTH COL Montague M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management MOUNIR SA, 1998, P INT C PAR DISTR PR Nuray R, 2006, INFORM PROCESS MANAG, V42, P595, DOI 10.1016/j.ipm.2005.03.023 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rao A. R., 1992, LINEAR ALGEBRA Renda M. E., 2003, P 2003 ACM S APPL CO, P841 Richardson M, 2002, ADV NEUR IN, V14, P1441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1998, P 20 BCS IRSG C YOUNG HP, 1974, J ECON THEORY, V9, P1 NR 30 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 EI 1558-2191 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JAN PY 2009 VL 21 IS 1 BP 21 EP 34 DI 10.1109/TKDE.2008.111 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 373UN UT WOS:000260999100002 ER PT S AU Charrad, M Lechevallier, Y ben Ahmed, M Saporta, G AF Charrad, Malika Lechevallier, Yves ben Ahmed, Mohamed Saporta, Gilbert BE Corchado, E Yin, H TI Block Clustering for Web Pages Categorization SO INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009) CY SEP 23-26, 2009 CL Burgos, SPAIN SP Junta Castilla Leon, Univ Burgos, Diputac Burgos, Ayuntamiento Burgos, GCI, CSA, FAE, FEC DE Web content mining; text mining; block clustering AB With the growth of web-based applications and the increased popularity of the World Wide Web (WWW), the WWW became the greatest source of information available in the world leading to all increased difficulty of extracting relevant information. Moreover, the content of web sites is constantly changing leading to continual changes in Web users' behaviours. Therefore, there is significant interest; in analysing web content, data. to better, serve users. Our proposed approach, which is grounded oil automatic textual analysis of a web site independently from the usage attempts to define groups of documents dealing With the same topic. Both document clustering and word clustering are well studied problems. However, most existing algorithms cluster documents and words separately hut not simultaneously. In this paper, we propose to apply a block clustering algorithm to categorize a web site pages according to their content. We report results of our recent testing of CROKI2 algorithm on a tourist web site. C1 [Charrad, Malika] Natl Sch Comp Sci, Manouba 2010, Tunisia. RP Charrad, M (reprint author), Natl Sch Comp Sci, Manouba 2010, Tunisia. EM malika.charrad@riadi.rnu.tn; yves.lechevallier@inria.fr; mohamed.benahmed@riadi.rnu.tn; gilbert.saporta@cnam.fr CR CHARRAD M, 2008, ACT 8 JOURN FRANC EX, V1, P43 CHARRAD M, 2008, ECOL IA 2008 Chen HC, 1996, J VIS COMMUN IMAGE R, V7, P88, DOI 10.1006/jvci.1996.0008 CRIMMINS F, 1999, J IEEEXPERT, P55 CUTTING DR, 1992, ACM SIGIR Dhillon I., 2001, P 7 ACM SIGKDD INT C, P269, DOI DOI 10.1145/502512.502550 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 FORGY EW, 1965, BIOMETRICS, V21, P768 Govaert G., 1983, THESIS PARIS LANDAUER TK, 1996, BASIC APPL MEMORY RE, V1, P105 Madeira SC, 2004, IEEE ACM T COMPUT BI, V1, P24, DOI 10.1109/TCBB.2004.2 PIROLLI P, 1996, P C HUM FACT COMP SY Prelic A., 2006, BIOINFORMATICS, P122 ROSSI F, 2005, P ESANN 2005 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMID H, 1997, NEW METHODS LANGUAGE SCHUTZE H, 1997, ACM SIGIR STREHL A, 2000, AAAI 2000 WORKSH AI STRICKER M, 2000, THESIS ESPCI van Rijsbergen C. J., 1979, INFORM RETRIEVAL VOORHEES EM, 1986, THESIS CORNELL U NR 21 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04393-2 J9 LECT NOTES COMPUT SC PY 2009 VL 5788 BP 260 EP 267 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BND11 UT WOS:000274188700032 ER PT S AU Carchiolo, V Longheu, A Malgeri, M Mangioni, G AF Carchiolo, Vincenza Longheu, Alessandro Malgeri, Michele Mangioni, Giuseppe BE Papadopoulos, GA Badica, C TI The Assessment of Expertise in Social Networks SO INTELLIGENT DISTRIBUTED COMPUTING III SE Studies in Computational Intelligence LA English DT Proceedings Paper CT 3rd International Symposium on Intelligent and Distributed Computing (IDC 2009) CY OCT 13-14, 2009 CL Syia Napa, CYPRUS SP Univ Craiova, Software Engn Dept, Fac Automat, Comp & Elect HO Univ Cyprus, Dept Comp Sci AB Experts finding is a frequent issue in social (even web-based) networks. This work proposes a method to globally rank people according to their expertise in a set of topics. We also introduce contexts similarity to allow related contexts to be exploited during ranks assessment. These ideas are applied to the expertise network built from Epinions.com dataset, showing first significant results. C1 [Carchiolo, Vincenza; Longheu, Alessandro; Malgeri, Michele; Mangioni, Giuseppe] Univ Catania, Fac Ingn, Dip Ingn Informat & Telecomunicaz, I-95125 Catania, Italy. RP Carchiolo, V (reprint author), Univ Catania, Fac Ingn, Dip Ingn Informat & Telecomunicaz, I-95125 Catania, Italy. EM car@diit.unict.it; alongheu@diit.unict.it CR Artz D, 2007, J WEB SEMANT, V5, P58, DOI 10.1016/j.websem.2007.03.002 Banerjee S., 2002, COMPUTATIONAL LINGUI, P117 Berkhin P, 2005, INTERNET MATH, V2, P73, DOI 10.1080/15427951.2005.10129098 BROWN JJ, 1987, J CONSUM RES, V14, P350, DOI 10.1086/209118 Fang H, 2007, LECT NOTES COMPUT SC, V4425, P418 FOO N, 1992, SEMANTIC DISTANCE CO GARNER BJ, 1987, P 7 INT WORKSH EXP S, P1285 Grandison T., 2000, SURVEY TRUST INTERNE John A., 2006, P WWW 2006 Katz E, 2005, PERSONAL INFLUENCE P Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Motwani R., 1995, RANDOMIZED ALGORITHM Page L, 1999, PAGERANK CITATION RA Resnik P, 1995, INT JOINT CONF ARTIF, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sepandar D., 2003, P 12 INT WORLD WID W Zhang J, 2007, LECT NOTES COMPUT SC, V4443, P1066 NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-642-03213-4 J9 STUD COMP INTELL PY 2009 VL 237 BP 53 EP 62 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BNE71 UT WOS:000274283000006 ER PT B AU Chen, JX AF Chen, Jianxue BE Zhang, S TI FORECASTING FINANCIAL MARKET VOLATILITY BASED ON WEB MINING SO INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE & TECHNOLOGY, PROCEEDINGS LA English DT Proceedings Paper CT International Symposium on Computer Science and Technology CY NOV 20-22, 2009 CL Ningbo, PEOPLES R CHINA DE Web Mining; Machine Learning; Support Vector Machine AB A new method for predicting short term fluctuation of financial market is proposed in this paper, which is based on web mining technology. A Chinese web textual mining system is developed to predict the daily stock price fluctuation, which aim to manage web-based financial bulletin news or report, and to integrate web mining, Chinese information processing, and support vector machines learning technology. The experimental result shows that this method can produce good precision when applied to Chinese stock market, which can lead to further study on financial market short-term behavior. C1 [Chen, Jianxue] Shanghai Univ Engn Sci, Coll Elect & Elect Engn, Shanghai 201620, Peoples R China. RP Chen, JX (reprint author), Shanghai Univ Engn Sci, Coll Elect & Elect Engn, Shanghai 201620, Peoples R China. CR JOACHIMS T, 2001, C RES DEV INF RETR S Joachims T., 1998, P 10 EUR C MACH LEAR, P137 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wuthrich B., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining NR 4 TC 0 Z9 0 PU AARDVARK GLOBAL PUBL, LLC PI SALT LAKE CITY PA 9587 SO GRANDVIEW DR, SALT LAKE CITY, UTAH 84092 USA BN 978-1-4276-4030-7 PY 2009 BP 500 EP 503 PG 4 WC Computer Science, Theory & Methods SC Computer Science GA BUY19 UT WOS:000290688600135 ER PT S AU Aguilar-Lopez, D Lopez-Arevalo, I Sosa, V AF Aguilar-Lopez, Dulce Lopez-Arevalo, Ivan Sosa, Victor BE Corchado, JM Rodriguez, S Llinas, J Molina, JM TI Usage of Domain Ontologies for Web Search SO INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE 2008 SE ADVANCES IN SOFT COMPUTING LA English DT Proceedings Paper CT International Symposium on Distributed Computing and Artificial Intelligence CY OCT 22-24, 2008 CL Salamanca, SPAIN HO Univ Salamanca DE Semantic search; ontologies AB The Web is a wide repository of information available, but its heterogeneity, size and human oriented semantic supposes an obstacle in the search for desired information. Web search engines are a great help for accessing Web resources, nevertheless their classification algorithms are still limited since they only check the presence of a specific keyword or links, they do not analyse the semantic content of the resources. In recent years, several works are being related to convert the Web from an information space to a knowledge space by using common plans. One of the ways to achieve this purpose is the use of ontologies. The present paper proposes a methodology, where previously defined domain ontologies and the WordNet thesaurus axe used to perform semantic searches obtaining suitable Web page results that really belong to the expected domain. C1 [Aguilar-Lopez, Dulce; Lopez-Arevalo, Ivan; Sosa, Victor] Cinvestav Tamaulipas, Informat Technol Lab, Victoria 87276, Tamaulipas, Mexico. RP Aguilar-Lopez, D (reprint author), Cinvestav Tamaulipas, Informat Technol Lab, Km 6 Carretera Victoria Monterrey, Victoria 87276, Tamaulipas, Mexico. EM daguilar@tamps.cinvestav.mx; ilopez@tamps.cinvestav.mx; vjsosa@tamps.cinvestav.mx CR Bocio J, 2004, FR ART INT, V113, P349 DROEGEMEIER KK, 2005, IEEE COMPUTING SCI E, V7, P24 DUJMOVIC J, 2006, COMP MEAS GROUPS INT, V2, P711 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 GAO M, 2005, ICITA 2005, V2, P256 Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Mierswa I., 2006, P 12 ACM SIGKDD INT, P935, DOI DOI 10.1145/1150402.1150531 Morato J, 2003, GWC 2004: SECOND INTERNATIONAL WORDNET CONFERENCE, PROCEEDINGS, P270 Patwardhan S., 2006, P EACL 2006 WORKSH M, P1 Ramachandran R., 2006, 22 INT C INT INF PRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZRUENES D, 2007, THESIS U POLITECNICA WURST M, 2008, WORD VECTOR TOOL USE NR 13 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1615-3871 BN 978-3-540-85862-1 J9 ADV SOFT COMP PY 2009 VL 50 BP 319 EP 328 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BIR68 UT WOS:000262358600038 ER PT J AU Hou, JL Wu, YJ AF Hou, Jiang-Liang Wu, Yu-Jen TI AN AUTOMATIC QUESTION ANSWERING AND KNOWLEDGE SUMMARIZATION APPROACH FOR Q&A SERVICES SO JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS LA English DT Article DE knowledge summarization; automatic question answering; Q&A; FAQ; customer relationship management (CRM) ID MODEL; MANAGEMENT; ORGANIZATION; INTERNET; LANGUAGE; DOCUMENT AB With regard to the drastic development of information technologies, the communication channels between enterprises and customers have become more and more complicated. Many enterprises have realized that electronic CRM mechanisms can provide efficient and cost-effective customer services. In order to automatically answer customer queries via contact centers, this paper proposes an integrated model with automatic question answering and knowledge summarization mechanisms. The integrated model consists of six main phases including keyword distribution analysis, question correlation analysis, meaning unit (MU) analysis, information intensity analysis, MU correlation analysis and MU linkage. The basic idea of the model is to explore the correlation between the target question and FAQs based on their keyword distribution. According to the derived correlation, the candidate answers of the target question can be determined. Furthermore, based on the information intensity of MUs in the candidate answers, critical MUs are extracted and linked to derive the summarized answer to the target question. A web-based platform is established and the proposed approach is evaluated via an LSP e-training case. By application of the automatic question answering mechanism, interaction relationships between users and e-platforms can be improved. The proposed model can be incorporated into CRM, e-training, or knowledge management systems for efficient question answering and accurate information retrieval. C1 [Hou, Jiang-Liang; Wu, Yu-Jen] Natl Tsing Hua Univ, Hsinchu 300, Taiwan. RP Hou, JL (reprint author), Natl Tsing Hua Univ, Hsinchu 300, Taiwan. EM admhou@ie.nthu.edu.tw CR Allan James, 2001, P 24 ANN INT ACM SIG, P10, DOI 10.1145/383952.383954 APPELT DE, 1999, INT JOINT C ART INT Azzam S., 1999, P ACL 99 COLL PARK M, P77, DOI 10.3115/1608810.1608825 Barzilay R., 1997, P ACL WORKSH INT SCA, P10 Berger A, 2000, P 23 ANN INT ACM SIG, P192, DOI 10.1145/345508.345576 BURKE R, 1997, 1997 AAAI SPRING S T, P97 Chen C, 2008, CHINESE J CATAL, V29, P4, DOI 10.1016/S1872-2067(08)60010-8 CHIEN LF, 1998, P 1997 ACM SIGIR PHI, P50 Choudary C, 2007, IEEE T MULTIMEDIA, V9, P1443, DOI 10.1109/TMM.2007.906602 DAY MY, 2007, 2007 IEEE INT C INF, P203, DOI 10.1109/IRI.2007.4296621 EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 Filman RE, 1998, IEEE INTERNET COMPUT, V2, P21, DOI 10.1109/MIC.1998.707686 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Gaizauskas R., 1998, COMPUTATIONAL LINGUI, V3, P17 Greenberg I, 1999, COMPUTER, V32, P4 GUAN T, 1999, 8 INT WORLD WID WEB, P1495 GUDUVADA VN, 1997, IEEE INTERNET COMPUT, V1, P58 HAMMOND K, 1995, PR CONF ART INT APPL, P80, DOI 10.1109/CAIA.1995.378787 HIRSCHMAN L, 2001, NATURAL LANGUAGE QUE, P15 Hou J. -L., 2003, INT J PROD RES, V42, P1747 Hou JL, 2004, J COMPUT INFORM SYST, V44, P1 Hou JL, 2005, INT J ADV MANUF TECH, V26, P169, DOI 10.1007/s00170-003-1971-9 Hou JL, 2006, J COMPUT INFORM SYST, V46, P64 HOU JL, 2006, INT C BUS INF SING Hou JL, 2006, COMPUT IND ENG, V51, P44, DOI 10.1016/j.cie.2006.06.012 Hou JL, 2006, INT J ADV MANUF TECH, V28, P417, DOI 10.1007/s00170-004-2230-4 HUANG CC, 2003, THESIS I SHOU U TAIW HUANG CM, 2006, 2006 INF MAN RES APP, P213 Huang SJ, 2008, CMES-COMP MODEL ENG, V34, P1 Kherfi ML, 2007, IEEE T MULTIMEDIA, V9, P893, DOI 10.1109/TMM.2007.893349 Lee CS, 2003, EXPERT SYST APPL, V25, P431, DOI 10.1016/S0957-4174(03)00062-9 LIGHT M, 2001, J NATURAL LANGUAGE E, V7, P15 Lopez-Ostenero F, 2008, INFORM PROCESS MANAG, V44, P66, DOI 10.1016/j.ipm.2006.10.006 LUHN HP, 1958, IBM J RES DEV, V2, P159 MOLDOVAN D, 1999, 8 TEXT RETR C TREC 8, P175 PLANT RT, 1994, J COMPUT INFORM SYST, V35, P64 Prager J., 2000, P 23 ANN INT ACM SIG, P184, DOI 10.1145/345508.345574 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Rezailashkajani M, 2008, INT J MED INFORM, V77, P122, DOI 10.1016/j.ijmedinf.2006.12.001 RICARDO BY, 1999, MODERN INFORM RETRIE, P118 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Tan X, 2002, J COMPUT INFORM SYST, V42, P77 WEI LY, 2006, J INFORM MANAGEMENT, V13, P153 WHITTEN D, 2003, J COMPUTER INFORM SY, V43, P48 WU YJ, 2005, 2005 C E BUS IND U C WUNG HC, 2001, THESIS NATL TAIWAN U You JY, 2007, IEEE T CIRC SYST VID, V17, P273, DOI 10.1109/TCSVT.2007.890857 ZHOU HH, 1992, J COMPUTER INFORMATI, V33, P73 NR 49 TC 2 Z9 2 PU CHINESE INST ENGINEERS PI TAIPEI PA #1, 4TH FL, SEC 2, JEN-AI RD, TAIPEI 10019, TAIWAN SN 0253-3839 J9 J CHIN INST ENG JI J. Chin. Inst. Eng. PY 2009 VL 32 IS 2 BP 193 EP 213 DI 10.1080/02533839.2009.9671497 PG 21 WC Engineering, Multidisciplinary SC Engineering GA 447NR UT WOS:000266198700004 ER PT S AU Pirro, G Ruffolo, M Talia, D AF Pirro, Giuseppe Ruffolo, Massimo Talia, Domenico BE Spaccapietra, S TI SECCO: On Building Semantic Links in Peer-to-Peer Networks SO JOURNAL ON DATA SEMANTICS XII SE Lecture Notes in Computer Science LA English DT Article; Book Chapter DE Ontology mapping in Peer-to-Peer networks; semantic mapping; semantic P2P applications; semantic web ID ONTOLOGY ALIGNMENT AB Ontology Mapping is a mandatory requirement for enabling semantic interoperability among different agents and services relying on different ontologies. This aspect becomes more critical in Peer-to-Peer (P2P) networks for several reasons: (i) the number of different ontologies can dramatically increase; (ii) mappings among peer ontologies have to be discovered on the fly and only on the parts of ontologies "contextual" to a specific interaction in which peers are involved; (iii) complex mapping strategies (e.g., structural mapping based on graph matching) cannot be exploited since peers are not aware of one another's ontologies. In order to address these issues, we developed a new ontology mapping algorithm called Semantic Coordinator (SECCO). SECCO is composed by three individual matchers: syntactic, lexical and contextual. The syntactic matcher, in order to discover mappings, exploits different kinds of linguistic information (e.g., comments, labels) encoded in ontology entities. The lexical matcher enables discovering mappings in a semantic way since it "interprets" the semantic meaning of concepts to be compared. The contextual matcher relies on a "how it fits" strategy, inspired by the contextual theory of meaning, and by taking into account the contexts in which the concepts to be compared are used refines similarity values. We show through experimental results that SECCO fulfills two important requirements: fastness and accuracy (i.e., quality of mappings). SECCO, differently from other semantic P2P applications (e.g., Piazza, GridVine) that assume the preexistence of mappings for achieving semantic interoperability, focuses on the problem of finding mappings. Therefore, if coupled with a P2P platform, it paves the way towards a comprehensive semantic P2P solution for content sharing and retrieval, semantic query answering and query routing. We report on the advantages of integrating SECCO in the K-link+system. C1 [Pirro, Giuseppe; Talia, Domenico] Univ Calabria, DEIS, I-87036 Arcavacata Di Rende, Italy. [Ruffolo, Massimo] Exeura, I-87036 Arcavacata Di Rende, Italy. RP Pirro, G (reprint author), Univ Calabria, DEIS, I-87036 Arcavacata Di Rende, Italy. EM gpirro@deis.unical.it; ruffolo@exeura.it; talia@deis.unical.it CR Aberer K, 2004, LECT NOTES COMPUT SC, V3298, P107 Aberer K., 2003, P 12 INT WORLD WID W, P197 Berkovsky S., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence Berners-lee T., 2001, SCI AM BERNERSLEE T, 2005, CONSORTIUM STANDARDS, V4 BETHEA WL, 2006, P OAEI 2006 ATH GEOR Bouquet P, 2003, LECT NOTES COMPUT SC, V2870, P130 Castano S., 2006, P INT WORKSH ONT MAT, P134 Castano S., 2003, P 1 INT WORKSH SEM W, P231 Castano S., 2004, Proceedings. 15th International Workshop on Database and Expert Systems Applications, DOI 10.1109/DEXA.2004.1333463 Choi N, 2006, SIGMOD RECORD, V35, P34 Conrath D. W., 1997, P ROCLING 10 TAIW Davies J., 2006, SEMANTIC WEB TECHNOL Devore J. L., PROBABILITY STAT ENG Do H., 2002, P 28 INT C VER LARG, P610 Do H.-H., 2002, P 2 INT WORKSH WEB D, P221 EHRIG M, 2003, P WOW LUZ SWITZ EHRIG M, 2004, LNCS, V3298, P289 Ehrig M, 2004, LECT NOTES COMPUT SC, V3053, P76 EUZENAT J, 2006, P OM 2006 ATH GEORG Euzenat J, 2004, LECT NOTES COMPUT SC, V3298, P698 GOBLE CA, 2004, P ECAI, P1129 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 HALEVY A. Y., 2003, P 12 INT WORLD WID W, P556 Hayes J., 2004, P 16 EUR C ART INT V, P1089 Hu Wei, 2006, P ONT MATCH OM 2006, P124 KLYNE G, 2007, RESOURCE DESCRIPTION KOTIS K, 2006, P OM 2006 ATH GEORG Lauer M., 1995, P 33 ANN M ASS COMP, P47, DOI 10.3115/981658.981665 Le Coche E, 2006, LECT NOTES ARTIF INT, V4333, P166 Levenshtein I. V, 1966, SOV PHYS DOKL, P707 *LUC, 2007, LUC AP LUC PROJ Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 MITRA P, 2004, P WORKSH MEAN COORD, P71 Pan R, 2005, LECT NOTES COMPUT SC, V3729, P563 PATELSCHNEIDER PF, 2007, OWL WEB ONTOLOGY LAN Patwardhan S., 2006, P EACL 2006 WORKSH M, P1 PIRRO G, 2007, P SWAE 2007 REG GERM, P407 Qu Y., 2006, P 15 INT C WORLD WID, P23, DOI DOI 10.1145/1135777.1135786 Resnik P., 1995, P 14 INT JOINT C ART, P448 RODGERS JL, 1988, AM STAT, V42, P59, DOI 10.2307/2685263 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafer G., 1976, MATH THEORY EVIDENCE Shvaiko P., 2007, ONTOLOGY MATCHING STAAB S, 2006, DECENTRALIZED MANAGE Stoilos G, 2005, LECT NOTES COMPUT SC, V3729, P624 SU X, 2006, DATA KHOWL ENG, V1, P47 WEI H, 2005, P K CAP 2005 BANFF C, P43 WINKLER WE, 1999, STAT INCOME DIVISION, V4 *WORDNET, 2007, WORDNET LEX DAT ENG *WORDNET, 2007, WORDNET SIM BIBL YI L, 2006, P OM 2006 ATH GEORG, P181 JAVA WORDNET SIMILAR NR 54 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00684-5 J9 LECT NOTES COMPUT SC PY 2009 VL 5480 BP 1 EP 36 D2 10.1007/978-3-642-00685-2 PG 36 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BLJ40 UT WOS:000270302000001 ER PT S AU Gadzicki, K AF Gadzicki, Konrad BA Zaheer, A BF Zaheer, A BE Mertsching, B Hund, M TI Hierarchical Clustering of Sensorimotor Features SO KI 2009: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 32nd Annual German Conference on Artificial Intelligence CY SEP 15-18, 2009 CL Paderborn, GERMANY SP A3plus GmbH, Ctr Excellence Cognit Interact Technol, Deutsch Forsch Zentrum Kunstlich Intelligenz, Hamburger Informat Technol - Ctr e.V, Inst Ind IT, Linguatec Sprachtechnol GmbH, O Reilly Media Inc, Sci Direct, Springer, Semant Technol Inst, t3n Magazin, Univ Paderborn ID MAPS AB In this paper a method for clustering patterns represented by sets of sensorimotor features is introduced. Sensorimotor features as, a biologically inspired representation have proofed to be working for the recognition task, but a method for unsupervised learning of classes from a set of patterns has been missing yet. By utilization of Self-Organizing Maps as a intermediate step, a hierarchy call be build with standard agglomerative clustering methods. C1 Univ Bremen, D-28359 Bremen, Germany. RP Gadzicki, K (reprint author), Univ Bremen, D-28359 Bremen, Germany. EM konny@informatik.uni-bremen.de CR ANDERSON JA, 1990, NEUROCOMPUTING DIREC, V2 COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 Ghosh J., 2006, GROUPING MULTIDIMENS JOHNSON NF, 1970, PSYCHOL LANGUAGE MOT, V4 Kangas J, 1994, THESIS HELSINKI U TE KNUDSEN EI, 1987, ANNU REV NEUROSCI, V10, P41 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Kohonen T., 2001, SELF ORG MAPS Larsen B., 1999, KDD 99, P16 Nene S. A., 1996, CUCS00596 Oesterreich R., 1981, HANDLUNGSREGULATION REED SK, 1974, MEM COGNITION, V2, P329, DOI 10.3758/BF03209004 REINEKING T, 2008, THESIS U BREMEN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schill K, 2001, J ELECTRON IMAGING, V10, P152, DOI 10.1117/1.1329627 STEVENS A, 1978, COGNITIVE PSYCHOL, V10, P422, DOI 10.1016/0010-0285(78)90006-3 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 Zetzsche C, 2005, NETWORK-COMP NEURAL, V16, P191, DOI 10.1080/09548980500463982 Zetzsche C, 2008, COGN PROCESS, V9, P283, DOI 10.1007/s10339-008-0214-2 ZETZSCHE C, 2001, NONLINEAR IMAGE PROC, P403, DOI 10.1016/B978-012500451-0/50014-X NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04616-2 J9 LECT NOTES ARTIF INT PY 2009 VL 5803 BP 331 EP 338 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BMY12 UT WOS:000273869000042 ER PT S AU Watabe, H Yoshimura, E Tsuchiya, S AF Watabe, Hirokazu Yoshimura, Eriko Tsuchiya, Seiji BE Velasquez, JD Rios, SA Howlett, RJ Jain, LC TI Degree of Association between Documents Using Association Mechanism SO KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT I, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 13th International Conference on Knowledge-Based Intelligent Information and Engineering Systems CY SEP 28-30, 2009 CL Santiago, CHILE SP Millennium Inst Complex Engn Syst, Chilean Govt, Millennium Sci Initiat, Univ Chile, Dept Ind Engn HO Univ Chile, Fac Phys Sci & Math DE Document search; Concept-Base; Earth Mover's Distance; Degree of association AB This paper proposes a method that quantifies the similarity between documents based on the level of relevance among terms in order to deliver search that captures the meaning of documents. More specifically. this paper proposes a method that uses a concept-base to look for relevance among different terms and calculates the degree of association between documents using the Earth Mover's Distance. When the proposed methods Were subjected to comparision tests with other methods using the NTClR3-WEB, they achieved good results. C1 [Watabe, Hirokazu; Yoshimura, Eriko; Tsuchiya, Seiji] Doshisha Univ, Dept Intelligent Informat Engn & Sci, Kyoto 6100394, Japan. RP Watabe, H (reprint author), Doshisha Univ, Dept Intelligent Informat Engn & Sci, Kyoto 6100394, Japan. EM hwatabe@mail.doshisha.ac.jp; eyoshimura@indy.doshisha.ac.jp; stsuchiy@mail.doshisha.ac.jp CR Millet S, 2006, VET J, V171, P301, DOI 10.1016/j.tvjl.2004.11.019 Okumura N, 2007, LECT NOTES COMPUT SC, V4692, P604 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 5 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04594-3 J9 LECT NOTES ARTIF INT PY 2009 VL 5711 BP 243 EP 250 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BMP40 UT WOS:000273270800030 ER PT S AU Rebolledo, LV Velasquez, JD AF Rebolledo, L. Victor Velasquez, Juan D. BE Velasquez, JD Rios, SA Howlett, RJ Jain, LC TI A Platform for Extracting and Storing Web Data SO KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT II, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 13th International Conference on Knowledge-Based Intelligent Information and Engineering Systems CY SEP 28-30, 2009 CL Santiago, CHILE SP Millennium Inst Complex Engn Syst, Chilean Govt, Millennium Sci Initiat, Univ Chile, Dept Ind Engn HO Univ Chile, Fac Phys Sci & Math DE Platform; Web mining; Knowledge Base ID VISITOR BEHAVIOR; SITE AB Web data or data originated on the Web contain information and knowledge which allows to improve web site efficiency and effectiveness to attract and retain visitors. However, web data have many irrelevant data inside. Consequently, it is necessary to preprocess them to model and understand the web user browsing behavior inside them. Further, due to frequent changes in the visitor's behavior, as well as in the web site itself, the discovered knowledge may become obsolete in a short period of time. In this paper, we introduce a platform which extracts, preprocesses and stores web data to enabling the utilization of web mining techniques. In other words, there is an Information Repository (IR) which stores preprocessed web data and it facilitates the patterns extraction. Likewise, there is a Knowledge Base (KB) for storing the discovered patterns which have been validated by a domain expert. The proposed structure was tested using a real web site to prove the effectiveness of our approach. EM vireboll@gmail.com; jvelasqu@dii.uchile.cl; jvelasqu@dii.uchile.cl CR Cooley R.W., 2000, THESIS U MINNESOTA M Kimball Ralph, 2000, DATA WEBHOUSE TOOLKI Kosala R., 2000, SIGKDD EXPLORATIONS Larose D. T., 2005, DISCOVERING KNOWLEDG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srivastava J., 2000, SIGKDD EXPLORATIONS, V1, P12 Theodoridis S., 1999, PATTERN RECOGNITION Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 Velasquez JD, 2003, LECT NOTES ARTIF INT, V2773, P951 VELASQUEZ JD, 2008, ADAPTIVE WEB SITE KN Yao YY, 2005, Proceedings of the 2005 International Conference on Active Media Technology (AMT 2005), P3, DOI 10.1109/AMT.2005.1505254 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04591-2 J9 LECT NOTES ARTIF INT PY 2009 VL 5712 BP 838 EP 845 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BMS40 UT WOS:000273459700104 ER PT B AU Ewy, BJ Swink, MT Pennington, SG Evans, JB Kim, JM Ling, C Earp, SL Maeda, M AF Ewy, Benjamin J. Swink, Michael T. Pennington, Steven G. Evans, Joseph B. Kim, Jin M. Ling, Chun (Steve) Earp, Samuel L. Maeda, Mari GP IEEE TI TIGR IN IRAQ AND AFGHANISTAN: NETWORK-ADAPTIVE DISTRIBUTION OF MEDIA RICH TACTICAL DATA SO MILCOM 2009 - 2009 IEEE MILITARY COMMUNICATIONS CONFERENCE, VOLS 1-4 SE IEEE Military Communications Conference LA English DT Proceedings Paper CT IEEE Military Communications Conference (MILCOM 2009) CY OCT 18-21, 2009 CL Boston, MA SP IEEE AB The Tactical Ground Reporting (TIGR) system provides a unique multimedia patrol preparation and debriefing tool tailored to the needs of the patrol leader, and is used by all U.S. Army brigades in Iraq and Afghanistan. The distribution of data from geographically dispersed patrols requires network awareness and policy flexibility. The TIGR system utilizes an application overlay on to the tactical network that is tailored to the distribution of rich media. This architecture results in the theater-wide replication of compressed reports and meta-data, while the storage of full-quality media is distributed to the node closest to where the media was created. Thumbnail and other compressed media representations are replicated according to policy to meet soldier needs. Full-quality media can be accessed on demand from any location in the network. Network utilization is controlled by a policy-based content subscription system, a priority queuing mechanism, and the discrete scheduling of network traffic. In this paper, we focus on the requirements for media rich tactical applications, detail a system that meets those requirements, and discuss experiences using TIGR in theater. C1 [Ewy, Benjamin J.; Swink, Michael T.; Pennington, Steven G.; Kim, Jin M.; Ling, Chun (Steve)] Ascend Intelligence LLC, Arlington, VA USA. RP Ewy, BJ (reprint author), Ascend Intelligence LLC, Arlington, VA USA. CR CHIARELLI PW, 2005, MILITARY REV JUL, P16 Date C., 1997, GUIDE SQL STANDARD, V4th E C M A International, 1999, ECMA262 *FMI, 2006, 60260 FMI *JEITA, CP3451 JEITA Kleinrock L., 1975, QUEUEING SYSTEMS, V1 Open Geospatial Consortium Inc, 2006, OPENGIS WEB MAP SERV Paulson LD, 2005, COMPUTER, V38, P14, DOI 10.1109/MC.2005.330 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Talbot D, 2008, TECHNOL REV, V111, P70 TURNER JS, 1986, IEEE COMMUN MAG, V24, P8, DOI 10.1109/MCOM.1986.1092946 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5238-5 J9 IEEE MILIT COMMUN C PY 2009 BP 1150 EP 1156 PG 7 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BQA52 UT WOS:000280509900170 ER PT B AU Zakaria, C Cure, O Smaili, K AF Zakaria, Chahnez Cure, Olivier Smaili, Kamel BE Sharp, B Zock, M TI Identifying Conflicts through eMails by using an Emotion Ontology SO NATURAL LANGUAGE PROCESSING AND COGNITIVE SCIENCE, PROCEEDINGS LA English DT Proceedings Paper CT 6th International Workshop on Natural Language Processing and Cognitive Science (NLPCS 2009) CY MAY, 2009 CL Milan, ITALY AB In the logic of text classification, this paper presents an approach to detect emails conflict exchanged between colleagues, who belong to a geographically distributed enterprise. The idea is to inform a team leader of such situation, hence to help him in preventing serious disagreement between team members. This approach uses the vector space model with TF*IDF weight to represent email; and a domain ontology of relational conflicts to determine its categories Our study also addresses the issue of building ontology, which is made up of two phases First we conceptualize the domain by hand, then we enrich it by using the triggers model that enables to find out terms in corpora which correspond to different conflicts C1 [Zakaria, Chahnez; Cure, Olivier] Univ Paris Est, Terre Digital IGM LabInfo, F-77454 Marne La Vallee, France. RP Zakaria, C (reprint author), Univ Paris Est, Terre Digital IGM LabInfo, 5 Bd Descartes Champs Marne, F-77454 Marne La Vallee, France. EM chahnez.zakaria@univ-mlv.fr; olivier.cure@univ-mlv.fr; smaili@loria.fr CR BROCHES RS, 2008, THESIS U WESLEYAN CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 DEBOIS N, 2007, EP S, V1, P7 DENOUAL E, 2006, THESIS U J FOURIER DIAS G, 2002, THESIS U ORLEANS ENGUERARD C., 1995, J QUANT LINGUIST, V2, P27, DOI 10.1080/09296179508590032 Friedman N, 1997, MACH LEARN, V29, P131, DOI 10.1023/A:1007465528199 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 HATON J-P, 2006, RECONNAISSANCE AUTOM LARIVEY M, 1998, GENRES EMOTIONS LETT LAVECCHIA C, 2007, 8 C INTERSPEECH McGuiness D. L., 2002, SPINNING SEMANTIC WE, P171 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOLTZ R, 2003, ALT TRACK P 12 INT W, P139 Whittaker S., 1996, C P HUM FACT COMP SY, P276 NR 15 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-92-0 PY 2009 BP 46 EP 54 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BQL09 UT WOS:000281234100005 ER PT S AU Xu, QQ Shen, HT Cui, B Ho, XX Dai, YF AF Xu, Quanqing Shen, Heng Tao Cui, Bin Ho, Xiaoxiao Dai, Yafei BE Fratta, L Schulzrinne, H Takahashi, Y Spaniol, O TI A Novel Content Distribution Mechanism in DHT Networks SO NETWORKING 2009 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 8th International IFIP TC 6 Network Conference 2009 CY MAY 11-15, 2009 CL Aachen, GERMANY DE DHT; Content Distribution; Vector Space Model; Search Frequency AB DHT (Distributed Hash Table) is a structured overlay network that is widely utilized in P2P systems. Existing content distribution approaches do not completely exploit, features of DHT and incur heavy network bandwidth consumption. This paper analyzes existing content distribution approaches including synchronous content distribution method in eMule based on DHT overlay networks and points out that their network loads are too heavy. We propose a novel content distribution algorithm: asynchronous distribution, in DHT networks. Compared with traditional distribution approaches, it is more effective and scalable with lower network load. We apply the techniques of vector space model and search frequency to the asynchronous distribution algorithm, which effectively improves search hit ratio and reduces network load. Simulation results based on real data from Maze system show that this approach has low network overhead and publishing cost, high search and download hit ratio. C1 [Xu, Quanqing; Cui, Bin; Dai, Yafei] Peking Univ, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China. RP Xu, QQ (reprint author), Peking Univ, State Key Lab Adv Opt Commun Syst & Networks, Beijing 100871, Peoples R China. EM xqq@net.pku.edu.cn; shenht@itee.uq.edu.au; bin.cui@pku.edu.cn; xhou@cs.sunysb.edu; dyf@net.pku.edu.cn RI Cui, Bin/A-4554-2012 CR Dabek R, 2003, LECT NOTES COMPUT SC, V2735, P33 Freedman M. J., 2004, NSDI, P239 Maymounkov P, 2002, LECT NOTES COMPUT SC, V2429, P53 PARK K, 2006, NSDI Rhea S., 2005, SIGCOMM 05, P73 Rowstron A., 2001, LECT NOTES COMPUTER, V2218, P329, DOI DOI 10.1007/3-540-45518-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steiner M, 2008, P2P'08: EIGHTH INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING, PROCEEDINGS, P195 Stoica Ion, 2001, SIGCOMM 01, P149, DOI DOI 10.1145/383059.383071 STRIBLING J, 2006, NSDI Tang C., 2004, NSDI, P211 Yang XY, 2007, P2P: SEVENTH INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING, PROCEEDINGS, P185 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-01398-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5550 BP 742 EP 755 PG 14 WC Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BJX88 UT WOS:000267377300058 ER PT S AU Usui, S Kamiji, NL Taniguchi, T Ueda, N AF Usui, Shiro Kamiji, Nilton L. Taniguchi, Tatsuki Ueda, Naonori BE Leung, CS Lee, M Chan, JH TI RAST: A Related Abstract Search Tool SO NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 16th International Conference on Neural Information Processing (ICONIP 2009) CY DEC 01-05, 2009 CL Bangkok, THAILAND SP Asia Pacific Neural Network Assembly, Int Neural Network Soc, Japanese Neural Network Soc, European Neural Network Soc, IEEE Computat Intelligence Soc HO King Mongluts Univ Technol Thonburi, Sch Informat Technol DE text data mining; knowledge visualization; neuroinformatics; bipartite graph; dimensionality reduction AB With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only search abstracts by the given keyword(s), but also displays abstracts related to a single or multiple selection. It also displays information on the similarity between all abstracts on a list, providing users with more information to take into account to support finding relevant abstracts. It also suggests possible keywords to help refine their search, and an interface for storing and sorting selected abstracts for future review and/or printing. C1 [Usui, Shiro; Kamiji, Nilton L.] RIKEN, Brain Sci Inst, Lab Neuroinformat, Wako, Saitama 3510198, Japan. RP Usui, S (reprint author), RIKEN, Brain Sci Inst, Lab Neuroinformat, 2-1 Hirosawa, Wako, Saitama 3510198, Japan. EM usuishiro@riken.jp; nilton@brain.riken.jp; tatsuki.taniguchi@ivis.co.jp; ueda@cslab.kecl.ntt.co.jp CR Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Iwata T, 2007, NEURAL COMPUT, V19, P2536, DOI 10.1162/neco.2007.19.9.2536 Naud Antoine, 2007, Front Neuroinform, V1, P7, DOI 10.3389/neuro.11.007.2007 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SAITO K, 2004, 14 JAP NEUR NETW SOC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 USUI S, 2008, 15 INT C NEUR INF PR Usui S, 2007, BIOSYSTEMS, V88, P334, DOI 10.1016/j.biosystems.2006.08.015 Usui S, 2007, IEEE IJCNN, P1103, DOI 10.1109/IJCNN.2007.4371112 USUI S, 2008, SFN 2008 WASH DC 2008, REL SEARCHB SFN 2008 NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-10682-8 J9 LECT NOTES COMPUT SC PY 2009 VL 5864 BP 189 EP 195 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BPM40 UT WOS:000279253400021 ER PT S AU Zakaria, C Cure, O Salzano, G Smaili, K AF Zakaria, Chahnez Cure, Olivier Salzano, Gabriella Smaili, Kamel BE Meersman, R Dillon, T Herrero, P TI Formalized Conflicts Detection Based on the Analysis of Multiple Emails: An Approach Combining Statistics and Ontologies SO ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS: OTM 2009, PT 1 SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT On the Move Confederated International Conference CY NOV 01-06, 2009 CL Vilamoura, PORTUGAL SP Algardata SA, Collibra, Lecture Notes Comp Sci, Univ Algarve AB In Computer Supported Cooperative Work (CSCW), it is crucial for project leaders to detect conflicting situations as early as possible. Generally, this task is performed manually by studying a set of documents exchanged between team members. In this paper, we propose a full-fledged automatic solution that identifies documents, subjects and actors involved in relational conflicts. Our approach detects conflicts in emails, probably the most popular type of documents in CSCW, but the methods used can handle other text-based documents. These methods rely on the combination of statistical and ontological operations. The proposed solution is decomposed in several steps: (i) we enrich a simple negative emotion ontology with terms occuring in the corpus of entails, (ii) we categorize each conflicting email according to the concepts of this ontology and (iii) we identify emails, subjects and team members involved in conflicting emails using possibilistic description logic and a set of proposed measures. Each of these steps are evaluated and validated on concrete examples. Moreover, this approach's framework is generic and can be easily adapted to domains other than conflicts, e.g. security issues, and extended with operations making use of our proposed set of measures. C1 [Zakaria, Chahnez; Cure, Olivier; Salzano, Gabriella] Univ Paris Est, IGM Terre Digitale, Marne La Vallee, France. RP Zakaria, C (reprint author), Univ Paris Est, IGM Terre Digitale, Marne La Vallee, France. EM chahnez.zakaria@univ-mlv.fr; olivier.cure@univ-mlv.fr; gabriella.salzano@univ-mlv.fr; smaili@loria.fr CR Baader F., 2003, DESCRIPTION LOGIC HD BIGI B, 2001, P STRING PROC INF RE BROCHES RS, 2008, THESIS U WESLEYAN CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Crammer K., 2001, J MACHINE LEARNING R, V2, P2001 DENOUAL E, 2006, THESIS U J FOURIER DIAS G, 2002, THESIS U ORLEANS Dubois D., 1994, HDB LOGIC ARTIFICIAL, V3, P439 EDEN L, 1994, MULTINATIONALES AMRI ENGUERARD C., 1995, J QUANT LINGUIST, V2, P27, DOI 10.1080/09296179508590032 Francisco V, 2007, LECT NOTES COMPUT SC, V4524, P88 Garcia-Rojas A., 2006, P 1 INT WORKSH SHAP, P63 HATON J-P, 2006, RECONNAISSANCE AUTOM LARIVERY M, 1998, LETTRE PSY, V2 LAVECCHIA C, 2007, 8 C INTERSPEECH Mathieu YY, 2005, LECT NOTES COMPUT SC, V3784, P350 McIntyre G, 2007, LECT NOTES COMPUT SC, V4552, P411 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOLTZ R, 2003, P 12 INT WORLD WID W, P139 Weston J., 1998, MULTICLASS SUPPORT V Whittaker S., 1996, C P HUM FACT COMP SY, P276 Xu LH, 2007, LECT NOTES COMPUT SC, V4738, P723 Yan J., 2008, ENG LETT, V16, P166 Yang Y., 1999, REEXAMINATION TEXT C Zacklad M., 2003, GROUP 2003, P190 Zhu B, 2008, DECIS SUPPORT SYST, V45, P778, DOI 10.1016/j.dss.2008.02.004 NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-05147-0 J9 LECT NOTES COMPUT SC PY 2009 VL 5870 BP 94 EP 111 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BPL24 UT WOS:000279119400006 ER PT J AU Kim, SW Chung, CW Kim, D AF Kim, Sea Woo Chung, Chin-Wan Kim, DaeEun TI An opinion-based decision model for recommender systems SO ONLINE INFORMATION REVIEW LA English DT Article DE Information retrieval; Skills; Worldwide web AB Purpose - A good recommender system helps users find items of interest on the web and can provide recommendations based on user preferences. In contrast to automatic technology-generated recommender systems, this paper aims to use dynamic expert groups that are automatically formed to recommend domain-specific documents for general users. In addition, it aims to test several effectiveness measures of rank order to determine if the top-ranked lists recommended by the experts were reliable. Design/methodology/approach - In the approach, expert groups evaluate web documents to provide a recommender system for general users. The authority and make-up of the expert group are adjusted through user feedback. The system also uses various measures to gauge the difference between the opinions of experts and those of general users to improve the evaluation effectiveness. Findings - The proposed system is efficient when there is major support from experts and general users. The recommender system is especially effective where there is a limited amount of evaluation data from general users. Originality/value - This is an original study of how to effectively recommend web documents to users based on the opinions of human experts. Simulation results were provided to show the effectiveness of the dynamic expert group for recommender systems. C1 [Kim, DaeEun] Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea. [Chung, Chin-Wan] Korea Adv Inst Sci & Technol, Dept Comp Sci, Seoul, South Korea. [Kim, Sea Woo] Korea Adv Inst Sci & Technol, Div Informat & Commun Engn, Seoul, South Korea. RP Kim, D (reprint author), Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea. EM daeeun@yonsei.ac.kr RI Chung, Chin-Wan/C-2029-2011 FU Ministry of Knowledge Economy, Korea under the Information Technology Research Center; Institute of Information Technology Advancement [IITA-2008-C1090-0801-0031] FX This research was supported by the Ministry of Knowledge Economy, Korea under the Information Technology Research Center support programme supervised by the Institute of Information Technology Advancement (grant number IITA-2008-C1090-0801-0031). CR BAIZEYATES R, 2005, P 14 INT C WORLD WID, P2 BIRNBAUM MH, 1979, J PERS SOC PSYCHOL, V37, P48, DOI 10.1037/0022-3514.37.1.48 Breese J S, 1998, P 14 C UNC ART INT, P43 Brin S., 1998, WWW7, P107 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 DELGADO J, 1998, P INT WORKSH COOP IN, P206 FERN A, 2007, P INT JOINT C ART IN, P2 Hawkins D. I., 2004, CONSUMER BEHAV BUILD Haykin S., 1999, NEURAL NETWORKS COMP Hill Will, 1995, P ACM CHI 95 C HUM F, P194, DOI 10.1145/223904.223929 Kim D.E, 2001, P EUR C RES ADV TECH, P275 KIM SW, 2001, P 9 INT C HUM COMP I, P415 Kleinberg J., 1998, P ACM SIAM S DISCR A, P604 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Liere R., 1997, P 14 C AM ASS ART IN, P591 NAKAJIMA S, 2005, P WWW 2005 2 ANN WOR RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 RESNICK P, 2001, P COMP SUPP COOP WOR, P175 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rocha C., 2004, P 13 INT C WORLD WID, P374, DOI 10.1145/988672.988723 Ross S. M., 2000, INTRO PROBABILITY MO, V7th SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEBASTIANI F, 1999, IEIB4311999 CNR Setiono R, 1997, LECT NOTES ARTIF INT, V1224, P221 SHARKOV EV, 1995, PLATINA ROSSII, V2, P10 SOBOROFF I, 1999, P SIGIR 99 WORKSH RE SOBOROFF I, 2006, P 29 ANN INT ACM SIG Taylor H.M., 1998, INTRO STOCHASTIC MOD TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 33 TC 2 Z9 2 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 1468-4527 J9 ONLINE INFORM REV JI Online Inf. Rev. PY 2009 VL 33 IS 3 BP 584 EP 602 DI 10.1108/14684520910969970 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 480BC UT WOS:000268705700012 ER PT S AU Oommen, BJ Granmo, OC Liang, ZY AF Oommen, B. John Granmo, Ole-Christoffer Liang, Zuoyuan BE Chien, BC Hong, TP TI A Novel Multidimensional Scaling Technique for Mapping Word-Of-Mouth Discussions SO OPPORTUNITIES AND CHALLENGES FOR NEXT-GENERATION APPLIED INTELLIGENCE SE Studies in Computational Intelligence LA English DT Proceedings Paper CT 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUL 24-27, 2009 CL Tainan, TAIWAN AB The techniques which utilize Multidimensional Scaling (MDS) as a fundamental statistical tool have been well developed since the late 1970's. In this paper we show how an MDS scheme can be enhanced by incorporating into it a Stochastic Point Location (SPL) strategy (one which optimizes the former's gradient descent learning phase) and a new Stress function. The enhanced method, referred to as MDS_SPL, has been used in conjunction with a combination of the TF-IDF and Cosine Similarities on a very noisy Word-Of-Mouth (WoM) discussion set consisting of postings concerning mobile phones, yielding extremely satisfying results. C1 [Oommen, B. John; Liang, Zuoyuan] Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada. RP Oommen, BJ (reprint author), Carleton Univ, Sch Comp Sci, Ottawa, ON K1S 5B6, Canada. EM oommen@scs.carleton.ca; ole.granmo@uia.no CR KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 OOMMEN BJ, 1988, IEEE T COMPUT, V37, P2, DOI 10.1109/12.75146 OOMMEN BJ, 2008, MAPPING LIT WO UNPUB Oommen BJ, 1997, IEEE T SYST MAN CY B, V27, P733, DOI 10.1109/3477.604122 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TORGERSON WS, 1952, PSYCHOMETRIKA, V17, P401 NR 7 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1860-949X BN 978-3-540-92813-3 J9 STUD COMPUT INTELL PY 2009 VL 214 BP 317 EP 322 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BKC59 UT WOS:000267755700049 ER PT B AU Buford, JF Yu, H Lua, EK AF Buford, JF Yu, H Lua, EK TI P2P Networking and Applications SO P2P NETWORKING AND APPLICATIONS SE Morgan Kaufmann Series in Networking LA English DT Book ID TO-PEER SYSTEMS; CONTENT-ADDRESSABLE NETWORK; OVERLAY NETWORKS; INTERNET APPLICATIONS; CONTENT DELIVERY; CENTRIC WARFARE; LOOKUP SERVICE; MULTICAST; SEARCH; WEB CR 3GPP, 2006, 23228 TS 3GPP Abad C, 2004, P SOC PHOTO-OPT INS, V5441, P215, DOI 10.1117/12.539416 Abdul-Rahman A., 2000, P 33 HAW INT C SYST Aberer K., 2001, P 10 INT C INF KNOWL Aberer K., 2005, P 5 IEEE INT C PEER, P11 Aberer K., 2003, SIGMOD RECORD, V32 Aberer K., 2004, IEEE T KNOWLEDGE, V16 Aberer K., 2005, P 5 IEEE INT C PEER Aberer K., 2005, 5 IEEE INT C PEER TO Aberer K., 2003, P2P SYSTEMS, V26 Abraham I., 2005, 4 ANN INT WORKSH PEE Adamic LA, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.046135 Adar E., 2000, 1 MONDAY Aggarwal C, 1999, IEEE T KNOWL DATA EN, V11, P94, DOI 10.1109/69.755618 Ahmed R, 2007, IEEE J SEL AREA COMM, V25, P73, DOI 10.1109/JSAC.2007.070108 Alima L., 2004, LECT NOTES COMPUTER, V3267, P223 Alima L. O., 2003, 3 INT WORKSH GLOB P2 Alimal L. O., 2003, P 3 IEEE ACM INT S C, P344, DOI [10.1109/CCGRID.2003.1199386, DOI 10.1109/CCGRID.2003.1199386] Al-Shraideh F., 2006, ICN ICONS MCI 2006, P203 Ambient Networks Deliverable, 2007, SYST DES SATO ASI Andersen D., 2003, P 3 ACM SIGCOMM C IN Andersen D., 2001, P 18 ACM S OP SYST P, P131 Anderson D., 2000, P IEEE 19 ANN JOINT Anderson J. W., 2001, HYPERBOLIC GEOMETRY Anderson T., 2005, GDD0501 Androutsellis-Theotokis S, 2004, ACM COMPUT SURV, V36, P335, DOI 10.1145/1041680.1041681 Androutsellis-Theotokis S., 2004, ACM COMPUTING SURVEY, V36 Andrzejak A., 2002, P 2 IEEE INT C PEER [Anonymous], 2001, TRUSTED COMPUTING PL [Anonymous], 2007, PROL DISTR DEN SERV [Anonymous], CISCO PIX 500 SER SE Anwar Z, 2005, P SOC PHOTO-OPT INS, V5820, P33, DOI 10.1117/12.604716 Aspnes J., 2002, P 21 ANN S PRINC DIS Associated Press, 2007, SKYPE SERVICE OUTAGE Balazinska M., 2002, P PERV 2002 Balm H., 2002, IEEE COMPUT, V35, P65 Banerjee S., 2002, P ACM SIGCOMM2002 SE Banerjee S., 2003, P IEEE 22 ANN JOINT Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 Barabasi A.-L., 2000, PHYSICA A, V281 Barabasi Albert-Laszlo, 2001, PHYS WEB 0701, P33 Barbosa R., 2007, PERFORMANCE EVALUATI Baset S., 2007, PEERTOPEER PROTOCOL Baset S., 2004, CUCS03904 COL U DEP Baset S. A., 2004, CUCS039046 Baumgart I, 2007, P 10 IEEE GLOB INT S Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Bettstetter C., 2000, P EUNICE 2000 SEPT Bharambe A., 2006, P INFOCOM Bharambe A., 2004, P ACM SIGCOMM2004 AU Birman KP, 1999, ACM T COMPUT SYST, V17, P41, DOI 10.1145/312203.312207 Bisignano M., 2007, 18 INT C DAT EXP SYS, P814 Biskupski B, 2007, ACM T AUTON ADAPT SY, V2 Blake S., 1998, 2475 IETF RFC Bless R., 2008, SPONTANEOUS VIRTUAL Bluetooth Special Interest Group, 2001, SEPC BLUET SYST COR Bluetooth Special Interest Group, 2001, SDP SEPC E, V1 Bollobas B, 2003, HANDBOOK OF GRAPHS AND NETWORKS: FROM THE GENOME TO THE INTERNET, P1 Bornholdt S., 2003, HDB GRAPHS NETWORKS BOURGAIN J, 1985, ISRAEL J MATH, V52, P46, DOI 10.1007/BF02776078 Braden R., 1994, 1633 IETF RFC Bradley W. B., 2004, P 37 HAW INT C SYST Braun D., 2008, IEEE COMMUNICAT DEC Brooks R. R., 2005, DISRUPTIVE SECURITY Buford John F, 2008, International Journal of Internet Protocol Technology, V3, DOI 10.1504/IJIPT.2008.019291 Buford J., 2007, IEEE GLOB 2007 GEN S Buford J., 2001, J UNIVERSAL COMPUTER Buford J, 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications Workshops, Proceedings, P124, DOI 10.1109/PERCOMW.2006.93 Buford J., 2006, IEEE WORKSH PERVAS C BUFORD J, 2007, P IEEE GLOB TEL C IE, P81 Buford J., 2007, IEEE GLOBECOM 2007 N BUFORD J, 2008, J COMPUTER COMMUNICA, V31, P452, DOI DOI 10.1016/J.COMCOM.2007.08.019 Buford J., 2007, IEEE GLOB 2007 NOV Buyya R, 2008, LECT NOTES ELECTR EN, V9, P1, DOI 10.1007/978-3-540-77887-5 Byers J., 2003, P IPTPS BERK CA FEB Cai HL, 2006, IEEE T PARALL DISTR, V17, P1189 Camarillo G., 2006, 3G IP MULTIMEDIA SUB Camp J., 2003, P TEL POL RES C WASH Cannon J. W., 1997, HYPERBOLIC GEOMETRY Cao P, 1997, PROCEEDINGS OF THE USENIX SYMPOSIUM ON INTERNET TECHNOLOGIES AND SYSTEMS, P193 Carlsson B., 2001, LECT NOTES COMPUTER, V2252, P347 Carpenter B., 2002, 3234 RFC Carpenter B., 2001, 3056 IETF RFC Carton B., 2004, P EUR AUG SEPT Castro M, 2002, IEEE J SEL AREA COMM, V20, P1489, DOI 10.1109/JSAC.2002.803069 Castro M., 2005, P 2 S NETW SYS DES I Castro M., 2002, SIGOPS EUR WORKSH FR Castro M., 2002, P 10 WORKSH ACM SIGO Castro M., 2004, COMPUT COMMUN REV, V34, P1331 Castro M., 2002, Proceedings of the Fifth Symposium on Operating Systems Design and Implementation (OSDI'02), DOI 10.1145/1060289.1060317 Castro M., 2004, MSRTR200473 Castro M., 2003, 200394 MSRTR Castro M., 2005, P 2 C S NETW SYST DE, V2, P7 Castro M., 2003, P 20 AC7 S 3P SYS PR Castro M., 2002, MSRTR200282 Castro M., 2003, P 20 ACM S OP SYS PR Castro M., 2003, P IEEE 22 ANN JOINT Chang C.-Y., 1999, P AS PAC WEB C Chawathe Y., 2003, ACM SIGCOMM 2003 Chawathe Y., 2000, P IEEE 19 ANN JOINT Chawathe Yatin Dilip, 2000, THESIS U CALIFORNIA Christel M., 2005, JCDL05 DENV JUN 7 11 Christense K., 2004, IEEE COMPUTER AUG, V37, P91 Chu YH, 2002, IEEE J SEL AREA COMM, V20, P1456, DOI 10.1109/JSAC.2002.803066 CHUANG JCI, 2001, J TELECOMMUNICATION, V17, P281 Clark D., 2005, 33 TEL POL RES C SEP Clark D., COMMUNICATIONS STRAT, V63 Clark Dave, 2006, COMMUNICATIONS STRAT, V63 Clarke I., 2000, P ICSI WORKSH DES IS Clarke I., 2001, P INT WORKSH DES ISS Clarke I., 2003, FREENETS NEXT GENERA Clarke I., 1999, DISTRIBUTED DE UNPUB Clip D, 2007, P ANN TECHN C US 06 Cohen B., 2003, P IPTPS Cohen B., 2008, BITTORRENT PROTOCOL Conti M, 2005, P 6 ACM INT S MOB AD, P343, DOI 10.1145/1062689.1062733 Cooper E., 2007, IETFP2PSIP Costa M., 2004, P 24 IEEE INT C DIST Cox R, 2002, LECT NOTES COMPUT SC, V2429, P155 CRAMER C, 2005, P 24 IEEE INT PERF C, P365, DOI 10.1109/PCCC.2005.1460589 Crespo A, 2002, INT CON DISTR COMP S, P23 Dabek E, 2003, P 2 INT WORKSH PEER Dabek F., 2001, P 18 ACM S OP SYST P Dabek F., 2001, P 18 ACM S OP SYST P, P202 Dabek F., 2004, P ACM SIGCOMM 2004 Dabek F., 2003, P INT WORKSH PEER TO Dabek F., 2003, P 2 ANN INT WORKSH P Dabek F., 2001, Proceedings Eighth Workshop on Hot Topics in Operating Systems, DOI 10.1109/HOTOS.2001.990065 Dabek F., 2001, P 8 WORKSH HOT TOP O, P81 DALAL YK, 1978, COMMUN ACM, V21, P1040, DOI 10.1145/359657.359665 DCIA, 2008, PROACT NETW PROV PAR de Bruijn N. D., 1946, COMBINATORIAL PROB A, V49, P758 de Bruijn N.G., 1946, P K NED AKAD WETENSC, V49, P758 Despotovic Z, 2006, COMPUT NETW, V50, P485, DOI 10.1016/j.comnet.2005.07.003 Devine R., 1993, LNCS, V730, P101 Dilley J, 2002, IEEE INTERNET COMPUT, V6, P50, DOI 10.1109/MIC.2002.1036038 Divac-Kmic L., 2005, PEER TO PEER SYSTEMS, P529 Douceur J., 2002, P 1 INT WORKSH PEER Druschel P., 2001, P 18 IFIP ACM MIDDL Druschel P., 2001, P IFIP ACM INT C DIS, P329 Druschel P., 2001, PAST LARGESCALE PERS Duan ZH, 2003, IEEE ACM T NETWORK, V11, P870, DOI 10.1109/TNET.2003.820436 Eberspacher J., 2004, P 2 INT WORK C PERF El-Ansary S., 2006, HDB THEORETICAL ALGO, P665 El-Ansary S., 2003, 2 INT WORKSH PEER TO El-Sayed A, 2003, IEEE NETWORK, V17, P46, DOI 10.1109/MNET.2003.1174177 es-Erice L. Garc, 2003, P INT C PAR DISTR CO Fahmy S., 2003, IEEE ACM T NETWORKIN Fielding R., 1999, 2616 IETF RFC Floyd S, 1997, IEEE ACM T NETWORK, V5, P784, DOI 10.1109/90.650139 Ford B., 2005, P 2005 USENIX ANN TE Fraigniaud P, 2006, THEOR COMPUT SCI, V355, P65, DOI 10.1016/j.tcs.2005.12.006 Freedman M. J., 2002, P ACM C COMP COMM SE Gai AT, 2004, FOURTH INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING, PROCEEDINGS, P167 Garber L, 2000, COMPUTER, V33, P12, DOI 10.1109/MC.2000.839316 Gerla M., 2005, PERSP WORKSH PEER TO Giaccio G., 2007, IEEE INT PAR DISTR P, P1 Gibbon D., 2006, CCNC 2006 Goldberg I., 2003, PRIVACY ENHANCING TE Gong L, 2001, IEEE INTERNET COMPUT, V5, P88 Goyal VK, 2001, IEEE SIGNAL PROC MAG, V18, P74, DOI 10.1109/79.952806 Gruber I., 2004, P IEEE INT S CLUST C, P363 Gu X., 2004, P 13 IEEE INT S HIGH, P110 GUHA S, 2006, IPTPS Guha S., 2006, 5 ANN INT WORKSH PEE Gummadi K., 2003, P ACM SIGCOMM 2003 Gupta A., 2004, P 1 USENIX S NETW SY Gupta A., 2004, P 1 S NETW SYST DES, P113 Gupta G., 2003, P 1 BIENN C INN DAT Gupta I, 2003, P 2 INT WORKSH PEER Gurun S., 2006, P 1 INT WORKSH DEC R Halabi S., 2000, INTERNET ROUTING ARC HE Q, 2005, J TELECOMMUNICATION, V28, P409 Heckmann O., 2002, 082002 KOM Hei X., 2006, P WORKSH INT PROT TV Hei X., 2006, P W3R5SH PR3T TV 2PT Helal A. Choonhwa Lee, 2003, IEEE T SYST MAN CYB, V33, P682 Held G., 2005, PRACTICAL GUIDE CONT Henderson T., 2002, P ACM SIGMULTIMEDIA Hildrum K., 2002, P SPAA Hildrum K, 2004, THEOR COMPUT SYST, V37, P405, DOI 10.1007/s00224-004-1146-6 Hodes TD, 2002, WIREL NETW, V8, P213, DOI 10.1023/A:1013772027164 Hossfeld T., 2006, 4 INT WORKSH INT PER Hsiao H., 2005, P 1 INT WORKSH MOB P, V08, P799 Hsiao H.-C., 2003, P PAR DISTR PROC S I HSIAO HC, 2005, 29 ANN INT COMP SOFT, P533 Hsiao H.-C., 2003, P INT PAR DISTR PROC Huitema C., 2006, 4380 IETF RFC Hull S., 2002, CONTENT DELIVERY NET IETF HIP WG, 2008, RFC5206 IETF HIP Institute for Electrical and Electronics Engineers, 1903, P1903 IEEE International Engineering Consortium, VOIC INT PROT ITU-T, 2006, H323 ITUT Izal M., 2004, 5 ANN PASS ACT MEAS Jannotti J., 2000, P S OP SYST DES IMPL Jelasity M., 2006, P INT ICDCS WORKSH D Jennings C., 2008, IETE RESOURCE LOCATI Jennings C., 2007, ADDRESS SET IN PRESS Jeronimo M., 2003, UPNP DESIGN EXAMPLE Johnson G., 2007, US TODAY 0907 Johnson M. Eric, FILE SHARING NETWORK Johnson M. Eric, 2007, P 6 WORKSH EC INF SE Johnson M. Eric, 2007, INADVERTENT FILE SHA Johnson W. B., 1984, AM MATH SOC, P46 Johnston A., 2007, SESSION INITIT UNPUB Jung Jaeyeon, 2001, P ACM SIGCOMM INT ME Kaashoek F., 2003, IPTPS FEB Kaashoek F., 2003, P 2 INT WORKSH PEER Kaashoek M. F., 2003, P 2 IPTPS BERK CA Kalnis P., 2002, P 2002 ACM SIGMOD IN, P25 Kalogeraki V., 2002, P 11 ACM C INF KNOWL Karacali B., 2006, INT C INT COMP, P224 Karger D., 1999, P 8 INT WORLD WID WE Karger D, 1997, P 29 ANN ACM S THEOR, P654, DOI 10.1145/258533.258660 Karger D. R., 2002, P 34 ANN ACM S THEOR, P741 Karp B, 2000, P 6 ANN INT C MOB CO KERSCH P, 2008, ELSEVIER J COMPUTER, V31, P603, DOI DOI 10.1016/J.COMCOM.2007.08.017 Khambatti M., 2003, P INT C PAR DISTR PR Khlifi H, 2006, IEEE COMMUN MAG, V44, P93, DOI 10.1109/MCOM.2006.1668388 Kinateder M., 2003, ECMMERCE WEB TECHNOL Kleinberg J., 2000, Proceedings of the Thirty Second Annual ACM Symposium on Theory of Computing, DOI 10.1145/335305.335325 Kleis M., 2005, Proceedings. 10th IEEE Symposium on Computers and Communications Klemkin A., 2004, UNOFFICIAL EDO UNPUB Klemm A., 2003, P WORKSH MOB AD HOC, P41 Klingberg T., 2002, GNUTELLA PROTOCOL 0 Kolberg M., 2007, P IEEE INT C COMM IC Kong J. S., 2006, P INT C DEP SYST NET Krishnamurthy B., 1999, Proceedings. 19th IEEE International Conference on Distributed Computing Systems (Cat. No.99CB37003), DOI 10.1109/ICDCS.1999.776535 Krishnamurthy S., 2005, 4 ANN INT WORKSH PEE Kronfol A. Z., THESIS PRINCETON U Kruskal J. B., 1978, MULTIDIMENSIONAL SCA KUBIATOWICZ J, 2000, SIGPLAN, V35, P190 Kulbak Y., 2005, EMULE PROTOCOL UNPUB Kumar A., P IEEE ICNP 2003 Kumar A., 2003, Proceedings. 11th IEEE International Conference on Network Protocols Kumar V., 2006, IP TELEPHONY H323 AR Kundur D., 2007, P IEEE INT C MULT EX Lan J., 2003, P 3 IEEE WORKSH INT, P90 Landsiedel O., 2007, IEEE GLOBECOM Landsiedel O., 2006, 6 INT IEEE C PEER TO Lao L., 2005, P 8 IEEE GLOB INT S Ledlie J., 2006, P 26 IEEE INT C DIST Lee C., 2002, INT J COMPUTER RES, V11, P1 Lee S.-J., 2008, INFOCOM 2008 27 C CO, P1732 Lee S.-J., 2005, PAM Leeper D., 2006, IEEE GLOB 2006 NOV Legout A., 2006, P IMC Leong B., COMPUTER COMMUNICATI, V29, P1243 Levy A. Y., 1995, Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1995, DOI 10.1145/212433.220198 Lewis L., 2001, MANAGING BUSINESS SE Lewis L., 1999, SERVICE LEVEL MANAGE LI D, 2005, P IEEE INFOCOM MIAM, P1677 Li J., 2005, P 2 USENIX S NETW SY Li J., 2004, P 3 INT WORKSH PEER Li J., 2008, PEEER TO PEER NETWOR, V1 Li J., 2005, PERFORMANCE VS COST Li J., 2004, P IPTPS Li J, 2003, 2 INT WORKSH PEER TO LI J, 2005, COMPUT COMMUN REV, V35, P81 Li J., 2005, P 24 IEEE INF MAR Li J., 2005, P ACM SIGC AS WORKSH Li L, 2005, THEORY SCALE FREE GR Li S., HIGH PERFORMANCE MAS Li X., 2006, AUERBACH PUBLICATION, P617 Li X., 2002, P 2 ACM INT WORKSH P, P82 Li Z, 2004, IEEE J SEL AREA COMM, V22, P29, DOI 10.1109/JSAC.2003.818782 Li Z., SURVEY OVERLAY MULTI Lian Q, 2005, P 13 IEEE INT C NETW, P132 Liang J, 2006, COMPUT NETW, V50, P842, DOI 10.1016/j.comnet.2005.07.014 Liang J., 2005, P INF 05 MIAM Liben-Nowell D., 2002, P ACM PODC JUL Liebeherr J., OVERLAY NETWORKS LEC Liebeherr J., 2003, MASTERING NETWORKS I Lim H., 2003, P ACM SIGCOMM JINT M Lin C.-J., 2007, 10 IEEE GLOB INT S Liotta A, 2007, IEEE COMMUN MAG, V45, P76, DOI 10.1109/MCOM.2007.382664 Litwin W., 1993, ACM SIGMOD RECORD, V22, P327, DOI 10.1145/170036.170084 Litwin W, 1996, ACM T DATABASE SYST, V21, P480, DOI 10.1145/236711.236713 LIU Y, 2004, 1 WORKSH NETW GRID A Loewenstern A., 2008, DHT PROTOCOL LOGUINOV D, 2003, P 2003 C APPL TECHN, V33, P395 Loo B. T., 2004, P 3 INT WORKSH PEER Loser A, 2007, IEEE J SEL AREA COMM, V25, P5, DOI 10.1109/JSAC.2007.070102 Lu J., 2003, P ACM CIKM 03 NEW OR Lua E. K., 2006, P 4 IEEE INT C PEER, P266 Lua E. K., 2005, IEEE COMMUNICATIONS, V7 Lua E. K., 2005, P ACM SIGCOMM US INT Lua E. K., 2006, P 25 IEEE INFOCOM C Lua EK, 2007, IEEE IC COMP COM NET, P141 LUA EK, 2008, ELSEVIER J COMPUTER, V31, P464, DOI DOI 10.1016/J.COMCOM.2007.08.046 Lue M., 2006, P 1 INT C SCAL INF S, V152, P51 Lv Q., 2002, P 16 ACM INT C SUP Mache J., 2002, P 2 IEEE INT C PEER MacQuire A., 2006, P 4 IEEE INT C PERV Malkhi D, 2002, P 21 ACM S PRINC DIS, P183, DOI DOI 10.1145/571825.571857 Mangioni G., 2006, INT S PAR DISTR PROC Mangioni G., 2006, LNCS, V3996, P172 Manku G., 2003, 4 USENIX S INT TECHN, P127 Mao Yun, 2004, P 4 ACM SIGCOMM C IN, P278, DOI 10.1145/1028788.1028827 Marocco E., 2008, IETF P2P INFR WORKSH Max H., 2006, RAY SKYPE DEFINITIVE MAYMOUNKOV P, 2002, SPRINGER LECT NOTES, V2429, P53 Maymounkov P., 2002, P IPTPS02 CAMBR MA M McBride S., 2006, WALL STREET J E 0504 Microsoft Corporation, 2006, PEER NAM RES PROT Microsoft Corporation, 2006, INTR WIND PEER TO PE Milgram S, 1967, PSYCHOL TODAY, V2, P60, DOI DOI 10.1145/335305.335325 Milojicic DS, 2003, HPL200257R1 Mirkovic J., 2004, COMPUTER COMMUNICATI, V34 Moen D. M., 2004, OVERVIEW OVERLAY MUL Monnerat L. R., 2006, P 20 INT PAR DISTR P Monnerat L. R., 2006, P 20 IEEE INT PAR DI Morselli R., 2005, P 24 ANN ACM S PRINC, P77, DOI 10.1145/1073814.1073828 Moskowitz R., 2006, 4425 IETF RFC MOSKOWITZ R, 2007, HOST IDENTI IN PRESS Moskowitz S., 2006, INTRO DIGITAL RIGHTS Muthitacharoen Athicha, 2002, 5 S OP SYST DES IMPL Nandi A., 2004, P 2 BERT WORKSH FUT Naoumov N., 2006, P 1 INT C SCAL INF S Narayanan S., 2007, IEEE COMMUNICATI JUN NELDER JA, 1965, COMPUT J, V7, P308 Ng T. S. Eugene, 2002, P IEEE INFOCOM 2002 Ngan T., 2003, P 2 INT WORKSH PEER Nikander P., 2005, RFC4225 IETF Oppenheimer D., 2004, 04025 PLANETLAB Padmanabhan V. N., 2002, P 12 2NT W3R5SH NETW Padmanabhan V.N., 2002, P 12 INT WORKSH NETW Pang J, 2004, P 4 ACM C INT MEAS I, P1, DOI 10.1145/1028788.1028790 Pappas V., 2006, IEEE INFOCOM 2006 AP Perkins C., 2002, 3344 RFC Peterson L., 2005, IEEE COMPUTER APR, P62 Pias M., 2003, P 2 INT WORKSH PEER Piatek M., 2007, 4 USENIX S NETW SYST Pietzuch P., 2006, P IWDDS 2006 LISB PO Pietzuch P., 2005, P 2 WORKSH REAL LARG Pietzuch P., 2006, P 22 INT C DAT ENG I Plaxton C. G., 1997, P 9 ANN ACM S PAR AL, P311, DOI DOI 10.1145/258492.258523 Podlipnig S, 2003, ACM COMPUT SURV, V35, P374, DOI 10.1145/954339.954341 Pouwelse J., P 5 INT WORKSH PEER Pucha H., 2004, P 10 ACM INT C MOB C Pucha H, 2006, IEEE T PARALL DISTR, V17, P1452, DOI 10.1109/TPDS.2006.174 Pucha H., 2004, Proceedings. Sixth IEEE Workshop on Mobile Computing Systems and Applications Puttaswamy Krishna P. N., 2007, P IEEE GLOB INT S AN Qazi S., 2007, P IEEE INT C COMM IC Qiu D., 2004, P SIGCOMM QU CT, 2004, CAYLEY DHTS GROUPTHE, V3358, P914 Ramasubramanian V., 2004, P NETW SYST DES IMPL Ramasubramanian V, 2004, ACM SIGCOMM COMP COM, V34, P331, DOI 10.1145/1030194.1015504 Rao A., 2003, P IPTPS BERK CA FEB Ratnasamy S, 2001, ACM SIGCOMM COMP COM, V31, P161, DOI 10.1145/964723.383072 Ratnasamy S., 2002, P 21 ANN JOINT C IEE Ren S., 2006, P 26 INT C DISTR COM Rescorlak E., 2008, IETF P2P INFR WORKSH Rhea S., 2004, P ANN C USENIX ANN T Rhea S., 2005, P USENIX WORLDS 2005 Rhea S., 2005, P ACM SIGCOMM 2005 A Richard III G. G., 2000, IEEE INTERNET COMPUT, V4 Richard III G. G., 2002, SERVICE DEVICE DISCO Risson J, 2006, COMPUT NETW, V50, P3485, DOI 10.1016/j.comnet.2006.02.001 Rodrigues R., 2004, P 3 INT WORKSH PEER Rosenberg J., 2007, OBTAINING RE IN PRES Rosenberg J., 2007, SESSION TRAVERSAL UT Rosenberg J., 2003, 3489 IETF RFC Rosenberg J., 2002, 3261 IETF RFC Rosenberg J., 2007, INTERACTIVE CONNECTI Ross KW, 1997, IEEE NETWORK, V11, P37, DOI 10.1109/65.642358 Rostami H., 2006, P 39 ANN HAW INT C S, V9, P223 Rowstron A., 2001, P ACM IFIP INT MIDDL Rowstron A., 2001, P ACM IFIP USENIX IN Rowstron A. I. T., 2001, P IFIP ACM INT C DIS Rowstron Antony I.T., 2001, P IFIP ACM INT C DIS, P329 Salkintzis A., 2005, WIRELESS MULTIMEDIA Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sandvine Corp, 2008, IETF WORKSH PEER TO Saroiu S., 2002, Proceedings of the Fifth Symposium on Operating Systems Design and Implementation (OSDI'02), DOI 10.1145/1060289.1060319 Schmidt C., 2004, WORLD WIDE WEB J, V7 Schollmeier Rudiger, 2001, DEFINITION PEERTOPEE Schreiber F. R., 1973, SYBIL Schutt T, 2008, COMPUT COMMUN, V31, P280, DOI 10.1016/j.comcom.2007.08.027 Schutt T., 2006, P 6 IEEE INT S CLUST, V00, P8 Shaker Ayman, 2005, P 5 IEEE INT C PEER, P39 Shavitt Y., 2004, P IEEE INFOCOM 2004 Shavitt Y, 2008, IEEE ACM T NETWORK, V16, P25, DOI 10.1109/TNET.2007.899021 Shavitt Y., 2003, P IEEE INFOCOM 2003 Shen HY, 2006, PERFORM EVALUATION, V63, P195, DOI 10.1016/j.peva.2005.01.004 Shen H., 2005, J PERFORMANCE EVALUA Shen H., 2004, P 18 IEEE INT PAR DI Sherwood R., 2005, Journal of Computer Security, V13 Shi S., 2002, P IEEE 21 ANN JOINT Shudo K, 2008, COMPUT COMMUN, V31, P402, DOI 10.1016/j.comcom.2007.08.002 Shudo K., COMPUTER CO IN PRESS SHUI C, 2006, PRIMA, V4088, P386 Sia K. C., 2006, DDOS VULNERABILITY A Simeonov P. L., 2002, P 5 IEEE INT S WIR P, P1385 Simpson W., 2005, VIDEO OVER IP PRACTI Sinnreich H., 2006, INTERNET COMMUNICATI Skevik K.-A., 2004, ANAL BIT TORRENT ITS Skevik KA, 2004, LECT NOTES COMPUT SC, V3311, P206 Skogh H.-E., 2006, P 6 INT WORKSH GLOB, P9 Spatscheck O., 2002, WEB CACHING REPLICAT Sripanidkulchai K., 2003, 2 INT C IEEE COMP CO Srisuresh P., 2002, 3303 RFC Stacey R., 2006, 80211N IEEE Stalling W., 1998, SNMP SNMPV2 SNMPV3 R Stallings W., 1995, NETWORK INTERNETWORK Stiemerling M., 2006, 4540 IETF RFC Stoica I, 2001, ACM SIGCOMM COMP COM, V31, P149, DOI 10.1145/964723.383071 Stoica I, 2003, IEEE ACM T NETWORK, V11, P17, DOI 10.1109/TNET.2002.808407 Stoica I., 2001, P ACM SIGCOMM TECHN Stolarz D., 2004, MASTERING INTERNET V Strauss J., 2003, INT MEAS C FLOR Stutzbach D., 2006, P 6 ACM SIGCOMM C IN Stutzbach D., 2006, P 6 ACM SIGCOMM C IN, P189, DOI DOI 10.1145/1177080.1177105 Subramanian L., 2004, P 1 C S NETW SYST DE, V1 Suel Torsten, 2003, 6 INT WORKSH WEB DAT Suh K., 2006, P IEEE INF INF 2006 Surana S, 2006, PERFORM EVALUATION, V63, P217, DOI 10.1016/j.peva.2005.01.003 Szymaniaka M., 2008, ELSEVIER J COMPUTER, V52, P1343 Tang Bin, 2005, P IEEE INT C WIR MOB Tang C., 2002, PEERSEARCH EFFICIENT TANG CQ, 2003, P ACM SIGCOMM KARLSR, V33, P175 Tang L., 2003, P ACM SIGCOMM INT ME Tannenbaum A., 2002, COMPUTER NETWORKS TeleGeography Report and Database, 2007, TELEGEOGRAPHY REPORT Templin E, 2005, 4214 IETF RTC Terplan K., 2001, OSS ESSENTIALS SUPPO The SpoVNet Consortium, SPONET ARCH SUPP FUT Thu Y., 2006, PEEER TO PEER SYST H, P643 Thu Y., 2004, EFFICIENT SEMANTIC S Thu Y., 2003, P 3 INT C PEER TO PE, P118 Thurston WP, 1997, PRINCETON MATH SERIE, V1 Traversat M. A., 2003, LOOSELYCONSISTENT DH Tsoumakos D., 2003, P 2 INT WORKSH PEER UPnP Forum, 2001, INT GAT DEV IGD STAN US National Institute of Standards, 2004, FED INF PROC STAND Vakali A, 2003, IEEE INTERNET COMPUT, V7, P68, DOI 10.1109/MIC.2003.1250586 Van Roy P., 2008, SELF MANAGEMENT LARG Venkatesh P., 2005, ACAD OPEN INTERNET J, V14, P8 Verma D. C., 2001, CONTENT DISTRIBUTION Vischnevsky V., 2006, 6 IEEE INT C PEER TO Wallach D., 2002, P INT S SOFTW SEC TH, P42 Wang J., 1999, ACM COMPUTER COMM RE, V29, P36, DOI DOI 10.1145/505696.505701 Wang SQ, 2005, J PARALLEL DISTR COM, V65, P207, DOI 10.1016/j.jpdc.2004.09.009 Wang W., 2007, IEEE T PARALL DISTR, V18, P145 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Wepiwe G., 2006, IEICE T INF SYST, V2, P570 Wepiwe G., 2005, P 4 IEEE INT S NETW Wierzbicki A, 2004, EUR T TELECOMMUN, V15, P559, DOI 10.1002/ett.1011 Wierzbicki A., 2004, P 4 INT WORKSH GLOB, P182, DOI DOI 10.1109/CCGRID.2004.1336565 WONG B, 2005, COMPUT COMMUN REV, V35, P85 Wongrujira K., 2004, P 6 INT C ADV COMM T Xie C., 2007, GLOB INT S Xie H., 2007, 6 INT WORKSH PEER TO Xie H., 2008, IETF P2P INRF WORKSH Xie H, 2008, P ACM SIGCOMM SEATTL Xiong L., 2003, P IEEE INT C DISTR C Xu D., 2003, P SPIEACM C MULT COM Xu D., 2006, ACM SPRINGER MULTIME, V11 Xu D., CDNP2P HYBRID ARCHIT Xu J., 2003, IEEE J SELECTED NOV Xu J, 2004, IEEE J SEL AREA COMM, V22, P151, DOI 10.1109/JSAC.2003.818805 Yang B., 2002, P 22 INT C DISTR COM Yang C., 2003, PEERTOPEER ARCHITECT Yang C., 2003, P 2 INT WORKSH GRID Yang K., 2006, P IEEE INT C WEB INT, P702 Yu H., 2007, P 2007 INT IN PRESS Yu H., 2008, ENCY WIRELESS MOBILE Yu H., 2007, P IEEE INT C MULT EX Zahn T, 2008, COMPUT COMMUN, V31, P643, DOI 10.1016/j.comcom.2007.08.021 Zahn T., 2005, 5 WORKSH APPL SERV W Zahn T., 2008, COMPUTER COMMUNICATI, V31 Zhang BC, 2006, COMPUT NETW, V50, P781, DOI 10.1016/j.comnet.2005.07.016 Zhang C., SKIPINDEX SCALABLE P Zhang H, 2004, COMPUT NETW, V46, P555, DOI 10.1016/j.comnet.2004.05.004 Zhang M., 2005, P 13 ANN ACM INT C M, P287, DOI DOI 10.1145/1101149.1101206 Zhang X., 2004, IEEE J SELECTED AREA, V22 Zhang X., 2005, P 24 ANN JOINT C IEE Zhao B. Y., 2004, P 3 INT WORKSH PEER Zhao BY, 2004, IEEE J SEL AREA COMM, V22, P41, DOI 10.1109/JSAC.2003.818784 Zheng H., 2000, P PASS ACT MEAS WORK Zheng H., 2005, P PASS ACT MEAS WORK Zhou J., 2007, IEEE WORKSH SERV DIS Zhu F., 2002, MSUCSE0224 Zhu Y., 2006, COMPUTER NETWORKS, V50, P742 Zhuang S.Q., 2001, P 11 INT WORKSH NETW NR 486 TC 11 Z9 11 PU MORGAN KAUFMANN PUB INC PI SAN FRANCISCO PA 340 PINE STR, 6TH FLR, SAN FRANCISCO, CA 94104-3205 USA BN 978-0-08-092119-8; 978-0-12-374214-8 J9 MOR KAUF NETW PY 2009 BP 1 EP 415 PG 415 WC Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BGW67 UT WOS:000324393700017 ER PT S AU Moskovitch, R Nissim, N Elovici, Y AF Moskovitch, Robert Nissim, Nir Elovici, Yuval BE Bonchi, F Ferrari, E Jiang, W Malin, B TI Malicious Code Detection Using Active Learning SO PRIVACY, SECURITY, AND TRUST IN KDD SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Workshop on Privacy, Security and Trust held In Conjunction with the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining CY AUG 24-27, 2008 CL Las Vegas, NV SP ACM SIGKDD AB The recent growth in network usage has motivated the creation of new malicious code for various purposes, including economic and other malicious purposes. Currently, dozens of new malicious codes are created every day and this number is expected to increase in the coining years. Today's signature-based anti-viruses and heuristic-based methods are accurate, but cannot detect new malicious code. Recently, classification algorithms were used successfully for the detection of malicious code. We present a complete methodology for the detection of unknown malicious code, inspired by text categorization concepts. However, this approach can be exploited further to achieve a more accurate and efficient acquisition method of unknown malicious files. We use an Active-Learning framework that enables the selection of the unknown files for fast acquisition. We performed ail extensive evaluation of a test collection consisting of more than 30,000 files. We present a rigorous evaluation setup, consisting of real-life scenarios, in which the malicious file content is expected to be low, at about 10% of the files in the stream. We define specific evaluation measures based oil the known precision and recall measures, which show the accuracy of the acquisition process and the improvement in the classifier resulting from the efficient acquisition process. C1 [Moskovitch, Robert; Nissim, Nir; Elovici, Yuval] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. EM robertmo@bgu.ac.il; nirni@bgu.ac.il; elovici@bgu.ac.il RI ELOVICI, YUVAL/F-1468-2012 CR Abou-Assaleh Tony, 2004, P 28 ANN INT COMP SO Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Chang C.-C., 2001, LIBSVM LIB SUPPORT V COLUB TR, 1999, SCIENCE, V286, P531 GRYAZNOV D, 2000, P 5 INT VIR B 1999 HENCHIRI O, 2006, P ICDM 2006 HONG KON, P891, DOI DOI 10.1109/ICDM.2006.4 Joachims T., 1998, ADV KERNEL METHODS S KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Mitchell TM, 1997, MACHINE LEARNING MOSKOVITCH R, 2008, IEEE INTELLIGENCE SE Roy N., 2001, ICML SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schult M., 2001, P IEEE S SEC PRIV, P178 SHIN S, 2006, INT MEAS C IMC BRAZ TONG S, 2001, J MACHINE LEARNING R, V2, P45, DOI DOI 10.1162/153244302760185243 NR 18 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-01717-9 J9 LECT NOTES COMPUT SC PY 2009 VL 5456 BP 74 EP 91 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLK14 UT WOS:000270349300006 ER PT B AU Cao, HH Hu, DH Shen, D Jiang, DX Sun, JT Chen, EH Yang, Q AF Cao, Huanhuan Hu, Derek Hao Shen, Dou Jiang, Daxin Sun, Jian-Tao Chen, Enhong Yang, Qiang BE Sanderson, M Zhai, CX Zobel, J Allan, J Aslam, JA TI Context-Aware Query Classification SO PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2009 CL Boston, MA SP ACM SIGIR, Microsoft Res, Yahoo, Google, IBM Res, Sun DE Search context; Query classification AB Understanding users' search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries without considering their context information. However, as exemplified by the well-known example on query "jaguar", many Web queries are short and ambiguous, whose real meanings are uncertain without the context information. In this paper, we incorporate context information into the problem of query classification by using conditional random field (CRF) models. In our approach, we use neighboring queries and their corresponding clicked URLs (Web pages) in search sessions as the context information. We perform extensive experiments on real world search logs and validate the effectiveness and efficiency of our approach. We show that we can improve the F, score by 52% as compared to other state-of-the-art baselines. C1 [Cao, Huanhuan; Chen, Enhong] Univ Sci & Technol China, Hefei, Peoples R China. RP Cao, HH (reprint author), Univ Sci & Technol China, Hefei, Peoples R China. EM caohuan@ustc.edu.cn; derekhh@cse.ust.hk; doushen@microsoft.com; djiang@microsoft.com; jtsun@microsoft.com; cheneh@ustc.edu.cn; qyang@cse.ust.hk CR ANGRAWAL R, 1993, SIGMOD 93, P206 Beitzel SM, 2004, SIGIR 2004, P321 Beitzel S.M., 2005, ICDM 2005, P42 BRODER A, 2002, SIGIR FORUM, P3 Broder A., 2007, SIGIR 07, P231 CAO H, 2009, WWW 09 IN PRESS, P67203 Cao Huanhuan, 2008, KDD 08, P875 Cui H., 2002, WWW, P325 Dai H.K., 2006, WORLD WID WEB C WWW, P829 Fonseca B. M., 2005, CIKM 05, P696 GUO J, 2008, SIGIR 08, P379 He D., 2000, P BCS IRSG 22 ANN C, P57 HE Q, 2009, ICDE 09 IN PRESS, P67203 Jones R., 2008, CIKM 08, P699, DOI DOI 10.1145/1458082.1458176 KANG IH, 2003, SIGIR 2003, P64 Lafferty J.D., 2001, ICML, P282 Lee U., 2005, WWW 05, P391 Li X., 2008, SIGIR, P339 McCallum A., 2003, UA1, P403 Olsson J.S., 2005, SIGIR 05, P645 Rose D.E., 2004, WWW 2004, P13 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Settles B, 2005, BIOINFORMATICS, V21, P3191, DOI 10.1093/bioinformatics/bti475 Shen D, 2005, SIGKDD EXPLOR NEWSL, V7, P100 Shen D., 2006, SIGIR 2006, P131 SUTTON C, 2004, ICML 04, P67203 Wen Ji rong, 2001, WWW, P162 NR 27 TC 17 Z9 18 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-483-6 PY 2009 BP 3 EP 10 DI 10.1145/1571941.1571945 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLT26 UT WOS:000270976500002 ER PT B AU Cummins, R O'Riordan, C AF Cummins, Ronan O'Riordan, Colm BE Sanderson, M Zhai, CX Zobel, J Allan, J Aslam, JA TI Learning in a Pairwise Term-Term Proximity Framework for Information Retrieval SO PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CY JUL 19-23, 2009 CL Boston, MA SP ACM SIGIR, Microsoft Res, Yahoo, Google, IBM Res, Sun DE Information Retrieval; Learning to Rank; Proximity AB Traditional ad hoc retrieval models do not take into account the closeness or proximity of terms. Document scores in these models are primarily based on the occurrences or non-occurrences of query-terms considered independently of each other. Intuitively, documents in which query-terms occur closer together should be ranked higher than documents in which the query-terms appear far apart. This paper outlines several term-term proximity measures and develops an intuitive framework in which they can be used to fully model the proximity of all query-terms for a particular topic. As useful proximity functions may be constructed from many proximity measures, we use a learning approach to combine proximity measures to develop a useful proximity function in the framework. An evaluation of the best proximity functions show that there is a significant improvement over the baseline ad hoc retrieval model and over other more recent methods that employ the use of single proximity measures. C1 [Cummins, Ronan] NUI Galway, Digital Enterprise Res Inst, Galway, Ireland. RP Cummins, R (reprint author), NUI Galway, Digital Enterprise Res Inst, Galway, Ireland. EM ronan.cummins@deri.ora; colmor@it.nuigalway.ie CR BAI J, 2008, WWW 08, P1183 BEIGBEDER M, 2005, SAC 05, P1018 Bhatia MPS, 2007, LECT NOTES ARTIF INT, V4798, P267 Buttcher S., 2006, SIGIR, P621 Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6 CUMMINS R, 2007, LEARN RANK INF RETR, P67203 Fang H., 2005, SIGIR 05, P480 FRASER A, 1994, 040 U SALF CYB RES I, P67203 Jones K. S., 2000, Information Processing & Management, V36 KOZA JR, 1992, GENETIC PROGRAMMING, P67203 NA SH, 2008, SIGIR 08, P821 Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Possas B., 2002, String Processing and Information Retrieval. 9th International Symposium, SPIRE 2002. Proceedings (Lecture Notes in Computer Science Vol.2476) RASOLOFO Y, 2003, ADV INFORM RETRIEVAL, P79 ROBERTSON SE, 1995, 3 TEXT RETRIEVAL C T, P67203 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tao T., 2007, SIGIR 2007, P295 Trotman A, 2005, INFORM RETRIEVAL, V8, P359, DOI 10.1007/s10791-005-6991-7 NR 19 TC 15 Z9 16 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-483-6 PY 2009 BP 251 EP 258 DI 10.1145/1571941.1571986 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLT26 UT WOS:000270976500033 ER PT J AU Wan, JA Yu, WM Xu, XH AF Wan, Jian Yu, Wenming Xu, Xianghua BE Yu, F Yue, G Shu, J Liu, Y TI Suffix Tree Based Chinese Document Feature Extraction and Clustering in RSS Aggregator SO PROCEEDINGS OF INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY (ISCSCT 2009) LA English DT Proceedings Paper CT International Symposium on Computer Science and Computational Technology CY DEC 26-28, 2009 CL Huangshan, PEOPLES R CHINA SP Univ Jiaxing, Univ Russia, Peoples Friendship, Univ Nanchang Hangkong, Univ Feng Chia, Qingdao Univ Sci & Technol, Univ Human Agr, Guangdong Univ Business Studies, Acad Publisher DE suffix tree; feature extraction; document clustering; RSS AB In RSS aggregator, the important issue is how to make the feeds information more manageable for RSS subscriber. In this paper, we propose a suffix tree based RSS feeds document clustering in Chinese RSS aggregator. We construct a suffix tree with meaningful Chinese words, and choose the phrases with high score given by a formula as document features. We cluster document using group-average algorithm with a new document similarity measure. The experiment results show that the new method can improve the quality of clustering in document "snippets" scenario, and the speed can meet the demand of "on the fly" clustering. C1 [Wan, Jian; Yu, Wenming; Xu, Xianghua] Hangzhou Dianzi Univ, Grid & Serv Comp Lab, Sch Comp Sci & Technol, Hangzhou 310037, Zhejiang, Peoples R China. RP Wan, JA (reprint author), Hangzhou Dianzi Univ, Grid & Serv Comp Lab, Sch Comp Sci & Technol, Hangzhou 310037, Zhejiang, Peoples R China. EM yuwenming_001@163.com CR Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 HUANG YG, 2007, J CHINESE INFORM PRO HUNG C, 2007, P 16 INT C WORLD WID JIANGNING W, 2008, P IEEE 8 INT C COMP Oren Z., 1998, P 21 ANN INT ACM SIG OREN Z, 1999, P 8 INT C WORLD WID [å½­äŗ¬ PENG Jing], 2007, [č®”ē®—ęœŗå­¦ęŠ„, Chinese Journal of Computers], V30, P1354 PETER W, 1988, INFORM PROCESS MANAG, V24, P577 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOMNATH B, 2007, P 30 ANN INT ACM SIG NR 10 TC 0 Z9 0 PU ACAD PUBL PI OULU PA PO BOX 40, OULU, FIN-90571, FINLAND BN 978-952-5726-07-7 PY 2009 BP 462 EP 466 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BRA62 UT WOS:000282276100106 ER PT B AU Cleger-Tamayo, S Fernandez-Luna, JM Huete, JF Perez-Vazquez, R Rodriguez-Cano, JC AF Cleger-Tamayo, Sergio Fernandez-Luna, Juan M. Huete, Juan F. Perez-Vazquez, Ramiro Rodriguez-Cano, Julio C. BE McQuay, W Smari, WW TI A Proposal for an Experimental Platform on Collaborative Information Retrieval SO PROCEEDINGS OF THE 2009 INTERNATIONAL SYMPOSIUM ON COLLABORATIVE TECHNOLOGIES AND SYSTEMS LA English DT Proceedings Paper CT International Symposium on Collaborative Technologies and Systems CY MAY 18-22, 2009 CL Baltimore, MD SP IEEE, ACM, IFIP DE Collaborative Information Retrieval; Collaborative search; Computer-Supported Cooperative Work; Human-Computer Interaction; Personalization; Recommendation ID MODEL AB Current research on Collaborative Information Retrieval area reveals a significant synergy among Information Retrieval, Human-Computer Interaction and Computer-Supported Cooperative Work disciplines. This fact has a high incidence in the modernization of Information Retrieval Systems. In this paper, we describe both out current research efforts to design an experimental platform for collaborative information retrieval, and our progress towards implementing it as a pluggable groupware. The plaform' score has been designed as a framework, to maximize its reusability and adaptability to experiment with collaborative information retrieval techniques, with a minimal programming effort. C1 [Cleger-Tamayo, Sergio] Univ Holguin, Dept Informat, Holguin, Cuba. RP Cleger-Tamayo, S (reprint author), Univ Holguin, Dept Informat, Holguin, Cuba. EM sergio@facinf.uho.edu.cu; jmfluna@decsai.ugr.es; jhg@decsai.ugr.es; rperez@uclv.edu.cu; jcrodriguez@facinf.uho.edu.cu RI Huete, Juan/C-2428-2012; Fernandez Luna, Juan Manuel/C-6845-2012 CR Adomavicius G., 1999, P 5 ACM SIGKDD INT C, P377, DOI 10.1145/312129.312287 AMERSHI S, 2008, P CHI 08, P1647, DOI 10.1145/1357054.1357311 BAROUNIEBRAHIMI M, 2008, CAN C AI, P60 CANTADOR I, 1997, IEEE WIC ACM INT C W, P562 Cheng L.T., 2003, ECLIPSE 03, P45 FOLEY C, 2008, JCDL WORKSH COLL INF FOLEY C, 2006, COLLABORATECOM 2006, P1 Galegher J., 1990, CSCW 90 Los Angeles. Proceedings of the Conference on Computer-Supported Cooperative Work GARRIDO JL, 2005, REQUIREMENTS ENG SOC, P226 GAUCH S, 2007, USER PROFILES PERSON GOLOVCHINSKY G, 2008, INF SEEK SUPP SYST W GOLOVCHINSKY G, 2008, P CSCW C SAN DIEG CA GREENBERG S, 1996, ACM SIGCHI96 C HUM F, P271 Greenberg S., 1996, ACM T COMPUT-HUM INT, V3, P66, DOI 10.1145/226159.226162 GROSS T, 1998, COLL COOP INF SEEK D GRUDIN J, 1994, IEEE COMPUT, V27, P19 Hansen P, 2005, INFORM PROCESS MANAG, V41, P1101, DOI 10.1016/j.ipm.2004.04.016 Henning M., 2008, DISTRIBUTED PROGRAMM Jameson A., 2007, LNCS, V4321, P596 Linde G, 2003, IEEE INTERNET COMPUT, P76 Manning C.D., 2008, INTRO INFORM RETRIEV Marchionini G., 2006, Bulletin of the American Society for Information Science and Technology, V32, DOI 10.1002/bult.2006.1720320508 MORRIS MR, 2007, P 20 ANN ACM S US IN, P3, DOI 10.1145/1294211.1294215 MORRIS MR, 2008, P CSCW C, P481, DOI 10.1145/1460563.1460640 MORRIS MR, 2007, COLLABORATING ALONE MORRIS MR, 2008, P 26 ANN SIGCHI C HU, P1657, DOI 10.1145/1357054.1357312 PAVLOV V, 2004, IEEE INTELL SYST APP, V19, P40 PICKENS J, 2007, COLLABORATIVE EXPLOR, P21 PICKENS J, 2008, SIGIR Reddy MC, 2008, INFORM PROCESS MANAG, V44, P256, DOI 10.1016/j.ipm.2006.12.010 Reddy MC, 2008, INFORM PROCESS MANAG, V44, P242, DOI 10.1016/j.ipm.2006.12.003 RODRIGUEZ M, 2007, P CRIWG 07, P157 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schafer J. B., 2007, The Adaptive Web. Methods and Strategies of Web Personalization SCHLICHTER J, 1997, COORDINATION TECHNOL, P199 SMEATON AF, 2006, SPIE ELECT IMAGING M, P15 Staab S, 2005, IEEE INTELL SYST, V20, P80, DOI 10.1109/MIS.2005.16 Storey M.-A.D., 2005, SOFTVIS 05, P193 Tiessen E. L., 1997, Journal of Interactive Learning Research, V8 van Rijsbergen C. J., 1979, INFORM RETRIEVAL WELLS D, 1996, GROUPWARE COLLABORAT 2009, APACHE MAHOUT TASTE NR 43 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4585-1 PY 2009 BP 485 EP 493 PG 9 WC Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary SC Computer Science; Engineering GA BLS38 UT WOS:000270937300063 ER PT B AU Zhang, QR Tan, JH Zhou, HY Tao, WY He, KJ AF Zhang, Qirui Tan, Jinghua Zhou, Huaying Tao, Weiye He, Kejing BE Luo, Q TI Machine Learning Methods for Medical Text Categorization SO PROCEEDINGS OF THE 2009 PACIFIC-ASIA CONFERENCE ON CIRCUITS, COMMUNICATIONS AND SYSTEM LA English DT Proceedings Paper CT Pacific-Asia Conference on Circuits, Communications and Systems CY MAY 16-17, 2009 CL Chengdu, PEOPLES R CHINA SP IEEE, Intelligent Informat TechnolApplicat Res Assoc, Int Journal Intelligent Informat Technol Applica, Wuhan Inst Technol DE medical text categorization; machine learning; immune algorithm; document indexing AB This paper reports a comparative study for medical text categorizations on four machine learning methods: k Nearest Neighbor (kNN), Support Vector Machines (SVM), Naive Bayes (NB) and Clonal Selection Algorithm Based on Antibody Density (CSABAD). CSABAD is an improved immune algorithm proposed by us. According to the clonal selection principle and density control mechanism, only those cells that have higher affinity and lower density are selected to proliferate. In addition, we propose an improved approach, called Term Frequency, Inverted Document Frequency and Inverted Entropy (TFIDFIE), to compute term weights in document indexing. It considers the distribution of documents in the training set in which the term occurs. Our experiments show that SVM and CSABAD outperform significantly kNN and Naive Bayes, and TFIDFIE is more effective than TFIDF on OHSCAL data set. C1 [Zhang, Qirui; Zhou, Huaying; Tao, Weiye] Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. RP Zhang, QR (reprint author), Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. EM qiruizhang@163.com; tanjh@gsta.com RI He, Kejing/ OI He, Kejing/0000-0003-4116-037X CR De Castro L. N., 2000, P GECCO 00 WORKSH AR, P36 Fabrizio S., 2002, ACM COMPUT SURV, V34, P147 Han E.-H., 2000, 4 EUR C PRINC PRACT, P424 MCCALLUM A, 1998, P AAAI 98 WORKSH LEA, P509 Mitchell T., 1996, MACHINE LEARNING *OHSUMED, 2008, OHSUMED DAT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnic VN, 1995, NATURE STAT LEARNING Xu Yan, 2008, Journal of Software, V19, DOI 10.3724/SP.J.1001.2008.00082 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, REEXAMINATION TEXT C, P42 Zhang QR, 2005, PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, P3792 ZHANG QR, 2007, J MICROCOMPUTER INFO, V23, P210 2008, MEDLINE DATABASE INF NR 14 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3614-9 PY 2009 BP 494 EP 497 DI 10.1109/PACCS.2009.156 PG 4 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BMV82 UT WOS:000273677100124 ER PT B AU Stein, B Anderka, M AF Stein, Benno Anderka, Maik BE Tjoa, AM Wagner, RR TI Collection-Relative Representations A Unifying View to Retrieval Models SO PROCEEDINGS OF THE 20TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATION LA English DT Proceedings Paper CT 20th International Conference on Database and Expert Systems Applications CY AUG 31-SEP 04, 2009 CL Linz, AUSTRIA SP DEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, Johannes Kepler Univ Linz ID INFORMATION-RETRIEVAL AB Various retrieval models have been developed and analyzed so far, but less research aims to an integration of the different models within a common framework. This paper introduces the idea of collection-relative retrieval models, a paradigm where several important retrieval models fit in. Our unifying view helps to better understand retrieval models, and it can be considered as a step towards a common theoretical framework for text retrieval. Collection-relative retrieval models employ a so-called index collection. We present an evaluation that shows how particular characteristics of the underlying index collection affect the retrieval performance of a collection-relative model. Based on such insights tailored index collections can be constructed in order to address specialized retrieval tasks. C1 [Stein, Benno; Anderka, Maik] Bauhaus Univ Weimar, Fac Media, D-99421 Weimar, Germany. RP Stein, B (reprint author), Bauhaus Univ Weimar, Fac Media, D-99421 Weimar, Germany. EM benno.stein@uni-weimar.de; maik.anderka@uni-weimar.de CR Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gabrilovich E., 2006, THESIS TECHNION ISRA Gabrilovich Evgeniy, P IJCAI 2007 LEE M, P COGSCI 2005 Nolan JP, STABLE DISTRIBUTIONS Ponte J. M., P SIGIR 1998 POTTHAST M, P ECIR 2008 ROBERTSON SE, 1997, PROBABILIT RANKING P Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srikanth M, 2004, THESIS STATE U NEW Y VOORHEES EM, 1999, P TREC 8 WONG SKM, P SIGIR 1985 NR 14 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3763-4 PY 2009 BP 383 EP 387 DI 10.1109/DEXA.2009.50 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BNV26 UT WOS:000275655300072 ER PT S AU Yen, NY Chao, LR Jin, Q Ma, JH Shih, TK AF Yen, Neil Y. Chao, Louis R. Jin, Qun Ma, Jianhua Shih, Timothy K. GP IEEE TI Resource Retrieval Service to Enhance U-Learning Environment SO PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION TECHNOLOGIES & APPLICATIONS (ICUT 2009) SE Proceedings of the International Conference on Ubiquitous Information Technologies & Applications LA English DT Proceedings Paper CT 4th International Conference on Ubiquitous Information Technologies CY DEC 20-22, 2009 CL Fukuoka, JAPAN SP IEEE DE resource retrieval; learning object; web mining; ubiquitous learning; object ranking AB E-Learning makes it possible for learners to study at any time. With the improvement of ubiquitous technologies and corresponding devices, learners can conduct their learning activities without any limitations from the environment. Instructors can utilize various kinds of resources to create more plentiful learning materials for learners. In this situation, instructors or learners have to spend more time on collecting relevant resources for a specific purpose. In our previous works, we utilized the geographical information and RFID (Radio Frequency Identification) to develop an outdoor adventure game to support learning procedures. In this paper, we go further to propose and develop a resource retrieval mechanism to assist learners in collecting relevant learning resources. This mechanism can be regarded as a search procedure. We aim at providing search results in the order of relevant degree based on learners' current geographical information. It can make resource retrieval more efficient in ubiquitous learning environments. C1 [Yen, Neil Y.; Chao, Louis R.] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan. RP Yen, NY (reprint author), Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan. EM neil219@gmail.com; sally@mail.tku.edu.tw; jin@waseda.jp; jianhua@hosei.ac.jp; timothykshih@gmail.com CR BROWN R, 2006, 18 ANN C AUSTR COMP HAIBO G, 2008, 2008 INT C INF AUT J, P697 Herlocker J, 2002, INFORM RETRIEVAL, V5, P287, DOI 10.1023/A:1020443909834 HINZE A, 2006, 29 AUSTR COMP SCI C *IEEE, IEEE PUBL PRIV INF P *IMS, IMS COMM CARTR SPEC IMS, IMS LEARN INF PACK L JARVELIN K, 2000, 23 ANN INT ACM SIGIR McCarey F, 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Proceedings, P154, DOI 10.1109/IAT.2006.23 OGATA H, 2006, 6 INT C ADV LEAN TEC, P928, DOI 10.1109/ICALT.2006.1652595 Ogata H., 2004, IEEE INT WORKSH WIR, P27 QAMAR AM, 2008, 8 IEEE INT C DAT MIN, P983 Rhodes BJ, 1997, FIRST INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS - DIGEST OF PAPERS, P123 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARWAR B, 2010, 10 INT C WORLD WID W WARTENA C, 2008, 19 INT C DAT EXP SYS, P54 WU CI, 2007, INT C UB COMP 2007, P235 YANG MJ, 2009, 9 IEEE INT C ADV LEA YOUNIS AA, 2007, 7 IEEE INT C BIOINF, P1139 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1976-0035 BN 978-1-4244-5131-9 J9 P INT C UBIQ INFORM PY 2009 BP 365 EP 370 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BRS47 UT WOS:000283532900067 ER PT B AU Chen, X Chen, SH AF Chen, Xi Chen, Shihong BE Hu, ZB Liu, QT TI Subsequence-based Text Segmentation and Labeling SO PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND COMPUTER SCIENCE, VOL I LA English DT Proceedings Paper CT 1st International Workshop on Education Technology and Computer Science CY MAR 07-08, 2009 CL Wuhan, PEOPLES R CHINA SP Huazhong Univ Sci & Technol, Harbin Inst Technol, IEEE Harbin Sect, IEEE Tech Comm Learning Technol, Huazhong Normal Univ, Wuhan Univ DE text segmentation; passage labeling; subsequence; sentence coherence; maximum cut AB Text segmentation is important for many natural language processing tasks, such as passage retrieval and summarization. This paper uses suffix tree model for the text representation and introduces a new measure, subsequence-based coherence, to represent the coherence between sentences and utilize the word order information. This paper also introduces a text segmentation algorithm, subsequence-based maximum cut, and a passage labeling approach based on subsequences. The educational text segmentation results show that our method outperforms some of the existing methods, and the passage labeling result is approving. C1 [Chen, Xi; Chen, Shihong] Wuhan Univ, Comp Sch, Wuhan, Peoples R China. EM sydchen@hotmail.com CR Beeferman D, 1999, MACH LEARN, V34, P177, DOI 10.1023/A:1007506220214 Cavnar W. B., 1994, TREC, P269 Chim H, 2007, P 16 INT WORLD WID W CHOI F, 2000, P NAACL CHRISTENSEN H, 2005, P ICASSP Eissen S. M., 2005, P 5 INT C KNOWL MAN, P596 HALLIDAY MAK, 1976, LONGMAN Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 MALIOUTOV I., 2006, P 21 INT C COMP LING, P25, DOI 10.3115/1220175.1220179 MEI Q, 2007, P 13 ACM SIGKDD INT, P490, DOI 10.1145/1281192.1281246 Pevzner L, 2002, COMPUT LINGUIST, V28, P19, DOI 10.1162/089120102317341756 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 14 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3557-9 PY 2009 BP 582 EP 587 DI 10.1109/ETCS.2009.138 PG 6 WC Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific Disciplines; Social Sciences, Mathematical Methods SC Computer Science; Education & Educational Research; Mathematical Methods In Social Sciences GA BKI84 UT WOS:000268236900131 ER PT J AU Klein, ST Shapira, D AF Klein, Shmuel T. Shapira, Dana BE Holub, J Zdarek, J TI On the Usefulness of Backspace SO PROCEEDINGS OF THE PRAGUE STRINGOLOGY CONFERENCE 2009 LA English DT Proceedings Paper CT 14th Prague Stringology Conference (PSC) CY AUG 31-SEP 02, 2009 CL Prague, CZECH REPUBLIC SP Prague Stringol Club HO Czech Tech Univ, Dept Comp Sci & Engn AB The usefulness of a backspace character in various applications of Information Retrieval Systems is investigated. While not being a character in the initial sense of the word, a backspace can be defined as being a part of an extended alphabet, thereby enabling the enhancement of various algorithms related to the processing of queries in Information retrieval. We bring examples of three different application areas. C1 [Klein, Shmuel T.] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. RP Klein, ST (reprint author), Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. EM tomi@cs.biu.ac.il; shapird@ash-college.ac.il CR Chang K. C.-C., 1999, ACM T INFORM SYSTEMS, V17, P1 Choueka Y., 1985, P 8 ANN INT ACM SIGI, P122, DOI 10.1145/253495.342777 Choueka Y., 1989, IEEE DATA ENG B, V14, P22 Fraenkel A. S., 1976, Jurimetrics Journal, V16 Frakes W., 1992, INFORMATION RETRIEVA, P131 HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898 JONES S., 2000, INFORM PROCESSING MA, V36, P779 Klein ST, 2009, INFORM PROCESS MANAG, V45, P298, DOI 10.1016/j.ipm.2008.12.003 Moffat A., 1995, SOFTWARE PRACTICE EX, V25, P891 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIEMINSKI A, 1988, INFORM PROCESS LETT, V26, P237, DOI 10.1016/0020-0190(88)90146-9 Tryfonopoulos C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009047 Veronis J., 2000, PARALLEL TEXT PROCES, P369 Witten I.H., 1994, MANAGING GIGABYTES C NR 14 TC 0 Z9 0 PU CZECH TECHNICAL UNIV PRAGUE PI PRAGUE 6 PA ZIKOVA 4, PRAGUE 6 166 35, CZECH REPUBLIC BN 978-80-01-04403-2 PY 2009 BP 80 EP 89 PG 10 WC Logic SC Science & Technology - Other Topics GA BHB19 UT WOS:000324864200008 ER PT J AU Aguilar-Lopez, D Lopez-Arevalo, I Sosa-Sosa, V AF Aguilar-Lopez, Dulce Lopez-Arevalo, Ivan Sosa-Sosa, Victor TI Use of ontologies to enhance the results of web search engines SO PROFESIONAL DE LA INFORMACION LA Spanish DT Article DE Ontologies; Semantic search; WordNet AB With the increasing number of web sites, the time spent by users reviewing the results also increases. In addition, the nature of web content is semantically heterogeneous and oriented to people who will be able to understand it. Frequently the results from search engines do not correspond to the expected topic. One approach to improve the results is to match the content of the web pages with a formal vocabulary on the topic (ontology) and with the informal vocabulary (common terms of the topic but not in the ontology). This paper describes a web search method that takes advantage of ontologies to reduce the search area of certain topics. With this approach the relevance of search engine results is enhanced by filtering the content through the integration of domain ontologies, the WordNet thesaurus, and a hierarchical similarity Measure. Thus, the improvement on the relevance of results reduces the time required to review such results. C1 [Aguilar-Lopez, Dulce; Lopez-Arevalo, Ivan; Sosa-Sosa, Victor] CINVESTAV, Lab Tecnol Informac, Mexico City, DF, Mexico. RP Aguilar-Lopez, D (reprint author), CINVESTAV, Lab Tecnol Informac, Mexico City, DF, Mexico. EM daguilar@tamps.cinvestav.mx; ilopez@tamps.cinvestav.mx; vjsosa@tamps.cinvestav.mx RI Sosa-Sosa, Victor/B-4525-2008 CR AGUILARLOPEZ D, 2008, P DCAI, P319 AGUILARLOPEZ D, 2008, 15 INT MULT ADV COMP Bocio J, 2004, FR ART INT, V113, P349 Bow Andrew McCallum, BOW TOOLKIT STAT LAN Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chignell MH, 1999, INFORM PROCESS MANAG, V35, P337, DOI 10.1016/S0306-4573(98)00065-X DROEGEMEIER KK, 2005, IEEE COMPUTING SCI E, V7, P24 DUMONTIER M, 2008, 4 INT WORKSH OWL EXP Fernandez-Lopez M, 1997, P AAAI97 SPRING S SE, P33 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 GAO MX, 2005, ICITA 05, P256 Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 LINDESAY V, SCHEMA WEB DIRECTORY MORATO J, 2007, P 2 GLOB WORDNET C, P270 NOY N, 2000, 12 INT C KNOWL ENG K, P17 Patwardhan S., 2006, P EACL 2006 WORKSH M, P1 Ramachandran R., 2006, 22 INT C INT INF PRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZRUENES D, 2007, THESIS U POLITECNICA SELBERG E, 1997, IEEE EXPERT JAN, P11 Silverstein C., 1999, SIGIR Forum, V33 Wackerly D., 2002, ESTADISTICA MATEMATI WURST M, WORD VECTOR TOOL USE NR 25 TC 0 Z9 0 PU EPI PI BARCELONA PA APARTADO 32 280, BARCELONA, 08080, SPAIN SN 1386-6710 J9 PROF INFORM JI Prof. Inf. PD JAN-FEB PY 2009 VL 18 IS 1 BP 34 EP 40 DI 10.3145/epi.2009.ene.05 PG 7 WC Information Science & Library Science SC Information Science & Library Science GA 408SX UT WOS:000263456400005 ER PT J AU Sirisha, BS Jeevan, VKJ Kumar, RVR Goswami, A AF Sirisha, B. S. Jeevan, V. K. J. Kumar, R. V. Raja Goswami, A. TI A personalised information support system for searching portals and e-resources SO PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS LA English DT Article DE India; Higher education; Data collection; Modelling; Information retrieval ID WORLD-WIDE-WEB; DISCOVERY AB Purpose - The purpose of this paper is to describe the development of a personalised information Support System to help faculty members to search various portals and e-resources without typing the search terms in different interfaces and to obtain results re re-ordered without human intervention. Design/methodology/approach - After a careful survey of various tools and techniques available for computerised client-centred information services, the study selected to apply web usage mining, proxy level data collection and a vector space retrieval model to develop the personalised information Support for teaching and research in a higher education institution. Findings - There are practical constraints in the implementation stage. There is considerable difficulty ill getting real and correct User interests and mapping them effectively into the products and services offered by the library. Also the interests Of users change continuously. If multiple users share the same PC, it is difficult to identify the user as there is no one-to-one mapping between user and IP address. Research limitations/implications - The paper has not considered cases for all the faculty members due to time constraints. The results obtained from the system also need correlation with the sources actually used by the faculty to test its efficacy in a highly fluid research situation like higher academics. Practical implications - A pragmatic client-centred information support prototype described in this paper may find use in other institutions needing similar information support. Originality/value - This paper demonstrates the pragmatic application of ICT for linking users and e-resources in an academic library. C1 [Jeevan, V. K. J.] Indira Gandhi Natl Open Univ, Lib & Documentat Div, New Delhi, India. [Kumar, R. V. Raja] Indian Inst Technol, Dept Elect & Elect Commun Engn, Kharagpur 721302, W Bengal, India. [Goswami, A.] Indian Inst Technol, Dept Math, Kharagpur 721302, W Bengal, India. RP Jeevan, VKJ (reprint author), Indira Gandhi Natl Open Univ, Lib & Documentat Div, New Delhi, India. EM vkjj@rediffmail.com CR Agrawal R., 1994, P 20 INT C VER LARG, P478 Anand SS, 2005, LECT NOTES ARTIF INT, V3169, P1 ARORA J, 2003, MAPPING TECHNOLOGY L, P271 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 CATLEDGE LD, 1995, COMPUT NETWORKS ISDN, V27, P1065, DOI 10.1016/0169-7552(95)00043-7 CHEN CC, 2001, P US MOD Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Cooley R., 1999, Knowledge and Information Systems, V1 DIEBOLD B, 2001, P 2001 AS PAC S INF, V9, P159 Eirinaki M, 2003, ACM T INTERNET TECHN, V3, P1, DOI DOI 10.1145/643477.643478 Han E.-H., 1998, P 2 INT C AUT AG, P408, DOI DOI 10.1145/280765.280872 HAN JW, 1993, IEEE T KNOWL DATA EN, V5, P29, DOI 10.1109/69.204089 Jeevan V. K. J., 2006, Library Review, V55, DOI 10.1108/00242530610706761 Jeevan VKJ, 2006, ELECTRON LIBR, V24, P680, DOI 10.1108/02640470610707277 Joachims T., 1997, P 15 INT JOINT C ART Kramer J., 2000, Communications of the ACM, V43, DOI 10.1145/345124.345139 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Manber U, 2000, COMMUN ACM, V43, P35, DOI 10.1145/345124.345136 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahabi C, 2002, LECT NOTES ARTIF INT, V2356, P113 Srivastava J., 2000, ACM SIGKDD EXPLORATI, V1, P12, DOI 10.1145/846183.846188 Steinbach M., 2000, COMP DOCUMENT CLUSTE VOZALIS E, 2001, INTELLIGENT TECHNIQU ZHANG F, 2002, P 1 IEEE INT C MACH, P986 NR 26 TC 1 Z9 1 PU EMERALD GROUP PUBLISHING LIMITED PI BINGLEY PA HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND SN 0033-0337 J9 PROGRAM-ELECTRON LIB JI Program-Electron. Libr. Inf. Syst. PY 2009 VL 43 IS 1 BP 77 EP 93 DI 10.1108/00330330910934129 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 416ZR UT WOS:000264044600007 ER PT S AU Lin, J Li, Y AF Lin, Jessica Li, Yuan BE Winslett, M TI Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation SO SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 21st International Conference on Scientific and Statistical Database Management CY JUN 02-04, 2009 CL New Orleans, LA SP LATG, Diamond Data Syst, Sun Microsyst, NOVACES, Univ New Orleans DE Data mining; Time series; Similarity Search AB For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets. C1 [Lin, Jessica; Li, Yuan] George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. RP Lin, J (reprint author), George Mason Univ, Dept Comp Sci, Fairfax, VA 22030 USA. EM jessica@cs.gmu.edu; ylif@gmu.edu CR Agrawal R., 1993, P 4 INT C FDN DAT OR, P69 Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Chan KP, 1999, PROC INT CONF DATA, P126 Deng K., 1997, IEEE INT S COMP INT, V1, P246 Ekambaram A, 2003, LECT NOTES COMPUT SC, V2869, P196 Faloutsos C., 1994, SIGMOD Record, V23 Gavrilov M., 2000, P 6 ACM SIGKDD Ge X., 2000, P 6 ACM SIGKDD INT C, P81, DOI 10.1145/347090.347109 Geurts P., 2001, LECT NOTES COMPUTER, V2168, P115 Goldberger A. L., 1997, DISCOVERY, V1 Johnson SC, 1967, PSYCHOMETRIKA, V2, P241 Keogh E., 2004, P 10 ACM SIGKDD INT Keogh E., 2002, UCR TIME SERIES DATA Keogh E., 2002, P 28 INT C VER LARG Keogh E, 2002, P 8 ACM SIGKDD INT C, P102 KEOGH E, 2004, TUTORIAL SIGKDD 2004 Keogh E., 2006, KNOWLEDGE INFORM SYS Keogh E. J., 2001, P ACM SIGMOD C MAN D, P151, DOI 10.1145/375663.375680 Li M., 1997, INTRO KOLMOGOROV COM Lin J., 2007, DATA MINING KNOWLEDG Lin J, 2004, LECT NOTES COMPUT SC, V2992, P106 McQueen J., 1967, P 5 BERK S MATH STAT, V1, P281 Nanopoulos A, 2001, INFORMATION PROCESSING AND TECHNOLOGY, P49 Ratanamahatana Chotirat Ann, 2004, P SIAM INT C DAT MIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang XZ, 2006, DATA MIN KNOWL DISC, V13, P335, DOI 10.1007/s10618-005-0039-x NR 26 TC 11 Z9 11 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-02278-4 J9 LECT NOTES COMPUT SC PY 2009 VL 5566 BP 461 EP 477 PG 17 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BKL72 UT WOS:000268444900032 ER PT S AU Stajner, T Mladenic, D AF Stajner, Tadej Mladenic, Dunja BE GomezPerez, A Yu, Y Ding, Y TI Entity Resolution in Texts Using Statistical Learning and Ontologies SO SEMANTIC WEB, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th Asian Semantic Web Conference CY DEC 06-09, 2009 CL Shanghai, PEOPLES R CHINA SP Google, STI Int DE Entity resolution; text mining; semantic annotation; ontology mapping ID INTEGRATION; WEB AB Ambiguities, which are inherently present in natural languages represent a challenge of determining the actual identities of entities mentioned in a document (e.g., Paris can refer to a city in France but it can also refer to a small city in Texas, USA or to a 1984 film directed by Wim Wenders having title Paris, Texas). Disambiguation is a problem that can be successfully solved by entity resolution methods. This paper studies various methods for estimating relatedness between entities, used in collective entity resolution. We define a unified entity resolution approach, capable of using implicit as well as explicit relatedness for collectively identifying in-text entities. As a relatedness measure, we propose a method, which expresses relatedness using the heterogeneous relations of a domain ontology. We also experiment with other relatedness measures, such as using statistical learning of co-occurrences of two entities or using content similarity between them. Evaluation on real data shows that the new methods for relatedness estimation give good results. C1 [Stajner, Tadej; Mladenic, Dunja] Jozef Stefan Inst, Ljubljana 1000, Slovenia. RP Stajner, T (reprint author), Jozef Stefan Inst, Jamova 39, Ljubljana 1000, Slovenia. EM tadej.stajner@ijs.si; dunja.mladenic@ijs.si CR Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722 BHATTACHARYA I, 2007, COLLECTIVE ENTITY RE Bizer C., 2004, LNCS, V3298 BUNESCU R, 2006, P 11 C EUR CHAPT ASS, P3 BUNESCU R, 2006, P BIONLP WORKSH LINK, V6, P49 Chen ZQ, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P204, DOI 10.1145/1255175.1255215 Cohen W. W., 2003, P IJCAI 2003 WORKSH Cucerzan S., 2007, P 2007 JOINT C EMP M, P708 Decker S, 2000, IEEE INTERNET COMPUT, V4, P63, DOI 10.1109/4236.877487 ELMAGARMID A, 2006, DUPLICATE RECORD DET Fellegi I., 1969, J AM STAT ASSOC, P1183 FINKEL J, 2005, ANN ARBOR, V100 Fortuna B., 2005, Informatica, V29 Haass LM, 1999, IEEE DATA ENG B, V22, P31 Church K. W., 1990, Computational Linguistics, V16 JANG M.-G., 1999, P 37 ANN M ASS COMP, P223, DOI 10.3115/1034678.1034718 KALASHNIKOV D, 2005, SIAM INT C DAT MIN S, P21 Klyne G., 2004, RESOURCE DESCRIPTION, P10 LI H, 1998, P 36 ANN M ASS COMP, V2, P749 Li X, 2005, AI MAG, V26, P45 LLOYD L, 2005, DISAMBIGUATION REFER Manning C. D., 1999, FDN STAT NATURAL LAN McCallum Andrew, 2005, QUEUE, V3, P48, DOI DOI 10.1145/1105664.1105679 Mihalcea R., 2005, P C HUM LANG TECHN E, P411, DOI 10.3115/1220575.1220627 MLADENIC D, 2006, ENCY DATA WAREHOUSIN, P1109 Overell S, 2006, CLEF 2006 WORKSH WOR Ramakrishnan C, 2005, SIGKDD EXPLORATIONS, V7, P56 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDHAUS E, 2008, NY TIMES, P40 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Singla P, 2006, P 6 IEEE INT C DAT M, P572 STAJNER T, 2009, UNSTRUCTURED LINKED Suchanek F. M., 2007, P 16 INT C WORLD WID, P697, DOI DOI 10.1145/1242572.1242667 Suchanek F. M., 2009, WWW 2009, P631 Tejada S, 2001, INFORM SYST, V26, P607, DOI 10.1016/S0306-4379(01)00042-4 Winkler W. E., 1999, STATE RECORD LINKAGE Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 Yates A., 2007, P NAACL HLT ROCH NY, P121 NR 38 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-10870-9 J9 LECT NOTES COMPUT SC PY 2009 VL 5926 BP 91 EP 104 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BPY96 UT WOS:000280398800007 ER PT S AU Carrillo, M Eliasmith, C Lopez-Lopez, A AF Carrillo, Maya Eliasmith, Chris Lopez-Lopez, A. BE Matousek, V Mautner, P TI Combining Text Vector Representations for Information Retrieval SO TEXT, SPEECH AND DIALOGUE, PROCEEDINGS SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 12th International Conference on Text, Speech and Dialogue CY SEP 13-17, 2009 CL Pilsen, CZECH REPUBLIC SP Univ W Bohemia, Fac Appl Sci, Masary Univ, Int Speech Commun Assoc, Czech Soc Cybernet & Informat AB This paper suggests a novel representation for documents that is intended to improve precision. This representation is generated by combining two central techniques: Random Indexing; and Holographic Reduced Representations (HRRs). Random indexing uses co-occurrence information among words to generate semantic context vectors that are the sum of randomly generated term identity vectors. HRRs are used to encode textual structure which can directly capture relations between words (e.g., compound terms, subject-verb, and verb-object). By using the random vectors to capture semantic information, and then employing HRRs to capture structural relations extracted from the text, document vectors are generated by summing all such representations in a document. In this paper, we show that these representations can be successfully used in information retrieval, can effectively incorporate relations, and can reduce the dimensionality of the traditional vector space model (VSM). The results of our experiments show that, when a representation that uses random index vectors is combined with different contexts, such as document occurrence representation (DOR), term co-occurrence representation (TCOR) and HRRs, the VSM representation is outperformed when employed in information retrieval tasks. C1 [Carrillo, Maya; Lopez-Lopez, A.] INAOE, Coordinac Ciencias Computac, Puebla 72840, Mexico. RP Carrillo, M (reprint author), INAOE, Coordinac Ciencias Computac, Luis Enrique Erro 1, Puebla 72840, Mexico. EM cmaya@inaoep.mx; celiasmith@uwaterloo.ca; allopez@inaoep.mx CR Fishbein JM, 2008, LECT NOTES COMPUT SC, V4956, P514 Grinberg D., 1995, CMUCS95125 LIU HG, 2004, MONTYLINGUA END TO E Plate T.A., 2003, HOLOGRAPHIC REDUCED Sahlgren M., 2004, P 20 INT C COMP LING, P487, DOI 10.3115/1220355.1220425 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 6 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-04207-2 J9 LECT NOTES ARTIF INT PY 2009 VL 5729 BP 24 EP 31 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BLL39 UT WOS:000270445700005 ER PT S AU Belimpasakis, P Roimela, K You, Y AF Belimpasakis, Petros Roimela, Kimmo You, Yu BE AlBegain, K Balakrishna, C Casado, AC TI Experience Explorer: a Life-Logging Platform Based on Mobile Context Collection SO THIRD INTERNATIONAL CONFERENCE ON NEXT GENERATION MOBILE APPLICATIONS, SERVICES, AND TECHNOLOGIES, PROCEEDINGS SE International Conference on Next Generation Mobile Applications Services and Technologies LA English DT Proceedings Paper CT 3rd International Conference on Next Generation Mobile Applications, Services, and Technologies CY SEP 16-18, 2009 CL Cardiff, WALES SP IEEE UKRI Commun Chapter, IEEE UKRI Comp Chapter, SIP Forum, European Council Modelling & Simulat HO Wales Millennium Ctr AB As mobile smart phones are being equipped with multiple radio interfaces and sensors, they are becoming capable of continuously collecting context information related to the users and the environment. This information can be utilized in different applications and services for providing rich, context-aware experiences. In this paper, we present a client-server platform that enables life logging, via mobile context collection, and processes the data so that meaningful higher-level context can be derived. This can then be mashed-up with 3(rd) party Internet services, for providing richer social networking and content associations, keeping privacy and security in mind. We present our core use cases, the proposed end-to-end architecture and current implementation, as well as an explorer user interface that visualizes users' life experience. C1 [Belimpasakis, Petros; Roimela, Kimmo; You, Yu] Nokia Res Ctr, Mixed Real Solut, Tampere Lab, Tampere, Finland. RP Belimpasakis, P (reprint author), Nokia Res Ctr, Mixed Real Solut, Tampere Lab, Tampere, Finland. CR ATWOOD M, 2007, OAUTH CORE 1 0 BYRNE D, 2008, IHCI 2008 CANTOR S, LIBERTY ID FF PROTOC ERICKSON T, 2003, CHI 03 HUM FACT COMP Gemmell J, 2006, COMMUN ACM, V49, P88, DOI 10.1145/1107458.1107460 Li K. A., 2008, P 6 INT C MOB SYST A Marmasse N, 2004, LECT NOTES COMPUT SC, V3205, P214 *NOK, NOK LIFEBLOG *NOK RES CTR, 2009, NOK TECHN INS SER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sellen A.J., 2007, P SIGCHI C HUM FACT Smith I., 2005, P 3 INT C PERV COMP NR 12 TC 7 Z9 7 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 2161-2897 BN 978-0-7695-3786-3 J9 INT CONF NEXT GEN PY 2009 BP 77 EP 82 DI 10.1109/NGMAST.2009.49 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BVF54 UT WOS:000291372700013 ER PT S AU Biancalana, C Lapolla, A Micarelli, A AF Biancalana, Claudio Lapolla, Antonello Micarelli, Alessandro BE Cordeiro, J Hammoudi, S Filipe, J TI Personalized Web Search Using Correlation Matrix for Query Expansion SO WEB INFORMATION SYSTEMS AND TECHNOLOGIES SE Lecture Notes in Business Information Processing LA English DT Proceedings Paper CT 4th International Conference on Web Information Systems and Technologies CY MAY 04-07, 2008 CL Funchal, PORTUGAL SP Inst Syst & Technol Informat, Control & Commun, Univ Madeira, Workflow Management Coalit DE Personalization; Information retrieval; Query expansion ID RETRIEVAL; SPACE AB In this work we present a comparing analysis of four Query Expansion (QE) techniques. Sharing the concept of term co-occurrence, we start from a simple system based on bigrams, then we moved onto a system based on term proximity through an approach known in the literature as Hyperspace Analogue to Language (HAL), and eventually developing a solution based on co-occurrence at page level. We have implemented the methods in a system prototype, which has been used to conduct several experiments that have produced interesting results. C1 [Biancalana, Claudio; Lapolla, Antonello; Micarelli, Alessandro] Univ Roma Tre, Artificial Intelligence Lab, Dept Comp Sci & Automat, I-00146 Rome, Italy. RP Biancalana, C (reprint author), Univ Roma Tre, Artificial Intelligence Lab, Dept Comp Sci & Automat, Via Vasca Navale 79, I-00146 Rome, Italy. EM biancalana@dia.uniroma3.it; lapolla@dia.uniroma3.it; micarel@dia.uniroma3.it CR Anick P., 2003, SIGIR 03, P88 Bai J., 2005, CIKM, P688 BRUZA P, 2002, CIKM, P260 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Gao MC, 2004, DESALINATION, V170, P177, DOI 10.1016/j.desal.2004.02.099 Gasparetti F, 2007, INT J PATTERN RECOGN, V21, P207, DOI 10.1142/S0218001407005429 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Porter MF, 1997, ALGORITHM SUFFIX STR, P313 RADLINSKI F, 2005, QUERY CHAINS LEARNIN Salton G., 1997, IMPROVING RETRIEVAL, P355 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Teevan J., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval NR 13 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-1348 BN 978-3-642-01343-0 J9 LECT NOTES BUS INF PY 2009 VL 18 BP 186 EP 198 PG 13 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BKB77 UT WOS:000267703400014 ER PT S AU Berendt, B Trumper, D AF Berendt, Bettina Trumper, Daniel BE Ting, IH Wu, HJ TI Semantics-Based Analysis and Navigation of Heterogeneous Text Corpora: The Porpoise News and Blogs Engine SO WEB MINING APPLICATIONS IN E-COMMERCE AND E-SERVICES SE Studies in Computational Intelligence LA English DT Article; Book Chapter DE text mining; semantic text analysis; ontology learning; news analysis; blogs analysis; visualisation AB Many information sites such as news services and search engine, offer options beyond keyword search to help people group and identify relevant sources of information. However, their search options are limited to fixed and mostly syntactic criteria (like date or source identify) or semantic criteria that are opaque ("similar documents"). Also, these interfaces do not support the systematic comparing and contrasting of different information sources that is central to information literacy. In this paper, we describe Porpoise, a system that, provides users with a toolkit for doing an in-depth, semantic analysis of a collection of archives of their choice. Users are able to analyse, compare and contrast corpora in "global" and in "local" fashion. Clustering, semi-automatic ontology learning, a new form of multidimensional nearest-neighbour search and visualisation help users to navigate a multilingual corpus of news and blogs by semantic criteria of their own design. C1 [Berendt, Bettina] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium. Humboldt Univ, Inst Informat Syst, D-10178 Berlin, Germany. RP Berendt, B (reprint author), Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium. CR BERENDT B, 2007, ISSI NEWSLETTER, V3, P55 Berendt B, 2006, LECT NOTES COMPUT SC, V4172, P495 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Cunningham H., 2002, P 40 ANN M ASS COMP DAVIDSON GS, 2002, J AM SOC INFORM SCI, V53, P764 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fortuna B., 2005, Informatica, V29 Fortuna B, 2006, LECT NOTES COMPUT SC, V4289, P121 Gibson D., 2005, WWW2005, P830 He Q, 2007, IEEE DATA MINING, P493 Herman D., 2005, ROUTLEDGE ENCY NARRA Kohonen T., 2001, SELF ORGANIZING MAPS KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Maedche A, 2002, WEB INTELLIGENCE Osinski S, 2004, ADV SOFT COMP, P359 Osinski S, 2006, LECT NOTES COMPUT SC, V3936, P167 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THELWALL M, 2006, P 15 INT WWW C 3 ANN Wise J., 1995, INFORMATION VISUALIZ, P51 NR 20 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-540-88080-6 J9 STUD COMPUT INTELL PY 2009 VL 172 BP 45 EP 64 D2 10.1007/978-3-540-88081-3 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BJL87 UT WOS:000266779500003 ER PT S AU Gupchup, J Musaloiu-E, R Szalay, A Terzis, A AF Gupchup, Jayant Musaloiu-E, Razvan Szalay, Alex Terzis, Andreas BE Roedig, U Sreenan, CJ TI Sundial: Using Sunlight to Reconstruct Global Timestamps SO WIRELESS SENSOR NETWORKS, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th European Conference on Wireless Sensor Networks CY FEB 11-13, 2009 CL Cork, IRELAND SP Lancaster Univ, Univ Coll Cork AB This paper investigates postmortem timestamp reconstruction in environmental monitoring networks. In the absence of a time-synchronization protocol, these networks use multiple pairs of (local, global) timestamps to retroactively estimate the motes' clock drift and offset and thus reconstruct the measurement time series. We present Sundial, a novel offline, algorithm for reconstructing global timestamps that is robust to unreliable global clock sources. Sundial reconstructs timestamps by correlating annual solar patterns with measurements provided by the motes' inexpensive light sensors. The Surprising ability to accurately estimate. the length of (lay using light intensity measurements enables Sundial to be robust to arbitrary mote clock restarts. Experimental results, based on multiple environmental network deployments spanning a period of over 2.5 years, show that, Sundial achieves accuracy as high as 10 parts per million (ppm), using solar radiation readings recorded it 20 minute intervals. C1 [Gupchup, Jayant; Musaloiu-E, Razvan; Terzis, Andreas] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA. RP Gupchup, J (reprint author), Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA. EM gupchup@jhu.edu; razvanm@jhu.edu; szalay@jhu.edu; terzis@jhu.edu CR CHANG M, 2008, P 2 INT WORKSH WIR S *CROSSB CORP, MICAZ SPEC DUDA RO, 1972, COMMUN ACM, V15, P11, DOI 10.1145/361237.361242 Duda R.O., 2001, PATTERN CLASSIFICATI FORSYTHEA WC, 1994, SCIENCEDIRECT, V80 Polastre J, 2005, P 4 INT C INF PROC S SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SZLAVECZ K, 2007, P AM GEOPH UN FALL M TERZIS A, INT J SENSOR NETWORK TOLLE G, 2005, P 3 ACM SENSYS C NOV Werner-Allen G., 2006, P 7 USENIX S OP SYST NR 11 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-642-00223-6 J9 LECT NOTES COMPUT SC PY 2009 VL 5432 BP 183 EP 198 PG 16 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Remote Sensing SC Computer Science; Remote Sensing GA BJA66 UT WOS:000264183400012 ER PT B AU Bravo-Alcobendas, D Sorzano, COS AF Bravo-Alcobendas, D. Sorzano, C. O. S. GP IEEE TI Clustering of Biomedical Scientific Papers SO WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS SE International Symposium on Intelligent Signal Processing-WISP LA English DT Proceedings Paper CT 6th IEEE International Symposium on Intelligent Signal Processing CY AUG 26-28, 2009 CL Budapest, HUNGARY SP IEEE Hungary Sect, IEEE Hungary I&M Soc Joint Chapter, IEEE Hungary CIS Chapter, IEEE Instrumentat & Measurement Soc ID NONNEGATIVE MATRIX FACTORIZATION AB In this paper we present a methodology for document clustering based on Non-negative Matrix Factorization (NMF) and ensemble clustering. Thanks to the ensemble clustering the algorithm is less prone to get into a local minimum caused by the initialization of the NMF. Despite the ensemble clustering, the algorithm keeps the semantic interpretability of the NMF and constructs a coocurrence matrix that allows the projection of the documents onto a two-dimensional space suitable for visualization. The algorithm is freely available for the information retrieval community from the Bioengineering Laboratory web page. C1 [Bravo-Alcobendas, D.; Sorzano, C. O. S.] Univ San Pablo CEU, Bioengn Lab, Madrid, Spain. RP Bravo-Alcobendas, D (reprint author), Univ San Pablo CEU, Bioengn Lab, Campus Urb Monteprincipe S-N, Madrid, Spain. EM coss.eps@ceu.es RI S. Sorzano, Carlos Oscar/ OI S. Sorzano, Carlos Oscar/0000-0002-9473-283X CR Bellman R. E., 1957, DYNAMIC PROGRAMMING Bishop C. M., 2006, PATTERN RECOGNITION DING C, 2006, P 21 NAT C ART INT A Lee DD, 1999, NATURE, V401, P788 LI T, 2007, P 7 IEEE INT C DAT M Manning C. D., 1999, FDN STAT NATURAL LAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahnaz F, 2006, INFORM PROCESS MANAG, V42, P373, DOI 10.1016/j.ipm.2004.11.005 SHINNOU H, 2007, P 45 ANN M ASS COMP XU W, 2003, P ACM SPEC INT GROUP ZHU S, 2008, P 2 INT S OPT SYST B NR 11 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-5058-9 J9 I S INTELL SIG PR PY 2009 BP 205 EP 209 PG 5 WC Engineering, Electrical & Electronic; Instruments & Instrumentation SC Engineering; Instruments & Instrumentation GA BOD85 UT WOS:000276341800036 ER PT B AU Ayodele, T Zhou, SK Khusainov, R AF Ayodele, Taiwo Zhou, Shikun Khusainov, Rinat BE Ao, SI Gelman, L Hukins, DWL Hunter, A Korsunsky, AM TI Applying Machine Learning Techniques for Email Reply Prediction SO WORLD CONGRESS ON ENGINEERING 2009, VOLS I AND II SE Lecture Notes in Engineering and Computer Science LA English DT Proceedings Paper CT World Congress on Engineering CY JUL 01-03, 2009 CL London, ENGLAND SP Int Assoc Engineers HO Imperial Coll London DE Email reply prediction; email messages; machine learning; email features AB For several years now, email has grown rapidly as the most-used communications tool on the internet. One advantage of the Internet is the ease with which people can communicate online. The popularity of online communication has created an explosion of users who regularly access the internet to connect with others. Many people use email to stay in touch with relatives and friends who live far away geographically. We propose a new framework to help prioritised email better using machine learning techniques; an intelligent email reply prediction system. Our goal is to provide concise, highly structured and prioritised emails, thus saving the user from browsing through thousands of emails and help to reduce time spent on checking and reading email messages. CR BRADLEY JR, 1996, P 1 INT C PRACT APPL, P486 BRYAN K, 2004, EUR C MACH LEARN Ducheneaut N., 2001, Interactions, V8, DOI 10.1145/382899.383305 JOSHUA RT, 2003, P 8 C EUR C COMP SUP, P238 KRAUT RE, 1997, MEDIA USE GLOBAL COR, P323 LAURA D, 2003, C HUM FACT COMP SYST, P924 MACKAY W, 1998, ACM T OFFIC INFORM S, V6, P30 MARK D, 2000, REPLY EXPECTATION PR MARY S, 1985, P ACM SIGOIS IEEECS, P96 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sproull L., 1991, CONNECTIONS NEW WAYS STEVE W, 1996, P ACM CHI 96 HUM FAC, P276 NR 12 TC 0 Z9 0 PU INT ASSOC ENGINEERS-IAENG PI HONG KONG PA UNIT1, 1-F, 37-39 HUNG TO ROAD, KWUN TONG, HONG KONG, 00000, PEOPLES R CHINA BN 978-988-17012-5-1 J9 LECT NOTES ENG COMP PY 2009 BP 31 EP 36 PG 6 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BLZ79 UT WOS:000271615700006 ER PT J AU Wang, HC Chang, CY Li, TY AF Wang, Hao-Chuan Chang, Chun-Yen Li, Tsai-Yen TI Assessing creative problem-solving with automated text grading SO COMPUTERS & EDUCATION LA English DT Article DE Computer-aided assessment; Automated grading; Creative problem-solving; Science learning assessment; Machine learning application ID VECTOR-SPACE MODEL; ABILITY; SCIENCE AB The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students' constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students' natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen's Kappa = .65-72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson's r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning. (C) 2008 Elsevier Ltd. All rights reserved. C1 [Wang, Hao-Chuan; Chang, Chun-Yen] Natl Taiwan Normal Univ, Sci Educ Ctr, Taipei 116, Taiwan. [Chang, Chun-Yen] Natl Taiwan Normal Univ, Dept Earth Sci, Taipei 116, Taiwan. [Li, Tsai-Yen] Natl Chengchi Univ, Dept Comp Sci, Taipei 11623, Taiwan. [Wang, Hao-Chuan] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Wang, HC (reprint author), Natl Taiwan Normal Univ, Sci Educ Ctr, 88 Sec 4,Ting Chou Rd, Taipei 116, Taiwan. EM haochuan@cs.cmu.edu; changcy@ntnu.edu.tw RI Chang, Chun-Yen/B-1307-2008 OI Chang, Chun-Yen/0000-0003-2373-2004 CR ALEVEN V, 2001, P INT C ART INT ED Anderson JR, 1995, J LEARN SCI, V4, P167, DOI 10.1207/s15327809jls0402_2 BASADUR M, 1995, CREATIVITY RES J, V8, P63, DOI 10.1207/s15326934crj0801_5 Bishop C. M., 2006, PATTERN RECOGNITION Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Chang CY, 2005, INT J SCI EDUC, V27, P625, DOI 10.1080/0950069042000323764 Chang CY, 2007, COMPUT HUM BEHAV, V23, P1971, DOI 10.1016/j.chb.2006.02.004 Chang CY, 2002, INT J SCI EDUC, V24, P441, DOI 10.1080/09500690110066502 Chang S.-N., 2005, INT J SCI MATH EDUC, V3, P117, DOI 10.1007/s10763-004-5239-0 Chi M. T. H., 1985, HUMAN ABILITIES INFO, P227 CHI MTH, 1994, COGNITIVE SCI, V18, P439, DOI 10.1207/s15516709cog1803_3 Cohen W.W., 2003, P IJCAI 03 WORKSH IN, P73 Cristianini N., 2000, INTRO SUPPORT VECTOR DAMORE RJ, 1985, P 8 ANN INT ACM SIGI Donmez P, 2005, CSCL 2005: Computer Supported Collaborative Learning 2005: The Next 10 Years, Proceedings, P125 FOGEL D, 1993, P 1993 ACM C COMP SC, P522 Frank E, 2005, DATA MINING PRACTICA GRASSER AC, 2005, IEEE T EDUC, V48, P612 GRASSER AC, 2001, AI MAG, V22, P39 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Hastie T, 2001, ELEMENTS STAT LEARNI Hearst MA, 2000, IEEE INTELL SYST APP, V15, P22, DOI 10.1109/5254.889104 HUANG CC, 2006, P 10 ANN GLOB CHIN C Joachims T, 1998, P EUR C MACH LEARN E Kohavi R., 1995, P INT JOINT C ART IN Ma W, 2003, P 2 SIGHAN WORKSH CH Ministry of Education, 2001, 1 9 GRAD SCI LIF TEC Mitchell T., 2006, CMUML06108 Mitchell TM, 1997, MACHINE LEARNING National Research Council, 1996, NAT SCI ED STAND Nijstad BA, 2006, PERS SOC PSYCHOL REV, V10, P186, DOI 10.1207/s15327957pspr1003_1 Osborn A. F., 1963, APPL IMAGINATION PRI, V3rd OSTERLIND SJ, 1994, ED ASSESSMENT, V2, P133, DOI 10.1207/s15326977ea0202_2 Platt J. C., 1998, ADV KERNEL METHODS S Popescu O., 2000, Building Dialogue Systems for Tutorial Applications. Papers from the 2000 AAAI Fall Symposium (Technical Report FS-00-01) RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Roediger HL, 2006, PSYCHOL SCI, V17, P249, DOI 10.1111/j.1467-9280.2006.01693.x Roediger HL, 2005, J EXP PSYCHOL LEARN, V31, P1155, DOI 10.1037/0278-7393.31.5.1155 ROUSSEEUW PJ, 1984, J AM STAT ASSOC, V79, P871, DOI 10.2307/2288718 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shermis M. D., 2003, AUTOMATED ESSAY SCOR Shermis MD, 2002, EDUC PSYCHOL MEAS, V62, P5, DOI 10.1177/0013164402062001001 SIMON HA, 1973, ARTIF INTELL, V4, P181, DOI 10.1016/0004-3702(73)90011-8 Singley M., 1995, J SCI EDUC TECHNOL, V4, P7, DOI 10.1007/BF02211577 Tsai CC, 2005, J RES SCI TEACH, V42, P1089, DOI 10.1002/tea.20089 Vapnik V., 1995, NATURE STAT LEARNING WANG HC, 2006, P 8 INT TUT SYST C WANG HC, 2007, P COMPUTER SUPPORTED Zenisky AL, 2002, APPL MEAS EDUC, V15, P337, DOI 10.1207/S15324818AME1504_02 NR 51 TC 12 Z9 12 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0360-1315 EI 1873-782X J9 COMPUT EDUC JI Comput. Educ. PD DEC PY 2008 VL 51 IS 4 BP 1450 EP 1466 DI 10.1016/j.compedu.2008.01.006 PG 17 WC Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA 358VI UT WOS:000259945200004 ER PT J AU Zhou, AY Zhang, R Qian, WN Vu, QH Hu, TM AF Zhou, Aoying Zhang, Rong Qian, Weining Vu, Quang Hieu Hu, Tianming TI Adaptive indexing for content-based search in P2P systems SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE Peer-to-Peer computing; Adaptive indexing; Term aggregation and classification; Information retrieval; Content-based search AB One of the major challenges in Peer-to-Peer (P2P) file sharing systems is to support content-based search. Although there have been some proposals to address this challenge, they share the same weakness of using either servers or super-peers to keep global knowledge, which is required to identify importance of terms to avoid popular terms in query processing. As a result, they are not scalable and are prone to the bottleneck problem, which is caused by the high visiting load at the global knowledge maintainers. To that end, in this paper, we propose a novel adaptive indexing approach for content-based search in P2P systems, which can identify importance of terms without keeping global knowledge. Our method is based on an adaptive indexing structure that combines a Chord ring and a balanced tree. The tree is used to aggregate and classify terms adaptively, while the Chord ring is used to index terms of nodes in the tree. Specifically, at each node of the tree, the system classifies terms as either important or unimportant. Important terms, which can distinguish the node from its neighbor nodes, are indexed in the Chord ring. On the other hand, unimportant terms, which are either popular or rare terms, are aggregated to higher level nodes. Such classification enables the system to process queries on the fly without the need for global knowledge. Besides, compared to the methods that index terms separately, term aggregation reduces the indexing cost significantly. Taking advantage of the tree structure, we also develop an efficient search algorithm to tackle the bottleneck problem near the root. Finally, our extensive experiments on both benchmark and Wikipedia datasets validated the effectiveness and efficiency of the proposed method. (c) 2008 Published by Elsevier B.V. C1 [Zhou, Aoying; Zhang, Rong; Qian, Weining; Hu, Tianming] E China Normal Univ, Inst Software Engn, Shanghai 200062, Peoples R China. [Zhou, Aoying; Zhang, Rong] Fudan Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China. [Vu, Quang Hieu] Natl Univ Singapore, Singapore MIT Alliance, Singapore 117548, Singapore. RP Zhou, AY (reprint author), E China Normal Univ, Inst Software Engn, 3663 Zhongshan Rd N, Shanghai 200062, Peoples R China. EM ayzhou@sei.ecnu.edu.cn; rongzh@fudan.edu.cn; wnqian@sei.ecnu.edu.cn; hieuvq@nus.edu.sg; tmhu@ieee.org FU National Natural Science Foundation of China [60496325, 60673137, 60673134]; National Hi-tech R&D Program of China [2006AA01Z103] FX The work is partially supported by National Natural Science Foundation of China (Grant Nos. 60496325, 60673137 and 60673134) and National Hi-tech R&D Program of China (Grant No. 2006AA01Z103). CR ABERER K., 2006, P P2PIR, P41, DOI 10.1145/1183579.1183588 BENDER M, P BTW 2005, P125 BUCKLEY C, 1995, TREC, V4, P25 Byers J, 2003, LECT NOTES COMPUT SC, V2735, P80 Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Chun B, 2003, ACM SIGCOMM COMP COM, V33, P3, DOI 10.1145/956993.956995 Crainiceanu A, 2004, P 7 INT WORKSH WEB D, P25, DOI 10.1145/1017074.1017082 CUENCAACUNA FM, 2002, DCSTR483 RUTG U Cuenca-Acuna F. M., 2003, Proceedings 12th IEEE International Symposium on High Performance Distributed Computing Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 Karger D., 2001, P 2001 ACM SIGCOMM C, P149 Karger DR, 2004, LECT NOTES COMPUT SC, V3279, P131 Lu J., 2005, P 27 EUR C INF RETR, P52 Lu Jie, 2003, P 12 INT C INF KNOWL, P199 LUPU M, 2006, P ICDE, P133 Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] RENADA ME, 2004, P CIKM, P562 REYNOLDS P, 2003, P INT MIDDL C JUN, P21 Sahin O. D., 2004, P DBISP2P, P61 Sahin O.D., 2005, P 13 ANN ACM INT C M, P946, DOI 10.1145/1101149.1101349 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEN HT, 2003, IEEE T KNOWLEDGE DAT Stribling J, 2005, LECT NOTES COMPUT SC, V3640, P69 SUEL T, 2003, P WEBDB Tang C., 2004, P 1 S NETW SYST DES, P211 Tang C., 2003, P 2003 C APPL TECHN, P175 TRYFONOPOULOS C, 2005, P 9 EUR C RES ADV TE, P25 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 Zhou AY, 2008, PROC INT CONF DATA, P1544, DOI 10.1109/ICDE.2008.4497617 ZHOU S, 2006, P ICDE, P161 NR 30 TC 3 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X EI 1872-6933 J9 DATA KNOWL ENG JI Data Knowl. Eng. PD DEC PY 2008 VL 67 IS 3 BP 381 EP 398 DI 10.1016/j.datak.2008.06.013 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 377NX UT WOS:000261259000003 ER PT J AU Vazquez, PP Gotzelmann, T Hartmann, K Nurnberger, A AF Vazquez, Pere-Pau Goetzelmann, Timo Hartmann, Knut Nuernberger, Andreas TI An interactive 3D framework for anatomical education SO INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY LA English DT Article DE Computer-assisted instruction (LB 1028.5); Medical educational materials (W 18.2); Computer graphics (T 385) AB Object This paper presents a 3D framework for Anatomy teaching. We are mainly concerned with the proper understanding of human anatomical 3D structures. Materials and methods The main idea of our approach is taking an electronic book such as Henry Gray's Anatomy of the human body, and a set of 3D models properly labeled, and constructing the correct linking that allows users to perform mutual searches between both media. Results We implemented a system where learners can interactively explore textual descriptions and 3D visualizations. Conclusion Our approach allows easily performing two search tasks: first, the user may select a text region and get a view showing the objects that contain the selected structures, and second, using the interactive exploration of a 3D model the user may automatically search for the textual description of the structures visible in the current view. C1 [Vazquez, Pere-Pau] Univ Politecn Cataluna, Modeling Visualizat & Graph Interact Grp, ES-08034 Barcelona, Spain. [Goetzelmann, Timo] Univ Magdeburg, Graph & Interact Syst Grp, D-39106 Magdeburg, Germany. [Hartmann, Knut] Univ Appl Sci, Graph & Interact Syst Grp, Flenshburg, Germany. [Nuernberger, Andreas] Univ Magdeburg, Informat Retrieval Grp, D-39106 Magdeburg, Germany. RP Vazquez, PP (reprint author), Univ Politecn Cataluna, Modeling Visualizat & Graph Interact Grp, Campus Nord,Edifici Omega C Jordi Girona,1-3, ES-08034 Barcelona, Spain. EM ppau@lsi.upc.edu; timo@isg.cs.uni-magdeburg.de; knut@isg.cs.uni-magdeburg.de; nuernb@iti.cs.uni-magdeburg.de RI Vazquez, Pere-Pau/L-4697-2014; Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 Feiner SK, 1993, INTELLIGENT MULTIMED, P117 Furnas GW, 1986, CHI 86, P16 Gotze M, 2005, SIMULATION VISUALISI, P195 Gotzelmann T, 2006, LECT NOTES COMPUT SC, V4073, P24 Gotzelmann T, 2007, LECT NOTES COMPUTER Gotzelmann T, 2007, J WSCG, V15, P1 Gotzelmann T, 2007, SIGGRAPH P 23 INT SP Gray H., 1918, ANATOMY HUMAN BODY Hartmann K, 2002, 2 INT S SMART GRAPH, P39 Lieberman H, 2004, AI MAG, V25, P63 Muhler K, 2007, EG IEEE VGTC S VIS SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlechtweg S, 1999, INT C COMP HUM INT I, P466 Schlechtweg S, 2000, AAAI SPRING S SMART, P8 Tortora G. J., 1997, INTRO HUMAN BODY ESS Vazquez P, 2001, VIS MOD VIS C, P273 Viola I, 2006, IEEE T VIS COMPUT GR, V12, P933, DOI 10.1109/TVCG.2006.152 WAHLSTER W, 1993, ARTIF INTELL, V63, P387, DOI 10.1016/0004-3702(93)90022-4 NR 20 TC 4 Z9 4 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1861-6410 EI 1861-6429 J9 INT J COMPUT ASS RAD JI Int. J. Comput. Assist. Radiol. Surg. PD DEC PY 2008 VL 3 IS 6 BP 511 EP 524 DI 10.1007/s11548-008-0251-4 PG 14 WC Engineering, Biomedical; Radiology, Nuclear Medicine & Medical Imaging; Surgery SC Engineering; Radiology, Nuclear Medicine & Medical Imaging; Surgery GA V33AE UT WOS:000208990900005 ER PT J AU Peng, Y Kou, G Shi, Y Chen, ZX AF Peng, Yi Kou, Gang Shi, Yong Chen, Zhengxin TI A DESCRIPTIVE FRAMEWORK FOR THE FIELD OF DATA MINING AND KNOWLEDGE DISCOVERY SO INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING LA English DT Article DE Grounded theory; knowledge discovery; descriptive framework; document clustering ID DATABASES AB Despite the rapid development, the field of data mining and knowledge discovery (DMKD) is still vaguely defined and lack of integrated descriptions. This situation causes difficulties in teaching, learning, research, and application. This paper surveys a large collection of DMKD literature to provide a comprehensive picture of current DMKD research and classify these research activities into high-level categories using grounded theory approach; it also evaluates the longitudinal changes of DMKD research activities during the last decade. C1 [Peng, Yi; Kou, Gang] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China. [Shi, Yong] CAS Res Ctr Fictitious Econ & Data Sci, Beijing 100080, Peoples R China. [Shi, Yong; Chen, Zhengxin] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA. RP Kou, G (reprint author), Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China. EM pengyi@uestc.edu.cn; kougang@yahoo.com; yshi@gscas.ac.cn; zchen@mail.unomaha.edu RI Kou, Gang/G-3869-2010 OI Kou, Gang/0000-0002-9220-8647 CR AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415 Agrawal R, 2000, P ACM SIGMOD C MAN D, P439, DOI DOI 10.1145/342009.335438 Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 ANCONA DG, 1990, ACAD MANAGE J, V33, P334, DOI 10.2307/256328 Bacon C. J., 2001, Data Base for Advances in Information Systems, V32 Bar-Or A, 2005, IEEE T KNOWL DATA EN, V17, P1138, DOI 10.1109/TKDE.2005.129 Baskerville R., 1999, ACCOUNTING MANAGEMEN, V9, P1, DOI 10.1016/S0959-8022(98)00017-4 Beck C T, 1996, Issues Compr Pediatr Nurs, V19, P1, DOI 10.3109/01460869609026851 Chen ZX, 2006, INT J INF TECH DECIS, V5, P703, DOI 10.1142/S0219622006002271 Cheng SW, 2006, INT J INF TECH DECIS, V5, P585, DOI 10.1142/S021962200600226X CONRAD JG, 2005, P ICAIL 2005, P177, DOI 10.1145/1165485.1165513 Denzin N. K., 1978, RES ACT Dick B., 2005, GROUNDED THEORY THUM DNUGGETS K, 2005, POLLS DATA MINING TX Dzeroski S., 2003, ACM SIGKDD EXPLORATI, V5, P1, DOI DOI 10.1145/959242.959245 Fayyad U, 1996, COMMUN ACM, V39, P27, DOI 10.1145/240455.240464 Fayyad U, 1996, COMMUN ACM, V39, P24, DOI 10.1145/240455.240463 FAYYAD U, 1998, ADV KNOWLEDGE DISCOV Fayyad UM, 1996, ADV KNOWLEDGE DISCOV FRIEDMAN J, 1997, P 29 S INT COMP SCI GEIST I, 2002, P ACM S APPL COMP, P508 Getoor L, 2003, ACM SIGKDD EXPLORATI, V5, P84, DOI 10.1145/959242.959253 Glaser B., 1967, DISCOVERY GROUNDED T Han J., 2006, DATA MINING CONCEPTS Han J, 2000, DATA MINING CONCEPTS HO TB, 2005, ADV KNOWLEDGE DISCOV, V3518 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JICK TD, 1979, ADMIN SCI QUART, V24, P602, DOI 10.2307/2392366 KDNUGGETS, 2007, M C DAT MIN KNOWL DI Kleinberg J, 1998, DATA MIN KNOWL DISC, V2, P311, DOI 10.1023/A:1009726428407 KOGAN J, 2003, TUT ACM 14 C INF KNO Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 Lin W., 2002, P 1 AUSTR DAT MIN WO, P83 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Macskassy S. A., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Mannila H, 1999, LECT NOTES ARTIF INT, V1634, P14 MARTIN PY, 1986, J APPL BEHAV SCI, V22, P141, DOI 10.1177/002188638602200207 *MICR CORP, 2005, MICR SQL SERV Nolan RL, 1980, MIS QUART, V4, P1, DOI 10.2307/249333 ORLIKOWSKI WJ, 1993, MIS QUART, V17, P309, DOI 10.2307/249774 PECHENIZKIY M, 2004, P ICDM 04 FDN DAT MI, P139 PENG Y, 2006, P 3 IEEE SSSM 2006 I PENG Y, 2006, P WORKSH 6 IEEE INT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarker S., 2001, Data Base for Advances in Information Systems, V32 Schuster A, 2004, DATA MIN KNOWL DISC, V8, P171, DOI 10.1023/B:DAMI.0000015870.80026.6a Shi Y., 2002, INT J INFORMATION TE, V1, P131, DOI DOI 10.1142/S0219622002000038 SHI Y, 2005, INT J INFORM TECH DE, V4 SI L, 2005, P 9 PAC AS C, P622 SIMOFF SJ, 2002, SIGKDD EXPLOR NEWSLE, V4, P118 Strauss A., 1990, BASICS QUALITATIVE R Thurston WE, 2005, HEALTH POLICY, V73, P237, DOI 10.1016/j.healthpol.2004.11.013 *UCI, 2006, MACH LEARN REP *UCI, 2006, KDD ARCH Yang Q, 2006, INT J INF TECH DECIS, V5, P597, DOI 10.1142/S0219622006002258 YAO YY, 2004, P ICDM 04 WORKSH FDN, P215 Zhao Y, 2001, 0140 U MINN DEP COMP Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 Zhao Y., 2003, FUNCTIONAL GENOMICS NR 60 TC 158 Z9 158 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0219-6220 J9 INT J INF TECH DECIS JI Int. J. Inf. Technol. Decis. Mak. PD DEC PY 2008 VL 7 IS 4 BP 639 EP 682 PG 44 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA 389NO UT WOS:000262100300005 ER PT J AU Li, ST Chang, WC AF Li, Sheng-Tun Chang, Won-Chen TI DESIGN AND EVALUATION OF A LAYERED THEMATIC KNOWLEDGE MAP SYSTEM SO JOURNAL OF COMPUTER INFORMATION SYSTEMS LA English DT Article DE Knowledge map; Knowledge management; Visualization; Hierarchical self-organizing map network; Text mining ID SELF-ORGANIZING MAP; VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; TREE; VISUALIZATIONS; ORGANIZATION; EXPLORATION; NETWORK; WEB AB Knowledge maps (K-maps) have been recognized as an expressive, efficient and effective way to enhance access and navigation of information in a large knowledge repository. However, its capability is limited by the lack of a way to systematically discover the hidden structure of codified knowledge and compactly visualize the map morphology to improve effective navigation. This paper presents a new hybrid approach to tackle these issues and proposes a layered thematic K-map system. The knowledge objects arc firstly categorized and labeled according to their hidden conceptual architecture using a hierarchical self-organizing map network and then thematically navigated through a spatially expandable stacked view with corresponding information. The system is tested with a real-world soil remediation patent corpus with various map topologies. A pair-wise evaluation consisting of clustering performance and usability assessment was conducted. Compared with a typical file/directory view, the results of usability evaluation showed that the proposed system enhances the efficiency of comparison tasks while preserving comparable performance of identification and association tasks. C1 [Li, Sheng-Tun; Chang, Won-Chen] Natl Cheng Kung Univ, Tainan 701, Taiwan. RP Li, ST (reprint author), Natl Cheng Kung Univ, Tainan 701, Taiwan. FU NSC, Taiwan [NSC95-2416-H-006-039] FX This study was supported in part by NSC95-2416-H-006-039, Taiwan, R.O.C. The authors would like to thank Uranus Huang, who assisted us in integrating the IT tools and developing the prototype system for this study. Appreciation also goes to the anonymous reviewers for their constructive comments. CR AAS L, 1999, 941 NR NORW COMP CTR Allan J, 2001, INFORM PROCESS MANAG, V37, P435, DOI 10.1016/S0306-4573(00)00056-X Asahi T, 1995, INFORM SYST RES, V6, P357, DOI 10.1287/isre.6.4.357 Bederson BB, 2002, ACM T GRAPHIC, V21, P833, DOI 10.1145/571647.571649 Berry M.W., 2004, SURVEY TEXT MINING C BLADH T, 2004, P 6 AS PAC C COMP HU BURKHARD R, 2005, P 9 INT C INF VIS IV Card S. K., 1999, READINGS INFORM VISU CHAN A, 2002, P 9 INT C NEUR INF P, V5, P2537 CHUNG W, 2003, P 36 ANN HAW INT C S Correa RF, 2006, NEUROCOMPUTING, V70, P62, DOI 10.1016/j.neucom.2006.07.007 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Davenport T. H., 1998, WORKING KNOWLEDGE Demian P., 2006, Information Visualization, V5, DOI 10.1057/palgrave.ivs.9500114 Dittenbach M, 2002, NEUROCOMPUTING, V48, P199, DOI 10.1016/S0925-2312(01)00655-5 de Oliveira MCF, 2003, IEEE T VIS COMPUT GR, V9, P378, DOI 10.1109/TVCG.2003.1207445 Fischer M, 1998, VISUALISIERUNG MANAG Forti A, 2006, NEURAL NETWORKS, V19, P1568, DOI 10.1016/j.neunet.2006.02.009 Freeman RT, 2005, NEUROCOMPUTING, V63, P415, DOI 10.1016/j.neucom.2004.07.005 Hou JL, 2006, J COMPUT INFORM SYST, V46, P64 Jetter A., 2006, KNOWLEDGE INTEGRATIO Johnson B., 1991, Proceedings Visualization '91 (Cat. No.91CH3046-0), DOI 10.1109/VISUAL.1991.175815 JUNGMEISTER WA, 1992, CARTR648 U MAR Keim D, 2001, COMMUN ACM, V44, P38, DOI 10.1145/381641.381656 Kim S, 2003, J KNOWLEDGE MANAGEME, V7, P34, DOI 10.1108/13673270310477270 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kohonen T., 2001, SPRINGER SERIES INFO, V30 Koshman S, 2006, LIBR INFORM SCI RES, V28, P192, DOI 10.1016/j.lisr.2006.03.017 LEE AHS, 1997, J CELL PATHOL, V2, P67 Lin FR, 2006, INFORM PROCESS MANAG, V42, P551, DOI 10.1016/j.ipm.2005.03.026 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 LOGAN D, 2000, KNOWLEDGE MAPPING 5 Losiewicz P, 2000, J INTELL INF SYST, V15, P99, DOI 10.1023/A:1008777222412 Miller T.W., 2005, DATA TEXT MINING BUS Morse E, 2000, INT J HUM-COMPUT ST, V53, P637, DOI 10.1006/ijhc.2000.0412 Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008 Pampalk E., 2004, Intelligent Data Analysis, V8 Pillat R.M., 2005, P 2005 LAT AM C HUM, P20, DOI 10.1145/1111360.1111363 Plaisant C., 2004, P WORK C ADV VIS INT, VThe challenge of information visualization evaluation, P109, DOI 10.1145/989863.989880 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rasmussen E., 1992, CLUSTERING ALGORITHM Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 Rekimoto J., 1993, P 3 ANN WORKSH INF T, P125 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEDBROOK TA, 2005, J COMPUTER INFORM SY, V45, P63 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768 SONGER AD, 2003, INTEGRATION INNOVATI Spence R., 2001, INFORM VISUALIZATION Stasko J, 2000, INT J HUM-COMPUT ST, V53, P663, DOI 10.1006/ijhc.2000.0420 Trappey AJC, 2006, EXPERT SYST APPL, V31, P755, DOI 10.1016/j.eswa.2006.01.013 van Wijk, 1999, IEEE S INF VIS SAN F Wexler M.N., 2001, J KNOWLEDGE MANAGEME, V5, P249, DOI 10.1108/EUM0000000005868 Woo JH, 2004, AUTOMAT CONSTR, V13, P203, DOI 10.1016/j.autcon.2003.09.003 Xiang Y, 2005, DECIS SUPPORT SYST, V41, P69, DOI 10.1016/j.dss.2004.02.006 Yang CC, 2003, DECIS SUPPORT SYST, V35, P89, DOI 10.1016/S0167-9236(02)00101-X Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yoon BU, 2002, R&D MANAGE, V32, P291, DOI 10.1111/1467-9310.00261 NR 57 TC 0 Z9 0 PU INT ASSOC COMPUTER INFO SYSTEM PI STILLWATER PA OKLAHOMA ST UNIV COLLEGE OF BUSINESS, STILLWATER, OK 74078 USA SN 0887-4417 J9 J COMPUT INFORM SYST JI J. Comput. Inf. Syst. PD WIN PY 2008 VL 49 IS 2 BP 92 EP 103 PG 12 WC Computer Science, Information Systems SC Computer Science GA 400AY UT WOS:000262841600011 ER PT J AU Pirro, G Talia, D AF Pirro, Giuseppe Talia, Domenico TI LOM: a linguistic ontology matcher based on information retrieval SO JOURNAL OF INFORMATION SCIENCE LA English DT Article DE ontology mapping; linguistic ontology mapping; information retrieval for ontology mapping; Lucene ID WEB AB Ontology mapping is a key problem to be solved for the success of the Semantic Web and related technologies. An ontology mapping algorithm aims at finding correspondences (or mappings) between entities of the source and target ontologies by combining several matching components, i.e., individual matchers, that exploit one or more sources of information encoded within the ontologies. In this paper, we investigate linguistic techniques for ontology mapping and underline their importance in paving the way to other matching techniques. We define a general mapping model architecture and discuss an implementation in the Lucene ontology matcher (LOM). LOM leverages the features of the Lucene search engine library. The basic idea is to gather the different kinds of linguistic information of the source ontology entities in Lucene documents that will be stored into an index. Mappings are discovered by using the values of entities in the target ontology as search arguments against the index created from the source ontology. Extensive experimental results using a popular benchmark test suite show the effectiveness of this approach in terms of precision, recall, F-measure and execution time as compared to other linguistic approaches. C1 [Pirro, Giuseppe; Talia, Domenico] Univ Calabria, DEIS, I-87036 Arcavacata Di Rende, CS, Italy. RP Pirro, G (reprint author), Univ Calabria, DEIS, Via Pietro Bucci Cubo 41-C, I-87036 Arcavacata Di Rende, CS, Italy. EM gpirro@deis.unical.it CR [Anonymous], RDF RESOURCE DESCRIP [Anonymous], OWL ONTOLOGY WEB LAN Aumueller D., 2005, P 2005 ACM SIGMOD IN, P906, DOI 10.1145/1066157.1066283 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BERNERSLEE T, SCI AM 2001 Blondel VD, 2004, SIAM REV, V46, P647, DOI 10.1137/S0036144502415960 BOUQUET P, D2 2 1 SPECIFICATION BOUQUET P, 2003, P 1 INT SEM WEB C SA, P20 Castano S, 2001, IEEE T KNOWL DATA EN, V13, P277, DOI 10.1109/69.917566 Castano S., 2003, P 1 INT WORKSH SEM W, P231 Choi N, 2006, SIGMOD RECORD, V35, P34 Do H., 2002, P 28 INT C VER LARG, P610 Do H.-H., 2002, P 2 INT WORKSH WEB D, P221 Doan A, 2003, VLDB J, V12, P303, DOI 10.1007/S00778-003-0104-2 Euzenat J., 2004, P 3 INT SEM WEB C HI, P698 EUZENAT J, 2006, P INT WORKSH ONT MAT Euzenat J., 2005, P INT ONT WORKSH P K, P97 GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Jiang J., 1997, P INT C RES COMP LIN KOTIS K, 2004, P 11 INT C ART INT M, P22 Levenshtein I. V, 1966, SOV PHYS DOKL, P707 Madhavan J., 2001, Proceedings of the 27th International Conference on Very Large Data Bases MAIER D, 2003, P 2 INT WORKSH WEB D, P1 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MITRA P, 2002, P WORKSH ONT SEM INT PIRRO G, 2007, P SWAE 2007 REG GERM, P407 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoilos G., 2005, P 4 INT SEM WEB C IS, P623 Su XM, 2006, DATA KNOWL ENG, V58, P47, DOI 10.1016/j.datak.2005.05.012 APACHE LUCENE PROJEC ONTOLOGY ALIGNMENT E PROTEGE API NR 34 TC 6 Z9 6 PU SAGE PUBLICATIONS LTD PI LONDON PA 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND SN 0165-5515 J9 J INF SCI JI J. Inf. Sci. PD DEC PY 2008 VL 34 IS 6 BP 845 EP 860 DI 10.1177/0165551508091014 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 377GK UT WOS:000261239500006 ER PT J AU Chan, B Wu, LL Talbot, J Cammarano, M Hanrahan, P AF Chan, Bryan Wu, Leslie Talbot, Justin Cammarano, Mike Hanrahan, Pat TI Vispedia*: Interactive Visual Exploration of Wikipedia Data via Search-Based Integration SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article DE Information visualization; Data integration; Wikipedia; Semantic web; Search interfaces ID VISUALIZATION AB Wikipedia is an example of the collaborative, semi-structured data sets emerging on the Web. These data sets have large, non-uniform schema that require costly data integration into structured tables before visualization can begin. We present Vispedia, a Web-based visualization system that reduces the cost of this data integration. Users can browse Wikipedia, select an interesting data table, then use a search interface to discover, integrate, and visualize additional columns of data drawn from multiple Wikipedia articles. This interaction is supported by a fast path search algorithm over DBpedia, a semantic graph extracted from Wikipedia's hyperlink structure. Vispedia can also export the augmented data tables produced for use in traditional visualization systems. We believe that these techniques begin to address the "long tail" of visualization by allowing a wider audience to visualize a broader class of data. We evaluated this system in a first-use formative lab study. Study participants were able to quickly create effective visualizations for a diverse set of domains, performing data integration as needed. C1 [Chan, Bryan; Wu, Leslie; Talbot, Justin; Cammarano, Mike; Hanrahan, Pat] Stanford Univ, Stanford, CA 94305 USA. RP Chan, B (reprint author), Stanford Univ, Stanford, CA 94305 USA. EM bryanc@stanford.edu; lwu2@stanford.edu; jtalbot@stanford.edu; mcammara@stanford.edu; hanrahan@cs.stanford.edu FU Department of Energy; Max Planck Institute FX This work was Supported in part by an RVAC grant from the Department of Energy and the Max Planck Institute. Thanks to Bjorn Hartmann for figure assistance and inspiration, and to Mark James for the use of his icons. CR AUER S, P ISWC 2007 Cammarano M, 2007, IEEE T VIS COMPUT GR, V13, P1200, DOI 10.1109/TVCG.2007.70617 Doan A., 2006, IEEE DATA ENG B, V29, P64 Dong X., 2007, P ACM SIGMOD INT C M, P43, DOI DOI 10.1145/1247480.1247487 Dontcheva M., 2007, P 20 ANN ACM S US IN, P61, DOI 10.1145/1294211.1294224 GOLDMAN R, 1997, P VLDB ATH GREEC GOLDMAN R, 1998, P VLDB Heer J., 2007, P ACM CHI, P1029, DOI 10.1145/1240624.1240781 Huynh D. F., 2007, P 16 INT C WORLD WID, P737, DOI 10.1145/1242572.1242672 HUYNH DF, P ISWC 2007 Kacholia V, 2005, P 31 INT C VER LARG, P505 LEHMANN J, 2007, LNI, V113, P99 Lenzerini M., 2002, P 21 ACM SIGACT SIGM, P233 Madhavan J, 2005, PROC INT CONF DATA, P57 Madhavan J., 2007, CIDR, P342 *MIT, SIM TIM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shneiderman B., 1997, D LIB MAGAZINE Suchanek F. M., 2007, P 16 INT C WORLD WID, P697, DOI DOI 10.1145/1242572.1242667 SUH B, 2007, P VAST 2007 OCT 30 2, P163 Viegas FB, 2007, IEEE T VIS COMPUT GR, V13, P1121, DOI 10.1109/TVCG.2007.70577 *W3C, TAB World Wide Web Consortium (W3C), SPARQL QUER LANG RDF Wu F., 2007, P 16 ACM C INF KNOWL, P41, DOI 10.1145/1321440.1321449 LIFTING VEIL IMPROVI NR 25 TC 8 Z9 8 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1077-2626 J9 IEEE T VIS COMPUT GR JI IEEE Trans. Vis. Comput. Graph. PD NOV-DEC PY 2008 VL 14 IS 6 BP 1213 EP 1220 DI 10.1109/TVCG.2008.178 PG 8 WC Computer Science, Software Engineering SC Computer Science GA 365CY UT WOS:000260384700012 PM 18988966 ER PT J AU Bjelica, M AF Bjelica, Milan TI Experiment with User Modeling for Communication Service Retrieval SO IEEE COMMUNICATIONS LETTERS LA English DT Article DE Communication systems; personalization; user modeling ID VECTOR-SPACE MODEL AB We consider user modeling for personalized service/content retrieval in multiservice networks. Simulation results show the existence of local suboptimum which presents a good compromise between computational costs and achieved performances. This feature could be of interest for future practical implementations. C1 Sch Elect Engn ETF, Dept Commun, Belgrade, Serbia. RP Bjelica, M (reprint author), Sch Elect Engn ETF, Dept Commun, Belgrade, Serbia. EM milan@etf.rs CR Bjelica M, 2007, IEEE COMMUN LETT, V11, P637, DOI 10.1109/LCOMM.2007.070278 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 1979, INFORM RETRIEVAL NR 4 TC 0 Z9 0 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1089-7798 J9 IEEE COMMUN LETT JI IEEE Commun. Lett. PD OCT PY 2008 VL 12 IS 10 BP 797 EP 799 DI 10.1109/LCOMM.2008.080444 PG 3 WC Telecommunications SC Telecommunications GA 359PI UT WOS:000260000200033 ER PT J AU Hammouda, K Kamel, M AF Hammouda, Khaled Kamel, Mohamed TI Distributed collaborative Web document clustering using cluster keyphrase summaries SO INFORMATION FUSION LA English DT Article DE distributed clustering; collaborative clustering; document clustering; keyphrase extraction ID CATEGORIZATION AB For the past few decades the mainstream data clustering technologies have been fundamentally based oil centralized operation; data sets were of small manageable sizes, and usually resided oil one site that belonged to one organization. Today, data is of enormous sizes and is usually located oil distributed sites; the primary example being the Web. This created a need for performing clustering in distributed environments. Distributed clustering solves two problems: infeasibility of collecting data at a central site, due to either technical and/or privacy limitations, and intractability of traditional clustering algorithms oil huge data sets. In this paper we propose a distributed collaborative clustering approach for clustering Web documents in distributed environments. We adopt a peer-to-peer model, where the main objective is to allow nodes in a network to first form independent opinions of local document clusterings, then collaborate with peers to enhance the local clusterings. Information exchanged between peers is minimized through the use of cluster summaries in the form of keyphrases extracted from the Clusters. This summarized view of peer data enables nodes to request merging of remote data selectively to enhance local clusters. Initial clustering, as well as merging peer data with local clusters, utilizes a clustering method, called similarity histogram-based clustering, based oil keeping a tight similarity distribution within clusters. This approach achieves significant improvement in local clustering solutions without the cost of centralized clustering, while maintaining the initial local clustering structure. Results show that larger networks exhibit larger improvements, tip to 15%, improvement in clustering quality, albeit lower absolute clustering quality than smaller networks. (C) 2007 Elsevier B.V. All rights reserved. C1 [Hammouda, Khaled] Univ Waterloo, Dept Syst Design Engn, PAMI Res Grp, Waterloo, ON N2L 3G1, Canada. [Kamel, Mohamed] Univ Waterloo, Dept Elect & Comp Engn, PAMI Res Grp, Waterloo, ON N2L 3G1, Canada. RP Hammouda, K (reprint author), Univ Waterloo, Dept Syst Design Engn, PAMI Res Grp, Waterloo, ON N2L 3G1, Canada. EM hammouda@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Barzilay R., 1999, P 37 ANN M ASS COMP, P550, DOI 10.3115/1034678.1034760 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Chakrabarti Soumen, 2003, MINING WEB DISCOVERI Clifton C, 2004, IEEE T KNOWL DATA EN, V16, P949, DOI 10.1109/TKDE.2004.32 Eisenhardt M, 2003, INFORMATIK 2003 INNO, P286 Hammouda K, 2003, 2003 IEEE WIC INT C, P597 Hammouda KM, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P203, DOI 10.1109/ICDM.2002.1183904 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hammouda KM, 2004, KNOWL INF SYST, V6, P710, DOI 10.1007/s10115-003-0118-5 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kargupta H., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 Li J, 2002, 2 MIT STUD OX WORKSH PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 NR 21 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1566-2535 J9 INFORM FUSION JI Inf. Fusion PD OCT PY 2008 VL 9 IS 4 BP 465 EP 480 DI 10.1016/j.inffus.2006.12.001 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA 351PV UT WOS:000259437300005 ER PT J AU Naud, A Usui, S AF Naud, Antoine Usui, Shiro TI Exploration of a collection of documents in neuroscience and extraction of topics by clustering SO NEURAL NETWORKS LA English DT Article DE Neuroinformatics; Document clustering; Text mining; Knowledge domain visualization AB This paper presents a preliminary analysis of the neuroscience knowledge domain, and an application of cluster analysis to identify topics in neuroscience. A collection of posters presented at the Society for Neuroscience (SfN) Annual Meeting in 2006 is first explored by viewing existing topics and poster sessions using multidimensional scaling. Based on the Vector Space Model, several Term Spaces were built on the basis of a set of terms extracted from the posters' abstracts and titles, and a set of free keywords assigned to the posters by their authors. The ensuing Term Spaces were Compared from the point of view of retrieving the genuine category titles. Topics were extracted from the abstracts of posters by clustering the documents using a bisecting k-means algorithm and selecting the most salient terms for each cluster by ranking. The terms extracted as topic descriptors were evaluated by comparing them to existing titles assigned to thematic categories defined by human experts in neuroscience. A comparison of two approaches for terms ranking (Document Frequency and Log-Entropy) resulted in better performance of the Log-Entropy scores, allowing to retrieve 31.0% of original title terms in clustered documents (and 37.1% in original thematic categories). (C) 2008 Elsevier Ltd. All rights reserved. C1 [Naud, Antoine] Nicholas Copernicus Univ, Dept Informat, PL-87100 Torun, Poland. [Usui, Shiro] RIKEN, Brain Sci Inst, Lab Neuroinformat, Wako, Saitama 3510198, Japan. RP Naud, A (reprint author), Nicholas Copernicus Univ, Dept Informat, Ul Grudziadzka 5, PL-87100 Torun, Poland. EM naud@is.umk.pl; usuishiro@riken.jp FU Laboratory for Neuroinformatics, RIKEN Brain Science Institute, Wake, Japan FX This research was performed while the author was at the Laboratory for Neuroinformatics, RIKEN Brain Science Institute, Wake, Japan. CR BANERJEE A, 2005, SIGKDD INT C KNOWL D, P532 BORG L, 2005, SPRINGER SERIES STAT DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon I. S., 2001, KDD-2001. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Dhillon IS, 2003, P 9 ACM SIGKDD INT C, P89 KOLDA TG, 1997, CSTR3806, P59 Naud A, 2007, LECT NOTES COMPUT SC, V4881, P115 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN Tan P., 2006, INTRO DATAMINING, P6 Usui S, 2007, BIOSYSTEMS, V88, P334, DOI 10.1016/j.biosystems.2006.08.015 USUI S, 2007, IJCNN 2007 Xu W., 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485 Yoo I., 2006, P 12 ACM SIGKDD INT, P791, DOI 10.1145/1150402.1150505 Zhao Y, 2005, DATA MIN KNOWL DISC, V10, P141, DOI 10.1007/s10618-005-0361-3 NR 17 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0893-6080 J9 NEURAL NETWORKS JI Neural Netw. PD OCT PY 2008 VL 21 IS 8 SI SI BP 1205 EP 1211 DI 10.1016/j.neunet.2008.05.009 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA 374FJ UT WOS:000261028300016 PM 18603406 ER PT J AU Chim, H Deng, X AF Chim, Hung Deng, Xiaotie TI Efficient phrase-based document similarity for clustering SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE suffix tree; document model; similarity measure; document clustering ID SUFFIX ARRAYS; CONSTRUCTION AB Phrase has been considered as a more informative feature term for improving the effectiveness of document clustering. In this paper, we propose a phrase-based document similarity to compute the pairwise similarities of documents based on the Suffix Tree Document (STD) model. By mapping each node in the suffix tree of STD model into a unique feature term in the Vector Space Document (VSD) model, the phrase-based document similarity naturally inherits the term tf-idf weighting scheme in computing the document similarity with phrases. We apply the phrase-based document similarity to the group-average Hierarchical Agglomerative Clustering (HAC) algorithm and develop a new document clustering approach. Our evaluation experiments indicate that the new clustering approach is very effective on clustering the documents of two standard document benchmark corpora OHSUMED and RCV1. The quality of the clustering results significantly surpasses the results of traditional single-word tf-idf similarity measure in the same HAC algorithm, especially in large document data sets. Furthermore, by studying the property of STD model, we conclude that the feature vector of phrase terms in the STD model can be considered as an expanded feature vector of the traditional single-word terms in the VSD model. This conclusion sufficiently explains why the phrase-based document similarity works much better than the single-word tf-idf similarity measure. C1 [Chim, Hung; Deng, Xiaotie] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. RP Chim, H (reprint author), City Univ Hong Kong, Dept Comp Sci, 83 Tat Chee Ave, Kowloon, Hong Kong, Peoples R China. EM h.chim@cityu.edu.hk; csdeng@cityu.edu.hk CR Allen P. O. R., 1993, P ACM C ORG COMP SYS, P166 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BIEGANSKI JRP, 1994, P 2M ANN HAW INT C S, P35 CARLSON JJ, 2000, SAND2000312 SANDIA Charniak E., 1993, STAT LANGUAGE LEARNI Cios K. J., 1998, DATA MINING METHODS Croft W. B., 1978, THESIS U CAMBRIDGE EHRENFEUCHT A, 1988, DISCRETE APPL MATH, V40 EISEN M, CLUSTER 3 0 EISSEN DSS, 2005, P 5 INT C KNOWL MAN, P596 Giegerich R, 1997, ALGORITHMICA, V19, P331, DOI 10.1007/PL00009177 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Jelinek F., 1997, STAT METHODS SPEECH Lang K, 1995, P 12 INT C MACH LEAR, P331 LARSEN B, 1999, P KDD WORKSH WEB US Lewis DD, 2004, J MACH LEARN RES, V5, P361 MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 PAMPAPATHI BMR, 2006, MACHINE LEARNING, V65 PORTER M, 1980, 5587 BRIT LIB RES DE Salton G., 1988, P 11 ANN INT ACM SIG, p147 , DOI 10.1145/62437.62447 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331 van Rijsbergen C. J, 1975, INFORM RETRIEVAL WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Yamamoto M, 2001, COMPUT LINGUIST, V27, P1, DOI 10.1162/089120101300346787 ZAMIR O, 1998, P 21 ANN INT ACM SIG Zamir O., 1997, P 3 INT C KNOWL DISC Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 NR 29 TC 21 Z9 23 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD SEP PY 2008 VL 20 IS 9 BP 1217 EP 1229 DI 10.1109/TKDE.2008.50 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 327XB UT WOS:000257760700006 ER PT J AU Sidorova, A Evangelopoulos, N Valacich, JS Ramakrishnan, T AF Sidorova, Anna Evangelopoulos, Nicholas Valacich, Joseph S. Ramakrishnan, Thiagarajan TI Uncovering the intellectual core of the information systems discipline SO MIS QUARTERLY LA English DT Article DE IS identity; IS research issues; IS research agenda; organizational identity; latent semantic analysis ID LATENT SEMANTIC ANALYSIS; ORGANIZATIONAL IDENTITY; OPTIMAL AGREEMENT; HIGH-IMPACT; RETRIEVAL; DIVERSITY; IMAGE; RELEVANCE; EVOLUTION; FRAMEWORK AB What is the intellectual core of the information systems discipline? This study uses latent semantic analysis to examine a large body of published IS research in order to address this question. Specifically, the abstracts of all research papers over the time period from 1985 through 2006 published in three top IS research journals-MIS Quarterly, Information Systems Research, and Journal of Management Information Systems-were analyzed. This analysis identified five core research areas: (1) information technology and organizations; (2) IS development; (3) IT and individuals; (4) IT and markets; and (5) IT and groups. Over the time frame of our analysis, these core topics have remained quite stable. However, the specific research themes within each core area have evolved significantly, reflecting research that has focused less on technology development and more on the social context in which information technologies are designed and used. As such, this analysis demonstrates that the information systems academic discipline has maintained a relatively stable research identity that focuses on how IT systems are developed and how individuals, groups, organizations, and markets interact with IT. C1 [Sidorova, Anna; Evangelopoulos, Nicholas; Ramakrishnan, Thiagarajan] Univ N Texas, Coll Business Adm, Denton, TX 76203 USA. [Valacich, Joseph S.] Washington State Univ, Coll Business Adm, Pullman, WA 99164 USA. RP Sidorova, A (reprint author), Univ N Texas, Coll Business Adm, Denton, TX 76203 USA. EM sidorova@unt.edu; evangeln@unt.edu; jsv@wsu.edu; ramakrit@unt.edu CR Agarwal R, 2005, MIS QUART, V29, P381 Albert S., 1985, RES ORGAN BEHAV, V7, P263 Alter S., 2003, COMMUNICATIONS AIS, V12, P607 ASHFORTH BE, 1989, ACAD MANAGE REV, V14, P20, DOI 10.2307/258189 Avgerou C, 1999, EUR J INFORM SYST, V8, P136, DOI 10.1057/palgrave.ejis.3000319 Banker RD, 2004, MANAGE SCI, V50, P281, DOI 10.1287/mnsc.1040.0206 BANVILLE C, 1989, COMMUN ACM, V32, P48, DOI 10.1145/63238.63241 Benbasat I, 1999, MIS QUART, V23, P3, DOI 10.2307/249403 Benbasat I, 2003, MIS QUART, V27, P183 Benbasat I, 1996, INFORM SYST RES, V7, P389, DOI 10.1287/isre.7.4.389 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Brickson S, 2000, ACAD MANAGE REV, V25, P82, DOI 10.2307/259264 Brown AD, 1997, ACAD MANAGE REV, V22, P643, DOI 10.2307/259409 Brown TJ, 2006, J ACAD MARKET SCI, V34, P99, DOI 10.1177/0092070305284969 CHENG X, 1995, J CLIMATE, V5, P2631 CHEW PA, 2007, P 13 ACM SIGKDD INT, P143, DOI 10.1145/1281192.1281211 Chua C., 2002, J ASSOC INF SYST, V3, P145 Cohen I. B., 1985, REVOLUTION SCI CRAWFORD C, 1970, PSYCHOMETRIKA, V35, P32 CULNAN MJ, 1986, MANAGE SCI, V32, P156, DOI 10.1287/mnsc.32.2.156 CULNAN MJ, 1987, MIS Q, V11, P340 Davenport TH, 1999, MIS QUART, V23, P19, DOI 10.2307/249405 Deans P.C., 2003, COMMUNICATIONS AIS, V12, P546 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dennis AR, 2006, MIS QUART, V30, P1 DESANCTIS C, 2003, J AIS, V4, P360 DESANCTIS G, 1993, MIS QUART, V17, pR6 Dufner D., 2003, COMMUNICATIONS AIS, V12, P527 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 DUTTON JE, 1991, ACAD MANAGE J, V34, P517, DOI 10.2307/256405 El Sawy O.A., 2003, COMMUNICATIONS AIS, V12, P588 Fox C., 1992, INFORMATION RETRIEVA, P102 Frakes W., 1992, INFORMATION RETRIEVA, P131 Galliers RD, 2003, J ASSOC INF SYST, V4, P337 George J.F., 2005, COMMUNICATIONS AIS, V16, P219 Gioia DA, 2000, ACAD MANAGE REV, V25, P63, DOI 10.2307/259263 Grover V, 2006, J ASSOC INF SYST, V7, P270 Guthrie R. A., 2003, COMMUNICATIONS AIS, V12, P557 Han J., 2006, DATA MINING CONCEPTS Harman D., 1992, INFORMATION RETRIEVA, P363 Hirschheim R, 2003, J ASSOC INF SYST, V4, P237 Holland C., 2003, COMMUNICATIONS AIS, V12, P599 HUSBANDS P, 2001, COMPUTATIONAL INFORM, P1445 IIVARI J, 2003, COMMUNICATIONS AIS, V12 IVES B, 1980, MANAGE SCI, V26, P910, DOI 10.1287/mnsc.26.9.910 Ives B., 2004, J ASSOC INF SYST, V5, P108 Kaplan B., 2004, INFORM SYSTEMS RES R, P1 Keen P.G.W., 1980, P 1 INT C INF SYST P, P9 Kiers HAL, 1997, PSYCHOMETRIKA, V62, P545, DOI 10.1007/BF02294642 KING LJ, 2006, INFORM SYSTEMS STATE Kintsch W, 2002, METAPHOR SYMBOL, V17, P249, DOI 10.1207/S15327868MS1704_1 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 2002, PSYCHOL LEARN MOTIV, V41, P43, DOI 10.1016/S0079-7421(02)80004-4 Landry M., 1992, ACCOUNTING MANAGEMEN, V2, P77, DOI 10.1016/0959-8022(92)90002-A LEE AS, 1989, MIS QUART, V13, P33, DOI 10.2307/248698 Lyytinen K., 2004, J ASSOC INF SYST, V5, P220 Lyytinen K, 2007, EUR J INFORM SYST, V16, P317, DOI 10.1057/palgrave.ejis.3000695 MASON RO, 1973, MANAGE SCI, V19, P475, DOI 10.1287/mnsc.19.5.475 McCubbrey D.J., 2003, COMMUNICATIONS AIS, V12, P553 Meyers J, 2002, GLOBAL TELEPH, V10, P4 Mingers J, 2001, INFORM SYST RES, V12, P240, DOI 10.1287/isre.12.3.240.9709 Myers M. D., 2003, COMMUNICATIONS AIS, V12, P582 Nolan R., 1980, MIS Q, P1 Orlikowski WJ, 2001, INFORM SYST RES, V12, P121, DOI 10.1287/isre.12.2.121.9700 Park H, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P3 PAUL RJ, 2007, EUROPEAN J INFORM SY, V16, P317 PEAY ER, 1988, PSYCHOMETRIKA, V53, P199, DOI 10.1007/BF02294132 Petter S, 2007, MIS QUART, V31, P623 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Power D.J., 2003, COMMUNICATIONS AIS, V12, P539 Rainer RK, 2005, COMMUN ACM, V48, P91, DOI 10.1145/1042091.1042096 Robey D., 2003, J AIS, V4, P352 Robey D, 1996, INFORM SYST RES, V7, P400, DOI 10.1287/isre.7.4.400 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott SG, 2000, ACAD MANAGE REV, V25, P43, DOI 10.2307/259262 VANGIGCH JP, 1989, BEHAV SCI, V34, P128, DOI 10.1002/bs.3830340203 Vessey I, 2002, J MANAGE INFORM SYST, V19, P129 Wade M, 2006, J ASSOC INF SYST, V7, P247 Weber R, 2003, MIS QUART, V27, pIII Westland J. C., 2004, COMMUNICATIONS AIS, V13, P136 Whetten D.A., 1992, ANN M AC MAN LAS VEG Whitley R., 1984, INTELLECTUAL SOCIAL Wolfe MBW, 2003, BEHAV RES METH INS C, V35, P22, DOI 10.3758/BF03195494 WU AY, 2003, COMMUNICATIONS AIS, V12, P562 Zhang P., 2005, J ASSOC INF SYST, V6, P227 NR 87 TC 59 Z9 59 PU SOC INFORM MANAGE-MIS RES CENT PI MINNEAPOLIS PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455 USA SN 0276-7783 J9 MIS QUART JI MIS Q. PD SEP PY 2008 VL 32 IS 3 BP 467 EP 482 PG 16 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 343NB UT WOS:000258859700002 ER PT J AU Martinet, J Satoh, S Chiaramella, Y Mulhem, P AF Martinet, Jean Satoh, Shin'ichi Chiaramella, Yves Mulhem, Philippe TI Media objects for user-centered similarity matching SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article DE image and video indexing; similarity matching; human perception; weighting scheme; evaluation ID IMAGE RETRIEVAL; CLASSIFICATION; SCENE; MODEL AB The increase of digital image and video acquisition devices, combined with the growth of the World Wide Web, requires the definition of user-relevant similarity matching methods providing meaningful access to documents searched by users among large amounts of data. The aim of our work is to define media objects for document description suited to images and videos, integrating a user-centered definition of importance for similarity matching. The importance is defined according to criteria and hypotheses, which have been experimentally validated. This leads to a definition of a weighting scheme for media objects (based on objects size, position, and scene homogeneity), which has also been validated with users in a second experiment. This model allows for meaningful similarity matching between document pairs and between users' queries and documents. C1 [Martinet, Jean; Satoh, Shin'ichi] Natl Inst Informat, Tokyo, Japan. [Chiaramella, Yves; Mulhem, Philippe] MRIM CLIPS IMAG, Grenoble, France. RP Martinet, J (reprint author), Natl Inst Informat, Tokyo, Japan. EM jean@nii.ac.jp; satoh@nii.ac.jp; Yves.Chiaramella@imag.fr; Philippe.Mulhem@imag.fr CR AYACHE S, 2007, EUR C INF RETR ECIR, P494 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAILLARGEON G, 1998, PROBABILITIES STAT T BASTAN M, 2006, P CIVR 2006 PHOEN AZ CARSON C, 1999, INT C VIS INF INF SY Haar Romeny B., 2003, FRONT END VISION MUL HOERSTER E, 2007, P CIVR 2007 AMST NET, P17 Hollink L, 2004, INT J HUM-COMPUT ST, V61, P601, DOI 10.1016/j.ijhcs.2004.03.002 Itti L, 1998, IEEE T PATTERN ANAL, V20, P1254, DOI 10.1109/34.730558 LEE JH, 1997, SIGIR 97 P 20 ANN IN, P267 Lim JH, 2001, PATTERN ANAL APPL, V4, P125, DOI 10.1007/PL00014574 Lim JH, 2000, NEW GENERAT COMPUT, V18, P147 LOTHAR KH, 2001, HIERARCHY INTERACTIO Lowe D. G., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, DOI 10.1109/ICCV.1999.790410 Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 LU Y, 1999, INT C IM AN PROC, P933 Martin P., 1986, MEASURING BEHAV INTR, V2nd MARTINET J, 2007, ECIR 07 MARTINET J, 2005, ACM CIKM, P760 MARTINET J, 2003, P CBMI 03, P335 MITRA M, 1998, RES DEV INFROM RETRI, P206 MULHEM P, 2003, MULTIMEDIA SYSTEMS C OSBERGER W, 1998, ICPR OUNIS I, 1998, P IEEE KNOWL DAT ENG, P50 Preece J., 1994, HUMAN COMPUTER INTER Qiu Y., 1993, SIGIR, P160 QUELHAS P, 2006, CIVR 2006 Quelhas P, 2007, IEEE T PATTERN ANAL, V29, P1575, DOI 10.1109/TPAMI.2007.1155 RODDEN K, 1999, P 25 BCS IRSG C IR RODDEN K, 2003, ACM C HUM FACT COMP, P409 ROJER AS, 1990, 10TH INT C PATT REC, V2, P278 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shannon E. C., 1948, BELL SYST TECH J, V27, P623 Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888 Sivic J., 2003, 9 IEEE INT C COMP VI, V2, P1470 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Snoek CGM, 2005, MULTIMED TOOLS APPL, V25, P5, DOI 10.1023/B:MTAP.0000046380.27575.a5 STENTIFORD F, 2003, P SPIE STOR RETR MED, V5021 TIAN Q, 2003, IEEE MULTIMED SPEC I, V10, P28 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vapnik V., 1995, NATURE STAT LEARNING Vogel J, 2007, INT J COMPUT VISION, V72, P133, DOI 10.1007/s11263-006-8614-1 Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109 WANG JZ, 2001, ICIAP, P380 YAHIAOUI I, 2003, EURASIP J APPL SIG P, V1, P48 ZHENG QF, 2006, ACM MULTIMEDIA 2006 NR 49 TC 2 Z9 2 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1380-7501 J9 MULTIMED TOOLS APPL JI Multimed. Tools Appl. PD SEP PY 2008 VL 39 IS 2 BP 263 EP 291 DI 10.1007/s11042-008-0200-9 PG 29 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 322NA UT WOS:000257381400007 ER PT J AU Ajoka, S Tsuge, S Shishibori, M Kita, K AF Ajoka, Shiro Tsuge, Satoru Shishibori, Masami Kita, Kenji TI Fast multidimensional nearest neighbor search algorithm using priority queue SO ELECTRICAL ENGINEERING IN JAPAN LA English DT Article DE nearest neighbor search; high-dimensional space; fast linear search; priority queue; multimedia information retrieval AB Nearest neighbor search in high-dimensional spaces is an interesting and important problem which is relevant for a wide variety of applications, including multimedia information retrieval, data mining, and pattern recognition. For such applications, the curse of high dimensionality tends to be a major obstacle in the development of efficient search methods. This paper addresses the problem of designing an efficient algorithm for high-dimensional nearest neighbor search using a priority queue. The proposed algorithm is based on a simple linear search algorithm and eliminates unnecessary arithmetic operations from distance computations between multidimensional vectors. Moreover, we propose two techniques, a dimensional sorting method and a PCA-based method, to accelerate multidimensional search. Experimental results indicate that our scheme scales well even lor a very large number of dimensions. (c) 2008 Wiley Periodicals, Inc. C1 [Ajoka, Shiro; Shishibori, Masami; Kita, Kenji] Univ Tokushima, Tokushima, Japan. RP Ajoka, S (reprint author), Univ Tokushima, Tokushima, Japan. CR Aggarwal C. C., 2001, P 8 INT C DAT THEOR, P420 BARNEA DI, 1972, IEEE T COMPUT, VC 21, P179 BECKMANN N, 1990, SIGMOD REC, V19, P322, DOI 10.1145/93597.98741 Berchtold S, 1996, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P28 Beyer K., 1999, P 7 INT C DAT THEOR, P217 BLOTT S, 1997, 19 ESPRIT HERMES Bozkaya T, 1999, ACM T DATABASE SYST, V24, P361, DOI 10.1145/328939.328959 Ciaccia P., P 23 INT C VER LARG, P426 Flickner M., 1995, IEEE COMPUT, V28, P23 Gaede V, 1998, ACM COMPUT SURV, V30, P170, DOI 10.1145/280277.280279 Guttman A, 1984, P ACM SIGMOD INT C M, P47 HELOR Y, P 9 IEEE INT C COMP, P1486 JIN JS, 2003, MULTIMEDIA INFORM RE Katayama N., 1997, P ACM SIGMOD INT C M, P369, DOI 10.1145/253260.253347 KOIZUMI D, FIT2003, P31 KOIZUMI D, 2002, DEV IMAGE KEYWORD RE KURNIAWATI R, 1997, P SPIE STOR RETR IM, P110 Mount D., ANN LIB APPROXIMATE Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sedgewick R, 1988, ALGORITHMS Sellis T., 1987, Proceedings of the Thirteenth International Conference on Very Large Data Bases: 1987 13th VLDB TAKEMOTO K, 2003, NEAREST NEIGHBOR CLA, V103, P1 White DA, 1996, PROC INT CONF DATA, P516, DOI 10.1109/ICDE.1996.492202 YIANILOS PN, 1993, PROCEEDINGS OF THE FOURTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P311 NR 25 TC 1 Z9 1 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 0424-7760 J9 ELECTR ENG JPN JI Electr. Eng. Jpn. PD AUG PY 2008 VL 164 IS 3 BP 69 EP 77 DI 10.1002/eej.20502 PG 9 WC Engineering, Electrical & Electronic SC Engineering GA 308LB UT WOS:000256390400009 ER PT J AU Farah, M Vanderpooten, D AF Farah, Mohamed Vanderpooten, Daniel TI An outranking approach for information retrieval SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT 13th International Conference on String Processing and Information Retrieval CY OCT 11-13, 2006 CL Glasgow, SCOTLAND DE information retrieval; relevance; outranking approach; multiple criteria; aggregation ID SEARCH ENGINE; MODEL; WEB AB Over the last three decades, research in Information Retrieval (IR) shows performance improvement when many sources of evidence are combined to produce a ranking of documents. Most current approaches assess document relevance by computing a single score which aggregates values of some attributes or criteria. They use analytic aggregation operators which either lead to a loss of valuable information, e.g., the min or lexicographic operators, or allow very bad scores on some criteria to be compensated with good ones, e.g., the weighted sum operator. Moreover, all these approaches do not handle imprecision of criterion scores. In this paper, we propose a multiple criteria framework using a new aggregation mechanism based on decision rules identifying positive and negative reasons for judging whether a document should get a better ranking than another. The resulting procedure also handles imprecision in criteria design. Experimental results are reported showing that the suggested method performs better than standard aggregation operators. C1 [Farah, Mohamed; Vanderpooten, Daniel] Univ Paris 09, Paris, France. [Farah, Mohamed] Fac Sci Monastir, Monastir, Tunisia. RP Farah, M (reprint author), Univ Paris 09, Paris, France. EM farah@lamsade.dauphine.fr; vdp@lamsade.dauphine.fr CR Amento B., 2000, SIGIR 00, P296 ANH VN, 2002, ACDS 2002 P 7 AUSTR Bordogna G, 2003, INT J APPROX REASON, V34, P105, DOI 10.1016/j.ijar.2003.07.003 Bordogna G, 2004, FUZZY SET SYST, V148, P105, DOI 10.1016/j.fss.2004.03.008 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 BOUGHANEM M, 2005, LECT NOTES COMPUTER, V3877, P44 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Cao G., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Craswell N., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval CRASWELL N, 2004, P TREC 2004 Dubois D, 1984, TIMS STUDIES MANAGEM, V20, P209 Fang H., 2004, SIGIR 2004, P49, DOI 10.1145/1008992.1009004 FOX EA, 1994, P TREC 3 Frakes W. B., 1992, INFORM RETRIEVAL DAT GRANKA LA, 2004, SIGIR 2004 Hawking D, 2001, INFORM RETRIEVAL, V4, P33, DOI 10.1023/A:1011468107287 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kraaij W., 2002, SIGIR 02, P27 Leclercq J. P., 1984, REV BELGE RECHERCHE, V24, P32 Pasi G., 2007, SIGIR 2007 P 30 ANN, P775 Robertson S. E., 1994, P TREC 3 ROY B, 1982, TRANSPORT RES A-POL, V16, P301, DOI 10.1016/0191-2607(82)90057-7 ROY B, 1989, MATH COMPUT MODEL, V12, P1245, DOI 10.1016/0895-7177(89)90366-X ROY B, 1991, THEOR DECIS, V31, P49, DOI 10.1007/BF00134132 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1983, INTRO MODERN IFORM R SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SAVOY J, 2000, P TREC 9 van Rijsbergen C. J., 1979, INFORM RETRIEVAL NR 30 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD AUG PY 2008 VL 11 IS 4 BP 315 EP 334 DI 10.1007/s10791-008-9046-z PG 20 WC Computer Science, Information Systems SC Computer Science GA 302ZT UT WOS:000256009500004 ER PT J AU Sjoberg, M Laaksonen, J Honkela, T Polla, M AF Sjoberg, Mats Laaksonen, Jorma Honkela, Timo Polla, Matti TI Inferring semantics from textual information in multimedia retrieval SO NEUROCOMPUTING LA English DT Article DE multimodal databases; multimedia semantics; content-based retrieval; self-organizing maps; symbol grounding problem ID WORDS; MODEL AB We propose a method for inferring semantic information from textual data in content-based multimedia retrieval. Training examples of images and videos belonging to a specific semantic class are associated with their low-level visual and aural descriptors augmented with textual features such as frequencies of significant words. A fuzzy mapping of a semantic class in the training set to a class of similar objects in the test set is created by using Self-Organizing Maps (SOMs) trained from the low-level descriptors. Experiments with two databases and different textual features show promising results, indicating the usefulness of the approach in bridging the gap from low-level visual features to semantic concepts. (C) 2008 Elsevier B.V. All rights reserved. C1 [Sjoberg, Mats; Laaksonen, Jorma; Honkela, Timo; Polla, Matti] Helsinki Univ Technol, Adapt Informat Res Ctr, Helsinki 02015, Finland. RP Sjoberg, M (reprint author), Helsinki Univ Technol, Adapt Informat Res Ctr, POB 5400, Helsinki 02015, Finland. EM mats.sjoberg@tkk.fi RI Sjoberg, Mats/E-8332-2012 CR Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 CARBONETTO P, 2004, P 8 EUR C COMP VIS P DAMERAU FJ, 1993, INFORM PROCESS MANAG, V29, P433, DOI 10.1016/0306-4573(93)90039-G Davis S. B., 1990, READINGS SPEECH RECO, P65 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FAN J, 2004, P 12 ANN ACM INT C M FINCH S, 1992, ARTIFICIAL NEURAL NE, V2 Church K. W., 1990, Computational Linguistics, V16 HARNAD S, 1990, PHYSICA D, V42, P335, DOI 10.1016/0167-2789(90)90087-6 HONKELA T, 2007, TERMINOLOGY EXTRACTI Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 HONKELA T, 1995, P ICANN 95 INT C ART, V2 *ISO IEC, 2002, 1593832002E ISOIEC *ISO IEC, 2001, JTC1SC29WG11N3914 IS KASKI S, 1998, P IEEE INT JOINT C N, V1 KHOTANZAD A, 1990, IEEE T PATTERN ANAL, V12, P489, DOI 10.1109/34.55109 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kohonen T., 2001, SPRINGER SERIES INFO, V30 KOSKELA M, 2007, P TRECVID 2007 WORKS KOSKELA M, 2005, P TRECVID 2005 WORKS KOSKELA M, 2004, P 3 INT C IM VID RET LAAKSONEN J, 2005, P INT INT C AD KNOWL LAAKSONEN J, 2006, P 9 SCAND C ART INT Laaksonen J, 2002, IEEE T NEURAL NETWOR, V13, P841, DOI 10.1109/TNN.2002.1021885 LEGRADY G, 2002, VISUAL COMMUNICATION, V1, P163 LIN X, 1991, P 14 ANN INT ACM SIG Manning C. D., 1999, FDN STAT NATURAL LAN MIIKKULAINEN R, 1990, P 12 ANN C COGN SCI Miikkulainen R., 1993, SUBSYMBOLIC NATURAL Miikkulainen R, 1997, BRAIN LANG, V59, P334, DOI 10.1006/brln.1997.1820 MONAY F, 2003, P 11 ACM INT C MULT MONTAGUE R, 1970, APPROACHES NATURAL L Mori Y., 1999, P 1 INT WORKSH MULT Naphade M, 2005, LIGHT SCALE CONCEPT PAN JY, 2004, P 2004 IEEE INT C MU PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 Roy D, 2005, TRENDS COGN SCI, V9, P389, DOI 10.1016/j.tics.2005.06.013 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton G., 1983, COMPUTER SCI SERIES SCHUTZE H, 1992, P SUP SJOBERG M, 2006, P 3 IFIP C ART INT A SJOBERG M, 2006, P 16 INT C ART NEUR SJOBERG M, 2006, P TRECVID 2006 WORKS VIITANIEMI V, 2006, STUDIES FUZZINESS SO, V210, P121 VIITANIEMI V, 2005, P 2 EUR WORKSH INT K von Foerster H, 2003, UNDERSTANDING UNDERS NR 47 TC 1 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD AUG PY 2008 VL 71 IS 13-15 BP 2576 EP 2586 DI 10.1016/j.neucom.2008.01.029 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA 347DP UT WOS:000259121100018 ER PT J AU Chang, JM Su, ECY Lo, A Chiu, HS Sung, TY Hsu, WL AF Chang, Jia-Ming Su, Emily Chia-Yu Lo, Allan Chiu, Hua-Sheng Sung, Ting-Yi Hsu, Wen-Lian TI PSLOoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysis SO PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS LA English DT Article DE protein subcellular localization; document classification; vector space model; gapped-dipeptides; probabilistic latent semantic analysis; support vector machines ID SECONDARY STRUCTURE PREDICTION; GRAM-NEGATIVE-BACTERIA; SUPPORT VECTOR MACHINES; LOCATION; CLASSIFICATION; INFORMATION; DATABASE; HYPROSP; SPACE; PSORT AB Prediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836-2847, Yu et al., Proteins 2006;64:643-651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.219% respectively, and it compares favorably with that of CELLO H (Yu et al., Proteins 2006,64:643-651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005,21:617-623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/similar to bioapp/PSLDoc/. C1 [Chang, Jia-Ming; Chiu, Hua-Sheng; Sung, Ting-Yi; Hsu, Wen-Lian] Acad Sinica, Inst Informat Sci, Bioinformat Lab, Taipei 115, Taiwan. [Su, Emily Chia-Yu; Lo, Allan] Acad Sinica, Taiwan Int Grad Program, Bioinformat Program, Taipei 115, Taiwan. [Su, Emily Chia-Yu] Natl Chiao Tung Univ, Inst Bioinformat, Hsinchu, Taiwan. [Lo, Allan] Natl Tsing Hua Univ, Dept Life Sci, Hsinchu, Taiwan. RP Sung, TY (reprint author), Acad Sinica, Inst Informat Sci, Bioinformat Lab, 128 Acad Rd,Sect 2, Taipei 115, Taiwan. EM tsung@iis.sinica.edu.tw RI Su, Emily Chia-Yu/A-9169-2010; Chang, Jia-Ming/A-5642-2008 OI Chang, Jia-Ming/0000-0002-6711-1739 CR ALTSCHUL SF, 1997, NUCLEIC ACIDS RES, V25, P3402 Bhasin M, 2005, BIOINFORMATICS, V21, P2522, DOI 10.1093/bioinformatics/bti309 BOOTH IR, 1985, MICROBIOL REV, V49, P359 Cedano J, 1997, J MOL BIOL, V266, P594, DOI 10.1006/jmbi.1996.0804 Chang C.-C., 2001, LIBSVM LIB SUPPORT V Cheng BYM, 2005, PROTEINS, V58, P955, DOI 10.1002/prot.20373 Chou KC, 1999, PROTEIN ENG, V12, P107, DOI 10.1093/protein/12.2.107 Cohen J., 1988, STAT POWER ANAL BEHA, pxxi COSTA EP, 2007, BRAZ S BIOINF BRAZ, P126 Cuff JA, 1999, PROTEINS, V34, P508, DOI 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gardy JL, 2003, NUCLEIC ACIDS RES, V31, P3613, DOI 10.1093/nar/gkg602 Gardy JL, 2005, BIOINFORMATICS, V21, P617, DOI 10.1093/bioinformatics/bti057 Garg A, 2005, J BIOL CHEM, V280, P14427, DOI 10.1074/jbc.M411789200 Hessa T, 2005, NATURE, V433, P377, DOI 10.1038/nature03216 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 HOFMANN T, 1999, P 15 C UNC ART INT U, P289 Hua SJ, 2001, BIOINFORMATICS, V17, P721, DOI 10.1093/bioinformatics/17.8.721 Hua SJ, 2001, J MOL BIOL, V308, P397, DOI 10.1006/jmbi.2001.4580 Jones DT, 1999, J MOL BIOL, V292, P195, DOI 10.1006/jmbi.1999.3091 King BR, 2007, GENOME BIOL, V8, DOI 10.1186/gb-2007-8-5-r68 Kumar C. A., 2006, Journal of Computing and Information Technology - CIT, V14, DOI 10.2498/cit.2006.03.02 Liang HK, 2005, PROTEINS, V59, P58, DOI 10.1002/prot.20386 Lin HN, 2005, BIOINFORMATICS, V21, P3227, DOI 10.1093/bioinformatics/bti524 Lu Z, 2004, BIOINFORMATICS, V20, P547, DOI 10.1093/bioinformatics/btg447 MANNING CD, 1999, FDN STAT NATURAL LAN, pR37 MATTHEWS BW, 1975, BIOCHIM BIOPHYS ACTA, V405, P442, DOI 10.1016/0005-2795(75)90109-9 McGuffin LJ, 2000, BIOINFORMATICS, V16, P404, DOI 10.1093/bioinformatics/16.4.404 MYERS EW, 1988, COMPUT APPL BIOSCI, V4, P11 Nair R, 2005, J MOL BIOL, V348, P85, DOI 10.1016/j.jmb.2005.02.025 Nair R, 2003, PROTEINS, V53, P917, DOI 10.1002/prot.10507 Nair R, 2002, PROTEIN SCI, V11, P2836, DOI 10.1110/ps.0207402 Nakai K, 1999, TRENDS BIOCHEM SCI, V24, P34, DOI 10.1016/S0968-0004(98)01336-X NAKAI K, 1991, PROTEINS, V11, P95, DOI 10.1002/prot.340110203 NAMBURU S, 2005, IEEE AER C BIG SKY M, P1 Park KJ, 2003, BIOINFORMATICS, V19, P1656, DOI 10.1093/bioinformatics/btg222 Reinhardt A, 1998, NUCLEIC ACIDS RES, V26, P2230, DOI 10.1093/nar/26.9.2230 Rey S, 2005, NUCLEIC ACIDS RES, V33, pD164, DOI 10.1093/nar/gki027 Ritchie MD, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-28 Robert V, 2006, PLOS BIOL, V4, P1984, DOI 10.1371/journal.pbio.0040377 ROST B, 1993, J MOL BIOL, V232, P584, DOI 10.1006/jmbi.1993.1413 Sal-Man N, 2007, J BIOL CHEM, V282, P19753, DOI 10.1074/jbc.M610368200 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIVIC J, 2005, P IEEE INT C COMP VI, P1331 Ulmschneider MB, 2005, PROTEINS, V59, P252, DOI 10.1002/prot.20334 Valdes-Perez RE, 2000, INT J HUM-COMPUT ST, V53, P411, DOI 10.1006/ijhc.2000.0397 Wang JR, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-174 Wheeler DL, 2000, NUCLEIC ACIDS RES, V28, P10, DOI 10.1093/nar/28.1.10 Wu KP, 2004, NUCLEIC ACIDS RES, V32, P5059, DOI 10.1093/nar/gkh836 Wu TF, 2004, J MACH LEARN RES, V5, P975 Yu CS, 2006, PROTEINS, V64, P643, DOI 10.1002/prot.21018 Yu CS, 2004, PROTEIN SCI, V13, P1402, DOI 10.1110/ps.03479604 NR 53 TC 22 Z9 22 PU WILEY-LISS PI HOBOKEN PA DIV JOHN WILEY & SONS INC, 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0887-3585 J9 PROTEINS JI Proteins PD AUG 1 PY 2008 VL 72 IS 2 BP 693 EP 710 DI 10.1002/prot.21944 PG 18 WC Biochemistry & Molecular Biology; Biophysics SC Biochemistry & Molecular Biology; Biophysics GA 319IE UT WOS:000257156500014 PM 18260102 ER PT J AU Perez, JM Berlanga, R Aramburu, MJ Pedersen, TB AF Manuel Perez, Juan Berlanga, Rafael Jose Aramburu, Maria Pedersen, Torben Bach TI Integrating data warehouses with web data: A survey SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE data warehouse repository; XML/XSL/RDF ID XML DTDS; UML AB This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research lines. C1 [Manuel Perez, Juan; Berlanga, Rafael] Univ Jaume 1, Dept Lenguajes & Sistemas Informat, E-12071 Castellon de La Plana, Spain. [Jose Aramburu, Maria] Univ Jaume 1, Dept Ingn & Ciencia Computadores, E-12071 Castellon de La Plana, Spain. [Pedersen, Torben Bach] Univ Aalborg, Dept Comp Sci, DK-9220 Aalborg O, Denmark. RP Perez, JM (reprint author), Univ Jaume 1, Dept Lenguajes & Sistemas Informat, Campus Riu Sec, E-12071 Castellon de La Plana, Spain. EM JuanMa.Perez@lsi.uji.es; berlanga@lsi.uji.es; aramburu@icc.uji.es; tbp@cs.aau.dk RI Berlanga, Rafael/K-9590-2014 OI Berlanga, Rafael/0000-0002-9155-269X CR Avnurand R., 2000, P 2000 ACM SIGMOD IN, P261, DOI 10.1145/342009.335420 Badia A., 2006, PROCESSING MANAGING, P96 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Beyer K., 2005, P 2005 ACM SIGMOD IN, P503, DOI 10.1145/1066157.1066215 BHOWMICK SS, 2001, THESIS NANYANG TECHN Bhowmick SS, 2003, IEEE T KNOWL DATA EN, V15, P423, DOI 10.1109/TKDE.2003.1185843 Bruckner R.M., 2001, P INT C WEB INF SYST, P109 CALRK J, 1999, XML PATH LANGUAGE XP Chauret N., 1997, DRUG METAB DISPOS, V26, P1 Cobena G, 2002, PROC INT CONF DATA, P41, DOI 10.1109/ICDE.2002.994696 Codd E. F., 1993, PROVIDING OLAP USER Daconta M. C., 2003, SEMANTIC WEB GUIDE F DEACH S, 2001, EXTENSIBLE SYLESHEET DeRose S., 2001, XML LINKING LANGUAGE FALLSIDE DC, XML SCHEMA 0 Foster I, 1998, GRID BLUEPRINT NEW C Golfarelli M., 2001, P ACM 4 INT WORKSH D, P40, DOI 10.1145/512236.512242 Hummer W., 2003, P 6 ACM INT WORKSH D, P33 Inmon W. H., 2005, BUILDING DATA WAREHO, V4th JAGADISH HV, 2002, 8 INT WORKSH DAT PRO, P149 Jensen MR, 2003, DATA KNOWL ENG, V44, P323, DOI 10.1016/S0169-023X(02)00142-8 Jensen RA, 2001, GENOME BIOL, V2 KAMPS J, 2004, P 7 INT WORKSH WEB D, P55, DOI 10.1145/1017074.1017089 Kimball R., 2002, DATA WAREHOUSE TOOLK KRISHNAPRASAD M, 2004, P 30 INT C VER LARG LAFON Y, 2007, SOAP VERSION 1 2 0 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LEE C, 1995, ACM SIGMOD RECORD, V24, P28, DOI 10.1145/211990.212011 LEE J, 2002, P INT C INF TECHN CO, P224 LEE J, 2003, P 12 INT C INF KNOWL, P572 Liu Z.H., 2005, P ACM SIGMOD C MAN D, P828, DOI 10.1145/1066157.1066259 Lu SY, 2005, DATA KNOWL ENG, V52, P231, DOI 10.1016/j.datak.2004.05.007 MALER E, 2006, EXTENSIBLE MARKUP LA MANGISENGI O, 2001, P 3 INT C DAT WAR KN, P328 Marian A., 2001, Proceedings of the 27th International Conference on Very Large Data Bases MCMCCABE J, 2000, P ACM SIGIR 00, P363 *MICR CORP HYP SOL, 2001, XML AN SPEC Mothe J, 2003, J AM SOC INF SCI TEC, V54, P650, DOI 10.1002/asi.10257 NGUYEN B, 2001, P ACM SIGMOD SANT BA, P437, DOI 10.1145/375663.375723 NGUYEN T, 2000, P 1 INT C ADV INF SY, P24 NGUYEN TB, 2001, P 3 INT WORKSH DES M NIEMI T, 2003, P 5 INT WORKSH DES M NIEMI T, 2003, P 5 ACM INT WORKSH D Niemi T, 2002, P 5 ACM INT WORKSH D, P22, DOI 10.1145/583890.583894 *OMG, 2004, UN MOD LANG UML Pal S., 2005, P 31 INT C VER LARG, P1175 PARK BK, 2005, P DAWAK, P32 Pedersen D., 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management, DOI 10.1109/SSDM.2002.1029721 PEDERSEN D, 2003, P ACM DOLAP DAT WAR, P25 PEDERSEN D, 2002, P DAWAK, P245 Pedersen D, 2002, P ACM 5 INT WORKSH D, P57, DOI 10.1145/583890.583899 Pedersen D., 2004, Proceedings. International Database Engineering and Applications Symposium, DOI 10.1109/IDEAS.2004.1319809 Pedersen D, 2004, PROC INT CONF DATA, P778, DOI 10.1109/ICDE.2004.1320045 PEDERSEN TB, 2005, IND INFORM TECHNOLOG, P1 Pedersen TB, 2001, INFORM SYST, V26, P383, DOI 10.1016/S0306-4379(01)00023-0 Pepper S., XML TOPIC MAPS XTM 1 Perez J. M., 2005, P 8 ACM INT WORKSH D, P19, DOI 10.1145/1097002.1097007 PEREZ JM, 2004, P 6 INT C ENT INF SY, P579 PEREZ JM, 2005, P ECIR, P536 PEREZ JM, 2004, P DEXA, P318 POKORNY J, 2001, P 4 ACM INT WORKSH D, P24, DOI 10.1145/512236.512240 Ponniah Paulraj, 2001, DATA WAREHOUSING FUN Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Priebe T., 2003, P 12 INT C INF KNOWL, P216 ROBIE J, 2006, XQUERY 1 0 XML QUERY ROMERO O, 2007, P 10 ACM INT WORKSH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANZ I, 2003, P DEXA, P109 Sheth A., 1990, ACM COMPUT SURV, V22, P183, DOI 10.1145/96602.96604 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 SPOFFORD G, 2001, MDX SOLUTIONS MICROS Trujillo J, 2004, J DATABASE MANAGE, V15, P41, DOI 10.4018/jdm.2004010102 Tseng FSC, 2005, J INF SCI, V31, P209, DOI 10.1177/0165551505052467 VRDOLJAK B, 2003, P 5 INT C DAT WAR KN, P89 *WEB WAR MIN GROUP, 2007, WHOW Widom J., 1995, Proceedings of the 1995 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/221270.221319 Wiwatwattana N., 2007, P 23 INT C DAT ENG I, P916 Xyleme Lucie, 2001, IEEE DATA ENG B, V24, P40 YINYAN C, 2000, P 11 INT C DAT EXP S, P457 Zhuge Y, 1998, PROC INT CONF DATA, P116, DOI 10.1109/ICDE.1998.655767 NR 80 TC 23 Z9 23 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD JUL PY 2008 VL 20 IS 7 BP 940 EP 955 DI 10.1109/TKDE.2007.190746 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 303CN UT WOS:000256017300006 ER PT J AU Fersini, E Messina, E Archetti, F AF Fersini, E. Messina, E. Archetti, F. TI Enhancing web page classification through image-block importance analysis SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE term weighting; vector space model; visual layout analysis; document classification AB We present a term weighting approach for improving web page classification, based on the assumption that the images of a web page are those elements which mainly attract the attention of the user. This assumption implies that the text contained in the visual block in which an image is located, called image-block, should contain significant information about the page contents. In this paper we propose a new metric, called the Inverse Term Importance Metric, aimed at assigning higher weights to important terms contained into important image-blocks identified by performing a visual layout analysis. We propose different methods to estimate the visual image-blocks importance, to smooth the term weight according to the importance of the blocks in which the term is located. The traditional TFxIDF model is modified accordingly and used in the classification task. The effectiveness of this new metric and the proposed block evaluation methods have been validated using different classification algorithms. (C) 2007 Elsevier Ltd. All rights reserved. C1 [Fersini, E.; Messina, E.; Archetti, F.] Univ Milano Bicocca, Dipartimento Informat Sistemist & Commun, Milan, Italy. [Archetti, F.] Consorzio Milano Ric, Milan, Italy. RP Fersini, E (reprint author), Univ Milano Bicocca, Dipartimento Informat Sistemist & Commun, Milan, Italy. EM fersini@disco.unimib.it CR AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470 CAI D, MSRTR200379, P2003 Cai D., 2003, P 5 AS PAC WEB C, P406 Frank E., 1999, DATA MINING PRACTICA Gao Y., 2006, P 14 ANN ACM INT C M, P901, DOI 10.1145/1180639.1180840 HUANG S, 2005, P 14 INT C WORLD WID, P1042 John G.H., 1995, P 11 C UNC ART INT, P338 Kovacevic M., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183910 Li F F, 2005, P 2005 IEEE COMP SOC, P524 MCCALLUM A, 1998, P 15 NAT C ART INT MEHTA RR, 2005, P 14 INT C WORLD WID, P928 NICHOLAS C, 2003, COMPREHENSIVE SURVEY Pearl J., 1982, P AM ASS ART INT NAT, P133 Platt JC, 1999, ADVANCES IN KERNEL METHODS, P185 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 Yi L., 2003, INT JOINT C ART INT, P43 NR 20 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2008 VL 44 IS 4 BP 1431 EP 1447 DI 10.1016/j.ipm.2007.11.003 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 321AP UT WOS:000257276800003 ER PT J AU Ortega, JL Aguillo, IF AF Ortega, Jose Luis Aguillo, Isidro F. TI Visualization of the Nordic academic web: Link analysis using social network tools SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information visualization; social network analysis; webometrics; co-link analysis; Nordic universities ID IMPACT FACTORS; COMMUNICATION; COLLABORATION; INTERLINKING; ENVIRONMENT; INFORMATION; SPACES; MODEL AB The aim of this paper is to study the link relationships in the Nordic academic web space - comprised of 23 Finnish, 11 Danish and 28 Swedish academic web domains with the European one. Through social networks analysis we intend to detect sub-networks within the Nordic network, the position and role of the different university web domains and to understand the structural topology of this web space. Co-link analysis, with asymmetrical matrices and cosine measure, is used to identify thematic clusters. Results show that the Nordic network is a cohesive network, set up by three well-defined sub-networks and it rests on the Finnish and Swedish sub-networks. We conclude that the Danish network has less visibility than other Nordic countries. The Swedish one is the principal Nordic sub-network and the Finland network is a slightly isolated from Europe, with the exception of the University of Helsinki. (C) 2007 Elsevier Ltd. All rights reserved. C1 [Ortega, Jose Luis; Aguillo, Isidro F.] CSIC, CINDOC, Cybermetr Lab, Madrid 28002, Spain. RP Ortega, JL (reprint author), CSIC, CINDOC, Cybermetr Lab, Joaquin Costa 22, Madrid 28002, Spain. EM jortega@cindoc.csic.es; isidro@cindoc.csic.es RI Aguillo, Isidro/A-7280-2008 OI Aguillo, Isidro/0000-0001-8927-4873 CR Aguillo IF, 2006, J AM SOC INF SCI TEC, V57, P1296, DOI 10.1002/asi.20433 AGUILLO IF, 2006, P INT WORKSH WEB INF Almind TC, 1997, J DOC, V53, P404, DOI 10.1108/EUM0000000007205 Bjorneborn L., 2001, P 12 ACM C HYP HYP, P133 Bjorneborn L, 2006, SCIENTOMETRICS, V68, P395, DOI 10.1007/s11192-006-0119-8 Borgatti S. P., 2002, NETDRAW GRAPH VISUAL Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 Cothey V., 2005, P 10 INT C INT SOC S COTHEY V, 2006, CYBERNIETRICS, V10, P3 Faba-Perez C, 2005, J AM SOC INF SCI TEC, V56, P779, DOI 10.1002/asi.20161 HEIMERIKS G, 2006, CYBERMETRICS, V10 Heimeriks G, 2003, SCIENTOMETRICS, V58, P391, DOI 10.1023/A:1026296812830 Heimeriks G., 2005, KNOWLEDGE PRODUCTION Holmberg K., 2007, P 11 INT C INT SOC S Ingwersen P, 1998, J DOC, V54, P236, DOI 10.1108/EUM0000000007167 KAMADA T, 1989, INFORM PROCESSING LE Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kretschmer H, 2004, SCIENTOMETRICS, V61, P405, DOI 10.1023/B:SCIE.0000045118.68430.fd KRETSCHMER H, 2006, INT WORKSH WEB INF S Leydesdorff L, 2006, J AM SOC INF SCI TEC, V57, P1616, DOI 10.1002/asi.20335 LEYDESDORFF L, 2007, J AM SOC INFORM SCI Musgrove PB, 2003, SCIENTOMETRICS, V58, P657, DOI 10.1023/B:SCIE.0000006886.37828.4a Nooy W., 2005, EXPLORATORY SOCIAL N ORTEGA JL, 2008, SCIENTOMETRICS, V74 Polanco X., 2001, NTTS ETK 2001 C HERS SALTON G, 1971, AUTOMATIC DOCUMENT P SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHARNHORST A, 2006, CYBERMETRICS, V10, P6 Smith AG, 1999, J DOC, V55, P577 Thelwall M, 2002, J DOC, V58, P683, DOI 10.1108/00220410210448219 Thelwall M, 2003, SCIENTOMETRICS, V56, P417, DOI 10.1023/A:1022387105904 Thelwall M, 2001, J AM SOC INF SCI TEC, V52, P1157, DOI 10.1002/asi.1182 Vaughan L, 2006, J AM SOC INF SCI TEC, V57, P1178, DOI 10.1002/asi.20398 NR 33 TC 25 Z9 25 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2008 VL 44 IS 4 BP 1624 EP 1633 DI 10.1016/j.ipm.2007.09.010 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 321AP UT WOS:000257276800015 ER PT J AU Oyarce, G AF Oyarce, Guillermo TI Using the shape recovery method to evaluate indexing techniques SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID UNKNOWN DISTANCE FUNCTION; INFORMATION-RETRIEVAL; TEXT CATEGORIZATION; VISUAL EXPLORATION; SYSTEM; MODEL; CLASSIFICATION; VISUALIZATION; PROXIMITIES; SELECTION AB Text representation, central to information processing, must be descriptive and discriminative. Although some of the many techniques to construct document representations may outperform others for certain tasks, no one is consistently better than others. Representations are still problematic. Evaluation techniques are needed to penetrate foundational questions about term behavior in representation. A study that applies the shape recovery analysis method is reported here as an evaluative tool to compare different indexing schemes. Three weight coefficients are used to rank indexing terms and are compared to the documents' full text. Two of the weight coefficients are novel and the third relies on the chi-squared distribution. Multidimensional scaling reduces the dimensional space of the document surrogates into a two-dimensional Cartesian space. Ten concentric circles evenly separated at 10% intervals of relevant data points starting at the centroid are used to construct a precision-recall curve. ANOVA is used for a straightforward computation of the 4 x 11 matrix of test data to see whether the four treatments yield the same P-R result. A post hoc HSD Tukey multiple comparisons test among pairwise treatments is also used to discover homogeneous groups. The findings show the value of the methodology to study term weighting schemes, and their descriptiveness and discriminative power, as well as the potential strength of the novel coefficients introduced. C1 Univ N Texas, Denton, TX 76202 USA. RP Oyarce, G (reprint author), Univ N Texas, POB 276, Denton, TX 76202 USA. EM oyga@unt.edu CR Bakus J, 2006, KNOWL INF SYST, V9, P468, DOI 10.1007/s10115-005-0209-6 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 BOYCE BR, 1985, ANNU REV INFORM SCI, V20, P153 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 Crestani F., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980) CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 CROUCH DB, 1986, 9 INT C RES DEV INF, P5 DOUCET A, 2006, ACM SIGIR FORUM, V40, P71, DOI 10.1145/1147197.1147212 Fuhr N., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980) Goodrum AA, 2001, J AM SOC INF SCI TEC, V52, P174, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1580>3.0.CO;2-V Gordon A. D., 1981, CLASSIFICATION METHO GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 HARMAN D, 1993, P TIPSTER TEXT PROGR, P17, DOI 10.3115/1119149.1119157 HARMAN D, 1996, 4 TEXT RETR C TREC 4, P1 Havre S., 2001, Proceedings IEEE Symposium on Information Visualization 2001. INFOVIS 2001 HAYES RM, 1993, INFORM PROCESS MANAG, V29, P1, DOI 10.1016/0306-4573(93)90019-A Herrera-Viedma E, 2001, J AM SOC INF SCI TEC, V52, P460, DOI 10.1002/1532-2890(2001)9999:9999<::AID-ASI1087>3.0.CO;2-Q Jarvelin K, 2007, J AM SOC INF SCI TEC, V58, P971, DOI 10.1002/asi.20589 JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1975, 5266 U CAMBR COMP LA Kamber M., 2001, DATA MINING CONCEPTS Kang BY, 2005, INFORM PROCESS MANAG, V41, P1065, DOI 10.1016/j.ipm.2004.08.005 KATTER R, 1967, STUDY DOCUMENT REPRE KATTER R, 1971, INTERPRETATIVE OVERL Khoo CSG, 2001, INFORM PROCESS MANAG, V37, P119, DOI 10.1016/S0306-4573(00)00022-4 KORFHAGE R, 1995, P 18 ANN INT ACM SIG, P377 Kwon OW, 2003, INFORM PROCESS MANAG, V39, P25, DOI 10.1016/S0306-4573(02)00022-5 LAFFERTY J, 2003, KLUWER INT SERIES IN, V13, P1 Lamb R, 2003, J AM SOC INF SCI TEC, V54, P97, DOI 10.1002/asi.10182 LEWIS DD, 1992, PUBLICATION U MASSAC LUHN HP, 1958, IBM J RES DEV, V2, P314 LUHN HP, 1957, IBM J RES DEV, V1, P309 Ma W, 2002, J AM SOC INF SCI TEC, V53, P567, DOI 10.1002/asi.10067 MARON ME, 1977, J AM SOC INFORM SCI, V28, P38, DOI 10.1002/asi.4630280107 O'Connor B. C., 1996, EXPLORATIONS INDEXIN OYARCE GA, 2004, P ASIST ANNU MEET, P166 OYARCE GA, 2004, NATL CORE J INFORM M, V24, P24 OYARCE GA, 2006, CREATING COLLABORATI, P263 OYARCE GA, 2000, PUBLICATION U N TEXA Pettigrew KE, 2001, J AM SOC INF SCI TEC, V52, P62, DOI 10.1002/1532-2890(2000)52:1<62::AID-ASI1061>3.3.CO;2-A RAMSAY JO, 1982, J ROY STAT SOC A STA, V145, P285, DOI 10.2307/2981865 ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J Rorvig M, 1999, J AM SOC INFORM SCI, V50, P652, DOI 10.1002/(SICI)1097-4571(1999)50:8<652::AID-ASI3>3.3.CO;2-J RORVIG M, 2000, NIST SPECIAL PUBLICA, P669 Rorvig M, 1999, J AM SOC INFORM SCI, V50, P639, DOI 10.1002/(SICI)1097-4571(1999)50:8<639::AID-ASI2>3.0.CO;2-C Rorvig M, 2000, J AM SOC INFORM SCI, V51, P1205, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1032>3.3.CO;2-# RORVIG ME, 1996, P 19 ANN INT ACM SIG, P343, DOI 10.1145/243199.243305 RORVIG ME, 1988, ANNU REV INFORM SCI, V23, P157 RORVIG ME, 1998, INFORM PROCESSING MA, V35, P135 RORVIG ME, 1999, P 1998 ANN M AM SOC, V35, P130 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 Salton G., 1975, THEORY INDEXING SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHEPARD RN, 1962, PSYCHOMETRIKA, V27, P125, DOI 10.1007/BF02289630 SHEPARD RN, 1962, PSYCHOMETRIKA, V27, P219, DOI 10.1007/BF02289621 Sormunen E, 2001, J DOC, V57, P358, DOI 10.1108/EUM0000000007087 Sparck-Jones K, 2004, J DOC, V60, P521, DOI 10.1108/00220410410560591 Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P779, DOI 10.1016/S0306-4573(00)00015-7 SPARKJONES K, 2003, ADV INFORM RETRIEVAL, P1 Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0 Thurstone LL, 1927, AM J PSYCHOL, V38, P368, DOI 10.2307/1415006 Thurstone LL, 1927, PSYCHOL REV, V34, P273, DOI 10.1037/h0070288 VANRIJSBERGEN CJ, 1989, SIGIR FORUM, V23, P77, DOI 10.1145/75334.75344 Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 Voorhees EM, 2000, INFORM PROCESS MANAG, V36, P3, DOI 10.1016/S0306-4573(99)00043-6 WEIS R, 1967, TECHNICAL MEMORANDUM WHITE H, 1998, J AM SOC INFORM SCI, V32, P163 White HD, 1997, ANNU REV INFORM SCI, V32, P99 WILKINSON I, 1982, P C HUM FACT COMP SY, P202 WU HC, 2006, GEN CHAIR P 15 ACM I, P854 Yu H., 2003, P 2003 ACM CIKM INT, P232 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zhang J, 2005, INFORM PROCESS MANAG, V41, P1003, DOI 10.1016/j.ipm.2004.03.005 NR 80 TC 2 Z9 2 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUL PY 2008 VL 59 IS 9 BP 1479 EP 1492 DI 10.1002/asi.20830 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 319MQ UT WOS:000257168200010 ER PT J AU Wan, XJ Yang, HW Xiao, HG AF Wan, Xiaojun Yang, Hanwu Xiao, Hanguo TI Towards a unified approach to document similarity search using manifold-ranking of blocks SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE document similarity search; web similarity search; manifold-ranking; document segmentation; web page segmentation ID TEXT AB Document similarity search (i.e. query by example) aims to retrieve a ranked list of documents similar to a query document in a text corpus or on the Web. Most existing approaches to similarity search first compute the pairwise similarity score between each document and the query using a retrieval function or similarity measure (e.g. Cosine), and then rank the documents by the similarity scores. In this paper, we propose a novel retrieval approach based on manifold-ranking of document blocks (i.e. a block of coherent text about a subtopic) to re-rank a small set of documents initially retrieved by some existing retrieval function. The proposed approach can make full use of the intrinsic global manifold structure of the document blocks by propagating the ranking scores between the blocks on a weighted graph. First, the TextTiling algorithm and the VIPS algorithm are respectively employed to segment text documents and web pages into blocks. Then, each block is assigned with a ranking score by the manifold-ranking algorithm. Lastly, a document gets its final ranking score by fusing the scores of its blocks. Experimental results on the TDT data and the ODP data demonstrate that the proposed approach can significantly improve the retrieval performances over baseline approaches. Document block is validated to be a better unit than the whole document in the manifold-ranking process. (C) 2007 Elsevier Ltd. All rights reserved. C1 [Wan, Xiaojun; Yang, Hanwu; Xiao, Hanguo] Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China. RP Wan, XJ (reprint author), Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China. EM wanxiaojun@icst.pku.edu.cn CR Allan J., 1998, P DARPA BROADC NEWS, P194 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Cai D., 2003, MSRTR200379 CAI D, 2004, P 27 ANN INT ACM SIG Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CHEN J, 2001, P 10 WORKD WID WEB C CHOI F, 1999, J TEXT TILE FREE PLA CROFT B, 2003, LANGUAGE MODELING IN Cruz I.F., 1998, P 7 INT C EL PUBL, P513 Dean J., 1999, P 8 INT WORLD WID WE DIAZ F, 2005, P 14 ACM INT C INF K Erkan G, 2004, J ARTIF INTELL RES, V22, P457 FOGARAS D, 2004, SCALING LINK BASED S Haveliwala T. H., 2002, P 11 INT WORLD WID W, P432 HEARST MA, 1994, P 32 M ASS COMP LING HEARST MA, 1993, P 16 ANN INT ACM SIG Hearst MA, 1997, COMPUT LINGUIST, V23, P33 IWAYAMA M, 2003, P SIGIR 2003 Jeh G., 2002, P 8 ACM SIGKDD INT C Joshi S., 2003, P 9 ACM SIGKDD C, P577 KASZKIEL M, 1997, ACM SIGIR FORUM, V31, P178, DOI 10.1145/278459.258561 Kaufmann Stefan, 1999, P 37 ANN M ASS COMP, P591, DOI 10.3115/1034678.1034686 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kovacevic M., 2002, P 2002 IEEE INT C DA KURLAND O, 2005, P SIGIR 2005 KURLAND O, 2006, P SIGIR 2006 Kurland Oren, 2005, P SIGIR, P306, DOI 10.1145/1076034.1076087 LIN Z, 2006, P 15 INT WORLD WID W Mihalcea Rada, 2004, P EMNLP, P404 OTTERBACHER J, 2005, P HIT EMNLP 2005 Page L, 1998, PAGERANK CITATION RA PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 QIN T, 2005, P SIGIR 2005 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON S, 1999, P TREC 99 SALTON G, 1991, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1996, P SIGIR 96 Smucker M. D., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148250 Song R., 2004, P 13 INT C WORLD WID, P203, DOI 10.1145/988672.988700 TOMBROS A, 2005, P ECIR 2005 van Rijsbergen C. J., 1979, INFORM RETRIEVAL XUE GR, 2004, P CIKM 2004 YU S, 2003, P 12 INT WORLD WID W ZHANG B, 2005, P SIGIR 2005 Zhou D., 2003, P NIPS 2003 NR 46 TC 13 Z9 14 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAY PY 2008 VL 44 IS 3 BP 1032 EP 1048 DI 10.1016/j.ipm.2007.07.012 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 292LN UT WOS:000255267600004 ER PT J AU Santos, RLT Roberto, PA Goncalves, MA Laender, AHF AF Santos, Rodrygo L. T. Roberto, Pablo A. Goncalves, Marcos Andre Laender, Alberto H. F. TI A Web services-based framework for building componentized digital libraries SO JOURNAL OF SYSTEMS AND SOFTWARE LA English DT Article DE digital libraries; web services; component-based software development; software evaluation ID MODEL AB We present a new Web services-based framework for building componentized digital libraries (DLs). We particularly demonstrate how traditional RDBMS technology can be easily deployed to support several common digital library services. Configuration and customization of the framework to build specialized systems is supported by a wizard-like tool which is based on a generic metamodel for DLs. Such a tool implements a workflow process that segments the DL design tasks into well-defined steps and drives the designer along these steps. Both the framework and the configuration tool are evaluated in terms of several performance and usability criteria. Our experimental evaluation demonstrates the feasibility and superior performance of our framework, as well as the effectiveness of the wizard tool for setting up DLs. (C) 2007 Elsevier Inc. All rights reserved. C1 [Santos, Rodrygo L. T.; Roberto, Pablo A.; Goncalves, Marcos Andre; Laender, Alberto H. F.] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, Brazil. RP Santos, RLT (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, Brazil. EM rodrygo@dcc.ufmg.br; pabloa@dcc.ufmg.br; mgoncalv@dcc.ufmg.br; laender@dcc.ufmg.br RI InWeb, Inct/J-9839-2013 CR Buchanan G, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P23, DOI 10.1145/1065385.1065392 Burbeck S., 1987, APPL PROGRAMMING SMA EKENSTIERNA M, 2002, THESIS DEP COMMUNICA Fox E., 1998, COMMUN ACM, V41, P29, DOI [10.1145/273035.273043, DOI 10.1145/273035.273043] Goncalves MA, 2004, ACM T INFORM SYST, V22, P270, DOI 10.1145/984321.984325 Grossman DA, 1997, J AM SOC INFORM SCI, V48, P122, DOI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# Laender A. H. F., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996357 Lagoze C, 2006, INT J DIGITAL LIB, V6, P124, DOI DOI 10.1007/S00799-005-0130-3 Lagoze C., 2001, P 1 ACM IEEE CS JOIN, P54, DOI 10.1145/379437.379449 RAGHAVAN A, 2005, P ECDL, P139 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANTOS RLT, 2006, P 10 EUR C RES ADV T, P135 SULEMAN H, 2005, P 8 INT C AS DIG LIB, P33 SULEMAN H, 2002, THESIS STATE U BLACK NR 14 TC 2 Z9 2 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0164-1212 J9 J SYST SOFTWARE JI J. Syst. Softw. PD MAY PY 2008 VL 81 IS 5 BP 809 EP 822 DI 10.1016/j.jss.2007.07.029 PG 14 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 292WD UT WOS:000255295900015 ER PT J AU Wang, W Ravishankar, CV AF Wang, Wei Ravishankar, Chinya V. TI Adaptive broadcasting for similarity queries in wireless content delivery systems SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE broadcast dissemination; client feedback; power management; approximate indexing; similarity matching ID SCHEDULING DATA BROADCAST; ENVIRONMENTS; MOBILE AB We present a new adaptive and energy-efficient broadcast model to support flexible responses to client queries. Clients do not have to request documents by name, since they may know the characteristics of the documents but not the document names or IDs. In our model, clients specify requirements through attributes, and servers broadcast documents that match client requests at a prespecified level of similarity. A given document may satisfy several clients, so the server broadcasts a minimal set of documents that achieves a desired level of satisfaction in the client population. The server obtains randomized feedback from clients and adapts its broadcast program accordingly. Clients use a selective tune-in scheme based on approximate indexing to conserve energy. Our model captures client interest patterns efficiently and accurately and scales very well with the number of clients while reducing the overall client average waiting times. The selective tune-in scheme reduces client energy consumption greatly, with a modest wait time increase. C1 [Wang, Wei; Ravishankar, Chinya V.] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA. RP Wang, W (reprint author), Univ Calif Riverside, Dept Comp Sci & Engn, Bourns Hall, Riverside, CA 92521 USA. EM wangw@cs.ucr.edu; ravi@cs.ucr.edu CR ACHARYA S, 1995, IEEE PERSONAL COMM, V2 Acharya S., 1998, P 4 ANN ACM IEEE INT, P43, DOI 10.1145/288235.288248 Acharya S., 1995, P ACM SIGMOD Aksoy D, 1999, IEEE ACM T NETWORK, V7, P846, DOI 10.1109/90.811450 Aksoy D., 1998, P IEEE INFOCOM, V2, P651, DOI 10.1109/INFCOM.1998.665086 AMMAR MH, 1985, PERFORM EVALUATION, V5, P235, DOI 10.1016/0166-5316(85)90024-0 [Anonymous], 2007, 80211 IEEE [Anonymous], 2007, LOS ANGELES TIMES Bertsekas D. P., 1996, CONSTRAINED OPTIMIZA BOWEN TF, 1992, COMM ACM, V35 BRESLAU L, 1999, P IEEE INFOCOM 99, P126 Buckley C., 1994, P 3 TEXT RETR C TREC, P69 Cetintemel U., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839477 CHERNOFF H, 1952, ANN MATH STAT, V23, P493, DOI 10.1214/aoms/1177729330 *CSIM, CSIM 19 SIM DEOLASEE P, 2001, P 10 INT WORLD WID W DHILLON I., 2001, DATA MINING SCI ENG DUFF IS, 1989, ACM T MATH SOFTWARE, V15, P1, DOI 10.1145/62038.62043 Faloutsoss M, 1999, P ACM SIGCOMM, P251, DOI 10.1145/316188.316229 Frakes W. B., 1992, INFORM RETRIEVAL DAT FRANKLIN AM, 1997, P ACM SIGMOD Hameed S, 1999, WIREL NETW, V5, P183, DOI 10.1023/A:1019194826654 Hameed Sohail, 1997, P MOBICOM 97, P90, DOI 10.1145/262116.262136 Hara T., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Herman G, 1987, P ACM SIGMOD, P97, DOI 10.1145/38713.38729 HU CL, 2002, P 22 INT C DISTR COM HU JH, 2000, IEEE INT C COMM 2000, V3, P1778 Hu QL, 2001, DISTRIB PARALLEL DAT, V9, P151, DOI 10.1023/A:1018944523033 HU Q, 1998, P WORKSH MOB DAT ACC, P213 HUANG JL, 2005, P 6 ACM INT C MOB DA, P234, DOI 10.1145/1071246.1071282 Imielinski T., 1994, P 1994 ACM SIGMOD IN, P25, DOI 10.1145/191839.191846 IMIELINSKI T, 1994, COMMUN ACM, V37, P18, DOI 10.1145/194313.194317 IRNIELINSKI T, 1997, IEEE T KNOWLEDGE DAT, V9 Knuth D.E., 1981, ART COMPUTER PROGRAM, VII Lee SD, 2003, PROC INT CONF DATA, P726 Lee WC, 1996, DISTRIB PARALLEL DAT, V4, P205, DOI 10.1007/BF00140950 Lewis D.D., REUTERS 21578 DISTRI Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Motwani R., 1995, RANDOMIZED ALGORITHM OH J, 2000, P IEEE C PERF COMP C, P361 Porter M., PORTER STEMMING ALGO Salton G., 1988, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIMUNIC T, 2000, P INT S LOW POW EL D Stathatos K., 1997, P 23 INT C VER LARG Su CJ, 1999, WIREL NETW, V5, P137, DOI 10.1023/A:1019134607998 SU CJ, 1998, P 4 ACM IEEE INT C M, P33, DOI 10.1145/288235.288246 Vaidya N. H., 1996, P 1 INT WORKSH SAT B, P38 Vaidya NH, 1999, WIREL NETW, V5, P171, DOI 10.1023/A:1019142809816 Velez B, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P6, DOI 10.1145/258525.258528 Wang SB, 2004, MACROMOL BIOSCI, V4, P27, DOI 10.1002/mabi.200300043 WU KL, 1996, P 12 IEEE INT C DAT XU B, 2001, P 8 INT C DAT THEOR, P114 Xu B, 2002, MOBILE NETW APPL, V7, P49, DOI 10.1023/A:1013277313373 YEE WG, 2002, IEEE T COMPUTERS OCT 2007, CNNS NEWSWATCH 2007, BATTLEFIELD FUTURE NR 58 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD APR PY 2008 VL 20 IS 4 BP 504 EP 518 DI 10.1109/TKDE.2007.190717 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 275AT UT WOS:000254045000007 ER PT J AU Crasso, M Zunino, A Campo, M AF Crasso, Marco Zunino, Alejandro Campo, Marcelo TI Easy web service discovery: A query-by-example approach SO SCIENCE OF COMPUTER PROGRAMMING LA English DT Article DE web services; web service discovery; distributed systems; information filtering; query-by-example; information retrieval ID VECTOR-SPACE MODEL; SEMANTIC WEB; ONTOLOGIES; ARCHITECTURE; SIMILARITY; RETRIEVAL; SYSTEMS AB Web services have acquired enormous popularity among software developers. This popularity has motivated developers to publish a large number of Web service descriptions in UDDI registries. Although these registries provide search facilities, they are still rather difficult to use and often require service consumers to spend too much time manually browsing and selecting service descriptions. This paper presents a novel search method for Web services called WSQBE that aims at both easing query specification and assisting discoverers by returning a short and accurate list of candidate services. In contrast with previous approaches, WSQBE discovery process is based on an automatic search space reduction mechanism that makes this approach more efficient. Empirical evaluations of WSQBE search space reduction mechanism, retrieval performance, processing time and memory usage, using a registry with 391 service descriptions, are presented. (C) 2008 Elsevier B.V. All rights reserved. C1 [Crasso, Marco; Zunino, Alejandro; Campo, Marcelo] UNICEN Univ, ISISTAN Res Inst, Buenos Aires, DF, Argentina. [Crasso, Marco; Zunino, Alejandro; Campo, Marcelo] Consejo Nacl Invest Cient & Tecn, Buenos Aires, DF, Argentina. RP Zunino, A (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,Tandil B7001BBO, Buenos Aires, DF, Argentina. EM azunino@exa.unicen.edu.ar RI Zunino, Alejandro/ OI Zunino, Alejandro/0000-0002-9537-3541 CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BUCKLEY C, 1994, SIGIR 94 Burstein M, 2005, IEEE INTERNET COMPUT, V9, P72, DOI 10.1109/MIC.2005.96 Campo M., 2008, REV IBEROAMERICANA I, V12, P25 Christensen E., 2001, NOTEWSDL20010315 WOR Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DOAN A, 2003, INT J VERY LARGE DAT, V12, P303 DONG X, 2004, P 13 INT C VER LARG Fallside D. C., 2004, XML SCHEMA Feldman R, 2006, TEXT MINING HDB ADV Fensel D, 2006, ENABLING SEMANTIC WE Garofalakis J, 2006, J WEB ENG, V5, P265 Gomez-Perez A., 2003, ONTOLOGICAL ENG Grefen P, 2006, INFORM SOFTWARE TECH, V48, P1115, DOI 10.1016/j.infsof.2006.03.010 Hearst M. A, 1999, P 37 ANN M ASS COMP, P3, DOI DOI 10.3115/1034678.1034679 Hess A., 2004, INT SEM WEB C HESS A, 2003, INT SEM WEB C HUANG KT, 1984, GRAPH INT 84 P Huhns MN, 2005, IEEE INTERNET COMPUT, V9, P75, DOI 10.1109/MIC.2005.21 JIAN W, 2005, IEEE INT C SERV COMP, V1, P287 JOACHIMS T, 1997, INT C MACH LEARN Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 KITTREDGE R, 1982, SUBLANGUAGES, V8 KOKASH N, 2006, 3 EUR START AI RES S, P220 KOKASH N, 2006, INT C SERV OR COMP Korfhage R., 1997, INFORM STORAGE RETRI Kozlenkov A, 2007, INT J WEB SERV RES, V4, P82, DOI 10.4018/jwsr.2007040104 Kuhn H. W., 1955, NAV RES LOG, V2, P83, DOI DOI 10.1002/NAV.3800020109 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Lee K.-H, 2007, INT C MULT UB ENG SE, P773 Lewis D, 1998, 10 EUR C MACH LEARN LOSEE RM, 1995, J AM SOC INFORM SCI, V46, P519, DOI 10.1002/(SICI)1097-4571(199508)46:7<519::AID-ASI5>3.0.CO;2-R MAHMOUD EAM, INT C COMP COMM NETW Mateos C, 2008, FUTURE GENER COMP SY, V24, P99, DOI 10.1016/j.future.2007.04.011 Mateos C., 2006, LECT NOTES ARTIFICIA, V4140 McCool R, 2005, IEEE INTERNET COMPUT, V9, P88, DOI 10.1109/MIC.2005.133 McIlraith SA, 2003, IEEE INTELL SYST, V18, P90, DOI 10.1109/MIS.2003.1179199 *OASIS CONS, 2004, UDDI VERS 3 0 2 Paolucci M, 2003, IEEE INTERNET COMPUT, V7, P34, DOI 10.1109/MIC.2003.1232516 Platzer C, 2005, Third European Conference on Web Services, Proceedings, P62 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rocchio J., 1971, SMART RETRIEVAL SYST Sabou M, 2005, J WEB SEMANT, V3, P340, DOI 10.1016/j.websem.2005.09.008 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] Shen J, 2007, FUTURE GENER COMP SY, V23, P283, DOI 10.1016/j.future.2006.05.007 Sivashanmugam K., 2003, 2003 INT C WEB SERV Stroulia E, 2005, INT J COOP INF SYST, V14, P407, DOI 10.1142/S0218843005001213 Tang CQ, 2003, ACM SIGCOMM COMP COM, V33, P89, DOI 10.1145/774763.774777 TOMA I, 2005, P 2 INT C GRID SERV van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vaughan-Nichols SJ, 2002, COMPUTER, V35, P18, DOI 10.1109/2.982908 Vega-Gorgojo G, 2006, FUTURE GENER COMP SY, V22, P709, DOI 10.1016/j.future.2006.02.012 Wang H, 2004, J WEB SEMANT, V1, P309 WHANG KY, 1987, ACM T INFORM SYST, V5, P393, DOI 10.1145/42196.42200 Witten I. H., 2005, M KAUFMANN SERIES DA Zhuge H, 2003, J SYST SOFTWARE, V67, P201, DOI 10.1016/S0164-1212(02)001309 ZHUGE H, 2004, J SYST SOFTWARE, V70, P107, DOI DOI 10.1016/S0164-1212(03)00003-7 NR 62 TC 27 Z9 28 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6423 EI 1872-7964 J9 SCI COMPUT PROGRAM JI Sci. Comput. Program. PD APR 1 PY 2008 VL 71 IS 2 BP 144 EP 164 DI 10.1016/j.scico.2008.02.002 PG 21 WC Computer Science, Software Engineering SC Computer Science GA 305LX UT WOS:000256181000003 ER PT J AU Rorissa, A Clough, P Deselaers, T AF Rorissa, Abebe Clough, Paul Deselaers, Thomas TI Exploring the relationship between feature and perceptual visual spaces SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID IMAGE RETRIEVAL; INFORMATION-RETRIEVAL; SIMILARITY MEASURE; TEXTURAL FEATURES; DISTANCE; CLASSIFICATION; PERSPECTIVES; EXPLORATION; DATABASES; SYSTEM AB The number and size of digital repositories containing visual information (images or videos) is increasing and thereby demanding appropriate ways to represent and search these information spaces. Their visualization often relies on reducing the dimensions of the information space to create a lower-dimensional feature space which, from the point-of-view of the end user, will be viewed and interpreted as a perceptual space. Critically for information visualization, the degree to which the feature and perceptual spaces correspond is still an open research question. In this paper we report the results of three studies which indicate that distance (or dissimilarity) matrices based on low-level visual features, in conjunction with various similarity measures commonly used in current CBIR systems, correlate with human similarity judgments. C1 SUNY Albany, Dept Informat Studies, Albany, NY 12222 USA. [Clough, Paul] Univ Sheffield, Dept Informat Studies, Sheffield S1 4DP, S Yorkshire, England. [Deselaers, Thomas] Rhein Westfal TH Aachen, Human Language Technol & Pattern Recognit Grp, D-52056 Aachen, Germany. RP Rorissa, A (reprint author), SUNY Albany, Dept Informat Studies, Albany, NY 12222 USA. EM arorissa@albany.edu; p.d.clough@sheffield.ac.uk; deselaers@informatik.rwth-aachen.de CR Barnard K., 2001, P INT C COMP VIS, V2, P408, DOI DOI 10.1109/ICCV.2001.937654 Bonnet E, 2002, J STAT SOFTW, V7, P1 Cai D., 2004, P 12 ANN ACM INT C M, P952, DOI 10.1145/1027527.1027747 Chang M., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996426 CHEN C, 2000, P INT C INF VIS IV20, P13 Combs T., 1999, P 4 ACM C DIG LIB, P130, DOI 10.1145/313238.313286 Coxon A. P. M., 1999, SORTING DATA COLLECT CRONBACH LJ, 1951, PSYCHOMETRIKA, V16, P297 Datta R., 2005, P 7 ACM SIGMM INT WO, P253, DOI 10.1145/1101826.1101866 del Bimbo A., 1999, VISUAL INFORM RETRIE DESEAERS T, 2004, LECT NOTES COMPUTER, V3491, P688 DESEAERS T, IN PRESS INFORM RETR Deselaers T., 2005, P INT C COMP VIS PAT, V2, P157 Deselaers T, 2007, INT J COMPUT VISION, V74, P51, DOI 10.1007/s11263-006-0007-y Deselaers T, 2006, LECT NOTES COMPUT SC, V4174, P202 DORI D, 2000, P 15 INT C PATT REC, P1042 DORKO G, 2006, THESIS I NATL POLYTE Dunn-Rankin P., 2002, SCALING METHODS Enser P, 2000, J INFORM SCI, V26, P199, DOI 10.1177/0165551004233212 Faloutsos C., 1994, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V3, DOI 10.1007/BF00962238 FAUQUEUR J, 2003, 3 INT WORKSH CONT BA Goldstone RL, 1999, MIT ENCY COGNITIVE S, P757 Goodrum AA, 2001, J AM SOC INF SCI TEC, V52, P174, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1580>3.0.CO;2-V Greisdorf H, 2002, J DOC, V58, P6, DOI 10.1108/00220410210425386 GREISDORF H, 2002, P 65 ANN M AM SOC IN, P383 GU ZQ, 1989, RADAR SIGNAL PROCESS, V136, P236 GUPTA A, 1997, COMMUN ACM, V40, P35 Haberacker P, 1995, PRAXIS DIGITALEN BIL HARALICK RM, 1973, IEEE T SYST MAN CYB, VSMC3, P610, DOI 10.1109/TSMC.1973.4309314 HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075 Ingwersen P., 1992, INFORM RETRIEVAL INT Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 JANECEK P, 2004, IC2000442 EFPL Jorgensen C, 1998, INFORM PROCESS MANAG, V34, P161, DOI 10.1016/S0306-4573(97)00077-0 JORGENSEN C, 2001, J AM SOC INFORM SCI, V52, P933 Jorgensen C, 2005, J AM SOC INF SCI TEC, V56, P1346, DOI 10.1002/asi.20229 KEYSERS D, 2007, IEEE T PATTERN ANAL LAINEHERNANDEZ M, 2006, P 69 ANN M AM SOC IN Legendre P, 1998, NUMERICAL ECOLOGY Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005 Li BT, 2003, MULTIMEDIA SYST, V8, P512, DOI 10.1007/s00530-002-0069-9 Liu H., 2004, P 1 INT C QUAL SERV, P84, DOI 10.1145/1026711.1026726 Lyman P., 2003, MUCH INFORM 2003 Manly BFJ, 2005, MULTIVARIATE STAT ME, V3rd MANTEL N, 1967, CANCER RES, V27, P209 Melara R. D, 1992, PSYCHOPHYSICAL APPRO, P303 MULLER H, 2000, ACM SIGKDD INT C KNO Neumann D., 2006, ACM T APPL PERCEPT, V3, P31, DOI 10.1145/1119766.1119769 Newby GB, 2001, J AM SOC INF SCI TEC, V52, P1026, DOI 10.1002/asi.1172 O'Connor B. C., 2004, PHOTO PROVOCATIONS T O'Connor BC, 1999, J AM SOC INFORM SCI, V50, P681, DOI 10.1002/(SICI)1097-4571(1999)50:8<681::AID-ASI6>3.0.CO;2-J Park G, 2005, INFORM PROCESS MANAG, V41, P177, DOI 10.1016/j.ipm.2003.08.002 Puzicha J., 1999, P 7 IEEE INT C COMP, V2, P1165 Qin J, 2000, J AM SOC INFORM SCI, V51, P166, DOI 10.1002/(SICI)1097-4571(2000)51:2<166::AID-ASI8>3.3.CO;2-Q RODDEN K, 2000, P CHALLENGES IMAGE R Rodden K., 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99), DOI 10.1109/INFVIS.1999.801855 Rogowitz BE, 1998, P SOC PHOTO-OPT INS, V3299, P576, DOI 10.1117/12.320148 RORISSA A, IN PRESS J AM SOC IN Rubner Y., 1999, THESIS STANFORD U ST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Santini S, 1999, IEEE T PATTERN ANAL, V21, P871, DOI 10.1109/34.790428 Siggelkow S., 2001, LNCS PATTERN RECOGNI, V2191, P9 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith JR, 1996, P SOC PHOTO-OPT INS, V2670, P426, DOI 10.1117/12.234781 Squire DM, 1998, PATTERN RECOGN, V31, P1905 Stan D, 2003, INFORM PROCESS MANAG, V39, P335, DOI 10.1016/S0306-4573(02)00131-0 STEVENS SS, 1975, PSYCHOPHYSICS INITRO SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 Tversky Amos, 1978, COGNITION CATEGORIZA, P79 VASCONCELOS N, 2000, P 15 INT C PATT REC, V4, P1038 YAVLINSKHY A, 2004, IEEE INT C AC SPEECH, P1040 Zachary J, 2001, J AM SOC INF SCI TEC, V52, P840, DOI 10.1002/asi.1138 Zachary J. M., 2000, THESIS LOUISIANA STA Zhang J, 1999, J AM SOC INFORM SCI, V50, P772, DOI 10.1002/(SICI)1097-4571(1999)50:9<772::AID-ASI5>3.0.CO;2-E Zhang J, 2001, INFORM PROCESS MANAG, V37, P279, DOI 10.1016/S0306-4573(00)00027-3 Zhang J, 1999, J AM SOC INFORM SCI, V50, P779, DOI 10.1002/(SICI)1097-4571(1999)50:9<779::AID-ASI6>3.0.CO;2-R Zhu B, 2000, J AM SOC INFORM SCI, V51, P625, DOI 10.1002/(SICI)1097-4571(2000)51:7<625::AID-ASI5>3.0.CO;2-L NR 79 TC 5 Z9 5 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD MAR PY 2008 VL 59 IS 5 BP 770 EP 784 DI 10.1002/asi.20792 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 268AN UT WOS:000253550400009 ER PT J AU Ortega, JL Aguillo, I Cothey, V Scharnhorst, A AF Ortega, Jose Luis Aguillo, Isidro Cothey, Viv Scharnhorst, Andrea TI Maps of the academic web in the European Higher Education Area - an exploration of visual web indicators SO SCIENTOMETRICS LA English DT Article ID WORLD-WIDE-WEB; NETWORK STRUCTURE; GRAPH STRUCTURE; SCIENCE; COLLABORATION; SPACE AB This paper shows maps of the web presence of the European Higher Education Area (EHEA) on the level of universities using hyperlinks and analyses the topology of the European academic network. Its purpose is to combine methods from Social Network Analysis (SNA) and cybermetric techniques in order to ask for tendencies of integration of the European universities visible in their web presence and the role of different universities in the process of the emergence of an European Research Area. We find as a main result that the European network is set up by the aggregation of well-defined national networks, whereby the German and British networks are dominant. The national networks are connected to each other through outstanding national universities in each country. C1 [Ortega, Jose Luis; Aguillo, Isidro] CSIC, CINDOC, Cybermetr Lab, Madrid 28002, Spain. [Cothey, Viv] Wolverhampton Univ, Sch Comp & Informat Technol, Wolverhampton, England. [Scharnhorst, Andrea] Royal Netherlands Acad Arts & Sci, Virtual Knowledge Studio, Amsterdam, Netherlands. RP Ortega, JL (reprint author), CSIC, CINDOC, Cybermetr Lab, Joaquin Costa 22, Madrid 28002, Spain. EM jortega@cindoc.csic.es RI Scharnhorst, Andrea/A-7802-2010; Aguillo, Isidro/A-7280-2008; Scharnhorst, Andrea/ OI Aguillo, Isidro/0000-0001-8927-4873; Scharnhorst, Andrea/0000-0001-8879-8798 CR Adamic L.A., 2002, GLOTTOMETRICS, V3, P143 AGUILLO IF, 2006, 9 INT C SCI TECHN IN Albert R, 1999, NATURE, V401, P130 Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2 BJORNEBORN L, 2003, SMALL WORLD LINK STR BJORNEBORN L, 2001, P 12 ACM C HYP HYP N BONITZ M, 2000, GLOBALISIERUNG WISSE, P173 BONITZ M, 1993, SCIENTOMETRICS, V26, P33 Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 Chen C., 2003, MAPPING SCI FRONTIER Cothey V., 2005, P 10 INT C INT SOC S Cothey V, 2004, J AM SOC INF SCI TEC, V55, P1228, DOI 10.1002/asi.20078 Dodge M, 2001, ATLAS OF CYBERSPACE DODGE M, 2004, CYBER GEOGRAPHY RES GLANZEL W, 2001, SCIENTOMETRICS, V51 Heimeriks G, 2003, SCIENTOMETRICS, V58, P391, DOI 10.1023/A:1026296812830 Heimeriks G., 2005, KNOWLEDGE PRODUCTION Kamada Tomihisa, 1989, INFORM PROCESSING LE, V31, P7 Katz JS, 2006, RES EVALUAT, V15, P85, DOI 10.3152/147154406781775922 LARSON R, 1996, P 59 ANN M AM SOC IN, P71 Leydesdorff L, 2006, J AM SOC INF SCI TEC, V57, P1616, DOI 10.1002/asi.20335 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q Nooy W., 2005, EXPLORATORY SOCIAL N NOYONS ECM, 1999, BIBLIOMETRICS MAPPIN ORGETA JL, 2007, VISUALIZACION WEB U PATERSON R, 1992, VISUALIZATION STUDY Polanco X., 2001, NTTS ETK 2001 C HERS SALTON G, 1971, AUTOMATIC DOCUMENT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scharnhorst A., 2003, J COMPUTER MEDIATED, V8 SEIDMAN SB, 1983, SOC NETWORKS, V5, P269, DOI 10.1016/0378-8733(83)90028-X SMALL H, 1974, SCI STUD, V4, P17, DOI 10.1177/030631277400400102 Small H, 2003, J AM SOC INF SCI TEC, V54, P394, DOI 10.1002/asi.10225 Thelwall M, 2006, J AM SOC INF SCI TEC, V57, P60, DOI 10.1002/asi.20253 Thelwall M, 2003, SCIENTOMETRICS, V56, P417, DOI 10.1023/A:1022387105904 Thelwall M, 2003, J AM SOC INF SCI TEC, V54, P706, DOI 10.1002/asi.10267 Thelwall M., 2004, LINK ANAL INFORM SCI Tufte E., 1997, VISUAL DISPLAY QUANT Vaughan L, 2006, J AM SOC INF SCI TEC, V57, P1178, DOI 10.1002/asi.20398 VAUGHEN L, 2005, P 10 INT C INT SOC S Wagner C. S., 2005, INT J TECHNOLOGY GLO, V1, P185, DOI 10.1504/IJTG.2005.007050 Wagner CS, 2005, RES POLICY, V34, P1608, DOI 10.1016/j.respol.2005.08.002 WHITE HD, 1981, J AM SOC INFORM SCI, V32, P163, DOI 10.1002/asi.4630320302 YOOK S, 2002, P NATL ACAD SCI USA, V21, P13382 NR 44 TC 29 Z9 31 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD FEB PY 2008 VL 74 IS 2 BP 295 EP 308 DI 10.1007/s11192-008-0218-9 PG 14 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 257ON UT WOS:000252808500008 ER PT B AU Hou, XS Yuan-Da, C Guan, ZT AF Hou, Xiang-Song Yuan-da, Cao Guan, Zhi-tao GP ETRI TI A semantic search model based on locality-sensitive hashing in mobile P2P SO 10TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY, VOLS I-III: INNOVATIONS TOWARD FUTURE NETWORKS AND SERVICES LA English DT Proceedings Paper CT 10th International Conference on Advanced Communication Technology CY FEB 17-20, 2008 CL Phoenix Pk, SOUTH KOREA SP Minist Informat & Commun, IEEE Commun Soc, IEEE Reg 10, Korean Inst Commun Sci, IEEK Commun Soc, Open Stand & Internet Assoc, Korea Inst Informat Scientists & Engineers, Inst Informat Technol Advancement, Korea Res Fdn, Elect & Telecommun Res Inst, Natl Informat Soc Agcy, Global IT Res Inst, IEEE Daejeon Sect ID INFORMATION-RETRIEVAL AB In this paper we have explored the issue of how to integrate semantics searching into a Mobile P2P system. Our semantic searching approach is based on LSH where the semantically close documents are clustered to the same peers with high probability. For a query, we chose several points randomly from the neighborhood of the query as the semantic searching key. Our approach only adds index information to Super Nodes, and Mobile Node only needs small energy and CPU consumption. C1 [Hou, Xiang-Song] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China. RP Hou, XS (reprint author), Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China. EM houxsc@bit.edu.cn; ydcao@bit.edu.cn; guanzhitao@bit.edu.cn CR ANDERSEN FU, WORKSH INF 2004 ALG BAWA M, 2005, P 14 INT C WORLD WID Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 BHATTACHARYA I, 2005, DISTR COMP SYST 2005 CLARKE L, 2001, P INT WORKSH DES ISS HSIAO HC, 2003, PAR DISTR PROC S 200 INDYK P, 1998, ACM S THEOR COMP 199 Johnson D., 1996, MOBILE COMPUTING, V353, P153, DOI DOI 10.1007/978-0-585-29603-6_ Kortuem G., 2002, ACM SIGMOBILE MOBILE, V6, P62, DOI 10.1145/643550.643557 Mayur Datar, 2004, P 20 ANN S COMP GEOM Panigrahy R, 2006, P 17 ANN ACM SIAM S RATNASAMY S, 2001, ACM SIGCOMM 2001 SAN Ripeanu Matei, 2001, PEER TO PEER COMP 20 Rowstron A., 2001, ACM INT C DISTR SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoica I., 2001, ACM SIGCOMM 2001 SAN Tang CQ, 2003, ACM SIGCOMM COMP COM, V33, P89, DOI 10.1145/774763.774777 Zhao BY, 2004, IEEE J SEL AREA COMM, V22, P41, DOI 10.1109/JSAC.2003.818784 NR 18 TC 0 Z9 0 PU IEEE PI PISCATAWAY PA 445 HOES LN, PISCATAWAY, NJ 08854 USA BN 978-89-5519-135-6 PY 2008 BP 1635 EP 1640 PG 6 WC Computer Science, Software Engineering; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BHO98 UT WOS:000254974602031 ER PT S AU Turaga, P Chellappa, R AF Turaga, Pavan Chellappa, Rama GP IEEE TI LEARNING ACTION DICTIONARIES FROM VIDEO SO 2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5 SE IEEE International Conference on Image Processing (ICIP) LA English DT Proceedings Paper CT 15th IEEE International Conference on Image Processing (ICIP 2008) CY OCT 12-15, 2008 CL San Diego, CA SP IEEE Signal Proc Soc DE Video Summarization; Activity Analysis AB Summarizing the contents of a video containing human activities is an important problem in computer vision and has important applications in automated surveillance systems. Summarizing a video requires one to identify and learn a 'vocabulary' of action-phrases corresponding to specific events and actions occurring in the video. We propose a generative model for dynamic scenes containing human activities as a composition of independent action-phrases - each of which is derived from an underlying vocabulary. Given a long video sequence, we propose a completely unsupervised approach to learn the vocabulary. Once the vocabulary is learnt, a video segment can be decomposed into a collection of phrases for summarization. We then describe methods to learn the correlations between activities and sequentiality of events. We also propose a novel method for building invariances to spatial transforms in the summarization scheme. C1 [Turaga, Pavan; Chellappa, Rama] Univ Maryland, Ctr Automat Res, College Pk, MD 20742 USA. RP Turaga, P (reprint author), Univ Maryland, Ctr Automat Res, College Pk, MD 20742 USA. EM pturaga@umiacs.umd.edu; rama@umiacs.umd.edu CR Bobick AF, 2001, IEEE T PATTERN ANAL, V23, P257, DOI 10.1109/34.910878 Brand M., 1997, COUPLED HIDDEN MARKO Hyvarinen A, 1999, IEEE T NEURAL NETWOR, V10, P626, DOI 10.1109/72.761722 Hyvarinen A., 2001, INDEPENDENT COMPONEN Niebles J., 2006, BRIT MACH VIS C ROYCHOWDHURY AK, 2003, CVPR WORKSH EV MIN RUI Y, 2004, TR2004115 MERL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ZELNIKMANOR L, 2001, EVENT BASED VIDEO AN ZHONG H, 2004, DETECTING UNUSUAL AC NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1522-4880 BN 978-1-4244-1765-0 J9 IEEE IMAGE PROC PY 2008 BP 1704 EP 1707 DI 10.1109/ICIP.2008.4712102 PG 4 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BJH64 UT WOS:000265921400427 ER PT B AU Hidayat, SS Kim, BK Ohba, K AF Hidayat, Sidiq S. Kim, B. K. Ohba, Kohtaro GP IEEE TI Affordance-based Ontology Design for Ubiquitous Robots SO 2008 17TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1 AND 2 LA English DT Proceedings Paper CT 17th IEEE International Symposium on Robot and Human Interactive Communications CY AUG 01-03, 2008 CL Munich, GERMANY SP IEEE, IEEE Syst, Man & Cybernet Soc, Korea Robot Soc, Robot Soc Japan, IEEE Ind Elect Soc, IEEE Robot & Automat Soc, Deutsch Gesell Robot AB Affordance concept is very powerful in coupling perception and action. However, this concept originally came from ecological approach and it is suitable for living creatures such as animals, therefore it's difficult to identify and apply in practice for robots. In order for a robot to get benefits from this concept, this paper proposes ontology approach based affordance theory for ubiquitous robots. The goal of this affordance-based ontology effort is to develop and begin to populate a knowledge representation capturing relevant information about robots capabilities and what the environment afford to assist human life in ubiquitous robots environment. C1 [Hidayat, Sidiq S.] Univ Tsukuba, Tsukuba, Ibaraki 3058572, Japan. RP Hidayat, SS (reprint author), Univ Tsukuba, Tsukuba, Ibaraki 3058572, Japan. EM s.hidayat@aist.go.jp; bk.kim@aist.go.jp; k.ohba@aist.go.jp RI Kim, Bong Keun/K-3816-2012 CR Arkin R. C., 1998, BEHAV BASED ROBOTICS Baader F., 2003, DESCRIPTION LOGIC HD COSAGUILERA I, 2003, P 9 INT C COGN MOD I Fitzpatrick P., 2003, P 2003 IEEE INT C RO, P3140 Fritz G, 2006, LECT NOTES COMPUT SC, V4095, P52 Gardner H., 1987, MINDS NEW SCI Gibson J.J., 1979, ECOLOGICAL APPROACH, P127 Hidayat Sidiq S, 2007, 16th IEEE International Conference on Robot and Human Interactive Communication Kim D., 2006, IEEE INT C ROB AUT LANGEGGER A, 2006, P IIWAS, P49 LORKEN C, 2007, INTRO AFFORDANCES RO McIlraith SA, 2001, IEEE INTELL SYST APP, V16, P46, DOI 10.1109/5254.920599 Murphy RR, 1999, IEEE T SYST MAN CY A, V29, P105, DOI 10.1109/3468.736365 MURPHY RR, 2000, INTELLIGENT ROBOTS A Norman D. A., 1999, Interactions, V6, DOI 10.1145/301153.301168 OHARA K, 2006, 2 INT C UB ROB AMB I, P66 OHARA K, UBIQUITOUS SPOT SERV OHBA K, 2007, 8 SYST INT DIV ANN C, P926 Paolucci M., 2002, 1 INT SEM WEB C ISWC ROME E, 2006, MULTISENSOR AFFORDAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STOYTCHEV A, 2005, P IEEE INT C ROB AUT, P18 STOYTCHEV A, 2005, P AAAI S DEV ROB, P21 SUH IH, 2007, ONTOLOGY BASED MULTY, P429 SUZUKI T, 2007, 8 SYST INT DIV ANN C, P107 UGUR E, 2006, MACSIM PHYS BASED SI Warren W., 1984, J EXP PSYCHOL, V10, P683 NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2212-8 PY 2008 BP 622 EP 627 PG 6 WC Computer Science, Cybernetics; Engineering, Electrical & Electronic; Robotics SC Computer Science; Engineering; Robotics GA BIP68 UT WOS:000261700900104 ER PT B AU Zhang, ZW Phan, XH Horiguchi, S AF Zhang, Zhiwei Phan, Xuan-Hieu Horiguchi, Susumu GP IEEE TI An Efficient Feature Selection using Hidden Topic in Text Categorization SO 2008 22ND INTERNATIONAL WORKSHOPS ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS, VOLS 1-3 LA English DT Proceedings Paper CT 22nd International Workshops on Advanced Information Networking and Applications CY MAR 25-28, 2008 CL Ginowan, JAPAN AB Text categorization is an important research area in information retrieval. In order to save the storage space and get better accuracy, efficient and effective feature selection methods for reducing the data before analysis are highly desired Usual v, researches on feature selection use only a proper measurement such as information gain. In this paper, we propose a new feature selection method by adopting an attractive hidden topic analysis and entropy-based feature ranking. Experiments dealing with the well-known Reuters-21578 and Ohsumed datasets show that our method can achieve a better classification accuracy while reducing the feature dimension dramatically. C1 [Zhang, Zhiwei; Phan, Xuan-Hieu; Horiguchi, Susumu] Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 980, Japan. RP Zhang, ZW (reprint author), Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 980, Japan. EM zzw@ecei.tohoku.ac.jp; hieuxuan@ecei.tohoku.ac.jp; susumu@ecei.tohoku.ac.jp CR Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116 Berger AL, 1996, COMPUT LINGUIST, V22, P39 BHATTACHARYA I, 2006, P SIAM SDM Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616 Heinrich G, 2005, PARAMETER ESTIMATION Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 Hofmann T., 1999, P UAI MINKA T, 2002, P UAI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 WEI X, 2006, P ACM SIGIR Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4233-1 PY 2008 BP 1223 EP 1228 DI 10.1109/WAINA.2008.137 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BLX52 UT WOS:000271293300205 ER PT B AU Zhu, DY Dreher, H AF Zhu, Dengya Dreher, Heinz GP IEEE TI Personalized Information Retrieval in Digital Ecosystems SO 2008 2ND IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES SE IEEE International Conference on Digital Ecosystems and Technologies LA English DT Proceedings Paper CT 2nd IEEE International Conference on Digital Ecosystems and Technologies CY FEB 26-29, 2008 CL Phitsanuloke, THAILAND SP IEEE DE information retrieval; personalization; user profile; machine learning; kNN ID SEARCH; WEB AB Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning users' search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting the weight of category which represents users' search preference. By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match users' information needs. C1 [Zhu, Dengya; Dreher, Heinz] Curtin Univ Technol, Perth, WA 6845, Australia. RP Zhu, DY (reprint author), Curtin Univ Technol, GPO Box U1987, Perth, WA 6845, Australia. EM dengya.zhu@postgrad.curtin.edu.au; h.dreher@curtin.edu.au CR [Anonymous], OPEN DIRECTORY PROJE Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BOLEY H, 2007, P IN IEEE DIG EC TEC, P401 Chakrabarti Soumen, 2003, MINING WEB DISCOVERI Chirita P.A., 2005, P 28 ANN INT ACM SIG, P178, DOI 10.1145/1076034.1076067 Chowdhury GG, 2004, INTRO MODERN INFORM DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gauch S., 2003, Web Intelligence and Agent Systems, V1 Glover E, 2001, COMMUN ACM, V44, P97, DOI 10.1145/501317.501319 Godoy D, 2006, INFORM SYST, V31, P247, DOI 10.1016/j.is.2005.02.008 *GOOGL DESKT, GOOGL DESKT SDK HOFGESANT PI, 2006, P WEBKDD 2006 KDD WO Jansen BJ, 2006, INFORM PROCESS MANAG, V42, P248, DOI 10.1016/j.ipm.2004.10.007 KOYCHEV I, P UM 2001 WORKSH MAC, P49 Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 *LIP, IMS LEARN INF PACK Mitchell TM, 1997, MACHINE LEARNING Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Pierrakos D, 2003, USER MODEL USER-ADAP, V13, P311, DOI 10.1023/A:1026238916441 Pitkow J, 2002, COMMUN ACM, V45, P50 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAFER R, 2001, P 9 GI WORKSH AG BAS Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 STERMSEK G, 2007, P INT C INF KNOWL EN Tsymbal A., 2004, TCDCS200415 Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 *YAH, YAH WEB DIR Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 ZHU D, P IN IEEE DIG EC TEC, P367 ZHU D, 2007, THESIS CURTIN U NR 32 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1489-5 J9 I C DIGIT ECOSYST TE PY 2008 BP 451 EP 456 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BJC56 UT WOS:000264749700072 ER PT B AU Ayodele, T Zhou, SK AF Ayodele, Taiwo Zhou, Shikun GP IEEE TI Applying Machine learning Algorithms for Email Management SO 2008 3RD INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND APPLICATIONS, VOLS 1 AND 2 LA English DT Proceedings Paper CT 3rd International Conference on Pervasive Computing and Applications CY OCT 06-08, 2008 CL Alexandria, EGYPT SP IEEE, Birmingham City Univ, Arab Acad Sci, Technol & Maritime Transport, Guilin Univ Technol, Beijing Univ Posts & Telecomm, Zhejiang Inst Modern Design Method DE emails; email grouping; summarization; reply prediction; unsupervised learning AB This paper presents the design and implementation of a new system to predict whether email received require a reply, group emails and summarize email messages. The system uses not only subjects and headers fields but also content of email messages to classify emails based on users' activities and generate summaries of each incoming message with unsupervised learning approach. Our framework tackles the problem of email overload, congestion, difficulties in prioritizing and difficulties in finding previously archived messages in the mail box. C1 [Ayodele, Taiwo; Zhou, Shikun] Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 2UP, Hants, England. RP Ayodele, T (reprint author), Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 2UP, Hants, England. EM taiwo.ayodele@port.ac.uk; shikun.zhou@port.ac.uk CR Boone G., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280791 CARENINI G, 2007, SUMMARZING EMAILS CO DREDZE M, 2005, REPLY EXPECTATION PR Farrell R., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Helfman J.I., 1995, ISHMAIL IMMEDIATE ID SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUFF D, 2007, IEEE COMPUT, V40, P31 TYLER J, 2003, P 8 C EUR C COMP SUP ZHOU L, 2005, P AAAI 2006 SPRING S NR 9 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2020-9 PY 2008 BP 339 EP 344 DI 10.1109/ICPCA.2008.4783606 PG 6 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJK54 UT WOS:000266697900065 ER PT B AU Xuan, ZG Chen, L Dang, YZ Yu, J AF Xuan, Zhaoguo Chen, Li Dang, Yanzhong Yu, Jiang GP IEEE TI Research Field Discovery Based on Text Clustering SO 2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31 SE International Conference on Wireless Communications, Networking and Mobile Computing LA English DT Proceedings Paper CT 4th International Conference on Wireless Communications, Networking and Mobile Computing CY OCT 12-17, 2008 CL Dalian, PEOPLES R CHINA SP IEEE Commun Soc, IEEE Antennas & Propagat Soc, Dalian Univ Technol, Wuhan Univ, Sci Res Publishing DE research field; text mining; text clustering; analysis AB For the need of revealing the structure and dynamics of science, text mining technologies have been widely used in extracting technical intelligence from research literature. To analyze the status of research fields, this paper applies text clustering to Chinese national science research proposals. By using vector-space model to represent the proposals, an improved Newman fast clustering algorithm has been carried out to explore the clusters of each year, which indicate the yearly research fields. After that, the paper gives an insightful representation of the whole status of the research fields. Then the similarities of clusters of different years are calculated to discover the groups, which identify the major research fields and illustrate their stability and changes over the time. These analyses will be helpful to discover the development trends of basic research fields. C1 [Xuan, Zhaoguo; Chen, Li; Dang, Yanzhong; Yu, Jiang] Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China. RP Xuan, ZG (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China. EM xzg@dl.cn; chenli_stu@163.com; yzhdang@dlut.edu.cn; yujiang_999@hotmail.com CR Bhattacharya S, 1998, SCIENTOMETRICS, V43, P359, DOI 10.1007/BF02457404 Delen D, 2008, EXPERT SYST APPL, V34, P1707, DOI 10.1016/j.eswa.2007.01.035 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 McKnight W., 2005, DM REV, P21 Miller T.W., 2005, DATA TEXT MINING BUS Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133 SALTON G, 2004, PHYS REV E, V69 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Small H., 1973, J AM SOC INFORM SCI, V24, P256 Van Den Besselaar P, 2006, SCIENTOMETRICS, V68, P377, DOI 10.1007/s11192-006-0118-9 XUAN ZG, 2007, INT J KNOWLEDGE SYST, V4, P36 2007, CHINESE CLASSIFIED T NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2107-7 J9 I C WIREL COMM NETW PY 2008 BP 11345 EP 11348 PG 4 WC Telecommunications SC Telecommunications GA BIW81 UT WOS:000263466105136 ER PT B AU Yu, J Dang, YZ AF Yu, Juan Dang, Yanzhong GP IEEE TI Learning Domain Feature from Text Corpora SO 2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31 SE International Conference on Wireless Communications, Networking and Mobile Computing LA English DT Proceedings Paper CT 4th International Conference on Wireless Communications, Networking and Mobile Computing CY OCT 12-17, 2008 CL Dalian, PEOPLES R CHINA SP IEEE Commun Soc, IEEE Antennas & Propagat Soc, Dalian Univ Technol, Wuhan Univ, Sci Res Publishing DE domain feature; length first segment; DFP analysis ID ONTOLOGIES AB For improving performance in automatically electronic documents processing, this paper proposes a concept of domain feature, which is defined as terms that can represent topics of a certain domain. Then it presents a non-lexicon-based approach automatically learning domain feature from text corpora. This approach combines the length first segment algorithm and domain feature possibility(DFP) algorithm. The former segments domain foreground corpora and extracts words and phrases in a satisfying recall rate, while the latter enhances the precision rate of learning by comparing different statistic properties that domain feature shows between foreground and background corpora. Experiments verify that given appropriate foreground and background corpora, this approach significantly improves efficiency in domain feature building and gets better result than manually building does. Algorithms combined in this approach can be widely used in other research domains of knowledge management. C1 [Yu, Juan; Dang, Yanzhong] Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China. RP Yu, J (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian, Peoples R China. EM bethyu888@hotmail.com; yzhdang@dlut.edu.cn CR Bouquet P, 2004, SPECIFICATION COMMON GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 JIANG SH, 2005, 6 INT S KNOWL SYST S KONG J, 2007, DATA SCI J, P500 MAEDCHE A, 2000, P 12 INT C SOFTW ENG Navigli R, 2004, COMPUT LINGUIST, V30, P151, DOI 10.1162/089120104323093276 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001] YUAN JP, 2006, APPL RES COMPUTERS, P1 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2107-7 J9 I C WIREL COMM NETW PY 2008 BP 11478 EP 11481 PG 4 WC Telecommunications SC Telecommunications GA BIW81 UT WOS:000263466105167 ER PT B AU Aguilar-Lopez, D Lopez-Arevalo, I Sosa-Sosa, V AF Aguilar-Lopez, Dulce Lopez-Arevalo, Ivan Sosa-Sosa, Victor GP IEEE TI Toward the semantic search by using ontologies SO 2008 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTING SCIENCE AND AUTOMATIC CONTROL (CCE 2008) LA English DT Proceedings Paper CT 5th International Conference on Electrical Engineering, Computing Science and Automatic Control CY NOV 12-14, 2008 CL Mexico City, MEXICO SP IEEE DE Hierarchical measure; ontologies; semantic search; WordNet ID MODEL AB This work describes a Web search approach taking into account the semantic content of Web pages. Eliminating irrelevant Web pages, the time-consuming task of revise the obtained results from actual search engines is reduced. The proposed approach is focused on Web pages that are not defined with semantic Web structure (most of the actual Web pages are in this format). The challenge is extract the semantic content from heterogeneous and human oriented Web pages. The approach integrates structures of ontologies, WordNet, and a hierarchical similarity measure to determine the relevance of a Web page. C1 [Aguilar-Lopez, Dulce; Lopez-Arevalo, Ivan; Sosa-Sosa, Victor] Cinvestav Tamaulipas, Informat Technol Lab, Victoria, Tamaulipas, Mexico. RP Aguilar-Lopez, D (reprint author), Cinvestav Tamaulipas, Informat Technol Lab, Victoria, Tamaulipas, Mexico. EM daguilar@tamps.cinvestav.mx; ilopez@tamps.cinvestav.mx; vjsosa@tamps.cinvestav.mx RI Sosa-Sosa, Victor/B-4525-2008 CR BAEZAYATES R, 2005, CRAWLING COUNTRY BET, P864 Bocio J, 2004, FR ART INT, V113, P349 DROEGEMEIER KK, 2005, IEEE COMPUTING SCI E, V7, P24 DUMONTIER M, 2008, 4 INT WORKSH Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 LINDESAY V, 2008, SCHEMAWEB DIRECTORY Morato J., 2004, P 2 GLOB WORDN C, P270 Noy NF, 2000, LECT NOTES ARTIF INT, V1937, P17 Patwardhan S., 2006, P EACL 2006 WORKSH M, P1 Ramachandran R., 2006, 22 INT C INT INF PRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALVADOROLIVAN JA, 2000, DOCUMENTACION CIENCI, P93 SANCHEZRUENES D, 2007, THESIS U POLITECNICA WURST M, 2008, WORD VECTOR TOOL USE NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2498-6 PY 2008 BP 128 EP 133 PG 6 WC Automation & Control Systems; Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BJK49 UT WOS:000266688800025 ER PT B AU Wong, WS Aghvami, H Wolak, SJ AF Wong, Wing Shun Aghvami, Hamid Wolak, Stephen J. GP IEEE TI Context-Aware Personal Assistant Agent Multi-Agent System SO 2008 IEEE 19TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS LA English DT Proceedings Paper CT 19th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications CY SEP 15-18, 2008 CL Cannes, FRANCE SP IEEE ID MODEL AB The main goal of this article is to show a Context-Aware PAA Multi-Agent System (CAPMAS) framework and how it conveys context-aware computing and agent technologies into ubiquitous system. We use context-awareness to provide computing environments with the ability to usefully adapt the services or information they provide. The 'agent technologies' take into account of different service solutions, which form the ultimate link to the context aware computing. In this article, we shall explore some of the basic agent technologies that lead us to develop our context awareness Personal Assistant Agent (PAA) based systems and concepts. C1 [Wong, Wing Shun; Aghvami, Hamid] Kings Coll London, Ctr Telecommun Res, London WC2R 2LS, England. RP Wong, WS (reprint author), Kings Coll London, Ctr Telecommun Res, London WC2R 2LS, England. EM wing.s.wong@kcl.ac.uk; hamid.aghvami@kcl.ac.uk; stephen.wolak@vodafone.com CR CAGLAYAN A, 1997, AGENT SOURCE BOOK CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/362384.362685 Coutaz J, 2005, COMMUN ACM, V48, P49, DOI 10.1145/1047671.1047703 Dey AK, 2001, HUM-COMPUT INTERACT, V16, P97, DOI 10.1207/S15327051HCI16234_02 Homayounvala E., 2005, 6th IEE International Conference on 3G and Beyond, DOI 10.1049/cp:20050249 HOMAYOUNVALA E, 2004, MIGRATION POLICIES P LLOYD SJ, 2001, MVCESOUWA13PII131 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiaffino S, 2006, IEEE INTELL SYST, V21, P12, DOI 10.1109/MIS.2006.15 TURNER PJ, 2002, MVCESOTONWA13ICR1 VLASSIS N, 2003, THESIS U AMSTERDAM WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2643-0 PY 2008 BP 2406 EP 2409 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BRH60 UT WOS:000282721801165 ER PT S AU Zhou, AY Zhang, R Vu, QH Qian, WN AF Zhou, Aoying Zhang, Rong Vu, Quang Hieu Qian, Weining GP IEEE TI COSTA: Adaptive indexing for terms in a large-scale distributed system SO 2008 IEEE 24TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, VOLS 1-3 SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT 24th IEEE International Conference on Data Engineering CY APR 07-12, 2008 CL Cancun, MEXICO SP IEEE, invent, Microsoft, ORACLE, IBM, SAP, Google, YAHOO C1 [Zhou, Aoying; Zhang, Rong] Fudan Univ, Dept Comp Sci & Engn, Shanghai 200433, Peoples R China. RP Zhou, AY (reprint author), Fudan Univ, Dept Comp Sci & Engn, Shanghai 200433, Peoples R China. EM ayzhou@fudan.edu.cn; rongzh@fudan.edu.cn; hieuvq@nus.edu.sg; wnqian@sei.ecnu.edu.cn CR BENDER M, 2005, P 31 INT C VER LARG Karger D., 2001, P 2001 ACM SIGCOMM C, P149 Loo B. T., 2004, P 30 INT C VER LARG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TANG C, 2003, P ACM SIMGCOMM C ZHOU S, 2006, P 22 ICDE NR 6 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1084-4627 BN 978-1-4244-1836-7 J9 PROC INT CONF DATA PY 2008 BP 1544 EP 1547 DI 10.1109/ICDE.2008.4497617 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BHX71 UT WOS:000257282600215 ER PT B AU Cojocar, GS Czibula, G AF Cojocar, Grigoreta Sofia Czibula, Gabriela (Serban) BE Letia, IA TI On Clustering based Aspect Mining SO 2008 IEEE 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING, PROCEEDINGS SE International Conference on Intelligent Computer Communication and Processing LA English DT Proceedings Paper CT IEEE 4th International Conference on Intelligent Computer Communication and Processing CY AUG 28-30, 2008 CL Cluj Napoca, ROMANIA SP IEEE AB Clustering is a division of data into groups of similar objects. Aspect mining is a process that tries to identify crosscutting concerns in existing software systems. The goal is to refactor the existing systems to use aspect oriented programming in order to make them easier to maintain and to evolve. The aim of this paper is to comparatively analyze and evaluate the results obtained by different clustering algorithms in aspect training. The evaluation is performed on an open source case study using four measures. C1 [Cojocar, Grigoreta Sofia; Czibula, Gabriela (Serban)] Univ Babes Bolyai, Dept Comp Sci, R-3400 Cluj Napoca, Romania. RP Cojocar, GS (reprint author), Univ Babes Bolyai, Dept Comp Sci, 1 Mihail Kogalniceanu St, R-3400 Cluj Napoca, Romania. EM grigo@cs.ubbcluj.ro; gabis@cs.ubbcluj.ro RI Czibula, Gabriela/C-3332-2012; Cojocar, Grigoreta-Sofia/C-4366-2015 OI Cojocar, Grigoreta-Sofia/0000-0003-0066-2752 CR ALBAYRAK S, 2003, TURK S ART INT NEUR BABU GP, 1993, PATTERN RECOGN LETT, V14, P763 Berkhin P., 2002, SURVEY CLUSTERING DA Bhuyan J.N., 1991, P 4 INT C GEN ALG, P408 Breu S., 2006, 21 IEEE ACM INT C AU Breu S., 2004, Proceedings. 19th International Conference on Automated Software Engineering, DOI 10.1109/ASE.2004.1342754 Bruntink M, 2004, PROC IEEE INT CONF S, P200, DOI 10.1109/ICSM.2004.1357804 Gamma E., JHOTDRAW PROJECT HANNEMANN J, 2001, ADV SEP CONC WORKSH He L., 2006, INT J COMPUTER SCI N, V6, P247 HENDERSONSELLER.B, 1996, OBJECT ORIENTED METR Irwin J., 1997, P EUR C OBJ OR PROGR, V1241, P220 Jain A.K., 1998, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kamber M., 2001, DATA MINING CONCEPTS Laddad Ramnivas, 2003, ASPECTJ ACTION PRACT LUNG CH, 1998, ISAW 98, P101 Mancoridis S., 1999, ICSM, P50 MANCORIDIS S, 1998, INT WORKSH PROGR COM, P45 MARIN M, 2004, SENR0413 CTR WISK IN Marin M, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P132, DOI 10.1109/WCRE.2004.23 Mitchell M, 1998, INTRO GENETIC ALGORI MOLDOVAN GS, 2006, P LINK ASP TECHN EV MOLDOVAN GS, 2006, STUD U BABES BOLYAI, V51, P53 Moldovan G. S., 2007, WSEAS Transactions on Computers, V6 RAGHAVAN VV, 1979, SIGIR FORUM, V14, P10 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMPAIO A, 2005, EARLY ASPECTS 2005 SERBAN G, 2007, P 3 BALK C INF BCI 2, P143 Serban G., 2007, WSEAS Transactions on Computers, V6 SERBAN G, 2006, P 8 INT S SYMB NUM A, P69 SHEPHERD D, 2005, P LINK ASP TECHN EV Tarr P., 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002), DOI 10.1109/ICSE.1999.841000 Tonella P., 2004, Proceedings. 11th Working Conference on Reverse Engineering XU X, 2004, SCAM 04, P75 Zadeh L. A., 1965, FUZZY SETS INFORM CO, V8, P338, DOI DOI 10.1016/S0019-9958(65)90241-X NR 36 TC 3 Z9 3 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2673-7 J9 INT C INTELL COMP CO PY 2008 BP 129 EP 136 DI 10.1109/ICCP.2008.4648364 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BIP24 UT WOS:000261519200018 ER PT B AU Dang, YZ Xuan, ZG Chen, L AF Dang, Yanzhong Xuan, Zhaoguo Chen, Li GP IEEE TI The analysis of research fields of Chinese natural science funds based on text classification SO 2008 IEEE 8TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY, VOLS 1 AND 2 LA English DT Proceedings Paper CT 8th IEEE International Conference on Computer and Information Technology CY JUL 08-11, 2008 CL Sydney, AUSTRALIA SP IEEE Comp Soc, IEEE TCSC, BL 21 Informat Technol Div, Korea Univ, Federat Chinese Scholars Australia, Univ Technol, Korea Univ, ARC Res Network Enterprise Informat Infrastruct, Australian Chinese ICT Profess Soc AB This paper applies text classification to mining research proposals for Chinese natural science funds. By using vector-space model to represent the proposals, the centroid-based classification has been carried out. And then the change of category members is analyzed through three variables defined to measure the stability, variability and overlapping of research fields, as the research field Management Science and Engineering is taken as a case study. The resulting figures give a direct representation of characteristics of research fields, which are helpful to find out the interrelationship of research fields and to adjust the field code system to reflect the real status more accurately. C1 [Dang, Yanzhong; Xuan, Zhaoguo; Chen, Li] Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. RP Dang, YZ (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. CR BURKS SD, 2004, SOCIAL NETWORKS ITS Han E., 2000, PRINCIPLES DATA MINI, P424 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 2005, CHINESE CLASSIFIED T NR 5 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2357-6 PY 2008 BP 89 EP 94 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIH67 UT WOS:000259565600015 ER PT B AU Cao, YJ Niu, ZD Wang, WQ Zhao, K AF Cao, YuJuan Niu, ZhenDong Wang, WeiQiang Zhao, Kun GP IEEE TI The study on Detecting near-Duplicate WebPages SO 2008 IEEE 8TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY, VOLS 1 AND 2 LA English DT Proceedings Paper CT 8th IEEE International Conference on Computer and Information Technology CY JUL 08-11, 2008 CL Sydney, AUSTRALIA SP IEEE Comp Soc, IEEE TCSC, BL 21 Informat Technol Div, Korea Univ, Federat Chinese Scholars Australia, Univ Technol, Korea Univ, ARC Res Network Enterprise Informat Infrastruct, Australian Chinese ICT Profess Soc AB Reprinting information among websites produces a great deal redundant Webpages. To improve search efficiency and user satisfaction, an algorithm to Detect near-Duplicate WebPages (DDW) is proposed. In the course of developing a near-duplicate detection system for a multi-billion page repository, we make two research contributions. First, we consider both syntactic and semantic information to present and compute documents' similarities. Second after classifying web-pages into different categories, we index feature in each category then search for near-duplicates only in the same category. From Google searching results for 72 queries, we select 5835 near-duplicate WebPages manually. Then insert them into an existing collection which contains about 768,763 WebPages, as the test data. The experimental results demonstrate that our approach outperforms I-Match algorithms. In large-scale test, approximate linear time and space complexity are gotten. C1 [Cao, YuJuan; Niu, ZhenDong; Wang, WeiQiang; Zhao, Kun] Beijing Inst Technol, Sch Comp Sci Technol, Beijing, Peoples R China. RP Cao, YJ (reprint author), Beijing Inst Technol, Sch Comp Sci Technol, Beijing, Peoples R China. CR Bharat K, 2000, J AM SOC INFORM SCI, V51, P1114, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1025>3.0.CO;2-0 Bharat K, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P501 BRIN S, 1995, P SPEC INT GROUP MAN, P298 Broder A., 1997, P 6 INT WORLD WID WE, P391 CHO J, 2000, P 2000 ACM SIGMOD IN, P355, DOI 10.1145/342009.335429 Chowdhury A, 2002, ACM T INFORM SYST, V20, P171, DOI 10.1145/506309.506311 Conrad J., 2003, P 12 INT C INF KNOWL, P443 Cooper J. W., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Fetterly D., 2003, Proceedings. First Latin American Web Congress, DOI 10.1109/LAWEB.2003.1250280 Heintze N, 1996, PROCEEDINGS OF THE SECOND USENIX WORKSHOP ON ELECTRONIC COMMERCE, P191 Hoad TC, 2003, J AM SOC INF SCI TEC, V54, P203, DOI 10.1002/asi.10170 Kolcz A., 2004, P 10 ACM SIGKDD INT, P605, DOI 10.1145/1014052.1014127 MANKU GS, 2007, INT WORLD WID WEB C, P141 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHI PL, 2006, P 21 INT C COMP LING SHIVAKUMAR, 1998, P WORKSH WEB DAT WEB, P204 YE SZ, 2006, P 10 PAC AS C KNOWL, P275 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2357-6 PY 2008 BP 95 EP 100 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIH67 UT WOS:000259565600016 ER PT S AU Rasiwasia, N Vasconcelos, N AF Rasiwasia, Nikhil Vasconcelos, Nuno GP IEEE TI A Study of Query by Semantic Example SO 2008 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, VOLS 1-3 SE PROCEEDINGS - IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION LA English DT Proceedings Paper CT IEEE Conference on Computer Vision and Pattern Recognition CY JUN 23-28, 2008 CL Anchorage, AK SP IEEE Comp Soc ID RELEVANCE FEEDBACK; IMAGE RETRIEVAL; SPACE AB In recent years, query-by-semantic-example (QBSE) has become a popular approach to do content based image retrieval [20, 23, 18]. QBSE extends the well established query-by-example retrieval paradigm to the semantic domain. While various authors have pointed out the benefits of QBSE, there are still various open questions with respect to this paradigm. These include a lack of precise understanding of how the overall performance depends on various different parameters of the system. In this work, we present a systematic experimental study of the QBSE framework. This can be broadly divided into three categories. First, we examine the space of low-level visual features for its effects on the retrieval performance. Second, we study the space of learned semantic concepts, herein denoted as the "semantic space", and show that not all semantic concepts are equally informative for retrieval. Finally, we present a study of the intrinsic structure of the semantic space, by analyzing the contextual relationships between semantic concepts and show that this intrinsic structure is crucial for the performance improvements. C1 [Rasiwasia, Nikhil; Vasconcelos, Nuno] Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92103 USA. RP Rasiwasia, N (reprint author), Univ Calif San Diego, Dept Elect & Comp Engn, San Diego, CA 92103 USA. CR Barnard K., 2001, INT C COMP VIS, V2, P408 BLEI D, 2003, P ACM SIGIR Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61 Cover T. M., 1991, ELEMENTS INFORM THEO Datta R., 2007, ACM COMPUT SURV, V39 Duygulu P., 2002, ECCV Feng SL, 2004, IEEE CVPR He XF, 2003, IEEE T CIRC SYST VID, V13, P39, DOI 10.1109/TCSVT.2002.808087 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Jain A. K., 1996, PATTERN RECOGNITION, V29 Kiyoki Y., 1994, SIGMOD Record, V23 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P115, DOI 10.1007/BF02289694 LAZEBNIK S, 2005, P IEEE CVPR Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94 LU J, 2005, IEEE NLP KE, P455 Lu Y, 2003, IEEE T MULTIMEDIA, V5, P339, DOI 10.1109/TMM.2003.813280 MAGALHAES J, 2007, SEMANTIC VECTOR SPAC MAGALHAES J, 2007, P 6 ACM INT C IM VID, P619 Natsev A., 2004, P 10 ACM SIGKDD INT, P641, DOI 10.1145/1014052.1014133 NIBLACK W, 1993, P SOC PHOTO-OPT INS, V1908, P173 Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 RASIWASIA N, 2006, CIVR, P51 Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMITH J, 2003, MULTIMEDIA SEMANTIC, P445 Smith J. R., 1996, ACM MULTIMEDIA, P87 SMITH JR, 2003, P SOC PHOTO-OPT INS, P271 VASCONCELOS N, 2001, P IEEE CVPR KAW HAW VASCONCELOS N, 2004, IEE T SIGNAL PROCESS, V52 NR 29 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1063-6919 BN 978-1-4244-2339-2 J9 PROC CVPR IEEE PY 2008 BP 727 EP 734 PG 8 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BIK29 UT WOS:000260371900097 ER PT B AU Niu, L Lu, J Zhang, GQ AF Niu, Li Lu, Jie Zhang, Guangquan GP IEEE TI Improved Business Intelligence Analytics on Manager's Experience SO 2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 SE IEEE Congress on Evolutionary Computation LA English DT Proceedings Paper CT IEEE Congress on Evolutionary Computation CY JUN 01-06, 2008 CL Hong Kong, PEOPLES R CHINA SP IEEE DE business intelligence; experience management; mental models; decision support system ID STRATEGIC DECISION-MAKING; MENTAL MODELS; INFORMATION EXTRACTION; SUPPORT-SYSTEMS AB Current business intelligence (BI) systems bring the manager with powerful data analysis functions. However the manager is either limited to predefined queries or feels lost within torrents of data. Thus, decision making is still a task with high cognitive load. BI analytics are based on queries into the data warehouse. Rather than predefining queries or less guided ad hoc analysis, we use the manager's experience, together with decision problem statement, business ontology and heuristics to automatically construct data retrieval queries. Our method is expected to provide the manager better decision support on cognitive orientation. C1 [Niu, Li; Lu, Jie; Zhang, Guangquan] Univ Technol Sydney, Fac Informat Technol, Sydney, NSW 2007, Australia. RP Niu, L (reprint author), Univ Technol Sydney, Fac Informat Technol, POB 123, Sydney, NSW 2007, Australia. EM aoilieniu@hotmail.com; jielu@it.uts.edu.au; zhangg@it.uts.edu.au CR ARJONA JL, 2002, LECT NOTES COMPUTER, V2348, P697 Arnott D, 2005, J INF TECHNOL, V20, P67, DOI 10.1057/palgrave.jit.2000035 Cardie C, 1997, AI MAG, V18, P65 CARLEY K, 1992, SOC FORCES, V70, P601, DOI 10.2307/2579746 Carley KM, 1997, J ORGAN BEHAV, V18, P533, DOI 10.1002/(SICI)1099-1379(199711)18:1+<533::AID-JOB906>3.3.CO;2-V CARLSON DA, 1990, COMMUN ACM, V32, P311 CASTELLS P, 2007, IEEE T KNOWL DATA EN, V17, P261 Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152 Chen CH, 2006, BRIT J EDUC TECHNOL, V37, P299, DOI 10.1111/j.1.1467-8535.2005.00480.x Chen JQ, 2003, DECIS SUPPORT SYST, V36, P147, DOI 10.1016/S0167-9236(02)00139-2 Embley DW, 1999, DATA KNOWL ENG, V31, P227, DOI 10.1016/S0169-023X(99)00027-0 GNYAWALI DR, 2005, RES METHODLOGY STRAT, P225 Guarino N., 1998, 1 INT C FORM ONT INF, P3 Ingwersen P., 1996, INFORM RETRIEVAL INT Johnson-Laird P. N., 1983, MENTAL MODELS Johnson-Laird P. N., 1998, SISTEMI INTELLIGENTI, V9, P68 Knoblock C.A., 2000, IEEE DATA ENG B, V23, P33 Kuo FY, 1998, DECIS SUPPORT SYST, V24, P89, DOI 10.1016/S0167-9236(98)00056-6 Laender AHF, 2002, SIGMOD REC, V31, P84 MINTZBERG, 1973, NATURE MANAGERIAL WO PORAC JF, 1990, ACAD MANAGE REV, V15, P224, DOI 10.2307/258155 RESNICK ML, 2003, HUM FACT ERG SOC 47 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWENK CR, 1984, STRATEGIC MANAGE J, V5, P111, DOI 10.1002/smj.4250050203 Shim JP, 2002, DECIS SUPPORT SYST, V33, P111, DOI 10.1016/S0167-9236(01)00139-7 Sutcliffe KM, 2003, HARVARD BUS REV, V81, P74 YADAV SB, 1992, DECIS SUPPORT SYST, V8, P55, DOI 10.1016/0167-9236(92)90037-P NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1822-0 J9 IEEE C EVOL COMPUTAT PY 2008 BP 726 EP 730 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BIW46 UT WOS:000263406500109 ER PT S AU Gadia, V Rosen, G AF Gadia, Vinay Rosen, Gail BE Chen, Y He, J Reddy, CK Yang, J Yoo, I Zhang, X Gao, J Huang, Y Song, M Yang, J Wu, Z TI A TEXT-MINING APPROACH FOR CLASSIFICATION OF GENOMIC FRAGMENTS SO 2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS SE IEEE International Conference on Bioinformatics and Biomedicine Workshop-BIBMW LA English DT Proceedings Paper CT IEEE International Conference on Bioinformatics and Biomedicine CY NOV 03-05, 2008 CL Philadelphia, PA SP IEEE AB Genome identification is an emerging area of interest due to the study of environmental DNA samples. We show that performance approaches 50% for classifying 500bp fragments when using 12mer features, but more importantly, the performance linearly increases for large N. Secondly, we determine that an inverted TF-IDF measure performs 16% better when only using 80% of the words, as opposed to taking the fullset (100%). This increase implies that while too sparse of a feature subset does not produce good results, a carefully selected set has the potential to improve genome classification over a random feature set. Computing even 80% of all possible features can result in a significant savings in computation. The Euclidean classifier and TF-IDF measures will pave the way for more discriminative classification techniques. C1 [Gadia, Vinay; Rosen, Gail] Drexel Univ, Dept Elect & Comp Engn, Philadelphia, PA 19104 USA. RP Gadia, V (reprint author), Drexel Univ, Dept Elect & Comp Engn, Philadelphia, PA 19104 USA. EM vg482@drexel.edu; gailr@ece.drexel.edu CR Benson DA, 2008, NUCL ACIDS RES, V36 BOHANNON J, 2008, SCI MAGAZINE FOFANOV Y, 2004, BIOINFORMATICS KIRZHNER V, 2005, ELSEVIER J BIOSYSTEM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turnbaugh PJ, 2007, NATURE, V449, P804, DOI 10.1038/nature06244 VOLKOVICH Z, 2005, PATTERN RECOGNITION WEISS S, 2005, PREDICTIVE METHODS A 2008, NATURE STRUCTURAL MO, V15 NR 9 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 2163-6966 BN 978-1-4244-2890-8 J9 IEEE INT C BIO BIO W PY 2008 BP 107 EP 108 DI 10.1109/BIBMW.2008.4686216 PG 2 WC Engineering, Biomedical SC Engineering GA BIQ62 UT WOS:000262067000015 ER PT B AU Nikesh, PL Sumam, MI David, PS AF Nikesh, P. L. Sumam, Mary Idicula David, Peter S. GP IEEE TI English-Malayalam Cross-Lingual Information Retrieval - an experience SO 2008 IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY SE International Conference on Electro Information Technology LA English DT Proceedings Paper CT IEEE International Conference on Electro/Information Technology CY MAY 18-20, 2008 CL Ames, IA SP IEEE DE Cross-Lingual Information Retrieval; vector space model; Malayalam; document ranking; bilingual dictionary; content based retrieval AB This paper describes about an English-Malayalam Cross-Lingual Information Retrieval system. The system retrieves Malayalam documents in response to query given in English or Malayalam. Thus monolingual information retrieval is also supported in this system. Malayalam is one of the most prominent regional languages of Indian subcontinent. It is spoken by more than 37 million people and is the native language of Kerala state in India. Since we neither had any full-fledged online bilingual dictionary nor any parallel corpora to build the statistical lexicon, we used a bilingual dictionary developed in house for translation. Other language specific resources like Malayalam stemmer, Malayalam morphological root analyzer etc developed in house were used in this work. C1 [Nikesh, P. L.; Sumam, Mary Idicula; David, Peter S.] Cochin Univ Sci & Technol, Dept Comp Sci, Cochin 682016, Kerala, India. RP Sumam, MI (reprint author), Cochin Univ Sci & Technol, Dept Comp Sci, Cochin 682016, Kerala, India. EM nikesh.pl@gmail.com; sumam123@yahoo.com; davidpeter123@yahoo.com CR Aljlayl M., 2002, Proceedings International Conference on Information Technology: Coding and Computing, DOI 10.1109/ITCC.2002.1000351 BALLESTEROS L, 1996, P 7 INT DEXA C DAT E, P791 Becker J., P BIS 2003 COL SPRIN Croft W. B., 1989, Proceedings of the 13th International Conference on Research and Development in Information Retrieval DIEKEMA AR, 2004, ACM SIGIR FORUM, V3 IDICULA SM, J S ASIAN RES, V27, P173 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 LARKEY LS, 2003, ACM T ASIAN LANGUAGE, V2, P130, DOI 10.1145/974740.974746 LEE AHS, 1997, J CELL PATHOL, V2, P67 MANDAL D, BENGALI HINDI ENGLIS NEUMANN D, CROSS LANGUAGE QUEST OGORMAN A, FRENCH CROSS LANGUAG PINGALI P, 2006, INT WORLD WID WEB C POLETTINI N, 2004, VECTOR SPECE MODEL I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEETHA A, 2007, 10 INT C INF TECHN I, P56 YU F, 2006, INT C COMP INT SEC 3, V2, P1460 NR 17 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2029-2 J9 INT CONF ELECTRO INF PY 2008 BP 271 EP 275 DI 10.1109/EIT.2008.4554312 PG 5 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BIG42 UT WOS:000259300100055 ER PT B AU Li, M Zheng, YT Neo, SY Wang, XD Tang, S Lin, SX AF Li, Ming Zheng, Yantao Neo, Shi-Yong Wang, Xiangdong Tang, Sheng Lin, Shou-Xun GP IEEE TI PERSONALIZED EVENT-BASED NEWS VIDEO RETRIEVAL WITH DYNAMIC USER-LOG SO 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4 LA English DT Proceedings Paper CT IEEE International Conference on Multimedia and Expo (ICME 2008) CY JUN 23-26, 2008 CL Hannover, GERMANY SP IEEE Signal Proc Soc, IEEE Circuits & Syst Soc, IEEE Commun Soc, IEEE Comp Soc DE Personalized retrieval; user-log AB Personalization especially in the domain of information retrieval is essentially important, as users might pose the same query even when they are searching for different information. It is thus necessary to create a retrieval engine which takes into consideration the dynamic information needs of different users. This paper presents our personalized news video retrieval engine, which exploits the individual user's previous browsing history to customize and enhance their future search results. Specifically, the system utilizes the news topic hierarchy, a hierarchical news topic structure derived from unsupervised clustering on the news video corpus and event entities from news video and online news articles. We then dynamically project user's browsing history onto this topic hierarchy to provide the basis for re-ranking relevant news videos. This system is tested on one month of TRECVID 2006 dataset consisting of 80 hours news video and found to return results in a more intuitive and personalized manner. C1 [Li, Ming; Zheng, Yantao; Neo, Shi-Yong] Natl Univ Singapore, Sch Comp, Singapore 117548, Singapore. RP Li, M (reprint author), Natl Univ Singapore, Sch Comp, Singapore 117548, Singapore. CR CHUA TS, 2006, TRECVD 2006 GHOSH H, INT MULT C 2007 HSU WH, 2005, COLUMBIA IBM NEWS VI Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 NEO SY, 2007, USE TOPIC EVOLUTION NEO SY, 2006, ACM MULTIMEDIA 2006 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang H., 2003, P 11 ACM INT C MULT, P632 ZHANG Y, 2007, MIR 07 Zhao Ying, 2002, CIKM 02, P515 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2570-9 PY 2008 BP 1157 EP 1160 PG 4 WC Computer Science, Interdisciplinary Applications; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications SC Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications GA BIP21 UT WOS:000261514000290 ER PT S AU Meng, XJ Chen, QC Wang, XL AF Meng, Xianjun Chen, Qingcai Wang, Xiaolong GP IEEE TI Semantic Feature Reduction in Chinese Document Clustering SO 2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6 SE IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on System, Man, and Cybernetic CY OCT 12-15, 2008 CL Singapore, SINGAPORE SP IEEE DE text clustering; feature selection; part-of-speech; synonym AB Text clustering techniques were usually used to structure the text documents into topic related groups which can facilitate users to get a comprehensive understanding on corpus or results from information retrieval system. Most of existing text clustering algorithm which derived from traditional formatted data clustering heavily rely on term analysis methods and adopted Vector Space Model (VSM) as their document representation. But because of the essential characteristic underlying text such as high dimensionality features vector space, the problem of sparseness has a strong impact on the clustering algorithm. So feature reduction is an important preprocess step for improving the efficiency and accuracy of clustering algorithm by removing redundant and irrelevant terms from corpus. Even the clustering is considered as an unsupervised learning method, but in text, there is still some priori knowledge we can use from NLP analysis based approach. In this paper, we propose a semantic analysis based feature reduction method which used in Chinese text clustering. Our method bases on a dedicated Part-of-Speech tags selection and synonyms consolidation and can reduce the feature space of documents more effectively compared with traditional feature reduction method tfidf and stopwords removal, meanwhile it preserves or sometimes even improves the accuracy of clustering algorithm. In our experiment, we tested our feature reduction method using bisecting k-means algorithm which was proved be efficient in text clustering. The results show that our method can reduce the feature space significantly, and meanwhile have a better clustering accuracy in terms of the purity. C1 [Meng, Xianjun; Chen, Qingcai; Wang, Xiaolong] Harbin Inst Technol, Shenzhen Grad Sch, ICRC Lab, Shenzhen, Peoples R China. RP Meng, XJ (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, ICRC Lab, Shenzhen, Peoples R China. EM mengxj@gmail.com; qingcai.chen@gmail.com; wangxl@insun.hit.edu.cn CR Aggarwal CC, 2000, SIGMOD RECORD, V29, P70 Arriaga RI, 2006, MACH LEARN, V63, P161, DOI 10.1007/s10994-006-6265-7 Basili R., 2000, P RIAO 00 6 INT C RE, P331 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dong Zh.D., 1999, HOWNET GAUCH SMR, 2000, FEATURE REDUCTION DO Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 Hotho A, 2003, P SIGIR 2003 SEM WEB HOTHO A, 2001, P IJCAI 2001 WORKSH, P30 Jain A. K., 1988, ALGORITHMS CLUSTERIN KARYPIS G, CLUTO CLUSTERING TOO, P2 Kaufman L, 1990, FINDING GROUPS DATA Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LIU T, 2003, EVALUATION FEATURE S MAGGINI M, 2004, P WEB INT IEEE WIC A, P363 Page L, 1998, PAGERANK CITATION RA Richardson M., 2006, P 15 INT C WORLD WID, P707, DOI DOI 10.1145/1135777.1135881 Rigutini L., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEDDING J, 2004, WORDNET BASED TEXT D, V113 Steinbach M., 2000, KDD WORKSH TEXT MIN, V34, P35 Van R. C. J., 1979, INFORM RETRIEVAL Yu S., 2003, J CHINESE LANGUAGE C, V13, P121 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 27 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-2383-5 J9 IEEE SYS MAN CYBERN PY 2008 BP 3720 EP 3725 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BKT54 UT WOS:000269197301317 ER PT S AU Aseervatham, S AF Aseervatham, Sujeevan GP IEEE TI A Local Latent Semantic Analysis-based Kernel for Document Similarities SO 2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT International Joint Conference on Neural Networks CY JUN 01-08, 2008 CL Hong Kong, PEOPLES R CHINA SP IEEE AB The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorization. In this paper, we present a semantic space constructed from latent concepts. The concepts are extracted using the Latent Semantic Analysis (LSA). To take into account of the specificity of each document category, we use the local LSA to define the global semantic space. Furthermore, we propose a weighted semantic kernel for the global space. The experimental results of the kernel, on text categorization tasks, show that this kernel performs better than global LSA kernels and especially for small LSA dimensions. C1 Univ Paris 13, CNRS, LIPN, UMR 7030, F-93430 Villetaneuse, France. RP Aseervatham, S (reprint author), Univ Paris 13, CNRS, LIPN, UMR 7030, 99 Av JB Clement, F-93430 Villetaneuse, France. EM Sujeevan.Aseervatham@lipn.univ-paris13.fr CR Cristianini N, 2002, J INTELL INF SYST, V18, P127, DOI 10.1023/A:1013625426931 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gliozzo A., 2005, P 9 C COMP NAT LANG, P56, DOI 10.3115/1706543.1706553 HULL D, 1995, THESIS STANFORD U Hull D., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Liu T., 2004, ICDM 04, P162 McCallum A, 1998, P AAAI 98 WORKSH LEA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Vapnik V., 1995, NATURE STAT LEARNING Wong S. K. Michael, 1985, SIGIR, P18, DOI DOI 10.1145/253495.253506 Yang Y., 2001, P 24 ANN INT ACM SIG, P137, DOI 10.1145/383952.383975 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4244-1820-6 J9 IEEE IJCNN PY 2008 BP 214 EP 219 DI 10.1109/IJCNN.2008.4633792 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIY81 UT WOS:000263827200035 ER PT B AU Hidayat, SS Kim, BK Ohba, K AF Hidayat, Sidiq S. Kim, B. K. Ohba, Kohtaro BE Chatila, R Kelly, A Merlet, JP TI Learning Affordance for Semantic Robots Using Ontology Approach SO 2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE/RSJ International Conference on Intelligent Robots and Systems CY SEP 22-26, 2008 CL Nice, FRANCE SP IEEE, Robot Soc Japan, IEEE Robot & Automat Soc, IEEE Ind Elect Soc, Soc Instrument & Control Engineers, Inst Natl Rech Informat & Automat, CNRS, Inst Control, Robot & Syst AB Learning affordance is very important for autonomous robots to know what the necessary actions should be performed to complete a given task with its abilities. However, it's difficult to identify and apply affordances in practice for robots. In order for a robot to get benefits from this concept, this paper proposes ontology approach based affordance concept or ubiquitous robots. The goal of this affordance-based ontology effort is to develop and begin to populate a neutral knowledge representation capturing relevant information about robots capabilities and what the environment afford to assist human life in ubiquitous robots environment. We propose a new method to 'teach' robot learn objects and context affordances using ontology approach for Semantic Robots in Ubiquitous Robots space. C1 [Hidayat, Sidiq S.] Univ Tsukuba, Tsukuba, Ibaraki 3058572, Japan. RP Hidayat, SS (reprint author), Univ Tsukuba, Tsukuba, Ibaraki 3058572, Japan. EM s.hidayat@aist.go.jp; bk.kim@aist.go.jp; k.ohba@aist.go.jp RI Kim, Bong Keun/K-3816-2012 CR ARKIN R, 1998, BEHAV BASED ROBOTIS Baader F., 2003, DESCRIPTION LOGIC HD COSAGUILERA I, 2003, P 9 INT C COGN MOD I, P4 Fitzpatrick P., 2003, P 2003 IEEE INT C RO, P3140 Fritz G, 2006, LECT NOTES COMPUT SC, V4095, P52 Gibson J.J., 1979, ECOLOGICAL APPROACH, P127 Hidayat Sidiq S, 2007, 16th IEEE International Conference on Robot and Human Interactive Communication Kim D., 2006, IEEE INT C ROB AUT Lopes M., 2007, IEEE RSJ INT C INT R, P1015 LORKEN C, 2007, PUBLICATIONS I COGNI, V2 Murphy RR, 1999, IEEE T SYST MAN CY A, V29, P105, DOI 10.1109/3468.736365 MURPHY RR, 2000, INTELLIGENT ROBOTS A Nishide S, 2007, IEEE INT CONF ROBOT, P2501, DOI 10.1109/ROBOT.2007.363841 Norman D. A., 1999, Interactions, V6, DOI 10.1145/301153.301168 OHARA K, 2006, 2 INT C UB ROB AMB I, P66 OHBA K, 2007, 8 SYST INT DIV ANN C, P926 ROME E, 2006, MULTISENSOR AFFORDAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STOYTCHEV A, 2005, P IEEE INT C ROB AUT, P18 STOYTCHEV A, 2005, P AAAI S DEV ROB, P21 SUH H, 2007, IEEE RSJ INT C INT R, P429 UGUR E, 2006, MACSIM PHYS BASED SI Warren W., 1984, J EXP PSYCHOL, V10, P683 NR 23 TC 3 Z9 3 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2057-5 PY 2008 BP 2630 EP 2636 DI 10.1109/IROS.2008.4651193 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Robotics SC Computer Science; Engineering; Robotics GA BIJ26 UT WOS:000259998201199 ER PT B AU Alla, HAHMA Al-Ghreimil, N AF Alla, Hanan Ahmed Hosni Mahmoud Abd Al-Ghreimil, Nadia BE Mohammadian, M TI A Novel Efficient Classification Algorithm for Search Engines SO 2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2 LA English DT Proceedings Paper CT International Conference on Computational Intelligence for Modelling, Control and Automation CY DEC 10-12, 2008 CL Vienna, AUSTRIA DE Information Processing; Supervised Classification; Document Classification ID INFORMATION AB In this paper a new classification algorithm of Web documents into a set of categories, is proposed. The proposed technique is based on analyzing relationships between different documents and the terms they contain by producing a set of rules relating the category of the document, its terms and their frequencies. Each document is represented by a graph that correlates its most frequent combined words and its category. The relationships among these graphs and the documents' categories are captured. The proposed technique has three phases. The first phase is a training phase where human experts determines the categories of different web pages and articles and combine these categories with appropriate weighted index. The second phase is the blind categorization phase to build a database that will be categorized according to the result of the first phase. The third phase is applying the proposed graph representation technique on the whole set of documents per category to determine its final graph representation. The third phase will produce better classification rules because the sample size is larger with no additional cost of supervised categorization. Experiments using data sets collected from different Web portals are conducted. C1 [Alla, Hanan Ahmed Hosni Mahmoud Abd; Al-Ghreimil, Nadia] King Saud Univ, Dept Informat Technol, Coll Comp & Informat Sci, Riyadh 11451, Saudi Arabia. RP Alla, HAHMA (reprint author), King Saud Univ, Dept Informat Technol, Coll Comp & Informat Sci, Riyadh 11451, Saudi Arabia. EM Hanan.hosni@yahoo.com CR Broder A., 1997, P 6 INT WORLD WID WE, P391 BUYUKKOKTEN O, 2001, P WWW10 HONG KONG CH CHAKRABARTI S, 1998, P ACM SIGMOD CHEKURI C, 1997, P 6 INT WORLD WID WE CHEN D, 2006, BMC BIOINFORMATICS, P367 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 CHEN JL, 2001, P WWW10 HK CHIN Chen N, 2007, INT J DOC ANAL RECOG, V10, P1, DOI 10.1007/s10032-006-0020-2 CHEN N, 2006, P 2006 C CTR ADV STU, P369 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Han B, 2006, BIOINFORMATICS, V22, P2136, DOI 10.1093/bioinformatics/btl350 JOACHIMS T, 1999, P ICML 99 BLED SLOV KER SJ, 2000, 38 ANN M ASS COMP LI KO YJ, 2002, P COLING KOLCZ A, 2001, P CIKM01 MIYAMOTO S, 1986, IEEE T SYST MAN CYB, V16, P278, DOI 10.1109/TSMC.1986.4308950 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Pitkow J., 1999, P 2 USENIX S INT TEC, P139 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 SALTON G, 1971, PRENTICE HALL SERIES, pCH14 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHATKAY H, 2006, ISMB SUPPLEMENT BIOI, P446 SHYU ML, 2001, P 17 INT C DISTR MUL SHYU ML, 2001, IEEE INT C SYST MAN, P1717 NR 24 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-3329-2 PY 2008 BP 773 EP 778 DI 10.1109/CIMCA.2008.68 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BON50 UT WOS:000277076500134 ER PT B AU Chamielec, G Weiss, D AF Chamielec, Grazyna Weiss, Dawid BE Ganzha, M Paprzycki, M PelechPilichowski, T TI Modeling the Frequency of Phrasal Verbs with Search Engines SO 2008 INTERNATIONAL MULTICONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (IMCSIT), VOLS 1 AND 2 LA English DT Proceedings Paper CT International Multiconference on Computer Science and Information Technology CY OCT 20-22, 2008 CL Wisla, POLAND AB There are well over a thousand phrasal verbs in English. For non-native speakers they are notoriously difficult to remember and use in the right context. We tried to construct a ranking of phrasal verbs according to their estimated occurrence frequency, based on quantitative information available from the public indexable Web. Technically, we used major Web search engines to acquire phrase-occurrence statistics, measured consistency between the rankings implied by their results and confirmed that a rough set of 'classes' of phrasal verbs can be distinguished. While this technique relies on inaccurate and possibly biased estimation functions, we show that the overall distribution of ranks seems to be consistent among all the queried search engines operated by different vendors. C1 [Chamielec, Grazyna] SuperMemo, Poznan, Poland. RP Chamielec, G (reprint author), SuperMemo, Poznan, Poland. EM ika.chamielec@gmail.com; dawid.weiss@cs.put.poznan.pl CR [Anonymous], 1995, OXFORD ADV LEARNERS BALDWIN T, 2002, COLING 02, P98 Cambridge, 2006, CAMBRIDGE PHRASAL VE CHAMIELEC G, 2008, RA0508 POZN U TECHN Kilgarriff A, 2007, COMPUT LINGUIST, V33, P147, DOI 10.1162/coli.2007.33.1.147 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WATERS NL, 2007, COMMUNICATION ACM, V50 2007, OXFORD PHRASAL VERBS NR 8 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-8-36-8-1014-9 PY 2008 BP 351 EP 358 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BKP15 UT WOS:000268848500057 ER PT B AU Jin, Y Lin, ZY Lin, HW AF Jin, Yi Lin, Zhuying Lin, Hongwei BA Zhou, Q BF Zhou, Q BE Luo, J TI The Research of Search Engine Based on Semantic Web SO 2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION WORKSHOP: IITA 2008 WORKSHOPS, PROCEEDINGS LA English DT Proceedings Paper CT 2nd International Symposium on Intelligent Information Technology Application CY DEC 21-22, 2008 CL Shanghai, PEOPLES R CHINA SP IEEE Comp Soc, IEEE, Intelligent Informat Technol Applicat Res, IEEE Circuits & Syst Soc AB Search engines play important roles in the success of the Web, Search engines helps any Internet user to rapidly find relevant information. But the unsolved problems of current search engines have led to the development of the Semantic Web. In the environment of Semantic Web, the search engines should be more useful and efficient for searching the relevant web information. In this paper we have presented the architecture of a semantic search engine, and our work shows how the fundamental elements of the semantic search engine can be used in the fundamental task of information retrieval. And then an improved algorithm based on TFIDF algorithm was proposed to guarantee the retrieve information resources in a more efficient way. C1 [Jin, Yi; Lin, Zhuying; Lin, Hongwei] Guizhou Normal Univ, Sch Math & Comp Sci, Guiyang 550001, Guizhou, Peoples R China. RP Jin, Y (reprint author), Guizhou Normal Univ, Sch Math & Comp Sci, Guiyang 550001, Guizhou, Peoples R China. EM genekim@126.com; gylinying@126.com CR Alani H, 2003, IEEE INTELL SYST, V18, P14, DOI 10.1109/MIS.2003.1179189 ARNOLD S, P 26 ONL INF C 2002, P51 BAEZAYATES R, 1999, MODEM INFORM RETRIEV FININ T, INFORM RETRIEVAL SEM FLORESCU D, 2000, INT J COMPUTER TELEC, V33, P119 Manevitz L., 2001, J MACHINE LEARNING R, V2, P139 Rocchio J. J., 1971, PRENTICE HALL SERIES, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THORSTEN J, 1996, P 14 INT C MACH LEAR, P78 NR 9 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3505-0 PY 2008 BP 360 EP 363 DI 10.1109/IITA.Workshops.2008.193 PG 4 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIY43 UT WOS:000263690400086 ER PT B AU Godoy, D Amandi, A AF Godoy, Daniela Amandi, Analia GP IEEE Computer Society TI Hybrid Content and Tag-based Profiles for Recommendation in Collaborative Tagging Systems SO 2008 LATIN AMERICAN WEB CONFERENCE (LA-WEB) LA English DT Proceedings Paper CT 6th Latin American Web Conference CY OCT 28-30, 2008 CL Vitoria, BRAZIL SP IEEE, Dept Informat, UFES, Brazil & Ctr Web Res, CS Dept, Univ Chile ID WEB; AGENTS; SEARCH; MODEL AB Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed. C1 [Godoy, Daniela] UNICEN Univ, ISISTAN Res Inst, RA-7000 Tandil, Buenos Aires, Argentina. RP Godoy, D (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ, RA-7000 Tandil, Buenos Aires, Argentina. EM dgodoy@exa.unicen.edu.ar; amandi@exa.unicen.edu.ar CR Au Yeung C. M., 2008, SOCIAL WEB KNOWLEDGE Cantador I., 2008, P 1 INT WORKSH COLL Carmagnola F, 2007, LECT NOTES ARTIF INT, V4511, P445 CAYZER S., 2007, P 1 INT C WEBL SOC M Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 Diederich J., 2006, P 1 INT WORKSH BUILD Farooq U., 2007, P 2007 INT ACM C SUP, P351, DOI 10.1145/1316624.1316677 Firan C., 2007, P 2007 LAT AM WEB C, P32, DOI DOI 10.1109/LA-WEB.2007.24 Godoy D, 2005, KNOWL ENG REV, V20, P329, DOI 10.1017/S0269888906000397 Godoy D, 2006, INFORM SYST, V31, P247, DOI 10.1016/j.is.2005.02.008 Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Huang Y-C., 2008, AAAI SPRING S SOC IN, P36 Kennedy L., 2008, P 17 INT C WORLD WID, P297, DOI 10.1145/1367497.1367539 Kim H., 2003, P 8 INT C INT US INT, P101 Li X, 2008, P 17 INT C WORLD WID, P675, DOI 10.1145/1367497.1367589 Li YF, 2006, IEEE T KNOWL DATA EN, V18, P554 Lieberman H, 1997, P ACM C COMP HUM INT, P67, DOI 10.1145/258549.258592 Lieberman H, 2001, COMMUN ACM, V44, P69, DOI 10.1145/381641.381661 Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 Mathes A., 2004, COMPUTER MEDIATED CO Michalski R. S., 1983, MACHINE LEARNING ART, P331 Michlmayr E., 2007, P WORKSH TAGG MET SO Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Nanas N, 2003, P 26 ANN INT ACM SIG, P198 Noll MG, 2007, LECT NOTES COMPUT SC, V4825, P367 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoyanovich J, 2008, AAAI SPRING S SOC IN, P104 SZOMSZOR M, 2007, P 4 EUR SEM WEB C BR THOMPSON K, 1991, MOR KAUF M, P127 Zukerman I, 2001, USER MODEL USER-ADAP, V11, P5, DOI 10.1023/A:1011175525451 NR 33 TC 4 Z9 4 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4244-4203-4 PY 2008 BP 58 EP 65 DI 10.1109/LA-WEB.2008.15 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJU03 UT WOS:000267157100008 ER PT B AU Anh, NT Anh, TQ Binh, NN AF Anh, Nguyen Tuan Anh, Tran Quang Binh, Nguyen Ngoc GP IEEE TI Vietnamese spam detection based on language classification SO 2008 SECOND INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND ELECTRONICS LA English DT Proceedings Paper CT 2nd International Conference on Communications and Electronics CY JUN 04-06, 2008 CL Hoi An, VIETNAM DE spam; language classification; Bayesian anti-spam; Vietnamese segmentation AB Language classification is the process of identifying the disposition of a presented text, such as classifying an email or a text document into a particular category. Classifying text can involve determining the genre of a book, categorizing a document, or in our case deciding whether an email is spam. The idea behind language classification is to teach the computer to be a filing clerk Spam filters using a Bayesian combination of the spam probabilities of individual words that employ language classification read and filter your email by learning your personal email behavior (what you think is and isn't spam). There are many spam filters written based on this technology and applied effectively for English and other languages. But they got a low effect when applied directly at Vietnamese spam. Because the token segmentation of the Bayesian filters is not suitable for Vietnamese specific characteristics. We, therefore, propose a Vietnamese segmentation for using token selection for building a Vietnamese spam filter based on language classification and Bayesian combination to sufficiently support Vietnamese. The result is very satisfactory. Thanks to this technique, our filter for Vietnamese spam is 9% more accurate when compared to other filters which use other segmentation technical. C1 [Anh, Nguyen Tuan] Hanoi Univ Technol, Lib & Informat Network Ctr, Hanoi, Vietnam. RP Anh, NT (reprint author), Hanoi Univ Technol, Lib & Informat Network Ctr, Hanoi, Vietnam. CR Androutsopoulos I., 2000, P WORKSH MACH LEARN Dinh Dien, 2001, 6 NAT LANG PROC PAC, P749 Graham P., 2002, A PLAN FOR SPAM OBRIEN C, 2003, INT S INF COMM TECHN, P298 PHAN TL, 2004, THESIS HA NOI U TECH ROBINSON G, 2003, STAT APPROACH SPAM P Sahami M, 1998, LEARNING TEXT CATEGO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tran Q.A., 2006, IJCSNS INT J COMPUTE, V6, P178 YERAZUNIS B, 2002, BETTER HUMAN Zdziarski J.A., 2005, ENDING SPAM BAYESIAN NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2425-2 PY 2008 BP 73 EP 78 PG 6 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BIE87 UT WOS:000259024400014 ER PT B AU Zamanifar, A Minaei-Bidgoli, B Kashefi, O AF Zamanifar, Azadeh Minaei-Bidgoli, Behrouz Kashefi, Omid GP IEEE TI A New Technique for Detecting Similar Documents based on Term Co-occurrence and Conceptual Property of the Text SO 2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2 LA English DT Proceedings Paper CT 3rd International Conference on Digital Information Management CY NOV 13-16, 2008 CL London, ENGLAND SP IEEE HO Univ E London ID WORDNET AB The importance of detecting similar documents grows rapidly as the amount of information increases exponentially. This paper presents a new technique for identifying similar documents. It combines statistical properties of documents with Persian linguistic features. The proposed technique is mostly suited for detecting similar documents in specific fields. The proposed method is built on lexical chain of important words and based on term co-occurrence property of the text. It prevents the irrelevant documents to be identified similar due to polysemy property of the words. It also considers the order of words in identifying the similar documents. If a document consists of more than one subject, it could also be founded and similar documents according to different topics of the text could be detected. Our results shows improved performance compared to existing word-based methods like LSI and VSM. C1 [Zamanifar, Azadeh; Minaei-Bidgoli, Behrouz; Kashefi, Omid] Iran Univ Sci & Technol, Dept Comp Engn, Tehran, Iran. EM az_zamanifar@comp.iust.ac.ir; b_minaei@iust.ac.ir; kashefi@iust.ac.ir CR CHIN OS, 2006, IEEE WIC ACM INT C W, P1046 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dey L, 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence, (WI 2006 Main Conference Proceedings), P23, DOI 10.1109/WI.2006.86 FORTUNA B, SEMIAUTOMATIC CONSTR Geng HT, 2006, Proceedings of the Fifth IEEE International Conference on Cognitive Informatics, Vols 1 and 2, P601 Hammouda K. M., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183904 LEE CS, IEEE T SYSTEMS MAN B, V35, P859 LEE JW, 2007, 4 INT C FUZZ SYST KN Lee M.D., 2005, P 27 ANN C COGN SCI, P1254 Liu Y, 2007, LECT NOTES COMPUT SC, V4489, P781 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mohanty S., 2006, P GWC, P321 OGAWA T, 2004, LECT NOTES ARTIF INT, V3215, P95 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCOTT S, P WORKSH COLING ACL, P45 SELVI P, 2007, IJCSNS, V7 Wan XJ, 2005, LECT NOTES COMPUT SC, V3689, P635 Wang JZ, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P395 NR 18 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2916-5 PY 2008 BP 537 EP 542 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BME94 UT WOS:000272049500084 ER PT B AU Johnston, A Lelli, F Decker, S AF Johnston, Ann Lelli, Francesco Decker, Stefan GP IEEE TI Using the Augmented Vector Space Model to Support the Knowledge Worker in Document Filtering SO 2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2 LA English DT Proceedings Paper CT 3rd International Conference on Digital Information Management CY NOV 13-16, 2008 CL London, ENGLAND SP IEEE HO Univ E London ID RETRIEVAL AB In the current age the discovery of relevant information is becoming extremely difficult. Users must manually search through the high-recall, low-precision results produced by current search engines. We propose a process which incorporates benefits of automated and manual information extraction. The HSC workbench (Hierarchical Step-wise Categorization) is a tool developed by the Hewlett-Packard Research Group in HP, ESC Galway. It implements this process supporting the user in information filtering tasks. automating segments of the process but giving the user control of the categorization. EM Ann.Johnston@hp.com; Francesco.Lelli@lu.unisi.ch; Stefan.Decker@deri.org CR Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 Grossman D. A., 2004, INFORM RETRIEVAL ALG O'Day V, 2001, P 7 EUR C COMP SUPP, P399 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 RAPAPORT L, 2002, TRANSFORM MAGAZI MAR, V11 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORM, P132 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2916-5 PY 2008 BP 727 EP 732 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BME94 UT WOS:000272049500113 ER PT B AU Ayodele, T Zhou, SK AF Ayodele, Taiwo Zhou, Shikun GP IEEE TI Email Reply Prediction: Unsupervised Leaning Approach SO 2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2 LA English DT Proceedings Paper CT 3rd International Conference on Digital Information Management CY NOV 13-16, 2008 CL London, ENGLAND SP IEEE HO Univ E London DE Email reply prediction; emails; interrogative words; require reply; questions AB With the ever increasing popularity of emails, email over-load and prioritization becomes a major problem for many email users. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organised and prioritized email better; email reply prediction. The goal is to provide concise, highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time. In this paper, we discuss the features used to differentiate emails, show promising initial results with unsupervised machine learning model, and outline future directions for this work. C1 [Ayodele, Taiwo; Zhou, Shikun] Univ Portsmouth, Dept Elect & Comp Engn, Portsmouth PO1 2UP, Hants, England. EM taiwo.ayodele@port.ac.uk; shikun.zhou@port.ac.uk CR Dabbish L., 2003, CHI 2003, P924 DREDZE M, 2005, CEAS 2005 2 C EM ANT Ducheneaut N., 2001, Interactions, V8, DOI 10.1145/382899.383305 Kraut R. E., 1997, CULTURE INTERNET, P323 MACKAY WE, 1988, ACM T INFORM SYST, V6, P380, DOI 10.1145/58566.58567 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sproull L., 1991, CONNECTIONS NEW WAYS SUMNER M, 1988, P ACM SIGOIS IEEECS, P96 TYLER J, 2003, P ECSCW 2003, P238 WHITTAKER S, 1996, EMAIL OVERLOAD EXPLO, P276 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2916-5 PY 2008 BP 865 EP 870 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BME94 UT WOS:000272049500134 ER PT B AU Yu, WD Wei, YT Liu, SJ Chang, PL AF Yu, Wen-der Wei, Yu-teh Liu, Shen-jung Chang, Pei-lun BE Zavadskas, EK Kaklauskas, A Skibniewski, MJ TI Preliminary study on the automatic lessons-learned file generator SO 25TH INTERNATIONAL SYMPOSIUM ON AUTOMATION AND ROBOTICS IN CONSTRUCTION - ISARC-2008 LA English DT Proceedings Paper CT 25th International Symposium on Automation and Robotics in Construction CY JUN 26-29, 2008 CL Vilnius, LITHUANIA SP Int Assoc Automat & Robit Construct, Vilnius Gediminas Tech Univ, Inst Internet & Intelligent Technologies, Int Council Res & Innovat Bldg & Construct, Lithuanian Acad Sci, Russian Acad Engn, Int Acad Engn DE text mining; lessons-learned; corpus; knowledge management AB Lessons-learned file (LLF) is commonly adopted to retain previous knowledge and experiences for future use in many construction organizations. Current practice in capturing LLF is mainly through the costly and time-consuming manual processes conducted by the construction engineers or managers. Moreover, many construction knowledge accumulated from previous projects is berried in the construction documents such as construction journals, proposals, as-built drawings, SPECs, plans, etc. It is impossible to develop LLFs from these documents manually. This paper presents the work of a preliminary attempt to develop an Automatic Lessons-Learned File Generator (ALLFG) based on text mining techniques. A prototype system is programmed. Case study is conducted to extract meaningful LLF from sample Chinese construction document automatically. Although the results are still experimental, promising potentials can be envisioned for practical applications. CR *CONSTR IND I, 1993, PUBL CONSTR IND I, V341 Dorre J., 1999, P 5 ACM SIGKDD INT C Feldman R., 1995, P 1 INT C KNOWL DISC, P112 FISHER D, 1998, 12311 CONSTR IND I KARTAM N, 1994, P COMP CIV ENG NEW Y, P1139 Kartam N., 1997, J PERFORM CONSTR FAC, V11, P178, DOI 10.1061/(ASCE)0887-3828(1997)11:4(178) Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333 Mao W., 2006, DATA KHOWL ENG, V61, P76 MCCULLOUCH B, 1994, P 1 C COMP CIV ENG, P1397 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 Pullwitt D, 2002, NEURAL NETWORKS, V15, P1099, DOI 10.1016/S0893-6080(02)00082-5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sullivan D., 2001, DOCUMENT WAREHOUSING NR 13 TC 0 Z9 0 PU VILNIUS GEDIMINAS TECHNICAL UNIV PRESS, TECHNIKA PI VILNIUS-40 PA SAULETEKIO A1. 11, VILNIUS-40, LT-10233, LITHUANIA BN 978-9955-28-304-1 PY 2008 BP 422 EP 428 DI 10.3846/isarc.20080626.422 PG 7 WC Automation & Control Systems; Construction & Building Technology; Robotics SC Automation & Control Systems; Construction & Building Technology; Robotics GA BID71 UT WOS:000258743700060 ER PT J AU Losada, DE Azzopardi, L AF Losada, David E. Azzopardi, Leif TI Assessing multivariate Bernoulli models for information retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE theory; experimentation; information retrieval; language models; multinomial; multivariate Bernoulli AB Although the seminal proposal to introduce language modeling in information retrieval was based on a multivariate Bernoulli model, the predominant modeling approach is now centered on multinomial models. Language modeling for retrieval based on multivariate Bernoulli distributions is seen inefficient and believed less effective than the multinomial model. In this article, we examine the multivariate Bernoulli model with respect to its successor and examine its role in future retrieval systems. In the context of Bayesian learning, these two modeling approaches are described, contrasted, and compared both theoretically and computationally. We show that the query likelihood following a multivariate Bernoulli distribution introduces interesting retrieval features which may be useful for specific retrieval tasks such as sentence retrieval. Then, we address the efficiency aspect and show that algorithms can be designed to perform retrieval efficiently for multivariate Bernoulli models, before performing an empirical comparison to study the behaviorial aspects of the models. A series of comparisons is then conducted on a number of test collections and retrieval tasks to determine the empirical and practical differences between the different models. Our results indicate that for sentence retrieval the multivariate Bernoulli model can significantly outperform the multinomial model. However, for the other tasks the multinomial model provides consistently better performance (and in most cases significantly so). An analysis of the various retrieval characteristics reveals that the multivariate Bernoulli model tends to promote long documents whose nonquery terms are informative. While this is detrimental to the task of document retrieval (documents tend to contain considerable nonquery content), it is valuable for other tasks such as sentence retrieval, where the retrieved elements are very short and focused. C1 [Losada, David E.] Univ Santiago de Compostela, Dept Electron & Computac, Grp Sistemas Inteligentes, Santiago De Compostela, Spain. [Azzopardi, Leif] Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Losada, DE (reprint author), Univ Santiago de Compostela, Dept Electron & Computac, Grp Sistemas Inteligentes, Santiago De Compostela, Spain. EM dlosada@usc.es; leif@dcs.gla.ac.uk RI Losada, David/A-3202-2009 CR Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 AMATI G, 2006, P 28 EUR C INF RETR, P13 AZZOPARDI L, 2005, THESIS U PAISLEY GLA AZZOPARDI L, 2006, P 28 EUR C INF RETR, P480 AZZOPARDI L, 2007, P INT C THEOR INF RE, P125 BAILLIE M, 2007, P 29 EUR C INF RETR Bernardo JM, 1994, BAYESIAN THEORY Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000 CALLAN J, 2001, ACM T INFORM SYST, V19, P2 Callan J, 2001, ACM T INFORM SYST, V19, P97, DOI 10.1145/382979.383040 Croft W. B., 2000, ADV INFORM RETRIEVAL Croft W. B., 2003, LANGUAGE MODELING IN DEGROOT MH, 1988, PROBABILITY STAT Harman D, 2002, P 11 TEXT RETR C TRE Hauff C, 2005, LECT NOTES COMPUT SC, V3408, P552 Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hiemstra D., 2001, THESIS U TWENTE ENSC KAMPS J, 2005, P ACM C INF KNOWL MA Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval KRAAIJ W, 2004, THESIS U TWENTE ENSC LAPLACE P, 1995, SOURCES HIST MATH PH LARKEY L, 2002, P 11 TEXT RETR C TRE Lavrenko V., 2004, THESIS U MASSACHUSET *LEMUR, 2008, LEM TOOLK Lidstone George James, 1920, T FACULTY ACTUARIES, V8, P182 LOSADA D, 2008, INF RETRIEV IN PRESS Losada D, 2005, INF RETR THEOR WORKS MANNING CD, 2000, FDN STAT LANGUAGE PR McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Metzler D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009110 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Murdock V, 2006, THESIS U MASSACHUSET Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROSENFELD R, 2000, P IEEE, V88, P8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soboroff I, 2004, P 13 TEXT RETR C TRE Soboroff Ian, 2003, P 12 TEXT RETR C TRE Stokes N, 2004, LECT NOTES COMPUT SC, V2997, P209 Voorhees E, 1999, P 8 TEXT RETR C TREC Voorhees Ellen M., 2005, TREC EXPT EVALUATION Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 Zhai, 2002, THESIS CARNEGIE MELL Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 NR 51 TC 8 Z9 8 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PY 2008 VL 26 IS 3 AR 17 DI 10.1145/1361684.1361690 PG 46 WC Computer Science, Information Systems SC Computer Science GA 317OC UT WOS:000257028500006 ER PT S AU Nurnberger, A Stober, S AF Nuernberger, Andreas Stober, Sebastian BE Boujemaa, N Detyniecki, M Nurnberger, A TI User modelling for interactive user-adaptive collection structuring SO ADAPTIVE MULTIMEDIAL RETRIEVAL: RETRIEVAL, USER, AND SEMANTICS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop on Adaptive Multimedia Retrieval CY JUL 05-06, 2007 CL Paris, FRANCE HO Univ Pierre & Marie Curie ID VECTOR-SPACE MODEL; TEXT RETRIEVAL AB Automatic structuring is one means to ease access to document collections, be it for organization or for exploration. Of even greater help would be a presentation that adapts to the user's way of structuring and thus is intuitively understandable. We extend an existing user-adaptive prototype system that is based on a growing self-organizing map and that learns a feature weighting scheme from a user's interaction with the system resulting in a personalized similarity measure. The proposed approach for adapting the feature weights targets certain problems of previously used heuristics. The revised adaptation method is based on quadratic optimization and thus we are able to pose certain contraints on the derived weighting scheme. Moreover, thus it is guaranteed that an optimal weighting scheme is found if one exists. The proposed approach is evaluated by simulating user interaction with the system on two text datasets: one artificial data set that is used to analyze the performance for different user types and a real world data set - a subset of the banksearch dataset - containing additional class information. C1 [Nuernberger, Andreas; Stober, Sebastian] Otto VonGuericke Univ Magdegurg, Inst Knowledge & Language Engn, Fac Comp Sci, D-39106 Magdeburg, Germany. RP Nurnberger, A (reprint author), Otto VonGuericke Univ Magdegurg, Inst Knowledge & Language Engn, Fac Comp Sci, D-39106 Magdeburg, Germany. RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR GREIFF WR, 1998, 21 ANN INT ACM SIGIR Hotho A., 2005, GLDV J COMPUTATIONAL, V20, P19 Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 NURNBERGER A, 2003, ARTIFICIAL NEURAL NE NURNBERGER A, 2002, P 9 INT C INF PROC M Nurnberger A, 2006, APPL SOFT COMPUT, V6, P357, DOI 10.1016/j.asoc.2005.11.008 Porter M.F., 1980, ALGORITHM SUFFIX STR, P130 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 SINKA MP, 2002, SOFT COMPUTING SYSTE, V87, P881 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-79859-0 J9 LECT NOTES COMPUT SC PY 2008 VL 4918 BP 95 EP 108 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BIA97 UT WOS:000258024200008 ER PT J AU Velasquez, JD Palade, V AF Velasquez, JD Palade, V TI Adaptive Web Sites: A Knowledge Extraction from Web Data Approach SO ADAPTIVE WEB SITES: A KNOWLEDGE EXTRACTION FROM WEB DATA APPROACH SE Frontiers in Artificial Intelligence and Applications LA English DT Book ID SOFT COMPUTING FRAMEWORK; WORLD-WIDE-WEB; OF-THE-ART; RECOMMENDER SYSTEMS; VISITOR BEHAVIOR; TEXT CATEGORIZATION; SOCIAL NETWORKS; DATA MARTS; PERSONALIZATION; CLASSIFICATION CR Aas K., 1999, TEXT CATEGORISATION Abraham Ajith, 2003, P IEEE C EV COMP CEC, P1384 Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99 Adomavicius G, 2005, COMMUN ACM, V48, P83, DOI 10.1145/1089107.1089109 Adriaans P., 1996, DATA MINING AGRAWAL R, 1997, P 13 INT C DAT ENG I Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Agrawal R., 1994, P 20 INT C VER LARG, P487 Amitay E., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354816 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Ardissono L, 2001, AI COMMUN, V14, P129 ASIRVATHAM AP, 2003, WEB PAGE CATEGORIZAT Baeza-Yates R., 2004, WEB MINING APPL TECH, P307 Baeza-Yates R., 2004, WEB MINING APPL TECH Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAEZAYATES RA, 2005, P 27 ECIR SANT COMP, P7 BALL GH, 1967, BEHAV SCI, V12, P153, DOI 10.1002/bs.3830120210 Batty M, 1997, INT PLANNING STUDIES, V2, P155, DOI 10.1080/13563479708721676 Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 BERENDT B, 2002, 1 INT SEM WEB C, P264 Berendt B, 2000, VLDB J, V9, P56, DOI 10.1007/s007780050083 BERENDT B, 2002, ECMML PKDD C AUG BERNERSLEE T, 1994, COMMUN ACM, V37, P76, DOI 10.1145/179606.179671 Berry M. J. A., 2000, MASTERING DATA MININ Berry M. J. A., 1997, DATA MINING TECHNIQU Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Bertsimas D, 2005, SIAM J OPTIMIZ, V15, P780, DOI 10.1137/S1052623401399903 BESTAVROS A, 1995, P 4 INT C ACM INT C, P782 BHOWMICK S, 1998, P INT WORKSH DAT WAR, P93 BOEHNLEIN M, 2004, P INT ACM 2 INT WORK, P15 Bonchi F, 2001, DATA KNOWL ENG, V39, P165, DOI 10.1016/S0169-023X(01)00038-6 Bonifati A, 2001, ACM T SOFTW ENG METH, V10, P452, DOI 10.1145/384189.384190 Bonino D., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) BORGES J, 1999, WORKSH WEB US AN US, P31 BOURAS C, 2001, P 2 INT C INT COMP I, V2, P238 Boving K. B., 2004, Scandinavian Journal of Information Systems, V16 Breiman L., 1984, CLASSIFICATION REGRE Brusilovsky P, 1996, USER MODEL USER-ADAP, V6, P87, DOI 10.1007/BF00143964 BRUSILOVSKY P, 2003, IEEE WEB INT INT C O Burges C., 1998, DATA MIN KNOWL DISC, V2, P1, DOI DOI 10.1023/A:1009715923555 Buttler D., 2004, P INT C INT COMP LAS, P3 Buyukkokten O., 2001, P 10 INT C WORLD WID, P652, DOI 10.1145/371920.372178 Cadoli M, 1997, AI COMMUN, V10, P137 CATLEDGE LD, 1995, COMPUT NETWORKS ISDN, V27, P1065, DOI 10.1016/0169-7552(95)00043-7 CAVERO JM, 2004, MANAGING DATA MINING, P188 Chakrabart S., 2000, SIGKDD EXPLORATIONS, V1, P1 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S., 1998, P 7 INT WORLD WID WE, P65 Chakrabarti S., 1999, IEEE COMPUTER, V32 Chan PK, 2000, LECT NOTES COMPUT SC, V1836, P39 Chang G., 2003, MINING WORLD WIDE WE Chaudhuri S., 1997, SIGMOD Record, V26 Chen MS, 1998, IEEE T KNOWL DATA EN, V10, P209 Chenoweth T, 2003, COMMUN ACM, V46, P93, DOI 10.1145/953460.953465 CHUANG WT, 2000, P INT C ACM SIGIR AT Codd E., 1993, PROVIDING OLAP ON LI, P32 CODD EF, 1982, COMMUN ACM, V25, P109, DOI 10.1145/358396.358400 COENEN F, 2000, P 4 PAKDD PAC AS C K Cohen MD, 2001, INTERFACES, V31, P109, DOI 10.1287/inte.31.2.109.10625 COLLIAT S, 1996, SIGMOD REC, V25, P64 Cooley R., 1997, Proceedings. 1997 IEEE Knowledge and Data Engineering Exchange Workshop (Cat. No.97TB100208), DOI 10.1109/KDEX.1997.629824 Cooley R., 2000, THESIS U MINNESOTA Cooley R., 1999, J KNOWLEDGE INFORM S, V1, P5 CORREIA N, 2001, WORKSH PERS EL PUBL Cranor L. F., 2003, P 2003 ACM WORKSH PR, P111, DOI DOI 10.1145/1005140.1005158 Datta A, 2001, VLDB J, V10, P104 DAVIS R, 1993, AI MAG, V14, P17 Davison B. D., 2002, P 13 ACM C HYP HYP H, P159 DAVISON BD, 2003, P 12 INT WORLD WID W Davison BD, 2001, IEEE INTERNET COMPUT, V5, P38, DOI 10.1109/4236.939449 DEBENHAM J, 2001, P 12 INT C DAT EXP S, P599 DELLMANN F, 2004, STAT ANAL WEB LOG FI DENNING PJ, 1982, COMMUN ACM, V25, P163, DOI 10.1145/358453.358454 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 DEVEDZIC V, 2002, KNOWLEDGE DISCOVERY Dhyani D, 2002, ACM COMPUT SURV, V34, P469, DOI 10.1145/592642.592645 Dikaiakos M, 2003, LECT NOTES COMPUT SC, V2738, P369 DUCHAMP D, 1999, P 2 INT USENIX S INT Duda R., 2000, PATTERN CLASSIFICATI, V2nd Dunkel B, 1997, FUTURE GENER COMP SY, V13, P231, DOI 10.1016/S0167-739X(97)00023-X Eirinaki M, 2003, ACM T INTERNET TECHN, V3, P1, DOI DOI 10.1145/643477.643478 Famili A., 1997, INTELL DATA ANAL, V1, P3, DOI 10.1016/S1088-467X(98)00007-9 Fayyad U., 1996, P 2 INT C KNOWL DISC, P82 Fayyad U, 1996, AI MAG, V17, P37 Feldman R., 1995, P 1 INT C KNOWL DISC, P112 Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 FISHER DH, 1996, J ARTIFICIAL INTELLI, P147 Flake G. W., 2000, KDD, P150 Flake GW, 2002, COMPUTER, V35, P66, DOI 10.1109/2.989932 Foster I, 1998, GRID BLUEPRINT NEW C FRAWLEY WJ, 1992, AI MAG, V13, P57 Fulkerson D. R., 1956, CAN J MATH, V8, P399, DOI 10.4153/CJM-1956-045-5 Gallaugher JM, 1996, INFORM SYST MANAGE, V13, P7, DOI 10.1080/10580539608906981 Gefen D., 2002, J ASSOC INF SYST, V3, P27 Gibson D., 1998, P 9 ACM C HYP HYP, P225, DOI 10.1145/276627.276652 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Golfarelli M, 1999, J COMPUTER SCI INFOR, V2, P1 Gyssens M, 1997, VLDB 97, P106 Ha SH, 2002, IEEE INTELL SYST, V17, P34 Hagen P., 1999, SMART PERSONALIZATIO Hahn U, 2000, COMPUTER, V33, P29, DOI 10.1109/2.881692 Han E., 1997, WORKSH RES ISS DAT M, P9 Hartigan J, 1979, J ROYAL STAT SOC C, V28, P100, DOI DOI 10.2307/2346830 Hay B, 2004, KNOWL INF SYST, V6, P150, DOI 10.1007/s10115-003-0109-6 HINNEBURG A, 2003, ICDM INT C MELB FLOR HOFMANN T, 1999, 6 INT JOINT C ART IN, P682 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 Hu XH, 2004, INT J INTELL SYST, V19, P585, DOI 10.1002/int.20012 HUANG Z, 2001, P WEBKDD 2001, P47 HURTADO CA, 1999, INT WORKSH DAT WAR O, P60 Inmon W., 1996, BUILDING DATA WAREHO, V2nd IVES B, 1984, COMMUN ACM, V27, P1193, DOI 10.1145/2135.2137 Iwayama M., 1995, P 18 ANN INT ACM SIG, P273, DOI 10.1145/215206.215371 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jang J-SR, 1997, NEUROFUZZY SOFT COMP Ji Junzhong, 2003, P 2003 IEEE WIC INT, P68 JIANG Z, 2000, RETRIEVER IMPROVING Joachims T, 1998, EUR C MACH LEARN ECM, P137 Joachims T., 1997, P 15 INT JOINT C ART, P770 Johansen A, 2000, EUR PHYS J B, V17, P319, DOI 10.1007/s100510070147 Joshi A., 2000, P ACM SIGMOD WORKSH, P63 KARYPIS G, 1999, IEEE COMPUT, V32, P68, DOI DOI 10.1109/2.781637 Kilfoil M., 2003, P ANN C COMM NETW SE, P119 Kim W., 2002, J OBJECT TECHNOLOGY, V1, P29 Kimball R., 1998, DATA WAREHOUSE LIFEC Kimball Ralph, 2000, DATA WEBHOUSE TOOLKI Kleinberg J., 1999, LECT NOTES COMPUTER, V1627, P1, DOI DOI 10.1007/3-540-48686-0_1 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kobsa A, 2001, KNOWL ENG REV, V16, P111, DOI 10.1017/S0269888901000108 Kobsa A., 2001, P US MOD 2001 8 INT, P303 KODRATOFF Y, 1995, P INT C ALG LEARN TH, V997 Kohonen T., 1987, SELF ORG ASS MEMORY, V2nd Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 Kotler P, 2000, MARKETING INTRO KOUTRI M, 2002, P 4 INT WORKSH COMP Koutri M, 2004, ADAPTABLE ADAPTIVE H Kraiss A, 1998, VLDB J, V7, P141, DOI 10.1007/s007780050060 Kumar R, 2002, COMPUTER, V35, P32, DOI 10.1109/MC.2002.1046971 KUMAR SR, 1998, P 39 IEEE S FDN COMP, P664 Kwon OW, 2003, INFORM PROCESS MANAG, V39, P25, DOI 10.1016/S0306-4573(02)00022-5 Lagus K, 2004, INFORM SCIENCES, V163, P135, DOI 10.1016/j.ins.2003.03.017 Lawrie D, 2001, P 24 ANN INT ACM SIG, P349, DOI 10.1145/383952.384022 Lee JH, 2004, ADV ENG INFORM, V18, P129, DOI 10.1016/j.aei.2004.09.007 Levene M, 2003, INFORM SYST, V28, P225, DOI 10.1016/S0306-4379(02)00021-2 LEVENSHTEIN VI, 1966, SOV PHYS DOKL, P705 LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Li QB, 2003, LECT NOTES COMPUT SC, V2822, P16 LI S, 2002, 2003E25 GSIA CARN ME LIDDY ED, 1993, P INT C ARPA WORKSH Linoff G. S., 2001, MINING WEB Loh S., 2000, SIGKDD EXPLORATIONS, V2, P29 LU Z, 2003, WEB INTELLIGENCE Mani I., 1999, ADV AUTOMATIC TEXT S Martyn T, 2004, SIGMOD RECORD, V33, P83 MCCALLUM A, 1998, AAAI 98 WORKSH LEARN, P349 Meuter ML, 2000, J MARKETING, V64, P50, DOI 10.1509/jmkg.64.3.50.18024 MIKA P, 2004, WEB INTELLIGENCE, P285 Mitra S, 2002, IEEE T NEURAL NETWOR, V13, P3, DOI 10.1109/72.977258 Mobasher B., 1999, P INT C IEEE KNOWL D Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Mobasher B, 2000, P INT C E COMM WEB T, P165 MOBASHER B, 2001, KDD C SEPT Moody D. L., 2000, P 2 INT WORKSH DMDW, P1 MORTAZAVIASL B, 2001, THESIS S FRASER U CA Muller KR, 2001, IEEE T NEURAL NETWOR, V12, P181, DOI 10.1109/72.914517 Mulvenna MD, 2000, COMMUN ACM, V43, P123 MYATT GJ, 2007, MAKING SENSE DATA Ngu DSW, 1997, COMPUT NETWORKS ISDN, V29, P1249 Nielsen J, 2006, QUANTITATIVE STUDIES Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 *OR CORP, 2002, OR ED OR 9I ADM GUID Padmanabhan V. N., 1996, ACM SIGCOMM COMPUTER, V26, P22, DOI 10.1145/235160.235164 Page L, 1998, PAGERANK CITATION RA Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 PATTERSON DA, 1998, P ACM SIGMOD INT C M, P109 Pazzani M. J., 1999, Proceedings of the Third International Conference on Autonomous Agents, DOI 10.1145/301136.301256 Pereira Jr A.R., 2004, J WEB ENG, V2, P247 PERKOWITZ M, 2001, THESIS U WASHINGTON Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 Perkowitz M., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence PERUGINI S, 2002, RECOMMENDATION PERSO Pierrakos D, 2003, USER MODEL USER-ADAP, V13, P311, DOI 10.1023/A:1026238916441 PINC BJ, 1993, MASS CUSTOMIZATION PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Riecken D, 2000, COMMUN ACM, V43, P27 Rios S. A., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence RIOS SA, 2006, INT J HYBRID INTELLI, V3, P219 Rios S.A., 2006, INT J COMPUTATIONAL, V1, P159 Rosenblatt F., 1962, PRINCIPLES NEURODYNA Runkler TA, 2003, INT J APPROX REASON, V32, P217, DOI 10.1016/S0888-613X(02)00084-1 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAPIA C, 1999, GI WORKSH DAT MIN DA Schafer J. B., 2001, Data Mining and Knowledge Discovery, V5, DOI 10.1023/A:1009804230409 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 SCHWARZKOPF E, 2001, P UM2001 WORKSH MACH, P77 SEBASTIANI F, 1999, P ASAI 99 1 ARG S AR Sen A, 2005, COMMUN ACM, V48, P79, DOI 10.1145/1047671.1047673 Silverman BG, 2001, INT J HUM-COMPUT ST, V55, P815, DOI 10.1006/ijhc.2001.0500 SOW DM, 2003, P INT C IFIP ACM USE, P262 Spiliopoulou M, 2003, INFORMS J COMPUT, V15, P171, DOI 10.1287/ijoc.15.2.171.14445 Spiliopoulou M, 1999, LECT NOTES ARTIF INT, V1704, P588 Srivastava J., 2000, SIGKDD EXPLORATIONS, V1, P12 Staab S, 2005, IEEE INTELL SYST, V20, P80, DOI 10.1109/MIS.2005.16 Strehl A, 2000, P SOC PHOTO-OPT INS, V4057, P33, DOI 10.1117/12.381756 Strehl A., 2000, WORKSH ART INT WEB S, P58 Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 TESTE O, 2001, 5 E EUR C ADV DAT IN, P25 Theodoridis S., 1999, PATTERN RECOGNITION THOR A, 2004, P INT C VLDB, P384, DOI 10.1016/B978-012088469-8/50036-X Tonella P., 2001, Proceedings 3rd International Workshop on Web Site Evolution. WSE 2001, DOI 10.1109/WSE.2001.988780 Tonella P, 2002, PROC IEEE INT CONF S, P290, DOI 10.1109/ICSM.2002.1167784 TOWELL GG, 1994, ARTIF INTELL, V70, P119, DOI 10.1016/0004-3702(94)90105-8 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 Vapnik V., 1979, ESTIMATION DEPENDENC Vapnik V., 1995, NATURE STAT LEARNING Vassiliadis P, 2002, P 14 INT C ADV INF S, P782 Vassiliadis P., 1999, SIGMOD Record, V28 VASSILIOU C, 2002, P INT C ADV INFR EL VASSILIOU C, 2003, CREATING ADAPTIVE WE, P261 VELASQUEZ J, 2002, P 2 INT C HYBR INT S, P480 Velasquez J. D., 2004, Proceedings. 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service Velasquez JD, 2005, IEICE T INF SYST, VE88D, P993, DOI 10.1093/ietisy/e88-d.5.993 VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 VELASQUEZ JD, 2003, P 7 INT C KNOWL BAS, V1, P951 Velasquez JD, 2007, INT J ARTIF INTELL T, V16, P793, DOI 10.1142/S0218213007003552 Velasquez JD, 2004, LECT NOTES COMPUT SC, V3213, P140 Velasquez J. D., 2003, Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003) Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 Velasquez JD, 2003, LECT NOTES ARTIF INT, V2773, P951 VELASQUEZ JD, 2007, P INT WORKSH INT WEB VELASQUEZ JD, 2004, THESIS U TOKYO JAPAN VELASQUEZ JD, 2004, P 3 IEEE WIC INT C W, P636 VELASQUEZ JD, 2003, DATA MINING, P389 Velasquez JD, 2006, LECT NOTES ARTIF INT, V4253, P487 VELASQUEZ JD, 2003, P 3 IEEE INT C DAT M, P699 Velasquez JD, 2007, KNOWL-BASED SYST, V20, P238, DOI 10.1016/j.knosys.2006.05.015 VELASQUEZ JD, 2004, P 4 IEEE INT C DAT M, P551 VELASQUEZ JD, 2003, P INT C COMP COMM CO, V4, P64 Wang JQ, 2002, PROCEEDINGS OF 2002 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, VOLS I AND II, P222 Wen Ji rong, 2001, WWW, P162 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 WILLIAMS GJ, 1996, TRDM96013 CSIRO DIV Wong C., 2001, WORKSH SOFT COMP CAS Wu Y., 2002, WORLD WIDE WEB, V5, P67, DOI 10.1023/A:1015750423727 Xiao J., 2001, ADC 01, P107 YANG Q, 2001, KNOWLEDGE DISCOVERY, P473 Yuan F, 2004, LECT NOTES COMPUT SC, V3213, P1030 Zechner K., 1996, P 16 INT C COMP LING, P986 Zhang T, 2002, J MACH LEARN RES, V2, P313, DOI 10.1162/153244302760200641 ZHENG T, 2005, P 19 INT C ART INT W, P80 Zhong N, 2003, LECT NOTES ARTIF INT, V2663, P1 ZHU J, 2002, P INT C ACM SIGWEB H, P298 NR 257 TC 23 Z9 23 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-58603-831-1 J9 FRONT ARTIF INTEL AP PY 2008 VL 170 BP 1 EP 272 PG 272 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA BMH44 UT WOS:000272350700011 ER PT S AU Alfred, R AF Alfred, Rayner BE Tang, C Ling, CX Zhou, XF Cercone, NJ Li, X TI A Genetic-Based Feature Construction Method for Data Summarisation SO ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 4th International Conference on Advanced Data Mining and Applications CY OCT 08-10, 2008 CL Chengdu, PEOPLES R CHINA SP Natl Sci Fdn China, Sichuan, Univ, WiseSoft Co Ltd DE Feature Construction; Data Summarisation; Genetic Algorithm; Clustering ID INDUCTION AB The importance of input representation has been recognised already in machine learning This paper discusses the application of genetic-based feature construction methods to generate input, data for the data summarisation method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied ill order to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarise data stored ill the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored ill a target, table, This paper addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features. C1 Univ Malaysia Sabah, Sch Informat Technol & Engn, Kota Kinabalu 88999, Sabah, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Sch Informat Technol & Engn, Locked Bag 2073, Kota Kinabalu 88999, Sabah, Malaysia. RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR ALFRED R, 2006, 2 ADMA INT C, P889 Amaldi E, 1998, THEOR COMPUT SCI, V209, P237, DOI 10.1016/S0304-3975(97)00115-1 BENSUSAN H, 1996, ICML 1996 BLOCKEEL H, 1999, TILDE WARMR USER MAN DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Frank E., 1999, DATA MINING PRACTICA Freitas AA, 2001, ARTIF INTELL REV, V16, P177, DOI 10.1023/A:1011996210207 HOLLAND JH, 1975, ADAPTATION NATURAL A Hu Y-J, 1998, P 3 ANN GEN PROGR C, P146 HU YJ, 1996, AAAI IAAI, V1, P806 KOZA JR, 1994, STAT COMPUTING, V4 Krawiec K., 2002, Genetic Programming and Evolvable Machines, V3, DOI 10.1023/A:1020984725014 Lavrac N., 2001, ACM T COMPUT LOG, V2, P458, DOI 10.1145/383779.383781 Otero FEB, 2003, LECT NOTES COMPUT SC, V2610, P384 PAGALLO G, 1990, MACH LEARN, V5, P71, DOI 10.1023/A:1022611825350 Quinlan Ross, 1993, M KAUFMANN SERIES MA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafti LS, 2003, LECT NOTES COMPUT SC, V2810, P599, DOI 10.1007/978-3-540-45231-7_55 Shannon C. E., 1948, BELL SYSTEM TECHNICA, V27 Srinivasan A, 1996, ARTIF INTELL, V85, P277, DOI 10.1016/0004-3702(95)00122-0 Vafaie H, 1998, IEEE INTELL SYST APP, V13, P57, DOI 10.1109/5254.671093 Wiener N., 2000, CYBERNETICS CONTROL ZHENG Z, 1996, ICTAI, P254 Zheng ZJ, 2000, MACH LEARN, V40, P35, DOI 10.1023/A:1007626017208 NR 24 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-88191-9 J9 LECT NOTES ARTIF INT PY 2008 VL 5139 BP 39 EP 50 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BIL02 UT WOS:000260453000005 ER PT S AU Alfred, R AF Alfred, Rayner BE Tang, C Ling, CX Zhou, XF Cercone, NJ Li, X TI Rules Extraction Based on Data Summarisation Approach Using DARA SO ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 4th International Conference on Advanced Data Mining and Applications CY OCT 08-10, 2008 CL Chengdu, PEOPLES R CHINA SP Natl Sci Fdn China, Sichuan, Univ, WiseSoft Co Ltd AB This paper helps the understanding and development of a data summarisation approach that summarises structured data stored in a non-target table that has many-to-one relations with the target table. In this paper, the feasibility of data summarisation techniques, borrowed from the Information Retrieval Theory, to summarise patterns obtained from data summarisation techniques, borrowed from the Information Retrieval Theory, to summarise patterns obtained from data stored across multiple tables with one-to-many relations is demonstrated. The paper describes the Dynamic Aggregation of Relational Attributes (DARA) framework, which summarises data stored in non-target tables in order to facilitate data modelling efforts in a multi-relational setting. The application of the DARA algorithm involving structured data is presented in order to show the adaptability of this algorithm involving structured data is presented in order to show the adaptability of this algorithm to real world problems. C1 Univ Malaysia Sabah, Sch Engn & Informat Technol, Sabah 88999, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Sch Engn & Informat Technol, Locked Bag 2073, Sabah 88999, Malaysia. RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR Alfred R, 2006, LECT NOTES ARTIF INT, V4093, P889 ALFRED R, 2007, 11 E EUR C ADV DAT I, P55 ALFRED R, 2008, 2 AS INT C MOD SIM A BLATAK J, 2004, P ECML PKDD 2004 CHA CLAUDIA P, 2006, MACH LEARN, V62, P65 CLAUDIA P, 2003, KDD, P167 CORREA E, 2004, P ECML PKDD 2004 CHA COUTURIER O, 2004, P ECML PKDD 2004 CHA Frank E., 1999, DATA MINING PRACTICA Knobbe A.J., 2001, LNCS LNAI, V2168, P277 KUENTZER J, 2007, BMC BIOINFORMATICS, V8 Salton G., 1984, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOON SC, 1996, INTEGRATION RELATION Tremblay MC, 2007, DECIS SUPPORT SYST, V43, P1305, DOI 10.1016/j.dss.2006.02.008 VANASSCHE A, 2003, P ECML PKDD 200O WOR, P108 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-88191-9 J9 LECT NOTES ARTIF INT PY 2008 VL 5139 BP 540 EP 547 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BIL02 UT WOS:000260453000053 ER PT S AU van Dongen, B Dijkman, R Mendling, J AF van Dongen, Boudewijn Dijkman, Remco Mendling, Jan BE Bellahsene, Z Leonard, M TI Measuring similarity between business process models SO ADVANCED INFORMATION SYSTEMS ENGINEERING, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 20th International Conference on Advanced Information Systems Engineering CY JUN 16-20, 2008 CL Montpellier, FRANCE SP Google, MICROSOFT, European Res Consort Informat & Math, Univ Montpellier 2, LIRMM, Lab Informat Robot Microelect Montpellier, Ctr Natl Rech Sci DE business process modeling; event-driven process chains; similarity; equivalence ID VIEW INTEGRATION; PROCESS DESIGN; LANGUAGE; BEHAVIOR AB Quality aspects become increasingly important when business process modeling is used in a large-scale enterprise setting. In order to facilitate a storage without redundancy and an efficient retrieval of relevant process models in model databases it is required to develop a theoretical understanding of how a degree of behavioral similarity can be defined. In this paper we address this challenge in a novel way. We use causal footprints as an abstract representation of the behavior captured by a process model, since they allow us to compare models defined in both formal modeling languages like Petri nets and informal ones like EPCs. Based on the causal footprint derived from two models we calculate their similarity based on the established vector space model from information retrieval. We validate this concept with an experiment using the SAP Reference Model and an implementation in the ProM framework. C1 [van Dongen, Boudewijn; Dijkman, Remco] Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands. RP van Dongen, B (reprint author), Eindhoven Univ Technol, NL-5600 MB Eindhoven, Netherlands. RI van Dongen, Boudewijn/C-2133-2011; Dijkman, Remco/B-2721-2014; Mendling, Jan/ OI Mendling, Jan/0000-0002-7260-524X CR BAEZAYATES R, 1999, MODEM INFORM RETRIEV Becker J., 2000, BUSINESS PROCESS MAN, P30 Biskup J, 1998, LECT NOTES COMPUT SC, V1358, P29 Dijkman R, 2007, P 11 IEEE INT ENT DI, P37 Eertink H, 1999, LECT NOTES COMPUT SC, V1708, P76 Ehrig M., 2007, P 4 AS PAC C CONC MO, P71 Green P, 2000, INFORM SYST, V25, P73, DOI 10.1016/S0306-4379(00)00010-7 Grossmann G, 2005, LECT NOTES COMPUT SC, V3649, P186 JOHNSON R, 1994, P ACM SIGPLAN 94 C P, V29, P171 Keller G., 1992, SEMANTISCHE PROZESSM Klein M, 2004, IEEE INTERNET COMPUT, V8, P30, DOI 10.1109/MIC.2004.1260701 Krogstie J, 2006, EUR J INFORM SYST, V15, P91, DOI 10.1057/palgrave.ejis.3000598 Levenshtein I. V., 1966, CYBERNETICS CONTROL, V10, P707 Manna Z., 1991, TEMPORAL LOGIC REACT Mendling J, 2006, LECT NOTES COMPUT SC, V4103, P55 Mendling J, 2007, LECT NOTES COMPUT SC, V4495, P439 Momotko M, 2004, LECT NOTES COMPUT SC, V3255, P306 PANKRATIUS V, 2005, FORMAL FDN WORKFLOW PESIC M, 2007, CONSTRAINT BASED WOR, P77 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Preuner G, 2001, DATA KNOWL ENG, V36, P153, DOI 10.1016/S0169-023X(00)00043-4 Recker J, 2006, LECT NOTES COMPUT SC, V4001, P369 Rosemann M., 2006, BUSINESS PROCESS MAN, V12, P377, DOI 10.1108/14637150610668024 Rosemann M, 2007, INFORM SYST, V32, P1, DOI 10.1016/j.is.2005.05.003 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van der Aalst WMP, 2006, LECT NOTES COMPUT SC, V4102, P129 VANDONGEN BF, 2007, 233 BETA EINDH U TEC VANDONGEN BF, 2006, P 10 IEEE INT ENT DI, P116 NR 28 TC 55 Z9 56 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-69533-2 J9 LECT NOTES COMPUT SC PY 2008 VL 5074 BP 450 EP 464 PG 15 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BHW75 UT WOS:000257089900034 ER PT S AU Zhao, X Jiang, ZL AF Zhao, Xu Jiang, Zongli BE Huang, DS Wunsch, DC Levine, DS Jo, KH TI An indexing matrix based retrieval model SO ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF THEORETICAL AND METHODOLOGICAL ISSUES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 4th International Conference on Intelligent Computing CY SEP 15-18, 2008 CL Shanghai, PEOPLES R CHINA ID INFORMATION AB The Latent Semantic Indexing, based standard retrieval usually computes semantic similarity between the query and every document, which is sensitive to the noise. Furthermore, it will take much more time when the scale of the corpus becomes larger. We propose a retrieval method which is based on an indexing matrix. The noises can be wiped off at a certain extent and it can get a better result than the traditional ways. The time cost of our method is much less than the standard retrieval method. RP Zhao, X (reprint author), Beijing Univ Technol, Lab Comp Software & Theory, PingLeYuan 100, Beijing, Peoples R China. CR Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 CRISTIANINI N, 2000, LATENT SEMANTIC KERN DEBOLE F, 2004, SAC 2003, P784 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shawe-Taylor J., 2004, KERNEL METHODS PATTE TAN S, 2005, NOVEL REFINEMENT APP, P469 TAN S, 2005, NOVEL REFINEMENT APP [唐ē„•ēŽ² Tang Huanling], 2005, [č®”ē®—ęœŗē ”ē©¶äøŽå‘展, Journal of Computer Research and Development], V42, P47, DOI 10.1360/crad20050106 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-87440-9 J9 LECT NOTES COMPUT SC PY 2008 VL 5226 BP 1001 EP 1008 PG 8 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BIH57 UT WOS:000259555200123 ER PT B AU Mesleh, AM AF Mesleh, Abdelwadood Moh'd BE Sobh, T TI Support Vector Machines Based Arabic Language Text Classification System: Feature Selection Comparative Study SO ADVANCES IN COMPUTER AND INFORMATIOM SCIENCES AND ENGINEERING LA English DT Proceedings Paper CT International Conference on Systems, Computing Sciences and Software Engineering CY MAY 31, 2007 CL Bridgeport, CT SP IEEE, Univ Bridgeport HO Univ Bridgeport ID CATEGORIZATION; MODEL AB feature selection (FS) is essential for effective and more accurate text classification (TC) systems. This paper investigates the effectiveness of five commonly used FS methods for our Arabic language TC System. Evaluation used an in-house collected Arabic TC corpus. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F(1) measure. C1 Balqa Appl Univ, Dept Comp Engn, Fac Engn Technol, Amman, Jordan. RP Mesleh, AM (reprint author), Balqa Appl Univ, Dept Comp Engn, Fac Engn Technol, Amman, Jordan. CR ALSHALABI R, 2006, P 4 INT MULT COMP SC, V4 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Benkhalifa M, 2001, INT J INTELL SYST, V16, P929, DOI 10.1002/int.1042 Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 CIRAVEGNA F, 2000, P PAIS 2000 PREST AP, P696 Das S., 2001, P 18 INT C MACH LEAR, P74 Dash M., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183893 Dash M, 1997, INTELL DATA ANAL, V1, P131, DOI DOI 10.1016/S1088-467X(97)00008-5 Duwairi R., 2005, P 2005 INT C DAT MIN, P187 Duwairi RM, 2006, J AM SOC INF SCI TEC, V57, P1005, DOI 10.1002/asi.20360 El-Halees A., 2007, ISLAMIC U J, V15, P157 El-Kourdi M., 2004, P COLING 20 WORKSH C, P51, DOI 10.3115/1621804.1621819 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 Gao S, 2006, ACM T INFORM SYST, V24, P190, DOI 10.1145/1148020.1148022 Guo GD, 2004, LECT NOTES COMPUT SC, V2945, P559 He J, 2003, APPL INTELL, V18, P311, DOI 10.1023/A:1023202221875 HOFMANN T, 2003, INTRO MACHINE LEARNI Joachims T., 1998, P 10 EUR C MACH LEAR, P137 KANAAN G, 2006, P 4 INT MULT COMP SC, V4 Khreisat L, 2006, P 2006 INT C DAT MIN, P78 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Liu H, 2005, IEEE INTELL SYST, V20, P64, DOI 10.1109/MIS.2005.105 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 MESLEH AM, 2007, P 2 INT C SOFTW DAT, P235 Mesleh AMA, 2007, J COMPUTER SCI, V3, P430 Mitra P, 2002, IEEE T PATTERN ANAL, V24, P301, DOI 10.1109/34.990133 Mladenic D., 1999, P 16 INT C MACH LEAR, P258 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 PENG F, 2003, P 6 INT WORKSH INF R, P41 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMIR AM, 2005, P 5 C INT INF TECHN, P13 SAWAF H, AR NAT LANG PROC WOR Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Syiam M. M., 2006, INT J INTELLIGENT CO, V6, P1 Taira H., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Vapnik V., 1995, NATURE STAT LEARNING Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 Yang Y., 1999, 22 ANN INT ACM SIGIR, P42 Zhang JN, 2003, PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL II, P888 NR 50 TC 4 Z9 4 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS BN 978-1-4020-8740-0 PY 2008 BP 11 EP 16 DI 10.1007/978-1-4020-8741-7_3 PG 6 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BIT20 UT WOS:000262481300003 ER PT S AU Alfred, R AF Alfred, Rayner BE Atzeni, P Caplinskas, A Jaakkola, H TI Dynamic aggregation of relational attributes based on feature construction SO ADVANCES IN DATABASES AND INFORMATION SYSTEMS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 12th East European Conference on Advances in Databases and Information Systems CY SEP 05-09, 2008 CL Pori, FINLAND DE feature construction; data summarisation; genetic algorithm; clustering ID INDUCTION AB The importance of input representation has been recognised already in machine learning. This paper discusses the application of genetic-based feature construction methods to generate input data for the data summarisation method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarise data stored in the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored in a target table. This paper addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features. C1 Univ Malaysia Sabah, Sch Engn & Informat Technol, Kota Kinabalu 88999, Sabah, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, Sch Engn & Informat Technol, Locked Bag 2073, Kota Kinabalu 88999, Sabah, Malaysia. RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR ALFRED R, 2006, 2 ADMA INT C, P889 Amaldi E, 1998, THEOR COMPUT SCI, V209, P237, DOI 10.1016/S0304-3975(97)00115-1 BENSUSAN H, 1996, ICML 1996 EV COMP MA BLOCKEEL H, 1999, TILDE WARMR USER MAN DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Frank E., 1999, DATA MINING PRACTICA Freitas AA, 2001, ARTIF INTELL REV, V16, P177, DOI 10.1023/A:1011996210207 HOLLAND JH, 1975, ADAPTATION NATURAL A Hu Y-J, 1998, P 3 ANN GEN PROGR C, P146 HU YJ, 1996, AAAI IAAI, V1, P806 KOZA JR, 1994, STAT COMPUTING, V4 Krawiec K., 2002, Genetic Programming and Evolvable Machines, V3, DOI 10.1023/A:1020984725014 Lavrac N., 2001, ACM T COMPUT LOG, V2, P458, DOI 10.1145/383779.383781 Otero FEB, 2003, LECT NOTES COMPUT SC, V2610, P384 PAGALLO G, 1990, MACH LEARN, V5, P71, DOI 10.1023/A:1022611825350 Quinlan Ross, 1993, M KAUFMANN SERIES MA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafti LS, 2003, LECT NOTES COMPUT SC, V2810, P599, DOI 10.1007/978-3-540-45231-7_55 Shannon C. E., 1948, BELL SYSTEM TECHNICA, V27 Srinivasan A, 1996, ARTIF INTELL, V85, P277, DOI 10.1016/0004-3702(95)00122-0 Vafaie H, 1998, IEEE INTELL SYST APP, V13, P57, DOI 10.1109/5254.671093 Wiener N., 2000, CYBERNETICS CONTROL ZHENG Z, 1996, ICTAI, P254 Zheng ZJ, 2000, MACH LEARN, V40, P35, DOI 10.1023/A:1007626017208 NR 24 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85712-9 J9 LECT NOTES COMPUT SC PY 2008 VL 5207 BP 2 EP 13 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIG79 UT WOS:000259393600002 ER PT S AU Yeh, JB Wu, CH AF Yeh, Jun-Bin Wu, Chung-Hsien BE Huang, YMR Xu, C Cheng, KS Yang, JFK Swamy, MNS Li, S Ding, JW TI Video News Retrieval Incorporating Relevant Terms Based on Distribution of Document Frequency SO Advances in Multimedia Information Processing - PCM 2008, 9th Pacific Rim Conference on Multimedia SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 9th Pacific Rim Conference on Multimedia CY DEC 09-13, 2008 CL Tainan, TAIWAN HO Natl Cheng Kung Univ DE Relevant event term; Video retrieval; boxplot; TFIDF; Vector Space Model; Term Extraction AB This paper presents an approach to video news retrieval within an event by integrating visual and textual features. A set of histogram bins of key frames in a shot is adopted as the visual feature, while the term frequency is used as the textual feature. A term scoring method is proposed to enhance the weights of relevant terms in an event by considering the windowed document frequency distribution. The weight for a given term is determined by mean of the difference between usual and unusual term groups which are quantized by the boxplot method. The first experiment evaluate the performance of the proposed method by giving generated document frequency distributions, while the second experiment gives the desired retrieval results for relevant terms in the real data. It concludes the proposed method can increase the performance of retrieving video news stories within an event using relevant terms. C1 [Yeh, Jun-Bin; Wu, Chung-Hsien] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan. RP Yeh, JB (reprint author), Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 70101, Taiwan. EM kennyou@csie.ncku.edu.tw; chwu@csie.ncku.edu.tw CR BEKCER J, 2003, BIS 2003 LI X, 2007, CIVR 2007 LIU Y, 2008, ICME 2008 Peng YX, 2006, IEEE T CIRC SYST VID, V16, P612, DOI 10.1109/TCSVT.2006.873157 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOLKMER T, 2006, ICME 2006 Walpole R. E., PROBABILITY STAT ENG Wang J, 2005, CONSTRAINTS, V10, P219, DOI 10.1007/s10601-005-2238-x WU HC, 2008, ACM T INFORM SYSTEMS, V26, P1, DOI 10.1145/1361684.1361686 Yeo BL, 1995, IEEE T CIRC SYST VID, V5, P533 NR 10 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89795-8 J9 LECT NOTES COMPUT SC PY 2008 VL 5353 BP 583 EP 592 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIS97 UT WOS:000262476800060 ER PT S AU Lim, JH Chevallet, JP Le, DTH Goh, H AF Lim, Joo-Hwee Chevallet, Jean-Pierre Le, Diem Thi Hoang Goh, Hanlin BE Satoh, S Nack, F Etoh, M TI Bi-modal conceptual indexing for Medical Image Retrieval SO ADVANCES IN MULTIMEDIA MODELING, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 14th International Multimedia Modeling Conference (MMM 2008) CY JAN 09, 2007 CL Kyoto, JAPAN HO Kyoto Univ AB To facilitate the automatic indexing and retrieval of large medical image databases, images and associated texts are indexed using concepts from the Unified Medical Language System (UMLS) metathesaurus. We propose a structured learning framework for learning medical semantics from images. Two complementary global and local visual indexing approaches are presented. Two fusion approaches are also used to improve textual retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. Using the ImageCLEFmed database, we demonstrate that our framework is superior when compared to the automatic runs evaluated in 2005 on the same Medical Image Retrieval task. C1 [Lim, Joo-Hwee; Chevallet, Jean-Pierre; Le, Diem Thi Hoang; Goh, Hanlin] Inst Infocomm Res, CNRS, UMI 2955, I2R,NUS,UJF,French Singapore IPAL Joint Lab, Singapore 119613, Singapore. RP Lim, JH (reprint author), Inst Infocomm Res, CNRS, UMI 2955, I2R,NUS,UJF,French Singapore IPAL Joint Lab, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. CR Aronson A R, 2001, Proc AMIA Symp, P17 Bishop C. M., 1995, NEURAL NETWORKS PATT CLOUGH P, 2006, LNCS, V4022 Dy JG, 2003, IEEE T PATTERN ANAL, V25, P373, DOI 10.1109/TPAMI.2003.1182100 Guld MO, 2002, P SOC PHOTO-OPT INS, V4685, P280 LIM J, 2005, P AIRS 2005, P84 Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shyu CR, 2002, COMPUT VIS IMAGE UND, V88, P119, DOI 10.1006/cviu.2002.0972 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-77407-5 J9 LECT NOTES COMPUT SC PY 2008 VL 4903 BP 456 EP 465 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BHD52 UT WOS:000252344900043 ER PT S AU Boudin, F Torres-Moreno, JM Velazquez-Morales, P AF Boudin, Florian Torres-Moreno, Juan-Manuel Velazquez-Morales, Patricia BE Nordstrom, B Ranta, A TI An efficient statistical approach for automatic Organic Chemistry summarization SO ADVANCES IN NATURAL LANGUAGE PROCESSING, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 6th International Conference on Natural Language Processing CY AUG 25-27, 2008 CL Gothenburg, SWEDEN SP Ctr Language Technol, City Gothenburg, Lingsoft Ltd HO Chalmers Univ Technol ID ABSTRACTS AB In this paper, we propose an efficient strategy for summarizing scientific documents in Organic Chemistry that concentrates on numerical treatments. We present its implementation named YACHS (Yet Another Chemistry Summarizer) that combines a specific document preprocessing with a sentence scoring method relying on the statistical properties of documents. We show that YACHS achieves the best results among several other summarizers on a corpus made of Organic Chemistry articles. C1 [Boudin, Florian; Torres-Moreno, Juan-Manuel; Velazquez-Morales, Patricia] Lab Informat Avignon, F-84911 Avignon 9, France. RP Boudin, F (reprint author), Lab Informat Avignon, 339 Chemin Meinajaries, F-84911 Avignon 9, France. RI Torres-Moreno, Juan-Manuel/K-5137-2012 CR Boudin F, 2008, LECT NOTES COMPUT SC, V4919, P334, DOI 10.1007/978-3-540-78135-6_28 CLIMENSON W, 1961, AM DOC, V12, P178, DOI 10.1002/asi.5090120303 DANG HT, 2005, DOC UND C DUC EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 JONES KS, 1996, EVALUATIONG NATURAL KUPIEC J, 1995, 18 ACM SIGIR C RES D, P68 Lin C.-Y., 2004, WORKSH TEXT SUMM BRA, P25 LUHN HP, 1958, IBM J RES DEV, V2, P159 Mani I., 1999, ADV AUTOMATIC TEXT S MANI I, 1998, 15 NAT C ART INT AAA, P820 POLLOCK JJ, 1975, J CHEM INF COMP SCI, V15, P226, DOI 10.1021/ci60004a008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RADEV DR, 2001, DOC UND C DUC Reeve LH, 2007, INFORM PROCESS MANAG, V43, P1765, DOI 10.1016/j.ipm.2007.01.026 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936 Torres-Moreno J. M., 2002, JADT, V2, P723 WINKLER WE, 1999, STAT INCOME DIVISION, V4, P73 Yatsko V A, 2007, Automatic Documentation and Mathematical Linguistics, V41, DOI 10.3103/S0005105507030041 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85286-5 J9 LECT NOTES ARTIF INT PY 2008 VL 5221 BP 89 EP 99 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BIE63 UT WOS:000258935200009 ER PT B AU Ma, H Amor, R Tempero, E AF Ma, Homan Amor, Robert Tempero, Ewan GP IEEE Computer Society TI Indexing the Java API using source code SO ASWEC 2008: 19TH AUSTRALIAN SOFTWARE ENGINEERING CONFERENCE, PROCEEDINGS LA English DT Proceedings Paper CT 19th Australian Software Engineering Conference CY MAR 25-28, 2008 CL Perth, CANADA SP Australian Comp Soc, Engineers Australia, Curtin Univ Technol, L3 Commun Nautronix, Murdoch Univ, ECU, Thales, Motorola, Davies Collison Cave, Project Performance Int, Object Consulting, SYSTEC DE Java Standard API; software repositories; source code analysis; reuse AB The basic idea behind software reuse is that software developers use reusable components found in software repositories to reduce the amount of code that has to be written and so increase productivity. A problem arises, however if the repository is too big - it becomes difficult to find relevant components. What is needed is an effective means to query repositories. Most approaches to developing such means involves creating a good index to which the queries can be applied. Developing a good index requires identifying the relevant information on which to base the index. In this paper we present the results of a project that used source code as the basis for the index. C1 [Ma, Homan; Amor, Robert; Tempero, Ewan] Univ Auckland, Dept Comp Sci, Auckland 1, New Zealand. RP Ma, H (reprint author), Univ Auckland, Dept Comp Sci, Auckland 1, New Zealand. RI Tempero, Ewan/D-2621-2009 OI Tempero, Ewan/0000-0002-3786-1707 CR Berry M., 1999, UNDERSTANDING SEARCH Bradshaw J., 1997, INTRO SOFTWARE AGENT DUMAIS ST, 21236 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Golub G. H., 1989, MATRIX COMPUTATIONS, VSecond HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P Hiemstra D., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900025 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 JENSEN LR, 2004, THESIS U AARHUS DENM LIN MY, 2005, THESIS U AUCKLAND LIN MY, 2006, AUSTR SOFTW ENG C AP MA H, 2006, 13 AS PAC SOFTW ENG, P342 MA HKC, 2007, THESIS U AUCKLAND MAAREK YS, 1991, IEEE T SOFTWARE ENG, V17, P800, DOI 10.1109/32.83915 Manning C. D., 1999, FDN STAT NATURAL LAN MCILROY MD, 1969, P NATO SOFTW ENG C, V1, P138 Michail A., 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002), DOI 10.1109/ICSE.1999.841036 Miller George Armitage, 1990, INT J LEXICOGRAPHY MOFFAT A, 2007, UNPUB RANK BIASED PR Porter M. F., 1997, READINGS INFORM RETR, P313 *QUAL RES GROUP, 2007, QUAL CORP REW R, 1990, IEEE COMPUT GRAPH, V10, P76, DOI 10.1109/38.56302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck Jones K., 1988, DOCUMENT RETRIEVAL S, P132 STEELE O, 1998, JWORDNET *SUN MICR, 2003, JAV 2 PLATF STAND ED YE Y, 2000, 6 INT CON SOFTW REUS, P302 YE Y, 2001, THESIS U COLORADO Zaremski A. M., 1995, ACM Transactions on Software Engineering and Methodology, V4, DOI 10.1145/210134.210179 2007, WORDNET LEXICAL DATA NR 30 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3100-7 PY 2008 BP 451 EP 460 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BHP24 UT WOS:000255154800047 ER PT J AU Chu, WW Liu, ZY Mao, WL Zou, QH AF Chu, Wesley W. Liu, Zhenyu Mao, Wenlei Zou, Qinghua BE Feng, DD TI KMeX: A Knowledge-Based Digital Library for Retrieving Scenario-Specific Medical Text Documents SO BIOMEDICAL INFORMATION TECHNOLOGY SE Academic Press Series in Biomedical Engineering LA English DT Article; Book Chapter ID QUERY EXPANSION; QUESTIONS; MEDLINE; CARE C1 [Chu, Wesley W.; Liu, Zhenyu; Mao, Wenlei; Zou, Qinghua] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA. RP Chu, WW (reprint author), Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA. CR Agrawal R., 1994, P 20 VLDB VER LARG D Aronson A. R., 2001, P AMIA ANN S Aronson A. R., 1997, P AMIA ANN S Buckley C., 1994, P 3 TEXT RETRIEVAL C Buckley C, 1995, P 4 TEXT RETRIEVAL C Chu S., 2002, YB MED INFORM Chu W. W., 1998, ENCY ELECT ELECT ENG Chu WW, 1998, IEEE T KNOWL DATA EN, V10, P872, DOI 10.1109/69.738355 Denny J. C., 2002, P AMIA ANN S Efthimiadis E. N., 1993, P 2 TEXT RETRIEVAL C Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Elkin P. L., 1988, Proceedings. The Twelfth Annual Symposium on Computer Applications in Medical Care (IEEE Cat. No.88CH2616-1) Ely JW, 1999, BRIT MED J, V319, P358 Ely JW, 2000, BRIT MED J, V321, P429, DOI 10.1136/bmj.321.7258.429 Friedman C, 1998, METHOD INFORM MED, V37, P334 Gonzalo J., 1998, P COLING ACL 98 WORK Guo Y., 2004, P 13 TEXT RETR C TRE Han J., 2000, P 2000 ACM SIGMOD SP HAYNES RB, 1990, ANN INTERN MED, V112, P78 Hersh W., 1994, P 22 ACM SIGIR C, P191 Hersh W. H., 2000, P AMIA ANN S Ide N, 1998, COMPUT LINGUIST, V24, P1 Jing Y., 1994, P RIAO 94, P146 Liu Z. L., 2006, 060019 UCLA COMP SCI Liu ZY, 2007, INFORM RETRIEVAL, V10, P173, DOI 10.1007/s10791-006-9020-6 LOVINS JB, 1968, MECH TRANSL, V11, P22 Lovins J., 1968, MECH TRANSLATION COM, V11, P11 Lyons John, 1977, SEMANTICS Mao W., 2002, P AM MED INF ASS AMI Mitra M., 1998, P ACM SIGIR 98 Montori V. M., 2003, BMC MED, V1 National Library of Medicine, 2003, UMLS KNOWL SOURC Plovnick R. M., 2004, J MED INTERNET RES, V6 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 Robertson S.E., 1994, P 3 TEXT RETRIEVAL C SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1975, J AM SOC INFORM SCI, V23, P74 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 Srinivasan S., 2002, P AMIA ANN S Steinbach M., 2000, P KDD KNOWL DISC DAT Tuttle MS, 1998, METHOD INFORM MED, V37, P373 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Wilczynski N. L., 2003, P AMIA ANN S WILCZYNSKI NL, 2001, INT J MED INFORM, V84, P390 Wong S.-L., 2003, P AMIA ANN S Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Zhao Y., 2002, 02022 TR U MINN DEP Zieman Y. L., 1997, P AMIA ANN S Zou Q., 2002, P IEEE INT C DAT MIN Zou Q., 2003, P AMIA ANN S NR 52 TC 1 Z9 1 PU ELSEVIER ACADEMIC PRESS INC PI SAN DIEGO PA 525 B STREET, SUITE 1900, SAN DIEGO, CA 92101-4495 USA BN 978-0-08-055072-5 J9 ACAD PR SER BIOM ENG PY 2008 BP 307 EP 341 DI 10.1016/B978-012373583-6.50018-9 PG 35 WC Medical Informatics SC Medical Informatics GA BGB38 UT WOS:000322159600015 ER PT S AU Sunayama, W AF Sunayama, Wataru BE Iwata, S Ohsawa, Y Tsumoto, S Zhong, N Shi, Y Magnani, L TI Discussion Visualization on a Bulletin Board System SO COMMUNICATIONS AND DISCOVERIES FROM MULTIDISCIPLINARY DATA SE Studies in Computational Intelligence LA English DT Article; Book Chapter DE bulletin board system; discussion visualization; discussion divergence; discussion convergence; communication support AB It is important for a collaborative community to decide its next action. The leader of a collaborative community must choose an action that increases rewards and reduces risks. When a leader cannot make this decision, action will be determined through community member discussion. However, this decision cannot be made in blind discussions, so systematic discussion is necessary to choose effective action in a limited time. In this paper, we propose a bulletin board system framework in which the effective discussion is established through the visualized discussion logs. C1 Hiroshima City Univ, Asaminami Ku, Hiroshima 7313194, Japan. RP Sunayama, W (reprint author), Hiroshima City Univ, Asaminami Ku, 3-4-1 Ozuka Higashi, Hiroshima 7313194, Japan. CR ASAHARA M, 2000, P 18 INT C COMP LING, V1, P21, DOI 10.3115/990820.990824 Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Lapata Mirella, 2003, P 41 M ASS COMP LING, P545 MATSUMURA N, 2002, P 11 INT WORLD WID W Morris J., 1991, Computational Linguistics, V17 OHSAWA Y, 2002, P 11 INT WORLD WID W SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SUNAYAMA W, 2006, SYSTEMS COMPUTERS JA NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-540-78732-7 J9 STUD COMPUT INTELL PY 2008 VL 123 BP 95 EP 109 D2 10.1007/978-3-540-78733-4 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BJM73 UT WOS:000266822600006 ER PT J AU Bogan-Marta, A Hategan, A Pitas, I AF Bogan-Marta, A. Hategan, A. Pitas, I. BE Kelemen, A Abraham, A Liang, Y TI Language engineering and information theoretic methods in protein sequence similarity studies SO COMPUTATIONAL INTELLIGENCE IN MEDICAL INFORMATICS SE Studies in Computational Intelligence LA English DT Article; Book Chapter ID ACID SUBSTITUTION MATRICES; LATENT SEMANTIC ANALYSIS; GENE ONTOLOGY; COMPRESSION; DISTANCE; CLASSIFICATION; EVOLUTIONARY; ALGORITHM; TAXONOMY; TOOLS AB The representation of biological data as text information opened new perspectives in the evolution of biological research. Many biological sequence databases are providing detailed information about sequences allowing investigations like searches, comparison, the establishment of relations between different sequences and species. The algorithmic procedures used for data sequence analysis are coming from many areas of computational sciences. Within this book chapter, we are bringing together a diversity of language engineering techniques and those involving information theoretic principles in analyzing protein sequences from similarity perspective. After we are proposing a state of the art in the subject, presenting a survey of the different approaches identified, the attention is oriented to the two methods we experimented. The description of these methods and the experiments performed open discussions addressed to the interested reader that may think about new ideas of improvement. C1 [Bogan-Marta, A.] Univ Oradea, Dept Comp, Oradea 410087, Romania. [Hategan, A.] Tampere Univ Technol, Inst Signal Proc, FIN-33101 Tampere, Finland. [Bogan-Marta, A.; Pitas, I.] Aristotle Univ Thessaloniki, Dept Informat, Artificial Intelligence & Informat Anal Lab, GR-54006 Thessaloniki, Greece. RP Bogan-Marta, A (reprint author), Univ Oradea, Dept Comp, Univ 1, Oradea 410087, Romania. EM alinab@uoradea.ro; andrea.hategan@tut.fi; pitas@aiia.csd.auth.gr CR ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999 APOSTOLICO A, 2000, DAT COMPR C Barton GJ, 1996, PROTEIN STRUCTURE PR Bellegarda JR, 2000, P IEEE, V88, P1279, DOI 10.1109/5.880084 Benedetto D, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.048702 Bennett CH, 1998, IEEE T INFORM THEORY, V44, P1407, DOI 10.1109/18.681318 Bennett CH, 2003, SCI AM, V288, P76 Berman HM, 2000, NUCLEIC ACIDS RES, V28, P235, DOI 10.1093/nar/28.1.235 Boeckmann B, 2003, NUCLEIC ACIDS RES, V31, P365, DOI 10.1093/nar/gkg095 BOGANMARATA A, 2005, IEEE IEE 2 INT C INT, P122 BOGANMARTA A, 2006, IEEE C ART INT THEOR, V11, P1 BOGANMARTA A, 2005, IEEE INT WORKSH GEN Bolshoy Alexander, 2003, Appl Bioinformatics, V2, P103 BURKHARDT S, 1999, 3 ANN INT C COMP MOL CAO MD, 2007, DAT COMPR C, P43 CHEN X, 1999, GENOME INFORM Chen X, 2002, BIOINFORMATICS, V18, P1696, DOI 10.1093/bioinformatics/18.12.1696 Chen X, 2004, IEEE T INFORM THEORY, V50, P1545, DOI 10.1109/TIT.2004.830793 Chen X, 2001, IEEE ENG MED BIOL, V20, P61 CHENG B, 2004, 15 INT S METH INT SY Cheng BYM, 2005, PROTEINS, V58, P955, DOI 10.1002/prot.20373 Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059 Coin L, 2003, P NATL ACAD SCI USA, V100, P4516, DOI 10.1073/pnas.0737502100 Cover T. M., 1991, ELEMENTS INFORM THEO DONG Q, 2005, BIOINFORMATICS, DOI DOI 10.1093/BIOINFORMATICS/BTI801 Durbin R., 1998, BIOL SEQUENCE ANAL P Finn RD, 2006, NUCLEIC ACIDS RES, V34, pD247, DOI 10.1093/nar/gkj149 GANAPATHIRAJU M, 2002, P HLT SAN DIEG Ganapathiraju MK, 2004, IEEE SIGNAL PROC MAG, V21, P78, DOI 10.1109/MSP.2004.1296545 Ganapathiraju Madhavi, 2004, Appl Bioinformatics, V3, P193, DOI 10.2165/00822942-200403020-00013 Ganapathiraju M, 2005, LECT NOTES COMPUT SC, V3345, P25 Ashburner M, 2001, GENOME RES, V11, P1425 GROZEA C, 2004, CDMTCS RES REPORT SE GRUMBACH S, 1993, DAT COMPR C GUO X, 2005, COMP SYST BIOINF C, P149 HALEY D, 2003, EL WORKSH COMP LEGE HATEGAN A, 2005, INT WORKSH GEN SIGN, P95 HATEGAN A, 2004, NORSIG2005, P1992 HENIKOFF S, 1992, P NATL ACAD SCI USA, V89, P10915, DOI 10.1073/pnas.89.22.10915 HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898 Jiang J. J., 1998, P INT C RES COMP LIN Jurafsky D, 2000, SPEECH LANGUAGE PROC KANG S, 2004, INT S CHIN SPOK LANG, P305 Kocsor A, 2006, BIOINFORMATICS, V22, P407, DOI 10.1093/bioinformatics/bti806 Kolmogorov A. N., 1965, PROBL PEREDACHI INF, V1, P3 Koonin E. V., 2002, SEQUENCE EVOLUTION F Korodi G, 2005, ACM T INFORM SYST, V23, P3, DOI 10.1145/1055709.1055711 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LI M, 2001, BIOINFORMATICS, V17, P49154 Li M., 1997, INTRO KOLMOGOROV COM Li M, 2004, IEEE T INFORM THEORY, V50, P3250, DOI 10.1109/TIT.2004.838101 Liao L, 2003, J COMPUT BIOL, V10, P857, DOI 10.1089/106652703322756113 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 LIU Y, 2004, ACM SIGIR C Lord P W, 2003, Pac Symp Biocomput, P601 Lord PW, 2003, BIOINFORMATICS, V19, P1275, DOI 10.1093/bioinformatics/btg153 MAGUITMAN AG, 2006, PAC S BIOCOMPUT, V11, P76 MANNING CD, 2000, FDN STAT NATURAL LAN, P554 METFESSEL BA, 1993, P 26 HAW INT C SYST, V1, P679 RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Mulder NJ, 2006, BIOFIZIKA+, V51, P656 NEVILLMANNING CG, 1999, DAT COMPR C Otu HH, 2003, BIOINFORMATICS, V19, P2122, DOI 10.1093/bioinformatics/btg295 PEARSON WR, 1988, P NATL ACAD SCI USA, V85, P2444, DOI 10.1073/pnas.85.8.2444 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P, 1995, INT JOINT CONF ARTIF, P448 RISSANEN JJ, 1976, IBM J RES DEV, V20, P198 Ross S. M., 1996, STOCHASTIC PROCESSES, V2 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarkar Indra N, 2002, Proc AMIA Symp, P677 Schaffer AA, 2001, NUCLEIC ACIDS RES, V29, P2994, DOI 10.1093/nar/29.14.2994 Schlicker A, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-302 Searls DB, 2002, NATURE, V420, P211, DOI 10.1038/nature01255 TABUS I, 2003, DAT COMPR C Tueyu Fu, 2003, Proceedings 2003 Joint Conference on Digital Libraries VANCOMPERNOLLE D, 2003, SPOKEN LANGUAGE SCI WANG S, 2003, IEEE INT C AC SPEECH Wang SJ, 2005, MACH LEARN, V60, P229, DOI 10.1007/s10994-005-0928-7 WU KP, 2003, IEEE COMP SOC BIOINF, P347 Yu YK, 2003, P NATL ACAD SCI USA, V100, P15688, DOI 10.1073/pnas.2533904100 YUAN Y, 2005, IEEE EMBS 27 ANN INT, P7738 NR 81 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-540-75766-5 J9 STUD COMPUT INTELL PY 2008 VL 85 BP 151 EP 183 D2 10.1007/978-3-540-75767-2 PG 33 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA BLZ10 UT WOS:000271477600008 ER PT S AU Matsuo, T Fujimoto, T AF Matsuo, Tokuro Fujimoto, Takayuki BE Lee, R Kim, HK TI An Analogical Thinking Based New Software Engineering Education Methodology SO COMPUTER AND INFORMATION SCIENCE SE Studies in Computational Intelligence LA English DT Proceedings Paper CT 7th IEEE/ACIS International Conference on Computer and Information Science in Conjunction with 2nd IEEE/ACIS International Workshop on e-Activity CY MAY 14-16, 2008 CL Portland, OR SP IEEE Comp Soc, Int Assoc Comp & Informat Sci C1 [Matsuo, Tokuro] Yamagata Univ, Grad Sch Sci & Engn, Yamagata 9928510, Japan. RP Matsuo, T (reprint author), Yamagata Univ, Grad Sch Sci & Engn, Jonan 4-3-16, Yamagata 9928510, Japan. EM tokuro@tokuro.net; me@fujimotokyo.net CR BAKER SJD, 2007, P ACM SIGCHI C HUM F MIYAKE N, 2000, 4 INT C LEARN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SUNAYAMA W, 2004, 18 ANN C JAP SOC ART NR 4 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1860-949X BN 978-3-540-79186-7 J9 STUD COMP INTELL PY 2008 VL 131 BP 77 EP 86 PG 10 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BIO32 UT WOS:000261332300007 ER PT S AU Gernmell, J Shepitsen, A Mobasher, B Burke, R AF Gernmell, Jonathan Shepitsen, Andriy Mobasher, Bamshad Burke, Robin BE Song, IY Eder, J Nguyen, TM TI Personalizing navigation in folksonomies using hierarchical tag clustering SO DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 10th International Conference on Data Warehousing and Knowledge Discovery CY SEP 02-05, 2008 CL Turin, ITALY DE collaborative tagging; hierarchical clustering; personalization ID RETRIEVAL; MODEL AB The popularity of collaborative tagging, otherwise known as "folksonomies". emanate from the flexibility they afford users in navigating large information spaces for resources, tags, or other users, unencumbered by a predefined navigational or conceptual hierarchy. Despite its advantages, social tagging also increases user overhead in search and navigation: users are free to apply any tag they wish to a resource, often resulting in a large number of tags that are redundant, ambiguous, or idiosyncratic. Data mining techniques such as clustering provide a means to overcome this problem by learning aggregate user models, and thus reducing noise. In this paper we propose a method to personalize search and navigation based on unsupervised hierarchical agglomerative tag clustering. Given a user profile, represented as a vector of tags, the learned tag clusters provide the nexus between the user and those resources that correspond more closely to the user's intent. We validate this assertion through extensive evaluation of the proposed algorithm using data from a real collaborative tagging Web site. C1 [Gernmell, Jonathan; Shepitsen, Andriy; Mobasher, Bamshad; Burke, Robin] Depaul Univ, Sch Comp, Ctr Web Intelligence, Chicago, IL 60604 USA. RP Gernmell, J (reprint author), Depaul Univ, Sch Comp, Ctr Web Intelligence, Chicago, IL 60604 USA. CR Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 BEGELMAN G, 2006, P COLL WEB TAGG WORK, V6 Choy S. O., 2006, P IEEE WIC ACM INT C, P352 Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 Hammond T., 2005, D LIB MAGAZINE, V11, P1082 Heymann P., 2006, 200610 COMP SCI DEP Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 MATHES A, 2004, THESIS U ILLINOIS UR Mika P, 2007, J WEB SEMANT, V5, P5, DOI 10.1016/j.websem.2006.11.002 Millen D.R., 2006, P SIGCHI C HUM FACT, P111, DOI 10.1145/1124772.1124792 Niwa S., 2006, P 3 INT C INF TECHN, P388 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Voorhees E. M., 1999, P TREC, V8, P77 Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 Xu Z., 2006, COLL WEB TAGG WORKSH Yan R., 2007, WORKSH MULT INF RETR, P13, DOI 10.1145/1290067.1290071 NR 18 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85835-5 J9 LECT NOTES COMPUT SC PY 2008 VL 5182 BP 196 EP 205 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIH48 UT WOS:000259488400019 ER PT S AU Rieck, K Holz, T Willems, C Dussel, P Laskov, P AF Rieck, Konrad Holz, Thorsten Willems, Carsten Duessel, Patrick Laskov, Pavel BE Zamboni, D TI Learning and classification of malware behavior SO DETECTION OF INTRUSIONS AND MALWARE, AND VULNERABILITY ASSESSMENT SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Conference on Detection of Intrusion and Malware, and Vulnerability Assessment CY JUL 10-11, 2008 CL Paris, FRANCE SP German Informat Soc, Secur Intrusion Detect & Response Special Interest Grp AB Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware. families share typical behavioral patterns reflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifier is trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with different heterogeneous test data collected over several months using honeypots demonstrate the effectiveness of our method, especially in detecting novel instances of malware families previously not recognized by commercial anti-virus software. C1 [Rieck, Konrad; Duessel, Patrick; Laskov, Pavel] Fraunhofer Inst FIRST, Intelligent Data Anal Dept, Berlin, Germany. RP Rieck, K (reprint author), Fraunhofer Inst FIRST, Intelligent Data Anal Dept, Berlin, Germany. RI Holz, Thorsten/H-5546-2013 CR *AV, 2007, ANTIVIR PERSONALEDIT Baecher P, 2006, LECT NOTES COMPUT SC, V4219, P165 Bailey M, 2007, LECT NOTES COMPUT SC, V4637, P178 BAYER U, 2006, P EICAR 2006 APR Bayer U., 2006, J COMPUTER VIROLOGY, V2, P67, DOI 10.1007/s11416-006-0012-2 Burges CJC, 1998, KNOWLEDGE DISCOVERY, V2, P121 CHRISTODORESCU M, 2005, IEEE S SEC PRIV, P32 Christodorescu M., 2003, P 12 USENIX SEC S, P12 Christodorescu M., 2007, P 6 JOINT M EUR SOFT Egele M., 2007, P USENIX ANN TECHN C Flake H., 2004, P DET INTR MALW VULN Fogla P., 2006, Proceedings of the 15th USENIX Security Symposium Hunt G, 1999, PROCEEDINGS OF THE 3RD USENIX WINDOWS NT SYMPOSIUM, P135 Jiang X., 2004, P 13 USENIX SEC S Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Karim M. E., 2005, J COMPUTER VIROLOGY, V1, P13, DOI 10.1007/s11416-005-0002-9 Kirda E., 2006, P 15 USENIX SEC S, P19 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 KRUEGEL C, 2004, P 20 ANN COMP SEC AP LEE T, 2006, P EICAR 2006 APR LEITA C, 2006, LNCS, V4219 Microsoft, MICR SEC INT REP MOSER A, 2007, P 23 ANN CO IN PRESS Moser A., 2007, P 2007 IEEE S SEC PR *NORM SANDB INF CT, NORM PLATT JC, 2001, ADV LARGE MARGIN CLA POUGET F, 2005, ECCE 2005 Rieck K, 2008, J MACH LEARN RES, V9, P23 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B., 2002, LEARNING KERNELS Shawe-Taylor J., 2004, KERNEL METHODS PATTE Sonnenburg S, 2006, J MACH LEARN RES, V7, P1531 Szor P., 2005, ART COMPUTER VIRUS R Vapnik V., 1998, STAT LEARNING THEORY *VIR B, 2007, AVK TOPS LAT AV TEST Vrable M., 2005, SIGOPS OPER SYST REV, V39, P148 Wagner David, 2002, P 9 ACM C COMP COMM, P255 Willems C., 2007, IEEE SECURITY PRIVAC, V5 Yin H., 2007, P ACM C COMP COMM SE NR 40 TC 43 Z9 44 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-70541-3 J9 LECT NOTES COMPUT SC PY 2008 VL 5137 BP 108 EP 125 PG 18 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BIA11 UT WOS:000257880000006 ER PT S AU Bonino, D Como, F AF Bonino, Dario Como, Fulvio BE Tjoa, AM Wagner, RR TI Self-similarity metric for index pruning in conceptual vector space models SO DEXA 2008: 19TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS SE International Workshop on Database and Expert Systems Applications - DEXA LA English DT Proceedings Paper CT 19th International Conference on Database and Expert Systems Applications CY SEP 01-05, 2008 CL Turin, ITALY AB One of the critical issues in search engines is the size of search indexes: as the number of documents handled by an engine increases, the search must preserve its efficiency, despite the growth of indexing structures. A widely agreed solution to this problem is the adoption of smaller, or pruned, indexes that allow increasing the retrieval speed while keeping the search quality as high as possible. This paper extends the notion of pruned index to semantic search systems based on conceptual vector space models and proposes a new self-similarity metric for index pruning. A conceptual vector space model represents documents as vectors in a n-dimensional space where each dimension corresponds to an ontology concept. The pruning algorithm proposed in this paper acts on the basis of document self-similarity, preserving only the most significant components of a document conceptual vector. Unlike many already proposed algorithms, the self-similarity metric is only based on local information and does not require to recompute the whole pruned index when new documents are added, i.e., it can be used on-line, possibly combined with other off-line pruning policies. The proposed metric is tested against two benchmark sets respectively related to Siderurgy (250 documents annotated with respect to the e-Class ontology) and Disability (2500 documents annotated with respect to the Passepartout ontology). Results show that the compression ratio achieved by this technique is satisfying (50%), while ranking similarity with results coming from non-pruned indexes remains sufficiently high (80%), thus preserving the quality of provided results. C1 [Bonino, Dario; Como, Fulvio] Politecn Turin, Turin, Italy. RP Bonino, D (reprint author), Politecn Turin, Turin, Italy. RI Corno, Fulvio/ OI Corno, Fulvio/0000-0002-9818-0999 CR Blanco R, 2007, LECT NOTES COMPUT SC, V4425, P64 Bonino D, 2004, PROC INT C TOOLS ART, P680 Bonino D, 2003, PROC INT C TOOLS ART, P580, DOI 10.1109/TAI.2003.1250244 CARNEL D, 2001, SIGIR 01, P43 Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22 Gardenfors P., 2004, FORMAL ONTOLOGY INFO, V114, P17 Raubal M., 2004, FORMAL ONTOLOGY INFO, P153 Ricardo A., 1999, MODERN INFORM RETRIE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1529-4188 BN 978-0-7695-3299-8 J9 INT WORKSHOP DATABAS PY 2008 BP 225 EP 229 PG 5 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIH38 UT WOS:000259487400038 ER PT S AU Kienreich, W Kraker, P AF Kienreich, Wolfgang Kraker, Peter BE Boulicaut, JF Berthold, MR Horvath, T TI Comparative Evaluation of Two Systems for the Visual Navigation of Encyclopedia Knowledge Spaces SO DISCOVERY SCIENCE, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 11th International Conference on Discovery Science CY OCT 13-16, 2008 CL Budapest, HUNGARY DE Knowledge Visualization; Digital Encyclopedias; Visual Navigation; User Interfaces; Human-Computer Interaction; Formal Experiments AB Modern digital encyclopedias contain hundreds of thousands of textual articles and multimedia elements. Alternative discovery techniques facilitating the visual exploration of encyclopedia knowledge spaces have recently received much attention. We present the results of a comparative usability evaluation of a two-dimensional and a three-dimensional visualization system integrated with the Brockhaus encyclopedia. Both systems enable visual navigation of article context. Results indicate that both systems perform comparably and that users prefer the threedimensional visualization system. C1 [Kienreich, Wolfgang; Kraker, Peter] Know Ctr, Competence Ctr Knowledge Based Applicat & Syst, A-8010 Graz, Austria. RP Kienreich, W (reprint author), Know Ctr, Competence Ctr Knowledge Based Applicat & Syst, Inffeldgasse 21A, A-8010 Graz, Austria. EM wkien@know-center.at; pkraker@know-center.at CR Bailey R. W., 1996, HUMAN PERFORMANCE EN *BROCKH MULT BIBL, BROCKH ENZYKL DIG Burkhard RA, 2005, LECT NOTES COMPUT SC, V3426, P238 Burkhard RA, 2005, J UNIVERS COMPUT SCI, V11, P473 Card S. K., 1999, INFORM VISUALIZATION Dumas J. S., 1999, PRACTICAL GUIDE USAB EIBL M, 2001, PANH C HUM COMP INT GRANITZER M, 2004, 10 IEEE S INF VIS, P127 Helander M., 1997, HDB HUMAN COMPUTER I Kienreich W., 2006, MIA J INFORM MODELLI, P7 KIENREICH W, 2005, 9 INT C INF VIS KIENREICH W, 2006, P 10 INT C INF VIS NARDI BA, 1991, 24 ANN HAW INT C SYS, P47 Perez C, 2004, IEEE INFOR VIS, P317 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEBRECHTS M, 1999, 22 ANN INT ACM SIGIR VIRZI RA, 1992, HUM FACTORS, V34, P457 NR 18 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-88410-1 J9 LECT NOTES COMPUT SC PY 2008 VL 5255 BP 160 EP 171 PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BIP33 UT WOS:000261537600015 ER PT S AU Kaban, A AF Kaban, Ata BE Boulicaut, JF Berthold, MR Horvath, T TI A Probabilistic Neighbourhood Translation Approach for Non-standard Text Categorisation SO DISCOVERY SCIENCE, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 11th International Conference on Discovery Science CY OCT 13-16, 2008 CL Budapest, HUNGARY AB The need for non-standard text categorisation; i.e. based on some subtle criterion other than topics, may arise in various circumstances. In this study, we consider written responses to a standardised psychometric test for determining the personality trait of human subjects. A number of state-of-the-art text classifiers that have been very successful in standard topic-based classification problems turn out to perform poorly in this task. Here we propose a very simple probabilistic approach, which is able to achieve accurate predictions, and demonstrates this peculiar problem is still solvable by simple statistical text representation means. We then extend this approach to include a latent variable, in order to obtain additional explanatory information beyond a black-box prediction. C1 Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England. RP Kaban, A (reprint author), Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England. EM A.Kaban@cs.bham.ac.uk CR Colas F, 2006, INT FED INFO PROC, V217, P169 Eyheramendy S, 2003, SPARSE BAYESIAN CLAS Fawcett T., 2004, ROC GRAPHS NOTES PRA Goldberger J., 2004, NEURAL INFORM PROCES, V17, P513 Hofmann Thomas, 1999, P 15 C UNC ART INT U Joachims T., 1998, P EUR C MACH LEARN Madsen RE, 2005, P 22 INT C MACH LEAR MCCALLUM AK, 1996, TOOLKIT STAT LANGUAG Mitchell TM, 1997, MACHINE LEARNING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saul L, 1997, P 2 C EMP METH NAT L, P81 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHEVADE SK, 2002, CD0222 NAT U SING DE Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-88410-1 J9 LECT NOTES COMPUT SC PY 2008 VL 5255 BP 332 EP 343 PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BIP33 UT WOS:000261537600029 ER PT S AU Awad, A Polyvyanyy, A Weske, M AF Awad, Ahmed Polyvyanyy, Artem Weske, Mathias TI Semantic Querying of Business Process Models SO EDOC 2008: 12TH IEEE INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING, PROCEEDINGS SE IEEE International Enterprise Distributed Object Computing (EDOC) Conference LA English DT Proceedings Paper CT 12th IEEE International Enterprise Distributed Object Computing CY SEP 15-19, 2008 CL Munich, GERMANY SP IEEE AB Determining similarity between business process models has recently gained interest in the business process management community. So far similarity was addressed separately either at semantic or structural aspect of process models. Also, most of the contributions that measure similarity of process models assume an ideal case when process models are enriched with semantics-a description of meaning of process model elements. However in real life this results in a heavy human effort consuming pre-processing phase which is often not feasible. In this paper we propose an automated approach for querying a business process model repository for structurally and semantically relevant models. Similar to the search on the Internet, a user formulates a BPMN-Q query and as a result receives a list of process models ordered by relevance to the query. We provide a business process model search engine implementation for evaluation of the proposed approach. C1 [Awad, Ahmed; Polyvyanyy, Artem; Weske, Mathias] Univ Potsdam, Hasso Plattner Inst, Business Proc Technol Grp, D-14482 Potsdam, Germany. RP Awad, A (reprint author), Univ Potsdam, Hasso Plattner Inst, Business Proc Technol Grp, D-14482 Potsdam, Germany. CR Aalst W.M.P., 1998, J CIRCUIT SYST COMP, V8, P21 Antoniou G, 2003, WEB ONTOLOGY LANGUAG Awad A., 2007, EMISA, P115 Awad A, 2008, LECT NOTES BUS INF, V7, P239 Bae J, 2006, ICWS 2006: IEEE International Conference on Web Services, Proceedings, P479 Becker J, 2003, P 6 INT C BUS INF SY, P7 Berry M. W., 1994, UTCS94270 *BPMI ORG, 2004, BUS PROC MOD NOT Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dijkman R., 2007, EDOC, P37 Ehrig M., 2007, APCCM 2007, P71 Hofmann T., 1999, P UNC ART INT UAI 99 Keller F., 2003, Proceedings 10th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems KELLER G, 1992, 89 VER I WIRTSCH FOR KOSCHMIDER A, 2007, SAC, P1263 KUROPKA D, 2003, MODELLE REPRASENTATI Lu RP, 2006, LECT NOTES COMPUT SC, V4102, P426 MENDLING J, 2007, CEUR WORKSH, P39 NUTTGENS M, 2002, PROMISE *OBJ MAN GROUP INC, 2003, MET OBJ FAC MOF 2 0 POLYVYANYY A, 2007, 19 HASS PLATTN I SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weske M., 2007, BUSINESS PROCESS MAN NR 24 TC 20 Z9 20 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1541-7719 BN 978-0-7695-3373-5 J9 IEEE EDOC CONF PY 2008 BP 85 EP 94 PG 10 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BIK52 UT WOS:000260405700009 ER PT B AU Hadi, WM Thabtah, F Hawari, SAL Ababneh, J AF Hadi, Wa'el Musa Thabtah, Fadi Hawari, Samer A. L. Ababneh, Jafar BE Bertelle, C Ayesh, A TI NAIVE BAYESIAN AND K-NEAREST NEIGHBOUR TO CATEGORIZE ARABIC TEXT DATA SO EUROPEAN SIMULATION AND MODELLING CONFERENCE 2008 LA English DT Proceedings Paper CT European Simulation and Modelling Conference CY OCT 27-29, 2008 CL Havre, FRANCE SP European Simulat Soc, Ghent Univ, Univ Havre, LITIS HO European Technol Inst DE Text Categorization; Naive Bayesian; Arabic Text Data; K-Nearest Neighbour ID MODEL AB Text classification is a supervised learning technique that uses labelled training data to derive a classification system (classifier) and then automatically classifies unlabelled text data using the derived classifier. This paper investigates Naive Bayesian method (NB) and K-Nearest Neighbour algorithm (KNN) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorisation data sets reveal that NB algorithm outperforms the KNN based on Cosine Coefficient approach with regards to all measures. EM whadi81@students.aabfs.org; ffayez@philadelphia.edu.jo; Samer.alhawari@yahoo.com; Jafar_ababneh@yahoo.com CR Benkhalifa M, 2001, INT J INTELL SYST, V16, P929, DOI 10.1002/int.1042 ELHALEES A, 2007, ARABIC TEXT CLASSIFI, V15, P157 El-Halees A., 2006, P 1 INT C M IN PRESS, P15 ELKOURDI M, 2004, 20 INT C COMP LING A Guo GD, 2004, LECT NOTES COMPUT SC, V2945, P559 Hammo B., 2002, WORKSH COMP APPR SEM, P55 Joachims T., 1998, P EUR C MACH LEARN E, P173 KHREISAT L, 2006, ARABIC TEXT CLASSIFI, P78 Mesleh AMA, 2007, J COMPUTER SCI, V3, P430 MOULINIER I, 1996, P 5 ANN S DOC AN INF Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sawaf H., 2001, AR NAT LANG PROC WOR Sebastiani Fabrizio, 1999, P ASAI 99 1 ARG S AR, P7 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 Van Rijsbergan C., 1979, INFORM RETRIEVAL BUT Wiener E, 1995, P 4 ANN S DOC AN INF, P317 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 19 TC 3 Z9 3 PU EUROSIS PI GHENT PA GHENT UNIV, COUPURE LINKS 653, GHENT, B-9000, BELGIUM BN 978-90-77381-44-1 PY 2008 BP 196 EP 200 PG 5 WC Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA BJC53 UT WOS:000264749400029 ER PT S AU Godoy, D Amandi, A AF Godoy, Daniela Amandi, Analia BE Nayak, R Ichalkaranje, N Jain, LC TI Modeling Interests of Web Users for Recommendation: A User Profiling Approach and Trends SO EVOLUTION OF THE WEB IN ARTIFICIAL INTELLIGENCE ENVIRONMENTS SE Studies in Computational Intelligence LA English DT Article; Book Chapter ID PERSONAL INFORMATION AGENTS; PATTERNS AB In order to personalize Web-based tasks, personal agents rely oil representations of user interests and preferences contained in user profiles. In consequence, a critical component for these agents is their capacity to acquire and model user interest categories as well as adapt them to changes in user interests over time. In this chapter, we address the problem of modeling the information preferences of Web users and its distinctive characteristics. We discuss the limitations of current profiling approaches and present a novel user profiling technique, named WebProfiler, developed to support incremental learning and adaptation of user profiles in agents assisting users with Web-based tasks. This technique aims at acquiring comprehensible user profiles that accurately capture user interests starting from observation of user behavior on the Web. C1 [Godoy, Daniela] Univ Nacl Ctr Prov Bs As, ISISTAN Res Inst, Buenos Aires, DF, Argentina. Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Godoy, D (reprint author), Univ Nacl Ctr Prov Bs As, ISISTAN Res Inst, CP 7000, Buenos Aires, DF, Argentina. EM dgodoy@exa.unicen.edu.ar; amandi@exa.unicen.edu.ar CR Agrawal R., 1994, P 20 INT C VER LARG, P487 AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470 BELKIN N, 1997, USER MODELING USER A, V7 BILLSUS D, 1999, P 7 INT C US MOD, P99 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Chen MS, 1998, IEEE T KNOWL DATA EN, V10, P209 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 DYKE NWV, 1999, P 1999 INT C INT US, P39 GAUCH S, 2003, J WEB INTELLIGENCE A, V1, P219 Gluck M., 1985, P 7 ANN C COGN SCI S, P283 GODOY D, 2008, COMPUT J, DOI DOI 10.1093/COMJNL/BXM107 Godoy D, 2006, AI COMMUN, V19, P207 Godoy D, 2006, INT FED INFO PROC, V217, P61 Godoy D, 2005, KNOWL ENG REV, V20, P329, DOI 10.1017/S0269888906000397 Herlocker JL, 2000, P 2000 ACM C COMP SU, P241, DOI DOI 10.1145/358916.358995 Kolodner J., 1993, CASE BASED REASONING Michalski Ryszard S., 1984, MACHINE LEARNING ART, P331 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Mitchell T., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176798 Mitchell TM, 1997, MACHINE LEARNING MLADENIC D, 2001, P 9 INT C HUM COMP I, V1, P893 Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 Muller ME, 2004, KNOWL ENG REV, V19, P61, DOI 10.1017/S0269888904000141 MULLER ME, 2001, P UM2001 WORKSH MACH, P67 Payne TR, 1997, IEEE T KNOWL DATA EN, V9, P329, DOI 10.1109/69.591456 Pazzani M. J., 1996, P 13 NAT C ART INT 8, V1, P54 R Srikant, 1995, P 21 INT C VER LARG, ppp Rennie J.D.M., 2000, P 6 ACM SIGKDD INT C Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schwab I., 2000, P 2000 INT C INT US, P241, DOI 10.1145/325737.325858 SIEG A, 2007, P 5 WORKSH INT TECHN, P84 THOMPSON K, 1991, CONCEPT FORMATION KN Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 Widmer G, 1996, MACH LEARN, V23, P69, DOI 10.1007/BF00116900 Widyantoro DH, 2001, J AM SOC INF SCI TEC, V52, P212, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1615>3.0.CO;2-O WIDYANTORO DH, 2003, ACM CIKM INT C INF K, P548 Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Zuckerman I., 2001, USER MODEL USER-ADAP, V11, P5 NR 39 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1860-949X BN 978-3-540-79139-3 J9 STUD COMPUT INTELL PY 2008 VL 130 BP 41 EP 68 D2 10.1007/978-3-540-79140-9 PG 28 WC Computer Science, Artificial Intelligence; Telecommunications SC Computer Science; Telecommunications GA BJO23 UT WOS:000266888400003 ER PT B AU Zhang, QR Luo, M Wang, HX Tan, JH AF Zhang, Qirui Luo, Man Wang, Hexian Tan, Jinghua BE Luo, Q TI A Medical Text Classification System Based on Immune Algorithm SO FBIE: 2008 INTERNATIONAL SEMINAR ON FUTURE BIOMEDICAL INFORMATION ENGINEERING, PROCEEDINGS LA English DT Proceedings Paper CT International Seminar on Future BioMedical Information Engineering CY DEC 18, 2008 CL Wuhan, PEOPLES R CHINA SP Engn Technol Press, Intelligent Informat Technol Applicat Assoc DE medical information; text categorization; immune algorithm; clonal selection; affinity; memory cells AB This paper proposes a new method of text categorization called the Clonal Selection Algorithm Based on Antibody Density (CSABAD). In this method, antigen, B cell and antibody are respectively corresponded with training texts, a possible individual of classifier and the affinity between the individual and training texts. B cells consist of general cells, fresh cells and memory cells. General cells are used to store various individuals, fresh cells are used to replace the degraded general cells, and memory cells are used to record the best individuals. According to the clonal selection principle and density control mechanism, only those cells that have higher affinity and lower density are selected to proliferate. The ultimate classifier is composed with many memory cells. Considering the characters of medical information, we realize a medical text classifier based on CSABAD, and tests the system on OHSUMED data set. The experiment results show that it can obtain the better classification performance. C1 [Zhang, Qirui; Luo, Man; Wang, Hexian] Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. RP Zhang, QR (reprint author), Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. EM qiruizhang@163.com; tanjh@gsta.com CR De Castro L. N., 2000, P GECCO 00 WORKSH AR, P36 Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 Han E.-H., 2000, 4 EUR C PRINC PRACT, P424 Mitchell T., 1996, MACHINE LEARNING *OHSUMED, OHSUMED DAT SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Xu Yan, 2008, Journal of Software, V19, DOI 10.3724/SP.J.1001.2008.00082 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 ZHANG QR, 2007, J MICROCOMPUTER INFO, V23, P210 ZHANG QR, 2008, P INT S INT IN PRESS MEDLINE DATABASE INF NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3561-6 PY 2008 BP 433 EP 436 DI 10.1109/FBIE.2008.82 PG 4 WC Engineering, Biomedical SC Engineering GA BKH73 UT WOS:000268135100106 ER PT B AU Zhang, QR Xue, YG Zhou, HY Tan, JH AF Zhang, Qirui Xue, Yonggang Zhou, Huaying Tan, Jinghua BE Luo, Q TI Research on Medical Document Categorization SO FBIE: 2008 INTERNATIONAL SEMINAR ON FUTURE BIOMEDICAL INFORMATION ENGINEERING, PROCEEDINGS LA English DT Proceedings Paper CT International Seminar on Future BioMedical Information Engineering CY DEC 18, 2008 CL Wuhan, PEOPLES R CHINA SP Engn Technol Press, Intelligent Informat Technol Applicat Assoc DE medical information; document categorization; information entropy; document indexing; Naive Bayes; Rocchio AB Medical document categorization is the process of automatically assigning one or more predefined category labels to medical documents. Document indexing plays a very important role in the process of classification. This paper proposes an improved method of computing term weights which is called tfidfie (term frequency, inverted document tfidf frequency and inverted entropy). In comparison with the I h (term frequency and inverted document frequency) function, the tfidfie function adds an information entropy factor, H, which represents the distribution of documents in the training set in which the term occurs. Then, we discuss the effects of training set in medical document categorization. An imbalanced training set decreases the performance of classifier. Considering the characteristics of medical documents, the medical classifiers are constructed by the methods of Naive Bayes and Rocchio respectively. The experiment results show that tfidfie improves the classification performance and Naive Bayes outperforms Rocchio. C1 [Zhang, Qirui; Xue, Yonggang; Zhou, Huaying] Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. RP Zhang, QR (reprint author), Guangdong Pharmaceut Univ, Coll Med Informat Engn, Guangzhou, Guangdong, Peoples R China. EM qiruizhang@163.com; tanjh@gsta.com CR Chawla N. V., 2004, ACM SIGKDD EXPLORATI, V6, P1, DOI DOI 10.1145/1007730.1007733 Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 Han E.-H., 2000, 4 EUR C PRINC PRACT, P424 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Jiang Dan, 2004, INFORM THEORY CODING Mitchell T., 1996, MACHINE LEARNING *OHSUMED, OHSUMED DAT SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yan X., 2008, J SOFTWARE, V19, P82 YIMING Y, 1997, P ICML 97 14 INT C M, P412 YIMING Y, 1999, SIGIR FORUM, P42 Zhang QR, 2005, PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, P3792 MEDLINE DATABASE INF NR 14 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3561-6 PY 2008 BP 437 EP 440 DI 10.1109/FBIE.2008.83 PG 4 WC Engineering, Biomedical SC Engineering GA BKH73 UT WOS:000268135100107 ER PT B AU Shi, XF Zhao, Y Dong, XJ AF Shi, Xuefin Zhao, Ying Dong, Xiangjun BE Ma, J Yin, YL Yu, J Zhou, SG TI Web Page Categorization Based on k-NN and SVM Hybrid Pattern Recognition Algorithm SO FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS LA English DT Proceedings Paper CT 5th International Conference on Fuzzy Systems and Knowledge Discovery CY OCT 18-20, 2008 CL Jinan, PEOPLES R CHINA SP Shandong Univ, Int Nat Computat & Knowledge Discovery Assoc ID SELECTION AB Traditional Information Retrieval (IR) method use keywords matching to filter the documents, but usually retrieves unrelated web pages. In order to effectively classify web pages, we present a Web page categorization algorithm, named WebPSC (Web Page Similarity Categorization). This algorithm uses Latent Semantic Indexing (LSI) to model Web pages and implement categorization based on hybrid pattern recognition algorithm of the k-NN and Support Vector Machine (SVM). an implementation of WebPSC, an intelligent agent system to acquire user interest and help user retrieving Web pages is presented. Empirical results of using this algorithm indicate our method can reach high levels of accuracy in Web page classification. C1 [Shi, Xuefin; Zhao, Ying] Beijing Univ Chem Technol, Sch Informat Sci & Technol, Beijing 100029, Peoples R China. RP Shi, XF (reprint author), Beijing Univ Chem Technol, Sch Informat Sci & Technol, Beijing 100029, Peoples R China. EM shixl@mailbuct.edu.cn; zhaoy@mail.buct.edu.cn; dxj@sdili.edu.cn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bow Andrew McCallum, BOW TOOLKIT STAT LAN Chakrabart S., 2000, SIGKDD EXPLORATIONS, V1, P1 Chen RC, 2006, EXPERT SYST APPL, V31, P427, DOI 10.1016/j.eswa.2005.09.079 Dasarathy BV, 1991, MCGRAW HILL COMPUTER DEERWESTER S, J SOC INFORM SCI, V41, P391 DUMAIS ST, NATL I STANDARDS TEC, P219 Giacinto G, 2000, LECT NOTES COMPUT SC, V1876, P87 Godoy D, 2006, INFORM SYST, V31, P247, DOI 10.1016/j.is.2005.02.008 KWOK TY, P 5 INT C NEUR INF 1, P347 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUTZE H, P SIGIR 97, P74 Shima K, 2004, PATTERN RECOGN LETT, V25, P1051, DOI 10.1016/j.patrec.2004.03.002 Vapnic VN, 1995, NATURE STAT LEARNING WANG Q, CHINESE HIGH TECHNOL, V15, P19 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 17 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3305-6 PY 2008 BP 523 EP 527 DI 10.1109/FSKD.2008.574 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Mathematics, Applied; Statistics & Probability SC Computer Science; Engineering; Mathematics GA BJA89 UT WOS:000264268600104 ER PT B AU Qu, Q Qiu, JN Sun, CY Wang, YZ AF Qu, Qiang Qiu, Jiangnan Sun, Chenyan Wang, Yanzhang BE Ma, J Yin, YL Yu, J Zhou, SG TI Graph-based Knowledge Representation Model and Pattern Retrieval SO FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS LA English DT Proceedings Paper CT 5th International Conference on Fuzzy Systems and Knowledge Discovery CY OCT 18-20, 2008 CL Jinan, PEOPLES R CHINA SP Shandong Univ, Int Nat Computat & Knowledge Discovery Assoc ID VECTOR-SPACE MODEL AB Knowledge representation and pattern retrieval are the basis of knowledge discovery and reasoning. Different from many knowledge representation models such as production rules, graph model used to present context information in text has been envisioned as an appropriate solution to solve complex relevance more acceptably by the user. In this paper, a novel graph model, Feature Event Dependency Graph (FEDG) is proposed FEDG emphasizes on representing the fact level knowledge compressively without losing important information. Meanwhile, based on this model, we propose retrieval and rank strategies for knowledge pattern retrieval which is meaningful for effective reasoning and latent knowledge discovery on large volumes of text knowledge. Extensive experiments on real knowledge sets, containing hundreds of domain specific rule based knowledge, demonstrate the. feasibility and effectiveness of the proposed scheme. C1 [Qu, Qiang; Qiu, Jiangnan; Wang, Yanzhang] Dalian Univ Technol, Sch Management, Dalian 116024, Peoples R China. RP Qu, Q (reprint author), Dalian Univ Technol, Sch Management, Dalian 116024, Peoples R China. EM nicky.jcoder@gmail.com CR CLAIFF ME, 1997, COMPUTATIONAL NATURA, P9 GILLESPIE GH, 1993, PART ACC C WASH DC U, V1, P86 Mao WL, 2007, DATA KNOWL ENG, V61, P76, DOI 10.1016/j.datak.2006.02.008 Meditskos G, 2008, IEEE T KNOWL DATA EN, V20, P397, DOI 10.1109/TKDE.2007.190699 Montes-y-Gomez M., 2000, LECT NOTES COMPUTER, V1873, P312 Nasukawa T, 2001, IBM SYST J, V40, P967 Persson A., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications Pires TT, 2007, DATA KNOWL ENG, V63, P270, DOI 10.1016/j.datak.2007.02.005 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAROJ S, 2007, P 25 IASTED INT MULT, P435 SCHENKER A, 2003, P 7 INT C DOC AN REC, P312 Sowa J., 1984, SYSTEMS PROGRAMMING SOWA JF, 1976, IBM J RES DEV, V20, P336 NR 13 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3305-6 PY 2008 BP 541 EP 545 DI 10.1109/FSKD.2008.7 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Mathematics, Applied SC Computer Science; Engineering; Mathematics GA BJA92 UT WOS:000264270500100 ER PT S AU Crouch, CJ Crouch, DB Kamat, N Malik, V Mone, A AF Crouch, Carolyn J. Crouch, Donald B. Kamat, Nachiket Malik, Vikram Mone, Aditya BE Fuhr, N Kamps, J Lalmas, M Trotman, A TI Dynamic Element Retrieval in the Wikipedia Collection SO FOCUSED ACCESS TO XML DOCUMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-19, 2007 CL Dagstuhl, GERMANY AB This paper describes the successful adaptation of our methodology for the dynamic retrieval of XML elements to a semi-structured environment. Working with text that contains both tagged and untagged elements presents particular challenges in this context. Our system is based on the Vector Space Model; basic functions are performed using the Smart experimental retrieval system. Dynamic element retrieval requires only a single indexing of the document collection at the level of the basic indexing node (i.e., the paragraph). It returns a rank-ordered list of elements identical to that produced by the same query against an all-element index of the collection. Experimental results are reported for both the 2006 and 2007 Ad-hoc tasks. C1 [Crouch, Carolyn J.; Crouch, Donald B.; Kamat, Nachiket; Malik, Vikram; Mone, Aditya] Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. EM ccrouch@d.umn.edu CR Crouch CJ, 2007, LECT NOTES COMPUT SC, V4518, P82 Crouch CJ, 2006, ACM T INFORM SYST, V24, P437, DOI 10.1145/1185877.1185880 Crouch CJ, 2006, LECT NOTES COMPUT SC, V3977, P268 FOX EA, 1983, THESIS CORNELL U GANAPATHIBHOTLA M, 2006, THESIS U MINNESOTA D KAMAT N, 2007, THESIS U MINNESOTA D Khanna S, 2005, THESIS U MINNESOTA D MALIK V, 2007, THESIS U MINNESOTA D MONE A, 2007, THESIS U MINNESOTA D Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 NR 13 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85901-7 J9 LECT NOTES COMPUT SC PY 2008 VL 4862 BP 70 EP 79 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIP02 UT WOS:000261444400006 ER PT S AU Tanioka, H AF Tanioka, Hiroki BE Fuhr, N Kamps, J Lalmas, M Trotman, A TI A Fast Retrieval Algorithm for Large-Scale XML Data SO FOCUSED ACCESS TO XML DOCUMENTS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 6th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-19, 2007 CL Dagstuhl, GERMANY ID INEX AB This paper proposes a novel approach for retrieving large-scale XML data using the vector space model. The vector space model is commonly used in the information retrieval community. Last year, for the Evaluation of XML Retrieval (INEX) 2006 Adhoc Track, we developed a system using fragment elements. The system made it possible to search over XML elements for queries with varying constraints on XML elements to be included in the search, without the need for reindexing the collection, supporting more flexible queries. However the system took significant time to unitize the fragment elements. To solve the problem, our new system is composed of an inverted-file list and a relative inverted-path list on the INEX 2007 Adhoc Track corpus. C1 JustSyst Corp, Innovat Technol R&D, Tokushima, Japan. RP Tanioka, H (reprint author), JustSyst Corp, Innovat Technol R&D, 108-4 Hiraishi Wakamatsu,Kawauchi Cho, Tokushima, Japan. EM hiroki.tanioka@justsystems.com CR Amer-Yahia S, 2006, SIGMOD RECORD, V35, P16 [Anonymous], XQUERY 1 0 XML QUERY [Anonymous], XML PATH LANGUAGE XP BAEZAYATES R, 1999, ACM PRESS SERIES, P1 CLARKE C, 2006, INEX 2006 RETRIEVAL EUGEN P, 2007, LNCS, V4518, P185 EVANS DA, 1994, TREC, P137 GEVA S, 2006, LNCS, V4518, P137 HATANO K, 2006, IEICE T INF SYST, V89, P422 Kazai G, 2006, LECT NOTES COMPUT SC, V3977, P16 PEHCEVSKI J, 2007, INEX 2007 E IN PRESS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIMIZU T, 2007, IEICE T INF SYST, V99, P159 SHIN D, 1998, DL 1998, P235 Tanioka H, 2007, LECT NOTES COMPUT SC, V4518, P45 WEIGEL F, 2004, WEBDB 04 P 7 INT WOR, P67 EXTENSIBLE MARKUP LA NR 17 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85901-7 J9 LECT NOTES COMPUT SC PY 2008 VL 4862 BP 129 EP 137 PG 9 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BIP02 UT WOS:000261444400012 ER PT S AU Olivas, JA Garces, PJ de la Mata, J Romero, FP Serrano-Guerrero, J AF Olivas, Jose A. Garces, Pablo J. de la Mata, Javier Romero, Francisco P. Serrano-Guerrero, Jesus BE Nikravesh, M Kacprzyk, J Zadeh, LA TI Conceptual soft-computing based web search: FIS-CRM, FISS metasearcher and GUMSe architecture SO FORGING NEW FRONTIERS: FUZZY PIONEERS II SE Studies in Fuzziness and Soft Computing LA English DT Proceedings Paper CT BISCE International Conference 2005 CY NOV 03-06, 2005 CL Berkeley, CA HO Univ Calf Berkeley DE soft computing; Internet search; agents ID MODEL AB In this work it is presented a brief summary of FIS-CRM (Fuzzy Interrelations and Synonymy Conceptual Representation Model), its first application FISS (Fuzzy Interrelations and Synonymy Searcher), and a new platform based on agents GUMSe (GUM Search), developed with the aim of improving the semantic capabilities of Web search engines. C1 [Olivas, Jose A.; de la Mata, Javier; Romero, Francisco P.; Serrano-Guerrero, Jesus] Univ Castilla La Mancha, Dept Comp Sci, E-13071 Ciudad Real, Spain. RP Olivas, JA (reprint author), Univ Castilla La Mancha, Dept Comp Sci, Paseo Univ 4, E-13071 Ciudad Real, Spain. EM JoseAngel.Olivas@uclm.es; pgarces@dccia.ua.es; fpromero@soluziona.com RI Serrano-Guerrero, Jesus/A-4137-2013 OI Serrano-Guerrero, Jesus/0000-0002-6177-8188 CR ABASOLO C, 2002, P C CAT INT LIG ART Bordogna G., 1995, FUZZINESS DATABASE M, P231 BREZEALE D, 1999, THESIS U TEXAS ARLIN Bruza Peter, 2000, P 23 ANN INT ACM SIG, P280, DOI 10.1145/345508.345598 CHOI D, 2001, P BISC INT WORKSH FU, P68 COOLEY R, 1997, 97021 TR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELGADO M, 2003, P 3 C EUSFLAT, P49 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 FERNANDEZ S, 2000, REV SOC ESPANOLA PRO, P89 FERNANDEZ S, 2001, THESIS U SANTIAGO CO GLOVER EJ, 1999, ACM, P210 GONZALO J, 1998, P COLING ACL WORK US Herrera-Viedma E, 2001, INT J UNCERTAIN FUZZ, V9, P77, DOI 10.1142/S0218488501001009 HERRERAVIEDMA R, 2003, P 3 EUSFLAT C ZITT G, P25 KERSCHBERG L, 2001, 2 INT C WEB INF SYST KINGIP L, 2001, P 7 INT C DAT SYS AD KIRYAKOV A, 1999, P NODALIDA 99 NORD C LAFOURCADE M, 2001, P 6 NAT LANG PROC PA, P127 LEACOCK C, 1998, WORDNET ELECT LEXICA, P285 LI Z, 2001, P 2001 AM C INF SYST LOUPY C, 2002, P LREC2002 WORKSH LI MARTINBAUTISTA MJ, 2001, P BISC INT WORKSH FU, P19 Miller G., 1990, INT J LEXICOGRAPHY, V3 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MIYAMOTO S, 1983, IEEE T SYST MAN CYB, V13, P62 MIYAMOTO S, 1990, FUZZY SET SYST, V38, P191, DOI 10.1016/0165-0114(90)90149-Z OHGAYA R, 2002, P 2002 NAFIPS ANN M, P274 Olivas JA, 2003, INT J APPROX REASON, V34, P201, DOI 10.1016/j.ijar.2003.07.008 Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAMAKRISHNAN G, 2004, 2 GLOB WORDNET C RICARTE I, 2001, P BISC INT WORKSH FU, P80 ROMERO FP, 2002, P INT C ART INT ICAI, P225 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SERRANOGUERRERO.J, 2004, 2004 ANN M N AM FUZZ, P647 SUN J, 2003, IEEE INT C NATL LANG TAKAGI T, 2001, P BISC INT WORKSH FU, P53 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 VOORHESS E, 1998, WORDNET ELECT LEXICA WHALEY JM, 1999, PCSTR99352 Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 Zadeh L. A., 1987, FUZZY SETS APPL ZADEH LA, 2003, P ALT WEB INT C AWIC ZAMIR O, 1999, P WWW8 NR 46 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1434-9922 BN 978-3-540-73184-9 J9 STUD FUZZ SOFT COMP PY 2008 VL 218 BP 107 EP 134 PG 28 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BHB64 UT WOS:000252100200005 ER PT S AU Soto, A Olivas, JA Prieto, ME AF Soto, Andres Olivas, Jose A. Prieto, Mannel E. BE Bello, R Falcon, R Pedrycz, W Kacprzy, J TI Fuzzy approach of synonymy and polysemy for Information Retrieval SO GRANULAR COMPUTING: AT THE JUNCTION OF ROUGH SETS AND FUZZY SETS SE STUDIES IN FUZZINESS AND SOFT COMPUTING LA English DT Proceedings Paper CT 1st International Symposium on Fuzzy and Rough Sets CY DEC 05-08, 2006 CL Santa Clara, CUBA DE web information retrieval; fuzzy set; query expansion; vector space model; synonymy; polysemy ID MODEL AB Development of methods for Information Retrieval based oil conceptual aspects is vital to reduce the quantity of unimportant documents retrieved by tire search engines. In this chapter, a method for expanding user queries is presented, such that for each term in the original query, all of its synonymys by a certain meaning with maximum concept frequency are introduced. To measure the degree of concept. presence in a document (or even in a document collection), a concept frequency formula is introduced. New fuzzy formulas are also introduced to calculate the synonymy degree between terms to manage with concepts (meanings). With them, even though a certain term does riot appear in a document, some degree of its presence could be estimated based on its degree of synonymy with terms that do appear in the document. A polysemy index is also introduced in order to simplify the treatment of weak and strong words. C1 [Soto, Andres] Univ Autonoma Carmen, Dept Comp Sci, Ciudad Del Carmen 24160, Campeche, Mexico. RP Soto, A (reprint author), Univ Autonoma Carmen, Dept Comp Sci, Ciudad Del Carmen 24160, Campeche, Mexico. RI Prieto Mendez, Manuel/A-8047-2011 OI Prieto Mendez, Manuel/0000-0003-0343-1482 CR [Anonymous], 1998, WORDNET ELECT LEXICA Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barrett R., 1995, AIM NEW APPROACH M I BLECUA J, 1997, DICCIONARIO AVANZADO, P647 de la Mata J, 2004, IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS, P62 DELGADO M, 2003, P 3 C EUSFLAT, P49 FERNANDEZ S, 2001, THESIS SANTIAGO COMP FERNANDEZ S, 2002, JOTRI 2002 ACTAS JOR GARCES P, 2002, P ICAI 2002, P219 Garces PJ, 2006, J AM SOC INF SCI TEC, V57, P564, DOI 10.1002/asi.20310 GONZALO J, 1998, P COLING ACL WORK US GOOLEY R, 1997, 97021 TR U MINN HAMDI M, 2006, APPL SOFT C IN PRESS Herrera-Viedma E., 2003, P 3 C EUSFLAT, P25 KIRYAKOV A, 1999, P NODALIDA 1999 NORD Kowalski G., 1997, INFORM RETRIEVAL SYS LAFOURCADE M, 2003, LNCS, V2817, P19 LAFOURCADE M, 2001, P 6 NAT LANG PROC PA, V202, P127 LARSEN B, 1999, P KDD 1999 LEACOCK C, 1998, WORDNET ELECT LEXICA, P285 Lin HC, 2006, EXPERT SYST APPL, V31, P397, DOI 10.1016/j.eswa.2005.09.078 LOUPY C, 2002, P LREC 2002 WORKSH L MARTINBAUTISTA MJ, 2002, J SOFT COMPUTING, V6, P365 MENDES M, 2003, FUZZ IEEE 2003 MILLER G, 1995, COMMUN ACM, V11, P39 OLIVAS J, 2007, STUDIES FUZZINESS SO OLIVAS J, 2006, RECUPERACION INFORM, P89 Olivas JA, 2003, INT J APPROX REASON, V34, P201, DOI 10.1016/j.ijar.2003.07.008 Olivas JA, 2006, LECT NOTES COMPUT SC, V4027, P681 Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 Pedrycz W, 1996, PATTERN RECOGN LETT, V17, P625, DOI 10.1016/0167-8655(96)00027-X Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 RICARTE I, 2001, P BISC INT WORKSH FU, P80 Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582] ROBERTSON S, 2006, 356 U CAMBR COMP LAB ROMERO F, 2007, P EUROFUSE WORKSH NE ROMERO F, 2003, 2003 INT C ART INT I Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck J. K., 2000, INFORMATION PROCESSI, V36, P779 Sparck Jones Karen, 1972, J DOC, V28, P11 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 Van Rijsbergen C. J., 1989, INFORM RETRIEVAL WHALEY J, 1999, PCSTR99352 DARTM COL Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZAMIR O, 1999, P WWW8 NR 48 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1434-9922 BN 978-3-540-76972-9 J9 STUD FUZZ SOFT COMP PY 2008 VL 224 BP 179 EP 198 PG 20 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BID14 UT WOS:000258502600012 ER PT B AU Martin-Toral, S Sainz-Palmero, GI AF Martin-Toral, Susana Sainz-Palmero, Gregorio I. BE Cordeiro, J Filipe, J TI DETECTION OF INCOHERENCES IN A TECHNICAL AND NORMATIVE DOCUMENT CORPUS SO ICEIS 2008: PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS: ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS LA English DT Proceedings Paper CT 10th International Conference on Enterprise Information Systems CY JUN 12-16, 2008 CL Barcelona, SPAIN SP Inst Syst & Technologies Informat, Control & Commun, Workflow Management Coalit, Assoc Adv Artificial Intelligence DE Document corpus; content incoherence; natural language processing; text mining; artificial intelligence; document engineering AB This paper is focused on the problems and effects generated by the use of a document corpus with mistakes, content incoherences amongst its connected documents and other errors. The problem introduced in this paper is very relevant in any area of human activity when this corpus is used as base element in the relationships between company partners, legal support, etc., and the way in which these incoherences can be detected. These problems can appear in several ways, and the produced effects are different, but a common situation exists in those areas of activity where many linked documents must be generated, managed and updated by different authors. This paper describes some examples of this problem in the case of a technical document corpus used amongst partners, and the solution framework developed for this case. Several types of incoherence have been detected and formulated, connected with problems described in other research areas such as information extraction and retrieval, text mining, document interpretation and others, but all of them have been bounded and introduced from the point of view of document incoherences and their effects, specially in a company context. Finally the computational architecture and methodology uses are described and some initial results of incoherence detection are discussed. C1 [Martin-Toral, Susana; Sainz-Palmero, Gregorio I.] Fdn CARTIF, Comp & Informat Technol Div, Valladolid 47151, Spain. RP Martin-Toral, S (reprint author), Fdn CARTIF, Comp & Informat Technol Div, Parque Tecnol Boecillo, Valladolid 47151, Spain. EM susmar@cartif.es; gresai@eis.uva.es CR ARANGO F, 2003, THESIS Berry M.W., 2004, SURVEY TEXT MINING C CARTIF F, 2006, FIT3501002006272 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Kasturi R., 1995, DOCUMENT IMAGE ANAL Krulwich B, 1997, IEEE EXPERT, V12, P22, DOI 10.1109/64.621224 Mani I., 1999, Information Retrieval, V1, DOI 10.1023/A:1009930203452 Mannila H, 1997, DATA MIN KNOWL DISC, V1, P259, DOI 10.1023/A:1009748302351 McCallum A.K., 1996, BOW TOOLKIT STAT LAN MINGSHAN L, 2002, J MENTAL HLTH POLICY, V5, P191 NAHM UY, 2004, TEXT MINING INFORM E *OASIS, 2007, ORG ADV STRUCT INF S Otterbacher JC, 2002, P ACL WORKSH AUT SUM, P27 RUIZ M, 2002, SISTEMAS JURDICOS CO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 15 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-37-1 PY 2008 BP 282 EP 287 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BIH44 UT WOS:000259488000044 ER PT B AU Long, S Wang, HJ Cai, JH Liu, CJ AF Long, Shun Wang, Hui-Jin Cai, Jian-Hua Liu, Chan-Juan BE Guo, MZ Zhao, L Wang, LP TI Enhancing Intelligent Agents with Information Retrieval Techniques SO ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS LA English DT Proceedings Paper CT 4th International Conference on Natural Computation (ICNC 2008) CY OCT 18-20, 2008 CL Jian, PEOPLES R CHINA SP Int Nat Computat & Knowledge Discovery Assoc, Shandong Univ AB Learning is an integral part of an intelligent agent's problem-solving ability. Most research works on learning assume that information are provided in a complete and well-structured manner and/or when rules are clearly specified, therefore have difficulties in handling cases with incomplete and unstructured data. This paper presents a novel approach to deal with this difficulty. It uses information retrieval techniques to enhance an agent's reasoning and problem-solving ability when only incomplete and unstructured information are available. Preliminary experimental results show that the integration of information retrieval can effectively help an agent to analyze the problem before solving it. C1 [Long, Shun; Wang, Hui-Jin; Cai, Jian-Hua; Liu, Chan-Juan] JiNan Univ, Dept Comp Sci, Guangzhou 510632, Guangdong, Peoples R China. RP Long, S (reprint author), JiNan Univ, Dept Comp Sci, Guangzhou 510632, Guangdong, Peoples R China. EM tlongshun@jnu.edu.cn; twanghj@jnu.edu.cn; kinwat@163.com; liuchanjuan0612@163.com CR BRIDGE D, 2007, P 4 WORKSH TEXT CAS ETZIONI O, 1995, IEEE EXPERT, V10, P44, DOI 10.1109/64.403956 Grossman D. A., 2004, INFORM RETRIEVAL ALG GUPTA KM, 2007, P 4 WORKSH TEXT CAS Jones K. S., 1997, READINGS INFORM RETR Kuokka DR, 1996, INT J COOP INF SYST, V5, P251, DOI 10.1142/S0218843096000105 Mitchell TM, 1997, MACHINE LEARNING NIST, 2003, NIST SPEC PUBL, V800-59 ORECCHIONNI A, 2007, P 4 WORKSH TEXT CAS RODRIGUES L, 2007, P 4 WORKSH TEXT CAS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weiss G., 2001, MULTIAGENT SYSTEMS M Wooldridge M., 2001, INTRO MULTIAGENT SYS NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3304-9 PY 2008 BP 627 EP 631 DI 10.1109/ICNC.2008.15 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BJB49 UT WOS:000264527000126 ER PT B AU El-Haj, MO Hammo, BH AF El-Haj, Mahmoud O. Hammo, Bassam H. TI Evaluation of Query-Based Arabic Text Summarization System SO IEEE NLP-KE 2008: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING LA English DT Proceedings Paper CT IEEE International Conference on Natural Language Processing and Knowledge Engineering CY OCT 19-22, 2008 CL Beijing, PEOPLES R CHINA SP IEEE DE Arabic query-based text summarization; Arabic single document summarization; concordance; stemming ID ABSTRACTS AB In this paper, we present and analyze the results of the application of Arabic Query-Based Text Summarization System - AQBTSS - in an attempt to produce a query-oriented summary for a single Arabic document. For this task, we adapted the traditional Vector Space Model (VSM) and the cosine similarity measure to find the most relevant passages extracted form Arabic document to produce a text summary. We aim at using the short summaries in some Natural Language (NL) tasks such as generating answers for Arabic open domain Question Answering System (AQAS) as well as experimenting with categorizing Arabic scripts. The obtained results indicate that our simple approach for text summarization is promising. C1 [El-Haj, Mahmoud O.; Hammo, Bassam H.] Univ Jordan, CIS Dept, Amman, Jordan. RP El-Haj, MO (reprint author), Univ Jordan, CIS Dept, Amman, Jordan. EM madhajj@hotmail.com; b.hammo@ju.edu.jo RI Hammo, Bassam/F-8388-2015 OI Hammo, Bassam/0000-0002-5270-7409 CR Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Grossman D. A., 1998, INFORM RETRIEVAL ALG Hammo B., 2002, P ACL 02 WORKSH COMP, P1, DOI 10.3115/1118637.1118644 Hammo B, 2004, COMPUT HUMANITIES, V38, P397, DOI 10.1007/s10579-004-1917-3 HARABAGIU S, 2000, P 18 INT C COMP LING, P292 HOUSE MD, 1999, P 9 C EUR CHAPT ASS Hovy E, 1999, ADVANCES IN AUTOMATIC TEXT SUMMARIZATION, P81 Jing H., 2000, P 6 C APPL NAT LANG, P310, DOI 10.3115/974147.974190 KAN M, 2001, P 8 EUR WORKSH NAT L, V8, P1, DOI 10.3115/1117840.1117853 Ker S.J., 2000, P ACL 2000 WORKSH RE, P79 LUHN HP, 1958, IBM J RES DEV, V2, P159 RATH GJ, 1961, AM DOC, V12, P139, DOI 10.1002/asi.5090120210 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sekine S., 2003, P HLT NAACL 03 TEXT, V5, DOI DOI 10.3115/1119467.1119476 Srihari R. R., 2000, P 6 APPL NAT LANG PR, P166, DOI 10.3115/974147.974170 Tellex S., 2003, P 26 ANN INT ACM SIG, P41 White M., 2001, P 1 INT C HUM LANG T, P1, DOI 10.3115/1072133.1072206 Wikipedia contributors, FREE ENCY Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 21 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2779-6 PY 2008 BP 88 EP 94 PG 7 WC Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BJL58 UT WOS:000266767300014 ER PT B AU Andrysco, N Benes, B Gurney, K AF Andrysco, Nathan Benes, Bedrich Gurney, Kevin BE Ebert, D Ebert, D Ertl, T TI Interactive Poster: Visual Analytic Techniques for CO(2) Emissions and Concentrations in the United States SO IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY 2008, PROCEEDINGS SE IEEE Symposium on Visual Analytics Science and Technology LA English DT Proceedings Paper CT IEEE Symposium on Visual Analytics Science and Technology CY OCT 21-23, 2008 CL Columbus, OH SP IEEE DE Visual Analytics; CO(2); Multivariate and Spatio-Temporal Data; Volume Visualization; GIS; Interactive Visualization ID SYSTEM AB Climate Change has emerged as one of the grand global challenges facing humanity. The dominant anthropogenic greenhouse gas that seems to be contributing to the climate change problem, carbon dioxide (COD, has a complex cycle through the atmosphere, oceans and biosphere. The combustion of fossil fuels (power production, transportation, etc.) remains the largest source of anthropogenic CO(2) to the Earth's atmosphere. Up until very recently, the quantification of fossil fuel CO(2) was understood only at coarse space and time scales. A recent research effort has greatly improved this space/time quantification resulting in source data at a resolution of less than 10 km(2)/hr at the surface of North America. By providing visual tools to examine this new, high resolution CO(2) data, we can better understand the way that CO(2) is transmitted within the atmosphere and how it is exchanged with other components of the Earth System. We have developed interactive visual analytic tools, which allows for easy data manipulation, analysis, and extraction. The Visualization system is aimed for a wide range Of users which include researchers and political leaders. The goal is to help assist these people in analyzing data and enabling new policy options in mitigation of fossil fuel CO(2) emissions in the U.S. C1 [Andrysco, Nathan] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. RP Andrysco, N (reprint author), Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA. EM nandrysc@purdue.edu; bbenes@purdue.edu; kgurney@purdue.edu RI Benes, Bedrich/ OI Benes, Bedrich/0000-0002-5293-2112 CR AHN J, 2008, WWW 08 AHN J, 2007, WEB INT IEEE WIC ACM, P706 Gauch S., 2007, LNCS, V4321, P54 Micarelli A, 2004, USER MODEL USER-ADAP, V14, P159, DOI 10.1023/B:USER.0000028981.43614.94 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69, DOI 10.1016/0306-4573(93)90024-8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 6 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-1-4244-2935-6 J9 IEEE S VIS ANAL PY 2008 BP 173 EP 176 PG 4 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BJC49 UT WOS:000264748600022 ER PT S AU Melucci, M AF Melucci, Massimo BE Agosti, M TI Modeling Retrieval and Navigation in Context SO INFORMATION ACCESS THROUGH SEARCH ENGINES AND DIGITAL LIBRARIES SE Information Retrieval Series LA English DT Article; Book Chapter DE vector spaces; information retrieval in context; personalization; implicit feedback; Vector Space Model ID INFORMATION-RETRIEVAL AB There is a growing realization that context can constrain Information Retrieval thereby reducing the complexity of a retrieval system. At this aim, a system has to retrieve documents by considering time, place, interaction, task, and many other factors that are implicit in the user environment. Instead of resorting to heuristics, a principled approach to Information Retrieval in Context may help understand how to design these systems. In this chapter, a principled approach to context-aware navigation and retrieval is presented. C1 [Melucci, Massimo] Univ Padua, Fac Stat, I-35131 Padua, Italy. [Melucci, Massimo] Univ Padua, Dept Informat Engn, I-35131 Padua, Italy. EM melo@dei.unipd.it CR BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 BELKIN NJ, 2003, DISC GROUP CONT BAS DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dubin D, 2004, LIBR TRENDS, V52, P748 Ingwersen P., 1992, INFORM RETRIEVAL INT MELUCCI M, 2007, P ACM C INF IN PRESS Melucci M., 2005, P ACM C INF KNOWL MA, P808, DOI 10.1145/1099554.1099745 MELUCCI M, 2007, P 2 WORKSH IN PRESS MELUCCI M, 2006, P ACM C INF KNOWL MA, P477 MELUCCI M, 2007, P AAAI SPRING S QUAN Rocchio J., 1971, SMART RETRIEVAL SYST Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 2004, GEOMETRY INFORM RETR van Rijsbergen C. J., 1979, INFORM RETRIEVAL White RW, 2005, ACM T INFORM SYST, V23, P325, DOI 10.1145/1080343.1080347 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Zigoris P., 2006, P 15 ACM INT C INF K, P397, DOI 10.1145/1183614.1183672 NR 19 TC 0 Z9 0 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-540-75133-5 J9 INFORM RETRIEVAL SER PY 2008 VL 22 BP 43 EP 57 DI 10.1007/978-3-540-75134-2_3 D2 10.1007/978-3-540-75134-2 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BJW76 UT WOS:000267327400003 ER PT S AU Costantino, M Coletti, P AF Costantino, M Coletti, P TI Information Extraction in Finance SO INFORMATION EXTRACTION IN FINANCE SE Advances in Management Information LA English DT Book ID GENETIC ALGORITHMS; RETRIEVAL; MODEL CR ABERDEEN J, 1993, P 5 MESS UND C MUC 5 ABERDEEN J, 1995, P 6 MESS UND C MUC 6 ACKLEY DH, 1985, COGNITIVE SCI, V9, P147 Almas Y., 2006, P WORKSH INF EXTR DO, P56, DOI 10.3115/1641408.1641415 ANDERSEN T, 2002, 8959 NAT BUR EC RES Aone C., 1998, P 7 MESS UND C MUC 7 Appel G., 1985, MOVING AVERAGE CONVE Azoff E. M., 1994, NEURAL NETWORK TIME BAUM LE, 1970, ANN MATH STAT, V41, P164, DOI 10.1214/aoms/1177697196 BEINHOCKER ERIC D., 2006, ORIGIN WEALTH EVOLUT BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Black William J., 1998, P 7 MESS UND C MUC 7 BOGURAEV B, 1995, J NATURAL LANGUAGE E, V1, P1 BOKMA AF, 1992, INT C INF PROC MAN U Bontcheva K., 2003, P WORKSH INF EXTR SL Borthwick A, 1998, P 7 MESS UND C MUC 7 BRILL E, 1994, NAT C ART INT Broekstra J., 2001, SEMANTICS WWW BUCKLEY C, 2006, TREE EVALUATION PACK CALIFF ME, 1999, P 16 NAT C ART INT O CALLAGHAN PC, 1998, THESIS U DURHAM Chen S. H., 2002, GENETIC ALGORITHMS G CHINCHOR N, 1995, P 6 MESS UND C MUC 6 CHINCHOR N, 1993, P 5 MESS UND C MUC 5 Chinchor N, 1992, P 4 MESS UND C, P22, DOI 10.3115/1072064.1072067 Colander David, 2000, COMPLEXITY VISION TE COSTANTINO M, 1996, P 18 INT INF TECHN I COSTANTINO M, 1997, THESIS U DURHAM COSTANTINO M, 1996, NEURO VEST J, V4, P14 COSTANTINO M, 1996, P 2 M ART INT ACC FI COSTANTINO M, 1996, MAGAZINE ARTIFICIAL, V2, P42 COSTANTINO M, 1999, J COMPUTATIONAL INTE, V7, P34 COSTANTINO M, 1999, P INT C INF TECHN IN COWELL RG, 2005, PROBABILISTIC NETWOR CROFT WB, 1992, TEXT BASED INTELLIGE Cunningham H., 2002, P 40 ANN M ASS COMP CUNNINGHAM H, 2007, GATE USER GUIDE Cunningham H, 2002, COMPUT HUMANITIES, V36, P223, DOI 10.1023/A:1014348124664 CUNNINGHAM H, 2000, CS0010 U SHEFF I LAN *DARPA, 1991, P 3 MESS UND C MUC 3 *DARPA, 1993, P 5 MESS UND C MUC 5 *DARPA, 1998, P 7 MESS UND C MUC 7 *DARPA, 1992, P 4 MESS UND C MUC 4 Defense Advanced Research Projects Agency (DARPA), 1995, P 6 MESS UND C MUC 6 DEJONG G, 1982, STRATEGIES NATURAL L, P149 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 EDWARDS RD, 2001, TECHNICALANALYSIS ST EGELI B, 2003, HAW INT C BUS ENGLE RF, 1993, J FINANC, V48, P1749, DOI 10.2307/2329066 Fellbaum C, 1998, WORDNET ELECT LEXICA Fellbaum C., 1998, ELECT LEXICAL DATABA Fine S, 1998, MACH LEARN, V32, P41, DOI 10.1023/A:1007469218079 FINN A, 2004, P EUR C MACH LEARN FINN A, 2004, P WORKSH AD TEXT EXT Freitag D., 1998, THESIS CARNEGIE MELL FUKUMOTO J, 1998, P 7 MESS UND C MUC 7 GAIZAUSKAS R, 1995, P 6 MESS UND C MUC 6 GARIGLIANO R, 1998, P 7 MESS UND C MUC 7 Garigliano R, 1993, P 13 INT C ART INT E GILBERT J, 1995, ARTIFICIAL INTELLIGE Grishman R., 1995, P 6 MESS UND C MUC 6 GRISHMAN R, 1993, P 5 MESS UND C MUC 5 GRISHMAN R, 1989, SPEECH AND NATURAL LANGUAGE, P71 Grishman R., 1995, TIPSTER PHASE 2 ARCH GROSSMAN DA, 2006, INFORM RETRIEVAL ALG HARMAN DK, 1994, P 2 TEXT RETR C TREC HARMAN DK, 1996, P 5 TEXT RETR C TREC HARMAN DK, 1995, P 4 TEXT RETR C TREC HARMAN DK, 1994, P 3 TEXT RETR C TREC HATAMIAN T, 1995, MAGAZINE ARTIFICIAL, V2, P48 HEDERMAN L, 1992, NATURAL LANGUAGE PRO Hepple M., 2000, P 38 ANN M ASS COMP HOBBS J, 1993, P 5 MESS UND C MUC 5 HOBBS J, 1994, ARPA WORKSH HUM LANG, P172 Humphreys K., 1998, P 7 MESS UND C MUC 7 JACOBS L, 2005, TRADERS WORLD, V39, P29 JACOBS PS, 1993, P 5 MESS UND C MUC 5 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 JONES KS, 1992, TEXT BASED INTELLIGE KAN M, 2001, P DOC UND WORKSH DUC KAN M, 2001, P 8 EUR WORKSH NAT L KINGDON J, 1994, NEURAL NETWORKS CAPI, P261 KIRKPATRICK S, 1983, SCIENCE, V220, P671, DOI 10.1126/science.220.4598.671 KIRYAKOV A, 2005, P 2 EUR SEM WEB C ES KRUPKA GR, 1995, P 6 MESS UND C MUC 6 LEE R, 1995, P 6 MESS UND C MUC 6 LEVITT ME, 1994, NEURAL NETWORKS CAPI, P233 Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LISBOA PJG, 2000, BUSINESS APPL WORLD LONG D, 1994, REASONING ANALOGY CA LUCONI FL, 1994, DECIS SUPPORT SYST, P365 LYTINEN SL, 1986, 9 INT JOINT CONC ART, P821 Mahfoud S, 1996, APPL ARTIF INTELL, V10, P543, DOI 10.1080/088395196118425 MATSUMOTO Y, 1997, JAPANESE MORPHOLOGIC MAYNARD D, 2000, 0006 CS U SHEFF DEP McCarthy J, 1955, PROPOSAL DARTMOUTH S McCarthy John, 1969, MACH INTELL, V4, P463 MCCARTHY W, 1992, INT J INTELL SYST, V1, P53 MCKEOWN K, 2003, P ANN C N AM CHAPT A McKeown K., 2001, P DOC UND WORKSH DUC McKeown K., 2002, P HUM LANG TECHN C S Meadow CT, 2007, LIBR INFORM SCI SER, P1 Meyer CD, 2006, GOOGLES PAGERANK SCI MILLER S, 1998, P 7 MESS UND C MUC 7 MITCHEL C, 1996, NEWS FORMULA CONCISE Moens M. -F., 2006, INFORM EXTRACTION AL Morgan R., 1995, P 6 MESS UND C MUC 6 MURATA M, 1994, P IREX WORKSH NAVIGLI R, 2004, COMP LINGUISTICS, V30, P8 NEWQUIST H, 1995, MAGAZINE ARTIFICIAL, V2 OGDEN W, 1994, MCCS94264 NEW MEX ST OKEEFE RM, 1993, INT J INTELL SYST, V2, P177 ONYSHKEVYCH B, 1993, P 5 MESS UND C MUC 5 PARKER B, 1994, THESIS U DURHAM PATTEN T, 1998, P 7 MESS UND C MUC 7 Patwardhan S., 2006, P WORKSH INF EXTR DO, P66, DOI 10.3115/1641408.1641416 PATWARDHAN S, 2007, P 2007 JOINT C EMP C PETERSON DD, 2006, TECHNICAL ANAL STOCK, V24, P68 *PLUM SYST GROUP, 1993, P 5 MESS UND C MUC 5 POPOV B, 2003, P 2 INT SEM WEB C IS RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 RADEV D, 2005, COMMUNICATIONS ASS C, V10 RADEV DR, 2001, P 1 INT C HUM LANG T RAY S, 2001, P 17 INT JOINT C ART REFENES AP, 1996, NEURAL NETWORKS CAPI REFENES AP, 1994, NEURAL NETWORKS CAPI, P101 *REUT, 2001, REUT 3000 XTRA GETT Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd RILOFF E, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P811 RILOFF E, 1994, ACM T INFORM SYST, V12, P296, DOI 10.1145/183422.183428 RILOFF E, 2004, UUCS04015 U UT SCH C RUEY T, 2005, ANAL FINANCIAL TIME Russell S. J., 2003, ARTIFICIAL INTELLIGE, V2nd SABBAGH D, 2006, TIMES SALTON G, 1986, ASS COMP MACHINERY A, V29, P548 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHANK RC, 1981, INSIDE COMP UNDERSTA Schmitt LM, 2001, THEOR COMPUT SCI, V259, P1, DOI 10.1016/S0304-3975(00)00406-0 Sclano F., 2007, P 3 INT C INT ENT SO Sekine S., 1998, P 6 WORKSH VER LARG SELL S, 1986, EXPERT SYSTEMS SEN TK, 1994, NEURAL NETWORKS CAPI, P325 SHADBOLT J, 2006, NEURAL NETWORKS FINA SKOUNAKIS M, 2003, P 18 INT JOINT C ART Smith J., 2003, INTRO EVOLUTIONARY C SMITH MH, 1996, THESIS U DURHAM SMITH MH, 1994, 7 INT WORKSH NAT LAN SOOFI AS, 2002, STUDIES COMP FIN SPR Sowa J. F., 2006, SEMANTIC NETWORKS, V2nd Sowa J. F., 1999, KNOWLEDGE REPRESENTA Sowa J.F., 1991, PRINCIPLES SEMANTIC STANFILL C, 1992, TEXT BASED INTELLIGE STEURER E, 1994, NEURAL NETWORKS CAPI, P199 Stevenson M., 2006, P WORKSH INF EXTR DO, P12, DOI 10.3115/1641408.1641410 Stevenson M., 2005, P 43 ANN M ASS COMP STRZALKOWSKI T, 1994, ARPA WORKSH HUM LANG, P328 SUDO K, 2003, P 41 ANN M ASS COMP Sudo K., 2001, P 1 INT C HUM LANG T SUNDHEIM BM, 1993, P 5 MESS UND C MUC 5 TAIT JI, 1982, THESIS U CAMBRIDGE Tan P. N., 2005, INTRO DATA MINING Tomita M., 1986, EFFICIENT PARSING NA *TPS GROUP BBN, 1995, P 6 MESS UND C MUC 6 Vapnik V., 1995, NATURE STAT LEARNING VOORHEES E, 2006, 15 TEXT RETRIEVAL C VOORHEES E. M., 2005, P 14 TEXT RETR C TRE WALCZAK S, 1995, MAGAZINE ARTIFICIAL, V2, P48 WATKINS PR, 1993, EXPERT SYSTEMS BUSIN WRIGHT G, 1993, INT J INTELL SYST, V2, P113 YANGARBER R, 1998, P 7 MESS UND C MUC 7 Zahedi F., 1993, INTELLIGENT SYSTEMS NR 171 TC 0 Z9 0 PU WIT PRESS/COMPUTATIONAL MECHANICS PUBLICATIONS PI SOUTHAMPTON PA ASHURST LODGE, ASHURST, SOUTHAMPTON SO40 7AA, ENGLAND SN 1742-0172 BN 978-1-84564-146-7 J9 ADV MANAG INFORM PY 2008 VL 8 BP 1 EP 174 PG 174 WC Computer Science, Information Systems SC Computer Science GA BWY30 UT WOS:000295290300008 ER PT J AU Efron, M AF Efron, Miles TI Query expansion and dimensionality reduction: Notions of optimality in Rocchio relevance feedback and latent semantic indexing SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE latent semantic indexing (LSI); relevance feedback; information retrieval ID MODEL AB Rocchio relevance feedback and latent semantic indexing (LSI) are well-known extensions of the vector space model for information retrieval (IR). This paper analyzes the statistical relationship between these extensions. The analysis focuses on each method's basis in least-squares optimization. Noting that LSI and Rocchio relevance feedback both alter the vector space model in a way that is in some sense least-squares optimal, we ask: what is the relationship between LSI's and Rocchio's notions of optimality? What does this relationship imply for IR? Using an analytical approach, we argue that Rocchio relevance feedback is optimal if we understand retrieval as a simplified classification problem. On the other hand, LSI's motivation comes to the fore if we understand it as a biased regression technique, where projection onto a low-dimensional orthogonal subspace of the documents reduces model variance. (c) 2007 Elsevier Ltd. All rights reserved. C1 Univ Texas Austin, Sch Informat, Austin, TX 78712 USA. RP Efron, M (reprint author), Univ Texas Austin, Sch Informat, Sanchez Bldg 564,1 Univ Stn D7000, Austin, TX 78712 USA. EM miles@ischool.utexas.edu CR Anderson T.W., 2003, INTRO MULTIVARIATE S, V3rd Bast H., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Buckley C., 1994, OV 3 RETR C TREC 3 N, P69 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Duda R.O., 2001, PATTERN CLASSIFICATI DUMAIS ST, 1992, TEXT RETR C, P137 Dumais ST, 2004, ANNU REV INFORM SCI, V38, P189 Eckart C, 1936, PSYCHOMETRIKA, V1, P211, DOI 10.1007/BF02288367 Efron M, 2005, J AM SOC INF SCI TEC, V56, P969, DOI 10.1002/asi.20188 Golub G. H., 1996, MATRIX COMPUTATIONS, V3rd Hastie T, 2001, ELEMENTS STAT LEARNI Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 HULL D, 1995, THESIS STANFORD U Hyvarinen A., 2001, INDEPENDENT COMPONEN Jolliffe IT, 2002, PRINCIPAL COMPONENT, V2 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Losee R.M., 1998, TEXT RETRIEVAL FILTE Neter J., 1996, APPL LINEAR STAT MOD PLACKETT RL, 1972, BIOMETRIKA, V59, P239, DOI 10.2307/2334569 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638 Salton G., 1997, IMPROVING RETRIEVAL, P355 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Story RE, 1996, INFORM PROCESS MANAG, V32, P329, DOI 10.1016/0306-4573(95)00055-0 Wong S. K. Michael, 1985, SIGIR, P18, DOI DOI 10.1145/253495.253506 NR 25 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2008 VL 44 IS 1 BP 163 EP 180 DI 10.1016/j.ipm.2006.12.008 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 238IT UT WOS:000251441600011 ER PT S AU Xu, Y Wang, B Li, JT Jing, HF AF Xu, Yan Wang, Bin Li, JinTao Jing, Hongfang BE Li, H Liu, T Ma, WY Sakai, T Wong, KF Zhou, GD TI An extended document frequency metric for feature selection in text categorization SO INFORMATION RETRIEVAL TECHNOLOGY SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 4th Asia Information Retrieval Symposium CY JAN 15-18, 2008 CL Harbin, PEOPLES R CHINA DE rough set; text categorization; feature selection; document frequency AB Feature selection plays an important role in text categorization. Many sophisticated feature selection methods such as Information Gain (IG), Mutual Information (MI) and chi(2) statistic measure (CHI) have been proposed. However, when compared to these above methods, a very simple technique called Document Frequency thresholding (DF) has shown to be one of the best methods either on Chinese or English text data. A problem is that DF method is usually considered as an empirical approach and it does not consider Term Frequency (TF) factor. In this paper, we put forward an extended DF method called TFDF which combines the Term Frequency (TF) factor. Experimental results on Reuters-21578 and OHSUMED corpora show that TFDF performs much better than the original DF method. C1 [Xu, Yan; Wang, Bin; Li, JinTao; Jing, Hongfang] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China. RP Xu, Y (reprint author), Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan S Rd, Beijing, Peoples R China. CR Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 ITNER DJ, 1995, P 4 ANN S DOC AN INF, P301 KOMOROWSKI J, 1999, NEW TREND DECISION M, P3 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 LIULING D, 2005, J CHINESE INFORM PRO, V18, P26 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 *REUT, 21578 REUT SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SONGWEI S, 2003, J COMPUTER ENG APPL, V39, P146 Yang S. M., 2002, Proceedings of 2002 International Conference on Machine Learning and Cybernetics (Cat.No.02EX583), DOI 10.1109/ICMLC.2002.1167443 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 ZHANG H, 2004, 17 INT FLAIRS C MIAM NR 17 TC 9 Z9 9 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-68633-0 J9 LECT NOTES COMPUT SC PY 2008 VL 4993 BP 71 EP 82 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BHV70 UT WOS:000256869500008 ER PT S AU Sawyer, P Gacitua, R Stone, A AF Sawyer, Pete Gacitua, Ricardo Stone, Andrew BE Paech, B Martell, C TI Profiling and Tracing Stakeholder Needs SO INNOVATIONS FOR REQUIREMENTS ANALYSIS: FROM STAKEHOLDERS' NEEDS TO FORMAL DESIGNS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 14th Monterey Workshop CY SEP 10-OCT 13, 2007 CL Monterey, CA DE Requirements engineering; natural language processing; information retrieval; tacit knowledge ID REQUIREMENTS; KNOWLEDGE AB The first stage in transitioning from stakeholders' needs to formal designs is the synthesis of user requirements from information elicited from the stakeholders. In this paper we show how shallow natural language techniques can be used to assist analysis of the elicited information and so inform the synthesis of the user requirements. We also show how related techniques can be used for the subsequent management of requirements and even help detect the absence of requirements' motivation by identifying unprovenanced requirements. C1 [Sawyer, Pete; Gacitua, Ricardo; Stone, Andrew] Univ Lancaster, Lancaster LA1 4WA, England. RP Sawyer, P (reprint author), Univ Lancaster, Lancaster LA1 4WA, England. EM sawyer@comp.lancs.ac.uk; gacitur1@comp.lancs.ac.uk; a.stone1@lancs.ac.uk CR AGUILERA C, 1990, J SYST SOFTWARE, V13, P209, DOI 10.1016/0164-1212(90)90097-6 ARCHER D, 2004, 11 EURALEX INT C EUR, P817 BERRY DM, 1987, J SYST SOFTWARE, V7, P221, DOI 10.1016/0164-1212(87)90044-6 BUSCH P, 2003, AS PAC S INF VIS APV, P37 CHANTREE F, 2006, 14 IEEE INT C REQ EN, P56 Cleland-Huang J., 2007, IEEE COMPUT, V40, P27 Collins H. M., 2001, PRACTICE TURN CONT T, P115 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FABRINI F, 2001, 7 INT WORKSH REQ ENG Fantechi A., 2003, Requirements Engineering, V8, DOI 10.1007/s00766-003-0174-0 Gacitua R, 2008, 7TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE IN CONJUNCTION WITH 2ND IEEE/ACIS INTERNATIONAL WORKSHOP ON E-ACTIVITY, PROCEEDINGS, P328, DOI 10.1109/ICIS.2008.94 GACITUA R, 2007, KNOWL-BASED SYST, V21, P192 GARIGLIANO R, 1994, 13 INT C ART INT EXP Garside R., 1997, CORPUS ANNOTATION LI, P102 Goldin L., 1997, Automated Software Engineering, V4, DOI 10.1023/A:1008617922496 Gotel OCZ, 1994, 1 INT C REQ ENG, P94 GRUNBACHER P, 2001, 34 HAW INT C SYST SC, P8 Huffman-Hayes J, 2006, IEEE T SOFTWARE ENG, V32, P4 Lecoeuche R, 2000, FIFTEENTH IEEE INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING, PROCEEDINGS, P55 LUQI KF, 2008, LNCS, V5320, P15 MAAREK Y, 1989, 5 INT WORKSH SOFTW S, P196 MCARTHER T, 1981, LONGMAN LEXICON CONT MICH L, 2002, DIT020047 U TRENT MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 Oakes M. P., 1998, STAT CORPUS LINGUIST och Dag J. N., 2002, REQUIR ENG, V7, P20 Polanyi M., 1983, TACIT DIMENSION RAVICHANDAR R, 2007, INT S GRAND CHALL TR RYAN K, 1993, 1 IEEE INT S REQ ENG, P240 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sawyer P, 2005, IEEE T SOFTWARE ENG, V31, P969, DOI 10.1109/TSE.2005.129 Stone A, 2006, IEE PROC-SOFTW, V153, P211, DOI 10.1049/ip-sen:20060034 Yu E, 1997, 3 IEEE INT S REQ ENG, P226 NR 34 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89777-4 J9 LECT NOTES COMPUT SC PY 2008 VL 5320 BP 196 EP 213 PG 18 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BJB56 UT WOS:000264553300011 ER PT J AU Gao, XY Vuong, LPB Zhang, MJ AF Gao, Xiaoying Vuong, Le Phong Bao Zhang, Mengjie TI Detecting data records in semi-structured web sites based on text token clustering SO INTEGRATED COMPUTER-AIDED ENGINEERING LA English DT Article DE automatic data detection; web information extraction; text token clustering; HTML tags; semi-structured web sites ID INFORMATION EXTRACTION AB This paper describes a new approach to the use of clustering for automatic data detection in semi-structured web pages. Unlike most exiting web information extraction approaches that usually apply wrapper induction techniques to manually labelled web pages, this approach avoids the pattern induction process by using clustering techniques on unlabelled pages. In this approach, a variant Hierarchical Agglomerative Clustering (HAC) algorithm called K-neighbours-HAC is developed which uses the similarities of the data format (HTML tags) and the data content (text string values) to group similar text tokens into clusters. We also develop a new method to label text tokens to capture the hierarchical structure of HTML pages and an algorithm for mapping labelled text tokens to XML. The new approach is tested and compared with several common existing wrapper induction systems on three different sets of web pages. The results suggest that the new approach is effective for data record detection and that it outperforms these common existing approaches examined on these web sites. Compared with the existing approaches, the new approach does not require training and successfully avoids the explicit pattern induction process, and accordingly the entire data detection process is simpler. C1 [Gao, Xiaoying; Vuong, Le Phong Bao; Zhang, Mengjie] Victoria Univ Wellington, Sch Math Stat & Comp Sci, Wellington, New Zealand. RP Zhang, MJ (reprint author), Victoria Univ Wellington, Sch Math Stat & Comp Sci, POB 600, Wellington, New Zealand. EM mengjie.zhang@mcs.vuw.ac.nz CR Alvarez M, 2008, DATA KNOWL ENG, V64, P491, DOI 10.1016/j.datak.2007.10.002 BAO LP, 2007, AUSTR C ART INT, P415 CARME J, 2006, BNCOD, P1 CAVERLEE J, 2004, ICDE, P103 Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152 Cheeseman P., 1996, ADV KNOWLEDGE DISCOV, P153 Crescenzi V., 2001, Proceedings of the 27th International Conference on Very Large Data Bases Frank E, 2005, DATA MINING PRACTICA Gao XY, 2004, INT J UNCERTAIN FUZZ, V12, P447, DOI 10.1142/S0218488504002928 Gao XY, 2004, LECT NOTES ARTIF INT, V3157, P585 Gao XY, 2007, LECT NOTES ARTIF INT, V4798, P349 GAZEN B, 2006, AAAI Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kaufman L, 1990, FINDING GROUPS DATA Kushmerick N, 1997, INT JOINT CONF ARTIF, P729 Laender A., 2002, SIGMOD RECORD, V31 LERMAN K, 2001, P WORKSH ADV TEXT EX Liu B., 2003, P 9 ACM SIGKDD INT C, P601 Mitchell TM, 1997, MACHINE LEARNING Muslea I., 1999, P 3 INT C AUT AG, P190, DOI 10.1145/301136.301191 MUSLEA I, 2005, REPOSITORY ONLINE IN Muslea I., 1999, P AAAI WORKSH MACH L *OBJ MAN GROUP, 2005, W3C DOC OBJ MOD Papadakis NK, 2005, IEEE T KNOWL DATA EN, V17, P1638, DOI 10.1109/TKDE.2005.203 REIS DC, 2004, WWW 04, P502, DOI DOI 10.1145/988672.988740 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 TURBO J, 2006, ACM COMPUTING SURVEY, V38 VUONG LPB, 2006, WEB INTELLIGENCE, P374 ZHAI Y., 2005, WWW 05, P76 NR 31 TC 12 Z9 12 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 1069-2509 J9 INTEGR COMPUT-AID E JI Integr. Comput.-Aided Eng. PY 2008 VL 15 IS 4 BP 297 EP 311 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary SC Computer Science; Engineering GA 342ZG UT WOS:000258821500003 ER PT S AU Moskovitch, R Feher, C Tzachar, N Berger, E Gitelman, M Dolev, S Elovici, Y AF Moskovitch, Robert Feher, Clint Tzachar, Nir Berger, Eugene Gitelman, Marina Dolev, Shlomi Elovici, Yuval BE OrtizArroyo, D Larsen, HL Zeng, DD Hicks, D Wagner, G TI Unknown Malcode Detection Using OPCODE Representation SO INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 1st European Conference on Intelligence and Security Informatics CY DEC 03-05, 2008 CL Esbjerg, DENMARK SP Aalborg Univ, Esbjerg Inst Technol, Obel Family Fdn, XSIS ApS, IEEE Syst, Man & Cybernet Soc, European Joint Res Ctr DE Malicious Code Detection; OpCode; Classification AB The recent growth in network usage has motivated the creation of new malicious code for various purposes, including economic ones. Today's signature-based anti-viruses are very accurate, but cannot detect new malicious code. Recently, classification algorithms were employed successfully for the detection of unknown malicious code. However, most of the studies use byte sequence n-grams representation of the binary code of the executables. We propose the use of (Operation Code) OpCodes, generated by disassembling the executables. We then use n-grams of the OpCodes as features for the classification process. We present a full methodology for the detection of unknown malicious code, based on text categorization concepts. We performed an extensive evaluation of a test collection of more than 30,000 files, in which we evaluated extensively the OpCode n-gram representation and investigated the imbalance problem, referring to real-life scenarios, in which the malicious file content is expected to be about 10% of the total files. Our results indicate that greater than 99% accuracy can be achieved through the use of a training set that has a malicious file percentage lower than 15%, which is higher than in our previous experience with byte sequence n-gram representation [1]. C1 [Moskovitch, Robert; Feher, Clint; Tzachar, Nir; Berger, Eugene; Gitelman, Marina; Dolev, Shlomi; Elovici, Yuval] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. EM robertmo@bgu.ac.il; clint@bgu.ac.il; tzachar@bgu.ac.il; bergere@bgu.ac.il; marinag@bgu.ac.il; dolev@bgu.ac.il; elovici@bgu.ac.il RI ELOVICI, YUVAL/F-1468-2012 CR Abou-Assaleh Tony, 2004, P 28 ANN INT COMP SO Bishop C. M., 1995, NEURAL NETWORKS PATT Chawla N., 2004, SIGKDD EXPLORATIONS, V6, P1 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Frank E, 2005, DATA MINING PRACTICA FREUND Y, 1999, INT JOINT C ART INT Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 GRYAZNOV D, 1999, 5 INT VIRUS B HENCHIRI O, 2006, P ICDM 2006 HONG KON, P891, DOI DOI 10.1109/ICDM.2006.4 KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 Mitchell TM, 1997, MACHINE LEARNING MOSKOVITCH R, 2008, IEEE INTELLIGENCE SE Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz M., 2001, P IEEE S SEC PRIV SHIN S, 2006, INT MEAS C IMC BRAZ NR 17 TC 13 Z9 13 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89899-3 J9 LECT NOTES COMPUT SC PY 2008 VL 5376 BP 204 EP 215 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BIW71 UT WOS:000263445500018 ER PT B AU Rahman, HA Noah, SA Jimenez-Salazar, H AF Rahman, Hayati Abd Noah, Shahrul Azman Jimenez-Salazar, Hector BE Zaman, HB Sembok, TMT VanRijsbergen, K Zadeh, L Buza, P Shih, T Taib, MN TI Measuring the Representativeness of Index Terms in Literary Texts: An Experiment on the Quran SO INTERNATIONAL SYMPOSIUM OF INFORMATION TECHNOLOGY 2008, VOLS 1-4, PROCEEDINGS: COGNITIVE INFORMATICS: BRIDGING NATURAL AND ARTIFICIAL KNOWLEDGE LA English DT Proceedings Paper CT International Symposium on Information Technology CY AUG 26-29, 2008 CL Kuala Lumpur, MALAYSIA SP IEEE HO Univ Kebangsaan, Fac Informat Sci & Technol AB Concept hierarchy is a hierarchically organized collection of domain concepts. It is particularly useful in many applications such as information retrieval, document browsing and document classification. One of the important tasks in the construction of concept hierarchy is the identification of suitable terms with appropriate size of domain vocabulary. One way of achieving such a size is by using term reduction. The aim of this paper is to examine the effectiveness of the reduction approach to reduce the size of vocabulary using term selection methods. An experiment has been conducted on the Quran which is assumed to be a literary text. The experiment compares the entropy method, the transition point method and the hybrid of transition point and entropy methods with the Vector Space Model (VSM). Results indicate the effectiveness of the Transition Point method as compared to the others in reducing the size of the vocabulary but at the same time preserve those important terms that exist in the literary documents. C1 [Rahman, Hayati Abd; Noah, Shahrul Azman] Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Malaysia. RP Rahman, HA (reprint author), Univ Kebangsaan Malaysia, Fac Informat Sci & Technol, Bangi 43600, Malaysia. EM hayatiar@gmail.com; samn.ukm@gmail.com; hgimenezs@gmail.com CR ALI YY, 1934, MEANING HOLY QURAN Eagleton T., 1996, LIT THEORY JIMENEZSALAZAR H, 2005, RES COMPUTING SCI, V14, P163 Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d MILLER GA, 2007, ELECT SOURCES WORDNE MOYOTL E, 2004, 20 C SOC ESP PROC LE, P141 ROJAS F, 2006, RES COMPUTING SCI, V20, P107 ROJASLOPEZ F, 2007, LNCS, V4394, P468 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 URBIZAGASTEGUI AR, 1999, POSIBILIDADES LEY ZI Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2327-9 PY 2008 BP 1104 EP 1108 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Telecommunications SC Computer Science; Telecommunications GA BIK22 UT WOS:000260353300171 ER PT B AU Huang, HH Yang, HC Kuo, YH AF Huang, Hsun-Hui Yang, Horng-Chang Kuo, Yau-Hwang GP IEEE Computer Society BE Pan, JS Abraham, A Chang, CC TI A Sense Based Similarity Measure for Cross-Lingual Documents SO ISDA 2008: EIGHTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, PROCEEDINGS LA English DT Proceedings Paper CT 8th International Conference on Intelligent Systems Design and Applications (ISDA 2008) CY NOV 26-28, 2008 CL Kaohsiung, TAIWAN SP IEEE, IEEE SMC Soc, Natl Sci Council, Minist Educ ID SEMANTIC SIMILARITY AB As cross-lingual information retrieval attracts increasing attention, tools that measure cross-lingual document similarity become desirable. Since the way that people convey thoughts at the abstract concept level makes little, if any, difference in the languages they use, it is possible to measure semantic similarity between different lingual documents based on the concepts conveyed by the documents. In this paper, we use senses for document representation to alleviate the barrier of different languages and adopt fuzzy set functions to cope with the inherent fuzziness among senses and propose two document similarity measures- one based on Tversky's notion on similarity and the other on the much used information retrieval criterion. Their performances are compared experimentally. We only focus on documents in English and Chinese. But the proposed approach can be easily extended to process documents in other languages. C1 [Huang, Hsun-Hui; Kuo, Yau-Hwang] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, CREDIT, Tainan 70101, Taiwan. RP Huang, HH (reprint author), Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, CREDIT, Tainan 70101, Taiwan. EM hhhuang@ismp.csie.ncku.edu.tw; hcyang@nttu.edu.tw; kuoyh@ismp.csie.ncku.edu.tw CR BUCKLEY C, 1993, HLT 93, P392 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 HUANG HH, 2006, P JOINT 3 INT C SOFT LITTMAN M, 1997, CROSS LANGUAGE INFOR, P237 MONTALVO S, 2006, ACL 06, P1145 Resnik P, 1995, INT JOINT CONF ARTIF, P448 Rodriguez MA, 2003, IEEE T KNOWL DATA EN, V15, P442, DOI 10.1109/TKDE.2003.1185844 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sowa J., 1992, ENCY ARTIFICIAL INTE Srinivasan P, 2001, INFORM PROCESS MANAG, V37, P15, DOI 10.1016/S0306-4573(00)00014-5 Steinberger R., 2002, Computational Linguistics and Intelligent Text Processing. Third International Conference, CICLing 2002. Proceedings (Lecture Notes in Computer Science Vol.2276) TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 UCHIDA H, 2004, 4 NTCIR WORKSH M TOK NR 14 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3382-7 PY 2008 BP 9 EP 13 DI 10.1109/ISDA.2008.284 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIT85 UT WOS:000262692600002 ER PT B AU Moskovitch, R Stopel, D Feher, C Nissim, N Elovici, Y AF Moskovitch, Robert Stopel, Dima Feher, Clint Nissim, Nir Elovici, Yuval BE Yang, CC Chen, WH Hsu, WL Wu, TC Zeng, D TI Unknown malcode detection via text categorization and the imbalance problem SO ISI 2008: 2008 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS LA English DT Proceedings Paper CT IEEE International Conference on Intelligence and Security Informatics (ISI 2008) CY JUN 17-20, 2008 CL Taipei, TAIWAN SP Cent Police Univ, Natl Taiwan Univ Sci & Technol, Academia Sinica, Inst Informat Sci, Natl Taiwan Univ, IEEE Intelligent Transportat Syst Soc, IEEE Syst, Man, & Cybernte Soc, Hewlett Packard Taiwan Ltd, IBM Taiwan Corp, SolventoSOFT Tech Corp, Thales Transport & Secur, Noetech Photoelect Inc, Alpha Pricing Co Ltd, Natl Secur Agcy, Minist Interior, Minist Econ Affairs, Coast Guard Adm, Off Homeland Secur, Natl Immigrat Bur, Telecom Technol Ctr, Police Res Assoc, Minist Educ, Natl Sci Council, Sci & Technol Advisory Grp Execut Yuan, Res, Dev & Evaluat Commiss, Natl Police Agcy, Criminal Invest Bur, Investigat Bur, Minist Justice, Chung Shan Inst Sci & Technol, Taiwan Stock Exchang Corp, Natl Sci Fdn, Henry C Lee Forens Sci Fdn DE malicious code detection; classification algorithms AB Today's signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Currently, dozens of new malicious codes are created every day, and this number is expected to increase in the coming years. Recently, classification algorithms were used successfully for the detection of unknown malicious code. These studies used a test collection with a limited size where the same malicious-benign-file ratio in both the training and test sets, which does not reflect real-life conditions. In this paper we present a methodology for the detection of unknown malicious code, based on text categorization concepts. We performed an extensive evaluation using a test collection that contains more than 30,000 malicious and benign riles, in which we investigated the imbalance problem. In real-life scenarios, the malicious file content is expected to be low, about 10% of the total riles. For practical purposes, it is unclear as to what the corresponding percentage in the training set should be. Our results indicate that greater than 95% accuracy can be achieved through the use of a training set that contains below 20% malicious file content. C1 [Moskovitch, Robert; Stopel, Dima; Feher, Clint; Nissim, Nir; Elovici, Yuval] Ben Gurion Univ Negev, Deutche Telecom Labs, IL-84105 Beer Sheva, Israel. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Deutche Telecom Labs, IL-84105 Beer Sheva, Israel. RI ELOVICI, YUVAL/F-1468-2012 CR Abou-Assaleh Tony, 2004, P 28 ANN INT COMP SO Bishop C. M., 1995, NEURAL NETWORKS PATT Chawla N., 2004, SIGKDD EXPLORATIONS, V6, P1 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Frank E, 2005, DATA MINING PRACTICA Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 Gryaznov D, 1999, P 5 INT VIR B HENCHIRI O, 2006, P ICDM 2006 HONG KON, P891, DOI DOI 10.1109/ICDM.2006.4 KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 Mitchell TM, 1997, MACHINE LEARNING Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schult M., 2001, P IEEE S SEC PRIV, P178 SHIN S, 2006, INT MEAS C IMC BRAZ NR 15 TC 12 Z9 13 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2414-6 PY 2008 BP 156 EP 161 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BID97 UT WOS:000258854400027 ER PT J AU Rieck, K Laskov, P AF Rieck, Konrad Laskov, Pavel TI Linear-time computation of similarity measures for sequential data SO JOURNAL OF MACHINE LEARNING RESEARCH LA English DT Article DE string kernels; string distances; learning with sequential data ID SUPPORT VECTOR MACHINES; CONSTRUCTION ALGORITHM; NATURAL-LANGUAGE; STRING KERNELS; PAIRWISE DATA; CLASSIFICATION; DISTANCES; TEXT; CATEGORIZATION; REPRESENTATION AB Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of similarity measures for sequences, covering various kernel, distance and non-metric similarity functions. The basis for comparison is embedding of sequences using a formal language, such as a set of natural words, k-grams or all contiguous subsequences. As realizations of the framework we provide linear-time algorithms of different complexity and capabilities using sorted arrays, tries and suffix trees as underlying data structures. Experiments on data sets from bioinformatics, text processing and computer security illustrate the efficiency of the proposed algorithms - enabling peak performances of up to 10(6) pairwise comparisons per second. The utility of distances and non-metric similarity measures for sequences as alternatives to string kernels is demonstrated in applications of text categorization, network intrusion detection and transcription site recognition in DNA. C1 [Rieck, Konrad; Laskov, Pavel] Fraunhofer FIRST IDA, D-12489 Berlin, Germany. RP Rieck, K (reprint author), Fraunhofer FIRST IDA, Kekulestr 7, D-12489 Berlin, Germany. EM KONRAD.RIECK@FIRST.FRAUNHOFER.DE; PAVEL.LASKOV@FIRST.FRAUNHOFER.DE RI Rieck, Konrad/F-2233-2010 CR Abouelhoda M. I., 2004, Journal of Discrete Algorithms, V2, DOI 10.1016/S1570-8667(03)00065-0 Anderberg M. R., 1973, CLUSTER ANAL APPL Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, DOI 10.1145/130385.130401 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 Chapelle O, 1999, IEEE T NEURAL NETWOR, V10, P1055, DOI 10.1109/72.788646 Cherkassky V, 1999, IEEE T NEURAL NETWOR, V10, P1075, DOI 10.1109/72.788648 Collins M, 2002, ADV NEUR IN, V14, P625 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Duda R.O., 2001, PATTERN CLASSIFICATI FREDKIN E, 1960, COMMUN ACM, V3, P490, DOI 10.1145/367390.367400 Gartner T, 2004, MACH LEARN, V57, P205, DOI 10.1023/B:MACH.0000039777.23772.30 GRAEPEL T, 1999, ADV NEURAL INFORM PR, V11 Gusfield D., 1997, ALGORITHMS STRINGS T Haasdonk B, 2005, IEEE T PATTERN ANAL, V27, P482, DOI 10.1109/TPAMI.2005.78 HAMMING RW, 1950, AT&T TECH J, V29, P147 Harmeling S, 2003, NEURAL COMPUT, V15, P1089, DOI 10.1162/089976603765202677 Harmeling S, 2006, NEUROCOMPUTING, V69, P1608, DOI 10.1016/j.neucom.2005.05.015 Hopcroft J. E., 2001, INTRO AUTOMATA THEOR Jaakkola T, 2000, J COMPUT BIOL, V7, P95, DOI 10.1089/10665270050081405 Jacobs DW, 2000, IEEE T PATTERN ANAL, V22, P583, DOI 10.1109/34.862197 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE KASAI T, 2001, 185 KYUSH U DEP INF Kasai T., 2001, Combinatorial Pattern Matching. 12th Annual Symposium, CPM 2001. Proceedings (Lecture Notes in Computer Science Vol. 2089) Knuth D. E., 1973, ART COMPUTER PROGRAM, V3 Laub J, 2006, PATTERN RECOGN, V39, P1815, DOI 10.1016/j.patcog.2006.04.016 Laub J, 2004, J MACH LEARN RES, V5, P801 Leslie Christina, 2002, Pac Symp Biocomput, P564 Leslie C, 2004, J MACH LEARN RES, V5, P1435 Leslie C.S., 2003, BIOINFORMATICS, V1, P1 LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845 Lewis D., 1997, REUTERS 21578 TEXT C Liao L, 2003, J COMPUT BIOL, V10, P857, DOI 10.1089/106652703322756113 Lippmann R, 2000, COMPUT NETW, V34, P579, DOI 10.1016/S1389-1286(00)00139-0 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 MANISCALCO M, 2007, J EXPT ALGORITHMICS, V12, DOI ARTN 1.2 Manzini G, 2004, ALGORITHMICA, V40, P33, DOI 10.1007/s00453-004-1094-1 MASEK WJ, 1980, J COMPUT SYST SCI, V20, P18, DOI 10.1016/0022-0000(80)90002-1 MCCREIGHT EM, 1976, J ACM, V23, P262, DOI 10.1145/321941.321946 McHugh J., 2000, ACM Transactions on Information and Systems Security, V3, DOI 10.1145/382912.382923 METSIS V, 2006, P 3 C EM ANT CEAS Muller KR, 2001, IEEE T NEURAL NETWOR, V12, P181, DOI 10.1109/72.914517 NEEDLEMA.SB, 1970, J MOL BIOL, V48, P443, DOI 10.1016/0022-2836(70)90057-4 O'Donovan Claire, 2002, Brief Bioinform, V3, P275, DOI 10.1093/bib/3.3.275 Odone F, 2005, IEEE T IMAGE PROCESS, V14, P169, DOI 10.1109/TIP.2004.840701 Ratsch G., 2004, KERNEL METHODS COMPU, P277 Ratsch G, 2007, PLOS COMPUT BIOL, V3, P313, DOI 10.1371/journal.pcbi.0030020 Rieck K., 2007, J COMPUTER VIROLOGY, V2, P243, DOI 10.1007/s11416-006-0030-0 Rieck K., 2007, ADV NEURAL INFORM PR, V19, P1177 RIECK K, 2006, LNCS, P374 Roth V, 2003, IEEE T PATTERN ANAL, V25, P1540, DOI 10.1109/TPAMI.2003.1251147 Rousu J, 2005, J MACH LEARN RES, V6, P1323 SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sankoff D., 1983, TIME WRAPS STRING ED Scholkopf B, 1998, NEURAL COMPUT, V10, P1299, DOI 10.1162/089976698300017467 Scholkopf B, 1998, ADV NEUR IN, V10, P640 Scholkopf B., 2002, LEARNING KERNELS Shawe-Taylor J., 2004, KERNEL METHODS PATTE SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 Sokal Robert, 1963, PRINCIPLES NUMERICAL Sonnenburg S, 2006, BIOINFORMATICS, V22, pE472, DOI 10.1093/bioinformatics/btl250 SONNENBURG S, 2007, LARGE SCALE KERNEL M Soon Ong C., 2004, P 21 INT C MACH LEAR, P639 SUEN CY, 1979, IEEE T PATTERN ANAL, V1, P164 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P1 TEO C, 2006, P ICML, P939 Tsuda K, 2002, NEURAL COMPUT, V14, P2397, DOI 10.1162/08997660260293274 UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331 Vapnik V., 1995, NATURE STAT LEARNING Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Vert J. P., 2004, KERNEL METHODS COMPU, P131 Vishwanathan S, 2004, KERNELS BIOINFORMATI, P113 von Luxburg U, 2004, J MACH LEARN RES, V5, P669 Watkins C, 2000, ADV NEUR IN, P39 Webb A. R., 2002, STAT PATTERN RECOGNI Weiner P., 1973, P 14 IEEE S SWITCH A, P1, DOI DOI 10.1109/SWAT.1973.13 Zien A, 2000, BIOINFORMATICS, V16, P799, DOI 10.1093/bioinformatics/16.9.799 NR 79 TC 31 Z9 33 PU MICROTOME PUBL PI BROOKLINE PA 31 GIBBS ST, BROOKLINE, MA 02446 USA SN 1532-4435 J9 J MACH LEARN RES JI J. Mach. Learn. Res. PD JAN PY 2008 VL 9 BP 23 EP 48 PG 26 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 312AL UT WOS:000256641400002 ER PT J AU Levy, M Sandler, M AF Levy, Mark Sandler, Mark TI Learning Latent Semantic Models for Music from Social Tags SO JOURNAL OF NEW MUSIC RESEARCH LA English DT Article AB In this paper we describe how to build a variety of information retrieval models for music collections based on social tags. We discuss the particular nature of social tags for music and apply latent semantic dimension reduction methods to co-occurrence counts of words in tags given to individual tracks. We evaluate the performance of various latent semantic models in relation to both previous work and a simple full-rank vector space model based on tags. We investigate the extent to which our low-dimensional semantic spaces respect traditional catalogue organization by artist and genre, and how well they generalize to unseen tracks, and we illustrate some of the concepts expressed by the learned dimensions. C1 [Levy, Mark; Sandler, Mark] Univ London, Ctr Digital Mus, London E1 4NS, England. RP Levy, M (reprint author), Univ London, Ctr Digital Mus, Mile End Rd, London E1 4NS, England. EM mark.levy@elec.qmul.ac.uk FU EPSRC [EP/E017614/1] FX The authors would like to thank Last. fm and MyStrands for the use of their tag data, Elias Pampalk and Paul Lamere for their insights into social tagging for music, and the anonymous reviewers for their helpful comments on this paper. This research was supported by EPSRC grant EP/E017614/1 (Online Music Recognition And Searching). CR ABERER K, 2004, P DAT SYST ADV APPL Aucouturier J. -J., 2006, THESIS U PARIS 6 FRA BAUMANN S, 2003, P WED LEEDS UK Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 DAS A, 2007, P WORLD WID WEB C BA DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eck D., 2007, NEUR INF PROC SYST C GELEIJNSE G, 2007, P ISMIR VIENN AUSTR Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hofmann Thomas, 1999, P 15 C UNC ART INT U KNEES P, 2004, P ISMIR BARC SPAIN KNEES P, 2004, THESIS VIENNA U TECH KREUTZ G, 2008, COMPUTER MUSIC UNPUB LEVY M, 2007, P ISMIR VIENN AUSTR Mandel MI, 2006, MULTIMEDIA SYST, V12, P3, DOI 10.1007/s00530-006-0032-2 Manning C.D., 2008, INTRO INFORM RETRIEV Monay F, 2007, IEEE T PATTERN ANAL, V29, P1802, DOI 10.1109/TPAMI.2007.1097 Pampalk E., 2006, THESIS VIENNA U TECH POPESCUL A, 2001, 17 C UNC ART INT SEA, P437 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scaringella N, 2006, IEEE SIGNAL PROC MAG, V23, P133, DOI 10.1109/MSP.2006.1598089 SCHMITZ P, 2006, P WORLD WID WEB C ED TURNBULL D, 2007, P SIGIR AMST NETH WHITMAN B, 2003, P IEEE WASPAA NEW PA WHITMAN B, 2005, THESIS MIT US WU X, 2006, P WORLD WID WEB C ED Yoshii K, 2008, IEEE T AUDIO SPEECH, V16, P435, DOI 10.1109/TASL.2007.911503 ZHANG L, 2005, P INT C COMP INF TEC NR 28 TC 19 Z9 19 PU ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND SN 0929-8215 J9 J NEW MUSIC RES JI J. New Music Res. PY 2008 VL 37 IS 2 BP 137 EP 150 AR PII 906008523 DI 10.1080/09298210802479292 PG 14 WC Computer Science, Interdisciplinary Applications; Music SC Computer Science; Music GA 376OL UT WOS:000261192800005 ER PT J AU Klie, S Martens, L Vizcaino, JA Cote, R Jones, P Apweiler, R Hinneburg, A Hermjakob, H AF Klie, Sebastian Martens, Lennart Vizcaino, Juan Antonio Cote, Richard Jones, Phil Apweiler, Rolf Hinneburg, Alexander Hermjakob, Henning TI Analyzing large-scale proteomics projects with latent semantic indexing SO JOURNAL OF PROTEOME RESEARCH LA English DT Article DE bioinformatics; data mining; proteomics; latent semantic analysis ID MASS-SPECTROMETRY; PLASMA-PROTEOME; IDENTIFICATIONS DATABASE; PILOT PHASE; PRIDE; REPOSITORY; STRATEGY; MODEL AB Since the advent of public data repositories for proteomics data, readily accessible results from high-throughput experiments have been accumulating steadily. Several large-scale projects in particular have contributed substantially to the amount of identifications available to the community. Despite the considerable body of information amassed, very few successful analyses have been performed and published on this data, leveling off the ultimate value of these projects far below their potential. A prominent reason published proteomics data is seldom reanalyzed lies in the heterogeneous nature of the original sample collection and the subsequent data recording and processing. To illustrate that at least part of this heterogeneity can be compensated for, we here apply a latent semantic analysis to the data contributed by the Human Proteome Organization's Plasma Proteome Project (HUPO PPP). Interestingly, despite the broad spectrum of instruments and methodologies applied in the HUPO PPP, our analysis reveals several obvious patterns that can be used to formulate concrete recommendations for optimizing proteomics project planning as well as the choice of technologies used in future experiments. It is clear from these results that the analysis of large bodies of publicly available proteomics data by noise-tolerant algorithms such as the latent semantic analysis holds great promise and is currently underexploited. C1 [Klie, Sebastian; Hinneburg, Alexander] Univ Halle Wittenberg, Halle An Der Saale, Germany. [Martens, Lennart; Vizcaino, Juan Antonio; Cote, Richard; Jones, Phil; Apweiler, Rolf; Hermjakob, Henning] European Bioinformat Inst, EMBL Outstn, Cambridge, England. RP Klie, S (reprint author), Univ Halle Wittenberg, Halle An Der Saale, Germany. EM sebklie@googlemail.com; lennart.martens@ebi.ac.uk RI Martens, Lennart/E-8816-2010; Vizcaino, Juan Antonio/C-3691-2009 OI Martens, Lennart/0000-0003-4277-658X; Vizcaino, Juan Antonio/0000-0002-3905-4335 CR Adkins JN, 2005, PROTEOMICS, V5, P3454, DOI 10.1002/pmic.200401333 Aebersold R, 2003, NATURE, V422, P198, DOI 10.1038/nature01511 Anderson NL, 2002, MOL CELL PROTEOMICS, V1, P845, DOI 10.1074/mcp.R200007-MCP200 Craig R, 2004, J PROTEOME RES, V3, P1234, DOI 10.1021/pr049882h DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Desiere F, 2005, GENOME BIOL, V6 Domon B, 2006, SCIENCE, V312, P212, DOI 10.1126/science.1124619 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Gevaert K, 2007, PROTEOMICS, V7, P2698, DOI 10.1002/pmic.200700114 Hamacher M, 2006, PROTEOMICS, V6, P4890, DOI 10.1002/pmic.200600295 Hermjakob H, 2006, EXPERT REV PROTEOMIC, V3, P1, DOI 10.1586/14789450.3.1.1 Jones P, 2006, NUCLEIC ACIDS RES, V34, pD659, DOI 10.1093/nar/gkj138 KATZ SM, 1987, IEEE T ACOUST SPEECH, V35, P400, DOI 10.1109/TASSP.1987.1165125 Kersey PJ, 2004, PROTEOMICS, V4, P1985, DOI 10.1002/pmic.200300721 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mallick P, 2007, NAT BIOTECHNOL, V25, P125, DOI 10.1038/nbt1275 Martens L, 2005, BIOINFORMATICS, V21, P3584, DOI 10.1093/bioinformatics/bti588 Martens L, 2005, PROTEOMICS, V5, P3537, DOI 10.1002/pmic.200401303 Martens L, 2006, PROTEOMICS, V6, P5076, DOI 10.1002/pmic.200600291 Nesvizhskii AI, 2005, MOL CELL PROTEOMICS, V4, P1419, DOI 10.1074/mcp.R500012-MCP200 Omenn GS, 2005, PROTEOMICS, V5, P3226, DOI 10.1002/pmic.200500358 Prince JT, 2004, NAT BIOTECHNOL, V22, P471, DOI 10.1038/nbt0404-471 Reidegeld KA, 2006, PROTEOMICS, V6, P4997, DOI 10.1002/pmic.200600305 Salton G., 1983, INTRO MODERN INFORM, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smellie A, 2004, J CHEM INF COMP SCI, V44, P1929, DOI 10.1021/ci0499222 STEINLEY D, 2006, J MATH STAT PSYCHOL, V59, P1 TEAM DC, 2005, R LANGUAGE ENV STAT Zolotarjova N, 2005, PROTEOMICS, V5, P3304, DOI 10.1002/pmic.200402021 NR 30 TC 25 Z9 25 PU AMER CHEMICAL SOC PI WASHINGTON PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA SN 1535-3893 J9 J PROTEOME RES JI J. Proteome Res. PD JAN PY 2008 VL 7 IS 1 BP 182 EP 191 DI 10.1021/pr070461k PG 10 WC Biochemical Research Methods SC Biochemistry & Molecular Biology GA 248LG UT WOS:000252154200022 PM 18047271 ER PT B AU Zhang, QG Zhang, CZ AF Zhang Qingguo Zhang Chengzhi BE Zhao, C Wu, C Wang, Y Liu, Q TI Automatic Chinese Keyword Extraction Based on KNN for Implicit Subject Extraction SO KAM: 2008 INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING, PROCEEDINGS LA English DT Proceedings Paper CT International Symposium on Knowledge Acquisition and Modeling CY DEC 21-22, 2008 CL Wuhan, PEOPLES R CHINA SP IEEE Comp Soc, IEEE AB In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by Vector Space Model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of keyword to meet readers' requirements Experimental results show the method proposed can not only improve the precision and recall of keyword extraction, but also extract implicit subject efficiently. C1 [Zhang Qingguo] Tongfang Knowledge Network Technol Co Ltd Beijing, Beijing 100084, Peoples R China. RP Zhang, QG (reprint author), Tongfang Knowledge Network Technol Co Ltd Beijing, Beijing 100084, Peoples R China. EM qgzhang@cnki.net; zhangchz@istic.ac.cn CR COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964 Edmundson H.P., 1959, R126 PRC, P1 Frank E, 1999, P 16 INT JOINT C ART, P668 LOIS LE, 1970, INFORM STORAGE RETRI, V6, P313 LUHN HP, 1957, IBM J RES DEV, V1, P309 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney P.D., 1999, ERB1057 NRC, P1 TURNEY PD, 1997, ERB1051 NAT RES COUN Zhang Qingguo, 2006, Journal of the China Society for Scientific and Technical Information, V25 NR 9 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3488-6 PY 2008 BP 689 EP 692 DI 10.1109/KAM.2008.87 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIV54 UT WOS:000263156800142 ER PT S AU Kasper, W Steffen, J Zhang, YJ AF Kasper, Walter Steffen, Joerg Zhang, Yajing BE Dengel, AR Berns, K Breuel, TM Boimarius, F RothBerghofere, T TI News Annotations for Navigation by Semantic Similarity SO KI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 31st Annual German Conference on Artificial Intelligence CY SEP 23-26, 2008 CL Kaiserslautern, GERMANY SP Microsoft Deutschland GmbH, Deutsch Telekom AG Lab, SAP AG, IDS Scheer AG, Living-e AG, Springer- Verlag GmbH, DFKI GmbH AB We present it system for browsing a news repository that is based on semantic similarity of documents. News documents get automatically annotated semantically using information extraction. Annotations are displayed to a user who can easily retrieve crosslingual semantically related documents by selecting interesting content items. C1 [Kasper, Walter; Steffen, Joerg; Zhang, Yajing] DFKI GmbH, D-66123 Saarbrucken, Germany. RP Kasper, W (reprint author), DFKI GmbH, Stuhlsatzenhausweg 3, D-66123 Saarbrucken, Germany. CR Allan J., 2002, TOPIC DETECTION TRAC BERNARDI A, 2004, NEWS ENGINE WEB SERV Bernstein A., 2005, SIMPACK GENERIC JAVA Bunke H, 1998, PATTERN RECOGN LETT, V19, P255, DOI 10.1016/S0167-8655(97)00179-7 DROZDZYNSKI W, 2004, KUNSTLICHE INTELLIGE, V1, P17 *EMM, 2007, EUR NEWS MON NEWS BR *EMM, 2007, EUR MED MON NEWS EXP Fellbaum C, 1998, WORDNET ELECT LEXICA KASPER W, 2008, ANCHORING TEMP UNPUB Oleshchuk V., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications *OPENCALAIS, 2008, CAL PEREZ G, 2006, RES COMPUTING SCI, V18, P3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seaborne A, 2004, SPARQL QUERY LANGUAG Valiente G., 2002, ALGORITHMS TREES GRA NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-85844-7 J9 LECT NOTES ARTIF INT PY 2008 VL 5243 BP 233 EP 240 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA BIL84 UT WOS:000260632500028 ER PT S AU Rajaram, S Scholz, M AF Rajaram, Shyamsundar Scholz, Martin BE Daelemans, W Goethals, B Morik, K TI Client-Friendly Classification over Random Hyperplane Hashes SO MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PART II, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT European Conference on Principles of Data Mining and Knowledge Discovery CY SEP 15-19, 2008 CL Antwerp, BELGIUM SP Univ Antwerp, Computat Linguist Flanders, Google, hp, VADIS, COGNOS, European Off Aerosp, SPSS, textkernel, Data Mining & Knowledge Discovery, IBM, Machine Learning AB In this work, we introduce a powerful and general feature representation based on a locality sensitive hash scheme called random hyperplane hashing. We are addressing the problem of centrally learning (linear) classification models from data that is distributed on a number of clients, and subsequently deploying these models on the same clients. Our main goal is to balance the accuracy of individual classifiers and different kinds of costs related to their deployment, including communication costs and computational complexity. We hence systematically study how well schemes for sparse high-dimensional data adapt to the much denser representations gained by random hyperplane hashing, how much data has to be transmitted to preserve enough of the semantics of each document, and how the representations affect the overall computational complexity. This paper provides theoretical results in the form of error bounds and margin based bounds to analyze the performance of classifiers learnt over the hash-based representation. We also present empirical evidence to illustrate the attractive properties of random hyperplane hashing over the conventional baseline representation of bag of words with and without feature selection. C1 [Rajaram, Shyamsundar; Scholz, Martin] Hewlett Packard Labs, Palo Alto, CA USA. RP Rajaram, S (reprint author), Hewlett Packard Labs, 1501 Page Mill Rd, Palo Alto, CA USA. CR Achlioptas D., 2001, S PRINC DAT SYST POD, P274 ANDONI A, 2006, S FDN COMP SCI, P459 ARRIAGA R, 1999, IEEE S FDN COMPUTER, P616 Bingham E, 2001, INT C KNOWL DISC DAT, P245 Charikar M. S., 2002, STOC, P380 Datar M., 2004, SCG 04, P253 ESHGHI K, 2008, INT C KNOWL DISC DAT Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 FRADKIN D, 2003, INT C KNOWL DISC DAT, P517 Goel N., 2005, SPIE C BIOM TECHN HU, P426 Goemans MX, 1995, J ACM, V42, P1115, DOI 10.1145/227683.227684 Indyk P., 1998, STOC, P604 Johnson W. B., 1984, CONT MATH, V26, P189 KUMAR K, 2007, ADV NEURAL INFORM PR, P793 MIERSWA L, 2006, INT C KNOWL DISC DAT Ravichandran D, 2005, ACL, P622 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGH K, 2003, CONTENT BASED IMAGE Vempala S., 2004, RANDOM PROJECTION ME NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-87480-5 J9 LECT NOTES ARTIF INT PY 2008 VL 5212 BP 250 EP 265 PN II PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Information Systems SC Computer Science GA BIJ50 UT WOS:000260075900017 ER PT J AU Suzuki, T Yokoi, H Fujita, S Takabayashi, K AF Suzuki, T. Yokoi, H. Fujita, S. Takabayashi, K. TI Automatic DPC Code Selection from Electronic Medical Records Text Mining Trial of Discharge Summary SO METHODS OF INFORMATION IN MEDICINE LA English DT Article DE Text mining; discharge summary; DPC; electronic medical record; natural language processing AB Objectives: We extracted index terms related to diseases recorded in hospital discharge summaries and examined the capability of the vector space model to select a suitable diagnosis with these terms. Methods: By morphological analysis, we extracted index terms and constructed an original dictionary for the discharge summary analysis. We chase 125 different DPC (Japanese DRG system) codes for the diseases, each of which had more than 20 cases. We divided them into two groups. One group consisted of 5927 cases from 2004 fiscal year and was used to generate the document vector space according to the DPC. The other group of 3187 cases was collected to verify the automatic DPC selection by using data from 2005 fiscal year. The top 200 extracted index terms for each disease were used to calculate the weight of each disease. Results: The DPC code obtained by the calculated similarity was compared with the original codes of patients for 125 DPCs of 3187 cases. Eighty percent of the cases matched the diagnosis of the DPC (first six digits) and 56% of the cases completely matched all 14 digits of the DPC. Conclusions:We demonstrated that we could extract suitable terms for each disease and obtain characteristics, such as the diagnosis, from the calculated vectors. This technique can be used to measure the qualification of discharge summaries and to integrate discharge summaries among different facilities. By the text mining technique, we can characterize the contents of electronic discharge summaries and deduce diagnoses with the data. C1 [Suzuki, T.] Chiba Univ Hosp, Dept Med Informat & Management, Chuo Ku, Chiba 260, Japan. [Yokoi, H.] Kagawa Univ Hosp, Dept Med Informat, Kagawa, Japan. [Fujita, S.] Chiba Univ Hosp, Dept Welf & Med Intelligence, Chiba 260, Japan. RP Suzuki, T (reprint author), Chiba Univ Hosp, Dept Med Informat & Management, Chuo Ku, 1-8-1 Inohana, Chiba 260, Japan. EM suzuki@ho.chiba-u.ac.jp CR Buckley C., 1995, NIST SPECIAL PUBLICA, P69 Chen ES, 2008, J AM MED INFORM ASSN, V15, P87, DOI 10.1197/jamia.M2401 Erdal Selnur, 2006, AMIA Annu Symp Proc, P918 FRANCESCONI E, 2007, P 11 INT C ART INT L, P131, DOI 10.1145/1276318.1276343 FUJITA S, 2004, P 2 ANN C JAP ASS ME, V1, P908 Goldman JA, 1999, METHOD INFORM MED, V38, P96 HEINZE DT, 2002, P AMIA ANN S, P254 HOWARD T, 1995, ARTIF INTELL, V3, P5 IWAHASHI Y, 2004, P 2 ANN C JAP ASS ME, V1, P804 KLAR R, 2004, METHOD INFORM MED, V43, P543 Mamlin Burke W, 2003, AMIA Annu Symp Proc, P420 Matsumoto Y., 2000, JAPANESE MORPHOLOGIC McCowan IA, 2007, J AM MED INFORM ASSN, V14, P736, DOI 10.1197/jamia.M2130 MendonƧa E A, 2000, Proc AMIA Symp, P575 MULLER M, 2007, MED 2007 P, V129, P340 ONO H, 2004, EXTRACTION DIAGNOSIS, P1786 Pakhomov Serguei V, 2002, Proc AMIA Symp, P587 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan P, 2001, Proc AMIA Symp, P642 TAKEMURA T, 2003, JAPAN J MED INFORM, V23, P95 TAKEMURA T, 2004, P 5 ANN C JAP ASS ME, V1, P464 NR 21 TC 13 Z9 13 PU SCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN PI STUTTGART PA HOLDERLINSTRASSE 3, D-70174 STUTTGART, GERMANY SN 0026-1270 J9 METHOD INFORM MED JI Methods Inf. Med. PY 2008 VL 47 IS 6 BP 541 EP 548 DI 10.3414/ME9128 PG 8 WC Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics SC Computer Science; Health Care Sciences & Services; Medical Informatics GA 386LM UT WOS:000261884700011 PM 19023491 ER PT S AU Dominich, S AF Dominich, Sandor BA Dominich, S BF Dominich, S TI The Modern Algebra of Information Retrieval Introduction SO MODERN ALGEBRA OF INFORMATION RETRIEVAL SE Information Retrieval Series LA English DT Editorial Material; Book Chapter ID AUTOMATIC TEXT-RETRIEVAL; WORLD-WIDE-WEB; CONCEPT HIERARCHIES; SEARCH ENGINES; LATTICE; PROBABILITY; RELEVANCE; NETWORKS; TERM; COMPLEXITY C1 Univ Pannonia, Dept Comp Sci, H-8200 Veszprem, Hungary. RP Dominich, S (reprint author), Univ Pannonia, Dept Comp Sci, Egyetem U 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu CR Aberer K, 2003, LECT NOTES COMPUT SC, V2642, P213 Adamic L., 2000, SCIENCE, V287, p2115a, DOI 10.1126/science.287.5461.2115a ADAMIC LA, 2003, ZIPF POWER LAWS PARE ALBERT R, 2000, THESIS U NOTRE DAME GODIN R, 1993, INT J MAN MACH STUD, V38, P747, DOI 10.1006/imms.1993.1035 BaezaYates R, 2003, INT J APPROX REASON, V34, P97, DOI 10.1016/j.ijar.2003.07.002 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2 BELEW KK, 2000, FINDING OUT ABOUT Berry W., 1999, UNDERSTANDING SEARCH Birkhoff G, 1936, ANN MATH, V37, P823, DOI 10.2307/1968621 BLAIR D, 2006, BACK ROUGH GROUND WI BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B Borlund P., 2003, INFORM RES, V8, P1 Brin S., 1998, P 7 INT WORLD WID WE, P107 Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9 Burris S., 2000, COURSE UNIVERSAL ALG CALADO P, 2003, ACM T INFORM SYSTEMS, V21, P1 Carpineto C, 2005, LECT NOTES ARTIF INT, V3626, P161 Carriere SJ, 1997, COMPUT NETWORKS ISDN, V29, P1257, DOI 10.1016/S0169-7552(97)00062-7 Cheung KSK, 2005, INFORM RETRIEVAL, V8, P285, DOI 10.1007/s10791-005-5663-y Chu H.T., 1996, P 59 ANN M AM SOC IN, P127 Clarke SJ, 1997, ASLIB PROC, V49, P184, DOI 10.1108/eb051463 Cole C, 2005, NEW DIRECTIONS COGNI COOPER GF, 1990, ARTIF INTELL, V42, P393, DOI 10.1016/0004-3702(90)90060-D COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CRESTANI F, 1998, INFORM RETRIEVAL UNC Croft W. B., 1998, P ACM SIGIR, P275 Croft W.B, 2003, LANGUAGE MODELLING I Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6 De Wilde P., 1996, NEURAL NETWORK MODEL DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dewey J., 1929, EXPERIENCE NATURE Dominich S, 2007, J AM SOC INF SCI TEC, V58, P1108, DOI 10.1002/asi.20586 DOMINICH S, 2006, SOFT COMPUTING WEB R, P161 Dominich S, 2003, INFORM PROCESS MANAG, V39, P167, DOI 10.1016/S0306-4573(02)00046-8 Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 Dominich S, 2005, LECT NOTES COMPUT SC, V3408, P67 Dominich S, 2004, J AM SOC INF SCI TEC, V55, P613, DOI 10.1002/asi.20008 Dominich S., 2001, MATH FDN INFORM RETR DOMINICH S, 2005, ALKALMAZOTT NYELVTUD, V1, P5 Doob JL, 1994, MEASURE THEORY Egghe L, 2000, SCIENTOMETRICS, V47, P237, DOI 10.1023/A:1005634925734 Faloutsoss M, 1999, P ACM SIGCOMM, P251, DOI 10.1145/316188.316229 Farahat A, 2006, SIAM J SCI COMPUT, V27, P1181, DOI 10.1137/S1064827502412875 FELDMAN JA, 1982, COGNITIVE SCI, V6, P205, DOI 10.1207/s15516709cog0603_1 Feynman RP, 1964, FEYNMAN LECT PHYS FOX EA, 1994, NIST PUBLICATION, V243 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GARFIELD E, 1972, SCIENCE, V178, P471, DOI 10.1126/science.178.4060.471 GARFIELD E, 1955, SCIENCE, V108 GELLER NL, 1978, INFORM PROCESS MANAG, V14, P93, DOI 10.1016/0306-4573(78)90066-3 GODIN R, 1989, SIGIR FORUM, V23, P32, DOI 10.1145/75334.75339 Grinbaum A, 2005, FOUND PHYS LETT, V18, P563, DOI 10.1007/s10702-005-1129-0 GROSSBERG S, 1976, BIOL CYBERN, V23, P121, DOI 10.1007/BF00344744 Grossman D. A., 2004, INFORM RETRIEVAL ALG Guilmi C D, 2003, EC B, V15, P1 GWIZDKA J, 1999, 9901 U TOR HAYS DD, 1966, READINGS AUTOMATIC L HEINE MH, 1999, P WORKSH LOG UNC MOD, P55 HOPFIELD JJ, 1984, P NATL ACAD SCI-BIOL, V81, P3088, DOI 10.1073/pnas.81.10.3088 JAMES W, 1988, NEUROCOMPUTING FDN R, P253 Jarvelin K, 2005, TURN INTEGRATION INF Jauch J. M., 1968, FDN QUANTUM MECH KAHNG B, 2002, PHYS REV, V66, P1 Kalogeraki V., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Kim M, 2004, INT J HUM-COMPUT ST, V60, P201, DOI 10.1016/j.ijhcs.2003.10.004 KIYOSI I, 2000, ENCY DICT MATH Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 KNEALE K, 1962, DEV LOGIC Knuth KH, 2005, NEUROCOMPUTING, V67, P245, DOI 10.1016/j.neucom.2004.11.039 Koester B., 2006, FOOCA WEB INFORM RET KOESTER B, 2005, P LWA 2005 LERN WISS, P178 Koester B, 2006, LECT NOTES ARTIF INT, V4065, P176 Kolmogorov A., 1956, FDN THEORY PROBABILI, V2nd Korfhage R., 1997, INFORM STORAGE RETRI Kowalski G., 1997, INFORM RETRIEVAL SYS KUMAR R, 1998, P WWW8 C Kurtz M., 1991, HDB APPL MATH ENG SC Lanczos K., 1970, SPACE AGES Langville A N, 2006, GOOGLES PAGERANK Langville AN, 2005, SIAM REV, V47, P135, DOI 10.1137/S0036144503424786 LEIGHTON HV, 1999, J AM SOC INFORM SCI, V50, P882 Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041 LEQUAN H, 2003, COMPUTATIONAL LINGUI, V1, P77 LESK M, 2007, 7 AGES INFORM RETRIE LUHN HP, 1966, READINGS AUTOMATED L, P159 MARON ME, 1960, J ASSOC COMPUT MACH, V7, P219 MARTIN WT, 1961, ELEMENTARY DIFFERENT Meadow C. T., 1999, TEXT INFORM RETRIEVA MESSAI N, 2006, P 4 INT C CONC LATT Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 MOOERS CN, 1959, P INT C SCI INF WASH, P1327 NG AY, 2001, P 17 INT JOINT C ART Nielson J, 1993, USABILITY ENG Over DE, 2007, COGNITIVE PSYCHOL, V54, P62, DOI 10.1016/j.cogpsych.2006.05.002 Pearl J., 1988, PROBABILISTIC REASON Pennock DM, 2002, P NATL ACAD SCI USA, V99, P5207, DOI 10.1073/pnas.032085699 PIZIAK R, 1978, MATH MAG, V51, P299 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Priss U, 2000, KNOWL ORGAN, V27, P132 Rajapakse RK, 2006, INFORM PROCESS MANAG, V42, P1260, DOI 10.1016/j.ipm.2005.12.005 Recski A., 1989, MATROID THEORY ITS A Redei M., 1996, STUD HIST PHILOS M P, V27, P493, DOI 10.1016/S1355-2198(96)00017-2 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 2002, P ACM SIGIR MF IR WO ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1974, CONTRIBUTIONS THEORY, V74, P584 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1966, READINGS AUTOMATIC L, P169 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J., 1991, Electronic Publishing: Origination, Dissemination and Design, V4 Shafi S. M., 2005, WEBOLOGY, V2 Shannon C. E., 1949, MATH THEORY COMMUNIC Shiode N, 2000, POWER LAW DISTRIBUTI Silva I. R., 2004, Proceedings. International Database Engineering and Applications Symposium, DOI 10.1109/IDEAS.2004.1319782 Silverstein C., 1998, 1998014 COMPAQ SYST Simmonds JG, 1982, BRIEF TENSOR ANAL SKOBELTSYN G., 2007, P WWW 2007 C MAY 8 1, P1185, DOI 10.1145/1242572.1242757 SMITH FJ, 1985, INFORM PROCESS MANAG, V21, P215, DOI 10.1016/0306-4573(85)90106-2 Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022 Stalnaker R., 1968, AM PHILOS Q MONOGRAP, V2, P98 Strogatz SH, 2001, NATURE, V410, P268, DOI 10.1038/35065725 SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1 SU LT, 1998, P 61 ANN M AM SOC IN, P348 TAIT JI, 2005, CHARTING NEW COURSE TANG MC, 2003, LIBRES, V13 Thelwall M, 2004, ASLIB PROC, V56, P24, DOI 10.1108/00012530410516840 Thorndike EL, 1937, PSYCHOL REC, V1, P399 Tsikrika T, 2004, INFORM PROCESS MANAG, V40, P751, DOI 10.1016/j.ipm.2004.04.008 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Ullman J.D., 1980, PRINCIPLES DATABASE van Rijsbergen C. J., 1979, INFORM RETRIEVAL VANRIJSBERGEN CJ, 1992, COMPUT J, V35, P291, DOI 10.1093/comjnl/35.3.291 VANRIJSBERGEN CJ, 2004, GEOMETRY IR Vegas J, 2007, J INF SCI, V33, P77, DOI 10.1177/0165551506067123 Wang Z, 1991, FUZZY MEASURE THEORY Weiss M. A., 1995, DATA STRUCTURES ALGO WELLISH H, 1991, INDEXING A Z WIDDOWS D, 2003, P MATH LANG BLOOM JU, P141 Widdows D., 2003, P 41 ANN M ASS COMP, P136 Widdows Dominic, 2004, GEOMETRY AND MEANING Wille R, 2005, LECT NOTES ARTIF INT, V3626, P1 Wolf K., 1993, SOFTSTAT 93 ADV STAT, V4, P429 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Yang A, 1996, DATA KNOWL ENG, V20, P211, DOI 10.1016/S0169-023X(96)00006-7 YU CT, 1989, ACM T DATABASE SYST, V14, P147, DOI 10.1145/63500.63519 YULE GU, 1924, PHILOS T, P213 Zimmerman HJ, 1996, FUZZY SET THEORY ITS Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 158 TC 7 Z9 7 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1387-5264 BN 978-3-540-77658-1 J9 INFORM RETRIEVAL SER PY 2008 VL 24 BP 1 EP + PG 40 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BJW77 UT WOS:000267327600001 ER PT B AU Kasper, W Steffen, J Zhang, YJ AF Kasper, Walter Steffen, Joerg Zhang, Yajing BE Sharp, B Zock, M TI Semantic navigation of news SO NATURAL LANGUAGE PROCESSING AND COGNITIVE SCIENCE, PROCEEDINGS LA English DT Proceedings Paper CT 5th International Workshop on Natural Language Processing and Cognitive Science CY JUN 12, 2008 CL Barcelona, SPAIN AB We present a system for browsing a news repository that is based on semantic similarity of documents. News documents get automatically annotated semantically using information extraction. Annotations are displayed to a user who can easily retrieve crosslingual semantically related documents by selecting interesting content items. C1 [Kasper, Walter; Steffen, Joerg; Zhang, Yajing] DFKI GmbH, D-66123 Saarbrucken, Germany. EM kasper@dfki.de; steffen@dfki.de; yajing.zhang@dfki.de CR Allan J., 2002, TOPIC DETECTION TRAC ALLEN JF, 1983, COMMUN ACM, V26, P832, DOI 10.1145/182.358434 BECKER M, 2002, INT C NLP MUMB IND BERNARDI A, 2004, NEWS ENGINE WEB SERV Bernstein A., 2005, SIMPACK GENERIC JAVA Bunke H, 1998, PATTERN RECOGN LETT, V19, P255, DOI 10.1016/S0167-8655(97)00179-7 DROZDZYNSKI W, 2004, KUNSTLICHE INTELLIGE, V1, P17 *EMM, 2007, EUR NEWS MON NEWS BR *EMM, 2007, EUR MED MON NEWS EXP Fellbaum C, 1998, WORDNET ELECT LEXICA Han Benjamin, 2006, P 13 INT S TEMP REPR, P196 HOBBS JR, 2006, TIME ONTOLO IN PRESS Hobbs J.R., 2004, ACM T ASIAN LANGUAGE, V3, P66, DOI DOI 10.1145/1017068.1017073 KOOMEN JAG, 1989, 231 U ROCH COMP SCI Oleshchuk V., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications *OPENCALAIS, 2008, CAL PEREZ G, 2006, RES COMPUTING SCI, V18, P3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seaborne A, 2004, SPARQL QUERY LANGUAG Sirin E., 2006, J WEB SEMANTICS NR 20 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-45-6 PY 2008 BP 42 EP 51 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science; Language & Linguistics; Psychology, Experimental SC Computer Science; Information Science & Library Science; Linguistics; Psychology GA BIC84 UT WOS:000258477900005 ER PT J AU Liu, YC Wang, XL Wu, C AF Liu, Yuanchao Wang, Xiaolong Wu, Chong TI ConSOM: A conceptional self-organizing map model for text clustering SO NEUROCOMPUTING LA English DT Article DE SOM; neuron vector; weight modification; HowNet; text clustering ID NETWORK AB In the novel conceptional self-organizing map model (ConSOM) proposed for text clustering in this paper, neurons and documents can be represented by two vectors: one in extended concept space, and the other in traditional feature space, and weight modification of neuron vector is guided by combination of similarities in both traditional and extended spaces. Experimental results show that by utilizing concept relevance knowledge effectively, ConSOM performs better than traditional "SOM plus VSM" mode in text clustering due to its semantic sensitivity. (c) 2007 Elsevier B.V. All rights reserved. C1 [Liu, Yuanchao; Wang, Xiaolong] Harbin Inst Technol, Dept Comp Sci & Technol, Harbin 150001, Peoples R China. [Liu, Yuanchao; Wu, Chong] Harbin Inst Technol, Dept Adm, Harbin 150001, Peoples R China. RP Liu, YC (reprint author), Harbin Inst Technol, Dept Comp Sci & Technol, Harbin 150001, Peoples R China. EM lyc@insun.hit.edu.cn CR Blackmore J., 1993, P IEEE INT C NEUR NE, V1, P450 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DITTENBACH M, 2000, P INT JOINT C NEUR N, P24 DONG Z, 2003, HOWNET KNOWLEDGE DAT FRITZKE B, 1995, NEURAL PROCESS LETT, V2, P9, DOI 10.1007/BF02332159 HARPER DJ, 1999, P 21 ANN BCS IRSG C, P92 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Lagus K, 1999, ARTIF INTELL REV, V13, P345, DOI 10.1023/A:1006586221250 NIKLASSON L, 1998, P ICANN98 8 INT C AR, P65 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strehl A., 2000, AAAI WORKSH AI WEB S, P58 Wang BB, 2003, P 26 AUSTR COMP SCI, P69 Yeh MF, 2005, NEUROCOMPUTING, V67, P281, DOI 10.1016/j.neucom.2005.02.007 NR 13 TC 15 Z9 16 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0925-2312 J9 NEUROCOMPUTING JI Neurocomputing PD JAN PY 2008 VL 71 IS 4-6 BP 857 EP 862 DI 10.1016/j.neucom.2007.03.006 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA 269QK UT WOS:000253663800044 ER PT S AU Yang, SY AF Yang, Sheng-Yuan BE Nguyen, NT Katarzyniak, R TI Ontological Query Processing and Maintaining Techniques for FAQ Systems SO NEW CHALLENGES IN APPLIED INTELLIGENCE TECHNOLOGIES SE STUDIES IN COMPUTATIONAL INTELLIGENCE LA English DT Proceedings Paper CT 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 18-20, 2008 CL Wroclaw, POLAND SP Inst Info Sci & Engn, Wroclaw Univ Technol, Int Soc Appl Intelligence, ISAI, Amer Assoc Artificial Intelligence, Assoc Comp Mach, Canadian Soc Computat Studies Intelligence, European Neural Network Soc, Japanese Soc Artificial Intelligence, Taiwanese Assoc Artificial Intelligence, Texas State Univ, San Marcos DE Ontology; Template-based Processing; User Modeling; Interface Agents; FAQ Systems AB This paper illustrated an Interface Agent which works as an assistant between the users and FAQ systems to retrieve FAQs on the domain of Personal Computer. It can effectively tackle the problems associated with traditional FAQ retrieval systems. Specifically, we addressed how ontology helps interface agents to provide better FAQ services and related algorithms described in details. Our preliminary experimentation demonstrated that user intention and focus of up to eighty percent of the user queries can be correctly understood by the system, and accordingly provided the query solutions with higher user satisfaction. C1 St Johns Univ, Dept Comp & Commun Engn, Tamsui 25135, Taipei County, Taiwan. RP Yang, SY (reprint author), St Johns Univ, Dept Comp & Commun Engn, 499,Sec 4 Tamking Rd, Tamsui 25135, Taipei County, Taiwan. CR Chiu Y.H., 2003, THESIS NATL TAIWAN U NOY NF, 2001, KSL0105 STAND KNOWL Ravichandran D., 2002, P 2 INT C HUM LANG T, P247, DOI 10.3115/1289189.1289206 Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soubbotin M.M., 2001, P TREC 10 C NIST GAI, P293 Tsai C. H., 2000, MMSEG WORD IDENTIFIC Winiwarter W, 2000, DATA KNOWL ENG, V35, P181, DOI 10.1016/S0169-023X(00)00032-X Yang S, 2005, P 17 INT C SOFTW ENG, P639 Yang S. Y., 1999, P 4 C ART INT APPL C, P248 YANG SY, 2006, 2006 S APPL DEV MAN, P168 YANG SY, 2006, P 2006 INT COMP S TA, P874 YANG SY, 2006, WSEAS T COMPUTERS, V5, P1843 Yang SY, 2007, J INTELL INF SYST, V28, P233, DOI 10.1007/s10844-006-0009-9 YANG SY, 2004, P 2004 INT WORKSH DI, P451 YANG SY, 2005, P 2005 INT WORKSH DI, P411 YANG SY, 2006, INT AC C 2006 SPEC S, P2 NR 18 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1860-949X BN 978-3-540-79354-0 J9 STUD COMP INTELL PY 2008 VL 134 BP 97 EP 109 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BIJ49 UT WOS:000260074000010 ER PT S AU David, J AF David, Joern BE Nguyen, NT Borzemski, L Grzech, A Ali, M TI Recommending software artifacts from repository transactions SO NEW FRONTIERS IN APPLIED ARTIFICIAL INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 18-20, 2008 CL Wroclaw, POLAND SP Inst Info Sci & Engn, Wroclaw Univ Technol, Int Soc Appl Intelligence, ISAI, Amer Assoc Artificial Intelligence, Assoc Comp Mach, Canadian Soc Computat Studies Intelligence, European Neural Network Soc, Japanese Soc Artificial Intellgence, Taiwanese Assoc Artificial Intelligence, Texas State Univ, San Marcos DE change impact analysis; recurrent neural networks; latent semantic indexing; repository mining; artifact recommendation AB The central problem addressed by this interdisciplinary paper is to predict related software artifacts that are usually changed together by a developer. The working focus of programmers is revealed by means of their interactions with a software repository that receives a set of cohesive artifact changes within one commit transaction. This implicit knowledge of interdependent changes can be exploited in order to recommend likely further changes, given a set of already changed artifacts. We suggest a hybrid approach based on Latent Semantic Indexing (LSI) and machine learning methods to recommend software development artifacts, that is predicting a sequence of configuration items that were committed together. As opposed to related approaches to repository mining that are mostly based on symbolic methods like Association Rule Mining (ARM), our connectionist method is able to generalize onto unseen artifacts. Text analysis methods are employed to consider their textual attributes. We applied our technique to three publicly available datasets from the PROMISE Repository of Software Engineering Databases. The evaluation showed that the connectionist LSI-approach achieves a significantly higher recommendation accuracy than existing methods based on ARM. Even when generalizing onto unseen artifacts, our approach still provides an accuracy of up to 72.7% on the given datasets. C1 Tech Univ Munich, Inst Comp Sci 2, D-85748 Garching, Germany. RP David, J (reprint author), Tech Univ Munich, Inst Comp Sci 2, Boltzmannstr 3, D-85748 Garching, Germany. CR ACHTERT E, 2005, WORKSH MIN COMPL DAT Agrawal R., 1994, P 20 INT C VER LARG Bohner S. A., 1996, SOFTWARE CHANGE IMPA, P1 Bruegge B., 2004, OBJECT ORIENTED SOFT BUDZIK J, 2000, P 5 INT C INT US INT, P106 CALLAN R, 2003, NEURONALE NETZE KLAR Cubranic D, 2003, PROC INT CONF SOFTW, P408, DOI 10.1109/ICSE.2003.1201219 Deerwester S., 1990, INDEXING LATENT SEMA GALL H, 1998, INT C SOFTW MAINT IC MASSEY B, 2005, NICKLE REPOSITORY T Porter M.F., 1980, ALGORITHM SUFFIX STR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmidhuber J, 1997, NEURAL NETWORKS, V10, P857, DOI 10.1016/S0893-6080(96)00127-X SHERRIFF M, 2007, EMPIRICAL SOFTWARE C Shirabad J. S., 2005, PROMISE REPOSITORY S SINGER J, 2005, P INT C SOFTW MAINT SYU I, 1996, P ACM C INF KNOWL MA, P145, DOI 10.1145/238355.238475 ZIMMERMANN T, 2004, INT C SOFTW ENG ICSE NR 18 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-69045-0 J9 LECT NOTES ARTIF INT PY 2008 VL 5027 BP 189 EP 198 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BHV18 UT WOS:000256666400020 ER PT S AU Masuda, T Yamamoto, D Ohira, S Nagao, K AF Masuda, Tomoki Yamamoto, Daisuke Ohira, Shigeki Nagao, Katashi BE Satoh, K Inokuchi, A Nagao, K Kawamura, T TI Video scene retrieval using online video annotation SO NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT Annual Conference of the Japanese-Society-for-Artificial-Intelligence CY JUN 18-22, 2007 CL Miyazaki, JAPAN SP Japanese Soc Artificial Intelligence AB In this paper, we propose an efficient method for extracting scene tags from online video annotation (e.g., comments about video scenes). To evaluate this method by applying extracted information to video scene retrieval, we have developed a video scene retrieval system based on scene tags (i.e., tags associated with video scenes). We have also developed a tag selection system that enables online users to select appropriate scene tags from data created automatically from online video annotation. Furthermore, we performed experiments on tag selection and video scene retrieval. We found that scene tags extracted by using our tag selection system had better cost performance than ones created using a conventional client-side video annotation tool. C1 [Masuda, Tomoki; Yamamoto, Daisuke] Nagoya Univ, Grad Sch Informat Sci, Nagoya, Aichi 4648601, Japan. RP Masuda, T (reprint author), Nagoya Univ, Grad Sch Informat Sci, Nagoya, Aichi 4648601, Japan. CR KUDO T, 2003, ACL 2003 P 41 ANN M, P24 Nagao K., 2003, DIGITAL CONTENT ANNO NAGAO K, 2002, COLING 2002 P 19 INT RIVADENEIRA AW, 2007, P SIGCHI C HUM FACT, P995, DOI 10.1145/1240624.1240775 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yamamoto D., 2005, P 1 INT C AUT PROD C NR 6 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-78196-7 J9 LECT NOTES ARTIF INT PY 2008 VL 4914 BP 54 EP 62 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BHK38 UT WOS:000253835500007 ER PT S AU Poesio, M Almuhareb, A AF Poesio, Massimo Almuhareb, Abdulrahman BE Buitelaar, P Cimiano, P TI Extracting concept descriptions from the Web: the importance of attributes and values SO ONTOLOGY LEARNING AND POPULATION: BRIDGING THE GAP BETWEEN TEXT AND KNOWLEDGE SE Frontiers in Artificial Intelligence and Applications LA English DT Article; Book Chapter DE Ontology learning from text; relation extraction; attributes and values AB When extracting information about concepts from the Web, the problem is not recall, but precision: trying to identify which properties of a concept are genuinely distinctive. We discuss a series of experiments in empirical ontology using both unsupervised and supervised methods, showing that not all semantic relations we can extract from text are equally useful, and suggesting that attempting to identify concept attributes (parts, qualities, and the like) and their values results in better concept descriptions than those obtained by being less selective. C1 [Poesio, Massimo] Univ Trent, Ctr Mind Brain Sci, I-38100 Trento, Italy. [Poesio, Massimo] Univ Essex, Language & Computat Grp, Colchester CO4 3SQ, Essex, England. [Almuhareb, Abdulrahman] KACST, King Abdulaziz City, Saudi Arabia. RP Poesio, M (reprint author), Univ Trent, Ctr Mind Brain Sci, I-38100 Trento, Italy. CR ALMUHAREB A, P INT C EMP METH NAT, P158 ALMUHAREB A, 2007, ONTOLOGY LEARN UNPUB ALMUHAREB A, 2006, THESIS U ESSEX ALMUHAREB A, 2006, P EUR C ART INT ECAI Almuhareb Abdulrahman, 2005, P ANN M COGN SCI SOC Baader F., 2003, DESCRIPTION LOGIC HD Berland M., 1999, P 37 ANN M ASS COMP, P57, DOI 10.3115/1034678.1034697 Brachman R.J., 1979, ASS NETWORKS REPRESE, P3 Caraballo S., 1999, P 37 ANN M ASS COMP, P120, DOI 10.3115/1034678.1034705 Carroll J. A., 2002, P 3 INT C LANG RES E, P1499 Cimiano P., 2004, P 13 INT C WORLD WID, P462, DOI 10.1145/988672.988735 CIMIANO P, 2005, P ACL WORKSH DEEP LE Curran James R., 2002, P WORKSH UNS LEX ACQ, P59, DOI DOI 10.3115/1118627.1118635 Dixon Robert M.W., 1991, NEW APPROACH ENGLISH Etzioni O, 2005, ARTIF INTELL, V165, P91, DOI 10.1016/j.artint.2005.03.001 Firth J. R., 1957, STUDIES LINGUISTIC A, P1 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Guarino N., 1992, Data & Knowledge Engineering, V8, DOI 10.1016/0169-023X(92)90025-7 Hatzivassiloglou Vasileios, 1993, P 31 ANN M ASS COMP, P172, DOI 10.3115/981574.981597 Hearst M., 1998, WORDNET ELECT LEXICA Hearst M. A., 1992, P 14 INT C COMP LING, P539 KARYPIS G, 2002, 02017 U MINN Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LEVESQUE HJ, 1985, READINGS KNOWLEDGE R, P42 LIN D, 1998, P INT C COMP LING CO Lund K., 1995, P 17 ANN C COGN SCI, P660 MANN G, 2003, P INT C COMP NAT LAN Moldovan Dan, 2003, P HUM LANG TECHN C H Navigli R., 2004, COMPUTATIONAL LINGUI, V30 PANTEL P, 2006, P 21 INT C COMP LING, P113, DOI 10.3115/1220175.1220190 Poesio M., 2005, P ACL WORKSH DEEP LE, P18, DOI 10.3115/1631850.1631853 POESIO M, 2004, P 3 INT C FORM ONT I POESIO M, 2002, P 3 LEX RES EV C LRE Pustejovsky J., 1995, GENERATIVE LEXICON RILOFF E, 1997, P INT C EMP METH NAT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1997, AMBIGUITY RESOLUTION Turney Peter D., 2001, P 12 EUR C MACH LEAR, P491 Voelker J., 2005, P 10 INT C APPL NAT WOODS WA, 1975, STUDIES COGNITIVE SC, P35 Zhao Y., 2003, 0140 U MINN DEP COMP NR 41 TC 0 Z9 0 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 978-1-58603-818-2 J9 FRONT ARTIF INTEL AP PY 2008 VL 167 BP 29 EP 44 PG 16 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Software Engineering SC Computer Science GA BMA39 UT WOS:000271659100004 ER PT B AU Gasparetti, F Micarelli, A AF Gasparetti, Fabio Micarelli, Alessandro BE Uchyigit, G Ma, MY TI A Deep Evaluation of Two Cognitive User Models for Personalized Search SO PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS LA English DT Proceedings Paper CT International Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces CY OCT, 2005 CL Reading, ENGLAND ID INFORMATION; LANGUAGE; MEMORY; REPRESENTATION; RETRIEVAL; CONTEXT; WORDS; SPACE AB Personalized retrieval of documents is a research field that has been gaining interest, since it is a possible solution to the information overload problem. The ability to adapt the retrieval process to the current user needs increases the accuracy and reduces the time users spend to formulate and sift through result lists. In this chapter we show two instances of user modeling. One is based on the human memory theory named Search of Associative Memory, and a further approach based on the Hyperspace Analogue to Language model. We prove how by implicit feedback techniques we are able to unobtrusively recognize user needs and monitor the user working context. This is important to provide personalization during traditional information retrieval and for recommender system development. We discuss an evaluation comparing the two cognitive approaches, their similarities and drawbacks. An extended analysis reveals interesting evidence about the good performance of SAM-based user modeling, but it also proves how HAL-based models evaluated in the Web browsing context shows slightly higher degree of precision. C1 [Gasparetti, Fabio; Micarelli, Alessandro] Roma Tre Univ, Artificial Intelligence Lab, Dept Comp Sci & Automat, I-00146 Rome, Italy. RP Gasparetti, F (reprint author), Roma Tre Univ, Artificial Intelligence Lab, Dept Comp Sci & Automat, Via Vasca Navale 79, I-00146 Rome, Italy. EM gaspare@dia.uniroma3.it; micarel@dia.uniroma3.it CR Alison J, 2003, BRAIN COGNITION, V53, P125, DOI 10.1016/S0278-2626(03)00093-9 ANDERSON JR, 2000, ATOMIC COMPONENTS TH Anick P., 2003, SIGIR 03, P88 Asnicar F.A., 1997, P 1 INT WORKSH AD SY, P3 ATCHLEY RA, 2001, BRAIN COGNITION, V46, P16, DOI 10.1016/S0278-2626(00)91268-5 Atkinson R. C., 1968, PSYCHOL LEARN MOTIV, P89, DOI DOI 10.1016/S0079-7421(08)60422-3 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BLACKMON MH, 2003, P ACM C HUM FACT COM, P463 BRUZA P, 2002, CIKM, P260 Budzik J, 2001, KNOWL-BASED SYST, V14, P37, DOI 10.1016/S0950-7051(00)00105-2 BURGESS C, 1998, RIGHT HEMISPHERE LAN, P245 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Burgess C, 1998, BEHAV RES METH INS C, V30, P188, DOI 10.3758/BF03200643 Chan PK, 2000, LECT NOTES COMPUT SC, V1836, P39 Chi E. H., 2001, P ACM CHI 2001 C HUM, P490, DOI 10.1145/365024.365325 CHI EH, 2004, P WORKSH US PROF CHI Claypool M., 2001, INTELLIGENT USER INT, V1, ppp Conley P, 2001, BRAIN COGNITION, V46, P86, DOI 10.1016/S0278-2626(01)80040-3 GASPARETTI F, 2005, P 1 INT WORKSH WEB P, P59 Gasparetti F, 2007, INT J PATTERN RECOGN, V21, P207, DOI 10.1142/S0218001407005429 GASPARETTI F, 2007, IUI 07, P325 Hearst MA, 1997, COMPUT LINGUIST, V23, P33 Joachims T., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Joachims T., 1997, P 15 INT JOINT C ART, P770 Jones K. S., 2004, Journal of Documentation, V60, DOI 10.1108/00220410410560591 KOENEMANN J, 1996, CHI, P205 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lieberman H., 1998, P 4 INT C INT US INT, P65 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Magnini B, 2004, USER MODEL USER-ADAP, V14, P239, DOI 10.1023/B:USER.0000028980.13669.44 Micarelli A., 2007, LNCS, V4321, P195 Micarelli A, 2004, USER MODEL USER-ADAP, V14, P159, DOI 10.1023/B:USER.0000028981.43614.94 MILLER GA, 1993, ADV NEURAL INFORM PR, P197 MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037//0033-295X.101.2.343 MLADENIC D, 2001, P 9 INT C HUM COMP I Montaner M, 2003, ARTIF INTELL REV, V19, P285, DOI 10.1023/A:1022850703159 Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 PIROLLI P, 2003, P 9 INT C US MOD JOH Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643 Pitkow J, 2002, COMMUN ACM, V45, P50 RAAIJMAKERS JGW, 1981, PSYCHOL REV, V88, P93, DOI 10.1037//0033-295X.88.2.93 RADLINSKI F, 2005, KDD 05 Rhodes BJ, 2000, THESIS MIT CAMBRIDGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Song D., 2004, ACM SIGIR 2004 WORKS SPERETTA M, 2005, WEB INTELLIGENCE WI2 Kelly D., 2003, SIGIR Forum, V37 Teevan J., 2004, CHI 04, P415 Teevan J., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Waern A, 2004, USER MODEL USER-ADAP, V14, P201 WHITE R, 2001, TREC White RW, 2002, LECT NOTES COMPUT SC, V2291, P93 NR 53 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 978-981-279-701-8 PY 2008 BP 33 EP 60 PG 28 WC Computer Science, Artificial Intelligence SC Computer Science GA BQS29 UT WOS:000281716200002 ER PT S AU Cao, TH Le, KC Ngo, VM AF Cao, Tru H. Le, Khanh C. Ngo, Vuong M. BE Ho, TB Zhou, ZH TI Exploring Combinations of Ontological Features and Keywords for Text Retrieval SO PRICAI 2008: TRENDS IN ARTIFICIAL INTELLIGENCE SE Lecture Notes in Artificial Intelligence LA English DT Proceedings Paper CT 10th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2008) CY DEC 15-19, 2008 CL Hanoi, VIETNAM SP Vietnamese Acad Sci & Technol, Minist Sci & Technol Vietnam, Hanoi Univ Technol, Vietnam Natl Univ, Air Force Off Sci Res, Asian Off Aerosp Res & Dev ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL AB Named entities have been considered and combined with keywords to enhance information retrieval performance. However, there is not yet a formal and complete model that takes into account entity names, classes, and identifiers together. Our work explores various adaptations of the traditional Vector Space Model that combine different ontological features with keywords, and in different ways. It shows better performance of the proposed models as compared to the keyword-based Lucene, and their advantages for both text retrieval and representation of documents and queries. C1 [Cao, Tru H.; Ngo, Vuong M.] Ho Chi Minh City Univ Technol, Fac Comp Sci & Engn, Ho Chi Minh City, Vietnam. RP Cao, TH (reprint author), Ho Chi Minh City Univ Technol, Fac Comp Sci & Engn, Ho Chi Minh City, Vietnam. EM tru@cse.hcmut.edu.vn; lckhanh@tma.com.vn; vuong@cse.hcmut.edu.vn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Buckley Christopher, 1985, 85686 CORN U Cao T., 2008, P 17 IEEE INT C FUZZ, P2028 Castells P., 2006, IEEE T KNOWL DATA EN, P261 Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 Goncalves A., 2006, P 7 INT C WEB AG INF Gospodnetic P., 2003, J IBM DEVELOPERWORKS Guha R. V., 2003, P 12 INT C WORLD WID, P700 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Khalid MA, 2008, LECT NOTES COMPUT SC, V4956, P705 Kiryakov A., 2005, J WEB SEMANTICS, V2 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 MEIJ E, 2007, P 16 TEXT RETRIEVAL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sekine S., 2004, NAMED ENTITY HIST FU VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 Varelas G., 2005, P 7 ANN ACM INT WORK, P10, DOI 10.1145/1097047.1097051 Zhou W., 2006, P 15 TEXT RETRIEVAL NR 19 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89196-3 J9 LECT NOTES ARTIF INT PY 2008 VL 5351 BP 603 EP 613 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BIT57 UT WOS:000262624600051 ER PT S AU Chen, X Zhou, J AF Chen, Xuan Zhou, Jian BE Gen, M Zhao, X Gao, JW TI Spherical credibilistic clustering algorithm for document data SO PROCEEDING OF THE SEVENTH INTERNATIONAL CONFERENCE ON INFORMATION AND MANAGEMENT SCIENCES SE Series of Information and Management Sciences LA English DT Proceedings Paper CT 7th International Conference on Information and Management Sciences CY AUG 12-19, 2008 CL Urumchi, PEOPLES R CHINA SP Int Assoc Informat & Management Sci, Waseda Univ, Tsinghua Univ, KAIST, Chinese Univ Hong Kong DE document clustering; fuzzy clustering; credibilistic clustering algorithm AB Credibilistic clustering is a new approach of fuzzy clustering, in which the credibility weights are used to measure the compactness of data. Motivated from their efficiency to noise data, this paper introduces the credibilistic clustering algorithms into the document clustering area to obtain a spherical credibilistic clustering algorithm, in which the cosine similarity are adopted to measure the distances among document data in the proposed algorithm to demonstrate the degree of belonging or compatibility. C1 [Chen, Xuan; Zhou, Jian] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China. CR Bezdek J. C., 1981, PATTERN RECOGNITION BOLEY D, 1999, DECISION SUPPORT SYS Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 FRAKES WB, 1992, INFORMATION RETRIEVA, P419 Jing LP, 2005, LECT NOTES ARTIF INT, V3518, P802 KRISHNAPURAM R, 1993, INT J FUZZY SYST, V2, P98 Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lin King-Ip, 2001, P 7 INT C DAT SYST A, P40 Liu B., 2004, UNCERTAINTY THEORY I Liu BD, 2002, IEEE T FUZZY SYST, V10, P445, DOI 10.1109/TFUZZ.2002.800692 Liu YJ, 2006, IEEE ANTENN WIREL PR, V5, P4, DOI 10.1109/LAWP.2005.863611 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P ACM SIGIR Steinbach M., 2000, KDD WORKSH TEXT MIN STREHL A, 2000, P HIPC Tjhi WC, 2006, PATTERN RECOGN LETT, V27, P151, DOI 10.1016/j.patrec.2005.07.012 WONG W, 2000, P INT C INF SOC 21 C Zadeh L. A., 1978, Fuzzy Sets and Systems, V1, DOI 10.1016/0165-0114(78)90029-5 ZAMIR O, 1998, P ACM SIGIR Zhao Y., CRITERION FUNCTIONS ZHAO Y, COMP AGGLOMERATIVE P ZHOU J, 2008, FURTHER INVESTIGATIO Zhou J, 2007, SER INF MANAGE SCI, V6, P404 NR 26 TC 0 Z9 0 PU CALIFORNIA POLYTECHNIC STATE UNIV PI SAN LUIS OBISPO PA CAL POLY, SAN LUIS OBISPO, CA 93407 USA SN 1539-2023 J9 SER INF MANAGE SCI PY 2008 VL 7 BP 295 EP 300 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA BID70 UT WOS:000258741800053 ER PT S AU Abadi, A Nisenson, M Simionovici, Y AF Abadi, Aharcin Nisenson, Mordechai Simionovici, Yahalomit BE Krikhaar, R Lammel, R Verhoef, C TI A traceability technique for specifications SO PROCEEDINGS OF THE 16TH IEEE INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION SE International Conference on Program Comprehension LA English DT Proceedings Paper CT 16th IEEE International Conference on Program Comprehension CY JUN 10-13, 2008 CL Amsterdam, NETHERLANDS SP IEEE Comp Soc, IEEE Comp Soc TCSE ID CODE AB Traceability in software involves discovering links between different artifacts, and is useful for a myriad of tasks in the software life cycle. We compare several different Information Retrieval techniques for this task, across two datasets involving real-world software with the accompanying specifications and documentation. The techniques compared include dimensionality reduction methods, probabilistic and information theoretic approaches, and the standard vector space model. C1 [Abadi, Aharcin; Nisenson, Mordechai; Simionovici, Yahalomit] IBM Haifa Res Lab, IL-31905 Haifa, Israel. RP Abadi, A (reprint author), IBM Haifa Res Lab, Haifa Univ Campus, IL-31905 Haifa, Israel. EM aharona@il.ibm.com; motinis@il.ibm.com; yahalom@il.ibm.com CR Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053 Cover T. M., 1991, ELEMENTS INFORM THEO CSISZAR I, 1975, ANN PROBAB, V3, P146, DOI 10.1214/aop/1176996454 Cullum J. K., 1985, LANCZOS ALGORITHMS L DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Etzkorn LH, 1997, COMPUTER, V30, P66, DOI 10.1109/2.625311 Frakes W. B., 1992, INFORM RETRIEVAL DAT Globerson A, 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753689 GUTMAN M, 1989, IEEE T INFORM THEORY, V35, P401, DOI 10.1109/18.32134 Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50 Lucia A. D., 2007, ACM T SOFTW ENG METH, V16, P13, DOI 10.1145/1276933.1276934 MARCUS A, 2003, ICSE 03 P 25 INT C S, P125 NAHER S, 1990, P INT C AUT LANG PRO, P1 Poshyvanyk D, 2007, INT C PROGRAM COMPRE, P37 Salton G., 1987, TERM WEIGHTING APPRO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 PETERS CODE DATASET NR 18 TC 25 Z9 25 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1092-8138 BN 978-0-7695-3176-2 J9 INT C PROGRAM COMPRE PY 2008 BP 103 EP 112 DI 10.1109/ICPC.2008.30 PG 10 WC Computer Science, Software Engineering SC Computer Science GA BHZ28 UT WOS:000257638100012 ER PT B AU Kudelka, M Snasel, V Lehecka, O El-Qawasmeh, E AF Kudelka, Milos Snasel, Vaclav Lehecka, Ondrej El-Qawasmeh, Eyas GP IEEE TI Web content mining using web design patterns SO PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION LA English DT Proceedings Paper CT IEEE International Conference on Information Reuse and Integration (IRI-2008) CY JUL 13-15, 2008 CL Las Vegas, NV SP IEEE Syst, Man & Cybernet Soc AB One of the artifacts important for reuse is design pattern. Our article concentrates on the usage of web design patterns while analyzing the structures and contents of web pages. We have created our own Pattrio method of pattern detection on web pages. The detected patterns on web pages describe the web page architecture from the external point of view of the user. The knowledge of this architecture can be used in various connections. Experiments have been presented in several conferences using the method of knowing the structure of web pages as automatically found. The article compares our approach with other selected approaches. Our comparison shows that web design patterns can play an important role in the area of analysis of web page structure and contents. C1 [Kudelka, Milos] Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic. RP Kudelka, M (reprint author), Tech Univ Ostrava, Dept Comp Sci, Ostrava, Czech Republic. RI Snasel, Vaclav/B-8094-2009; Kudelka, Milos/D-8457-2014 OI Kudelka, Milos/0000-0002-7219-1008 CR BECKER J, 2003, INT C BUS INF SYST B, P7 DONG J, 2007, INT WORKSH PRED MOD *GOOGL, GOOGL EXP GOOGLE INC, 2007, Patent No. 20070118165 Han J., 2006, DATA MINING CONCEPTS KOCIBOVA J, 2007, WEB INT INT AG TECHN, P221 KOHONEN T, 2006, SELF ORG MAPS Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 KUDELKA M, 2006, SEMANTIC ANAL WEB PA, P329 KUDELKA M, 2008, WEB PAGES REORDERING, P731 LEE CC, 2003, INTELLIGENT WEB TOPI, P584 MICROSOFT CORP, 2007, Patent No. 2007005116 NIE Z, 2007, OBJECT LEVEL VERTICA, P235 NOVELL INC, 2007, Patent No. 2007033171 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMIDT S, 2005, GI JAHRESTAGUNG, P246 SNASEL V, 2007, SEMANTIC ANAL WEB PA, P328 Tidwell J., 2006, DESIGNING INTERFACES Tsantalis N, 2006, IEEE T SOFTWARE ENG, V32, P896, DOI 10.1109/TSE.2006.112 Van Duyne D., 2002, DESIGN SITES PATTERN van Welie M., 2008, PATTERN LIB INTERACT WONG TL, 2005, HOT ITEM MINING SUMM, P797 NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2659-1 PY 2008 BP 232 EP 237 PG 6 WC Computer Science, Cybernetics; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIC73 UT WOS:000258429100041 ER PT B AU Hawkins, B Giraud-Carrier, C AF Hawkins, B. Giraud-Carrier, C. BE Zhang, K Ajhajj, R TI Ranking Search Results for Translated Content SO PROCEEDINGS OF THE 2009 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION LA English DT Proceedings Paper CT IEEE International Conference on Information Reuse and Integration CY AUG 10-12, 2009 CL Las Vegas, NE SP IEEE Syst, Man & Cybernet Soc AB Translation Memory (TM) consists of a collection of previously translated units of text that may prove useful in the translation of new text. The main problem faced by translators who wish to take advantage of TM is that, although search tools do exist, there is no standardized way of effectively ranking search results. This paper proposes a method for effectively ranking TM search results together with a novel approach to efficiently finding common substrings. C1 [Hawkins, B.] Lingotek Inc, Draper, UT 84020 USA. RP Hawkins, B (reprint author), Lingotek Inc, Draper, UT 84020 USA. EM brianhks1@gmail.com; cgc@cs.byu.edu CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bellman R., 2003, DYNAMIC PROGRAMMING BOYER RS, 1977, COMMUN ACM, V20, P762, DOI 10.1145/359842.359859 Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Gusfield D., 1997, ALGORITHMS STRINGS T Hatcher E., 2009, LUCENE ACTION HODASZ G, 2004, P 9 EAMT WORKSH, P82 MAIZEL JV, 1981, P NATL ACAD SCI-BIOL, V78, P7665, DOI 10.1073/pnas.78.12.7665 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIMARD M, 2001, P MT SUMM 8, P335 STEPHEN G. A., 1994, STRING SEARCHING ALG SUMITA E, 2001, P ACL 2001 WORKSH DA, P1 UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-4115-0 PY 2008 BP 242 EP 245 PG 4 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BNF36 UT WOS:000274330000044 ER PT J AU Moskovitch, R Elovici, Y AF Moskovitch, Robert Elovici, Yuval BE Remenyi, D TI Unknown Malicious Code Detection - Practical Issues SO PROCEEDINGS OF THE 7TH EUROPEAN CONFERENCE ON INFORMATION WARFARE AND SECURITY LA English DT Proceedings Paper CT 7th European Conference on Information Warfare and Security (ECIW) CY JUN 30-JUL 01, 2008 CL Plymouth, ENGLAND HO Univ Plymouth DE Malicious code detection; anti virus; machine learning; text categorization; imbalance problem AB The recent growth in Internet usage has motivated the creation of new malicious code for various purposes, including information warfare. Today's signature-based anti-viruses can detect accurately known malicious code but are very limited in detecting new malicious code. New malicious codes are being created every day, and their number is expected to increase in the coming years. Recently, machine learning methods, such as classification algorithms, were used successfully for the detection of unknown malicious code. These studies were based on a test collection with a limited size of less than 3,000 files, and the proportions of malicious and benign files in both the training and test sets were identical. These test collections do not correspond to real life conditions, in which the percentage of malicious files is significantly lower than that of the benign files. In this study we present a methodology for the detection of unknown malicious code. The executable binary code is represented by n-grams. We performed an extensive evaluation using a test collection of more than 30,000 files, in which we investigated the imbalance problem. Five levels of Malicious Files Percentage (MFP) in the training set (16.7, 33.4, 50, 66.7 and 83.4%) were used to train classifiers. 17 levels of MFP (5, 7.5, 10, 12.5, 15, 20, 30, 40, 50, 60, 70, 80, 85, 87.5, 90, 92.5 and 95%) were set in the test set to represent various benign/malicious files ratio during the detection. Our evaluation results suggest that varying classification algorithms react differently to the various benign/malicious files ratio. For 10% MFP in the test set, representing real life conditions, in general the highest performance achieved for the use of less than 33.3% MFP in the training set, and in specific classifiers was above 95% of accuracy was achieved. Additionally we present a chronological evaluation, in which the dataset from 2000 to 2007 was divided to training sets and tests sets. Evaluation results show that an update in the training set is needed. C1 [Moskovitch, Robert; Elovici, Yuval] Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Deutsch Telekom Labs, IL-84105 Beer Sheva, Israel. EM robertmo@bgu.ac.il; elovici@bgu.ac.il CR Abou-Assaleh Tony, 2004, P 28 ANN INT COMP SO Bishop C. M., 1995, NEURAL NETWORKS PATT Chawla N. V., 2004, ACM SIGKDD EXPLORATI, V6, P1, DOI DOI 10.1145/1007730.1007733 Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Frank E, 2005, DATA MINING PRACTICA Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 Golub TR, 1999, SCIENCE, V286, P531, DOI 10.1126/science.286.5439.531 Gryaznov D, 1999, P 5 INT VIR B Henchiri O, 2006, IEEE DATA MINING, P891 Jung Shin S J, 2006, INT MEAS C IMC BRAZ KOLTER J, 2004, P 10 ACM SIGKDD INT, P470, DOI 10.1145/1014052.1014105 Kolter JZ, 2006, J MACH LEARN RES, V7, P2721 Mitchell TM, 1997, MACHINE LEARNING Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schult M., 2001, P IEEE S SEC PRIV, P178 NR 16 TC 1 Z9 1 PU ACAD CONFERENCES LTD PI NR READING PA CURTIS FARM, KIDMORE END, NR READING, RG4 9AY, ENGLAND BN 978-1-906638-06-1 PY 2008 BP 145 EP 152 PG 8 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBQ55 UT WOS:000307869300018 ER PT B AU Hosni, HA Alla, MA AF Hosni, Hanan Ahmed Alla, Mahmoud Abd BE Mastorakis, NE Demiralp, M Mladenov, V Bojkovic, Z TI A Novel Efficient Classification Algorithm for Search Engines SO PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON APPLIED INFORMATICS AND COMMUNICATIONS, PTS I AND II SE Recent Advances in Computer Engineering LA English DT Proceedings Paper CT 8th WSEAS International Conference on Applied Informatics and Communications CY AUG 20-22, 2008 CL Rhodes, GREECE SP WSEAS DE Information Processing on the Web; Supervised Classification; Document Classification ID INFORMATION AB In this paper a new classification algorithm of Web documents into a set of categories, is proposed. The proposed technique is based on analyzing relationships between different documents and the terms they contain by producing a set of rules relating the category of the document, its terms and their frequencies. Each document is represented by a graph that correlates its most frequent combined words and its category. The relationships among these graphs and the documents' categories are captured. The proposed technique has three phases. The first phase is a training phase where human experts determines the categories of different web pages and articles and the supervised classification algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the blind categorization phase where a web crawler will crawl through the World Wide Web to build a database that will be categorized according to the result of the first phase. This data base contains URLs and their categories. The third phase is applying the proposed graph representation technique on the whole set of documents per category to determine its final graph representation. The third phase will produce better classification rules because the sample size is larger with no additional cost of supervised categorization. Experiments using data sets collected from different Web portals are conducted. C1 [Hosni, Hanan Ahmed; Alla, Mahmoud Abd] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Technol, Riyadh 11451, Saudi Arabia. RP Hosni, HA (reprint author), King Saud Univ, Coll Comp & Informat Sci, Dept Informat Technol, Riyadh 11451, Saudi Arabia. CR Broder A., 1997, P 6 INT WORLD WID WE, P391 BUYUKKOKTEN O, 2001, P WWW10 HONG KONG CH CHAKRABARTI S, 1998, P ACM SIGMOD CHEKURI C, 1997, P 6 INT WORLD WID WE CHEN D, 2006, BMC BIOINFORMATICS, P367 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 CHEN JL, 2001, P WWW10 HK CHIN Chen N, 2007, INT J DOC ANAL RECOG, V10, P1, DOI 10.1007/s10032-006-0020-2 CHEN N, 2006, P 2006 C CTR ADV STU, P369 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Han B, 2006, BIOINFORMATICS, V22, P2136, DOI 10.1093/bioinformatics/btl350 JOACHIMS T, 1999, P ICML 99 BLED SLOV KER SJ, 2000, 38 ANN M ASS COMP LI KO YJ, 2002, P COLING KOLCZ A, 2001, P CIKM01 MIYAMOTO S, 1986, IEEE T SYST MAN CYB, V16, P278, DOI 10.1109/TSMC.1986.4308950 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Pitkow J., 1999, P 2 USENIX S INT TEC, P139 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 SALTON G, 1971, PRENTICE HALL SERIES, pCH14 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHATKAY H, 2006, ISMB SUPPLEMENT BIOI, P446 SHYU ML, 2001, P 7 INT C DISTR MULT SHYU ML, 2001, IEEE INT C SYST MAN, P1717 NR 24 TC 0 Z9 0 PU WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC PI ATHENS PA AG LOANNOU THEOLOGOU 17-23, 15773 ZOGRAPHOU, ATHENS, GREECE BN 978-960-6766-94-7 J9 REC ADV COMPUT ENG PY 2008 BP 356 EP 362 PG 7 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BJF66 UT WOS:000265456100057 ER PT B AU Murakami, T Suehisa, Y AF Murakami, Tamotsu Suehisa, Yasushi GP ASME TI Quantity dimension indexing for design knowledge management SO PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2007, VOL 6, PTS A AND B LA English DT Proceedings Paper CT ASME International Design Engineering Technical Conferences/Computers and Information in Engineering Conference CY SEP 04-07, 2007 CL Las Vegas, NV SP ASME, Design & Engn Div, ASME, Comp & Informat Engn Div AB Although many knowledge management techniques based on text expression have been developed, they are not necessarily sufficient for managing engineering design knowledge. In this paper, we propose quantity dimension indexing of design knowledge as a fundamental method for design knowledge management. Physical quantities describing physical phenomena can be represented as vectors in a seven-dimensional space where the orthogonal axes are the seven base units of the SI (The International System of Units). Because of the generality, objectivity and universality of the SI, this space covers all physical quantities that appear in the past, present and future design knowledge and design problems, and the same quantities are represented as the same vectors regardless of the differences in people, products, domains, organizations, nations and languages. We assume that the similarities of physical phenomena lead to similarities in the dimensions of quantities describing the phenomena, and propose to use this seven-dimensional vector for estimating the similarity of design knowledge from the viewpoint of physical phenomena. Based on this basic idea, we mathematically define similarity between two quantities using quantity dimensions. We prepared design knowledge examples and retrieval keys and conducted design knowledge retrieval and design knowledge similarity estimation by quantity dimension indexing and confirmed that we obtained adequate results without using a concept dictionary or thesaurus elaborated in advance, which are indispensable in the text approach. C1 [Murakami, Tamotsu; Suehisa, Yasushi] Univ Tokyo, Dept Engn Synth, Bunkyo Ku, Tokyo 1138656, Japan. RP Murakami, T (reprint author), Univ Tokyo, Dept Engn Synth, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1138656, Japan. CR Ahmed S, 2005, DESIGN STUD, V26, P565, DOI 10.1016/j.destud.2005.02.005 BHASKAR R, 1990, ARTIF INTELL, V45, P73, DOI 10.1016/0004-3702(90)90038-2 GRUBER TR, 1994, 4 INT C PRINC KNOWL, P1 Hirtz J, 2002, RES ENG DES, V13, P65, DOI 10.1007/S00163-001-0008k-3 *INT ORG STAND, 1992, 31 ISO *JAP IND STAND, 2000, B00012000 JIS Kitamura Y, 2004, ADV ENG INFORM, V18, P115, DOI 10.1016/j.aei.2004.09.002 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Szykman S, 2000, IEEE INTELL SYST APP, V15, P48, DOI 10.1109/5254.846285 Umeda Y, 1997, IEEE EXPERT, V12, P42, DOI 10.1109/64.585103 Umeda Y, 1996, AI EDAM, V10, P275 Webb A., 2002, Engineering Management Journal, V12, DOI 10.1049/em:20020302 2006, BUREAU INT POIDS MES, P94 NR 13 TC 0 Z9 0 PU AMER SOC MECHANICAL ENGINEERS PI NEW YORK PA THREE PARK AVENUE, NEW YORK, NY 10016-5990 USA BN 978-0-7918-4807-4 PY 2008 BP 601 EP 610 PG 10 WC Engineering, Multidisciplinary; Engineering, Manufacturing; Engineering, Mechanical SC Engineering GA BHM76 UT WOS:000254345300056 ER PT B AU Li, XF Zhao, LL AF Li, Xin-fu Zhao, Lei-lei TI A Multilayer Method of Text Feature Extraction Based on CILIN SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY LA English DT Proceedings Paper CT International Conference on Computer Science and Information Technology CY AUG 29-SEP 02, 2008 CL Singapore, SINGAPORE SP IACSIT, Sichuan Comp Federat China, Nanyang Technol Univ, Sichuan Univ China, Meiji Univ AB The feature extraction is the most critical technology of text categorization. The method of feature extraction from Chinese text based on CILIN is different from the conventional feature extraction, which uses two feature extraction methods. This method is good at dealing with synonyms and polysemes, and reducing the dimension. Firstly, it uses the method of feature extraction from Chinese text based on CILIN to analyze the meaning of key words. Secondly, use the mutual information to extract the feature, it can give the relation between class and lemma. The experiment results proposed that comprehend to the meaning of key words can distinctively improve the text classification precision. C1 [Li, Xin-fu; Zhao, Lei-lei] Hebei Univ, Fac Math & Comp, Baoding, Hebei, Peoples R China. RP Li, XF (reprint author), Hebei Univ, Fac Math & Comp, Baoding, Hebei, Peoples R China. CR Belhumeur PN, 1997, IEEE T PATTERN ANAL, V19, P711, DOI 10.1109/34.598228 Dunning T., 1993, Computational Linguistics, V19 EYHERAMENDY S, 2003, 9 INT WORKSH ART INT JAN B, 2005, HIGHER ORDER FEATURE, P9 MEI JJ, 1996, CILIN SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *SEBASTIANIF, 2002, ACM COMPUTER SURVEY, V34, P1 Vapnik V., 1998, STAT LEANING THEORY Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 ZHU QM, 2005, TECHNOLOGY DEALING I NR 12 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3308-7 PY 2008 BP 48 EP 52 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIL06 UT WOS:000260454800010 ER PT B AU Jia, T Yi, W AF Jia, Tu Yi, Wu TI Improvement of Supervised Isomap Algorithm and Its Application to Visualization and Categorization of Web Chinese Text SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY LA English DT Proceedings Paper CT International Conference on Computer Science and Information Technology CY AUG 29-SEP 02, 2008 CL Singapore, SINGAPORE SP IACSIT, Sichuan Comp Federat China, Nanyang Technol Univ, Sichuan Univ China, Meiji Univ ID NONLINEAR DIMENSIONALITY REDUCTION AB It is important to reduce the dimensionality of features in Web Chinese text categorization. Isomap algorithm is an unsupervised manifold learning technique. SIIsomap algorithm, an extension of Isomap to supervised feature extraction, is proposed in this paper. It uses adding constant method and a direct embedding technique of Isomap algorithm for testing data to make the embedding more reasonable and easier. SIIsomap algorithm is applied to visualization and classification experiments of Web Chinese text as a feature extraction method. In contrast with existed methods, it gets better visualization and classification effects and illustrates the effectiveness of our method. C1 [Jia, Tu; Yi, Wu] Natl Univ Def Technol, Dept Math & Syst Sci, Coll Sci, Changsha 410073, Hunan, Peoples R China. RP Jia, T (reprint author), Natl Univ Def Technol, Dept Math & Syst Sci, Coll Sci, Changsha 410073, Hunan, Peoples R China. CR Bingham Ella, 2001, P 7 ACM SIGKDD INT C, P245, DOI 10.1145/502512.502546 CHOI HY, 2005, P 4 IEEE INT C DEV L, P208 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Geng X, 2005, IEEE T SYST MAN CY B, V35, P1098, DOI 10.1109/TSMCB.2005.850151 Gu R. J., 2006, IN C COMP INT SEC, P674 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shi LK, 2005, PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, P1707 Smith L. I., 2002, TUTORIAL PRINCIPAL C Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Vlachos M., 2002, P 8 ACM SIGKDD INT C, P645 [å‘Ø茜 Zhou Qian], 2004, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V18, P17 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3308-7 PY 2008 BP 157 EP 161 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIL06 UT WOS:000260454800031 ER PT B AU Liu, YX Liu, ZJ AF Liu, Yongxin Liu, Zhijng TI An Improved Hierarchical K-Means Algorithm for Web Document Clustering SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY LA English DT Proceedings Paper CT International Conference on Computer Science and Information Technology CY AUG 29-SEP 02, 2008 CL Singapore, SINGAPORE SP IACSIT, Sichuan Comp Federat China, Nanyang Technol Univ, Sichuan Univ China, Meiji Univ AB In order to conquer the major challenges of current web document clustering, i.e. huge volume of documents, high dimensional process, we proposed a simple agglomerative hierarchical K-Means clustering (SAHKC) algorithm based on H-K (hierarchical K-Means) algorithm, and a new model was used in this paper to describe the web document, named as multiple feature vector space model (MFVSM). Experimental results indicate that: the MFVSM is helpful in improving the quality of clustering result, and compare with the H-K algorithm, the SAHKC algorithm running time reduce nearly 30%, however the average precision of clustering result only reduce about 10%. C1 [Liu, Yongxin; Liu, Zhijng] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China. RP Liu, YX (reprint author), Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China. CR Chen T.-S., 2005, P 2005 INT S INT SIG, P405 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Kaufman L, 1990, FINDING GROUPS DATA Li WC, 2007, PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 4, P605 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Xu Rui, 2005, IEEE T NEURAL NETWOR, V16, P634 NR 7 TC 1 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3308-7 PY 2008 BP 606 EP 610 PG 5 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIL06 UT WOS:000260454800118 ER PT S AU Santos, E Lins, L Ahrens, JP Freire, J Silva, CT AF Santos, Emanuele Lins, Lauro Ahrens, James P. Freire, Juliana Silva, Claudio T. BE Freire, J Koop, D Moreau, L TI A First Study on Clustering Collections of Workflow Graphs SO PROVENANCE AND ANNOTATION OF DATA AND PROCESSES SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 2nd International Provenance and Annotation Workshop CY JUN 17-18, 2008 CL Salt Lake City, UT SP Microsoft Corp, Univ Utah, Sci Comp & Imaging Inst, Springer, Univ Utah ID SEGMENTATION; SUBGRAPH AB As workflow systems get more widely used, the number of workflows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of workflow and provenance graphs. We propose two different representations for these graphs and present an experimental evaluation, using a collection of 1,700 workflow graphs, where we study the trade-offs of these representations and the effectiveness of alternative clustering techniques. C1 [Santos, Emanuele; Lins, Lauro; Silva, Claudio T.] Univ Utah, Sci Comp & Imaging Inst, Salt Lake City, UT 84112 USA. RP Santos, E (reprint author), Univ Utah, Sci Comp & Imaging Inst, Salt Lake City, UT 84112 USA. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbosa L, 2007, P IEEE 23 INT C DAT, P326 Bunke H, 1998, PATTERN RECOGN LETT, V19, P255, DOI 10.1016/S0167-8655(97)00179-7 CUTTING DR, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P318 Ester M, 2000, DATA MIN KNOWL DISC, V4, P193, DOI 10.1023/A:1009843930701 Freire J, 2006, LECT NOTES COMPUT SC, V4145, P10 Greco G, 2006, IEEE T KNOWL DATA EN, V18, P1010, DOI 10.1109/TKDE.2006.123 Hastie T., 2001, SPRINGER SERIES STAT Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Makrogiannis S, 2005, IEEE T SYST MAN CY A, V35, P224, DOI 10.1109/TSMCA.2004.832820 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANFELIU A, 1983, IEEE T SYST MAN CYB, V13, P353 ULLMANN JR, 1976, J ACM, V23, P31, DOI 10.1145/321921.321925 *VIS TOOLK, 2008, KITW WU Z, 1993, IEEE T PATTERN ANAL, V15, P1101, DOI 10.1109/34.244673 NR 16 TC 4 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-89964-8 J9 LECT NOTES COMPUT SC PY 2008 VL 5272 BP 160 EP 173 PG 14 WC Computer Science, Theory & Methods SC Computer Science GA BIU90 UT WOS:000262977000016 ER PT B AU Shepitsen, A Gemmell, J Mobasher, B Burke, R AF Shepitsen, Andriy Gemmell, Jonathan Mobasher, Bamshad Burke, Robin GP ACM TI Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering SO RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS LA English DT Proceedings Paper CT ACM Conference on Recommender Systems CY OCT 23-25, 2008 CL Lausanne, SWITZERLAND SP ACM SIGCHI, ACM SIGIR, ACM SIGWEB, ACM SIGKDD, ACM SIGART DE Collaborative tagging; clustering; personalization; recommender systems ID RETRIEVAL AB Collaborative tagging applications allow Internet users to annotate resources with personalized tags. The complex network created by many annotations, often called a folksonomy, permits users the freedom to explore tags, resources or even other user's profiles unbound from a rigid predefined conceptual hierarchy. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag redundancy and ambiguity hindering navigation. Data mining techniques, such as clustering, provide a means to remedy these problems by identifying trends and reducing noise. Tag clusters can also be used as the basis for effective personalized recommendation assisting users in navigation. We present a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters. Our basic recommendation framework is independent of the clustering method, but we use a context-dependent variant of hierarchical agglomerative clustering which takes into account the user's current navigation context in cluster selection. We present extensive experimental results on two real world dataset. While the personalization algorithm is successftil in both cases, our results suggest that folksonomics encompassing only one topic domain, rather than many topics, present an easier target for recommendation, perhaps because they are more focused and often less sparse. Furthermore, context dependent cluster selection, an integral step in our personalization algorithm, demonstrates more utility for recommendation in multi-topic folksonomies than in single-topic folksonomies. This observation suggests that topic selection is an important strategy for recommendation in multi-topic folksonomies. C1 [Shepitsen, Andriy; Gemmell, Jonathan; Mobasher, Bamshad; Burke, Robin] Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA. RP Shepitsen, A (reprint author), Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA. EM ashepits@cs.depaul.edu; jgemmell@cs.depaul.edu; mobasher@cs.depaul.edu; rburke@cs.depaul.edu CR Bao S., 2007, P 16 INT C WORLD WID, P501, DOI DOI 10.1145/1242572.1242640 BEGELMAN G, 2006, P COLL WEB TAGG WORK, P6 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 GEMMELL J, 2008, P 10 INT C DAT WAR K GEMMELL J, 2008, INTELLIGENT TECHNIQU Hammond T., 2005, D LIB MAGAZINE, V11, P1082 Hayes C., 2007, INT C WEBL SOC MED Heymann P., 2006, 200610 COMP SCI DEP Hotho A, 2006, LECT NOTES COMPUT SC, V4011, P411 Mathes A., 2004, COMPUTER MEDIATED CO Mika P., 2007, WEB SEMANTICS SCI SE, V5, P5, DOI 10.1016/j.websem.2006.11.002. Millen D.R., 2006, P SIGCHI C HUM FACT, P111, DOI 10.1145/1124772.1124792 Niwa S., 2006, P 3 INT C INF TECHN, P388 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Voorhees E. M., 1999, P TREC, V8, P77 Wu X., 2006, P 15 INT C WORLD WID, P417, DOI 10.1145/1135777.1135839 Xu Z., 2006, COLL WEB TAGG WORKSH YAN R, 2007, EFFICIENT MANUAL IMA NR 20 TC 77 Z9 77 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-60558-093-7 PY 2008 BP 259 EP 266 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BJS80 UT WOS:000267113700033 ER PT B AU Rahal, I AF Rahal, Imad GP ACM TI Undergraduate Research Experiences in Data Mining SO SIGCSE'08: PROCEEDINGS OF THE 39TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION LA English DT Proceedings Paper CT 39th ACM Technical Symposium on Computer Science Education CY MAR 12-15, 2008 CL Portland, OR SP ACM SIGCSE, ACM SIGACCESS DE Data mining; machine learning; pattern recognition; undergraduate course design AB The new interdisciplinary field of Data Mining emerged in the early 1990s as a response to the profusion of digital data generated in numerous fields such as biology, chemistry, astronomy, advertising, banking and finance, retail market, stock market, and the WWW. In this paper, I describe an undergraduate course in Data Mining offered at the College of Saint Benedict and Saint John's University in Spring of 2007 as a CSCI-317-upper-division "Topics in Computer Science"- course, entitled "Data Intelligence." One of the main objectives of the course was to engage students in experimental computing research through a number of carefully planned research activities resulting in better understanding of the course contents and deeper insights into the challenges faced by the data mining community. C1 [Rahal, Imad] Coll St Benedict, Dept Comp Sci, Collegeville, MN 56321 USA. EM irahal@csbsju.edu CR Agrawal R., 1994, P 20 INT C VER LARG, P487 ANTONIE ML, P INT WORKSH MULT DA Frank E, 2005, DATA MINING PRACTICA Han J., 2000, P 2000 ACM SIGMOD IN, P1, DOI 10.1145/342009.335372 LOPEZ D, 2001, P MIDW INSTR COMP S MUSICANT D, 2006, P SIGCSE HOUST TX MA NG R, 1994, P VLDB SANT CHIL ROIGER RJ, 2005, P SIGCSES C INN TECH, P415, DOI 10.1145/1067445.1067620 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAN PN, 2005, INTRO DATA MINGING NR 11 TC 3 Z9 3 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-59593-947-0 PY 2008 BP 461 EP 465 PG 5 WC Computer Science, Interdisciplinary Applications; Education & Educational Research; Education, Scientific Disciplines; Social Sciences, Interdisciplinary SC Computer Science; Education & Educational Research; Social Sciences - Other Topics GA BJH00 UT WOS:000265741800107 ER PT J AU Pennacchiotti, M De Cao, D Marocco, P Basili, R AF Pennacchiotti, Marco De Cao, Diego Marocco, Paolo Basili, Roberto GP European Language Resources Association TI Towards a Vector Space Model for FrameNet-like Resources SO SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008 LA English DT Proceedings Paper CT 6th International Conference on Language Resources and Evaluation (LREC) CY MAY 28-30, 2008 CL Marrakech, MOROCCO SP European Language Resources Assoc (ELRA), Evaluat & Language Resources Distribut Agcy (ELDA), Ist Linguistica Computazionale (ILC), Nuance, Inst Nederlandse Lexicologie (INL), Microsoft, European Media Lab GmBH (EML), Linguatec, Connexor, Orange, Telisma, AAMT, AMTA, ACL, AFNLP, ALTA, COCOSDA, Oriental COCOSDA, EACL, EAMT, ELSNET, EURALEX, GWA, IAMT, ISCA, KnowledgeWeb, LDC, NEMLAR Network, SIGLEX, TEI, Technolangue French Program, WRITE, Informat Soc & Media, European Commiss, Unit E 2 Content & Knowledge AB In this paper, we present an original framework to model frame semantic resources (namely, FrameNet) using minimal supervision. This framework can be leveraged both to expand an existing FrameNet with new knowledge, and to induce a FrameNet in a new language. Our hypothesis is that a frame semantic resource can be modeled and represented by a suitable semantic space model. The intuition is that semantic spaces are an effective model of the notion of "being characteristic of a frame" for both lexical elements and full sentences. The paper gives two main contributions. First, it shows that our hypothesis is valid and can be successfully implemented. Second, it explores different types of semantic VSMs, outlining which one is more suitable for representing a frame semantic resource. In the paper, VSMs are used for modeling the linguistic core of a frame, the lexical units. Indeed, if the hypothesis is verified for these units, the proposed framework has a much wider application. C1 [Pennacchiotti, Marco] Univ Saarland, D-66123 Saarbrucken, Germany. EM pennacchiotti@coli-uni.sb.de; decao@info.uniroma2.it; marocco@info.uniroma2.it; basili@info.uniroma2.it CR Baker Collin F., 1998, P CLING ACL MONTR CA Bar-Haim Roy, 2005, DEFINITION ANAL INTE Basili Roberto, 2006, 17 EUR C ART INT ECA Boas HC, 2005, INT J LEXICOGR, V18, P445, DOI 10.1093/ijl/eci043 Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Burchardt Aljoscha, 2006, P PASCAL RTE2 WORKSH Burchardt Aljoscha, 2006, P 5 INT C LANG RES E Clark P., 2007, P ACL PASCAL WORKSH, P54, DOI 10.3115/1654536.1654549 Dagan I, 2006, LECT NOTES ARTIF INT, V3944, P177 Fillmore Charles J., 1985, QUADERNI SEMANTICA, V6, P222 Garoufi K., 2007, THESIS SAARLAND U Giampiccolo D., 2007, P ACL PASCAL WORKSH, P1, DOI 10.3115/1654536.1654538 Gildea D, 2002, COMPUT LINGUIST, V28, P245, DOI 10.1162/089120102760275983 Gordon A., 2007, P 45 ANN M ASS COMP, P192 Green D. M., 1996, SIGNAL DETECTION THE Harris Z., 1964, PHILOS LINGUISTICS Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lapata M., 2007, COMPUTATIONAL LINGUI, V33 Lin Dekang, 2001, P ACM C KNOWL DISC D Lin Dekang, 1998, P JOINT INT C COMP L Mohammad Saif, 2005, DISTRIBUTIONAL UNPUB Ohara K., 2004, P WORKSH BUILD LEX R Pado Sebastian, 2007, CROSS LINGUAL ANNOTA Palmer M., 2005, COMPUTATIONAL LINGUI, V31 Pantel Patrick, 2004, P HLT NAACL 04 BOST Pitel Guillaume, 1998, CROSS LINGUAL LABELI Pitel Guillaume, 2006, WORKSH MULT SEM ANN Sahlgren Magnus, 2006, WORLD SPACE MODEL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Subirats C., 2003, P WORKSH FRAM SEM 17 Weeds J., 2004, P COLING 2004 GEN SW Weeds Julie, 2007, THESIS Yarowsky David, 2001, P 1 HUM LANG TECHN C NR 33 TC 0 Z9 0 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE PY 2008 BP 790 EP 796 PG 7 WC Linguistics SC Linguistics GA BGS66 UT WOS:000324028900138 ER PT J AU Anick, P Murthi, V Sebastian, S AF Anick, Peter Murthi, Vijay Sebastian, Shaji GP European Language Resources Association TI Similar Term Discovery using Web Search SO SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008 LA English DT Proceedings Paper CT 6th International Conference on Language Resources and Evaluation (LREC) CY MAY 28-30, 2008 CL Marrakech, MOROCCO SP European Language Resources Assoc (ELRA), Evaluat & Language Resources Distribut Agcy (ELDA), Ist Linguistica Computazionale (ILC), Nuance, Inst Nederlandse Lexicologie (INL), Microsoft, European Media Lab GmBH (EML), Linguatec, Connexor, Orange, Telisma, AAMT, AMTA, ACL, AFNLP, ALTA, COCOSDA, Oriental COCOSDA, EACL, EAMT, ELSNET, EURALEX, GWA, IAMT, ISCA, KnowledgeWeb, LDC, NEMLAR Network, SIGLEX, TEI, Technolangue French Program, WRITE, Informat Soc & Media, European Commiss, Unit E 2 Content & Knowledge AB We present an approach to the discovery of semantically similar terms that utilizes a web search engine as both a source for generating related terms and a tool for estimating the semantic similarity of terms. The system works by associating with each document in the search engine's index a weighted term vector comprising those phrases that best describe the document's subject matter. Related terms for a given seed phrase are generated by running the seed as a search query and mining the result vector produced by averaging the weights of terms associated with the top documents of the query result set. The degree of similarity between the seed term and each related term is then computed as the cosine of the angle between their respective result vectors. We test the effectiveness of this approach for building a term recommender system designed to help online advertisers discover additional phrases to describe their product offering. A comparison of its output with that of several alternative methods finds it to be competitive with the best known alternative. C1 [Anick, Peter; Murthi, Vijay; Sebastian, Shaji] Yahoo Inc, Santa Clara, CA USA. EM panick@yahoo-inc.com; murthij@yahoo-inc.com; colleague@shaji.com CR Ananiadou S, 2002, COMPUTERM 2002, P1 Anick P, 2003, P SIGIR 2003 TOR CAN SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Davison AC, 1997, CAMBRIDGE SERIES STA Grefenstette G., 1994, EXPLORATIONS AUTOMAT Hearst M. A., 1992, P 14 C COMP LING, V2, P539, DOI 10.3115/992133.992154 Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835 Lin D., 1998, P 17 INT C COMP LING, P768 Metzler D, 2007, LECT NOTES COMPUT SC, V4425, P16 Sahami Mehran, 2006, P 15 INT C WORLD WID, P377, DOI DOI 10.1145/1135777.1135834 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastian S, 2006, P EC 06 ANN ARB MICH NR 12 TC 0 Z9 0 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE PY 2008 BP 1209 EP 1213 PG 5 WC Linguistics SC Linguistics GA BGS66 UT WOS:000324028901050 ER PT J AU Biemann, C Quasthoff, U Heyer, G Holz, F AF Biemann, Chris Quasthoff, Uwe Heyer, Gerhard Holz, Florian GP European Language Resources Association TI ASV Toolbox - A Modular Collection of Language Exploration Tools SO SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008 LA English DT Proceedings Paper CT 6th International Conference on Language Resources and Evaluation (LREC) CY MAY 28-30, 2008 CL Marrakech, MOROCCO SP European Language Resources Assoc (ELRA), Evaluat & Language Resources Distribut Agcy (ELDA), Ist Linguistica Computazionale (ILC), Nuance, Inst Nederlandse Lexicologie (INL), Microsoft, European Media Lab GmBH (EML), Linguatec, Connexor, Orange, Telisma, AAMT, AMTA, ACL, AFNLP, ALTA, COCOSDA, Oriental COCOSDA, EACL, EAMT, ELSNET, EURALEX, GWA, IAMT, ISCA, KnowledgeWeb, LDC, NEMLAR Network, SIGLEX, TEI, Technolangue French Program, WRITE, Informat Soc & Media, European Commiss, Unit E 2 Content & Knowledge ID SMALL-WORLD; NETWORKS; CODES; MODEL AB ASV Toolbox is a modular collection of tools for the exploration of written language data both for scientific and educational purposes. It includes modules that operate on word lists or texts and allow to perform various linguistic annotation, classification and clustering tasks, including language detection, POS-tagging, base form reduction, named entity recognition, and terminology extraction. On a more abstract level, the algorithms deal with various kinds of word similarity, using pattern-based and statistical approaches. The collection can be used to work on large real-world data sets as well as for studying the underlying algorithms. Each module of the ASV Toolbox is designed to work either on a plain text files or with a connection to a MySQL database. While it is especially designed to work with corpora of the Leipzig Corpora Collection, it can easily be adapted to other sources. C1 [Biemann, Chris; Quasthoff, Uwe; Heyer, Gerhard; Holz, Florian] Univ Leipzig, Dept Comp Sci, NLP Grp, D-04009 Leipzig, Germany. EM biem@informatik.uni-leipzig.de; quasthoff@informatik.uni-leipzig.de; heyer@informatik.uni-leipzig.de; holz@informatik.uni-leipzig.de CR [Anonymous], 2003, LINGPIPE Biemann C., 2005, LNCS, V3406 Biemann C., 2006, P HLT NAACL 06 WORKS Biemann Chris, 2006, P COLING ACL 06 STUD Bird S, 2004, P ACL DEM SESS BARC, P214 Bordag S., 2006, P UNS SEG WORDS MORP Bordag S., 2007, P CLEF WORKSH 2007 B Bourigault D., 1992, P 14 INT C COMP LING, P977, DOI DOI 10.3115/993079.993111 Braschler M, 2003, LECT NOTES COMPUT SC, V2633, P177 COHEN JD, 1995, J AM SOC INFORM SCI, V46, P162, DOI 10.1002/(SICI)1097-4571(199504)46:3<162::AID-ASI2>3.0.CO;2-6 Cunningham H., 2002, P 40 ANN M ASS COMP Daille B., 1994, P 15 INT C COMP LING, P515 Cancho RFI, 2001, P ROY SOC B-BIOL SCI, V268, P2261, DOI 10.1098/rspb.2001.1800 Heyer Gerhard, 2006, TEXT MINING WISSENSR Holz F, 2008, LECT NOTES COMPUT SC, V4919, P117, DOI 10.1007/978-3-540-78135-6_11 Kazakov D, 2001, MACH LEARN, V43, P121, DOI 10.1023/A:1007629103294 Kazakov D., 1997, ECML MLNET WORKSH EM, P105 Larson M., 2000, P 6 INT C SPOK LANG LEVENSHT.VI, 1965, DOKL AKAD NAUK SSSR+, V163, P845 MONZ C, 2002, CLEF 2001, V2406, P262 Quasthoff U., 2006, P LREC Quasthoff U., 2002, P CONNL 2002 TAIP TA RABINER LR, 1989, P IEEE, V77, P257, DOI 10.1109/5.18626 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 VITERBI AJ, 1967, IEEE T INFORM THEORY, V13, P260, DOI 10.1109/TIT.1967.1054010 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Witschel F., 2005, P NODALIDA Witschel Hans Friedrich, 2004, TEXT WORTER MORPHEME Witten I. H., 2000, WEKA PRACTICAL MACHI Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 31 TC 0 Z9 0 PU EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA PI PARIS PA 55-57, RUE BRILLAT-SAVARIN, PARIS, 75013, FRANCE PY 2008 BP 1760 EP 1767 PG 8 WC Linguistics SC Linguistics GA BGS66 UT WOS:000324028901144 ER PT S AU Kudelka, M Snasel, V Lehecka, O El-Qawasmeh, E Pokorny, J AF Kudelka, Milos Snasel, Vaclav Lehecka, Ondrej El-Qawasmeh, Eyas Pokorny, Jaroslav BE Geffert, V Karhumaki, J Bertoni, A Preneel, B Navrat, P Bielikova, M TI Web pages reordering and clustering based on web patterns SO SOFSEM 2008: THEORY AND PRACTICE OF COMPUTER SCIENCE SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 34th Conference on Current Trends in Theory and Practice of Computer Science CY JAN 19-25, 2008 CL Novy Smokovec, SLOVAKIA SP Asseco Slovakia, Data Informat Technol & Expert Consulting, European Res Consortium Informat & Math, Hewlett Packard Slovakia, IBM Slovakia, Siemens Slovakia, SOFTEC AB In this paper was proposed a method for the description of web pages using web patterns. We will explain what we mean by the term "web pattern". We will present a taxonomy web patterns and a description of some their types. In the description of web patterns we will focus on properties which are useful for automatic detection on web pages. As a result of the detection we get a description of a web page using found web patterns. The description can be used for reordering and clustering of a web page set. C1 [Kudelka, Milos; Snasel, Vaclav; Lehecka, Ondrej] Tech Univ Ostrava, Dept Comp Sci, Ostrava 70833, Czech Republic. RP Kudelka, M (reprint author), Tech Univ Ostrava, Dept Comp Sci, 17 Listopadu 15, Ostrava 70833, Czech Republic. EM milos.kudelka@vsb.cz; vaclav.snasel@vsb.cz; ondrej.lehecka@vsb.cz; eyas@just.edu.jo; pokorny@ksi.ms.mff.cuni.cz RI Snasel, Vaclav/B-8094-2009; Kudelka, Milos/D-8457-2014 OI Kudelka, Milos/0000-0002-7219-1008 CR Alexander C, 1977, PATTERN LANGUAGE TOW Chakrabarti Soumen, 2003, MINING WEB DISCOVERI Chang CH, 2006, IEEE T KNOWL DATA EN, V18, P1411, DOI 10.1109/TKDE.2006.152 DEARDEN FJ, 2006, HUMAN COMPUTER INTER, V21, P49 Dong J, 2007, PROMISE 2007 3 INT W, P12 Ivory MY, 2005, ACM T INFORM SYST, V23, P463, DOI 10.1145/1095872.1095876 Kiyavitskaya N, 2006, LECT NOTES ARTIF INT, V4333, P143 KOCIBOVA J, 2007, WEB INT INT AG TECHN, P221 KOHONEN T, 2006, SELFORGANIZING MAPS KUDELKA M, 2006, WI 2006 INT C WEB IN, P329 Nie Z., 2007, WWW, P81 Nie Z., 2007, CIDR, P235 PIVK A, 2005, THESIS U MARIBOR REIS DC, 2004, WWW 04, P502, DOI DOI 10.1145/988672.988740 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SNASEL V, 2007, AWIC 2007 FONT FRANC SNASEL V, 2007, CISIM 2007, P14 Tidwell J., 2006, DESIGNING INTERFACES Tsantalis N, 2006, IEEE T SOFTWARE ENG, V32, P896, DOI 10.1109/TSE.2006.112 VANDUYNE DK, 2002, DESIGN PATTERNS PRIN YU S, 2003, WORLD WID WEB C WWW, P203 NR 21 TC 7 Z9 7 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-77565-2 J9 LECT NOTES COMPUT SC PY 2008 VL 4910 BP 731 EP 742 PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BHJ33 UT WOS:000253521400063 ER PT B AU Alves, V Schwanninger, C Barbosa, L Rashid, A Sawyer, P Rayson, P Pohl, C Rummler, A AF Alves, Vander Schwanninger, Christa Barbosa, Luciano Rashid, Awais Sawyer, Peter Rayson, Paul Pohl, Christoph Rummler, Andreas BA Geppert, B BF Geppert, B BE Pohl, K TI An Exploratory Study of Information Retrieval Techniques in Domain Analysis SO SPLC 2008: 12TH INTERNATIONAL SOFTWARE PRODUCT LINE CONFERENCE, PROCEEDINGS LA English DT Proceedings Paper CT 12th International Software Product Line Conference CY SEP 08-12, 2008 CL Limerick, IRELAND SP Software Engn Inst, Carnegie Mellon, Irish Comp Soc, BigLever, MKS, Syst Log, Pure Syst, Telelogic ID REQUIREMENTS; KNOWLEDGE AB Domain analysis involves not only looking at standard requirements documents (e.g., use case specifications) but also at customer information packs, market analyses, etc. Looking across all these documents and deriving, in a practical and scalable way, a feature model that is comprised of coherent abstractions is a fundamental and non-trivial challenge. We conduct an exploratory study to investigate the suitability of Information Retrieval (IR) techniques for scalable identification of commonalities and variabilities in requirement specifications for software product lines. Accordingly, based on observations derived from industrial experience and on state-of-the-art research and practice, we also propose an initial framework, leveraging IR to systematically abstract requirements from existing specifications of a given domain into a feature model. We evaluate this framework, present a roadmap for its further extension, and formulate hypotheses to guide future work in exploring IR techniques for domain analysis. CR Bosch J, 2000, DESIGN USE SOFTWARE BRAGANCA A, 2007, P 11 INT SOFTW PROD Chen K., 2005, P 13 IEEE INT C REQ, P31 CZARNECKI K, 2000, GENERATIVE PROGRAMM DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 GACITUA R, 2007, P 27 SGAI INT C INN, P153 Gray R. M., 1991, ENTROPY INFORM THEOR Gurp J.V., 2001, P WORK IEEE IFIP C S, P45 JOHN I, 2003, 00404E IESE KIT L, 2006, OOPSLA 06, P383 KOVACEVIC J, 2007, D11 AMPLE OCHDAG JN, 2006, EMSE EMPIRICAL SOFTW, V11, P303 RIEBISCH M, 2002, INTEGRATED DESIGN PR, P45 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sawyer P, 2005, IEEE T SOFTWARE ENG, V31, P969, DOI 10.1109/TSE.2005.129 Stone A, 2006, IEE PROC-SOFTW, V153, P211, DOI 10.1049/ip-sen:20060034 Tan P. N., 2005, INTRO DATA MINING NR 17 TC 10 Z9 10 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3303-2 PY 2008 BP 67 EP 76 PG 10 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BII25 UT WOS:000259669800007 ER PT B AU Liang, GM AF Liang Guangmin GP IEEE COMPUTER SOC TI Modeling Unknown Web Attacks in Network Anomaly Detection SO Third 2008 International Conference on Convergence and Hybrid Information Technology, Vol 2, Proceedings LA English DT Proceedings Paper CT 3rd International Conference on Convergence and Hybrid Information Technology (ICCIT 2008) CY NOV 11-13, 2008 CL Busan, SOUTH KOREA AB Due to the unknown web attacks are hardly be detected and the early warning and response mechanism cannot be established, many of intrusion detection systems (IDSs) are only effective in detecting known web attacks and cannot evaluate the risk of web service. In order to conquer these limitations and inspired by immune principles, this paper presents an immune-based active defense model for web attacks which is on the basis of the clone selection and hyper-mutation. Therefore, the immune learning algorithm and the attack detection mechanism are given. The risk of web attacks is quantitatively analyzed on the relationship between the antibody concentration and the state of an illness in biological immune system (BIS). Theoretical analysis and experimental evaluation demonstrate that the model is more suitable for detecting unknown attacks, and provides an active defense mechanism for detecting network anomalies. C1 Shenzhen Polytech, Dept Comp Engn, Shenzhen 518055, Peoples R China. RP Liang, GM (reprint author), Shenzhen Polytech, Dept Comp Engn, Shenzhen 518055, Peoples R China. EM gmliang@oa.szpt.net CR ALMGREN M, 2001, RAID 00, P22 ALMGREN M, 2000, NDSS *CERT CC, 2002, CA200227 CERTCC *CERT CC, 2002, CA200119 CERTCC D'haeseleer P., 1996, IEEE S SEC PRIV, P110 GARCIA VH, 2006, IFIP PPAI, P323 GIANNI T, 2006, WSEAS T COMPUTERS, V5, P186 GONZALEZ FA, 2002, IEEE T EVOLUTIONARY, V6, P281 JERNE NK, 1974, ANN INST PASTEUR IMM, VC125, P373 Ji Z, 2006, GECCO 2006: Genetic and Evolutionary Computation Conference, Vol 1 and 2, P111 KLEIN DV, 1999, WORKSH INTR DET NETW, P81 KRUGEL C, 2005, COMPUTER NETWORKS, V48, P717 Li T., 2004, COMPUTER IMMUNOLOGY Roesch M., 1999, LISA 99, P229 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vigna G., 2003, ACSAC, P34 NR 16 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3407-7 PY 2008 BP 112 EP 116 DI 10.1109/ICCIT.2008.151 PG 5 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BIR65 UT WOS:000262355500020 ER PT S AU Qin, WJ Zhang, DQ Shi, YC Du, KJ AF Qin, Weijun Zhang, Daqing Shi, Yuanchun Du, Kejun BE Sandnes, FE Zhang, Y Rong, C Yang, LT Ma, J TI Combining user profiles and situation contexts for spontaneous service provision in smart assistive environments SO UBIQUITOUS INTELLIGENCE AND COMPUTING, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Conference on Ubiquitous Intelligence and Computing CY JUN 23-25, 2008 CL Oslo, NORWAY AB Wireless hotspots are permeating our workplace, home and public places bringing interesting services and spontaneous connectivity to mobile users. Since mobile users will be immersed in thousands of different kinds of pervasive services, it's of paramount importance to provide appropriate services to the right person in the right form. Existing approaches offer a rather simplistic solution to the problem and seldom consider the multi-dimensional contextual information. In this paper we present a service provision mechanism which can enable effective service provision based on semantic similarity measure with the combination of user profiles and situation context in WLAN enabled environment. We illustrate our ideas through a scenario of service discovery and provision in public places (e.g. shopping mall) where a disabled person can select appropriate services with the constraints of his mobile device. C1 [Qin, Weijun; Shi, Yuanchun] Tsinghua Univ, Dept Comp Sci & Technol, Minist Educ, Key Lab Pervas Comp, Beijing 100084, Peoples R China. RP Qin, WJ (reprint author), Tsinghua Univ, Dept Comp Sci & Technol, Minist Educ, Key Lab Pervas Comp, Beijing 100084, Peoples R China. CR [Anonymous], OWL S SEMANTIC MARKU CARMAGNOLA F, 2008, P IUI 2008 WORKSH UB Gu T, 2005, J NETW COMPUT APPL, V28, P1, DOI 10.1016/j.jnca.2004.06.002 Lee C, 2006, IEEE PERVAS COMPUT, V5, P16 Lin Z., 2007, P IEEE INT C AUT SCI, P759 NAKAJIMA T, 2006, PERS UBIQUIT COMPUT, P379 QIN WJ, 2006, P GPC, P644 RUKZIO E, 2006, P 8 INT C UB COMP UB, P87 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strang T., 2004, P UBICOMP WORKSH ADV *UPNP FOR, 2003, UPNP DEV ARCH VERS 1 Yu S., 2005, WORKSH SEM MOB ENV S NR 12 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-69292-8 J9 LECT NOTES COMPUT SC PY 2008 VL 5061 BP 187 EP 200 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BHX58 UT WOS:000257237300016 ER PT S AU Thabtah, F Hadi, WM Al-shammare, G AF Thabtah, Fadl Hadi, Wa'el Musa Al-shammare, Gaith GP Int Assoc Engineers TI VSMs with K-Nearest Neighbour to Categorise Arabic Text Data SO WCECS 2008: WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE SE Lecture Notes in Engineering and Computer Science LA English DT Proceedings Paper CT World Congress on Engineering and Computer Science (WCECS 2008) CY OCT 11-24, 2008 CL San Francisco, CA SP Int Assoc Engineers DE Data sets; Data mining; Text categorisation; Term weighting; VSM AB Text categorisation is a popular problem that has been studied extensively in the last four decades. This paper investigates different variations of vector space models (VSMs) and term weighting approaches using KNN algorithm. The base of our comparison in the experiments we conduct is the F1 evaluation measure. The Experimental results against different Arabic text categorisation data sets provide evidence that Dice and Jaccard Coefficient outperform the Cosine Coefficient approach with regards to F1 results, and the Dice-based TF.IDF achieves the highest average scores. C1 [Thabtah, Fadl] Philadelphia Univ, MIS Dept, Amman, Jordan. RP Thabtah, F (reprint author), Philadelphia Univ, MIS Dept, Amman, Jordan. EM ffayez@philadelphia.edu.jo; whadi81@students.aabfs.org; gaithshammare@yahoo.com CR ELHALEES A, 2007, SERIES NATURAL STUDI, V15, P157 El-Halees A., 2006, P 1 INT C M IN PRESS, P15 ELKOURDI M, 2004, 20 INT C COMP LING A Hammo B., 2002, WORKSH COMP APPR SEM, P55 Joachims T., 1998, P EUR C MACH LEARN E, P173 JUNKER M, 1999, P 5 INT C DOC AN REC KHREISAT L, 2006, ARABIC TEXT CLASSIFI, P78 MOULINIER G, 1996, P 5 ANN S DOC AN INF QUINLAN J, C4 5 PROGR MACH LEAR Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAWAF H, ARAB NAT LANG PROC W Sebastiani Fabrizio, 2005, TEXT MINING ITS APPL, P109 THABTAH F, P 4 IEEE INT C DAT M, P217 TOKUNAGA T, 1994, TEXT CATEGORISATION Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 VANRIJSBERGAN CJ, 1979, INFORM RETRIEVAL Wiener E, 1995, P 4 ANN S DOC AN INF, P317 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 20 TC 7 Z9 7 PU INT ASSOC ENGINEERS-IAENG PI HONG KONG PA UNIT1, 1-F, 37-39 HUNG TO ROAD, KWUN TONG, HONG KONG, 00000, PEOPLES R CHINA SN 2078-0958 BN 978-988-98671-0-2 J9 LECT NOTES ENG COMP PY 2008 BP 778 EP 781 PG 4 WC Computer Science, Interdisciplinary Applications; Engineering, Multidisciplinary SC Computer Science; Engineering GA BIW70 UT WOS:000263417100146 ER PT B AU Blancalana, C Lapolla, A Micarelli, A AF Blancalana, Claudio Lapolla, Antonello Micarelli, Alessandro BE Cordeiro, J Filipe, J Hammoudi, S TI Query Expansion with matrix correlation techniques - A systematic approach SO WEBIST 2008: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2 LA English DT Proceedings Paper CT 4th International Conference on Web Information Systems and Technologies CY MAY 04-07, 2008 CL Funchal, PORTUGAL SP Inst Syst & Technol Informat, Control & Commun, Univ Madeira, Workflow Management Coalit DE Information Retrieval; Query Expansion; personalization ID RETRIEVAL; SPACE AB This paper presents an Information Retrieval system that employs techniques based on Personalization and Query Expansion (QE). The system was developed in an incremental and iterative way, starting from a simpler system and reaching a more complex one, to the point that it is possible to talk about several systems each based on a specific, deeply analyzed approach: four systems sharing the concept of term co-occurrence. Starting from a simple system based on bigrams, we moved onto a system based on term proximity, through an approach known in the literature as Hyperspace Analogue to Language (HAL), and eventually developing a solution based on co-occurrence at page level. The latter presents a hybrid approach based on term proximity. This novel architecture is shown here for the first time to our knowledge. C1 [Blancalana, Claudio; Lapolla, Antonello; Micarelli, Alessandro] Roma Tre Univ, Artificial Intelligence Lab, Dept Comp Sci & Automat, I-000146 Rome, Italy. EM claudio.biancalana@dia.uniroma3.it; lapolla@dia.uniroma3.it; micarel@dia.uniroma3.it CR ANICK P, 2003, P 26 ANN INT ACM SIG, P88 Bai J., 2005, CIKM, P688 BRUZA P, 2002, CIKM, P260 Burgess C, 1998, DISCOURSE PROCESS, V25, P211 Gao Jianfeng, 2004, SIGIR 04 P 27 ANN IN, P170, DOI DOI 10.1145/1008992.1009024 Gasparetti F, 2007, INT J PATTERN RECOGN, V21, P207, DOI 10.1142/S0218001407005429 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Porter MF, 1997, ALGORITHM SUFFIX STR, P313 RADLINSKI F, 2005, QUERY CHAINS LEARNIN Salton G., 1997, IMPROVING RETRIEVAL, P355 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 Teevan J., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval NR 13 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-27-2 PY 2008 BP 34 EP 41 PG 8 WC Business; Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science; Social Issues SC Business & Economics; Computer Science; Information Science & Library Science; Social Issues GA BIE96 UT WOS:000259033100007 ER PT B AU Atkison, T AF Atkison, Travis BE Callaos, N Lesso, W Zinn, CD Baralt, J Rutkauskas, AV Stasytyte, V TI Using an Information Retrieval Technique to Discover Malicious Software SO WMSCI 2008: 12TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL VI, PROCEEDINGS LA English DT Proceedings Paper CT 12th World Multi-Conference on Systemics, Cybernetics and Informatics/14th International Conference on Information Systems Analysis and Synthesis CY JUN 29-JUL 02, 2008 CL Orlando, FL SP Int Inst Informat & System, Int Federat Syst Res DE malicious software detection; information retrieval; n-gram analysis; cosine similarity AB This paper describes a research effort to detect unknown, known or variances of known malicious software using an information retrieval technique known as cosine similarity. Document similarity techniques, such as cosine similarity, have been used with great success in several document retrieval applications. By following the standard information retrieval methodology, software, in machine readable format, is regarded as documents in the corpus. These "documents" may or may not have a known malicious intent. The query is a piece of software, again in machine readable format, which contains a certain type of malicious software. This methodology provides an ability to search the corpus with a query and retrieve/identify potentially malicious software as well as other instances of the same type of vulnerability. This retrieval is based on the similarity of the query to a given document in the corpus. The subsequent use of an information visualization technique will allow for quickly and clearly finding the malicious software and will provide the ability for finding similar, potentially new types or variances of malicious behavior. C1 Mississippi State Univ, Dept Comp Sci & Engn, Starkville, MS 39762 USA. CR ABOUASSALEH T, 2004, P 28 ANN INT C SOFTW, V2 Abou-Assaleh T., 2004, P 2 ANN C PRIV SEC T, P193 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE HENCHIRI O, 2006, DAT MIN 2006 ICDM 06, P891 KEPHART JO, 1995, P IJCAI 95 MONTR AUG, P985 Liu N., 2004, P 13 ACM INT C INF K, P334, DOI 10.1145/1031171.1031240 Marceau C., 2000, P 2000 WORKSH NEW SE Reddy D., 2006, J COMPUTER VIROLOGY, V2, P231, DOI 10.1007/s11416-006-0027-8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, IEEE DATA ENG B, V24, P35 *SYM, 2005, SYM INT SEC THREAT R NR 11 TC 0 Z9 0 PU INT INST INFORMATICS & SYSTEMICS PI ORLANDO PA 14269 LORD BARCLAY DR, ORLANDO, FL 32837 USA BN 978-1-934272-36-7 PY 2008 BP 284 EP 289 PG 6 WC Business, Finance; Computer Science, Information Systems; Computer Science, Theory & Methods; Education & Educational Research; Information Science & Library Science SC Business & Economics; Computer Science; Education & Educational Research; Information Science & Library Science GA BIZ14 UT WOS:000263875200052 ER PT B AU Yang, CY Hsieh, CW Chan, YW Liao, YC AF Yang, Che-Yu Hsieh, Cheng-Wei Chan, Yu-Wei Liao, Yi-Chun BE Li, L Shih, TK Li, Q Yang, SQ Zhou, QG Wang, YH Lau, RWH Hung, JC Chang, WC TI Using Online Automated FAQ System to Promote Community Learning SO 2008 FIRST IEEE INTERNATIONAL CONFERENCE ON UBI-MEDIA COMPUTING AND WORKSHOPS, PROCEEDINGS LA English DT Proceedings Paper CT 1st IEEE International Conference on Ubi-Media Computing and Workshops CY JUL 15-16, 2008 CL Lanzhou, PEOPLES R CHINA SP IEEE, Lanzhou Univ DE automated frequently asked question; community learning; knowledge sharing; distance learning AB Knowledge is a product out of interaction. However, interaction between learners and between learner and instructor addresses a problem the instructor cannot be online all the time and it is not possible for the instructor to deal with lots of questions proposed from learners in a timely manner. Also, in online community learning, learners are encouraged to ask questions and discuss the answers with each others. The value of participation and sharing during learning is emphasized Therefore, there is a need of an automated FAQ system to support learning efficiency of community learning that everybody in the learning system can share knowledge with and learn from each others. This article examines how to adopt online FAQ mechanism to facilitate knowledge sharing and demonstrates how a FAQ system can help knowledge acquisition, accumulating and sharing in an online community learning environment. C1 [Yang, Che-Yu] Ching Kuo Inst Management & Hlth, Dept Informat Technol, Chilung, Taiwan. RP Yang, CY (reprint author), Ching Kuo Inst Management & Hlth, Dept Informat Technol, Chilung, Taiwan. EM cyyang@ems.cku.edu.tw CR Johnson D. W., 1989, COOPERATION COMPETIT KUUTTI K, 1991, PROCEEDINGS OF THE SECOND EUROPEAN CONFERENCE ON COMPUTER-SUPPORTED COOPERATIVE WORK : ECSCW 91, P249 Lave J, 1991, SITUATED LEARNING Lewis David D., 1992, THESIS U MASSACHUSET Pfister H.-R., 2001, P 2001 INT ACM SIGGR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slavin RE, 1996, CONTEMP EDUC PSYCHOL, V21, P43, DOI 10.1006/ceps.1996.0004 Watzlawick P., 1967, PRAGMATICS HUMAN COM NR 8 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1865-7 PY 2008 BP 535 EP 540 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BJA76 UT WOS:000264210400093 ER PT B AU Dai, WH Jiao, CZ He, TT AF Dai, Wenhua Jiao, Cuizhen He, Tingting BE Li, JP Bloshanskii, I Ni, LM Pandey, SS Yang, SX TI Research of Adaptive Text Fuzzy Clustering Method Based on Genetic Algorithm SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE INFORMATION COMPUTING AND AUTOMATION, VOLS 1-3 LA English DT Proceedings Paper CT International Conference on Information Computing and Automation CY DEC 20-22, 2007 CL Hefei, PEOPLES R CHINA HO Univ Elect Sci & Technol China DE Fuzzy Clustering; Genetic Algorithm; text clustering; variable-length chromosome AB As Fuzzy C-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it's difficult to determine the cluster number, we proposed an Adaptive Text Fuzzy Clustering Method Based on Genetic Algorithm. According to the principle of Vector Space Model, documents were represented as vectors. Then we adopted a new strategy of variable-length chromosome encoding and randomly chose initial clustering centers to form chromosomes among document vectors. Combining the efficiency of Fuzzy C-means Algorithm with the global optimization ability of Genetic Algorithm, the local optimal solution was avoided and the optimum number and the optimum result of cluster were obtained by means of genetic evolution. Experiments indicated that this algorithm was efficient and accurate. C1 [Dai, Wenhua; Jiao, Cuizhen] Xianning Coll, Dept Comp, Xianning, Peoples R China. RP Dai, WH (reprint author), Xianning Coll, Dept Comp, Xianning, Peoples R China. CR Andre J., 2001, Advances in Engineering Software, V32, DOI 10.1016/S0965-9978(00)00070-3 Li JP, 2003, EUR J OPER RES, V147, P334, DOI 10.1016/S0377-2217(02)00564-7 Ramze RM, 1998, PATTERN RECOGN, V19, P237 Rezaee MR, 1998, PATTERN RECOGN LETT, V19, P237 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 XIAO L, 2002, P 19 COLING, P598 Xizhao Wang, 2004, Pattern Recognition Letters, V25, DOI 10.1016/j.patrec.2004.03.008 YANG MS, 1993, MATH COMPUT MODEL, V18, P1, DOI 10.1016/0895-7177(93)90202-A NR 8 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 978-981-279-948-7 PY 2008 BP 1270 EP 1273 PG 4 WC Automation & Control Systems; Computer Science, Information Systems; Computer Science, Theory & Methods SC Automation & Control Systems; Computer Science GA BIE31 UT WOS:000258893000317 ER PT J AU Diaz, F AF Diaz, Fernando TI Regularizing query-based retrieval scores SO INFORMATION RETRIEVAL LA English DT Article DE regularization; cluster hypothesis; cluster-based retrieval; pseudo-relevance feedback; query expansion; document expansion ID INFORMATION-RETRIEVAL; MANIFOLDS; RANKING; MODEL AB We adapt the cluster hypothesis for score-based information retrieval by claiming that closely related documents should have similar scores. Given a retrieval from an arbitrary system, we describe an algorithm which directly optimizes this objective by adjusting retrieval scores so that topically related documents receive similar scores. We refer to this process as score regularization. Because score regularization operates on retrieval scores, regardless of their origin, we can apply the technique to arbitrary initial retrieval rankings. Document rankings derived from regularized scores, when compared to rankings derived from un-regularized scores, consistently and significantly result in improved performance given a variety of baseline retrieval algorithms. We also present several proofs demonstrating that regularization generalizes methods such as pseudo-relevance feedback, document expansion, and cluster-based retrieval. Because of these strong empirical and theoretical results, we argue for the adoption of score regularization as general design principle or post-processing step for information retrieval systems. C1 Univ Massachusetts, Dept Comp Sci, Amherst, MA 01003 USA. RP Diaz, F (reprint author), Univ Massachusetts, Dept Comp Sci, 140 Governors Dr, Amherst, MA 01003 USA. EM fdiaz@cs.umass.edu CR Balinski J, 2005, INFORM PROCESS MANAG, V41, P759, DOI 10.1016/j.ipm.2004.01.006 BELEW RK, 1989, SIGIR 89, P11 Belkin M., 2002, ADV NEURAL INFORM PR, V15, P929 BELKIN M, 2005, COLT, P486 BELKIN M, 2004, COLT, P624 Belkin M, 2004, MACH LEARN, V56, P209, DOI 10.1023/B:MACH.0000033120.25363.1e Brin S., 1998, P 7 INT C WORLD WID, P107 Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 Carmel D., 2006, SIGIR 06, P390 Chen BL, 2002, J ENVIRON SCI-CHINA, V14, P12 CHUNG F, 2004, ANN COMB, V9, P1 Chung F., 1997, SPECTRAL GRAPH THEOR Cohn D. A, 2000, NIPS, P430 CONNELL M, 2004, IR357 CIIR U MASS DE CORFT WB, 1988, SIGIR 88, P481 CROFT WB, 2003, LANG MODELING INFORM Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FANG H, 2005, SIGIR 05 Fang H., 2004, SIGIR 2004, P49, DOI 10.1145/1008992.1009004 HARMAN DK, 1993, INFORM PROCESS MANAG, V29, P411, DOI 10.1016/0306-4573(93)90037-E Kleinberg J.M., 1998, SODA 98, P668 Krovetz R., 1993, SIGIR Forum Kurland O., 2004, SIGIR 2004, P194 Kurland O., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Kurland O., 2005, SIGIR 05 KWOK KL, 1989, SIGIR 89, P21 Lafferty J, 2005, J MACH LEARN RES, V6, P129 Lafon S., 2004, THESIS YALE U LAVRENKO V, 2006, IR473 U MASS Liu X., 2004, SIGIR 2004, V186-193 McCallum A.K., 1996, BOW TOOLKIT STAT LAN Metzler D., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 Montague M., 2001, CIKM 01, P427 Ng A. Y., 2001, SIGIR Forum PETERSEN KB, 2005, MATRIX COOKBOOK Platt J. C., 2000, ADV LARGE MARGIN CLA Qin T., 2005, SIGIR 05, P408 RICHARDSON M, 2006, WWW 06, P707 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x Robertson S. E., 1981, SIGIR 80, P35 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rolleke T, 2006, INFORM PROCESS MANAG, V42, P4, DOI 10.1016/j.ipm.2004.11.006 Salton G., 1988, SIGIR 88, P147 Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savoy J, 1997, J AM SOC INFORM SCI, V48, P235, DOI 10.1002/(SICI)1097-4571(199703)48:3<235::AID-ASI5>3.0.CO;2-Y Singhal A, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P34 STROHMAN T, 2004, P INT C INT AN Tao T., 2006, HLT NAACL 2006, P407 TURTLE H, 1990, SIGIR 90 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VOORHEES E. M., 2004, P 13 TEXT RETR C TRE VOORHEES EM, 2001, TREC EXPT EVALUATION Wei X., 2006, SIGIR 06, P178 Wilkinson R., 1991, SIGIR Forum Xu JX, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P254 Yom-Tov E., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Zhou D., 2005, ADV NEURAL INFORM PR, V17, P1633 Zhou DY, 2004, ADV NEUR IN, V16, P169 NR 61 TC 9 Z9 9 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD DEC PY 2007 VL 10 IS 6 BP 531 EP 562 DI 10.1007/s10791-007-9034-8 PG 32 WC Computer Science, Information Systems SC Computer Science GA 224SB UT WOS:000250465600003 ER PT J AU Korenius, T Laurikkala, J Juhola, M AF Korenius, Tuomo Laurikkala, Jorma Juhola, Martti TI On principal component analysis, cosine and Euclidean measures in information retrieval SO INFORMATION SCIENCES LA English DT Article DE information retrieval; cosine measure; Euclidean distance measure; principal component analysis; clustering; documents ID GRADED RELEVANCE ASSESSMENTS; VECTOR-SPACE MODEL; TEXT CATEGORIZATION; LANGUAGES; WORDS AB Clustering groups document objects represented as vectors. An extensive vector space may cause obstacles to applying these methods. Therefore, the vector space was reduced with principal component analysis (PCA). The conventional cosine measure is not the only choice with PCA, which involves the mean-correction of data. Since mean-correction changes the location of the origin, the angles between the document vectors also change. To avoid this, we used a connection between the cosine measure and the Euclidean distance in association with PCA, and grounded searching on the latter. We applied the single and complete linkage and Ward clustering to Finnish documents utilizing their relevance assessment as a new feature. After the normalization of the data PCA was run and relevant documents were clustered. (C) 2007 Elsevier Inc. All rights reserved. C1 Univ Tampere, Dept Comp Sci, FIN-33104 Tampere, Finland. RP Juhola, M (reprint author), Univ Tampere, Dept Comp Sci, Kanslerinrinne 1, FIN-33104 Tampere, Finland. EM Martti.Juhola@cs.uta.fi CR Alkula R, 2001, INFORM RETRIEVAL, V4, P195, DOI 10.1023/A:1011942104443 ALKULA R, 2000, THESIS U TAMPERE FIN BELEW R, 2000, FINDING ABOUT COGNIT Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 ELHAMDOUCHI A, 1989, COMPUT J, V32, P220, DOI 10.1093/comjnl/32.3.220 Everitt Brian S., 2001, CLUSTER ANAL Grossman D. A., 2004, INFORM RETRIEVAL ALG Hand D., 2001, PRINCIPLES DATA MINI Horn R. A., 1990, MATRIX ANAL Jain A. K., 1988, ALGORITHMS CLUSTERIN Jolliffe IT, 1986, PRINCIPAL COMPONENTS Kang BY, 2005, INFORM SCIENCES, V170, P443, DOI 10.1016/j.ins.2004.03.013 Kaufman L, 1990, FINDING GROUPS DATA KEKALAINEN J, 1999, THESIS U TAMPERE FIN Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Korenius T, 2006, INFORM RETRIEVAL, V9, P33, DOI 10.1007/s10791-005-5720-6 LANCE GN, 1967, COMPUT J, V9, P373 Lertnattee V, 2006, INFORM SCIENCES, V176, P1712, DOI 10.1016/j.ins.2005.05.010 Manning CD, 2000, FDN STAT NATURAL LAN, V2nd *MATH WORKS INC, 2002, MATL STAT TOOLB US G Maucec MS, 2004, INFORM SCIENCES, V166, P249, DOI 10.1016/j.ins.2003.12.004 Mitchell TM, 1997, MACHINE LEARNING Qian G., 2004, ACM S APPL COMP, P1232 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Rencher A., 2002, METHODS MULTIVARIATE RU L, 2004, 13 TEXT RETR C TREC Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sharma S., 1996, APPL MULTIVARIATE TE Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Spath H., 1980, CLUSTER ANAL ALGORIT van der Weide TP, 2006, INFORM SCIENCES, V176, P91, DOI 10.1016/j.ins.2004.09.015 WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Zadrozny S, 2006, INFORM SCIENCES, V176, P415, DOI 10.1016/j.ins.2005.07.017 NR 37 TC 27 Z9 29 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD NOV 15 PY 2007 VL 177 IS 22 BP 4893 EP 4905 DI 10.1016/j.ins.2007.05.027 PG 13 WC Computer Science, Information Systems SC Computer Science GA 218LE UT WOS:000250016200005 ER PT J AU Lin, J Wilbur, WJ AF Lin, Jimmy Wilbur, W. John TI PubMed related articles: a probabilistic topic-based model for content similarity SO BMC BIOINFORMATICS LA English DT Article AB Background: We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance-but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH (R) in MEDLINE (R). Results: The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of pmra over bm25 in terms of precision. Conclusion: Our experiments suggest that the pmra model provides an effective ranking algorithm for related article search. C1 [Lin, Jimmy] Univ Maryland, Coll Informat Studies, College Pk, MD 20742 USA. [Lin, Jimmy; Wilbur, W. John] Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA. RP Lin, J (reprint author), Univ Maryland, Coll Informat Studies, College Pk, MD 20742 USA. EM jimmylin@umd.edu; wilbur@ncbi.nlm.nih.gov CR Berger AL, 1996, COMPUT LINGUIST, V22, P39 CLEVERDON CW, 1968, ASLIB CRANFIELD RES Croft W. B., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115 Harman D.K., 2005, TREC EXPT EVALUATION, P21 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HERSH W, 2005, P 14 TEXT RETR C TRE Jones K. S., 2000, Information Processing & Management, V36 LIN J, 2007, LAMPTR145CSTR4877UMI MARGULIS EL, 1993, INFORM PROCESS MANAG, V29, P215, DOI 10.1016/0306-4573(93)90005-X Robertson S., 1994, P 3 TEXT RETR C TREC Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Smucker M. D., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148250 STROHMAN T, 2006, 29 ANN INT ACM SIGIR Voorhees Ellen M., 2005, TREC EXPT EVALUATION VOORHEES EM, 1997, P 9 TEXT RETR C TREC WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X WILBUR WJ, 2005, MED INFORM KNOWLEDGE, P277 Zhai C, 2006, 29 ANN INT ACM SIGIR NR 21 TC 50 Z9 50 PU BIOMED CENTRAL LTD PI LONDON PA 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND SN 1471-2105 J9 BMC BIOINFORMATICS JI BMC Bioinformatics PD OCT 30 PY 2007 VL 8 AR 423 DI 10.1186/1471-2105-8-423 PG 14 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology GA 253XF UT WOS:000252550200003 PM 17971238 ER PT J AU Liu, H AF Liu, Hugo TI Social network profiles as taste performances SO JOURNAL OF COMPUTER-MEDIATED COMMUNICATION LA English DT Article AB This Study examines how a social network profile's lists of interests-music, books, movies, television shows, etc.-can function as an expressive arena for taste performance. By composing interest tokens around a theme, profile users craft their "taste statements." First, socioeconomic and aesthetic influences on taste are considered, and the expressivity of interest tokens is analyzed using a semiotic framework. Then, a grounded theory approach is taken to identify four types of taste statements-those that convey prestige, differentiation, authenticity, and theatrical persona. The semantics of taste and taste statements are further investigated through a statistical analysis of 127,477 profiles collected from the MySpace social network site between November 2006 and January 2007. The major findings of the analysis include statistical evidence for prestige and differentiation taste statements and an interpretation of the taste semantics underlying the MySpace community-its motifs, paradigms, and demographic structures. C1 [Liu, Hugo] MIT, Media Lab, Cambridge, MA 02139 USA. [Liu, Hugo] MIT, Program Comparat Media Studies, Cambridge, MA 02139 USA. RP Liu, H (reprint author), MIT, Media Lab, 20 Ames St, Cambridge, MA 02139 USA. CR Barthes R., 1977, ELEMENTS SEMIOLOGY Bourdieu P., 1984, DISTINCTION SOCIAL C boyd D., 2006, 1 MONDAY, P11 *CONSCORE, 2007, SOC NETW GOES GLOB CSIKSZENTMIHALY.M, 1981, MEANING THINGS DOMES Davis Fred, 1992, FASHION CULTURE IDEN Donath J, 2004, BT TECHNOL J, V22, P71, DOI 10.1023/B:BTTJ.0000047585.06264.cc Donath J. S., 1999, COMMUNITIES CYBERSPA, P29 FLORE A, 2005, P C HUM FACT COMP SY, P1371 Gans Herbert J., 1999, POPULAR CULTURE HIGH, V2nd Glaser B., 1967, DISCOVERY GROUNDED T Goffman E., 1959, PRESENTATION SELF EV GRODIN D, 1996, CONSTRUCTING SELF ME, P3 Church K. W., 1990, Computational Linguistics, V16 HAWKINS T, 1978, ANGELES TIMES 1208, pL8 Lenhart A, 2007, PEW INTERNET PROJECT Levine Donald N., 1971, INDIVIDUALITY SOCIAL, P6 Liu H., 2005, P IUI BEYOND PERSONA, P54 Liu H, 2006, INT J SEMANT WEB INF, V2, P42, DOI 10.4018/jswis.2006010102 MCCRACKEN G, 2006, PLENTITUDE BLOOMINGT McCracken G., 1988, CULTURE CONSUMPTION *NIELS NETRATINGS, 2005, SOC NETW SIT GROW 47, P229 PAOLILLO JC, 2005, VISUALIZING SEMANTIC, P229 PERKEL D, IN PRESS INFORMAL LE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIMMEL G, 1971, INDIVIDUALITY SOCIAL, P251 SIMPSON TA, 1996, CONSTRUCTING SELF ME, P107 Solomon Michael R., 1987, MARKETING SEMIOTICS, P189 Sunden Jenny, 2003, MAT VIRTUALITIES APP Thornton Sarah, 1996, CLUB CULTURES MUSIC TODOROV T, 1981, INTRO POETICS MINNEA U.S. Census Bureau, 2007, 2007 STAT ABSTR Veblen T., 1899, THEORY LEISURE CLASS NR 33 TC 52 Z9 52 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1083-6101 J9 J COMPUT-MEDIAT COMM JI J. Comput.-Mediat. Commun. PD OCT PY 2007 VL 13 IS 1 AR 13 PG 25 WC Communication; Information Science & Library Science SC Communication; Information Science & Library Science GA 263UF UT WOS:000253241300013 ER PT J AU Kajikawa, Y Ohno, J Takeda, Y Matsushima, K Komiyama, H AF Kajikawa, Yuya Ohno, Junko Takeda, Yoshiyuki Matsushima, Katsumori Komiyama, Hiroshi TI Creating an academic landscape of sustainability science: an analysis of the citation network SO SUSTAINABILITY SCIENCE LA English DT Article DE Sustainability science; Research on research; Citation network; Overview map; Network analysis AB Sustainability is an important concept for society, economics, and the environment, with thousands of research papers published on the subject annually. As sustainability science becomes a distinctive research field, it is important to define sustainability clearly and grasp the entire structure, current status, and future directions of sustainability science. This paper provides an academic landscape of sustainability science by analyzing the citation network of papers published in academic journals. A topological clustering method is used to detect the sub-domains of sustainability science. Results show the existence of 15 main research clusters: Agriculture, Fisheries, Ecological Economics, Forestry (agroforestry), Forestry (tropical rain forest), Business, Tourism, Water, Forestry (biodiversity), Urban Planning, Rural Sociology, Energy, Health, Soil, and Wildlife. Agriculture, Fisheries, Ecological Economics, and Forestry (agroforestry) clusters are predominant among these. The Energy cluster is currently developing, as indicated by the age of papers in the cluster, although it has a relatively small number of papers. These results are compared with those obtained by natural language processing. Education, Biotechnology, Medical, Livestock, Climate Change, Welfare, and Livelihood clusters are uniquely extracted by natural language processing, because they are common topics across clusters in the citation network. C1 [Kajikawa, Yuya; Ohno, Junko; Takeda, Yoshiyuki; Matsushima, Katsumori] Univ Tokyo, Sch Engn, Inst Engn Innovat, Bunkyo Ku, Tokyo 1138656, Japan. [Komiyama, Hiroshi] Univ Tokyo, IR3S, Bunkyo Ku, Tokyo 1138654, Japan. RP Kajikawa, Y (reprint author), Univ Tokyo, Sch Engn, Inst Engn Innovat, Bunkyo Ku, 2-11-16 Yayoi, Tokyo 1138656, Japan. EM kaji@biz-model.t.u-tokyo.ac.jp RI Kajikawa, Yuya/C-1996-2015 OI Kajikawa, Yuya/0000-0003-3577-5167 FU Ministry of Education, Science, Sports and Culture [18700240] FX We are grateful to anonymous referees for their valuable comments, owing to which our manuscript was greatly improved. We thank Associate Professor Hideki Mima for his help in natural language processing. We also thank Dr Ai Hiramatsu, Professor Yoshihisa Murasawa, and Professor Shuichiro Asao at The University of Tokyo for encouraging our research. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 18700240, 2006. CR Adai AT, 2004, J MOL BIOL, V340, P179, DOI 10.1016/j.jmb.2004.04.047 BARBIER EB, 1987, ENVIRON CONSERV, V14, P101 BORRNER K, 2003, ANN REV INFO SCI TE, V37, P179 Boyack KW, 2005, SCIENTOMETRICS, V64, P351, DOI 10.1007/s11192-005-0255-6 BROWN BJ, 1987, ENVIRON MANAGE, V11, P713, DOI 10.1007/BF01867238 Callicott JB, 1997, CONSERV BIOL, V11, P32, DOI 10.1046/j.1523-1739.1997.95468.x Christensen NL, 1996, ECOL APPL, V6, P665, DOI 10.2307/2269460 Clark WC, 2003, P NATL ACAD SCI USA, V100, P8059, DOI 10.1073/pnas.1231333100 Costanza R, 2004, ECOL ECON, V50, P261, DOI 10.1016/j.ecolecon.2004.06.001 DOVERS SR, 1993, ENVIRON CONSERV, V20, P217 GOODLAND R, 1995, ANNU REV ECOL SYST, V26, P1 Kajikawa Y, 2006, J INF SCI, V32, P511, DOI 10.1177/0165551506067125 Kates RW, 2001, SCIENCE, V292, P641, DOI 10.1126/science.1059386 Komiyama H, 2006, SUSTAIN SCI, V1, P1, DOI 10.1007/s11625-006-0007-4 Lele S, 1996, CONSERV BIOL, V10, P354, DOI 10.1046/j.1523-1739.1996.10020354.x Lele Sharahchandra M., 1991, WORLD DEV, V19 Ma CB, 2006, ECOL ECON, V58, P491, DOI 10.1016/j.ecolecon.2005.07.023 MACROBERTS MH, 1989, J AM SOC INFORM SCI, V40, P342, DOI 10.1002/(SICI)1097-4571(198909)40:5<342::AID-ASI7>3.0.CO;2-U Marcuse P, 1998, ENVIRON URBAN, V10, P103, DOI 10.1177/095624789801000201 Meadows D. H., 1972, THE LIMITS OF GROWTH Mihelcic JR, 2003, ENVIRON SCI TECHNOL, V37, P5314, DOI 10.1021/es034605h Mima H., 2000, Terminology, V6 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.026113 Newman MEJ, 2004, PHYS REV E, V69, DOI 10.1103/PhysRevE.69.066133 REDCLIFT M, 1992, GEOFORUM, V23, P395, DOI 10.1016/0016-7185(92)90050-E Reitan P, 2005, SUSTAINABILITY SCI P, V1, p[77, 77] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEARMAN R, 1990, ENVIRON MANAGE, V14, P1, DOI 10.1007/BF02394014 SIMON D, 1989, ENVIRON CONSERV, V16, P41 Soule ME, 1998, BIOSCIENCE, V48, P397, DOI 10.2307/1313379 Thelwall M, 2006, J AM SOC INF SCI TEC, V57, P1303, DOI 10.1002/asi.20434 WCED, 1987, OUR COMM FUT NR 32 TC 54 Z9 61 PU SPRINGER TOKYO PI TOKYO PA 1-11-11 KUDAN-KITA, CHIYODA-KU, TOKYO, 102-0073, JAPAN SN 1862-4065 J9 SUSTAIN SCI JI Sustain. Sci. PD OCT PY 2007 VL 2 IS 2 BP 221 EP 231 DI 10.1007/s11625-007-0027-8 PG 11 WC Environmental Sciences SC Environmental Sciences & Ecology GA V93FW UT WOS:000206302000007 ER PT J AU Han, H Ko, Y Seo, J AF Han, Hyoungdong Ko, Youngjoong Seo, Jungyun TI Using the revised EM algorithm to remove noisy data for improving the one-against-the-rest method in binary text classification SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE binary text classification; the one-against-the-rest method; the EM algorithm; the sliding window technique AB Automatic text classification is the problem of automatically assigning predefined categories to free text documents, thus allowing for less manual labors required by traditional classification methods. When we apply binary classification to multi-class classification for text classification, we usually use the one-against-the-rest method. In this method, if a document belongs to a particular category, the document is regarded as a positive example of that category; otherwise, the document is regarded as a negative example. Finally, each category has a positive data set and a negative data set. But, this one-against-the-rest method has a problem. That is, the documents of a negative data set are not labeled manually, while those of a positive set are labeled by human. Therefore, the negative data set probably includes a lot of noisy data. In this paper, we propose that the sliding window technique and the revised EM (Expectation Maximization) algorithm are applied to binary text classification for solving this problem. As a result, we can improve binary text classification through h extracting potentially noisy documents from the negative data set using the sliding window technique and removing actually noisy documents using the revised EM algorithm. The results of our experiments showed that our method achieved better performance than the original one-against-the-rest method in all the data sets and all the classifiers used in the experiments. (c) 2006 Elsevier Ltd. All rights reserved. C1 Dong A Univ, Dept Comp Engn, Pusan 604714, South Korea. Sogang Univ, Program Integrated Biotechnol, Seoul 121742, South Korea. Sogang Univ, Dept Comp Sci, Seoul 121742, South Korea. RP Ko, Y (reprint author), Dong A Univ, Dept Comp Engn, Pusan 604714, South Korea. EM hdhan@sogang.ac.kr; yjko@dau.ac.kr; seojy@sogang.ac.kr CR Brill E, 1995, COMPUT LINGUIST, V21, P543 Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 Dempster AP, 1997, J ROYAL STAT SOC B, V39, P1 Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Ko Y., 2004, P 42 ANN M ASS COMP, DOI [10.3115/1218955.1218988, DOI 10.3115/1218955.1218988] Ko Y, 2004, INFORM PROCESS MANAG, V40, P191, DOI 10.1016/S0306-4573(03)00029-3 Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 Lee C-H, 2001, P ACM 10 INT C INF K, P263 LEWIS DD, 1998, P EUR C MACH LEARN Li X. L., 2003, P 18 INT JOINT C ART, P587 Liu B., 2002, P 19 INT C MACH LEAR, P8 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Merialdo B., 1994, Computational Linguistics, V20 Mitchell TM, 1997, MACHINE LEARNING Roy N., 2001, P 18 INT C MACH LEAR, P441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schohn G., 2000, P 17 INT C MACH LEAR, P839 TONG S, 2001, J MACHINE LEARNING R, V2, P45, DOI DOI 10.1162/153244302760185243 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang Y., 2002, J INTELLIGENT INFORM, V18 YU H, 2002, P INT C KNOWL DISC D Zadrozny B., 2001, P 18 INT C MACH LEAR ZADROZNY B, 2002, P INT C KNOWL DISC D NR 25 TC 3 Z9 4 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2007 VL 43 IS 5 BP 1281 EP 1293 DI 10.1016/j.ipm.2006.11.003 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 173JR UT WOS:000246869800009 ER PT J AU Almpanidis, G Kotropoulos, C Pitas, I AF Almpanidis, G. Kotropoulos, C. Pitas, I. TI Combining text and link analysis for focused crawling - An application for vertical search engines SO INFORMATION SYSTEMS LA English DT Article DE focused crawling; information retrieval; latent semantic indexing; text categorisation; vertical search engines ID WEB; INFORMATION; ALGORITHM; MODEL AB The number of vertical search engines and portals has rapidly increased over the last years, making the importance of a topic-driven (focused) crawler self-evident. In this paper, we develop a latent semantic indexing classifier that combines link analysis with text content in order to retrieve and index domain-specific web documents. Our implementation presents a different approach to focused crawling and aims to overcome the limitations imposed by the need to provide initial data for training, while maintaining a high recall/precision ratio. We compare its efficiency with other well-known web information retrieval techniques. (c) 2006 Elsevier B.V. All rights reserved. C1 Aristotle Univ Thessaloniki, Dept Informat, GR-54124 Thessaloniki, Greece. RP Kotropoulos, C (reprint author), Aristotle Univ Thessaloniki, Dept Informat, Box 451, GR-54124 Thessaloniki, Greece. EM galba@aiia.csd.auth.gr; costas@aiia.csd.auth.gr; pitas@aiia.csd.auth.gr RI Kotropoulos, Constantine/B-7928-2010 OI Kotropoulos, Constantine/0000-0001-9939-7930 CR Aggarwal C.C., 2001, P 10 INT WORLD WID W, P96, DOI DOI 10.1145/371920.371955 Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Baeza-Yates R., 2005, P 14 INT C WORLD WID, P864 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bergmark D., 2002, P 6 EUR C RES ADV TE, P91 Berry M., 1999, UNDERSTANDING SEARCH Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Boldi P, 2004, SOFTWARE PRACT EXPER, V34, P711, DOI 10.1002/spe.587 Boldi P., 2004, P ALG MOD WEB GRAPH BRIN S, 1998, WWW7 COMPUTER NETWOR, V30, P107 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chakrabarti S., 2002, MINING WEB DISCOVERI Chakrabarti S., 2001, P 10 INT C WORLD WID, P211, DOI 10.1145/371920.372054 CHAU M, 2003, IEEE COMPUT MAG, V36, P55 CHO J, 1998, P 7 INT WORLD WID WE, P161 Cohn D., 2000, P 17 INT C MACH LEAR, P167 Cohn D, 2001, ADV NEUR IN, V13, P430 DAVISON B, 2003, P WORKSH TEXT MIN LI Davison BD, 2000, P 23 ANN INT ACM SIG, P272, DOI 10.1145/345508.345597 DEBRA P, 1994, J COMPUT NETWORKS IS, P183 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diligenti M., 2000, P 26 INT C VER LARG, P527 FISHER M, 2003, P ECIR 03 25 EUR C I, P41 FOLTZ P, 1995, P C HUM FACT COMP SY, P101 Garfield E., 1986, INFORM PROCESSING MA, V22, P445, DOI 10.1016/0306-4573(86)90091-9 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Grams E., 1998, P DIG LIB 98 3 ACM C, P99, DOI 10.1145/276675.276686 Haveliwala T. H., 2002, P 11 INT C WORLD WID, P517, DOI DOI 10.1145/511446.511513 Henzinger M.R., 2000, IEEE DATA ENG B, V23, P3 Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 HYNEK J, 2000, P 34 INT C MOSIS, P97 *IBM ALM RES CTR, CLEVER PROJ Jones K. S., 1979, Journal of Documentation, V35, DOI 10.1108/eb026672 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Kontostathis A, 2006, INFORM PROCESS MANAG, V42, P56, DOI 10.1016/j.ipm.2004.11.007 Leskovec J, 2005, P 11 ACM SIGKDD INT, P177, DOI [10.1145/1081870.1081893, DOI 10.1145/1081870.1081893] Lewandowski D, 2006, J INF SCI, V32, P131, DOI 10.1177/0165551506062326 McCallum AK, 2000, INFORM RETRIEVAL, V3, P127, DOI 10.1023/A:1009953814988 Menczer F., 2001, P 24 ANN INT ACM SIG, P241, DOI DOI 10.1145/383952.383995 Menczer F., 1997, P 14 INT C MACH LEAR Najork M., 2001, P 10 INT WORLD WID W, P114, DOI DOI 10.1145/371920.371965 Ng A., 2001, P 24 ANN INT ACM SIG, P258, DOI 10.1145/383952.384003 O'Brien G.W., 1994, THESIS U TENNESSEE K PANT G, 2002, P 2 INT WORKSH WEB D PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rennie J., 1999, P 16 INT C MACH LEAR, P335 RIJSBERGEN K, INFORM RETRIEVAL ONL Robertson S.E., 1992, P TEXT RETRIEVAL C T, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIGNORINI A., 2005, P 14 INT C WORLD WID, P902 SIZOV S, 2003, P 1 C INN DAT SYST R SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SRINIVASAN P, 2002, P ACM INT C RES DEV STEELE R, 2001, P INT COMP 01 LSA VE SULLIVAN D, VORTALS ARE COMING V SULLIVAN D, NOW ITS VECTORIES AR Tang C., 2004, P 27 ANN INT ACM SIG, P112, DOI 10.1145/1008992.1009014 VARLAMIS I, 2004, IEEE T KNOWL DATA EN, V16, P585 Wasserman S., 1994, SOCIAL NETWORK ANAL Xi Wensi, 2005, P 28 ANN INT ACM SIG, P130, DOI 10.1145/1076034.1076059 YANG K, 2002, P 10 TEXT RER C TREC ZEINALIPOURYATZ.D, 2001, P 8 PANH C INF, V2, P377 ZELIKOVITZ S, WORKSH NOT TEXT LEAR NR 65 TC 26 Z9 31 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD SEP PY 2007 VL 32 IS 6 BP 886 EP 908 DI 10.1016/j.is.2006.09.004 PG 23 WC Computer Science, Information Systems SC Computer Science GA 177MM UT WOS:000247157200004 ER PT J AU Song, FX Zhang, D Xu, Y Wang, JH AF Song, Fengxi Zhang, David Xu, Yong Wang, Jizhong TI Five new feature selection metrics in text categorization SO INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE LA English DT Article DE feature selection; text categorization; support vector machines; multiple comparative test; pattern recognition AB Feature selection has been extensively applied in statistical pattern recognition as a mechanism for cleaning up the set of features that are used to represent data and as a way of improving the performance of classifiers. Four schemes commonly used for feature selection are Exponential Searches, Stochastic Searches, Sequential Searches, and Best Individual Features. The most popular scheme used in text categorization is Best Individual Features as the extremely high dimensionality of text feature spaces render the other three feature selection schemes time prohibitive. This paper proposes five new metrics for selecting Best Individual Features for use in text categorization. Their effectiveness have been empirically tested on two well-known data collections, Reuters-21578 and 20 Newsgroups. Experimental results show that the performance of two of the five new metrics, Bayesian Rule and F-one Value, is not significantly below that of a good traditional text categorization selection metric, Document Frequency. The performance of another two of these five new metrics, Low Loss Dimensionality Reduction and Relative Frequency Difference, is equal to or better than that of conventional good feature selection metrics such as Mutual Information and Chi-square Statistic. C1 [Song, Fengxi; Wang, Jizhong] New Star Res Inst Appl Technol Hefei City, Dept Automat & Simulat, Hefei 230031, Anhui, Peoples R China. [Song, Fengxi; Xu, Yong] Harbin Inst Technol, Shenzhen Grad Sch, Hefei 230031, Anhui, Peoples R China. [Zhang, David] City Polytech Hong Kong, Dept Automat & Simulat, Hefei 230031, Anhui, Peoples R China. RP Song, FX (reprint author), New Star Res Inst Appl Technol Hefei City, Dept Automat & Simulat, 451 Huang Shan Rd, Hefei 230031, Anhui, Peoples R China. EM songfengxi@yahoo.com CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Battiti R., 1994, IEEE T NEURAL NETWOR, V5 DOWNEY TJ, 1994, INTELLIGENT ENG SYST, V4, P363 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 FUHR N, 1999, CATEGORIZATION TOOL GALAVOTTI L, 1999, THESIS U PISA PISA Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427 Jain A, 1997, IEEE T PATTERN ANAL, V19, P153, DOI 10.1109/34.574797 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Joachims T., 1998, P 10 EUR C MACH LEAR KWAK N, 2002, IEEE T NEURAL NETWOR, V13 LANG K, 1995, P 12 INT C MACH LEAR, P131 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lewis D.D., REUTERS 21578 DISTRI Ma J., OSU SVM CLASSIFIER M MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 NARENDRA P, 1977, IEEE T COMPUT, V26, P917 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUTZE H, P SIGIR 95 18 ACM IN, P229 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 [å®‹ęž«ęŗŖ Song Fengxi], 2004, [ęؔ式čƆ别äøŽäŗŗå·„ę™ŗčƒ½, Pattern recognition and artificial intelligence], V17, P161 Song FX, 2005, PATTERN ANAL APPL, V8, P199, DOI 10.1007/s10044-005-0256-3 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yang Y., 1997, MACH LEARN INT WORKS, P412 NR 26 TC 4 Z9 4 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE SN 0218-0014 J9 INT J PATTERN RECOGN JI Int. J. Pattern Recognit. Artif. Intell. PD SEP PY 2007 VL 21 IS 6 BP 1085 EP 1101 DI 10.1142/S0218001407005831 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 242IW UT WOS:000251719800008 ER PT J AU Arazy, O Woo, C AF Arazy, Ofer Woo, Carson TI Enhancing information retrieval through statistical natural language processing: A study of collocation indexing SO MIS QUARTERLY LA English DT Article DE document management; information retrieval (IR); word ambiguity; natural language processing (NLP); collocations; distance; directionality; weighting; general settings ID DOCUMENT-RETRIEVAL; SYSTEMS; COOCCURRENCE AB In this paper, we provide preliminary evidence for the usefulness of statistical natural language processing (NLP) techniques, and specifically of collocation indexing, for IR in general settings. We investigate the effect of three key parameters on collocation indexing performance: directionality, distance, and weighting. We build on previous work in IR to (1) advance our knowledge of key design elements for collocation indexing, (2) demonstrate gains in retrieval precision from the use of statistical NLP for general-settings IR, and, finally, (3) provide practitioners with a useful cost benefit analysis of the methods under investigation. Although the management of information assets-specifically, of text documents that make up 80 percent of these assets an provide organizations with a competitive advantage, the ability of information retrieval (IR) systems to deliver relevant information to users is severely hampered by the difficulty of disambiguating natural language. The word ambiguity problem is addressed with moderate success in restricted settings, but continues to be the main challenge for general settings, characterized by large, heterogeneous document collections. C1 Univ Alberta, Edmonton, AB T6G 2R6, Canada. Univ British Columbia, Sauder Sch Business, Vancouver, BC V6T 1Z2, Canada. RP Arazy, O (reprint author), Univ Alberta, 3-23 Business Bldg, Edmonton, AB T6G 2R6, Canada. EM ofer.arazy@ualberta.ca; carson.woo@ubc.ca CR Alvarez C., 2004, P RIAO RECH INF ASS, P686 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BUCKLEY C, 1996, P 4 TEXT RETR C CARMEL D, 2001, P 10 TEXT RETR C CROGT WB, 1991, P 14 ANN C RES DEV I, P32 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FAGAN J, 1987, EXPT AUTOMATIC PHRAS FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Firth J. R., 1968, SELECTED PAPERS J R, p[168, 1] Church K. W., 1990, Computational Linguistics, V16 Hevner AR, 2004, MIS QUART, V28, P75 Hsinchun Chen, 2001, KNOWLEDGE MANAGEMENT JAIN H, 1998, MIS Q, V22, P77 JIANG M, 2004, 8 S AI MATH SPECIAL KHOO C, 2001, INFORMATION PROCESSI, P119 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LUHN HP, 1958, IBM J RES DEV, V2, P314 MAAREK YS, 1991, IEEE T SOFTWARE ENG, V17, P800, DOI 10.1109/32.83915 Manning C. D., 2003, FDN STAT NATURAL LAN MARTIN W, 1983, LEXICOGRAPHY PRINCIP, P77 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Mittendorf E, 2000, INFORM RETRIEVAL, V3, P243, DOI 10.1023/A:1026520926673 Morita K, 2004, INFORM PROCESS MANAG, V40, P957, DOI 10.1016/j.ipm.2003.08.009 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Salton Gerard, 1991, P 14 ANN INT ACM SIG, P21, DOI 10.1145/122860.122863 Spangler S, 2003, J MANAGE INFORM SYST, V19, P191 SPARCKJONES KS, 1999, P 8 TEXT RETR C SPRAGUE RH, 1995, MIS QUART, V19, P29, DOI 10.2307/249710 Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Stokoe C, 2003, P 26 ANN INT ACM SIG, P159, DOI DOI 10.1145/860435.860466 STRZALKOWSKI T, 1996, P 5 TEXT RETR C VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 NR 40 TC 8 Z9 8 PU SOC INFORM MANAGE-MIS RES CENT PI MINNEAPOLIS PA UNIV MINNESOTA-SCH MANAGEMENT 271 19TH AVE SOUTH, MINNEAPOLIS, MN 55455 USA SN 0276-7783 J9 MIS QUART JI MIS Q. PD SEP PY 2007 VL 31 IS 3 BP 525 EP 546 PG 22 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 200XW UT WOS:000248797100006 ER PT J AU Stavrianou, A Andritsos, P Nicoloyannis, N AF Stavrianou, Anna Andritsos, Periklis Nicoloyannis, Nicolas TI Overview and semantic issues of text mining SO SIGMOD RECORD LA English DT Article ID CATEGORIZATION; SIMILARITY; SENSE AB Text mining refers to the discovery of previously unknown knowledge that can be found in text collections. In recent years, the text mining field has received great attention due to the abundance of textual data. A researcher in this area is requested to cope with issues originating from the natural language particularities. This survey discusses such semantic issues along with the approaches and methodologies proposed in the existing literature. It covers syntactic matters, tokenization concerns and it focuses on the different text representation techniques, categorisation tasks and similarity measures suggested. C1 Univ Lyon 2, F-69365 Lyon 07, France. Univ Trent, I-38100 Trento, Italy. RP Stavrianou, A (reprint author), Univ Lyon 2, F-69365 Lyon 07, France. EM anna.stavrianou@univ-lyon2.fr; periklis@dit.unitn.it; nicolas.nicoloyannis@univ-lyon2.fr RI Cong , Gao/A-3726-2011 CR Abadi Daniel J., 2007, P 33 INT C VER LARG, P411 ANANIADOU S, 2005, ARIADNE, V42 Ando RK, 2005, P 43 ANN M ASS COMP, P1, DOI 10.3115/1219840.1219841 ANTONELLIS I, 2006, P SIAM TEXT MIN WORK APTE C, 1998, C AUT LEARN DISC Blake C., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989501 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 Bloehdoen S., 2004, P 4 IEEE INT C DAT M, P331 BLOEHDORN S, 2005, P 29 ANN C GERM CLAS, P334 Bourigault D., 1992, P 14 INT C COMP LING, P977, DOI DOI 10.3115/993079.993111 Budanitsky A, 2001, WORKSH WORDN OTH LEX Buitelaar P, 2005, ONTOLOGY LEARNING TE CARENINI G, 2005, 3 KCAP BANN ALB CAN, P11 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 COHEN KB, 2004, ARTIFICIAL INTELLIGE CONG G, 2004, 9 DASFAA JES ISL KOR, P482 CULOTTA A, 2006, HUMAN LANGUAGE TECHN, P296 Daille B., 1994, P 15 INT C COMP LING, P515 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fan W., 2006, COMMUN ACM, V49, P76, DOI DOI 10.1145/1151030.1151032 Firth J. R., 1957, STUDIES LINGUISTIC A, P1 Fortuna B., 2006, P 15 INT C WORLD WID, P949, DOI 10.1145/1135777.1135959 FORTUNA B, 2005, JOINT INT WORKSH EWM, P121 Freitag D., 1998, THESIS CARNEGIE MELL Furnkranz J, 1998, AAAI ICML WORKSH LEA, P5 GOMEZPEREZ A, 1999, P IJCAI 99 WOKRSH ON Halevy A.Y., 2006, P 32 INT C VER LARG, P9 Hatzivassiloglou Vasileios, 1997, P 35 ANN M ASS COMP, P174, DOI DOI 10.3115/976909.979640 Hayes J., 2004, P 16 EUR C ART INT V, P1089 Hearst M. A, 1999, P 37 ANN M ASS COMP, P3, DOI DOI 10.3115/1034678.1034679 Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Hirschman L, 2002, BIOINFORMATICS, V18, P1553, DOI 10.1093/bioinformatics/18.12.1553 Hoskinson A, 2005, COMPUTER, V38, P97, DOI 10.1109/MC.2005.375 Jindal N., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148215 Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag Kamps J., 2004, P 4 INT C LANG RES E, V4, P1115 KAO A, 2006, SIGKDD EXPLORATIONS, V7, P1 KAO A, 2004, SIGKDD EXPLORATIONS, V6, P132 Kehagias A, 2003, J INTELL INF SYST, V21, P227, DOI 10.1023/A:1025554732352 Kozima H., 1993, P 31 ANN M ASS COMP, P286, DOI DOI 10.1016/S0306-4573(02)00035-3 LAFFERTY J, 2001, P 18 INT C MACH LEAR, P282 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Manning C. D., 1999, FDN STAT NATURAL LAN MANSURI IR, 2006, P 22 ICDE MCCALLUM A, 2005, ACM QUEUE NOV, V3 Metzler D., 2005, P 14 INT C INF KNOWL, P517, DOI 10.1145/1099554.1099695 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Mitra M, 1997, P RIAO97 COMP ASS IN, P200 Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 MOONEY RJ, 2006, ACM SIGKDD EXPLORATI, V7, P3 NENADIC G, 2006, ACM T ASIAN LANGUAGE, V5, P22, DOI 10.1145/1131348.1131351 NIGAM K, 2000, 8 CIKM KANS CIT MI, P86 Niles Ian, 2003, P IEEE INT C INF KNO, P412 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 RAJMAN M, 1999, P 9 ASMDA LISB PORT, P80 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 SALTON G, 1988, P 26 ANN M ASS COMP, P120 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sapir Edward, 1921, LANGUAGE INTRO STUDY Schapire R. E., 1999, P 16 INT JOINT C ART, P1401 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sebastiani F., 2006, ENCY LANGUAGE LINGUI, V14, P457 SHANNON CE, 1948, AT&T TECH J, V27, P379 SMALHEISER NR, 1994, NEUROSCI RES COMMUN, V15, P1 Spasic I, 2005, BRIEF BIOINFORM, V6, P239, DOI 10.1093/bib/6.3.239 Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8 Turney PD, 2003, ACM T INFORM SYST, V21, P315, DOI 10.1145/944012.944013 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Varelas G., 2005, P 7 ANN ACM INT WORK, P10, DOI 10.1145/1097047.1097051 Witten I. H., 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096), DOI 10.1109/DCC.1999.755669 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 Yeh AS, 2003, BIOINFORMATICS, V19, P331 ZAIANE OR, 1998, 199813 TR S FRAS U NR 78 TC 11 Z9 11 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0163-5808 J9 SIGMOD RECORD JI Sigmod Rec. PD SEP PY 2007 VL 36 IS 3 BP 23 EP 34 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 235EG UT WOS:000251215700003 ER PT J AU Rasiwasia, N Moreno, PJ Vasconcelos, N AF Rasiwasia, Nikhil Moreno, Pedro J. Vasconcelos, Nuno TI Bridging the gap: Query by semantic example SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE content-based image retrieval; Gaussian mixtures; image similarity; multiple instance learning; query by example; semantic retrieval; semantic space ID USERS RELEVANCE FEEDBACK; IMAGE RETRIEVAL; SPACE AB A combination of query-by-visual-example (QBVE) and semantic retrieval (SR), denoted as query-by-semantic-example (QBSE), is proposed. Images are labeled with respect to a vocabulary of visual concepts, as is usual in SR. Each image is then represented by a vector, referred to as a semantic multinomial, of posterior concept probabilities. Retrieval is based on the query-by-example paradigm: the user provides a query image, for which 1) a semantic multinomial is computed and 2) matched to those in the database. QBSE is shown to have two main properties of interest, one mostly practical and the other philosophical. From a practical standpoint, because it inherits the generalization ability of SR inside the space of known visual concepts (referred to as the semantic space) but performs much better outside of it, QBSE produces retrieval systems that are more accurate than what was previously possible. Philosophically, because it allows a direct comparison of visual and semantic representations under a common query paradigm, QBSE enables the design of experiments that explicitly test the value of semantic representations for image retrieval. An implementation of QBSE under the minimum probability of error (MPE) retrieval framework, previously applied with success to both QBVE and SR, is proposed, and used to demonstrate the two properties. In particular, an extensive objective comparison of QBSE with QBVE is presented, showing that the former significantly outperforms the latter both inside and outside the semantic space. By carefully controlling the structure of the semantic space, it is also shown that this improvement can only be attributed to the semantic nature of the representation on which QBSE is based. C1 Univ Calif San Diego, Dept Elect & Comp Engn, Stat Visual Comp Lab, La Jolla, CA 92093 USA. Google Inc, Google Res & Dev Ctr, New York, NY 10011 USA. RP Rasiwasia, N (reprint author), Univ Calif San Diego, Dept Elect & Comp Engn, Stat Visual Comp Lab, La Jolla, CA 92093 USA. EM nikux@ucsd.edu; pedro@google.com; nuno@ece.ucsd.edu CR Amir A., 2005, P NIST TRECVID WORKS Auer P, 1997, P 14 INT C MACH LEAR, P21 Barnard K., 2001, P INT C COMP VIS, V2, P408, DOI DOI 10.1109/ICCV.2001.937654 BLEI D, 2003, P ACM SIGIR C RES DE CARNEIO G, 2005, P IEEE CVPR SAN DIEG Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61 Cover T. M., 1991, ELEMENTS INFORM THEO COX IJ, 1997, P IEEE CVPR Dempster A. P., 1977, J R STAT SOC B, V39 Duda R.O., 2001, PATTERN CLASSIFICATI DUYGULU P, 2002, P ECCV COP DENM Fellbaum C, 1998, WORDNET ELECT LEXICA FENG S, 2004, P IEEE CVPR WASH DC Forsyth DA, 1997, PROC CVPR IEEE, P678, DOI 10.1109/CVPR.1997.609399 Haering N., 1997, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.97TB100175), DOI 10.1109/IVL.1997.629716 HAN J, 2002, P INT C IM PROC He XF, 2003, IEEE T CIRC SYST VID, V13, P39, DOI 10.1109/TCSVT.2002.808087 JAIN A, 1996, PATTERN RECOGNIT J, V29 Kiyoki Y., 1994, SIGMOD Record, V23 Kraaij W., 2006, P TRECVID KUECK PCH, 2004, P ECCV PRAG CZECH RE LAVRENKO V, 2003, MODEL LEARNING SEMAN Lee CS, 1999, PROC SPIE, V3846, P294, DOI 10.1117/12.360434 LU LWY, 2003, IEEE T MULTIMEDIA, V5, P339 LU MZJ, 2005, P IEEE NLP KE MANMATHA R, 1997, P SPIE, V3016 Maron O., 1998, FRAMEWORK MULTIPLE I Muller H., 2002, P INT C IM VID RETR, P38 NIBLACK W, 1993, P SOC PHOTO-OPT INS, V1908, P173 Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143 PICARD R, 1995, P AS C COMP VIS SING PLATT J, 1999, ADV LARGE MARGIN CLA, V6174 Rui Y., 2000, P IEEE C COMP VIS PA Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith J. R., 2003, ICME, P445 Smith J. R., 1998, P IEEE WORKSH CONT B Smith J. R., 2003, Proceedings of the SPIE - The International Society for Optical Engineering, V5307, DOI 10.1117/12.536579 SMITH JR, 1996, VISUALSEEK FULLY AUT, P87 SNOEK C, 2006, P CIVR, P11 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 Szummer M., 1998, P WORKSH CONT BAS AC Tahaghoghi SMM, 2001, AUST COMP S, V23, P138, DOI 10.1109/ADC.2001.904476 Thorpe S, 1996, NATURE, V381, P520, DOI 10.1038/381520a0 Vailaya A, 1998, PATTERN RECOGN, V31, P1921, DOI 10.1016/S0031-3203(98)00079-X VASCONCELOS N, 2001, P INT C PATT REC BAR VASCONCELOS N, 2000, P ECCV DUBL IR VASCONCELOS N, 2001, P INT C IM PROC THES Vasconcelos N, 2004, IEEE T SIGNAL PROCES, V52, P2322, DOI 10.1109/TSP.2004.831125 WESTERVELD T, 2003, P MULT INF RETR WORK NR 51 TC 67 Z9 71 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD AUG PY 2007 VL 9 IS 5 BP 923 EP 938 DI 10.1109/TMM.2007.900138 PG 16 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA 193ZX UT WOS:000248314800003 ER PT J AU Snoek, CGM Huurnink, B Hollink, L de Rijke, M Schreiber, G Worring, M AF Snoek, Cees G. M. Huurnink, Bouke Hollink, Laura de Rijke, Maarten Schreiber, Guus Worring, Marcel TI Adding semantics to detectors for video retrieval SO IEEE TRANSACTIONS ON MULTIMEDIA LA English DT Article DE concept learning; content analysis and indexing; knowledge modeling; multimedia information systems; video retrieval ID IMAGE RETRIEVAL; MULTIMEDIA RETRIEVAL; FEATURES; QUERY; MODEL; GAP AB In this paper, we propose an automatic video retrieval method based on high-level concept detectors. Research in video analysis has reached the point where over 100 concept detectors can be learned in a generic fashion, albeit with mixed performance. Such a set of detectors is very small still compared to ontologies aiming to capture the full vocabulary a user has. We aim to throw a bridge between the two fields by building a multimedia thesaurus, i.e., a set of machine learned concept detectors that is enriched with semantic descriptions and semantic structure obtained from WordNet. Given a multimodal user query, we identify three strategies to select a relevant detector from this thesaurus, namely: text matching, ontology querying, and semantic visual querying. We evaluate the methods against the automatic search task of the TRECVID 2005 video retrieval benchmark, using a news video archive of 85 h in combination with a thesaurus of 363 machine learned concept detectors. We assess the influence of thesaurus size on video search performance, evaluate and compare the multimodal selection strategies for concept detectors, and finally discuss their combined potential using oracle fusion. The set of queries in the TRECVID 2005 corpus is too small for us to be definite in our conclusions, but the results suggest promising new lines of research. C1 Univ Amsterdam, Inst Informat, Intelligent Syst Lab Amsterdam, NL-1098 SJ Amsterdam, Netherlands. Free Univ Amsterdam, FEW, Dept Comp Sci, NL-1081 HV Amsterdam, Netherlands. RP Snoek, CGM (reprint author), Univ Amsterdam, Inst Informat, Intelligent Syst Lab Amsterdam, Kruislaan 403, NL-1098 SJ Amsterdam, Netherlands. EM cgmsnoek@science.uva.nl RI Snoek, Cees/ OI Snoek, Cees/0000-0001-9092-1556 CR ADAMS B, 2002, P 11 TEXT RETR C GAI AMIR A, 2003, P TRECVID WORKSH GAI Bertini M., 2005, P 13 ANN ACM INT C M, P395, DOI 10.1145/1101149.1101235 Brown M. G., 1995, P ACM MULT SAN FRANC Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13 Chang C.-C., 2001, LIBSVM LIB SUPPORT V Chang SF, 1998, IEEE T CIRC SYST VID, V8, P602 CHRISTE Y, 1995, CAH CIVILIS MEDIEVAL, V38, P4 CHUA TS, 2004, P TRECVID WORKSH GAI DelBimbo A, 1997, IEEE T PATTERN ANAL, V19, P121, DOI 10.1109/34.574790 Fan JP, 2004, IEEE T MULTIMEDIA, V6, P70, DOI 10.1109/TMM.2003.819583 Fellbaum C, 1998, WORDNET ELECT LEXICA Fox EA, 1994, TEXT RETRIEVAL C TRE, V500-215, P243 Gevers T, 2000, IEEE T IMAGE PROCESS, V9, P102, DOI 10.1109/83.817602 Hauptmann A, 2007, IEEE T MULTIMEDIA, V9, P958, DOI 10.1109/TMM.2007.900150 HOLLINK L, 2006, THESIS VRIJE U AMSTE HOLLINK L, 2003, P K CAP 2003 WORKSH Hollink L., 2005, P 13 ANN ACM INT C M, P479, DOI 10.1145/1101149.1101256 Hollink V, 2004, INFORM RETRIEVAL, V7, P33, DOI 10.1023/B:INRT.0000009439.19151.4c HOOGS A, 2003, IEEE COMP SOC C COMP, V2, P327 HYVONEN E, 2004, P ECAI WORKSH APPL S Iyengar G, 2005, P 13 ACM INT C MULT, P21, DOI 10.1145/1101149.1101154 KAMPS J, 2004, P 2004 ACM S APPL CO, P1073, DOI 10.1145/967900.968118 Kennedy L.S., 2005, P 13 ANN ACM INT C M, P882, DOI 10.1145/1101149.1101339 Lenat D., 1990, BUILDING LARGE KNOWL Lienhart R, 1997, IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS '97, PROCEEDINGS, P509 Lin C., 2003, P TRECVID WORKSH GAI Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d Ma WY, 1999, MULTIMEDIA SYST, V7, P184, DOI 10.1007/s005300050121 Miller G. A., 1993, P ARPA WORKSH HUM LA, P303, DOI 10.3115/1075671.1075742 Naphade M, 2006, IEEE MULTIMEDIA, V13, P86, DOI 10.1109/MMUL.2006.63 Naphade MR, 2001, IEEE T MULTIMEDIA, V3, P141 Natsev A. P., 2005, P ACM MM 2005, P598, DOI 10.1145/1101149.1101288 Neo SY, 2006, LECT NOTES COMPUT SC, V4071, P143 Platt JC, 2000, ADV NEUR IN, P61 Porter M. F., 1997, READINGS INFORM RETR, P313 Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138 Resnik P, 1995, INT JOINT CONF ARTIF, P448 Rui Y., 2000, Proceedings ACM Multimedia 2000, DOI 10.1145/354384.354443 SALTON G, 1972, COMMUN ACM, V15, P658, DOI 10.1145/361454.361509 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schroer R, 2001, IEEE AERO EL SYS MAG, V16, P3 SMEATON A, 2005, CIVR, V3568, P19 SMEATON AF, 1996, P 19 ANN INT ACM SIG, P174, DOI 10.1145/243199.243261 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Smith E, 1997, ENVIRON TOXICOL PHAR, V4, P3, DOI 10.1016/S1382-6689(97)10035-7 Snoek C. G., 2006, P 14 ANN ACM INT C M, P421, DOI 10.1145/1180639.1180727 Snoek CGM, 2007, IEEE T MULTIMEDIA, V9, P280, DOI 10.1109/TMM.2006.886275 Snoek CGM, 2006, IEEE T PATTERN ANAL, V28, P1678, DOI 10.1109/TPAMI.2006.212 Tjong Kim Sang E., 2002, J MACH LEARN RES, V2, P559 VANGEMERT JC, 2006, P INT WORKSH SEM LEA Vapnik V. N., 2000, NATURE STAT LEARNING Volkmer T, 2005, P 13 ANN ACM INT C M, P892, DOI DOI 10.1145/1101149.1101341 Volkmer T, 2007, IEEE T MULTIMEDIA, V9, P967, DOI 10.1109/TMM.2007.900153 Westerveld T, 2003, EURASIP J APPL SIG P, V2003, P186, DOI 10.1155/S111086570321101X Worring M, 2007, IEEE T MULTIMEDIA, V9, P909, DOI 10.1109/TMM.2007.898913 Yan R, 2004, P 12 ANN ACM INT C M, P548, DOI 10.1145/1027527.1027661 2006, LUCENE SEARCH ENGINE NR 58 TC 53 Z9 55 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1520-9210 J9 IEEE T MULTIMEDIA JI IEEE Trans. Multimedia PD AUG PY 2007 VL 9 IS 5 BP 975 EP 986 DI 10.1109/TMM.2007.900156 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA 193ZX UT WOS:000248314800007 ER PT J AU Chen, ZX Fu, B AF Chen, Zhixiang Fu, Bin TI On the complexity of Rocchio's similarity-based relevance feedback algorithm SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID VECTOR-SPACE MODEL; RETRIEVAL; PERCEPTRON AB Rocchio's similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive learning algorithm from examples in searching for documents represented by a linear classifier. Despite its popularity in various applications, there is little rigorous analysis of its learning complexity in literature. In this article, the authors prove for the first time that the learning complexity of Rocchio's algorithm is 0(d + d(2)(log d + log n)) over the discretized vector space (0,..., n - 1}(d) when the inner product similarity measure is used. The upper bound on the learning complexity for searching for documents represented by a monotone linear classifier ((q) over right arrow, 0) over {0,..., n - 1}(d) can be improved to, at most, 1 + 2k (n - 1) (log d + log(n - 1)), where k is the number of nonzero components in (q) over right arrow. Several lower bounds on the learning complexity are also obtained for Rocchio's algorithm. For example, the authors prove that Rocchio's algorithm has a lower bound Omega(((2)(d))log n) on its learning complexity over the Boolean vector space {0, 1)(d). C1 Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78451 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, 1201 W Univ Dr, Edinburg, TX 78451 USA. EM chen@cs.panam.edu; binfu@cs.panam.edu CR Baeza-Yates R., 1999, MODERN INFORM RETREI BSHOUTY N, 1995, P 18 ANN C COMP LEAR, V3559, P198 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN Z, 2004, COMPUTATIONAL WEB IN, P303 Chen ZX, 2005, LECT NOTES COMPUT SC, V3595, P955 Chen ZX, 2001, LECT NOTES COMPUT SC, V2108, P540 Chen ZX, 2002, INFORM RETRIEVAL, V5, P61, DOI 10.1023/A:1012730924277 Frakes W. B., 1992, INFORM RETRIEVAL DAT HAMPSON SE, 1990, IEEE T SYST MAN CYB, V20, P67, DOI 10.1109/21.47810 Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Kivinen J, 1997, ARTIF INTELL, V97, P325, DOI 10.1016/S0004-3702(97)00039-8 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 MAASS W, 1994, COMPUTATIONAL LEARNI, V1, P381 Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANVIJSBERGEN CJ, 1988, INFORM RETRIEVAL WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 23 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD AUG PY 2007 VL 58 IS 10 BP 1392 EP 1400 DI 10.1002/asi.20612 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 193BD UT WOS:000248247400002 ER PT J AU Lacoste, C Lim, JH Chevallet, JP Diem, THL AF Lacoste, Caroline Lim, Joo-Hwee Chevallet, Jean-Pierre Diem, Thi Hoang Le TI Medical-image retrieval based on knowledge-assisted text and image indexing SO IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY LA English DT Article DE content-based image retrieval; knowledge-based image indexing; Unified Medical Language System (UMLS); visual ontology AB Voluminous medical images are generated daily. They are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical-image databases, both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework based on support vector machines to facilitate modular design and learning of medical semantics from images. We present two complementary visual indexing approaches within this framework: a global indexing to access image modality and a local indexing to access semantic local features. Two fusion approaches are developed to improve textual retrieval using the UMLS-based image indexing. First, a simple fusion of the textual and visual retrieval approaches is proposed, improving significantly the retrieval results of both text and image retrieval. Second, a visual modality filtering is designed to remove visually aberrant images according to the query modality concept(s). Using the ImageCLEFmed database, we demonstrate the effectiveness of our framework which is superior when compared with the automatic runs evaluated in 2005 on the same medical-image retrieval task. C1 Inst Infocomm Res, French Singapore IPAL Joint Lab, CNRS, UMI 2955,I2R,NUS,UJF, Singapore 119613, Singapore. RP Lacoste, C (reprint author), Inst Infocomm Res, French Singapore IPAL Joint Lab, CNRS, UMI 2955,I2R,NUS,UJF, Singapore 119613, Singapore. EM joohwee@i2r.a-star.edu.sg CR Aronson A R, 2001, Proc AMIA Symp, P17 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Bishop C. M., 1995, NEURAL NETWORKS PATT Bodenreider O, 2003, J BIOMED INFORM, V36, P414, DOI 10.1016/j.jbi.2003.11.002 Chevallet JP, 2006, LECT NOTES COMPUT SC, V4022, P642 CHVALLET JP, 2004, P AIRS, V3211, P263 CLOUGH P, 2005, LNCS, V4022, P535 Dy JG, 2003, IEEE T PATTERN ANAL, V25, P373, DOI 10.1109/TPAMI.2003.1182100 Guld MO, 2002, P SOC PHOTO-OPT INS, V4685, P280 HUANG T, 1997, P IEEE INT C IM PROC, P815 Joachims Thorsten, 2002, LEARNING CLASSIFY TE LIM J, 2005, P AIRS 2005, P84 LIM J, 2006, EURASIP J APPL SIG P, V21, P1 Lim JH, 2005, PATTERN RECOGN, V38, P847, DOI 10.1016/j.patcog.2004.11.002 Lim JH, 2005, MULTIMEDIA SYST, V10, P317, DOI 10.1007/s00530-004-0158-z Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024 RADHOUANI S, 2006, P IEEE ICME, P1853 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schmid H., 1994, P INT C NEW METH LAN, P44 SERASSET G, 1994, P 15 C COMP LING MOR, P278 Shyu CR, 2002, COMPUT VIS IMAGE UND, V88, P119, DOI 10.1006/cviu.2002.0972 Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972 Vapnik V., 1995, NATURE STAT LEARNING 2006, CROSS LANG EV FOR NO NR 24 TC 12 Z9 13 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1051-8215 J9 IEEE T CIRC SYST VID JI IEEE Trans. Circuits Syst. Video Technol. PD JUL PY 2007 VL 17 IS 7 BP 889 EP 900 DI 10.1109/TCSVT.2007.897114 PG 12 WC Engineering, Electrical & Electronic SC Engineering GA 186NR UT WOS:000247785900009 ER PT J AU Lee, KS Kageura, K AF Lee, Kyung-Soon Kageura, Kyo TI Virtual relevant documents in text categorization with support vector machines SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE virtual document; prior knowledge; topical representation; text categorization; support vectors AB This paper explores the incorporation of prior knowledge into support vector machines as a means of compensating for a shortage of training data in text categorization. The prior knowledge about transformation invariance is generated by a virtual document method. The method applies a simple transformation to documents, i.e., making virtual documents by combining relevant document pairs for a topic in the training set. The virtual document thus created not only is expected to preserve the topic, but even improve the topical representation by exploiting relevant terms that are not given high importance in individual real documents. Artificially generated documents result in the change in the distribution of training data without the randomization. Experiments with support vector machines based on linear, polynomial and radial-basis function kernels showed the effectiveness on Reuters-21578 set for the topics with a small number of relevant documents. The proposed method achieved 131%, 34%, 12% improvements in micro-averaged F-1 for 25, 46, and 58 topics with less than 10, 30, and 50 relevant documents in learning, respectively. The result analysis indicates that incorporating virtual documents contributes to a steady improvement on the performance. (c) 2006 Elsevier Ltd. All rights reserved. C1 Chonbuk Natl Univ, Div Elect & Informat Engn, Jeonju 561756, South Korea. Univ Tokyo, Grad Sch Educ, Bunkyo Ku, Tokyo 1130033, Japan. RP Lee, KS (reprint author), Chonbuk Natl Univ, Div Elect & Informat Engn, 664-14 Duckjin Gu, Jeonju 561756, South Korea. EM selfsolee@chonbuk.ac.kr; kyo@p.u-tokyo.ac.jp CR ALLAN J, 1996, P 4 TEXT RETR C TREC BALCAN MF, 2005, P 18 ANN C LEARN THE, P111 Blum A., 1998, P 11 ANN C COMP LEAR CRISTIANINI N, 2000, INTRO SUPPORT SECTOR Decoste D, 2002, MACH LEARN, V46, P161, DOI 10.1023/A:1012454411458 FOGGIO T, 1992, 1347 MIT ART INT LAB Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 Freund Y., 1996, P 13 INT C MACH LEAR, P148 Joachims T., 2002, P ACM C KNOWL DISC D Joachims T., 1998, P 10 EUR C MACH LEAR, P137 KAWATANI T, 2002, INT ACM SIGIR C RES KWOK K, 1997, P 5 TEXT RETR C LEE KS, 2003, P P INT C AS DIG LIB, P62 LEE KS, 2002, P 11 TEXT RETR C TRE Lewis DD, 1999, REUTERS 21578 TEXT C NIGAM K, 2000, P 6 ACM SIGKDD INT C Robertson S. E., 2002, P 11 TEXT RETR C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sassano M, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P208 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Scholkopf B., 1999, ADV KERNEL METHODS S Scholkopf B., 1996, LECT NOTES COMPUT SC, V1112, P47 Scholkopf B., 1995, P 1 INT C KNOWL DISC Schwenk H., 1997, P INT C ART NEUR NET, P967 SINGHAL A, 1997, P 20 ACM SIGIR C RES, P21 Skurichina M, 2002, PATTERN ANAL APPL, V5, P121, DOI 10.1007/s100440200011 Tan P. N., 2005, INTRO DATA MINING Vapnick V., 1998, STAT LEARNING THEORY Vapnik V., 1995, NATURE STAT LEARNING YANG Y, 1999, INT ACM SIGIR C RES NR 30 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2007 VL 43 IS 4 BP 902 EP 913 DI 10.1016/j.ipm.2006.08.010 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 155UY UT WOS:000245605100004 ER PT J AU Wang, TY Chiang, HM AF Wang, Tai-Yue Chiang, Huei-Min TI Fuzzy support vector machine for multi-class text categorization SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE fuzzy support vector machine; feature selection; membership functions; OAA-FSVM ID PERCEPTRON; RETRIEVAL; SELECTION; MODEL AB Document classification, with the blooming of the Internet information delivery, has become indispensable required and is expected to be disposed by an automatic text categorization. This paper presents a text categorization system to solve the multi-class categorization problem. The system consists of two modules: the processing module and the classifying module. In the first module, ICF and Uni are used as the indictors to extract the relevant terms. While the fuzzy set theory is incorporated into the OAA-SVM in the classifying module, we specifically propose an OAA-FSVM classifier to implement a multi-class classification system. The performances of OAA-SVM and OAA-FSVM are evaluated by macro-average performance index. Also the statistical significance test is examined by the McNemar's test. The results from the empirical study show that the proposed OAA-FSVM method has out-performed OAA-SVM in the multi-class text categorization problem. (c) 2006 Elsevier Ltd. All rights reserved. C1 Natl Cheng Kung Univ, Dept Ind & Informat Management, Tainan 70101, Taiwan. RP Wang, TY (reprint author), Natl Cheng Kung Univ, Dept Ind & Informat Management, 1,Univ Rd, Tainan 70101, Taiwan. EM tywang@mail.ncku.edu.tw CR Abe S., 2002, P 10 EUR S ART NEUR, P113 ALLWEIN E, 2000, REDUCING MULTICLASS APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Breiman L., 1984, CLASSIFICATION REGRE Cawley G. C., 2000, MATLAB SUPPORT VECTO Chen JH, 2002, IEEE T NEURAL NETWOR, V13, P1364, DOI 10.1109/TNN.2002.804311 Chung FL, 2004, APPL SOFT COMPUT, V5, P67, DOI 10.1016/j.asoc.2004.03.012 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 Cristianini N., 2000, INTRO SUPPORT VECTOR DIETTERICH TG, 1998, NEURAL COMPUT, V10, P7 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Foody GM, 2004, PHOTOGRAMM ENG REM S, V70, P627 Fuhr N., 1991, P RIAO 91, P606 Haykin S., 1999, NEURAL NETWORKS, VSecond Huang H.P., 2002, INT J FUZZY SYST, V4, P826 Huang Y.L., 1998, J LIBR INF SCI, V24, P1023 Inoue T., 2001, P INT JOINT C NEUR N, V2, P1449 Jayadeva, 2004, NEUROCOMPUTING, V61, P401, DOI 10.1016/j.neucom.2004.02.004 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Kikuchi T, 2005, PATTERN RECOGN LETT, V26, P1937, DOI 10.1016/j.patrec.2005.03.014 Kohavi R., 1995, P 14 INT JOINT C ART, P1137 Lee CK, 2006, INFORM PROCESS MANAG, V42, P155, DOI 10.1016/j.ipm.2004.08.006 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lin CF, 2002, IEEE T NEURAL NETWOR, V13, P464, DOI 10.1109/72.991432 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 NILSBACK ME, 2004, P INT C COMP VIS PAT Peng FC, 2006, INFORM PROCESS MANAG, V42, P963, DOI 10.1016/j.ipm.2005.09.002 Platt J. C., 1998, ADV KERNEL METHODS S, P185 RICARDOM BY, 1999, MODERN INFORM RETRIE Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHIFFRIN EL, 1995, AM J HYPERTENS, V8, P229, DOI 10.1016/0895-7061(95)96211-2 Schwenker F., 2000, P 4 INT C KNOWL BAS, P561 SHANNON CE, 1949, AT&T TECH J, V28, P656 SHAO J, 1993, J AM STAT ASSOC, V88, P486, DOI 10.2307/2290328 Takahashi F, 2002, P 9 INT C NEUR INF P, P1418, DOI 10.1109/ICONIP.2002.1202854 Tao Q., 2004, NEURAL PROCESS LETT, V20, P139, DOI 10.1007/s11063-004-1640-5 Tsujinishi D, 2003, NEURAL NETWORKS, V16, P785, DOI 10.1016/S0893-6080(03)00110-2 Wei L. L., 2003, P 2 INT C MACH LEARN, P3082 WEISS SM, 1994, PROCEEDINGS OF THE TWELFTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P626 Weston J., 1998, CSDTR9804 U LOND DEP Wiener E, 1995, P 4 ANN S DOC AN INF, P317 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yang Y., 1994, SIGIR 94 P 17 ANN IN, V94, P13 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 ZHENG CH, 2002, P 1 INT C MACH LEARN, P4 NR 49 TC 34 Z9 37 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2007 VL 43 IS 4 BP 914 EP 929 DI 10.1016/j.ipm.2006.09.011 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 155UY UT WOS:000245605100005 ER PT J AU Yu, H Lee, M Kaufman, D Ely, J Osheroff, JA Hripcsak, G Cimino, J AF Yu, Hong Lee, Minsuk Kaufman, David Ely, John Osheroff, Jerome A. Hripcsak, George Cimino, James TI Development, implementation, and a cognitive evaluation of a definitional question answering system for physicians SO JOURNAL OF BIOMEDICAL INFORMATICS LA English DT Article DE question answering; information retrieval; question analysis; text summarization; machine-learning; evaluation ID WORLD-WIDE-WEB; CLINICAL QUESTIONS; HEALTH INFORMATION; MEDICAL INFORMATION; ONLINE EVIDENCE; PATIENT-CARE; INTERNET; QUALITY; RETRIEVAL; USABILITY AB The published medical literature and online medical resources are important sources to help physicians make patient treatment decisions. Traditional sources used for information retrieval (e.g., PubMed) often return a list of documents in response to a user's query. Frequently the number of returned documents from large knowledge repositories is large and makes information seeking practical only "after hours" and not in the clinical setting. This study developed novel algorithms, and designed, implemented, and evaluated a medical definitional question answering system (MedQA). MedQA automatically analyzed a large number of electronic documents to generate short and coherent answers in response to definitional questions (i.e., questions with the format of "What is X?"). Our preliminary cognitive evaluation shows that MedQA out-performed three other online information systems (Google, OneLook, and PubMed) in two important efficiency criteria; namely, time spent and number of actions taken for a physician to identify a definition. It is our contention that question answering systems that aggregate pertinent information scattered across different documents have the potential to address clinical information needs within a timeframe necessary to meet the demands of clinicians. Published by Elsevier Inc. C1 Univ Wisconsin, Dept Hlth Sci, Milwaukee, WI 53211 USA. Columbia Univ, Dept Biomed Informat, New York, NY 10027 USA. Univ Iowa, Coll Med, Dept Family Med, Iowa City, IA 52242 USA. Thomson Healthcare, Durham, NC USA. RP Yu, H (reprint author), Univ Wisconsin, Dept Hlth Sci, Enderis Hall 939,2400 E Hartford Ave,POB 413, Milwaukee, WI 53211 USA. EM HongYu@uwm.edu CR Allen ES, 1999, NATURE, V402, P722, DOI 10.1038/45370 Alper BS, 2005, ANN FAM MED, V3, P507, DOI 10.1370/afm.370 Alper BS, 2001, J FAM PRACTICE, V50, P960 BARZILAY R, 1999, 37 ASS COMP LING 199 BARZILAY R, 2002, INFERRING STRATEGIES BARZILAY R, 1997, ACL INT SCAL TEXT SU Benigeri M, 2003, HEALTH PROMOT INT, V18, P381, DOI 10.1093/heapro/dag409 BERKOWITZ L, 2002, REV EVALUATION INTER Berland GK, 2001, JAMA-J AM MED ASSOC, V285, P2612, DOI 10.1001/jama.285.20.2612 BETHARD S, 2004, AAAI 2004 SPRING S E Bhavnani SK, 2005, J AM SOC INF SCI TEC, V56, P989, DOI 10.1002/asi.20189 Blair-Goldensohn S., 2004, NEW DIRECTIONS QUEST Childs S., 2005, Performance Measurement and Metrics, V6, DOI 10.1108/14678040510607803 Cimino James J, 2003, AMIA Annu Symp Proc, P175 Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671 Cui H., 2005, 28 ANN INT ACM SIGIR Culver JD, 1997, J GEN INTERN MED, V12, P466, DOI 10.1046/j.1525-1497.1997.00084.x D'Alessandro DM, 2004, PEDIATRICS, V113, P64, DOI 10.1542/peds.113.1.64 Davison K, 1997, Clin Perform Qual Health Care, V5, P64 Day R., 1998, WRITE PUBLISH SCI PA DELBECQUE T, 2005, CONNECTING MED INFOR EDMUNDSO.HP, 1969, J ACM, V16, P264, DOI 10.1145/321510.321519 ELHADAD N, 2004, J ARTIF INTELL MED ELHADAD N, 2005, FACILITATING PHYS AC Ely JW, 1999, BRIT MED J, V319, P358 Ely JW, 2005, J AM MED INFORM ASSN, V12, P217, DOI 10.1197/jamia.M1608 Ely JW, 2000, BRIT MED J, V321, P429, DOI 10.1136/bmj.321.7258.429 Ely JW, 1997, J FAM PRACTICE, V45, P382 Eysenbach G, 2002, BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573 FISZMAN M, 2004, HLT NAACL 2004 COMP GABBAY I, 2004, ACL 2004 WORKSH QUES GLENNIE E, 2006, J DIAGN RADIOGR IMAG, V6, P25, DOI 10.1017/S1460472806000034 Gosling AS, 2004, INT J MED INFORM, V73, P391, DOI 10.1016/j.ijmedinf.2004.12.017 Griffiths KM, 2000, BRIT MED J, V321, P1511, DOI 10.1136/bmj.321.7275.1511 Halliday M. A. K., 1976, COHESION ENGLISH HATZIVASSILOGLO.V, 2000, INVESTIGATION LINGUI Hersh WR, 2002, J AM MED INFORM ASSN, V9, P283, DOI 10.1197/jamia.M0996 HOVY E, 1997, ACM EACL WORKSH INT Impicciatore P, 1997, BRIT MED J, V314, P1875 Jacquemart Pierre, 2003, Stud Health Technol Inform, V95, P463 Jadad AR, 1998, JAMA-J AM MED ASSOC, V279, P611, DOI 10.1001/jama.279.8.611 Kaufman DR, 2003, J BIOMED INFORM, V36, P45, DOI [10.1016/S1532-0464(03)00056-X, 10.1016/S1532-0464(03)0056-X] KLAVANS J, 2001, P AM MED INF ASS S Kushniruk AW, 2004, J BIOMED INFORM, V37, P56, DOI 10.1016/j.jbi.2004.01.003 Lee M, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-140 LIGHT M, 2004, HLT NAACL 2004 WORKS LIN J, 2007, INFORM PROCESS MANAG LIN J, 2006, HLT NAACL BIONLP Luhn Hans P., 1958, IBM J RES DEV, V2, P159 Marchionini G., 1995, INFORM SEEKING ELECT MARCU D, 1997, RHETORICAL PARSING S McClung HJ, 1998, PEDIATRICS, V101, DOI 10.1542/peds.101.6.e2 MCKEOWN K, 2001, P 1 ACM IEEE CS JOIN MCKNIGHT L, 2003, CATEGORIZATION SENTE Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 MIZUTA Y, 2004, POST COLING WORKSH Mizuta Y., 2004, P 4 INT C LANG RES E NIU Y, 2004, ACL 2004 WORKSH QUES Norman D.A., 1986, COGNITIVE ENG USER C, P31 Purcell GP, 2002, BRIT MED J, V324, P557, DOI 10.1136/bmj.324.7337.557 Radev D. R., 2000, ANLP NAACL WORKSH SU RILOFF E, 1996, AUTOMATICALLY GENERA Riloff E., 2004, INTRO SUNDANCE AUTOS RINALDI F, 2004, ACL 2004 WORKSH QUES Sackett D., 2000, EVIDENCE BASED MED P, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schilling LM, 2005, ACAD MED, V80, P51, DOI 10.1097/00001888-200501000-00013 SEOL Y, 2004, SCENARIO BASED ASSES Silberg WM, 1997, JAMA-J AM MED ASSOC, V277, P1244, DOI 10.1001/jama.277.15.1244 Straus SE, 1999, ANN ONCOL, V10, P29, DOI 10.1023/A:1008308211595 Sutcliffe A, 1998, INTERACT COMPUT, V10, P321, DOI 10.1016/S0953-5438(98)00013-7 SYSENBACH G, 2002, JAMA-J AM MED ASSOC, V287, P2691 Takeshita H., 2002, Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting Thelwall M, 2001, J AM SOC INF SCI TEC, V52, P1157, DOI 10.1002/asi.1182 Voorhees E., 2004, NIST SPECIAL PUBLICA, V500-261 WALTZ DL, 1978, COMMUN ACM, V21, P526, DOI 10.1145/359545.359550 Westbrook JI, 2004, J AM MED INFORM ASSN, V11, P113, DOI 10.1197/jamia.M1385 Westbrook JI, 2005, J AM MED INFORM ASSN, V12, P315, DOI 10.1197/jamia.M1717 Wyatt JC, 1997, BRIT MED J, V314, P1879 XU A, 2003, QA BBN ANSWERING DEF XUANG X, 2006, ANN S AM MED INF ASS YU H, 2005, P 19 INT JOINT C ART YU H, 2003, P EMP METH NAT LANG YU H, 2006, ANSWERING BIOL QUEST YU H, 2003, BIOINFORMATICS S1, V19, P340 YU H, 2006, HLT NAACL BIONLP 200 Zhu H., 2005, P AAAI 2005 WORKSH Q ZHU H, 2005, BIOLINK SIG LINKING ZWEIGENBAUM P, 2003, QUESTION ANSWERING B, P1 ZWEIGENBAUM P, 2005, MIE 2005 WORKSH NR 90 TC 29 Z9 30 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1532-0464 J9 J BIOMED INFORM JI J. Biomed. Inform. PD JUN PY 2007 VL 40 IS 3 BP 236 EP 251 DI 10.1016/j.jbi.2007.03.002 PG 16 WC Computer Science, Interdisciplinary Applications; Medical Informatics SC Computer Science; Medical Informatics GA 188XZ UT WOS:000247953900003 PM 17462961 ER PT J AU Yang, SY Chuang, FC Ho, CS AF Yang, Sheng-Yuan Chuang, Fang-Chen Ho, Cheng-Seen TI Ontology-supported FAQ processing and ranking techniques SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE FAQ; ontology; ranking method AB This paper describes an FAQ system on the Personal Computer (PC) domain, which employs ontology as the key technique to pre-process FAQs and process user query. It is also equipped with an enhanced ranking technique to present retrieved, query-relevant results. Basically, the system bases on the wrapper technique to help clean, retrieve, and transform FAQ information collected from a heterogeneous environment and stores it in an ontological database. During retrieval of FAQs, the system trims irrelevant query keywords, employs either full keywords match or partial keywords match to retrieve FAQs, and removes conflicting FAQs before turning the final results to the user. Ontology plays the key role in all the above activities. To produce a more effective presentation of the search results, the system employs an enhanced ranking technique, which includes Appearance Probability, Satisfaction Value, Compatibility Value, and Statistic Similarity Value as four measures properly weighted to rank the FAQs. Our experiments show the system does improve precision rate and produces better ranking results. The proposed FAQ system manifests the following interesting features. First, the ontology-supported FAQ extraction from webpages can clean FAQ information by removing redundant data, restore missing data, and resolve inconsistent data. Second, the FAQs are stored in an ontology-directed internal format, which supports semantics-constrained retrieval of FAQs. Third, the ontology-supported natural language processing of user query helps pinpoint user's intent. Finally, the partial keywords match-based ranking method helps present user-most-wanted, conflict-free FAQ solutions for the user. C1 Hwa Hsia Inst Technol, Dept Elect Engn, Taipei, Taiwan. Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan. Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei, Taiwan. St Johns Univ, Dept Comp & Commun Engn, Taipei, Taiwan. RP Ho, CS (reprint author), Hwa Hsia Inst Technol, Dept Elect Engn, Taipei, Taiwan. EM ysy@mail.sju.edu.tw; fcchuang@ailab2.et.ntust.edu.tw; csho@cc.hwh.edu.tw CR ATZENI P, 1997, ACM SIGMOD RECORD, V26, P16, DOI 10.1145/271074.271080 BURKE R, 1997, AAAI SPRING S NAT LA, P17 Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902 Chiu Y.H., 2003, THESIS NATL TAIWAN U CHUANG FC, 2003, THESIS NATL TAIWAN U Duineveld AJ, 2000, INT J HUM-COMPUT ST, V52, P1111, DOI 10.1006/ijhc.1999.0366 FLORESCU D, 1998, ACM SIGMOD RECORD, V27, P59, DOI 10.1145/290593.290605 GRUBER TR, 1993, P INT WORKSH FORM ON Hammer J., 1997, P ACM SIGMOD INT C M, P532, DOI 10.1145/253260.253395 HO CS, 2001, NSC892218E011014 HSU WL, 2001, 2001 IEEE INT WORKSH JACOBS PS, 1994, P 15 INT C COMP LING, P665 Katz B., 2002, P 7 INT WORKSH APPL, P230 Katz B., 1997, P 5 RIAO C COMP ASS Knoblock CA, 2001, INT J COOP INF SYST, V10, P145, DOI 10.1142/S0218843001000291 Kupiec J., 1993, P 16 ANN INT ACM SIG, P181, DOI 10.1145/160688.160717 LIN ICY, 1999, ARTIF INTELL, V13, P37 Lin J., 2003, P 12 INT C INF KNOWL, P116 LIN J, 2002, 11 TEXT RETR C TREC, P447 LYTINEN S, 2002, AAAI SPRING S MIN AN, P46 Noy NF, 1997, AI MAG, V18, P53 NOY NF, 2000, ONTOLOGY DEV 101 GUI Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SNEIDERS E, 1999, AAAI FALL S STRZALKOW T, 1999, NATURAL LANGUAGE INF Tsai C. H., 2000, MMSEG WORD IDENTIFIC VAN HL, 2002, P 14 INT C SOFTW ENG, P557 Winiwarter W, 2000, DATA KNOWL ENG, V35, P181, DOI 10.1016/S0169-023X(00)00032-X Yang S. Y., 1999, P 4 C ART INT APPL C, P248 YANG SY, 2004, 2004 12 NAT C FUZZ T, P181 NR 31 TC 12 Z9 12 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD JUN PY 2007 VL 28 IS 3 BP 233 EP 251 DI 10.1007/s10844-006-0009-9 PG 19 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 169TE UT WOS:000246613800002 ER PT J AU Dominich, S Kiezer, T AF Dominich, Sandor Kiezer, Tamas TI A measure theoretic approach to information retrieval SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID AUTOMATIC TEXT-RETRIEVAL AB The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy-based, and probability-based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory. C1 Univ Veszprem, Dept Comp Sci, H-8200 Veszprem, Hungary. RP Dominich, S (reprint author), Univ Veszprem, Dept Comp Sci, Egyetem 10, H-8200 Veszprem, Hungary. EM dominich@dcs.vein.hu CR BaezaYates R, 2003, INT J APPROX REASON, V34, P97, DOI 10.1016/j.ijar.2003.07.002 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BELEW RK, 2000, FINDING OUT ABOUT Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Berry W., 1999, UNDERSTANDING SEARCH DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Doob JL, 1994, MEASURE THEORY DUBIN D, 2004, LIB TRENDS SPR FAYNMAN RP, 1964, FEYNMAN LECT PHYS Folland G. B., 1984, REAL ANAL MODERN TEC KOLMOGOROFF A, 1950, FDN PROBABILITY LANCZOS K, 1970, SPACE THROUGH AGES Meadow C. T., 1999, TEXT INFORM RETRIEVA NALLAPATI R, 2004, P ACM SIGIR INT C RE Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rudin W., 1966, REAL COMPLEX ANAL SALTON G, 1975, READINGS AUTOMATIC, V18, P613 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1966, READINGS AUTOMATIC L, P169 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Simmonds JG, 1982, BRIEF TENSOR ANAL van Rijsbergen C. J., 1979, INFORM RETRIEVAL VANRIJSBERGEN CJ, 2004, GEOMETRY IR VONNEUMANN J, 1927, MATH PHYS KLASSE, P1 Wang Z, 1991, FUZZY MEASURE THEORY WON KM, 1996, KANGWEON KYUNGKI MAT, V4, P135 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 ZIMMERMAN HJ, 1996, FUZZY SET THEORY AND NR 31 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2007 VL 58 IS 8 BP 1108 EP 1122 DI 10.1002/asi.20586 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 176IO UT WOS:000247078400004 ER PT J AU Cathey, RJ Jensen, EC Beitzel, SM Frieder, O Grossman, D AF Cathey, Rebecca J. Jensen, Eric C. Beitzel, Steven M. Frieder, Ophir Grossman, David TI Exploiting parallelism to support scalable hierarchical clustering SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID VECTOR-SPACE MODEL; ALGORITHMS; PERFORMANCE; RETRIEVAL AB A distributed memory parallel version of the group average hierarchical agglomerative clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard Text REtrieval Conference (TREC) test collection, our parallel hierarchical clustering algorithm is shown to be scalable in terms of processors efficiently used and the collection size. Results show that our algorithm performs close to the expected O(n(2)/p) time on p processors rather than the worst-case O(n(3)/p) time. Furthermore, the O(n(2)/p) memory complexity per node allows larger collections to be clustered as the number of nodes increases. While partitioning algorithms such as k-means are trivially parallelizable, our results confirm those of other studies which showed that hierarchical algorithms produce significantly tighter clusters in the document clustering task. Finally, we show how our parallel hierarchical agglomerative clustering algorithm can be used as the clustering subroutine for a parallel version of the buckshot algorithm to cluster the complete TREC collection at near theoretical runtime expectations. C1 IIT, Informat Retrieval Lab, Dept Comp Sci, Chicago, IL 60616 USA. RP Cathey, RJ (reprint author), IIT, Informat Retrieval Lab, Dept Comp Sci, 10 W 31st St, Chicago, IL 60616 USA. EM cathey@ir.itt.edu; jensen@ir.itt.edu; beitzel@ir.itt.edu; frieder@ir.itt.edu; grossman@ir.itt.edu CR Anderberg M. R., 1973, CLUSTER ANAL APPL Baker M., 1999, INT WORKSH JAV PAR D Bradley P. S., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Chen K., 2004, P ACM C INF KNOWL MA, P285, DOI 10.1145/1031171.1031233 Chow CL, 2000, INT J DAMAGE MECH, V9, P241, DOI 10.1106/L2YK-N0YQ-G5NL-3VBB Cui B., 2003, P 2003 ACM SIGMOD IN, P479 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DASH M, 2004, P 10 INT C PAR DISTR, P363 DAY WHE, 2005, J CLASSIF, V1, P7 DHILLON I., 2001, DATA MINING SCI ENG Dhillon IS, 2000, LECT NOTES ARTIF INT, V1759, P245, DOI 10.1007/3-540-46502-2_13 Dubes R.C., 1988, ALGORITHMS CLUSTERIN Duda R.O., 2001, PATTERN CLASSIFICATI Duda R.O., 1973, PATTERN CLASSIFICATI FASULO D., 1999, UWCSE010302 Ghosh J, 2003, HUM FAC ER, P247 Globerson A, 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753689 Gropp W, 1996, PARALLEL COMPUT, V22, P789, DOI 10.1016/0167-8191(96)00024-5 Gropp W., 1996, USING MPI PORTABLE P Grossman D. A., 2004, INFORM RETRIEVAL ALG Guha S., 1998, P ACM SIGMOD INT C M, P73, DOI 10.1145/276304.276312 Hartigan J, 1975, CLUSTERING ALGORITHM He X, 2004, P 27 ANN INT ACM SIG, P96, DOI 10.1145/1008992.1009012 INFANTESMORRIS T, 2003, P ACM C INF KNOWL MA, P500 KARYPIS G, 2002, CLUTO A CLUSTERING T Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 LI XB, 1990, IEEE T PATTERN ANAL, V12, P1088, DOI 10.1109/34.61708 MAO JC, 1995, IEEE T NEURAL NETWOR, V6, P296 Nassar S., 2004, P 2004 ACM SIGMOD IN, P467, DOI 10.1145/1007568.1007621 *NIST TEXT RETR C, ENGL DOC COLL OLSON CF, 1995, PARALLEL COMPUT, V21, P1313, DOI 10.1016/0167-8191(95)00017-I Ordonez C, 2004, IEEE T KNOWL DATA EN, V16, P909, DOI 10.1109/TKDE.2004.25 Orlandia R., 2005, P ACM C INF KNOW MAN, P201, DOI 10.1145/1099554.1099592 Rajasekaran S, 2005, IEEE T PARALL DISTR, V16, P497, DOI 10.1109/TPDS.2005.72 Ruocco AS, 1997, J AM SOC INFORM SCI, V48, P932, DOI 10.1002/(SICI)1097-4571(199710)48:10<932::AID-ASI7>3.0.CO;2-2 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUTZE C, 1997, P 20 ACM SIGIR C RES, P74 Snir M., 1997, MPI COMPLETE REFEREN Steinbach M., 2000, P KDD 2000 WORKSH TE Thakur R, 2003, LECT NOTES COMPUT SC, V2840, P257 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Wu CH, 2000, J PARALLEL DISTR COM, V60, P1137, DOI 10.1006/jpdc.2000.1644 Xu J., 1999, P 22 ANN INT ACM SIG, P254, DOI 10.1145/312624.312687 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 Zhang T., 1996, P 1996 ACM SIGMOD IN, P103, DOI DOI 10.1145/235968.233324 Zhao Y, 2002, INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS, P16 Zhao Y., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 Zhong Shi, 2005, IEEE INT JOINT C NEU, V5, P3180 NR 52 TC 3 Z9 3 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2007 VL 58 IS 8 BP 1207 EP 1221 DI 10.1002/asi.20596 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 176IO UT WOS:000247078400012 ER PT J AU Gaulton, KJ Mohlke, KL Vision, TJ AF Gaulton, Kyle J. Mohlke, Karen L. Vision, Todd J. TI A computational system to select candidate genes for complex human traits SO BIOINFORMATICS LA English DT Article ID MACULAR DEGENERATION; FUNCTIONAL VARIANT; RHEUMATOID-ARTHRITIS; TYROSINE-PHOSPHATASE; FACTOR-H; ASSOCIATION; DISEASE; RISK; SUSCEPTIBILITY; DISORDERS AB Motivation: Identification of the genetic variation underlying complex traits is challenging. The wealth of information publicly available about the biology of complex traits and the function of individual genes permits the development of informatics-assisted methods for the selection of candidate genes for these traits. Results: We have developed a computational system named CAESAR that ranks all annotated human genes as candidates for a complex trait by using ontologies to semantically map natural language descriptions of the trait with a variety of gene-centric information sources. In a test of its effectiveness, CAESAR successfully selected 7 out of 18 (39%) complex human trait susceptibility genes within the top 2% of ranked candidates genome-wide, a subset that represents roughly 1% of genes in the human genome and provides sufficient enrichment for an association study of several hundred human genes. This approach can be applied to any well-documented mono- or multi-factorial trait in any organism for which an annotated gene set exists. C1 Univ N Carolina, Curriculum Genet & Mol Biol, Chapel Hill, NC 27516 USA. Univ N Carolina, Dept Genet, Chapel Hill, NC 27516 USA. Univ N Carolina, Dept Biol, Chapel Hill, NC 27516 USA. RP Gaulton, KJ (reprint author), Univ N Carolina, Curriculum Genet & Mol Biol, Chapel Hill, NC 27516 USA. EM kgaulton@email.unc.edu RI Vision, Todd/B-4867-2010 OI Vision, Todd/0000-0002-6133-2581 CR Adie EA, 2006, BIOINFORMATICS, V22, P773, DOI 10.1093/bioinformatics/btk031 Aerts S, 2006, NAT BIOTECHNOL, V24, P537, DOI 10.1038/nbt1203 Alfarano C, 2005, NUCLEIC ACIDS RES, V33, pD418, DOI 10.1093/nar/gki051 Apweiler R, 2000, BIOINFORMATICS, V16, P1145, DOI 10.1093/bioinformatics/16.12.1145 Bairoch A., 2005, NUCLEIC ACIDS RES, V33, P154 Becker KG, 2004, NAT GENET, V36, P431, DOI 10.1038/ng0504-431 Begovich AB, 2004, AM J HUM GENET, V75, P330, DOI 10.1086/422827 Birney E., 2006, NUCLEIC ACIDS RES, V34, P556 Blake JA, 2003, NUCLEIC ACIDS RES, V31, P193, DOI 10.1093/nar/gkg047 Bottini N, 2004, NAT GENET, V36, P337, DOI 10.1038/ng1323 Camon E, 2003, GENOME RES, V13, P662, DOI 10.1101/gr.461403 Dean M, 2003, HUM MUTAT, V22, P261, DOI 10.1002/humu.10259 Franke L, 2006, AM J HUM GENET, V78, P1011, DOI 10.1086/504300 Freudenberg J, 2002, BIOINFORMATICS, V18, pS110 Gharani N, 2004, MOL PSYCHIATR, V9, P474, DOI 10.1038/sj.mp.4001498 Gold B, 2006, NAT GENET, V38, P458, DOI 10.1038/ng1750 Grant SFA, 2006, NAT GENET, V38, P320, DOI 10.1038/ng1732 Grupe A, 2006, AM J HUM GENET, V78, P78, DOI 10.1086/498851 Guo DH, 2004, NAT GENET, V36, P837, DOI 10.1038/ng1391 Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514 HARRIS MA, 2004, NUCLEIC ACIDS RES, V32, P258 Helgadottir A, 2006, NAT GENET, V38, P68, DOI 10.1038/ng1692 Hirschman L, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S11 Kanehisa M., 2004, NUCLEIC ACIDS RES, V32, P277 Kelso J, 2003, GENOME RES, V13, P1222, DOI 10.1101/gr.985203 Klein RJ, 2005, SCIENCE, V308, P385, DOI 10.1126/science.1109557 Kochi Y, 2005, NAT GENET, V37, P478, DOI 10.1038/ng1540 Laitinen T, 2004, SCIENCE, V304, P300, DOI 10.1126/science.1090010 Maglott D, 2005, NUCLEIC ACIDS RES, V33, P54 Maraganore DM, 2005, AM J HUM GENET, V77, P685, DOI 10.1086/496902 Monsuur AJ, 2005, NAT GENET, V37, P1341, DOI 10.1038/ng1680 Newton-Cheh C, 2005, MUTAT RES-FUND MOL M, V573, P54, DOI 10.1016/j.mrfmmm.2005.01.006 Peltonen L, 2001, SCIENCE, V291, P1224, DOI 10.1126/science.291.5507.1224 Perez-Iratxeta C, 2002, NAT GENET, V31, P316, DOI 10.1038/ng895 PERI S, 2004, NUCLEIC ACIDS RES, V32, pS497 Pimm J, 2005, AM J HUM GENET, V76, P902, DOI 10.1086/430095 Rivera A, 2005, HUM MOL GENET, V14, P3227, DOI 10.1093/hmg/ddi353 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith CL, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2004-6-1-r7 Thomas DC, 2006, CANCER EPIDEM BIOMAR, V15, P595, DOI 10.1158/1055-9965.EPI-06-0146 Turner FS, 2003, GENOME BIOL, V4, DOI 10.1186/gb-2003-4-11-r75 UEDA H, 2003, NATURE, V423, P503 van Driel MA, 2003, EUR J HUM GENET, V11, P57, DOI 10.1038/sj.ejhg.5200918 Vella A, 2005, AM J HUM GENET, V76, P773, DOI 10.1086/429843 WHEELER D, 2006, NUCLEIC ACIDS RES, V22, pD173 NR 45 TC 55 Z9 57 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 1367-4803 J9 BIOINFORMATICS JI Bioinformatics PD MAY 1 PY 2007 VL 23 IS 9 BP 1132 EP 1140 DI 10.1093/bioinformatics/btm001 PG 9 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics GA 171ZG UT WOS:000246773300012 PM 17237041 ER PT J AU Zhu, YW Hu, Y AF Zhu, Yingwu Hu, Yiming TI Efficient semantic search on DHT overlays SO JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING LA English DT Article; Proceedings Paper CT 3rd International Conference on Peer-to-Peer Computing (P2P 2003) CY SEP 01-03, 2003 CL LINKOPING, SWEDEN SP IEEE Sweden, Intelligent Informat Syst Lab HO LINKOPINGS UNIV DE peer-to-peer; vector space model; locality sensitive hashing; semantic indexing; semantic locating; top term; recall AB Distributed hash tables (DHTs) excel at exact-match lookups, but they do not directly support complex queries such as semantic search that is based on content. In this paper, we propose a novel approach to efficient semantic search on DHT overlays. The basic idea is to place indexes of semantically close files into same peer nodes with high probability by exploiting information retrieval algorithms and locality sensitive hashing. A query for retrieving semantically close files is answered with high recall by consulting only a small number (e.g., 10-20) of nodes that stores the indexes of the files semantically close to the query. Our approach adds only index information to peer nodes, imposing only a small storage overhead. Via detailed simulations, we show that our approach achieves high recall for queries at very low cost, i.e., the number of nodes visited for a query is about 10-20, independent of the overlay size. (C) 2007 Elsevier Inc. All rights reserved. C1 Seattle Univ, Dept CSSE, Seattle, WA 98122 USA. Univ Cincinnati, Dept ECECS, Cincinnati, OH 45221 USA. RP Zhu, YW (reprint author), Seattle Univ, Dept CSSE, Seattle, WA 98122 USA. EM zhuy@seattleu.edu CR Bawa M., 2003, P 26 ANN INT ACM SIG, P306 Broder AZ, 2000, J COMPUT SYST SCI, V60, P630, DOI 10.1006/jcss.1999.1690 Buckley C., 1985, TR85686 CORN U DEP C Charikar M. S., 2002, P 34 ANN ACM S THEOR, P380 COHEN E, 2003, P INFOCOM 2003 MAR A, V2, P1261 Crespo A., 2002, Proceedings 22nd International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2002.1022239 Druschel P., 2001, P IFIP ACM INT C DIS, P329 Gupta Abhishek, 2003, P 1 BIENN C INN DAT Harren M., 2002, P 1 INT WORKSH PEER Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 LEMPEL R, 2001, P VLDB Li JY, 2003, LECT NOTES COMPUT SC, V2735, P207 Linial N., 1996, Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, DOI 10.1145/237814.237999 Lv Q., 2002, P 16 INT C SUP, P84, DOI 10.1145/514191.514206 MAHALINGHAM M, 2003, 9 INT WORKSH FUT TRE Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Muthitacharoen A., 2001, P 18 ACM S OP SYST P, P174, DOI DOI 10.1145/502034.502052 Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] REYNOLDS P, 2003, P INT MIDDL C JUN, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 SPRIPANIDKULCHA.K, 2003, P IEEE INFOCOM MAR, V3, P2166 Stading T., 2002, P 1 INT WORKSH PEER Stoica I, 2001, P 2001 ACM SIGCOMM C, P149, DOI DOI 10.1145/383059.383071 TANG C, 2004, P USENIX ACM S NETW Tang C., 2004, P 1 S NETW SYST DES, P211 Tang C., 2003, P 2003 C APPL TECHN, P175 Zhao Ben Y., 2001, UCBCSD011141 ZHU Y, 2006, IEEE T PARALLEL DIST ZHU Y, 2005, P INT C PAR PROC ICP NR 31 TC 11 Z9 11 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 0743-7315 EI 1096-0848 J9 J PARALLEL DISTR COM JI J. Parallel Distrib. Comput. PD MAY PY 2007 VL 67 IS 5 BP 604 EP 616 DI 10.1016/j.jpdc.2007.01.005 PG 13 WC Computer Science, Theory & Methods SC Computer Science GA 165ZD UT WOS:000246345500008 ER PT J AU Beitzel, SM Jensen, EC Lewis, DD Chowdhury, A Frieder, O AF Beitzel, Steven M. Jensen, Eric C. Lewis, David D. Chowdhury, Abdur Frieder, Ophir TI Automatic classification of Web queries using very large unlabeled query logs SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE algorithms; experimentation ID SELECTIONAL PREFERENCES AB Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such classification becomes critical if the system must route queries to a subset of topic-specific and resource-constrained back-end databases. Successful query classification poses a challenging problem, as Web queries are short, thus providing few features. This feature sparseness, coupled with the constantly changing distribution and vocabulary of queries, hinders traditional text classification. We attack this problem by combining multiple classifiers, including exact lookup and partial matching in databases of manually classified frequent queries, linear models trained by supervised learning, and a novel approach based on mining selectional preferences from a large unlabeled query log. Our approach classifies queries without using external sources of information, such as online Web directories or the contents of retrieved pages, making it viable for use in demanding operational environments, such as large-scale Web search services. We evaluate our approach using a large sample of queries from an operational Web search engine and show that our combined method increases recall by nearly 40% over the best single method while maintaining adequate precision. Additionally, we compare our results to those from the 2005 KDD Cup and find that we perform competitively despite our operational restrictions. This suggests it is possible to topically classify a significant portion of the query stream without requiring external sources of information, allowing for deployment in operationally restricted environments. C1 IIT, Dept Comp Sci, Chicago, IL 60616 USA. David D Lewis Consulting, Chicago, IL 60614 USA. RP Beitzel, SM (reprint author), Telcordia Technol, 1 Telcordia Dr,RRC-1K329, Piscataway, NJ 08854 USA. EM steve@research.telcordia.com; ej@ir.iit.edu; davelewis@daviddlewis.com; abdur@ir.iit.edu; ophir@ir.iit.edu CR ADAMO JM, 2000, DATA MIN ING ASS RUL Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176 Beitzel S. M., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009048 Beitzel S. M., 2005, P 28 ANN INT ACM SIG, P581, DOI 10.1145/1076034.1076138 BEITZEL SM, 2006, IN PRESS J AM SOC IN Beitzel S.M., 2005, P 5 IEEE INT C DAT M, P42 BOT RS, 2005, P 14 ACM INT C INF K, P281, DOI 10.1145/1099554.1099627 Broder A., 2002, SIGIR Forum, V36 CHO J, 1998, P 7 INT WORLD WID WE, P161 Chowdhury Abdur, 2003, P 12 INT C INF KNOWL, P435 Cover T. M., 1991, ELEMENTS INFORM THEO Craswell N., 2004, P 13 TEXT RETR C TRE, P89 DASNEVES F, 2005, P CIKM 2005 BREM GER, P91, DOI 10.1145/1099554.1099573 Eastman CM, 2003, ACM T INFORM SYST, V21, P383, DOI 10.1145/944012.944015 Manmatha R., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Glover EJ, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P210, DOI 10.1145/319950.319980 Gravano L., 2003, P 12 INT C INF KNOWL, P325 Grossman D. A., 2004, INFORM RETRIEVAL ALG Jansen BJ, 2005, J AM SOC INF SCI TEC, V56, P559, DOI 10.1002/asi.20145 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Joachims T, 1999, ADV KERNEL METHODS S Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Kardkovacs Z. T., 2005, SIGKDD EXPLOR, V7, P111 KOWALCZYK P, 2004, 17 AUSTR JOINT C ART, P550 Krauth W., 1987, J PHYSICS A, V20, P745 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 Levy Steven, 2010, SEARCH ENGINE WATCH, P96 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Li Y., 2002, P 19 INT C MACH LEAR, P379 Light M, 2002, COGNITIVE SCI, V26, P269 Manning C. D., 1999, FDN STAT NATURAL LAN Martin A., 1997, P EUROSPEECH, P1895 McCarthy D, 2003, COMPUT LINGUIST, V29, P639, DOI 10.1162/089120103322753365 Mitchell TM, 1997, MACHINE LEARNING Ntoulas A., 2004, P 13 INT C WORLD WID, P1, DOI 10.1145/988672.988674 RESNIK P, 1993, UNPUB SELECTION INFO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Shen D, 2005, SIGKDD EXPLOR NEWSL, V7, P100 Spink A., 2004, WEB SEARCH PUBLIC SE Spink A., 2002, SIGIR Forum, V36 SPINK A, 2002, IEEE COMPUT, V35, P107 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R Tague J. M., 1981, INFORMATION RETRIEVA, P59 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Vogel D., 2005, SIGKDD EXPLOR NEWSL, V7, P117 VOORHEES E. M., 2004, P 13 TEXT RETR C TRE Wen J. R., 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974 Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108 WEN JR, 2001, P 24 ANN INT ACM SIG, P442, DOI 10.1145/383952.384083 Wilkinson R., 2003, P 12 TEXT RETR C TRE, P78 NR 51 TC 11 Z9 11 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2007 VL 25 IS 2 AR 9 DI 10.1145/1229179.1229183 PG 29 WC Computer Science, Information Systems SC Computer Science GA 170VD UT WOS:000246693400004 ER PT J AU Can, AB Baykal, N AF Can, Aysu Betin Baykal, Nazife TI MedicoPort: A medical search engine for all SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE LA English DT Article DE medical information retrieval; UMLS; topical crawler; Internet ID INFORMATION-RETRIEVAL; HEALTH INFORMATION AB We present a new next generation domain search engine called MedicoPort. MedicoPort is a medical search engine designed for the users with no medical expertise. It is enhanced with the domain knowledge obtained from Unified Medical Language System (UMLS) to increase the effectiveness of the searches. The power of the system is based on the ability to understand the semantics of web pages and the user queries. MedicoPort transforms a keyword search into a conceptual search. Through our system we present a topical web crawling technique and indexing techniques empowered by the semantics information. MedicoPort aims to generate maximum output with semantic value using minimum input from the user. Since MedicoPort is designed to help people seeking information about health on the web, our target users are not medical specialists who can effectively use the special jargon of medicine and access medical databases. Medical experts have the advantage of shrinking the answerset by expressing several terms using medical terminology. MedicoPort provides the same advantage to its users through the automated use of the medical domain knowledge in the background. The results of our experiments indicate that, expanding the queries with domain knowledge, such as using the synonyms and partially or contextually relevant terms from UMLS, increase dramatically the relevance of an answer set produced by MedicoPort and the number of retrieved web pages that are relevant to the user request. (c) 2007 Elsevier Ireland Ltd. All rights reserved. C1 Middle E Tech Univ, Inst Informat, TR-06531 Ankara, Turkey. RP Can, AB (reprint author), Middle E Tech Univ, Inst Informat, TR-06531 Ankara, Turkey. EM aysu@ii.metu.edu.tr; baykal@ii.metu.edu.tr RI Betin Can, Aysu/C-2035-2013 CR ABASOLO JM, 2000, P ECDL 2000 WORKSH S Aggarwal CC, 2001, ACM T INFORM SYST, V19, P286, DOI 10.1145/502115.502119 Baujard O, 1998, COMPUT BIOL MED, V28, P589, DOI 10.1016/S0010-4825(98)00036-5 Boyer C, 1997, INT J MED INFORM, V47, P27, DOI 10.1016/S1386-5056(97)00081-6 CUCINA RJ, 2001, MEDINFO2001, V10, P181 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eysenbach G, 2002, BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573 Gaudinat A, 2006, INT J MED INFORM, V75, P73, DOI 10.1016/j.ijmedinf.2005.11.002 Kagolovsky Y, 1998, INT J MED INFORM, V51, P181, DOI 10.1016/S1386-5056(98)00114-2 KAYISOGLU A, 2005, LOKMAN MED ONTOLOGY KAYISOGLU A, 2005, P INT S HLTH INF BIO KUBILAY M, 2005, SPECIAL INDEX RETRIE Leroy G, 2001, IEEE T INF TECHNOL B, V5, P261, DOI 10.1109/4233.966101 Plovnick RM, 2004, J MED INTERNET RES, V6, P98, DOI 10.2196/jmir.6.3.e27 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Zeng QT, 2006, J AM MED INFORM ASSN, V13, P80, DOI 10.1197/jamia.M1820 Zhou YL, 2006, DECIS SUPPORT SYST, V42, P1431, DOI 10.1016/j.dss.2005.11.006 NR 17 TC 15 Z9 16 PU ELSEVIER IRELAND LTD PI CLARE PA ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND SN 0169-2607 J9 COMPUT METH PROG BIO JI Comput. Meth. Programs Biomed. PD APR PY 2007 VL 86 IS 1 BP 73 EP 86 DI 10.1016/j.cmpb.2007.01.007 PG 14 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Biomedical; Medical Informatics SC Computer Science; Engineering; Medical Informatics GA 154YV UT WOS:000245545600010 PM 17321002 ER PT J AU Mao, WL Chu, WW AF Mao, Wenlei Chu, Wesley W. TI The phrase-based vector space model for automatic retrieval of free-text medical documents SO DATA & KNOWLEDGE ENGINEERING LA English DT Article DE information storage and retrieval/methods; computing methodologies; vector space model; concept-based vector space model; phrase-based vector space model; information systems; unified medical language system ID ALGORITHM AB Objective: To develop a document indexing scheme that improves the retrieval effectiveness for free-text medical documents. Design: The phrase-based vector space model (VSM) uses multi-word phrases as indexing terms. Each phrase consists of a concept in the unified medical language system (UMLS) and its corresponding component word stems. The similarity between concepts are defined by their relations in a hypernym hierarchy derived from UMLS. After defining the similarity between two phrases by their stem overlaps and the similarity between the concepts they represent, we define the similarity between two documents as the cosine of the angle between their corresponding phrase vectors. This paper reports the development and the validation of the phrase-based VSM. Measurement: We compare the retrieval effectiveness of different vector space models using two standard test collections, OHSUMED and Medlars. OHSUMED contains 105 queries and 14,430 documents, and Medlars contains 30 queries and 1033 documents. Each document in the test collections is judged by human experts to be either relevant or non-relevant to each query. The retrieval effectiveness is measured by precision and recall. Results: The phrase-based VSM is significantly more effective than the current gold standard-the stem-based VSM. Such significant retrieval effectiveness improvements are observed in both the exhaustive search and cluster-based document retrievals. Conclusion: The phrase-based VSM is a better indexing scheme than the stem-based VSM. Medical document retrieval using the phrase-based VSM is significantly more effective than that using the stem-based VSM. (c) 2006 Elsevier B.V. All rights reserved. C1 Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA. RP Chu, WW (reprint author), Univ Calif Los Angeles, Dept Comp Sci, 3731H Boelter Hall, Los Angeles, CA 90095 USA. EM wenlei-m@yahoo.com; wwc@cs.ucla.edu CR AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855 ARAMPATZIS A, 2000, P BCSIRSG 2000 *ASTM, 2003, E138402A ATM INT SUB Buckle JS, 2004, WA SCI TECHNOL, V4, P25 Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 FAGAN J, 1988, THESIS CORNELL U Fellbaum C., 1998, WORDNET ELECT LEXICA Gonzalo J, 1998, P COLING ACL 98 WORK, P38 Gusfield D., 1997, ALGORITHMS STRINGS T Hersh William, 1994, P 17 ANN INT ACM SIG, P192 HERSH WR, 1992, P 16 ANN S COMP APPL, V16, P644 HULL D, 1996, P 5 TEXT RETR C GAIT, P167 Ide N, 1998, COMPUT LINGUIST, V24, P1 *JAMIA, 2003, J AM MED INFORM ASSN, V10, P110 Johnson D B, 1999, Proc AMIA Symp, P814 Lewis D., 1990, P 13 ANN INT ACM SIG, P385, DOI 10.1145/96749.98244 LOVINS JB, 1968, MECH TRANSL, V11, P22 Lyons John, 1977, SEMANTICS Mao Wenlei, 2002, Proc AMIA Symp, P489 *MED REC I, 2002, 4 ANN MRI SURV EL HL Mihalcea R., 2000, P ACL WORKSH IR NLP Miller G., 1993, 5 PAPERS WORDNET Mitra M, 1997, P RIAO97 COMP ASS IN, P200 *NLM, 2001, UMLS KNOWL SOURC *NLM, 1987, MED SUBJ HEAD NAT TE *NLM, 2003, PUBM OV PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RADA R, 1989, J AM SOC INFORM SCI, V40, P304, DOI 10.1002/(SICI)1097-4571(198909)40:5<304::AID-ASI2>3.0.CO;2-6 Richardson R., 1995, CA0395 DUBL CIT U Salton G., 1983, MCGRAWHILL COMPUTER SALTON G, 1972, J AM SOC INFORM SCI, V23, P75, DOI 10.1002/asi.4630230202 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEINBACH M, 2000, P KDD WORKSH TEXT MI SUSSNA MJ, 1997, THESIS U CALIFORNIA *US NAT LIB MED, 2003, NLM GAT *US NAT LIB MED NA, 2003, MEDLINEPLUS van Rijsbergen C. J., 1979, INFORM RETRIEVAL Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 YANG YM, 1993, P 17 ANN S COMP APPL, V17, P685 ZHAI CX, 1996, P 5 TEXT RETR C TREC, P347 Zhao Y., 2002, 02022 U MINN DEP COM NR 42 TC 21 Z9 22 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0169-023X J9 DATA KNOWL ENG JI Data Knowl. Eng. PD APR PY 2007 VL 61 IS 1 BP 76 EP 92 DI 10.1016/j.datak.2006.02.008 PG 17 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 151AP UT WOS:000245261000006 ER PT J AU Bhamidipati, NL Pal, SK AF Bhamidipati, Narayan L. Pal, Sankar K. TI Stemming via distribution-based word segregation for classification and retrieval SO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS LA English DT Article DE precision and recall; prototype selection; stemming; text categorization ID ALGORITHM; MODEL AB A novel corpus-based method for stemmer refinement, which can provide improvement in both classification and retrieval, is described. The method models the given words as generated from a multinomial distribution over the topics available in the corpus and includes a procedurelike sequential hypothesis testing that enables grouping together distributionally similar words. The system can refine any stemmer, and its strength can be controlled with parameters that reflect the amount of tolerance to be allowed in computing the similarity between the distributions of two words. Although obtaining the morphological roots of the given words is not the primary objective, the algorithm automatically does that to some extent. Despite a huge reduction in dictionary size, classification accuracies are seen to improve significantly when the proposed system is applied on some existing stemmers for classifying 20 Newsgroups and -WebKB data. The refinements obtained are also suitable for cross-corpus stemming. Regarding retrieval, its superiority is extensively demonstrated with respect to four existing methods. C1 Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India. RP Bhamidipati, NL (reprint author), Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India. EM bln_r@isical.ac.in; sankar@isical.ac.in RI Pal, Sankar /G-2243-2010 CR Bacchin M, 2005, INFORM PROCESS MANAG, V41, P121, DOI 10.1016/j.ipm.2004.04.006 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 Chakrabarti S., 2002, MINING WEB DISCOVERI COHEN AM, 2005, P 14 ANN TEXT RETRIE Dawson J.L., 1974, B ASS LIT LINGUISTIC, V2, P33 DEMADARIAGA RS, 2005, P 12 INT C STRING PR, P228 Frakes W. B., 2003, ACM SIGIR FORUM, V37, P26, DOI 10.1145/945546.945548 GUSTAD T, 2002, LANG COMPUT, V45, P104 Harman D., 1995, P 3 TEXT RETR C TREC, P1 HARMAN D, J AM SOC INF SCI, V42, P7 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Johnson N. L., 1997, DISCRETE MULTIVARIAT Kantrowitz M., 2000, P 23 ANN INT ACM SIG, P357, DOI 10.1145/345508.345650 Kraaij W., 1996, P 19 ANN INT ACM SIG, P40, DOI 10.1145/243199.243209 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Kullback S, 1959, INFORM THEORY STAT LANG Y, 1999, P 22 ACM SIGIR C BER, P42 Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Lehmann E. L., 1997, TESTING STAT HYPOTHE Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lin D., 1998, P 17 INT C COMP LING, P768 LOVINS JB, 1968, MECH TRANSL, V11, P22 McCallum A.K., 1996, BOW TOOLKIT STAT LAN Nigam K., 1999, P IJCAI 99 WORKSH MA, P61 Paice C. D., 1990, SIGIR Forum, V24 Paice CD, 1996, J AM SOC INFORM SCI, V47, P632, DOI 10.1002/(SICI)1097-4571(199608)47:8<632::AID-ASI8>3.0.CO;2-U Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SPITTERS M, 2000, P 6 C CONT BAS MULT, P1124 Vapnik V., 1995, NATURE STAT LEARNING WALD ABRAHAM, 1947, SEQUENTIAL ANAL Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 YAMOUT F, 2004, P KI2004 WORKSH MACH, P7 20 NEWS GROUPS 20NG WEBKB DATA SET NR 39 TC 3 Z9 3 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA SN 1083-4419 EI 1941-0492 J9 IEEE T SYST MAN CY B JI IEEE Trans. Syst. Man Cybern. Part B-Cybern. PD APR PY 2007 VL 37 IS 2 BP 350 EP 360 DI 10.1109/TSMCB.2006.885307 PG 11 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Automation & Control Systems; Computer Science GA 148WY UT WOS:000245109300010 PM 17416163 ER PT J AU Lancieri, L Boubchir, L AF Lancieri, Luigi Boubchir, Larbi TI Using multiple uncertain examples and adaptative fuzzy reasoning to optimize image characterization SO KNOWLEDGE-BASED SYSTEMS LA English DT Article DE uncertainty; data mining; collective intelligence; probabilistic reasoning; vision AB This article proposes an automatic characterization method by comparing unknown images with examples more or less known. Our approach allows to use uncertain examples but easy to obtain (e.g. by automatic retrieval on the Internet). The use of fuzzy logic and adaptive clustering makes it possible to reduce automatically the noise from this database by preserving only the examples having a strong level of redundancy in the dominant shapes. To validate this method, we compared our artificial process of recognition with the estimation of human operators. The tests show that the automatic process gives an average accuracy of the characterization near, to 95%. (c) 2006 Elsevier B.V. All rights reserved. C1 Univ Caen, France Telecom, R&D, F-14032 Caen, France. RP Lancieri, L (reprint author), Univ Caen, France Telecom, R&D, F-14032 Caen, France. EM luigi.lancieri@orange-ft.com CR BARALDI A, TR98038 INT COMP SCI Bezdek J. C., 1981, PATTERN RECOGNITION BEZDEK JC, 1993, IEEE T FUZZY SYSTEMS BLOCH I, 1999, PATTERN RECOGNITION BOBICK F, 1995, IEEE WORKSH ON CONT BOUJEMAA N, 2002, ACM C MIR02 JUAN PIN CLOUARD R, WEB SITE PANDORA FRE DABBAGH N, 1999, GESTALT INSTRUCTIONA DORTIER F, 1999, HUMAN SCI JAWAHAR CV, 1996, IEEE SIGNAL PROCESSI LANCIERI L, 2004, COGNITION COOPERATIO, P28 LESAUX B, 2002, RECENT ADV VISUAL IN MEILHAC C, 1999, INT IEEE MULT MED C RONKAINEN P, 1998, C199842 U HELS SALTON G, 1963, J ACM, V10, P440, DOI 10.1145/321186.321188 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VERTAN C, 2000, 19 INT M N AM FUZZ I NR 17 TC 4 Z9 4 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD APR PY 2007 VL 20 IS 3 BP 266 EP 276 DI 10.1016/j.knosys.2006.05.018 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA 158HW UT WOS:000245783400007 ER PT J AU Sanfilippo, A Posse, C Gopalan, B Riensche, R Beagley, N Baddeley, B Tratz, S Gregory, M AF Sanfilippo, Antonio Posse, Christian Gopalan, Banu Riensche, Rick Beagley, Nathaniel Baddeley, Bob Tratz, Stephen Gregory, Michelle TI Combining hierarchical and associative gene ontology relations with textual evidence in estimating gene and gene product similarity SO IEEE TRANSACTIONS ON NANOBIOSCIENCE LA English DT Article DE BLAST; gene ontology; gene product similarity; GoPubMed; ontology alignment ID DATABASE; BLAST AB Two approaches have recently emerged where the similarity between two genes or gene products is obtained by comparing Gene Ontology (GO) annotations associated with the genes or gene products. One approach captures GO-based similarity in terms of hierarchical relations within each gene subontology, while the other relies on associative relations across the three gene subontologies. We propose a novel methodology where the two approaches can be merged and enriched by textual evidence extracted from biomedical literature with ensuing benefits in coverage and stronger correlation with sequence-based similarity. C1 Pacific NW Natl Lab, Richland, WA 99352 USA. RP Sanfilippo, A (reprint author), Pacific NW Natl Lab, Richland, WA 99352 USA. EM antonio.sanfilippo@pnl.gov; christian.posse@pnl.gov; banu.gopalan@pnl.gov; rick.riensche@pnl.gov; nathaniel.beagley@pnl.gov; bob.baddeley@pnl.gov; stephen.tratz@pnl.gov; michelle.gregory@pnl.gov CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Andrade M A, 1999, Proc Int Conf Intell Syst Mol Biol, P28 Andrade MA, 1997, P 5 INT C INT SYST M, P25 Azuaje F., 2006, P IEEE INT C DAT MIN, P114 Azuaje F., 2005, P ISMB 2005 SIG M BI, P9 Beissbarth T, 2004, BIOINFORMATICS, V20, P1464, DOI 10.1093/bioinformatics/bth088 BODENREIDER O, 2005, P PAC S BIOC, P104 Buckley Christopher, 1985, 85686 CORN U BUDANITSKY A, 1999, CSRG390 Chang J T, 2001, Pac Symp Biocomput, P374 Couto F.M., 2003, IMPLEMENTATION FUNCT DELFS R, 2004, GERM BIOINF C BIEL G Doms A, 2005, NUCLEIC ACIDS RES, V33, pW783, DOI 10.1093/nar/gki470 Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487 GOPALAN B, 2006, UNPUB ALIGNING ONTOL HOOK SE, 2005, AQUAT TOXICOL, V77, P372 Jiang J, 1997, INT C RES COMP LING KENDALL MG, 1949, BIOMETRIKA, V36, P177, DOI 10.2307/2332540 Lin D., 1998, 15 INT C MACH LEARN Lord P W, 2003, Pac Symp Biocomput, P601 LORD PW, 2002, BIOINFORMATICS, V19, P1275 MacCallum RM, 2000, BIOINFORMATICS, V16, P125, DOI 10.1093/bioinformatics/16.2.125 Mulder NJ, 2003, NUCLEIC ACIDS RES, V31, P315, DOI 10.1093/nar/gkg046 Oehmen C, 2006, IEEE T PARALL DISTR, V17, P740, DOI 10.1109/TPDS.2006.112 PEARSON WR, 1988, P NATL ACAD SCI USA, V85, P2444, DOI 10.1073/pnas.85.8.2444 Pedersen T., 2005, 200525 UMSI Popescu M, 2006, IEEE ACM T COMPUT BI, V3, P263, DOI 10.1109/TCBB.2006.37 Popescu M., 2005, Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 05CH37680) POSSE C, 2006, P INT WORKSH BIOINF PUSTEJOVSKY J, 2002, ACL 2002 WORKSH NAT Raychaudhuri S, 2003, NUCLEIC ACIDS RES, V31, P4553, DOI 10.1093/nar/gkg636 Resnik P., 1995, P 14 INT JOINT C ART, P448 ROMEIJN EH, 1992, TINBERGEN I RES SERI, V32 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANFILIPPO A, P BIONLP 06 LINK NAT, P25 SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 SPEER N, P 2004 IEEE S COMP I, P252 Weisberg S., 2005, APPL LINEAR REGRESSI, V3rd NR 38 TC 4 Z9 6 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1536-1241 J9 IEEE T NANOBIOSCI JI IEEE Trans. Nanobiosci. PD MAR PY 2007 VL 6 IS 1 BP 51 EP 59 DI 10.1109/TNB.2007.891886 PG 9 WC Biochemical Research Methods; Nanoscience & Nanotechnology SC Biochemistry & Molecular Biology; Science & Technology - Other Topics GA 146OT UT WOS:000244944600008 PM 17393850 ER PT J AU Liu, SZ Hu, HP AF Liu, Shi-Zhu Hu, He-Ping TI Text classification using sentential frequent itemsets SO JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY LA English DT Article DE text classification; sentential frequent itemsets; variable precision rough set model ID MODEL AB Text classification techniques mostly rely on single term analysis of the document data set, while more concepts, especially the specific ones, are usually conveyed by set of terms. To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset's contribution to the classification. Experiments over the Reuters and newsgroup corpus are carried out, which validate the practicability of the proposed system. C1 Huazhong Univ Sci & Technol, Coll Comp Sci, Wuhan 430074, Peoples R China. RP Liu, SZ (reprint author), Huazhong Univ Sci & Technol, Coll Comp Sci, Wuhan 430074, Peoples R China. EM stoneboo@126.com RI Hu, Hanping/H-3942-2015 CR APTE C, 1994, ACM T INFORM SYSTE, V12, P232 COHEN W, 1999, ACM T INFORM SYST, V17, P146 Cohen W. W., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Hull D. A., 1994, P 17 ACM SIGIR C, P282 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li W., 2001, P IEEE INT C DAT MIN, P369 Liu B., 1998, P 4 INT C KNOWL DISC, P80 MARIALUIZA A, 2002, P IEEE 2002 INT C DA, P19 Meretakis D., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354768 MOUNLINIER I, 1996, CONNECTIONIST STAT, V1040, P343 Ruiz M. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312700 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 YANG Y, 1997, CUMCS97127 ZAIANE OR, 2002, P 13 AUSTR DAT C ADC, P215 ZIARKO W, 1993, J COMPUT SYST SCI, V46, P39, DOI 10.1016/0022-0000(93)90048-2 NR 20 TC 2 Z9 2 PU SCIENCE CHINA PRESS PI BEIJING PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA SN 1000-9000 J9 J COMPUT SCI TECHNOL JI J. Comput. Sci. Technol. PD MAR PY 2007 VL 22 IS 2 BP 334 EP 337 DI 10.1007/s11390-007-9041-7 PG 4 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering SC Computer Science GA 152PS UT WOS:000245374700020 ER PT J AU Ding, C Patra, JC AF Ding, Chen Patra, Jagdish C. TI User modeling for personalized web search with self-organizing map SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID DOCUMENT-RETRIEVAL; INFORMATION; RELEVANCE AB The widely used Web search engines index and recommend individual Web pages in response to a few keywords queries to assist users in locating relevant documents. However, the Web search engines give different users the same answer set, although the users may have different preferences. A personalized Web search would carry out the search for each user according to his or her preferences. To conduct the personalized Web search, the authors provide a novel approach to model the user profile with a self-organizing map (SOM). Their results indicate that SOM is capable of helping the user to find the related category for each query used in the Web search to make a personalized Web search effective. C1 Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore. RP Ding, C (reprint author), Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore. EM PG04159157@ntu.edu.sg; aspatra@ntu.edu.sg RI Patra, Jagdish/D-2652-2009 CR Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balabanovic M., 1995, P AAAI SPRING S INF, P13 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BEZDEK JC, 1995, IEEE T NEURAL NETWOR, V6, P1029, DOI 10.1109/72.410347 Bollacker K.D., 1999, P 4 ACM C DIG LIB, P105, DOI 10.1145/313238.313270 Brin S., 1998, COMPUTER NETWORKS IS, V30, P1 BUDZIK J, 1999, P 62 ANN M AM SOC IN, P27 Cetintemel U., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839477 Chen GX, 2002, STAT SINICA, V12, P241 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 CHOI DI, 1994, IEEE T NEURAL NETWOR, V5, P561 Cui H, 2003, IEEE T KNOWL DATA EN, V15, P829 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Diamantaras K., 1996, PRINCIPAL COMPONENT DOLIN R, 1998, DLIB MAGAZINE, P558 FINKELSTEIN L, 2002, ACM T INFORM SYST, V20, P1046 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 Foltz PW, 1990, P C OFF INF SYST CAM, P40, DOI 10.1145/91474.91486 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 *GOOGL, 2004, GOOGL PERS SEARCH Hou JY, 2003, IEEE T KNOWL DATA EN, V15, P940 Joachims T., 1997, P 15 INT JOINT C ART, P770 Jolliffe IT, 1996, PRINCIPAL COMPONENT Kao B, 2000, IEEE T SYST MAN CY C, V30, P364, DOI 10.1109/5326.885118 Kohonen T., 2001, SELF ORG MAPS Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28 Mauldin ML, 1997, IEEE EXPERT, V12, P8, DOI 10.1109/64.577466 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 Pitkow J, 2002, COMMUN ACM, V45, P50 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G., 1997, READINGS INFORM RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scime A., 2000, Proceedings 2000 Kyoto International Conference on Digital Libraries: Research and Practice, DOI 10.1109/DLRP.2000.942176 STADNYK I, 1992, COMMUN ACM, V35, P49, DOI 10.1145/138859.138864 Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764 TRAJKOVA J, 2004, RECH INF ASS ORD RIA Yu S., 2003, P 12 INT C WORLD WID, P11 NR 43 TC 12 Z9 12 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 15 PY 2007 VL 58 IS 4 BP 494 EP 507 DI 10.1002/asi.20497 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 166LF UT WOS:000246379600004 ER PT J AU Jensen, R Shen, Q AF Jensen, Richard Shen, Qiang TI Fuzzy-rough sets assisted attribute selection SO IEEE TRANSACTIONS ON FUZZY SYSTEMS LA English DT Article DE attribute selection; dimensionality reduction; fuzzy-rough sets; rough selection ID REDUCTION; CATEGORIZATION; RULES AB Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribute selectors preserve the original meaning of the attributes after reduction. This has found application in tasks that involve datasets containing huge numbers of attributes (in the order of tens of thousands) which, for some learning algorithms, might be impossible to process further. Recent examples include text processing and web content classification. AS techniques have also been applied to small and medium-sized datasets in order to locate the most informative attributes for later use. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in AS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. FRFS is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other AS techniques in the comparative study. C1 Univ Wales, Dept Comp Sci, Aberystwyth SY23 3DB, Ceredigion, Wales. RP Jensen, R (reprint author), Univ Wales, Dept Comp Sci, Aberystwyth SY23 3DB, Ceredigion, Wales. EM rkj@aber.ac.uk; qqs@aber.ac.uk CR ALMUALLIM H, 1991, PROCEEDINGS : NINTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P547 ALPIGINI JJ, 2002, P 3 INT C Bellman R. E., 1961, ADAPTIVE CONTROL PRO Blake C., 1998, UCI REPOSITORY MACHI CARREIRAPERPINN.MA, 2001, THESIS U SHEFFIELD S Chen SM, 2001, APPL ARTIF INTELL, V15, P645, DOI 10.1080/088395101750363984 Chouchoulas A, 2001, APPL ARTIF INTELL, V15, P843, DOI 10.1080/088395101753210773 Dash M., 1997, INTELL DATA ANAL, V1 Dubois D., 1992, INTELLIGENT DECISION, P203 Duntsch I., 2000, ROUGH SET DATA ANAL Everitt B., 1984, MONOGRAPHS STAT APPL HOLTE RC, 1993, MACH LEARN, V11, P63, DOI 10.1023/A:1022631118932 Hunt Earl B, 1966, EXPT INDUCTION Jensen R, 2004, IEEE T KNOWL DATA EN, V16, P1457, DOI 10.1109/TKDE.2004.96 Jensen R, 2005, FUZZY SET SYST, V149, P5, DOI 10.1016/j.fss.2004.07.014 Jensen R, 2004, FUZZY SET SYST, V141, P469, DOI 10.1016/S0165-0114(03)00021-6 Kira K., 1992, P 10 NAT C ART INT, P129 Langley P., 1994, P AAAI FALL S REL, P1 Liu H. C., 1995, Proceedings of the SPIE - The International Society for Optical Engineering Lozowski A, 1996, P INT C NEUR NETW VO, P94 Marin-Blazquez JG, 2002, IEEE T FUZZY SYST, V10, P484, DOI 10.1109/TFUZZ.2002.800687 Pal S. K., 1999, ROUGH FUZZY HYBRIDIZ Pawlak Z., 1991, ROUGH SETS THEORETIC Pedrycz W, 2002, PATTERN RECOGN, V35, P825, DOI 10.1016/S0031-3203(01)00102-9 Polkowski L., 2000, STUDIES FUZZINESS SO, V56 Polkowski L., 2002, ADV SOFT COMPUTING Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Quinlan J. R., 1993, M KAUFMANN SERIES MA SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlimmer J. C., 1993, P 10 INT C MACH LEAR, P284 Sebban M, 2002, PATTERN RECOGN, V35, P835, DOI 10.1016/S0031-3203(01)00084-X Shen Q., 2002, PATTERN RECOGN, V35, P341 Shen Q, 2004, PATTERN RECOGN, V37, P1351, DOI 10.1016/j.patcog.2003.10.016 Siedlecki W., 1988, International Journal of Pattern Recognition and Artificial Intelligence, V2, DOI 10.1142/S0218001488000145 Skowron A., 1996, Fundamenta Informaticae, V27 Slowinski R., 1992, INTELLIGENT DECISION Slowinski R., 1997, ADV MACHINE INTELLIG, VIV, P17 Stefanowski J., 2000, ROUGH SETS CURRENT T, P212 Swiniarski RW, 2003, PATTERN RECOGN LETT, V24, P833, DOI 10.1016/S0167-8655(02)00196-4 THIELE H, 1998, CI3098 U DORTM Witten I. H., 2000, DATA MINING PRACTICA Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yao YY, 1998, INFORM SCIENCES, V109, P21, DOI 10.1016/S0020-0255(98)00012-7 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZADEH LA, 1975, INFORM SCIENCES, V9, P43, DOI 10.1016/0020-0255(75)90017-1 ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5 NR 47 TC 153 Z9 176 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI PISCATAWAY PA 445 HOES LANE, PISCATAWAY, NJ 08855 USA SN 1063-6706 J9 IEEE T FUZZY SYST JI IEEE Trans. Fuzzy Syst. PD FEB PY 2007 VL 15 IS 1 BP 73 EP 89 DI 10.1109/TFUZZ.2006.889761 PG 17 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 144NP UT WOS:000244803400007 ER PT S AU Lim, JH Jang, H Lim, J Park, SJ AF Lim, Joon-Ho Jang, Hyunchul Lim, Jaesoo Park, Soo-Jun GP IEEE TI Normalization of gene/protein names in biological literatures using Vector-Space Model SO 2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16 SE PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY LA English DT Proceedings Paper CT 29th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society CY AUG 22-26, 2007 CL Lyon, FRANCE SP IEEE Engn Med & Biol Soc ID GENE LISTS; IDENTIFICATION; PROTEIN AB As the number of biological literatures grows exponentially, needs for text mining system are increased. In text mining area, normalization is mapping gene/protein names to a database. It is necessary to combine extracted information from various literatures and to curate a database or an ontology using literatures. Previous normalization researches used direct comparison methods between a database and literatures, but it is weak to extremely variational gene/protein names in literatures. Therefore, in this paper, we propose a normalization method using Vector-Space Model. For each gene/protein name, we rank identifiers using Vector-Space Model, and find the most similar identifier with the name. Experimental result shows the proposed method has 70.7% f-measure. C1 [Lim, Joon-Ho; Jang, Hyunchul; Lim, Jaesoo; Park, Soo-Jun] Elect & Telecommun Res Inst, Lifeinformat Team, Taejon 305700, South Korea. RP Lim, JH (reprint author), Elect & Telecommun Res Inst, Lifeinformat Team, Taejon 305700, South Korea. EM joonho.lim@etri.re.kr CR COHEN AM, 2005, P JOINT ACL WORKSH B Crim J, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S13 Fundel K, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S15 Hanisch D, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S14 Hirschman L, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S11 *IEEE INT SYST STA, 2002, MIN INF FUNCT GEN, V17, P66 Kim J-D, 2004, P INT JOINT WORKSH N, P70, DOI 10.3115/1567594.1567610 Kim J-D, 2003, BIOINFORMATICS S1, V19, pi180, DOI DOI 10.1093/BIOINFORMATICS/BTG1023 Krauthammer M, 2004, J BIOMED INFORM, V37, P512, DOI 10.1016/j.jbi.2004.08.004 MORGAN AA, 2007, PAC S BIOCOMPUT, V12, P281 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WELLNER B, 2005, P JOINT ACL WORKSH B Yeh A, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S2 NR 13 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1094-687X BN 978-1-4244-0787-3 J9 P ANN INT IEEE EMBS PY 2007 BP 390 EP 393 DI 10.1109/IEMBS.2007.4352306 PG 4 WC Engineering, Biomedical; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging SC Engineering; Imaging Science & Photographic Technology; Radiology, Nuclear Medicine & Medical Imaging GA BHI92 UT WOS:000253467000099 ER PT B AU Karamolegkos, PN Patrikakis, CZ Doulamis, ND Tragos, EZ AF Karamolegkos, Pantelis N. Patrikakis, Charalampos Z. Doulamis, Nikolaos D. Tragos, Elias Z. GP IEEE TI User-profile based communities assessment using clustering methods SO 2007 IEEE 18TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, VOLS 1-9 LA English DT Proceedings Paper CT 18th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication CY SEP 03-07, 2007 CL Athens, GREECE SP IEEE Commun Soc, Natl Tech Univ Athens ID VECTOR-SPACE AB In this paper, we introduce and evaluate a framework for a user profile-based socializing application. We model the user profile as an unordered set composed of n keywords that represent users' preferences. We evaluate the effectiveness of two clustering algorithms, k-means and spectral clustering in the scope of social groups' assessment. Through experimental results we substantiate the applicability of spectral clustering in the examined service and we evaluate the impact of profile size in terms of the quality of partitions yielded by spectral clustering. The above are performed using case studies and scenarios developed in the context of IST project's MAGNET Beyond pilot services definition. C1 [Karamolegkos, Pantelis N.; Patrikakis, Charalampos Z.; Doulamis, Nikolaos D.; Tragos, Elias Z.] Natl Tech Univ Athens, Telecommun Lab, GR-10682 Athens, Greece. RP Karamolegkos, PN (reprint author), Natl Tech Univ Athens, Telecommun Lab, GR-10682 Athens, Greece. CR Bach FR, 2004, ADV NEURAL INFORM PR, V16 BRESLAU L, 1999, P IEEE INF 1999 Duda R., 2000, PATTERN CLASSIFICATI, V2nd *ETSI, 2005, US PROF MAN ETSI GUI HENDRICKSON B, 1995, SIAM J SCI COMPUT, V16, P452, DOI 10.1137/0916028 HUANG JL, 2004, P IEEE INF HONG KONG, V2, P2050 Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 Joachims Thorsten, 2002, LEARNING CLASSIFY TE KOBSA, 2006, USER MODELING USERAD, V16, P129 LANCIERI L, 2003, P IEEE INT S COMP IN, P130 LI WT, 1992, IEEE T INFORM THEORY, V38, P1842, DOI 10.1109/18.165464 LING C, 2002, INT J PATTERN RECOGN, V16 Liu B, 2001, ADV ENG SOFTW, V32, P519, DOI 10.1016/S0965-9978(01)00004-7 Meila M., 2004, 442 U WASH DEP STAT Meila M., 2001, P INT WORKSH ART INT, P8 Nouali O, 2004, EXPERT SYST APPL, V26, P171, DOI 10.1016/S0957-4174(03)00118-0 Phatak D., 2002, P 2002 IEEE INT C FU, V1, P705 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiaffino S, 2006, IEEE INTELL SYST, V21, P12, DOI 10.1109/MIS.2006.15 SEITZ C, 2004, P 2 IEEE ANN C PERV, P4 SHORTREED S, P UAI 2005 Smith M, 2002, COMMUN ACM, V45, P51, DOI 10.1145/505248.505272 Sotiropoulos D. N., 2006, P 2 INT WORKSH WEB P TONKIN E, 2006, 17TH SIG CR CLASS RE VONLUXBURG U, TR149 MAX PLANCK I B X LI, 2005, TECHNOVATION, V25, P9 NR 26 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1143-6 PY 2007 BP 237 EP 242 PG 6 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BHI16 UT WOS:000253402500048 ER PT S AU Barbosa, L Freire, J Silva, A AF Barbosa, Luciano Freire, Juliana Silva, Altigran GP IEEE TI Organizing hidden-Web databases by clustering visible Web documents SO 2007 IEEE 23RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING, VOLS 1-3 SE IEEE International Conference on Data Engineering LA English DT Proceedings Paper CT IEEE 23rd International Conference on Data Engineering CY APR 11-15, 2007 CL Istanbul, TURKEY AB In this paper we address the problem of organizing hidden-Web databases. Given a heterogeneous set of Web forms that serve as entry points to hidden-Web databases, our goal is to cluster the forms according to the database domains to which they belong. We propose a new clustering approach that models Web forms as a set of hyperlinked objects and considers visible information in the form context-both within and in the neighborhood of forms-as the basis for similarity comparison. Since the clustering is performed over features that can be automatically extracted, the process is scalable. In addition, because it uses a rich set of metadata, our approach is able to handle a wide range of,forms, including content-rich forms that contain multiple attributes, as well as simple keyword-based search inter-faces. An experimental evaluation over real Web data shows that our strategy generates high-quality clusters-measured both in terms of entropy and F-measure. This indicates that our approach provides an effective and general solution to the problem of organizing hidden-Web databases. C1 Univ Utah, Salt Lake City, UT 84112 USA. RP Barbosa, L (reprint author), Univ Utah, Salt Lake City, UT 84112 USA. EM lbarbosa@cs.utah.edu; juliana@cs.utah.edu; alti@dcc.ufam.edu.br CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbosa L., 2005, WEBDB, P1 Barbosa L., 2004, SBBD, P309 BERGHOLZ A, 2003, WISE, P126 Bharat K, 1998, COMPUT NETWORKS ISDN, V30, P469, DOI 10.1016/S0169-7552(98)80047-0 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chang K. C.-C., 2005, CIDR, P44 Chang KCC, 2004, SIGMOD RECORD, V33, P61 Cope Jared, 2003, ADC, P181 GALPERIN MY, 2005, NUCL ACIDS RES, V33 GIBSON D, 1998, UK C HYP, P225 Gravano L, 2003, ACM T INFORM SYST, V21, P1, DOI 10.1145/635484.635485 GRAVANO L, 1999, ACM TODS, V24 HALVEY AY, 2005, ACM QUEUE, V3 HE B, 2003, SIGMOD 03, P217 He B., 2004, CIKM, P22 He H., 2003, VLDB, P357 He H, 2004, VLDB J, V13, P256, DOI 10.1007/s00778-004-0126-4 He X, 2002, COMPUT STAT DATA AN, V41, P19, DOI 10.1016/S0167-9473(02)00070-1 HEss A, 2003, P IIWEB, P111 Hsieh W.C., 2006, SIGMOD, P725 Huang S, 2004, LECT NOTES COMPUT SC, V3306, P253 Kumar R, 1999, COMPUT NETW, V31, P1481, DOI 10.1016/S1389-1286(99)00040-7 Larsen B., 1999, KDD 99, P16 P Wu, 2006, ICDE Raghavan S., 2001, VLDB, P129 Ru YB, 2005, ONLINE INFORM REV, V29, P249, DOI 10.1108/14684520510607579 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN Tan P. N., 2005, INTRO DATA MINING *UIUC, UIUC WEB INT REP Wang Y., 2002, CIKM, P499 WEISS R, 1996, ACM HYPERTEXT, P180 Wu W, 2006, ICDE Wu W., 2004, SIGMOD, P95 YU C, 2002, IEEE TKDE Zamir O., 1998, SIGIR, P46 SEARCH ENGINES DIRCT PROFUSION SEARCH ENG BRIGHTPLANETS SEARCH NR 40 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1084-4627 BN 978-1-4244-0802-3 J9 PROC INT CONF DATA PY 2007 BP 301 EP 310 PG 10 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BGQ56 UT WOS:000249779900031 ER PT S AU Nurminen, M Honkaranta, A Karkkainen, T AF Nurminen, Miika Honkaranta, Anne Karkkainen, Tommi GP IEEE TI ProcMiner: Advancing process analysis and management SO 2007 IEEE 23RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, VOLS 1-2 SE IEEE International Conference on Data Engineering Workshop LA English DT Proceedings Paper CT IEEE 23rd International Conference on Data Engineering Workshop CY APR 17-20, 2007 CL Istanbul, TURKEY SP IEEE AB This paper contributes both to research and practice on process mining. Previous research on process mining has focused on mining patterns from event log files to generate process models. The process mining approach adopted in this paper is focused on producing patterns about process models, not the models themselves. The approach is demonstrated by ProcMiner - an explorative research prototype for management, consolidating, publishing, retrieving, and analyzing process models. Content-based document clustering is applied to process models represented as XML database in order to find topical groups from models. In practice, organizations face numerous challenges in managing their process models. The models may be heterogeneous or ambiguous. The modeling software may change over time or due to differences in departmental purchases. ProcMiner was used in quality system development initiative at the University of Jyvaskyla. The findings support previous model engineering research, showing that multiple actions are needed to ensure consistency of process models, and to make them efficiently manageable. C1 [Nurminen, Miika; Honkaranta, Anne; Karkkainen, Tommi] Univ Jyvaskyla, Fac Informat Technol, SF-40351 Jyvaskyla, Finland. RP Nurminen, M (reprint author), Univ Jyvaskyla, Fac Informat Technol, SF-40351 Jyvaskyla, Finland. EM minurmin@rnitJyu.fi; anne.honkaranta@itjyu.fi; tka@mit.jyu.fi RI Nurminen, Miika/ OI Nurminen, Miika/0000-0003-0117-2691 CR Andrews T., 2003, BUSINESS PROCESS EXE Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BERNSTEIN A, 2003, MONOTONIC INHERITANC BRAY T, 2004, EXTENSIBLE MARKUP LA Clark J, 1999, XSL TRANSFORMATIONS Cockburn A., 2000, WRITING EFFECTIVE US Dalal NP, 2004, COMMUN ACM, V47, P83, DOI 10.1145/971617.971620 Davenport TH, 1993, PROCESS INNOVATION R Dorre J., 1999, P 5 ACM SIGKDD INT C, P398, DOI 10.1145/312129.312299 ELLMER E, 1996, 7 INT WORKSH DAT EXP, P320 Fayyad U, 1996, AI MAG, V17, P37 FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 Grigori D, 2004, COMPUT IND, V53, P321, DOI 10.1016/j.compind.2003.10.007 Han J, 2000, DATA MINING CONCEPTS Huang K, 2004, LECT NOTES COMPUT SC, V3252, P107 KARJALAINEN A, 2000, HICSS 00, V3, P3013 KLEIN M, 2001, SEARCHING SERVICES S Luk RWP, 2002, J AM SOC INF SCI TEC, V53, P415, DOI 10.1002/asi.10056 Madria S. K., 1999, Data Warehousing and Knowledge Discovery. First International Conference, DaWaK'99. Proceedings (Lecture Notes in Computer Science Vol.1676) Malone T.W., 2003, MIT PROCESS HDB MILI H, 2004, P WORKSH LEG TRANSF NURMINEN M, 2005, INT WORKSH INT DAT M, P1036 *OMG, 2005, 050704 OMG OSTERWEIL L, 1997, ICSE 9 AW P ICSE97 M, P540 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sharp A., 2001, WORKFLOW MODELING SMITH H, 2004, WORKFLOW JUST PI PRO SOFFER P, 2003, 8 CAISE IFIP8 1 INT, P126 van der Aalst WMP, 2003, LECT NOTES COMPUTER, P1 van der Aalst WMP, 2003, DATA KNOWL ENG, V47, P237, DOI 10.1016/S0169-023X(03)00066-1 WfMC, 2005, WFMCTC1025 White S. A., 2004, BUSINESS PROCESS MOD WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 33 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1943-2895 BN 978-1-4244-0831-3 J9 I C DATA ENGIN WORKS PY 2007 BP 760 EP 769 DI 10.1109/ICDEW.2007.4401065 PG 10 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHM59 UT WOS:000254288100090 ER PT S AU Suzuki, Y Nakatani, K Kawagoe, K AF Suzuki, Yu Nakatani, Keigo Kawagoe, Kyoji GP IEEE TI An automatic page link generation method based on users' behavior SO 2007 IEEE 23RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, VOLS 1-2 SE IEEE International Conference on Data Engineering Workshop LA English DT Proceedings Paper CT IEEE 23rd International Conference on Data Engineering Workshop CY APR 17-20, 2007 CL Istanbul, TURKEY SP IEEE AB In this paper, we propose a novel method for generating personalized page links. The page links which are generated by our proposed method are useful if users look for web pages related to the current web pages. To generate these page links, we proposed the following two steps, such as 1) divide current web pages into the signi cant minimal blocks, and 2) nd web pages related to these blocks. We con rmed in our experimental evaluation that users can nd web pages related to the users' interests. C1 [Suzuki, Yu; Nakatani, Keigo; Kawagoe, Kyoji] Ritsumeikan Univ, Coll Informat Sci & technol, Shiga 5258577, Japan. RP Suzuki, Y (reprint author), Ritsumeikan Univ, Coll Informat Sci & technol, 1-1-1 Noji Higashi, Shiga 5258577, Japan. EM suzuki@ics.ritsurnei.ac.jp; nakatani@coms.ics.ritsumei.ac.jp; kawagoe@coms.ics.ritsumei.ac.jp CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE CAI D, 2003, P 5 AS PAC C WEB C Dwork C, 2001, P 10 INT C WORLD WID, P613, DOI DOI 10.1145/371920.372165 Fagin R., 2003, P 2003 ACM SIGMOD IN, P301, DOI DOI 10.1145/872757.872795 FUKUMURA S, 2004, IPSJ T DATABASE SYST, P57 *HIT INC, 1999, 18 IPA WORKSH KIYOMITSU H, 2001, TOD8, V42, P10 KIYOMITSU H, 2001, TOD8, V42, P139 Ko Y., 2000, P 18 INT C COMP LING, P453 Pelleg D., 2000, P 17 INT C MACH LEAR, P727 Queen J.M., 1967, P 5 BERK S MATH STAT, P281 SALTON G, 2000, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANO R, 1999, IPSJ WORKSH DAT SYST NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1943-2895 BN 978-1-4244-0831-3 J9 I C DATA ENGIN WORKS PY 2007 BP 826 EP 834 DI 10.1109/ICDEW.2007.4401073 PG 9 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHM59 UT WOS:000254288100097 ER PT S AU Kashef, R Kamel, MS AF Kashef, R. Kamel, M. S. GP IEEE TI Hard-fuzzy clustering: A cooperative approach SO 2007 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-8 SE IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics CY OCT 07-10, 2007 CL Montreal, COOK ISLANDS SP IEEE ID PERFORMANCE; ALGORITHMS AB Data clustering plays an important role in many disciplines, where there is a need to learn the inherent grouping structure of the data in an unsupervised manner. It is wen known that no clustering method can adequately handle all sorts of cluster structures and properties (e.g. shape, size, overlapping, and density). Combining multiple clustering methods is an approach to overcome the deficiency of single algorithms and further enhance their performances. Current approaches to multiple clusterings use ensemble clustering to generate aggregated solution from multiple clusterings or using a hybrid cascaded refinement to enhance the end-result clusters produced by a former clustering algorithm(s). A disadvantage of the cluster ensemble is the highly computational load of combing the clustering results especially for large and high dimensional datasets. A drawback of the hybrid approaches is that, one (or more) of the clustering algorithms stays idle until the previous algorithm(s) finishes its clustering. In this paper we propose a Cooperative Hard-Fuzzy Clustering (CHFC) model based on intermediate cooperation between the hard k-means (KM) and fuzzy c-means (FM to produce better clustering solutions. Our experimental results over artificial, real, and text documents datasets show that the quality of the clustering solutions obtained from the CHFC model is better than those obtained from both the KM and the FCM and also better than those obtained from hybrid cascaded models. C1 [Kashef, R.; Kamel, M. S.] Univ Waterloo, Dept Elect & Comp Engn, Pattern Anal & Machine Intelligence PAMI Res Grp, Waterloo, ON N2L 3G1, Canada. RP Kashef, R (reprint author), Univ Waterloo, Dept Elect & Comp Engn, Pattern Anal & Machine Intelligence PAMI Res Grp, Waterloo, ON N2L 3G1, Canada. RI Kamel, Mohamed/D-9323-2011 CR AYAD H, 2007, IEEE T PATTERN ANAL BEZDEK JC, 1984, COMPUT GEOSCI, V10, P191, DOI 10.1016/0098-3004(84)90020-7 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Duda R.O., 1973, PATTERN CLASSIFICATI ENG Y, 2004, INT C ART INT SCI TE, P138 Ester M., 1996, P 2 INT C KNOWL DISC, P226 Gropp W., 1996, USING MPI PORTABLE P HAMMOUDA, 2006, 2006 SIAM C DAT MIN, P453 Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 ISMAIL MA, 1989, PATTERN RECOGN, V22, P75, DOI 10.1016/0031-3203(89)90040-X Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Maulik U, 2002, IEEE T PATTERN ANAL, V24, P1650, DOI 10.1109/TPAMI.2002.1114856 Mendes MES, 2003, IEEE INT CONF FUZZY, P648 QIAN Y, 2000, INT C PATT REC ICPR, V2, P732 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savares S.M., 2001, P 1 SIAM INT C DAT M, P1 Steinbach M., 2000, P KDD WORKSH TEXT MI, P109 Strehl A., 2002, C ART INT AAAI 2002, P93 *UCI MAC LEARN DAT, U CAL IRV DEP INF CO Xu R, 2005, IEEE T NEURAL NETWOR, V16, P645, DOI 10.1109/TNN.2005.845141 Xu ST, 2004, J SUPERCOMPUT, V30, P117, DOI 10.1023/B:SUPE.0000040611.25862.d9 NR 22 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 978-1-4244-0990-7 J9 IEEE SYS MAN CYBERN PY 2007 BP 1870 EP 1875 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BHP06 UT WOS:000255016301141 ER PT S AU Wen, GZ Guo, XY Huang, DS Liu, KH AF Wen, Guo-Zhu Guo, Xiao-Yong Huang, De-Shuang Liu, Kun-Hong GP IEEE TI Application of self-organizing map in aerosol single particles data clustering SO 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT International Joint Conference on Neural Networks CY AUG 12-17, 2007 CL Orlando, FL SP IEEE ID MASS-SPECTROMETRY AB In this paper, Self-Organizing Map (SOM) is used to visualize and cluster the data set of aerosol single particle mass spectrum, which was collected by aerosol time-of-flight mass spectrometry (ATOFMS). In view of the characteristic feature of aerosol particle data, the TF-IDF scheme used widely in document clustering is employed to preprocess. Subsequently for data clustering analysis, a two-level clustering framework is proposed, wherein SOM is firstly used to cluster input data and get the primary results, and then the results are again clustered by semiautomatic k-means algorithm. In order to demonstrate the validity of clustering, the chemical significance for cluster centroid is also investigated, wherein inorganic salts, "Calcium-Containing" particles, biogenic soot particles, and carbonaceous particles etc. are identified. C1 [Wen, Guo-Zhu; Huang, De-Shuang] Chinese Acad Sci, Inst Intelligent Machine, Intelligent Comp Lab, Beijing 100864, Peoples R China. RP Huang, DS (reprint author), Chinese Acad Sci, Inst Intelligent Machine, Intelligent Comp Lab, Beijing 100864, Peoples R China. CR DOCKERY DW, 1993, NEW ENGL J MED, V329, P1753, DOI 10.1056/NEJM199312093292401 Han J, 2006, DATA MINING CONCEPTS, P70 Hinz KP, 1999, J AM SOC MASS SPECTR, V10, P648, DOI 10.1016/S1044-0305(99)00028-8 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 Kohonen T., 2001, SELF ORG MAPS KRAAIJVELD MA, 1995, IEEE T NEURAL NETWOR, V6, P548, DOI 10.1109/72.377962 Phares DJ, 2001, ANAL CHEM, V73, P2338, DOI 10.1021/ac0015063 QIU Z, 2001, ENV SCI, V22, P51 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 Salt K, 1996, ANAL CHEM, V68, P230, DOI 10.1021/ac950396a SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ULTSCH A, 1992, P C SOC INF CLASS Ultsch A., 1990, P INT NEUR NETW C IN, P305 Vesanto J, 2000, IEEE T NEURAL NETWOR, V11, P586, DOI 10.1109/72.846731 Vesanto J., 1999, Intelligent Data Analysis, V3, DOI 10.1016/S1088-467X(99)00013-X Vesanto J, 2000, SOM TOOLBOX MATLAB Wang JD, 1997, SPECTROSC LETT, V30, P99, DOI 10.1080/00387019708002593 Yin HJ, 2002, NEURAL NETWORKS, V15, P1005, DOI 10.1016/S0893-6080(02)00075-8 ZHANG XG, 1993, P 1993 INT JOINT C N, V3, P2448 NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4244-1379-9 J9 IEEE IJCNN PY 2007 BP 991 EP 996 DI 10.1109/IJCNN.2007.4371093 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BHM64 UT WOS:000254291100173 ER PT S AU Arevian, G Panchev, C AF Arevian, Garen Panchev, Christo GP IEEE TI Optimising the hystereses of a two context layer RNN for text classification SO 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT International Joint Conference on Neural Networks CY AUG 12-17, 2007 CL Orlando, FL SP IEEE ID NETWORKS; CATEGORIZATION AB Established techniques from Information Retrieval (IR) and Machine Learning (ML) have shown varying degrees of success in the automatic classification of real-world text. The capabilities of an extended version of the Simple Recurrent Network (SRN) for classifying news titles from the Reuters-21578 Corpus are explored. The architecture is composed of two hidden layers where each layer has an associated context layer that takes copies of previous activation states and integrates them with current activations. This results in improved performance, stability and generalisation by the adjustment of the percentage of previous activation strengths kept "in memory" by what is defined as the hysteresis parameter. The study demonstrates that this partial feedback of activations must be carefully fine-tuned to maintain optimal performance. Correctly adjusting the hysteresis values for very long and noisy text sequences is critical as classification performance degrades catastrophically when values are not optimally set. C1 [Arevian, Garen] Univ Sunderland, Sch Comp & Technol, Sunderland SR6 0DD, England. RP Arevian, G (reprint author), Univ Sunderland, Sch Comp & Technol, Sunderland SR6 0DD, England. CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Arevian G, 2003, INT J APPROX REASON, V32, P237, DOI 10.1016/S0888-613X(02)00085-3 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BAXT WG, 1995, NEURAL COMPUT, V7, P624, DOI 10.1162/neco.1995.7.3.624 BODEN M, 2000, GECCO, P1033 Elman J. L., 1989, 8903 CRL U CAL ELMAN JL, 1988, 8901 CRL U CAL FRASCONI P, 1992, NEURAL COMPUT, V4, P120, DOI 10.1162/neco.1992.4.1.120 Giles CL, 2001, MACH LEARN, V44, P161, DOI 10.1023/A:1010884214864 Grossberg S., 1988, NEURAL NETWORKS NATU Hochreiter S, 1997, NEURAL COMPUT, V9, P1735, DOI 10.1162/neco.1997.9.8.1735 HUANG BQ, 2005, ICEIS, P27 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Johnston D., 1995, FDN CELLULAR NEUROPH Jurafsky D, 2000, SPEECH LANGUAGE PROC Kaikhah K., 2004, TEXT SUMMARIZATION U Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Koch C., 1989, METHODS NEURONAL MOD Kohonen T., 1995, SELF ORGANIZING MAPS KREMER SC, 1995, IEEE T NEURAL NETWOR, V6, P1000, DOI 10.1109/72.392262 LEWIS D, 1994, SEQUENTIAL ALFORITHM Lewis D., 1997, REUTERS 21578 TEXT C Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 O'Reilly R. C., 2000, COMPUTATIONAL EXPLOR Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd Salton G., 1983, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneider Karl-Michael, 2004, NEW FEATURE SELECTIO Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 [ę—¶å°č™Ž Shi Xiaohu], 2003, [č½Æä»¶å­¦ęŠ„, Journal of Software], V14, P1110 Tibshirani R, 1996, NEURAL COMPUT, V8, P152, DOI 10.1162/neco.1996.8.1.152 Wang BY, 2005, PDCAT 2005: SIXTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS AND TECHNOLOGIES, PROCEEDINGS, P913 Weisss S. M., 1991, COMPUTER SYSTEMS LEA Wermter S, 2000, PROCEEDINGS OF THE FIFTH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1 AND 2, P880 Wermter S., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) WERMTER S, 1999, P INT C ART NEUR NET, P898 WILES J, 1999, ABILITY RECURRENT NE WILES J, 1995, LEARNING COUNT WITHO NR 39 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 978-1-4244-1379-9 J9 IEEE IJCNN PY 2007 BP 2941 EP 2946 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BHM64 UT WOS:000254291102148 ER PT B AU Konishi, R Takahashi, Y Kiyoki, Y AF Konishi, Ryosuke Takahashi, Yusuke Kiyoki, Yasushi GP IEEE TI Dynamic and personalized curriculum-generation method analysing causality and dependency between learning objects SO 2007 IEEE International Workshop on Databases for Next Generation Researchers LA English DT Proceedings Paper CT 3rd IEEE International Workshop on Databases for Next Generation Researchers CY APR 15, 2007 CL Istanbul, TURKEY SP IEEE ID VECTOR-SPACE MODEL AB In this paper, we show a dynamic and personalized curriculum generation method that analyses the causality and dependency between learning objects. This method calculates the strength and extracts the rules of causality and dependency between individual learning objects, described beforehand, using the results of testing, trends of understanding within a particular community, and definitions of learning objects with a learning order. This method determines the important learning objects for an individual user, and an order for effective learning, by analysing the results of an individual and those of a particular community. It dynamically computes changing relationships between learning objects and the learning situation of individual users, and enables generation of a personalized curriculum that supports effective learning. C1 Keio Univ, Fac Policy Management, Kanagawa 2528520, Japan. RP Konishi, R (reprint author), Keio Univ, Fac Policy Management, 5322 Endo, Kanagawa 2528520, Japan. CR HERLOCKER J, 1999, P ACM SIGIR 99 KARYPIS G, 2001, 10 ANN C INF KNOWL M, P247 KONISHI R, 2006, DBWS2006, P27 *MIN ED CULT SPORT, JUN HIGH SCH GOV GUI MITA Y, 2004, DEWS2004 OKAMOTO K, SPREADS FUTURE MATH Perl J., 1988, PROBABILISTIC REASON RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANO Y, 2002, DEWS2002 SARWAR B, 2001, P INT WWW C 10 HONGK SHIMIZU N, 2002, IPSJCEO2065006, P41 TAKANO K, 2005, SIG 5TOD25, V46 TAKATNO K, 2002, DBSJ LETT, V1, P55 ZUSH T, 2004, SIG7TOD22, V45, P124 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-0903-7 PY 2007 BP 91 EP 96 DI 10.1109/SWOD.2007.353204 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BGN17 UT WOS:000248568300016 ER PT B AU Zhou, AY Zhang, R Vu, QH Qian, WN AF Zhou, Aoying Zhang, Rong Vu, Quang Hieu Qian, Weining BE Li, K Xiang, Y Jin, H Qu, WY Cao, ZY TI Content-based search using term aggregation and classification over hybrid Peer-to-Peer systems SO 2007 IFIP INTERNATIONAL CONFERENCE ON NETWORK AND PARALLEL COMPUTING WORKSHOPS, PROCEEDINGS SE IFIP International Conference on Network and Parallel Computing NPC LA English DT Proceedings Paper CT IFIP International Conference on Network and Parallel Computing CY SEP 18-21, 2007 CL Dalian, PEOPLES R CHINA SP IFIP WG 10 3 Concurrent Syst, Chinese Acad Sci, Inst Comp Technol, Dalian Maritime Univ, Huazhong Univ Sci & Technol, Cent Queensland Univ AB In this paper we propose a method to support content-based search, one of the challenges in Peer-to-Peer file sharing system. The proposed method is based on a hybrid structure, which is a combination of a Chord ring and a balanced tree. The tree is used to aggregate and classify terms while the Chord ring is used to index terms of nodes in the tree. At every node in the tree, the system classifies terms as either important or unimportant. Important terms of a node, which can distinguish the node from its neighbor nodes, are indexed in the Chord ring while unimportant terms that are either popular or rare terms are aggregated to higher level nodes. Based on the classification, the system can process queries on the fly without the need of global knowledge. Therefore, our system can avoid the problem of bottleneck at nodes keeping global knowledge and the expensive cost of synchronization global knowledge among these nodes. We have done extensive experiments to validate the effectiveness and efficiency of our proposal. C1 [Zhou, Aoying; Zhang, Rong] Fudan Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China. RP Zhou, AY (reprint author), Fudan Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China. EM ayzhou@fudan.edu.cn; rongzh@fudan.edu.cn; hieuvq@nus.edu.sg; wnqian@sei.ecnu.edu.cn CR BUCKLEY C, 1995, TREC, V4, P25 Byers J., 2003, P IPTPS CHUN B, 2003, ACM SIGCOMM COMPUTER, V33 Crainiceanu A, 2004, P 7 INT WORKSH WEB D, P25, DOI 10.1145/1017074.1017082 KARGER DR, 2004, P IPTPS LU J, 2005, P ECIR LUPA M, 2006, P ICDE Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] RENADA ME, 2004, P CIKM REYNOLDS P, 2003, P INT MIDDL C JUN, P21 Sahin O.D., 2005, P 13 ANN ACM INT C M, P946, DOI 10.1145/1101149.1101349 SAHIN OD, 2004, P DBISP2P SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEN HT, 2003, IEEE T KNOWLEDGE DAT TANG C, 2003, P SIGCOMM Tang Chunqiang, 2004, P NSDI WONG SKM, 1987, ACM T DATABASE SYSTE, V12 ZHOU S, 2006, P ICDE NR 18 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2943-1 J9 IFIP INT C NETW PARA PY 2007 BP 28 EP 35 DI 10.1109/NPC.2007.158 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BGY79 UT WOS:000251390000007 ER PT B AU Zhu, D Dreher, H AF Zhu, Dengya Dreher, Heinz GP IEEE TI An integrating text retrieval framework for digital ecosystems paradigm SO 2007 INAUGURAL IEEE INTERNATIONAL CONFERENCE ON DIGITAL ECOSYSTEMS AND TECHNOLOGIES SE IEEE International Conference on Digital Ecosystems and Technologies LA English DT Proceedings Paper CT IEEE International Conference on Digital Ecosystems and Technologies CY FEB 21-23, 2007 CL Cairns, AUSTRALIA SP IEEE DE information retrieval; self-organizing search; categorization; crawler ID INFORMATION-RETRIEVAL; SEARCH AB The purpose of the research is to provide effective information retrieval services for digital 'organisms' in a digital ecosystem by leveraging the power of Web searching technology. A novel integrating digital ecosystem search framework (a new digital organism) is proposed which employs the Web search technology and traditional database searching techniques to provide economic organisms with comprehensive, dynamic, and organization-oriented information retrieval ranging from the Internet to personal (semantic) desktop. C1 Curtin Univ Technol, Perth, WA 6845, Australia. RP Zhu, D (reprint author), Curtin Univ Technol, GPO Box U1987, Perth, WA 6845, Australia. EM dengya.zhu@postgrad.curtin.edu.au; h.dreher@curtin.edu.au CR ALBRO EN, 2006, GOOGLE MINI MIGHTY S ALLEN J, P 19 ACM INT C RES D, P307 [Anonymous], OPEN DIRECTORY PROJE Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BARROWS R, 2006, ACM QUEUE MAY, P30 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Cho J., 2002, P 11 INT C WORLD WID, P124 Dolog P., 2003, P 12 INT WORLD WID W, P99 DREHER H, 2006, P 7 INT C FLEX QUER, P282 DUMAIS S, P SIGCHI C HUM FACT, P277 Gauch S., 2003, Web Intelligence and Agent Systems, V1 GEDDA R, 2006, COMPUTER WORLD 0622 Gospodnetic O, 2005, LUCENE ACTION GUIDE *ISYS SEARCH SOFTW, 2006, SEL INTR SEARCH ENG Jones K. S., 2000, Information Processing & Management, V36 KLAS CP, 2000, P 22 ANN C BRIT COMP Meng WY, 2002, ACM COMPUT SURV, V34, P48, DOI 10.1145/505282.505284 Mizzaro S, 1998, INTERACT COMPUT, V10, P303, DOI 10.1016/S0953-5438(98)00012-5 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Mladenic D., 1998, P 13 EUR C ART INT E, P473 Notess G, 1998, DATABASE, V21, P32 Pitkow J, 2002, COMMUN ACM, V45, P50 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SILBERSCHATZ A, 1993, DATABASE SYSTEM CONC, P633 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Zamir O., 1998, P 21 ANN INT ACM SIG, P45 ZHU D, 2007, THESIS CURTIN U TECH XAPIAN PROJECT LUCENE APACHE JAKART LIP IMS LEARNER INFO NR 32 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-0467-4 J9 I C DIGIT ECOSYST TE PY 2007 BP 165 EP 170 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Industrial SC Computer Science; Engineering GA BGQ12 UT WOS:000249667300029 ER PT B AU Weichselbraun, A Wohlgenannt, G Scharl, A Granitzer, M Neidhart, T Juffinger, A AF Weichselbraun, Albert Wohlgenannt, Gerhard Scharl, Arno Granitzer, Michael Neidhart, Thomas Juffinger, Andreas GP IEEE TI Applying vector space models to ontology link type suggestion SO 2007 INNOVATIONS IN INFORMATION TECHNOLOGIES, VOLS 1 AND 2 LA English DT Proceedings Paper CT 4th International Conference on Innovations in Information Technology CY NOV 18-20, 2007 CL Dubai, U ARAB EMIRATES SP IEEE AB The identification and labeling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes an approach for suggesting ontology relationship types to domain experts based on implicitly learned relations from a domain corpus. The learning process extracts verb-vectors from sentences containing domain concepts. It computes centroids for known relationship types and stores them in the knowledge base. Vectors of unknown relationships are compared to the stored centroids using the cosine similarity metric. The system then suggests the relationship type of the most similar centroid. Domain experts evaluate these suggestions to refine the knowledge base and constantly improve the component's accuracy. Using four sample ontologies on "energy sources", this paper demonstrates how link type suggestion aids the ontology design process. It also provides a statistical analysis on the accuracy and average ranking performance of batch learning versus online learning. C1 [Weichselbraun, Albert; Wohlgenannt, Gerhard] Vienna Univ Econ, A-1090 Vienna, Austria. RP Weichselbraun, A (reprint author), Vienna Univ Econ, A-1090 Vienna, Austria. CR BERLAND M, 1999, ACL 99 Cimiano P., 2005, ONTOLOGY LEARNING TE, P59 Fellbaum C, 1998, COMPUTATIONAL LINGUI, V25, P292 FINKELSTEINLAND.M, 1999, INT WORKSH ONT ENG G Hearst M. A., 1992, COLING 92, P539 Kavalec M, 2005, ONTOLOGY LEARNING TE, P44 Liu W., 2005, J UNIVERSAL KNOWLEDG, P50 Maedche A, 2002, WEB INTELLIGENCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 1999, 22 ANN INT ACM SIGIR, P206 NR 10 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1840-4 PY 2007 BP 349 EP 353 PG 5 WC Engineering, Multidisciplinary; Engineering, Electrical & Electronic SC Engineering GA BHS69 UT WOS:000255983000071 ER PT S AU Martinet, J Satoh, S AF Martinet, Jean Satoh, Shin'ichi GP IEEE TI A study of intra-modal association rules for visual modality representation SO 2007 INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING, PROCEEDINGS SE International Workshop on Content-Based Multimedia Indexing LA English DT Proceedings Paper CT International Workshop on Content-Based Multimedia Indexing (CBMI 2007) CY JUN 25-27, 2007 CL Bordeaux, FRANCE SP IEEE, LaBRI, CNRS, IBM, Reg Aquitaine, INRIA, ENSEIRC, COST 292, Centre Natl Cinematographie, Semant Multimodal Anal Digital Media AB This paper presents a study of the use of association rules in document representation. It consists of an approach for building a compact and meaningful representation of low level features in the documents of a multimedia database. The proposed method is based on the discovery and the study of intra-modal association rules between individual objects in the context of a spatial-temporal window. The discovered relations are used to build mid-level objects capturing the most frequently occurring patterns in the database, possibly closer to high level concepts. We show the results of an evaluation assessing the quality of the representation with regards to its compactness. C1 Natl Inst Informat, Multimedia Informat Res Div, Tokyo, Japan. RP Martinet, J (reprint author), Natl Inst Informat, Multimedia Informat Res Div, Tokyo, Japan. EM jean@nii.ac.jp; satoh@nii.ac.jp CR Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Agrawal R., 1994, P 20 INT C VER LARG, P487 Carson C., 1999, 3 INT C VIS INF SYST HADDAD H, 2001, 2001 INT C ART INT HADDAD H, 2000, 4 EUR C PRINC PRACT Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 HAN J, 1997, P 1997 ACM SIGMOD IN HANG CW, SURVEY SPATIAL DATA HASSAN H, 2006, ICWE, P48 Jeon J, 2004, LECT NOTES COMPUT SC, V3115, P24 JING Y, 1994, P RIAO 94 4 INT C RE Koperski K, 1995, LECT NOTES COMPUT SC, V951, P47 ORDONEZ C, 1999, P IEEE ADDV DIG LIB Quelhas P., 2005, 10 IEEE INT C COMP V, V1, P883, DOI 10.1109/ICCV.2005.152 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Snoek Cees GM, 2005, MULTIMED TOOLS APPL, V25, P5 Williams HE, 2004, ACM T INFORM SYST, V22, P573, DOI 10.1145/1028099.1028102 ZEITOUNI K, 2002, SURVEY SPATIAL DATA ZHENG QF, 2006, P 14 ANN ACM INT C M NR 19 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1949-3983 BN 978-1-4244-1010-1 J9 INT WORK CONTENT MUL PY 2007 BP 344 EP 350 DI 10.1109/CBMI.2007.385432 PG 7 WC Computer Science, Software Engineering SC Computer Science GA BGN61 UT WOS:000248622600048 ER PT B AU Dai, WH Jiao, CZ He, TT AF Dai, Wenhua Jiao, Cuizhen He, Tingting BE Liao, BY Pan, JS Jain, LE Liao, M Noda, H Ho, ATS TI Research of K-means clustering method based on parallel genetic algorithm SO 2007 THIRD INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, VOL II, PROCEEDINGS LA English DT Proceedings Paper CT 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing CY NOV 26-28, 2007 CL Kaohsiung, TAIWAN SP Natl Kaohsiung Univ Appl Sci, Natl Univ Kaohsiung, Kaohsiung City Govt, Minist Educ, Taiwanese Assoc Artificial Intelligence, Knowledge Based Intelligent Eng Syst Ctr, Image Proc Pattern Recognition Soc Taiwan, Jinwen Univ Sci Technol, Chinese Inst Engineers AB As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it's difficult to determine the cluster number, we proposed a K-means Clustering Method Based on Parallel Genetic Algorithm. In the method, we adopted a new strategy of Variable-Length Chromosome Encoding and randomly chose initial clustering centers to form chromosomes among samples. Combining the efficiency of K-means Algorithm with the global optimization ability of Parallel Genetic Algorithm, the local optimal solution was avoided and the optimum number and optimum result of cluster were obtained by means of heredity, mutation in the community, and parallel evolution, intermarriage among communities. Experiments indicated that this algorithm was efficient and accurate. C1 [Dai, Wenhua; Jiao, Cuizhen] Xianning Coll, Dept Comp, Xianning 437005, Peoples R China. RP Dai, WH (reprint author), Xianning Coll, Dept Comp, Xianning 437005, Peoples R China. CR Andre J., 2001, Advances in Engineering Software, V32, DOI 10.1016/S0965-9978(00)00070-3 Li JP, 2003, EUR J OPER RES, V147, P334, DOI 10.1016/S0377-2217(02)00564-7 MAKOTO I, 1995, TR950015 TOK I TECHN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEINBACH M, 2000, COMP DOCUMENT CLUSTE, P1 XIAO L, 2002, P 19 COLING, P598 Zhao Y., 2001, CRITERION FUNCTIONS, P1 NR 7 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA PY 2007 BP 158 EP 161 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHG10 UT WOS:000252884900037 ER PT B AU Matsuo, T Fujimoto, T AF Matsuo, Tokuro Fujimoto, Takayuki BE Lee, R Chowdhury, MU Ray, S Lee, T TI A new cooperative communication system in distance education SO 6th IEEE/ACIS International Conference on Computer and Information Science, Proceedings LA English DT Proceedings Paper CT 6th IEEE/ACIS International Conference on Computer and Information Science in Conjunction with 1st IEEE/ACIS International Workshop on e-Activity CY JUL 11-13, 2007 CL Melbourne, AUSTRALIA SP IEEE Comp Soc, Int Assoc Comp & Informat Sci AB We propose an interactive edit web system, that is, users can, edit and revise documents cooperatively and interactively. The le-web is constructed based on conceptions of special issue edit and pantographic issue edit. The goal of this study is not only developing cooperative document making system by many users, but research/report paper making system by mentors and other students. In recent years, computer-based tele-learning systems are developed. Thus, we propose a useful cooperative document making support system to develop a system used at university educations. C1 Yamagata Univ, Yamagata 990, Japan. RP Matsuo, T (reprint author), Yamagata Univ, Yamagata 990, Japan. CR KITADA J, 2001, IPSJ J CONTENTS, V42 MIYAKE N, 2000, 4 INT C LEARN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SUNAYAMA W, 2004, 18 ANN C JAP SOC ART Yamamoto Y., 1999, Journal of Japanese Society for Artificial Intelligence, V14 NR 5 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2841-0 PY 2007 BP 943 EP 948 DI 10.1109/ICIS.2007.35 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Software Engineering SC Computer Science GA BGN43 UT WOS:000248597700157 ER PT S AU Davis, D Jiang, E AF Davis, Darren Jiang, Eric BE Alhajj, R Gao, H Li, X Li, JZ Zaiane, OR TI Exploring content and linkage structures for searching relevant web pages SO Advanced Data Mining and Applications, Proceedings SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 3rd International Conference on Advanced Data Mining and Applications CY AUG 06-08, 2007 CL Harbin, PEOPLES R CHINA SP Harbin Inst Technol DE web mining; hyperlink analysis; information retrieval; singular value decomposition ID INFORMATION AB This work addresses the problem of Web searching for pages relevant to a query URL. Based on an approach that uses a deep linkage analysis among vicinity pages, we investigate the Web page content structures and propose two new algorithms that integrate content and linkage analysis for more effective page relationship discovery and relevance ranking. A prototypical Web searching system has recently been implemented and experiments on the system have shown that the new content and linkage based searching methods deliver improved performance and are effective in identifying semantically relevant Web pages. C1 Univ San Diego, San Diego, CA 92110 USA. RP Davis, D (reprint author), Univ San Diego, 5998 Alcala Pk, San Diego, CA 92110 USA. CR Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Dean J, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P389 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Hou JY, 2003, IEEE T KNOWL DATA EN, V15, P940 KLEINBERG J, 1998, P 9 ACMSIAM S DISC A Kleinberg J.M., 1999, COCOON 1999, V1627, P1, DOI 10.1007/3-540-48686-0_1 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 9 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73870-1 J9 LECT NOTES ARTIF INT PY 2007 VL 4632 BP 15 EP 22 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BGS80 UT WOS:000250366700002 ER PT S AU Hao, YN Zhang, YC Cao, JL AF Hao, Yanan Zhang, Yanchun Cao, Jinli BE Krogstie, J Opdahl, A Sindre, G TI WSXplorer: Searching for desired Web services SO ADVANCED INFORMATION SYSTEMS ENGINEERING, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 19th International Conference on Advanced Information Systems Engineering CY JUN 11-15, 2007 CL Trondheim, NORWAY SP Google, SINTEF, Telenor, European Res Consortium Informat & Math, City Trondheim, NTNU AB With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. A main problem that remains is how to discover desired web services. In this paper, we propose WSXplorer, a novel scheme for identifying potentially relevant web services given a textual description of services. In particular, we propose a new schema matching algorithm for supporting web-service operations matching. The matching algorithm catches not only structures, but even better semantic information of schemas. Based on service operations matching, the concept of attribute closure is introduced to identify associations between web-service operations. We also propose a ranking strategy to satisfy a user's top-k requirements. Experimental evaluation shows that our approach can achieve high precision and recall ratio. C1 Victoria Univ, Sch Comp Sci & Math, Melbourne, Vic 8001, Australia. RP Hao, YN (reprint author), Victoria Univ, Sch Comp Sci & Math, POB 14428, Melbourne, Vic 8001, Australia. EM haoyn@csm.vu.edu.au; yzhang@csm.vu.edu.au; j.cao@latrobe.edu.au CR AGRAWAL S, 2002, P INT C DAT ENG Bhiri S., 2005, P 14 INT C WORLD WID, P138, DOI 10.1145/1060745.1060769 Booth D., 2004, WEB SERVICES ARCHITE Dong X., 2004, P 30 INT C VER LARG, P372, DOI 10.1016/B978-012088469-8/50035-8 HAO Y, 2007, P AUSTR COMP SCI C Hristidis V, 2003, P 29 INT C VER LARG, P850, DOI 10.1016/B978-012722442-8/50080-X Hristidis V., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases Kaufman L, 1990, FINDING GROUPS DATA Limthanmaphon B., 2003, P 14 AUSTR DAT C DAT, P201 Limthanmaphon Benchaphon, 2004, P 15 AUSTR DAT C, P171 Moormann A. Zaremski, 1997, ACM T SOFTW ENG METH, V6, P333, DOI 10.1145/261640.261641 PU K, 2006, P INT C DAT ENG, V31 Reis D., 2004, P 13 INT C WORLD WID, P502, DOI 10.1145/988672.988740 ROMAN D, 2005, 10 WSMO Sajjanhar A, 2004, LECT NOTES COMPUT SC, V3007, P665 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Z., 2005, P 2005 IEEE INT C SE, V01, P279 Sycara K, 2002, AUTON AGENT MULTI-AG, V5, P173, DOI 10.1023/A:1014897210525 Wang H, 2003, IEEE T SYST MAN CY A, V33, P697, DOI 10.1109/TSMCA.2003.819917 Wang H, 2005, IEEE T KNOWL DATA EN, V17, P425 WANG Y, 2003, P INT C WEB INF SYST Xie T, 2006, LECT NOTES COMPUT SC, V3841, P319 ZHANG KZ, 1989, SIAM J COMPUT, V18, P1245, DOI 10.1137/0218082 NR 24 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-72987-7 J9 LECT NOTES COMPUT SC PY 2007 VL 4495 BP 173 EP 187 PG 15 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BGI41 UT WOS:000247331300013 ER PT S AU Du, YJ Li, HM Liao, ZW AF Du, YaJun Li, HaiMing Liao, Zhenwen BE Huang, DS Heutte, L Loog, M TI The strategy of matching user queries with web pages based on formal concept analysis SO ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES SE COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE LA English DT Proceedings Paper CT 3rd International Conference on Intelligent Computing CY AUG 21-24, 2007 CL Qingdao, PEOPLES R CHINA SP IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; CONCEPT LATTICES AB A searching result can be returned for submitting user query to Search Engine(SE). However, large numbers of Web pages in the searching result are not interested by users. To overcome this problem, from automated reasoning perspective, we put forward a matching model how to match user query with Web pages. According as the basic theory of formal concept lattice, we defined OR-RULE and AND-RULE, put forward AND-MATCH and OR-MATCH. Two reasoning methods based on AND-MATCH and OR-MATCH, and their algorithms of the matching model, are proposed. The experiment demonstrates that these method is efficient. C1 Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. RP Du, YJ (reprint author), Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Peoples R China. CR BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I Carpineto C, 2004, J UNIVERS COMPUT SCI, V10, P985 CROUCH DB, 1989, HYPERTEXT 89 PROCEEDINGS, P225 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DU YJ, 2005, 17 IMACS WORLD C SCI, P1 DU YJ, 2005, P 2005 INT C MACH LE, V4, P2368 GERARD S, 1983, COMMUN ACM, V26, P1022 KWOK KL, 1990, ACM T INFORM SYST, V8, P363, DOI 10.1145/102675.102677 Li Z., 2004, Proceedings of 2004 6th International Conference on Transparent Optical Networks (IEEE Cat. No.04EX804), DOI 10.1109/ICTON.2004.1362033 LINDIG C, 1995, P INT JOINT C ART IN, P21 LOSADA DE, 2005, LECT NOTES COMPUTER Losada DE, 2001, COMPUT J, V44, P410, DOI 10.1093/comjnl/44.5.410 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rajapakse R, 2005, NEURAL COMPUT, V17, P2291, DOI 10.1162/0899766054615662 RAJAPAKSE RK, 2003, P 7 WORLD MULT SYST Robertson S., 1996, TREC 4 4 TEXT RETR C, P73 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 XU Y, 2003, LATTICE VALUE LOGIC, P305 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1865-0929 BN 978-3-540-74281-4 J9 COMM COM INF SC PY 2007 VL 2 BP 489 EP 498 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BGX46 UT WOS:000251134500055 ER PT S AU Wu, JN Wang, XH AF Wu, Jiangning Wang, Xiaohuan BE Huang, DS Heutte, L Loog, M TI A knowledge navigation method for the domain of customers' services of mobile communication corporations in China SO ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 3rd International Conference on Intelligent Computing CY AUG 21-24, 2007 CL Qingdao, PEOPLES R CHINA SP IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China DE topic map; social network analysis; knowledge navigation; explicit knowledge; tacit knowledge ID MODEL AB Rapidly increasing amount of mobile phone users and types of services leads to a great accumulation of complaining information. How to use this information to enhance the quality of customers' services is a big issue at present. To handle this kind of problem, the paper presents an approach to construct a domain knowledge map for navigating the explicit and tacit knowledge in two ways: building the Topic Map-based explicit knowledge navigation model, which includes domain TM construction, a semantic topic expansion algorithm and VSM-based similarity calculation; building Social Network Analysis-based tacit knowledge navigation model, which includes a multi-relational expert navigation algorithm and the criterions to evaluate the performance of expert networks. In doing so, both the customer managers and operators in call centers can find the appropriate knowledge and experts quickly and exactly. The experimental results show that the above method is very powerful for knowledge navigation. C1 Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Liaoning, Peoples R China. RP Wu, JN (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Liaoning, Peoples R China. CR asserman Stanley, SOCIAL NETWORK ANAL, P199 HU YQ, 1998, INTRO OPERATIONS RES *INV TOOLS TECHN K, SOC NETW AN KM TOOLK JIANG SH, 2006, SEGMENTATION ALGORIT, V25, P74 Liu J., 2004, INTRO SOCIAL NETWORK Polanyi Michael, 1958, PERSONAL KNOWLEDGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEVEN P, TAO TOPIC MAP FINDIN Wu JN, 2006, LECT NOTES CONTR INF, V344, P753 NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74201-2 J9 LECT NOTES ARTIF INT PY 2007 VL 4682 BP 340 EP 349 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Operations Research & Management Science SC Computer Science; Operations Research & Management Science GA BGU28 UT WOS:000250577100038 ER PT B AU Mesleh, A AF Mesleh, Abdelwadood BE Demiralp, M Udriste, C Bognar, G Soni, R Nassar, H TI Support vector machines based arabic language text classification system: Feature selection comparative study SO APPLIED MATHEMATICS FOR SCIENCE AND ENGINEERING LA English DT Proceedings Paper CT 12th WSEAS International Conference on APPLIED MATHEMATICS CY DEC 29-31, 2007 CL Cairo, EGYPT SP WSEAS DE SVM; feature selection; information gain; CHI; odd ratio; GSS; NGL; mutual information; Arabic text classification; Arabic text categorization ID CATEGORIZATION; MODEL AB Feature selection is essential for effective and accurate text classification systems. This paper investigates the effectiveness of six commonly used feature selection methods, Evaluation used an in-house collected Arabic text classification corpus, and classification is based on Support Vector Machine Classifier. The experimental results are presented in terms of precision, recall and Macroaveraged F(1) measure. C1 Balqa Appl Univ, Dept Comp Engn, Fac Engn Technol, Amman 11134, Jordan. RP Mesleh, A (reprint author), Balqa Appl Univ, Dept Comp Engn, Fac Engn Technol, POB 15008, Amman 11134, Jordan. EM wadood@fet.edu.jo CR ALSHALABI R, 2006, P 4 INT MULT COMP SC, V4 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Benkhalifa M, 2001, INT J INTELL SYST, V16, P929, DOI 10.1002/int.1042 CIRAVEGNA F, 2000, P PAIS 2000 PREST AP, P696 Duwairi R., 2005, P 2005 INT C DAT MIN, P187 Duwairi RM, 2006, J AM SOC INF SCI TEC, V57, P1005, DOI 10.1002/asi.20360 El-Halees A., 2007, ISLAMIC U J, V15, P157 El-Kourdi M., 2004, P COLING 20 WORKSH C, P51, DOI 10.3115/1621804.1621819 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 Gao S, 2006, ACM T INFORM SYST, V24, P190, DOI 10.1145/1148020.1148022 Guo GD, 2004, LECT NOTES COMPUT SC, V2945, P559 He J, 2003, APPL INTELL, V18, P311, DOI 10.1023/A:1023202221875 HOFMANN T, 2003, INTRO MACHINE LEARNI Joachims T., 1998, P 10 EUR C MACH LEAR, P137 KANAAN G, 2006, P 4 INT MULT COMP SC, V4 Khreisat L, 2006, P 2006 INT C DAT MIN, P78 Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Liu H, 2005, IEEE INTELL SYST, V20, P64, DOI 10.1109/MIS.2005.105 Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 MESLEH AM, 2007, P 2 INT C SOFTW DAT, V1, P235 Mesleh AMA, 2007, J COMPUTER SCI, V3, P430 Mladenic D., 1999, P 16 INT C MACH LEAR, P258 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 PENG F, 2003, P 6 INT WORKSH INF R, P41 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMIR AM, 2005, P 5 C INT INF TECHN, P13 SAWAF H, ARAB NAT LANG PROC W Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Syiam M. M., 2006, INT J INTELLIGENT CO, V6, P1 Taira H., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Vapnik V., 1995, NATURE STAT LEARNING Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 Yang Y., 1999, 22 ANN INT ACM SIGIR, P42 Zhang JN, 2003, PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL II, P888 NR 44 TC 1 Z9 1 PU WORLD SCIENTIFIC AND ENGINEERING ACAD AND SOC PI ATHENS PA AG LOANNOU THEOLOGOU 17-23, 15773 ZOGRAPHOU, ATHENS, GREECE BN 978-960-6766-27-5 PY 2007 BP 228 EP 234 PG 7 WC Engineering, Electrical & Electronic; Mathematics, Applied SC Engineering; Mathematics GA BHF80 UT WOS:000252771600039 ER PT S AU Hinneburg, A Porzel, A Wolfram, K AF Hinneburg, Alexander Porzel, Andrea Wolfram, Karina BE Hochreiter, S Wagner, R TI An evaluation of text retrieval methods for similarity search of multi-dimensional NMR-spectra SO Bioinformatics Research and Development, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 1st International Conference on Bioinformatics Research and Development CY MAR 12-14, 2007 CL Berlin, GERMANY AB Searching and mining nuclear magnetic resonance (NMR)spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete text-like data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful "topics" in the NMR-data. C1 Univ Halle Wittenberg, Inst Comp Sci, D-4010 Halle, Germany. RP Hinneburg, A (reprint author), Univ Halle Wittenberg, Inst Comp Sci, D-4010 Halle, Germany. CR Barros AS, 2005, CHEMOMETR INTELL LAB, V78, P125, DOI 10.1016/j.chemolab.2005.01.003 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 CAI L, 2003, SIGIR 03 FARKAS M, 1988, ANAL CHIM ACTA, V206, P173, DOI 10.1016/S0003-2670(00)80840-5 Hofmann T., 1999, SIGIR 99 Krishnan P, 2005, J EXP BOT, V56, P255, DOI 10.1093/jxb/eri010 MEI Q, KDD 05 POPESCUL A, UAI 2001 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbeck C, 2003, J CHEM INF COMP SCI, V43, P1733, DOI 10.1021/ci0341363 STEYVERS M, KDD 04 TSIPOURAS A, 1995, ANAL CHIM ACTA, V316, P161, DOI 10.1016/0003-2670(95)00322-Q WOLFRAM K, 2006, PKDD 06 NR 13 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-71232-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4414 BP 424 EP 438 PG 15 WC Biochemical Research Methods; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Biochemistry & Molecular Biology; Computer Science GA BGE03 UT WOS:000246237400033 ER PT S AU Tanioka, H AF Tanioka, Hiroki BE Fuhr, N Lalmas, M Trotman, A TI A method of preferential unification of plural retrieved elements for XML retrieval task SO Comparative Evaluation of XML Information Retrieval Systems SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-20, 2006 CL Dagstuhl, GERMANY SP DELOS Network Excellence Digital Lib AB We developed a passage retrieval system for XML documents using the vector space model. To be more flexible for the query, we also developed a method of unification of multiple retrieved elements and a fragment indexing system. Our system is composed of an inverted file and an XML Path Language (XPath) path list. The validity of the method was tested as part of the ad hoc track in the Initiative for the Evaluation of XML Retrieval (INEX) 2006. C1 JustSystems Corp, Innovat Technol R&D, Tokushima, Japan. CR [Anonymous], XML PATH LANGUAGE XP BAEZAYATES R, 1999, MODERN INFORM RETRIE, P1 EVANS D, 1994, P 2 TEXT RETR C TREC, P500 GEVA S, 2003, INEX 2003 WORKSH P, P110 Grabs T, 2003, LECT NOTES COMPUT SC, V2818, P95 Kazai G, 2003, LECT NOTES COMPUT SC, V2818, P279 KELLY W, 2003, INEX WORKSH P, P126 Mihajlovic V, 2006, LECT NOTES COMPUT SC, V3977, P72 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIGURBJORNSSON B, 2003, INEX 2003 WORKSH P, P19 TANIOKA H, 2005, 5 NTCIR WORKSH M Trotman A., 2006, P SIGIR 2006 WORKSH, P43 Zobel J., 2006, ACM COMPUT SURV, V38, P6, DOI DOI 10.1145/1132956.1132959 NR 13 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73887-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4518 BP 45 EP 56 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGP47 UT WOS:000249566700005 ER PT S AU Crouch, CJ Crouch, DB Ganapathibhotla, M Bakshi, V AF Crouch, Carolyn J. Crouch, Donald B. Ganapathibhotla, Murthy Bakshi, Vishal BE Fuhr, N Lalmas, M Trotman, A TI Dynamic element retrieval in a semi-structured collection SO Comparative Evaluation of XML Information Retrieval Systems SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 5th International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 17-20, 2006 CL Dagstuhl, GERMANY SP DELOS Network Excellence Digital Lib ID VECTOR-SPACE MODEL AB This paper describes our methodology for the dynamic retrieval of XML elements, an overview of its implementation in a structured environment, and the challenges introduced by applying it to the INEX Wikipedia [4] collection, which can more aptly be described as semi-structured. Our system is based on the vector space model [9] and its basic functions are performed using the Smart experimental retrieval system [8]. A major change in the system this year is the incorporation of a method for the dynamic computation of query term weights [6] to be correlated with the dynamically generated and weighted element vectors. Dynamic element retrieval requires only a single indexing of the document collection at the level of the basic indexing node (in this case, the paragraph). It returns a rank-ordered list of elements equivalent to that produced by the same query against an all-element index of the collection. (A detailed description of this method appears in [1].) As we move from a well structured collection, such as the INEX IEEE documents, to Wikipedia, changes in the structure of the articles must be accommodated. C1 Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. CR Crouch CJ, 2005, LECT NOTES COMPUT SC, V3493, P292 Crouch CJ, 2006, ACM T INFORM SYST, V24, P437, DOI 10.1145/1185877.1185880 Crouch CJ, 2006, LECT NOTES COMPUT SC, V3977, P268 Denoyer L., 2006, INEX WORKSHOP PRE P, P367 FOX EA, 1983, THESIS CORNELL U GANAPATHIBHOTLA M, 2006, THESIS U MINNEOTA DU Khanna S, 2005, THESIS U MINNESOTA D SALTON G, 1971, SMART RRETRIEVAL SYS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1998, 6 TEXT RETR C TREC 6, V500, P215 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 NR 11 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73887-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4518 BP 82 EP 88 PG 7 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BGP47 UT WOS:000249566700009 ER PT S AU Lopez, FR Jimenez-Salazar, H Pinto, D AF Lopez, Franco Rojas Jimenez-Salazar, Hector Pinto, David BE Gelbukh, A TI A competitive term selection method for information retrieval SO Computational Linguistics and Intelligent Text Processing SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 8th International Conference on Intelligent Text Processing and Computational Linguistics CY FEB 18-24, 2007 CL Mexico City, MEXICO SP IPN, Nat Language & Text Proc Lab AB Term selection process is a very necessary component for most natural language processing tasks. Although different unsupervised techniques have been proposed, the best results are obtained with a high computational cost, for instance, those based on the use of entropy. The aim of this paper is to propose an unsupervised term selection technique based on the use of a bigram-enriched version of the transition point. Our approach reduces the corpus vocabulary size by using the transition point technique and, thereafter, it expands the reduced corpus with bigrams obtained from the same corpus, i.e., without external knowledge sources. This approach provides a considerable dimensionality reduction of the TREC-5 collection and, also has shown to improve precision for some entropy-based methods. C1 BUAP, Fac Comp Sci, Puebla 72570, Mexico. CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BOOTH A, 1967, INFORM CONTR, V10, P383 GELBUKH A, 1999, LECT NOTES ARTIF INT, V1692, P130 INTO D, 2006, LNCS, V3878, P536 JIMENEZSALAZAR H, 2005, RES COMPUTING SCI, V14, P163 MONTEMURRO MA, 2001, ARXIVCONDMAT0109218, V1, P12 MOYOTL E, 2005, LNCS, V3406, P706 MOYOTL E, PROCESAMIENTO LENGUA, P141 MOYOTL E, 2005, MASTER COMPUTER SCI Perez-Carballo J, 2000, INFORM PROCESS MANAG, V36, P155, DOI 10.1016/S0306-4573(99)00049-7 PINTO D, 2006, LNCS, V4022 ROJAS F, 2006, P CLEF 2006, P53 ROJAS F, 2006, RES COMPUTING SCI, V20, P107 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHANNON CE, 1948, AT&T TECH J, V27, P379 URBIZAGASTEGUI AR, POSIBILIDADES LEY ZI Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 19 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-70938-1 J9 LECT NOTES COMPUT SC PY 2007 VL 4394 BP 468 EP 475 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA BFS87 UT WOS:000244441100041 ER PT S AU Wang, KT Jeng, YL Huang, YM Wang, TI AF Wang, Kun-Te Jeng, Yu-Lin Huang, Yueh-Min Wang, Tzone-I BE Gervasi, O Gavrilova, ML TI Applying dynamic blog-based learning map in web tutoring assistances SO Computational Science and Its Applications - ICCSA 2007, Pt 1, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT International Conference on Computational Science and Its Applications (ICCSA 2007) CY AUG 26-29, 2007 CL Kuala Lumpur, MALAYSIA SP Univ Perugia, Univ Calgary, Optima Numerics, Spark Planner Pte Ltd, Univ Calgary, SPARCS Lab, MASTER-UP DE problem-based Learning; blog; information retrieval; dynamic learning map AB Web tutoring provides teachers with a variety of pedagogical options and is a convenient platform motivating learning materials for learners. This paper begins by retrieving relevant blog articles, and then integrating a learning map as a dynamic social learning model. Because these retrieved blog articles pertain to course materials, they can be used to promote learner engagement in their interactions with a learning map and hence, achieve their goals more easily. An experimental course has been launched and the results show that learners do make use of the blog-based learning and can eventually cross the specified test thresholds. Lecturers using the proposed approach can apply the principles of dynamic learning in ways which not only reduce teacher workload, but also enhance student learning through the active construction of knowledge supported by alternative perspectives within meaningful blog contexts. C1 Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan. RP Wang, KT (reprint author), Natl Cheng Kung Univ, Dept Engn Sci, 1 Ta Hsueh Rd, Tainan 701, Taiwan. RI Huang, Yueh Min/B-4563-2009 CR *ADL, 2004, ADV DISTR LEARN ADL BAEZAYATES R, 1999, RIBEIRONETO B MODERN BLOOD R, 2002, WEBLOG HDB PRACTICE Brusilovsky P., 1996, LNCS, V1086, P261 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chen JN, 2005, INTERACT LEARN ENVIR, V13, P159, DOI 10.1080/10494820500382810 CHENG SC, 2005, IEEE LEARNING TECHNO, V7, P37 David R., 1993, LECT NOTES COMPUTER, V674, P71 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DeSanctis G, 2001, COMMUN ACM, V44, P80, DOI 10.1145/501317.501352 Jonassen D, 1999, LEARNING TECHNOLOGY LIN FH, 2001, P IEEE PAC RIM C COM, V1, P212 MEDLEY MD, 1999, J CONTINUING HIGHER, V47, P25 Murata T., 1989, P IEEE, V77 Park Y. C., 1995, Proceedings of the 1995 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/221270.221571 PETERSN JL, 1981, PETRI THEORY MODELIN PETRI CA, 1962, THESIS IIM QIU Y, 1993, SIGIR 93 PITTSB PA Ray W.H., 1981, ADV PROCESS CONTROL RICARDO BY, 1999, MODERN INFORM RETRIE, P23 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scardamalia M, 2004, ED TECHNOLOGY ENCY Singhal A., 2001, IEEE DATA ENG B, V24, P35 SU JM, 2005, J COMPUTER STANDARDS TSENG SS, 2005, NEW APPROACH CONSTRU NR 26 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74468-9 J9 LECT NOTES COMPUT SC PY 2007 VL 4705 BP 1119 EP 1132 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BGT71 UT WOS:000250429300093 ER PT S AU Du, Y Pei, Z Li, H Xiang, D Li, K AF Du, Yajun Pei, Zheng Li, HaiMing Xiang, Dan Li, Kai BE Gervasi, O Gavrilova, ML TI New fast algorithm for constructing concept lattice SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2007, PT 2, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT International Conference on Computational Science and Its Applications (ICCSA 2007) CY AUG 26-29, 2007 CL Kuala Lumpur, MALAYSIA SP Univ Perugia, Univ Calgary, Optima Numerics, Spark Planner Pte Ltd, Univ Calgary, SPARCS Lab, MASTER UP AB Concept lattice is an important method and technology for data analysis and knowledge discovery, however, in this research field, many researchers focus on two-valued formal context. In this paper, we transform continuous-valued formal context into many-valued formal context first, then on the basis of many-valued formal context, the definition of the equivalence class of single attribute and formal pairs of single attribute are given. We discuss constructing formal concept lattice based on single attribute concepts, uniting concepts, updating concept and adding concepts, all these concepts are generated by union, intersection and equality of objects of formal pairs. At last, we present a fast algorithm for constructing concept lattice and analyze its complexity. It has demonstrated that the algorithm is effective by our experiments. C1 Xihua Univ, Sch Math & Comp Sci, Sichuan 610039, Peoples R China. RP Du, Y (reprint author), Xihua Univ, Sch Math & Comp Sci, Sichuan 610039, Peoples R China. EM duyajun@mail.xhu.edu.cn CR Bordat J.P., 1986, MATH SCI HUMAINES, V96, P31 Chein M., 1969, B MATH R S ROUMANIE, V13, P21 DU YJ, 2005, P 2005 INT C MACH LE, V4, P2368 Ganter B., 1999, FORMAL CONCEPT ANAL GODIN R, INT J MANMACHINE STU, V38, P747 HETTICH S, 1999, UCI KDD ARCHIEVE Hu K., 2000, J SOFTWARE, V11, P1479 KUZNETSOV SO, 1993, NAUCH-TEKHN INFORM 2, P17 MERZ CJ, 2004, UCI REPOSITORY MACHI Missaoui R., 1994, Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD'93) Norris E.M., 1978, REV ROUMAINE MATH PU, V23, P243 Nourine L, 1999, INFORM PROCESS LETT, V71, P199, DOI 10.1016/S0020-0190(99)00108-8 QIAO SY, 2003, P 2 INT C MACH LEARN, P63 Rajapakse R, 2005, NEURAL COMPUT, V17, P2291, DOI 10.1162/0899766054615662 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STUMME G, 2001, P GIF MASCH LERN U D, V763 STUMME G, 2002, J KNOWLEDGE DATA ENG, V42, P189 Wille R., 1982, ORDERED SETS, P445 XIE ZP, 2000, J COMPUTER RES DEV, V37, P1415 ZHANG WX, 2003, INFORM SYSTEM KNOWLE, P7 NR 20 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74475-7 J9 LECT NOTES COMPUT SC PY 2007 VL 4706 BP 434 EP 447 PG 14 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BGT73 UT WOS:000250429600041 ER PT S AU Parapar, J Casanova, JM Barreiro, A AF Parapar, Javier Casanova, Jose M. Barreiro, Alvaro BE Diaz, RM Pichler, F Arencibia, AQ TI NowOnWeb: News search and summarization SO COMPUTER AIDED SYSTEMS THEORY- EUROCAST 2007 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 11th International Conference on Computer Aided Systems Theory CY FEB 12-16, 2007 CL Las Palmas, SPAIN HO Elder Museum Sci & Technol AB Agile access to the huge amount of information published by the thousands of news sites available on-line leads to the application of Information Retrieval techniques to this problem. The aim of this paper is to present NowOnWeb, a news retrieval system that obtains the articles from different on-line sources providing news searching and browsing. The main points solved during the development of NowOnWeb were: article recognition and extraction, redundancy detection and text summarization. For these points we provided effective solutions that put all them together had risen to a system that satisfies, in a reasonable way, the daily information needs of the user. C1 [Parapar, Javier; Casanova, Jose M.; Barreiro, Alvaro] Univ A Coruna, Dept Comp Sci, IRLab, La Coruna 15071, Spain. RP Parapar, J (reprint author), Univ A Coruna, Dept Comp Sci, IRLab, Campus Elvina S-N, La Coruna 15071, Spain. CR Allan J., 2003, SIGIR 2003, P314 Cafarella M., 2004, QUEUE, V2, P54, DOI 10.1145/988392.988408 Crescenzi V, 2004, J ACM, V51, P731, DOI 10.1145/1017460.1017462 Hatcher E., 2004, ACTION SERIES Hovy E., 2005, OXFORD HDB COMPUTATI, P583 Radev D, 2005, COMMUN ACM, V48, P95, DOI 10.1145/1089107.1089111 REIS DC, 2004, WWW 04, P502, DOI DOI 10.1145/988672.988740 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sigelman S., 2002, P HUM LANG TECHN C Zhang Y., 2002, SIGIR 2002, P81 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-75866-2 J9 LECT NOTES COMPUT SC PY 2007 VL 4739 BP 225 EP 232 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BGZ50 UT WOS:000251543000029 ER PT S AU Ngomo, ACN AF Ngomo, Axel-Cyrille Ngonga BE Hawemdeh, S TI Adaptive and context-sensitive information retrieval SO Creating Collaborative Advantage Through Knowledge and Innovation SE Series on Innovation and Knowledge Management LA English DT Proceedings Paper CT 3rd International Conference on Knowledge Management CY 2006 CL London, ENGLAND AB Retrieving information stored electronically has become a more and more time consuming task, especially in IT-driven companies. We assume this is partially due to the commonly accepted "one-fits-all" construction of search engines. In order to increase the quality of Information Retrieval (IR) in the company environment, we developed a system that increases the accuracy of standard IR engines by two means: One, expanding user queries using a description of the user context and two, learning terms describing user contexts out of implicit user feedback. This paper presents a formal description of the architecture of the system, the implementation details, and our results. C1 Univ Leipzig, Dept Comp Sci, D-04109 Leipzig, Germany. CR Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Claypool M., 2001, P 6 INT C INT US INT, P33, DOI DOI 10.1145/359784.359836 GOODENOUGH W, 1957, LANGUAGE, V32, P195 Morita M., 1994, P 17 ANN INT ACM SIG, P272 MUEHLHOFF T, 2004, P KNOW TECH 2004, P45 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAGHVA K, 1998, TR9801 INF SCI RES I Kelly D., 2003, SIGIR Forum, V37 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VOORHEES E, 1992, P 1 TEXT RETR C WASH, P343 VOORHEES EM, 1994, P 2 TEXT RETR C TREC, P223 Zell A, 1997, SIMULATION NEURONALE NR 13 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE SN 1793-1533 BN 978-981-270-451-1 J9 SER INNOVAT KNOWL MA PY 2007 VL 5 BP 289 EP 300 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Management SC Computer Science; Business & Economics GA BFY46 UT WOS:000245434900019 ER PT B AU Pirro, G Talia, D AF Pirro, Giuseppe Talia, Domenico BE Tjoa, AM Wagner, RR TI An approach to Ontology Mapping based on the Lucene search engine library SO DEXA 2007: 18TH INTERNATIONAL CONFERENCE ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 18th International Conference on Database and Expert Systems Applications CY SEP 03-07, 2007 CL Regensburg, GERMANY SP DEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, Univ Regensburg HO Univ Regensburg AB Recently ontology mapping has been identified as a key issue in semantic-based technologies. A mapping algorithm aims at finding correspondences, and matching, between URIrefs (classes, relationships, instances) of a source ontology and a target ontology by combining several matching components (matchers) each of which relies on one or more ontology features (linguistic, structural, etc..). In this paper we propose an approach to ontology mapping based on the Lucene search engine library. We exploit Lucene features to build an index from a source ontology in which Lucene documents, gathering different kinds of information (name, value, comment, label, etc.) about URIrefs, are stored. Therefore mappings are derived by using values of the URIrefs of the target ontology as search arguments against the index created from the source ontology. Experimental results show the suitability of this approach in terms of Precision, Recall, F-Measure and execution time, as compared to other four approaches. C1 Univ Calabria, DEIS, I-87030 Commenda Di Rende, Italy. RP Pirro, G (reprint author), Univ Calabria, DEIS, I-87030 Commenda Di Rende, Italy. EM gpirro@deis.unical.it; talia@deis.unical.it CR Castano S, 2001, IEEE T KNOWL DATA EN, V13, P277, DOI 10.1109/69.917566 Choi N, 2006, SIGMOD RECORD, V35, P34 DO HH, VLDB 2002, P610 Doan A, 2003, VLDB J, V12, P303, DOI 10.1007/S00778-003-0104-2 EUZENAT J, 2005, K CAP WORKSH INT ONT, P97 EUZENAT J, 2004, ISWC 2004, P698 Giunchiglia F, 2004, ESWS, P61 Klyne G., 2004, RESOURCE DESCRIPTION Kotis K, 2004, LECT NOTES COMPUT SC, V3192, P22 Levenshtein I. V, 1966, SOV PHYS DOKL, P707 Madhavan J., 2001, VLDB J, P49 Noy NF, 2003, INT J HUM-COMPUT ST, V59, P983, DOI 10.1016/j.ijhcs.2003.08.002 Palopoli L, 2003, SOFTWARE PRACT EXPER, V33, P847, DOI 10.1002/spe.531 Patel-Schneider P.F., 2004, OWL WEB ONTOLOGY LAN Qu Y., 2006, WWW, P23 Rahm E, 2001, VLDB J, V10, P334, DOI 10.1007/s007780100057 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STOILOS G, 2005, ISWC 2005, P623 Thanh Le B, 2004, ICEIS, P236 Winkler W. E., 1999, STAT INCOME DIVISION, VR99/04 NR 20 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2932-5 PY 2007 BP 407 EP 411 DI 10.1109/DEXA.2007.29 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BGW55 UT WOS:000250954300082 ER PT B AU Scaffidi, C Bierhoff, K Chang, E Felker, M Ng, H Jin, C AF Scaffidi, Christopher Bierhoff, Kevin Chang, Eric Felker, Mikhael Ng, Herman Jin, Chun GP ACM TI Red Opal: Product-Feature Scoring from Reviews SO EC'07: PROCEEDINGS OF THE EIGHTH ANNUAL CONFERENCE ON ELECTRONIC COMMERCE LA English DT Proceedings Paper CT 8th Annual Conference on Electronic Commerce (EC 2007) CY JUN 11-15, 2007 CL San Diego, CA SP ACM SIGecom AB Online shoppers are generally highly task-driven: they have a certain goal in mind, and they are looking for a product with features that are consistent with that goal. Unfortunately, finding a product with specific features is extremely time-consuming using the search functionality provided by existing web sites. In this paper, we present a new search system called Red Opal that enables users to locate products rapidly based on features. Our fully automatic system examines prior customer reviews, identifies product features, and scores each product on each feature. Red Opal uses these scores to determine which products to show when a user specifies a desired product feature. We evaluate our system on four dimensions: precision of feature extraction, efficiency of feature extraction, precision of product scores, and estimated time savings to customers. On each dimension, Red Opal performs better than a comparison system. C1 [Scaffidi, Christopher; Bierhoff, Kevin; Ng, Herman; Jin, Chun] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Scaffidi, C (reprint author), Carnegie Mellon Univ, Sch Comp Sci, 5000 Forbes Ave, Pittsburgh, PA 15213 USA. EM cscaffid@andrew.cmu.edu; kbierhof@andrew.cmu.edu; wenchiec@andrew.cmu.edu; mfelker@andrew.cmu.edu; ngherman@andrew.cmu.edu; cjin@andrew.cmu.edu CR ALLEN J, 1995, NATURAL LANGUAGE UND Barnard L., 2003, P SAICSIT 2003 ACM, P258 DAMERAU FJ, 1965, AM DOC, V16, P283, DOI 10.1002/asi.5090160403 Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226 Hu Minqing, 2004, P 10 ACM SIGKDD INT, P168, DOI DOI 10.1145/1014052.1014073 Hu N., 2006, P 7 ACM C EL COMM, P324, DOI 10.1145/1134707.1134743 Kageura K., 1996, Terminology, V3, DOI 10.1075/term.3.2.03kag Kim S., 2004, P 20 INT C COMP LING, P1367, DOI DOI 10.3115/1220355.1220555 Leech G., 2001, WORD FREQUENCIES WRI Liu B., 2005, P 14 INT C WORLD WID, P342, DOI DOI 10.1145/1060745.1060797 LIU H, MONTYLINGUA END TO E Matsuo Y, 2003, P 16 INT FLOR AI RES, P392 Morinaga S., 2002, P 8 ACM SIGKDD INT C, P341, DOI DOI 10.1145/775047.775098 Nakagawa H., 2002, P 2 INT WORKSH COMP, P29 PAREKH R., 2003, P 5 WEBKDD WORKSH Popescu A., 2005, P C HUM LANG TECHN E, P339, DOI 10.3115/1220575.1220618 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 Robertson S., 1981, P 3 ANN ACM C RES DE, P35 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCAFFIDI C, 2006, CMUISRI06111 SCH COM Sista S., 2004, P INT C MACH LEARN M Wackerly D, 2001, MATH STAT APPL, V6th WOLFINBARGER M, 2000, P AM C INF SYST LONG, P1362 Wu H., 1981, P 4 ANN INT ACM SIGI, P30, DOI 10.1145/511754.511759 ZAHIR S, 2002, P INT C COMP INF TEC, P381 NR 25 TC 12 Z9 12 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-59593-653-0 PY 2007 BP 182 EP 191 PG 10 WC Computer Science, Interdisciplinary Applications; Mathematics, Applied; Telecommunications SC Computer Science; Mathematics; Telecommunications GA BKJ81 UT WOS:000268330200022 ER PT S AU Teixeira, C Respicio, A AF Teixeira, Carlos Respicio, Ana BE Ma, LH Rauterberg, M Nakatsu, R TI See, hear or read the film SO Entertainment Computing - ICEC 2007 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 6th International Conference on Entertainment Computing (ICES 2007) CY SEP 15-17, 2007 CL Shanghai, PEOPLES R CHINA SP Nat Sci Fdn China, Autodesk Japan, Microsoft Japan HO Shanghai Jiao Tong Univ AB Films have been the most entertaining art form during the past century. Sometimes they were inspired in written novels; sometimes they have inspired new written novels. Film scripts are halfway between the film in the screen and the pure world of written imagination. Real time is one of the dimensions lost in the script, breaking the anchors to the time signals of what films are made. This paper presents a full approach for merging these two worlds in the real time dimension. Using subtitles time stamping and a new parallel text alignment algorithm, a time stamped script is produced. This is used to create new enriched narrative films, also presented in the paper. C1 Univ Lisbon, DI, P-1699 Lisbon, Portugal. RI Respicio, Ana/K-4715-2012 OI Respicio, Ana/0000-0003-2758-7035 CR CHAMBEL T, 2002, P ACM HYPERTEXT 02 C DEMIROS I, 2004, LREC P 4 INT LANG RE Gibbon D. C., 1998, Proceedings Eighth International Workshop on Research Issues in Data Engineering. Continuous-Media Databases and Applications (Cat. No.98TB100223), DOI 10.1109/RIDE.1998.658275 MARTONE A, 2005, P CBMI 4 INT WORKSH MARTONE A, 2004, P SPIE INT C STOR RE, P108 RONFARD R, 2003, P IEEE INT C MULT EX, V1, P121 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turetsky R, 2004, P IEEE INT C MULT EX, V3, P1659 NR 8 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74872-4 J9 LECT NOTES COMPUT SC PY 2007 VL 4740 BP 271 EP 281 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Information Science & Library Science; Social Sciences, Interdisciplinary SC Computer Science; Information Science & Library Science; Social Sciences - Other Topics GA BGQ60 UT WOS:000249782900033 ER PT B AU Li, Q Li, ZS Liang, G Zhang, JY Chen, LY Li, BL AF Li, Qing Li, Zhishu Liang, Gang Zhang, Jing-yu Chen, Liang-yin Li, Baolin BE Lei, J Yu, J Zhou, SG TI IDGSC: An immunity-based approach for dynamic grid service classification SO FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS LA English DT Proceedings Paper CT 4th International Conference on Fuzzy Systems and Knowledge Discovery CY AUG 24-27, 2007 CL Haikou, PEOPLES R CHINA SP Hainan Univ, IEEE Reliabil Soc, Asia Pacific Neural Network Assembly AB Service classification is not only an indispensable method for manually browsing and managing service repositories, but can in fact be used for simple but efficient forms of service annotation and semi-automated discovery, complementary aid for automatic selection. In order to classify services in a service taxonomy dynamically, a new service classification model based on immune theory is proposed in this paper The concepts of "self", "nonself" and "filter" are given first. Then the model is described in detail. Finally, a simulation experiment is performed, and the important parameters of the model are analyzed The experimental result shows that the new model possess better adaptability and stability. C1 [Li, Qing; Li, Zhishu; Liang, Gang; Zhang, Jing-yu; Chen, Liang-yin; Li, Baolin] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China. RP Li, Q (reprint author), Sichuan Univ, Sch Comp Sci, Chengdu 610065, Peoples R China. CR Bruno M., 2005, P IEEE INT C E TECHN DIK L, 1997, IEEE SOFTWARE, P67 FORSTER I, 2002, COMPUTER, V35, P37 Forster I., 2001, INT J SUPERCOMPUTER, V15, P200 FORSTER I, 1998, GRID BLUEPRINT NEW C Keller U, 2005, LECT NOTES COMPUT SC, V3532, P1 Kim J., 2005, P 3 INT C AIS ICARIS, P168 Kim J., 2002, THESIS U COLL LONDON Kim J., 2004, J GENETIC PROGRAMMIN, V5, P361 LI T, 2004, COMPUTE IMMUNOLOGYU *OASIS UDDI, 2004, UDDI TECHN WHIT PAP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEBASTIANI F, 2002, ACM COMPUT SURV, P11 NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA PY 2007 BP 160 EP 165 DI 10.1109/FSKD.2007.351 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHE43 UT WOS:000252460300032 ER PT B AU Gotzelmann, T Hartmann, K Nurnberger, A Strothotte, T AF Goetzelmann, Timo Hartmann, Knut Nuernberger, Andreas Strothotte, Thomas BE Braz, J Vazquez, PP Pereira, JM TI 3D spatial data mining on document sets for the discovery of failure causes in complex technical devices SO GRAPP 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL AS/IE LA English DT Proceedings Paper CT 2nd International Conference on Computer Graphics Theory and Applications CY MAR 08-11, 2007 CL Barcelona, SPAIN SP Inst Syst & Technol Informat, Control & Commun, EuroGraph Spanish Sect, Inst Natl Rech Informat Automat DE spatial data mining; visualization; association rules; discovery of error causes; product improvement ID KNOWLEDGE DISCOVERY AB The retrospective fault analysis of complex technical devices based on documents emerging in the advanced steps of the product life cycle can reveal error sources and problems, which have not been discovered by simulations or other test methods in the early stages of the product life cycle. This paper presents a novel approach to support the failure analysis through (i) a semi-automatic analysis of databases containing product-related documents in natural language (e. g., problem and error descriptions, repair and maintenance protocols, service bills) using information retrieval and text mining techniques and (ii) an interactive exploration of the data mining results. Our system supports visual data mining by mapping the results of analyzing failure-related documents onto corresponding 3D models. Thus, visualization of statistics about failure sources can reveal problem sources resulting from problematic spatial configurations. RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR Agrawal R., 1993, C MAN DAT WASH DC, P207 Bernhardsen T., 2002, GEOGRAPHIC INFORM SY Berthold M, 2003, INTELLIGENT DATA ANA BORGELT C, 2003, I WS FREQUENT ITEM S DIEPSTRATEN J, 2002, TRANSPARENCY INTERAC Ester M, 2000, DATA MIN KNOWL DISC, V4, P193, DOI 10.1023/A:1009843930701 Fellbaum C, 1998, WORDNET ELECT LEXICA FLAVIN PG, 1996, BT TELECONN SERIES Frank E., 1999, DATA MINING PRACTICA FRAWLEY WJ, 1992, AI MAG, V13, P57 Gooch B., 2001, NONPHOTOREALISTIC RE Hand D., 2001, PRINCIPLES DATA MINI Hartmann K, 2002, 2 INT S SMART GRAPH, P39 HARTMANN K, 2002, INT WORK C ADV VIS I, P300 Hipp J., 2002, IND C DAT MIN, P15 Hotho A., 2005, GLDV J COMPUTATIONAL, V20, P19 Keim D.A., 2002, IEEE T VISUALIZATION, V7, P100 McDonald JM, 1998, ARCH PATHOL LAB MED, V122, P409 Miller H. J., 2001, GEOGRAPHIC DATA MINI MOORE GW, 2000, MED DATA MINING KNOW, P61 ORFOR S, 1997, REV VISUALIZATION SO SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMIDT VS, 1998, ESRI EUR US GROUP C Shneiderman B, 1996, IEEE SYMPOSIUM ON VISUAL LANGUAGES, PROCEEDINGS, P336 Strothotte T., 2002, NON PHOTOREALISTIC C Tan P. N., 2005, INTRO DATA MINING Tufte E., 1997, VISUAL EXPLANATIONS Viola I., 2004, P IEEE VIS 2004, P139, DOI DOI 10.1109/VIS.2004.48 ZAKI MJ, 1997, 3 INT C KNOWL DISC D, P283 Zienkiewicz O., 2000, FINITE ELEMENT METHO, V2 NR 31 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-972-8865-72-6 PY 2007 BP 137 EP 145 PG 9 WC Computer Science, Software Engineering SC Computer Science GA BHE23 UT WOS:000252426600018 ER PT B AU Lin, ZD Deng, K Hong, YF AF Lin, Zhongda Deng, Kun Hong, Yanfen BE Lin, TY Hu, X Han, J Shen, X Li, Z TI Research of web pages categorization SO GRC: 2007 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Granular Computing CY NOV 02-04, 2007 CL San Jose, CA SP IEEE Computat Intelligence Soc AB The web is a huge repository of information and there is a need for categorizing web pages to facilitate the indexing, search, and retrieval of pages. In this paper, we discuss several issues related to automated classification of web pages, especially classification of textual web pages. We analyze features selection and categorization algorithms of web pages and give some suggestions for web pages categorization. C1 [Lin, Zhongda; Deng, Kun; Hong, Yanfen] Jiangxi Univ Finance & Econ, Dept Comp Sci & Technol, Nanchang Univ, Jiangxi 330029, Peoples R China. RP Lin, ZD (reprint author), Jiangxi Univ Finance & Econ, Dept Comp Sci & Technol, Nanchang Univ, Jiangxi 330029, Peoples R China. CR Aas K., 1999, TEXT CATEGORIZATION ANL AJ, 2004, T ROUGH SETS, V2 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Craven M, 2001, MACH LEARN, V43, P97, DOI 10.1023/A:1007676901476 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 JOACHIM ST, 2001, COMPOSITE KERNELS HY, P250 MLADENIC B, 2004, 27 ANN INT ACM SIGIR, P234 RIBONI D, EURASIA ICT 2002 P W SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sun Jiantao, 2004, Journal of Tsinghua University (Science and Technology), V44 Yang Y., 1997, 14 INT C MACH LEARN, P412 NR 12 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3032-1 PY 2007 BP 691 EP 694 PG 4 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHG71 UT WOS:000252984500136 ER PT B AU Mesleh, AM Kanaan, G AF Mesleh, Abdelwadood Moh'd Kanaan, Ghassan BE Fahmy, HMA Wahba, AM ElKharashi, MW ElDin, AMB Sobh, MA Taher, M TI Support Vector Machine Text Classification System: Using Ant Colony Optimization Based Feature Subset Selection SO ICCES: 2008 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING & SYSTEMS LA English DT Proceedings Paper CT International Conference on Computer Engineering and Systems CY NOV 25-27, 2008 CL Cairo, EGYPT SP Ain Shams Univ, Fac Engn, Ain Shams Univ, Comp Engn & Syst Dept ID ALGORITHMS AB Feature subset selection (FSS) is an important step for effective text classification systems. In this work, we have implemented a support vector machine (SVM) text classifier for Arabic articles. Moreover, we have implemented a novel FSS method based on Ant Colony Optimization (ACO) and Chi-square statistic. The proposed ACO-Based FSS method adapted Chi-square statistic as heuristic information and the effectiveness of the SVM classifier as a guide to improve the selection of features for each category. Compared to the six state-of-the-art FSS methods, our ACO Based-FSS algorithm achieved better TC effectiveness. Evaluation used an in-house Arabic text classification corpus that consists of 1445 documents independently classified into nine categories. The experimental results were presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F, measures. C1 [Mesleh, Abdelwadood Moh'd] Blaqa Appl Univ, Fac Engn Technol, Amman, Jordan. RP Mesleh, AM (reprint author), Blaqa Appl Univ, Fac Engn Technol, Amman, Jordan. CR Al-Ani A., 2005, INT J COMPUTATIONAL, V2, P53 ALJLAYL M, 2001, 10 TEXT RETR C GAITH, P265 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BLUM AL, 1992, NEURAL NETWORKS, V5, P117, DOI 10.1016/S0893-6080(05)80010-3 Dorigo M, 1991, 91016 DIP EL POL MIL Dorigo M, 1996, IEEE T SYST MAN CY B, V26, P29, DOI 10.1109/3477.484436 Dorigo M, 2004, ANT COLONY OPTIMIZATION, P1, DOI 10.1007/b99492 Dorigo M., 1996, IEEE T SYST MAN CY B, V26, P1 Elbeltagi E, 2005, ADV ENG INFORM, V19, P43, DOI 10.1016/j.aei.2005.01.004 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 GAIAVOTTI L, 2000, P ECDL 00 4 EUR C RE, P59 Goldberg D. E., 1989, GENETIC ALGORITHMS S Jensen R., 2003, P 2003 UK WORKSH COM, P15 Liu H, 1998, FEATURE SELECTION KN Liu H, 2005, IEEE T KNOWL DATA EN, V17, P491 Manning C. D., 1999, FDN STAT NATURAL LAN MESLEH A, 2008, P INT JOINT IN PRESS Mesleh AMA, 2007, J COMPUTER SCI, V3, P430 Moens M. -F., 2006, INFORM EXTRACTION AL Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schreyer M., 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), DOI 10.1109/CEC.2002.1004475 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sivagaminathan RK, 2007, EXPERT SYST APPL, V33, P49, DOI 10.1016/j.eswa.2006.04.010 Vapnik V., 1995, NATURE STAT LEARNING YAHYA A, 1989, 1 GREAT LAK COMP SCI Yang Y., 1997, 14 INT C MACH LEARN, P412 NR 28 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2115-2 PY 2007 BP 143 EP 148 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BJB91 UT WOS:000264588800024 ER PT B AU Yanagimoto, H Omatu, S AF Yanagimoto, Hidekazu Omatu, Sigeru BE Cardoso, J Cordeiro, J Filipe, J TI Text categorization using earth mover's distance as similarity measure SO ICEIS 2007: PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS LA English DT Proceedings Paper CT 9th International Conference on Enterprise Information Systems (ICEIS 2007) CY JUN 12-16, 2007 CL Funchal, PORTUGAL SP INSTICC, Univ Madeira, FCT, ACM SIGMIS, Assoc Advancement Artificial Intelligence DE text categorization; Earth Mover's Distance ID RETRIEVAL AB We propose a text categorization system using Earth Mover's Distance (EMD) as similarity measure between documents. Many text categorization systems adopt the Vector Space Model and use cosine similarity as similarity measure between documents. There is an assumption that each of words included in documents is uncorrelated because of an orthogonal vector space. However, the assumption is not desirable when a document includes a lot of synonyms and polysemic words. The EMD does not demand the assumption because it is computed as a solution of a transportation problem. To compute the EMD in consideration of dependency among words, we define the distance between words, which needs to compute the EMD, using a co-occurrence frequency between the words. We evaluate the proposing method with ModApte split of Reuters-21578 text categorization test collection and confirm that the proposing method improves a precision rate for text categorization. C1 [Yanagimoto, Hidekazu; Omatu, Sigeru] Osaka Prefecture Univ, Osaka, Japan. RP Yanagimoto, H (reprint author), Osaka Prefecture Univ, 1-1 Sakai, Osaka, Japan. EM hidekazu@cs.osakafu-u.ac.jp; omatu@cs.osakafu-u.ac.jp CR Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Mitchell TM, 1997, MACHINE LEARNING PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 WAN X, 2005, 14 ACM INT C INF KNO, P301 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 NR 8 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-972-8865-90-0 PY 2007 BP 632 EP 635 PG 4 WC Computer Science, Information Systems SC Computer Science GA BHH64 UT WOS:000253307500102 ER PT B AU ELdesoky, AE Saleh, M Sakr, NA AF ELdesoky, A. E. Saleh, M. Sakr, N. A. BE Fahmy, HMA Salem, A ElKharashi, MW BahaaElDin, AM Taher, M TI Novel Similarity Measure for Document Clustering Based on Topic Phrases SO ICNM: 2009 INTERNATIONAL CONFERENCE ON NETWORKING & MEDIA CONVERGENCE LA English DT Proceedings Paper CT International Conference on Networking and Media Convergence CY MAR 24-25, 2009 CL Cairo, EGYPT SP Ain Shams Univ ID SUFFIX ARRAYS AB Document clustering is a subset of the data clustering field which categorizes large set of documents into similar and related groups. In the traditional Vector Space Model (VSM) researchers have considered the unique word which occurs in the document set as the candidate feature. Recently a new trend which considered the phrase to be a more informative feature has taken place; the matter which contributes in improving the document clustering accuracy and effectiveness. This paper proposes a new approach for computing the similarity measure of the traditional VSM by considering the topic phrases of the document as the constituting terms for the VSM instead of the traditional term "word" and applying the new approach to the Buckshot method, which is a mix of the Hierarchical Agglomerative Clustering (HAC) algorithm and the K-means partitioning algorithm. Such a mechanism may raise the effectiveness of the clustering by increasing the evaluation metrics values. C1 [ELdesoky, A. E.; Sakr, N. A.] Mansoura Univ, Dept Comp & Syst, Mansoura, Egypt. RP ELdesoky, AE (reprint author), Mansoura Univ, Dept Comp & Syst, Mansoura, Egypt. EM Noha_sakr@mans.edu.eg CR Berkhin P., SURVEY CLUSTERING DA CHIM H, 2007, IEEE T KNOWLEDGE DAT, V3 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Huang1 Joshua Zhexue, 2006, TEXT CLUSTERING ALGO MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 MOSCHITTI A, 2004, ECIR, P181 Nicholas Andrews O., 2007, RECENT DEV DOCUMENT PENG Y, 6 IEEE INT C DAT MIN PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN SURDEANU M, 1921, HYBRID UNSUPERVISED WANG Y, 2006, P 39 HAW INT C SYST Yamamoto M, 2001, COMPUT LINGUIST, V27, P1, DOI 10.1162/089120101300346787 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zhi-biao Wu, 1994, P 32 ANN M ASS COMP NR 17 TC 0 Z9 0 PU IEEE, ELECTRON DEVICES SOC & RELIABILITY GROUP PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-3776-4 PY 2007 BP 92 EP 96 PG 5 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BJW10 UT WOS:000267291400012 ER PT B AU Mesleh, AMA AF Mesleh, Abdelwadood Moh'd A. BE Filipe, J Helfert, M Shishkov, B TI Chi square feature extraction based SVMS Arabic text categorization system SO ICSOFT 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFTWARE AND DATA TECHNOLOGIES, VOL PL/DPS/KE/MUSE LA English DT Proceedings Paper CT 2nd International Conference on Software and Data Technologies CY JUL 22-25, 2007 CL Barcelona, SPAIN SP INSTICC, Workflow Management Coalit, Interdisciplinary Inst Collaborat & Res Enterprise Syst & Technol DE text classification; Arabic language; SVMs; CHI square ID RETRIEVAL AB This paper aims to implement a Support Vector Machines (SVMs) based text classification system for Arabic language articles. This classifier uses CHI square method as a feature selection method in the preprocessing step of the Text Classification system design procedure. Comparing to other classification methods, our classification system shows a high classification effectiveness for Arabic articles term of Macroaveraged F1 = 88.11 and Microaveraged F1 = 90.57. C1 Arab Acad Banking & Financial Sci, Fac Informat Syst & Technol, Amman, Jordan. RP Mesleh, AMA (reprint author), Arab Acad Banking & Financial Sci, Fac Informat Syst & Technol, Amman, Jordan. CR ALSHALABI R, 2006, P 4 INT MULT COMP SC Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Benkhalifa M, 2001, INT J INTELL SYST, V16, P929, DOI 10.1002/int.1042 Cristianini N., 2000, INTRO SUPPORT VECTOR El-Kourdi M., 2004, P COLING 20 WORKSH C, P51, DOI 10.3115/1621804.1621819 HOFMANN H, 2003, INTRO MACHINE LEARNI Hofmann T, 2000, ADV NEUR IN, V12, P914 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Larkey L. S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Manning C. D., 1999, FDN STAT NATURAL LAN Mitchell T., 1996, MACHINE LEARNING SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samir A., 2005, 5 IBIMA C INT INF TE SCHAPIRE RE, 2000, MACHINE LEARNING, V39 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 TAKAMURA H, 2004, P COMPUTATIONAL NATU, P57 Vapnik V., 1995, NATURE STAT LEARNING Vapnik V., 1998, STAT LEARNING THEORY Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y., 1999, 22 ANN INT ACM SIGIR, P42 Yang YM, 1996, J AM SOC INFORM SCI, V47, P357 NR 23 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-989-8111-05-0 PY 2007 BP 235 EP 240 PG 6 WC Computer Science, Software Engineering SC Computer Science GA BHE56 UT WOS:000252479400037 ER PT B AU Alfred, R Kazakov, D AF Alfred, Rayner Kazakov, Dimitar GP IEEE TI Handling datasets in a multi-relational environment: Cluster dispersion vs cluster purity SO IDAACS 2007: PROCEEDINGS OF THE 4TH IEEE WORKSHOP ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS SE IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems-Technology and Applications-IDAACS LA English DT Proceedings Paper CT 4th IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems CY SEP 06-08, 2007 CL Dortmund, GERMANY SP Ternopil Natl Econ Univ, Res Inst Intelligent Comp Syst, Univ Appl Sci Dortmund, Deutsch Forschungsgemeinsch, REW Syst AG, IEEE Instrumentat & Management Soc, Res Inst Intelligent Comp Syst DE data aggregation; clustering; semi-supervised clustering; genetic algorithm; relational data mining; data pre-processin AB Clustering multiple-instances in a multi-relational environment requires data transformations (e.g. data aggregation) from datasets stored in multiple tables into a single table. Unfortunately, most relational databases are limited to a few basic methods of aggregation (e.g. max, min, sum, count, ave) to aggregate continuous and categorical values. Therefore, data transformation is limited only to aggregation of continuous and categorical values. In this paper, to get the best number Of clusters, we propose a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations, in which a single record has multiple instances that are associated with it. The clustering algorithm can be used in two ways. One is the unsupervised clustering, where the user may control the result of clustering by optimizing the value of cluster dispersion. The other is a semi-supervised clustering, where the user uses an unsupervised clustering method optimized with a genetic algorithm incorporating a measure of classification accuracy used in decision tree algorithm, the GINI index. In this paper, we examine both methods to dynamically cluster multiple instances, as a means of aggregating them, and illustrate the effectiveness of the semi-supervised genetic algorithm-based clustering technique. C1 [Alfred, Rayner] Univ Malaysia Sabah, SEIT, Sabah 88999, Malaysia. RP Alfred, R (reprint author), Univ Malaysia Sabah, SEIT, Sabah 88999, Malaysia. EM ralfred@ums.edu.my; kazakov@cs.york.ac.uk RI Alfred, Rayner /M-8376-2013 OI Alfred, Rayner /0000-0002-3080-3264 CR Agrawal R., 1998, P ACM SIGMOD INT C M, P94, DOI 10.1145/276304.276314 ALFRED R, 2006, P 2 INT C ADV DAT MI, P889 ALFRED R, 2006, P DMIN 2006, P296 Breiman L., 1984, CLASSIFICATION REGRE DAVIES DL, 1979, IEEE T PATTERN ANAL, V1, P224 Dietterich TG, 1997, ARTIF INTELL, V89, P31, DOI 10.1016/S0004-3702(96)00034-3 Filho J. L. R., 1994, IEEE COMPUT, V27, P28 Frank E., 1999, DATA MINING PRACTICA Goldberg D. E., 1989, GENETIC ALGORITHMS S Hartigan J, 1975, CLUSTERING ALGORITHM HOFMANN T, 1998, ADV NEURAL INFORM PR, V10 Holland JH, 1975, ADAPTION NATURAL ART KIRSTEN M, 2000, ILP KIRSTEN M, 2000, P ILP 2000 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRINIVASAN A, 1996, ARTIFICIAL INTELLIGE, V85 Tseng LY, 2001, PATTERN RECOGN, V34, P415, DOI 10.1016/S0031-3203(00)00005-4 ZHAO Y, 2003, HIERARCHICAL CLUSTER 1999, WORKSHOP NOTES DISCO NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1347-8 J9 INT WORKSH INT DATA PY 2007 BP 196 EP 201 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BHO06 UT WOS:000254690400041 ER PT B AU Jeong, B Cho, H Kulvatunyou, B Jones, A AF Jeong, Buhwan Cho, Hyunbo Kulvatunyou, Boonserm Jones, Albert BE Chang, W Joshi, JBD TI A multi-criteria web services composition problem SO IRI 2007: Proceedings of the 2007 IEEE International Conference on Information Reuse and Integration LA English DT Proceedings Paper CT IEEE International Conference on Information Reuse and Integration CY AUG 13-15, 2007 CL Las Vegas, NV SP IEEE, SMC Soc AB With its prevalence in enterprise applications integration, the service-oriented approach has been. studied in various ways. The popularity, however, results in a number of different standards and implementations. The approach needs agreed-upon definitions and assumptions. To this end, the paper presents a multi-criteria service composition problem and positions to state service engineering functions associated with the problem in a formal form. The paper also introduces an information. compatibility concept to measure the degree of interoperable data exchanges between services. C1 Pohang Univ Sci & Technol, Dept Ind Engn & Management, Pohang 290784, South Korea. RP Jeong, B (reprint author), Pohang Univ Sci & Technol, Dept Ind Engn & Management, San 31 Hyoja, Pohang 290784, South Korea. CR Christensen Erik, 2001, WEB SERVICE DESCRIPT Clement L, 2004, UNIVERSAL DESCRIPTIO GOU H, 2000, P IEEE INT C SYST MA, P3183 JEONG B, 2006, THESIS POHNAG U SCI JEONG B, 2007, P 20 INT C IND ENG O KOKASH N, 2006, P EUR START AI RES S, P220 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Mani A., 2002, UNDERSTANDING QUALIT Martin D, 2003, OWL S SEMANTIC MARKU MENASCE DA, 2002, IEEE INTERNET COMPUT, P72 O'Brien Liam, 2005, CMUSEI2005TN014 Peterson J. L., 1977, ACM COMPUT SURV, V9, P223, DOI DOI 10.1145/356698.356702 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wu J., 2005, P 2005 IEEE INT C SE, P287 Wu Z., 1994, P 32 ANN M ASS COMP, P133, DOI DOI 10.3115/981732.981751 Zeng L., 2003, P 12 INT C WORLD WID, P411, DOI DOI 10.1145/775152.775211 NR 16 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1499-4 PY 2007 BP 379 EP 384 PG 6 WC Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Remote Sensing SC Computer Science; Remote Sensing GA BGU19 UT WOS:000250559700063 ER PT B AU Gomez, JM Palacios, RC Alor-Hernandez, G Posada-Gomez, R Crespo, AG AF Miguel Gomez, Juan Colomo Palacios, Ricardo Alor-Hernandez, Giner Posada-Gomez, Ruben Garcia Crespo, Angel BE Almeida, V BaezaYates, R TI Search in the eye of the beholder: Using the Personal Social Dataset and Ontology-guided Input to improve Web search efficiency SO LA-WEB 2007: 5TH LATIN AMERICAN WEB CONGRESS, PROCEEDINGS LA English DT Proceedings Paper CT 5th Latin American Web Congress CY OCT 31-NOV 02, 2007 CL Santiago, CHILE SP IW3C2 Int WWW Conf Comm AB Among the challenges of searching the vast information source the Web has become, improving Web search efficiency by different strategies using semantics and the user generated data from Web 2.0 applications remains a promising and interesting approach. In this paper, we present the Personal Social Dataset and Ontology-guided Input strategies and couple them together, providing a proof-of-concept implementation. C1 [Miguel Gomez, Juan; Colomo Palacios, Ricardo; Garcia Crespo, Angel] Univ Carlos III Madrid, Dept Informat, E-28903 Getafe, Spain. RP Gomez, JM (reprint author), Univ Carlos III Madrid, Dept Informat, E-28903 Getafe, Spain. RI Colomo-Palacios, Ricardo/E-5139-2010; Alor-Hernandez, Giner/ OI Colomo-Palacios, Ricardo/0000-0002-1555-9726; Alor-Hernandez, Giner/0000-0003-3296-0981 CR Ankolekar A., 2007, P 16 INT C WORLD WID, P825, DOI 10.1145/1242572.1242684 Brin S., 1998, COMPUTER NETWORKS IS, V30, P1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FENSEL D, 2002, SILVER BULLET KNOWLE GIUNCHIGLIA F, 2005, P AAAI WORKSH CONT O Goldberg D., 1992, COMMUN ACM, V35, P12 GOMEZ JM, 2006, P INT C INT WEB APPL GOMEZ JM, 2007, P 17 INT C EL COMM C GOMEZ JM, 2006, P INT MULT C COMP GL Heyman P., 2006, COLLABORATIVE CREATI KRUK S, 2005, SOCIAL SEMANTIC SEAR KRUK SR, 2005, P SEM DESKT WORKSH I MALTZ D, 1995, P C COMP HUM INT O'Reilly T., 2005, WHAT IS WEB 2 0 DESI OREN E, 2006, P INT SEM WEB C ISWC Ranganathan S.R., 1962, ELEMENTS LIB CLASSIF SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMITZ P, 2006, P 15 WWW C SCHRAEFEL M, 2006, COMMUNICATIONS ACM, V49 Shadbolt N., 2006, SEMANTIC WEB REVISIT SHARDANAND U, 1995, P ACM CHI95 SUGIYAMA K, 2004, P WWW04 YEE KP, 2003, P INT C HUM INT NR 23 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3008-6 PY 2007 BP 50 EP 56 PG 7 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHC99 UT WOS:000252244100006 ER PT J AU Elden, L AF Elden, Lars TI Matrix Methods in Data Mining and Pattern Recognition SO MATRIX METHODS IN DATA MINING AND PATTERN RECOGNITION SE Fundamentals of Algorithms LA English DT Article; Book ID LEAST-SQUARES; INFORMATION-RETRIEVAL; LINEAR ALGEBRA; SEARCH ENGINE; FACTORIZATION; REGRESSION; ALGORITHM; DISCOVERY; MODEL; FACES C1 Linkoping Univ, Linkoping, Sweden. RP Elden, L (reprint author), Linkoping Univ, Linkoping, Sweden. CR Anderson E., 1999, LAPACK USERS GUIDE [Anonymous], 1985, 754 ANSIIEEE Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bai Z., 2000, TEMPLATES SOLUTION A Barrett R, 1994, TEMPLATES SOLUTION L, V1st Bergeron B, 2002, BIOINFORMATICS COMPU Berkhin P, 2005, INTERNET MATH, V2, P73, DOI 10.1080/15427951.2005.10129098 Berry M. W., 2005, Computational & Mathematical Organization Theory, V11, DOI 10.1007/s10588-005-5380-5 Berry M. J. A., 2000, MASTERING DATA MININ Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164 Berry M. W., 2001, COMPUTATIONAL INFORM BERRY MW, 2006, ALGORITHMS APPL APPR Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bjorck A., 2004, Acta Numerica, V13, DOI 10.1017/S0962492904000169 Bjorck A, 1996, NUMERICAL METHODS LE BLOM K., 2005, SIAM J MATRIX ANAL A, V26, P566 Blondel VD, 2004, SIAM REV, V46, P647, DOI 10.1137/S0036144502415960 Boutsidis C., 2005, HPCLABSCG60805 U PAT Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Brunet JP, 2004, P NATL ACAD SCI USA, V101, P4164, DOI 10.1073/pnas.0308531101 Burl MC, 1998, MACH LEARN, V30, P165, DOI 10.1023/A:1007400206189 Businger P. A., 1965, NUMER MATH, V7, P269, DOI 10.1007/BF01436084 CHELLAPPA R, 1995, P IEEE, V83, P705, DOI 10.1109/5.381842 CHRISTIANINI N., 2000, INTRO SUPPORT VECTOR Cios K. J., 1998, DATA MINING METHODS CONROY JM, 2006, DUC 02 C P Davis TA, 2006, FUND ALGORITHMS, V2, P1, DOI 10.1137/1.9780898718881 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 De Lathauwer L, 2000, SIAM J MATRIX ANAL A, V21, P1253, DOI 10.1137/S0895479896305696 Demmel J.W., 1997, APPL NUMERICAL LINEA Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Duda R.O., 2001, PATTERN CLASSIFICATI Elden L, 2004, COMPUT STAT DATA AN, V46, P11, DOI 10.1016/S0167-9473(03)00138-5 Elden L, 2006, ACT NUMERIC, V15, P327, DOI 10.1017/S0962492906240017 Elden L., 2004, INTRO NUMERICAL COMP Fayyad UM, 1996, ADV KNOWLEDGE DISCOV Fowler J.H., 2005, AUTHORITY SUPREME CO Gao Y, 2005, BIOINFORMATICS, V21, P3970, DOI 10.1093/bioinformatics/bti653 Giles J, 2003, STAT DATA MINING KNO, P455 GOHARIAN N, 2003, J SYSTEM CYBERNET IN, V1 Golub GH, 2006, BIT, V46, P759, DOI 10.1007/s10543-006-0091-y Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Golub G.H., 1965, SIAM J NUMER ANAL, V2, P205 Grossman D. A., 1998, INFORM RETRIEVAL ALG Gyongi Z., 2004, P 30 INT C VER LARG, P576, DOI 10.1016/B978-012088469-8/50052-8 Hand D., 2001, PRINCIPLES DATA MINI Hastie T, 2001, ELEMENTS STAT LEARNI Haveliwala T., 2003, ANAL COMPARISON APPR Hegland M, 2001, ACT NUMERIC, V10, P313 Higham N. J., 2002, ACCURACY STABILITY N, V2nd Ipsen ICF, 2006, SIAM J MATRIX ANAL A, V27, P952, DOI 10.1137/S0895479804439808 Jessup ER, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P121 Kamber M., 2001, DATA MINING CONCEPTS Kamvar S. D., 2003, P 12 INT WORLD WID W, P261 Kamvar S.D., 2003, EXPLOITING BLOCK STR KAMVAR SD, 2003, LINEAR ALGEBRA APPL, V386, P51 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LANGVILLE AN, 2005, INT MATH, V1, P355 Langville AN, 2005, SIAM REV, V47, P135, DOI 10.1137/S0036144503424786 Lawson C.L., 1995, CLASSICS APPL MATH, V15 Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791 Lee DD, 1999, NATURE, V401, P788 Lehoucq R. B., 1998, ARPACK USERS GUIDE S Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041 Mangasarian OL, 1990, SIAM NEWS, V23, P1 Mani I., 2001, AUTOMATIC SUMMARIZAT *MATHW INC, 1996, MATL US GUID Mena Jesus, 1999, DATA MINING YOUR WEB Meyer CD, 2000, MATRIX ANAL APPL LIN Meyer CD, 2006, GOOGLES PAGERANK SCI MOLER C, 2002, MATLAB NEWS NOTE OCT, P12 Morrison JL, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-233 PAATERO P, 1994, ENVIRONMETRICS, V5, P111, DOI 10.1002/env.3170050203 Page L, 1998, PAGERANK CITATION RA PAIGE CC, 1982, ACM T MATH SOFTWARE, V8, P43, DOI 10.1145/355984.355989 Park H, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P3 Park H, 2003, BIT, V43, P427, DOI 10.1023/A:1026039313770 Pauca VP, 2006, LINEAR ALGEBRA APPL, V416, P29, DOI 10.1016/j.laa.2005.06.025 Saad Y., 1992, NUMERICAL METHODS LA Saad Y., 2003, ITERATIVE METHODS SP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVAS B, 2002, THESIS LINKOPING U SCHLESINGER JD, 2002, DUC 02 C P Serra-Capizzano S, 2005, SIAM J MATRIX ANAL A, V27, P305, DOI 10.1137/S0895479804441407 Shahnaz F, 2006, INFORM PROCESS MANAG, V42, P373, DOI 10.1016/j.ipm.2004.11.005 Simard P., 1993, ADV NEURAL INFORMATI, V5, P50 Simard P.Y., 2001, INT J IMAGING SYSTEM, V11, p[181, 194] SIROVICH L, 1987, J OPT SOC AM A, V4, P519, DOI 10.1364/JOSAA.4.000519 Sjostrom M., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Smaragdis P., 2003, P IEEE WORKSH APPL S, P177, DOI DOI 10.1109/ASPAA.2003.1285860 Smilde A, 2004, MULTIWAY ANAL APPL C Stewart G, 1998, MATRIX ALGORITHMS BA Stewart G. W., 1990, MATRIX PERTURBATION Stewart G.W., 2001, MATRIX ALGORITHMS, VII Tenenbaum JB, 2000, NEURAL COMPUT, V12, P1247, DOI 10.1162/089976600300015349 TOTTY M, 2003, WALL STREET J 0226, V39 Trefethen L.N., 1997, NUMERICAL LINEAR ALG Tucker L.R, 1964, CONTRIBUTIONS MATH P, P109 TUCKER LR, 1966, PSYCHOMETRIKA, V31, P279, DOI 10.1007/BF02289464 TURK M, 1991, J COGNITIVE NEUROSCI, V3, P71, DOI 10.1162/jocn.1991.3.1.71 van den Bergen GJA, 2004, COLLISION DETECTION Vasilescu M. A. O., 2003, P IEEE C COMP VIS PA, P93 Vasilescu M.A.O., 2002, LNCS, V2350, P447 VASILESCU MAO, 2002, P INT C PATT REC ICP Vasilescu M. A. O., 2002, Proceedings 16th International Conference on Pattern Recognition, DOI 10.1109/ICPR.2002.1048350 Wedin P.-A., 1973, BIT (Nordisk Tidskrift for Informationsbehandling), V13, DOI 10.1007/BF01951945 Wilkinson J.H, 1968, LINEAR ALGEBRA APPL, V1, P409, DOI 10.1016/0024-3795(68)90017-7 Witten I. H., 2000, DATA MINING PRACTICA WOLD H., 1975, PERSPECTIVES PROBABI Wold S, 2001, CHEMOMETR INTELL LAB, V58, P109, DOI 10.1016/S0169-7439(01)00155-1 WOLD S, 1984, SIAM J SCI STAT COMP, V5, P735, DOI 10.1137/0905052 Wolfram S., 1999, MATH BOOK, Vfourth Zeimpekis D, 2005, P WORKSH CLUST HIGH, P38 Zha H., 2002, P 25 ANN INT ACM SIG, P113 NR 114 TC 74 Z9 74 PU SIAM PI PHILADELPHIA PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA BN 978-0-898716-26-9 J9 FUND ALGORITHMS PY 2007 VL 4 BP IX EP + PG 218 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Mathematics, Applied SC Computer Science; Mathematics GA BOM43 UT WOS:000276998900001 ER PT S AU Sukvaree, T Kawtrakul, A Caelen, J AF Sukvaree, Thana Kawtrakul, Asanee Caelen, Jean BE Kokinov, B Richardson, DC RothBerghofer, TR Vieu, L TI Thai text coherence structuring with coordinating and subordinating relations for text summarization SO Modeling and Using Context SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Proceedings Paper CT 6th International Interdisciplinary Conference on Modeling and Using Context (CONTEXT 2007) CY AUG 20-24, 2007 CL Roskilde, DENMARK SP Nokia Res Ctr, Danish Res Council Humanities, Apple, Denmark, Roskilde Univ, Programming, Logic & Intelligent Syst Res Grp, Roskilde Univ, Dept Commun, Business & Informat Technologies ID DISCOURSE; MODEL AB Text summarization with the consideration of coherence can be achieved by using discourse processing with the Rhetorical Structure Theory (RST). Additional problems on relational ambiguity may arise, especially in Thai. For example, the use of cue words, i.e. "tae/(sic)" (meaning "but"), can be identified as a contrast relation or an elaboration relation. Therefore, we propose the reduction of the ambiguity level by reducing the relation types to two, namely Coordinating and Subordinating relation. Our framework is to concentrate on coherence structuring which requires the following 3 steps: (1) identify an attachment point for an incoming discourse unit by using our Adaptive Right-frontier algorithm; (2) extract Coordinating and Subordinating relations through the identification of linguistic coherence features in the lexical and phrasal level, using Bayesian techniques; (3) construct coherence tree structures, The accuracy is 70.45% for the first step, 77.47% and 79.89% for COR and SUBR extraction respectively in the second step and 64.94% in constructing coherent tree of the third. C1 Kasetsart Univ, Dept Comp Engn, Bangkok, Thailand. RP Sukvaree, T (reprint author), Kasetsart Univ, Dept Comp Engn, Bangkok, Thailand. CR ASHER N, 2005, LOGICS CONVERSATION CRISTEA D, SUMMARISATION DISCOU, P632 EDMUNDSON HP, 1969, ACM, V16, P264 GELBUKH A, 2005, LNCS, V3406 GROSZ BJ, 1995, COMPUT LINGUIST, V21, P203 HOVY E, 1977, P WORKSH INT SCAL TE, P18 HOVY E, 1977, DISCOURSE PROCESS, P18 KONGWA A, 2005, P 6 S NAT LANG PROC Mann W, 1988, TEXT, V8, P243, DOI 10.1515/text.1.1988.8.3.243 Marcu D., 1997, M ASS COMP LING, P96 Moore J. D., 1992, Computational Linguistics, V18 POLANYI L, 1988, J PRAGMATICS, V12, P601, DOI 10.1016/0378-2166(88)90050-1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SASSEN C, RIGHT FRONTIER CONST, P222 WATTANAMETHANON M, 2005, P 6 S NAT LANG PROC NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-74254-8 J9 LECT NOTES ARTIF INT PY 2007 VL 4635 BP 453 EP 466 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BGV07 UT WOS:000250748900034 ER PT S AU Hu, Y Lu, RZ Chen, YQ Liu, H Zhang, DY AF Hu, Yi Lu, Ruzhan Chen, Yuquan Liu, Hui Zhang, Dongyi BE Kedad, Z Lammari, N Metais, E Meziane, F Rezgui, Y TI The dictionary-based quantified conceptual relations for hard and soft chinese text clustering SO Natural Language Processing and Information Systems, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 12th International Conference on Applications of Natural Language to Information Systems CY JUN 27-29, 2007 CL Paris, FRANCE SP Univ Versailles, St Quentin, Conservatoire Natl Arts & Metiers, Univ Salford AB In this paper we present a new similarity of text on the basis of combining cosine measure with the quantified conceptual relations by linear interpolation for text clustering. These relations derive from the entries and the words in their definitions in a dictionary, which are quantified under the assumption that the entries and their definitions are equivalent in meaning. This kind of relations is regarded as "knowledge" for text clustering. Under the framework of k-means algorithm, the new interpolated similarity improves the performance of clustering system significantly in terms of optimizing hard and soft criterion functions. Our results show that introducing the conceptual knowledge from the un-structured dictionary into the similarity measure tends to provide potential contributions for text clustering in future. C1 Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. RP Hu, Y (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. RI Liu, Hui/ OI Liu, Hui/0000-0003-1679-6560 CR Anderberg M. R., 1973, CLUSTER ANAL APPL Bloehdoen S., 2004, P 4 IEEE INT C DAT M, P331 Caraballo S., 1999, P 37 ANN M ASS COMP, P120, DOI 10.3115/1034678.1034705 Cimiano P, 2005, J ARTIF INTELL RES, V24, P305 DING C, 2001, TR2001XX U CAL LAWR Hindle D., 1990, P 28 ANN M ASS COMP, P268, DOI 10.3115/981823.981857 Hotho A., 2003, P SEM WEB WORKSH SIG JING L, 2006, P SIAM SDM TEXT MIN Jing L, 2005, P PAC AS C KNOWL DIS, P802 Kamber M., 2001, DATA MINING CONCEPTS LI XJ, 2004, MODERN CHINESE STAND Mitchell Tom. M., 1997, MACH LEARN, P191 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEINBACH M, 2000, P KDD WORKSH TEXT MI Velardi P., 2001, P INT C FORM ONT INF, P270 ZHAO Y, 2002, COMP AGGLOMERATIVE P, P2 ZHAO Y, 2001, TR0140 U MINN DEP CO NR 18 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73350-8 J9 LECT NOTES COMPUT SC PY 2007 VL 4592 BP 96 EP 106 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BGK39 UT WOS:000247910300009 ER PT S AU Schumacher, K AF Schumacher, Kinga BE Kedad, Z Lammari, N Metais, E Meziane, F Rezgui, Y TI Four methods for supervised word sense disambiguation SO Natural Language Processing and Information Systems, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 12th International Conference on Applications of Natural Language to Information Systems CY JUN 27-29, 2007 CL Paris, FRANCE SP Univ Versailles, St Quentin, Conservatoire Natl Arts & Metiers, Univ Salford DE word sense disambiguation; term weighting; machine learning AB Word sense disambiguation is the task to identify the intended meaning of an ambiguous word in a certain context, one of the central problems in natural language processing. This paper describes four novel supervised disambiguation methods which adapt some familiar algorithms. They built on the Vector Space Model using an automatically generated stop list and two different statistical methods of finding index terms. These proceedings allow a fully automated and language independent disambiguation. The first method is based upon Latent Semantic Analysis, an automatic indexing method employed for text retrieval. The second one disambiguates via co-occurrence vectors of the target word. Disambiguation relying on Naive Bayes uses the Naive Bayes Classifier and disambiguation relying on SenseClusters(1) uses an unsupervised word sense discrimination technique. These methods were implemented and evaluated to experience their performance, to compare the different approaches and to draw conclusions about the main characteristic of supervised disambiguation. The results show that the classification approach using Naive Bayes is the most efficient, scalable and successful method. C1 German Res Ctr Artificial Intelligence, Knowledge Management Dept, Kaiserslautern, Germany. RP Schumacher, K (reprint author), German Res Ctr Artificial Intelligence, Knowledge Management Dept, Kaiserslautern, Germany. CR BAGGA A, 1996, 16 C COMP LING Banerjee S., 2002, LNCS, V2276, P136 Banko M, 2001, P 39 ANN M ASS COMP BERRY MW, 1994, CS94270 COMP SCI DEP DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Frank E, 2005, DATA MINING PRACTICA KAROV Y, 1998, COMPUTATIONAL LINGUI, V24 KONTOSTATHIS A, 2002, LUCSE02010 LEH U DEP LESK M, 1986, 5 INT C SYST DOC LEVOW GA, 1997, AIM1637 MIT AI LAB MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Pedersen T., 2005, 200525 UMSI Purandare A., 2004, P C COMP NAT LANG LE, P41 PURANDARE A, 2004, UNSUPERVISED WORD SE Russel S., 2003, ARTIFICIAL INTELLIGE, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 NR 17 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-73350-8 J9 LECT NOTES COMPUT SC PY 2007 VL 4592 BP 317 EP 328 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BGK39 UT WOS:000247910300028 ER PT B AU Lin, FR Yu, JH AF Lin, Fu-ren Yu, Jen-Hung GP PACIS TI Visualized Cognitive Knowledge Map Integration for P2P Networks SO PACIFIC ASIA CONFERENCE ON INFORMATION SYSTEMS 2007, SECTIONS 1-6 LA English DT Proceedings Paper CT 11th Pacific Asia Conference on Information Systems CY JUL 03-06, 2007 CL Auckland, NEW ZEALAND DE Self-Organizing Map (SOM); Knowledge Map; Peer-to-Peer (P2P) Architecture; Egocentric SOM (ESOM) AB In the knowledge management field, knowledge map created under the client-server architecture has been widely used to direct the knowledge sharing process. Peer-to-peer (P2P) architecture has been practicable for file sharing, distributed computing, instant messaging, etc. by virtue of the increases of Internet bandwidth and personal computer capability. P2P architecture attracts researchers and practitioners to study knowledge sharing issues in its autonomy and self-organization characters. It calls for academic efforts to design knowledge management supporting system as that for client-server architecture may not be applicable. This study proposes a visualized cognitive knowledge map integration system to facilitate knowledge management on P2P networks. By using the SOM (Self-Organized Map)-like model, called Egocentric SOM (ESOM), the prototyping system can merge the external knowledge under a focal peer's knowledge structure and present the cognitive knowledge map visually. In evaluating the proposed integration method this study allocates abstracts of industrial research reports from an industrial technology research institute according to their corresponding author peers and generates individual cognitive knowledge maps. The results from the evaluation experiments reveal that ESOM is capable to retain individual peer's knowledge structure while articulating with that of other peers in the cognitive knowledge network. C1 [Lin, Fu-ren; Yu, Jen-Hung] Natl Tsing Hua Univ, Hsinchu, Taiwan. EM frlin@mx.nthu.edu.tw; jhyu@gmail.com CR Agrawal R., 2000, P 7 INT C EXT DAT TE, P365 BONIFACIO M, 2002, P 3 INT S MULT AG SY Castano S., 2003, Proceedings. 14th International Workshop on Database and Expert Systems Applications Fritzke B., 1995, ADV NEURAL INFORMATI, V7, P625 *HUNG C, 2003, P 23 SGAI INT C INN Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Lin FR, 2006, INFORM PROCESS MANAG, V42, P551, DOI 10.1016/j.ipm.2005.03.026 LUHN HP, 1957, IBM J RES DEV, V1, P309 Mangisengi O., 2003, DEXA 03, P787 Rauber A, 2002, IEEE T NEURAL NETWOR, V13, P1331, DOI 10.1109/TNN.2002.804221 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wei C.-P., 2001, P 34 HAW INT C SYST NR 13 TC 0 Z9 0 PU NATL SUN YAT-SEN UNIV PI KAOHSIUNG PA ELECTRONIC COMMERCE RES CTR, 70 LIEN-HAI RD, KAOHSIUNG, 804, TAIWAN PY 2007 PG 14 WC Business; Computer Science, Information Systems; Management SC Business & Economics; Computer Science GA BKS82 UT WOS:000269121600091 ER PT B AU Rhodes, J Boyer, S Kreulen, J Chen, Y Ordonez, P AF Rhodes, James Boyer, Stephen Kreulen, Jeffrey Chen, Ying Ordonez, Patricia BE Altman, RB Dunker, AK Hunter, L Murray, T Klein, TE TI Mining patents using molecular similarity search SO Pacific Symposium on Biocomputing 2007 LA English DT Proceedings Paper CT 13th Pacific Symposium on Biocomputing (PSB) CY JAN 03-07, 2007 CL Maui, HI DE chemical similarity; data mining; patents; search engine; InChI ID CHEMICAL SIMILARITY; SMILES AB Text analytics is becoming an increasingly important tool used in biomedical research, While advances continue to be made in the core algorithms for entity identification and relation extraction, a need for practical applications of these technologies arises. We developed a system that allows users to explore the US Patent corpus using molecular information. The core of our system contains three main technologies: A high performing chemical annotator which identifies chemical terms and converts them to structures, a similarity search engine based on the emerging IUPAC International Chemical Identifier (InChI) standard, and a set of on demand data mining tools. By leveraging this technology we were able to rapidly identify and index 3, 623, 248 unique chemical structures from 4,375,036 US Patents and Patent Applications. Using this system a user may go to a web page, draw a molecule, search for related Intellectual Property (IP) and analyze the results. Our results prove that this is a far more effective way for identifying IP than traditional keyword based approaches. C1 IBM Corp, Almaden Serv Res, San Jose, CA 95120 USA. RP Rhodes, J (reprint author), IBM Corp, Almaden Serv Res, San Jose, CA 95120 USA. CR Brecher J, 1999, J CHEM INF COMP SCI, V39, P943, DOI 10.1021/ci990062c DALBY A, 1992, J CHEM INF COMP SCI, V32, P244, DOI 10.1021/ci00007a012 Flower DR, 1998, J CHEM INF COMP SCI, V38, P379, DOI 10.1021/ci970437z *GNU FDL, 2006, OP BAB *HUM, 2005, TOP 50 BRAND NAM DRU *INC DAYL CHEM INF, 2006, DAYL CHEM SMILES *INC DAYL CHEM INF, 2005, DAYL THEOR FING Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876 *IUPAC, 2005, IUPAC INT CHEM ID IN KRAMER S, 2001, KDD 01 P 7 ACM SIGKD *MURR RUST RES GRO, 2006, UN INCHI FAQ *NAT CTR BIOT INF, 2006, PUBCH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stein S. E., 2003, P 2003 INT CHEM INF WEININGER D, 1988, J CHEM INF COMP SCI, V28, P31, DOI 10.1021/ci00057a005 WEININGER D, 1989, J CHEM INF COMP SCI, V29, P97, DOI 10.1021/ci00062a008 Willett P, 1998, J CHEM INF COMP SCI, V38, P983, DOI 10.1021/ci9800211 NR 17 TC 12 Z9 12 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 981-270-417-5 PY 2007 BP 304 EP 315 PG 12 WC Biochemistry & Molecular Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical SC Biochemistry & Molecular Biology; Computer Science; Engineering GA BFX58 UT WOS:000245296300029 PM 17990501 ER PT B AU Tang, J Du, YJ Qi, L AF Tang, Jun Du, Ya-Jun Qi, Liang TI Research on concept lattice based personalized information retrieval SO PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 LA English DT Proceedings Paper CT 6th International Conference on Machine Learning and Cybernetics CY AUG 19-22, 2007 CL Hong Kong, PEOPLES R CHINA SP Machine Learning & Cybernet Res Inst, Hebei Univ, IEEE Syst Man & Cybernet Soc, Harbin Inst Technol Shenzhen Grad Sch, Chinese Univ Hong Kong, City Univ Hong Kong, Hong Kong Baptist Univ, Hong Kong Univ Sci & Technol, Int Fuzzy Syst Assoc, Hebei Univ Sci & Technol DE concept lattice; information retrieval model; user modeling; personalized services AB The information retrieval model is the core of the information retrieval. Nowadays, with the recent advances in the theory and practice of concept lattice, many researchers are using concept lattice as a basis for constructing information retrieval model, the representative of which is the concept lattice based ranking model (CLR). However, the CLR is only based on the shortest path linking the query to the documents. It is not well defined. To resolve the existing problem, first, this paper presents a concept lattice based user model to represent user interest in concept lattice structure. A novel concept lattice based user modeling algorithm is also provided. Second, this paper proposes a concept lattice based personalized ranking model (CLPR), which measures the similarity among query, user interest model and documents according to the relation between query and user interest based on concept lattice. The similarity computation of CLPR contains more information than traditional information retrieval models, and moreover, the CLPR can identify the new interest of users and find the documents of new interest. Our experiments show that documents retrieved by CLPR achieve steadily higher measures of precision than the CLR and the traditional vector space model. And CLPR is feasible to common search engine users. C1 [Tang, Jun] Qingdao Agr Univ, Sch Commun, Qingdao 266109, Peoples R China. RP Tang, J (reprint author), Qingdao Agr Univ, Sch Commun, Qingdao 266109, Peoples R China. CR BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BELKIN NJ, 1997, P 6 INT C US MOD CHI, P213 CARPINETO C, 2005, LECT NOTES COMPUTER, P161 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DU Y, 2005, THESIS SW JIAOTONG U, P47 Ganter B., 1999, FORMAL CONCEPT ANAL MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 MCTEAR MF, 1993, ARTIF INTELL REV, V7, P157, DOI 10.1007/BF00849553 Romano G., 2004, CONCEPT DATA ANAL TH Salton G., 1997, SIGIR Forum, V31 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wille R., 1982, ORDERED SETS, P445 NR 12 TC 3 Z9 3 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-0972-3 PY 2007 BP 4032 EP 4037 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Theory & Methods SC Computer Science GA BGZ09 UT WOS:000251433406071 ER PT B AU Yu, J Kang, JC Meng, XY AF Yu Jing Kang Jian-chu Meng Xiang-yi BE Zhang, SY Wang, F TI Research on genetic classifier in process method of decision information SO Proceedings of the 2007 Chinese Control and Decision Conference LA Chinese DT Proceedings Paper CT Chinese Control Decision Conference CY JUL 03-06, 2007 CL Wuxi, PEOPLES R CHINA DE genetic classifier; intelligence decision support system; cloud model; discretization method AB As an attempt of intelligence decision support system(IDSS), genetic classifier is applied to IDSS. Genetic algorithm is used to enhance the learning ability of the whole system. Besides, aiming at the limitations of the discretize attributes of genetic classifier, a cloud based on discretization method is introduced and the rule matching procedure is improved. Experiments show the effectiveness of this method. C1 Beihang Univ, Coll Comp, Beijing 100083, Peoples R China. RP Yu, J (reprint author), Beihang Univ, Coll Comp, Beijing 100083, Peoples R China. CR APTE C, 1994, P 17 ANN ACM SIGIR C BARRY A, DATA MINING USING LE Li D. Y., 2005, ARTIFICIAL INTELLIGE, VFirst Li Darryl, 2003, P 2003 NAT C ART INT, P6 MURPHY PM, REPOSITORY MACHINE L SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tiyun Huang, 2001, INTELLIGENCE DECISIO Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 8 TC 0 Z9 0 PU NORTHEASTERN UNIV PR PI BOSTON PA PO BOX 116, BOSTON, MA 02117 USA BN 978-7-81102-396-1 PY 2007 BP 937 EP 940 PG 4 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA BGJ64 UT WOS:000247648200250 ER PT B AU Zeng, LM Zhang, Y Qiu, RG AF Zeng, Limin Zhang, Yong Qiu, Robin G., Sr. GP IEEE TI Adaptive user profiling in enhancing RSS-based information services SO Proceedings of the 2007 IEEE International Conference on Service Operations and Logistics, and Informatics LA English DT Proceedings Paper CT IEEE International Conference on Service Operations and Logistics and Informatics CY AUG 27-29, 2007 CL Philadelphia, PA SP IEEE Intelligent Transportat Syst Soc, INFORMS, CAS, Key Lab Complex Syst & Intelligence Sci DE user profile; Rocchio; content filtering; concept drift; RSS ID CONCEPT DRIFT; TEXT AB Although there are many web-based information service systems available, it is still not an easy job in navigating through the Internet to get the needed information service in a timely manner. This paper investigates a practical approach to deploying just-in-time assistive information services on the Internet. As a preliminary exploration, the RSS technology is used to assure that the information service is up-to-date. When combined with one-time Rocchio feedback and many-times Rocchio feedback, information services can be well filtered and aggregated to meet the needs of personalization, and become more just-in-time assistive. A prototyped news service system is built to validate the applicability of the proposed methodology. C1 Nanjing Univ Aeronaut & Astronaut, Coll Informat Sci & Technol, Nanjing 210016, Peoples R China. CR Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BILLSUS D, 1999, 7 INT C US MOD BANFF, P99 Bollacker KD, 2000, IEEE INTELL SYST APP, V15, P42, DOI 10.1109/5254.850826 Degemmis M, 2006, LECT NOTES ARTIF INT, V4093, P661 Diaz A., 2005, Web Intelligence and Agent Systems, V3 Fdez-Riverola F, 2007, EXPERT SYST APPL, V33, P36, DOI 10.1016/j.eswa.2006.04.011 Gauch S., 2003, Web Intelligence and Agent Systems, V1 GODOY D, KNOWLEDGE ENG REV, V20, P329 GRIMNES GA, 2003, 9 INT C US MOD JOACHIMS T, 1997, 14 INT C MACH LEARN KLINKENBERG R, 2000, 7 INT C MACH LEARN, P487 Klinkenberg R., 2004, Intelligent Data Analysis, V8 KUBAT M, 1989, PATTERN RECOGN LETT, V10, P223, DOI 10.1016/0167-8655(89)90092-5 Lang K., 1995, 12 INT C MACH LEARN, P331 Lazarescu M. M., 2004, Intelligent Data Analysis, V8 MAGNINI B, 2001, 8 INT C US MOD, P74 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MOSCHITTI A, 2003, 25 EUR C INF RETR RE PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Widmer G, 1996, MACH LEARN, V23, P69, DOI 10.1007/BF00116900 NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1117-7 PY 2007 BP 63 EP 67 PG 5 WC Business; Engineering, Electrical & Electronic; Information Science & Library Science; Management; Operations Research & Management Science SC Business & Economics; Engineering; Information Science & Library Science; Operations Research & Management Science GA BGV34 UT WOS:000250780200011 ER PT B AU Richter, Y Naveh, Y Gresh, DL Connors, DP AF Richter, Yossi Naveh, Yehuda Gresh, Donna L. Connors, Daniel P. GP IEEE TI Optimatch: Applying constraint programming to workforce management of highly-skilled employees SO PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS LA English DT Proceedings Paper CT IEEE International Conference on Service Operations and Logistics and Informatics CY AUG 27-29, 2007 CL Philadelphia, PA SP IEEE Intelligent Transportat Syst Soc, INFORMS, CAS, Key Lab Complex Syst & Intelligence Sci AB Today many companies face the challenge of matching highly-skilled professionals to high-end positions in large organizations and human deployment agencies. Unlike traditional Workforce Management problems such as shift scheduling, highly-skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work specifically focuses on the services industry, where much of the revenue comes from the assignment of highly professional workers. Here, non-accurate matches may result in significant monetary losses and other negative effects. We deal with very large pools of both positions and employees, where optimal decisions should be made rapidly in a dynamic environment. Since traditional Operations Research (OR) methods fail to answer this problem, we employ Constraint Programming (CP), a subfield of Artificial Intelligence with strong algorithmic foundations. Our CP model builds on new constraint propagators designed for this problem (but applicable elsewhere), as well as on information retrieval methods used for analyzing the complex text describing high-end professionals and positions. Optimatch, which is based on this technology and is being used by IBM services organizations, provides strong experimental results. C1 IBM Haifa Res Lab, IL-31905 Haifa, Israel. EM richter@il.ibm.com; naveh@il.ibm.com; gresh@us.ibm.com; dconnors@us.ibm.com CR Dechter R., 2003, CONSTRAINT PROCESSIN GILAT D, 2006, P 6 INT C PRACT THEO, P518 GRESH DL, 2007, IBM J RES DEV, V51 Hatcher Erik, 2004, LUCENE ACTION MUNAF D, 1993, WP121203 Naveh Y, 2007, IBM J RES DEV, V51, P263 Richter Y, 2006, LECT NOTES COMPUT SC, V4204, P468 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANHENTENRYCK P, 1999, OPL OPTIMICATION PRO VANHENTENRYCK P, 1997, NUMERICA MODELING LA Verfaillie G, 2005, CONSTRAINTS, V10, P253, DOI 10.1007/s10601-005-2239-9 YANG R, 1996, 2 INT C PRACT APPL C, P373 APACHE LUCENE NR 14 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-1117-7 PY 2007 BP 173 EP 178 PG 6 WC Business; Engineering, Electrical & Electronic; Information Science & Library Science; Management; Operations Research & Management Science SC Business & Economics; Engineering; Information Science & Library Science; Operations Research & Management Science GA BGV34 UT WOS:000250780200030 ER PT B AU Ke, WM Mostafa, J Fu, YY AF Ke, Weimao Mostafa, Javed Fu, Yueyu BE Larson, R Rasmussen, E Sugimoto, S Toms, E TI Collaborative Classifier Agents: Studying the Impact of Learning in Distributed Document Classification SO PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES LA English DT Proceedings Paper CT 7th ACM/IEEE Joint Conference on Digital Libraries CY JUN 18-23, 2007 CL Vancouver, CANADA SP ACM SIG Informat Retrieval, ACM SIG Hypertex, Hypermedia & Web, IEEE TCDL, Amer Soc Informat Sci & Technol, Coalit Networked Informat DE Information retrieval; distributed classification; multi-agent system; learning; collaboration; effectiveness; efficiency ID TEXT CATEGORIZATION; DIGITAL LIBRARIES AB We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms. C1 [Ke, Weimao; Mostafa, Javed; Fu, Yueyu] Indiana Univ, Lab Appl Informat Res, Bloomington, IN 47405 USA. EM wke@indiana.edu; jm@indiana.edu; yufu@indiana.edu RI Ke, Weimao/K-7462-2014 CR Baeza-Yates R., 2004, MODERN INFORM RETRIE Borner K., 2005, COMPLEXITY, V10, P58 CALLAN J, 2003, SIGIR FORUM, V37, P33 Chakrabarti S, 1999, COMPUTER, V32, P60, DOI 10.1109/2.781636 Davis JR, 2000, J AM SOC INFORM SCI, V51, P273, DOI 10.1002/(SICI)1097-4571(2000)51:3<273::AID-ASI6>3.0.CO;2-6 Dushay N., 1999, Digital 99 Libraries. Fourth ACM Conference on Digital Libraries Fox EA, 2004, JCDL 2004: PROCEEDINGS OF THE FOURTH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P374 Frank E, 2005, DATA MINING PRACTICA FU Y, 2005, JCDL 05, P157 Gorodetsky V, 2003, IEEE/WIC INTERNATIONAL CONFERENCE ON INTELLIGENT AGENT TECHNOLOGY, PROCEEDINGS, P438, DOI 10.1109/IAT.2003.1241116 HELSINGER A, 2004, P IEEE C SYST MAN CY Ke WM, 2005, LIBR COLLECT ACQUIS, V29, P220, DOI 10.1016/j.lcats.2005.04.015 Knight K, 1999, COMMUN ACM, V42, P58, DOI 10.1145/319382.319394 Korfhage R., 1997, INFORM STORAGE RETRI Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 LEWIS DD, 1992, TEXT-BASED INTELLIGENT SYSTEMS : CURRENT RESEARCH AND PRACTICE IN INFORMATION EXTRACTION AND RETRIEVAL, P179 Lewis DD, 2004, J MACH LEARN RES, V5, P361 MENCZER F, 1998, AGENTS 98, P157 Modi P. J., 2001, AGENTS 01, P37 Mostafa J, 2000, INFORM PROCESS MANAG, V36, P415, DOI 10.1016/S0306-4573(99)00033-3 MUKHOPADHYAY S, 1989, IEEE T SYST MAN CYB, V19, P1008, DOI 10.1109/21.44015 Mukhopadhyay S, 2005, J AM SOC INF SCI TEC, V56, P834, DOI 10.1002/asi.20176 Mukhopadhyay S, 2003, J AM SOC INF SCI TEC, V54, P966, DOI 10.1002/asi.10292 Paepcke A, 1998, COMMUN ACM, V41, P33, DOI 10.1145/273035.273044 PENG S, 2001, IPDPS 01 PEREIRA FB, 2002, P S APPL COMP MARCH REDKO VG, 2002, ICAIS, P279 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 TAULBEE OE, 1965, P 1965 20 NAT C NEW, P119 WIMMER MA, 2000, DEXA 2000, P340 Yang K., 2002, THESIS U N CAROLINA Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 YU ES, 2000, AGENTS 00, P388 NR 34 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036-9998 USA BN 978-1-59593-644-8 PY 2007 BP 428 EP 437 DI 10.1145/1255175.1255263 PG 10 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Information Science & Library Science SC Computer Science; Engineering; Information Science & Library Science GA BJI20 UT WOS:000266062800068 ER PT B AU De Souza, AF Pedroni, F Oliveira, E Ciarelli, PM Henrique, WF Veronese, L AF De Souza, Alberto F. Pedroni, Felipe Oliveira, Elias Ciarelli, Patrick M. Henrique, Wallace F. Veronese, Lucas BE Mourelle, LD Nedjah, N Kacprzyk, J Abraham, A TI Automated free text classification of economic activities using VG-RAM Weightless Neural Networks SO PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS LA English DT Proceedings Paper CT 7th International Conference on Intelligent Systems Design and Applications (ISDA 2007) CY OCT 22-24, 2007 CL Rio de Janeiro, BRAZIL SP FAPERJ, Int Fuzzy Syst Assoc, Brazilian Comp Soc, Brazilian Soc Automat, IEEE Syst Man & Cybernet Soc, European Neural Network Soc, European Soc Fuzzy Log & Technol, World Federat Soft Comp HO Univ Estado Rio de Janeiro AB We tackle the problem of automating the categorization of companies according to their economic activities using business descriptions in free text format as input. This categorization is vital to fundamental aspects of national governmental administration such as short, medium and long term planning and taxation. As the number of categories considered is very large (more than 1000 in the Brazilian scenario), the automatic text categorization problem targeted here is challenging. We have applied and compared the use of two different techniques to deal with it: the Vector Space Model, a well known text categorization technique; and Virtual Generalizing Random Access Memory Weightless Neural Network, or VG-RAM WNN. To our knowledge, this is the first report on using VG-RAM WNN for text categorization. C1 [De Souza, Alberto F.; Pedroni, Felipe; Henrique, Wallace F.; Veronese, Lucas] Dept Informat, BR-29060970 Vitoria, Brazil. RP De Souza, AF (reprint author), Dept Informat, Av Fernando Ferrari S-N, BR-29060970 Vitoria, Brazil. RI De Souza, Alberto/C-4546-2013 CR ALEKSANDER I, 1966, IEE ELECT LETT, P231 ALEKSANDER I, 1997, BRAIN LIKE COMPUTING, P513 Aleksander I., 1998, RAM BASED NEURAL NET, P18 CARNEIRO R, 2006, LECT NOTES COMPUTETR, V4231, P427 *CNAE, 2003, FISC IBGE KOMATI KS, 2003, APPL BIONICS BIOMECH, V1, P21, DOI 10.1533/abib.2003.1.1.21 Ludermir T. B., 1999, NEURAL COMPUT SURV, V2, P41 Mitchell R. J., 1998, RAM BASED NEURAL NET, P61 *NOV TECHN SOL, 2007, NEUR REPR MOD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2976-9 PY 2007 BP 782 EP 787 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHC83 UT WOS:000252223600127 ER PT B AU Arevian, G AF Arevian, Garen BA Young, T BF Young, T BE Haas, LL Kacprzyk, J Motwani, R Broder, A Ho, H TI Recurrent neural networks for robust real-world text classffication SO PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE: WI 2007 LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY NOV 02-05, 2007 CL Silicon Valley, CA SP IEEE Comp Soc, Web Intelligence Consortium, ACM, YAHOO, IBM, San Jose State Univ ID CATEGORIZATION AB This paper explores the application of recurrent neural networks for the task of robust text classification of a real-world benchmarking corpus. There are many well-established approaches which are used for text classification, but they fail to address the challenge from a more multi-disciplinary viewpoint such as natural language processing and artificial intelligence. The results demonstrate that these recurrent neural networks can be a viable addition to the many techniques used in web intelligence for tasks such as context sensitive email classification and web site indexing. C1 Univ Sunderland, Sch Comp & Engn, Sunderland SR6 0DD, Durham, England. RP Arevian, G (reprint author), Univ Sunderland, Sch Comp & Engn, St Peter Campus,St Peters Way, Sunderland SR6 0DD, Durham, England. CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 AREVIAN G, 2007, P INT JOINT C NEUR N AREVIAN G, 2007, P INT C ART NEUR NET ELMAN JL, 1988, 8901 CRL Fu XJ, 2003, IEEE T SYST MAN CY B, V33, P399, DOI 10.1109/TSMCB.2003.810911 HUANG BQ, 2005, ICEIS, P27 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Kaikhah K., 2004, TEXT SUMMARIZATION U Lewis D., 1997, REUTERS 21578 TEXT C Rijsbergen CV, 1979, INFORM RETRIEVAL, V2nd SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 [ę—¶å°č™Ž Shi Xiaohu], 2003, [č½Æä»¶å­¦ęŠ„, Journal of Software], V14, P1110 Wang L, 2005, SUPPORT VECTOR MACHI WERMTER S, 1995, HYBRID CONNECTIONIST Wermter S., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) WERMTER S, 1999, P INT C ART NEUR NET, P898 NR 19 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3026-0 PY 2007 BP 326 EP 329 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHH66 UT WOS:000253307900054 ER PT B AU Wang, JZ Taylor, W AF Wang, James Z. Taylor, William BA Young, T BF Young, T BE Haas, LL Kacprzyk, J Motwani, R Broder, A Ho, H TI Concept forest: A new ontology-assisted text document similarity measurement method SO PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE: WI 2007 LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY NOV 02-05, 2007 CL Silicon Valley, CA SP IEEE Comp Soc, Web Intelligence Consortium, ACM, YAHOO, IBM, San Jose State Univ ID DISAMBIGUATION AB Although using ontologies to assist information retrieval and text document processing has recently attracted more and more attention, existing ontology-based approaches have not shown advantages over the traditional keywords-based Latent Semantic Indexing (LSI) method This paper proposes an algorithm to extract a concept forest (CF) from a document with the assistance of a natural language ontology, the WordNet lexical database. Using concept forests to represent the semantics of text documents, the semantic similarities of these documents are then measured as the commonalities of their concept forests. Performance studies of text document clustering based on different document similarity measurement methods show that the CF-based similarity measurement is an effective alternative to the existing key-words-based methods. In particular, this CF-based approach has obvious advantages over the existing keywords-based methods, including LSI, in processing short text documents or in P2P or live news environments where it is impractical to collect the entire document corpus for analysis. C1 [Wang, James Z.; Taylor, William] Clemson Univ, Sch Comp, Clemson, SC 29634 USA. RP Wang, JZ (reprint author), Clemson Univ, Sch Comp, Box 340974, Clemson, SC 29634 USA. CR CHIN OS, 2006, IEEE WIC ACM INT C W, P1046 CHUNG YJ, 2005, MACHINE TRANSLATION, V19, P59, DOI 10.1007/s10590-005-2559-y DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 De Luca EW, 2006, INT J INTELL SYST, V21, P693, DOI 10.1002/int.20155 DESIMONE T, 2005, RECENT ADV NATURAL L, P21 Dey L, 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence, (WI 2006 Main Conference Proceedings), P23, DOI 10.1109/WI.2006.86 Fellbaum C, 1998, WORDNET ELECT LEXICA FORTUNA B, 2005, SEMIAUTOMATIC CONSTR Hotho Andreas, 2003, P 3 IEEE INT C DAT M, P541 Hung CL, 2004, IEEE INTELL SYST, V19, P68 JING L, SIAM TEXT MIN 2006 W Kazakov D., 2004, P 3 WORKSH ROB METH, P104, DOI 10.3115/1621445.1621458 KEHAGIAS A, 2001, J INTELLIGENT INFORM, V21 Koller D., 1998, P 14 INT C MACH LEAR Lee CS, 2005, IEEE T SYST MAN CY B, V35, P859, DOI 10.1109/TSMCB.2005.845032 Liu Y, 2007, LNCS, V4489, P780 Mavroeidis D, 2005, LECT NOTES ARTIF INT, V3721, P181 Navigli R, 2003, IEEE INTELL SYST, V18, P22, DOI 10.1109/MIS.2003.1179190 SABOU M, 2005, LEARNING WEB SERVICE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1998, USE WORDNET NATURAL, P38 SUGUMARAN V, INT J DATA KNOWLEDGE, V42, P251 NR 22 TC 3 Z9 4 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-3026-0 PY 2007 BP 395 EP 401 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BHH66 UT WOS:000253307900064 ER PT S AU Grinberg, I Wiseman, Y AF Grinberg, Ilan Wiseman, Yair BE DeFigueiredo, RJP TI Scalable parallel collision detection simulation SO PROCEEDINGS OF THE NINTH IASTED INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING SE IASTED International Conference on Signal and Image Processing LA English DT Proceedings Paper CT 9th IASTED International Conference on Signal and Image Processing CY AUG 20-22, 2007 CL Honolulu, HI SP Int Assoc Sci & Technol Dev, IASTED TCIP, IASTED TCSP DE motion detection and estimation; scalability; geometry models; parallel and distributed simulation AB Several simulations for parallel collision detection have been suggested during the last years. The algorithms usually greatly depend on the parallel infrastructure and this dependency causes in many times non-scalability performance. The dependency also harms the portability of the simulation. This paper suggests a scalable and portable parallel algorithm for collision detection simulation that fits both clusters and MPI machines. C1 [Grinberg, Ilan; Wiseman, Yair] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. RP Grinberg, I (reprint author), Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel. CR ASSARSSON U, 2001, LECT NOTES COMPUTER, V2150, P663 Brown S., 2000, COMPUTER METHODS APP, V184, P375 Cohen J. D., 1995, Proceedings 1995 Symposium on Interactive 3D Graphics, DOI 10.1145/199404.199437 Curless Brian, 1996, P SIGGRAPH 96, P303, DOI 10.1145/237170.237269 FIGUEIREDO M, 2004, ICPADS 04 Funfzig C, 2006, IEEE COMPUT GRAPH, V26, P64, DOI 10.1109/MCG.2006.17 GHANEM TM, 2004, P 20 ANN IEEE INT C Gottschalk S., 2000, THESIS U N CAROLINA JAMES DL, 2004, ACM T GRAPHICS TOG, V23, P391 Jimenez P, 2001, COMPUT GRAPH-UK, V25, P269, DOI 10.1016/S0097-8493(00)00130-8 Klosowski JT, 1998, IEEE T VIS COMPUT GR, V4, P21, DOI 10.1109/2945.675649 LAWLOR O, 2001, THESIS U ILLINOIS UR Lawlor O.S., 2002, P 16 INT C SUP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAN TS, 1999, P ACM S INT 3D GRAPH, P63, DOI 10.1145/300523.300530 TRUMBORE B, 1992, GRAPHICS GEMS, V3, P295 van den Bergen G., 1998, J GRAPHICS TOOLS, V2, P1 VANDERBERGEN G, 2004, SUPPORT MAPPING, P130 VANDERBERGEN G, 2005, SPATIAL DATA STRUCTU, P171 NR 19 TC 5 Z9 5 PU ACTA PRESS ANAHEIM PI ANAHEIM PA PO BOX 5124, ANAHEIM, CA 92814-5124 USA SN 1482-7921 BN 978-0-88986-675-1 J9 IASTED INT CONF SIGN PY 2007 BP 380 EP 385 PG 6 WC Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Engineering; Imaging Science & Photographic Technology GA BGZ03 UT WOS:000251419000068 ER PT B AU Dai, WH Rao, GZ He, TT AF Dai, Wenhua Rao, Guizhen He, Tingting BE Zeng, S Liu, Y Zhang, Q Kang, L TI Research of text clustering based on hybrid Parallel Genetic Algorithm SO PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 2nd International Symposium on Intelligence Computation and Application (ISICA 2007) CY SEP 21-23, 2007 CL Wuhan, PEOPLES R CHINA DE Parallel Genetic Algorithm; K-means Clustering; text clustering; vector space model; feature extraction AB K-means Clustering Algorithm is sensitive to the choice of the initial cluster center, and easy to fall into a local optimal solution. In order to avoid this kind of flaw, we proposed Hybrid Parallel Genetic Algorithm. In this method, we expressed the documents set into Vector Space Model and randomly chose initial clustering center to form chromosome among document vectors. Combined with the efficiency of K-means Algorithm and the global optimization ability of Parallel Genetic Algorithm, we can provide a higher efficiency and precision for text clustering by means of heredity, mutation in the community, and parallel evolution, intermarriage among communities. Experiments indicate that Hybrid Parallel Genetic Algorithm has higher accuracy and global optimization ability than the other text clustering methods like K-means Algorithm, Genetic Algorithm and so on. C1 [Dai, Wenhua; Rao, Guizhen] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China. RP Dai, WH (reprint author), Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China. CR IWAYAMA M, 1995, TR950015 DEP COMP SC JANG N, 2002, J COMPUTER RES DEV, V39, P580 Liu J, 2001, Genome Inform, V12, P14 Liu J. H., 2003, PROGR ASIAN SOCIAL P, P43 Macqueen J. B., 1967, P 5 BERK S MATH STAT, P281 Pettey C.B., 1987, P 2 INT C GEN ALG, P155 POTTS JC, 1994, IEEE T SYST MAN CYB, V24, P73, DOI 10.1109/21.259687 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tanese R, 1987, P 2 INT C GEN ALG, P177 NR 10 TC 0 Z9 0 PU CHINA UNIV GEOSCIENCES PRESS PI WUHAN PA 388 LUMO ROAD, WUHAN, HUBEI 430074, PEOPLES R CHINA BN 978-7-5625-2204-1 PY 2007 BP 28 EP 31 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BHD91 UT WOS:000252386600007 ER PT J AU Jones, MN Mewhort, DJK AF Jones, Michael N. Mewhort, Douglas J. K. TI Representing word meaning and order information in a composite holographic lexicon SO PSYCHOLOGICAL REVIEW LA English DT Review DE semantic memory; mental lexicon; latent semantic analysis; statistical learning; holographic models ID LATENT SEMANTIC ANALYSIS; SIMPLE RECURRENT NETWORKS; DISTRIBUTED-MEMORY MODEL; SPREADING ACTIVATION; ASSOCIATIVE INFORMATION; SENTENCE COMPREHENSION; AMBIGUITY RESOLUTION; EYE-MOVEMENTS; CONTEXT; RETRIEVAL AB The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level. C1 Univ Colorado, Inst Cognit Sci, Boulder, CO 80309 USA. Queens Univ, Dept Psychol, Kingston, ON K7L 3N6, Canada. RP Jones, MN (reprint author), Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN 47405 USA. EM jonesmn@indiana.edu CR BALOTA DA, 1986, J EXP PSYCHOL LEARN, V12, P336, DOI 10.1037//0278-7393.12.3.336 BATTIG WF, 1969, J EXP PSYCHOL, V80, P1, DOI 10.1037/h0027577 BOOTH TL, 1969, 10 ANN IEEE S SWITCH, P74, DOI 10.1109/SWAT.1969.17 Bower G. H., 1973, HUMAN ASS MEMORY Brooks L., 1978, COGNITION CATEGORIZA, P169 BURGESS C, 1989, J EXP PSYCHOL LEARN, V15, P620, DOI 10.1037/0278-7393.15.4.620 Burgess C, 2000, COGNITIVE DYNAMICS, P117 CHIARELLO C, 1990, BRAIN LANG, V38, P75, DOI 10.1016/0093-934X(90)90103-N Chomsky N., 1980, RULES REPRESENTATION Chomsky N., 1965, ASPECTS THEORY SYNTA Christiansen MH, 1999, COGNITIVE SCI, V23, P157, DOI 10.1207/s15516709cog2302_2 Chwilla DJ, 2000, J MEM LANG, V42, P314, DOI 10.1006/jmla.1999.2680 Collins A. M., 1972, ORG MEMORY, P309 COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 COLLINS AM, 1969, J VERB LEARN VERB BE, V8, P240, DOI 10.1016/S0022-5371(69)80069-1 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dennis S, 2001, PSYCHOL REV, V108, P452, DOI 10.1037//0033-295X.108.2.452 ELMAN JL, 1991, MACH LEARN, V7, P195, DOI 10.1007/BF00114844 Elman J. L., 1995, MIND MOTION EXPLORAT, P195 Elman JL, 2004, TRENDS COGN SCI, V8, P301, DOI 10.1016/j.tics.2004.05.003 ELMAN JL, 1993, COGNITION, V48, P71, DOI 10.1016/0010-0277(93)90058-4 Estes William K., 1994, CLASSIFICATION COGNI Fellbaum C, 1998, WORDNET ELECT LEXICA Firth J. R., 1957, STUDIES LINGUISTIC A, P1 FISCHLER I, 1979, J VERB LEARN VERB BE, V18, P1, DOI 10.1016/S0022-5371(79)90534-6 FISCHLER IS, 1985, MEM COGNITION, V13, P128, DOI 10.3758/BF03197005 Fleischman M., 2005, P 27 ANN C COGN SCI, P684 FOSS DJ, 1970, J VERB LEARN VERB BE, V9, P699, DOI 10.1016/S0022-5371(70)80035-4 Franklin DRJ, 2002, KLUWER INT SER ENG C, V657, P465 FRAZIER L, 1982, COGNITIVE PSYCHOL, V14, P178, DOI 10.1016/0010-0285(82)90008-1 Gabel R. A., 1973, SIGNALS LINEAR SYSTE Gentner D., 1982, LANGUAGE DEV, V2, P301 Gibson JJ, 1966, SENSES CONSIDERED PE Gillette J, 1999, COGNITION, V73, P135, DOI 10.1016/S0010-0277(99)00036-0 GLENBERG AM, IN PRESS SYMBOLS EMO GRIFFITHS TL, 2005, TOPICS SEMANTIC REPR GRITTIFHS TL, 2002, P 24 AN C COGN SCI S, P381 GRITTITHS TL, 2005, ADV NEURAL INFORM PR, V17, P537 HEISER WJ, 1988, BRIT J MATH STAT PSY, V41, P37 HINTZMAN DL, 1984, BEHAV RES METH INS C, V16, P96 Hoffman T., 2001, MACH LEARN, V42, P177 Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388 HUCHINSON KA, 2003, PSYCHONOMIC B REV, V10, P785 Jelinek F., 1991, Computational Linguistics, V17 Jones MN, 2006, J MEM LANG, V55, P534, DOI 10.1016/j.jml.2006.07.003 Kintsch W., 1998, COMPREHENSION PARADI Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1 KINTSCH W, 1988, PSYCHOL REV, V95, P163, DOI 10.1037/0033-295X.95.2.163 KOUNIOS J, 1992, J EXP PSYCHOL GEN, V121, P459, DOI 10.1037/0096-3445.121.4.459 Kwantes PJ, 2005, PSYCHON B REV, V12, P703, DOI 10.3758/BF03196761 LAHAM RD, 2000, THESIS U COLORADO BO Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LANDAUER TK, 1996, BASIC APPL MEMORY RE, V1, P105 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LANDAUER TK, 1994, ED TEST SERV C NAT L LIVESAY K, 1998, BRAIN COGNITION, V28, P203 Lucas M, 2000, PSYCHON B REV, V7, P618, DOI 10.3758/BF03212999 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 Maki WS, 2004, BEHAV RES METH INS C, V36, P421, DOI 10.3758/BF03195590 Marcus M. P., 1994, COMPUTATIONAL LINGUI, V19, P313 MASSON MEJ, 1995, J EXP PSYCHOL LEARN, V21, P3, DOI 10.1037/0278-7393.21.1.3 McFalls EL, 1996, READ WRIT, V8, P235, DOI 10.1007/BF00420277 MCKOON G, 1992, J EXP PSYCHOL LEARN, V18, P1155, DOI 10.1037/0278-7393.18.6.1155 McNamara T. P., 2005, SEMANTIC PRIMING PER McRae K, 1997, J EXP PSYCHOL GEN, V126, P99, DOI 10.1037/0096-3445.126.2.99 Metcalfe-Eich J., 1982, PSYCHOL REV, V89, P627 MEWHORT DJK, 1991, RELATING THEORY AND DATA : ESSAYS ON HUMAN MEMORY IN HONOR OF BENNET B MURDOCK, P425 Mewhort DJK, 2005, MEMORY, V13, P300, DOI 10.1080/09658210344000242 MEYER DE, 1971, J EXP PSYCHOL, V90, P227, DOI 10.1037/h0031564 Miller G., 1990, INT J LEXICOGRAPHY, V3 Miller G. A., 1999, WORDNET ELECT LEXICA, P23 Miller G. A., 1951, LANGUAGE COMMUNICATI MILLER GA, 1950, AM J PSYCHOL, V63, P176, DOI 10.2307/1418920 Murdock B, 2001, J MATH PSYCHOL, V45, P564, DOI 10.1006/jmps.2000.1339 MURDOCK BB, 1992, J MATH PSYCHOL, V36, P68, DOI 10.1016/0022-2496(92)90053-A MURDOCK BB, 1982, PSYCHOL REV, V89, P609, DOI 10.1037/0033-295X.89.6.609 MURDOCK BB, 1993, J MATH PSYCHOL, V37, P421, DOI 10.1006/jmps.1993.1025 Neath I, 1997, MEM COGNITION, V25, P256, DOI 10.3758/BF03201116 Neath I., 1998, HUMAN MEMORY INTRO R Neely J. H., 1991, BASIC PROCESSES READ, P264 Nelson D. L., 1998, U S FLORIDA WORD ASS NELSON K, 1981, DEV PSYCHOL, V17, P170, DOI 10.1037/0012-1649.17.2.170 NELSON K, 1974, PSYCHOL REV, V81, P267, DOI 10.1037/h0036592 ONIFER W, 1991, MEM COGNITION, V15, P225 Osgood CE, 1941, J SOC PSYCHOL, V14, P403 OSGOOD CE, 1971, J SOC ISSUES, V27, P5 OSGOOD CE, 1952, PSYCHOL BULL, V49, P197, DOI 10.1037/h0055737 Paivio A., 1986, MENTAL REPRESENTATIO Paivio A., 1968, J EXPT PSYCHOL MONOG, V76, P2 Palmer M, 2005, COMPUT LINGUIST, V31, P71, DOI 10.1162/0891201053630264 Perfetti CA, 1998, DISCOURSE PROCESS, V25, P363 Pexman PM, 2002, PSYCHON B REV, V9, P542, DOI 10.3758/BF03196311 Pexman PM, 2003, MEM COGNITION, V31, P842, DOI 10.3758/BF03196439 Plate T. A., 2003, CSLI LECT NOTES, V150 PLATE TA, 1995, IEEE T NEURAL NETWOR, V6, P623, DOI 10.1109/72.377968 Plaut D. C., 1995, P 17 ANN C COGN SCI, P37 Pustejovsky J., 1996, GENERATIVE LEXICON PYLYSHYN ZW, 1981, PSYCHOL REV, V88, P16, DOI 10.1037//0033-295X.88.1.16 PYLYSHYN ZW, 1973, PSYCHOL BULL, V80, P1, DOI 10.1037/h0034650 RATCLIFF R, 1978, PSYCHOL REV, V85, P59, DOI 10.1037//0033-295X.85.2.59 RIPS LJ, 1973, J VERB LEARN VERB BE, V12, P1, DOI 10.1016/S0022-5371(73)80056-8 ROSCH E, 1975, J EXP PSYCHOL GEN, V104, P192, DOI 10.1037//0096-3445.104.3.192 Rosch Eleanor, 1973, COGNITIVE DEV ACQUIS, P111 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUBERTH RE, 1977, J EXP PSYCHOL HUMAN, V3, P27, DOI 10.1037//0096-1523.3.1.27 Semon R., 1923, MNEMIC PSYCHOL Serafin R., 2004, P 42 ANN M ASS COMP, P692, DOI 10.3115/1218955.1219043 Sereno SC, 2006, J EXP PSYCHOL HUMAN, V32, P335, DOI 10.1037/0096-1523.32.2.335 SERVANSCHREIBER D, 1991, MACH LEARN, V7, P161, DOI 10.1007/BF00114843 SHELTON JR, 1992, J EXP PSYCHOL LEARN, V18, P1191, DOI 10.1037//0278-7393.18.6.1191 Simon H. A., 1996, SCI ARTIFICIAL Smith AE, 2006, BEHAV RES METHODS, V38, P262, DOI 10.3758/BF03192778 SMITH EE, 1974, PSYCHOL REV, V81, P214, DOI 10.1037/h0036351 SMOLENSKY P, 1990, ARTIF INTELL, V46, P159, DOI 10.1016/0004-3702(90)90007-M Snedeker J., 2004, WEAVING LEXICON, P257 Solan Z, 2005, P NATL ACAD SCI USA, V102, P11629, DOI 10.1073/pnas.0409746102 STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037//0033-2909.87.2.245 STEYVERS M, IN PRESS LATENT SEMA Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 STEYVERS M, 2000, MODELING SEMANTIC OR TABOSSI P, 1988, J MEM LANG, V27, P324, DOI 10.1016/0749-596X(88)90058-7 TANENHAUS MK, 1979, COGNITIVE MODELS SPE, P383 TARABAN R, 1988, J MEM LANG, V27, P597, DOI 10.1016/0749-596X(88)90011-3 Turney Peter D., 2001, P 12 EUR C MACH LEAR, P491 VANDERHEIJDEN AHC, 1984, PERCEPT PSYCHOPHYS, V35, P85 VANPETTEN C, 1987, J MEM LANG, V26, P188, DOI 10.1016/0749-596X(87)90123-9 WHITTLESEA BWA, 1993, J EXP PSYCHOL LEARN, V19, P1235, DOI 10.1037//0278-7393.19.6.1235 Whittlesea BWA, 2002, J EXP PSYCHOL GEN, V131, P325, DOI 10.1037//0096-3445.131.3.325 Wiemer-Hastings P., 2001, P 23 ANN C COGN SCI, P1140 Wiemer-Hastings P, 2000, PROCEEDINGS OF THE TWENTY-SECOND ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P989 WILLIAMS EJ, 1959, J ROY STAT SOC B, V21, P396 Williams JN, 1996, EUR J COGN PSYCHOL, V8, P113, DOI 10.1080/095414496383121 NR 134 TC 132 Z9 132 PU AMER PSYCHOLOGICAL ASSOC PI WASHINGTON PA 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA SN 0033-295X J9 PSYCHOL REV JI Psychol. Rev. PD JAN PY 2007 VL 114 IS 1 BP 1 EP 37 DI 10.1037/0033-295X.114.1.1 PG 37 WC Psychology; Psychology, Multidisciplinary SC Psychology GA 125OV UT WOS:000243452500001 PM 17227180 ER PT B AU Scekic, O Omerovic, S Tomazic, S Milutinovic, V AF Scekic, Ognjen Omerovic, Sanida Tomazic, Sago Milutinovic, Veljko BE Milovanovic, BD TI Concept modeling and its application to patent modeling SO TELSIKS 2007: 8TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS IN MODERN SATELLITE, CABLE AND BROADCASTING SERVICES, VOLS 1 AND 2 LA English DT Proceedings Paper CT 8th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services CY SEP 26-28, 2007 CL Nis, SERBIA SP Fac Elect Engn, Natl Soc MicrowaveTheory & Tech, MTT, AP-s, Com Soc, IEEE Reg, Serbian Acad Sci & Arts, Serbian Minist Sci, Serbian Minist Telecommun & Informat Soc, Acad Engn Sci Serbia & Montenegro, IEEE Serbia & Montenegro Sect, SCG IEEE Commun Chapter, SCG IEEE MTT Chapter, Natl Sci Telecommun, ETRAN Soc DE concept-modeling; knowledge-modeling; patents; indexing; RDF AB This paper briefly introduces the notion of concept modeling, explore some existing techniques, and discuss their advantages and drawbacks. In an effort to find a general concept model, focus of our research was primarily on documents describing patents. Proposed is model for patents which allows efficient detection of individual concepts and relations among them, both within and across patent boundaries. This approach is based on a hybrid solution - employing existing conceptual indexing technique for extraction of individual concepts, and RDF/OWL descriptions for patent-specific data. C1 Univ Belgrade, Fac Elect Engn, Belgrade 11000, Serbia. RP Scekic, O (reprint author), Univ Belgrade, Fac Elect Engn, Bulevar Kralja Aleksandra 73, Belgrade 11000, Serbia. CR Gomez-Perez A, 2002, IEEE INTELL SYST, V17, P54, DOI 10.1109/5254.988453 Hayes P., 2004, RDF SEMANTICS OMEROVIC S, 2006, RES SUN MICROSYSTEMS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCEKIC O, 2006, YU INFO 06 SOWA J, 2000, P LING CONC STRUCT L, P55 VOSS A, 1999, INT WORKSH EN TECHN, P245 WOODS WA, 2000, P APPL NATURAL LANGU NR 8 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-86-85195-54-9 PY 2007 BP 3 EP 6 PG 4 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA BGW93 UT WOS:000251017900001 ER PT S AU Negishi, Y Kawaguchi, N AF Negishi, Yuya Kawaguchi, Nobuo BE Ichikawa, H Cho, WD Satoh, I Youn, HY TI Instant learning sound sensor: Flexible real-world event recognition system for ubiquitous computing SO UBIQUITOUS COMPUTING SYSTEMS, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Proceedings Paper CT 4th International Symposium on Ubiquitous Computing Systems CY NOV 25-28, 2007 CL Tokyo, JAPAN SP Ubiquitous Networking Forum, Nokia, NTT, Support Ctr Adv Telecommun Technol Res Fdn, Int Informat Sci Fdn, Telecomm Advancement Fdn ID ALGORITHM AB We propose a smart sound sensor for building context-aware systems that instantly learn and detect events from various kinds of everyday sounds and environmental noise by using small and low-cost device. The proposed system automatically analyzes and selects an appropriate sound recognition process, using sample sounds and a parameter templates database in the event learning phase. A user is only required to input target event sounds from a microphone or sound files. Using the proposed sensor, the developer of ubiquitous service can easily utilize real world sounds as event triggers to control appliances or human's activity monitors for presence services without a signal processing programming. C1 [Negishi, Yuya; Kawaguchi, Nobuo] Nagoya Univ, Grad Sch Engn, Chikusa Ku, Nagoya, Aichi 4648601, Japan. RP Negishi, Y (reprint author), Nagoya Univ, Grad Sch Engn, Chikusa Ku, 1 Furo Cho, Nagoya, Aichi 4648601, Japan. EM negishi@el.itc.nagoya-u.ac.jp; kawaguti@nagoya-u.jp CR Bao L., 2004, ACTIVITY RECOGNITION, P1 CHEN J, 2005, BATHROOM ACTIVITY MO, P47 COTTERELL S, 2005, EBLOCKS ENABLING TEC Gray R. M., 1984, IEEE ASSP Magazine, V1, DOI 10.1109/MASSP.1984.1162229 LINDE Y, 1980, IEEE T COMMUN, V28, P84, DOI 10.1109/TCOM.1980.1094577 LUKOWICZ P, 2004, REC WORKSH ACT US BO, P18 MA L, 2003, ENV NOISE CLASSIFICA, P360 *MICR TECHN INC, DSPIC DIG SIGN CONTR MYERS CS, 1981, IEEE T ASSP, V26, P351 SAKOE H, 1978, IEEE T ACOUST SPEECH, V26, P43, DOI 10.1109/TASSP.1978.1163055 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tapia EM, 2006, LECT NOTES COMPUT SC, V3968, P117 NR 12 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-76771-8 J9 LECT NOTES COMPUT SC PY 2007 VL 4836 BP 72 EP 85 PG 14 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BHG89 UT WOS:000253086100006 ER PT S AU Herschel, S AF Herschel, Sven BE Benatallah, B Casati, F Georgakopoulos, D Bartolini, C Sadiq, W Godart, C TI Similarity-based document distribution for efficient distributed information retrieval SO WEB INFORMATION SYSTEMS ENGINEERING - WISE 2007, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 8th International Conference on Web Information Systems Engineering (WISE 2007) CY DEC 03, 2007 CL Nancy, FRANCE SP Nancy Univ, INRIA, Loria, Lorraine Reg, I 3, Grand Nancy, EMISA AB Performing information retrieval (IR) efficiently in a distributed environment is currently one of the main challenges in IR. Document representations are distributed among nodes in a manner that allows a query processing algorithm to efficiently direct queries to those nodes that contribute to the result. Existing term-based document distribution algorithms do not scale with large collection sizes or many-term queries because they incur heavy network traffic during the distribution and query phases. We propose a novel algorithm for document distribution, namely distance-based document distribution. The distribution obtained by our algorithm allows answering any IR query effectively by contacting only a few nodes, independent of both document collection size and network size, thereby improving efficiency. We accomplish this by linearizing the information retrieval search space such that it reflects the ranking formula which will be used for later retrieval. Our experimental evaluation indicates that effective information retrieval can be efficiently accomplished in distributed networks. C1 [Herschel, Sven] Humboldt Univ, D-10099 Berlin, Germany. RP Herschel, S (reprint author), Humboldt Univ, Unter Linden 6, D-10099 Berlin, Germany. RI Casati, Fabio/I-7952-2013 CR Aberer K., 2005, P 5 IEEE INT C PEER, P11 ABERER K, ARCHITECTURE PEER TO AGHBARI ZA, 2004, LNCS, V3129, P229 BENDER M, BOOKMARK DRIVEN QUER CALLAN J, 2004, P SIGIR WORKSH PEER Callan J., 2000, ADV INFORM RETRIEVAL, P127 NEUMANN T, 2006, EXPDB, P1 NOTTELMANN H, 2005, HETEROGENEOUS DISTRI Ratnasamy Sylvia, 2001, SIGCOMM 01, P161, DOI DOI 10.1145/383059.383072 Reynolds P, 2003, LECT NOTES COMPUT SC, V2672, P21 Rowstron A., 2001, LECT NOTES COMPUTER, V2218, P329, DOI DOI 10.1007/3-540-45518-3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Samet H., 1989, DESIGN ANAL SPATIAL Stoica Ion, 2001, SIGCOMM 01, P149, DOI DOI 10.1145/383059.383071 Tang C., 2003, SIGCOMM, P175 TANG C, 2003, SIGCOMM COMPUT COMMU, V33, P89 Zhao B. Y., 2001, TAPESTRY INFRASTRUCT NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 978-3-540-76992-7 J9 LECT NOTES COMPUT SC PY 2007 VL 4831 BP 99 EP 110 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BHG87 UT WOS:000253084500009 ER PT B AU Hubert, G Mothe, J Englmeier, K AF Hubert, Gilles Mothe, Josiane Englmeier, Kurt BE Filipe, J Cordeiro, J TI Tuning search engine to fit XML retrieval scenario SO WEBIST 2007: Proceedings of the Third International Conference on Web Information Systems and Technologies, Vol IT: INTERNET TECHNOLOGY LA English DT Proceedings Paper CT 3rd International Conference on Web Information Systems and Technologies CY MAR 03-06, 2007 CL Barcelona, SPAIN SP Inst Syst & Technologies Informat, Control & Commun, Open Univ Catalonia, Int World Wide Web Conf Comm DE information retrieval; XML; configurable engine; retrieval scenarios ID INFORMATION-RETRIEVAL AB XML usage is growing to describe documents and consequently systems to search in XML collections are necessary. Various proposals of systems intend to handle XML documents. This paper describes an XML approach based on direct contribution of the components constituting an information need. The search engine is largely configurable in order to be adapted to different context of search. Beyond being globally adapted to a collection of documents an important objective is to define a search engine that can be adapted to different retrieval scenarios and to identify how to adapt it. This paper presents first experiments on INEX testbeds that show how the engine can be adapted to better respond to different retrieval scenarios. C1 Univ Toulouse 3, IRIT, SIG EVI, F-31062 Toulouse, France. RP Hubert, G (reprint author), Univ Toulouse 3, IRIT, SIG EVI, 118 Route Narbonne, F-31062 Toulouse, France. CR AMERYAHIA S, 2004, ACM SIGMOD, P83 AUGE J, 2003, BDA 03 19 JOURN BAS, P69 BRAY T, 2004, EXTENSIBLE MARKUP LA CARMEL D, 2003, 2L INT C SIGIR TOR, P151 Clark J., 1999, XML PATH LANGUAGE XP CROUCH CJ, 2003, 2 INEX WORKSH DAGST, P89 FUHR N, 2003, 2 INEX WORKSH DAGST, P1 Fuhr N, 2004, ACM T INFORM SYST, V22, P313, DOI 10.1145/984321.984326 Geva S, 2005, LECT NOTES COMPUT SC, V3493, P211 Hubert G, 2005, LECT NOTES COMPUT SC, V3493, P183 KAZAI G, 2005, PRE P 4 INEX WORKSH, P401 LIU S, 2004, 27 INT C SIGIR SHEFF, P88 OGILVIE P, 2003, 2 INEX WORKSH DAGST, P12 Pehcevski J, 2005, LECT NOTES COMPUT SC, V3493, P153 PIWOWARSKI B, 2003, 2 INEX WORKSH DAGST, P33 Ponte J. M., 1998, 21 ACM SIGIR C RES D, P275 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sigurbjornsson B, 2005, LECT NOTES COMPUT SC, V3493, P196 NR 18 TC 0 Z9 0 PU INSTICC-INST SYST TECHNOLOGIES INFORMATION CONTROL & COMMUNICATION PI SETUBAL PA AVENIDA D MANUEL L, 27A 2 ESQUERDO, SETUBAL, 2910-595, PORTUGAL BN 978-972-8865-77-1 PY 2007 BP 228 EP 233 PG 6 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BGR32 UT WOS:000250084800031 ER PT J AU Risson, J Moors, T AF Risson, John Moors, Tim TI Survey of research towards robust peer-to-peer networks: Search methods SO COMPUTER NETWORKS LA English DT Review DE peer-to-peer network; distributed hash table; consistent hashing; scalable distributed data structure; vector model; latent semantic indexing; dependability; torus; butterfly network; skip graph; Plaxton tree; de Bruijn graph ID P2P SYSTEM; MANAGEMENT; DESIGN; COMMUNICATION; LIGHTWEIGHT; DISCOVERY; OVERLAY; TREES; POWER AB The pace of research on peer-to-peer (P2P) networking in the last five years warrants a critical survey. P2P has the makings of a disruptive technology-it can aggregate enormous storage and processing resources while minimizing entry and scaling costs. Failures are common amongst massive numbers of distributed peers, though the impact of individual failures may be less than in conventional architectures. Thus the key to realizing P2P's potential in applications other than casual file sharing is robustness. P2P search methods are first couched within an overall P2P taxonomy. P2P indexes for simple key lookup are assessed, including those based on Plaxton trees, rings, tori, butterflies, de Bruijn graphs and skip graphs. Similarly, P2P indexes for keyword lookup, information retrieval and data management are explored. Finally, early efforts to optimize range, multi-attribute, join and aggregation queries over P2P indexes are reviewed. Insofar as they are available in the primary literature, robustness mechanisms and metrics are highlighted throughout. However, the low-level mechanisms that most affect robustness are not well isolated in the literature. Furthermore, there has been little consensus on robustness metrics. Recommendations are given for future research. (c) 2006 Elsevier B.V. All rights reserved. C1 Univ New S Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia. RP Risson, J (reprint author), Univ New S Wales, Sch Elect Engn & Telecommun, High St, Sydney, NSW 2052, Australia. EM jr@tuffit.com; t.moors@unsw.edu.au RI Tavares, Antonio/A-7115-2008 CR ABERER K, 2005, P 5 INT C PEER PEER ABERER K, 2002, WORKSH DISTR DAT STR Aberer K, 2003, SIGMOD RECORD, V32, P29 ABERER K, 2002, IC200279 EPFL ABERER K, 2003, IC200332 ABERER K, 2001, P 8 EUR SOFTW ENG C, V26 ABERER K, 2004, INT J BUSINESS PROCE ABRAHAM I, 2004, P ACM SIAM S DISCR A Adamic LA, 2001, PHYS REV E, V64 Adler M., 2003, P 35 ACM S THEOR COM, P575 ADYA A, 2002, ACM SIGOPS OP SYST R, P1 Ahlgren B., 2004, P ACM SIGCOMM WORKSH, P65, DOI 10.1145/1016707.1016719 AJMANI S, 2002, 1 INT WORKSH PEER PE ALBRECHT K, 2003, 427 DEP COMP SCI ALBRECHT K, 2004, P 4 IEEE INT C PEER Alimal L. O., 2003, P 3 IEEE ACM INT S C, P344, DOI [10.1109/CCGRID.2003.1199386, DOI 10.1109/CCGRID.2003.1199386] ANAGNOSTAKIS K, 2004, P 2J INT C DISTR COM Andrzejak A., 2002, P 2 IEEE INT C PEER Annexstein FS, 2002, IEEE IC COMP COM NET, P10 [Anonymous], 2003, P 19 ACM S OP SYST P, DOI DOI 10.1145/945445.945450 ASPNES J, 2004, P 23 ANN ACM SIGACT Aspnes J, 2003, SIAM PROC S, P384 Avnurand R., 2000, P 2000 ACM SIGMOD IN, P261, DOI 10.1145/342009.335420 Awerbuch B., 2003, P 22 ANN S PRINC DIS, P123, DOI 10.1145/872035.872053 AWERBUCH B, 2004, 3 INT WORKSH PEER PE Awerbuch B., 2004, P 16 ACM S PAR ALG A, P44, DOI 10.1145/1007912.1007920 Babaoglu O., 2002, Proceedings 22nd International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2002.1022238 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BAILEY NTJ, 1975, EPIDEMIC THEORY INFE BALAKRISHNAN H, 2003, 2 INT WORKSH PEER PE Balakrishnan H., 2003, Communications of the ACM, V46, DOI 10.1145/606272.606299 BALAZINSKA M, 2002, P 1 INT C PERV COMP Banaei-Kashani F, 2003, P 3 IEEE ACM INT S C, P351 BAQUERO C, 2003, ACM SIGOPS OPERATING, V37, P90, DOI 10.1145/958965.958974 Bawa M., 2003, P 12 INT C WORLD WID, P577 Bawa M., 2003, P 26 ANN INT ACM SIG, P306 Bawa M., 2004, P ACM SIGMOD INT C M, P515, DOI 10.1145/1007568.1007627 Bawa M, 2003, SIGMOD RECORD, V32, P23 BENTLEY JL, 1975, COMMUN ACM, V18, P509, DOI 10.1145/361002.361007 BERKET K, 2004, P 4 IEEE INT C PEER BHAGWAN R, 2003, CS20030755 BHARAMBE A, 2004, SIGCOMM 04 AUG 30 SE Birman KP, 2003, LECT NOTES COMPUT SC, V2584, P97 BLAKE C, 2003, 9 WORKSH HOT TOP OP BLAZE M, 2003, TRUST REPUTATION P2P BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Buragohain C., 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003), DOI 10.1109/PTP.2003.1231503 BYERS J, 2003, 2 INT WORKSH PEER PE BYERS J, 2002, P 2002 C APPL TECHN, P47 Cai M., 2004, P 13 INT C WORLD WID, P650, DOI 10.1145/988672.988760 CAI M., 2003, P INT WORKSH GRID CO Cao P., 2004, PODC, P206, DOI 10.1145/1011767.1011798 Caronni G., 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003), DOI 10.1109/PTP.2003.1231512 Castiglione G, 2001, J HEPATOL, V34, P131, DOI 10.1016/S0168-8278(01)81352-0 CASTRO M, 2003, 2004 INT C DEP SYST Castro M, 2003, ACM T COMPUT SYST, V21, P236, DOI 10.1145/859716.859718 CASTRO P, 2004, P 24 INT C DISTR COM CATES J, 2003, THESIS ROBUST EFFICI CHANDER A, 2002, 2 IEEE ACM INT S CLU, P56 Chawathe Y, 2003, P 2003 C APPL TECHN, P407 CHOLVI V, 2004, P S PAR ALG ARCH JUL CHUN B, 2004, P 2004 ACM SIGMOD IN, P935, DOI 10.1145/1007568.1007705 *CIT, 2004, CIT SCI LIT DIG LIB CLARK D, 2002, C APPL TECHN ARCH PR, P347 CLARK D, ACM SIGCOMM COMPUTER, V18, P1988 CLARKE I, 1999, THESIS Clarke I., 2002, IEEE INTERNET COMPUT, V6 Clarke I., 2003, FREENETS NEXT GENERA Clarke Ian, 2001, FREENET DISTRIBUTED COHEN E, 2003, IEEE INF 2003 22 ANN Cohen E., 2002, P 2002 C APPL TECHN, P177 CONSIDINE J, 2003, PRAGMATIC APPROACH D COOPER B, 2003, 2 INT WORKSH PEER TO COOPER BF, 2003, AD HOC SELF SUPERVIS COSTA P, 2004, 24 INT C DISTR COMP Costa P., 2003, P 2 INT WORKSH DISTR, P1, DOI DOI 10.1145/966618.966629 Cox L., 2003, P 19 ACM S OP SYST P, P120 Cox R, 2004, ACM SIGCOMM COMP COM, V34, P113, DOI 10.1145/972374.972394 COX R, 2002, 1 INT WORKSH PEER PE CUENCAACUNA F, 2002, P 12 INT S HIGH PERF DABEK F, 2004, SIGCOMM 04 AUG 30 SE Dabek F., 2004, P 1 S NETW SYST DES, P85 Dabek F, 2001, P 18 ACM S OP SYST P, P202 Dabek F., 2003, P 2 INT WORKSH PEER Damiani E., 2002, P 9 ACM C COMP COMM, P207 DASWANI N, 2003, 9 INT C DAT THEOR IC Daswani N., 2002, P 9 ACM C COMP COMM, P181 Daswani S, 2002, GNUTELLA UDP EXTENSI DATTA A, 2004, P 4 IEEE INT C PEER DEBRUIJN ND, 1946, ACAD WETENSCHAPPEN, V49, P758 Demers A, 1987, P 6 ANN ACM S PRINC, P1, DOI [10.1145/41840.41841, DOI 10.1145/41840.41841] DePaoli F, 2004, IEEE INTERNET COMPUT, V8, P54, DOI 10.1109/MIC.2004.9 DEVINE R, 1993, P 4 INT C FDN DAT OR Eugster PT, 2003, ACM T COMPUT SYST, V21, P341, DOI 10.1145/945506.945507 Eugsters P, 2004, IEEE COMPUT, V37, P60 FEAMSTER N, 2003, P ACM SIGCOMM WORKSH, P289 FELDMAN M, 2004, 3 ANN WORKSH EC INF FELDMAN M, 2004, ACM E COMM C EC MAY FESSANT FL, 2004, 3 INT WORKSH PEER PE Fiat A, 2002, SIAM PROC S, P94 Fisk A., 2003, GNUTELLA ULTRAPEER Q Fisk A., 2003, GNUTELLA DYNAMIC QUE FOSTER I, 2003, 2 INT WORKSH PEER PE FRAIGNIAUD P, 2003, 1349 LAB RECH INF Freedman M., 2002, P 9 ACM C COMP COMM, P193 Freedman M J, 2004, P 1 S NETW SYST DES, P239 Freedman M.J., 2003, P 2 INT WORKSH PEER Furnas G. W., 1988, P 11 ANN INT ACM SIG, P465, DOI 10.1145/62437.62487 Ganesan P., 2004, P 30 INT C VER LARG Ganesan P., 2004, P INT C DISTR COMP S Ganesh AJ, 2003, IEEE T COMPUT, V52, P139, DOI 10.1109/TC.2003.1176982 Gao J, 2004, IEEE J SEL AREA COMM, V22, P54, DOI 10.1109/JSAC.2003.818794 GEDIK B, 2003, P 23 INT C DISTR COM Gedik B., 2003, Proceedings 22nd International Symposium on Reliable Distributed Systems GEELS D, 2003, P SIGOPS EUR WORKSH GIL T, 2003, P2PSIM SIMULATOR PEE GILBERT S, 2004, MITCSAILTR890 GNAWALI O, 2002, THESIS GOLD R, 2001, P 1 INT C PEER PEER, P62 Gopalakrishnan V., 2004, P 24 INT C DISTR COM GRAY J, 1981, P VLDB SEP GRIBBLE S, 2001, P 4 INT WORKSH DAT W Gribble S. D., 2000, P 4 S OP SYST DES IM Gummadi K, 2003, P 2003 C APPL TECHN, P381 GUMMADI KP, 2002, P SIGCOMM IMW 2002 N Gupta A., 2004, 1 S NETW SYST DES IM GUPTA A, 2003, 9 WORKSH HOT TOP OP Gupta Abhishek, 2003, P 1 BIENN C INN DAT GUPTA I, 2001, FIGHTING FIRE FIRE U GUPTA I, 2004, P ACM PODC, P216, DOI 10.1145/1011767.1011799 GUPTA I, 2003, 2 INT WORKSH PEER TO GUPTA I, 2003, THESIS CORNELL U HARREN J, 2002, P 1 INT WORKSH PEER Harvey N., 2003, P 4 USENIX S INT TEC HARVEY N, 2003, 2 INT WORKSH PEER PE HARVEY N, 2002, MSRTR200292 HAZEL S, 2002, P 2 INT C PEER PEER Hellerstein JM, 2003, SIGMOD RECORD, V32, P34 Hildrum K., 2002, P 14 ACM S PAR ALG A, P41 HILDRUM K, 2004, DISTRIBUTED OBJECT L HILDRUM K, 2004, P 16 ANN ACM S PAR A, P25, DOI 10.1145/1007912.1007918 Hodes TD, 2002, WIREL NETW, V8, P213, DOI 10.1023/A:1013772027164 Honicky R.J., 2003, P 17 INT PAR DISTR P HOSCHEK W, 2002, P 2002 ACM IEEE C SU, P1 HUEBSCH R, 2003, P 29 INT C VER LARG IAMNITCHI A, 2002, IEEE HIGH PERF DISTR IAMNITCHI A, 2003, THESIS IMASE M, 1981, IEEE T COMPUT, V30, P439 Iyer S., 2002, P 21 ACM S PRINC DIS, P213 JARKE M, 1984, COMPUT SURV, V16, P111 JELASITY M, 2004, P 12 EUR C PAR DISTR Joseph S, 2003, IEICE T COMMUN, VE86B, P1740 JOSEPHSON WK, 2004, 3 INT WORKSH PEER PE JOVANOVIC MA, 2001, THESIS Junginger MO, 2004, IEEE NETWORK, V18, P38, DOI 10.1109/MNET.2004.1265832 KAASHOEKM F, 2003, 2 INT WORKSH PEER TO Kalogeraki V., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 KAN G, 2001, 1 INT C PEER PEER CO, P4 KANGASHARJU J, 2003, P 3 INT IEEE COMP PE KARGER D, 2004, P 16 ACM S PAR ALG A Karger D., 1997, ACM S THEOR COMP Karger D. R., 2002, P 34 ANN ACM S THEOR, P741 KARGER DR, 2004, 3 INT WORKSH PEER PE KARP B, 2004, 3 INT WORKSH PEER PE *KAZAA, 2001, KAZAA MED DESKT KELEHER P, 2002, 1 INT WORKSH PEER PE KLAMPANOS I. A, 2004, P 2004 ACM S APPL CO, P1078, DOI 10.1145/967900.968119 Klingberg T., 2002, GNUTELLA 0 6 KOLONIARI G, 2004, P 9 INT C EXT DAT TE KOLONIARI G, 2003, P VLDB INT WORKSH DA, P232 KOLONIARI G, 2003, P 5 WORKSH DISTR DAT Kossmann D, 2000, ACM COMPUT SURV, V32, P422, DOI 10.1145/371578.371598 Krohn M., 2004, P IEEE S SEC PRIV MA Kronfol AZ, 2002, FASD FAULT TOLERANT Kubiatowicz J., 2000, P 9 INT C ARCH SUPP, P190, DOI 10.1145/378993.379239 LAPRIE JC, 1995, 25 INT S FAULT TOL C, P2, DOI 10.1109/FTCSH.1995.532603 Lee KY, 2000, J PARALLEL DISTR COM, V60, P1, DOI 10.1006/jpdc.1999.1591 LEI S, 2004, P 2J INT C DISTR COM LI G, 2002, IEEE INTERNET COMPUT, V6, P37, DOI 10.1109/MIC.2002.978367 LI J, 2004, 3 INT WORKSH PEER PE LI J, 2003, IMPLEMENTING AGGREGA LI J, 2003, 2 INT WORKSH PEER PE LI J, 2005, P IEEE INF MAR 13 17 LI X, 2004, 18 ANN C DISTR COMP Li X., 2002, P 2 ACM INT WORKSH P, P82 Liben-Nowell D., 2002, ANAL EVOLUTION PEER, P233 *LIM, 2004, LIM HOST COUNT LIN SD, 2004, 3 INT WORKSH PEER TO LITWIN W, 2004, 20040402 CERIA LITWIN W, 1994, P 20 INT C VER LARG LITWIN W, 2004, E COMMUNICATION 0323 Litwin W, 1996, ACM T DATABASE SYST, V21, P480, DOI 10.1145/236711.236713 Litwin W., 1993, P ACM INT C MAN DAT Litwin W, 1996, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED INFORMATION SYSTEMS, P120, DOI 10.1109/PDIS.1996.568674 LIU Y, 2004, P IEEE INF MAR 0711 LO V, 2004, 3 INT WORKSH PEER PE Loguinov D., 2003, P ACM SIGCOMM AUG, P395 Lomet D, 1996, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED INFORMATION SYSTEMS, P108, DOI 10.1109/PDIS.1996.568673 Loo AW, 2003, COMMUN ACM, V46, P56, DOI 10.1145/903893.903894 Loo B. T., 2004, P 30 INT C VER LARG LOO BT, 2004, CSD041305 LOO BT, 2004, CSD041301 LOO BT, 2004, 3 INT WORKSH PEER TO LUA EK, 2004, P 4 IEEE INT C PEER Lv Q., 2002, P 16 INT C SUP, P84, DOI 10.1145/514191.514206 Lv Q., 2002, P 1 INT WORKSH PEER LYNCH N, 2002, P INT PEER PEER S MA LYNCH N, 2004, MITLCSTR936 Mache J., 2002, P 2 IEEE INT C PEER MAHAJAN R, 2003, 2 INT WORKSH PEER PE Malkhi D, 2002, P 21 ACM S PRINC DIS, P183, DOI DOI 10.1145/571825.571857 Manku G. S., 2003, P 22 ANN S PRINC DIS, P133, DOI 10.1145/872035.872054 Manku G. S., 2004, P 23 ACM S PRINC DIS, P197, DOI 10.1145/1011767.1011797 Manku GS, 2003, P 4 USENIX S INT TEC MAO JW, 2003, THESIS MARTI S, 2004, 3 INT WORKSH PEER PE Massoulie L., 2003, Proceedings 22nd International Symposium on Reliable Distributed Systems Maymounkov P., 2002, P 1 INT WORKSH PEER MAYMOUNKOV P, 2003, 2 INT WORKSH PEER PE MCCANNE S, LBNL UCB NETWORK SIM Milojicic D. S., 2002, HPL200257 Mishchke J., 2004, IEEE NETWORK, V18, P30 MISLOVE A, 2004, 3 INT WORKSH PEER PE MISLOVE A, 2003, 9 WORKSH HOT TOP OP MIZRAK AT, 2003, IEEE WORKSH INT APPL MOHAN A, 2002, IEEE INT C COMM ICC MONTRESOR A, 2003, UBLCS200316 MULLER W, 2003, P 5 ACM SIGMM INT WO, P79, DOI 10.1145/973264.973278 MUTHITACHAROEN A, 2002, ACM SIGOPS OPERATING, P31 MUTHITACHAROEN A, 2002, P 5 S OP SYST DES IM NAOR M, 2004, 3 INT WORKSH PEER PE NAOR M, 2003, 2 INT WORKSH PEER PE Naor M., 2003, P 15 ACM S PAR ALG A, P50 Nejdl W, 2003, SIGMOD RECORD, V32, P41 NEJDL W, 2003, EDUTELLE PROJECT RDF NG TSE, 2002, IEEE INF 2002 21 ANN NGAN TWJ, 2003, 2 INT WORKSH PEER PE ODONNEL C, 2004, P 4 IEEE INT C PEER On G., 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003), DOI 10.1109/PTP.2003.1231504 OZSU MT, 1999, PRINC DISTR DAT SYST PAPAIOANNOU T, 2004, 4 INT WORKSH GLOB PE PIAS M, 2003, 2 INT WORKSH PEER PE Plank J., 2003, PARALLEL PROCESSING, V13, P207, DOI 10.1142/S0129626403001240 Plaxton C., 1997, ACM S PAR ALG ARCH PSALTOULIS D, 2004, P 23 ANN ACM SIGACT PUGH W, 1990, COMMUN ACM, V33, P668, DOI 10.1145/78973.78977 Pugh W, 1989, P WORKSH ALG DAT STR, P437 Ramabhadran S, 2004, P 23 ANN ACM S PRINC, P368, DOI DOI 10.1145/1011767.1011823 RAMASWAMY L, 2003, P 2003 HAW INT C SYS RAO A, 2003, P 2 INT WORKSH PEER RATNASAMY S, 2002, SCALABLE CONTENT ADD Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] RATNASAMY S, 2002, P 1 INT WORKSH PEER RATNASAMY S, 2003, IRBTR03009 Reddy S. M., 1980, DIRECT GRAPHS MINIMA REYNOLDS P, 2003, ACMIFP USENIX INT MI Rhea S, 2001, IEEE INTERNET COMPUT, V5, P40, DOI 10.1109/4236.957894 RHEA S, 2003, P USENIX ANN TECHN C RHEA S, 2003, P 2 INT WORKSH PEER Rhea S, 2004, P USENIX ANN TECHN C Rhea S., 2005, P ACM SIGCOMM 05 AUG, P73, DOI DOI 10.1145/1080091.1080102 Ripeanu M, 2002, IEEE INTERNET COMPUT, V6, P50 RISSON J, 2005, P 30 ANN IEEE C LOC, P18 Robertson S., 1999, NIST SPECIAL PUBLICA, P253 RODRIGUES R, 2002, 10 ACM SIGOPS EUR WO ROHRS C, 2002, QUERY ROUTING GNUTEL ROUSSOPOULOS M, 2004, 3 INT WORKSH PEER TO ROWLEY RA, 1993, IEEE T COMPUT, V42, P1480, DOI 10.1109/12.260637 Rowstron A., 2001, P 18 ACM S OP SYST P, P188 ROWSTRON A, 2001, IFIP ACM MIDDL 2001 RUHL M, 2003, EFFICIENT ALGORITHMS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANKARALINGAM K, 2003, P 12 INT S HIGH PERF Saroiu S., 2002, ACM SIGOPS OPERATING, V36, P315, DOI 10.1145/844128.844158 Saroius S, 2002, P MULT COMP NETW 200 SCHLOSSER M, 2002, SPRINGER LECT NOTES, V2530 SCHLUMBERGER ML, 1974, THESIS Schmidt C, 2004, IEEE INTERNET COMPUT, V8, P19, DOI 10.1109/MIC.2004.1297269 SCHNEIDMAN J, 2003, 2 INT WORKSH PEER PE SELCUK A, 2004, 4 INT WORKSH GLOB PE Sen S, 2004, IEEE ACM T NETWORK, V12, P219, DOI 10.1109/TNET.2004.826277 Sen S., 2002, Proceedings of the Second Internet Measurement Workshop. IMW 2002 SERJANTOV A, 2002, P 2 INT C PEER PEER SHENKER S, 2003, 29 INT C VER LARG DA SHI SM, 2004, 3 INT WORKSH PEER TO SIEKA B, 2004, P 4 IEEE INT C PEER SINGH A, 2002, P 3 INT IEEE C PEER SINGHAL A, 1999, P 7 TEXT RETR C TREC, P253 Singhal A., 2001, IEEE DATA ENG B, V24, P35 SINGLA A, 2002, ULTRAPEERS STEP GNUT SIT E, 2002, 1 INT WORKSH PEER PE SOLLINS K, 2003, P ACM SIGCOMM WORKSH Stavrou A, 2004, IEEE J SEL AREA COMM, V22, P6, DOI 10.1109/JSAC.2003.818778 Stoica I, 2003, IEEE ACM T NETWORK, V11, P17, DOI 10.1109/TNET.2002.808407 Stoica I, 2001, P 2001 ACM SIGCOMM C, P149, DOI DOI 10.1145/383059.383071 Stonebraker Michael, 1996, VLDB J, V5, P48, DOI 10.1007/s007780050015 Sunaga H., 2002, Proceedings Second International Conference on Peer-to-Peer Computing (P2P 2002), DOI 10.1109/PTP.2002.1046329 Surridge M., 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003), DOI 10.1109/PTP.2003.1231493 TANG C, 2002, 1 WORKSH HOT TOP NET, V33 TANG C, 2004, 2M ANN INT ACM SIGIR Tang C., 2004, P 1 S NETW SYST DES, P211 Tang C., 2003, P 2003 C APPL TECHN, P175 TANG C, 2005, P 2005 ACM SIGM INT, P14, DOI 10.1145/1064212.1064216 TANIN E, 2004, P NAT C DIG GOV RES, P81 Tatarinov I, 2003, SIGMOD RECORD, V32, P47 TEMPICH S, 2004, P 13 C WORLD WID WEB, P640 TERPSTRA W, 2004, P 1 INT WORKSH PEER THADANI S, 2001, META DATA SEARCHES G THADANI SM, 2001, META INFORMATION SEA Triantafillou P., 2003, P 1 INT WORKSH DAT I, P169 TSANGOU M, 2003, P 5 WORKSH DISTR DAT VANRENESSE R, 2003, SPRINGER VERLAG LECT, V2584 VANRENESSE R, 2004, 3 INT WORKSH PEER TO Van Renesse R, 2003, ACM T COMPUT SYST, V21, P164, DOI 10.1145/762483.762485 Vogels W, 2003, ACM SIGCOMM COMP COM, V33, P131, DOI 10.1145/774763.774784 VOULGARIS S, 2003, P 14 IFIP IEEE WORKS WALFISH M, 2004, P 1 S NETW SYST DES, P225 WANG X, 2001, P JOINT INT C MEAS M Weatherspoon H., 2002, P 1 INT WORKSH PEER WEATHERSPOON H, 2002, P INT WORKSH REL PEE WEATHERSPOON H, 2002, P SIGOPS EUR WORKSH YALAGANDULA P, 2004, SIGCOMM 04 AUT 30 SE Yang B., 2002, P 22 INT C DISTR COM YANG B, 2004, 24 INT C DISTR COMP YANG B, 2001, 27 INT C VER LARG DA Yang Beverly, 2003, P 19 INT C DAT ENG I Yang Beverly, 2002, P 22 IEEE INT C DIST YEAGER B, 2003, PROPOSED RES TRACKS YEAGER B, 2003, PEER TO PEER RES GRO Zhang XY, 2004, IEEE J SEL AREA COMM, V22, P18, DOI 10.1109/JSAC.2003.818780 ZHANG Z, 2003, 2 INT WORKSH PEER TO Zhao B., 2002, 1 INT WORKSH PEER TO Zhao Ben Y., 2001, UCBCSD011141 Zhao BY, 2004, IEEE J SEL AREA COMM, V22, P41, DOI 10.1109/JSAC.2003.818784 ZHOU L, 2004, 3 INT WORKSH PERR TO ZHUANG S, 2005, P IEEE INF MAR 13 17 NR 336 TC 101 Z9 105 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1389-1286 EI 1872-7069 J9 COMPUT NETW JI Comput. Netw. PD DEC 5 PY 2006 VL 50 IS 17 BP 3485 EP 3521 DI 10.1016/j.comnet.2006.02.001 PG 37 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA 093GA UT WOS:000241154900015 ER PT J AU Xu, F Wang, ZJ AF Xu, Feng Wang, Zhijian TI Kind of E-mail classification system based on cluster technology of the text SO DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS LA English DT Proceedings Paper CT International Conference on Sensing, Computing and Automation CY MAY 08-11, 2006 CL Chongqing, PEOPLES R CHINA AB E-Mail Classification is one of the research focuses in the Internet application at present. It had made certain progress in this respect in recent years. People mainly use the text classification technology to classify E-Mails, however, such Classification system had some defects. This paper mainly combined some additional properties of the E-mail to bring out an E-Mail Classification system based on cluster technology of the text to solve some problems in existing E-Mail Classification system. C1 Univ Hohai, Sch Comp & Informat Engn, Nanjing 210098, Peoples R China. RP Xu, F (reprint author), Univ Hohai, Sch Comp & Informat Engn, Nanjing 210098, Peoples R China. CR Alsabti K., 1998, P 1 WORKSH HIGH PERF Baeza-Yates RA, 1999, MODERN INFORM RETRIE CUTTING DR, 1992, ANN INT C RES DEV IN Jain A. K., 1988, ALGORITHMS CLUSTERIN JAIN AK, 2003, J COMPUTER RES DEV, V40, P1609 KILANDER F, 1997, INTELLIGENT INFORM F PANG JF, 2001, J COMPUT APPL RES, P23 RILOFF E, 1994, ACM T INFORM SYSTEM RISH I, 2001, EMPIRICAL STUDY NAIV, P215 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAPIRERE S, 1999, MACH LEARN, P297 Sebastiani Fabrizio, 2002, ACM COMPUT SURV, V34, p[11, 32] Sebastiani Fabrizio, 2002, ACM COMPUT SURV, V34, P32 WU X, 2003, J N JIAOTONG U, V27, P39 YANG Y, 1996, JASIS 96 J AM SOC IN, P357 ZHANG DL, 2003, J TSINGHUA U, V43, P1289 ZHENG Y, 2003, J COMPUTER RES DEV, V40, P1609 NR 17 TC 0 Z9 0 PU WATAM PRESS PI WATERLOO PA C/O DCDIS JOURNAL, 317 KAREN PLACE, WATERLOO, ONTARIO N2L 6K8, CANADA SN 1492-8760 J9 DYNAM CONT DIS SER B JI Dyn. Contin. Discret. Impuls. Syst. Ser. B-Appl. Algorithms PD DEC PY 2006 VL 13E SU S BP 3836 EP 3841 PN 8 PG 6 WC Mathematics, Applied SC Mathematics GA 201OX UT WOS:000248842900053 ER PT J AU Xia, HX Wang, SG Yoshida, T AF Xia, Haoxiang Wang, Shuguang Yoshida, Taketoshi TI A MODIFIED ANT-BASED TEXT CLUSTERING ALGORITHM WITH SEMANTIC SIMILARITY MEASURE SO JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING LA English DT Article DE Ant-based clustering; text clustering; ant movement rule; semantic similarity measure AB Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance. Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant's carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm. C1 [Xia, Haoxiang] Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. [Wang, Shuguang] BHR Frontline Technol Dalian Corp Ltd, Dalian 116023, Peoples R China. [Yoshida, Taketoshi] Japan Adv Inst Sci & Technol, Sch Knowledge Sci, Kanazawa, Ishikawa 9231292, Japan. RP Xia, HX (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. EM hxxia@dlut.edu.cn; yoshida@jaist.ac.jp FU National Natural Science Foundation of China [70301009, 70431001]; Ministry of Education, Culture, Sports, Science and Technology of Japan under the "Kanazawa Region, Ishikawa High-Tech Sensing Cluster of Knowledge-Based Cluster Creation Project" FX This work was supported in part by National Natural Science Foundation of China under Grants No. 70301009 and No. 70431001, and by Ministry of Education, Culture, Sports, Science and Technology of Japan under the "Kanazawa Region, Ishikawa High-Tech Sensing Cluster of Knowledge-Based Cluster Creation Project". CR ANKERST M, 1999, P ACM SIGMOD INT C M, P49, DOI 10.1145/304182.304187 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Beni G., 1989, P NATO ADV WORKSH RO Berry M., 2003, SURVEY TEXT MINING C Beyer K, 1999, LECT NOTES COMPUT SC, V1540, P217 Bonabeau E., 1999, SWARM INTELLIGENCE N Chen L, 2004, PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P1387 Chiou YC, 2001, EUR J OPER RES, V135, P413, DOI 10.1016/S0377-2217(00)00320-9 Deneubourg J., 1991, P 1 INT C SIM AD BEH, P356 Handl J, 2003, FR ART INT, V104, P204 Handl J., 2002, Parallel Problem Solving from Nature - PPSN VII. 7th International Conference. Proceedings (Lecture Notes in Computer Science Vol.2439) Hartigan J. A., 1979, Applied Statistics, V28, DOI 10.2307/2346830 Hoe K., 2002, P 3 INT WORKSH ANT A, P256 Hotho A., 2003, P SEM WEB WORKSH SIG Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Jing L., 2006, P 4 WORKSH TEXT MIN Kanade P., 2003, P 22 INT C N AM FUZZ, P227 Kuntz P., 1998, J HEURISTICS, V5, P327 Labroche N, 2002, FR ART INT, V77, P345 Lewis D., 2006, REUTERS 21578 TEXT C Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 LINDBERG DAB, 1993, JAMA-J AM MED ASSOC, V269, P3124, DOI 10.1001/jama.269.24.3124 Lumer E., 1994, P 3 INT C SIM AD BEH Megaputer Intelligence Inc, 2006, ONL INTR TEXTANALYST RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Monmarche N., 1999, AAAI 99 GECCO 99 WOR, P23 RAMOS V, 2005, P 4 IEEE INT WORKSH, P977 Ramos V., 2002, P 1 SPAN C EV BIOINS, P284 Rector A., 1994, P MED INF EUR MIE94 Resnik P, 1995, P 14 INT JOINT C ART SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Vizine A. L., 2005, Informatica, V29 WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967 Xia H., 2006, P 7 INT S KNOWL SYST, P159 NR 38 TC 7 Z9 8 PU SPRINGER HEIDELBERG PI HEIDELBERG PA TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY SN 1004-3756 J9 J SYST SCI SYST ENG JI J. Syst. Sci. Syst. Eng. PD DEC PY 2006 VL 15 IS 4 BP 474 EP 492 DI 10.1007/s11518-006-5029-z PG 19 WC Operations Research & Management Science SC Operations Research & Management Science GA V24PD UT WOS:000208421400007 ER PT J AU Zakos, J Verma, B AF Zakos, John Verma, Brijesh TI A novel context-based technique for web information retrieval SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article; Proceedings Paper CT 14th International World Wide Web Conference (WWW2005) CY MAY 10-14, 2005 CL Chiba, JAPAN DE web information retrieval; context-based; term weighting; query expansion ID SEARCH; MODEL AB In this paper we present context matching, a novel context-based technique for the ad-hoc retrieval of web documents. The aim of the technique is to dynamically generate a measure of document term significance during retrieval that can be used as a substitute or co-contributor of the term frequency measure. Unlike term frequency, which relies on a term occurring multiple times in a document to be considered significant, context matching is based on the notion that if a term in a given document occurs in that document in the context of the query, then that term is deemed to be significant. Context matching has the ability to potentially determine a term to be significant even if it occurs only once in a document. Vice versa, it also has the ability to determine a term to be insignificant, even if occurs frequently within a document. We show how expanded terms generated by a typical query expansion technique can be used effectively as query context for context matching. The technique is ideally suited to the nature of web information retrieval and we show how context matching significantly improves retrieval accuracy through experimental results on TREC web benchmark data. C1 Griffith Univ, Sch Informat Technol, Nathan, Qld 4215, Australia. RP Zakos, J (reprint author), Griffith Univ, Sch Informat Technol, Nathan, Qld 4215, Australia. EM j.zakos@griffith.edu.au; b.verma@cqu.edu.au CR ALLAN J, 1999, P 8 TEXT RETR C NOV, P637 Amitay E, 2002, P 11 TEXT RETR C TRE ANH V, 2003, P 12 TEXT RETR C TRE, P726 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Brin S., 1998, P 7 INT WORLD WID WE, P107 Craswell N., 2003, P 12 TEXT RETR C TRE, P193 Finkelstein L., 2001, P 10 INT C WORLD WID, P406, DOI 10.1145/371920.372094 Glover E, 2001, COMMUN ACM, V44, P97, DOI 10.1145/501317.501319 He Ben, 2003, P 12 INT C INF KNOWL, P10 HEZINGER M, 2000, IEEE DATA ENG B, V23, P38 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 Jing H, 1999, P 22 ANN INT ACM SIG, P90, DOI 10.1145/312624.312661 Kang I.-H., 2003, P 26 ANN INT ACM SIG, P64 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lawrence S, 1998, IEEE INTERNET COMPUT, V2, P38, DOI 10.1109/4236.707689 LUHN HP, 1957, IBM J RES DEV, V1, P309 NAMBA I, 1999, P 8 TEXT RETR C TREC, P275 Plachouras V., 2003, P 12 TEXT RETR C TRE, P636 PLACHOURIS V, 2002, ACM SIGIR C, P105 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOORHEES E, 1998, USING TEXT RETRIEVAL, P285 WALKER S, 1997, P 6 TEXT RETR C TREC, P125 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 YU S, 2003, P 12 INT WORLD WID W NR 29 TC 8 Z9 8 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD DEC PY 2006 VL 9 IS 4 BP 485 EP 503 DI 10.1007/s11280-006-0223-y PG 19 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 125VJ UT WOS:000243471000006 ER PT J AU Caverlee, J Liu, L Rocco, D AF Caverlee, James Liu, Ling Rocco, Daniel TI Discovering interesting relationships among deep web databases: A source-biased approach SO WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS LA English DT Article; Proceedings Paper CT 14th International World Wide Web Conference (WWW2005) CY MAY 10-14, 2005 CL Chiba, JAPAN DE deep web database; database ranking; probing; deep web; biased discovery ID INFORMATION-RETRIEVAL; SELECTION; MODEL AB The escalation of deep web databases has been phenomenal over the last decade, spawning a growing interest in automated discovery of interesting relationships among available deep web databases. Unlike the "surface" web of static pages, these deep web databases provide data through a web-based query interface and account for a huge portion of all web content. This paper presents a novel source-biased approach to efficiently discover interesting relationships among web-enabled databases on the deep web. Our approach supports a relationship-centric view over a collection of deep web databases through source-biased database analysis and exploration. Our source-biased approach has three unique features: First, we develop source-biased probing techniques, which allow us to determine in very few interactions whether a target database is relevant to the source database by probing the target with very precise probes. Second, we introduce source-biased relevance metrics to evaluate the relevance of deep web databases discovered, to identify interesting types of source-biased relationships for a collection of deep web databases, and to rank them accordingly. The source-biased relationships discovered not only present value-added metadata for each deep web database but can also provide direct support for personalized relationship-centric queries. Third, but not least, we also develop a performance optimization using source-biased probing with focal terms to further improve the effectiveness of the basic source-biased model. A prototype system is designed for crawling, probing, and supporting relationship-centric queries over deep web databases using the source-biased approach. Our experiments evaluate the effectiveness of the proposed source-biased analysis and discovery model, showing that the source-biased approach outperforms query-biased probing and unbiased probing. C1 Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA. RP Caverlee, J (reprint author), Georgia Inst Technol, Coll Comp, 801 Atlantic Dr, Atlanta, GA 30332 USA. EM caverlee@cc.gatech.edu CR AGICHTEIN E, 2003, P INT WORKSH WEB DAT Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bergman M.K., 2000, DEEP WEB SURFACING H CALLAN J, 1999, P 1999 ACM C MAN DAT Callan J, 2001, ACM T INFORM SYST, V19, P97, DOI 10.1145/382979.383040 Callan J.P., 1995, P 18 ANN INT ACM SIG CAVERLEE J, 2004, P 20 IEEE C DAT ENG Chakrabarti S, 1999, P 8 INT WORLD WID WE CHANG K, 2004, SIGMOD REC, V33 Cohen W.W., 1996, AAAI WORKSH INT BAS CRASWELL N, 2000, P 5 ACM C DIG LIB AC Croft W. B., 1996, P 19 ANN INT ACM SIG DOLIN R, 1999, P 4 ACM C DIG LIB AC FRENCH JC, 1999, P 22 AN INT ACM SIGI Fuhr N, 1999, ACM T INFORM SYST, V17, P229, DOI 10.1145/314516.314517 Gravano L., 1995 Gravano L, 1999, ACM T DATABASE SYST, V24, P229, DOI 10.1145/320248.320252 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 IPEIROTIS P, 2002, P 28 INT C VER LARG IPEIROTIS PG, 2003, ACM TOIS, V21, P1 IPEIROTIS PG, 2001, P 2001 ACM C MAN DAT IPEIROTIS PG, 2005, P 21 IEEE INT C DAT LIU L, 1999, P 15 IEEE INT C DAT Lyman Peter, 2003, MUCH INFORM MENG W, 1998, P 24 INT C VER LARG MENG W, 1999, P 4 IFCIS INT C COOP NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 POWELL AL, 2000, P 23 ANN INT ACM SIG QIU Y, 1993, P 16 ANN INT ACM SIG Raghavan S., 2001, P 27 INT C VER LARG ROCCO D, 2005, P 14 INT WORLD WID W Salton G., 1997, READINGS INFORM RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, INFORM PROCESS MANAG, V33, P307, DOI 10.1016/S0306-4573(96)00068-4 SUGIURA A, 2000, P 9 INT WORLD WID WE WANG W, 2000, P 1 INT C WEB INF SY WANG W, 2004, P 30 INT C VER LARG WU W, 2004, P 2004 ACM C MAN DAT YUWONO B, 1997, DATABASE SYSTEMS ADV ZHANG Z, 2004, P 2004 ACM C MAN DAT NR 41 TC 5 Z9 7 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-145X J9 WORLD WIDE WEB JI World Wide Web PD DEC PY 2006 VL 9 IS 4 BP 585 EP 622 DI 10.1007/s11280-006-0227-7 PG 38 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 125VJ UT WOS:000243471000010 ER PT J AU Losada, DE Barreiro, A AF Losada, David E. Barreiro, Alvaro TI Negations and document length in logical retrieval SO INFORMATION SYSTEMS LA English DT Article; Proceedings Paper CT 11th Symposium on String Processing and Information Retrieval CY OCT, 2004 CL Padua, ITALY DE information retrieval; logic-based information retrieval ID INFORMATION-RETRIEVAL; MODEL AB An unsolved problem in logic-based information retrieval is how to obtain automatically logical representations for documents and queries. This problem limits the impact of logical models for information retrieval because their full expressive power cannot be harnessed. In this paper we propose a method for producing logical document representations which goes further than other simplistic "bag-of-words" approaches. The suggested procedure adopts popular information retrieval heuristics, such as document length corrections and global term distribution. This work includes a report of several experiments applying partial document representations in the context of a propositional model of information retrieval. The benefits of this expressive framework, powered by the new logical indexing approach, become apparent in the evaluation. (C) 2005 Elsevier B.V. All rights reserved. C1 Univ Santiago Compostela, Grp Sistemas Inteligentes, Dept Elect & Computac, Santiago De Compostela, Spain. Univ A Coruna, Dept Computac, La Coruna, Spain. RP Losada, DE (reprint author), Univ Santiago Compostela, Grp Sistemas Inteligentes, Dept Elect & Computac, Santiago De Compostela, Spain. EM dlosada@dec.usc.es; barreiro@udc.es RI Losada, David/A-3202-2009 CR CRESTANI F, 1998, INFORM RETRIEVAL UNC Crestani F., 2000, Information Retrieval, V2 CROFT WB, 2003, LANGUAGE MODELING IN Dalal M, 1988, P AAAI 88, P475 Lalmas M, 1998, KNOWL ENG REV, V13, P263, DOI 10.1017/S0269888998002124 Lau R. Y. K., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009017 LOGAN B, 1994, P 17 ANN INT C RES D, P91 LOSADA D, 2003, LNCS, V2663, P219 LOSADA DE, 2001, THESIS U CORUNA Losada D. E., 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, DOI 10.1109/SPIRE.2000.878195 Losada D. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312654 Losada DE, 2001, COMPUT J, V44, P410, DOI 10.1093/comjnl/44.5.410 Losada DE, 2003, J AM SOC INF SCI TEC, V54, P285, DOI 10.1002/asi.10209 LOSADA DE, 2000, P SIGIR 2000 WORKSH, P16 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 NR 22 TC 0 Z9 0 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD NOV PY 2006 VL 31 IS 7 BP 610 EP 620 DI 10.1016/j.is.2005.11.001 PG 11 WC Computer Science, Information Systems SC Computer Science GA 087ZU UT WOS:000240782100005 ER PT J AU Jones, MN Kintsch, W Mewhort, DJK AF Jones, Michael N. Kintsch, Walter Mewhort, Douglas J. K. TI High-dimensional semantic space accounts of priming SO JOURNAL OF MEMORY AND LANGUAGE LA English DT Article DE semantic space models; models of word meaning; semantic priming; mediated priming ID AUTOMATIC SPREADING ACTIVATION; SIMPLE RECURRENT NETWORKS; LEXICAL DECISION TASK; ASSOCIATIVE INFORMATION; MODEL; REPRESENTATIONS; MEMORY; COOCCURRENCE; RETRIEVAL; ACQUISITION AB A broad range of priming data has been used to explore the structure of semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language. To best account for the variety of priming data, we introduce a holographic model of the lexicon that learns word meaning and order information from experience with a large text corpus. Both context and order information are learned into the same composite representation by simple summation and convolution mechanisms (cf. Murdock, B.B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609-626). We compare the similarity structure of representations learned by the holographic model, Latent Semantic Analysis (LSA; Landauer, T.K., & Dumais, ST. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240), and the Hyperspace Analogue to Language (HAL; Lund, K., & Burgess, C., (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203-208) at predicting human data in a variety of semantic, associated, and mediated priming experiments. We found that both word context and word order information are necessary to account for trends in the human data. The representations learned from the holographic system incorporate both types of structure, and are shown to account for priming phenomena across several tasks. (c) 2006 Elsevier Inc. All rights reserved. C1 Univ Colorado, Inst Cognit Sci, Boulder, CO 80309 USA. Queens Univ, Dept Psychol, Kingston, ON K7L 3N6, Canada. RP Jones, MN (reprint author), Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN 47405 USA. EM jonesmn@indiana.edu CR MCNAMARA TP, 1988, J MEM LANG, V27, P545, DOI 10.1016/0749-596X(88)90025-3 Audet C, 1998, PROCEEDINGS OF THE TWENTIETH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P66 BALOTA DA, 1986, J EXP PSYCHOL LEARN, V12, P336, DOI 10.1037//0278-7393.12.3.336 BATTIG WF, 1969, J EXP PSYCHOL, V80, P1, DOI 10.1037/h0027577 Burgess C, 2000, COGNITIVE DYNAMICS, P117 CHIARELLO C, 1990, BRAIN LANG, V38, P75, DOI 10.1016/0093-934X(90)90103-N Chwilla DJ, 2000, J MEM LANG, V42, P314, DOI 10.1006/jmla.1999.2680 Chwilla DJ, 2002, MEM COGNITION, V30, P217, DOI 10.3758/BF03195282 Collins A. M., 1972, ORG MEMORY COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 Cree GS, 1999, COGNITIVE SCI, V23, P371, DOI 10.1016/S0364-0213(99)00005-1 DEGROOT AMB, 1983, J VERB LEARN VERB BE, V22, P417, DOI 10.1016/S0022-5371(83)90273-6 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dennis S, 2005, COGNITIVE SCI, V29, P145, DOI 10.1207/s15516709cog0000_9 ELMAN JL, 1991, MACH LEARN, V7, P195, DOI 10.1007/BF00114844 ELMAN JL, 1990, COGNITIVE SCI, V14, P179, DOI 10.1207/s15516709cog1402_1 Ferrand L, 2003, MENTAL LEXICON: SOME WORDS TO TALK ABOUT WORDS, P25 FRANKLIN DRJ, 2002, HIGH PERFORMANCE COM Gabel R. A., 1973, SIGNALS LINEAR SYSTE GRIFFITHS TL, 2005, ADV NEURAL INFORM PR, V18, P2 Howell SR, 2005, J MEM LANG, V53, P258, DOI 10.1016/j.jml.2005.03.002 HUCHINSON KA, 2003, PSYCHONOMIC B REV, V10, P785 Hummel JE, 2003, PSYCHOL REV, V110, P220, DOI 10.1037/0033-295X.110.2.220 JONES MR, IN PRESS PSYCHOL REV Kintsch W., 1998, COMPREHENSION PARADI LAHAM RD, 2000, THESIS U COLORADO BO Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 LIVESAY K, 1998, P COGN SCI SOC Livesay K, 1998, BRAIN COGNITION, V37, P102 Lucas M, 2000, PSYCHON B REV, V7, P618, DOI 10.3758/BF03212999 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 MCKOON G, 1992, J EXP PSYCHOL LEARN, V18, P1155, DOI 10.1037/0278-7393.18.6.1155 MCKOON G, 1988, J MEM LANG, V28, P547 McNamara T. P., 2005, SEMANTIC PRIMING PER MCNAMARA TP, 1992, J EXP PSYCHOL LEARN, V18, P1173, DOI 10.1037//0278-7393.18.6.1173 McRae K, 1998, J EXP PSYCHOL LEARN, V24, P558, DOI 10.1037//0278-7393.24.3.558 McRae K, 1997, J EXP PSYCHOL GEN, V126, P99, DOI 10.1037/0096-3445.126.2.99 Metcalfe-Eich J., 1982, PSYCHOL REV, V89, P627 MEYER DE, 1971, J EXP PSYCHOL, V90, P227, DOI 10.1037/h0031564 MILLER GA, 1950, AM J PSYCHOL, V63, P176, DOI 10.2307/1418920 MILLER GA, 1995, SPECIAL ISSUE INT J, V3 MOSS HE, 1995, J EXP PSYCHOL LEARN, V21, P863, DOI 10.1037/0278-7393.21.4.863 Murdock B, 2001, J MATH PSYCHOL, V45, P564, DOI 10.1006/jmps.2000.1339 MURDOCK BB, 1992, J MATH PSYCHOL, V36, P68, DOI 10.1016/0022-2496(92)90053-A MURDOCK BB, 1982, PSYCHOL REV, V89, P609, DOI 10.1037/0033-295X.89.6.609 MURDOCK BB, 1993, J MATH PSYCHOL, V37, P421, DOI 10.1006/jmps.1993.1025 Neely J. H., 1991, BASIC PROCESSES READ, P264 Nelson D. L., 1998, U S FLORIDA WORD ASS NEWHORT DJK, 1991, RELATING THEORY DATA, P425 OSGOOD CE, 1971, J SOC ISSUES, V27, P5 OSGOOD CE, 1952, PSYCHOL BULL, V49, P197, DOI 10.1037/h0055737 Perea M, 2002, PSYCHOL RES-PSYCH FO, V66, P180, DOI 10.1007/s00426-002-0086-5 Perfetti CA, 1998, DISCOURSE PROCESS, V25, P363 Plate T. A., 2003, CSLI LECT NOTES, V150 PLATE TA, 1995, IEEE T NEURAL NETWOR, V6, P623, DOI 10.1109/72.377968 Plaut D. C., 1995, P 17 ANN C COGN SCI, P37 Plaut DC, 2000, PSYCHOL REV, V107, P786, DOI 10.1037//0033-295X.107.4.786 ROSCH E, 1975, J EXP PSYCHOL GEN, V104, P192, DOI 10.1037//0096-3445.104.3.192 SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SERVANSCHREIBER D, 1991, MACH LEARN, V7, P161, DOI 10.1007/BF00114843 SHELTON JR, 1992, J EXP PSYCHOL LEARN, V18, P1191, DOI 10.1037//0278-7393.18.6.1191 SMITH EE, 1974, PSYCHOL REV, V81, P214, DOI 10.1037/h0036351 Solomon KO, 2001, COGNITIVE PSYCHOL, V43, P129, DOI 10.1006/cogp.2001.0754 STEYVERS M, 2000, THESIS INDIANA U Steyvers M, 2005, COGNITIVE SCI, V29, P41, DOI 10.1207/s15516709cog2901_3 Wiemer P., 2000, P 22 ANN M COGN SCI WIEMERHASTINGS P, 2001, P 23 ANN M COGN SCI Williams JN, 1996, EUR J COGN PSYCHOL, V8, P113, DOI 10.1080/095414496383121 NR 71 TC 54 Z9 54 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 0749-596X J9 J MEM LANG JI J. Mem. Lang. PD NOV PY 2006 VL 55 IS 4 BP 534 EP 552 DI 10.1016/j.jml.2006.07.003 PG 19 WC Linguistics; Psychology; Psychology, Experimental SC Linguistics; Psychology GA 111DC UT WOS:000242430600007 ER PT J AU Benoit, G AF Benoit, Gerald TI Link analysis: An information science approach. SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Book Review C1 Simmons Coll, Boston, MA 02115 USA. RP Benoit, G (reprint author), Simmons Coll, 300 Fenway, Boston, MA 02115 USA. EM benoit@simmons.edu CR Baeza-Yates R., 1999, MODERN INFORM BATAGELJ V, 2003, PAJEK Belew R.K., 2000, FINDING OUT COGNITIV GETOOR L, 2001, IJCAI WORKSH TEXT LE Glanzel W, 2003, BIBLIOMETRICS RES FI Hand D., 2001, PRINCIPLES DATA MINI HENZIGER M, 2000, B IEEE COMPUTER SOC Korfhage R., 1997, INFORM STORAGE RETRI LARSON RR, 1996, BIBLIOMETRICS WORLD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Thelwall M, 2001, J AM SOC INF SCI TEC, V52, P1157, DOI 10.1002/asi.1182 Thelwall M., 2004, LINK ANAL INFORM SCI THELWALL M, 1993, INTERPRETING SOCIAL Van Rijsbergen K., 1979, INFORM RETRIEVAL NR 14 TC 0 Z9 0 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD NOV PY 2006 VL 57 IS 13 BP 1855 EP 1858 DI 10.1002/asi.20466 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 100SL UT WOS:000241688900016 ER PT J AU Roth, C Bourgine, P AF Roth, Camille Bourgine, Paul TI Lattice-based dynamic and overlapping taxonomies: The case of epistemic communities SO SCIENTOMETRICS LA English DT Article ID GALOIS LATTICES; NETWORKS; MODEL; MAP; ZEBRAFISH; SCIENCES; AUTHORS AB We present a method for describing taxonomy evolution. We focus on the structure of epistemic communities (ECs), or groups of agents sharing common knowledge concerns. Introducing a formal framework based on Galois lattices, we categorize ECs in an automated and hierarchically structured way and propose criteria for selecting the most relevant epistemic communities - for instance, ECs gathering a certain proportion of agents and thus prototypical of major fields. This process produces a manageable, insightful taxonomy of the community. Then, the longitudinal study of these static pictures makes possible an historical description. In particular, we capture stylized facts such as field progress, decline, specialization, interaction (merging or splitting), and paradigm emergence. The detection of such patterns in epistemic networks could fruitfully be applied to other contexts. C1 Ecole Polytech, CNRS, CREA, F-75005 Paris, France. RP Roth, C (reprint author), Ecole Polytech, CNRS, CREA, 1 Rue Descartes, F-75005 Paris, France. EM camille.roth@polytechnique.edu CR ARCHAMBAULT E, 2004, UTILISATION BIBLIOME Barbut M., 1970, ALGEBRE COMBINATOIRE, V2 Belohlavek R., 2000, DISCOVERING WORLD FU, P462 Berlin B., 1992, ETHNOBIOLOGICAL CLAS Birkhoff G., 1948, LATTICE THEORY Bradbury J, 2004, PLOS BIOL, V2, P568, DOI 10.1371/journal.pbio.0020148 Callon M., 1986, MAPPING DYNAMICS SCI COHENDET P., 2003, REV EC IND, V103, P15, DOI 10.3406/rei.2003.3105 *COLD SPRING HARB, 2003, ZEBR DEV GEN Cowan R., 2000, IND CORP CHANGE, V9, P212 Dooley K, 2000, CURR OPIN GENET DEV, V10, P252, DOI 10.1016/S0959-437X(00)00074-5 DUPOUET O, 2001, EC HETEROGENEOUS AGE Duquenne V, 2003, APPL ARTIF INTELL, V17, P243, DOI 10.1080/08839510390187152 FREEMAN LC, 1993, SOCIOL METHODOL, V23, P127, DOI 10.2307/271008 Godin R, 1998, THEOR PRACT OBJ SYST, V4, P117 Grunwald DJ, 2002, NAT REV GENET, V3, P717, DOI 10.1038/nrg892 HAAS PM, 1992, INT ORGAN, V46, P1 Ide N, 1998, COMPUT LINGUIST, V24, P1 Lander ES, 2001, NATURE, V409, P860, DOI 10.1038/35057062 Jain AK, 1999, ACM COMPUT SURV, V31 Johnson SC, 1967, PSYCHOMETRIKA, V2, P241 Klein Julie Thompson, 1990, INTERDISCIPLINARY HI Kohonen T., 2001, SELF ORG MAPS, V3rd Kreuzman H, 2001, SCIENTOMETRICS, V51, P525, DOI 10.1023/A:1019647103469 Kuhn T.S., 1970, STRUCTURE SCI REVOLU Kuznetsov SO, 2002, J EXP THEOR ARTIF IN, V14, P189, DOI 10.1080/09528130210164170 LEYDESDORFF L, 1991, SOC STUD SCI, V21, P75, DOI 10.1177/030631291021001005 Leydesdorff L, 1997, J AM SOC INFORM SCI, V48, P418 Lopez A, 1997, COGNITIVE PSYCHOL, V32, P251, DOI 10.1006/cogp.1997.0651 Lorrain F., 1971, J MATH SOCIOL, V1, P49, DOI [DOI 10.1080/0022250X.1971.9989788), 10.1080/0022250X.1971.9989788] MCCAIN KW, 1986, J AM SOC INFORM SCI, V37, P111, DOI 10.1002/(SICI)1097-4571(198605)37:3<111::AID-ASI2>3.0.CO;2-D McCain KW, 2005, SCIENTOMETRICS, V65, P131, DOI 10.1007/s11192-005-0262-7 Monjardet B, 2003, MATH SOC SCI, V46, P103, DOI 10.1016/S0165-4896(03)00072-6 Newman MEJ, 2004, EUR PHYS J B, V38, P321, DOI 10.1140/epjb/e2004-00124-y Noyons ECM, 1998, J AM SOC INFORM SCI, V49, P68, DOI 10.1002/(SICI)1097-4571(1998)49:1<68::AID-ASI9>3.0.CO;2-Z PERSSON O, 1995, SCIENTOMETRICS, V33, P351, DOI 10.1007/BF02017336 ROSCH E, 1978, AM PSYCHOL, V44, P1468 Roth C., 2005, MATH POPUL STUD, V12, P107, DOI 10.1080/08898480590931404 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sokal Robert, 1963, PRINCIPLES NUMERICAL Stumme G, 2002, DATA KNOWL ENG, V42, P189, DOI 10.1016/S0169-023X(02)00057-5 Wasserman S., 1994, SOCIAL NETWORK ANAL WHITE DR, 2006, MEDIEVAL MODERN CIVI WHITE HC, 1976, AM J SOCIOL, V81, P730, DOI 10.1086/226141 WHITTAKE.RH, 1969, SCIENCE, V163, P150, DOI 10.1126/science.163.3863.150 Wille R., 1982, ORDERED SETS, P445 WILLE R, 1992, COMPUT MATH APPL, V23, P493, DOI 10.1016/0898-1221(92)90120-7 NR 47 TC 7 Z9 7 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0138-9130 J9 SCIENTOMETRICS JI Scientometrics PD NOV PY 2006 VL 69 IS 2 BP 429 EP 447 DI 10.1007/s11192-006-0161-6 PG 19 WC Computer Science, Interdisciplinary Applications; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 083SE UT WOS:000240477700013 ER PT J AU Chen, DY Li, X Dong, ZY Chen, X AF Chen, Ding-Yi Li, Xue Dong, Zhao Yang Chen, Xia TI Fitness assessment of document model SO INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE LA English DT Article DE document classification; document model; fitness measurement ID TEXT CATEGORIZATION AB Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, "Resource selection for domain-specific cross-lingual IR", in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification. C1 Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. RP Chen, DY (reprint author), Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. EM dingyi@itee.uq.edu.au CR Akaike H., 1974, 2 INT S INF THEOR, P267 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 CHEN DY, 2005, ADC 2005 16 AUSTR DA, P125 Chickering D., 1997, P 13 C UNC ART INT U, P80 COOPER WS, 1973, J AM SOC INFORM SCI, V24, P87, DOI 10.1002/asi.4630240204 Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich T, 1995, ACM COMPUT SURV, V27, P326, DOI 10.1145/212094.212114 Dobra A., 2002, KDD 2002 P 8 ACM SIG, P481 Dumais S., 1998, CIKM 98, P148 FINCH S, 2003, REUTERS 21578 CORPUS FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Gao Jianfeng, 2004, SIGIR 04 P 27 ANN IN, P170, DOI DOI 10.1145/1008992.1009024 Heckerman D., 1994, KDD WORKSH, P85 Joachims T, 1998, EUR C MACH LEARN ECM, P137 KHMELEV DV, 2003, SIGIR 2003 P 26 ANN, P104 Krovetz R., 1993, SIGIR Forum LARKEY LS, 1998, SIGIR 1998 P 21 ANN, P90 Larkey LS, 2003, AUTOMATED ESSAY SCORING: A CROSS-DISCIPLINARY PERSPECTIVE, P55 Lewis D. D., 1995, SIGIR Forum Lewis David D., 1992, THESIS U MASSACHUSET Lewis D.D, 1998, EUR C MACH LEARN, P4 LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 PORTER MF, 1997, READINGS INFORMATION Quinlan J. R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence Reunanen J., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753715 RIJSBERGEN CJV, 1997, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROGATI M, 2004, SIGIR 2004 P 27 ANN, P154 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 VAPNIK VN, 1995, NATURE STAT LEARNING, P119 WARD G, 1996, MOBY WORD MOBY LEXIC WEISS SM, 1993, IEEE EXPERT, V8, P61, DOI 10.1109/64.248354 Yang Y., 1997, ICML 97, P412 NR 37 TC 0 Z9 0 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 0020-7721 J9 INT J SYST SCI JI Int. J. Syst. Sci. PD OCT 20 PY 2006 VL 37 IS 13 BP 893 EP 903 DI 10.1080/00207720600891539 PG 11 WC Automation & Control Systems; Computer Science, Theory & Methods; Operations Research & Management Science SC Automation & Control Systems; Computer Science; Operations Research & Management Science GA 096EZ UT WOS:000241361700004 ER PT J AU Crouch, CJ AF Crouch, Carolyn J. TI Dynamic element retrieval in a structured environment SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE design; experimentation; XML; structured retrieval; flexible retrieval; dynamic element retrieval; vector space model ID XML; RANKING AB This research examines the feasibility of dynamic element retrieval in a structured environment. Structured documents and queries are represented in extended vector form, based on a modification of the basic vector space model suggested by Fox [1983]. A method for the dynamic retrieval of XML elements, which requires only a single indexing of the documents at the level of the basic indexing node, is presented. This method, which we refer to as flexible retrieval, produces a rank ordered list of retrieved elements that is equivalent to the result produced by the same retrieval against an all-element index of the collection. Flexible retrieval obviates the need for storing either an all-element index or multiple indices of the collection. C1 Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. EM ccrouch@d.umn.edu CR CARMEL D, 2002, P 25 ANN ACM SPEC IN, P54 Carmel D., 2003, P 26 ANN INT ACM SIG, P151 CROUCH C, 1990, P 13 ANN INT ACM SIG, P369, DOI 10.1145/96749.98242 CROUCH C, 2003, P 2 WORKSH IN EV XML, P87 CROUCH C, 2005, P ADV SML RETR 3 INT, P292 CROUCH CJ, 2002, P 1 WORKSH IN EV XML, P95 Crouch CJ, 2006, LECT NOTES COMPUT SC, V3977, P268 Fox E., 1983, THESIS CORNELL U ITH FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 FUHR N, 2005, LECT NOTES COMPUTER, V3977 Fuhr N., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291005 GOVERT N, 2002, P 1 WORKSH IN EV XML, P26 GRABS T, 2002, P XML INF RETR WORKS, P53 GRABS T, 2002, P 1 WORKSH IN EV XML, P141 Kakade V, 2005, LECT NOTES COMPUT SC, V3408, P96 KARZI G, 2005, P INEX WORKSH EL RET, P22 Khanna S, 2005, THESIS U MINNESOTA D MALIK S, 2005, LECT NOTES COMPUTER, V3977, P1 Mass Y, 2005, LECT NOTES COMPUT SC, V3493, P73 MASS Y., 2002, P 1 WORKSH IN EV XML, P73 RAGHAVAN VV, 1989, SIGIR FORUM, V23, P59, DOI 10.1145/75334.75342 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1968, AUTOMATIC INFORM ORG SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 SINGHAL A, 1996, P 19 ANN INT ACM SPE, P19 WEIGEL F, 2005, LECT NOTES COMPUTER, V3493, P126 NR 30 TC 6 Z9 6 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD OCT PY 2006 VL 24 IS 4 BP 437 EP 454 DI 10.1145/1185877.1185880 PG 18 WC Computer Science, Information Systems SC Computer Science GA 112UN UT WOS:000242553000003 ER PT J AU Cummins, R O'Riordan, C AF Cummins, Ronan O'Riordan, Colm TI Evolved term-weighting schemes in Information Retrieval: an analysis of the solution space SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article; Proceedings Paper CT 17th Artificial Intelligence and Cognitive Science Conference (AICS-06) CY 2006 CL Belfast, NORTH IRELAND HO Queens Univ Belfast, Sch Elect, Elect Engn & Comp Sci DE genetic programming; Information Retrieval; term-weighting schemes AB Evolutionary computation techniques are increasingly being applied to problems within Information Retrieval (IR). Genetic programming (GP) has previously been used with some success to evolve term-weighting schemes in IR. However, one fundamental problem with the solutions generated by this stochastic, non-deterministic process, is that they are often difficult to analyse. In this paper, we introduce two different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. We show, using this framework, that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available. C1 [Cummins, Ronan; O'Riordan, Colm] Natl Univ Ireland, Dept Informat Technol, Galway, Ireland. RP Cummins, R (reprint author), Natl Univ Ireland, Dept Informat Technol, Galway, Ireland. EM ronan.cummins@nuigalway.ie; colm.oriordan@nuigalway.ie CR Buckley C., 2000, SIGIR 00, P33, DOI DOI 10.1145/345508.345543 CARTERETTE B, 2005, CIKM 2005, P680 Choi JH, 2000, BIOINFORMATICS, V16, P1056, DOI 10.1093/bioinformatics/16.11.1056 CHOWDHURY A, 2002, SIGIR 02, P381 CUMMINS R, 2006, 17 ART INT COGN SCI CUMMINS R, 2006, TIR 06 TEXT BAS INF, V9, P311 Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 Fang H., 2005, SIGIR 05, P480 HE B, 2003, CIKM 03, P10 Jones K. S., 2000, Information Processing & Management, V36 Koza J. R., 1992, GENETIC PROGRAMMING Luke S, 2001, P GEN EV COMP C GECC, P74 OREN N, 2002, P SAICSIT Ponte J. M., 1998, SIGIR 98, P275, DOI DOI 10.1145/290941.291008 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, IEEE DATA ENG B, V24, P35 Trotman A, 2005, INFORM RETRIEVAL, V8, P359, DOI 10.1007/s10791-005-6991-7 Zobel J., 1998, SIGIR Forum, V32 NR 19 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD OCT PY 2006 VL 26 IS 1-2 BP 35 EP 47 DI 10.1007/s10462-007-9034-5 PG 13 WC Computer Science, Artificial Intelligence SC Computer Science GA 240TA UT WOS:000251609100004 ER PT J AU Vechtomova, O AF Vechtomova, Olga TI Noun phrases in interactive query expansion and document ranking SO INFORMATION RETRIEVAL LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn DE interactive query expansion; noun phrases; document ranking ID INFORMATION-RETRIEVAL; MODEL AB The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase-based document ranking method. A combined syntactico-statistical method was used for the selection of phrases for query expansion. Several statistical measures of phrase selection were evaluated. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. One of the major problems in phrase-based document retrieval is weighting of overlapping and non-contiguous word sequences in documents. The paper presents a new method of phrase weighting, which addressed this problem, and its evaluation on the TREC dataset. C1 Univ Waterloo, Fac Engn, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. RP Vechtomova, O (reprint author), Univ Waterloo, Fac Engn, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. EM ovechtom@engmail.uwaterloo.ca CR ALLAN J, 2005, P 13 TEXT RETR C NIS Anick P. G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312670 Banerjee S., 2003, P 4 INT C INT TEXT P Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 BELY N, 1970, PROCEDURES ANAL SEMA Brill E, 1995, COMPUT LINGUIST, V21, P543 Bruza Peter, 2000, P 23 ANN INT ACM SIG, P280, DOI 10.1145/345508.345598 CLARKE CLA, 1995, CS9507 U WAT COMP SC Dunning T., 1993, Computational Linguistics, V19 EVANS DA, 1996, P ACL 96 34 ANN M AS Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Frantzi K., 1996, P 16 INT C COMP LING, P41 HULL D, 1996, P 5 TEXT RETR C GAIT, P167 Jones K. S., 2000, Information Processing & Management, V36 MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U Mitra M, 1997, P RIAO97 COMP ASS IN, P200 PICKENS J, 2000, P RIAO Ramshaw L.A., 1995, P 3 ACL WORKSH VER L ROBERTSON SE, 2004, P 13 C INF KNOWL MAN ROBERTSON SE, 2004, P 12 TEXT RETR C, P418 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1998, P 20 BCS IRSG C GREN STRZALKOWSKI T, 1999, NATURAL LANGUAGE INF, P113 STRZALKOWSKI T, 1995, INFORM PROCESS MANAG, V31, P397, DOI 10.1016/0306-4573(94)00055-8 VECHTOMOVA O, 2003, P 12 TEXT RETR C NIS VECHTOMOVA O, 2005, P 13 TEXT RETR C NIS Vintar S., 2004, P MEMURA 2004 WORKSH, P54 VOORHEES E, 2003, P 12 TEXT RETR C NIS Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 33 TC 4 Z9 4 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP PY 2006 VL 9 IS 4 BP 399 EP 420 DI 10.1007/s10791-006-6390-8 PG 22 WC Computer Science, Information Systems SC Computer Science GA 067RZ UT WOS:000239321600002 ER PT J AU Nurnberger, A Detyniecki, M AF Nuernberger, Andreas Detyniecki, Marcin TI Externally growing self-organizing maps and its application to e-mail database visualization and exploration SO APPLIED SOFT COMPUTING LA English DT Article DE growing self-organizing maps; e-mail; document classification; visualization; information retrieval ID LATENT SEMANTIC ANALYSIS; VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; TEXT RETRIEVAL AB In this paper we present an approach to organize and classify e-mails using self-organizing maps. The aim is on the one hand to provide an intuitive visual profile of the considered mailing lists and on the other hand to offer an intuitive navigation tool, were similar e-mails are located close to each other, so that the user can scan easily for e-mails similar in content. To be able to evaluate this approach we have developed a prototypical software tool that imports messages from a mailing list and arranges/ groups these e-mails based on a similarity measure. The tool combines conventional keyword search methods with a visualization of the considered e-mail collection. The prototype was developed based on externally growing self-organizing maps, which solve some problems of conventional self-organizing maps and which are computationally viable. Besides the underlying algorithms we present and discuss some system evaluations in order to show the capabilities of the approach. (C) 2005 Elsevier B.V. All rights reserved. C1 Univ Magdeburg, FIN IWS IR Grp, D-39106 Magdeburg, Germany. Univ Paris 06, CNRS, Lab Informat, F-75015 Paris, France. RP Nurnberger, A (reprint author), Univ Magdeburg, FIN IWS IR Grp, Universitatspl 2, D-39106 Magdeburg, Germany. EM nuernb@iws.cs.uni-magdeburg.de; Marcin.Detyniecki@lip6.fr RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR Alahakoon D, 2000, IEEE T NEURAL NETWOR, V11, P601, DOI 10.1109/72.846732 ALLINSON N, 2001, P 3 WORKSH SELF ORG Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BORGELT C, 2004, P PKDD WORKSH STAT A DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FRITZKE B, 1994, NEURAL NETWORKS, V7, P1441, DOI 10.1016/0893-6080(94)90091-4 GREIFF WR, 1998, P 21 ANN INT ACM SIG HONKELA T, 1996, NEWSGROUP EXPLORATIO Honkela T., 1997, SELF ORG MAPS NATURA ISBELL CL, 1998, P C NEUR INF PROC NI, P480 Kaski S., 1998, P IJCNN 98 INT JOINT, V1, P413 Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 Kohonen T., 1984, SELF ORG ASS MEMORY KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Lagus K., 1999, P ICANN99 9 INT C AR, V1, P371 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 LOCHBAUM KE, 1989, INFORM PROCESS MANAG, V25, P665, DOI 10.1016/0306-4573(89)90100-3 LYMAN P, 2000, MUCH INFORM PROJECT NURNBERGER A, 2001, P INT C EUR SOC FUZZ, P377 NURNBERGER A, 2002, P INT JOINT C NEUR N Nurnberger A, 2003, LECT NOTES COMPUT SC, V2714, P883 NURNBERGER A, 2000, DATA UTILIZATION SOF NURNBERGER A, 2001, P INT WORKSH INF RET, P61 *PITN BOW, 2000, MESS PRACT KNOWL EC Porter M.F., 1980, ALGORITHM SUFFIX STR, P130 RABUER A, 1999, P INT JOINT C NEUR N, P3527 RITTER H, 1989, BIOL CYBERN, V61 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, TR74218 CORN U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Steinbach M., 2000, KDD WORKSH TEXT MIN Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 L SOFT LISTSERV STAT NR 37 TC 9 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 1568-4946 J9 APPL SOFT COMPUT JI Appl. Soft. Comput. PD AUG PY 2006 VL 6 IS 4 BP 357 EP 371 DI 10.1016/j.asoc.2005.11.008 PG 15 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA 107KM UT WOS:000242169800004 ER PT J AU De Luca, EW Nurnberger, A AF De Luca, EW Nurnberger, A TI Using clustering methods to improve ontology-based query term disambiguation SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article AB In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or "Sense Folders" (prototype categories) derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user-specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology. (c) 2006 Wiley Periodicals, Inc. C1 Univ Magdeburg, D-39106 Magdeburg, Germany. RP Nurnberger, A (reprint author), Univ Magdeburg, Univ Pl 2, D-39106 Magdeburg, Germany. EM deluca@iws.cs.uni-magdeburg.de; nuernb@iws.cs.uni-magdeburg.de RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR Agirre E., 1996, P 16 INT C COMP LING, P16 AGOSTI M, 2001, LECT NOTES COMPUTER, V1980 BADE K, 2005, P 3 INT WORKSH AD MU, P91 Bentivogli L., 2002, P 1 INT C GLOB WORDN BERNERSLEE T, 1998, SEMANTIC WEB RAOD MA BORGELT C, 2004, P PRINC PRACT KNOWL CIRAVEGNA F, 1994, P INT WORKSH FUT DIC DELUCA EW, 2004, P 2 INT WORKSH AD MU, P72 DELUCA EW, 2004, P EUR S INT TECHN EU *DIG EQ CORP, 1995, ALT VIST Fellbaum C, 1998, WORDNET ELECT LEXICA FENSEL D, 2000, ON TO KNOWLEDGE ONTO Gangemi A., 2001, P INT C FORM ONT INF, P285 Gonzalo J., 1998, P COLING ACL 98 WORK Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066 Magnini B., 2002, P 1 INT WORDN C MYS, P146 OLTRAMARI A, 2002, P 3 INT C LANG RES E, P17 Pianta E., 2002, P 1 INT C GLOB WORDN, P293 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINKA MP, 2002, SOFT COMPUTING SYSTE, V87, P881 STEINBACH M, 2000, KNOWL DISC DAT WORKS Vossen P, 1998, COMPUT HUMANITIES, V32, P73, DOI 10.1023/A:1001175424222 YANG J, 1994, YAHOO HOME PAGE NR 25 TC 4 Z9 4 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 0884-8173 J9 INT J INTELL SYST JI Int. J. Intell. Syst. PD JUL PY 2006 VL 21 IS 7 BP 693 EP 709 DI 10.1002/int.20155 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 055DU UT WOS:000238431000003 ER PT J AU Ortega, JL Aguillo, I AF Ortega, Jose Luis Aguillo, Isidro TI Co-link analysis: a theoretical approach SO PROFESIONAL DE LA INFORMACION LA Spanish DT Article DE Webometrics; co-link analysis; information visualisation; maps of science; multidimensional scaling; search engines ID AUTHOR COCITATION ANALYSIS; SIMILARITY; SPACE AB This work provides a theoretical and practical walk-through of Co-link Analysis, one of the classic techniques for information visualisation that serves as a tool for constructing Maps of Science in Webometrics. An analysis is offered of the steps to be followed, the most suitable data resources, the different kinds of matrices, and how these have to be processed. The results permit distinguishing between the different steps taken and the differences in processing these matrices. The study finds that asymmetrical matrices provide more information than symmetrical ones, that there is greater discrimination with the cosine than with the correlation, and that crawlers obtain more accurate information than search engines. C1 Cindoc CSIC, Grp Invest Cibermetria, Madrid 28002, Spain. RP Ortega, JL (reprint author), Cindoc CSIC, Grp Invest Cibermetria, Joaquin Costa,22, Madrid 28002, Spain. EM jortega@cindoc.csic.es; isidro@cindoc.csic.es RI Aguillo, Isidro/A-7280-2008 OI Aguillo, Isidro/0000-0001-8927-4873 CR AGUILLO I, 2005, 9 JORN ESP DOC Ahlgren P, 2003, J AM SOC INF SCI TEC, V54, P550, DOI 10.1002/asi.10242 ANEGON FM, 2005, INFORM PROCESSING MA, V41, P1520 ARROYO N, 2004, METHODS HERRAMIENTAS Bar-Ilan J., 2005, CYBERMETRICS, V9, P1 BARILAN J, 1998, CYBERMETRICS, V1 Bensman SJ, 2004, J AM SOC INF SCI TEC, V55, P935, DOI 10.1002/asi.20028 BOUDOURIDES M, 2000, TRIPL HEL C CHAKRABARTI S, 2002, MINING WEB KNOWLEDGE Cothey V, 2004, J AM SOC INF SCI TEC, V55, P1228, DOI 10.1002/asi.20078 Faba-Perez C., 2004, FUNDAMENTOS TECNICAS HERREROSOLANA V, 2004, CIENCIA INFORM, V33 Jaccard P., 1912, NEW PHYTOL, V11, P37, DOI DOI 10.1111/J.1469-8137.1912.TB05611.X JIMENEZ AC, 1993, B DIGITAL FH, V2 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 LARSON R, 1996, P 59 ANN M AM SOC IN, P71 LARSON RRR, 2005, TERM WEIGHTING RANKI Leydesdorff L, 2005, J AM SOC INF SCI TEC, V56, P769, DOI 10.1002/asi.20130 LEYDESDORFF L, 2006, IN PRESS J AM SOC IN MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q ORTEGA JL, 2006, J INFORM SCI PRIEGO JLO, 2005, P 10 INT C INT SOC PRIEGO JLO, 2006, IN PRESS J AM SOC IN Prime C, 2002, SCIENTOMETRICS, V54, P291, DOI 10.1023/A:1016022214005 Rodriguez i Gairin J. M., 1997, REV ESP DOC CIENT, V20, P175 Rorvig M, 1999, J AM SOC INFORM SCI, V50, P639, DOI 10.1002/(SICI)1097-4571(1999)50:8<639::AID-ASI2>3.0.CO;2-C ROUSSEAU R, 1998, CYBERMETRICS, P2 ROUSSEAU R, 1997, CYBERMETRICS, V1, P1 SALTON G, 1971, AUTOMATIC DOCUMENT P SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMALL H, 1974, SCI STUD, V4, P17, DOI 10.1177/030631277400400102 Small H, 2003, J AM SOC INF SCI TEC, V54, P394, DOI 10.1002/asi.10225 SMALL HG, 1978, SOC STUD SCI, V8, P327, DOI 10.1177/030631277800800305 Sneath P.H.A., 1973, NUMERICAL TAXONOMY SPARCKJONES K, 1972, J DOC, V28, P111 THELWALL M, 2001, CYBERMETRICS, P1 NR 36 TC 3 Z9 3 PU EPI PI BARCELONA PA APARTADO 32 280, BARCELONA, 08080, SPAIN SN 1386-6710 J9 PROF INFORM JI Prof. Inform. PD JUL-AUG PY 2006 VL 15 IS 4 BP 270 EP 277 PG 8 WC Information Science & Library Science SC Information Science & Library Science GA 072TR UT WOS:000239696400004 ER PT J AU Cummins, R O'Riordan, C AF Cummins, R O'Riordan, C TI Evolving local and global weighting schemes in information retrieval SO INFORMATION RETRIEVAL LA English DT Article DE genetic programming; information retrieval; term-weighting schemes ID GENETIC ALGORITHMS; DOCUMENT-RETRIEVAL; SEARCH AB This paper describes a method, using Genetic Programming, to automatically determine term weighting schemes for the vector space model. Based on a set of queries and their human determined relevant documents, weighting schemes are evolved which achieve a high average precision. In Information Retrieval (IR) systems, useful information for term weighting schemes is available from the query, individual documents and the collection as a whole. We evolve term weighting schemes in both local (within-document) and global (collection-wide) domains which interact with each other correctly to achieve a high average precision. These weighting schemes are tested on well-known test collections and are compared to the traditional tf-idf weighting scheme and to the BM25 weighting scheme using standard IR performance metrics. Furthermore, we show that the global weighting schemes evolved on small collections also increase average precision on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as both high and low frequency terms are assigned low weights. However, the local weightings evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections. C1 Natl Univ Ireland, Dept Informat Technol, Galway, Ireland. RP Cummins, R (reprint author), Natl Univ Ireland, Dept Informat Technol, Galway, Ireland. EM ronan.cummins@nuigalway.ie; colmor@it.nuigalway.ie CR Bergstrom A., 2000, P 2000 INT C INT US, P29, DOI 10.1145/325737.325763 DARWIN C, 1859, ORIGIN SPECIES MEANS FAN W, 2004, INFORM PROCESSING MA Fan WG, 2004, J AM SOC INF SCI TEC, V55, P628, DOI 10.1002/asi.20009 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GREIFF W, 1998, P 21 INT ACM SIGIR C Gustafson S., 2004, THESIS U NOTTINGHAM Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X KIM S, 2001, P 2001 C EV COMP CEC, P1253 Koza J. R., 1992, GENETIC PROGRAMMING KUSCU I, 2000, P 2000 C EV COMP CEC, P1393 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 LEWIS DD, 1992, P SPEECH NAT LANG WO, P212, DOI 10.3115/1075527.1075574 Li L., 2002, P 11 INT C WORLD WID, P527 LUCAS JM, 1993, J ALGORITHM, V15, P343, DOI 10.1006/jagm.1993.1045 Luhn H. P., 1958, IBM J RES DEV, P159 OREN N, 2002, P SAICSIT Pirkola A, 2001, J AM SOC INF SCI TEC, V52, P575, DOI 10.1002/asi.1106.abs PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1998, 7 TEXT RETR C SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHULTZ C, 1968, PIONEER INFORM SCI Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Singhal A., 2001, IEEE DATA ENG B, V24, P35 SPARCKJONES K, 1972, J DOC, V28, P11 van Rijsbergen C. J., 1979, INFORM RETRIEVAL VRAJITORU D, 2000, SOFT COMPUTING INFOR, P199 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 YANG JJ, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P603 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 35 TC 11 Z9 11 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JUN PY 2006 VL 9 IS 3 BP 311 EP 330 DI 10.1007/s10791-006-1682-6 PG 20 WC Computer Science, Information Systems SC Computer Science GA 050PP UT WOS:000238100700005 ER PT J AU Godoy, D Amandi, A AF Godoy, D Amandi, A TI Modeling user interests by conceptual clustering SO INFORMATION SYSTEMS LA English DT Article DE personal information agents; conceptual clustering; ontology learning ID SEMANTIC WEB AB As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages. (c) 2005 Elsevier B.V. All rights reserved. C1 ISITAN Res Inst, RA-7000 Buenos Aires, DF, Argentina. Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina. RP Godoy, D (reprint author), ISITAN Res Inst, UNICEN Univ Campus Univ, RA-7000 Buenos Aires, DF, Argentina. EM dgodoy@exa.unicen.edu.ar; amandi@exa.unicen.edu.ar CR APTE C, 1994, INFORM SYST, V12, P233 Berendt B., 2002, P 1 INT SEM WEB C IS, P264 Berners-lee T., 2001, SCI AM CLERKIN P, 2001, SEM WEB MIN WORKSH DALESSIO S, 1998, P 3 C EMP METH NAT L DELTEIL A, 2001, WORKSH ONT LEARN P 2 Dom B., 2002, P 18 C UNC ART INT, P137 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 FAURE D, 1998, P AD LEX CORP RES SU, P1 Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 FRAKES WR, 1992, INFORMATION RETRIEVA Godoy D, 2000, LECT NOTES ARTIF INT, V1952, P43 Grimnes GA, 2003, LECT NOTES ARTIF INT, V2702, P414 John G. H., 1994, P 11 INT C MACH LEAR Koller D., 1997, P 14 INT C MACH LEAR, P170 LUGO GG, 2002, LECT NOTES COMPUTER, V2527, P546 Maedche A, 2002, LECT NOTES COMPUTER, V2431, P348 Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602 Michalski R. S., 1983, MACHINE LEARNING ART, P331 Mladenic D, 1999, IEEE INTELL SYST APP, V14, P44, DOI 10.1109/5254.784084 MUGGLETON S, 1995, NEW GENERAT COMPUT, V13, P3 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rodriguez M.A., 2000, THESIS U MAINE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shannon C. E, 1948, BELL SYST TECH J, V27, P398 SINKA M, 2002, FRONTIERS ARTIFICIAL, V87, P881 THOMPSON K, 1991, CONCEPT FORMATION KN VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 31 TC 28 Z9 32 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD JUN-JUL PY 2006 VL 31 IS 4-5 BP 247 EP 265 DI 10.1016/j.is.2005.02.008 PG 19 WC Computer Science, Information Systems SC Computer Science GA 026JC UT WOS:000236334200003 ER PT J AU Hobona, G James, P Fairbairn, D AF Hobona, G James, P Fairbairn, D TI Multidimensional visualisation of degrees of relevance of geographic data SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE LA English DT Article DE geographic information retrieval; geospatial metadata; ontology; multidimensional visualisation; ranking ID INFORMATION-RETRIEVAL; SIMILARITY; MODEL AB The ever-increasing number of spatial data sets accessible through spatial data clearinghouses continues to make geographic information retrieval and spatial data discovery major challenges. Such challenges have been addressed in the discipline of Information Retrieval through ranking of data according to inferred degrees of relevance. Spatial data, however, present an additional challenge as they are characteristically made up of geometry, attribute and, optionally, temporal components. As these components are mutually independent of one another, this paper suggests that they be ranked independently of one another. The representation of the results of the independent ranking of these three components of spatial data suggests that representation of the results of the ranking process requires an alternative approach to currently used textual ranked lists: visualisation of relevance in a three-dimensional visualisation environment. To illustrate the possible application of such an approach, a prototype browser is presented. C1 Univ Newcastle Upon Tyne, Sch Civil Engn & Geosci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. RP Hobona, G (reprint author), Univ Newcastle Upon Tyne, Sch Civil Engn & Geosci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. EM G.E.Hobona@ncl.ac.uk CR Agarwal P, 2005, INT J GEOGR INF SCI, V19, P501, DOI 10.1080/13658810500032321 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Borlund P, 2003, INFORM RES, V8 BROWN L, 1999, T GIS, V3, P207 BUCHER B, 2005, P 22 INT CART C A CO Chen C., 2004, INFORM VISUALIZATION, V2nd Cooper W. S., 1992, P 15 ANN INT ACM SIG, P198, DOI 10.1145/133160.133199 Crompvoets J, 2004, INT J GEOGR INF SCI, V18, P665, DOI 10.1080/13658810410001702030 Fabrikant SI, 2001, ANN ASSOC AM GEOGR, V91, P263, DOI 10.1111/0004-5608.00242 de Oliveira MCF, 2003, IEEE T VIS COMPUT GR, V9, P378, DOI 10.1109/TVCG.2003.1207445 *FGDC, 2005, FGDC CLEAR REG Frew J., 2000, International Journal on Digital Libraries, V2, DOI 10.1007/s007990050004 HEARST M, 1999, MODERN INFORM RETRIE *ISO, 1998, ISO239501998 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LARSON RR, 2004, P WORKSH GEOGR INF R LONGLEY PA, 2001, GEOGRAPHI INFORM SYS LUESKI A, 2004, USER MODEL USER-ADAP, V14, P259 Maguire D. J., 2004, COMPUTERS ENV URBAN, V29, P3 Nielsen J., 2000, DESIGNING WEB USABIL *OCG, 2001, 19115 ISOTC211 DIS PODOLAK I, 2004, P 12 INT C GEOINF U Rodriguez MA, 2003, IEEE T KNOWL DATA EN, V15, P442, DOI 10.1109/TKDE.2003.1185844 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sengupta RR, 2003, INT J GEOGR INF SCI, V17, P157, DOI 10.1080/1365881022000015990 Sullivan T., 2001, P 1 ACM IEEE CS JOIN, P251, DOI 10.1145/379437.379669 Tait M. G., 2005, Computers, Environment and Urban Systems, V29, DOI 10.1016/j.compenvurbsys.2004.05.011 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 van Kreveld M, 2005, GEOINFORMATICA, V9, P61, DOI 10.1007/s10707-004-5622-6 Ware C, 1996, ACM T GRAPHIC, V15, P121, DOI 10.1145/234972.234975 WEST L, 2005, GIGATEWAY 2004 2005 YAMUNA P, 2000, P ACM INT C MULT, P243, DOI 10.1145/357744.357955 NR 32 TC 4 Z9 4 PU TAYLOR & FRANCIS LTD PI ABINGDON PA 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND SN 1365-8816 J9 INT J GEOGR INF SCI JI Int. J. Geogr. Inf. Sci. PD MAY PY 2006 VL 20 IS 5 BP 469 EP 490 DI 10.1080/13658810600607634 PG 22 WC Computer Science, Information Systems; Geography; Geography, Physical; Information Science & Library Science SC Computer Science; Geography; Physical Geography; Information Science & Library Science GA 041VJ UT WOS:000237484100001 ER PT J AU Gancarski, AL Doucet, A Henriques, PR AF Gancarski, AL Doucet, A Henriques, PR TI Attribute grammar-based interactive system to retrieve information from XML documents SO IEE PROCEEDINGS-SOFTWARE LA English DT Article AB A system to interactively access extensible mark-up language documents aiming at information retrieval (IR) is described. The system has two main modules: the query editor/ processor, where the user specifies his/her needs and the document analyser, which performs operations for query evaluation. The interactive construction of queries is based on the manipulation of intermediate results during query edition and evaluation. Queries are written in IXDIRQL, a query language that extends XPath with selection operations to extract the interesting subset of elements from intermediate results. This helps the user in building queries to retrieve the desired results. Moreover, textual similarity search of traditional IR is also possible in LXDIRQL, leading to a ranked list of elements. To support a syntax-directed edition of queries and its incremental evaluation during the iterative process, IXDIRQL is specified by an attribute grammar (AG). This formalisation enables the use of an automatic generator of the desired working environment. In this system, documents are also represented by AG. This representation uniformly defines structure, content and operations over documents; this allows for a better interoperability between components. The system has been used by real users to check its correct behaviour and the correct specification of queries, using selection operations. C1 Univ Minho, Braga, Portugal. LIP6, Paris, France. RP Gancarski, AL (reprint author), Univ Minho, Braga, Portugal. EM aldalopes@di.uminho.pt RI Henriques, Pedro Rangel/M-5587-2013 OI Henriques, Pedro Rangel/0000-0002-3208-0207 CR Abiteboul S, 1998, VLDB J, V7, P96, DOI 10.1007/s007780050056 ABITEBOUL S, 1993, P 19 INT VLDB C DUBL AMERYAHIA S, 2005, IN PRESS XQUERY 1 0 Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bergman O, 2003, J HIGH ENERGY PHYS BRAY T, 2004, EXTENSIBLE MARKUP LA Chamberlin D., 2005, XQUERY 1 0 XML QUERY CHINENYANGA TT, 2001, P INT ACM SIGIR C SI Deutsch A., 1998, XML QL QUERY LANGUAG Fallside D.C., 2001, XML SCHEMA 0 FUHR N, 2004, INEX INITIATIVE EVAL FUHR N, 2001, P INT ACM SIGIR C SI GANCARSKI A, 2001, P 3 DELOS NETW EXC W GANCARSKI A, 2003, P 2003 ACM S DOC ENG GANCARSKI A, 2005, THESIS U P M CURIE P GANCARSKI A, 2003, P 7 ICCC IFIP INT C GOVERT N, 2002, P 1 ANN WORKSH IN EV Kuiper M, 1998, LECT NOTES COMPUT SC, V1383, P298 Le Hors A, 2004, DOCUMENT OBJECT MODE Neven F., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275489 NEVEN F, 1998, LECT NOTES COMPUT SC, V1341, P351 PSAILA G, 1999, P 2 INT WORKSH ATTR Raggett D., 1999, HTML 4 01 SPECIFICAT RAMALHO J, 1998, P INT C MARK TECHN M SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARAIVA J, LRC PURELY FUNCTIONA Van Herwijnen E., 1994, PRACTICAL SGML NR 27 TC 4 Z9 4 PU INSTITUTION ENGINEERING TECHNOLOGY-IET PI HERTFORD PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND SN 1462-5970 J9 IEE PROC-SOFTW JI IEE Proc-Softw PD APR PY 2006 VL 153 IS 2 BP 51 EP 60 DI 10.1049/ip-sen:20050042 PG 10 WC Computer Science, Software Engineering SC Computer Science GA 043SE UT WOS:000237621100001 ER PT J AU Norvag, K AF Norvag, K TI Granularity reduction in temporal document databases SO INFORMATION SYSTEMS LA English DT Article DE temporal databases; document databases; vacuuming; granularity reduction ID SYSTEM AB With rapidly decreasing storage costs, temporal document databases are now a viable solution in many contexts. However, storing an ever-growing database can still be too costly, and as a consequence it is desirable to be able to physically delete old versions of data. Traditionally, this has been performed by an operation called vacuuming, where the oldest versions are physically deleted or migrated from secondary storage to less costly tertiary storage. In temporal document databases on the other hand, it is often more appropriate to remove intermediate versions instead of removing the oldest versions. We call this operation granularity reduction. In this paper we describe the concept of granularity reduction, and present six strategies for selecting the document versions to eliminate. Three of the strategies have been implemented in the V2 temporal document database system, and in this context we discuss the cost of applying the strategies. (c) 2004 Elsevier B.V. All rights reserved. C1 Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, N-7491 Trondheim, Norway. RP Norvag, K (reprint author), Norwegian Univ Sci & Technol, Dept Comp & Informat Sci, Sem Soelands V 7-9, N-7491 Trondheim, Norway. EM kjetil.norvag@idi.ntnu.no CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE BRIN S, 1998, WWW7 COMPUTER NETWOR, V30, P107 BUNEMAN P, 2002, P 2002 ACM SIGMOD IN CHIEN SY, 2001, P 11 INT WORSH RES I COBENA G, 2002, P 18 INT C DAT ENG GARCIAMOLINA H, 1998, P 24 VLDB C JENSEN CS, 1995, TSQL2 TEMPORAL QUERY Marian A., 2001, Proceedings of the 27th International Conference on Very Large Data Bases MILLER W, 1985, SOFTWARE PRACT EXPER, V15, P1025, DOI 10.1002/spe.4380151102 Norvag K, 2004, INFORM SOFTWARE TECH, V46, P557, DOI 10.1016/j.infsof.2003.10.006 NORVAG K, 2002, EDBT 2002 WORKSH EDB Norvag K, 2004, DATA KNOWL ENG, V49, P105, DOI 10.1016/j.datak.2003.08.006 Olson M. A., 1999, P FREENIX TRACK 1999 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SKYT J, 2002, P 18 INT C DAT ENG Skyt J, 2003, DATA KNOWL ENG, V44, P1, DOI 10.1016/S0169-023X(02)00060-5 STONEBRAKER M, 1987, P 13 VLDB C TICHY WF, 1985, SOFTWARE PRACT EXPER, V15, P637, DOI 10.1002/spe.4380150703 TOMAN D, 2001, P TIME 01 WANG F, 2003, P 10 INT C TEMP REPR WANG Y, 2003, P 19 INT C DAT ENG NR 21 TC 0 Z9 1 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4379 J9 INFORM SYST JI Inf. Syst. PD APR PY 2006 VL 31 IS 2 BP 134 EP 147 DI 10.1016/j.is.2004.10.002 PG 14 WC Computer Science, Information Systems SC Computer Science GA 994YA UT WOS:000234065900003 ER PT J AU Bakus, J Kamel, MS AF Bakus, J Kamel, MS TI Higher order feature selection for text classification SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE feature selection; text classification ID CATEGORIZATION AB In this paper. we present the MIFS-C variant of the mutual information feature-selection algorithms. We present an algorithm to find the optimal value of the redundancy parameter, which is a key parameter in the MIFS-type algorithms. Furthermore, we present an algorithm that speeds up the execution time of all the MIFS variants. Overall, the presented MIFS-C has comparable classification accuracy (in some cases even better) compared with other MIFS algorithms, while its running time is faster. We compared this feature selector with other feature selectors, and found that it performs better in most cases. The MIFS-C performed especially well for the breakeven and F-measure because the algorithm can be tuned to optimise these evaluation measures. C1 Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada. RP Bakus, J (reprint author), Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. EM jbakus@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 BAKUS J, 2003, P 8 CAN WORKSH INF T, P147 BATTITI R, 1994, IEEE T NEURAL NETWOR, V5, P537, DOI 10.1109/72.298224 Devijver P. A., 1982, PATTERN RECOGNITION, V1st Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Duda R.O., 2001, PATTERN CLASSIFICATI Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fano RM, 1961, TRANSMISSION INFORMA Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Ghiselli E. E., 1964, THEORY PSYCHOL MEASU Hall M. A., 2000, P 17 INT C MACH LEAR, P359 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Joachims Thorsten, 2001, P 24 ANN INT ACM SIG, P128, DOI 10.1145/383952.383974 Ko Y., 2000, P 18 INT C COMP LING, P453 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X KOLLER D, 1996, P 13 INT C MACH LEAR, P170 KWAK N, 1994, IEEE T NEURAL NETWOR, V13, P143 LEWIS D, 1996, P 19 ANN INT ACM SIG, P307 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Liu H, 1998, FEATURE SELECTION KN McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Mladenic D., 1999, P 16 INT C MACH LEAR, P258 Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 Press W., 1988, NUMERICAL RECIPES C Quinlan R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SIDLECKI W, 1988, INT J PATTERN RECOGN, V2, P197 VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 Vapnik V., 1995, NATURE STAT LEARNING Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 YANG Y, 2002, J INTELL INFORM SYST, V18 NR 35 TC 10 Z9 15 PU SPRINGER LONDON LTD PI GODALMING PA SWEETAPPLE HOUSE CATTESHALL ROAD, GODALMING GU7 3DJ, SURREY, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD APR PY 2006 VL 9 IS 4 BP 468 EP 491 DI 10.1007/s10115-005-0209-6 PG 24 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 038PB UT WOS:000237233700004 ER PT J AU Chou, TC Cheng, SC AF Chou, TC Cheng, SC TI Design and implementation of a semantic image classification and retrieval of organizational memory information systems using analytical hierarchy process SO OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE LA English DT Article DE organizational memory information systems; AHP; content-based image retrieval; trademark retrieval systems; semantic image; vector space model ID KNOWLEDGE; COLOR; MANAGEMENT; DATABASES; FEATURES; MODEL AB With the fast growth of image database, managing image contents is becoming an increasingly important part of the development of organizational memory information systems. However, effective and precise image retrieval still remains a problem because of the extreme difficulty in image understanding, and high-level knowledge required. In this paper, a new semantic image retrieval method using the analytic hierarchical process is proposed, to provide a new way to incorporate experts' judgment into the system and to evaluate the fitness of a semantic description used to represent an image. The main aim of the proposed method is to facilitate mapping the perceptions of low-level features of the knowledge users to higher-level organizational knowledge. A prototype of this system is implemented and experimental results show that the performance of the proposed method is excellent when compared to traditional techniques. The implications, limitations, and future works of the proposed method for the development of organizational memory information systems are discussed. (c) 2004 Elsevier Ltd. All rights reserved. C1 Natl Kaohsiung First Univ Sci & Technol, Dept Informat Management, Kaohsiung 824, Taiwan. Natl Kaohsiung First Univ Sci & Technol, Dept Comp & Commun Engn, Kaohsiung 824, Taiwan. RP Chou, TC (reprint author), Natl Kaohsiung First Univ Sci & Technol, Dept Informat Management, 1 Univ Rd, Kaohsiung 824, Taiwan. EM tcchou@ccms.nkfust.edu.tw CR Boland RJ, 2001, ACAD MANAGE J, V44, P393 Buchanan DA, 2001, J MANAGE INQUIRY, V10, P151, DOI 10.1177/1056492601102018 Conci A, 2002, EXPERT SYST APPL, V23, P377, DOI 10.1016/S0957-4174(02)00073-8 Cross R, 2000, SLOAN MANAGE REV, V41, P69 DAFT RL, 1984, ACAD MANAGE REV, V9, P284, DOI 10.2307/258441 Ding Y, 2002, DATA KNOWL ENG, V41, P205, DOI 10.1016/S0169-023X(02)00041-1 Eakins JP, 2002, PATTERN RECOGN, V35, P3, DOI 10.1016/S0031-3203(01)00038-3 Faloutsos C, 1994, J INTELLIGENT SYSTEM, V1, P95 FLICKNER M, 1995, IEEE COMPUT, V28, P9 Forman E., 1990, READINGS MULTIPLE CR, P295 HUBER GP, 1990, ACAD MANAGE REV, V15, P47, DOI 10.2307/258105 Jain AK, 1996, PATTERN RECOGN, V29, P1233, DOI 10.1016/0031-3203(95)00160-3 LAKOFF G, 1980, COGNITIVE SCI, V4, P195, DOI 10.1207/s15516709cog0402_4 Li WS, 1998, DATA KNOWL ENG, V27, P139, DOI 10.1016/S0169-023X(97)00058-X Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803 Markus ML, 2001, J MANAGE INFORM SYST, V18, P57 METHER BM, 1998, INFORM PROCESSING MA, V34, P109 METHROTRA R, 1995, IEEE COMPUT, V28, P57 Muller W., 1996, Design Studies, V17, DOI 10.1016/0142-694X(95)00030-U Nonaka I, 1998, CALIF MANAGE REV, V40, P40 Pei SC, 1999, IEEE T CIRC SYST VID, V9, P501, DOI 10.1109/76.754779 PENTLAND A, 1994, P SOC PHOTO-OPT INS, V2185, P34, DOI 10.1117/12.171786 Pevtzow R, 1997, P SIMC 1997 INT WORK, P189 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Roth V, 1999, IMAGE VISION COMPUT, V17, P531, DOI 10.1016/S0262-8856(98)00144-9 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Satty TL, 1985, ANAL PLANNING ORG SY Shih JL, 2001, IMAGE VISION COMPUT, V19, P1011, DOI 10.1016/S0262-8856(01)00063-4 Simon H.A., 1960, NEW SCI MANAGEMENT D Socher G, 2000, IMAGE VISION COMPUT, V18, P155, DOI 10.1016/S0262-8856(99)00024-4 Stan D, 2003, INFORM PROCESS MANAG, V39, P335, DOI 10.1016/S0306-4573(02)00131-0 STEIN EW, 1995, INT J INFORM MANAGE, V15, P17, DOI 10.1016/0268-4012(94)00003-C STEIN EW, 1995, INFORM SYST RES, V6, P85, DOI 10.1287/isre.6.2.85 Tenkasi R., 1995, REV BEHAV SCI KNOWLE, P147 Thomas KW, 1982, ACAD MANAGE REV, V7, P345, DOI 10.2307/257326 Tuomi I, 1999, J MANAGE INFORM SYST, V16, P103 WALSH JP, 1991, ACAD MANAGE REV, V16, P57, DOI 10.2307/258607 Wan X, 1998, IEEE T CIRC SYST VID, V8, P628 Wang Z, 2000, IEE P-VIS IMAGE SIGN, V147, P9, DOI 10.1049/ip-vis:20000100 Watson J., 2000, Information Management Journal, V34 WHYTE L, 1969, HIERARCHIAL STRUCTUR Wijnhoven F., 1999, Journal of Management Information Systems, V16 Yin PY, 2002, PATTERN RECOGN LETT, V23, P113, DOI 10.1016/S0167-8655(01)00091-5 Zhu L, 2002, INFORM SYST, V27, P537, DOI 10.1016/S0306-4379(02)00020-0 NR 45 TC 7 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0305-0483 J9 OMEGA-INT J MANAGE S JI Omega-Int. J. Manage. Sci. PD APR PY 2006 VL 34 IS 2 BP 125 EP 134 DI 10.1016/j.omega.2004.08.004 PG 10 WC Management; Operations Research & Management Science SC Business & Economics; Operations Research & Management Science GA 985NY UT WOS:000233386000002 ER PT J AU Lin, FR Hsueh, CM AF Lin, FR Hsueh, CM TI Knowledge map creation and maintenance for virtual communities of practice SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE knowledge map; information retrieval; clustering; community of practice; professional community ID TEXT RETRIEVAL; MODEL; INFORMATION AB This paper proposes a knowledge map management system to facilitate knowledge management in virtual communities of practice. To realize the proposed knowledge map management, we develop knowledge map creation and maintenance functions by utilizing information retrieval and data mining techniques. The knowledge maps created respectively from the documents of the teachers' professional community, SCTNet, and the thesis repository at Taiwan's National Central Library, are evaluated by experts of these two domains. Knowledge maps generated by the system are accepted by domain experts from the evaluation since the degree of their modification of the automatically created knowledge maps is proportionally small. The knowledge structure representing the categories of community documents maintains its high purity, diversity, specificity, and structure adaptation by using the knowledge map maintenance function with limited Computational cost. Thus, the knowledge map creation and maintenance mechanisms developed in this research enable the dynamic knowledge management of communities of practice on the Internet. (c) 2005 Elsevier Ltd. All rights reserved. C1 Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan. Natl Sun Yat Sen Univ, Dept Informat Management, Kaohsiung 804, Taiwan. RP Lin, FR (reprint author), Natl Tsing Hua Univ, Inst Technol Management, Hsinchu 300, Taiwan. EM frlin@mx.nthu.edu.tw CR AGRAWAL R, 1998, SIGMOD98 AGRAWAL R, 1999, P 7 C EXT DAT TECHN BaezaYates RA, 1996, J ACM, V43, P915, DOI 10.1145/235809.235810 BIRU T, 1989, J DOC, V45, P85, DOI 10.1108/eb026840 Browne GJ, 1997, MANAGE SCI, V43, P1, DOI 10.1287/mnsc.43.1.1 Chien LF, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50, DOI 10.1145/258525.258534 CHIEN LF, 1996, COMPUTATIONAL LINGUI, V1, P205 CHOU C, 1998, J ED MULTIMEDIA HYPE, V7, P151 Gonnet G., 1992, INFORMATION RETRIEVA, P66 Kaufman L., 1990, FINDING GROUP DATA I Knuth D. E., 1973, ART COMPUTER PROGRAM, V3 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LI Z, 1998, P 1 AS DIG LIB WORKS, P42 Lin FR, 2001, J ORG COMP ELECT COM, V11, P155, DOI 10.1207/S15327744JOCE1103_02 MERKL D, 1999, P ICONIP 99 6 INT C, P37 MORRISON D, 1968, JACM, P514 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 Paolucci R., 1998, Journal of Educational Multimedia and Hypermedia, V7 PASK G, 1972, INT J MAN MACH STUD, V4, P217, DOI 10.1016/S0020-7373(72)80004-X PUNJ G, 1983, J MARKETING RES, V20, P134, DOI 10.2307/3151680 Roussinov DG, 1999, DECIS SUPPORT SYST, V27, P67, DOI 10.1016/S0167-9236(99)00037-8 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Steinbach M., 2000, KDD 2000 WORKSH TEXT Wei CP, 2002, EUR J INFORM SYST, V11, P208, DOI 10.1057/palgrave.ejis.3000429 WITKIN HA, 1977, REV EDUC RES, V47, P1 WONG KF, 1998, P 1 AS DIT LIB WORKS WU Z, 1995, J AM SOC INFORM SCI, V46, P532 WU ZM, 1993, J AM SOC INFORM SCI, V44, P532, DOI 10.1002/(SICI)1097-4571(199310)44:9<532::AID-ASI3>3.0.CO;2-M YANG CC, 1998, P 1 AS DIG LIB WORKS, P57 YANG H, 1999, 4 INT WORKSH INF RET NR 33 TC 31 Z9 34 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2006 VL 42 IS 2 BP 551 EP 568 DI 10.1016/j.ipm.2005.03.026 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 981BF UT WOS:000233061300014 ER PT J AU Garces, PJ Olivas, JA Romero, FP AF Garces, PJ Olivas, JA Romero, FP TI Concept-matching IR systems versus word-matching information retrieval systems: Considering fuzzy interrelations for indexing Web pages SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID MODEL AB This article presents a semantic-based Web retrieval system that is capable of retrieving the Web pages that are conceptually related to the implicit concepts of the query. The concept of "concept" is managed from a fuzzy point of view by means of semantic areas. In this context, the proposed system improves most search engines that are based on matching words. The key of the system is to use a new version of the Fuzzy Interrelations and Synonymy-Based Concept Representation Model (FIS-CRM) to extract and represent the concepts contained in both the Web pages and the user query. This model, which was integrated into other tools such as the Fuzzy Interrelations and Synonymy based Searcher (FISS) metasearcher and the fz-mail system, considers the fuzzy synonymy and the fuzzy generality interrelations as a means of representing word interrelations (stored in a fuzzy synonymy dictionary and ontologies). The new version of the model, which is based on the study of the cooccurrences of synonyms, integrates a soft method for disambiguating word senses. This method also considers the context of the word to be disambiguated and the thematic ontologies and sets of synonyms stored in the dictionary. C1 Univ Alicante, Dept Comp Sci & Artificial Intelligence, E-03080 Alicante, Spain. Univ Castilla La Mancha, Dept Comp Sci, E-13071 Ciudad Real, Spain. UCLM, Ctr Mixto Invest & Desarrollo Soluc, Ciudad Real 13003, Spain. RP Garces, PJ (reprint author), Univ Alicante, Dept Comp Sci & Artificial Intelligence, Carretera San Vicente del Raspeig S-N, E-03080 Alicante, Spain. EM pgarces@dccia.ua.es; JoseAngel.Olivas@ucim.es; fromero@uf-isf.es RI Lavbic, Dejan/G-1405-2010 CR Bordogna G., 1995, FUZZINESS DATABASE M, P231 BORDOGNA G, 2003, INTELLIGENT EXPLORAT, P38 CHOI D, 2001, P BISC INT WORKSH FU, P68 COOLEY R, 1997, 97021 TR U MINN Crestani F., 2000, Information Retrieval, V2 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DELGADO M, 2003, P 3 C EUSFLAT, P49 FERNANDEZ S, 2000, REV SOC ESPANOLA PRO, P89 FERNANDEZ S, 2001, THESIS U SANTIAGO CO Gonzalo J, 1998, P COLING ACL 98 WORK, P38 Herrera-Viedma E, 2001, INT J UNCERTAIN FUZZ, V9, P77, DOI 10.1142/S0218488501001009 HERRERAVIEDMA R, 2003, P 3 EUSFLAT C ZITT G, P25 King-Ip Lin, 2001, Proceedings Seventh International Conference on Database Systems for Advanced Applications. DASFAA 2001, DOI 10.1109/DASFAA.2001.916362 KIRYAKOV A, 1999, P NODALIDA 99 NORD C LAFOURCADE M, 2001, P 6 NAT LANG PROC PA, P127 LEACOCK C, 1998, WORDNET ELECT LEXICA, P285 LOUPY C, 2002, P LREC2002 WORKSH CR MARTINBAUTISTA MJ, 2001, P BISC INT WORKSH FU, P19 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MIYAMOTO S, 1990, FUZZY SET SYST, V38, P191, DOI 10.1016/0165-0114(90)90149-Z OHGAYA R, 2002, P 2002 NAFIPS ANN M, P274 Olivas JA, 2003, INT J APPROX REASON, V34, P201, DOI 10.1016/j.ijar.2003.07.008 Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 RAMAKRISHNAN G, 2004, P INT C GLOB WORDNET RIBEIRO A, 2003, INTELLIGENT EXPLORAT, P19 RICARTE I, 2001, P BISC INT WORKSH FU Richardson R., 1995, CA0395 DUBL CIT U SC ROMERO FP, 2002, P INT C ART INT IC A, V1, P225 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SUN J, 2003, IEEE INT C NAT LANG TAKAGI H, 2001, RRD PROTEIN 1, V1, P53 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 Voorhees E., 1998, WORDNET ELECT LEXICA WHALEY J, 1999, PCSTR99352 DARTM COL Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 ZADEH LA, 2003, P ATL WEB INT C AWIC, P15 Zamir O., 1999, P 8 INT C WORLD WID, P1361 NR 38 TC 14 Z9 14 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 15 PY 2006 VL 57 IS 4 BP 564 EP 576 DI 10.1002/asi.20310 PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 014DR UT WOS:000235459500012 ER PT B AU Hage, H Aimeur, E AF Hage, Hicham Aimeur, Esma GP IEEE TI ICE: A system for Identification of Conflicts in Exams SO 2006 IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, VOLS 1-3 SE International Conference on Computer Systems and Applications LA English DT Proceedings Paper CT 4th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-06) CY MAR 08-11, 2006 CL Sharjah, U ARAB EMIRATES SP IEEE, ACS AB Although E-learning has advanced considerably in the last decade, some of its aspects, such as E-testing, are still in the development phase. Authoring tools and test banks for E-tests are becoming an integral and indispensable part of E-learning platforms and with the implementation of E-learning standards, such as IMS QTI, E-testing material can be easily shared and reused across various platforms. With the knowledge available for re-use and exam automation comes a new challenge: making sure that created exams are free of conflicts. A Conflict exists in an exam if at least two questions within that exam are redundant in content, and/or if at least one question reveals the answer to another question within the same exam. In this paper we propose using Information Retrieval techniques to detect conflicts within an exam. Our solution, ICE (Identification of Conflicts in Exams), is based on the vector space model relying on tfi-df weighing and the cosine function to calculate similarity. ICE also combines the hybrid recommendation techniques of the EQRS (Exam Question Recommender System) in order to propose replacements for conflicting questions. C1 Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada. RP Hage, H (reprint author), Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ H3C 3J7, Canada. EM hagehich@iro.umontreal.ca; aimeur@iro.umontreal.ca CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564 Burke R, 2004, LECT NOTES COMPUT SC, V3155, P91 BUTLER DL, 2003, TECHNOLOGY SOURC JAN GAUDIOSI E, 2003, INT C ART INT ED AIE, P237 HAGE H, 2005, INT C ART INT ED AIE, P249 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MOHAN P, 2003, INT C ART INT ED AIE, P307 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 2001, IEEE DATA ENG B, V24, P35 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 1-4244-0211-5 J9 I C COMP SYST APPLIC PY 2006 BP 979 EP 986 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BFN66 UT WOS:000243337100149 ER PT J AU Zobel, J Moffat, A AF Zobel, Justin Moffat, Alistair TI Inverted files for text search engines SO ACM COMPUTING SURVEYS LA English DT Review DE algorithms; experimentation; performance; inverted file indexing; document database; text retrieval; information retrieval; Web search engine ID INFORMATION-RETRIEVAL SYSTEMS; PARTITIONED SIGNATURE FILES; DOCUMENT-RETRIEVAL; STATISTICAL RANKING; CACHING STRATEGIES; INDEX COMPRESSION; QUERY EVALUATION; ACCESS METHODS; BIT-VECTORS; PERFORMANCE AB The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature. C1 RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. Univ Melbourne, Dept Comp Sci & Software Engn, Parkville, Vic 3052, Australia. RP Zobel, J (reprint author), RMIT Univ, Sch Comp Sci & Informat Technol, Melbourne, Vic, Australia. EM jz@cs.rmit.edu.au CR Anh VN, 2005, INFORM RETRIEVAL, V8, P151, DOI 10.1023/B:INRT.0000048490.99518.5c Anh V.N., 2002, P 25 ANN INT ACM SIG, P3 Anh VN, 2001, P 24 ANN INT ACM SIG, P35, DOI 10.1145/383952.383957 ANH VN, 1998, P ACM SIGIR INT C RE, P291 ARUSU A, 2001, ACM T INTERNET TECHN, V1, P2 Badue C., 2001, Proceedings Eighth Symposium on String Processing and Information Retrieval, DOI 10.1109/SPIRE.2001.989733 Baeza-Yates R., 2002, HDB MASSIVE DATA SET, P195 BaezaYates R, 2000, J AM SOC INFORM SCI, V51, P69, DOI 10.1002/(SICI)1097-4571(2000)51:1<69::AID-ASI10>3.0.CO;2-C Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Barbara D, 1996, PROC INT CONF DATA, P172, DOI 10.1109/ICDE.1996.492103 Barroso LA, 2003, IEEE MICRO, V23, P22, DOI 10.1109/MM.2003.1196112 Bayer R., 1972, Acta Informatica, V1, DOI 10.1007/BF00288683 BEAULIEU M, 2002, P 25 ANN INT ACM SIG Bell T.C., 1990, TEXT COMPRESSION BELL TC, 1993, J AM SOC INFORM SCI, V44, P508, DOI 10.1002/(SICI)1097-4571(199310)44:9<508::AID-ASI2>3.0.CO;2-A Bertino E., 1997, INDEXING TECHNIQUES BIRD RM, 1978, P 4 WORKSH COMP ARCH, P42, DOI 10.1145/800128.804166 BIRD RM, 1977, P 3 NONN WORKSH SYR, P8, DOI 10.1145/800180.810247 Blandford D., 2002, Proceedings DCC 2002. Data Compression Conference, DOI 10.1109/DCC.2002.999972 BOOKSTEIN A, 2000, KLUWER INT J INFORM, V1, P315 BOOKSTEIN A, 1991, P 14 ANN INT ACM SIG, P63, DOI 10.1145/122860.122867 BOOKSTEIN A, 1990, INFORM PROCESS MANAG, V26, P525, DOI 10.1016/0306-4573(90)90072-A BOOKSTEIN A, 1992, INFORM PROCESS MANAG, V28, P735, DOI 10.1016/0306-4573(92)90065-8 BOOKSTEIN A, 1991, INFORM SYST, V16, P387, DOI 10.1016/0306-4379(91)90030-D BOOKSTEIN A, 1991, P DATA COMPRESSION B, P402 Brin S., 1998, COMPUTER NETWORKS IS, V30, P1 BRISABOA NR, 2003, P S STRING PROC INF, P122 Brown Eric W., 1995, P 18 ANN INT ACM SIG, P30, DOI 10.1145/215206.215329 Brown EricW., 1994, P 20 INT C VER LARG, P192 BROWN EW, 1994, P INT C ADV DAT TECH Buckley C., 1985, P 8 ANN INT ACM SIGI, P97, DOI 10.1145/253495.253515 BURKOWSKI FJ, 1990, P 13 INT C RES DEV I, P211, DOI 10.1145/96749.98226 BUTTCHER S., 2005, P ACM CIKM INT C INF, P317, DOI 10.1145/1099554.1099645 CACHEDA F, 2004, LECT NOTES COMPUTER, V2997, P395 Cahoon B, 2000, ACM T INFORM SYST, V18, P1, DOI 10.1145/333135.333136 CAHOON B, 1996, P 19 ANN INT ACM SIG, P110, DOI 10.1145/243199.243238 CAN F, 1994, INFORM PROCESS MANAG, V30, P343, DOI 10.1016/0306-4573(94)90049-3 CARDENAS AF, 1975, COMMUN ACM, V18, P253, DOI 10.1145/360762.360766 Carmel D., 2001, P 24 ANN INT ACM SIG, P43, DOI 10.1145/383952.383958 Choueka Y., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42039 Choueka Y., 1986, P 9 ACM SIGIR C, P88 CHOUEKA Y, 1988, P ACM SIGIR INT C RE, P597, DOI 10.1145/62437.62500 Ciaccia P, 1996, ACM T DATABASE SYST, V21, P295, DOI 10.1145/232753.232755 CIACCIA P, 1993, ACM T INFORM SYST, V11, P133, DOI 10.1145/130226.145014 CLARKE CL, 1994, CS9440 U WAT DEP COM CLARKE CLA, 1995, MT9501 U WAT DEP COM Clarke CLA, 2000, ACM T INFORM SYST, V18, P44, DOI 10.1145/333135.333137 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 COUVREUR TR, 1994, J AM SOC INFORM SCI, V45, P443, DOI 10.1002/(SICI)1097-4571(199408)45:7<443::AID-ASI1>3.0.CO;2-O CRINGEAN JK, 1990, P 13 INT C RES DEV I, P429, DOI 10.1145/96749.98249 CROFT WB, 1988, ACM T INFORM SYST, V6, P42, DOI 10.1145/42279.45947 CROFT WB, 2003, LANGUAGE MODELING IN CROFT WB, 1998, P 21 ANN INT ACM SIG CROFT WB, 2001, P 24 ANN INT ACM SIG Culpepper JS, 2005, LECT NOTES COMPUT SC, V3772, P1 Cutting D., 1990, P 13 INT C RES DEV I, P405, DOI 10.1145/96749.98245 de Kretser O., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312664 de Kretser O., 1998, Proceedings. 18th International Conference on Distributed Computing Systems (Cat. No.98CB36183), DOI 10.1109/ICDCS.1998.679488 de Kretser O, 2004, SOFTWARE PRACT EXPER, V34, P1011, DOI 10.1002/spe.601 Eastman C. M., 1983, Proceedings of the Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 ELIAS P, 1975, IEEE T INFORM THEORY, V21, P194, DOI 10.1109/TIT.1975.1055349 Faloutsos C., 1995, SURVEY INFORM RETRIE FALOUTSOS C, 1985, P ACM SIGMOD C AUSTI, P63, DOI 10.1145/318898.318903 FALOUTSOS C, 1992, LECT NOTES COMPUT SC, V580, P310 FALOUTSOS C, 1985, COMPUT SURV, V17, P49 FICICK JL, 1990, P 13 ANN INT ACM SIG FOX EA, 1991, 9110 TR VIRG POL I S FRAENKEL AS, 1985, NATO ASI SERIES F, V12, P169 Frakes W. B., 1992, INFORM RETRIEVAL DAT FREI HP, 1996, P 19 ANN INT ACM SIG GALLAGER RG, 1975, IEEE T INFORM THEORY, V21, P228, DOI 10.1109/TIT.1975.1055357 Garcia S., 2004, P AUSTR COMP SCI C, P7 GOLOMB SW, 1966, IEEE T INFORM THEORY, V12, P399, DOI 10.1109/TIT.1966.1053907 Grabs T., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Grossman D. A., 2004, INFORM RETRIEVAL ALG HARMAN D, 1991, INFORM PROCESS MANAG, V27, P449, DOI 10.1016/0306-4573(91)90062-Q HARMAN DK, 1992, INFORM RETRIEVAL DAT, P362 HARMAN D, 1990, J AM SOC INFORM SCI, V41, P581, DOI 10.1002/(SICI)1097-4571(199012)41:8<581::AID-ASI4>3.0.CO;2-U HARPER DJ, 1982, 7 CSIRO DIV COMP RES Haskin R., 1980, Papers of the Fifth Workshop on Computer Architecture for Non-Numeric Processing Hawking D, 1997, LECT NOTES COMPUT SC, V1324, P127 HAWKING D, 1998, ACM SIGIR FORUM, V32, P16, DOI 10.1145/305110.305119 Heaps H. S., 1978, INFORM RETRIEVAL COM HEARST M, 1999, P 22 ANN INT ACM SIG HEIN GE, 2003, MED WELT, V54, P8 IVIE EL, 1966, THESIS MIT CAMBRIDGE JAKOBSSON M, 1978, INFORM PROCESS LETT, V7, P304, DOI 10.1016/0020-0190(78)90023-6 JEONG BS, 1995, IEEE T PARALL DISTR, V6, P142 Jones K. S., 1997, READINGS INFORM RETR Jones K. S., 2000, Information Processing & Management, V36 Jonsson B. T., 1998, P ACM SIGMOD INT C M, P118, DOI 10.1145/276304.276316 KENT A, 1990, J AM SOC INFORM SCI, V41, P508, DOI 10.1002/(SICI)1097-4571(199010)41:7<508::AID-ASI5>3.0.CO;2-J KLEIN ST, 1989, ACM T INFORM SYST, V7, P230, DOI 10.1145/65943.65946 Kleinberg J.M., 1999, JACM, V46, P5 Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 Kocberbera S, 1997, INFORM PROCESS MANAG, V33, P353, DOI 10.1016/S0306-4573(96)00073-8 LANCASTER FW, 1973, INFORM RETRIEVAL ONL LEE DL, 1995, IEEE T KNOWL DATA EN, V7, P423 LEE DL, 1989, ACM T INFORM SYST, V7, P158, DOI 10.1145/65935.65937 Lee Y. K., 1996, P 1 ACM INT C DIG LI, P91, DOI 10.1145/226931.226950 Lempel R., 2003, P 12 INT C WORLD WID, P19 Lempel R., 2004, ACM T INTERNET TECHN, V4, P31, DOI 10.1145/967030.967032 LEMPEL R, 2005, THEORET COMPUT SCI, V324, P253 LESTER N, 2005, P CIKM INT C INF KNO Lester N, 2005, LECT NOTES COMPUT SC, V3806, P470 LESTER N, 2006, IN PRESS INFORM PROC Lim Lipyeow, 2003, P 12 INT C WORLD WID, P102, DOI DOI 10.1145/775152.775167 LINOFF G, 1993, P ACM SIGIR INT C RE, P88, DOI 10.1145/160688.160699 Lu ZH, 2003, INFORM RETRIEVAL, V6, P159, DOI 10.1023/A:1023947204209 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 LUHN HP, 1957, IBM J RES DEV, V1, P309 MacFarlane A., 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, DOI 10.1109/SPIRE.2000.878197 MACLEOD IA, 1987, INFORM PROCESS MANAG, V23, P511, DOI 10.1016/0306-4573(87)90056-2 MANBER U, 1994, US WINT 1994 TECHN C, P23 Manning C. D., 1999, FDN STAT NATURAL LAN Kaszkiel M, 1999, ACM T INFORM SYST, V17, P406, DOI 10.1145/326440.326445 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARTIN TP, 1990, INFORM PROCESS MANAG, V26, P227, DOI 10.1016/0306-4573(90)90028-Z MARTIN TP, 1991, INFORM SYST, V16, P1, DOI 10.1016/0306-4379(91)90045-B MATTHEWS FW, 1967, J CHEM DOC, V7, P49, DOI 10.1021/c160024a015 MCDONELL KJ, 1977, COMPUT J, V20, P116, DOI 10.1093/comjnl/20.2.116 Melnik S, 2001, ACM T INFORM SYST, V19, P217, DOI 10.1145/502115.502116 MOFFAT A, 1992, P 15 ANN INT ACM SIG, P274, DOI 10.1145/133160.133210 MOFFAT A, 1994, INFORM PROCESS MANAG, V30, P733, DOI 10.1016/0306-4573(94)90002-7 Moffat A, 2000, INFORM RETRIEVAL, V3, P25, DOI 10.1023/A:1013002601898 Moffat A., 1992, Computing Systems, V5 Moffat A, 2004, LECT NOTES COMPUT SC, V3306, P1 Moffat A, 1996, ACM T INFORM SYST, V14, P349, DOI 10.1145/237496.237497 Moffat A., 1992, P IEEE DAT COMPR C S, P72 MOFFAT A, 2005, UNPUB PIPELINED ARCH MOFFAT A, 2002, COMPRESSION CODING A MOFFAT A, 1995, J AM SOC INFORM SCI, V46, P537, DOI 10.1002/(SICI)1097-4571(199508)46:7<537::AID-ASI7>3.0.CO;2-P MOTZKIN D, 1994, INFORM PROCESS MANAG, V30, P93, DOI 10.1016/0306-4573(94)90026-4 NAVARRO G, 2000, KLUWER INF RETRIEVAL, V3, P49 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 Persin M, 1996, J AM SOC INFORM SCI, V47, P749, DOI 10.1002/(SICI)1097-4571(199610)47:10<749::AID-ASI3>3.0.CO;2-2 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 RABITTI F, 1986, P 9 ANN INT ACM SIGI REDDAWAY SF, 1991, INFORM PROCESS MANAG, V27, P311, DOI 10.1016/0306-4573(91)90086-2 RIBEIRONETO B, 1998, P ACM DIG LIB PITTSB, V3, P182 Ribeiro-Neto B., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312663 Rice R.F., 7922 JET PROP LAB Robertson S, 1994, NIST SPECIAL PUBLICA ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROGERS W, 1995, P ANN S DOC AN INF R SACKSDAVIS R, 1987, ACM T DATABASE SYST, V12, P655, DOI 10.1145/32204.32222 Salomon D., 2000, DATA COMPRESSION COM SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INDEX ORG SALTON G, 1962, ISR2 HARV COMP LAB Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1972, COMMUN ACM, V15, P658, DOI 10.1145/361454.361509 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saraiva Paricia Correia, 2001, P 24 ANN INT ACM SIG, P51, DOI 10.1145/383952.383959 Sayood K, 2000, INTRO DATA COMPRESSI Scholer F., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SCHUEGRAF EJ, 1976, INFORM PROCESS MANAG, V12, P377, DOI 10.1016/0306-4573(76)90035-2 Segesta J, 2002, IEEE ANN HIST COMPUT, V24, P23, DOI 10.1109/MAHC.2002.1024760 SEVERANCE DG, 1977, COMPUT SURV, V9, P259 Shieh WY, 2003, J INF SCI ENG, V19, P681 SHIEH WY, 2003, P INT C INF KNOWL EN, P64 Shieh WY, 2005, INFORM PROCESS MANAG, V41, P275, DOI 10.1016/j.ipm.2003.10.004 Shieh WY, 2003, INFORM PROCESS MANAG, V39, P117, DOI 10.1016/S0306-4573(02)00020-1 Shoens K., 1994, P 17 ANN INT ACM SIG, P329 Silvestri F., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009046 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Smeaton Alan F., 1981, P 4 INT C INF STOR R, P83, DOI 10.1145/511754.511767 SPINK A, 2000, INFORMAT RES, V6, P1 Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R STANFILL C, 1989, SIGIR FORUM, V23, P88, DOI 10.1145/75334.75345 Stanfill C, 1990, P 13 ANN INT ACM SIG, P413, DOI [10.1145/96749.98247, DOI 10.1145/96749.98247] STELLHORN WH, 1977, IEEE T COMPUT, V26, P1258 Strohman T., 2005, P 28 ANN INT ACM SIG, P219, DOI 10.1145/1076034.1076074 TAGUE J, 1985, P 8 ANN INT ACM SIGI TEUHOLA J, 1978, INFORM PROCESS LETT, V7, P308, DOI 10.1016/0020-0190(78)90024-8 Tomasic A, 1996, INFORM PROCESS MANAG, V32, P647, DOI 10.1016/S0306-4573(96)00019-2 Tomasic A., 1993, Proceedings of the Second International Conference on Parallel and Distributed Information Systems (Cat. No.93TH0493-7), DOI 10.1109/PDIS.1993.253078 Tomasic A., 1994, P 1994 ACM SIGMOD IN, P289, DOI 10.1145/191839.191896 Trotman A, 2003, INFORM RETRIEVAL, V6, P5, DOI 10.1023/A:1022949613039 Turtle H, 1995, INFORM PROCESS MANAG, V31, P831, DOI 10.1016/0306-4573(95)00020-H VANRIJSBERGEN CJ, 1979, INFORM RETRIEV VASANTHAKUMAR SR, 1996, B TECHNICAL COMMITTE, V19, P24 Voorhees Ellen M., 2005, TREC EXPT EVALUATION VOORHEES EM, 1986, P 9 ANN INT ACM SIGI, P164, DOI 10.1145/253168.253203 Williams H. E., 1999, P AUSTR DAT C AUCKL, P141 Williams HE, 2004, ACM T INFORM SYST, V22, P573, DOI 10.1145/1028099.1028102 Williams HE, 1999, COMPUT J, V42, P193, DOI 10.1093/comjnl/42.3.193 Witten I. H., 1999, MANAGING GIGABYTES C WITTEN IH, 1991, P DAT COMPR C, P23 WONG WYP, 1993, INFORM PROCESS MANAG, V29, P647, DOI 10.1016/0306-4573(93)90085-R Xi W., 2002, LNCS, V2458, P422 XI W, 2002, P DIG LIB IT OPP CHA, P404 ZEZULA P, 1991, ACM T INFORM SYST, V9, P336, DOI 10.1145/119311.119313 Zobel J., 1996, SIGMOD Record, V25 Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 ZOBEL J, 1993, P INT C VER LARG DAT, P290 ZOBEL J, 1995, INFORM PROCESS MANAG, V31, P361, DOI 10.1016/0306-4573(94)00052-5 ZOBEL J, 1992, PROC INT CONF VERY L, P352 Zobel J., 1998, SIGIR Forum, V32 Zobel Justin, 1993, P 4 AUSTR DAT C BRIS, P26 NR 205 TC 0 Z9 0 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PY 2006 VL 38 IS 2 DI 10.1145/1132956.1132959 PG 56 WC Computer Science, Theory & Methods SC Computer Science GA 076TJ UT WOS:000239980300003 ER PT S AU Kumar, A AF Kumar, Amruth BE Wade, V Ashman, H Smyth, B TI A scalable solution for adaptive problem sequencing and its evaluation SO ADAPTIVE HYPERMEDIA AND ADAPTIVE WEB-BASED SYSTEMS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems CY JUN 21-23, 2006 CL Dublin, IRELAND SP Trinity Coll, Univ Coll Dublin, Natl Col Ireland DE programming tutor; adaptive problem sequencing; evaluation AB We propose an associative mechanism for adaptive generation of problems in intelligent tutors. Our evaluations of the tutors that use associative adaptation for problem sequencing show that 1) associative adaptation targets concepts less well understood by students; and 2) associative adaptation helps students learn with fewer practice problems. Apart from being domain-independent, the advantages of associative adaptation compared to other adaptive techniques are that it is easier to build and is scalable. C1 Ramapo Coll, Mahwah, NJ 07430 USA. RP Kumar, A (reprint author), Ramapo Coll, Mahwah, NJ 07430 USA. CR AIMEUR E, 2002, P ITS 2002, P718 ARROYO I, 2001, P ARTIFICIAL INTELLI, P456 Bloom B. S., 1984, EDUC RES, V13, P3 Bloom B. S., 1956, TAXONOMY ED OBJECTIV Burton R. R., 1978, COGNITIVE SCI, V2, P155, DOI DOI 10.1207/S15516709COG0202_4 CZARKOWSKI M, 2003, P AI ED 2003, P404 DANCIK G, 2003, P FRONT ED C FIE 200 KALYUGA S, 2003, P AI ED 2003, P167 KAY J, 2000, P 5 INT C INT TUT SY, P19 KUMAR AN, 2006, P ITS 2000, P383 KUMAR AN, 2006, INT FLAIRS C ART INT KURHILA J, 2002, P ITS 2002, P129 Melis E., 2001, INT J ARTIFICIAL INT, V12, P385 MILLAN E, 2000, P ITS 2000, P534 REISER B, 1985, P IJCAI 1985 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wainer H., 1990, COMPUTERIZED ADAPTIV Weber G., 2001, INT J ARTIFICIAL INT, V12, P351 NR 18 TC 7 Z9 7 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34696-1 J9 LECT NOTES COMPUT SC PY 2006 VL 4018 BP 161 EP 171 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BET35 UT WOS:000239424300018 ER PT S AU Lee, LW Chen, SM AF Lee, Li-Wei Chen, Shyi-Ming BE Ali, M Dapoigny, R TI New methods for text categorization based on a new feature selection method and a new similarity measure between documents SO ADVANCES IN APPLIED ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems CY JUN 27-30, 2006 CL Annecy, FRANCE SP Univ Savoie, Dept Comp Sci, Int Soc Appl Intelligence, LISTIC/ESIA, AIAA, ACM SIGART, AFIA, CSCSI/SCEIO, ECCAI, ENNS, INNS, JSAI, TAAI, Texas State Univ AB In this paper, we present a new feature selection method based on document frequencies and statistical values. We also present a new similarity measure to calculate the degree of similarity between documents. Based on the proposed feature selection method and the proposed similarity measure between documents, we present three methods for dealing with the Reuters-21578 top 10 categories text categorization. The proposed methods get higher performance for dealing with the Reuters-21578 top 10 categories text categorization than that of the method presented in [4]. C1 Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan. RP Lee, LW (reprint author), Natl Taiwan Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taipei, Taiwan. EM smchen@et.ntust.edu.tw CR Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 CHAKRABARTI S, 2003, MINING WEB, P137 CHUA SKN, 2004, P IEEE WIC ACM INT C DOAN S, 2004, P IEEE 4 INT C HYBR Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 LAM W, 1998, P SIGIR 98 21 ACM IN, P195 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 Shima K, 2004, PATTERN RECOGN LETT, V25, P1051, DOI 10.1016/j.patrec.2004.03.002 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 24 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-35453-0 J9 LECT NOTES COMPUT SC PY 2006 VL 4031 BP 1280 EP 1289 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BEV62 UT WOS:000239623800135 ER PT S AU Kotropoulos, C Papaioannou, A AF Kotropoulos, C Papaioannou, A BE Antoniou, G Potamias, G Spyropoulos, C Plexousakis, D TI A novel updating scheme for probabilistic latent semantic indexing SO ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 4th Helenic Conference on Artifical Intelligence CY MAY 18-20, 2006 CL Heraklion, GREECE SP EETN HO Univ Crete AB Probabilistic Latent Semantic Indexing (PLSI) is a statistical technique for automatic document indexing. A novel method is proposed for updating PLSI when new documents arrive. The proposed method adds incrementally the words of any new document in the term-document matrix and derives the updating equations for the probability of terms given the class (i.e. latent) variables and the probability of documents given the latent variables. The performance of the proposed method is compared to that of the folding-in algorithm, which is an inexpensive, but potentially inaccurate updating method. It is demonstrated that the proposed updating algorithm outperforms the folding-in method with respect to the mean squared error between the aforementioned probabilities as they are estimated by the two updating methods and the original non-adaptive PLSI algorithm. C1 Aristotle Univ Thessaloniki, Dept Informat, GR-54124 Thessaloniki, Greece. RP Kotropoulos, C (reprint author), Aristotle Univ Thessaloniki, Dept Informat, GR-54124 Thessaloniki, Greece. EM costas@aiia.csd.auth.gr; apapaion@aiia.csd.auth.gr RI Kotropoulos, Constantine/B-7928-2010 OI Kotropoulos, Constantine/0000-0001-9939-7930 CR Almpanidis G, 2005, LECT NOTES COMPUT SC, V3686, P278 Bartell B., 1992, P 15 ANN INT ACM SIG, P161, DOI 10.1145/133160.133191 Berry M., 1999, UNDERSTANDING SEARCH DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Hofmann T., 1998, TR98042 INT COMP SCI Hofmann T., 1999, P UNC ART INT UAI 99 Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 Lang K, 1995, P 12 INT C MACH LEAR, P331 Neal R., 1999, LEARNING GRAPHICAL M, P355 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saul L, 1997, P 2 C EMP METH NAT L, P81 YATES RB, 1999, MODERN INFROM RETRIE NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34117-X J9 LECT NOTES COMPUT SC PY 2006 VL 3955 BP 137 EP 147 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BEL89 UT WOS:000238053100014 ER PT S AU Amati, G AF Amati, Giambattista BE Lalmas, M MacFarlane, A Ruger, S Tombros, A Tsikrika, T Yavlinsky, A TI Frequentist and Bayesian approach to information retrieval SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 28th European Conference on Information Retrieval (ECIR 2006) CY APR 10, 2005-APR 12, 2006 CL London, ENGLAND SP Queen Mary Univ London, City Univ, Engn & Phys Sci Res Council, CEPIS, Google, GCHQ, Microsoft Res, Yahoo Res, Sharp, Apriorie, Lemur Consulting, MMKM, Elsevier HO Imperial Coll London, S Kensington ID VECTOR-SPACE MODEL AB We introduce the hypergeometric models KL, DLH and DLLH using the DFR approach, and we compare these models to other relevant models of IR. The hypergeometric models are based on the probability of observing two probabilities: the relative within-document term frequency and the entire collection term frequency. Hypergeometric models are parameter-free models of IR. Experiments show that these models have an excellent performance with small and very large collections. We provide their foundations from the same IR probability space of language modelling (LM). We finally discuss the difference between DFR and LM. Briefly, DFR is a frequentist (Type I), or combinatorial approach, whilst language models use a Bayesian (Type II) approach for mixing the two probabilities, being thus inherently parametric in its nature. C1 Fdn Ugo Bordoni, Rome, Italy. RP Amati, G (reprint author), Fdn Ugo Bordoni, Rome, Italy. CR AMATI G, 2002, NIST SPECIAL PUBLICA, P182 Amati G, 2002, ACM T INFORM SYST, V20, P357, DOI 10.1145/582415.582416 AMATI G, 2004, NIST SPECIAL PUBLICA Amati Giambattista, 2003, THESIS U GLASGOW BAHL LR, 1983, IEEE T PATTERN ANAL, V5, P179 Berger A, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P222 Brown P. F., 1990, Computational Linguistics, V16 Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860 Feller W, 1968, INTRO PROBABILITY TH GOOD IJ, 1968, ESTIMATION PROBABILI, V30 Harter SP, 1974, THESIS U CHICAGO HE B, 2005, P 12 INT C INF KNOWL He B., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Ounis I, 2005, LECT NOTES COMPUT SC, V3408, P517 Plachouras V., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009069 PLACHOURAS V, 2004, NIST SPECIAL PUBLICA Ponte J. M., 1998, 21 ACM SIGIR C RES D, P275 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RENYI A, 1969, FDN PROBABILITY Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ZHAI C, 2004, ACM T INFORM SYSTEMS, V22 ZHAI C, 2001, CIKM 2001, P334 NR 27 TC 12 Z9 12 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-33347-9 J9 LECT NOTES COMPUT SC PY 2006 VL 3936 BP 13 EP 24 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BEM01 UT WOS:000238083200003 ER PT S AU Cheng, V Yeung, CS Li, CH AF Cheng, Victor Yeung, Chi-sum Li, Chun-hung BE Jiao, L Wang, L Gao, X Liu, J Wu, F TI Classification of online discussions via content and participation SO ADVANCES IN NATURAL COMPUTATION, PT 2 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd International Conference on Natural Computation (ICNC 2006) CY SEP 24-28, 2006 CL Xian, PEOPLES R CHINA SP Xidian Univ, Natl Nat Sci Fdn China, Int Neural Network Soc, Asia Pacific Neural Network Assembly, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, IEEE Computat Intelligence Singapore Chapter, Chinese Assoc Artificial Intelligence DE online community; support vector machine; information retrieval; feature extraction; classification AB Web forums and online communities are becoming increasingly important information sources. While there are significant research works on classification of Web pages, relatively less is known on classification of online discussions. By observing the special nature of user participation in web communities, we propose classification methods based on user participation and text content of online discussions. Support vector machines have been employed in this study to classify and analyze discussions based on content and participation. It is found that the accuracy of using participation as classification features can be very high in certain communities. The use of high-dimensional classifier can be effective in enhancing retrieval and classification of online discussion topics. C1 Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. RP Cheng, V (reprint author), Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. EM victor@comp.hkbu.edu.hk; csyeung@comp.hkbu.edu.hk; chli@comp.hkbu.edu.hk CR Dayhoff J., 1998, NEURAL NETWORKS PATT Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 OSUNA E, 1996, AIM1602 MIT ROGERS I, GOOGLE PAGE ALGORITH SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHOLKOPF B, 2000, 200023 MSR TR Vapnik V., 1995, NATURE STAT LEARNING Vapnik V., 1998, STAT LEARNING THEORY NR 10 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-45907-3 J9 LECT NOTES COMPUT SC PY 2006 VL 4222 BP 820 EP 828 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BFH68 UT WOS:000241892100105 ER PT J AU Godoy, D Amandi, A AF Godoy, Daniela Amandi, Analia TI A conceptual clustering approach for user profiling in personal information agents SO AI COMMUNICATIONS LA English DT Article DE conceptual clustering; user profiling; personal information agents ID TEXT CATEGORIZATION AB Information agents have emerged in the last decade as an alternative to assist users to cope with the increasing volume of information available on the Web. In order to provide personalized assistance, these agents rely on having some knowledge about users contained into user profiles, i.e., models of users preferences and interests gathered by observation of user behavior. User profiles have to summarize categories corresponding not only to diverse user information interests but also to different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, the discovery of interest categories using document clustering offers the advantage that an a priori knowledge of user interests is not needed, therefore the process of acquiring profiles is completely unsupervised. However, most document clustering algorithms are not applicable to the problem of incrementally acquiring and modeling interests because of either the kind of solutions they provide, which do not resemble user interests, or the way they build such solutions, which is generally not incremental. In this paper we describe and evaluate a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), designed to support learning of user interests by personal information agents. WebDCC algorithm carries out incremental, unsupervised concept learning over Web documents with the goal of building and maintaining both accurate and comprehensible user profiles. Empirical evaluation of using this algorithm for user profiling and its advantages with respect to other clustering algorithms are presented. C1 Univ Nacl Ctr Prov Bs As, ISISTAN Res Inst, RA-7000 Tandil, Buenos Aires, Argentina. RP Godoy, D (reprint author), Univ Nacl Ctr Prov Bs As, ISISTAN Res Inst, Campus Univ,Paraje Arroyo Seco, RA-7000 Tandil, Buenos Aires, Argentina. EM dgodoy@exa.unicen.edu.ar; amandi@exa.unicen.edu.ar CR Aggarwal C. C., 1999, P 5 ACM SIGKDD INT C, P352, DOI 10.1145/312129.312279 Basu S., 2002, P 19 INT C MACH LEAR, P19 BILLSUS D, 1999, P 7 INT C US MOD, P99 Boyapati V., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Dom B., 2002, P 18 C UNC ART INT, P137 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Fisher D., 1992, P 9 INT C MACH LEARN, P163 Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 FRAKES WR, 1992, INFORMATION RETRIEVA Godoy D, 2005, IEEE INTERNET COMPUT, V9, P56, DOI 10.1109/MIC.2005.90 Iba W., 2001, LNCS, V2049, P39 John G., 1994, P 11 INT C MACH LEAR, P121 Karypis G., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354772 Koller D., 1997, P 14 INT C MACH LEAR, P170 Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Lieberman H., 1995, P 14 INT JOINT C ART, P924 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P359 Michalski Ryszard S., 1984, MACHINE LEARNING ART, P331 Mladenic D, 1999, IEEE INTELL SYST APP, V14, P44, DOI 10.1109/5254.784084 MLADENIC D, 1998, WORKING NOTES LEARNI MLADENIC D, 2001, P 9 INT C HUM COMP I, V1, P893 Mladenic D, 2003, DECIS SUPPORT SYST, V35, P45, DOI 10.1016/S0167-9236(02)00097-0 Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pirolli Peter, 1996, P ACM SIGCHI C HUM F, P213, DOI 10.1145/238386.238489 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Ruiz ME, 2002, INFORM RETRIEVAL, V5, P87, DOI 10.1023/A:1012782908347 Russell S. J., 1995, ARTIFICIAL INTELLIGE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SHANNON CE, 1948, AT&T TECH J, V27, P379 THOMPSON K, 1991, CONCEPT FORMATION KN Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 Widyantoro D. H., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1184034 NR 39 TC 5 Z9 5 PU IOS PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0921-7126 J9 AI COMMUN JI AI Commun. PY 2006 VL 19 IS 3 BP 207 EP 227 PG 21 WC Computer Science, Artificial Intelligence SC Computer Science GA 085JT UT WOS:000240601100002 ER PT B AU Ohkura, K Goto, K Hanakawa, N Kawaguchi, S Lida, H AF Ohkura, Kimiharu Goto, Keita Hanakawa, Noriko Kawaguchi, Shinji Lida, Hajimu BE Jalote, P TI Project replayer with email analysis - Revealing contexts in software development SO ASPEC 2006: 13th Asia-Pacific Software Engineering Conference, Proceedings LA English DT Proceedings Paper CT 13th Asia-Pacific Software Engineering Conference CY DEC 06-08, 2006 CL Bangalore, INDIA SP IEEE Comp Soc, IEEE, IBM, Microsoft Res, Satyam Comp Serv Ltd, Infosys Technologies Ltd, Philips, TATA Consultancy Serv AB In many software development projects, people tend to repeat same mistakes due to lack of shared knowledge from past experiences. Generally, it is very difficult to manually find out valuable phenomena from huge data. Invisible context, which cannot be known directly from software documents or formal reports, is an important factor to these difficulties. We propose a new method to find contexts based on analysis to email archives in a project repository. In this method, we first apply natural language processing to extract keywords from email messages. Next, similarities among the messages are calculated based on the extracted keywords, and the messages are classified into clusters according to the similarities. The clustering result can be presented with other information such as code growth graph or schedule charts. This method is implemented as an extension to the Project Replayer, a tool to review past project data. Pilot analysis confirms that a researcher could grasp important contexts of failures in actual projects using the Project Replayer. C1 Nara Inst Sci & Technol, Grad Sch Informat Sci, Nara, Japan. Hannan Univ, Fac Management Informat, Matsubara, Osaka, Japan. RP Ohkura, K (reprint author), Nara Inst Sci & Technol, Grad Sch Informat Sci, Nara, Japan. CR Bird C., 2006, P INT WORKSH MIN SOF CONKLIN MS, 2005, P MSR205 ST LOUIS MI Cubranic D, 2005, IEEE T SOFTWARE ENG, V31, P446, DOI 10.1109/TSE.2005.71 GOTO K, 2006, P SPW PROS2006 LNCS, P72 MATSUSHITA M, 2005, P 12 AS PAC SOFTW EN, P821 MURAKOSHI H, 2001, 19 INT C COMP PROC O, P359 OHIRA M, 2004, P ISESE2004, P37 Robles G., 2005, SIGSOFT SOFTW ENG NO, V30, P1 SALTON G, 1988, TERM WEIGHTING APPRO, V24 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCACCHI W, 2005, P 1 INT C OP SOURC S Wagstrom P., 2005, P 1 INT C OP SOURC S, P16 NR 12 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2685-0 PY 2006 BP 453 EP 460 PG 8 WC Computer Science, Software Engineering SC Computer Science GA BFY98 UT WOS:000245509400054 ER PT S AU Liang, G Li, T Gong, X Jiang, Y Yang, J Ni, HC AF Liang, Gang Li, Tao Gong, Xun Jiang, Yaping Yang, Jin Ni, Hancheng BA Huang, DS BF Huang, DS BE Li, K Irwin, GW TI NASC: A novel approach for spam classification SO COMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Conference on Intelligent Computing (ICIC) CY AUG 16-19, 2006 CL Kunming, PEOPLES R CHINA SP IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China AB The technology of spam filters has recently received considerable attention as a powerful approach to Internet security management. The traditional spam filters almost adopted static measure, and filters need be updated and maintained frequently, so they can not adapt to dynamic spam. In order to get over the limitation of the traditional means, an immunity-based spam classification was proposed in this paper. A brief review about traditional technology of spam filter is given first, particularly, the Bayes probability model. Then the NASC is described in detail by introducing self, non-self, detector and detect algorithm. Finally, a simulation experiment was performed, and the important parameters of the model were analyzed. The experimental result shows that the new model increases the recall of filter greatly in condition that precision also increasing, and demonstrate that the model has the features of self-learning and self-adaptation. C1 Sichuan Univ, Dept Comp Sci & Technol, Chengdu 610065, Peoples R China. RP Liang, G (reprint author), Sichuan Univ, Dept Comp Sci & Technol, Chengdu 610065, Peoples R China. EM gangliang56@163.com CR Brutlag JD, 2000, ICML, P103 CRAWFORD E, 2002, P 19 INT C MACH LEAR DIAO Y, 2000, COMP STUDY CLASSIFIC FABRIZIO S, 2002, ACM COMPUT SURV, P11 Le Z., 2004, ACM T ASIAN LANGUAGE, V3, P243 LEE DL, 1997, IEEE SOFTWARE, P67 LI T, 2005, LECT NOTES COMPUTER, P72 LI T, 2004, COMPUTE IMMUNOLOGY LI T, 2005, SCI CHINA SER F, P557 LI T, 2005, CHINESE SCI BULL, P2650 LI T, 2005, IMMUNE BASED MODEL C, P59 LIU B, 2001, P 7 INT C KNOWL DISC SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHNEIDER KM, 2003, P 11 C EUR CHOP ACL NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-37277-6 J9 LECT NOTES COMPUT SC PY 2006 VL 4115 BP 672 EP 681 PG 10 WC Biochemical Research Methods; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Biochemistry & Molecular Biology; Computer Science GA BEY16 UT WOS:000240085400071 ER PT S AU Posse, C Sanfilippo, A Gopalan, B Riensche, R Beagley, N Baddeley, B AF Posse, C. Sanfilippo, A. Gopalan, B. Riensche, R. Beagley, N. Baddeley, B. BE Alexandrov, VN VanAlbada, GD Sloot, PMA TI Cross-ontological analytics: Combining associative and hierarchical relations in the gene ontologies to assess gene product similarity SO COMPUTATIONAL SCIENCE - ICCS 2006, PT 2, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 6th International Conference on Computational Science (ICCS 2006) CY MAY 28-31, 2006 CL Reading, ENGLAND SP Intel Corp, IBM, SGI, Microsoft Res, EPSRC, Springer, ACET Ctr, Univ Reading, SIAM, IMACS, UK e Sci Programme AB Gene and gene product similarity is a fundamental diagnostic measure in analyzing biological data and constructing predictive models for functional genomics. With the rising influence of the gene ontologies, two complementary approaches have emerged where the similarity between two genes/gene products is obtained by comparing gene ontology (GO) annotations associated with the gene/gene products. One approach captures GO-based similarity in terms of hierarchical relations within each gene ontology. The other approach identifies GO-based similarity in terms of associative relations across the three gene ontologies. We propose a novel methodology where the two approaches can be merged with ensuing benefits in coverage and accuracy. C1 Pacific NW Natl Lab, Richland, WA 99352 USA. RP Posse, C (reprint author), Pacific NW Natl Lab, 902 Battelle Blvd,POB 999, Richland, WA 99352 USA. EM Christian.Posse@pnl.gov; Antonio.Sanfilippo@pnl.gov; Banu.Gopalan@pnl.gov; Rick.Riensche@pnl.gov; Nathaniel.Beagley@pnl.gov; Bob.Baddeley@pnl.gov CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 Azuaje F., 2005, P ISMB 2005 SIG M BI, P9 BODENREIDER O, 2005, P PAC S BIOC, P104 BUDANITSKY A, 1999, CSRG390 U TOR DEP CO Couto F.M., 2003, IMPLEMENTATION FUNCT Jiang J., 1997, P INT C RES COMP LIN KENDALL MG, 1949, BIOMETRIKA, V36, P177, DOI 10.2307/2332540 Lin Dekang, 1998, P 15 INT C MACH LEAR Lord P W, 2003, Pac Symp Biocomput, P601 LORD PW, 2002, BIOINFORMATICS, V19, P1275 OEHMEN CS, 2006, IN PRESS IEEE T PARA PEARSON WR, 1988, P NATL ACAD SCI USA, V85, P2444, DOI 10.1073/pnas.85.8.2444 Popescu M., 2005, Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 05CH37680) Raychaudhuri S, 2003, NUCLEIC ACIDS RES, V31, P4553, DOI 10.1093/nar/gkg636 Resnik P., 1995, P 14 INT JOINT C ART, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 NR 17 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34381-4 J9 LECT NOTES COMPUT SC PY 2006 VL 3992 BP 871 EP 878 PG 8 WC Computer Science, Theory & Methods SC Computer Science GA BEO13 UT WOS:000238389400116 ER PT S AU Chernov, S Kohlschutter, C Nejdl, W AF Chernov, Sergey Kohlschuetter, Christian Nejdl, Wolfgang BE Sugimoto, S Hunter, J Rauber, A Morishima, A TI A plugin architecture enabling federated search for digital libraries SO Digital Libraries: Achievements, Challenges and Opportunities, Proceedings SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Proceedings Paper CT 9th International Conference on Asian Digital Libraries CY NOV 27-30, 2006 CL Kyoto, JAPAN SP Kyoto Univ, Grad Sch Informat, 21st Cent Ctr Excellence Program, Univ Tsukuba, Grad Sch Informat, Res Ctr Knowledge Comm AB Today, users expect a variety of digital libraries to be searchable from a single Web page. The German Vascoda project provides this service for dozens of information sources. Its ultimate goal is to provide search quality close to the ranking of a central database containing documents from all participating libraries. Currently, however, the Vascoda portal is based on a non-cooperative metasearch approach, where results from sources are merged randomly and ranking quality is sub-optimal. In this paper, we describe a Lucene-based plugin which replaces this method by a truly federated search across different search engines, where the exchange of document statistics improves document ranking. Preliminary evaluation results show ranking results equal to a centralized setup. C1 Univ Hannover, L3S Res Ctr, D-30539 Hannover, Germany. RP Chernov, S (reprint author), Univ Hannover, L3S Res Ctr, Expo Plaza 1, D-30539 Hannover, Germany. CR BALKE WT, 2005, ECDL 05 P 9 EUR C RE, P379 CALLAN J, 2000, DISTRIBUTED INFORM R, P127 CASTELLI D, 2004, ERCIM NEWS, V59 CRASWELL NE, 2001, THESIS ANU CROFT WB, 2000, DELOS WORKSH INFORM FUHR N, 2002, ECDL 2002, P597 Gospodnetic O., 2005, LUCENE ACTION Gravano Luis, 1997, STARTS STANFORD PROP, V26, P207 GREEN N, 2001, JCDL 01 P 1 ACM IEEE, P207 Lagoze C, OPEN ARCH INITIATIVE LIU X, 2002, J DIGITAL INFORM, V4 Meng WY, 2002, ACM COMPUT SURV, V34, P48, DOI 10.1145/505282.505284 *NAT INF STAND ORG, 2003, Z3950 APPL SERV DEF NEUROTH H, 2003, ECDL 03 P 7 EUR C RE, P257 SADEH T, 2006, HIGH ENERGY PHYS LIB, V12 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SI L, 2002, CIKM 02, P391 NR 17 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-49375-1 J9 LECT NOTES COMPUT SC PY 2006 VL 4312 BP 202 EP 211 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BFN08 UT WOS:000243132700022 ER PT S AU Archetti, F Campanelli, P Fersini, E Messina, E AF Archetti, F. Campanelli, P. Fersini, E. Messina, E. BE Larsen, HL Pasi, G OrtizArroyo, D Andreasen, T Christiansen, H TI A hierarchical document clustering environment based on the Induced Bisecting k-Means SO FLEXIBLE QUERY ANSWERING SYSTEMS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Flexible Query Answering Systems CY JUN 07-10, 2006 CL Milan, ITALY SP Assoc Italiana Informat Calcolo Automat, Univ Degli Studi Milano Bicoca, Univ Milano, Dipartment Tecnol Informaz AB The steady increase of information on WWW, digital library, portal, database and local intranet, gave rise to the development of several methods to help user in Information Retrieval, information organization and browsing. Clustering algorithms are of crucial importance when there are no labels associated to textual information or documents. The aim of clustering algorithms, in the text mining domain, is to group documents concerning with the same topic into the same cluster, producing a flat or hierarchical structure of clusters. In this paper we present a Knowledge Discovery System for document processing and clustering. The clustering algorithm implemented in this system, called Induced Bisecting k-Means, outperforms the Standard Bisecting k-Means and is particularly suitable for on line applications when computational efficiency is a crucial aspect. C1 Univ Milan Bicocca, DISCO, I-20126 Milan, Italy. Consorzio Milano Ric, I-20129 Milan, Italy. RP Archetti, F (reprint author), Univ Milan Bicocca, DISCO, Via Bicocca Arcimboldi,8, I-20126 Milan, Italy. EM archetti@milanoricerche.it; campanelli@milanoricerche.it; fersini@disco.unimib.it; messina@disco.unimib.it CR BOLEY D, TR97056 U MINN DEP C Cho J, 2000, P ACM SIGMOD INT C M, P117, DOI 10.1145/342009.335391 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Dhillon I., 2002, TEXT DATA MINING APP Ferragina Paolo, 2005, 14 INT C WORLD WID W, P801 Han E.-H., 1998, P 2 INT C AUT AG, P408, DOI DOI 10.1145/280765.280872 Kashyap V., 2005, International Journal of Web and Grid Services, V1, DOI 10.1504/IJWGS.2005.008322 Koller D., 1997, P 14 INT C MACH LEAR, P170 Pirolli Peter, 1996, P ACM SIGCHI C HUM F, P213, DOI 10.1145/238386.238489 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1983, INTRO MODERN RETRIEV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Savaresi M., 2001, 1 SIAM INT C DAT MIN, P1 Steinbach M., 2000, KDD WORKSH TEXT MIN Toda H, 2005, P 7 ACM INT WORKSH W, P81, DOI 10.1145/1097047.1097063 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining ZHANG D, 2004, P 6 AS PAC WEB C NR 17 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-34638-4 J9 LECT NOTES COMPUT SC PY 2006 VL 4027 BP 257 EP 269 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BEP51 UT WOS:000238575000022 ER PT S AU Hu, JN Deng, WH Guo, J AF Hu, Jiani Deng, Weihong Guo, Jun BE Wang, L Jiao, L Shi, G Li, X Liu, J TI Robust discriminant analysis of latent semantic feature for text categorization SO FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Conference on Fuzzy Systems and Knowledge Discovery CY SEP 24-28, 2006 CL Xian, PEOPLES R CHINA SP Xidian Univ, Natl Nat Sci Fdn China, Int Neural Network Soc, Asia-Pacific Neural Network Assembly, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, IEEE Computat Intelligence Soc, Singapore Chapter, Chinese Assoc Artificial Intelligence Tech ID DECOMPOSITION AB This paper proposes a Discriminative Semantic Feature (DSF) method for vector space model based text categorization. The DSF method, which involves two stages, first reduces the dimension of the document vector space by Latent Semantic Indexing (LSI), and then applies a Robust linear Discriminant analysis Model (RDM), which improves the classical LDA by a energy-adaptive regularization criteria, to extract the discriminative semantic feature with enhanced discrimination power. As a result, DSF method can not only uncover latent semantic structure but also capture the discriminative feature. Comparative experiments on various state-of-art dimension reduction schemes such as our DSF, LSI, orthogonal centroid, two-stage LSI+LDA, LDA/QR and LDA/GSVD, are also performed. Experiments using the Reuters-21578 text collection show the proposed method performs better than other algorithms. C1 Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China. RP Hu, JN (reprint author), Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China. EM cughu@126.com; cvpr_dwh@126.com; guojun@bupt.edu.cn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUDA R, 2000, PATERN CLASSIFICATIO Fukunaga K, 1990, INTRO STAT PATTERN R, V2nd Howland P, 2004, IEEE T PATTERN ANAL, V26, P995, DOI 10.1109/TPAMI.2004.46 Joachims Thorsten, 2002, LEARNING CLASSIFY TE Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 Lewis D.D., REUTERS 21578 TEXT C PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Thomaz CE, 2004, IEEE T CIRC SYST VID, V14, P214, DOI 10.1109/TCSVT.2003.821984 TORKKOLA K, 2001, IEEEINT C DAT MIN IC Ye JP, 2005, IEEE T PATTERN ANAL, V27, P929 Zhao Y, 2004, MACH LEARN, V55, P311, DOI 10.1023/B:MACH.0000027785.44527.d6 NR 15 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-45916-2 J9 LECT NOTES COMPUT SC PY 2006 VL 4223 BP 400 EP 409 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BFD14 UT WOS:000241106000046 ER PT S AU Doan, S Ha, QT Horiguchi, S AF Doan, Son Ha, Quang-Thuy Horiguchi, Susumu BE Wang, L Jiao, L Shi, G Li, X Liu, J TI A general fuzzy-based framework for text representation and its application to text categorization SO FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Conference on Fuzzy Systems and Knowledge Discovery CY SEP 24-28, 2006 CL Xian, PEOPLES R CHINA SP Xidian Univ, Natl Nat Sci Fdn China, Int Neural Network Soc, Asia-Pacific Neural Network Assembly, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, IEEE Computat Intelligence Soc, Singapore Chapter, Chinese Assoc Artificial Intelligence Tech ID INFORMATION-RETRIEVAL; MODEL AB In this paper we develop the general framework for text representation based on fuzzy set theory. This work is extended from our original ideas [5],[4], in which a document is represented by a set of fuzzy concepts. The importance degree of these fuzzy concepts characterize the semantics of documents and can be calculated by a specified aggregation function of index terms. Based on this representation, a general framework is proposed and applied to text categorization problem. An algorithm is given in detail for choosing fuzzy concepts. Experiments on the real-world data set show that the proposed method is superior to the conventional method for text representation in text categorization. C1 Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 9808579, Japan. Vietnam Natl Univ, Coll Technol, Hanoi, Vietnam. RP Doan, S (reprint author), Tohoku Univ, Grad Sch Informat Sci, Aoba 09, Sendai, Miyagi 9808579, Japan. EM s-doan@ecei.tohoku.ac.jp; thuyhq@vnu.edu.vn; susumu@ecei.tohoku.ac.jp RI Doan, Son/P-5396-2014 OI Doan, Son/0000-0002-7284-1306 CR Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 DAON S, 2002, P 1 INT C FUZZ SET K, V2, P514 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Doan S., 2005, Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE Cat. No. 05CH37680) Joachims T., 1998, P 10 EUR C MACH LEAR, P137 LEWIS D, 1991, THESIS U MASSACHUSET LUCARELLA D, 1991, J INFORM SCI, V17, P81, DOI 10.1177/016555159101700202 Manning C. D., 1999, FDN STAT NATURAL LAN Miyamoto S., 1990, FUZZY SETS INFORM RE Molinari A., 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE '96 (Cat. No.96CH35998), DOI 10.1109/FUZZY.1996.551727 MOULINIER I, 1996, AAAI S MACH LEARN IN Moulinier I., 1996, Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing MURAI T, 1989, FUZZY SET SYST, V30, P103, DOI 10.1016/0165-0114(89)90074-2 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SPARCKJONES K, 1972, J DOC, V28, P11 Witte R., 2003, P 2003 INT S REF RES, P43 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 23 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-45916-2 J9 LECT NOTES COMPUT SC PY 2006 VL 4223 BP 611 EP 620 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BFD14 UT WOS:000241106000073 ER PT J AU Amer-Yahia, S Botev, C Dorre, J Shanmugasundaram, J AF Amer-Yahia, S Botev, C Dorre, J Shanmugasundaram, J TI XQuery full-text extensions explained SO IBM SYSTEMS JOURNAL LA English DT Article AB There has been recent interest in developing XML query languages, such as XPath and XQuery, to tap the vast amount of information represented and stored in Extensible Markup Language (XML). These query languages, however, have focused mainly on querying the structure of XML documents and provide only rudimentary support for querying text content. To fill this void, XQuery Full-Text has been developed as a full-text extension to XQuery (and also XPath, which is a subset of XQuery). Consequently, XQuery Full-Text can be used to seamlessly query over both the structure and the text content of XML documents. This paper explains the design principles behind XQuery Full-Text, describes its evolution, and illustrates its core features with examples. it is intended as a reference that is shorter and more accessible than the current World Wide Web Consortium working draft. C1 AT&T Labs Res, Florham Pk, NJ 07932 USA. Cornell Univ, Dept Comp Sci, Ithaca, NY 14850 USA. IBM Deutschland Entwicklung GmbH, D-71032 Boblingen, Germany. RP Amer-Yahia, S (reprint author), AT&T Labs Res, 180 Pk Ave, Florham Pk, NJ 07932 USA. EM sihem@research.att.com; cbotev@cs.cornell.edu; doerre@de.ibm.com; jai@cs.cornell.edu CR *ACM SIGMOD, REC XML VERS Al-Khalifa S., 2003, P ACM SIGMOD INT C M, P4 AMERYAHIA S, 2004, XQUERY 1 0 XPATH 2 0 Amer-Yahia S., 2004, P 13 INT WORLD WID W, P583, DOI 10.1145/988672.988751 AMERYAHIA S, 2005, XQUERY 1 0 XPATH 2 0 [Anonymous], 59641985 ISO *ANSI NISO, Z3919 ANSI NISO BERGLUND A, 2005, XML PATH LAUNGUAGE X BREMER JM, 2002, P 5 INT WORKSH WEB D, P1 Brown LJ, 1998, THEOR PRACT OBJ SYST, V4, P227, DOI 10.1002/(SICI)1096-9942(1998)4:4<227::AID-TAPO3>3.3.CO;2-L BUXTON S, 2003, XQUERY XPATH FULL TE BWORN EW, 1995, P 18 ANN INT ACM SIG, P30 Carmel D., 2003, P 26 ANN INT ACM SIG, P151 CASE P, 2001, XML GOV WORK GROUP Chamberlin D., 2005, XQUERY 1 0 XML QUERY Chamberlin D, 2002, IBM SYST J, V41, P597 CHINENYANGA TT, 1991, P 4 INT WORKSH WEB D, P1 Cohen W. W., 1998, P ACM SIGMOD INT C M, P201, DOI 10.1145/276304.276323 FERNANDEZ M, 2005, XQUERY 1 0 XPATH 2 0 FLORESCU D, 2000, P 9 WWW C COMP NETW, P119 FUHR N, 2000, P ACM SIGIR 2000 WOR, P172 Guo L, 2003, P 2003 ACM SIGMOD IN, P16, DOI DOI 10.1145/872757.872762 HAYASHI Y, 2000, P ACM SIGIR WORKSH X, P32 HUNG E, 2004, P 2004 ACM SIGMOD IN, P719, DOI 10.1145/1007568.1007649 *ISO, 27881986 ISO KATZ H, 2003, XQUERY EXPERTS GUIDE MALHOTRA A, 2005, XQUERY 1 0 XPATH 2 0 Melton J, 2001, SIGMOD RECORD, V30, P97 Myaeng S. H., 1998, P 21 ANN INT ACM SIG, P138, DOI 10.1145/290941.290980 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Salton G., 1986, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THEOBALD A, 2002, P 8 INT C EXT DAT TE, P477 *UN C, 2003, UN STAND VERS 4 0 Witten I.H., 1994, MANAGING GIGABYTES C Zhang C., 2001, P ACM SIGMOD INT C M, V5, P425, DOI [10.1145/375663.375722, DOI 10.1145/375663.375722] NR 36 TC 9 Z9 9 PU IBM CORP PI ARMONK PA 1 NEW ORCHARD ROAD, ARMONK, NY 10504 USA SN 0018-8670 J9 IBM SYST J JI IBM Syst. J. PY 2006 VL 45 IS 2 BP 335 EP 351 PG 17 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA 053WA UT WOS:000238335400009 ER PT B AU Wang, JF Yuan, JS AF Wang Jianfeng Yuan Jinsha BE Li, M TI Web text categorization based on latent semantic analysis SO ICCSE'2006: Proceedings of the First International Conference on Computer Science & Education: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION LA English DT Proceedings Paper CT 1st International Conference on Computer Science and Education (ICCSE 2006) CY JUL 27-29, 2006 CL Xiamen, PEOPLES R CHINA SP China Natl Res Council Comp Educ Coll & Univ, IEEE Control Syst Chapter, Singapore, Univ Virginia, Univ Melbourne HO Xiamen Univ DE latent semantic analysis; Web text categorization; support vector machine AB Traditional text categorization methods are difficult to deal with the high dimensionality characteristic of the text document based on semantic concept of the words. This paper proposed a method to Web text categorization based on latent semantic analysis. Textual data was mapped into a lower space. The proposed approach used the singular-value decomposition to derive a latent semantic space. The SVM is used to text categorization in the semantic space. Experimental results show that the method is effective on the performance of the text categorization. C1 N China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Hebei Province, Peoples R China. RP Wang, JF (reprint author), N China Elect Power Univ, Dept Elect & Commun Engn, Baoding 071003, Hebei Province, Peoples R China. CR Berry M. W., 1995, P 1995 ACM IEEE C SU, P20, DOI 10.1145/224170.285569 Hofmann T., 1999, P 22 ANN ACM C RES D Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774 LI K, 1997, P 1 INT C MACH LEARN, P412 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Sebastiani Fabrizio, 2002, ACM COMPUT SURV, V34, p[11, 32] Sebastiani Fabrizio, 2002, ACM COMPUT SURV, V34, P32 Vapnik, 1995, NATURE STAT LEARNING YANG YM, P ICML 97 14 INT C NR 10 TC 0 Z9 0 PU XIAMEN UNIV PRESS PI FUJIAN PA XIAMEN, FUJIAN, 361005, PEOPLES R CHINA BN 7-5615-2582-6 PY 2006 BP 826 EP 828 PG 3 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Telecommunications SC Computer Science; Telecommunications GA BFD83 UT WOS:000241272800197 ER PT B AU Zhang, H Song, HT AF Zhang, Hui Song, Han-Tao BE Tsumoto, S Clifton, CW Zhong, N Wu, XD Liu, JM Wah, BW Cheung, YM TI Fuzzy related classification approach based on semantic measurement for web document SO ICDM 2006: Sixth IEEE International Conference on Data Mining, Workshops LA English DT Proceedings Paper CT 6th IEEE International Conference on Data Mining CY DEC 18-22, 2006 CL Hong Kong, PEOPLES R CHINA SP IEEE DE fuzzy classification; semantic measurement; semantic web; VSM ID RETRIEVAL; MODEL AB A fuzzy related classification approach for Web document based on ontology concept semantic measurement is presented in this paper. The features of ontology in the semantic web are explored, and a method to compute the semantic similarity between concepts is put forward. Motivated by the semantic similarity and the fuzzy characteristics of the Web document, the fuzzy related technology combined with YSM in classing Web documents into a predefined set of ontology categories is adopted. Finally some examples are presented to show that the results are satisfying. C1 Beijing Inst Technol, Dept Comp Sci & Engn, Beijing 100081, Peoples R China. RP Zhang, H (reprint author), Beijing Inst Technol, Dept Comp Sci & Engn, Beijing 100081, Peoples R China. CR Berners-Lee T., 1999, WEAVING WEB BETTINA B, 2004, 1 EUR WEB MIN FOR EW, P1 BLOEHDORN S, 2004, P MIN SEM WEB WORKSH Broder A., 1997, P 6 INT WORLD WID WE, P391 CHEKURI C, 1997, P 6 INT WORLD WID WE Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Haruechaiyasak C, 2004, PROCEEDINGS OF THE 2004 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI-2004), P357, DOI 10.1109/IRI.2004.1431487 Metzler D, 2004, INFORM PROCESS MANAG, V40, P735, DOI 10.1016/j.ipm.2004.05.001 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 978-0-7695-2702-4 PY 2006 BP 615 EP 619 PG 5 WC Computer Science, Information Systems SC Computer Science GA BFZ38 UT WOS:000245603100112 ER PT S AU Velivelli, A Huang, TS AF Velivelli, Atulya Huang, Thomas S. BE Clifton, CW Zhong, N Liu, JM Wah, BW Wu, XD TI Dirichlet aspect weighting: A generalized EM algorithm for integrating external data fields with semantically structured queries by using gradient projection method SO ICDM 2006: Sixth International Conference on Data Mining, Proceedings SE IEEE International Conference on Data Mining LA English DT Proceedings Paper CT 6th IEEE International Conference on Data Mining CY DEC 18-22, 2006 CL Hong Kong, PEOPLES R CHINA SP IEEE AB In this paper we address the problem of document retrieval with semantically structured queries - queries where each term has a tagged field label. We introduce Dirichlet Aspect Weighting model which integrates terms from external databases into the query language model in a bayesian learning framework. For this model, the dirichlet prior distribution is governed by parameters which depend on the number of fields in the external databases. This model needs additional examples to be augmented to the semantically structured query These examples are obtained using pseudo relevance feedback. We formulate a loglikelihood function for the Dirichlet Aspect Weighting model and maximize it using a novel Generalized EM algorithm. Compari. son of the results of Dirichlet Aspect Weighting model on TREC 2005 Genomics Track dataset with baseline methods using pseudo relevance feedback, while incorporating terms from external databases shows an improvement. C1 Univ Illinois, Beckman Inst Adv Sci & Technol, Dept Elect & Comp Engn, Urbana, IL 61801 USA. RP Velivelli, A (reprint author), Univ Illinois, Beckman Inst Adv Sci & Technol, Dept Elect & Comp Engn, Urbana, IL 61801 USA. CR Andrews G., 2001, SPECIAL FUNCTIONS Bertsekas D., 1999, NONLINEAR PROGRAMMIN Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 CROFT WB, 2003, LANGUAGE MODELING IN FUHR N, 2004, SIGIR FORUM, V38, P42 HERSH WR, 1999, P ACM SIGIR 99, P50 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 McLachlan G, 1996, EM ALGORITHM EXTENSI Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tropp JA, 2005, IEEE T INFORM THEORY, V51, P188, DOI 10.1109/TIT.2004.839492 Zangwill W., 1969, NONLINEAR PROGRAMMIN Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 Zhai C., 2001, 10 INT C INF KNOWL M, P403 NR 16 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1550-4786 BN 978-0-7695-2701-7 J9 IEEE DATA MINING PY 2006 BP 633 EP 644 PG 12 WC Computer Science, Information Systems SC Computer Science GA BFZ37 UT WOS:000245601900063 ER PT J AU Zhai, CX Lafferty, J AF Zhai, CX Lafferty, J TI A risk minimization framework for information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article; Proceedings Paper CT 4th ACM SIGIR Workshop on Mathematical/Formal Methods in IR CY 2003 CL Toronto, CANADA SP ACM DE retrieval models; risk minimization; statistical language models; bayesian decision theory ID PROBABILISTIC MODELS; RELEVANCE; SPACE; IR AB This paper presents a probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models, user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We discuss how this framework can unify existing retrieval models and accommodate systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive retrieval models for subtopic retrieval, which is concerned with retrieving documents to cover many different subtopics of a general query topic. These new models differ from traditional retrieval models in that they relax the traditional assumption of independent relevance of documents. (c) 2004 Elsevier Ltd. All rights reserved. C1 Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Zhai, CX (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM czhai@cs.uiuc.edu CR Allan James, 2001, P 24 ANN INT ACM SIG, P10, DOI 10.1145/383952.383954 Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Carbonell J., 1998, P SIGIR 1998 Cooper W., 1991, P 14 ANN INT ACM SIG, P57, DOI 10.1145/122860.122866 COOPER WS, 1994, P 17 ANN INT ACM C R, P242 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1981, J AM SOC INFORM SCI, P451 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dominich S, 2000, J AM SOC INFORM SCI, V51, P614, DOI 10.1002/(SICI)1097-4571(2000)51:7<614::AID-ASI4>3.0.CO;2-S Dominich S., 2002, P ACM SIGIR 2002 WOR, P43 DOMINICH S, 2001, MATH FDN INFORMATION Evans D. A., 1996, P 34 ANN M ASS COMP, P17, DOI 10.3115/981863.981866 FOX EA, 1983, THESIS CORNELL U FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 FUHR N, 2001, P LANG MOD IR WORKSH FUNG R, 1995, COMMUN ACM, V38, P42, DOI 10.1145/203330.203340 Gey FC, 1994, P 17 ANN INT ACM SIG, P222 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Hiemstra D., 1998, P 7 TEXT RETR C TREC Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 KALT T, 1996, 78 CIIR U MASS KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 LAFFERTY J, 2003, LANGUAGE MODELING IN Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 LEWIS DD, 1992, 9193 U MASS Lewis D.D, 1998, EUR C MACH LEARN, P4 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mittendorf E., 1994, P 17 ANN INT ACM SIG, P318 Over P., 1998, Sixth Text REtrieval Conference (TREC-6) (NIST SP 500-240) Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 Ribeiro B, 1996, P 19 ANN INT ACM SIG, P253, DOI 10.1145/243199.243272 RIBEIRONETO B, 2000, SOFT COMPUTING INFOR, P259 Robertson S. E., 1982, Information Technology: Research and Development, V1 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 Robertson SE, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P16, DOI 10.1145/258525.258529 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 STRZALKOWSKI T, 1997, P 5 TEXT RETR C TREC TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJBERGEN CJ, 1977, J DOC, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, COMPUTER J, V29 WONG SKM, 1989, INFORM PROCESS MANAG, V25, P39, DOI 10.1016/0306-4573(89)90090-3 WONG SKM, 1995, ACM T INFORM SYST, V13, P69 WONG SKM, 1991, INT J GEN SYST, V19, P101 Zhai, 2002, THESIS CARNEGIE MELL Zhai C., 2003, P 26 ANN INT ACM SIG, P10 Zhai C., 2001, 10 INT C INF KNOWL M, P403 Zhai C., 2002, P 25 ANN INT ACM SIG, P49 ZHAI C, 1997, K C APPL NAT LANG PR, P312 NR 67 TC 35 Z9 36 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2006 VL 42 IS 1 BP 31 EP 55 DI 10.1016/j.ipm.2004.11.003 PG 25 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 971BE UT WOS:000232355300003 ER PT J AU Zhu, X Cao, HL Yu, Y AF Zhu, X Cao, HL Yu, Y TI SDQE: towards automatic semantic query optimization in P2P systems SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; peer-to-peer; query expansion; SDQE ID PEER-TO-PEER; INFORMATION-RETRIEVAL; SEARCH; NETWORKS; WEB AB Conventional information retrieval technology (i.e. VSM) faces many difficulties when being implemented in complex P2P systems for the lack of global statistic information (e.g. IDF) and central services. In this paper, we suggest a novel query optimization scheme (Semantic Dual Query Expansion, SDQE) that makes full use of the context information supplied by the local document collection. Latent Semantic Indexing (LSI) is used to explore the local context information. By comparing the different local context information hidden in different document collections, it is possible to solve the synonymy-polysemy problem in VSM. The experiments prove that our scheme is effective to improve the retrieval performance in P2P systems without knowing the global statistic information. (c) 2004 Elsevier Ltd. All rights reserved. C1 Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. RP Zhu, X (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, 1954 Huashan Rd, Shanghai 200030, Peoples R China. EM zhuxing@sjtu.edu.cn; edward@apex.sjtu.edu.cn; yyu@sjtu.edu.cn CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Balakrishnan H., 2003, Communications of the ACM, V46, DOI 10.1145/606272.606299 BARBOSA MW, 2004, P 4 INT WORKSH SOFTW, P216, DOI 10.1145/974044.974079 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Callan J., 1998, P 21 ANN INT ACM SIG, P112, DOI 10.1145/290941.290974 COHEN E, 2003, P IEEE INFOCOM 2003 CUECAACUNA FM, 2002, LECT NOTES, V2376, P220 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Gauch S, 1999, ACM T INFORM SYST, V17, P250, DOI 10.1145/314516.314519 Golub G.H., 1996, MATRIX COMPUTATION Hull D., 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 Khambatti M, 2004, LECT NOTES COMPUT SC, V2944, P48 Lee JH, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/258525.258587 Li JY, 2003, LECT NOTES COMPUT SC, V2735, P207 Lu Jie, 2003, P 12 INT C INF KNOWL, P199 Milojicic D. S., 2002, HPL200257 Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995 Ogilvie P., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 Ratnasamy S, 2001, P 2001 C APPL TECHN, P161, DOI [10.1145/383059.383072, DOI 10.1145/964723.383072] SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoica I, 2001, P 2001 ACM SIGCOMM C, P149, DOI DOI 10.1145/383059.383071 Tang C., 2003, P 2003 C APPL TECHN, P175 Tang CQ, 2003, ACM SIGCOMM COMP COM, V33, P89, DOI 10.1145/774763.774777 Tiwana A, 2003, COMMUN ACM, V46, P76, DOI 10.1145/769800.769805 VOORHEES EM, 1996, 5 TEXT RETR C TREC 5 Waterhouse S, 2002, IEEE INTERNET COMPUT, V6, P68, DOI 10.1109/4236.978371 Yang B., 2002, Proceedings 22nd International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2002.1022237 Zhang XY, 2001, INFORM PROCESS MANAG, V37, P313, DOI 10.1016/S0306-4573(00)00032-7 NR 33 TC 3 Z9 4 PU ELSEVIER SCI LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND SN 0306-4573 EI 1873-5371 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2006 VL 42 IS 1 BP 222 EP 236 DI 10.1016/j.ipm.2004.08.004 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 971BE UT WOS:000232355300014 ER PT S AU Last, M Markov, A Kandel, A AF Last, M Markov, A Kandel, A BE Chen, H Wang, FY Yang, CC Zeng, D Chau, M Chang, K TI Multi-lingual detection of terrorist content on the web SO INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT Workshop on Intelligence and Security Informatics (WISI 2006) CY APR 09, 2006 CL Singapore, SINGAPORE SP Chinese Univ Hong Kong, Univ Arizona, Nanyang Technol Univ, Chinese Acad Sci ID CLASSIFICATION; INFORMATION AB Since the web is increasingly used by terrorist organizations for propaganda, disinformation, and other purposes, the ability to automatically detect terrorist-related content in multiple languages can be extremely useful. In this paper we describe a new, classification-based approach to multi-lingual detection of terrorist documents. The proposed approach builds upon the recently developed graph-based web document representation model combined with the popular C4.5 decision-tree classification algorithm. Evaluation is performed on a collection of 648 web documents in Arabic language. The results demonstrate that documents downloaded from several known terrorist sites can be reliably discriminated from the content of Arabic news reports using a simple decision tree. C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA. RP Last, M (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. EM mlast@bgu.ac.il; markov@bgu.ac.il; kandel@csee.usf.edu RI LAST, MARK/F-1424-2012 OI LAST, MARK/0000-0003-0748-7918 CR AHMED C, 2002, MULTILINGUAL INFORM ALJLAYL M, 2001, 10 INT C INF KNOWL M Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 CARREIRA R, 2004, 9 INT C INT US INT J CHENKER A, 2005, SERIES MACHINE PERCE, V62 DUMAIS S, 2000, 23 ANN INT ACM SIGIR EIRINAKI M, 2003, 9 ACM SIGKDD INT C K EIRINAKI M, 2003, ACM T INT TECHN TOIT Kamber M., 2001, DATA MINING CONCEPTS KURAMOCHI M, 2002, 0226 TR U MINN DEP C LARKEY LS, SIGIR 2002 LARKEY LS, 2004, 27 ANN INT C RES DEV LARSON F, 2002, 2 ACM IEEE CS JOINT MAMON O, 2000, MASSIVE COMPUTING SE MARIA N, 2000, 23 ANN INT ACM SIGIR Markov A, 2005, LECT NOTES COMPUT SC, V3528, P293 McCallum A., 1998, AAAI 98 WORKSH LEARN Mitchell TM, 1997, MACHINE LEARNING MULVENNA MD, 2000, COMMUNICATIONS A AUG Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 RAMAKRISHNA K, 2003, WORLD SCI REIS D, 2004, 13 INT C WORLD WID W Ripplinger B., 2000, WORKSH CROSS LANG EV SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A, 2004, INT J PATTERN RECOGN, V18, P475, DOI 10.1142/S0218001404003241 SEBASTIANI F, 1999, ACM COMPUTING SURVEY SLATON G, 1987, TR87881 Tauritz DR, 2000, INFORM SCIENCES, V122, P121, DOI 10.1016/S0020-0255(99)00123-1 TZERAS K, 1993, 16 ANN INT ACM SIGIR Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Yan X., 2002, IEEE INT C DAT MIN I YANG Y, 2002, J INTELLIGENT IN MAR NR 33 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-33361-4 J9 LECT NOTES COMPUT SC PY 2006 VL 3917 BP 16 EP 30 PG 15 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BEJ99 UT WOS:000237498000003 ER PT S AU Doan, S Horiguchi, S AF Doan, Son Horiguchi, Susumu BE Huang, DS Li, K Irwin, GW TI Dynamic feature selection in text classification SO INTELLIGENT CONTROL AND AUTOMATION SE LECTURE NOTES IN CONTROL AND INFORMATION SCIENCES LA English DT Article; Proceedings Paper CT International Conference on Intelligent Computing (ICIC) CY AUG 16-19, 2006 CL Kunming, PEOPLES R CHINA SP IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China AB We study the problem of feature selection in text classification. Previous researches use only a measurement such as information gain, mutual information, chi-square for selecting good features. In this paper we propose a new approach to feature selection - dynamic feature selection. A new algorithm for feature selection is proposed. In this algorithm, by combining different measurements for features and tuning parameters, several feature subsets are generated, the best feature set which achieves the best performance from a classifier is obtained from those. Experiments dealing with the real-world data set show that the proposed dynamic feature selection outperforms traditional feature selection methods. C1 Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 9808579, Japan. RP Doan, S (reprint author), Tohoku Univ, Grad Sch Informat Sci, 6-3-09 Aoba, Sendai, Miyagi 9808579, Japan. EM s-doan@ecei.tohoku.ac.jp; susumu@ecei.tohoku.ac.jp RI Doan, Son/P-5396-2014; Doan, Son/F-8266-2015 OI Doan, Son/0000-0002-7284-1306; Doan, Son/0000-0002-7284-1306 CR AMALDI E, 1998, THEORETICAL COMPUTER, V1, P237 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X Liu H, 1998, FEATURE SELECTION KN MCCALLUM AK, 1996, TOOLKIT STAT LANGUAG Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rogati M., 2002, INT C INF KNOWL MAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 14 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0170-8643 BN 3-540-37255-5 J9 LECT NOTES CONTR INF PY 2006 VL 344 BP 664 EP 675 PG 12 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Automation & Control Systems; Computer Science GA BEZ62 UT WOS:000240383400083 ER PT S AU Rios, SA Velasquez, JD Yasuda, H Aoki, T AF Rios, Sebastian A. Velasquez, Juan D. Yasuda, Hiroshi Aoki, Terumasa BE Gabrys, B Howlett, RJ Jain, LC TI Web site off-line structure reconfiguration: A web user browsing analysis SO KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems CY OCT 09-11, 2006 CL Bournemouth, ENGLAND ID IMPROVE AB The correct web site text content must be help to the visitors to find what they are looking for. However, the reality is quite different, many times the web page text content is ambiguous, without meaning and worst, it don't have relation with the topic that is shown as the main theme. One reason to this problem is the lack of contents with concept meaning in the web page, i.e., the utilization of words and sentences that show concepts, which finally is the visitor goal. In this paper, we introduce a new approach for improving the web site text content by extracting Concept-Based Knowledge from data originated in the web site itself. By using the concepts, a web page can be rewrite for showing more relevant information to the eventual visitor. C1 Univ Tokyo, Appl Informat Engn Lab, Tokyo, Japan. Univ Tokyo, Ctr Collaborat Res, Tokyo, Japan. RP Rios, SA (reprint author), Univ Tokyo, Appl Informat Engn Lab, Tokyo, Japan. EM srios@mpeg.rcast.u-tokyo.ac.jp; jvelasqu@dii.uchile.cl; yasuda@mpeg.rcast.u-tokyo.ac.jp; aoki@mpeg.rcast.u-tokyo.ac.jp RI Velasquez, Juan/H-3859-2013 OI Velasquez, Juan/0000-0003-4819-9680 CR BERENDT B, 2001, VLDB J, V9, P27 CHAKRABARTI S, 2000, SIGKDD EXPLORATIONS, P1 Eirinaki M., 2004, WORKSH WEB INF DAT M, P72 Eirinaki M, 2003, ACM T INTERNET TECHN, V3, P1, DOI DOI 10.1145/643477.643478 Loh S., 2000, SIGKDD EXPLORATIONS, V2, P29 Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Mulvenna M. D., 2000, Communications of the ACM, V43, DOI 10.1145/345124.345165 Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 PERKOWITZ M, 2001, THESIS U WASHINGTONG Perkowitz M, 2000, COMMUN ACM, V43, P152, DOI 10.1145/345124.345171 Rios SA, 2005, LECT NOTES ARTIF INT, V3809, P1162 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spiliopoulou M, 2003, INFORMS J COMPUT, V15, P171, DOI 10.1287/ijoc.15.2.171.14445 VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 NR 15 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-46537-5 J9 LECT NOTES ARTIF INT PY 2006 VL 4252 BP 371 EP 378 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications SC Computer Science GA BFI89 UT WOS:000242122600048 ER PT S AU Velasquez, JD Palade, V AF Velasquez, Juan D. Palade, Vasile BE Gabrys, B Howlett, RJ Jain, LC TI Testing online navigation recommendations in a web site SO KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems CY OCT 09-11, 2006 CL Bournemouth, ENGLAND ID PERSONALIZATION AB An online navigation recommendation system provides the prospective web site visitor with a set of pages that could be of his/her interest. Because the recommendations are given during the user session in the web site, it could be very damaging for the overall business of the company owning the web site, if the recommendations are erroneous. In this paper, we introduce an a priori method to estimate the success of an online navigation recommendation. The methodology was tested in a recommendation system that works with the data generated in a real web site, which proved the effectiveness of our approach. C1 Univ Chile, Dept Ind Engn, Santiago, Chile. Univ Oxford, Comp Lab, Oxford, England. RP Velasquez, JD (reprint author), Univ Chile, Dept Ind Engn, Santiago, Chile. EM jvelasqu@dii.uchile.cl; Vasile.Palade@comlab.ox.ac.uk RI Velasquez, Juan/H-3859-2013 OI Velasquez, Juan/0000-0003-4819-9680 CR BRUSILOVSKY P, 2003, IEEE WEB INT INT C H COENEN F, 2000, P 4 PAKDD PAC AS C K, P1 Eirinaki M, 2003, ACM T INTERNET TECHN, V3, P1, DOI DOI 10.1145/643477.643478 Kobsa A, 2001, KNOWL ENG REV, V16, P111, DOI 10.1017/S0269888901000108 Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Mobasher B, 2000, P INT C E COMM WEB T, P165 Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 Pierrakos D, 2003, USER MODEL USER-ADAP, V13, P311, DOI 10.1023/A:1026238916441 Runkler TA, 2003, INT J APPROX REASON, V32, P217, DOI 10.1016/S0888-613X(02)00084-1 Sae-Tang S., 2002, LECT NOTES COMPUTER, V2252, P36 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VELASQUE JD, 2005, LECT NOTES ARTIF INT, V3213, P993 Velasquez J. D., 2003, Proceedings. ITRE 2003. International Conference on Information Technology: Research and Education (IEEE Cat. No.03EX647) Velasquez JD, 2005, IEICE T INF SYST, VE88D, P993, DOI 10.1093/ietisy/e88-d.5.993 Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 YANG H, 2002, P INT C WEBKDD WORKS, P77 ZHENG T, 2005, P 19 INT C ART INT W, P80 NR 17 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-46542-1 J9 LECT NOTES ARTIF INT PY 2006 VL 4253 BP 487 EP 496 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BFI90 UT WOS:000242123100062 ER PT S AU Yoldas, U Nagypal, G AF Yoldas, Umit Nagypal, Gabor BE Meersman, R Tari, Z TI Ontology supported automatic generation of high-quality semantic metadata SO ON THE MOVE TO MEANINGFUL INTERNET SYSTEMS 2006: COOPIS, DOA, GADA, AND ODBAS, PT 1, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT On the Move Confederated International Conference on CoopIS/DOA/GADA, and ODBASE CY OCT 29-NOV 03, 2006 CL Montpellier, FRANCE SP Ctr Natl Res Sci, City Montpellier, Ecole Polytech Univ Montepellier, Univ Montpellier II, Lab Informat Robot Microelect Montpllier, RMIT Univ, Sch Comp Sci & Informat Technol, Vrije Univ Brussel, Dept Comp Sci ID MODEL; KNOWLEDGE; WEB AB Large amounts of data in modern information systems, such as the World Wide Web, require innovative information retrieval techniques to effectively satisfy users' information need. A promising approach is to exploit document semantics in the IR process. For this purpose, high-quality semantic metadata is needed. This paper introduces a method to automatically create semantic metadata by using ontologically enhanced versions of common information extraction methods, such as named entity recognition and coreference resolution. Furthermore, this work also proposes the application of ontology-specific heuristic rules to further improve the quality of generated metadata. The results of our method was evaluated using a small test collection. C1 Conemais AG, Karlsruhe, Germany. Univ Karlsruhe, FZI Res Ctr Informat Technol, Karlsruhe, Germany. RP Yoldas, U (reprint author), Conemais AG, Karlsruhe, Germany. EM yoldas@conemis.com; nagypal@fzi.de CR Berners-Lee T, 2001, SCI AM, V284, P34 Cimiano P., 2005, P 14 INT WORLD WID W, P332, DOI 10.1145/1060745.1060796 Davies J., 2004, P 37 HAW INT C SYST Dean M., 2004, OWL WEB ONTOLOGY LAN DECKER S, 1999, IFIP TC2 WG2 6 8 WOR, P351 Dill S., 2003, P 12 INT C WORLD WID, P178 FININ T, 2005, P 38 ANN HAW INT C S GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008 Handschuh S, 2002, LECT NOTES ARTIF INT, V2473, P358 HENDLER J, 2000, AAAI WORKSH, P345 Hustadt U., 2004, P 9 INT C PRINC KNOW, P152 Kahan J, 2002, COMPUT NETW, V39, P589, DOI 10.1016/S1389-1286(02)00220-7 KIRYAKOV A, 2005, J WEB SEMANT, V2, P49 Martin P, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P325 Nagypal G, 2005, LECT NOTES COMPUT SC, V3762, P780 Nagypal G, 2003, LECT NOTES COMPUT SC, V2888, P906 Nagypal G., 2005, Literary & Linguistic Computing, V20, DOI 10.1093/llc/fqi037 Rocha C., 2004, P 13 INT C WORLD WID, P374, DOI 10.1145/988672.988723 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 NR 21 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-48287-3 J9 LECT NOTES COMPUT SC PY 2006 VL 4275 BP 791 EP 806 PG 16 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BFN04 UT WOS:000243131600048 ER PT B AU Hammouda, K Kamel, M AF Hammouda, Khaled Kamel, Mohamed BE Ghosh, J Lambert, D Skillicorn, D Srivastava, J TI Collaborative Document Clustering SO PROCEEDINGS OF THE SIXTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING SE SIAM Proceedings Series LA English DT Proceedings Paper CT 6th SIAM International Conference on Data Mining CY APR 20-22, 2006 CL Bethesda, MD SP Lawrence Livermore Natl Lab, Ctr Appl Sci Comp, Amer Stat Assoc ID CATEGORIZATION; MODEL AB Document clustering has been traditionally studied as a centralized process. There are scenarios when centralized clustering does not serve the required purpose; e.g. documents spanning multiple digital libraries need not be clustered in one location, but rather clustered at each location, then enriched by receiving more information from other locations. A distributed collaborative approach for document clustering is proposed in this paper. The main objective here is to allow peers in a network to form independent opinions of local document grouping, followed by exchange of cluster summaries in the form of keyphrase vectors. The nodes then expand and enrich their local solution by receiving recommended documents from their peers based on the peer judgement of the similarity of local documents to the exchanged cluster summaries. Results show improvement in final clustering after merging peer recommendations. The approach allows independent nodes to achieve better local clustering by having access to distributed data without the cost of centralized clustering, while maintaining the initial local clustering structure and coherency. C1 [Hammouda, Khaled; Kamel, Mohamed] Univ Waterloo, Pattern Anal & Machine Intelligence Res Grp, Waterloo, ON N2L 3G1, Canada. RP Hammouda, K (reprint author), Univ Waterloo, Pattern Anal & Machine Intelligence Res Grp, Waterloo, ON N2L 3G1, Canada. EM hammouda@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Barzilay R., 1999, P 37 ANN M ASS COMP, P550, DOI 10.3115/1034678.1034760 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Chakrabarti Soumen, 2003, MINING WEB DISCOVERI Clifton C, 2004, IEEE T KNOWL DATA EN, V16, P949, DOI 10.1109/TKDE.2004.32 Eisenhardt M, 2003, INFORM 2003 INNOVATI HAMMOUDA K, 2005, IAPR 4 INT C MACH LE, P265 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hammouda KM, 2004, KNOWL INF SYST, V6, P710, DOI 10.1007/s10115-003-0118-5 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kargupta H., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 Li J, 2002, 2 MIT STUD OX WORKSH PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney P. D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009976227802 NR 20 TC 4 Z9 4 PU SIAM PI PHILADELPHIA PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA BN 978-0-89871-611-5 J9 SIAM PROC S PY 2006 BP 453 EP 463 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BUJ09 UT WOS:000289491100040 ER PT S AU Farsani, HK Nematbakhsh, M AF Farsani, Hadi Khosravi Nematbakhsh, Mohammadali BE Ardil, C TI A Semantic Recommendation Procedure for Electronic Product Catalog SO PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 16 SE Proceedings of World Academy of Science Engineering and Technology LA English DT Proceedings Paper CT Conference of the World-Academy-of-Science-Engineering-and-Technology CY NOV 24-26, 2006 CL Venice, ITALY DE Personalization; Recommendation; OWL Ontology; Electronic Catalogs; Classification AB To overcome the product overload of Internet shoppers, we introduce a semantic recommendation procedure which is more efficient when applied to Internet shopping malls. The suggested procedure recommends the semantic products to the customers and is originally based on Web usage mining, product classification, association rule mining, and frequently purchasing. We applied the procedure to the data set of MovieLens Company for performance evaluation, and some experimental results are provided. The experimental results have shown superior performance in terms of coverage and precision. C1 [Farsani, Hadi Khosravi; Nematbakhsh, Mohammadali] Univ Isfahan, Dept Comp Engn, Esfahan, Iran. RP Farsani, HK (reprint author), Univ Isfahan, Dept Comp Engn, Esfahan, Iran. CR AASHEIM C, SCANNING WO IN PRESS AGRAWAL R, 1993, P 1993 ACM SIGMOD IN Antoniou G., 2003, HDB ONTOLOGIES INFOR Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 BENJAMIN Y, 2003, ELECT COMMERCE RES A Cho YH, 2002, EXPERT SYST APPL, V23, P329, DOI 10.1016/S0957-4174(02)00052-0 Deshpande M, 2004, ACM T INFORM SYST, V22, P143, DOI 10.1145/963770.963776 FARSANI HK, 2006, P IADIS C Karypis G., 2001, P ACM C INF KNOWL MA Kim JK, 2002, ELECTRON COMMER R A, V1, P301, DOI 10.1016/S1567-4223(02)00022-4 Kowalski G., 1997, INFORM RETRIEVAL SYS LEE CH, 2001, EXPERT SYSTEMS APPL MOBASHER B, 2003, IJCAI 2003 WORKSH IT MOBASHER B, 2004, PRACTICAL HDB INTERN Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 RESNICK P, 1997, COMMUN ACM, V40, P3 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B, 2000, P 2 ACM C EL COMM, P158, DOI DOI 10.1145/352871.352887 Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Sarwar B.M., 2001, WWW10 Schafer J.B., 1999, P 1 ACM C EL COMM DE, P158, DOI DOI 10.1145/336992.337035 Smith Michael K., OWL WEB ONTOLOGY LAN YANG Y, 2005, J ELECT COMMERCE ZHANG Y, 2007, EXPERT SYST IN PRESS, V33 MOVIELENS 2003 3 AUTOMATIC CLASSIFI NR 26 TC 0 Z9 0 PU WORLD ACAD SCI, ENG & TECH-WASET PI CANAKKALE PA PO BOX 125, CANAKKALE, 17100, TURKEY SN 1307-6884 J9 PROC WRLD ACAD SCI E PY 2006 VL 16 BP 350 EP 355 PG 6 WC Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BII16 UT WOS:000259634000063 ER PT S AU Shaban, K Basir, O Kamel, M AF Shaban, Khaled Basir, Otman Kamel, Mohamed BE MartinezTrinidad, JF Ochoa, JAC Kittler, J TI Document mining based on semantic understanding of text SO PROGRESS IN PATTERN RECOGNITON, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 11th Iberoamerican Conference in Pattern Recognition CY NOV 14-17, 2006 CL Cancun, MEXICO SP Adv Technol Applicat Ctr Cuba, Mexican Assoc Pattern Recognit, Mexican Assoc Comp Vis, Neurocomp & Robotics, Cuban Assoc Pattern Recognit, Spanish Assoc Pattern Recognit & Image Anal, Portuguese Assoc Pattern Recognit, SIGPR-SBC, Chilean Assoc Pattern Recognit DE document mining; semantic understanding; text representation; similarity measure; document clustering ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; MATHEMATICAL-THEORY; CATEGORIZATION; COMMUNICATION AB This paper presents a new paradigm for mining documents by exploiting the semantic information of their texts. A formal semantic representation of linguistic inputs is introduced and utilized to build a semantic representation for documents. The representation is constructed through accumulation of syntactic and semantic analysis outputs. A new distance measure is developed to determine the similarities between contents of documents. The measure is based on inexact matching of attributed trees. It involves the computation of all distinct similarity common sub-trees, and can be computed efficiently. It is believed that the proposed representation along with the proposed similarity measure will enable more effective document mining processes. The proposed techniques to mine documents were implemented as components in a mining system. A case study of semantic document clustering is presented to demonstrate the working and the efficacy of the framework. Experimental work is reported, and its results are presented and analyzed. C1 Univ Waterloo, Waterloo, ON N2L 3G1, Canada. RP Shaban, K (reprint author), Univ Waterloo, Waterloo, ON N2L 3G1, Canada. EM kshaban@uwaterloo.ca; obasir@uwaterloo.ca; mkamel@uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Shaban, Khaled/M-2768-2014; Kamel, Mohamed/ OI Shaban, Khaled/0000-0002-5688-7515; Kamel, Mohamed/0000-0001-6173-8082 CR Aas K., 1999, 941 NORW COMP CTR BAEZAYATES R, 1999, MODERN INFROM RETRIE BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Chakrabarti Soumen, 2003, MINING WEB DISCOVERI Cios K. J., 1998, DATA MINING METHODS CUNNINGHAM H, 2002, P 40 ANN M ACL ACL 0 CUTTING DR, 1993, 16 INT ACM SIGIR C R, P126 DASARATHY BV, 1991, MCGRAWHILL COMP SCI Eikvil L., 1999, 945 NORW COMP CTR ERKHIN P, 2002, SURVEY CLUSTERING DA HILL DR, 1968, MECH INFORM STORAGE Jain A. K., 1988, ALGORITHMS CLUSTERIN Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976 Miller G. A., 1993, INTRO WORDNET ONLINE Mostafa J, 1997, ACM T INFORM SYST, V15, P368, DOI 10.1145/263479.263481 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G., 1984, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 Soclerland S., 1999, LEARNING INFORM EXTR Steinbach M., 2000, KDD 2000 WORKSH TEXT *TEXT AN INT INC, 2003, MULT MULT NLP Text Analysis International Inc, 2001, INT DEV ENV NAT LANG van Rijsbergen C. J., 1979, INFORM RETRIEVAL Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 30 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-46556-1 J9 LECT NOTES COMPUT SC PY 2006 VL 4225 BP 834 EP 843 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BFF53 UT WOS:000241598400086 ER PT S AU Combarro, EF Montanes, E Diaz, I Cortina, R Alonso, P Ranilla, J AF Combarro, E. F. Montanes, E. Diaz, I. Cortina, R. Alonso, P. Ranilla, J. BE Simos, T Maroulis, G TI NoW architectures, dimensionality reduction and self-organizing maps for information retrieval SO RECENT PROGRESS IN COMPUTATIONAL SCIENCES AND ENGINEERING, VOLS 7A AND 7B SE LECTURE SERIES ON COMPUTER AND COMPUTATIONAL SCIENCES LA English DT Proceedings Paper CT International Conference on Computational Methods in Science and Engineering CY OCT 27-NOV 01, 2006 CL Chania, GREECE DE information retrieval; self-organizing maps; dimensionality reduction; NoW Architectures AB The efficiency and effectiveness of topic or user query-related document. retrieval can be improved by clustering similar documents, by dimensionality reduction techniques and by incorporating parallel strategies. This paper explores the use of unsupervised learning with neural network-based clustering algorithms, several methods of feature reduction and the application of No W Architectures. a kind of low-cost parallel architecture. Our experiments on six different, corpora show that both the computational cost and the lexicon size can be reduced without degrading the overall retrieval performance. C1 [Combarro, E. F.; Montanes, E.; Diaz, I.; Cortina, R.; Ranilla, J.] Univ Oviedo, Dept Comp Sci, E-33271 Gijon, Spain. RP Combarro, EF (reprint author), Univ Oviedo, Dept Comp Sci, Campus Viesques, E-33271 Gijon, Spain. RI Ranilla, Jose/E-8012-2013; Combarro, Elias/H-2590-2015 OI Ranilla, Jose/0000-0003-2941-3741; Combarro, Elias/0000-0003-3808-4273 CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Johnson W. B., 1984, CONT MATH, V26, P189 Lawrence RD, 1999, DATA MIN KNOWL DISC, V3, P171, DOI 10.1023/A:1009817804059 Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOORHESS EM, 2000, P 8 TEXT RETR C TREC Whaley RC, 1997, UTCS97366 NR 7 TC 0 Z9 0 PU VSP BV-C/O BRILL ACAD PUBL PI LEIDEN PA PO BOX 9000, 2300 PA LEIDEN, NETHERLANDS SN 1573-4196 BN 978-90-04-15542-8 J9 LECT SER COMPUTER CO PY 2006 VL 7A-B BP 110 EP 113 PG 4 WC Mathematics, Applied SC Mathematics GA BHM78 UT WOS:000254378800027 ER PT S AU Albanese, M Chianese, A Penta, A Picariello, A Passariello, L AF Albanese, M. Chianese, A. Penta, A. Picariello, A. Passariello, L. TI A model for user profiling in an ASP environment SO Seventeenth International Conference on Database and Expert Systems Applications, Proceedings SE International Workshop on Database and Expert Systems Applications - DEXA LA English DT Proceedings Paper CT 17th International Conference on Database and Expert Systems Applications CY SEP 04-08, 2006 CL Cracow, POLAND SP DEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, Andrzej Frycz Modrzewski Cracow Coll, FAW Software Engn Gem GmbH AB Nowadays an ever increasing number of companies rely on Application Service Providers for hosting some or almost all software applications. In this domain, an effective way for customizing user interfaces would be highly desirable. We propose a profiling system interacting with the applications in order to capture users' behavior. The system is based on Bayesian Networks and we show a strategy for combining data collected in each session and previous knowledge. Experiments on a prototype show that the proposed approach is promising. C1 Univ Naples, Dipartimento Informat & Sistemist, Naples, Italy. RP Albanese, M (reprint author), Univ Naples, Dipartimento Informat & Sistemist, Naples, Italy. RI Picariello, Antonio/G-9062-2012 CR COOPER GF, 1992, MACH LEARN, V9, P309, DOI 10.1007/BF00994110 Joachims T., 1997, P 15 INT JOINT C ART, P770 Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Rangarajan SK, 2004, COMPUTER, V37, P34, DOI 10.1109/MC.2004.1297299 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schiaffino S. N., 2000, OP DISC TRACK P INT, P12 Subrahmanian V.S., 2000, HETEROGENEOUS AGENT Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 NR 9 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1529-4188 BN 0-7695-2641-1 J9 INT WORKSHOP DATABAS PY 2006 BP 15 EP 19 DI 10.1109/DEXA.2006.12 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BFI72 UT WOS:000242043300003 ER PT B AU Fujimoto, T Matsuo, T AF Fujimoto, Takayuki Matsuo, Tokuro GP IEEE BE Song, YT Lu, C TI A web-based cooperative research paper edit system SO SNPD 2006: Seventh ACIS International Conference on Software Engineering Artificial Intelligence, Networking, and Parallel/Distributed Computing, Proceedings LA English DT Proceedings Paper CT 7th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel and Distributed Computing/7th ACIS International Workshop on Self-Assembling Networks CY JUN 19-20, 2006 CL Las Vegas, NV SP Int Assoc Comp & Informat Sci, Towson Univ, Cent Michigan Univ AB We propose an interactive edit web system, that is, users can edit and revise documents cooperatively and interactively. The Ie-web is constructed based on conceptions of special issue edit and pantographic issue edit. The goal of this study is not only developing cooperative document making system by many users, but research paper making system by mentors and other students. In recent years, computer-based tele-learning systems are developed. Thus, we propose a useful cooperative document making support system to develop a system used at university educations. C1 Sonoda Womens Univ, Amagasaki, Hyogo 6618520, Japan. RP Fujimoto, T (reprint author), Sonoda Womens Univ, 7-29-1,Minami Tsukaguchi, Amagasaki, Hyogo 6618520, Japan. CR KITADA J, 2001, IPSJ J CONTENTS, V42 MIYAKE N, 2000, 4 INT C LEARN SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SUNAYAMA W, 2004, 18 ANN C JAP SOC ART Yamamoto Y., 1999, Journal of Japanese Society for Artificial Intelligence, V14 NR 5 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2611-X PY 2006 BP 354 EP 360 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BEQ48 UT WOS:000238913400059 ER PT S AU Farah, M Vanderpooten, D AF Farah, Mohamed Vanderpooten, Daniel BE Crestani, F Ferragina, P Sanderson, M TI A multiple criteria approach for information retrieval SO STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 13th International Conference on String Processing and Information Retrieval CY OCT 11-13, 2006 CL Glasgow, SCOTLAND DE information retrieval; relevance; multiple criteria AB Research in Information Retrieval shows -performance improvement when many sources of evidence are combined to produce a ranking of documents. Most current approaches assess document relevance by computing a single score which aggregates values of some attributes or criteria. We propose a multiple criteria framework using an aggregation mechanism based on decision rules identifying positive and negative reasons for judging whether a document should get a better ranking than another. The resulting procedure also handles imprecision in criteria design. Experimental results are reported. C1 Univ Paris 09, LAMSADE, F-75775 Paris 16, France. RP Farah, M (reprint author), Univ Paris 09, LAMSADE, F-75775 Paris 16, France. EM farah@lamsade.dauphine.fr; vdp@lamsade.dauphine.fr CR ANH VN, 2002, P AUSTR DOC COMP S S, P7 Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286 BOUGHANEM M, 2005, AMR05 3 INT WORKSH A Brin S., 1998, WWW7, P107 CRASWELL N, 2004, P TREC 2004 GAITH MA Craswell N., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval DUBOIS D, 1984, FUZZY SETS DECISION, P209 FOX EA, 1994, P TREC 3 GAITH MAR U Frakes W. B., 1992, INFORM RETRIEVAL DAT GAO G, 2005, P 28 INT C RES DEC I, P298, DOI 10.1145/1076034.1076086 Granka L.A., 2004, SIGIR 04, P478 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 ROBERTSON SE, 1994, P TREC 3 GAITH MAR U ROY B, 1982, TRANSPORT RES A-POL, V16, P301, DOI 10.1016/0191-2607(82)90057-7 ROY B, 1989, MATH COMPUT MODEL, V12, P1245, DOI 10.1016/0895-7177(89)90366-X ROY B, 1991, THEOR DECIS, V31, P49, DOI 10.1007/BF00134132 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SAVOY J, 2001, P TREC 9 GAITH US NI SHANNON CE, 1951, AT&T TECH J, V30, P50 NR 21 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-45774-7 J9 LECT NOTES COMPUT SC PY 2006 VL 4209 BP 242 EP 254 PG 13 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BFF50 UT WOS:000241594700020 ER PT S AU Daliri, MR Delponte, E Verri, A Torre, V AF Daliri, Mohammad Reza Delponte, Elisabetta Verri, Alessandro Torre, Vincent BE Yeung, DY Kwok, JT Fred, A Roli, F DeRidder, D TI Shape categorization using string kernels SO STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT Joint International Workshop on Structural, Syntactic, and Statistical Pattern Recognition CY AUG 17-19, 2006 CL Hong Kong, PEOPLES R CHINA SP Int Assoc Pattern Recognit, Tech Comm TC1 & TC2, Hong Kong Univ Sci & Technol ID 3-D OBJECTS; RECOGNITION AB In this paper, a novel algorithm for shape categorization is proposed. This method is based on the detection of perceptual landmarks, which are scale invariant. These landmarks and the parts between them are transformed into a symbolic representation. Shapes are mapped into symbol sequences and a database of shapes is mapped into a set of symbol sequences and therefore it is possible to use support vector machines for categorization. The method here proposed has been evaluated on silhouettes database and achieved the highest recognition result reported with a score of 97.85% for the MPEG-7 shape database. C1 SISSA, I-34014 Trieste, Italy. Univ Genoa, DISI, I-16146 Genoa, Italy. RP Daliri, MR (reprint author), SISSA, Via Beirut 2-4, I-34014 Trieste, Italy. CR ARBTER K, 1990, IEEE T PATTERN ANAL, V12, P640, DOI 10.1109/34.56206 Attalla E, 2005, PATTERN RECOGN, V38, P2229, DOI 10.1016/j.patcog.2005.02.009 Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558 BIEDERMAN I, 1988, COGNITIVE PSYCHOL, V20, P38, DOI 10.1016/0010-0285(88)90024-2 Blum H., 1967, MODELS PERCEPTION SP, P362 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Costa L. d. F. D., 2000, SHAPE ANAL CLASSIFIC, V1st Cristianini N., 2000, INTRO SUPPORT VECTOR Edelman S., 1999, REPRESENTATION RECOG Fan R.-E., 2005, WORKING SET SELECTIO Joachims T., 1998, LNCS, V1398, P137 Latecki LJ, 2000, PROC CVPR IEEE, P424, DOI 10.1109/CVPR.2000.855850 Lindeberg Tony, 1998, INT J COMPUT VISION, V30, P77 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 Majer P, 2001, LECT NOTES COMPUT SC, V2106, P245 MOKHTARIAN F, 1986, IEEE T PATTERN ANAL, V8, P34 MURASE H, 1995, INT J COMPUT VISION, V14, P5, DOI 10.1007/BF01421486 RIVLIN E, 1995, IEEE T PATTERN ANAL, V17, P226, DOI 10.1109/34.368188 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastian TB, 2004, IEEE T PATTERN ANAL, V26, P550, DOI 10.1109/TPAMI.2004.1273924 Sharvit D, 1998, J VIS COMMUN IMAGE R, V9, P366, DOI 10.1006/jvci.1998.0396 SUPER BJ, 2004, IEEE WORKSH LEARN CO, V6, P93 Ullman S, 1996, HIGH LEVEL VISION VAPUIK V, 1998, STAT LEARNING THEORY NR 24 TC 6 Z9 6 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-37236-9 J9 LECT NOTES COMPUT SC PY 2006 VL 4109 BP 297 EP 305 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BEY05 UT WOS:000240075100032 ER PT J AU Godoy, D Amandi, A AF Godoy, D Amandi, A TI User profiling in personal information agents: a survey SO KNOWLEDGE ENGINEERING REVIEW LA English DT Review ID WORLD-WIDE-WEB; RECOMMENDER SYSTEMS; ALGORITHM; MODELS; NEWS; CLASSIFICATION; CATEGORIZATION; RETRIEVAL; EXAMPLES AB Personal information agents have emerged in the last decade to help users to cope with the increasing amount of information available on the Internet. These agents are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context of intelligent information agents. Existing approaches and lines of research in the main dimensions of user profiling, such as acquisition, learning, adaptation and evaluation, are discussed. C1 UNICEN Univ, ISISTAN Res Inst, RA-7000 Buenos Aires, DF, Argentina. RP Godoy, D (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ, RA-7000 Buenos Aires, DF, Argentina. EM dgodoy@exa.unicen.edu.ar; amandi@exa.unicen.edu.ar CR AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1023/A:1022689900470 Angelides MC, 2003, IEEE MULTIMEDIA, V10, P12, DOI 10.1109/MMUL.2003.1237546 Ardissono L, 2004, USER MODEL USER-ADAP, V14, P1, DOI 10.1023/B:USER.0000010152.46361.e0 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Balabanovic M., 1995, P AAAI SPRING S INF, P13 Basu C., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Bauer M, 2000, COMMUN ACM, V43, P98, DOI 10.1145/330534.330547 Berners-lee T., 2001, SCI AM BILLSUS D, 1999, P 7 INT C US MOD, P99 Billsus D, 2000, USER MODEL USER-ADAP, V10, P147, DOI 10.1023/A:1026501525781 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boone G., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280791 BUDZIK J, 2000, AAAI 2000 WORKSH AI Cetintemel U., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839477 Chen L., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280789 Chin D. N., 1991, Intelligent User Interfaces Clark P., 1989, Machine Learning, V3, DOI 10.1007/BF00116835 Claypool M., 2001, P 6 INT C INT US INT, P33, DOI DOI 10.1145/359784.359836 Cohen W. W., 1996, P 13 NAT C ART INT 8, V1, P709 Cohens W, 1995, P 12 INT C MACH LEAR, P115 COST S, 1993, MACH LEARN, V10, P57, DOI 10.1007/BF00993481 COTTER P, 2001, P 17 NAT C ART INT 1, P957 DIAO Y, 2000, LECT NOTES COMPUTER, V2702, P408 Duda R.O., 1973, PATTERN CLASSIFICATI DYKE NWV, 1999, P 1999 INT C INT US, P39 Furnkranz J, 1999, LECT NOTES COMPUT SC, V1642, P487 GAUCH S, 2003, J WEB INTELLIGENCE A, V1, P219 GOYCHEV I, 2000, P ECAI 2000 WORKSH C, P101 Grimnes GA, 2003, LECT NOTES ARTIF INT, V2702, P414 HARMAN D, 1989, J AM SOC INFORM SCI, V42, P7 Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772 Herlocker JL, 2000, P 2000 ACM C COMP SU, P241, DOI DOI 10.1145/358916.358995 Herlocker J. L., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312682 Hoashi K., 2000, P 23 ANN INT ACM SIG, P176, DOI 10.1145/345508.345573 Hodgson J, 2001, IEEE INTERNET COMPUT, V5, P20, DOI 10.1109/4236.895138 Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775 Jameson A, 1995, USER MODEL USER-ADAP, V5, P193 JENNINGS A, 1992, IEICE T INF SYST, VE75D, P198 JOACHIMS T, 1997, P INT JOINT C ART IN, V1, P770 JOHNSONGENTILE K, 1994, J EDUC COMPUT RES, V11, P121 Kamba T, 1997, INT J HUM-COMPUT ST, V46, P789, DOI 10.1006/ijhc.1996.0113 Kelly D., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009057 Klinkenberg R, 1998, LEARNING TEXT CATEGO, P33 Klusch M, 2001, DATA KNOWL ENG, V36, P337, DOI 10.1016/S0169-023X(00)00049-5 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Kozierok Robyn, 1993, P ACM SIGCHI INT WOR, P81, DOI 10.1145/169891.169908 Krulwich B, 1997, AI MAG, V18, P37 Kuflik T, 2003, J AM SOC INF SCI TEC, V54, P243, DOI 10.1002/asi.10220 Letouzey F, 2000, LECT NOTES ARTIF INT, V1968, P71 Lewis David D., 1992, THESIS U MASSACHUSET Li X. L., 2003, P 18 INT JOINT C ART, P587 Lieberman H., 2001, YOUR WISH IS MY COMM Lieberman H., 1995, P 14 INT JOINT C ART, P924 Lieberman H., 1999, Proceedings of the Third International Conference on Autonomous Agents, DOI 10.1145/301136.301176 LOVINS JB, 1968, MECH TRANSL, V11, P22 MACSKASSY SA, 1999, IJCAI 99 WORKSH LEAR Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 MALONE T, 1986, P CHI C HUM FACT COM, P1, DOI 10.1145/22627.22340 MALOOF MA, 1995, AAAI FALL S ACT LEAR, P70 MASAYASU A, 1997, IPSJ SIGNOTES INTELL, V97, P13 Massa P, 2004, LECT NOTES COMPUT SC, V2995, P221 MCNEE SM, 2003, LECT NOTES COMPUTER, V2702 Michalski R. S., 1969, P 5 INT S INF PROC A, VA3, P125 Middleton S., 2003, THESIS U SOUTHAMPTON Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MINSKY M., 1987, SOC MIND Mitchell T., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176798 MLADENIC D, 2001, USABILITY EVALUATION, V1, P893 Mobasher B, 2002, DATA MIN KNOWL DISC, V6, P61, DOI 10.1023/A:1013232803866 MONTANER M, 2004, P 6 INT C ENT INF SY, P303 Montaner M., 2002, Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems Morita M., 1994, P 17 ANN INT ACM SIG, P272 Moukas A., 1998, AUTON AGENT MULTI-AG, V1, P59, DOI 10.1023/A:1010094506174 MULLER ME, 2001, P 8 INT C US MOD WOR Nakhaeizadeh G., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining NURI M, 2000, THESIS CARNEGIE MELL O'Donovan J., 2005, P 10 INT C INT US IN, P167, DOI DOI 10.1145/1040830.1040870 Oard D. W., 2001, P 64 ANN M AM SOC IN, P38 Orwant J., 1995, USER MODEL USER-ADAP, V4, P107, DOI 10.1007/BF01099429 Paice C. D., 1990, SIGIR Forum, V24 PALIOURAS G, 1999, P 7 INT C US MOD UM, P169 Payne TR, 1997, IEEE T KNOWL DATA EN, V9, P329, DOI 10.1109/69.591456 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN RAMIREZ JM, 2000, P AAAI 2000 WORKSH A, P52 Rennie J.D.M., 2000, P 6 ACM SIGKDD INT C Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 RICH E, 1983, INT J MAN MACH STUD, V18, P199, DOI 10.1016/S0020-7373(83)80007-8 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B., 2000, P WEBKDD WORKSH ACM Sarwar BM, 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071 Schein A. I., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/564376.564421 Schwab I., 2000, P 2000 INT C INT US, P241, DOI 10.1145/325737.325858 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Segal R.B., 2000, P 17 INT C MACH LEAR, P863 Seo Y.-W., 2000, P 2000 INT C INT US, P248, DOI 10.1145/325737.325859 SHAPIRO E, 1981, P 7 INT JOINT C ART, P446 Shardanand U., 1995, P ACM CHI 95 C HUM F, V1, P210 Sheth B. D., 1994, THESIS MIT Somlo G. L., 2001, Proceedings of the Fifth International Conference on Autonomous Agents, DOI 10.1145/375735.376306 SORENSEN H, 1995, P CIKM 95 WORKSH INT Stanfill C., 1986, Communications of the ACM, V29, DOI 10.1145/7902.7906 Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 Webb G., 1996, USER MODEL USER-ADAP, V5, P117, DOI 10.1007/BF01099758 Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 Widmer G, 1996, MACH LEARN, V23, P69, DOI 10.1007/BF00116900 Widyantoro DH, 2001, J AM SOC INF SCI TEC, V52, P212, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1615>3.0.CO;2-O WONG SKM, 2000, TECHNOLOGY LETT, V4, P50 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yu HJ, 2004, IEEE T KNOWL DATA EN, V16, P70 Zukerman I, 2001, USER MODEL USER-ADAP, V11, P5, DOI 10.1023/A:1011175525451 NR 117 TC 22 Z9 24 PU CAMBRIDGE UNIV PRESS PI NEW YORK PA 40 WEST 20TH ST, NEW YORK, NY 10011-4211 USA SN 0269-8889 J9 KNOWL ENG REV JI Knowl. Eng. Rev. PD DEC PY 2005 VL 20 IS 4 BP 329 EP 361 DI 10.1017/S0269888906000397 PG 33 WC Computer Science, Artificial Intelligence SC Computer Science GA 052AC UT WOS:000238201800001 ER PT J AU Cummins, R O'riordan, C AF Cummins, R O'riordan, C TI Evolving general term-weighting schemes for information retrieval: Tests on larger collections SO ARTIFICIAL INTELLIGENCE REVIEW LA English DT Article; Proceedings Paper CT 15th Artificial Intelligence and Cognitive Science Conference (AICS 2004) CY SEP, 2004 CL Castlebar, IRELAND DE genetic programming; information retrieval; term-weighting schemes ID GENETIC ALGORITHMS; DOCUMENT-RETRIEVAL AB Term-weighting schemes are vital to the performance of Information Retrieval models that use term frequency characteristics to determine the relevance of a document. The vector space model is one such model in which the weights assigned to the document terms are of crucial importance to the accuracy of the retrieval system. This paper describes a genetic programming framework used to automatically determine term-weighting schemes that achieve a high average precision. These schemes are tested on standard test collections and are shown to perform as well as, and often better than, the modern BM25 weighting scheme. We present an analysis of the schemes evolved to explain the increase in performance. Furthermore, we show that the global (collection wide) part of the evolved weighting schemes also increases average precision over idf on larger TREC data. These global weighting schemes are shown to adhere to Luhn's resolving power as middle frequency terms are assigned the highest weight. However, the complete weighting schemes evolved on small collections do not perform as well on large collections. We conclude that in order to evolve improved local (within-document) weighting schemes it is necessary to evolve these on large collections. C1 Natl Univ Ireland Univ Coll Galway, Dept Informat Technol, Galway, Ireland. RP Cummins, R (reprint author), Natl Univ Ireland Univ Coll Galway, Dept Informat Technol, Galway, Ireland. EM ronan.cummins@nuigalway.ie CR CUMMINS R, 2004, 15 ART INT COGN SCI CUMMINS R, 2004, LAT BREAK PAP 2004 G DARWIN C, 1859, ORIGIN SPECIES MEANS FAN W, 2004, INFORMATION PROCESSI Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GRIEFF W, 1998, P 21 INT ACM SIGIR C Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X KIM S, 2001, P 2001 C EV COMP CEC Koza J. R., 1992, GENETIC PROGRAMMING KUSCU I, 2000, P 2000 C EV COMP CEC, P1393 Luhn H. P., 1958, IBM J RES DEV, P159 OREN N, 2002, P SAICSIT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTS SB, 1995, OBES RES, V3, P3 ROBERTSON SE, 1997, P 20 ANN INT ACM SIG ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Trotman A., 2004, SIGIR, P603 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VRAJITORU D, 2000, SOFT COMPUTING INFOR, P199 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 YANG JJ, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P603 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 28 TC 6 Z9 6 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0269-2821 J9 ARTIF INTELL REV JI Artif. Intell. Rev. PD NOV PY 2005 VL 24 IS 3-4 BP 277 EP 299 DI 10.1007/s10462-005-9001-y PG 23 WC Computer Science, Artificial Intelligence SC Computer Science GA 994GI UT WOS:000234018800004 ER PT J AU Chu, WW Liu, ZY Mao, WL Zou, QH AF Chu, WW Liu, ZY Mao, WL Zou, QH TI A knowledge-based approach for retrieving scenario-specific medical text documents SO CONTROL ENGINEERING PRACTICE LA English DT Article; Proceedings Paper CT 5th IFAC Symposium on Modelling and Control in Biomedical Systems CY AUG 21-23, 2003 CL MELBOURNE, AUSTRALIA SP Int Federat Automat Control, IUPESM, IEEE Engn Med & Biol Soc, Int Measurement Confederat, Inst Engineers Australia, Univ Sydney, Hong Kong Polytech Univ DE knowledge-based information retrieval; indexing; vector space model; query expansion ID QUESTIONS; CARE AB Medical free-text queries often share the same scenario. A scenario represents a repeating task in healthcare. For example, a specific scenario is searching for treatment methods for a specific disease, where "treatment" is a term indicating the scenario. To support scenario-specific retrieval, in this paper we present a new knowledge-based approach to address these problems. In addition, we describe a testbed system developed using the approach. Our specific implementation uses the UMLS Metathesaurus and semantic structure to extract key concepts from a free text. The approach uses phrase-based indexing to represent similar concepts, and query expansion to improve matching query terms with the terms in the document. The system formulates the query based on the user's input and the selected scenario template such as "disease, treatment" or "disease, diagnosis." Thus, it is able to retrieve documents relevant to the specific scenario. Evaluating the system using the standard OSHMED corpus, our empirical results validate the effectiveness of this new approach over the traditional text retrieval techniques. (c) 2005 Elsevier Ltd. All rights reserved. C1 Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA. RP Liu, ZY (reprint author), Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA. EM wwc@cs.ucla.edu; vicliu@cs.ucla.edu; wenlei@cs.ucla.edu; zou@cs.ucla.edu CR Aho A.V., 1975, CACM, V18, P330 ARONSON AR, 2001, P AMIA ANN S 2001 Brin S., 1998, P WWW 98 CALLAN JP, 1992, P DEXA 92 CHU WW, 1998, COOPERATIVE INFORMAT Chu WW, 1998, IEEE T KNOWL DATA EN, V10, P872, DOI 10.1109/69.738355 CHU WW, 2003, 030039 UCLA Cigarran J., 1998, P COLING ACL WORKSH Denny J. C., 2002, P AMIA ANN S Elkin P. L., 1988, Proceedings. The Twelfth Annual Symposium on Computer Applications in Medical Care (IEEE Cat. No.88CH2616-1) Ely JW, 1999, BRIT MED J, V319, P358 Ely JW, 2000, BRIT MED J, V321, P429, DOI 10.1136/bmj.321.7258.429 Friedman C, 1998, METHOD INFORM MED, V37, P334 Gusfield D., 1997, ALGORITHMS STRINGS T HAUX R, 2002, YB MED INFORMATICS M HAYNES RB, 1990, ANN INTERN MED, V112, P78 Hersh W., 1994, P 22 ACM SIGIR C, P191 *I MED, 2001, CROSS QAUAL CHASM NE Ide N, 1998, COMPUT LINGUIST, V24, P1 LOVINS JB, 1968, MECH TRANSL, V11, P22 Lyons John, 1977, SEMANTICS MAO W, 2002, P AMIA ANN S 2002 *NAT LIB MED, 2001, UMLS KNOWL SOURC National Library of Medicine, 2003, UMLS KNOWL SOURC ROSE DE, 2004, P 13 INT WORLD WID W SALTON G, 1983, MCGRAWWHILL COMPUTER Salton G., 1975, J AM SOC INFORM SCI, V23, P74 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRINIVASAN S, 2002, P AMIA ANN S 2002 Tuttle MS, 1998, METHOD INFORM MED, V37, P373 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA ZIEMAN YL, 1997, P AMIA ANN S 1997 NR 32 TC 3 Z9 3 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0967-0661 J9 CONTROL ENG PRACT JI Control Eng. Practice PD SEP PY 2005 VL 13 IS 9 BP 1105 EP 1121 DI 10.1016/j.conengprac.2004.12.011 PG 17 WC Automation & Control Systems; Engineering, Electrical & Electronic SC Automation & Control Systems; Engineering GA 941JO UT WOS:000230211300004 ER PT J AU Chen, LH Chue, WL AF Chen, LH Chue, WL TI Using Web structure and summarisation techniques for Web content mining SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE knowledge representation of Web documents; Web structure; summarisation; Web content mining; content-based automatic Web document clustering ID RELEVANCE FEEDBACK; RETRIEVAL; CATEGORIZATION; DISCOVERY AB The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a "Tree-like" Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining. (c) 2004 Elsevier Ltd. All rights reserved. C1 Nanyang Technol Univ, Div Informat Engn, Sch Elect & Elect Engn, Singapore 639798, Singapore. RP Chen, LH (reprint author), Nanyang Technol Univ, Div Informat Engn, Sch Elect & Elect Engn, S Spine,Block S1,Nanyang Ave, Singapore 639798, Singapore. EM elhchen@ntu.edu.sg; wailian@pmail.ntu.edu.sg CR Brin S., 1998, P 7 INT WORLD WID WE, P107 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Butler D, 2000, NATURE, V405, P112, DOI 10.1038/35012148 Carpineto C, 2002, ACM T INFORM SYST, V20, P259, DOI 10.1145/568727.568728 Chakrabarti S, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P545 Chang CH, 1999, IEEE T KNOWL DATA EN, V11, P595 Chang CH, 2003, DECIS SUPPORT SYST, V35, P129, DOI 10.1016/S0167-9236(02)00100-8 Chen HC, 2001, J AM SOC INF SCI TEC, V52, P1134, DOI 10.1002/asi.1180 CHO J, 1998, P 7 INT WORLD WID WE, P161 Crimmins F, 1999, IEEE INTELL SYST APP, V14, P55, DOI 10.1109/5254.784085 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Cutting D.R., 1993, P 16 ANN INT ACM SIG, P126, DOI 10.1145/160688.160706 Etzioni O, 1996, COMMUN ACM, V39, P65, DOI 10.1145/240455.240473 Flake G. W., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347121 FRAUENFELDER M, 2001, TECHNOLOGY REV NOV FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GALLAGHER DF, 2002, TECHNOLOGY REV NOV GLOVER E. J., 2002, P 11 INT WORLD WID W, P562 *GRAPH VIS US, 1998, WWW US SURV Hearst Marti A., 1996, P 19 ANN INT ACM SIG, DOI 10.1145/243199.243216 KLEINBERG JM, 1999, P 9 ACM SIAM S DISCR, P668 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 Lam-Adesina AM, 2001, P 24 ANN INT ACM SIG, P1, DOI 10.114/383952.383953 LAWRENCE S, 1999, IEEE COMPUT, V32, P67 Marchionini G. M., 1995, INFORMATION SEEKING OLSEN S, 2002, GOOGLE ADDS NEW WEB Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Sahami M., 1998, P 3 ACM INT C DIG LI, P200, DOI 10.1145/276675.276697 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAMMON JW, 1969, IEEE T COMPUT, VC 18, P401, DOI 10.1109/T-C.1969.222678 Tombros A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290947 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 SINKA M, 2002, FRONTIERS ARTIFICIAL, V87, P881 SPINK A, 2000, INFORMATION RES INT, V6 Tolle KM, 2000, J AM SOC INFORM SCI, V51, P352, DOI 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.3.CO;2-# *VIV, 2000, DOC CLUST ENG VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X White RW, 2003, INFORM PROCESS MANAG, V39, P707, DOI 10.1016/S0306-4573(02)00033-X WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 Zamir O., 1999, P 8 INT C WORLD WID, P1361 NR 44 TC 6 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 2005 VL 41 IS 5 BP 1225 EP 1242 DI 10.1016/j.ipm.2004.08.003 PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 920OA UT WOS:000228698800013 ER PT J AU Trotman, A AF Trotman, A TI Learning to rank SO INFORMATION RETRIEVAL LA English DT Article DE searching; document ranking; genetic programming; machine learning ID TEXT RETRIEVAL AB New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learned function. C1 Univ Otago, Dept Comp Sci, Dunedin, New Zealand. RP Trotman, A (reprint author), Univ Otago, Dept Comp Sci, Dunedin, New Zealand. EM andrew@cs.otago.ac.nz RI Trotman, Andrew/A-4918-2008 CR ANH VN, 2002, AUSTR COMPUTER SCI C, V24, P41 Buckley C, 1991, TREC EVAL Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543 Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5 DeJong K., 1975, THESIS U MICHIGAN FAN W, 2004, P 37 ANN HAW INT C S FAN W, 1999, P 1999 AM C INF SYST Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001 GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288 Harman D., 1992, INFORMATION RETRIEVA, P363 Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HART WE, 1996, ADAPTIVE INDIVIDUALS, P483 HAWKING D, 1999, P 8 INT WORLD WID WE, P1321 HOLLAND JH, 1975, ADAPTATION NATURAL A Igel C., 1999, P GEN EV COMP C GECC, P1061 JONES S, 1999, P 8 TEXT RETRIEVAL C Kaszkiel M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291031 Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137 Khuri S., 1994, P 22 ANN ACM COMP SC, P66, DOI 10.1145/197530.197558 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Koza J. R., 1992, GENETIC PROGRAMMING OREN N, 2002, THESIS U WITWATERSRA OREN N, 2002, P SAICSIT, P224 Page L, 1998, PAGERANK CITATION RA Possas B., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Raghavan V. V., 1983, Proceedings of the Sixth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Robertson Stephen E., 1994, P 3 TEXT RETR C TREC, P109 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1995, P 4 TEXT RETRIEVAL C, P537 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SMART W, 2004, P 2 WORKSH AUSTR INF TONGCHIM S, 2000, P 5 INT S ART LIF RO, P251 Williams HE, 1999, COMPUT J, V42, P193, DOI 10.1093/comjnl/42.3.193 Witten I.H., 1994, MANAGING GIGABYTES C Zobel J, 1998, ACM T DATABASE SYST, V23, P453, DOI 10.1145/296854.277632 Zobel J., 1998, SIGIR Forum, V32 ZOBEL J, 1995, SOFTWARE PRACT EXPER, V25, P891, DOI 10.1002/spe.4380250804 NR 40 TC 24 Z9 24 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD SEP PY 2005 VL 8 IS 3 BP 359 EP 381 DI 10.1007/s10791-005-6991-7 PG 23 WC Computer Science, Information Systems SC Computer Science GA 907NW UT WOS:000227725800001 ER PT J AU Rousu, J Shawe-Taylor, J AF Rousu, J Shawe-Taylor, J TI Efficient computation of gapped substring kernels on large alphabets SO JOURNAL OF MACHINE LEARNING RESEARCH LA English DT Article DE kernel methods; string kernels; text categorization; sparse dynamic programming AB We present a sparse dynamic programming algorithm that, given two strings s and t, a gap penalty l, and an integer p, computes the value of the gap-weighted length-p subsequences kernel. The algorithm works in time O(p vertical bar M vertical bar log vertical bar t vertical bar), where M = {(i,j)vertical bar s(i) = t(j)} is the set of matches of characters in the two sequences. The algorithm is easily adapted to handle bounded length subsequences and different gap-penalty schemes, including penalizing by the total length of gaps and the number of gaps as well as incorporating character-specific match/gap penalties. The new algorithm is empirically evaluated against a full dynamic programming approach and a trie-based algorithm both on synthetic and newswire article data. Based on the experiments, the full dynamic programming approach is the fastest on short strings, and on long strings if the alphabet is small. On large alphabets, the new sparse dynamic programming algorithm is the most efficient. On medium-sized alphabets the trie-based approach is best if the maximum number of allowed gaps is strongly restricted. C1 Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England. RP Rousu, J (reprint author), Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. EM JUHO@CS.RHUL.AC.UK; JST@ECS.SOTON.AC.UK RI Rousu, Juho/E-8195-2012 OI Rousu, Juho/0000-0002-0705-4314 CR Agarwal P. K., 1999, CONT MATH, V23, P1 Alstrup S., 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science, DOI 10.1109/SFCS.2000.892088 BAKER BS, 1998, P 6 ANN EUR S ALG, P79 Cancedda N, 2003, J MACH LEARN RES, V3, P1059, DOI 10.1162/153244303322533197 Chazelle B., 1995, Proceedings of the Twenty-Seventh Annual ACM Symposium on the Theory of Computing, DOI 10.1145/225058.225291 Chazelle B., 1989, Proceedings of the Fifth Annual Symposium on Computational Geometry, DOI 10.1145/73833.73848 Cristianini N., 2000, INTRO SUPPORT VECTOR De Berg M., 1997, COMPUTATIONAL GEOMET EPPSTEIN D, 1992, J ACM, V39, P519, DOI 10.1145/146637.146650 Eppstein D., 1989, THESIS COLUMBIA U Haussler D., 1999, CONVOLUTION KERNELS Joachims T., 1999, P 10 EUR C MACH LEAR, P137 Leslie C, 2003, ADV NEURAL INFORMATI, V15, P1417 Leslie C., 2002, P PAC S BIOC, V7, P566 Leslie C, 2003, LECT NOTES ARTIF INT, V2777, P114, DOI 10.1007/978-3-540-45167-9_10 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 MAKINEN V, 2003, 20036 U HELSINKI DEP OVERMARS MH, 1988, J ALGORITHM, V9, P254, DOI 10.1016/0196-6774(88)90041-7 Poon CK, 2003, THEOR COMPUT SCI, V296, P487, DOI 10.1016/S0304-3975(02)00741-7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saunders C., 2002, P 19 INT C MACH LEAR, P530 Shawe-Taylor J., 2004, KERNEL METHODS PATTE Vapnik V., 1995, NATURE STAT LEARNING Vishwanathan S. V. N., 2003, ADV NEURAL INFORM PR, V15, P569 Watkins C, 2000, ADV NEUR IN, P39 NR 25 TC 16 Z9 17 PU MICROTOME PUBLISHING PI BROOKLINE PA 31 GIBBS STREET, BROOKLINE, MA 02446 USA SN 1532-4435 J9 J MACH LEARN RES JI J. Mach. Learn. Res. PD SEP PY 2005 VL 6 BP 1323 EP 1344 PG 22 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 026HN UT WOS:000236330100003 ER PT J AU Nielsen, FA Balslev, D Hansen, LK AF Nielsen, FA Balslev, D Hansen, LK TI Mining the posterior cingulate: Segregation between memory and pain components SO NEUROIMAGE LA English DT Review DE meta-analysis; PubMed; positron emission tomography; magnetic resonance imaging; cingulate gyrus; brain mapping; memory; pain; Alzheimer's disease; automatic data processing; bibliometrics ID POSITRON-EMISSION-TOMOGRAPHY; POSTTRAUMATIC-STRESS-DISORDER; CEREBRAL-BLOOD-FLOW; CHILDHOOD SEXUAL-ABUSE; NONNEGATIVE MATRIX FACTORIZATION; IRRITABLE-BOWEL-SYNDROME; CONSCIOUS RESTING STATE; EPISODIC MEMORY; HUMAN BRAIN; AUTOBIOGRAPHICAL MEMORY AB We present a general method for automatic meta-analyses in neuroscience and apply it on text data from published functional imaging studies to extract main functions associated with a brain area-the posterior cingulate cortex (PCC). Abstracts from PubMed are down-loaded, words extracted and converted to a bag-of-words matrix representation. The combined data are analyzed with hierarchical non-negative matrix factorization. We find that the prominent themes in the PCC corpus are episodic memory retrieval and pain. We further characterize the distribution in PCC of the Talairach coordinates available in some of the articles. This shows a tendency to functional segregation between memory and pain components where memory activations are predominantly in the caudal part and pain in the rostral part of PCC. (c) 2005 Elsevier Inc. All rights reserved. C1 Univ Copenhagen Hosp, Ctr Neurosci, Rigshosp, Dept Neurol,Neurobiol Res Unit, DK-2100 Copenhagen, Denmark. Danish Res Ctr Magnet Resonance, Hvidovre, Denmark. Tech Univ Denmark, DK-2800 Lyngby, Denmark. RP Nielsen, FA (reprint author), Univ Copenhagen Hosp, Ctr Neurosci, Rigshosp, Dept Neurol,Neurobiol Res Unit, Bldg 9201,Blegdamsvej 9, DK-2100 Copenhagen, Denmark. EM fn@imm.dtu.dk RI Hansen, Lars/E-3174-2013; Nielsen, Finn/L-4697-2013 OI Nielsen, Finn/0000-0001-6128-3356 CR Abel KM, 2003, NEUROREPORT, V14, P387, DOI 10.1097/01.wnr.0000058031.29600.31 Adler LJ, 1997, ANESTH ANALG, V84, P120, DOI 10.1097/00000539-199701000-00023 Andreasen NC, 1995, NEUROIMAGE, V2, P296, DOI 10.1006/nimg.1995.1037 ANDREASEN NC, 1995, AM J PSYCHIAT, V152, P1576 Arnold JT, 1997, BLOOD, V89, P823 Baciu MV, 1999, AM J NEURORADIOL, V20, P1920 Balslev D, 2002, HUM BRAIN MAPP, V15, P135, DOI 10.1002/hbm.10015 Bartels A, 2000, NEUROREPORT, V11, P3829, DOI 10.1097/00001756-200011270-00046 Becerra LR, 1999, MAGNET RESON MED, V41, P1044, DOI 10.1002/(SICI)1522-2594(199905)41:5<1044::AID-MRM25>3.0.CO;2-M Bentley DE, 2002, CLIN NEUROPHYSIOL, V113, P1351, DOI 10.1016/S1388-2457(02)00149-9 Berman RA, 1999, HUM BRAIN MAPP, V8, P209, DOI 10.1002/(SICI)1097-0193(1999)8:4<209::AID-HBM5>3.0.CO;2-0 Berthoz A, 1997, PHILOS T R SOC B, V352, P1437, DOI 10.1098/rstb.1997.0130 Binder JR, 1999, J COGNITIVE NEUROSCI, V11, P80, DOI 10.1162/089892999563265 Boxer AL, 2003, ARCH NEUROL-CHICAGO, V60, P949, DOI 10.1001/archneur.60.7.949 Bremner J Douglas, 2002, Curr Psychiatry Rep, V4, P254, DOI 10.1007/s11920-996-0044-9 Bremner JD, 1999, AM J PSYCHIAT, V156, P1787 Bremner JD, 2003, BIOL PSYCHIAT, V53, P879, DOI 10.1016/S0006-3223(02)01891-7 Bremner JD, 1999, BIOL PSYCHIAT, V45, P806, DOI 10.1016/S0006-3223(98)00297-2 Brett M, 2001, NEUROIMAGE, V13, P85, DOI 10.1016/S1053-8119(01)91428-4 Bromm B, 2001, NEWS PHYSIOL SCI, V16, P244 Burgess N, 2001, NEUROIMAGE, V14, P439, DOI 10.1006/nimg.2001.0806 Bush G, 2000, TRENDS COGN SCI, V4, P215, DOI 10.1016/S1364-6613(00)01483-2 Cabeza R, 2000, J COGNITIVE NEUROSCI, V12, P1, DOI 10.1162/08989290051137585 Chen ACN, 2002, NEUROSCI LETT, V328, P190, DOI 10.1016/S0304-3940(02)00512-8 Christoff K, 2000, PSYCHOBIOLOGY, V28, P168 Cipolotti L, 2003, NEUROPSYCHOLOGIA, V41, P1148, DOI 10.1016/S0028-3932(03)00032-0 Clark VP, 1996, NEUROIMAGE, V4, P1, DOI 10.1006/nimg.1996.0025 COMMITTERI G, 2003, NEUROIMAGE, P19 Cornette L, 1998, BRAIN, V121, P143, DOI 10.1093/brain/121.1.143 Creac'h C, 2000, AM J NEURORADIOL, V21, P1402 Daselaar SM, 2003, BRAIN, V126, P43, DOI 10.1093/brain/awg005 Desgranges B, 1998, NEUROIMAGE, V8, P198, DOI 10.1006/nimg.1998.0359 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 DOBROKHOTOV P, 2003, STUDIES HLTH TECHNOL, V95 DONOHO D, 2004, ADV NEURAL INFORM PR, P16 Duncan J, 2000, TRENDS NEUROSCI, V23, P475, DOI 10.1016/S0166-2236(00)01633-7 Duzel E, 1999, P NATL ACAD SCI USA, V96, P1794, DOI 10.1073/pnas.96.4.1794 DWASS M, 1957, ANN MATH STAT, V28, P181, DOI 10.1214/aoms/1177707045 EDGINGTON ES, 1986, STAT TXB MONOGRAPHS, V77 Elkashef AM, 2000, PSYCHIAT RES-NEUROIM, V100, P1, DOI 10.1016/S0925-4927(00)00064-0 Farris JT, 2001, HOUS POLICY DEBATE, V12, P1 Fink GR, 1996, J NEUROSCI, V16, P4275 Fox GC, 2001, CONCURR COMP-PRACT E, V13, P1, DOI 10.1002/cpe.582 Fox PT, 2002, NAT REV NEUROSCI, V3, P319, DOI 10.1038/nrn789 Francis ST, 2000, NEUROIMAGE, V11, P188, DOI 10.1006/nimg.2000.0541 FREDRIKSON M, 1995, PSYCHOPHYSIOLOGY, V32, P43, DOI 10.1111/j.1469-8986.1995.tb03404.x Fredrikson M, 1997, J CLIN PSYCHIAT, V58, P16 Fujii T, 2002, NEUROIMAGE, V15, P501, DOI 10.1006/nimg.2001.0995 Gaussier E, 2002, LECT NOTES COMPUT SC, V2291, P229 Gautier JF, 1999, AM J CLIN NUTR, V70, P806 Gelnar PA, 1999, NEUROIMAGE, V10, P460, DOI 10.1006/nimg.1999.0482 Ghaem O, 1997, NEUROREPORT, V8, P739, DOI 10.1097/00001756-199702100-00032 Goldman JA, 1999, METHOD INFORM MED, V38, P96 Goutte C, 2001, HUM BRAIN MAPP, V13, P165, DOI 10.1002/hbm.1031 Goutte C, 1999, NEUROIMAGE, V9, P298, DOI 10.1006/nimg.1998.0391 Gron G, 2000, NAT NEUROSCI, V3, P404 HANSEN LK, 2000, P ICASSP2000 TURK IE HOFMANN T, 1999, P 15 C UNC ART INT U, P289 HSIEH JC, 1995, PAIN, V63, P225, DOI 10.1016/0304-3959(95)00048-W Pol HEH, 2001, ARCH GEN PSYCHIAT, V58, P1118 Hunkin NM, 2002, NEUROPSYCHOLOGIA, V40, P1456, DOI 10.1016/S0028-3932(01)00200-7 Ingvar M, 1999, PHILOS T R SOC B, V354, P1347, DOI 10.1098/rstb.1999.0483 Inoue K, 1998, J NEUROPHYSIOL, V79, P117 Isbell CL, 1999, ADV NEUR IN, V11, P480 Jernigan TL, 1998, NEUROIMAGE, V8, P93, DOI 10.1006/nimg.1998.0350 Kertzman C, 1997, EXP BRAIN RES, V114, P170, DOI 10.1007/PL00005617 KILTS CD, 1996, NEUROIMAGE, V3, P227 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kobayashi S, 2000, BIOMACROMOLECULES, V1, P3, DOI 10.1021/bm990007c Kolenda T., 2000, ADV INDEPENDENT COMP Krause BJ, 1999, EUR J NEUROSCI, V11, P3293, DOI 10.1046/j.1460-9568.1999.00723.x Lange N, 1999, NEUROIMAGE, V10, P282, DOI 10.1006/nimg.1999.0472 Lanius RA, 2001, AM J PSYCHIAT, V158, P1920, DOI 10.1176/appi.ajp.158.11.1920 Lee DD, 2001, ADV NEUR IN, V13, P556 Lee DD, 1999, NATURE, V401, P788 Lekander M, 2000, NEUROSCI LETT, V282, P193, DOI 10.1016/S0304-3940(00)00901-0 Luna B, 2002, NEUROLOGY, V59, P834 Maddock RF, 2001, NEUROSCIENCE, V104, P667, DOI 10.1016/S0306-4522(01)00108-7 Maddock RJ, 1997, PSYCHIAT RES-NEUROIM, V75, P1, DOI 10.1016/S0925-4927(97)00018-8 Maddock RJ, 1999, TRENDS NEUROSCI, V22, P310, DOI 10.1016/S0166-2236(98)01374-5 Maddock RJ, 2003, HUM BRAIN MAPP, V18, P30, DOI 10.1002/hbm.10075 Maguire EA, 2001, SCAND J PSYCHOL, V42, P225, DOI 10.1111/1467-9450.00233 Maguire EA, 1999, HIPPOCAMPUS, V9, P54, DOI 10.1002/(SICI)1098-1063(1999)9:1<54::AID-HIPO6>3.0.CO;2-O Maguire EA, 2001, BRAIN, V124, P1156, DOI 10.1093/brain/124.6.1156 MARDIA KV, 1970, BIOMETRIKA, V57, P519, DOI 10.2307/2334770 Mardia KV, 1979, MULTIVARIATE ANAL PR Mazoyer B, 2001, BRAIN RES BULL, V54, P287, DOI 10.1016/S0361-9230(00)00437-8 MCDONALD CR, 2001, NEUROCASE, V7, P405 Mead LA, 2002, J INT NEUROPSYCH SOC, V8, P735, DOI 10.1017/S1355617702860015 Mesulam MM, 2001, NEUROIMAGE, V13, P1065, DOI 10.1006/nimg.2001.0768 MINOSHIMA S, 1994, LANCET, V344, P895, DOI 10.1016/S0140-6736(94)92871-1 Naliboff BD, 2001, PSYCHOSOM MED, V63, P365 Nichols T.E., 2001, HUM BRAIN MAPP, V15, P1, DOI DOI 10.1002/HBM.1058 Nielsen F., 2000, VDE2000 VISUALIZATIO, P76 NIELSEN FA, 2002, NEUROIMAGE, P16 NIELSEN FA, 2004, P 13 DANSK K MONST B, P121 Nielsen FA, 2004, ARTIF INTELL MED, V30, P141, DOI 10.1016/S0933-3657(03)00041-1 Nielsen FA, 2002, HUM BRAIN MAPP, V15, P146, DOI 10.1002/hbm.10012 NIELSEN FA, 2003, NEUROIMAGE, P19 NYBACK H, 1991, J NEUROL NEUROSUR PS, V54, P673 Nyberg L, 1996, J NEUROSCI, V16, P3753 Olson CR, 1996, J NEUROPHYSIOL, V76, P3285 Onoe H, 2001, NEUROIMAGE, V13, P37, DOI 10.1006/nimg.2000.0670 Pasca M. A., 2001, SIGIR Forum Peiffer C, 2001, AM J RESP CRIT CARE, V163, P951 Perez-Iratxeta C, 2001, TRENDS BIOCHEM SCI, V26, P573, DOI 10.1016/S0968-0004(01)01926-0 Petrovic P, 1999, PAIN, V83, P459, DOI 10.1016/S0304-3959(99)00150-5 Peyron R, 2000, NEUROPHYSIOL CLIN, V30, P263, DOI 10.1016/S0987-7053(00)00227-6 Phillips ML, 1998, PSYCHIAT RES-NEUROIM, V83, P127, DOI 10.1016/S0925-4927(98)00036-5 Piefke M, 2003, BRAIN, V126, P650, DOI 10.1093/brain/awg064 Pissiota A, 2002, EUR ARCH PSY CLIN N, V252, P68, DOI 10.1007/s004060200014 Raichle ME, 2001, P NATL ACAD SCI USA, V98, P676, DOI 10.1073/pnas.98.2.676 Rainville P, 1999, J COGNITIVE NEUROSCI, V11, P110, DOI 10.1162/089892999563175 Rehm K, 1998, Med Image Anal, V2, P215, DOI 10.1016/S1361-8415(98)80020-0 ROSEN SD, 1994, LANCET, V344, P147, DOI 10.1016/S0140-6736(94)92755-3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shah NJ, 2001, BRAIN, V124, P804, DOI 10.1093/brain/124.4.804 SHALLICE T, 1994, NATURE, V368, P633, DOI 10.1038/368633a0 SHIBATA H, 2003, EUR J NEUROSCI, V18, P1516 Shin LM, 1997, ARCH GEN PSYCHIAT, V54, P233 Shin LM, 1999, AM J PSYCHIAT, V156, P575 Shulman GL, 1997, J COGNITIVE NEUROSCI, V9, P648, DOI 10.1162/jocn.1997.9.5.648 Small DM, 2003, NEUROIMAGE, V18, P633, DOI 10.1016/S1053-8119(02)00012-5 Sowell ER, 2000, AM J PSYCHIAT, V157, P1475, DOI 10.1176/appi.ajp.157.9.1475 Sprengelmeyer R, 1998, P ROY SOC B-BIOL SCI, V265, P1927 Tillfors M, 2001, AM J PSYCHIAT, V158, P1220, DOI 10.1176/appi.ajp.158.8.1220 Tolle TR, 1999, ANN NEUROL, V45, P40, DOI 10.1002/1531-8249(199901)45:1<40::AID-ART8>3.0.CO;2-L Tournoux P., 1988, COPLANAR STEROTAXIC Tzourio-Mazoyer N, 2002, NEUROIMAGE, V15, P273, DOI 10.1006/nimg.2001.0978 Van Horn JD, 1998, BRAIN RES, V793, P29, DOI 10.1016/S0006-8993(98)00051-1 Verne GN, 2003, PAIN, V103, P99, DOI 10.1016/S0304-3959(02)00416-5 Vogt BA, 1996, EUR J NEUROSCI, V8, P1461, DOI 10.1111/j.1460-9568.1996.tb01608.x Vogt BA, 2001, J COMP NEUROL, V438, P353, DOI 10.1002/cne.1320 Vogt Brent A., 1992, Cerebral Cortex, V2, P435, DOI 10.1093/cercor/2.6.435 Vogt BA, 2000, TRENDS NEUROSCI, V23, P195, DOI 10.1016/S0166-2236(00)01579-4 Volkow ND, 2002, PSYCHIAT RES-NEUROIM, V114, P39, DOI 10.1016/S0925-4927(01)00130-5 Wiggs CL, 1999, NEUROPSYCHOLOGIA, V37, P103 WIK G, 1993, PSYCHIAT RES-NEUROIM, V50, P15 Willoch F, 2000, ANN NEUROL, V48, P842 Yamasaki H, 2002, P NATL ACAD SCI USA, V99, P11447, DOI 10.1073/pnas.182176499 NR 141 TC 52 Z9 55 PU ACADEMIC PRESS INC ELSEVIER SCIENCE PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA SN 1053-8119 J9 NEUROIMAGE JI Neuroimage PD SEP PY 2005 VL 27 IS 3 BP 520 EP 532 DI 10.1016/j.neuroimage.2005.04.034 PG 13 WC Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging SC Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging GA 959UA UT WOS:000231543600004 PM 15946864 ER PT J AU Efron, M AF Efron, M TI Eigenvalue-based model selection during latent semantic indexing SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID NUMBER; RULES; COMPONENTS; RETAIN AB In this study amended parallel analysis (APA), a novel method for model selection in unsupervised learning problems such as information retrieval (IR), is described. At issue is the selection of k, the number of dimensions retained under latent semantic indexing (LSI). Amended parallel analysis is an elaboration of Horn's parallel analysis, which advocates retaining eigenvalues larger than those that we would expect under term independence. Amended parallel analysis operates by deriving confidence intervals on these "null" eigenvalues. The technique amounts to a series of nonparametric hypothesis tests on the correlation matrix eigenvalues. In the study, APA is tested along with four established dimensionality estimators on six standard IR test collections. These estimates are evaluated with regard to two IR performance metrics. Additionally, results from simulated data are reported. In both rounds of experimentation APA performs well, predicting the best values of k on 3 of 12 observations, with good predictions on several others, and never offering the worst estimate of optimal dimensionality. C1 Univ Texas, Sch Informat, Austin, TX 78712 USA. RP Efron, M (reprint author), Univ Texas, Sch Informat, 1 Univ Stn D7000, Austin, TX 78712 USA. EM miles@ischool.utexas.edu CR Anderson TW, 1984, INTRO MULTIVARIATE S Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bishop C. M., 1995, NEURAL NETWORKS PATT COTA AA, 1993, EDUC PSYCHOL MEAS, V53, P585, DOI 10.1177/0013164493053003001 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding C. H. Q., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312652 DUMAIS ST, 1992, NIST SPECIAL PUBLICA, P137 EFRON B, 1979, ANN STAT, V7, P1, DOI 10.1214/aos/1176344552 Efron B, 1993, INTRO BOOTSTRAP EFRON M, 2002, THESIS U N CAROLINA Fukunaga K, 1982, HDB STAT, V2, P347, DOI 10.1016/S0169-7161(82)02018-5 GLORFELD LW, 1995, EDUC PSYCHOL MEAS, V55, P377, DOI 10.1177/0013164495055003002 Golub G. H., 1989, MATRIX COMPUTATIONS, VSecond GUTTMAN L, 1954, PSYCHOMETRIKA, V19, P149 GUTTMAN L, 1958, PSYCHOMETRIKA, V23, P297, DOI 10.1007/BF02289780 HAKSTIAN AR, 1982, MULTIVAR BEHAV RES, V17, P193, DOI 10.1207/s15327906mbr1702_3 Hastie T., 2001, SPRINGER SERIES STAT Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 HORN JL, 1965, PSYCHOMETRIKA, V30, P179, DOI 10.1007/BF02289447 Hyvarinen A., 2001, INDEPENDENT COMPONEN JACKSON DA, 1993, ECOLOGY, V74, P2204, DOI 10.2307/1939574 Jiang F., 2000, P 17 INT C MACH LEAR, P423 JOBSON JD, 1991, SPRINGER SERIES STAT Jolliffe IT, 2002, SPRINGER SERIES STAT Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Ledermann W, 1937, PSYCHOMETRIKA, V2, P85, DOI 10.1007/BF02288062 LINN RL, 1968, PSYCHOMETRIKA, V33, P37, DOI 10.1007/BF02289675 LONGMAN RS, 1989, MULTIVARIATE BEHAV R, V24, P56 Losee R.M., 1998, TEXT RETRIEVAL FILTE Mardia K. V., 1979, MULTIVARIATE ANAL MIHAIL M, 2002, 6 INT WORKSH RAND AP Muirhead R. J., 1982, ASPECTS MULTIVARIATE Rencher A., 1995, METHODS MULTIVARIATE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STRANG G, 1988, LINEAR ALGEBRA ITS A Subhash S., 1996, APPL MULTIVARIATE TE ten Berge JMF, 1999, PSYCHOMETRIKA, V64, P317 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 Wyse N., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Yates R.B., 1999, MODERN INFORMATION R ZWICK WR, 1986, PSYCHOL BULL, V99, P432, DOI 10.1037//0033-2909.99.3.432 NR 41 TC 8 Z9 8 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUL PY 2005 VL 56 IS 9 BP 969 EP 988 DI 10.1002/asi.20188 PG 20 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 936YV UT WOS:000229892500007 ER PT J AU Price, L Thelwall, M AF Price, L Thelwall, M TI The clustering power of low frequency words in academic webs SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID SCIENCE; COMMUNICATION; ALGORITHM; SITES AB The value of low frequency words for subject-based academic Web site clustering is assessed. A new technique is introduced to compare the relative clustering power of different vocabularies. The technique is designed for word frequency tests in large document clustering exercises. Results for the Australian and New Zealand academic Web spaces indicate that low frequency words are useful for clustering academic Web sites along subject lines; removing low frequency words results in sites becoming, on average, less dissimilar to sites from other subjects. C1 Wolverhampton Univ, Sch Comp & Informat Technol, Wolverhampton WV1 1SB, England. RP Price, L (reprint author), Wolverhampton Univ, Sch Comp & Informat Technol, Wulfruna St, Wolverhampton WV1 1SB, England. EM Liz.Price@wlv.ac.uk; m.thelwall@wlv.ac.uk RI Thelwall, Mike/C-1449-2013 OI Thelwall, Mike/0000-0001-6065-205X CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Baldi P., 2003, MODELLING INTERNET W Bharat K., 2001, Proceedings 2001 IEEE International Conference on Data Mining, DOI 10.1109/ICDM.2001.989500 BJORNEBORN L, 2001, UNPUB NECESSARY DAT Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3 Chen GT, 2003, RAIRO-OPER RES, V37, P179, DOI 10.1051/ro:2003020 Comeau DC, 2004, J AM SOC INF SCI TEC, V55, P169, DOI 10.1002/asi.10354 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Della Mea V, 2004, J AM SOC INF SCI TEC, V55, P530, DOI 10.1002/asi.10408 FLAKE GW, 2002, IEEE COMPUT, V35, P66 Glanzel W, 2001, SCIENTOMETRICS, V50, P199, DOI 10.1023/A:1010561321723 HAND P, 2001, PRINCIPLES DATA MINI Heimeriks G, 2003, SCIENTOMETRICS, V58, P391, DOI 10.1023/A:1026296812830 KOBAYASHI M, 2003, SURVEY TEXT MINING, P103 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LANCASTER EW, 1997, INFORMATION RETRIEVE Leydesdorff L, 2004, SCIENTOMETRICS, V60, P159, DOI 10.1023/B:SCIE.0000027678.31097.e0 LUHN HP, 1958, IBM J RES DEV, V2, P159 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.3.CO;2-7 Thelwall M, 2005, J AM SOC INF SCI TEC, V56, P609, DOI 10.1002/asi.20126 THELWALL M, IN PRESS COMMUNICATI Thelwall M, 2001, J INFORM SCI, V27, P319 Thelwall M., 2004, Proceedings. Eighth International Conference on Information Visualisation, DOI 10.1109/IV.2004.1320191 THELWALL M, 2003, CYBERMETRICS, V6 Thelwall M, 2002, J AM SOC INF SCI TEC, V53, P995, DOI 10.1002/asi.10135 Weeber M, 2000, COMPUT LINGUIST, V26, P301, DOI 10.1162/089120100561719 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 33 TC 7 Z9 7 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2005 VL 56 IS 8 BP 883 EP 888 DI 10.1002/asi.20177 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 926XU UT WOS:000229157800012 ER PT J AU Albrecht-Buehler, C Watson, B Shamma, DA AF Albrecht-Buehler, C Watson, B Shamma, DA TI Visualizing live text streams using motion and temporal pooling SO IEEE COMPUTER GRAPHICS AND APPLICATIONS LA English DT Article C1 Northwestern Univ, Realism Lab, Evanston, IL 60208 USA. Northwestern Univ, Intelligent Informat Lab, Evanston, IL 60208 USA. Northwestern Univ, Dept Comp Sci, Evanston, IL 60208 USA. RP Albrecht-Buehler, C (reprint author), Northwestern Univ, Realism Lab, Evanston, IL 60208 USA. EM conrad@cs.northwestern.edu; ayman@cs.northwestern.edu; watsonb@cs.northwestern.edu CR Bartram L, 2003, INT J HUM-COMPUT ST, V58, P515, DOI 10.1016/S1071-5819(03)00021-1 Havre S, 2002, IEEE T VIS COMPUT GR, V8, P9, DOI 10.1109/2945.981848 Ishizaki S., 1996, P SIGCHI C HUM FACT, P347, DOI 10.1145/238386.238566 Kerren A, 2002, LECT NOTES COMPUT SC, V2269, P1 Lum EB, 2003, IEEE T VIS COMPUT GR, V9, P115, DOI 10.1109/TVCG.2003.1196000 Manka JS, 2003, PROCEEDINGS OF THE TWENTY-NINTH ANNUAL CONFERENCE ON EXPLOSIVES AND BLASTING TECHNIQUE, VOL 1, P169 Miller N. E., 1998, Proceedings Visualization '98 (Cat. No.98CB36276), DOI 10.1109/VISUAL.1998.745302 NOWELL L, 2001, P IEEE S INF VIS, P11 O'Regan JK, 2000, VIS COGN, V7, P191 Rennison E., 1994, P ACM S US INT SOFTW, P3, DOI 10.1145/192426.192429 Rensink RA, 1997, PSYCHOL SCI, V8, P368, DOI 10.1111/j.1467-9280.1997.tb00427.x ROBERTSON G, 1993, P ACM C HUM FACT COM, P83 Rushall D. A., 1996, Proceedings. IEEE Symposium on Information Visualization '96 (Cat. No.96TB100083), DOI 10.1109/INFVIS.1996.559228 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEKULER R, 2002, STEVENS HDB EXPT PSY, V1, P569 SHAMMA DA, 2004, 13 INT C WORLD WID W, P41 Simons DJ, 1998, PSYCHON B REV, V5, P644, DOI 10.3758/BF03208840 Smith A. T., 1994, VISUAL DETECTION MOT Ware C., 2004, ACM T APPL PERCEPT, V1, P3, DOI DOI 10.1145/1008722.1008724 Wise J. A., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528686 Wong P. C., 2003, P IEEE S INF VIS, P97, DOI DOI 10.1109/INFVIS.2003.1249014 NR 21 TC 13 Z9 13 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 0272-1716 J9 IEEE COMPUT GRAPH JI IEEE Comput. Graph. Appl. PD MAY-JUN PY 2005 VL 25 IS 3 BP 52 EP 59 DI 10.1109/MCG.2005.70 PG 8 WC Computer Science, Software Engineering SC Computer Science GA 922CN UT WOS:000228813600009 PM 15943088 ER PT J AU Koike, A Niwa, Y Takagi, T AF Koike, A Niwa, Y Takagi, T TI Automatic extraction of gene/protein biological functions from biomedical text SO BIOINFORMATICS LA English DT Article ID GENE ONTOLOGY; ANNOTATION AB Motivation: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing. Results: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes. C1 Univ Tokyo, Grad Sch Frontier Sci, Dept Computat Sci, Chiba 2778561, Japan. Hitachi Ltd, Cent Res Lab, Kokubunji, Tokyo 1858601, Japan. RP Koike, A (reprint author), Univ Tokyo, Grad Sch Frontier Sci, Dept Computat Sci, Kiban 3A1 CB01 5-1-5,Kashiwanoha Kashiwa, Chiba 2778561, Japan. EM akoike@hgc.jp CR Ashburner M, 2000, NAT GENET, V25, P25 Blaschke Christian, 2002, Genome Inform, V13, P201 Camon E, 2003, GENOME RES, V13, P662, DOI 10.1101/gr.461403 Chiang JH, 2003, BIOINFORMATICS, V19, P1417, DOI 10.1093/bioinformatics/btg160 Friedman C, 2001, Bioinformatics, V17 Suppl 1, pS74 Fukuda K., 1998, P PAC S BIOC, P705 Humphreys K, 2000, Pac Symp Biocomput, P505 Jacquemin C., 1994, P 17 ANN INT ACM SIG, P132, DOI 10.1109/CMPCON.1994.282934 KIM JJ, 2004, IN PRESS LECT NOTES Koike A, 2003, GENOME RES, V13, P1231, DOI 10.1101/gr.835903 KOIKE A, 2004, P HLT NAACL BIOLINK, P9 KRALLINGER M, 2004, P BIOCREATIVE GRAN S KRYMOLOWSKI Y, 2004, P BIOCREATIVE NENADIC G, 2003, P ACL 2003 WORKSH NA, P121 Raychaudhuri S, 2002, GENOME RES, V12, P203, DOI 10.1101/gr.199701 Rindflesch TC, 2000, P PAC S BIOC, P514 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schug J, 2002, GENOME RES, V12, P648, DOI 10.1101/gr.222902 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 Tanabe L, 2002, BIOINFORMATICS, V18, P1124, DOI 10.1093/bioinformatics/18.8.1124 Tsujii J., 2000, P 18 INT C COMP LING, P201 Xie HQ, 2002, GENOME RES, V12, P785, DOI 10.1101/gr.86902 Yakushiji A, 2001, Pac Symp Biocomput, P408 NR 23 TC 27 Z9 28 PU OXFORD UNIV PRESS PI OXFORD PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND SN 1367-4803 J9 BIOINFORMATICS JI Bioinformatics PD APR 1 PY 2005 VL 21 IS 7 BP 1227 EP 1236 DI 10.1093/bioinformatics/bti084 PG 10 WC Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability SC Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics GA 911CB UT WOS:000227977800050 PM 15509601 ER PT J AU Sinka, MP Corne, DW AF Sinka, MP Corne, DW TI The BankSearch web document dataset: investigating unsupervised clustering and category similarity SO JOURNAL OF NETWORK AND COMPUTER APPLICATIONS LA English DT Article DE benchmark dataset; clustering; text classification; unsupervised learning; stemming; stoplists ID CATEGORIZATION AB Targeting useful and relevant information on the internet is a highly complicated research area, which is served in part by research into document clustering. A foundational aspect of such research (proven over and over again in other research disciplines) is the use of standard datasets, against which different techniques can be properly benchmarked and assessed. We argue that, so far in this broad area of research, as many datasets have been used as research papers written, thus preventing confident reasoning about the relative performance of different techniques used in different publications. We describe a solution to this problem with the compilation of the BankSearch dataset, a proposed standard dataset suitable for a wide range of web-intelligence related research activities. At the time of writing, this dataset has already become a popular download in the Statlib archive, and is in use for benchmarking of a variety of document processing and web search techniques. Herein we also use the dataset in experiments to investigate certain issues in unsupervised web document clustering. Our main interest is how unsupervised clustering performance varies with the relative 'distance' between the categories inherent in the data, and how this is affected by the use of stemming and stoplists. These issues relate to, among other things, the design of useful search engines. We use simple k-means clustering, and find, unsurprisingly, that performance improves as categories become more distant. However, we also find that very close categories can be distinguished with fair accuracy, and there are interesting results concerning the use of stemming. Stop-word removal is confirmed as universally helpful, but stemming is not always to be recommended on 'distant' categories. (C) 2004 Published by Elsevier Ltd. C1 Univ Exeter, Dept Comp Sci, Exeter EX4 4QF, Devon, England. Univ Reading, Dept Comp Sci, Reading RG6 6AY, Berks, England. RP Corne, DW (reprint author), Univ Exeter, Dept Comp Sci, Harrison Bldg, Exeter EX4 4QF, Devon, England. EM d.w.corne@exeter.ac.uk CR Aas K., 1999, TEXT CATEGORISATION APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Goldszmidt M., 1998, ITAD433MS98044 SRI I Lewis D., 1994, P 3 ANN S DOC AN INF, P81 LEWIS K, 1992, CAREER DEV EXCEPTION, V15, P37 Moore J., 1997, P 7 WORKSH INF TECHN PIERRE J, 2000, PRACTICAL ISSUES AUT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RILOFF E, 1997, P 18 ANN INT ACM SIG, P130 SABASTIANI F, 1999, IEIB4311999 CNR IT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani Fabrizio, 1999, P ASAI 99 1 ARG S AR, P7 Slonim N., 2001, P 23 EUR C INF RETR Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Tishby N, 1999, P 37 ANN ALL C COMM, P368 TSUKADA M, 2001, WEB INTELLIGENCE RES, V2198, P303, DOI 10.1007/3-540-45490-X_36 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA Wiener E, 1995, P 4 ANN S DOC AN INF, P317 WONG W, 2000, P IEEE 2000 INT C IN Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 ZAMIR O, 1998, P 21 ANN INT ACM SIG, P31 Zhao Y., 2002, CRITERION FUNCTIONS NR 24 TC 9 Z9 9 PU ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD PI LONDON PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND SN 1084-8045 J9 J NETW COMPUT APPL JI J. Netw. Comput. Appl. PD APR PY 2005 VL 28 IS 2 BP 129 EP 146 DI 10.1016/j.jnca.2004.01.002 PG 18 WC Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering SC Computer Science GA 889EH UT WOS:000226425800005 ER PT J AU Trotman, A AF Trotman, A TI Choosing document structure weights SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE structured information retrieval; genetic algorithms; vector space model; probability model ID GENETIC ALGORITHMS; INFORMATION-RETRIEVAL; OPTIMIZATION; RANKING AB Existing ranking schemes assume all term occurrences in a given document are of equal influence. Intuitively, terms occurring in some places should have a greater influence than those elsewhere. An occurrence in an abstract may be more important than an occurrence in the body text. Although this observation is not new, there remains the issue of finding good weight: for each structure. Vector space, probability, and Okapi BM25 ranking are extended to include structure weighting. Weights are then selected for the TREC WSJ collection using a genetic algorithm. The learned weights are then tested on an evaluation set of queries. Structure weighted vector :space inner product and structure weighted probabilistic retrieval show an about 5% improvement in mean average precision over their unstructured counterparts. Structure weighted BM25 shows nearly no improvement. Analysis suggests BM25 cannot be improved using structure weighting. (C) 2003 Elsevier Ltd. All rights reserved. C1 Univ Otago, Dept Comp Sci, Dunedin, New Zealand. RP Trotman, A (reprint author), Univ Otago, Dept Comp Sci, Dunedin, New Zealand. EM andrew@cs.otago.ac.nz RI Trotman, Andrew/A-4918-2008 CR Baeza-Yates R., 1998, Proceedings. String Processing and Information Retrieval: A South American Symposium (Cat. No.98EX207), DOI 10.1109/SPIRE.1998.712977 Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Boughanem M, 2002, J AM SOC INF SCI TEC, V53, P934, DOI 10.1002/asi.10119 Boyan J., 1996, P AAAI WORKSH INT BA BRAY T, 1988, EXTENSIBLE MARKUP LA Callan James P., 1994, P 17 ANN INT ACM SIG, P302 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S Chinenyanga T., 2001, P 24 ANN INT C RES D, P163, DOI 10.1145/383952.383982 DeJong K., 1975, THESIS U MICHIGAN Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 FUHR N, 2000, P ACM SIGIR 200 WORK FUHR N, 2002, P ACM SIGIR 2000 WOR Fuller M, 1993, P 16 ANN INT ACM SIG, P204, DOI 10.1145/160688.160720 Goldberg D. E., 1989, GENETIC ALGORITHMS S GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288 Harman DK, 1993, P 16 ANN INT ACM SIG, P36, DOI 10.1145/160688.160692 HOLLAND JH, 1975, ADAPTATION NATURAL A Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X Kaszkiel M, 2001, J AM SOC INF SCI TEC, V52, P344, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1075>3.3.CO;2-R Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 Khuri S., 1994, P 22 ANN ACM COMP SC, P66, DOI 10.1145/197530.197558 Kim S, 2000, P IEEE INT ASIC C&E, P13 KIM S, 2001, P 2001 C EV COMP CEC, P1253 KIM YH, 2000, P 9 TEXT RETR C TREC, P392 Kotsakis E., 2002, P 2002 ACM S APPL CO, P663 MEUSS H, 1999, P 21 ANN C IR RES IR Moody J., 1994, STAT NEURAL NETWORKS Morgan J., 1996, POLYMODEL 16 APPL AR, P142 Pathak P., 2000, P 33 HAW INT C SYST Rapela J, 2001, P 3 INT WORKSH WEB I, P61 Robertson S., 1996, P 4 TEXT RETR C TREC, P73 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1996, EVALUATION LEARNING SCHLIEDER T, 2000, P DELOS WORKSH INF S Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 Shin D, 1998, P 3 ACM INT C DIG LI, P235, DOI 10.1145/276675.276702 THOM JA, 1995, DESIGN INDEXES STRUC TROTMAN A, IN PRESS INFORMATION VRAJITORU D, 2000, SOFT COMPUTING INFOR, P199 Vrajitoru D, 1998, INFORM PROCESS MANAG, V34, P405, DOI 10.1016/S0306-4573(98)00015-6 Weisss S. M., 1991, COMPUTER SYSTEMS LEA WILKINSON R, 1994, P 3 TEXT RETR C TREC, P81 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 WOLFF J, 1999, IAITR9912 U BONN Wolff J. E., 2000, Proceedings IEEE Advances in Digital Libraries 2000, DOI 10.1109/ADL.2000.848377 Yang Jun, 1992, Proceedings of the IEEE International Symposium on Industrial Electronics (Cat. No.92TH0371-5), DOI 10.1109/ISIE.1992.279625 NR 48 TC 22 Z9 24 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR PY 2005 VL 41 IS 2 BP 243 EP 264 DI 10.1016/j.ipm.2003.10.003 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 874AK UT WOS:000225323100005 ER PT J AU Rubin, DL Thorn, CF Klein, TE Altman, RB AF Rubin, DL Thorn, CF Klein, TE Altman, RB TI A statistical approach to scanning the biomedical literature for pharmacogenetics knowledge SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION LA English DT Article ID GENE AB Objective: Biomedical databases summarize current scientific knowledge, but they generally require years of laborious curation effort to build, focusing on identifying pertinent literature and data in the voluminous biomedical literature. It is difficult to manually extract useful information embedded in the large volumes of literature, and automated intelligent text analysis tools are becoming increasingly essential to assist in these curation activities. The goal of the authors was to develop an automated method to identify articles in Medline citations that contain pharmacogenetics data pertaining to gene-drug relationships. Design: The authors built and evaluated several candidate statistical models that characterize pharmacogenetics articles in terms of word usage and the profile of Medical Subject Headings (MeSH) used in those articles. The best-performing model was used to scan the entire Medline article database (11 million articles) to identify candidate pharmacogenetics articles. Results: A sampling of the articles identified from scanning Medline was reviewed by a pharmacologist to assess the precision of the method. The authors' approach identified 4,892 pharmacogenetics articles in the literature with 92% precision. Their automated method took a fraction of the time to acquire these articles compared with the time expected to be taken to accumulate them manually. The authors have built a Web resource (http://pharmdemo.stanford. edu/pharmclb/main.spy) to provide access to their results. Conclusion: A statistical classification approach can screen the primary literature to pharmacogenetics articles with high precision. Such methods may assist curators in acquiring pertinent literature in building biomedical databases. C1 Stanford Univ, Sect Med Informat, Stanford, CA 94305 USA. Stanford Med Ctr, Dept Genet, Stanford, CA USA. RP Rubin, DL (reprint author), Stanford Univ, Sect Med Informat, MSOB X-215, Stanford, CA 94305 USA. EM rubin@smi.stanford.edu CR BACHRACH CA, 1978, MED INFORM, V3, P237 LECESSIE S, 1992, APPL STAT-J ROY ST C, V41, P191 Chang JT, 2004, BIOINFORMATICS, V20, P216, DOI 10.1093/bioinformatics/btg393 Chang JT, 2004, PHARMACOGENETICS, V14, P577, DOI 10.1097/00008571-200409000-00002 Coletti MH, 2001, J AM MED INFORM ASSN, V8, P317 EVANS WE, 1999, SCIENCE, V286, P5439 Evans WE, 2001, ANNU REV GENOM HUM G, V2, P9, DOI 10.1146/annurev.genom.2.1.9 FLOCKHART DA, DRUG INTERACTION DAT GEMAN S, 1992, NEURAL COMPUT, V4, P1, DOI 10.1162/neco.1992.4.1.1 HERSCH W, 2003, TREC 12 TEXT RETR C Hewett M, 2002, NUCLEIC ACIDS RES, V30, P163, DOI 10.1093/nar/30.1.163 Hirschman L, 2002, BIOINFORMATICS, V18, P1553, DOI 10.1093/bioinformatics/18.12.1553 John G.H., 1995, P 11 C UNC ART INT, P338 Klein T. E., 2001, Pharmacogenomics Journal, V1, P167 *NAT LIB MED, MEDL DAT National Library of Medicine, MED SUBJ HEAD NG AY, 2002, ADV NEURAL INFORM PR, V14, P609 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 REGEV Y, 2003, SIGKDD EXPLORATIONS, V4, P90 RUBIN DL, 2004, MEDINFO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shatkay H, 2003, J COMPUT BIOL, V10, P821, DOI 10.1089/106652703322756104 Shi M, 2003, LECT NOTES COMPUT SC, V2633, P597 Witten I. H., 2000, DATA MINING PRACTICA Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yeh AS, 2003, BIOINFORMATICS, V19, pi331, DOI 10.1093/bioinformatics/btg1046 Zhang MQ, 1998, HUM MOL GENET, V7, P919, DOI 10.1093/hmg/7.5.919 NR 27 TC 24 Z9 24 PU HANLEY & BELFUS INC PI PHILADELPHIA PA 210 S 13TH ST, PHILADELPHIA, PA 19107 USA SN 1067-5027 J9 J AM MED INFORM ASSN JI J. Am. Med. Inf. Assoc. PD MAR-APR PY 2005 VL 12 IS 2 BP 121 EP 129 DI 10.1197/jamia.M1640 PG 9 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Information Science & Library Science; Medical Informatics SC Computer Science; Information Science & Library Science; Medical Informatics GA 909EC UT WOS:000227842000003 PM 15561790 ER PT J AU Torvik, VI Weeber, M Swanson, DR Smalheiser, NR AF Torvik, VI Weeber, M Swanson, DR Smalheiser, NR TI A probabilistic similarity metric for Medline records: A model for author name disambiguation SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID MONOTONE BOOLEAN FUNCTIONS; RETRIEVAL AB We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service ("Author-ity": http://arrowsmith.psych.uic.edu) that takes as input an author's name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated. C1 Univ Illinois, Dept Psychiat, Chicago, IL 60612 USA. Univ Chicago, Div Humanities, Chicago, IL 60637 USA. RP Torvik, VI (reprint author), Univ Illinois, Dept Psychiat, MC912,1601 W Taylor St, Chicago, IL 60612 USA. EM vtorvik@uic.edu; marc@weeber.net; dswanson@uchicago.edu; smalheiser@psych.uic.edu RI Torvik, Vetle/A-2327-2008 CR CHURCHES T, 2002, BMC MED INFORMATICS French JC, 2000, J AM SOC INFORM SCI, V51, P774, DOI 10.1002/(SICI)1097-4571(2000)51:8<774::AID-ASI90>3.3.CO;2-G Garfield E., 1979, CITATION INDEXING Grossman J., 2002, C NUMERANTIUM, V158, P201 Holmes D. I., 2001, Literary & Linguistic Computing, V16, DOI 10.1093/llc/16.4.403 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jones K. S., 2000, Information Processing & Management, V36 JUDSON DH, 2002, ANN M CLASS SOC N AM KARYPIS G, 1999, IEEE COMPUT, V32, P68, DOI DOI 10.1109/2.781637 Lawrence S., 1999, Proceedings of the Third International Conference on Autonomous Agents, DOI 10.1145/301136.301255 Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.026118 *NOM, 2001, NLM TECHN B Noyons ECM, 1999, J AM SOC INFORM SCI, V50, P115, DOI 10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.0.CO;2-J ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON T, 1988, ORDER RESITRICTED ST Ruiz-Perez R, 2002, J MED LIBR ASSOC, V90, P411 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Swanson DR, 1997, ARTIF INTELL, V91, P183, DOI 10.1016/S0004-3702(97)00008-8 Taskar B, 2001, P 17 INT JOINT C ART, P870 Torvik VI, 2003, INFORM SCIENCES, V151, P171, DOI 10.1016/S0020-0255(03)00062-8 TORVIK VI, 2002, NIH HUM BRAIN PROJ A Torvik VI, 2002, INFORMS J COMPUT, V14, P144, DOI 10.1287/ijoc.14.2.144.117 WARNER JW, 2001, P 1 ACM IEEE CS JOIN, P21, DOI 10.1145/379437.379441 Wilbur WJ, 1996, COMPUT BIOL MED, V26, P209, DOI 10.1016/0010-4825(95)00055-0 WINKLER WE, 1995, WILEY S PRO, P355 Yu H, 2002, J AM MED INFORM ASSN, V9, P262, DOI 10.1197/jamia.M0913 NR 26 TC 57 Z9 59 PU WILEY-BLACKWELL PI HOBOKEN PA 111 RIVER ST, HOBOKEN 07030-5774, NJ USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JAN 15 PY 2005 VL 56 IS 2 BP 140 EP 158 DI 10.1002/asi/20105 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 890JU UT WOS:000226508300004 ER PT J AU Soucy, P Mineau, GW AF Soucy, Pascal Mineau, Guy W. BE Kaelbling, LP Saffotti, A TI Beyond TFIDF Weighting for Text Categorization in the Vector Space Model SO 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05) LA English DT Proceedings Paper CT 19th International Joint Conference on Artificial Intelligence (IJCAI 05) CY JUL 30-AUG 05, 2005 CL Edinburgh, SCOTLAND SP BCS, Scottish Enterprise, QinetiQ, BTO, Foresight, Microsoft Research, ERCIM, Intelligent Applications Ltd, IBM, CologNET, Intel, Google AB KNN and SVM are two machine learning approaches to Text Categorization (TC) based on the Vector Space Model. In this model, borrowed from Information Retrieval, documents are represented as a vector where each component is associated with a particular word from the vocabulary. Traditionally, each component value is assigned using the information retrieval TFIDF measure. While this weighting method seems very appropriate for IR, it is not clear that it is the best choice for TC problems. Actually, this weighting method does not leverage the information implicitly contained in the categorization task to represent documents. In this paper, we introduce a new weighting method based on statistical estimation of the importance of a word for a specific categorization problem. This method also has the benefit to make feature selection implicit, since useless features for the categorization problem considered get a very small weight. Extensive experiments reported in the paper shows that this new weighting method improves significantly the classification accuracy as measured on many categorization tasks. C1 [Soucy, Pascal] Coveo, Quebec City, PQ, Canada. RP Soucy, P (reprint author), Coveo, Quebec City, PQ, Canada. EM psoucy@coveo.com; guy.mineau@ift.ulaval.ca CR Brank J, 2002, P 19 C MACH LEARN IC Debole F., 2003, P SAC 03 18 ACM S AP, P784 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Han EH, 1999, THESIS U MINNESOTA Joachims T., 1998, P EUR C MACH LEARN JOACHIMS T, 1998, LS8 U DORTM, P24 Lewis D., 1997, REUTERS 21578 TEXT C Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 ROSE TG, 2001, P 3 INT C LANG RES E SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILSON EB, 1927, J AM STAT ASSOC, V22, P212 Wilson EB, 1927, J AM STAT ASSOC, V22, P209, DOI 10.2307/2276774 Yang Y, 1999, SIGIR 99 NR 13 TC 13 Z9 13 PU IJCAI-INT JOINT CONF ARTIF INTELL PI FREIBURG PA ALBERT-LUDWIGS UNIV FREIBURG GEORGES-KOHLER-ALLEE, INST INFORMATIK, GEB 052, FREIBURG, D-79110, GERMANY PY 2005 BP 1130 EP 1135 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BUS48 UT WOS:000290233000181 ER PT S AU Tsai, WH Cheng, SS Chao, YH Wang, HM AF Tsai, WH Cheng, SS Chao, YH Wang, HM GP IEEE TI Clustering speech utterances by speaker using eigenvoice-motivated vector space models SO 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING SE International Conference on Acoustics Speech and Signal Processing ICASSP LA English DT Proceedings Paper CT 30th IEEE International Conference on Acoustics, Speech, and Signal Processing CY MAR 19-23, 2005 CL Philadelphia, PA SP IEEE AB This study investigates the problem of automatically grouping unknown speech utterances based on their associated speakers. The proposed method utilizes the vector space model, which was originally developed in document-retrieval research, to characterize each utterance as a tf-idf-based vector of acoustic terms, thereby deriving a reliable measurement of similarity between utterances. To define the required acoustic terms that are most representative in terms of voice characteristics, the eigenvoice approach is applied on the utterances to be clustered, which creates a set of eigenvector-based terms. To further improve speaker-clustering performance, the proposed method encompasses a mechanism of blind relevance feedback for refining the inter-utterance similarity measure. C1 Acad Sinica, Inst Sci Informat, Taipei 115, Taiwan. RP Tsai, WH (reprint author), Acad Sinica, Inst Sci Informat, Taipei 115, Taiwan. EM wesley@iis.sinica.edu.tw; sscheng@iis.sinica.edu.tw; yschao@iis.sinica.edu.tw; whm@iis.sinica.edu.tw CR CHEN S, P ICASSP 98 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 FALTLHAUSER R, P ASRU 01 HUBERT L, 1985, J CLASSIF, V2, P193, DOI 10.1007/BF01908075 JIN H, P DARPA SPEECH REC W Kuhn R, 2000, IEEE T SPEECH AUDI P, V8, P695, DOI 10.1109/89.876308 Lapidot I, 2002, IEEE T NEURAL NETWOR, V13, P877, DOI 10.1109/TNN.2002.1021888 REYNOLDS D, P ICSLP 98 Reynolds DA, 2000, DIGIT SIGNAL PROCESS, V10, P19, DOI 10.1006/dspr.1999.0361 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOLOMONOFF A, P ICASSP 98 TSAI W, P ICSLP 04 NR 13 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1520-6149 BN 0-7803-8874-7 J9 INT CONF ACOUST SPEE PY 2005 BP 725 EP 728 PG 4 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BCI02 UT WOS:000229404200182 ER PT B AU Alguliev, RM Aliguliyev, RM AF Alguliev, RM Aliguliyev, RM GP IEEE Computer Society TI Effective summarization method of text documents SO 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Proceedings LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY SEP 19-22, 2005 CL Compiegne, FRANCE SP IEEE Comp Soc, Web Intelligence Consortium, Assoc Comp Machinery HO Compiegne Univ Technol AB In this paper, we propose text summarization method that creates text summary by definition of the relevance score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every sentence in the document with characteristic vector of words, which appear in the document, and calculation of relevance score for each sentence. The relevance score of sentence is determined through its comparison with all the other sentences in the document and with the document title by cosine measure. Prior to application of this method the scope of features is defined and then the weight of each word in the sentence is calculated with account of those features. The weights of features, influencing relevance of words, are determined using genetic algorithms. C1 Azerbaijan Natl Acad Sci, Inst Informat Technol, Baku, Azerbaijan. RP Alguliev, RM (reprint author), Azerbaijan Natl Acad Sci, Inst Informat Technol, Baku, Azerbaijan. RI Alguliev, Rasim/ OI Alguliev, Rasim/0000-0003-1223-7411 CR Chakarabarti S., 2000, ACM SIGKDD EXPLORATI, V1, P1, DOI 10.1145/846183.846187 Delort J.-Y., 2003, P 14 ACM C HYP HYP A, P208 EIBEN AE, 2003, INTRO EVOLUTIONARY Fresno V, 2004, J INTELL INF SYST, V22, P215, DOI 10.1023/B:JIIS.0000019277.82436.17 Goldberg D. E., 1989, GENETIC ALGORITHMS S Goldstein J., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312665 Gong Y., 2001, P 24 ANN INT ACM SIG, P19, DOI DOI 10.1145/383952.383955 Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58 Hu P., 2004, P 4 INT C COMP INF T, P1159 Jain AK, 1999, ACM COMPUT SURV, V31 Kim OJ, 2003, J KOREAN MED SCI, V18, P3 Ko Y, 2004, INFORM PROCESS MANAG, V40, P65, DOI 10.1016/S0306-4573(02)00056-0 Lam W, 2003, IEEE T PATTERN ANAL, V25, P628 Lee Z.-J., 2003, KNOWL INF SYST, V5, P503, DOI 10.1007/s10115-003-0082-0 Mani I, 1999, ADV AUTOMATED TEXT S Michalewicz Z, 1996, GENETIC ALGORITHMS D Mitra M., 1997, P ACL 97 EACL 97 WOR, P39 OLSEN AL, 1994, P 1 IEEE C EV COMP O, V2, P554 Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1986, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen D, 2004, P 27 ANN INT C RES D, P242, DOI 10.1145/1008992.1009035 TOLCHEEV VO, 2004, INFORMATION TECHNOLO, P6 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 NR 25 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2415-X PY 2005 BP 264 EP 271 DI 10.1109/WI.2005.57 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDM68 UT WOS:000234321300046 ER PT B AU Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T AF Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T GP IEEE Computer Society TI Establishing guidelines on how to improve the web site content based on the identification of representative pages SO 2005 IEEE/WIC/ACM International Conference on Web Intelligence, Proceedings LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY SEP 19-22, 2005 CL Compiegne, FRANCE SP IEEE Comp Soc, Web Intelligence Consortium, Assoc Comp Machinery HO Compiegne Univ Technol AB The internet has become a big battlefield where organizations are trying to keep their present clients and to gain new ones. Two important weapons that the organizations have are to make a good web site design and to have a content interesting for the visitors. To improve the web site content many tools have been developed However it is hard to figure out how to apply these changes. Furthermore, in complex web sites, this is a non trivial task. We propose a novel approach that helps to improve a web site content using a SOFM and performing a reverse clustering analysis that allows us to gather the most representative web pages from a web site, using this small set of pages as a guideline of how these enhancements should be performed The effectiveness of the method was tested in a real web site. C1 Univ Tokyo, Adv Sci & Technol Res Ctr, Meguro Ku, Tokyo, Japan. RP Rios, SA (reprint author), Univ Tokyo, Adv Sci & Technol Res Ctr, Meguro Ku, 4-6-1 Komaba, Tokyo, Japan. CR Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 LIU B, 2003, WEB INTELLIGENCE, P105 Mani I., 1999, Information Retrieval, V1, DOI 10.1023/A:1009930203452 MITRA M, 1997, P ACL EACL 97 WORKSH, P31 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turney P., 2003, P 18 INT JOINT C ART, P434 Velasquez J. D., 2004, Proceedings. 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2415-X PY 2005 BP 284 EP 288 DI 10.1109/WI.2005.60 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDM68 UT WOS:000234321300050 ER PT B AU Zushi, T Takano, K Kiyoki, Y AF Zushi, T Takano, K Kiyoki, Y GP IEEE COMPUTER SOCIETY TI A vector space retrieval method with causal relationship computation functions for event data SO 2005 SYMPOSIUM ON APPLICATIONS AND THE INTERNET WORKSHOPS, PROCEEDINGS LA English DT Proceedings Paper CT International Symposium on Applications and the Internet (SAINT 2005) CY JAN 31-FEB 04, 2005 CL Trento, ITALY SP IEEE Comp Soc, Informat Proc Soc Japan, Commun Soc, Informat & Syst Soc, Inst Elect, Informat & Commun Engineers AB In this paper, we propose a vector space retrieval method with causal relationship computation functions for event data. The method is used for the domain in which a query given by a user can be regarded as an event. By expressing an event as a vector, causal relationships between events can be described as numerical values. This feature enables to set a level of importance on a specific event flexibly. The method can be applied to document retrieval with causal relationship computation. We clarify effectiveness of the method by showing several experimental results using synthetic event and document data sets. C1 Keio Univ, Grad Sch Media & Governance, Tokyo 108, Japan. RP Zushi, T (reprint author), Keio Univ, Grad Sch Media & Governance, Tokyo 108, Japan. CR BOLLEN KA, 1989, STRUCTURA EQUATIONS DEERWESTER SC, 1991, J AM SOC INFORM SCI, V41, P391 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Pearl J., 1988, PROBABILISTIC REASON SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 5 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2263-7 PY 2005 BP 430 EP 433 DI 10.1109/SAINTW.2005.1620066 PG 4 WC Computer Science, Information Systems; Telecommunications SC Computer Science; Telecommunications GA BBR28 UT WOS:000227352600103 ER PT B AU Chatti, MA Klamma, R Quix, C Kensche, D AF Chatti, MA Klamma, R Quix, C Kensche, D BE Goodyear, P Sampson, DG Yang, DJT Kinshuk, X Okamoto, T Hartley, R Chen, NS TI LM-DTM: An environment for XML-based, LIP/PAPI-Compliant deployment, transformation and matching of learner models SO 5th IEEE International Conference on Advanced Learning Technologies, Proceedings LA English DT Proceedings Paper CT 5th IEEE International Conference on Advanced Learning Technologies CY JUL 05-08, 2005 CL Kaohsiung, TAIWAN SP IEEE TCLT, IEEE Comp Soc, Natl Kaohsiung Normal Univ, Microsoft Res, Natl Sun Yatsen Univ AB Our shared belief is that learning, like other human activities, cannot and will not be confined within rigidly defined course systems or learning repositories, inclosing learning resources which cannot be tailored to the different learner's needs, skills, interests, preferences, goals, etc. Therefore, a learning environment, besides supporting communication between knowledge providers and consumers, has to be organized in a flexible manner based on different learner profiles. Learner modeling has become a highly challenging task to provide personalized, adaptive and context-based learning. The work presented in this paper addresses this issue by providing a meta-level solution for the description, transformation and matching of learner models, based on standards such as IMS LIP, IEEE PAPI, XML to foster the reuse and exchange of learner models between learning platforms, both by universities and corporations. C1 Rhein Westfal TH Aachen, Aachen, Germany. CR HODGINS HW, FUTURE *IEEE, 1484121 IEEE *IEEE, P14842D8 *IMS, IMS LEARN INF PACK S SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 5 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2338-2 PY 2005 BP 567 EP 569 DI 10.1109/ICALT.2005.193 PG 3 WC Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA BCR30 UT WOS:000230898800169 ER PT B AU Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T AF Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T BE Kwasnicka, H Paprzycki, M TI Improving the web text content by extracting significant pages into a Web Site SO 5th International Conference on Intelligent Systems Design and Applications, Proceedings LA English DT Proceedings Paper CT 5th International Conference on Intelligent Systems Design and Applications (ISDA 2005) CY SEP 08-10, 2005 CL Wroclaw, POLAND SP World Federat Soft Comp, European Soc Fuzzy Log & Technol, European Neural Network Soc, Warsaw Sch Social Psychol, Polish Minist Sci Res & Informat Technol ID DIRECTIONS AB Web Systems have reached a very important role in today's business world. Every day organizations fight to keep their present clients and to gain new ones. In order to accomplish this goal it is very important to make precise changes in the web site content. However, the development of these improvements is a complex and specialized task because of the nature of the web data itself We propose a novel approach to successfully make changes to improve the web site content using text mining. We use a Self Organizing Feature Map (SOFM) to find the most relevant text content, and then we propose a reverse clustering analysis in order to extract the most significant pages of the whole web site. The effectiveness of this method was experimentally tested in a real web site. C1 Univ Tokyo, Res Ctr Adv Sci & Technol, Meguro Ku, Tokyo, Japan. RP Rios, SA (reprint author), Univ Tokyo, Res Ctr Adv Sci & Technol, Meguro Ku, 4-6-1 Komaba, Tokyo, Japan. CR BERENDT B, 2001, VLDB J, V9, P27 Buyukkokten O., 2001, P 10 INT C WORLD WID, P652, DOI 10.1145/371920.372178 Kosala R., 2000, SIGKDD EXPLORATIONS Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 Velasquez JD, 2003, LECT NOTES ARTIF INT, V2773, P951 NR 8 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2286-6 PY 2005 BP 32 EP 36 PG 5 WC Computer Science, Artificial Intelligence SC Computer Science GA BDE37 UT WOS:000233058700006 ER PT B AU Doan, S Horiguchi, S AF Doan, S Horiguchi, S BE Ribeiro, B Albrecht, RF Dobnikar, A Pearson, DW Steele, NC TI The use of multi-criteria in feature selection to enhance text categorization SO Adaptive and Natural Computing Algorithms SE SPRINGER COMPUTER SCIENCE LA English DT Proceedings Paper CT 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA) CY MAR 21-23, 2005 CL Coimbra, PORTUGAL HO Univ Coimbra AB Feature selection has been an interesting issue in text categorization up to now. Previous works in feature selection often used filter model in which features, after ranked by a measure, are selected based on a given threshold. In this paper, we present a novel approach to feature selection based on multi-criteria of each feature. Instead of only one criterion, multi-criteria of a feature are used; and a procedure based on each threshold of feature selection is proposed. This framework seems to be suitable for text data and applied to text categorization. Experimental results on Reuters-21578 benchmark data show that our approach has a promising scheme and enhances the performance of a text categorization system. C1 Japan Adv Inst Sci & Technol, Tatsunokuchi, Ishikawa, Japan. RP Doan, S (reprint author), Japan Adv Inst Sci & Technol, 1-1,Asahidai 1-8, Tatsunokuchi, Ishikawa, Japan. CR BLUM A, 1997, ARTIF INTELL, V1, P245 DEBOLE F, 2004, IN PRESS J AM SOC IN Huang SH, 2003, IEEE T KNOWL DATA EN, V15, P1364, DOI 10.1109/TKDE.2003.1245278 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X MCCALLUM AK, 1996, TOOLKIT STAT LANGUAG Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Rogati M., 2002, INT C INF KNOWL MAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SOUCY P, 2001, INT JOINT C ART INT Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG WIEN PI VIENNA PA SACHSENPLATZ 4-6, A-1201 VIENNA, AUSTRIA BN 3-211-24934-6 J9 SPRING COMP SCI PY 2005 BP 441 EP 444 DI 10.1007/3-211-27389-1_106 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BCH94 UT WOS:000229368400106 ER PT S AU Guo, J Keselj, V Gao, Q AF Guo, J Keselj, V Gao, Q BE Kegl, B Lapalme, G TI Integrating Web content clustering into Web log association rule mining SO ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 18th Conference of the Canadian-Society-for-Computational-Studies-of-Intelligence CY MAY 09-11, 2005 CL Victoria, CANADA SP Canadian Soc Computat Studies Intelligence ID WORLD-WIDE-WEB AB One of the effects of the general Internet growth is an immense number of user accesses to WWW resources. These accesses are recorded in the web server log files, which are a rich data resource for finding useful patterns and rules of user browsing behavior, and they caused the rise of technologies for Web usage mining. Current Web usage mining applications rely exclusively on the web server log files. The main hypothesis discussed in this paper is that Web content analysis can be used to improve Web usage mining results. We propose a system that integrates Web page clustering into log file association mining and uses the cluster labels as Web page content indicators. It is demonstrated that novel and interesting association rules can be mined from the combined data source. The rules can be used further in various applications, including Web user profiling and Web site construction. We experiment with several approaches to content clustering, relying on keyword and character n-gram based clustering with different distance measures and parameter settings. Evaluation shows that character n-gram based clustering performs better than word-based clustering in terms of an internal quality measure (about 3 times better). On the other hand, word-based cluster profiles are easier to manually summarize. Furthermore, it is demonstrated that high-quality rules are extracted from the combined dataset. C1 Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 1W5, Canada. RP Guo, J (reprint author), Dalhousie Univ, Fac Comp Sci, 6050 Univ Ave, Halifax, NS B3H 1W5, Canada. EM jguo@cs.dal.ca; vlado@cs.dal.ca; qggao@cs.dal.ca CR ASLTON G, 1988, INFORMATION PROCESSI, V24, P513 BORGES J, 1999, P WEBKDD, P92 Chakrabarti S, 1999, COMPUTER, V32, P60, DOI 10.1109/2.781636 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 EIRINAKI M, 2004, ACM WEB INF DAT MAN, P70 Etzioni O, 1996, COMMUN ACM, V39, P65, DOI 10.1145/240455.240473 JIN X, 2004, P AAAI 2004 WORKSH S, P26 JO T, 2001, P 2 INT S ADV INT SY, P95 Kamber M., 2001, DATA MINING CONCEPTS KATO H, 2000, P WORKSH WEB MIN E C, P95 Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 Madria S.K., 1999, P 1 INT C DAT WAR KN, P303 MIAO Y, 2004, COMP DOCUMENT CLUSTE Mobasher B, 2000, P INT C E COMM WEB T, P165 PANDEY A, 2001, TR01004 U MINN DEP C PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PUNIN J, 2001, WEB USAGE MINING LAN, P88 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, P TEXT MIN WORKSH KD Ypma A., 2002, WORKSH WEB KNOWL DIS, P31 NR 20 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25864-7 J9 LECT NOTES COMPUT SC PY 2005 VL 3501 BP 182 EP 193 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BCM07 UT WOS:000229965500019 ER PT S AU Vechtomova, O AF Vechtomova, O BE Losada, DE FernandezLuna, JM TI The role of multi-word units in interactive information retrieval SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 27th European Conference on Information Retrieval Research (ECIR 2005) CY MAR 21-23, 2005 CL Santiago Compostela, SPAIN SP Microsoft Res, SHARP, ERCIM, CEPIS, BCS HO Univ Santiago Compostele, Tech Sch Engn ID PROBABILISTIC MODEL AB The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase search method. A combined syntactico-statistical method was used for the selection of phrases. First, noun phrases were selected using a part-of-speech tagger and a noun-phrase chunker, and secondly, different statistical measures were applied to select phrases for query expansion. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. We analyse the problems of phrase weighting and suggest new ways of addressing them. A new method of phrase matching and weighting was developed, which specifically addresses the problem of weighting overlapping and non-contiguous word sequences in documents. C1 Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. RP Vechtomova, O (reprint author), Univ Waterloo, Dept Management Sci, Waterloo, ON N2L 3G1, Canada. EM ovechtom@engmail.uwaterloo.ca CR ALLAN J, 2004, P 12 TEXT RETR C NIS, P24 Banerjee S., 2003, P 4 INT C INT TEXT P Beaulieu M, 1998, INTERACT COMPUT, V10, P237, DOI 10.1016/S0953-5438(98)00008-3 BELY N, 1970, PROCEDURES ANAL SEMA Brill E, 1995, COMPUT LINGUIST, V21, P543 CLARKE CLA, 1995, CS9507 U WAT Dunning T., 1993, Computational Linguistics, V19 Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Frantzi K., 1996, P 16 INT C COMP LING, P41 Jones K. S., 2000, Information Processing & Management, V36 Jones K. S., 2000, Information Processing & Management, V36 Manning C. D., 1999, FDN STAT NATURAL LAN MARCHIONINI G, 1992, J AM SOC INFORM SCI, V43, P156, DOI 10.1002/(SICI)1097-4571(199203)43:2<156::AID-ASI8>3.0.CO;2-U Mitra M, 1997, P RIAO97 COMP ASS IN, P200 RAMSHAW L, 1995, P 3 ACL WOKSH VER LA ROBERTSON SE, 2004, P 12 TEXT RETR C, P418 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ruthven L, 2003, P 26 ANN INT ACM SIG, P213 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1968, COMPUTER EVALUATION, V15, P8 SMEATON AF, 1998, P 20 BCS IRSG C GREN STRZALKOWSKI T, 1999, NATURAL LANGUAGE INF, P113 VECHTOMOVA O, 2004, P 12 TEXT RETR C, P820 VECHTOMOVA O, 2005, IN PRESS P 13 TEXT R Vintar S., 2004, P MEMURA 2004 WORKSH, P54 VOORHEES E, 2004, P 12 TEXT RETR C NIS Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 28 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25295-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3408 BP 403 EP 420 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCF33 UT WOS:000229020900029 ER PT S AU Liu, T Guo, J AF Liu, T Guo, J BE Huang, DS Zhang, XP Huang, GB TI Text similarity computing based on standard deviation SO ADVANCES IN INTELLIGENT COMPUTING, PT 1, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Conference on Intelligent Computing CY AUG 23-26, 2005 CL Hefei, PEOPLES R CHINA SP Inst Intelligent Machines, Univ Sci Technol, IEEE Computat Intelligence Soc, Hong Kong Computat Intelligence Chapter AB Automatic text categorization is defined as the task to assign free text documents to one or more predefined categories based on their content. Classical method for computing text similarity is to calculate the cosine value of angle between vectors. In order to improve the categorization performance, this paper puts forward a new algorithm to compute the text similarity based on standard deviation. Experiments on Chinese text documents show the validity and the feasibility of the standard deviation-based algorithm. C1 Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China. RP Liu, T (reprint author), Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China. EM sdlclt@sohu.com CR APTE C, 1994, P 17 ANN INT ACM SIG BIN L, 2002, J CHINESE INFORMATIO, V16, P18 CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 DAVID DL, 1996, SIGIR 96, P298 Fano RM, 1961, TRANSMISSION INFORMA Fuhr N., 1991, P RIAO 91, P606 JAOCHIMS T, 1998, P 10 EUR C MACH LEAR KEENETH WC, 1989, P ACL, V27, P76 Lewis D., 1994, 3 ANN S DOC AN INF R, P81 Moulinier I, 1997, LAFORIALIP6 U PAR 6 MOULINIER I, 1996, P 5 ANN S DOC AN INF Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, ASS COMPUTING MACHIN, V15, P8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TOM M, 1996, MACHINE LEARNING Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 WIENER E, 1995, P 4 ANN S DOC AN INF WILLIAM WC, 1996, SIGIR 96, P307 YANG Y, 1994, ACM T INFORM SYST, V12, P253 YANG Y, 1992, P 14 INT C COMP LING, V2, P447, DOI 10.3115/992133.992139 YIMING Y, 1997, P ICML 97 14 INT C M, P412 YIMING Y, 1995, ACM SIGIR C RES DEV, P256 NR 25 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28226-2 J9 LECT NOTES COMPUT SC PY 2005 VL 3644 BP 456 EP 464 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BDC09 UT WOS:000232528800048 ER PT S AU Liu, J Yao, JX Wu, GF AF Liu, J Yao, JX Wu, GF BE Famili, AF Kok, JN Pena, JM Siebes, A Feelders, A TI Sentiment classification using Information Extraction technique SO ADVANCES IN INTELLIGENT DATA ANALYSIS VI, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 6th International Symposium on Intelligent Data Analysis CY SEP 08-10, 2005 CL Madrid, SPAIN AB This paper explores the sentiment classification with Information Extraction (IE) approach. The IE approach here is required to detect the sentiment expressions on specific subject (person, product, company and so on) and then to evaluate the sentiment strength and/or the validation of them. Our method can be illustrated logically as: (1) From a given text, extract the sentiment expressions on the specific subjects and attach certain sentiment tag and weight to each of them; (2) Calculate the sentiment indicator for each sentiment genre by accumulating the weights of all the expression with the corresponding tag; (3) Given the indicators on different sentiment genres, use a classifier to predict the sentiment label of the given text. To extract expression robustly when encounter some complex linguistic phenomena (such as ellipsis, anaphora), a new parsing idea named super parsing is proposed. It enables some non-adjacent linguistic constituents to be merged to deduce a new one. As an incremental implementation of super parsing, a system named Approximate Text Analysis (ATA) is described in this paper. As for the classification task, two different classifiers are used: simple linear classifier (called SLC here) and SVM. The experiments show the reasonable performance of our approach. C1 Shanghai Univ, Dept Comp Sci, Shanghai 200072, Peoples R China. RP Liu, J (reprint author), Shanghai Univ, Dept Comp Sci, 149 Yanchang Rd, Shanghai 200072, Peoples R China. EM liujian@mail.shu.edu.cn; jianxin_yao@mail.shu.edu.cn; gfwu@mail.shu.edu.cn CR Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, DOI 10.1145/130385.130401 Collobert R, 2001, J MACH LEARN RES, V1, P143, DOI 10.1162/15324430152733142 Das S, 2001, AS PAC FIN ASS ANN C Eikvil L., 1999, 945 NORW COMP CTR HEARST MA, 1992, TEXT-BASED INTELLIGENT SYSTEMS : CURRENT RESEARCH AND PRACTICE IN INFORMATION EXTRACTION AND RETRIEVAL, P257 Hu M., 2004, P 19 NAT C ART INT Hu M., 2004, P ACM SIGKDD INT C K Johnson M., 1985, P 23 C ASS COMP LING, P127, DOI 10.3115/981210.981225 Kobayashi N., 2004, P 1 INT JOINT C NAT, P584 LIU J, 2005, Patent No. 2005100235898 Muslea I., 1999, AAAI WORKSH MACH LEA NASUKAWA T, 2003, 2 INT C KNOWL CAPT K, P70 Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79 SACK W, 1995, P AAAI94 SEATTL WA, P1488 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Spertus E., 1997, P INN APPL ART INT I, P1058 Tatemura J., 2000, P 5 INT C INT US INT, P272, DOI 10.1145/325737.325870 Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 TONG RM, 2001, SIGIR WORKSH OP TEXT Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153 Yi J., 2003, 3 IEEE INT C DAT MIN NR 21 TC 2 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28795-7 J9 LECT NOTES COMPUT SC PY 2005 VL 3646 BP 216 EP 227 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDA54 UT WOS:000232273600020 ER PT S AU Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T AF Rios, SA Velasquez, JD Vera, ES Yasuda, H Aoki, T BE Wang, L Chen, K Ong, YS TI Using SOFM to improve web site text content SO ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 1st International Conference on Natural Computation (ICNC 2005) CY AUG 27-29, 2005 CL Changsha, PEOPLES R CHINA SP Xiangtang Univ, IEEE Circuits & Syst Soc, IEEE Computat Intelligence Soc, IEEE Control Syst Soc, Int Neural Network Soc, European Neural Network Soc, Chinese Assoc Artificial Intelligence, Japanese Neural Network Soc, Int Fuzzy Syst Assoc, Asia Pacific Neural Network Assembly, Fuzzy Math & Syst Assoc China, Hunan Comp Federat ID DIRECTIONS AB We introduce a new method to improve web site text content by identifying the most relevant free text in the web pages. In order to understand the variations in web page text, we collect pages during a period. The page text content is then transformed into a feature vector and is used as input of a clustering algorithm (SOFM), which groups the vectors by common text content. In each cluster, a centroid and its neighbor vectors are extracted. Then using a reverse clustering analysis, the pages represented by each vector are reviewed in order to find the similar. Furthermore, the proposed method was tested in a real web site, proving the effectiveness of this approach. C1 Univ Tokyo, Res Ctr Adv Sci & Technol, Tokyo, Japan. Univ Chile, Dept Ind Engn, Santiago, Chile. Univ Tokyo, Ctr Collaborat Res, Tokyo, Japan. RP Rios, SA (reprint author), Univ Tokyo, Res Ctr Adv Sci & Technol, Tokyo, Japan. EM srios@mpeg.rcast.u-tokyo.ac.jp; jvelasqu@dii.uchile.cl; esvera@vp.ccr.u-tokyo.ac.jp; yasuda@mpeg.rcast.u-tokyo.ac.jp; aoki@mpeg.rcast.u-tokyo.ac.jp RI Velasquez, Juan/H-3859-2013 OI Velasquez, Juan/0000-0003-4819-9680 CR BERENDT B, 2001, VLDB J, V9, P27 Buyukkokten O., 2001, P 10 INT C WORLD WID, P652, DOI 10.1145/371920.372178 CHAKRABARTI S, 2000, NEWSLETTER SPECIAL I Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 Loh S., 2000, SIGKDD EXPLORATIONS, V2, P29 Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 Pal SK, 2002, IEEE T NEURAL NETWOR, V13, P1163, DOI 10.1109/TNN.2002.1031947 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VELASQUEZ JD, 2005, INT J WEB INFORMATIO, V1, P11 Velasquez JD, 2003, LECT NOTES ARTIF INT, V2773, P951 VELASQUEZ JD, 2004, IEEE INT C E TECHN, P285, DOI 10.1109/EEE.2004.1287324 NR 11 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28325-0 J9 LECT NOTES COMPUT SC PY 2005 VL 3611 BP 622 EP 626 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BDA23 UT WOS:000232222500088 ER PT S AU Cui, LZ Wang, HY AF Cui, LZ Wang, HY BE Szczepaniak, PS Kacprzyk, J Niewiadomski, A TI Vector retrieval model for XML document based on dynamic partition of information units SO ADVANCES IN WEB INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Atlantic Web Intelligence Conference (AWIC 2005) CY JUN 06-09, 2005 CL Lodz, POLAND SP Tech Univ Lodz, Inst Comp Sci, Polish Acad Sci, Syst Res Inst, Polish Cybernet Soc, Lodz Div, Polish Neural Networks Soc AB XML document is applied in WEB application more and more. Because users can find what they need in numerous XML documents, technology of information retrieval based on XML document becomes a hot topic in information retrieval field now. Traditional technology of information retrieval based on XML document need define retrieval unit and retrieval result unit of the retrieval beforehand, and the dividing granularity is either too big or too small. In this paper we propose a retrieval method, which can dynamically partition information units in terms of the structure and semantic information of XML in vector space model. Therefore it reduces calculating workload efficiently and improves running efficiency of the entire retrieval system. The retrieval efficiency of this method is proved than the traditional one when they have the same accuracy. Finally, the results have been testified by experiment. C1 Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China. RP Cui, LZ (reprint author), Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China. EM clz@dareway.com.cn CR Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 HATANO K, 2002, P 13 INT C DAT EXP S, P758 HAYASHI Y, 2000, ACM SIGIR 2000 WORKS Lee Joon Ho, 1994, P 17 ANN INT ACM SIG, P182 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Theobald A., 2000, P WEBDB 2000, P105 NR 6 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26219-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3528 BP 114 EP 119 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BCN66 UT WOS:000230302600019 ER PT S AU Markov, A Last, M AF Markov, A Last, M BE Szczepaniak, PS Kacprzyk, J Niewiadomski, A TI A simple, structure-sensitive approach for web document classification SO ADVANCES IN WEB INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Atlantic Web Intelligence Conference (AWIC 2005) CY JUN 06-09, 2005 CL Lodz, POLAND SP Tech Univ Lodz, Inst Comp Sci, Polish Acad Sci, Syst Res Inst, Polish Cybernet Soc, Lodz Div, Polish Neural Networks Soc AB In this paper we describe a new approach to classification of web documents. Most web classification methods are based on the vector space document representation of information retrieval. Recently the graph based web document representation model was shown to outperform the traditional vector representation using k-Nearest Neighbor (k-NN) classification algorithm. Here we suggest a new hybrid approach to web document classification built upon both, graph and vector representations. K-NN algorithm and three benchmark document collections were used to compare this method to graph and vector based methods separately. Results demonstrate that we succeed in most cases to outperform graph and vector approaches in terms of classification accuracy along with a significant reduction in classification time. C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RP Markov, A (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. CR Jain A.K., 1999, ACM COMPUTING SURVEY, V31 Kamber M., 2001, DATA MINING CONCEPTS KURAMOCHI M, 2002, TR0226 U MINN DEP CO Maimon O., 2000, KNOWLEDGE DISCOVERY McCallum A., 1998, AAAI 98 WORKSH LEARN Mitchell TM, 1997, MACHINE LEARNING MUKUND D, 2003, ICDM 2003 3 IEEE INT Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A., 2003, THESIS U S FLORIDA Schenker A, 2004, INT J PATTERN RECOGN, V18, P475, DOI 10.1142/S0218001404003241 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Yan X., 2002, UIUCDCSR20022296 NR 14 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26219-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3528 BP 293 EP 298 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BCN66 UT WOS:000230302600046 ER PT S AU Crouch, CJ Mahajan, A Bellamkonda, A AF Crouch, CJ Mahajan, A Bellamkonda, A BE Fuhr, N Lalmas, M Malik, S Szlavik, Z TI Flexible retrieval based on the vector space model SO ADVANCES IN XML INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Workshop of the Initiative for the Evaluation of XML Retrieval CY DEC 06-08, 2004 CL Schloss Dagstuhl, GERMANY AB This paper describes the current state of our system for structured retrieval. The system itself is based on an extension of the vector space model initially proposed by Fox [5]. The basic functions are performed using the Smart experimental retrieval system [11]. The major advance achieved this year is the inclusion of a flexible capability, which allows the system to retrieve at a desired level of granularity (i.e., at the element level). The quality of the resultant statistics is largely dependent on issues (in particular, ranking) which have yet to be resolved. C1 Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. RP Crouch, CJ (reprint author), Univ Minnesota, Dept Comp Sci, Duluth, MN 55812 USA. EM ccrouch@d.umn.edu CR BELLAMKONDA A, 2004, THESIS U MINNESOTA D CROUCH C, 1990, P 13 ANN INT ACM SIG, P369, DOI 10.1145/96749.98242 CROUCH C, 2003, P 2 WORKSH IN EV XML, P87 CROUCH C, 2002, P 1 WORKSH IN EV XML, P99 FOX EA, 1983, THESIS CORNELL U FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 Kamps J., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009009 Liu S., 2004, P 27 ANN INT ACM SIG, P88, DOI 10.1145/1008992.1009010 MAHAJAN A, 2004, THESIS U MINNESOTA D SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1998, NIST SP, V500, P215 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 NR 15 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26166-4 J9 LECT NOTES COMPUT SC PY 2005 VL 3493 BP 292 EP 302 PG 11 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCO01 UT WOS:000230359500023 ER PT S AU Chen, ZX Fu, B AF Chen, ZX Fu, B BE Deng, X Du, D TI On the complexity of Rocchio's similarity-based relevance feedback algorithm SO ALGORITHMS AND COMPUTATION SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 16th International Symposium on Algorithms and Computations (ISAAC 2005) CY DEC 19-21, 2005 CL Hainan, PEOPLES R CHINA SP Chinese Acad Sci, Acad Math & Syst Sci, City Univ Hong Kong, Univ Hong Kong ID VECTOR-SPACE MODEL; PERCEPTRON AB In this paper, we prove for the first time that the learning complexity of Rocchio's algorithm is (d+d(2) (log d+log n)) over the discretized vector space {0,..., n-1}(d), when the inner product similarity measure is used. The upper bound on the learning complexity for searching for documents represented by a monotone linear classifier (q, 0) over {0,..., n-1}(d) can be improved to O(d+2k(n-1) (log d+log(n-1))), where k is the number of nonzero components in q. An Omega(((d)(2)) log n) lower bound on the learning complexity is also obtained for Rocchio's algorithm over {0,..., n-1}(d). In practice, Rocchio's algorithm often uses fixed query updating factors. When this is the case, the lower bound is strengthened to 20 (d) over the binary vector space {0,1}(d). In general, if the query updating factors are bounded by O(n(c)) for some constant c >= 0, an Omega(n(d-1-c)/(n-1)) lower bound is obtained over {0,...,n-1}(d). C1 Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA. Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA. Res Inst Children, New Orleans, LA 70118 USA. RP Chen, ZX (reprint author), Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA. EM chen@cs.panam.edu; fu@cs.uno.edu CR Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bshouty N. H., 1995, Proceedings of the Eighth Annual Conference on Computational Learning Theory, DOI 10.1145/225298.225322 CHEN Z, 2004, COMPUTATIONAL WEB IN, P303 CHEN Z, 2001, P 7 ANN INT COMP COM, P540 CHEN Z, 2005, P COCOON 05, P955 Chen ZX, 1997, THEOR COMPUT SCI, V180, P155, DOI 10.1016/S0304-3975(96)00144-2 Chen ZX, 2002, INFORM RETRIEVAL, V5, P61, DOI 10.1023/A:1012730924277 Frakes W. B., 1992, INFORM RETRIEVAL DAT HAMPSON SE, 1990, IEEE T SYST MAN CYB, V20, P67, DOI 10.1109/21.47810 Kivinen J, 1997, ARTIF INTELL, V97, P325, DOI 10.1016/S0004-3702(97)00039-8 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 MAASS W, 1994, COMPUTATIONAL LEARNI, V1, P381 Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANVIJSBERGEN CJ, 1979, INFORM RETRIEVAL WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 20 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-30935-7 J9 LECT NOTES COMPUT SC PY 2005 VL 3827 BP 216 EP 225 PG 10 WC Computer Science, Theory & Methods; Mathematics, Applied SC Computer Science; Mathematics GA BDQ35 UT WOS:000234885900023 ER PT S AU Yang, Y Jin, F Jiang, YQ AF Yang, Y Jin, F Jiang, YQ BE Li, DL Wang, B TI Ant-based document clustering and visualization SO Artificial Intelligence Applications and Innovations II SE INTERNATIONAL FEDERATION FOR INFORMATION PROCESSING LA English DT Proceedings Paper CT 2nd International Conference on Artificial Intelligence Applications and Innovations CY SEP 07-09, 2005 CL Beijing, PEOPLES R CHINA SP IFIP TC12, WG12 5, China Agr Univ & Informat Comm, Chinese Soc Agr Engn DE ant-based algorithm; document clustering; visualization AB This paper discusses the document clustering and visualization process: analyzing documents index, clustering document, and visualizing exploration. It focuses on ant-based clustering algorithm and some significant improvements. Clusterings are formed on the plane by ants walking, picking up or dropping down projected document vectors with different probability. It is shown that the similar documents appear in spatial proximity, whereas unrelated documents are clearly separated in visual space. C1 SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu, Peoples R China. RP Yang, Y (reprint author), SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu, Peoples R China. CR Deneubourg J., 1991, P 1 INT C SIM AD BEH, P356 HANDL J, 2002, 7 INT C PAR PROBL SO, P221 Himberg J, 2000, P IEEE INNS ENNS INT, V3, P587 Jin HD, 2004, INFORM SCIENCES, V163, P157, DOI 10.1016/j.ins.2003.03.020 Liao Guang-lan, 2003, COMPUTER ENG APPL, P35 Lumer E., 1994, P 3 INT C SIM AD BEH, V3, P499 Morris S, 2002, COMPUT IND ENG, V43, P841, DOI 10.1016/S0360-8352(02)00143-2 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sangole A, 2003, COMPUT GRAPH-UK, V27, P963, DOI 10.1016/j.cag.2003.08.012 Wu B, 2002, IEEE C EVOL COMPUTAT, P477 Yang Y, 2003, IEEE SWARM INTELLIGE, P65 NR 12 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES SN 1571-5736 BN 0-387-28318-8 J9 INT FED INFO PROC PY 2005 VL 187 BP 189 EP 196 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BCY94 UT WOS:000231956800020 ER PT S AU Li, Q Li, JH AF Li, Q Li, JH BE Gorodetsky, V Liu, J Skormin, VA TI Topic-specific text filtering based on multiple reducts SO AUTONOMOUS INTELLIGENT SYSTEMS: AGENTS AND DATA MINING, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT International Workshop on Autonomous Intelligent Systems - Agents and Data Mining (AIS-ADM 2005) CY JUN 06-08, 2005 CL St Petersburg, RUSSIA SP Russian Acad Sci, St Petersburg Inst Informat & Automat, Binghamton Univ, Web Intelligence Consortium, EOARD, USAF, USN Off Naval Res Global, USA Res Lab European Res Off, Russian Fdn Basic Res DE text filtering; content security; rough set; feature selection; statistical classification methods ID ROUGH; CLASSIFICATION AB Feature selection is a very important step in text preprocessing, a good selected feature subset can get the same performance than using full features, at the same time, it reduced the learning time. To make our system fit for the application and to embed this model gateway for real-time text filtering, we need to further select more accurate features. In this paper, we proposed a new feature selection method based on Rough set theory. It generate several reducts, but the special point is that between these reducts there are no common attributes, so these attributes have more powerfully capability to classify new objects, especially for real data set in application. We choose two data sets to evaluate our feature selection method, one is a benchmark data set from UCI machine learning archive, and another is captured from Web. We use statistical classification methods to classify these objects, in the benchmark testing set, we get good precision with a single reduct, but in real date set, we get good precision with several reducts, and the data set is used in our system for topic-specific text filtering. Thus we conclude our method is very effective in application. In addition, we also conclude that SVM and VSM methods get better performance, while Naive Bayes method get poor performance with the same selected features on non-balance data set. C1 Shanghai Jiao Tong Univ, Modern Commun Inst, Shanghai 200030, Peoples R China. RP Li, Q (reprint author), Shanghai Jiao Tong Univ, Modern Commun Inst, Shanghai 200030, Peoples R China. EM sjtuliqiang@163.com CR Bao YG, 2003, LECT NOTES COMPUT SC, V2690, P545 BURGES C, 1998, DATA MIN KNOWL DISC, V121, P284 CERCONE N, 1996, P ISMIS ZAK POL, P653 Chouchoulas A, 2001, APPL ARTIF INTELL, V15, P843, DOI 10.1080/088395101753210773 Chouchoulas A., 2002, P 2002 UK WORKSH COM, P18 Chouchoulas A, 1999, LECT NOTES ARTIF INT, V1711, P118 Han JC, 2004, LECT NOTES ARTIF INT, V3066, P176 Joachims T., 1998, P EUR C MACH LEARN John G., 1994, P 11 INT C MACH LEAR, P121 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Mitchell TM, 1997, MACHINE LEARNING Pawlak Z., 1991, ROUGH SETS THEORETIC PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 PUI Y, 2002, IEEE INTELL SYST APP, V17, P48 SALTON G, 1968, J ACM, V15, P8, DOI 10.1145/321439.321441 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Skowron A, 1992, INTELLIGENT DECISION, P331 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Zhou ZH, 2003, IEEE T INF TECHNOL B, V7, P37, DOI 10.1109/TITB.2003.808498 ZIZKA J, 2002, LECT NOTES ARTIF INT, P151 NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26164-8 J9 LECT NOTES COMPUT SC PY 2005 VL 3505 BP 175 EP 183 PG 9 WC Computer Science, Artificial Intelligence SC Computer Science GA BCN64 UT WOS:000230301200013 ER PT B AU Liu, SH AF Liu, SH BE Muller, G Lin, KJ TI Enhancing e-business-intelligence-service: A topic-guided text summarization framework SO CEC 2005: Seventh IEEE International Conference on E-Commerce Technology, Proceedings LA English DT Proceedings Paper CT 7th IEEE International Conference on E-Commerce Technology/1st IEEE International Workshop on Service Oriented Solutions for Cooperative Organizations/IEEE International Workshop on Business Transformation CY JUL 19, 2005 CL Munich, GERMANY SP IEEE TCEC AB Text summarization will be a very important function in next generation e-business-intelligence-service. While human beings have proven to be extremely capable summarizers, computer based automated abstracting and summarizing has proven to be extremely challenging tasks. The dominant approach to text summarization is selection-based, by which the most content-bearing sentences or passages are identified and selected to compose a summary. However, the results from such a process often suffer from flaws such as incoherent content and poor readability due to unclear relationships between the selected text excerpts, dangling references, and so on. In this paper we present a process model of topic guided text summarization - TIDE. By directing the content selection process with identified topic structure of the text, the logical relation between the selected sentences is captured and better presented, which will also be valuable resource for a more complete and precise understanding of text meaning in the next stage of processing. C1 Abo Akad Univ, IAMSR, IIS, FIN-20520 Turku, Finland. RP Liu, SH (reprint author), Abo Akad Univ, IAMSR, IIS, Lemminkaisenkatu 14 B,DataCity 6th Floor, FIN-20520 Turku, Finland. CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 EDMUNDSON, 1999, ADV AUTOMATIC TEXT S FAGAN JL, 1987, 87868 CORN U DEP COM GOLDSTEIN J, 2000, ANLP NAACL WORKSH AU Hovy E., 1999, ADV AUTOMATIC TEXT S KELLEDY F, 1997, 19 ANN BCSIRSG C IR LIU S, 2005, IN PRESS PUBLICATION LUHN HP, 1999, ADV AUTOMATIC TEXT S Mani I., 1999, ADV AUTOMATIC TEXT S PAICE CD, 1990, INFORM PROCESS MANAG, V26, P171, DOI 10.1016/0306-4573(90)90014-S RADEV D, 2003, MEAD MULTIDOCUMENT S Salton G, 1997, INFORM PROCESS MANAG, V33, P193, DOI 10.1016/S0306-4573(96)00062-3 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHERIDAN P, 1992, INFORM PROCESS MANAG, V28, P349, DOI 10.1016/0306-4573(92)90080-J Sparck-Jones K., 1999, ADV AUTOMATIC TEXT S NR 17 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2277-7 PY 2005 BP 493 EP 496 PG 4 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BCY99 UT WOS:000231979200073 ER PT S AU Liu, SZ Hu, HP AF Liu, SZ Hu, HP TI A hybrid text classification system using sentential frequent itemsets SO COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT International Conference on Computational Intelligence and Security CY DEC 15-19, 2005 CL Xi'an, PEOPLES R CHINA SP IEEE Computat Intelligence, Hong Kong Chapter, Xidian Univ, Hong Kong Baptist Univ, Natl Nat Sci Fdn China, Guangdong Univ Technol AB Text classification techniques mostly rely on single term analysis of the document data set, while more concepts especially the specific ones are usually conveyed by set of terms, To achieve more accurate text classifier, more informative feature including frequent co-occurring words in the same sentence and their weights are particularly important in such scenarios. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset's contribution to the classification. Experiments over the Reuters corpus are carried out, which validate the practicability of the proposed system. C1 Huazhong Univ Sci & Technol, Coll Comp Sci, Wuhan 430074, Peoples R China. RP Liu, SZ (reprint author), Huazhong Univ Sci & Technol, Coll Comp Sci, Wuhan 430074, Peoples R China. EM stoneboo@126.com RI Hu, Hanping/H-3942-2015 CR Antonie M.-L., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183881 COHEN W, 1999, ACM T INFORM SYST, V17, P146 COHEN W, 1998, 4 INT C KNOWL DISC D, P169 HULL DA, 1998, 17 ACM INT C MACH LE, P137 Joachimss T., 1998, 10 EUR C MACH LEARN, P137 Lewis D.D., 1998, 10 EUR C MACH LEARN, P4 LI W, 2001, IEEE INT C DAT MIN I, P2 Liu B, 1998, ACM INT C KNOWL DISC, P80 MERETAKIS D, 2000, P ACM CIKM POTER MF, 1980, ALGORITHM SUFFIX STR, V14, P130 RUIZ M, 1999, 22 ACM SIGIR INT C I, P282 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAN CM, 2002, J INFORMATION PROCES Yang Y., 1999, 22 ANN INT ACM SIGIR, P42 YANG Y, 1997, CUMCS97127 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-30818-0 J9 LECT NOTES ARTIF INT PY 2005 VL 3801 BP 442 EP 449 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDQ19 UT WOS:000234873700065 ER PT S AU Moyotl-Hernandez, E Jimenez-Salazar, H AF Moyotl-Hernandez, E Jimenez-Salazar, H BE Gelbukh, A TI Enhancement of DTP feature selection method for text categorization SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 6th Annual Conference on Intelligent Text Processing and Computational Linguistics CY FEB 13-19, 2005 CL Mexico City, MEXICO SP Natl Polytech Inst, Nat Language & Text Proc Lab, Ctr Comp Res AB This paper studies the structure of vectors obtained by using term selection methods in high-dimensional text collection. We found that the distance to transition point (DTP) method omits commonly occurring terms, which are poor discriminators between documents, but which convey important information about a collection. Experimental results obtained on the Reuters-21578 collection with the k-NN classifier show that feature selection by DTP combined with common terms outperforms slightly simple document frequency. C1 Univ Autonoma Puebla, Fac Ciencias Comp, Puebla 72570, Mexico. RP Moyotl-Hernandez, E (reprint author), Univ Autonoma Puebla, Fac Ciencias Comp, Puebla 72570, Mexico. EM emoyotl@mail.cs.buap.mx; hjimenez@fcfm.buap.mx CR BOOTH AD, 1967, INFORM CONTROL, V10, P386, DOI 10.1016/S0019-9958(67)90201-X DEBOLE F, P LREC 04 4 INT C LA, P971 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 MOYOTL E, 2004, P SEPLN 04 MOYOTL E, 2004, P CIC 04 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 URBIZAGASTEGUIA.R, 1999, POSIBILIDADES ZIPF I Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 ZIPF GK, 1949, HUMAN BEHAV PRINICPL NR 12 TC 2 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24523-5 J9 LECT NOTES COMPUT SC PY 2005 VL 3406 BP 719 EP 722 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BCD56 UT WOS:000228725100080 ER PT S AU Combarro, EF Ranilla, J Mones, R Vazquez, N Diaz, I Montanes, E AF Combarro, EF Ranilla, J Mones, R Vazquez, N Diaz, I Montanes, E BE Diaz, RM Pichler, F Arencibia, AQ TI Unsupervised learning in information retrieval using NOW Architectures SO COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2005 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 10th International Conference on Computer Aided Systems Theory CY FEB 07-11, 2005 CL Las Palmas, SPAIN ID ALGORITHM; MAPS AB The efficiency and effectiveness of the retrieval of documents which are relevant to a certain topic or user query can be improved by means of the clustering of similar documents as well as by introducing parallel strategies. In this paper we explore the use of unsupervised learning, using clustering algorithms based on neural networks, as well as the introduction of NOW Architectures, a kind of low-cost parallel architecture, and study the impact on Information Retrieval. C1 Univ Oviedo, Ctr Artificial Intelligence, Gijon, Asturias, Spain. RP Combarro, EF (reprint author), Univ Oviedo, Ctr Artificial Intelligence, Campus Viesques, Gijon, Asturias, Spain. EM ir@aic.uniovi.es RI Ranilla, Jose/E-8012-2013; Combarro, Elias/H-2590-2015; Diaz, Irene/K-9976-2014; Fernandez-Combarro, Elias/; Montanes, Elena/ OI Ranilla, Jose/0000-0003-2941-3741; Combarro, Elias/0000-0003-3808-4273; Diaz, Irene/0000-0002-3024-6605; Fernandez-Combarro, Elias/0000-0001-6742-2003; Montanes, Elena/0000-0003-0609-8945 CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FERNANDEZ J, 2003, CLEF 2003 WORKSH FERNANDEZ J, 2004, LECT NOTES COMPUTER, V3237, P358 Fritzke B., 1995, ADV NEURAL INFORMATI, V7, P625 Fritzke B., 1994, ARTIFICIAL NEURAL NE, V2, P1051 Kaski S., 1998, NEURAL COMPUTING SUR, V1, P1 Kohonen T., 1996, LECT NOTES COMPUTER, V1112, P269 Kohonen T., 2001, SPRINGER SERIES INFO, V30 Lagus K, 2002, NEURAL PROCESS LETT, V15, P21, DOI 10.1023/A:1013853012954 Lagus Krista, 1996, P 2 INT C KNOWL DISC, P238 Lawrence RD, 1999, DATA MIN KNOWL DISC, V3, P171, DOI 10.1023/A:1009817804059 MONES R, UNPUB SELF ORG MAPS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G., 1971, SMART RETRIEVAL SYST Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOORHESS EM, 2000, P 8 TEXT RETR C TREC NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29002-8 J9 LECT NOTES COMPUT SC PY 2005 VL 3643 BP 179 EP 188 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDJ76 UT WOS:000233888200023 ER PT S AU Ozgur, A Ozgur, L Gungor, T AF Ozgur, A Ozgur, L Gungor, T BE Yolum, P Gungor, T Gurgen, F Ozturan, C TI Text categorization with class-based and corpus-based keyword selection SO COMPUTER AND INFORMATION SICENCES - ISCIS 2005, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 20th International Symposium on Computer and Information Sciences CY OCT 26-28, 2005 CL Istanbul, TURKEY SP Sci & Tech Res Council Turkey, Inst Elec & Elect Engineers, Turkey Sect, Bogazici Univ Res Fund DE keyword selection; text categorization; SVM; Reuters-21578 AB In this paper, we examine the use of keywords in text categorization with SVM. In contrast to the usual belief, we reveal that using keywords instead of all words yields better performance both in terms of accuracy and time. Unlike the previous studies that focus on keyword selection metrics, we compare the two approaches for keyword selection. In corpus-based approach, a single set of keywords is selected for all classes. In class-based approach, a distinct set of keywords is selected for each class. We perform the experiments with the standard Reuters-21578 dataset, with both boolean and tf-idf weighting. Our results show that although tf-idf weighting performs better, boolean weighting can be used where time and space resources are limited. Corpus-based approach with 2000 keywords performs the best. However, for small number of keywords, class-based approach outperforms the corpus-based approach with the same number of keywords. C1 Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey. RP Ozgur, A (reprint author), Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey. EM ozgurarz@boun.edu.tr; ozgurlev@boun.edu.tr; gungort@boun.edu.tr; gungort@boun.edu.tr RI Ozgur, Arzucan/I-3147-2012 OI Ozgur, Arzucan/0000-0001-8376-1056 CR Aizawa A., 2001, P 6 NAT LANG PROC PA, P307 AZCARRAGA AP, 2002, INT J ARTIFICIAL INT, V11 Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Joachims T., 1998, EUR C MACH LEARN ECM JOACHIMS T., 1999, ADV KERNAL METHODS S Lewis D. D., REUTERS 21578 DOCUME Lin S.-H., 1998, P 21 ANN INT ACM SIG, P241, DOI 10.1145/290941.291001 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Mladenic D., 1999, P 16 INT C MACH LEAR, P258 Ozgur A., 2004, THESIS BOGAZICI U TU Ozgur L, 2004, PATTERN RECOGN LETT, V25, P1819, DOI 10.1016/j.patrec.2004.07.004 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 YANG Y, 1996, 22 ACM INT C RES DEV NR 18 TC 8 Z9 8 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29414-7 J9 LECT NOTES COMPUT SC PY 2005 VL 3733 BP 606 EP 615 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDL73 UT WOS:000234179600061 ER PT J AU Setlur, V Albrecht-Buehler, C Gooch, AA Rossoff, S Gooch, B AF Setlur, V Albrecht-Buehler, C Gooch, AA Rossoff, S Gooch, B TI Semanticons: Visual metaphors as file icons SO COMPUTER GRAPHICS FORUM LA English DT Article; Proceedings Paper CT 26th Annual Conference of the Eurographics-Association CY AUG, 2005 CL Dublin, IRELAND SP Eurograph Assoc, SCi Fdn Ireland & Eurograph ID MODEL AB Semanticons can enhance the representation of files by offering symbols that are both meaningful and easily distinguishable. The semantics of a file is estimated by parsing its name, location, and content to generate a 'context', which is used to query an image database. The resulting images are simplified by segmenting them, computing an importance value for each segmented region, and removing unimportant regions. The abstract look-and-feel of icons is achieved using non-photo realistic techniques for image stylization. We increase the effectiveness of the semanticons by compositing them with traditional and familiar interface icons. Two psychophysical studies using semanticons as stimuli demonstrate that semanticons decrease the time necessary to locate a file in a visual search task and enhance performance in a memory task. C1 Northwestern Univ, Evanston, IL 60208 USA. RP Setlur, V (reprint author), Northwestern Univ, Evanston, IL 60208 USA. RI Gooch, Bruce/B-7157-2009 CR ABED F, 1994, METAPHOR SYMBOLIC AC, V19, P45 ANQUETIL N, 1998, ICSE 98, P84 BEEFERMAN D, 1998, P WORKSH APPL WORDNE, P135 BYRNE MD, 1993, CHI 93, P446 Comaniciu D, 2002, IEEE T PATTERN ANAL, V24, P603, DOI 10.1109/34.1000236 Woodruff A., 2001, CHI 2001 Conference Proceedings. Conference on Human Factors in Computing Systems Fogarty James, 2001, UIST 2001, P141 Gooch B, 2004, ACM T GRAPHIC, V23, P27, DOI 10.1145/966131.966133 HEALY CG, 1996, PERCEPTUAL COLOUR SE HENRY TR, 1990, ACM T GRAPHIC, V9, P133, DOI 10.1145/77635.77641 Itti L, 1998, IEEE T PATTERN ANAL, V20, P1254, DOI 10.1109/34.730558 Lewis JP, 2004, ACM T GRAPHIC, V23, P416, DOI 10.1145/1015706.1015739 MOYES J, 1994, CHI 94, P283, DOI 10.1145/259963.260494 Rowley HA, 1996, ADV NEUR IN, V8, P875 RUBENS P, 1988, SIGDOC 88, P25 RUSSELL DM, 2003, HAW INT C SYST SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Suh B., 2003, UIST 03, P95 SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487 NR 19 TC 4 Z9 4 PU BLACKWELL PUBLISHING PI OXFORD PA 9600 GARSINGTON RD, OXFORD OX4 2DQ, OXON, ENGLAND SN 0167-7055 J9 COMPUT GRAPH FORUM JI Comput. Graph. Forum PY 2005 VL 24 IS 3 SI SI BP 647 EP 656 DI 10.1111/j.1467-8659.2005.00889.x PG 10 WC Computer Science, Software Engineering SC Computer Science GA 956TX UT WOS:000231323800045 ER PT S AU Chen, ZX Fu, B AF Chen, ZX Fu, B BE Wang, LH TI A quadratic lower bound for Rocchio's similarity-based relevance feedback algorithm SO COMPUTING AND COMBINATORICS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 11th Annual International Conference on Computing and Combinatorics (COCOON 2005) CY AUG 16-29, 2005 CL Kunming, PEOPLES R CHINA SP Chinese Acad Sci, Acad Math & Syst Sci ID VECTOR-SPACE MODEL; RETRIEVAL AB It is shown in [4] that Rocchio's similarity-based relevance feedback algorithm makes Omega(n) mistakes in searching for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}(n). In practice, Rocchio's algorithm often uses a fixed query updating factor and a fixed classification threshold. When this is the case, we strengthen the work in [4] in this paper and prove that Rocchio's algorithm makes Omega(k(n - k)) mistakes in searching for the same collection of documents over the binary vector space {0, 1}(n). A quadratic lower bound is obtained when k is proportional to n. An O(k(n - k)(2)) upper bound is also obtained. C1 Univ Texas, Dept Comp Sci, Edinburg, TX 78541 USA. Univ New Orleans, Dept Comp Sci, New Orleans, LA 70148 USA. Res Inst Children, New Orleans, LA USA. RP Chen, ZX (reprint author), Univ Texas, Dept Comp Sci, Edinburg, TX 78541 USA. EM chen@cs.panam.edu; fu@cs.uno.edu CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN Z, 2000, LNCS, V1969, P108 CHEN Z, 2004, COMPUTATIONAL WEB IN, P303 Chen ZX, 2001, LECT NOTES COMPUT SC, V2108, P540 Chen ZX, 2002, INFORM RETRIEVAL, V5, P61, DOI 10.1023/A:1012730924277 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANVIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 15 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28061-8 J9 LECT NOTES COMPUT SC PY 2005 VL 3595 BP 955 EP 964 PG 10 WC Computer Science, Theory & Methods SC Computer Science GA BCY51 UT WOS:000231866900096 ER PT S AU Doan, S Horiguchi, S AF Doan, S Horiguchi, S BE Zanasi, A Brebbia, CA Ebecken, NFF TI A multi-criteria decision making approach in feature selection for enhancing text categorization SO Data Mining VI: Data Mining, Text Mining and Their Business Applications SE WIT TRANSACTIONS ON INFORMATION AND COMMUNICATION TECHNOLOGIES LA English DT Proceedings Paper CT 6th Conference on Data Mining - Text Mining and Their Business Applications CY MAY 25-27, 2005 CL Skiathos, GREECE SP Wessex Inst Technol, Wessex Inst Technol, Transact Informat & Commun Technol AB This paper considers the problem of feature selection in text categorization. Previous works in feature selection often used a filter model in which features, after ranked by a measure, are selected based on a given threshold. In this paper, we present a novel approach to feature selection based on multi-criteria decision making of each feature. Instead of only one criterion, multi-criteria of a feature are used; and a procedure based on each threshold of the criterion is proposed. This framework seems to be suitable for text data and can be applied to feature selection in text categorization. Experimental results on Reuters-21578 benchmark data show that our approach has a promising scheme and enhances the performance of a text categorization system. C1 Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 980, Japan. CR Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 DEBOLE F, 2004, IN PRESS J AM SOC IN DOAN S, 2004, IN PRESS P 4 INT C H DOAN S, 2004, IN PRESS P 5 INT C R Huang SH, 2003, IEEE T KNOWL DATA EN, V15, P1364, DOI 10.1109/TKDE.2003.1245278 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X LEWIS D, REUTERS 21578 DATASE McCallum A.K., 1996, BOW TOOLKIT STAT LAN Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Rogati M., 2002, INT C INF KNOWL MAN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 SOUCY P, 2001, INT JOINT C ART INT Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 15 TC 0 Z9 0 PU WIT PRESS PI SOUTHAMPTON PA ASHURST LODGE, SOUTHAMPTON SO40 7AA, ASHURST, ENGLAND SN 1746-4463 BN 1-84564-017-9 J9 WIT TRANS INFO COMM PY 2005 BP 77 EP 87 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BCQ48 UT WOS:000230756400008 ER PT S AU Chen, DY Li, X Dong, ZY Chen, X AF Chen, DY Li, X Dong, ZY Chen, X BE Tjoa, AM Trujillo, J TI Effectiveness of document representation for classification SO DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2005) CY AUG 22-26, 2005 CL Copenhagen, DENMARK ID TEXT CATEGORIZATION; MODEL AB Conventionally, document classification researches focus on improving the learning capabilities of classifiers. Nevertheless, according to our observation, the effectiveness of classification is limited by the suitability of document representation. Intuitively, the more features that are used in representation, the more comprehensive that documents are represented. However, if a representation contains too many irrelevant features, the classifier would suffer from not only the curse of high dimensionality, but also overfitting. To address this problem of suitableness of document representations, we present a classifier-independent approach to measure the effectiveness of document representations. Our approach utilises a labelled document corpus to estimate the distribution of documents in the feature space. By looking through documents in this way, we can clearly identify the contributions made by different features toward the document classification. Some experiments have been performed to show how the effectiveness is evaluated. Our approach can be used as a tool to assist feature selection, dimensionality reduction and document classification. C1 Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. RP Chen, DY (reprint author), Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld 4072, Australia. EM dingyi@itee.uq.edu.au; xueli@itee.uq.edu.au; zdong@itee.uq.edu.au; chenxia@itee.uq.edu.au RI gao, xiaodan/K-3594-2015 CR Akaike H., 1974, 2 INT S INF THEOR, P267 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 CHEN DY, 2005, 16 AUSTR DAT C NEWC, P125 Chickering D., 1997, P 13 C UNC ART INT U, P80 Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dietterich T, 1995, ACM COMPUT SURV, V27, P326, DOI 10.1145/212094.212114 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Heckerman D., 1994, KDD WORKSH, P85 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS DD, REUTERS CORPUS Liu T., 2003, ICML, P488 Porter M. F., 1997, READINGS INFORM RETR Quinlan J. R., 1995, IJCAI-95. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 VAPNIK VN, 1995, NATURE STAT LEARNING, P119 WARD G, MOBY WORK MOBY LEXIC WEISS SM, 1993, IEEE EXPERT, V8, P61, DOI 10.1109/64.248354 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 28 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28558-X J9 LECT NOTES COMPUT SC PY 2005 VL 3589 BP 368 EP 377 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCY28 UT WOS:000231850500036 ER PT S AU Sahin, OD Emekci, F Agrawal, D El Abbadi, A AF Sahin, OD Emekci, F Agrawal, D El Abbadi, A BE Ng, WS Ooi, BC Ouksel, A Sartori, C TI Content-based similarity search over peer-to-peer systems SO DATABASES, INFORMATION SYSTEMS, AND PEER-TO-PEER COMPUTING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd International Worshop on Databases, Information Systems and Peer-to-Peer Computing CY AUG 29-30, 2004 CL Toronto, CANADA SP Microsoft Corp, Springer AB Peer-to-peer applications are used to share large volumes of data. An important requirement of these systems is efficient methods for locating the data of interest in a large collection of data. Unfortunately current peer-to-peer systems either offer exact keyword match functionality or provide inefficient text search methods through centralized indexing or flooding. In this paper we propose a method based on popular Information Retrieval techniques to facilitate content-based searches in peer-to-peer systems. A simulation of the proposed design was implemented and its performance was evaluated using some commonly used test collections, including Ohsumed which was used for the TREC-9 Filtering Track. The experiments demonstrate that our approach is scalable as it achieves high recall by visiting only a small subset of the peers. C1 Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA. RP Sahin, OD (reprint author), Univ Calif Santa Barbara, Dept Comp Sci, Santa Barbara, CA 93106 USA. EM fodsahin@cs.ucsb.edu; fatih@cs.ucsb.edu; agrawal@cs.ucsb.edu; amr@cs.ucsb.edu CR Bawa M., 2003, P 26 ANN INT ACM SIG, P306 Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035 Buckley Christopher, 1985, 85686 CORN U Crespo A., 2002, Proceedings 22nd International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2002.1022239 CUENCAACUNA FM, 2003, S HIGH PERF DISTR CO DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUMAIS S. T., 1995, P TEXT RETR C TREC 3, P219 ELANSARY S, 2003, P 2 INT WORKSH PEER GNAWALI OD, 2003, THESIS MIT Harren M., 2002, P 1 INT WORKSH PEER Hersh William, 1994, P 17 ANN INT ACM SIG, P192 JIANG F, 1999, CS995 DUK U KOLONIARI G., 2004, P 9 INT C EXT DAT TE, P29 Li Jinyang, 2003, P 2 INT WORKSH PEER Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 RATNASAMY S, 2001, P ACM SIGCOMM, V4, P161 ROBERTSON S, 2001, P 9 TEXT RETR C TREC, P25 Rowstron A., 2001, P MIDDL RYNOLDS P, 2003, P MIDDL, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoica I, 2001, P 2001 ACM SIGCOMM C, P149, DOI DOI 10.1145/383059.383071 Suel T, 2003, P INT WORKSH WEB DAT, P67 Tang C., 2003, P 2003 C APPL TECHN, P175 Yang B., 2002, Proceedings 22nd International Conference on Distributed Computing Systems, DOI 10.1109/ICDCS.2002.1022237 Zhao BY, 2004, IEEE J SEL AREA COMM, V22, P41, DOI 10.1109/JSAC.2003.818784 Zhu Y., 2003, Proceedings Third International Conference on Peer-to-Peer Computing (P2P2003), DOI 10.1109/PTP.2003.1231511 NR 26 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25233-9 J9 LECT NOTES COMPUT SC PY 2005 VL 3367 BP 61 EP 78 PG 18 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCC00 UT WOS:000228552500005 ER PT J AU Alfonseca, E Carro, RM Freire, M Ortigosa, A Perez, D Rodriguez, P AF Alfonseca, E Carro, RM Freire, M Ortigosa, A Perez, D Rodriguez, P TI Authoring of adaptive computer assisted assessment of free-text answers SO EDUCATIONAL TECHNOLOGY & SOCIETY LA English DT Article DE authoring; adaptive hypermedia; computer-assisted assessment; e-Learning; free-text answers ID COURSES; SYSTEM AB Adaptation techniques can be applied not only to the multimedia contents or navigational possibilities of a course, but also to the assessment. In order to facilitate the authoring of adaptive free-text assessment and its integration within adaptive web-based courses, Adaptive Hypermedia techniques and Free-text Computer Assisted Assessment are combined in what could be called Adaptive Computer Assisted Assessment of Free-text Answers. This paper focuses on the integration of this type of assessment within adaptive courses, as well as on the presentation of an authoring tool able to manage the insertion and modification of different question statements and reference answers for open-ended questions. The results of the evaluation of this tool with course authors show the feasibility of proposing and evaluating open-ended questions adapted to each student, as well as that of getting a better model of the student's progress. C1 Univ Autonoma Madrid, Dept Comp Sci, E-28049 Madrid, Spain. RP Alfonseca, E (reprint author), Univ Autonoma Madrid, Dept Comp Sci, Carretera Colmenar Viejo,Km 14-5, E-28049 Madrid, Spain. EM Enrique.Alfonseca@uam.es; Rosa.Carro@uam.es; Manuel.Freire@uam.es; Alvaro.Ortigosa@uam.es; Diana.Perez@uam.es; Pilar.Rodriguez@uam.es RI Carro, Rosa/E-8011-2013; Ortigosa, Alvaro/N-7324-2013; Perez-Marin, Diana/L-4100-2014 OI Ortigosa, Alvaro/0000-0002-7674-4132; Perez-Marin, Diana/0000-0003-3390-0251 CR Alfonseca E, 2004, LECT NOTES ARTIF INT, V3230, P25 ALFONSECA E, 2003, WRAETLIC USER GUIDE Brushe GD, 2001, DIGIT SIGNAL PROCESS, V11, P110, DOI 10.1006/dspr.2001.0389 Burstein J., 2001, P 6 INT COMP ASS ASS Carro RM, 2003, LECT NOTES ARTIF INT, V2774, P252 Carro RM, 1999, J NETW COMPUT APPL, V22, P249, DOI 10.1006/jnca.1999.0094 CARRO RM, 2002, VIRTUAL ENV TEACHING, P111, DOI 10.1142/9789812776570_0004 Chou C, 2000, IEEE T EDUC, V43, P266 CRISTEA D, 2004, 1 INT WORKSH AUTH AD DODDINGTON G, 2002, HUM LANG TECHN C MAR Glas C.A.W., 2000, COMPUTERIZED ADAPTIV Gutierrez S, 2004, LECT NOTES COMPUT SC, V3137, P401 GUZMAN E, 2002, INT C INF COMM TECHN Laham D., 2000, THESIS U COLORADO BO LILLEY M, 2003, P 7 COMP ASS ASS C L LIN CY, 2003, HUM LANG TECHN C MAY Lutticke R, 2004, LECT NOTES COMPUT SC, V3137, P417 Mason O., 2002, P 6 INT COMP ASS ASS MING Y, 2000, LEARNERS TOGETHER FE Mitrovic A, 2004, LECT NOTES COMPUT SC, V3137, P185 PANOS S, 2003, 3 IEEE INT C ADV LEA PAPINENI K, 2001, RC22176 W0109022 TJ PEREZ D, 2004, INT ASS D ASS C JUN PEREZ D, 2004, LANG RES EV C LREC 2 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SOSNOVSKY S, 2004, LECT NOTES COMPUTER, V3137, P365 Tzanavari A, 2004, LECT NOTES COMPUT SC, V3137, P340 Valenti S., 2003, J INF TECHNOL, V2, P319 WHITTINGTON D, 1999, P 6 INT COMP ASS ASS NR 29 TC 3 Z9 3 PU IEEE COMPUTER SOC, LEARNING TECHNOLOGY TASK FORCE PI PALMERSTON NORTH PA BAG 11-222, MASSEY UNIVERSITY, PALMERSTON NORTH, NEW ZEALAND SN 1436-4522 J9 EDUC TECHNOL SOC JI Educ. Technol. Soc. PY 2005 VL 8 IS 3 BP 53 EP 65 PG 13 WC Education & Educational Research SC Education & Educational Research GA 953VN UT WOS:000231109400006 ER PT B AU Liu, WY Huang, GL Liu, XY Deng, XT Min, Z AF Liu, WY Huang, GL Liu, XY Deng, XT Min, Z GP IEEE Computer Society TI Phishing webpage detection SO Eighth International Conference on Document Analysis and Recognition, Vols 1 and 2, Proceedings LA English DT Proceedings Paper CT 8th International Conference on Document Analysis and Recognition (ICDAR 2005) CY AUG 29-SEP 01, 2005 CL Seoul, SOUTH KOREA SP ABBYY Software House, BK 21 Sch Informat Technol KAIST, Elect & Telecommun Res Inst, Hitachi Cent Res Lab, IBM Corp, Int Assoc Pattern Recognit, Korea Adv Inst Sci & Technol, Korea Informat Sci Soc, Korea Sci & Engn Fdn, Minist Informat & Commun, Inst Informat Assessment, Microsoft DE anti-phishing; web document analysis; information filtering AB An approach to detection of phishing webpages based on visual similarity is proposed, which can be utilized as a part of an enterprise solution to anti-phishing. A legitimate webpage owner can use this approach to search the Web for suspicious webpages which are visually similar to the true webpage. The approach first decomposes the webpages into salient (visually distinguishable) block regions. The visual similarity between two webpages is then evaluated in three metrics: block level similarity, layout similarity, and overall style similarity. A webpage is reported as a phishing suspect if any of them (with regards to the true one) is higher than its corresponding preset threshold. Preliminary experiments show that the approach can successfully detect those phishing webpages with few false alarms at a speed adequate for online application. C1 City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China. RP Liu, WY (reprint author), City Univ Hong Kong, Dept Comp Sci, Tat Chee Ave, Hong Kong, Hong Kong, Peoples R China. RI LIU, Wenyin/C-1345-2012; Deng, Xiaotie/E-8607-2011 OI Deng, Xiaotie/0000-0003-3189-5989 CR BRODER A, P WWW97, P391 Chowdhury A, 2002, ACM T INFORM SYST, V20, P171, DOI 10.1145/506309.506311 Hoad TC, 2003, J AM SOC INF SCI TEC, V54, P203, DOI 10.1002/asi.10170 Liu Y, 2004, LECT NOTES COMPUT SC, V3129, P280 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 5 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2420-6 PY 2005 BP 560 EP 564 DI 10.1109/ICDAR.2005.190 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BCZ12 UT WOS:000232022600109 ER PT B AU Zakos, J Verma, B AF Zakos, J Verma, B GP IEEE Computer Society TI A novel context matching based technique for web document retrieval SO Eighth International Conference on Document Analysis and Recognition, Vols 1 and 2, Proceedings LA English DT Proceedings Paper CT 8th International Conference on Document Analysis and Recognition (ICDAR 2005) CY AUG 29-SEP 01, 2005 CL Seoul, SOUTH KOREA SP ABBYY Software House, BK 21 Sch Informat Technol KAIST, Elect & Telecommun Res Inst, Hitachi Cent Res Lab, IBM Corp, Int Assoc Pattern Recognit, Korea Adv Inst Sci & Technol, Korea Informat Sci Soc, Korea Sci & Engn Fdn, Minist Informat & Commun, Inst Informat Assessment, Microsoft ID MODEL AB This paper presents a novel context matching technique for the retrieval of web documents. The aim of the technique is to dynamically generate a context-based measure of document term significance during retrieval that can be used as a substitute or co-contributor of the term frequency measure. Unlike term frequency, which relies on a term to occur multiple times within a document to be considered significant, context matching is based on the notion that if a term in a given document occurs in that document in the context of the query, then that term is deemed to be significant. Context matching has the ability to potentially determine a term to be significant even if it occurs only once in a large document. The proposed technique has been implemented and the experiments were conducted using a TREC benchmark database. A comparative analysis shows that context matching significantly improves retrieval effectiveness and outperforms previously published results. CR ANH A, 2004, P 9 AUSTR DO COMP S, P25 Billerbeck B., 2004, P AUSTR DAT C DUN NZ, V27, P69 Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 Cai D, 2004, P 27 ANN INT ACM SIG, P456, DOI 10.1145/1008992.1009070 Craswell N., 2003, P 12 TEXT RETR C TRE, P193 Finkelstein L., 2001, P 10 INT C WORLD WID, P406, DOI 10.1145/371920.372094 Glover E, 2001, COMMUN ACM, V44, P97, DOI 10.1145/501317.501319 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 HU J, 2003, P 7 INT C DOC AN REC, P640 Jing H, 1999, P 22 ANN INT ACM SIG, P90, DOI 10.1145/312624.312661 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A., 2003, Proceedings Seventh International Conference on Document Analysis and Recognition WEN J, 2003, P 12 TEXT RETR, P408 WHITTEN I, 1999, MANAGING GIGABYTES Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 NR 16 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2420-6 PY 2005 BP 909 EP 913 DI 10.1109/ICDAR.2005.26 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BCZ12 UT WOS:000232022600178 ER PT B AU Beitzel, SM Jensen, EC Frieder, O Lewis, DD Chowdhury, A Kolcz, A AF Beitzel, SM Jensen, EC Frieder, O Lewis, DD Chowdhury, A Kolcz, A BE Han, J Wah, BW Raghavan, V Wu, X Rastogi, R TI Improving automatic query classification via semi-supervised learning SO Fifth IEEE International Conference on Data Mining, Proceedings LA English DT Proceedings Paper CT 5th IEEE International Conference on Data Mining CY NOV 27-30, 2005 CL Houston, TX SP IEEE Comp Soc, TCII, IEEE Comp Soc, TCPAMI, IBM Res, Knowledge & Informat Syst, Web Intelligence Consortium, Univ Louisiana Lafayette, Ctr Adv Comp Studies, Amer Discount ADS Inc, Univ Houston, Dept Comp Sci, Elder Res Inc AB Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose web search systems. Such classification becomes critical if the system is to return results not just from a general web collection but from topic-specific back-end databases as well. Maintaining sufficient classification recall is very difficult as web queries are typically short, yielding few features per query. This feature sparseness coupled with the high query volumes typical for a large-scale search service makes manual and supervised learning approaches alone insufficient. We use an application of computational linguistics to develop an approach for mining the vast amount of unlabeled data in web query logs to improve automatic topical web query classification. We show that our approach in combination with manual matching and supervised learning allows its to classify a substantially larger proportion of queries than any single technique. We examine the performance of each approach on a real web query stream and show that our combined method accurately classifies 46% of queries, out performing the recall of best single approach by nearly 20% with a 7% improvement in overall effectiveness. CR BEEFERMAN D, 2000, ACM SIGMOD BEITZEL S, 2004, ACM SIGIR BEITZEL SM, 2005, SIGIR 2005 SALV BRAZ Cover T. M., 1991, ELEMENTS INFORM THEO GRAVANO L, 2003, ACM CIKM Greiff W., 2002, COGNITIVE SCI, V87, P1 Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 KANG I, 2003, ACM SIGIR Krauth W., 1987, J PHYSICS A, V20, P745 KUPPER LL, 1989, AM STAT, V43, P101, DOI 10.2307/2684511 LEWIS DD, 1995, SIGIR 1995 SEATLL WA Manning C. D., 1999, FDN STAT NATURAL LAN McCarthy D, 2003, COMPUT LINGUIST, V29, P639, DOI 10.1162/089120103322753365 Mitchell TM, 1997, MACHINE LEARNING Resnik P. S., 1993, SELECTION INFORM CLA Rijsbergen C. J. v., 1979, INFORM RETRIEVAL SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sullivan D., 2003, SEARCH ENGINE WATCH Tague J. M., 1981, INFORMATION RETRIEVA, P59 WEN JR, 2001, ACM SIGIR WEN JR, 2001, WWW 2001 WEN JR, 2002, ACM T INFORM SYSTEMS, V20 NR 22 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2278-5 PY 2005 BP 42 EP 49 DI 10.1109/ICDM.2005.80 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDS21 UT WOS:000235162400006 ER PT S AU Markov, A Last, M AF Markov, Alex Last, Mark BE Last, M Kandel, A TI Identification of Terrorist Web Sites with Cross-Lingual Classification Tools SO FIGHTING TERROR IN CYBERSPACE SE Series in Machine Perception and Artificial Intelligence LA English DT Article; Book Chapter ID INFORMATION C1 [Markov, Alex; Last, Mark] Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RP Markov, A (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. EM markov@bgumail.bgu.ac.il; mlast@bgumail.bgu.ac.il; mlast@bgumail.bgu.ac.il RI LAST, MARK/F-1424-2012 OI LAST, MARK/0000-0003-0748-7918 CR Ahlayl M., 2001, P 10 INT C INF KNOWL Ahmed A., 2002, P MULT INF ACC NAT L Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 [Anonymous], 1999, J ACM COMPUTING SURV, V34, P1 Carreira R., 2004, P 9 INT C INT US INT Dumais S., 2000, P 23 ANN INT ACM SIG Eirinaki M., 2003, P 9 ACM SIGKDD INT C Eirinaki M., J ACM T INTERNET TEC, V3, P1 Kamber M., 2001, DATA MINING CONCEPTS Kuramochi M, 2004, IEEE T KNOWL DATA EN, V16, P1038, DOI 10.1109/TKDE.2004.33 Larkey L. S., 2004, P 27 ANN INT C RES D Larkey L. S., 2002, P SIGIR 2002 Larson R., 2002, P 2 ACM IEEE CS JOIN Maimon O., 2000, MASSIVE COMPUTING SE Maria N., 2000, P 23 ANN INT ACM SIG Markov A, 2005, LECT NOTES COMPUT SC, V3528, P293 McCallum A, 1998, P AAAI 98 WORKSH LEA Mitchell TM, 1997, MACHINE LEARNING Mulvenna M., 2000, J COMMUNICATIONS ACM, V43, P122 Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN QUINLAN JR, 1986, MACH LEARN, V1, P1, DOI DOI 10.1023/A:1022643204877 Ramakrishna K., 2003, BALI THREAT TERRORIS Reis D., 2004, P 13 INT C WORLD WID, P502, DOI 10.1145/988672.988740 Ripplinger B., 2000, WORKSH CROSS LANG EV Salton G., 1987, TR87881 CORN U SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schenker A, 2005, SER MACH PERCEPT ART, V62, P1, DOI 10.1142/9789812569455 Schenker A, 2004, INT J PATTERN RECOGN, V18, P475, DOI 10.1142/S0218001404003241 Tauritz DR, 2000, INFORM SCIENCES, V122, P121, DOI 10.1016/S0020-0255(99)00123-1 Tzeras K., 1993, P 16 ANN INT ACM SIG Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Yan X., 2002, P IEEE INT C DAT MIN Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 NR 33 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE SN 1793-0839 BN 978-9-81270-325-5 J9 SER MACH PERCEPT ART PY 2005 VL 65 BP 117 EP + D2 10.1142/9789812703255 PG 28 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BZB48 UT WOS:000301002200009 ER PT B AU Yang, F Wu, MH Li, W AF Yang, F Wu, MH Li, W BE Duserick, FG TI LF-vector: The research of representation of text document vector with local feature SO Fourth Wuhan International Conference on E-Business: The Internet Era & The Global Enterprise, Vols 1 and 2 LA English DT Proceedings Paper CT 4th Wuhan International Conference on E-Business - Global Business Interface CY JUN 04-05, 2005 CL Wuhan, PEOPLES R CHINA SP Huahzong Univ Sci & Technol Wuhan, Coll Management, Wuhan Univ, Business Sch, Wuhan Univ, Coll Informat Management, Wuhan Univ Technol, Coll Business, Zhongnan Univ Econ & Law, Xinhua Sch Banking & Insurance, Cent China Normal Univ, Informat Management Dept, Huahzong Univ Technol & Sci, Chinese Journal Management, Wuhan Univ, Inst Syst Engn, China Univ Geoscim Ctr Int Cooperat E Business, China Univ Geosci, Coll Management, Alfred Univ, Coll Business, Int Business Interface Inc DE document representation; VSM model; local feature; document vector; LF-Vector AB The key of organization and retrieval of textual document such as Web pages, science papers is their feature representation. The classical VSM technique is the most prominent approach. However, it has some shortcomings. For example it can not contain information about the spatial location or distribution of terms in a document. To provide more accurate description of a document and improve retrieval ability, this paper proposes the amendment of calculating the weight of terms. This method adds the information of local feature to the integral feature. It maintains the character of the classical VSM technology in the mean time, integrated the local feature. This technique can be used in various information systems, e-commerce and decision support systems. C1 Xinyang Normal Univ, Dept Comp Sci, Xinyang 464000, Henan, Peoples R China. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 CHISHOLM E, 1998, NEW TERM WEIGHTING F DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 YU Z, 2003, P 2 INT C MACH LEARN, P2 NR 7 TC 0 Z9 0 PU ALFRED UNIV PI ALFRED PA ONE SAXON DR, ALFRED, NY 14802 USA PY 2005 BP 508 EP 513 PG 6 WC Business; Computer Science, Interdisciplinary Applications SC Business & Economics; Computer Science GA BCP36 UT WOS:000230520800078 ER PT B AU Borgelt, C Nurnberger, A Kruse, R AF Borgelt, C Nurnberger, A Kruse, R BE Krishnapuram, R Pal, N TI Fuzzy learning vector quantization with size and shape parameters SO FUZZ-IEEE 2005: Proceedings of the IEEE International Conference on Fuzzy Systems: BIGGEST LITTLE CONFERENCE IN THE WORLD SE IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) LA English DT Proceedings Paper CT IEEE International Conference on Fuzzy Systems CY MAY 22-25, 2005 CL Reno, NV SP IEEE, IEEE Neural Networks Soc ID TEXT RETRIEVAL; ALGORITHM AB We study an extension of fuzzy learning vector quantization that draws on ideas from the more sophisticated approaches to fuzzy clustering, enabling us to find fuzzy clusters of ellipsoidal shape and differing size with a competitive learning scheme. This approach may be seen as a kind of online fuzzy clustering, which can have advantages w.r.t. the execution time of the clustering algorithm. We demonstrate the usefulness of our approach by applying it to document collections, which are, in general, difficult to cluster due to the high number of dimensions and the special distribution characteristics of the data. C1 Univ Magdeburg, Dept Knowledge Proc & Language Engn, D-39106 Magdeburg, Germany. RP Borgelt, C (reprint author), Univ Magdeburg, Dept Knowledge Proc & Language Engn, Univ Pl 2, D-39106 Magdeburg, Germany. RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bezdek J. C., 1981, PATTERN RECOGNITION Bezdek J. C., 1999, FUZZY MODELS ALGORIT Bilmes J., 1997, ICSITR97021 U BERK Bock H. H., 1974, AUTOMATISCHE KLASSIF DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Duda R.O., 1973, PATTERN CLASSIFICATI Everitt BS, 1981, FINITE MIXTURE DISTR FRAKES WR, 1992, INFORMATION RETRIEVA GATH I, 1989, IEEE T PATTERN ANAL, V11, P773, DOI 10.1109/34.192473 GREIFF WR, 1998, P 21 ANN INT ACM SIG Gustafson E.E., 1979, P IEEE CDC SAN DIEG, P761 Hoppner F., 1999, FUZZY CLUSTER ANAL Karayiannis NB, 1996, IEEE T NEURAL NETWOR, V7, P1196, DOI 10.1109/72.536314 Karayiannis NB, 1997, IEEE T FUZZY SYST, V5, P622, DOI 10.1109/91.649915 Klawonn F, 1997, FUZZY SET SYST, V85, P177, DOI 10.1016/0165-0114(95)00350-9 Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 KOCHBAUM KE, 1989, INFORMATION PROCESSI, V25, P665 Kohonen T., 1986, TKKFA601 HELS U TECH Kohonen T., 2001, SELF ORG MAPS Krishnapuram R., 1993, IEEE Transactions on Fuzzy Systems, V1, DOI 10.1109/91.227387 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 SINKA MP, 2002, SOFT COMPUTING SYSTE, P881 Timm H, 2004, FUZZY SET SYST, V147, P3, DOI 10.1016/j.fss.2003.11.009 TIMM H, 2002, P IEEE INT C FUZZ SY TSAO ECK, 1994, PATTERN RECOGN, V27, P757, DOI 10.1016/0031-3203(94)90052-3 Witten I. H., 1999, MANAGING GIGABYTES C NR 31 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9158-6 J9 IEEE INT CONF FUZZY PY 2005 BP 195 EP 200 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BCR92 UT WOS:000230981000034 ER PT B AU Doan, S AF Doan, S BE Krishnapuram, R Pal, N TI A fuzzy-based approach for text representation in text categorization SO FUZZ-IEEE 2005: Proceedings of the IEEE International Conference on Fuzzy Systems: BIGGEST LITTLE CONFERENCE IN THE WORLD SE IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) LA English DT Proceedings Paper CT IEEE International Conference on Fuzzy Systems CY MAY 22-25, 2005 CL Reno, NV SP IEEE, IEEE Neural Networks Soc ID INFORMATION-RETRIEVAL; MODEL AB Document representation is one of the most important tasks in text processing, especially in text categorization. This task has many applications that include document management, information retrieval, text routing, etc. In this paper, we proposes a novel scheme for text representation based on fuzzy set theory. A new algorithm for choosing a term set that characterizes a document in the corpus is given under the view of fuzzy set. Experimental results applied to text categorization problem using the relevance feedback technique show that our proposed method reduced the number of dimensions and achieves higher performances compared to other baseline methods. In addition, it also produces results that compare favorably to the result achieved with the all vocabulary method. RI Doan, Son/F-8266-2015 OI Doan, Son/0000-0002-7284-1306 CR Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032 BUELL DA, 1982, FUZZY SET SYST, V7, P35, DOI 10.1016/0165-0114(82)90038-0 *CTL GROUP, 20NEWSGROUPS DAT DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 LEWIS D, 1991, THESIS U MASSACHUSET LUCARELLA D, 1991, J INFORM SCI, V17, P81, DOI 10.1177/016555159101700202 Manning C. D., 1999, FDN STAT NATURAL LAN McCallum A.K., 1996, BOW TOOLKIT STAT LAN MIYAMOTO S, 1990, FUZZY SETS INFORMATI Molinari A., 1996, Proceedings of the Fifth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE '96 (Cat. No.96CH35998), DOI 10.1109/FUZZY.1996.551727 MOULINIER I, 1996, AAAI S MACH LEARN IN MURAI T, 1989, FUZZY SET SYST, V30, P103, DOI 10.1016/0165-0114(89)90074-2 Rochio J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P379 Witte R., 2003, P 2003 INT S REF RES, P43 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 19 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9158-6 J9 IEEE INT CONF FUZZY PY 2005 BP 1008 EP 1013 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BCR92 UT WOS:000230981000174 ER PT S AU Wu, JN Tanioka, H Wang, SZ Pan, DH Yamamoto, K Wang, ZT AF Wu, JN Tanioka, H Wang, SZ Pan, DH Yamamoto, K Wang, ZT BE Wang, L Jin, Y TI An improved VSM based information retrieval system and fuzzy query expansion SO FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Conference on Fuzzy Systems and Knowledge Discovery CY AUG 27-29, 2005 CL Changsha, PEOPLES R CHINA SP Xiangtan Univ, IEEE Circuits & Syst Soc, IEEE Computat IntelligenceSoc, IEEE Control Syst Soc, Int Neural Network Soc, European Neural Network Soc, Chinese Assoc Artifiical Intelligence, Japanese Neural Network Soc, Int Fuzzy Syst Assoc, Asia-Pacific Neural Network Assembly, Fuzzy Math & Syst Assoc Chine, Hunan Comp Federat AB In this paper, we proposean improved information retrieval model, where the integration of modification-, words and head-words is introduced into the representation of user queries and!,the traditional vector space model. We show how to calculate the weights of combined terms in vectors. We also propose a new strategy to construct the thesaurus in fuzzy way for query expansion. Through the developed information retrieval system, we can retrieve documents in a relatively narrow search-space and; meanwhile extend the coverage of the retrieval to the related documents that; do not necessarily contain the same terms as the given query. Experiments for 'testing the retrieval effectiveness have been implemented by using benchmark corpora. Experimental results show that the improved information retrieval system is capable of improving the retrieval performance both in precision and recall C1 Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. Justsyst Corp, Dept R&D Strategy, Tokushima 7710189, Japan. Dalian Justsyst Co Ltd, Dalian 116024, Peoples R China. RP Wu, JN (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. EM jnwu@dlut.edu.cn; hiroki_tanioka@justsystems.co.jp; wangzt@dlut.edu.cn; gyise@dlut.edu.cn; kenichi_yamamoto@justsystem.co.jp; wangshizhu@justsystem.cn CR CHANDRENMUNIYAN.R, 2000, P TENCON, V1, P156 Kim MC, 1999, INFORM PROCESS MANAG, V35, P19, DOI 10.1016/S0306-4573(98)00040-5 Kraft DH, 1997, FUZZY SET SYST, V90, P183, DOI 10.1016/S0165-0114(97)00085-7 Letsche TA, 1997, INFORM SCIENCES, V100, P105, DOI 10.1016/S0020-0255(97)00044-3 LIU G, 1994, INFORMATION SYSTEMS, V4, P928 Mandala R, 2000, INFORM PROCESS MANAG, V36, P361, DOI 10.1016/S0306-4573(99)00068-0 NIE JY, 2002, WORKSH MATH FORM MET Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 Qiu Yonggang, 1993, P 16 ANN INT ACM SIG, P160, DOI DOI 10.1145/160688.160713 RAMIREZ C, 1995, IEE C CAS BAS REAS P SALTON G, 1982, J ASSOC COMPUT MACH, V29, P152 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 12 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28312-9 J9 LECT NOTES ARTIF INT PY 2005 VL 3613 BP 537 EP 546 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDA15 UT WOS:000232217900068 ER PT S AU Wu, JN Yang, GF AF Wu, JN Yang, GF BE Wang, L Jin, Y TI An ontology-based method for project and domain expert matching SO FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 2, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Conference on Fuzzy Systems and Knowledge Discovery CY AUG 27-29, 2005 CL Changsha, PEOPLES R CHINA SP Xiangtan Univ, IEEE Circuits & Syst Soc, IEEE Computat IntelligenceSoc, IEEE Control Syst Soc, Int Neural Network Soc, European Neural Network Soc, Chinese Assoc Artifiical Intelligence, Japanese Neural Network Soc, Int Fuzzy Syst Assoc, Asia-Pacific Neural Network Assembly, Fuzzy Math & Syst Assoc Chine, Hunan Comp Federat AB In this paper, we present a novel method to find the right expert who matches a certain project well, The idea behind this method includes building domain ontologies to describe projects and experts and calculating similarities between projects and domain experts for matching. The developed system consists of four main components: ontology building, document formalization, similarity calculation and user interface. First, we utilize Protege to develop the predetermined domain ontologies; in which some related concepts are defined. Then, documents concerning experts and projects are formalized by means of concept trees with weights. This process can be done either automatically or manually. Finally, a new method that integrates node-based and edge-based approach is proposed to measure the semantic similarities between projects and experts with the help of the domain ontologies. The experimental results show that the developed information matching system can reach the satisfied recall and precision. C1 Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. RP Wu, JN (reprint author), Dalian Univ Technol, Inst Syst Engn, Dalian 116024, Peoples R China. EM jnwu@dlut.edu.cn; gfyang@student.dlut.edu.cn CR Decker S., 1999, SEMANTIC ISSUES MULT, P351 GRUBER T, 1993, COMMUN ACM, V37, P48 Guarino N, 1999, IEEE INTELL SYST APP, V14, P70, DOI 10.1109/5254.769887 Guarino N., 1995, VERY LARGE KNOWLEDGE HEFTIN J, 2000, ARTIF INTELL, P35 Jiang J., 1997, P INT C RES COMP LIN Leacock C., 1994, FILLING SPARSE TRAIN Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resnik P., 1995, P 14 INT JOINT C ART, P448 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28331-5 J9 LECT NOTES ARTIF INT PY 2005 VL 3614 BP 176 EP 185 PN 2 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDA17 UT WOS:000232218400022 ER PT S AU Guo, LT Yang, SB Wang, J Zhou, JY AF Guo, LT Yang, SB Wang, J Zhou, JY BE Zhuge, H Fox, GC TI Trust model based on similarity measure of vectors in P2P networks SO GRID AND COOPERATIVE COMPUTING - GCC 2005, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Symposium on Trustworthy Global Computing CY APR 07-09, 2005 CL Edinburgh, SCOTLAND AB For the rationality and selfishness of peers, the traditional security schemes can hardly solve the fake services providing and free-ridings in P2P networks. The trust model based on reputation can restrain the malicious behaviors, and avoid malicious peers abusing of resources. However, it suffers much from dishonest feedbacks and strategically altering behaviors of malicious peers. Recurring to the trust model of society network, a trust model based on similarity measure of vectors and a distributed implementation scheme based on R-Chain are proposed. In this model, "time-sensitive factor" has improved the sensitiveness of detecting peers' behaviors, "service-type factor" and recommendation trust based on similarity measure of vectors have prevented collusion and bad-mouthing among peers. Consequently, theoretic analysis and simulation show that this model is effective and has favorable feasibility of implementation. C1 Univ Sci & Technol China, Dept Comp Sci, Hefei 230026, Peoples R China. RP Guo, LT (reprint author), Univ Sci & Technol China, Dept Comp Sci, Hefei 230026, Peoples R China. EM ltguo@mail.ustc.edu.cn; syang@ustc.edu.cn; joycew@mail.ustc.edu.cn; jjyyzhou@mail.ustc.edu.cn CR ADAR E, 2000, FREE RIDING GNUTELLA, V5 Androutsellis-Theotokis S, 2004, ACM COMPUT SURV, V36, P335, DOI 10.1145/1041680.1041681 BETH T, P 3 EUR S RES COMP S Dellarocas C., 2000, ACM C EL COMM, P150 DESPOTOVIC Z, 2004, 8 INT WORKSH COOP IN FENG ZJ, 2004, J COMPUTER RES DEV, V41 KAMVAR S, 2003, P 12 WWW C Lam SK, 2004, P 13 WORLD WID WEB C LIANG Z, 2002, J COMPUTER RES DEV, V39, P986 Liu L, 2004, 17 INT C PAR DISTR C, P131 ORAM A, PEER PEER HARNESSING RESNICK P, 2001, NBER WORKSH EMP STUD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srivatsa M., 2005, WWW 05, P422, DOI DOI 10.1145/1060745.1060808 WEN D, 2004, RECOMMENDATION BASED, V15 XIONG L, 2003, IEEE C E COMM CEC 03 NR 16 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-30510-6 J9 LECT NOTES COMPUT SC PY 2005 VL 3795 BP 836 EP 847 PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BDQ17 UT WOS:000234857700103 ER PT B AU Doan, S Horiguchi, S AF Doan, S Horiguchi, S BE Ishikawa, M Hashimoto, S Paprzycki, M Barakova, E Yoshida, K Koppen, M Corne, DW Abraham, A TI An efficient feature selection using multi-criteria in text categorization SO HIS'04: Fourth International Conference on Hybrid Intelligent Systems, Proceedings LA English DT Proceedings Paper CT 4th International Conference on Hybrid Intelligent Systems (HIS 04) CY DEC 05-08, 2004 CL Kitakyushu, JAPAN SP IEEE Comp Soc, Computat Intelligence Soc, IEEE, Syst Man, & Cybernet Soc, BMFSA, SOFT, Int Fuzzy Syst Assoc, City Kitakyushu, World Federat Soft Comp AB Text categorization is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on multi-criteria ranking of features. Based on a threshold value for each criterion, a new procedure for feature selection is proposed and applied to a text categorization. Experiments dealing with the Reuters-21578 benchmark data and the naive Bayes algorithm show that the proposed approach outperforms performances in compare to conventional feature selection methods. C1 Japan Adv Inst Sci & Technol, Grad Sch Informat Sci, Tatsunokuchi, Ishikawa 9231292, Japan. RP Doan, S (reprint author), Japan Adv Inst Sci & Technol, Grad Sch Informat Sci, Asahidai 1-1, Tatsunokuchi, Ishikawa 9231292, Japan. RI Doan, Son/F-8266-2015 OI Doan, Son/0000-0002-7284-1306 CR AMALDI E, 1998, THEORETICAL COMPUTER, P237 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 Blum AL, 1997, ARTIF INTELL, V97, P245, DOI 10.1016/S0004-3702(97)00063-5 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X LEWIS D, 1991, THESIS GRADUATE SCH Liu H, 1998, FEATURE SELECTION KN McCallum A.K., 1996, BOW TOOLKIT STAT LAN Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2291-2 PY 2005 BP 86 EP 91 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BBW91 UT WOS:000228181100014 ER PT B AU Zakos, J Verma, B AF Zakos, J Verma, B BE Selvaraj, H Verma, B DeCarvalho, A TI Concept-based term weighting for web information retrieval SO ICCIMA 2005: Sixth International Conference on Computational Intelligence and Multimedia Applications, Proceedings LA English DT Proceedings Paper CT 6th International Conference on Computational Intelligence and Multimedia Applications CY AUG 16-18, 2005 CL Las Vegas, NV SP IEEE Comp Soc AB In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed the significance of this technique is that 1) unlike IDF, CBW is independent of document collection statistics, 2) it presents a new way of interpreting ontologies for retrieval, and 3) it introduces an additional source of term importance information that can be used for term weighting. C1 Griffith Univ, Sch Informat & Comm Technol, Southport, Qld 4215, Australia. RP Zakos, J (reprint author), Griffith Univ, Sch Informat & Comm Technol, Southport, Qld 4215, Australia. CR ANH V, 2003, P 12 TEXT RETR C TRE, P726 Anh V.N., 2002, P 25 ANN INT ACM SIG, P3 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Craswell N., 2003, P 12 TEXT RETR C TRE, P193 Li X., 1995, P IJCAI 95, P1368 Mihalcea R, 1998, P COLING ACL WORKSH, P16 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 NAMBA I, 1999, P 8 TEXT RETR C TREC, P275 PLACHOURIS V, 2002, WORKSH MATH FORM MET, P105 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1999, P 8 TEXT RETR C TREC, P151 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sparck Jones K., 1972, J DOC, V28, P111 VOORHEES EM, 1998, WORDNET ELECT LEXICA, P285 WEN J, 2003, P 12 TEXT RETR, P408 Zobel J., 1998, SIGIR Forum, V32 NR 19 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2358-7 PY 2005 BP 173 EP 178 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BDC11 UT WOS:000232529100027 ER PT J AU Xu, YJ Benaroch, M AF Xu, YJ Benaroch, M TI Information retrieval with a hybrid automatic query expansion and data fusion procedure SO INFORMATION RETRIEVAL LA English DT Article DE relevance-feedback; data fusion; multiple queries ID RELEVANCE FEEDBACK; MODEL AB We propose a hybrid information retrieval (IR) procedure that builds on two well-known IR approaches: data fusion and query expansion via relevance feedback. This IR procedure is designed to exploit the strengths of data fusion and relevance feedback and to avoid some weaknesses of these approaches. We show that our IR procedure is built on postulates that can be justified analytically and empirically. Additionally, we offer an empirical investigation of the procedure, showing that it is superior to relevance feedback on some dimensions and comparable on other dimensions. The empirical investigation also verifies the conditions under which the use of our IR procedure could be beneficial. C1 Natl Univ Singapore, Sch Comp, Dept Informat Syst, Singapore 117543, Singapore. Syracuse Univ, Sch Management, Syracuse, NY 13244 USA. RP Xu, YJ (reprint author), Natl Univ Singapore, Sch Comp, Dept Informat Syst, 3 Sci Dr 2, Singapore 117543, Singapore. EM xuyj@comp.nus.edu.sg CR Bartell B. T., 1994, P 17 ANN INT ACM SIG, P173 Belkin N. J., 1993, P 16 ANN INT ACM SIG, P339, DOI 10.1145/160688.160760 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BORLUND P, 2002, J AM SOC INFORM SCI, V54, P913 Buckley C., 1994, P 17 ANN INT ACM SIG, P292 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1997, NATL I STANDARDS TEC, P107 BUCKLEY C, 1993, NIST SPEC PUBL, P45 BUCKLEY C, 1995, MASSIVE QUERY EXPANS BUCKLEY C, 1992, NIST SPECIAL PUBLICA, P59 CHEN H, 1994, J AM SOC INFORM SCI, V46, P194 Cohen WW, 1999, J ARTIF INTELL RES, V10, P243 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 KANTOR PB, 1992, 13TH NATIONAL ONLINE MEETING : PROCEEDINGS - 1992, P147 Katzer J., 1982, Information Technology: Research and Development, V1 Lee JH, 1998, INFORM PROCESS MANAG, V34, P681, DOI 10.1016/S0306-4573(98)00034-X NG K, 1997, NIST SPECIAL PUBLICA NG K, 1998, P 61 ANN M AM SOC IN, P167 Ng KB, 2000, J AM SOC INFORM SCI, V51, P1177, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1030>3.0.CO;2-E Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROCCHIO J, 1965, ISR9 HARV COMP LAB Rorvig M, 1999, J AM SOC INFORM SCI, V50, P652, DOI 10.1002/(SICI)1097-4571(1999)50:8<652::AID-ASI3>3.3.CO;2-J SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1995, P 18 ANN INT ACM SIG, P351 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 32 TC 4 Z9 5 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PD JAN PY 2005 VL 8 IS 1 BP 41 EP 65 DI 10.1023/B:INRT.0000048496.31867.62 PG 25 WC Computer Science, Information Systems SC Computer Science GA 872AU UT WOS:000225178900003 ER PT S AU Li, W Wong, KF Yuan, CF Li, WJ Xia, YQ AF Li, W Wong, KF Yuan, CF Li, WJ Xia, YQ BE Lee, GG Yamada, A Meng, H Myaeng, SH TI Improving text similarity measurement by critical sentence vector model SO INFORMATION RETRIEVAL TECHNOLOGY, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd Asia Information Retrieval Symposium CY OCT 13-15, 2005 CL Jeju Isl, SOUTH KOREA ID SPACE MODEL AB We propose the Critical Sentence Vector Model (CSVM), a novel model to measure text similarity. The CSVM accounts for the structural and semantic information of the document. Compared to existing methods based on keyword vector, e.g. Vector Space Model (VSM), CSVM measures documents similarity by measuring similarity between critical sentence vectors extracted from documents. Experiments show that CSVM outperforms VSM in calculation of text similarity. C1 Chinese Univ Hong Kong, Dept Syst Engn, Shatin, Hong Kong, Peoples R China. Tsing Hua Univ, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. RP Li, W (reprint author), Chinese Univ Hong Kong, Dept Syst Engn, Shatin, Hong Kong, Peoples R China. EM wli@se.cuhk.edu.hk; kfwong@se.cuhk.edu.hk; ycf@tsinghua.edu.cn; cswjli@comp.polyu.edu.hk; yqxia@se.cuhk.edu.hk RI Li, Wenjie/ OI Li, Wenjie/0000-0002-7360-8864 CR ABNEY S, 1994, PARTIAL PARSING TUTO Abney S., 1996, Eighth European Summer School in Logic, Language and Information. Workshop on Robust Parsing. ESSLLI'96 Abney Steven, 2000, P 6 APPL NAT LANG PR, P296, DOI 10.3115/974147.974188 Cormen Thomas H., INTRO ALGORITHMS FRIBURGER N, 2002, TEXTUAL SIMILARITY B Jing H., 2000, P 6 C APPL NAT LANG, P310, DOI 10.3115/974147.974190 LEE DL, 1997, IEEE SOFTWARE, P67 MARIA N, 2000, P 3 INT WORKSH WEB D, P26 Pasca M., 2001, P 24 ANN INT ACM SIG, P366, DOI 10.1145/383952.384025 QUEK CY, 1996, CLASSIFICATION WORLD RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Salton G., 1993, ACM SIGIR C R D INF, P49 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEBASTIANI F, 1999, IEIB4311999 CONS NAT STREHL J, 2000, AAAI 2000 WORKSHOP A, P58 NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29186-5 J9 LECT NOTES COMPUT SC PY 2005 VL 3689 BP 522 EP 527 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDF93 UT WOS:000233302700044 ER PT S AU Ye, HL AF Ye, HL BE Gallagher, M Hogan, J Maire, F TI Multi-level document classifications with self-organising maps SO INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING IDEAL 2005, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005) CY JUL 06-08, 2005 CL Brisbane, AUSTRALIA SP Univ Queensland AB The Self-Organising Map (SOM) is widely used to classify document collections. Such classifications are usually coarse-grained and cannot accommodate accurate document retrieval. A document classification scheme based on Multi-level Nested Self-Organising Map (MNSOM) is proposed to solve the problem. An MNSOM consists of a top map and a set of nested maps organised at different levels. The clusters on the top map of an MNSOM are at a relatively general level achieving retrieval recall, and the nested maps further elaborate the clusters into more specific groups, thus enhancing retrieval precision. The MNSOM was tested by a software document collection. The experimental results reveal that the MNSOM significantly improved the retrieval performance in comparison with the single SOM based classification. C1 Univ Newcastle, Sch Elect Engn & Comp Sci, Newcastle, NSW 2308, Australia. RP Ye, HL (reprint author), Univ Newcastle, Sch Elect Engn & Comp Sci, Newcastle, NSW 2308, Australia. EM Huilin.Ye@newcastle.edu.au CR Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X HONKELA T, 1996, A32 HELS U TECHN HEL Kohonen T., 1997, SELF ORG MAPS KOHONEN T, 1998, P 8 INT C ART NEUR N, P55 Kohonen T, 1988, SELF ORG ASS MEMORY Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Maarek Y. S., 1991, SIGIR Forum, V25 MAAREK YS, 1989, THESIS ISRAEL I TECH MAAREK YS, 1991, IEEE T SOFTWARE ENG, V17, P800, DOI 10.1109/32.83915 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Orwig RE, 1997, J AM SOC INFORM SCI, V48, P157 RIJSBERGEN C, 1980, INFORMATION RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 13 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26972-X J9 LECT NOTES COMPUT SC PY 2005 VL 3578 BP 367 EP 374 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCR09 UT WOS:000230878800048 ER PT B AU Hamamoto, M Kitagawa, H Pan, JY Faloutsos, C AF Hamamoto, M Kitagawa, H Pan, JY Faloutsos, C GP IEEE Computer Society TI A comparative study of feature vector-based topic detection schemes for text streams SO International Workshop on Challenges in Web Information Retrieval and Integration, Proceedings LA English DT Proceedings Paper CT International Workshop on Challenges in Web Information Retrieval and Integration CY APR 08-09, 2005 CL Tokyo, JAPAN SP IEEE Comp Soc, Database Soc Japan, Informat Proc Soc Japan, Inst Elect, Informat & Commun Engn ID MODEL AB Topic detection is an important subject when voluminous text data is sent continuously to a user We examine a method to detect topics in text data using feature vectors. Feature vectors represent the main distribution of data and they are obtained by various data analysis methods. This paper examines three methods: Singular Value Decomposition (SVD), clustering, and Independent Component Analysis (ICA). SVD and clustering are popular existing methods. Clustering, especially, is applied to many topic detection methods. ICA was recently developed in signal processing research. In applications related to text data, however ICA has not been compared with SVD and clustering, nor has its relationship with them been explored. This paper reports comparative experiments for these three methods and then shows properties as they apply to text data. C1 Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 305, Japan. RP Hamamoto, M (reprint author), Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba, Ibaraki 305, Japan. RI Kitagawa, Hiroyuki/F-1438-2013 CR BINGHAM E, 2001, P 3 INT C IND COMP A, P546 Cardoso JF, 1996, SIAM J MATRIX ANAL A, V17, P161, DOI 10.1137/S0895479893259546 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FISCUS J, 1999, P DARPA BROADC NEWS, P19 Hyvarinen A., 2001, INDEPENDENT COMPONEN KABAN A, 2000, 10 U PAISL DEP COMP Kolenda T., 2001, P 3 INT C IND COMP A, P540 Li H, 2003, INFORM PROCESS MANAG, V39, P521, DOI 10.1016/S0306-4573(02)00035-3 MacQueen J., 1967, P 5 BERK S MATH STAT, V1, P281, DOI DOI 10.1234/12345678 Rajaraman K., 2001, P 5 PAC AS C KNOWL D, P102 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schultz J., 1999, P DARPA BROADC NEWS, P189 WALLS F, 1999, P DARPA BROADC NEWS, P193 Wayne C., 2000, P LANG RES EV C LREC, P1487 NR 14 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2414-1 PY 2005 BP 122 EP 127 DI 10.1109/WIRI.2005.1 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BCV03 UT WOS:000231381100017 ER PT B AU Bast, H Weber, I AF Bast, H Weber, I GP IEEE Computer Society TI Insights from viewing ranked retrieval as rank aggregation SO INTERNATIONAL WORKSHOP ON CHALLENGES IN WEB INFORMATION RETRIEVAL AND INTEGRATION, PROCEEDINGS LA English DT Proceedings Paper CT International Workshop on Challenges in Web Information Retrieval and Integration CY APR 08-09, 2005 CL Tokyo, JAPAN SP IEEE Comp Soc, Database Soc Japan, Informat Proc Soc Japan, Inst Elect, Informat & Commun Engn ID LATENT SEMANTIC ANALYSIS AB We view a variety of established methods for ranked retrieval from a common angle, namely as a process of combining query-independent rankings that were precomputed for certain attributes. Apart from a general insight into what effectively distinguishes various schemes from each other we obtain three specific results concerned with concept-based retrieval. First, we prove that latent semantic indexing (LSI) can be implemented to answer queries in time proportional to the number of words in the query, which improves over the standard implementation by an order of magnitude; a similar result is established for LSI's probabilistic sibling PLSI. Second, we give a simple and precise characterization of the extent, to which latent semantic indexing (LSI) can deal with polysems, and when it fails to do so. Third, we demonstrate that the recombination of the intricate, yet relatively cheap mechanism of PLSI for mapping queries to attributes, with a simplistic, easy-to-compute set of document rankings gives a retrieval performance which is at least as good as that of the most sophisticated concept-based retrieval schemes and which does not require any precomputation. C1 Max Planck Inst Informat, D-66123 Saarbrucken, Germany. RP Bast, H (reprint author), Max Planck Inst Informat, Stuhlsatzenhausweg 85, D-66123 Saarbrucken, Germany. EM bast@mpi-sb.mpg.de; iweber@mpi-sb.mpg.de RI Weber, Ingmar/A-3307-2009; Weber, Ingmar/ OI Weber, Ingmar/0000-0003-4169-2579 CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Dhillon IS, 2003, P 2003 IEEE INT C DA, P517 DUPRET G, 2003, P ACM C RES DEV INF, P221 Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6 Fagin R., 1996, Proceedings of the Fifteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1996, DOI 10.1145/237661.237715 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950 Husbands P, 2001, COMPUTATIONAL INFORMATION RETRIEVAL, P145 KAMVAR SD, 2003, 18 INT JOINT C ART I, P561 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Lee DD, 1999, NATURE, V401, P788 Lee DD, 2000, P NIPS, ppp Page L., 1998, PAGER CITATION RANKI Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIN K, 2003, P CLCLING 03, P619 Silverstein C., 1999, SIGIR Forum, V33 NR 18 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2414-1 PY 2005 BP 232 EP 237 DI 10.1109/WIRI.2005.19 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BCV03 UT WOS:000231381100032 ER PT S AU Schneider, KM AF Schneider, KM BE Jorge, A Torgo, L Brazdil, P Camacho, R Gama, J TI Weighted average pointwise mutual information for feature selection in text categorization SO KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2005 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 16th European Conference on Machine Learning (ECML)/9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) CY OCT 03-07, 2005 CL Oporto, PORTUGAL SP FCT, LIACC NIAAD AB Mutual information is a common feature score in feature selection for text categorization. Mutual information suffers from two theoretical problems: It assumes independent word variables, and longer documents are given higher weights in the estimation of the feature scores, which is in contrast to common evaluation measures that do not distinguish between long and short documents. We propose a variant of mutual information, called Weighted Average Pointwise Mutual Information (WAPMI) that avoids both problems. We provide theoretical as well as extensive empirical evidence in favor of WAPMI. Furthermore, we show that WAPMI has a nice property that other feature metrics lack, namely it allows to select the best feature set size automatically by maximizing an objective function, which can be done using a simple heuristic without resorting to costly methods like EM and model selection. C1 Univ Passau, Dept Gen Linguist, D-94030 Passau, Germany. RP Schneider, KM (reprint author), Univ Passau, Dept Gen Linguist, D-94030 Passau, Germany. EM schneide@phil.uni-passau.de CR Apte C., 1994, P 17 ANN INT ACM SIG, P23 Cover T. M., 1991, ELEMENTS INFORMATION Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 Dhillon I. S., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753661 Eyheramendy S., 2003, AI STAT 2003, P332 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Friedman JH, 1997, DATA MIN KNOWL DISC, V1, P55, DOI 10.1023/A:1009778005914 Church K. W., 1990, Computational Linguistics, V16 John G, 1994, MACH LEARN, P121 Lang K, 1995, P 12 INT C MACH LEAR, P331 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 Rennie J D, 2001, THESIS MIT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 14 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29244-6 J9 LECT NOTES ARTIF INT PY 2005 VL 3721 BP 252 EP 263 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDF43 UT WOS:000233235600027 ER PT S AU Gayo-Avello, D Alvarez-Gutierrez, D Gayo-Avello, J AF Gayo-Avello, D Alvarez-Gutierrez, D Gayo-Avello, J BE Peters, C Clough, P Gonzalo, J Jones, GJF Kluck, M Magnini, B TI Application of variable length N-gram vectors to monolingual and bilingual information retrieval SO MULTILINGUAL INFORMATION ACCESS FOR TEXT, SPEECH AND IMAGES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th Workshop of the Cross-Language Evaluation Forum CY SEP 15-17, 2004 CL Bath, ENGLAND SP Ist Sci Tecnol Informaz ID TEXT AB Our group in the Department of Informatics at the University of Oviedo has participated, for the first time, in two tasks at CLEF: monolingual (Russian) and bilingual (Spanish-to-English) information retrieval. Our main goal was to test the application to IR of a modified version of the n-gram vector space model (codenamed blindLight). This new approach has been successfully applied to other NLP tasks such as language identification or text summarization and the results achieved at CLEF 2004, although not exceptional, are encouraging. There are two major differences between the blindLight approach and classical techniques: (1) relative frequencies are no longer used as vector weights but are replaced by n-gram significances, and (2) cosine distance is abandoned in favor of a new metric inspired by sequence alignment techniques, not so computationally expensive. In order to perform cross-language IR we have developed a naive n-gram pseudo-translator similar to those described by McNamee and Mayfield or Pirkola et al. C1 Univ Oviedo, Dept Informat, Oviedo 33007, Spain. RP Gayo-Avello, D (reprint author), Univ Oviedo, Dept Informat, Calvo Sotelo S-N, Oviedo 33007, Spain. EM dani@uniovi.es RI Gayo-Avello, Daniel/D-3791-2014 OI Gayo-Avello, Daniel/0000-0002-4705-6891 CR DASILVA F, 1999, P MOL6 DASILVA F, 1999, P VEXTAL VEN IT Dunning T., 1993, Computational Linguistics, V19 Gayo-Avello D, 2004, LECT NOTES ARTIF INT, V3230, P267 Gayo-Avello D, 2004, LECT NOTES COMPUT SC, V3141, P440 KIMBRELL RE, 1988, BYTE, V13, P297 Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 MAH C. P., 1985, P 8 INT ACM C RES DE, P155, DOI 10.1145/253495.253521 MCNAMEEE P, CLEF 2003 WORKSH 21 PETERS C, IN PRESS LNCS PIRKOLA A, 2002, INFORMATION RES, V7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-27420-0 J9 LECT NOTES COMPUT SC PY 2005 VL 3491 BP 73 EP 82 PG 10 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCT55 UT WOS:000231117600007 ER PT S AU Chik, FCY Luk, RWP Chung, KFL AF Chik, FCY Luk, RWP Chung, KFL BE Montoyo, A Munoz, R Metais, E TI Text categorization based on subtopic clusters SO NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 10th International Conference on Applications of Natural Language to Information Systems CY JUN 15-17, 2005 CL Alicante, SPAIN HO Univ Alicante AB The distribution of the number of documents in topic classes is typically highly skewed. This leads to good micro-average performance but not so desirable macro-average performance. By viewing topics as clusters in a high dimensional space, we propose the use of clustering to determine subtopic clusters for large topic classes by assuming that large topic clusters are in general a mixture of a number of subtopic clusters. We used the Reuters News articles and support vector machines to evaluate whether using subtopic cluster can lead to better macro-average performance. C1 Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. RP Chik, FCY (reprint author), Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. EM cscychik@comp.polyu.edu.hk; csrluk@comp.polyu.edu.hk; cskchung@comp.polyu.edu.hk RI Luk, Robert/B-9382-2015 OI Luk, Robert/0000-0002-9310-8867 CR Aas K., 1999, 941 NORW COMP CTR Callan James P., 1994, P 17 ANN INT ACM SIG, P302 DUMAIS S, 1998, INDUCTIVE LEARNING A Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582 Jain AK, 1999, ACM COMPUT SURV, V31 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694 Lewis D.D., REUTERS 21578 TEXT C Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 Mitchell TM, 1997, MACHINE LEARNING NICHOLAS C, TUTORIAL CLUSTERING PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 SCHUTZE H, SINGLE LINK COMPLETE Takamura H, 2002, P CONLL 2002, P29 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 YANG YM, 1997, CMUCS97127 COMP SCI NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-26031-5 J9 LECT NOTES COMPUT SC PY 2005 VL 3513 BP 203 EP 214 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BCO46 UT WOS:000230413100019 ER PT S AU Yoshida, M Nakagawa, H AF Yoshida, M Nakagawa, H BE Su, KY Tsujii, J Lee, JH Kwong, OY TI Specification retrieval - How to find attribute-value information on the web SO NATURAL LANGUAGE PROCESSING - IJCNLP 2004 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 1st International Joint Conference on Natural Language Processing (IJCNLP 2004) CY MAR 22-24, 2004 CL Hainan Isl, PEOPLES R CHINA AB This paper proposes a method for retrieving Web pages according to objects described in them. To achieve that goal, ontologies extracted from HTML tables are used as queries. The system retrieves Web pages containing the type of objects described by a given ontology. We propose a simple and efficient algorithm for this task and show its performance on real-world Web sites. C1 Univ Tokyo, Ctr Informat Technol, Bunkyo Ku, Tokyo 1130033, Japan. RP Yoshida, M (reprint author), Univ Tokyo, Ctr Informat Technol, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1130033, Japan. CR Chakrabarti S., 2002, MINING WEB DISCOVERI Cormack G. V., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291009 CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Manning C. D., 1999, FDN STAT NATURAL LAN MASUDA K, 2003, P ACL 2003 STUD RES, P50 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Nigam K., 1999, P IJCAI 99 WORKSH MA, P61 Quinlan J., 1994, C4 5 PROGRAMS MACHIN Rivest R. L., 1987, Machine Learning, V2, DOI 10.1007/BF00058680 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M., 2000, INFORMATION RETRIEVA, V2, P49, DOI 10.1023/A:1009933700147 Yoshida M, 2001, P PAC ASS COMP LING, P332 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24475-1 J9 LECT NOTES COMPUT SC PY 2005 VL 3248 BP 338 EP 347 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BBZ57 UT WOS:000228359800036 ER PT S AU Uzuner, O Katz, B AF Uzuner, O Katz, B BE Dale, R Wong, KF Su, J Kwong, OY TI A comparative study of language models for book and author recognition SO NATURAL LANGUAGE PROCESSING - IJCNLP 2005, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Joint Conference on Natural Language Processing CY OCT 11-13, 2005 CL Jeju Isl, SOUTH KOREA SP Jeju Provine Local Govt, Korea Adv Inst Sci & Technol, Korea Inst Sci & Technol Informat, Elet & Telecomm Res Inst, Microsoft Korea, Microsoft Japan, Mobico & Sysmeta ID SHAKESPEARE; LENGTH; PROSE AB Linguistic information can help improve evaluation of similarity between documents; however, the kind of linguistic information to be used depends on the task. In this paper, we show that distributions of syntactic structures capture the way works are written and accurately identify individual books more than 76% of the time. In comparison, baseline features, e.g., tfidf-weighted keywords, function words, etc., give an accuracy of at most 66%. However, testing the same features on authorship attribution shows that distributions of syntactic structures are less successful than function words on this task; syntactic structures vary even among the works of the same author whereas features such as function words are distributed more similarly among the works of an author and can more effectively capture authorship. C1 MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA. RP Uzuner, O (reprint author), MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA. EM ozlem@csail.mit.edu; boris@csail.mit.edu CR ALEXANDER D, 1964, SOME CLASES VERBS EN BAKER JC, 1988, J ASS LIT LINGUISTIC, V3, P36 BIBER D, 1989, LINGUISTICS, V27, P3, DOI 10.1515/ling.1989.27.1.3 Biber Douglas, 1998, CORPUS LINGUISTICS I Brill E., 1992, P 3 C APPL NAT LANG Brinegar C. S., 1963, J AM STAT ASSOC, V58, P85, DOI 10.1080/01621459.1963.10500834 DIAB M, 1998, P 6 INT C ART INT AP THISTED R, 1987, BIOMETRIKA, V74, P445, DOI 10.1093/biomet/74.3.445 Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670 Halliday M. A. K., 1976, COHESION ENGLISH Halliday M. A. K, 1985, INTRO FUNCTIONAL GRA HATZIVASSILOGLO.V, 1999, 37 ANN M ACL HATZIVASSILOGLO.V, 2001, NAACL 01 AUT SUM WOR HOLMES DI, 1994, COMPUT HUMANITIES, V28, P87, DOI 10.1007/BF01830689 KATZ B, 1990, ARTIFICIAL INTELLIGE KATZ B, 1988, P 12 INT C COMP LING Khmelev D. V., 2001, Literary & Linguistic Computing, V16, DOI 10.1093/llc/16.3.299 KJETSAA G, 1984, AUTHORSHIP QUIET DON KOPPEL M, 2003, P IJCAI 03 WORKSH CO KUKUSHKINA OV, 2000, PROBLEMY PEREDACHI I, V37, P172 Levin Beth, 1993, ENGLISH VERB CLASSES Mendenhall TC, 1887, SCIENCE, V11, P237 Miller GA, 1958, INFORM CONTR, V1, P370, DOI 10.1016/S0019-9958(58)90229-8 MORTON AQ, 1965, J R STAT SOC SER A-G, V128, P169, DOI 10.2307/2344178 MOSTELLER F, 1963, J AM STAT ASSOC, V58, P275, DOI 10.2307/2283270 Peng RD, 2002, AM STAT, V56, P175, DOI 10.1198/000313002100 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire R., 2002, MSRI WORKSH NONL EST SICHEL HS, 1974, J ROY STAT SOC A STA, V137, P25, DOI 10.2307/2345142 Smith M. W. A., 1983, ASS LIT LINGUISTIC C, V11, P73 TALLENTIRE DR, 1972, THESIS U CAMBRIDGE UZUNER O, 2005, THESIS MIT UZUNER O, 2005, P 20 NAT C ART INT A van der Geest Thea, 1996, NEW WRITING ENV WRIT WILKINSON J, 1995, P IEEE 5 ANN DUAL US WILLIAMS CB, 1975, BIOMETRIKA, V62, P207, DOI 10.1093/biomet/62.1.207 Witten I. H., 2000, DATA MINING PRACTICA Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yule G., 1938, BIOMETRIKA, V30, P363 NR 40 TC 10 Z9 10 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-29172-5 J9 LECT NOTES ARTIF INT PY 2005 VL 3651 BP 969 EP 980 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BDF92 UT WOS:000233302600084 ER PT J AU Srinivas, PR Wei, SH Cristianini, N Jones, EG Gorin, FA AF Srinivas, PR Wei, SH Cristianini, N Jones, EG Gorin, FA TI Comparison of vector space model methodologies to reconcile cross-species neuroanatomical concepts SO NEUROINFORMATICS LA English DT Article DE neuroanatomical term extraction; information retrieval; vector space model; automated relevance feedback; neuroanatomical domains ID PERFORMANCE AB Generating informational thesauri that classify, cross-reference, and retrieve diverse and highly detailed neuroscientific information requires identifying related neuroanatomical terms and acronyms within and between species (Gorin et al., 2001) Manual construction of such informational thesauri is laborious, and we describe implementing and evaluating a neuroanatomical term and acronym reconciliation (NTAR) system to assist domain experts with this task. NTAR is composed of two modules. The neuroanatomical term extraction (NTE) module employs a hidden Markov model (HMM) in conjunction with lexical rules to extract neuroanatomical terms (NT) and acronyms (NA) from textual material. The output of the NTE is formatted into collections of term- or acronym-indexed documents composed of sentences and word phrases extracted from textual material. The second information retrieval (IR) module utilizes a vector space model (VSM) and includes a novel, automated relevance feedback algorithm. The IR module retrieves statistically related neuroanatomical terms and acronyms in response to queried neuroanatomical terms and acronyms. Neuroanatomical terms and acronyms retrieval obtained from term-based inquiries were compared with (1) term retrieval obtained by including automated relevance feedback and with (2) term retrieval using "document-to-document" comparisons (context-based VSM). The retrieval of synonymous and similar primate and macaque thalamic terms and acronyms in response to a query list of human thalamic terminology by these three IR approaches was compared against a previously published, manually constructed concordance table of homologous cross-species terms and acronyms. Term-based VSM with automated relevance feedback retrieved 70% and 80% of these primate and macaque terms and acronyms, respectively, listed in the concordance table. Automated feedback algorithm correctly identified 87% of the macaque terms and acronyms that were independently selected by a domain expert as being appropriate for manual relevance feedback. Context-based VSM correctly retrieved 97% and 98% of the primate and macaque terms and acronyms listed in the term homology table. These results indicate that the NTAR system could assist neuroscientists with thesauri creation for closely related, highly detailed neuroanatomical domains. C1 UC Davis, Ctr Neurosci, Davis, CA 95616 USA. UC Davis, Dept Neurol, Davis, CA USA. Univ Calif Davis, Dept Stat, Davis, CA USA. RP Gorin, FA (reprint author), UC Davis, Ctr Neurosci, Davis, CA 95616 USA. EM fagorin@ucdavis.edu CR Altschul SF, 1997, NUCLEIC ACIDS RES, V25, P3389, DOI 10.1093/nar/25.17.3389 [Anonymous], 2000, AM HERITAGE DICT ENG, V4 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BERROIS CD, 2002, JAMA, V9 Bowden DM, 2003, NEUROINFORMATICS, V1, P43, DOI 10.1385/NI:1:1:043 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CRESTANI F, UNCERTAINTY INTELLIG CRISTIANINI N, NATO ASI SERIES COMP FRAKES WR, 1992, INFORMATION RETRIEVA Gorin F, 2001, NEUROSCIENTIST, V7, P18 Grefenstette G., 1994, EXPLORATIONS AUTOMAT Gusfield D., 1997, ALGORITHMS STRINGS T HASSLER R, 1959, ANATOMY THALAMUS INT HOMAYOUNI R, 2003, MINING BIBLIOME IDEN JONES EG, 1998, HDB CHEM NEUROANATOM, V14 Lamping J., 1994, P ACM S US INT SOFTW, V1, P13, DOI 10.1145/192426.192430 Leroy G, 2003, J BIOMED INFORM, V36, P145, DOI 10.1016/S1532-0464(03)00039-X Liu HF, 2002, J AM MED INFORM ASSN, V9, P621, DOI 10.1097/jamia.M1101 Liu HF, 2004, J AM MED INFORM ASSN, V11, P320, DOI 10.1197/jamia.M1533 MAGNUS S, 2001, SEM KNOWL ACQ CAT WO Manning CD, 2000, FDN STAT NATURAL LAN, V2nd MAO W, 2002, P AMIA ANN S NOMADIC G, 2002, P 2 INT WORKSH COMP Olszewski J, 1952, THALAMUS MACACA MULA Patwardhan S., 2003, P 4 INT C INT TEXT P PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 PUSTEJOVSKY J, 2001, LINGUISTIC KNOWLEDGE RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 RATNAPARKH A, 1997, MXTERMINATOR Rindflesch T.C., 2000, P 6 APPL NAT LANG PR, P188, DOI 10.3115/974147.974173 RINDFLESH T, 1999, P AMIA ANN S SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRINIVAS PR, 2002, NEUROINFORMATICS, V1, P177 *U BIRM DEP ENGL S, QTAG V 3 01 PORT POS YAO D, P 2 C AS PAC BIOINF, V29, P53 SPECIALIST LEXICON N NR 38 TC 2 Z9 2 PU HUMANA PRESS INC PI TOTOWA PA 999 RIVERVIEW DRIVE SUITE 208, TOTOWA, NJ 07512 USA SN 1539-2791 J9 NEUROINFORMATICS JI Neuroinformatics PY 2005 VL 3 IS 2 BP 115 EP 131 DI 10.1385/NI:03:02:115 PG 17 WC Computer Science, Interdisciplinary Applications; Neurosciences SC Computer Science; Neurosciences & Neurology GA 942ZY UT WOS:000230322600004 PM 15988041 ER PT J AU Song, FX Liu, SH Yang, JY AF Song, FX Liu, SH Yang, JY TI A comparative study on text representation schemes in text categorization SO PATTERN ANALYSIS AND APPLICATIONS LA English DT Article DE text categorization; text representation; support vector machines; multi-way analysis of variance; pattern recognition AB It is well known that the classification effectiveness of the text categorization system is not simply a matter of learning algorithms. Text representation factors are also at work. This paper will consider the ways in which the effectiveness of text classifiers is linked to the five text representation factors: "stop words removal", "word stemming", "indexing", "weighting", and "normalization". Statistical analyses of experimental results show that performing "normalization" can always promote effectiveness of text classifiers significantly. The effects of the other factors are not as great as expected. Contradictory to common sense, a simple binary indexing method can sometimes be helpful for text categorization. EM songfengxi@yahoo.com CR Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 CHAKRABARTI S, 2002, P 28 VLDB C HONG KON Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 HSU C, 2002, IEEE T NEURAL NETW, V13 Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819 Joachims T., 1998, P 10 EUR C MACH LEAR Lang K, 1995, P 12 INT C MACH LEAR, P331 LEWIS, REUTERS21578 DISTRIB Ma J., OSU SVM CLASSIFIER M MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 PETRIDIS V, 2001, P 2001 INT JOINT C N PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 Yang Y., 1997, MACH LEARN INT WORKS, P412 Zhang T, 2001, INFORM RETRIEVAL, V4, P5, DOI 10.1023/A:1011441423217 NR 19 TC 6 Z9 7 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013 USA SN 1433-7541 J9 PATTERN ANAL APPL JI Pattern Anal. Appl. PY 2005 VL 8 IS 1-2 BP 199 EP 209 DI 10.1007/s10044-005-0256-3 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA 977BV UT WOS:000232778300017 ER PT S AU Almpanidis, G Kotropoulos, C AF Almpanidis, G Kotropoulos, C BE Singh, S Singh, M Apte, C Perner, P TI Combining text and link analysis for focused crawling SO PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Conference on Advances in Pattern Recognition CY AUG 22-25, 2005 CL Bath, ENGLAND ID MODEL AB The number of vertical search engines and portals has rapidly increased over the last years, making the importance of a topic-driven (focused) crawler evident. In this paper, we develop a latent semantic indexing classifier that combines link analysis with text content in order to retrieve and index domain specific web documents, We compare its efficiency with other well-known web information retrieval techniques. Our implementation presents a different approach to focused crawling and aims to overcome the limitations of the necessity to provide initial training data while maintaining a high recall/precision ratio, C1 Aristotle Univ Thessaloniki, Dept Informat, GR-54124 Thessaloniki, Greece. RP Almpanidis, G (reprint author), Aristotle Univ Thessaloniki, Dept Informat, Box 451, GR-54124 Thessaloniki, Greece. EM galba@aiia.csd.auth.gr; costas@aiia.csd.auth.gr RI Kotropoulos, Constantine/B-7928-2010 OI Kotropoulos, Constantine/0000-0001-9939-7930 CR Aggarwal C.C., 2001, P 10 INT WORLD WID W, P96, DOI DOI 10.1145/371920.371955 Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 Bergmark D., 2002, P 6 EUR C RES ADV TE, P91 Berry M., 1999, UNDERSTANDING SEARCH Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 BRIN S, 1998, WWW7 COMPUTER NETWOR, V30, P107 CALADO P, 2000, P 12 INT C INF KNOWL, P394 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chakrabarti S., 2002, MINING WEB DISCOVERI Chakrabarti S., 2001, P 10 INT C WORLD WID, P211, DOI 10.1145/371920.372054 Chau M, 2003, COMPUTER, V36, P56, DOI 10.1109/MC.2003.1198237 CHO J, 1998, P 7 INT WORLD WID WE, P161 Cohn D., 2000, P 17 INT C MACH LEAR, P167 Cohn D, 2001, ADV NEUR IN, V13, P430 DELIGENTI M, 2000, P 2L INT C VER LARG, P527 Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Menczer F., 2001, P 24 ANN INT ACM SIG, P241, DOI DOI 10.1145/383952.383995 Najork M., 2001, P 10 INT WORLD WID W, P114, DOI DOI 10.1145/371920.371965 Ng A., 2001, P 24 ANN INT ACM SIG, P258, DOI 10.1145/383952.384003 O'Brien G.W., 1994, THESIS U TENNESSEE K PANT G, 2002, P 2 INT WORKSH WEB D Rennie J., 1999, P 16 INT C MACH LEAR, P335 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRINIVASAN P, 2002, P INT C RES DEV INF TEELE R, 2001, P INT COMP LAS VEG VARLAMIS I, 2004, IEEE T KNOWL DATA EN, V16, P585 YANG K, 2002, P 10 TEXT RETR C TRE CMU WORLD WIDE KNOWL NR 29 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28757-4 J9 LECT NOTES COMPUT SC PY 2005 VL 3686 BP 278 EP 287 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BDA34 UT WOS:000232247900030 ER PT S AU Narayan, BL Pal, SK AF Narayan, BL Pal, SK BE Pal, SK Bandyopadhyay, S Biswas, S TI Distribution based stemmer refinement SO PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 1st International Conference on Pattern Recognition and Machine Intelligence CY DEC 20-22, 2005 CL Calcutta, INDIA SP Indian Statist Inst, ISI, Ctr Soft Comp Res-A Natl Fac, Govt India, Dept Sci & Technol, Int Ctr Pure & Appl Math, Int Assoc Pattern Recognit, Web Intelligence Consortium, Webel, Govt West Bengal IT Comp, Govt India, Council Sci & Ind Res, IEEE HO Statist Inst Kolkata AB Stemming is a common preprocessing task applied to text corpora. Errors in this process may be refined either manually or based on a corpus. We describe a novel corpus-based stemming technique which models the given words as being generated from a multinomial distribution over the topics available in the corpus. A sequential hypothesis testing like procedure helps us group together distributionally similar words. This stemmer refines any given stemmer and its strength can be controlled with the help of two thresholds. A refinement based on the 20 Newsgroups data set shows that the proposed method splits equivalence classes appropriately. C1 Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, W Bengal, India. RP Narayan, BL (reprint author), Indian Stat Inst, Machine Intelligence Unit, 203,BT Rd, Kolkata 700108, W Bengal, India. EM bln_r@isical.ac.in; sankar@isical.ac.in RI Pal, Sankar /G-2243-2010 CR Frakes W. B., 2003, ACM SIGIR FORUM, V37, P26, DOI 10.1145/945546.945548 Johnson N. L., 1997, DISCRETE MULTIVARIAT Kraaij W., 1996, P 19 ANN INT ACM SIG, P40, DOI 10.1145/243199.243209 Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 PAICE CD, 1996, J AM SOC INFORM SCI, V47, P28 PEREIRA F, 1993, 31ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P183 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnik V., 1995, NATURE STAT LEARNING WALD ABRAHAM, 1947, SEQUENTIAL ANAL Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 YAMOUT F, 2004, P KI2004 WORKSH MACH, P7 NR 12 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-30506-8 J9 LECT NOTES COMPUT SC PY 2005 VL 3776 BP 672 EP 677 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BDQ09 UT WOS:000234856700108 ER PT B AU Zhou, JG Wang, K Wu, J Yan, PL Wu, M AF Zhou, JG Wang, K Wu, J Yan, PL Wu, M GP IEEE TI A method of Chinese text categorization based on proximal support vector machine SO Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 LA English DT Proceedings Paper CT 4th International Conference on Machine Learning and Cybernetics CY AUG 18-21, 2005 CL Canton, PEOPLES R CHINA SP IEEE Systems, Man & Cybernet TCC, Hong Kong Polytechn Univ, Hebei Univ, S China Univ Technol, Chongqing Univ, Sun Yatsen Univ, Harbin Inst Technol, Int Univ Germany DE text categorization; proximal support vector machine (PSVM); recall; precision AB A Chinese text categorization method based on proximal support vector machine and similarity of words is studied in the paper. Firstly feature vectors are extracted, and then the text feature subset based on similarity of words is got, finally the text is categorized based on proximal support vector machine. The tests on the large-scale text show that the recall is comparatively low and the precision is comparatively high. C1 Wuhan Univ, Sch Elect Informat, Wuhan 430079, Hubei, Peoples R China. RP Zhou, JG (reprint author), Wuhan Univ, Sch Elect Informat, Wuhan 430079, Hubei, Peoples R China. CR CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411 Fung Glenn, 2001, P KDD 2001 KNOWL DIS Lee Y. J., 1999, 9903 U WISC COMP SCI LEE YJ, 2001, P 1 SLAM INT C DAT M LI K, 2002, P 1 INT C MACH LEARN LIU Q, 2002, 3 S CHIN SEM TAIB Mangasaian OL, 2001, J MACH LEARN RES, V1, P161, DOI 10.1162/15324430152748218 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnik V., 1998, STAT LEARNING THEORY WANG XH, 2001, 6 INT C YOUNG COMP S Zhang X-G, 2000, ACTA AUTOMATICA SINI, V26, P32 ZHANG YJ, 1998, MINIMICRO SYSTEMS, V19 NR 12 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9091-1 PY 2005 BP 1615 EP 1619 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BDT94 UT WOS:000235325602054 ER PT B AU Zhang, QR Zhang, L Dong, SB Tan, JH AF Zhang, QR Zhang, L Dong, SB Tan, JH GP IEEE TI Document indexing in text categorization SO PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9 LA English DT Proceedings Paper CT 4th International Conference on Machine Learning and Cybernetics CY AUG 18-21, 2005 CL Canton, PEOPLES R CHINA SP IEEE Systems, Man & Cybernet TCC, Hong Kong Polytechn Univ, Hebei Univ, S China Univ Technol, Chongqing Univ, Sun Yatsen Univ, Harbin Inst Technol, Int Univ Germany DE text categorization; document indexing; feature selection AB Aiming at the characteristic of text categorization, this paper proposes an improved method of computing term weights, tfidfie, based on the traditional tfidf function that is generally used in most classifiers. In comparison with the tfidf function, the tfidfie function adds an information entropy factor, H, which represents the distribution of documents in the training set in which the term occurs. The experiments show tfidfie outperforms tfidf. In addition, this paper analyses the difference of using information entropy factor H between document categorization and feature selection, also finds that both two phases are all necessary for text categorization, meanwhile it can reach the best performance of classification with up to 70% of the unique terms being removed. C1 S China Univ Technol, Guangdong Key Lab Comp Network, Guangzhou 510640, Peoples R China. RP Zhang, QR (reprint author), S China Univ Technol, Guangdong Key Lab Comp Network, Guangzhou 510640, Peoples R China. EM qrzhang@scut.edu.cn; ling@scut.edu.cn; sbdong@scut.edu.cn; tanjh@gsta.com CR [代六ēŽ² Dai Liuling], 2004, [äø­ę–‡äæ”ęÆå­¦ęŠ„, Journal of Chinese Information Processing], V18, P26 David Hull, 1994, 17 ACM C RES DEV INF, P282 Fabrizio S., 2002, ACM COMPUT SURV, V34, P1 JIANG D, 2004, INFORMATION THEORY Mitchell T., 1996, MACHINE LEARNING SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang YM, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P42 Yang Y., 1997, 14 INT C MACH LEARN, P412 NR 9 TC 3 Z9 5 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9091-1 PY 2005 BP 3792 EP 3796 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BDT94 UT WOS:000235325605095 ER PT B AU Liu, T Guo, J AF Liu, T Guo, J GP IEEE TI Automatic text categorization based on angle distribution SO Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9 LA English DT Proceedings Paper CT 4th International Conference on Machine Learning and Cybernetics CY AUG 18-21, 2005 CL Canton, PEOPLES R CHINA SP IEEE Systems, Man & Cybernet TCC, Hong Kong Polytechn Univ, Hebei Univ, S China Univ Technol, Chongqing Univ, Sun Yatsen Univ, Harbin Inst Technol, Int Univ Germany DE text categorization; similarity; angle distribution AB In order to improve the performance of Chinese text categorization, a new Chinese text categorization method based on angle distribution is presented. The new method describes the text with a more precise model and proposed a new categorization algorithm by employing angle distribution. Simulation results on open Chinese text collection show that the precision and recall of most classes have been increased with reference to the classical method, and the macro average of precision and recall are both about 72 percents, which certificating the effectiveness and feasibility of the angle distribution-based algorithm. C1 Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China. RP Liu, T (reprint author), Beijing Univ Posts & Telecommun, Sch Informat Engn, Beijing 100876, Peoples R China. CR BIN L, 2002, J CHINESE INFORMATIO, V16, P18 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, ASS COMPUTING MACHIN, V15, P8 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 NR 5 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9091-1 PY 2005 BP 3797 EP 3801 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BDT94 UT WOS:000235325605096 ER PT B AU Sun, YH He, PL Cheng, LL Wu, GY AF Sun, YH He, PL Cheng, LL Wu, GY BE Zhu, Q TI Study on Term Co-occurrence based on Vector Space Model and its application in text classification SO Proceedings of the 11th Joint International Computer Conference LA English DT Proceedings Paper CT 11th Joint International Computer Conference (JICC 2005) CY NOV 10-12, 2005 CL Chongqing, PEOPLES R CHINA SP China Comp Federat, Hong Kong Comp Soc, Chongqing Informat Ind Bur, Chongqing Univ, Chongqing Univ Post & Telecommun DE Term Co-occurrence Model; Vector Space Model; text classification AB A new Term Co-occurrence Model based on Vector Space Model (VSM) for improving the performance of text classification is proposed in this paper. The model selects a whole document as basic unit for obtaining term co-occurrence resources, which are then applied to text classification. Experimental results show that it improves the performance of text classification system greatly. C1 Tianjin Univ, Dept Comp Sci & Technol, Tianjin 300072, Peoples R China. RP Sun, YH (reprint author), Tianjin Univ, Dept Comp Sci & Technol, Tianjin 300072, Peoples R China. CR JAN WB, 2001, JCDL 1 ACM IEEE JOIN, P207 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1988, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUETZE H, 1997, DOCUMENT INFORM RETR THORSTEN J, 1998, EUR C MACH LEARN ECM, P137 NR 6 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 981-256-532-9 PY 2005 BP 387 EP 390 DI 10.1142/9789812701534_0087 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Telecommunications SC Computer Science; Imaging Science & Photographic Technology; Telecommunications GA BDF35 UT WOS:000233230700087 ER PT B AU Sun, YH He, PL Hou, YX Wei, JH AF Sun, YH He, PL Hou, YX Wei, JH BE Zhu, Q TI Linear and non-linear dimension reduction algorithms and their applications in document clustering SO Proceedings of the 11th Joint International Computer Conference LA English DT Proceedings Paper CT 11th Joint International Computer Conference (JICC 2005) CY NOV 10-12, 2005 CL Chongqing, PEOPLES R CHINA SP China Comp Federat, Hong Kong Comp Soc, Chongqing Informat Ind Bur, Chongqing Univ, Chongqing Univ Post & Telecommun DE linear and non-linear dimension reduction; SIE; document clustering AB Three linear and non-linear dimension reduction algorithms, LSI, Isomap and SIE for processing high dimensional data in natural language documents are presented in this paper. Document data processed by the three algorithms is then clustered by bisecting K-means clustering algorithm. Experimental results show that the performance of SIE algorithm and LSI algorithm in document clustering are basically equivalent and better than the benchmark, while the performance of Isomap algorithm is worse than the benchmark. C1 Tianjin Univ, Dept Comp Sci & Technol, Tianjin 300072, Peoples R China. RP Sun, YH (reprint author), Tianjin Univ, Dept Comp Sci & Technol, Tianjin 300072, Peoples R China. CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Lewis D.D., REUTERS 21578 TEXT C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEINBACH M, 2000, P KDD WORKSH TEXT MI Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 NR 5 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 981-256-532-9 PY 2005 BP 416 EP 419 DI 10.1142/9789812701534_0094 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Telecommunications SC Computer Science; Imaging Science & Photographic Technology; Telecommunications GA BDF35 UT WOS:000233230700094 ER PT B AU Cheng, YZ Zhang, J AF Cheng, YZ Zhang, J GP IEEE TI An algorithm for the illegal copying detection of digital documents SO Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'05) LA English DT Proceedings Paper CT International Conference on Natural Language Processing and Knowledge Engineering CY OCT 30-NOV 01, 2005 CL Wuhan, PEOPLES R CHINA SP IEEE, AAI, CIPSC, Chinese Assoc Artificial Intelligence, IEEE Signal Proc Soc, IEEE Beijing Sect AB Document copy detection has very important application in both intellectual property protection and information retrieval. In this paper, a novel reconstruction method and the corresponding overlap measuring algorithm based on the thought of Mathematical Expression will be presented. Firstly, the documents are expressed in the aspect of component granularities; and then the text attribute vector space model was established according to the statistic of component frequencies; after that, the overlap value is obtained. Results of experiments showed that the algorithm is available. C1 Hunan Int Econ Univ, Sch Elect & Informat Engn, Changsha 410205, Peoples R China. RP Cheng, YZ (reprint author), Hunan Int Econ Univ, Sch Elect & Informat Engn, Changsha 410205, Peoples R China. CR BRASSIL JT, 1995, P INF 95 APR, P853 Brin S., 1995, P 1995 ACM SIGMOD IN, P398, DOI 10.1145/223784.223855 Manber U., 1994, P WINT USENIX C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shivakumar Narayanan, 1995, P 2 INT C THEOR PRAC Si A., 1997, P ACM S APPL COMP, P70, DOI 10.1145/331697.335176 Song QB, 2001, J COMPUTER RES DEV, V38, P121 Sun XM, 2002, INT J PATTERN RECOGN, V16, P735, DOI 10.1142/S0218001402001939 Wise MJ, 1996, P 27 SIGCSE TECHN S, P130, DOI 10.1145/236452.236525 Zhang Chun-xia, 2005, Journal of System Simulation, V17 NR 10 TC 1 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9361-9 PY 2005 BP 384 EP 387 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDV06 UT WOS:000235577200072 ER PT B AU Liu, LY Kang, JC Yu, J Wang, ZL AF Liu, LY Kang, JC Yu, J Wang, ZL GP IEEE TI A comparative study on unsupervised feature selection methods for text clustering SO Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'05) LA English DT Proceedings Paper CT International Conference on Natural Language Processing and Knowledge Engineering CY OCT 30-NOV 01, 2005 CL Wuhan, PEOPLES R CHINA SP IEEE, AAI, CIPSC, Chinese Assoc Artificial Intelligence, IEEE Signal Proc Soc, IEEE Beijing Sect AB Text clustering is one of the central problems in text mining and information retrieval area. For the high dimensionality of feature space and the inherent data sparsity, performance of clustering algorithms will dramatically decline. Two techniques are used to deal with this problem: feature extraction and feature selection. Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, four unsupervised feature selection methods, DF, TC, TVQ, and a new proposed method TV are introduced. Experiments are taken to show that feature selection methods can improves efficiency as well as accuracy of text clustering. Three clustering validity criterions are studied and used to evaluate clustering results. C1 Beihang Univ, Sch Comp Sci, Beijing 100083, Peoples R China. RP Liu, LY (reprint author), Beihang Univ, Sch Comp Sci, 37 Xueyuan Rd,Haidian Dist, Beijing 100083, Peoples R China. CR AGGRAWAL CC, 2000, P SIGMOD 00, P70 Dash M, 1997, INTELL DATA ANAL, V1, P131, DOI DOI 10.1016/S1088-467X(97)00008-5 DHILLON I, FEATURE SELECTION DO DOUTHAT A, 1998, 7 MESS UND C Jolliffe I. T., 1986, SPRINGER SERIES STAT Lewis DD, 1999, REUTERS 21578 TEXT C LIU T, 2003, P ICML 2003 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Ruger S., 2000, FEATURE REDUCTION DO SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slonim N., 2000, P 23 ANN INT ACM SIG, P208, DOI DOI 10.1145/345508.345578 Theodoridis S., 1999, PATTERN RECOGNITION Wyse N., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 ZHAO Y, 2001, 140 U MINN DEP COMP NR 17 TC 8 Z9 8 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9361-9 PY 2005 BP 597 EP 601 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDV06 UT WOS:000235577200112 ER PT B AU Li, D Cao, YD Wu, SY AF Li, D Cao, YD Wu, SY GP IEEE TI A new weighted text filtering method SO PROCEEDINGS OF THE 2005 IEEE INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING (IEEE NLP-KE'05) LA English DT Proceedings Paper CT International Conference on Natural Language Processing and Knowledge Engineering CY OCT 30-NOV 01, 2005 CL Wuhan, PEOPLES R CHINA SP IEEE, AAI, CIPSC, Chinese Assoc Artificial Intelligence, IEEE Signal Proc Soc, IEEE Beijing Sect AB In order to describe the user's profile exactly and provide what they need in information filtering, a new method to weight the user's characterized demand and filter for him is put forward. In the paper, at first we define the terms' weight of the user's profile according to the order of the proposed sequence, then calculate the similarity between the user's profile and the documents using the semantic dictionaries of Synonym Dictionary and HowNet, and at last submit the results to the user sorted in descending order of the similarity. The experiments show that it can improve the precision of information filtering. C1 Beijing Inst Technol, Dept Comp Sci & Engn, Sch Informat Engn, Beijing 100081, Peoples R China. RP Li, D (reprint author), Beijing Inst Technol, Dept Comp Sci & Engn, Sch Informat Engn, Beijing 100081, Peoples R China. EM llidun@163.com CR Billerbeck B, 2004, LECT NOTES COMPUT SC, V3246, P30 Lu Song, 2000, J CHINESE INFORMATIO, V14, P8 LU Y, 2004, J NANJING AGR U, V27, P87 MEI JJ, 1983, SYNONYM DICT MEI LJ, 2005, J CHINESE INFORM PRO, V1, P63 PANTEL P, 2000, 6 APPL NAT LANG PROC, P78 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SENELLART P., 2003, SURVEY TEXT MINING Stamou S, 2005, LECT NOTES COMPUT SC, V3406, P604 WANG B, 1999, THESIS BEIJING CHINE Wang Daliang, 2005, Journal of Tsinghua University (Science and Technology), V45 WU J, 2005, CHINESE J COMPUTERS, V4, P597 Zhang XB, 2004, GLOBAL PLANET CHANGE, V41, P221, DOI 10.1016/j.gloplacha.2004.01.008 NR 14 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-9361-9 PY 2005 BP 695 EP 698 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BDV06 UT WOS:000235577200130 ER PT B AU Zeimpekis, D Gallopoulos, E AF Zeimpekis, Dimitrios Gallopoulos, Efstratios BE Kargupta, H Srivastava, J Kamath, C Goodman, A TI CLSI: A Flexible Approximation Scheme from Clustered Term-Document Matrices SO PROCEEDINGS OF THE FIFTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING SE SIAM Proceedings Series LA English DT Proceedings Paper CT 5th SIAM International Conference on Data Mining CY APR 21-23, 2005 CL Newport Beach, CA SP SIAM, Lawrence Livermore Natl Lab, Ctr Appl Sci Comp, Amer Stat Assoc DE Low rank approximations; Clustering; LSI ID SINGULAR-VALUE DECOMPOSITION; LOW-RANK APPROXIMATIONS; ALGORITHMS; FACTORIZATION; SVDS AB We investigate a methodology for matrix approximation and IR. A central feature of these techniques is an initial clustering phase on the columns of the term-document matrix, followed by partial S VD on the columns constituting each cluster. The extracted information is used to build effective low rank approximations to the original matrix as well as for IR. The algorithms can be expressed by means of rank reduction formulas. Experiments indicate that these methods can achieve good overall performance for matrix approximation and IR and compete well with existing schemes. C1 [Zeimpekis, Dimitrios; Gallopoulos, Efstratios] Univ Patras, Comp Engn & Informat Dept, GR-26110 Patras, Greece. RP Zeimpekis, D (reprint author), Univ Patras, Comp Engn & Informat Dept, GR-26110 Patras, Greece. EM dsz@hpclab.ceid.upatras.gr; stratis@hpclab.ceid.upatras.gr CR Achlioptas D., 2001, P 33 ANN ACM S THEOR, P611, DOI 10.1145/380752.380858 BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry M. W., 2004, CS04525 U TENN DEP C Castelli V, 2003, IEEE T KNOWL DATA EN, V15, P671, DOI 10.1109/TKDE.2003.1198398 Chu MT, 2002, SIAM J MATRIX ANAL A, V23, P1025, DOI 10.1137/S0895479800382555 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 Drineas P, 2004, MACH LEARN, V56, P9, DOI 10.1023/B:MACH.0000033113.59016.96 Drineas P., 2004, TR1270 YAL U DEP COM Frieze A, 1998, ANN IEEE SYMP FOUND, P370 Gao J., 2003, 38203 U KENT DEP COM Guttman L, 1944, PSYCHOMETRIKA, V9, P1, DOI 10.1007/BF02288709 Hubert L, 2000, SIAM REV, V42, P68, DOI 10.1137/S0036144598340483 Kokiopoulou E., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009013 Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 Lee DD, 2001, ADV NEUR IN, V13, P556 Park H., 2003, BIT, V43, P1 Park H., 2003, MATRIX RANK REDUCTIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stewart GW, 1999, NUMER MATH, V83, P313, DOI 10.1007/s002110050451 Zeimpekis D., 2004, 1604 HPCLABSCG COMP Zhang ZY, 2001, SIAM J MATRIX ANAL A, V22, P1245, DOI 10.1137/S0895479899357875 Zhang ZY, 2002, SIAM J MATRIX ANAL A, V23, P706, DOI 10.1137/S0895479899359631 NR 22 TC 8 Z9 8 PU SIAM PI PHILADELPHIA PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA BN 978-0-89871-593-4 J9 SIAM PROC S PY 2005 BP 631 EP 635 PG 5 WC Computer Science, Artificial Intelligence SC Computer Science GA BUJ08 UT WOS:000289491000075 ER PT S AU Yang, Y Kamel, M Jin, F AF Yang, Y Kamel, M Jin, F GP IEEE TI A model of document clustering using ant colony algorithm and validity index SO PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT IEEE International Joint Conference on Neural Networks (IJCNN 2005) CY JUL 31-AUG 04, 2005 CL Montreal, CANADA SP Int Neural Network Soc, IEEE Computat Intelligence Soc AB This paper discusses document clustering using ant colony algorithm and validity index. Clusterings are formed on the plane by ants walking, picking up or dropping down projected document vectors with different probability. The proposed model uses a clustering validity index not only to evaluate the performance of the algorithm, but also to find the optimal number of clusters and reduce outliers. Experiments on data from the Reuters-21578 collection show that the proposed model has better performance than that of LF algorithm and ART neural networks. C1 SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu 610031, Peoples R China. RP Yang, Y (reprint author), SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu 610031, Peoples R China. EM yangyan@nec.swjtu.edu.cn; mkamel@uwaterloo.ca; jinfan22@163.com RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Carpenter G., ADAPTIVE RESONANCE T Deneubourg J., 1991, P 1 INT C SIM AD BEH, P356 HALKIDI M, 2002, P SETN C APR HALKIDI M, 2000, P 4 EUR C PRINC PRAC, P165 Hussin MF, 2003, IEEE IJCNN, P2238 KOHONEN T, 1995, IEEE SWARM INTELLIGE Kohonen T., 1995, SELF ORG MAPS KONDADADI R, 2002, P 2002 INT JOINT C N, V3, P2545 Lumer E., 1994, P 3 INT C SIM AD BEH, V3, P499 MONMARCHE N, 213 LAB INF U TOURS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Theodoridis S., 1999, PATTERN RECOGNITION Wu B, 2002, IEEE C EVOL COMPUTAT, P477 Wu ST, 2003, NEURAL PROCESS LETT, V17, P253, DOI 10.1023/A:1026083612746 Yang Y, 2003, IEEE SWARM INTELLIGE, P65 NR 16 TC 0 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 0-7803-9048-2 J9 IEEE IJCNN PY 2005 BP 2730 EP 2735 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BDS40 UT WOS:000235178004013 ER PT S AU Almpanidis, G Kotropoulos, C Pitas, I AF Almpanidis, G Kotropoulos, C Pitas, I BE Rauber, A Christodoulakis, S Tjoa, AM TI Focused crawling using latent semantic indexing - An application for vertical search engines SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 9th European Conference on Research and Advanced Technology for Digital Libraries (ECDL 2005) CY SEP 18-23, 2005 CL Vienna, AUSTRIA SP Austrian Natl Lib, Austrian Comp Soc, Vienna Univ Technol ID ALGORITHM; MODEL AB Vertical search engines and web portals are gaining ground over the general-purpose engines due to their limited size and their high precision for the domain they cover. The number of vertical portals has rapidly increased over the last years, making the importance of a topic-driven (focused) crawler evident. In this paper, we develop a latent semantic indexing classifier that combines link analysis with text content in order to retrieve and index domain specific web documents. We compare its efficiency with other well-known web information retrieval techniques. Our implementation presents a different approach to focused crawling and aims to overcome the size limitations of the initial training data while maintaining a high recall/precision ratio. C1 Aristotle Univ Thessaloniki, Dept Informat, GR-54124 Thessaloniki, Greece. RP Almpanidis, G (reprint author), Aristotle Univ Thessaloniki, Dept Informat, Box 451, GR-54124 Thessaloniki, Greece. EM galba@aiia.csd.auth.gr; costas@aiia.csd.auth.gr; pitas@aiia.csd.auth.gr RI Kotropoulos, Constantine/B-7928-2010 OI Kotropoulos, Constantine/0000-0001-9939-7930 CR Arasu Arvind, 2001, ACM T INTERNET TECHN, V1, P2, DOI 10.1145/383034.383035 BERGMARK D, P 6 EUR C RES ADV TE, P91 Berry M., 1999, UNDERSTANDING SEARCH Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 BRIN S, 1998, WWW7 COMPUTER NETWOR, V30, P107 Calado P., 2003, P 12 INT C INF KNOWL, P394, DOI DOI 10.1145/956863.956938 Chakrabarti S, 1999, COMPUT NETW, V31, P1623, DOI 10.1016/S1389-1286(99)00052-3 Chakrabarti S., 2002, MINING WEB DISCOVERI Chakrabarti S., 2001, P 10 INT C WORLD WID, P211, DOI 10.1145/371920.372054 Chau M, 2003, COMPUTER, V36, P56, DOI 10.1109/MC.2003.1198237 CHO J, 1998, P 7 INT WORLD WID WE, P161 Cohn D., 2000, P 17 INT C MACH LEAR, P167 Cohn D, 2001, ADV NEUR IN, V13, P430 DAVIDSON B, 2003, P IJCAI 03 WORKSH TE Diligenti M., 2000, P 26 INT C VER LARG, P527 GGARWAL C, 2001, P 10 INT WORLD WID W, P96 Golub G.H., 1996, MATRIX COMPUTATIONS, V3rd Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Menczer F., 2001, P 24 ANN INT ACM SIG, P241, DOI DOI 10.1145/383952.383995 Najork M., 2001, P 10 INT WORLD WID W, P114, DOI DOI 10.1145/371920.371965 Ng A., 2001, P 24 ANN INT ACM SIG, P258, DOI 10.1145/383952.384003 O'Brien G.W., 1994, THESIS U TENNESSEE K PANT G, 2002, P 2 INT WORKSH WEB D PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Rennie J., 1999, P 16 INT C MACH LEAR, P335 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SRNIVASAN P, 2002, P INT C RES DEV IN R STEELE R, 2001, P INT COMP LAS VEG VARLAMIS I, 2004, IEEE T KNOWL DATA EN, V16, P585 YANG K, P 10 TEXT RETR C TRE NR 31 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-28767-1 J9 LECT NOTES COMPUT SC PY 2005 VL 3652 BP 402 EP 413 PG 12 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BDJ77 UT WOS:000233890800036 ER PT B AU Burek, G De Roeck, A Zdrahal, Z AF Burek, G De Roeck, A Zdrahal, Z GP IEEE Computer Society TI Hybrid mappings of complex questions over an integrated semantic space SO Sixteenth International Workshop on Database and Expert Systems Applications, Proceedings LA English DT Proceedings Paper CT 16th International Workshop on Database and Expert Systems Applications CY AUG 22-26, 2005 CL Copenhagen, DENMARK SP Danish Minist Sci, Technol & Innov, DEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, FAW Software Engn GmbH HO Copenhagen Business Sch AB We address the issue of measuring semantic similarity between ontologies and text by means of applying Latent Semantic Analysis. This method allows ranking of vector representations describing semantic relations according to their cosine similarity with a particular query. Our work is expected to make contributions including the introduction of reasoning about uncertainty when mapping between ontologies, an algorithm that can perform automatic mapping between concepts or relations derived from text and concepts or relations belonging to different ontologies, and the capability to infer implicit similarity between concepts or relations. C1 Open Univ, Comp Res Ctr, Milton Keynes MK7 6AA, Bucks, England. RP Burek, G (reprint author), Open Univ, Comp Res Ctr, Milton Keynes MK7 6AA, Bucks, England. CR BERROL S, 1992, PHYSICAL MED REHABIL, V6, P1 Buitelaar P., 2003, P ELSNET ENABLER WOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FLORESCU D, 2003, P 23 VLDB C ATH GREE GRUBER T, 2005, INT J HUMAN COMPUTER Hendler J., 2002, J I ELECT ENG JPN, V122, P676 JONES WP, 1987, J AM SOC INFORM SCI, V38, P420, DOI 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 MADHAVAN J, 2002, P AAAI 18 NAT C ART NOY NF, 2003, P SEM INT WORKSH COL SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tellex S., 2003, P 26 ANN INT ACM SIG NR 13 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2424-9 PY 2005 BP 557 EP 561 DI 10.1109/DEXA.2005.110 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDB89 UT WOS:000232421900095 ER PT B AU Nurminen, M Honkaranta, A Karkkainen, T AF Nurminen, M Honkaranta, A Karkkainen, T GP IEEE Computer Society TI ExtMiner: Combining multiple ranking and clustering algorithms for structured document retrieval SO Sixteenth International Workshop on Database and Expert Systems Applications, Proceedings LA English DT Proceedings Paper CT 16th International Workshop on Database and Expert Systems Applications CY AUG 22-26, 2005 CL Copenhagen, DENMARK SP Danish Minist Sci, Technol & Innov, DEXA Assoc, Austrian Comp Soc, Res Inst Appl Knowledge Proc, FAW Software Engn GmbH HO Copenhagen Business Sch AB This paper introduces ExtMiner a platform and potential tool for information management in SMEs (Small & Medium-size Enterprise), or for organizational work-groups. ExtMiner supports interactive and iterative clustering of documents. It provides users with a visual cluster and list views at the same time, supporting iterative search process. ExtMiner may also be applied as a platform for research on retrieval fusion, since it combines search, clustering and visualization algorithms. ExtMiner was evaluated with three document collections. Although the findings were encouraging the user interface and performance with large document repositories need further development. C1 Univ Jyvaskyla, Fac Informat Technol, SF-40351 Jyvaskyla, Finland. RP Nurminen, M (reprint author), Univ Jyvaskyla, Fac Informat Technol, SF-40351 Jyvaskyla, Finland. RI Nurminen, Miika/ OI Nurminen, Miika/0000-0003-0117-2691 CR BENAHARON Y, 2003, INEX 2003 WORKSHOP P, P46 Brin S., 1998, P 7 INT C WORLD WID, P107 CROUCH C, 2003, INEX 2003 WORKSHOP P, P89 Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 Ester M., 1996, P 2 INT C KNOWL DISC, P226 Faloutsos C., 1995, SIGMOD Record, V24 FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 Han J, 2000, DATA MINING CONCEPTS HANNAPPEL P, 1999, P 10 INF RES MAN ASS KOCHTANEK TR, 1982, INFORM PROCESS MANAG, V18, P33, DOI 10.1016/0306-4573(82)90049-8 Leuski A, 2000, IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2000, P125, DOI 10.1109/INFVIS.2000.885099 NURMINEN M, 2005, THESIS U JYVASKYLA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schlieder T, 2002, J AM SOC INF SCI TEC, V53, P489, DOI 10.1002/asi.10060 van Rijsbergen C. J., 1979, INFORM RETRIEVAL WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Wu MF, 2001, INFORM PROCESS MANAG, V37, P459, DOI 10.1016/S0306-4573(00)00057-1 Yang K., 2002, THESIS U N CAROLINA NR 18 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2424-9 PY 2005 BP 1036 EP 1040 DI 10.1109/DEXA.2005.91 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BDB89 UT WOS:000232421900175 ER PT S AU Sunayama, W Tachibana, K AF Sunayama, W Tachibana, K BE Abraham, A Dote, Y Furuhashi, T Koppen, M Ohuchi, A Ohsawa, Y TI Bulletin board system for scenario creation based on a sub-story model SO Soft Computing as Transdisciplinary Science and Technology SE ADVANCES IN SOFT COMPUTING LA English DT Proceedings Paper CT 4th IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST 05) CY 2005 CL Muroran, JAPAN SP IEEEE Syst Man & Cybernet Soc, World Federat Soft Comp, European Soc Fuzzy Log & Technol, Japan Soc Promot Sci, Soc Instrumentat & Control Engineers, Transdisciplinary Federat Sci & Technol, JSPS Int Meeting Series, Life Oriented Software Lab HO Muroran Inst Technol DE Bulletin Board System; discussion control; story model; scenario creation AB It is important to decide the next action for a collaborative community. The leader of a collaborative community must choose an action that increases rewards and reduces risks. When a leader cannot make this decision, the action will be determined through community member discussion. However, this decision cannot be made in blind discussions, so systematic discussion is necessary for choosing effective actions in limited time. In addition, the repetition of divergence and convergence in the discussion according to the members ' common knowledge and backgrounds will lead to an effective conclusion. In this paper, we propose a bulletin board system framework in which the scenario creation, a series of actions to be undertaken, is established through the discussion and exchange of opinions. C1 Hiroshima City Univ, Fac Informat Sci, Hiroshima 7313194, Japan. RP Sunayama, W (reprint author), Hiroshima City Univ, Fac Informat Sci, Hiroshima 7313194, Japan. CR Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 ISHIKAWA N, 2000, COMPETITIVE ADVANTAG Lapata Mirella, 2003, P 41 M ASS COMP LING, P545 MATUMURA N, 2002, P 11 INT WWW C Morris J., 1991, Computational Linguistics, V17 OHSAWA Y, 2002, P 11 INT WWW C SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 7 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1615-3871 BN 3-540-25055-7 J9 ADV SOFT COMP PY 2005 BP 501 EP 510 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA BCV56 UT WOS:000231416200056 ER PT B AU Cronin, B AF Cronin, B BE Turner, P Davenport, E TI High-fidelity mapping of intellectual space SO Spaces, Spatiality and Technology SE KLUWER INTERNATIONAL SERIES ON COMPUTER SUPPORTED COOPERATIVE WORK LA English DT Proceedings Paper CT Seminar on Spaces, Spatiality and Technology CY DEC, 2004 CL Edinburgh, SCOTLAND ID SCIENCE; COLLABORATION; COCITATION; VISUALIZATION; 20TH-CENTURY; TIME C1 Indiana Univ, Bloomington, IN 47405 USA. CR ALLEN RC, 1983, J ECON BEHAV ORGAN, V4, P1, DOI 10.1016/0167-2681(83)90023-9 ALLEN TJ, 1969, ANN REV INFORMATION, P3 BABER Z, 2003, SOCIAL EPISTEMOLOGY, V17, P95, DOI 10.1080/0269172032000144009 BARBASI AL, 2003, LINKED EVERYTHING LI Boisot M., 1999, KNOWLEDGE ASSETS SEC BOISTO MH, 1995, INFORMATION SPACE FR BORNER K, 2003, 99 ANN M ASS AM GEOG BOYD D, 2002, P HAW INT C SYST SCI BROWN JS, 2000, SOCIAL LIFE INFORMAT Castells M., 2000, RISE NETWORK SOC Chen GT, 2003, RAIRO-OPER RES, V37, P179, DOI 10.1051/ro:2003020 Christensen Clayton M., 2000, INNOVATORS DILEMMA N Clark Andy, 2003, NATURAL BORN CYBORGS COLLINS R, 2003, SOCIAL EPISTEMOLOGY, V17, P147, DOI 10.1080/0269172032000144090 Crane D., 1972, INVISIBLE COLL DIFFU CRONIN B., 2000, ASIS MONOGRAPH SERIE Cronin B, 2004, J AM SOC INF SCI TEC, V55, P160, DOI 10.1002/asi.10353 Cronin B, 2004, J AM SOC INF SCI TEC, V55, P557, DOI 10.1002/asi.10406 CRONIN B, 1995, SCH COURTESY ROLE AC Cronin B., 1984, CITATION PROCESS ROL Cronin B, 2003, J AM SOC INF SCI TEC, V54, P855, DOI 10.1002/asi.10278 CRONIN B, 2001, SCIENTOMETRICS, V54, P31 DAVENPORT E, 1990, J DOC, V46, P175, DOI 10.1108/eb026859 Davenport E, 2000, ASIST MON SER, P517 Dourish P., 2001, HUMAN COMPUTER INTER, V16 Dourish P., 2001, ACTION IS FDN EMBODI ERICKSON T, 2002, SOCIAL TRANSLUCENCE Etzkowitz H, 1998, MINERVA, V36, P271, DOI 10.1023/A:1004348123030 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 Geertz C, 1973, INTERPRETATION CULTU GLADWELL W, 2002, TIPPING POINT LITTLE Heimeriks G., 2002, STATE ART BIBLIOMETR HEPWORTH M, 1989, GEOGRAPHY INFORMATIO Hutchins E., 1995, COGNITION WILD KATZ JS, 1994, SCIENTOMETRICS, V31, P31, DOI 10.1007/BF02018100 KING DW, 1984, STUDY VALUE INFORMAT Knorr Cetina K., 1999, EPISTEMIC CULTURES S Lin X, 2003, INFORM PROCESS MANAG, V39, P689, DOI 10.1016/S0306-4573(02)00037-7 Nazer Nancy, 2001, AM BEHAV SCI, V44, P1750 Odlyzko A, 2002, LEARN PUBL, V15, P7, DOI 10.1087/095315102753303634 Page L, 1998, PAGERANK CITATION RA Porter M., 1998, HARVARD BUSINESS NOV, P77 PRICE DJD, 1965, SCIENCE, V149, P510 Putnam R., 2000, BOWLING ALONE COLLAP RAYWARD WB, 1992, CONCEPTIONS LIB INFO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Skupin A, 2000, P IEEE S INF VIS 200, P91 Skupin A, 2002, IEEE COMPUT GRAPH, V22, P50, DOI 10.1109/38.974518 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 Suchman L. A., 1987, PLANS SITUATED ACTIO, V2nd THELWALL M, 2005, ANN REV INFORMATION VONHIPPEL E, 1987, RES POLICY, V16, P291, DOI 10.1016/0048-7333(87)90015-1 White HD, 2001, J AM SOC INF SCI TEC, V52, P87, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1542>3.3.CO;2-K White HD, 2004, J AM SOC INF SCI TEC, V55, P111, DOI 10.1002/asi.10369 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 White HD, 1997, ANNU REV INFORM SCI, V32, P99 Williams JG, 1995, ANNU REV INFORM SCI, V30, P161 Wouters P., 1999, THE CITATION CULTURE Ziman J., 2000, REAL SCI Zook MA, 2003, ENVIRON PLANN A, V35, P1261, DOI 10.1068/a35105 NR 60 TC 1 Z9 1 PU SPRINGER PI DORDRECHT PA PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS BN 1-4020-3272-2 J9 KIS CO SUP COOP WORK PY 2005 VL 5 BP 117 EP 128 PG 12 WC Social Issues; Social Sciences, Interdisciplinary SC Social Issues; Social Sciences - Other Topics GA BDC08 UT WOS:000232525800009 ER PT J AU Milic-Frayling, N AF Milic-Frayling, N. BE Zanasi, A TI Text processing and information retrieval SO TEXT MINING AND ITS APPLICATIONS TO INTELLIGENCE, CRM AND KNOWLEDGE MANAGEMENT SE Advances in Management Information LA English DT Article; Book Chapter ID VECTOR-SPACE MODEL; DOCUMENT-RETRIEVAL; RELEVANCE; ALGORITHM; FEEDBACK; SYSTEM AB This chapter introduces the reader to various issues related to gathering, pre-processing, and retrieving textual information. It provides an overview of statistical natural language processing (NLP) techniques that are commonly used as a pre-cursor to text mining or integral parts of text mining applications. It also discusses various aspects of information retrieval, covering the main research issues and common practices. C1 [Milic-Frayling, N.] Microsoft Res Ltd, Cambridge, England. RP Milic-Frayling, N (reprint author), Microsoft Res Ltd, Cambridge, England. EM natasamf@microsoft.com CR ADAMSON GW, 1974, INFORM STORAGE RET, V10, P253, DOI 10.1016/0020-0271(74)90020-5 Allen J., 1995, NATURAL LANGUAGE UND [Anonymous], 1995, P 6 MESS UND C MUC 6 [Anonymous], 1991, P 3 MESS UND C MUC 3 [Anonymous], 1993, P 5 MESS UND C MUC 5 [Anonymous], 1992, P 4 MESS UND C MUC 4 [Anonymous], 1998, P 7 MESS UND C MUC 7 [Anonymous], DOCUMENT UNDERSTANDI Baeza-Yates R. A., 1999, MODERN INFORM RETRIE Bahl L.R., 1976, INT S INF THEOR RONN Baker JK, 1975, SPEECH RECOGNITION, P521 Beauliue M. M., 1995, J AM SOC INFORM SCI, V47, P85 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 Bowman M.C., 1994, P 2 INT WORLD WID WE, P763 Brants Th., 1998, P NEMLAP 98 SYDN AUS Brill E, 1995, COMPUT LINGUIST, V21, P543 Brin S., 1998, P 7 WWW C SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chakrabarti S., 1998, P 7 WWW C CHARNIAK E, 1993, PROCEEDINGS OF THE ELEVENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, P784 Cho J., 1998, P 7 WWW C BRISB AUST Church K., 1989, ACL, V27, P76 Church K., 1991, LEXICAL ACQUISITION, P115 Church K.W., 1988, ANLP, V2, P136 Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 Dawson J., 1982, WRITING EFFICIENT PR Demers A.J., 1977, P 4 ACM SIGACT SIGPL, P170, DOI 10.1145/512950.512966 DeRose S. J., 1988, Computational Linguistics, V14 DEROUAULT AM, 1986, IEEE T PATTERN ANAL, V8, P742 Doran Ch., 1994, COLING, V15, P922 EFTHIMIADIS EN, 1995, INFORM PROCESS MANAG, V31, P605, DOI 10.1016/0306-4573(95)00070-W ELLIS D, 1989, J INFORM SCI, V15, P237, DOI 10.1177/102831538901500406 ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 Evans D.A., 1995, INFORMATION PROCESSI, V31, P494 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Fox C., 1990, SIGIR Forum, V24 Frakes W. B., 1992, INFORM RETRIEVAL DAT Francis W. N., 1964, MANUAL INFORM ACCOMP Francis W. N., 1982, FREQUENCY ANAL ENGLI Garside Roger, 1987, COMPUTATIONAL ANAL E Goldfarb C.F., 1998, XML HDB Goldfarb Charles F., 1990, SGML HDB HAFER MA, 1974, INFORM STORAGE RET, V10, P371, DOI 10.1016/0020-0271(74)90044-8 HARMAN D, 1991, J AM SOC INFORM SCI, V42, P7, DOI 10.1002/(SICI)1097-4571(199101)42:1<7::AID-ASI2>3.0.CO;2-P Harman D., 1995, Hypertext - Information Retrieval - Multimedia. Proceedings HIM '95 Harman D.K., 1994, NIST SPECIAL PUBLICA Harman D.K., 1997, NIST SPECIAL PUBLICA HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Hauser R., 1998, FDN COMPUTATIONAL LI Hopcroft J. E., 1979, INTRO AUTOMATA THEOR Ide E., 1971, SMART RETRIEVAL SYST, P337 Ingwersen P., 1992, INFORM RETRIEVAL INT Jelinek F., 1985, NATO ASI SERIES E, VE91, P569 Johansson S., 1978, MANUAL INFORM ACCOMP Jones K. S., 1979, Information Processing & Management, V15, DOI 10.1016/0306-4573(79)90060-8 Justeson J. S., 1995, NATURAL LANGUAGE ENG, V1, P9 Kleinberg J.M., 1999, P 5 ANN INT C COMP C Kucera H., 1967, COMPUTATIONAL ANAL P Lauer M., 1995, ACL, V33, P47 Lauer M., 1995, THESIS MARCQUARIE U Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LEWIS DD, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P37 LIDDY ED, 1991, INFORM PROCESS MANAG, V27, P55, DOI 10.1016/0306-4573(91)90031-G LOVINS JB, 1968, MECH TRANSL, V11, P22 Manning D., 1999, FDN STAT NATURAL LAN MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Marshall I., 1987, COMPUTATIONAL ANAL E, P42 Paice C. D., 1990, SIGIR Forum, V24 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCHE E, 1995, COMPUT LINGUIST, V21, P227 Rosenkrantz S.J., 1970, IEEE 11 ANN S SWITCH, P139 Salton G., 1981, INFORM RETRIEVAL EXP, P316 Salton G., 1968, AUTOMATIC INFORM ORG Salton G., 1983, INTRO MODERN INFORM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y Sparck Jones K., 1998, READINGS INFORM RETR Sparck Jones K., 1976, J DOC, V32, P59 Sparck Jones K., 1981, INFORM RETRIEVAL EXP Sparck Jones K., 1995, INFORMATION PROCESSI, V31, P291 Strzalkowski T., 1994, P 4 C APPL NAT LANG, P168, DOI 10.3115/974358.974396 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 van Rijsbergen C. J., 1979, INFORM RETRIEVAL Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202 Zobel J., 1998, SIGIR Forum, V32 NR 91 TC 0 Z9 0 PU WIT PRESS/COMPUTATIONAL MECHANICS PUBLICATIONS PI SOUTHAMPTON PA ASHURST LODGE, ASHURST, SOUTHAMPTON SO40 7AA, ENGLAND SN 1742-0172 BN 978-1-84564-131-3 J9 ADV MANAG INFORM PY 2005 VL 2 BP 1 EP 45 D2 10.2495/978-1-85312-995-7 PG 45 WC Computer Science, Information Systems SC Computer Science GA BWX86 UT WOS:000295277400002 ER PT S AU Yang, Y Kamel, M Jin, F AF Yang, Y Kamel, M Jin, F BE Zhang, Y Tanaka, K Yu, JX Wang, S Li, M TI Topic discovery from document using ant-based clustering combination SO WEB TECHNOLOGIES RESEARCH AND DEVELOPMENT - APWEB 2005 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th Asia-Pacific Web Conference CY MAR 29-APR 01, 2005 CL Shanghai, PEOPLES R CHINA SP Natl Inst Informat & Commun Technol, Shanghai Jiao Tong Univ, Victoria Univ, Natl Nat Sci Fdn China, ARC Res Netwp=ork Enterprise Informat Infrastructure, Microsoft Res Asia, WISE Soc, Database Soc China Comp Federat AB This paper presents a topic discovery approach based on multi-ant colonies clustering combination. The algorithm consists of three parts. First, each document is represented as a vector of features in a vector space model. Then a hypergraph model is used to combine the clusterings produced by three kinds of ant-based algorithms with different moving speed. Finally, the topic of each cluster is extracted by re-computing the term weights. Test results show that the number of topics can be adaptively determined and clustering combination can improve the system performance. C1 SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu 610031, Sichuan, Peoples R China. Univ Waterloo, Pattern Anal & Machine Intelligence Lab, Waterloo, ON N2L 3G1, Canada. RP Yang, Y (reprint author), SW Jiaotong Univ, Sch Comp & Commun Engn, Chengdu 610031, Sichuan, Peoples R China. EM yangyan@nec.swjtu.edu.cn; mkamel@uwaterloo.ca RI Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR AYAD H, 2002, ADV ARTIFICIAL INTEL, P161 Berkhin P., 2002, SURVEY CLUSTERING DA Deneubourg J., 1991, P 1 INT C SIM AD BEH, P356 Larsen B., 1999, P 5 ACM SIGKDD INT C, P16, DOI DOI 10.1145/312129.312186 Lumer E., 1994, P 3 INT C SIM AD BEH, V3, P499 MONMARCHE N, 213 LAB INF U TOURS PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Ramos V, 2002, AEB 2002, P284 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Strehl A., 2002, P C ART INT AAAI 200, P93 Wu B, 2002, IEEE C EVOL COMPUTAT, P477 Wu KJ, 2004, INFORM PROCESS MANAG, V40, P239, DOI 10.1016/S0306-4573(03)00039-6 Yang Y, 2003, IEEE SWARM INTELLIGE, P65 NR 14 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25207-X J9 LECT NOTES COMPUT SC PY 2005 VL 3399 BP 100 EP 108 PG 9 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BCD84 UT WOS:000228780500011 ER PT S AU Li, Q Li, JH AF Li, Q Li, JH BE Zhang, Y Tanaka, K Yu, JX Wang, S Li, M TI An efficient topic-specific web text filtering framework SO WEB TECHNOLOGIES RESEARCH AND DEVELOPMENT - APWEB 2005 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th Asia-Pacific Web Conference CY MAR 29-APR 01, 2005 CL Shanghai, PEOPLES R CHINA SP Natl Inst Informat & Commun Technol, Shanghai Jiao Tong Univ, Victoria Univ, Natl Nat Sci Fdn China, ARC Res Netwp=ork Enterprise Informat Infrastructure, Microsoft Res Asia, WISE Soc, Database Soc China Comp Federat AB In this paper, an efficient topic-specific Web text filtering framework is proposed. This framework focuses on blocking some topic-specific Web text content. In this framework, a hybrid feature selection method is proposed, and a high efficient filtering engine is designed. In training, we select features based on CHI statistic and rough set theory, then to construct filter with Vector Space Model. We train our frame with huge datasets, and the result suggests our framework is more effective for the topic-specific text filtering. This framework runs at server such as gateway, and it is more efficient than a client-based system. C1 Shanghai Jiao Tong Univ, Modern Commun Inst, Shanghai 200030, Peoples R China. RP Li, Q (reprint author), Shanghai Jiao Tong Univ, Modern Commun Inst, Shanghai 200030, Peoples R China. EM 1iqiang@sjtu.edu.cn CR Chouchoulas A, 2001, APPL ARTIF INTELL, V15, P843, DOI 10.1080/088395101753210773 DING C, 1999, P IEEE INT C SYST MA, P115 FAN JJ, 1993, IEEE T KNOWL DATA EN, V5, P339 Lee PY, 2002, IEEE INTELL SYST, V17, P48, DOI 10.1109/MIS.2002.1039832 PANG JF, 2001, COMPUTER APPL RES, P23 PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang Y., 2002, CIKM 02, P659 NR 8 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-25207-X J9 LECT NOTES COMPUT SC PY 2005 VL 3399 BP 157 EP 163 PG 7 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BCD84 UT WOS:000228780500016 ER PT J AU Hammouda, KM Kamel, MS AF Hammouda, Khaled M. Kamel, Mohamed S. TI Document Similarity Using a Phrase Indexing Graph Model SO KNOWLEDGE AND INFORMATION SYSTEMS LA English DT Article DE Document similarity; Phrase matching; Phrase indexing; Document representation; Document Index Graph AB Document clustering techniques mostly rely on single term analysis of text, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the Document Index Graph, which indexes Web documents based on phrases rather than on single terms only. The semistructured Web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The Document Index Graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. However, using phrase indexing yields more accurate document similarity calculations. The similarity between documents is based on both single term weights and matching phrase weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, gives a more accurate measure of document similarity and thus significantly enhances Web document clustering quality. C1 [Hammouda, Khaled M.; Kamel, Mohamed S.] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RP Hammouda, KM (reprint author), Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. EM hammouda@pami.uwaterloo.ca RI Kamel, Mohamed/D-9323-2011 FU Natural Sciences and Engineering Research Council of Canada (NSERC) FX This research was partially supported by a strategic grant from the Natural Sciences and Engineering Research Council of Canada (NSERC). CR Aas K., 1999, 941 NORW COMP CTR Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Cios K. J., 1998, DATA MINING METHODS Frakes W. B., 1992, INFORM RETRIEVAL DAT Hammouda K. M., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183904 Hofmann T., 1999, P 16 INT JOINT C ART, P682 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 ISAACS JD, 1999, PCSTR99357 DARTM COL Jain A. K., 1988, ALGORITHMS CLUSTERIN JUNKER M, 1999, P 1 WORKSH LEARN LAN, P84 Kosala Raymond, 2000, ACM SIGKDD EXPLORATI, V2, P1, DOI DOI 10.1145/360402.360406 LU SY, 1978, IEEE T SYST MAN CYB, V8, P381, DOI 10.1109/TSMC.1978.4309979 Nahm U.Y., 2000, P 17 NAT C ART INT A, P627 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G., 1983, MCGRAW HILL COMPUTER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Steinbach M., 2000, P KDD 2000 WORKSH TE Strehl A, 2000, P AAAI WORKSH AI WEB, P58 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining ZAMIR O, 1999, P 8 INT WORLD WID WE, P8 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 25 TC 14 Z9 14 PU SPRINGER LONDON LTD PI ARTINGTON PA ASHBOURNE HOUSE, THE GUILDWAY, OLD PORTSMOUTH ROAD, ARTINGTON GU3 1LP, GUILDFORD, ENGLAND SN 0219-1377 J9 KNOWL INF SYST JI Knowl. Inf. Syst. PD NOV PY 2004 VL 6 IS 6 BP 710 EP 727 DI 10.1007/s10115-003-0118-5 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA V97IC UT WOS:000206578200004 ER PT J AU Hammouda, KM Kamel, MS AF Hammouda, KM Kamel, MS TI Efficient phrase-based document indexing for web document clustering SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE Web mining; document similarity; phrase-based indexing; document clustering; document structure; document index graph; phrase matching ID CATEGORIZATION AB Document clustering techniques mostly rely on single term analysis of the document data set, such as the Vector Space Model. To achieve more accurate document clustering, more informative features including phrases and their weights are particularly important in such scenarios. Document clustering is particularly useful in many applications such as automatic categorization of documents, grouping search engine results, building a taxonomy of documents, and others. This paper presents two key parts of successful document clustering. The first part is a novel phrase-based document index model, the Document Index Graph, which allows for incremental construction of a phrase-based index of the document set with an emphasis on efficiency, rather than relying on single-term indexes only. It provides efficient phrase matching that is used to judge the similarity between documents. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. The second part is an incremental document clustering algorithm based on maximizing the tightness of clusters by carefully watching the pair-wise document similarity distribution inside clusters. The combination of these two components creates an underlying model for robust and accurate document similarity calculation that leads to much improved results in Web document clustering over traditional methods. C1 Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RP Hammouda, KM (reprint author), Univ Waterloo, Dept Syst Design Engn, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada. EM hammouda@pami.uwaterloo.ca; mkamel@pami.uwaterloo.ca RI Hammouda, Khaled/A-1087-2008; Kamel, Mohamed/D-9323-2011; Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Aas K., 1999, 941 NORW COMP CTR Apostolico A., 1985, NATO ASI SERIES F, V12, P85 Beil F., 2002, P 8 ACM SIGKDD INT C, P436, DOI DOI 10.1145/775047.775110 Boley D, 1999, ARTIF INTELL REV, V13, P365, DOI 10.1023/A:1006592405320 Boley D, 1998, DATA MIN KNOWL DISC, V2, P325, DOI 10.1023/A:1009740529316 Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X Caropreso M. F., 2000, IEIB4072000 Charikar M., 1997, P 29 ANN ACM S THEOR, P626, DOI 10.1145/258533.258657 Cios K. J., 1998, DATA MINING METHODS Cohen W, 1995, P 5 INT WORKSH IND L, P3 Dasarathy B., 1991, MCGRAWHILL COMPUTER Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 FAGAN J, 1987, THESIS CORNELL U FRAKES WR, 1992, INFORMATION RETRIEVA Freitag D., 1999, P AAAI 99 WORKSH MAC, P31 Hammouda K. M., 2002, Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002, DOI 10.1109/ICDM.2002.1183904 HILL DR, 1968, MECH INFORMATION STO Hofmann T., 1999, P 16 INT JOINT C ART, P682 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 ISAACS JD, 1999, PCSTR99357 DARTM COL Jain A. K., 1988, ALGORITHMS CLUSTERIN Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 JUNKER M, 1999, P 1 WORKSH LEARN LAN, P84 Kargupta H., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 Kurtz S, 1999, SOFTWARE PRACT EXPER, V29, P1149, DOI 10.1002/(SICI)1097-024X(199911)29:13<1149::AID-SPE274>3.0.CO;2-O Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 LU SY, 1978, IEEE T SYST MAN CYB, V8, P381, DOI 10.1109/TSMC.1978.4309979 MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 Nahm U.Y., 2000, P 17 NAT C ART INT A, P627 Pantel P., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1983, MCGRAWHILL COMPUTER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scott S., 1999, P 16 INT C MACH LEAR, P379 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 Steinbach M., 2000, P KDD 2000 WORKSH TE Strehl A, 2000, P AAAI WORKSH AI WEB, P58 WONG W, 2000, P 2000 INT C INF SOC Yang SC, 1999, JAVA REP, V4, P32 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zamir O, 1999, COMPUT NETW, V31, P1361, DOI 10.1016/S1389-1286(99)00054-7 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 45 TC 99 Z9 111 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD OCT PY 2004 VL 16 IS 10 BP 1279 EP 1296 DI 10.1109/TKDE.2004.58 PG 18 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 845OC UT WOS:000223253200008 ER PT J AU Burstein, F Chodorow, M Leacock, C AF Burstein, F Chodorow, M Leacock, C TI Automated essay evaluation: The Criterion Online writing service SO AI MAGAZINE LA English DT Article; Proceedings Paper CT 15th Innovative Applications of Artificial Intelligence Conference (IAAI-03) CY AUG 12-14, 2003 CL Acapulco, MEXICO SP Amer Assoc Artificial Intelligence AB In this article, we describe a deployed educational technology application: the Criterion Online Essay Evaluation Service, a web-based system that provides automated scoring and evaluation of student essays. Criterion has two complementary applications: (1) Critique Writing Analysis Tools, a suite of pro-rams that detect errors in grammar, usage, and mechanics, that identify discourse elements in the essay, and that recognize potentially undesirable elements of style, and (2) e-rater version 2.0, an automated essay scoring system. Critique and e-rater provide students with feedback that is specific to their writing in order to help them improve their writing skills and is intended to be used under the instruction of a classroom teacher. Both applications employ natural language processing and machine learning techniques. All of these capabilities outperform baseline algorithms, and some of the tools agree with human judges in their evaluations as often as two judges agree with each other. C1 Educ Testing Serv, Princeton, NJ 08541 USA. CUNY Hunter Coll, New York, NY 10021 USA. CUNY, Grad Sch, New York, NY USA. RP Burstein, F (reprint author), Educ Testing Serv, Princeton, NJ 08541 USA. EM jburstein@ets.org; martin.chodorow@hunter.cuny.edu; cleacock@ets.org CR BRELAND HM, 1994, 9426 ETS Breland HM, 1996, PSYCHOL SCI, V7, P96, DOI 10.1111/j.1467-9280.1996.tb00336.x Burstein J, 2003, IEEE INTELL SYST, V18, P32, DOI 10.1109/MIS.2003.1179191 Burstein J., 1998, P 36 ANN M ASS COMP, P206 BURSTEIN J, 2003, P 10 C EUR CHAPT ASS Chodorow M., 2000, P 1 M N AM CHAPT ASS, P140 Elliott S., 2003, AUTOMATED ESSAY SCOR Foltz PW, 1998, DISCOURSE PROCESS, V25, P285 Golding Andrew, 1995, 3 WORKSH VER LARG CO, P39 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 MACDONALD NH, 1982, IEEE T COMMUN, V30, P105, DOI 10.1109/TCOM.1982.1095380 Page EB, 1966, PHI DELTA KAPPAN, V48, P238 Ratnaparkhi A., 1996, P EMP METH NAT LANG Rudner L. M., 2002, J TECHNOLOGY LEARNIN, V1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shermis M. D., 2003, AUTOMATED ESSAY SCOR NR 16 TC 11 Z9 11 PU AMER ASSOC ARTIFICIAL INTELL PI MENLO PK PA 445 BURGESS DRIVE, MENLO PK, CA 94025-3496 USA SN 0738-4602 J9 AI MAG JI AI Mag. PD FAL PY 2004 VL 25 IS 3 BP 27 EP 36 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA 857TH UT WOS:000224140700004 ER PT J AU Perugini, S Goncalves, MA Fox, EA AF Perugini, S Goncalves, MA Fox, EA TI Recommender systems research: A connection-centric survey SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Review DE recommendation; recommender systems; small-worlds; social networks; user modeling ID SMALL-WORLD NETWORKS; INFORMATION-RETRIEVAL; GRAPH ANALYSIS; USER PROFILES; WEB SITES; PERSONALIZATION; PRIVACY; INTERNET; IMPROVE; MODEL AB Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues-such as evaluation, targeting, and privacy and trust-which we also briefly address. C1 Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA. RP Perugini, S (reprint author), Virginia Tech, Dept Comp Sci, Blacksburg, VA 24061 USA. EM sperugin@cs.vt.edu; mgoncalv@cs.vt.edu; fox@cs.vt.edu CR Adamic LA, 1999, LECT NOTES COMPUT SC, V1696, P443 Adomavicius G., 2001, LECT NOTES COMPUTER, V2232, P180 Adomavicius G., 1999, P 5 ACM SIGKDD INT C, P377, DOI 10.1145/312129.312287 Aggarwal CC, 1999, P 5 ACM SIGKDD INT C, P201, DOI 10.1145/312129.312230 Agrawal R., 1993, P 1993 ACM SIGMOD IN, V22, P207, DOI DOI 10.1145/170035.170072 Albert R, 1999, NATURE, V401, P130 Alspector J., 1998, P 3 ACM C DIG LIB, P11, DOI 10.1145/276675.276677 Amaral LAN, 2000, P NATL ACAD SCI USA, V97, P11149, DOI 10.1073/pnas.200327197 Amento B, 2000, P 23 ANN INT ACM SIG, P296, DOI 10.1145/345508.345603 Andre E, 2002, COMMUN ACM, V45, P43 Avery C, 1997, COMMUN ACM, V40, P88, DOI 10.1145/245108.245127 Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 BASU C, 2001, P 24 ANN INT ACM SIG BAUDISCH P, 1999, P ACM CHI WORKSH INT BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143 Berghel H, 2001, COMMUN ACM, V44, P19, DOI 10.1145/374308.374320 BERGMAN E, 2000, MORGAN KAUFMANN SERI Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 BLOEDORN E, 1996, AAAI SPRING S MACH L Breese J S, 1998, P 14 C UNC ART INT, P43 Brin S, 1998, P 7 INT WORLD WID WE BRODER A, 2003, 26 ANN INT ACM C RES Broder A., 2000, P 9 INT WORLD WID WE Burke R., 1999, P WORKSH AI EL COMM, P69 Bush V., 1945, ATLANTIC MONTHLY, P101 Carlson JM, 1999, PHYS REV E, V60, P1412, DOI 10.1103/PhysRevE.60.1412 Carroll JM, 1996, COMMUN ACM, V39, P69, DOI 10.1145/240483.240498 Chakrabarti S, 1999, COMPUTER, V32, P60, DOI 10.1109/2.781636 Cingil I, 2000, COMMUN ACM, V43, P136, DOI 10.1145/345124.345168 CLAYPOOL M, 1999, P 22 ANN INT ACM SIG Claypool M, 2001, IEEE INTERNET COMPUT, V5, P32, DOI 10.1109/4236.968829 DENNING PJ, 1982, COMMUN ACM, V25, P163, DOI 10.1145/358453.358454 Ehrlich K., 1995, P ACM C HUM FACT COM, P202, DOI 10.1145/223904.223930 Erdos P., 1960, PUBL MATH I HUNG, V5, P17 FLAKE GW, 2002, IEEE COMPUT, V35, P66 FOLTZ PW, 1992, COMMUN ACM, V35, P51, DOI 10.1145/138859.138866 FRUEND Y, 1998, P 15 INT C MACH LEAR, P170 Goecks J., 2000, P 5 INT C INT US INT, P129, DOI 10.1145/325737.325806 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 GOLDBERG K, 2000, M0041 U CAL EL RES L GRASSO A, 1999, P INT ACM SIGGROUP C, P267, DOI 10.1145/320297.320329 Hayes B, 2000, AM SCI, V88, P9, DOI 10.1511/2000.15.3279 Hayes B, 2000, AM SCI, V88, P104, DOI 10.1511/2000.2.104 HAYES C, 2002, TCDCS200219 TRIN COL Herlocker JL, 2000, P 2000 ACM C COMP SU, P241, DOI DOI 10.1145/358916.358995 HILL W, 1996, P ACM 1996 CSCW C NO, P106, DOI 10.1145/240080.240229 HOUSMAN E, 1996, P IEEE T ENG WRIT SP, P100 Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 Kautz H, 1997, AI MAG, V18, P27 Kleinberg J, 2001, SCIENCE, V294, P1849, DOI 10.1126/science.1067014 Kleinberg J., 2000, Proceedings of the Thirty Second Annual ACM Symposium on Theory of Computing, DOI 10.1145/335305.335325 KLEINBERG JM, 1999, P INT C COMB COMP Kleinberg JM, 2000, NATURE, V406, P845, DOI 10.1038/35022643 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126 Krulwich B., 1996, P AAAI SPRING S MACH, P100 Kumar R., 1999, P 8 INT WORLD WID WE Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344 LOEB S, 1992, COMMUN ACM, V35, P26, DOI 10.1145/138859.138860 Lynch C., 2001, P JOINT DELOS NSF WO, P84 Manber U, 2000, COMMUN ACM, V43, P35, DOI 10.1145/345124.345136 MILGRAM S, 1967, PSYCHOL TODAY, V1, P61 Mintzer F, 2001, COMMUN ACM, V44, P52, DOI 10.1145/381641.381657 Mirza BJ, 2003, J INTELL INF SYST, V20, P131, DOI 10.1023/A:1021819901281 MIRZA BJ, 2001, THESIS VIRGINIA TECH Mobasher B, 2000, COMMUN ACM, V43, P142, DOI 10.1145/345124.345169 Mooney R.J., 2000, P 5 ACM C DIG LIB, P195, DOI DOI 10.1145/336597.336662 Mostafa J, 1997, ACM T INFORM SYST, V15, P368, DOI 10.1145/263479.263481 Mulvenna M. D., 2000, Communications of the ACM, V43, DOI 10.1145/345124.345165 Nevill-Manning C, 2001, COMMUN ACM, V44, P41, DOI 10.1145/374308.374335 Osareh F, 1996, LIBRI, V46, P149, DOI 10.1515/libr.1996.46.3.149 PAPATHEODOROU C, 2001, LECT NOTES ARTIF INT, V2049, P286 Pazzani M, 1997, MACH LEARN, V27, P313, DOI 10.1023/A:1007369909943 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 Perkowitz M, 2000, COMMUN ACM, V43, P152, DOI 10.1145/345124.345171 Perugini S, 2003, ADV COMPUT, V57, P323, DOI 10.1016/S0065-2458(03)57007-3 Ramakrishnan N, 2001, IEEE INTERNET COMPUT, V5, P54, DOI 10.1109/4236.968832 RCUKER J, 1997, COMMUN ACM, V40, P73 Resnick P, 1997, COMMUN ACM, V40, P56, DOI 10.1145/245108.245121 Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Resnick P, 2000, COMMUN ACM, V43, P45, DOI 10.1145/355112.355122 Riecken D, 2000, COMMUN ACM, V43, P27 Riedl J, 2001, IEEE INTERNET COMPUT, V5, P29, DOI 10.1109/4236.968828 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Russell S, 1995, PRENTICE HALL SERIES Salton G., 1983, MCGRAW HILL COMPUTER SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sarwar B, 2000, P 2 ACM C EL COMM, P158, DOI DOI 10.1145/352871.352887 Sarwar B. M., 1998, P ACM C COMP SUPP CO, P345, DOI 10.1145/289444.289509 Schafer J.B., 1999, P 1 ACM C EL COMM DE, P158, DOI DOI 10.1145/336992.337035 SCHWARTZ MF, 1993, COMMUN ACM, V36, P78, DOI 10.1145/163381.163402 SHAPIRO C, 1999, INFORMATION RULES ST Shardanand U., 1995, P C HUM FACT COMP SY, P210, DOI DOI 10.1145/223904.223931 Shardanand U., 1994, THESIS MASSACHUSETTS Shneiderman B, 2000, COMMUN ACM, V43, P57, DOI 10.1145/355112.355124 Singh MP, 2001, COMMUN ACM, V44, P49, DOI 10.1145/367211.367255 Sinha R., 2001, P JOINT DELOS NSF WO, P73 Sinha R., 2002, P C HUM FACT COMP SY, P830 Soboroff Ian M., 1999, P IJCAI 99 WORKSH MA, P86 Spiliopoulou M, 2000, COMMUN ACM, V43, P127, DOI 10.1145/345124.345167 Srinivasan S, 2002, COMPUTER, V35, P38, DOI 10.1109/MC.2002.993769 Srivastava J., 2000, SIGKDD EXPLORATIONS, V1, P12 Stewart GW, 2000, COMPUT SCI ENG, V2, P50, DOI 10.1109/5992.814658 Sutton R., 1998, ADAPTIVE COMPUTATION SWEARINGEN K, 2001, P 24 ANN INT ACM SIG SWEARINGEN K, 2002, P C DES INT SYST DIS TERVEEN L, 2002, HUMAN COMPUTER INTER, pCH22 Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 Wasserman S., 1994, SOCIAL NETWORK ANAL WASSERMAN S, 1994, ADV SOCIAL NETWORK A WATTS DJ, 1999, SANTA FE I B, V14 Watts DJ, 1998, NATURE, V393, P440, DOI 10.1038/30918 Webb GI, 2001, USER MODEL USER-ADAP, V11, P19, DOI 10.1023/A:1011117102175 Wellman B, 2001, SCIENCE, V293, P2031, DOI 10.1126/science.1065547 Wexelblat A., 1999, P SIGCHI C HUM FACT, P270, DOI 10.1145/302979.303060 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 Zimmerman J, 2002, P C HUM FACT COMP SY, P608 2004, IN PRESS ACM T COMP NR 120 TC 42 Z9 46 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD SEP PY 2004 VL 23 IS 2 BP 107 EP 143 DI 10.1023/B:JIIS.0000039532.05533.99 PG 37 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 849JR UT WOS:000223535400001 ER PT J AU Hakenberg, J Schmeier, S Kowald, A Klipp, E Leser, U AF Hakenberg, J Schmeier, S Kowald, A Klipp, E Leser, U TI Finding kinetic parameters using text mining SO OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY LA English DT Article ID BIOMEDICAL LITERATURE; SYSTEMS BIOLOGY; DATABASE; NETWORK AB The mathematical modeling and description of complex biological processes has become more and more important over the last years. Systems biology aims at the computational simulation of complex systems, up to whole cell simulations. An essential part focuses on solving a large number of parameterized differential equations. However, measuring those parameters is an expensive task, and finding them in the literature is very laborious. We developed a text mining system that supports researchers in their search for experimentally obtained parameters for kinetic models. Our system classifies full text documents regarding the question whether or not they contain appropriate data using a support vector machine. We evaluated our approach on a manually tagged corpus of 800 documents and found that it outperforms keyword searches in abstracts by a factor of five in terms of precision. C1 Humboldt Univ, Dept Comp Sci, D-10099 Berlin, Germany. Max Planck Inst Mol Genet, Kinet Modeling Grp, Berlin, Germany. RP Hakenberg, J (reprint author), Humboldt Univ, Dept Comp Sci, Unter Linden 6, D-10099 Berlin, Germany. EM hakenberg@informatik.hu-berlin.de RI Schmeier, Sebastian/D-4731-2011 CR Aizerman M., 1964, AUTOMAT REM CONTR, V25, P821 Blaschke Christian, 2002, Brief Bioinform, V3, P154, DOI 10.1093/bib/3.2.154 Cristianini N., 2000, INTRO SUPPORT VECTOR DEBRUIJN B, 2002, EFMI WORKSH NAT LANG Donaldson I, 2003, BMC BIOINFORMATICS, V4, DOI 10.1186/1471-2105-4-11 Ewens W., 2001, STAT METHODS BIOINFO FAULSTICH L, 2003, WORKSH DAT MIN TEXT Gelbart W, 2003, NUCLEIC ACIDS RES, V31, P172, DOI 10.1093/nar/gkg094 Glenisson P, 2003, Pac Symp Biocomput, P391 Gosset William Sealy, 1908, BIOMETRIKA, V6, P1, DOI [DOI 10.1093/BIOMET/6.1.1, 10.1093/biomet/6.1.1] GRIGORIEV A, 2003, NUCLEIC ACIDS RES, V14, P4157 Hucka M, 2003, BIOINFORMATICS, V19, P524, DOI 10.1093/bioinformatics/btg015 Ideker T, 2001, SCIENCE, V292, P929, DOI 10.1126/science.292.5518.929 Issel-Tarver L, 2002, METHOD ENZYMOL, V350, P329 Joachims T, 1998, EUR C MACH LEARN Joachims Thorsten, 2002, LEARNING CLASSIFY TE Kitano H, 2002, SCIENCE, V295, P1662, DOI 10.1126/science.1069492 Koike A, 2003, GENOME RES, V13, P1231, DOI 10.1101/gr.835903 LEWIS D, 1997, 5 TEXT RETR C Li SM, 2004, SCIENCE, V303, P540, DOI 10.1126/science.1091403 MARKOWETZ F, 2001, THESIS HEIDELBERG U Mitchell TM, 1997, MACHINE LEARNING *NAT LIB MED, 2003, MEDL NOONBURG DB, 1996, PDFTOTEXT *ORACLE, 2003, OR TEXT Proux D, 2000, Proc Int Conf Intell Syst Mol Biol, V8, P279 Raychaudhuri S, 2002, GENOME RES, V12, P203, DOI 10.1101/gr.199701 Raychaudhuri S, 2002, GENOME RES, V12, P1582, DOI 10.1101/gr.116402 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANTORINI B, 1990, MSCIS9047 U PENN SCHMEIER S, 2003, LLWA ARB KNOWL DISC SCHMITZ J, 1994, ANN PHYS-LEIPZIG, V3, P1 Schomburg I, 2002, NUCLEIC ACIDS RES, V30, P47, DOI 10.1093/nar/30.1.47 Stapley B J, 2002, Pac Symp Biocomput, P374 STRASBERG HR, 2000, 2000 AMIA ANN S Vapnik V., 1995, NATURE STAT LEARNING Wheeler DL, 2003, NUCLEIC ACIDS RES, V31, P28, DOI 10.1093/nar/gkg033 YEH AS, 2003, BIOINFORMATICS, V19, P1331 NR 38 TC 26 Z9 27 PU MARY ANN LIEBERT INC PUBL PI LARCHMONT PA 2 MADISON AVENUE, LARCHMONT, NY 10538 USA SN 1536-2310 J9 OMICS JI OMICS PD SUM PY 2004 VL 8 IS 2 BP 131 EP 152 DI 10.1089/1536231041388366 PG 22 WC Biotechnology & Applied Microbiology; Genetics & Heredity SC Biotechnology & Applied Microbiology; Genetics & Heredity GA 843FI UT WOS:000223063500005 PM 15268772 ER PT J AU Fresno, V Ribeiro, A AF Fresno, V Ribeiro, A TI An analytical approach to concept extraction in HTML environments SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE concept extraction; feature vector in HTML texts; Web page characterization; Web page representation; Web page classification AB The core of the Internet and World Wide Web revolution comes from their capacity to efficiently share the huge quantity of data, but the rapid and chaotic growth of the Net has extremely complicated the task of sharing or mining useful information. Each inference process, from Internet information, requires an adequate characterization of the Web pages. The textual part of a page is one of the most important aspects that should be considered to appropriately perform a page characterization. The textual characterization should be made through the extraction of an appropriate set of relevant concepts that properly represent the text included in the Web page. This paper presents a method to obtain such a set of relevant concepts from a Web page, essentially based on a relevance estimation of each word in the text of a Web page. The word-relevance is defined by a combination of criteria that take into account characteristics of the HTML language as well as more classical measures such as the frequency and the position of a word in a document. Besides, heuristic rules to obtain the most suitable fusion of criteria is achieved via a statistical study. Several experiments are conducted to test the performance of the proposed concept extraction method compared to other approaches including a commercial tool. The results obtained here exhibit a greater success in the concept extraction by the proposed technique against other tested methods. C1 Rey Juan Carlos Univ, Escuela Super Ciencias Expt & Tecnol, Madrid 28933, Spain. CSIC, Spanish Council Sci Res, IAI, Madrid 28500, Spain. RP Fresno, V (reprint author), Rey Juan Carlos Univ, Escuela Super Ciencias Expt & Tecnol, Madrid 28933, Spain. EM v.fresno@escet.urjc.es; angela@iai.csic.es RI Ribeiro, Angela/L-3519-2014; Ribeiro, Angela/ OI Ribeiro, Angela/0000-0001-5807-8132 CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Dunham M. H, 2002, DATA MINING INTRO AD FRESNO V, 2001, INT ICSC C COMP INT, P416 GALE WA, 1992, COMPUT HUMANITIES, V26, P415, DOI 10.1007/BF00136984 Gudivada V.N., 1997, IEEE INTERNET CO SEP, P58 HENZINGER M, 2000, B TECHNICAL COMMITTE, V23, P3 Hovy E., 1999, ADV AUTOMATIC TEXT S Koller D, 1996, INT C MACH LEARN, P284 Kosala R., 2000, SIGKDD EXPLORATIONS, V2, P1 Manning Christopher D., 2001, FDN STAT NATURAL LAN Mitchell TM, 1997, MACHINE LEARNING MLADENIC D, 1999, IEEE EXPERT JUL MUSCIANO C, 1997, HTML COMPLETE GUIDE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 UNCTAD, 2002, E COMM DEV REP 2002 Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 NR 17 TC 6 Z9 6 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD MAY PY 2004 VL 22 IS 3 BP 215 EP 235 DI 10.1023/B:JIIS.0000019277.82436.17 PG 21 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 801ZO UT WOS:000220134000002 ER PT J AU Boyack, KW AF Boyack, KW TI Mapping knowledge domains: Characterizing PNAS SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA LA English DT Article; Proceedings Paper CT Colloquium on Mapping Knowledge Domains CY MAY 09-11, 2003 CL Irvine, CA HO Natl Acad Sci & Engn, Arnold & Mable Beckman Ctr ID SCIENTIFIC JOURNALS; SCIENCE; INDICATORS; VISUALIZATION; NETWORKS; IMPACT; MAP; ISSUES AB A review of data mining and analysis techniques that can be used for the mapping of knowledge domains is given. Literature mapping techniques can be based on authors, documents, journals, words, and/or indicators. Most mapping questions are related to research assessment or to the structure and dynamics of disciplines or networks. Several mapping techniques are demonstrated on a data set comprising 20 years of papers published in PNAS. Data from a variety of sources are merged to provide unique indicators of the domain bounded by PNAS. By using funding source information and citation counts, it is shown that, on an aggregate basis, papers funded jointly by the U.S. Public Health Service (which includes the National Institutes of Health) and non-U.S. government sources outperform papers funded by other sources, including by the U.S. Public Health Service alone. Grant data from the National Institute on Aging show that, on average, papers from large grants are cited more than those from small grants, with performance increasing with grant amount. A map of the highest performing papers over the 20-year period was generated by using citation analysis. Changes and trends in the subjects of highest impact within the PNAS domain are described. Interactions between topics over the most recent 5-year period are also detailed. C1 Sandia Natl Labs, Computat Comp Informat & Math Ctr, Albuquerque, NM 87185 USA. RP Boyack, KW (reprint author), Sandia Natl Labs, Computat Comp Informat & Math Ctr, POB 5800, Albuquerque, NM 87185 USA. EM kboyack@sandia.gov RI Boyack, Kevin/ OI Boyack, Kevin/0000-0001-7814-8951 CR Bassecoulard E, 1999, SCIENTOMETRICS, V44, P323, DOI 10.1007/BF02458483 Batagelj V., 1998, CONNECTIONS, V212, P47 Borner K, 2003, ANNU REV INFORM SCI, V37, P179, DOI 10.1002/aris.1440370106 Boyack KW, 2003, J AM SOC INF SCI TEC, V54, P447, DOI 10.1002/asi.10230 Boyack KW, 2002, J AM SOC INF SCI TEC, V53, P764, DOI 10.1002/asi.10066 Butler L, 2001, RES EVALUAT, V10, P59, DOI 10.3152/147154401781777141 CALLON M, 1983, SOC SCI INFORM, V22, P191, DOI 10.1177/053901883022002003 CARD SK, 1999, READINGS INFORMATION CARPENTE.MP, 1973, J AM SOC INFORM SCI, V24, P425, DOI 10.1002/asi.4630240604 Chen C., 2003, MAPPING SCI FRONTIER CHEN C, 2003, J AM SOC INF SCI TEC, V54, P453 DAVIDSON GS, 2001, 7 IEEE S INF VIS INF, P23 Davidson GS, 1998, J INTELL INF SYST, V11, P259, DOI 10.1023/A:1008690008856 De Solla Price D. J., 1963, LITTLE SCI BIG SCI DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Erosheva E, 2004, P NATL ACAD SCI USA, V101, P5220, DOI 10.1073/pnas.0307760101 FRAME JD, 1976, FED PROC, V35, P2529 GARFIELD E, 1970, NATURE, V227, P669, DOI 10.1038/227669a0 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 Godin B, 2003, RES POLICY, V32, P679, DOI 10.1016/S0048-7333(02)00032-X Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101 Hood WW, 2001, J AM SOC INF SCI TEC, V52, P1242, DOI 10.1002/asi.1191 Ingwersen P, 1997, J AM SOC INFORM SCI, V48, P205, DOI 10.1002/(SICI)1097-4571(199703)48:3<205::AID-ASI3>3.0.CO;2-0 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kim SK, 2001, SCIENCE, V293, P2087, DOI 10.1126/science.1061603 KING J, 1987, J INFORM SCI, V13, P261, DOI 10.1177/016555158701300501 Landauer TK, 2004, P NATL ACAD SCI USA, V101, P5214, DOI 10.1073/pnas.0400341101 Lewison G, 1995, 5 INT C INT SOC SCI, P255 Lewison G, 1998, GUT, V43, P288 Lewison G, 1998, SCIENTOMETRICS, V41, P17, DOI 10.1007/BF02457963 Leydesdorff L, 1997, J AM SOC INFORM SCI, V48, P418 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Martin B. R., 1984, FORESIGHT SCI PICKIN MARTIN BR, 1983, RES POLICY, V12, P61, DOI 10.1016/0048-7333(83)90005-7 MCALLISTER PR, 1983, J AM SOC INFORM SCI, V34, P123, DOI 10.1002/asi.4630340205 Morris SA, 2003, J AM SOC INF SCI TEC, V54, P413, DOI 10.1002/asi.10227 Narin F, 1996, SCIENTOMETRICS, V36, P293, DOI 10.1007/BF02129596 National Science Board, 2002, SCI ENG IND 2002 Newman MEJ, 2001, P NATL ACAD SCI USA, V98, P404, DOI 10.1073/pnas.021544898 Noyons ECM, 1999, J AM SOC INFORM SCI, V50, P115, DOI 10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.0.CO;2-J PRICE DJD, 1965, SCIENCE, V149, P510 Rinia EJ, 2002, SCIENTOMETRICS, V54, P347, DOI 10.1023/A:1016078331752 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEFFE H, 1953, BIOMETRIKA, V40, P87, DOI 10.1093/biomet/40.1-2.87 Seglen PO, 1997, ALLERGY, V52, P1050, DOI 10.1111/j.1398-9995.1997.tb00175.x Seglen PO, 1997, BRIT MED J, V314, P498 Small H, 1997, SCIENTOMETRICS, V38, P275, DOI 10.1007/BF02457414 URATA H, 1990, SCIENTOMETRICS, V18, P309, DOI 10.1007/BF02017767 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 NR 50 TC 40 Z9 43 PU NATL ACAD SCIENCES PI WASHINGTON PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA SN 0027-8424 J9 P NATL ACAD SCI USA JI Proc. Natl. Acad. Sci. U. S. A. PD APR 6 PY 2004 VL 101 SU 1 BP 5192 EP 5199 DI 10.1073/pnas.0307509100 PG 8 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA 812EO UT WOS:000220823000003 PM 14963238 ER PT J AU Zhai, CX Lafferty, J AF Zhai, CX Lafferty, J TI A study of smoothing methods for language models applied to information retrieval SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article DE algorithms; experimentation; performance; statistical language models; term weighting; TF-IDF weighting; Dirichlet prior smoothing; Jelinek-Mercer smoothing; absolute discounting smoothing; backoff smoothing; interpolation smoothing; risk minimization; two-stage smoothing; leave-one-out; EM algorithm ID PROBABILITIES AB Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing, the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections. Experimental results show that not only is the retrieval performance generally sensitive to the smoothing parameters, but also the sensitivity pattern is affected by the query type, with performance being more sensitive to smoothing for verbose queries than for keyword queries. Verbose queries also generally require more aggressive smoothing to achieve optimal performance. This suggests that smoothing plays two different role - to make the estimated document language model more accurate and to "explain" the noninformative words in the query. In order to decouple these two distinct roles of smoothing, we propose a two-stage smoothing strategy, which yields better sensitivity patterns and facilitates the setting of smoothing parameters automatically. We further propose methods for estimating the smoothing parameters automatically. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to - or better than - the best results achieved using a single smoothing method and exhaustive parameter search on the test data. C1 Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA. RP Zhai, CX (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. EM czhai@cs.uiuc.edu; lafferty@cs.cmu.edu CR Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GOOD IJ, 1953, BIOMETRIKA, V40, P237, DOI 10.2307/2333344 Goodman J., 1998, TR1098 HARV U HIEMSTRA D, 1999, P 7 TEXT RETR C TREC, V7, P227 Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop JONES KS, 1997, READINGS INFORMATION KATZ SM, 1987, IEEE T ACOUST SPEECH, V35, P400, DOI 10.1109/TASSP.1987.1165125 Kneser R, 1995, P IEEE INT C AC SPEE, V1, P181 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Kwok K. L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291003 Lafferty J., 2001, P 24 ANN INT ACM SIG, P111, DOI DOI 10.1145/383952.383970 Lavrenko V, 2001, P 24 ANN INT ACM SIG, P120, DOI DOI 10.1145/383952.383972 MacKay D., 1995, NAT LANG ENG, V1, P289 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NEY H, 1994, COMPUT SPEECH LANG, V8, P1, DOI 10.1006/csla.1994.1001 NEY H, 1995, IEEE T PATTERN ANAL, V17, P1202, DOI 10.1109/34.476512 Ponte J. M., 1998, THESIS U MASSACHUSET Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 ROBERTS SB, 1995, OBES RES, V3, P3 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Song F., 1999, P 22 ANN INT ACM SIG, P279, DOI DOI 10.1145/312624.312698 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VOORHEES E, 2001, P TEXT RETR C TREC1, V9 WONG SKM, 1995, ACM T INFORM SYST, V13, P69 Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI DOI 10.1145/502585.502654 NR 30 TC 264 Z9 279 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2004 VL 22 IS 2 BP 179 EP 214 DI 10.1145/984321.984322 PG 36 WC Computer Science, Information Systems SC Computer Science GA 812QK UT WOS:000220853800001 ER PT J AU Goncalves, MA Fox, EA Watson, LT Kipp, NA AF Goncalves, MA Fox, EA Watson, LT Kipp, NA TI Streams, structures, spaces, scenarios, societies (5S): A formal model for digital libraries SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Review DE theory; foundations; taxonomy; definitions; applications ID INFORMATION-RETRIEVAL; SYSTEMS; TEXT; SCIENCE; RELEVANCE; FRAMEWORK; LANGUAGE; SEARCH; WEB AB Digital libraries (DLs) are complex information systems and therefore demand formal foundations lest development efforts diverge and interoperability suffers. In this article, we propose the fundamental abstractions of Streams, Structures, Spaces, Scenarios, and Societies (5S), which allow us to define digital libraries rigorously and usefully. Streams are sequences of arbitrary items used to describe both static and dynamic (e.g., video) content. Structures can be viewed as labeled directed graphs, which impose organization. Spaces are sets with operations on those sets that obey certain constraints. Scenarios consist of sequences of events or actions that modify states of a computation in order to accomplish a functional requirement. Societies are sets of entities and activities and the relationships among them. Together these abstractions provide a formal foundation to define, relate, and unify concepts - among others, of digital objects, metadata, collections, and services required to formalize and elucidate "digital libraries". The applicability, versatility, and unifying power of the 5S model are demonstrated through its use in three distinct applications: building and interpretation of a DL taxonomy, informal and formal analysis of case studies of digital libraries (NDLTD and OAI), and utilization as a formal basis for a DL description language. C1 Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24061 USA. RP Goncalves, MA (reprint author), Virginia Polytech Inst & State Univ, Dept Comp Sci, Blacksburg, VA 24061 USA. EM mgoncalv@vt.edu; fox@vt.edu; ltw@vt.edu; kipp@vt.edu CR Abiteboul S., 1999, DATA WEB RELATIONS S ABITEBOUL S, 1997, J DIGITAL LIB, V1, P5 Abrams M, 1999, COMPUT NETW, V31, P1695, DOI 10.1016/S1389-1286(99)00044-4 Anderson K. M., 2003, Proceedings 2003 Joint Conference on Digital Libraries ATKINS A, 2001, INTEROPERABILITY MET Atkins DL, 1999, IEEE T SOFTWARE ENG, V25, P334, DOI 10.1109/32.798323 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BAEZAYATES R, 2000, P ACM SIGIR 2000 WOR BAYLEY KD, 1994, TYPOLOGIES TAXONOMIE Bayraktar M., 1998, Digital 98 Libraries. Third ACM Conference on Digital Libraries Beeri C., 1990, Data & Knowledge Engineering, V5, DOI 10.1016/0169-023X(90)90020-E BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722 Berners-Lee Tim, 2001, SCI AM, V284, P5 Booch G, 1999, COMMUN ACM, V42, P26, DOI 10.1145/317665.317672 Borgman CL, 1999, INFORM PROCESS MANAG, V35, P227 Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198 CASTELLI D, 2002, LECT NOTES COMPUTER, V2458, P251 Cattell R.G.G., 1994, OBJECT DATABASE STAN CLARKE CLA, 1995, COMPUT J, V38, P43 Codd E. F., 1970, COMMUN ACM, V13, P6 COOMBS JH, 1987, COMMUN ACM, V30, P933, DOI 10.1145/32206.32209 Cormen TH, 1990, INTRO ALGORITHMS CRABTREE A, 1997, P 2 ACM INT C DIG LI, P221, DOI 10.1145/263690.263824 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 DAVIS L, 2001, JCDL 01 P 1 JOINT C, P146 DAVIS MD, 1994, COMPUTATION COMPLEXI de Moura ES, 2000, ACM T INFORM SYST, V18, P113 De Oliveira MCF, 2001, ACM T INFORM SYST, V19, P28, DOI 10.1145/366836.366869 *DOMTEAM, 2003, DOC OBJ MOD DOM HOM Dong A., 2001, P 1 ACM IEEE CS JOIN, P314, DOI 10.1145/379437.379699 DUSHAY N, 2001, CSDL0112017 CORR ELLIS D, 1992, J DOC, V48, P45, DOI 10.1108/eb026889 FLANAGAN JW, 2003, MANAGING COMPLEX INF FOSKETT DJ, 1980, ENCY LIB INFORMATION, P416 Fox E. A., 1995, Communications of the ACM, V38, DOI 10.1145/205323.205325 FOX EA, 1997, D LIB MAG, V3, P9 Fox EA, 1998, COMMUN ACM, V41, P28 FRANCE RK, 2002, MARIAN DIGITAL LIB S FUHR N, 2000, P ACM SIGIR 2000 WOR FUHR N, 2004, XIRQL XML QUERY LANG FURUTA R, 1994, P 1 ANN C THEOR PRAC FURUTA R, 1989, ELECT PUB ORIGINATIO, V1, P19 GARZASALAZAR DA, 2001, PHRONESIS Gladney H., 1994, P 1 ANN C THEOR PRAC, P101 Gladney HM, 2001, COMMUN ACM, V44, P63, DOI 10.1145/374308.374343 GODEMENT R, 1969, ALGEBRA GOH D, 2000, DL 00 P 5 ACM INT C, P153 Goldfarb C.F., 1998, XML HDB Goncalves M. A., 2002, P 2 ACM IEEE CS JOIN, P263 GONCALVES MA, 2001, P JOINT DESLOS NSF W GONCALVES MA, 2003, DIGITAL LIB FORMAL T GONCALVES MA, 2003, 0304 VIRG TECH GONCALVES MA, 2002, P 9 INT S STRING PRO, P194 GONCALVES MA, 2001, P 5 EUR C RES ADV TE, P173 HEATH LS, 1995, COMMUN ACM, V38, P52, DOI 10.1145/205323.376383 HSIA P, 1994, IEEE SOFTWARE, V11, P33, DOI 10.1109/52.268953 JONES KS, 1997, READINGS INFORMATION KALINICHENKO LA, 2000, P 2 RUSS SCI C DIG L Kautz H, 1997, COMMUN ACM, V40, P63, DOI 10.1145/245108.245123 KELAPURE R, 2003, LECT NOTES COMPUTER, V2760 Kochtanek TR, 1999, INFORM PROCESS MANAG, V35, P245, DOI 10.1016/S0306-4573(98)00060-0 KYING M, 1995, SCENARIO BASED DESIG Laender AHF, 2002, DATA KNOWL ENG, V40, P121, DOI 10.1016/S0169-023X(01)00047-7 Lagoze C., 2001, P 1 ACM IEEE CS JOIN, P54, DOI 10.1145/379437.379449 LAGOZE C, 1996, D LIB MAG, V2, P7 Lamsweerde A., 1998, IEEE T SOFTWARE ENG, V24, P1089 Lesk M., 1999, P INT S DIG LIB 1999 LEVY DM, 1995, COMMUN ACM, V38, P77, DOI 10.1145/205323.205346 *LIB C, 2003, METS MET ENC TRANS S Licklider J. C. R., 1965, LIB FUTURE Lorie Raymond A., 2001, P 1 ACM IEEE CS JOIN, P346, DOI 10.1145/379437.379726 Lucarella D, 1996, ACM T INFORM SYST, V14, P3, DOI 10.1145/214174.214175 MACKAY WE, 1998, P CHI 98, P416, DOI 10.1145/274644.274701 MILLER T, 1999, ANNOTATION SYSTEM CO Navarro G, 1997, ACM T INFORM SYST, V15, P400, DOI 10.1145/263479.263482 *NDLTD, 2003, NETW DIG LIB THES DI Nelson ML, 2001, COMMUN ACM, V44, P60, DOI 10.1145/374308.374342 Nestorov S., 1997, SIGMOD Record, V26 NEVES FD, 2000, P 5 ACM C DIG LIB AC, P103 *OAI, 2001, OP ARCH IN PROT MET OARD D, 1999, D LIB MAG, V5, P10 Oberweis A, 1996, ACM T INFORM SYST, V14, P380, DOI 10.1145/237496.237498 OGAWA R, 1990, P ECHT 90 1 EUR C HY, P38 PRINCE R, 1999, P INT JOINT C WORK A, P217, DOI 10.1145/295665.295692 Ramakrishnan N, 2000, IEEE INTERNET COMPUT, V4, P21, DOI 10.1109/4236.895012 RANGANATHAN SR, 1965, DESCRIPTIVE ACCOUNT Reddy R., 2001, DIGITAL LIB UNIVERSA ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rossman AY, 1999, STUD MYCOL, P1 ROSSON MB, 1996, P ACM CHI 96 C HUM F, P342 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 SCHAUBLE P, 1998, SUMMARY REPORT SERIE SCHWARTZ MF, 1993, COMMUN ACM, V36, P78, DOI 10.1145/163381.163402 SHACKELFORD DE, 1993, P ACM C HYP ECHT 93, P1, DOI 10.1145/168750.168753 SHANNON CE, 1948, AT&T TECH J, V27, P379 Singhal M, 1994, ADV CONCEPTS OPERATI Spivey J.M., 1988, INTRO Z SPECIFICATIO STAPLES T, 2003, D LIB MAGA, V9, P4 Sutcliffe A., 1997, Proceedings of the Third IEEE International Symposium on Requirements Engineering (Cat. No.97TB100086), DOI 10.1109/ISRE.1997.566843 Sutcliffe AG, 1998, IEEE T SOFTWARE ENG, V24, P1072, DOI 10.1109/32.738340 Tague J., 1991, P 14 ANN INT ACM SIG, P14, DOI 10.1145/122860.122862 Tansley R., 2003, Proceedings 2003 Joint Conference on Digital Libraries TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Ullman J. D., 1988, PRINCIPLES DATABASE Varian H. R., 2000, INTERNET PUBLISHING VICKERY BC, 1965, RUTGERS SERIES INTEL, V5 *W3C, 2001, MATH MARK LANG MATHM *W3C, 2000, XML SCHEM PART 0 PRI *W3C, 1998, RES DESCR FRAM RDF M *W3C, 2000, RES DESCR FRAM RDF S WANG B, 1999, INT J DIGITAL LIB, V2, P91, DOI 10.1007/s007990050040 Wang WP, 1998, ACM T INFORM SYST, V16, P372, DOI 10.1145/291128.291132 WIEDERHOLD G, 1995, COMMUN ACM, V38, P85, DOI 10.1145/205323.205347 WILKINSON R, 1996, INFORM RETRIEVAL HYP, P257 Winskel G., 1993, FDN COMPUTING SERIES Witten I. H., 2000, P 5 ACM C DIG LIB SA, P113, DOI 10.1145/336597.336650 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 ZHU Q, 2003, LECT NOTES COMPUTER, V2769 Zia LL, 2001, COMMUN ACM, V44, P83, DOI 10.1145/374308.375359 Ziviani N, 2000, COMPUTER, V33, P37, DOI 10.1109/2.881693 ZUBAIR M, 2000, P 9 INT WORLD WID WE NR 124 TC 79 Z9 80 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2004 VL 22 IS 2 BP 270 EP 312 DI 10.1145/984321.984325 PG 43 WC Computer Science, Information Systems SC Computer Science GA 812QK UT WOS:000220853800004 ER PT J AU Dubin, D AF Dubin, D TI The most influential paper Gerard Salton never wrote SO LIBRARY TRENDS LA English DT Article ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; SYSTEMS; SIMILARITY AB Gerard Salton is often credited with developing the vector space model (VSM) for information retrieval (IR). Citations to Salton give the impression that the VSM must have been articulated as an IR model sometime between 1970 and 1975. However, the VSM as it is understood today evolved over a longer time period than is usually acknowledged, and an articulation of the model and its assumptions did not appear in print until several years after those assumptions had been criticized and alternative models proposed. An often cited over-view paper titled "A Vector Space Model for Information Retrieval" (alleged to have been published in 1975) does not exist, and citations to it represent a confusion of two 1975 articles, neither of which were overviews of the VSM as a model of information retrieval. Until the late 1970s, Salton did not present vector spaces as models of IR generally but rather as models of specific computations. Citations to the phantom paper reflect an apparently widely held misconception that the operational features and explanatory devices now associated with the VSM must have been introduced at the same time it was first proposed as an IR model. C1 Univ Illinois, Grad Sch Lib & Informat Sci, Champaign, IL 61820 USA. RP Dubin, D (reprint author), Univ Illinois, Grad Sch Lib & Informat Sci, 501 E Daniel St, Champaign, IL 61820 USA. CR ARMAPATZIS A, 2000, BCS IRSG 2000 COLL I BOLLMANN P, 1991, NSF WORKSH FUT DIR T, P16 BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 BOOKSTEIN A, 1976, LIBR QUART, V46, P153 Cardie C., 2000, P 6 APPL NAT LANG PR, P180, DOI 10.3115/974147.974172 CHEN H, 2001, THESIS GUANGDONG U F Crouch C, 1996, J AM SOC INFORM SCI, V47, P108 Davis P. J., 1981, MATH EXPERIENCE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fraleigh J. B., 1987, LINEAR ALGEBRA Fuhr N., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980) Green P. E., 1976, MATH TOOLS APPL MULT Howland P, 2004, SURVEY OF TEXT MINING, P3 Jiang F., 2001, P 2 NTCIR WORKSH RES Kobayashi M, 2004, SURVEY OF TEXT MINING, P103 Koll M., 1979, ACM SIGIR FORUM, V13, P32, DOI 10.1145/1095366.1095368 LAY DC, 1994, LINEAR ALGEBRA ITS A LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 McCabe M. C., 2000, P 23 ANN INT ACM SIG, P363, DOI 10.1145/345508.345656 MCGILL MJ, 1979, ANNU REV INFORM SCI, V14, P93 MCGILL MJ, 1976, J AM SOC INFORM SCI, V27, P205, DOI 10.1002/asi.4630270403 MEINCKE PP, 1979, ORDERING SYSTEMS GLO, P158 Michell Joel, 1990, INTRO LOGIC PSYCHOL Nallapati R., 2003, P HLT NAACL 2003 STU, P1 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 Salton G., 1975, REGIONAL C SERIES AP, V18 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1963, J ACM, V10, P440, DOI 10.1145/321186.321188 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1965, IEEE SPECTRUM, V2, P90 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1979, J DOC, V35, P1, DOI 10.1108/eb026671 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1974, CUCSD74203 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, RES DEV INFORMATION, P151 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1965, NBS MISCELLANEOUS PU, V269, P201 SAMMON JW, 1968, RADCTR68178 SINGHAL A, 1995, P DUAL US TECHN APPL, P318 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Singhal A., 2001, IEEE DATA ENG B, V24, P35 SWITZER P, 1965, STAT ASS METHODS MEC, P163 THEOPHYLACTOU M, 1998, BCS IRSG 20 ANN COLL TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 Tversky A., 1978, COGNITION CATEGORIZA TVERSKY A, 1982, PSYCHOL REV, V89, P123, DOI 10.1037/0033-295X.89.2.123 Wong S., 1984, P 7 ANN INT ACM SIGI, P167 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 WONG SKM, 1986, P 9 ANN INT ACM SIGI, P175, DOI 10.1145/253168.253205 WONG SKM, 1987, ACM T DATABASE SYST, V12, P299, DOI 10.1145/22952.22957 NR 53 TC 9 Z9 9 PU GSLIS PUBLICATIONS PI CHAMPAIGN PA 501 E DANIEL ST, CHAMPAIGN, IL 61820-6211 USA SN 0024-2594 J9 LIBR TRENDS JI Libr. Trends PD SPR PY 2004 VL 52 IS 4 BP 748 EP 764 PG 17 WC Information Science & Library Science SC Information Science & Library Science GA 870UQ UT WOS:000225084500005 ER PT J AU Jensen, R Shen, Q AF Jensen, R Shen, Q TI Fuzzy-rough attribute reduction with application to web categorization SO FUZZY SETS AND SYSTEMS LA English DT Article DE attribute reduction; web categorization; data redundancy AB Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents a novel approach, based on an integrated use of fuzzy and rough set theories, to greatly reduce this data redundancy. Formal concepts of fuzzy-rough attribute reduction are introduced and illustrated with a simple example. The work is applied to the problem of web categorization, considerably reducing dimensionality with minimal loss of information. Experimental results show that fuzzy-rough reduction is more powerful than the conventional rough set-based approach. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. (C) 2003 Elsevier B.V. All rights reserved. C1 Univ Edinburgh, Div Informat, Ctr Intelligent Syst Applicat, Edinburgh, Midlothian, Scotland. RP Shen, Q (reprint author), Univ Edinburgh, Div Informat, Ctr Intelligent Syst Applicat, Edinburgh, Midlothian, Scotland. EM richjens@dai.ed.ac.uk; qiangs@dai.ed.ac.uk CR Beaubouef T., 1999, ROUGH FUZZY HYBRIDIZ Chouchoulas A., 2002, P 2002 UK WORKSH COM, P18 Dash M, 1997, INTELL DATA ANAL, V1, P131, DOI DOI 10.1016/S1088-467X(97)00008-5 Devijver P. A., 1982, PATTERN RECOGNITION, V1st Dubois D., 1992, INTELLIGENT DECISION, P203 DUNTSCH I, 1999, ROUGH SET DATA ANAL HOHLE U, 1988, FUZZY SET SYST, V27, P31, DOI 10.1016/0165-0114(88)90080-2 Jensen R., 2001, WEB INTELLIGENCE RES, P95 Jensen R, 2002, PROCEEDINGS OF THE 2002 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOL 1 & 2, P29 Kira K., 1992, P 10 NAT C ART INT, P129 Langley P., 1994, P AAAI FALL S REL, P1 Larson K., 1998, P CHI 98 HUM FACT CO, P25, DOI 10.1145/274644.274649 Marin-Blazquez JG, 2002, IEEE T FUZZY SYST, V10, P484, DOI 10.1109/TFUZZ.2002.800687 Mitchell TM, 1997, MACHINE LEARNING Pal S. K., 1999, ROUGH FUZZY HYBRIDIZ Pawlak Z., 1991, ROUGH SETS THEORETIC PEDRYCZ W, 1999, ROUGH FUZZY HYBRIDIZ SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shen Q, 2000, ENG APPL ARTIF INTEL, V13, P263, DOI 10.1016/S0952-1976(00)00010-5 Slowinski R., 1992, INTELLIGENT DECISION THIELE H, 1998, CI3098 U DORTM WYGRALAK M, 1989, FUZZY SET SYST, V29, P241, DOI 10.1016/0165-0114(89)90197-8 YAO YY, 1988, INFORM SCI, V109, P21 Zadeh L. A., 1965, FUZZY SETS INFORM CO, V8, P338, DOI DOI 10.1016/S0019-9958(65)90241-X NR 25 TC 170 Z9 203 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0165-0114 J9 FUZZY SET SYST JI Fuzzy Sets Syst. PD FEB 1 PY 2004 VL 141 IS 3 BP 469 EP 485 DI 10.1016/S0165-0114(03)00021-6 PG 17 WC Computer Science, Theory & Methods; Mathematics, Applied; Statistics & Probability SC Computer Science; Mathematics GA 764QA UT WOS:000188216500006 ER PT B AU Brahmi, D Ziou, D AF Brahmi, D Ziou, D GP ieee computer society TI Improving CBIR systems by integrating semantic features SO 1ST CANADIAN CONFERENCE ON COMPUTER AND ROBOT VISION, PROCEEDINGS LA English DT Proceedings Paper CT 1st Canadian Conference on Computer and Robot Vision CY MAY 17-19, 2004 CL London, CANADA SP Canadian Soc Image Proc & Pattern Recognit, Int Assoc Pattern Recognit, IEEE Comp Soc HO Univ Western Ontario ID GAP AB Nowadays, access to information requires to manage effectively multimedia databases, and among challenges offered to scientific community since last decades, multimedia retrieval techniques (particularly images retrieval) are became an active research direction. Introduced to overcome the main drawbacks encountered by text-based images retrieval, which are the subjective and manual annotation of images, content based images retrieval (CBIR) systems index images according to low-level visual features such as color, texture, shape to retrieve similar images. However, despite the progress achieved in the content based image retrieval, in particular with the relevance feedback approach where the user refine the search via the specification of relevant or not relevant items, the current CBIR systems still have a major difficulty that it has yet to overcome: how to negotiate the "semantic gap"? This problem comes from the mismatch between their capabilities and the needs of users. In this paper, we address the problem of how relate low-level features to high level to bring out semantic concepts from images. Our aim is to combine content-based and metadata-based approaches for image retrieval from a user perspective to yield better results and overcome to the lacks of these techniques when they are taken separately. To represent the semantic content of images, we propose a model which takes account of the interaction between the user and the metadata. In particular, we model the semantic user' preference by analyzing its answers through the Relevance Feedback process. Furthermore, we introduce a new machine learning technique that modify the weights (i.e. relative importance) of metadata representing the semantic content of images. C1 Univ Sherbrooke, Fac Sci, DMI, Sherbrooke, PQ J1K 2R1, Canada. RP Brahmi, D (reprint author), Univ Sherbrooke, Fac Sci, DMI, Sherbrooke, PQ J1K 2R1, Canada. CR Barnard K., 2001, P INT C COMP VIS, V2, P408, DOI DOI 10.1109/ICCV.2001.937654 BARNARD K, 2001, COMPUTER VISION PATT, P435 BENITEZ AB, 2002, INT C MULT EXP ICME Duygulu P, 2002, 7 EUR C COMP VIS, P97 Fielitz B. D., 1975, DECISION SCI, V6, P1, DOI 10.1111/j.1540-5915.1975.tb00992.x GRASHTEYN IS, 1965, TABLE INTEGRALS SERI GROSKY WI, 2001, 1 INT C INT MULT CON KHERFI ML, 2002, CONTENT BASED IMAGE LOWE S, 1999, P EUR 99 BUD SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sjolander K, 1996, COMPUT APPL BIOSCI, V12, P327 WENYIN L, 2001, HUMAN COMPUTER INTER, P326 YE HCL, 2000, ACM MULTIMEDIA 2000 Zhao R, 2002, PATTERN RECOGN, V35, P593, DOI 10.1016/S0031-3203(01)00062-0 Zhao R, 2002, IEEE T MULTIMEDIA, V4, P189 ZHOU XS, 2002, IEEE MULTIMEDIA NR 16 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2127-4 PY 2004 BP 233 EP 240 DI 10.1109/CCCRV.2004.1301449 PG 8 WC Computer Science, Artificial Intelligence; Robotics SC Computer Science; Robotics GA BAG13 UT WOS:000222055200032 ER PT B AU Jiang, MH Deng, BX Sheng, XW Tang, XF Ruan, QQ Yuan, BZ AF Jiang, MH Deng, BX Sheng, XW Tang, XF Ruan, QQ Yuan, BZ GP PHEI TI Automatic text classification based on rough set and improved quick-reduce algorithm SO 2004 7TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS 1-3 LA English DT Proceedings Paper CT 7th International Conference on Signal Processing CY AUG 31-SEP 04, 2004 CL Beijing, PEOPLES R CHINA SP Chinese Inst Elect, IEEE Signal Proc Soc, IEE, Union Radio Sci Int, IEEE Beijing Sect, Natl Nat Sci Fdn China, CIE Comm URSI, IEEE Comp Soc Beijing Chapter, IEEE SP Soc Beijing Chapter, CIE Signal Proc Soc, NO Jiaotong Univ DE automatic classification; rough set; decision table; reduction algorithm AB This paper proposes a fast dimensionality reduction algorithm for automatic text classifications (TC). which introduces Rough Set theory that can greatly reduce the document vector dimensions by the reduction algorithm. The experimental results prove that the proposed algorithm is very successful, it can not only keep important low-frequency words but also remove high-frequency words with no use in classification. Thus our algorithm reduces effectively the dimensional space, and reaches higher accuracy while losing less useful information compared with the conventional reduction methods. C1 Tsing Hua Univ, Dept Chinese Language, Lab Computat Linguist, Beijing 100084, Peoples R China. RP Jiang, MH (reprint author), Tsing Hua Univ, Dept Chinese Language, Lab Computat Linguist, Beijing 100084, Peoples R China. CR Pawlak Z., 1991, ROUGH SETS THEORETIC PAWLAK Z, 1982, INT J COMPUT INF SCI, V11, P341, DOI 10.1007/BF01001956 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Wang G. Y., 2001, ROUGH SET THEORY KNO NR 5 TC 0 Z9 0 PU PUBLISHING HOUSE ELECTRONICS INDUSTRY PI BEIJING PA PO BOX 173 WANSHOU ROAD, BEIJING 100036, PEOPLES R CHINA BN 7-121-00215-9 PY 2004 BP 2712 EP 2715 PG 4 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology; Telecommunications SC Computer Science; Engineering; Imaging Science & Photographic Technology; Telecommunications GA BBR53 UT WOS:000227412102225 ER PT B AU Davies, S Badem, S Williams, MD King, R AF Davies, S Badem, S Williams, MD King, R BE Zhang, LJ Li, M Sheth, AP Jeffery, KG TI "Google* by reformulation": Modeling search as successive refinement SO 2004 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING, PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Services Computing CY SEP 15-18, 2004 CL Shanghai, PEOPLES R CHINA SP IEEE Comp Soc, TSC SC AB Humans naturally employ a process of iterative refinement when they search for information, clarifying their intentions and adjusting their goals in response to what they encounter. We propose to develop tools specifically to facilitate this process, and hypothesize that theey, will help users converge on their desired information more quickly. This paper presents a layered extension to the Google Web search interface that illustrates the so-called "retrieval by reformulation" paradigm. The tool will serve as a launching point to a wider field of inquiry into user interaction with large information spaces. C1 Univ Colorado, Boulder, CO 80309 USA. RP Davies, S (reprint author), Univ Colorado, Boulder, CO 80309 USA. CR FAILS JA, 2003, P 8 2NT C INT US INT Grosky WI, 2002, SIGMOD RECORD, V31, P54 Jansen B. J., 1998, SIGIR Forum, V32 NECHES R, 1998, DLIB MAGAZINE OCT PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 2004, ENGLISH LANGUAGE STO Schohn G., 2000, P 17 INT C MACH LEAR STAAB S, 2002, IEEE INTELLIGENT JAN WILLIAMS MD, 1984, INT J MAN MACH STUD, V21, P333, DOI 10.1016/S0020-7373(84)80052-8 YAO KT, 1999, P INT WORKSH COLL MO NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2225-4 PY 2004 BP 435 EP 440 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BBB87 UT WOS:000224606900054 ER PT S AU Honkela, T Hyvarinen, A AF Honkela, T Hyvarinen, A GP ieee TI Linguistic feature extraction using independent component analysis SO 2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT IEEE International Joint Conference on Neural Networks (IJCNN) CY JUL 25-29, 2004 CL Budapest, HUNGARY SP IEEE, IEEE Neural Networks Soc, Hungarian Acad Sci, Comp & Automat Res Inst, Katholieke Univ Leuven, Republic Hungary, Natl Commun & Informat Council ID LATENT SEMANTIC ANALYSIS; MODEL AB Our aim is to find syntactic and semantic relationships of words based on the analysis of corpora. We propose the application of independent component analysis, which seems to have clear advantages over two classic methods: latent semantic analysis and self-organizing maps. Latent semantic analysis is a simple method for automatic generation of concepts that are useful, e.g., in encoding documents for information retrieval purposes. However, these concepts cannot easily be interpreted by humans. Self-organizing maps can be used to generate an explicit diagram which characterizes the relationships between words. The resulting map reflects syntactic categories in the overall organization and semantic categories in the local level. The self-organizing map does not, however, provide any explicit distinct categories for the words. Independent component analysis applied on word context data gives distinct features which reflect syntactic and semantic categories. Thus, independent component analysis gives features or categories that are both explicit and can easily be interpreted by humans. This result can be obtained without any human supervision or tagged corpora that would have some predetermined morphological, syntactic or semantic information. C1 Helsinki Univ Technol, Neural Networks Res Ctr, Lab Comp & Informat Sci, Helsinki, Finland. RP Honkela, T (reprint author), Helsinki Univ Technol, Neural Networks Res Ctr, Lab Comp & Informat Sci, Helsinki, Finland. CR Bingham E., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval COMON P, 1994, SIGNAL PROCESS, V36, P287, DOI 10.1016/0165-1684(94)90029-9 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fillmore Charles J., 1968, UNIVERSALS LINGUIST, P1 FINCH S, 1992, ARTIFICIAL NEURAL NE, V2, P1365 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Church K. W., 1990, Computational Linguistics, V16 Honkela T., 1995, P ICANN 95 INT C ART, V2, P3 HONKELA T, 1997, COMPUTING ANTICIPATO, P563 Hyvarinen A, 1999, IEEE T NEURAL NETWOR, V10, P626, DOI 10.1109/72.761722 Hyvarinen A., 2001, INDEPENDENT COMPONEN ISBELL C, 1998, ADV NEURAL INFORMATI, V11, P480 JUTTEN C, 1991, SIGNAL PROCESS, V24, P1, DOI 10.1016/0165-1684(91)90079-X KOLENDA T, 2000, ADV NEURAL INFORM PR, V13, P235 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Manning C. D., 1999, FDN STAT NATURAL LAN MIIKKULAINEN R, 1990, PROGRAM OF THE TWELFTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P447 MIIKKULAINEN R, 1993, P 12 ANN C COGN SCI Miikkulainen R, 1997, BRAIN LANG, V59, P334, DOI 10.1006/brln.1997.1820 MIIKULAINEN R, 1993, SUBSYMBOLIC NATURAL RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H., 1992, Proceedings. Supercomputing '92. (Cat. No.92CH3216-9), DOI 10.1109/SUPERC.1992.236684 NR 23 TC 2 Z9 2 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 0-7803-8359-1 J9 IEEE IJCNN PY 2004 BP 279 EP 284 DI 10.1109/IJCNN.2004.1379914 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA BBC97 UT WOS:000224941900047 ER PT S AU Caverlee, J Liu, L Buttler, D AF Caverlee, J Liu, L Buttler, D GP IEEE COMPUTER SOCIETY TI Probe, cluster, and discover: Focused extraction of QA-Pagelets from the Deep Web SO 20TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS SE PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (SERIES) LA English DT Proceedings Paper CT 20th International Conference on Data Engineering CY MAR 30-APR 02, 2004 CL Boston, MA SP Microsoft Res, bea, IBM, MITRE, Sun Microsyst ID MATHEMATICAL-THEORY; COMMUNICATION AB In this paper we introduce the concept of a QA-Pagelet to refer to the content region in a dynamic page that contains query matches. We present THOR, a scalable and efficient mining system for discovering and extracting QA-Pagelets from the Deep Web. A unique feature of THOR is its two-phase extraction framework. In the first phase, pages from a deep web site are grouped into distinct clusters of structurally-similar pages. In the second phase,pages from each page cluster are examined through a subtree filtering algorithm that exploits the structural and content similarity at subtree level to identify the QA-Pagelets. C1 Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA. RP Caverlee, J (reprint author), Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA. CR ARASU A, SIGMOD 03 BARYOSSEF Z, WWW 02 Beeferman D., 2000, KNOWLEDGE DISCOVERY, P407 BERGMAN MK, 2000, BRIGHT PLANET BHARAT K, SIGIR 98 BRODER AZ, WWW 97 CALLAN J, SIGMOD 99 CAVERLEE J, 2003, PROBE CLUSTER DISCOV COHEN W, AAAI 99 CRESCENZI V, VLDB 01 DEAN J, WWW 99 DHILLON I., 2001, DATA MINING SCI ENG Duda R.O., 2001, PATTERN CLASSIFICATI GIBSON D, HYPERTEXT 98 Hawking D, 1999, ACM T INFORM SYST, V17, P40, DOI 10.1145/297117.297123 IPEIROTIS PG, 2003, ACM TOIS, V21, P1 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 KUMAR R, 1999, WWW 99 MAY Levenshtein V. I., 1966, SOV PHYS DOKL, V10, P707 MENG W, COOPIS 99 NIERMAN A, 2002, WEBDB PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAGGETT D, 1999, CLEAN UP YOUR WEB PA SALTON G, 1997, READINGS INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P623 SHANNON CE, 1948, AT&T TECH J, V27, P379 Steinbach M., 2000, KDD WORKSH TEXT MINI ZAMIR O, SIGIR 98 Zhao Y., 2002, CRITERION FUNCTIONS NR 31 TC 8 Z9 11 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1063-6382 BN 0-7695-2065-0 J9 PROC INT CONF DATA PY 2004 BP 103 EP 114 DI 10.1109/ICDE.2004.1319988 PG 12 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA BY96R UT WOS:000189506500012 ER PT S AU Kosinov, S Marchand-Maillet, S AF Kosinov, S Marchand-Maillet, S BE Nurnberger, A Detyniecki, M TI Overview of approaches to semantic augmentation of multimedia databases for efficient access and content retrieval SO ADAPTIVE MULTIMEDIA RETRIEVAL SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 1st International Workshop on Adaptive Multimedia Retrieval CY SEP 15-16, 2003 CL Hamburg, GERMANY SP European Network Excellence Intelligent Technol Smart Adapt Syst ID IMAGE RETRIEVAL; EM ALGORITHM; PICTURES AB Ever-increasing amount of multimedia available online necessitates the development, of new techniques and methods that can overcome the semantic gap problem. The said problem, encountered due to major disparities between inherent representational characteristics of multimedia and its semantic content sought by the user, has been a prominent research direction addressed by a great number of semantic augmentation approaches originating from such areas as machine learning, statistics, natural language processing, etc. In this paper, we review several of these recently developed techniques that bring together low-level representation of multimedia and its semantics in order to improve the efficiency of access and retrieval. We also present a distance-based discriminant analysis (DDA) method that defines the design of a basic building block classifier for distinguishing among a selected number of semantic categories. In addition to that, we demonstrate how a set of DDA classifiers can be grouped into a hierarchical ensemble for prediction of an arbitrary set of semantic classes. C1 Univ Geneva, Comp Vis & Multimedia Lab, CH-1211 Geneva 4, Switzerland. RP Kosinov, S (reprint author), Univ Geneva, Comp Vis & Multimedia Lab, 24 Rue Gen Defour, CH-1211 Geneva 4, Switzerland. EM kosinov@cui.unige.ch; marchand@cui.unige.ch CR ARKADEV A, 1966, COMPUTERS PATTER REC Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 Barnard K., 2003, INTERNET IMAGING Blake C., 1998, UCI REPOSITORY MACHI Blei D. M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460 Borg I., 1997, MODERN MULTIDIMENSIO Chen Y., 2001, IEEE INT C IM PROC I Cox IJ, 2000, IEEE T IMAGE PROCESS, V9, P20, DOI 10.1109/83.817596 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Deng Y., 1999, P IEEE COMP SOC C CO, V2, P446 DEUN KV, 2003, 200335 EI Duda R.O., 1973, PATTERN CLASSIFICATI Fasulo D., 1999, ANAL RECENT WORK CLU Fisher RA, 1936, ANN EUGENIC, V7, P179 Fix E., 1951, 4 USAF SCH AV MED FUKUNAGA K, 1983, IEEE T PATTERN ANAL, V5, P671 HARDOON DR, 2003, CSDTR0302 ROY HOLL U Hastie T, 1996, IEEE T PATTERN ANAL, V18, P607, DOI 10.1109/34.506411 Heiser WJ., 1995, RECENT ADV DESCRIPTI, P157 HUBER PJ, 1964, ANN MATH STAT, V35, P73, DOI 10.1214/aoms/1177703732 Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459 JIGN F, 2003, IEEE INT C MULT EXP JING F, 2002, IEEE INT C AC SPEECH JING F, 2003, INT C IM VID RETR JORDAN MI, 1994, NEURAL COMPUT, V6, P181, DOI 10.1162/neco.1994.6.2.181 Kohrs A., 1999, P COMP INT MOD CONTR KOSINOV S, 2003, 0307 U GEN COMP VIS LANDAUER T, 1990, 6 ANN C UW CTR NEW O, P31 LEIBE B, 2003, INT C COMP VIS PATT, P409 Li J, 2003, IEEE T PATTERN ANAL, V25, P1075 Li J., 1999, DCC 1999, P23 LI Y, 2004, IN PRESS LECT NOTES MARCHANDMAILLET S, 2004, LECT NTOES COMPUTER MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Monay F., 2003, P ACM INT C MULT ACM MORI Y, 1999, 1 INT WORKSH MULT IN Muller H, 2004, INT J COMPUT VISION, V56, P65, DOI 10.1023/B:VISI.0000004832.02269.45 PRAKS P, 2003, P SIAM C APPL LIN AL Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI [10.1145/192844.192905, DOI 10.1145/192844.192905] Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANTINI S, 1998, ACM MULTIMEDIA, P345 SOLLA SA, 2000, ADV NEURAL INFORMATI, V12 Squire DM, 2000, PATTERN RECOGN LETT, V21, P1193, DOI 10.1016/S0167-8655(00)00081-7 Su Z, 2000, PROC INT C TOOLS ART, P258 Tong S., 2001, ACM MULTIMEDIA, V9, P107 Vinokourov A., 2003, ADV NEURAL INFORM PR, V15, P1473 VINOKOUROV A, 2003, 4 INT S IND COMP AN Zhao R., 2000, IEEE INT C MULT EXP, P679 NR 51 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-22163-8 J9 LECT NOTES COMPUT SC PY 2004 VL 3094 BP 19 EP 35 PG 17 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BAM57 UT WOS:000222846700002 ER PT S AU Bao, JP Shen, JY Liu, XD Liu, HY Zhang, XD AF Bao, JP Shen, JY Liu, XD Liu, HY Zhang, XD BE Li, Q Wang, G Feng, L TI Finding plagiarism based on common semantic sequence model SO ADVANCES IN WEB-AGE INFORMATION MANAGEMENT: PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th International Conference on Web-Age Information Management CY JUL 15-17, 2004 CL Dalian, PEOPLES R CHINA SP NO Univ China, Dalian Univ Technol, Database Soc CCF AB It is one of key problems in Text Mining to find document features. The string matching model and global word frequency model are two common models. But the former can hardly resist rewording noise, whereas the latter cannot find document details. We present Common Semantic Sequence Model (CSSM) and apply it to Document Copy Detection. CSSM combines the ideas of 2 models above, and it makes a trade-off between a document global features and local features. CSSM calculates the common words proportion between 2 documents semantic sequences to make a plagiarism score. A semantic sequence is indeed a continual word sequence after the low-density words are omitted. With the collection of 2 documents semantic sequences, we can detect plagiarism in a fine granularity. We test CSSM with several common copy types. The result shows that CSSM is excellent for detecting non-rewording plagiarism and valid even if documents are reworded to some extent. C1 Xian Jiaotong Univ, Dept Comp Sci & Engn, Xian 710049, Peoples R China. RP Bao, JP (reprint author), Xian Jiaotong Univ, Dept Comp Sci & Engn, Xian 710049, Peoples R China. EM baojp@mail.xjtu.edu.cn CR BAO JP, 2003, P INT C NAT LANG PRO, P250 BRODER AZ, 1997, 6 INT WEB C SANT CLA DENNING PJ, 1995, COMMUNICATIONS ACM, V38 MONOSTORI K, 2000, P INF RES MAN ASS IN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shivakumar Narayanan, 1995, P 2 INT C THEOR PRAC Si A., 1997, P ACM S APPL COMP, P70, DOI 10.1145/331697.335176 YHEINTZE N, 1996, P 2 USENIX WORKSH EL NR 8 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-22418-1 J9 LECT NOTES COMPUT SC PY 2004 VL 3129 BP 640 EP 645 PG 6 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BAK53 UT WOS:000222635400066 ER PT S AU Kim, YS Oh, JS Lee, JY Chang, JH AF Kim, YS Oh, JS Lee, JY Chang, JH BE Webb, GI Yu, X TI An intelligent grading system for descriptive examination papers based on probabilistic. Latent semantic analysis SO AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 17th Annual Australian Conference on Artificial Intelligence CY DEC 04-06, 2004 CL Cairns, AUSTRALIA HO Cent Queensland Univ AB In this paper, we developed an intelligent grading system, which scores descriptive examination papers automatically, based on Probabilistic Latent Semantic Analysis (PLSA). For grading, we estimated semantic similarity between a student paper and a model paper. PLSA is able to represent complex semantic structures of given contexts, like text passages, and are used for building linguistic semantic knowledge which could be used in estimating contextual semantic similarity. In this paper, we marked the real examination papers and we can acquire about 74% accuracy of a manual grading, 7% higher than that from the Simple Vector Space Model. C1 Hallym Univ, Div Informat Engn & Telecommun, Gangwon 200702, South Korea. Seoul Natl Univ, Sch Comp Sci & Engn, Seoul 151744, South Korea. RP Kim, YS (reprint author), Hallym Univ, Div Informat Engn & Telecommun, Gangwon 200702, South Korea. EM yskim01@hallym.ac.kr; bil78@hallym.ac.kr; jylee@hallym.ac.kr; jhchang@bi.snu.ac.kr CR BANG H, 2001, P KIPS CHRISTIANINI N, 2002, J INTELL INF SYST, V18, P127 HOFMANN T, 1999, ADV NEURAL INFORMATI, V11 Hofmann T., 1999, P 15 C UNC ART INT Hofmann T., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312649 KANG S, HANGULE MORPHOLOGICA KIM W, 2003, KOSASEONGEO ENCY SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wong S., 1985, P 8 ANN INT ACM SIGI, P18, DOI 10.1145/253495.253506 NR 9 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24059-4 J9 LECT NOTES ARTIF INT PY 2004 VL 3339 BP 1141 EP 1146 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BBM32 UT WOS:000226133600114 ER PT S AU Hong, M Kairmpour-Fard, A Russell, S Hunter, L AF Hong, M Kairmpour-Fard, A Russell, S Hunter, L BE Kim, TG TI Integrated term weighting, visualization, and user interface development for bioinformation retrieval SO ARTIFICIAL INTELLIGENCE AND SIMULATION SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 13th International Conference on Artificial Intelligence, Simulation and Planning in High Autonomy Systems (AIS 2004) CY OCT 04-06, 2004 CL Cheju Isl, SOUTH KOREA ID AUTOMATIC TEXT RETRIEVAL; INFORMATION; ALGORITHM AB This project implements an integrated biological information website that classifies technical documents, learns about users' interests, and offers intuitive interactive visualization to navigate vast information spaces. The effective use of modem software engineering principles, system environments, and development approaches is demonstrated. Straightforward yet powerful document characterization strategies are illustrated, helpful visualization for effective knowledge transfer is shown, and current user interface methodologies are applied. A specific success of note is the collaboration of disparately skilled specialists to deliver a flexible integrated prototype in a rapid manner that meets user acceptance and performance goals. The domain chosen for the demonstration is breast cancer, using a corpus of abstracts from publications obtained online from Medline. The terms in the abstracts are extracted by word stemming and a stop list, and are encoded in vectors. A TF-IDF technique is implemented to calculate similarity scores between a set of documents and a query. Polysemy and synonyms are explicitly addressed. Groups of related and useful documents are identified using interactive visual displays such as a spiral graph that represents of the overall similarity of documents. K-means clustering of the similarities among a document set is used to display a 3-D relationship map. User identities are established and updated by observing the patterns of terms used in their queries, and from login site locations. Explicit considerations of changing user category profiles, site stakeholders, information modeling, and networked technologies are pointed out. C1 Univ Colorado, Hlth Sci Ctr, Denver, CO 80262 USA. RP Hong, M (reprint author), Univ Colorado, Hlth Sci Ctr, 4200 E 9th Ave,Campus Box C-245, Denver, CO 80262 USA. EM Min.Hong@UCHSC.edu; Anis.Karimpour-Fard@UCHSC.edu; Steve-Russell@UCHSC.edu; Larry.Hunter@UCHSC.edu CR Anh VN, 2001, P 24 ANN INT ACM SIG, P35, DOI 10.1145/383952.383957 BERRIOS D, 2002, METHODS SEMIAUTOMATE BERRY M, 1995, P SUPERCOMPUTING 95 Blaschke C, 1999, INTELLIGENT SYSTEMS, P60 CAMPBELL K, UNPUB SPECIAL ISSUE CROUCH C, IR APPROACH XML RETR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fukuda K, 1998, Pac Symp Biocomput, P707 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 HERSH WR, 1990, COMPUT BIOMED RES, V23, P410, DOI 10.1016/0010-4809(90)90031-7 ILIOPOULOS AJ, 2001, P 6 ANN PAC S BIOC P, P384 LOVINS JB, 1968, MECH TRANSL, V11, P22 PRATT W, 2000, P AM MED INF ASS AMI Pratt W, 2000, J AM MED INFORM ASSN, V7, P605 Proux D., 1998, GENOME INFORM SER WO, V9, P72 RINDFLESCH TC, PAC S BIOC, P517 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 RUSSELL S, 1999, P KNOWLEDGE WORL JUL SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1968, AUTOMATIC CONTENT AN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEKIMIZU T, 1998, GENOME INFORMATICS, P62 Thomas J., 2000, PACIFIC S BIOCOMPUTI, P538 TOKUNAGA T, 1994, TR0001 TOK I TECHN D UGER S, 2000, FEATURE REDUCTION DO VOORHEES EM, 1998, WORDNET ELECT LEXICA, P285 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 Yoshida M, 2000, BIOINFORMATICS, V16, P169, DOI 10.1093/bioinformatics/16.2.169 NR 31 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24476-X J9 LECT NOTES COMPUT SC PY 2004 VL 3397 BP 673 EP 682 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BBZ55 UT WOS:000228359600071 ER PT S AU Jiang, M Jensen, E Beitzel, S Argamon, S AF Jiang, M Jensen, E Beitzel, S Argamon, S BE Banks, D House, L McMorris, FR Arabie, P Gaul, W TI Choosing the right bigrams for information retrieval SO CLASSIFICATION, CLUSTERING, AND DATA MINING APPLICATIONS SE STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION LA English DT Proceedings Paper CT Meeting of the International-Federation-of-Classifications-Societies (IFCS) CY JUL 15-18, 2004 CL Chicago, IL SP Int Federat Classificat Soc HO Illinois Inst Technol AB After more than 30 years of research in information retrieval, the dominant paradigm remains the "bag-of-words," in which query terms are considered independent of their coocurrences with each other. Although there has been some work on incorporating phrases or other syntactic information into IR, such attempts have given modest and inconsistent improvements, at best. This paper is a first step at investigating more deeply the question of using bigrams for information retrieval. Our results indicate that only certain kinds of bigrams are likely to aid retrieval. We used linear regression methods on data from TREC 6, 7, and 8 to identify which bigrams are able to help retrieval at all. Our characterization was then tested through retrieval experiments using our information retrieval engine, AIRE, which implements many standard ranking functions and retrieval utilities. C1 IIT, Informat Retrieval Lab, Chicago, IL 60616 USA. RP Jiang, M (reprint author), IIT, Informat Retrieval Lab, Chicago, IL 60616 USA. CR Arampatzis AT, 1998, INFORM PROCESS MANAG, V34, P693, DOI 10.1016/S0306-4573(98)00030-2 ARAMPATZIS AT, 2000, EVALUATION LINGUISTI CHOWCHURY A, 2000, IIT TREC 9 ENTITY BA CHOWDHURY A, 2001, INT S INF SYST ENG FAGAN J, 1987, 10 ANN INT ACM SIGIR Halliday M. A. K., 1994, INTRO FUNCTIONAL GRA, V2nd HIEMSTRA D, 2000, RELATING NEW LANGUAG HIEMSTRA D, 2001, USING LANGUAGE MODEL, P164 JIANG M, 2004, 2004 S AI MATH SPEC KOSTER CHA, 2003, TAMING WILD PHRASES KRAAIJ W, 1998, 2 EUR C RES ADV TECH LEWIS, 1990, TERM CLUSTERING SYNT MILLER DRH, 1999, 22 ACM C RES DEV INF MITRA M, 1997, 5 INT C RECH INF ASS NARITA M, 2000, 23 ACM C RES DEV INF ROBERTSON SE, 1995, 4 ANN TEXT RETR C SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1998, 20 BCS IRSG C SONG F, 1999, 8 INT C INF KNOWL MA Strzalkowski T., 1999, NATURAL LANGUAGE INF TURPIN A, 1999, 22 ACM C RES DEV INF VOORHEES E, 1999, NATURAL LANGUAGE PRO Xu JX, 1998, ACM T INFORM SYST, V16, P61, DOI 10.1145/267954.267957 ZHAI C, 2001, 24 ACM C RES DEV INF ZHAI C, 1997, NIST SPECIAL PUBLICA NR 26 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1431-8814 BN 3-540-22014-3 J9 ST CLASS DAT ANAL PY 2004 BP 531 EP 540 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science; Mathematics, Applied SC Computer Science; Operations Research & Management Science; Mathematics GA BAS44 UT WOS:000223368200050 ER PT S AU Carthy, J Wilson, DC Wang, RC Dunnion, J Drummond, A AF Carthy, J Wilson, DC Wang, RC Dunnion, J Drummond, A BE Gelbukh, A TI Using T-Ret system to improve incident report retrieval SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th International Conference on Intelligent Text Processing and Computational Linguistics CY FEB 15-21, 2004 CL Seoul, SOUTH KOREA SP Assoc Computat Linguist AB This papers describes novel research involving the development of Textual CBR techniques and applying them to the problem of Incident Report Retrieval. Incident Report Retrieval is a relatively new research area in the domain of Accident Reporting and Analysis. We describe T-Ret, an Incident Report Retrieval system that incorporates textual CBR techniques and outline preliminary evaluation results. C1 Univ Coll Dublin, Dept Comp Sci, Dublin 2, Ireland. RP Carthy, J (reprint author), Univ Coll Dublin, Dept Comp Sci, Dublin 2, Ireland. EM Joe.Carthy@ucd.ie; David.Wilson@ucd.ie; Rachel@ucd.ie; John.Dunnion@ucd.ie; Anne.Drummond@ucd.ie CR BRUNINGHAUS S, 1997, P 2 INT C CAS BAS RE, P303 CARTHY J, 2002, EUR C CAS BAS REAS E DANIELS J, 1997, P 2 INT C CAS BAS RE JOHNSON C, 1999, P INT US INT 99 RED, P127, DOI 10.1145/291080.291102 LENZ M, 1998, LECT NOTES ARTIFICIA, V1488 REASON J, 1998, MANAGING RISKS ORG A SALTON G, 1983, SMART SIRE EXPT RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WEBER R, 1998, P AAAI 98 WORKSH TEX, P40 WILSON DC, 1999, P 4 UK CAS BAS REAS, P67 NR 10 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-21006-7 J9 LECT NOTES COMPUT SC PY 2004 VL 2945 BP 468 EP 471 PG 4 WC Computer Science, Information Systems; Computer Science, Theory & Methods; Language & Linguistics SC Computer Science; Linguistics GA BY59Y UT WOS:000189417900057 ER PT B AU Robertson, S Jones, KS AF Robertson, S Jones, KS BE Herbert, A Jones, KS TI Retrieval system models: What's new? SO COMPUTER SYSTEMS: THEORY, TECHNOLOGY AND APPLICATIONS: A TRIBUTE TO ROGER NEEDHAM SE MONOGRAPHS IN COMPUTER SCIENCE LA English DT Proceedings Paper CT Roger Needham 50 and 5 Meeting CY FEB 17, 2003 CL Cambridge, ENGLAND ID INFORMATION-RETRIEVAL C1 Microsoft Res Ltd, Cambridge, England. RP Robertson, S (reprint author), Microsoft Res Ltd, Cambridge, England. CR Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 Croft W.B, 2003, LANGUAGE MODELLING I Jones K. S., 2000, Information Processing & Management, V36 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 NEEDHAM RM, 1963, INFORMATION PROCESSI, V62, P284 Robertson S. E., 1981, INFORMATION RETRIEVA, P35 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 NR 10 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING STREET, NEW YORK, NY 10013, UNITED STATES BN 0-387-20170-X J9 MG COMP SCI PY 2004 BP 237 EP 242 DI 10.1007/0-387-21821-1_35 PG 6 WC Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BY73H UT WOS:000189450000035 ER PT B AU Pan, F Rahal, I Cui, Y Perrizo, W AF Pan, F Rahal, I Cui, Y Perrizo, W BE Gupta, B TI Efficient ranking of keyword queries using P-trees SO COMPUTERS AND THEIR APPLICATIONS LA English DT Proceedings Paper CT 19th International Conference on Computers and Their Applications CY MAR 19-20, 2004 CL Seattle, WA SP Int Soc Comp & Their Applicat DE keyword query; information retrieval; P-tree AB In this paper, we describe the architecture, implementation, and evaluation of the P-RANK system built to address the requirement for extracting evidences of specific products of genes from biomedical papers. PRANK is a system that accepts user interests in the form of keywords, which integrate different depth weights into the ranking score and highlights that molecular biologists who review these papers looking mainly for the certain part of a paper to extract experimental evidences, in turn, returns a ranked list pertaining to the users' interests.. Our contributions include presenting a new efficient keyword query system using a data structure called the P-tree(1), and a fast weighted ranking method using the EIN-ring. C1 N Dakota State Univ, Dept Comp Sci, Fargo, ND 58105 USA. RP Pan, F (reprint author), N Dakota State Univ, Dept Comp Sci, Fargo, ND 58105 USA. CR Brin S., 1998, WWW C DING Q, 2002, P ACM S APPL COMP SA, P426 Kamber M., 2001, DATA MINING CONCEPTS Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 PAN F, 2003, PROXIMAL SUPPORT VEC PERRIZO W, 2001, NDSUCSTR011 Perrizo W, 2002, PAKDD 02, P517 RAHAL I, 2003, ACM S APPL COMP SALTON, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 0 Z9 0 PU INTERNATIONAL SOCIETY COMPUTER S & THEIR APPLICATIONS (ISCA) PI RALEIGH PA 8820 SIX FORKS ROAD, RALEIGH, NC 27615 USA BN 1-880843-50-1 PY 2004 BP 278 EP 281 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BY92Q UT WOS:000189496800063 ER PT S AU Jiang, JJ Wang, WN AF Jiang, JJ Wang, WN BE Chen, Z Chen, H Miao, Q Fu, Y Fox, E Lim, E TI Text-based P2P content search using a hierarchical architecture SO DIGITAL LIBRARIES: INTERNATIONAL COLLABORATION AND CROSS-FERTILIZATION, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Asian Digital Libraries (ICADL 2004) CY DEC 13-17, 2004 CL Shanghai, PEOPLES R CHINA AB As a scalable alternative to traditional server-based architecture, peer-to-peer (P2P) computing has become a popular distributed computing paradigm. However, efficient content search is absent, which hinders the wider deployment of the peer-to-peer systems. Earlier peer-to-peer systems such as Napster and Gnutella suffer from unscalability. Structured peer-to-peer networks achieve good scalability and high reliability, and yet they can only support a single-key based lookup instead of content search by means of DHTs (Distributed Hash Tables). In this paper, we propose a text-based peer-to-peer content search solution, which uses a hierarchical architecture. The heterogeneity of the popularity of terms both in documents and queries and the heterogeneity of hosts are both examined herein. There some techniques are employed to cope with the troubles arising from these heterogeneities. The experimental results show that the solution is feasible and efficient. C1 Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. Shanghai Jiao Tong Univ, Network & Informat Ctr, Shanghai 200030, Peoples R China. RP Jiang, JJ (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China. EM jjj@sjtu.edu.cn; wnwang@sjtu.edu.cn RI JIANG, Junjie/B-6081-2013 CR Li JY, 2003, LECT NOTES COMPUT SC, V2735, P207 Ramaswamy L, 2003, PEER TO PEER COMPUTI, P66, DOI 10.1109/PTP.2003.1231505 REYNOLDS P, 2003, P ACM IFIP USENIX MI, V2672, P21 Ribeiro-Neto B.A., 1998, P 3 ACM C DIG LIB, P182, DOI 10.1145/276675.276695 SALTON G, 1989, INFORMATION RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stading T, 2002, LECT NOTES COMPUT SC, V2429, P203 Stoica I., 2003, IEEE ACM T NETWORK, V11, P11 Yang B., 2001, Proceedings of the 27th International Conference on Very Large Data Bases NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24030-6 J9 LECT NOTES COMPUT SC PY 2004 VL 3334 BP 429 EP 439 PG 11 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBO59 UT WOS:000226741400046 ER PT B AU Sant, J AF Sant, J BE Cantoni, L McLoughlin, C TI Visualization of curricula using multi-dimensional scaling SO ED-MEDIA 2004: World Conference on Educational Multimedia, Hypermedia & Telecommunications, Vols. 1-7 LA English DT Proceedings Paper CT World Conference on Educational Multimedia, Hypermedia and Telecommunications CY JUN 21-26, 2004 CL Lugano, SWITZERLAND AB Curricula in many institutions of higher learning are represented either textually or graphically as linear sequences of courses to be taken within a program. The use of Two-Dimensional maps generated using Multi-Dimensional Scaling and Information Retrieval techniques is demonstrated as an alternative to more traditional techniques of representing curriculum. The preliminary implementation of a Curriculum Visualization System and the advantages and special problems of using this approach are discussed referencing real-world examples. The benefits of using this approach to compare course offerings within and between institutions and comparisons with human analysis of curriculum are also discussed. CR BUJA A, UNPUB J COMPUTATIONA Chalmers Matthew, 1992, P 15 ANN INT ACM SIG, P330, DOI DOI 10.1145/133160.133215 HUANG S, 2003, TR0314 WORC POL I CO Kruskal J. B., 1978, QUANTITATIVE APPL SO PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TORGERSON WS, 1952, PSYCHOMETRIKA, V17, P401 NR 7 TC 0 Z9 0 PU ASSOC ADVANCEMENT COMPUTING EDUCATION PI NORFOLK PA PO BOX 3728, NORFOLK, VA 23514 USA BN 1-880094-53-3 PY 2004 BP 1911 EP 1917 PG 7 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Education & Educational Research SC Computer Science; Education & Educational Research GA BCI56 UT WOS:000229602302057 ER PT B AU Velasquez, JD Bassi, A Yasuda, H Aoki, T AF Velasquez, JD Bassi, A Yasuda, H Aoki, T BE Rastogi, R Morik, K Bramer, M Wu, X TI Mining web data to create online navigation recommendations SO FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS LA English DT Proceedings Paper CT 4th IEEE International Conference on Data Mining CY NOV 01-04, 2004 CL Brighton, ENGLAND SP IEEE Comp Soc, TCCI, IEEE Comp Soc, TCPAMI, IBM Res, StatSoft Ltd, Web Intelligence Consortium AB A system to provide online navigation recommendation for web visitors is introduced. We call visitor the anonymous user i.e., when only data about her/his browsing behavior (web logs) are available. We first apply clustering techniques over a large sample of web data. Next, from the significant patterns that are discovered, a set of rules about how to use them is created Finally, comparing the current web visitor session with the patterns, online navigation recommendations are proposed using the mentioned rules. The system was tested using data from a real web site, showing its effectiveness. C1 Univ Tokyo, Res Ctr Adv Sci & Technol, Tokyo, Japan. RP Velasquez, JD (reprint author), Univ Tokyo, Res Ctr Adv Sci & Technol, Tokyo, Japan. CR B Mobasher, 1999, P IEEE KNOWL DAT ENG BRUSILOVSKY P, 2003, IEEE WEB INT INT C O Cadoli M, 1997, AI COMMUN, V10, P137 LU Z, 2003, WEB INTELLIGENCE Nielsen J, 1999, COMMUN ACM, V42, P65, DOI 10.1145/291469.291470 Runkler TA, 2003, INT J APPROX REASON, V32, P217, DOI 10.1016/S0888-613X(02)00084-1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Theodoridis S., 1999, PATTERN RECOGNITION Velasquez JD, 2004, IEICE T INF SYST, VE87D, P389 VELASQUEZ JD, 2004, IN PRESS P 3 IEEE WI NR 10 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2142-8 PY 2004 BP 551 EP 554 DI 10.1109/ICDM.2004.10019 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBI95 UT WOS:000225713000099 ER PT B AU Garces, PJ Castro-Schez, JJ Manjavacas, RM Olivas, JA AF Garces, PJ Castro-Schez, JJ Manjavacas, RM Olivas, JA BE Arabnia, HR Mun, Y TI A suppliers searcher agent based on FIS-CRM used in E-commerce websites SO IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS LA English DT Proceedings Paper CT International Conference on Artificial Intelligence/International Conference on Machine Learning, Models, Technologies and Applications CY JUN 21-24, 2004 CL Las Vegas, NV SP Comp Sci Res, Educ, & Applicat Press, Int Technol Inst, Korean Soc Internet Informat, World Acad Sci Informat Technol ID MODEL AB In this paper, we present an approach for the design of an intelligent agent capable of providing a list of suppliers of a product according to the restrictions defined by the buyer. This agent could be called by other intelligent agent, more particularly, the merchant broker agent. The agent suggested could be integrated in a multi-agent system inside the E-commerce environment. To design the agent we make use of the FIS-CRM model. It will be used to extract from the Internet a list of possible suppliers of a desired product. It searches in the WWW making use of the concepts associated to the words used by the seller to describe their products and the buyer's words to express what he want. C1 Univ Alicante, Dept Comp Sci & AI, Alicante 03080, Spain. RP Garces, PJ (reprint author), Univ Alicante, Dept Comp Sci & AI, Carretera San Vicente Raspeig S-N, Alicante 03080, Spain. RI Castro-Schez, Jose Jesus/E-8934-2012 OI Castro-Schez, Jose Jesus/0000-0002-0201-7653 CR Castro-Schez JJ, 2004, IEEE T FUZZY SYST, V12, P123, DOI 10.1109/TFUZZ.2003.822684 CASTROSCHEZ JJ, 2004, P IEEE INT C FUZZY S CASTROSCHEZ JJ, IN PRESS INT J HUMAN CASTROSCHEZ JJ, 2004, IN PRESS P IPMU04 PE FERNANDEZ S, 2001, THESIS U SANTIAGO CO Guttman RH, 1998, KNOWL ENG REV, V13, P147, DOI 10.1017/S0269888998002082 Lomuscio A. R., 2001, Agent mediated electronic commerce. European agentlink perspective (Lecture Notes in Artificial Intelligence Vol.1991), DOI 10.1007/3-540-44682-6_2 Maes P, 1999, COMMUN ACM, V42, P81, DOI 10.1145/295685.295716 Olivas JA, 2003, INT J APPROX REASON, V34, P201, DOI 10.1016/j.ijar.2003.07.008 OLIVAS JA, 2003, P 3 EUSFL C ZITT GER, P63 ROMERO FP, 2002, P INT C ART INT ICAI, P225 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHAW MJ, 2000, HDB ELECT COMMERCE, P3 Turban E., 1999, ELECT COMMERCE MANAG WIDYANTORO D, P BISC INT WORKSH FU, P155 WOOLDRIDGE M, 1995, KNOWL ENG REV, V10, P115 NR 16 TC 0 Z9 0 PU C S R E A PRESS PI ATHENS PA 115 AVALON DR, ATHENS, GA 30606 USA BN 1-932415-33-5 PY 2004 BP 50 EP 55 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BBL81 UT WOS:000226030400008 ER PT B AU Maggini, M Rigutini, L Turchi, M AF Maggini, M Rigutini, L Turchi, M BE Zhong, N Tirri, H Yao, YY Zhou, L Liu, J Cercone, N TI Pseudo-supervised clustering for text documents SO IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY SEP 20-24, 2004 CL Beijing, PEOPLES R CHINA SP IEEE Comp Soc, ACM, Web Intelligence Consortium, Beijing Univ Technol, NSFC AB Effective solutions for Web search engines can take advantage of algorithms for the automatic organization of documents into homogeneous clusters. Unfortunately, document clustering is not an easy task especially when the documents share a common set of topics, like in vertical search engines. In this paper we propose two clustering algorithms which can be tuned by the feedback of an expert. The feedback is used to choose an appropriate basis for the representation of documents, while the clustering is performed in the projected space. The algorithms are evaluated on a dataset containing papers from computer science conferences. The results show that an appropriate choice of the representation basis can yield better performance with respect to the original vector space model. C1 Univ Siena, Dipartimento Ingn Informaz, Siena, Italy. RP Maggini, M (reprint author), Univ Siena, Dipartimento Ingn Informaz, Via Roma 56, Siena, Italy. CR ATKINSON K, 2004, GNU ASPELL 0 50 5 MA Bradley P.S., 1998, P 4 INT C KNOWL DISC COVER T, 1991, ELEMENTS INFORMAYTIO DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dhillon IS, 2001, MACH LEARN, V42, P143, DOI 10.1023/A:1007612920971 DOM BE, 2001, INFORMATION THEORETI Jain A. K., 1988, ALGORITHMS CLUSTERIN Karypis G., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354772 LUHN H, 1959, INFORMATION RETRIEVA, V3, P1021 RASMUSSEN E, 1992, INFORMATION RETRIEVA SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEINBAC M, 2000, P KDD WORKSH TEXT MI VEMPALA S, 1999, P 40 ANN S FDN COMP, P616 NR 15 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2100-2 PY 2004 BP 363 EP 369 DI 10.1109/WI.2004.10138 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBB14 UT WOS:000224473600052 ER PT B AU Yang, J Wang, L Zhang, S Sui, X Zhang, N Xu, ZQ AF Yang, J Wang, L Zhang, S Sui, X Zhang, N Xu, ZQ BE Zhong, N Tirri, H Yao, YY Zhou, L Liu, J Cercone, N TI Building domain ontology based on web data and generic ontology SO IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY SEP 20-24, 2004 CL Beijing, PEOPLES R CHINA SP IEEE Comp Soc, ACM, Web Intelligence Consortium, Beijing Univ Technol, NSFC AB The automatic or semi-automatic construction of ontology has become a research topic of interest in recent years. This paper describes a mechanism for constructing domain specific ontologies automatically based on web data and generic ontology. Firstly, we employ the hierarchical agglomerative clustering(HAC) algorithm, clustering web pages hierarchically and resulting in a binary tree. Then an algorithm is proposed, which selects from the binary tree the significative nodes as topics implying concepts of domain interests. Lastly, the Chinese generic ontology, HowNet, is introduced to evolve the topics (together with their hierarchical structures) into domain ontology. We experiment our method in the field of computer hardware based on web pages collected from Chinese BtoC web sites. An in-depth discussion on the experiment results is also given. C1 Peking Univ, Sch EECS, Dept Comp Sci, Beijing 100871, Peoples R China. RP Yang, J (reprint author), Peking Univ, Sch EECS, Dept Comp Sci, Beijing 100871, Peoples R China. CR HE CLT, 2003, INFORMATION RETRIEVA, P613 Jain A.K., 1998, ALGORITHMS CLUSTERIN LIIM SJL, 2003, ISMIS 2003, P93 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 van der Vet PE, 1998, IEEE T KNOWL DATA EN, V10, P513, DOI 10.1109/69.706054 ZHANG YWS, 2003, IAT 2003, P461 ZHOU QB, 2002, P 35 HAW INT COMF SY NR 7 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2100-2 PY 2004 BP 686 EP 689 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBB14 UT WOS:000224473600122 ER PT B AU An, JY Chen, YPP AF An, JY Chen, YPP BE Zhong, N Tirri, H Yao, YY Zhou, L Liu, J Cercone, N TI Concept learning of text documents SO IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE (WI 2004), PROCEEDINGS LA English DT Proceedings Paper CT IEEE/WIC/ACM International Conference on Web Intelligence CY SEP 20-24, 2004 CL Beijing, PEOPLES R CHINA SP IEEE Comp Soc, ACM, Web Intelligence Consortium, Beijing Univ Technol, NSFC AB Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used These keywords should be fewer and comprehensible. A naive method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords, Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods. C1 Deakin Univ, Sch Informat Technol, Fac Sci & Technol, Melbourne, Vic 3125, Australia. RP An, JY (reprint author), Deakin Univ, Sch Informat Technol, Fac Sci & Technol, Melbourne, Vic 3125, Australia. RI Chen, Yi-Ping Phoebe/B-8844-2008 OI Chen, Yi-Ping Phoebe/0000-0002-4122-3767 CR Beyer K., 1999, INT C DAT THEOR, P217 HAMMOUDA KM, 2002, ICDM, P203 Jain A. K., 1988, ALGORITHMS CLUSTERIN Jong KAD, 1993, MACH LEARN, V13, P161 Li Y., 2003, ICDM, P593 LI Y, 2003, WI, P96 Li YF, 2003, APPL ARTIF INTELL, V17, P443, DOI 10.1080/08839510390219246 Michalski R, 1983, MACHINE LEARNING ART Michalski R. S., 1969, P 5 INT S INF PROC A, VA3, P125 MICHALSKI RS, 1986, UIUCDCSR861260 U ILL Mitchell TM, 1997, MACHINE LEARNING Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WINSTON P, 2002, C4 5 DECISION TREE NR 14 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2100-2 PY 2004 BP 698 EP 701 PG 4 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBB14 UT WOS:000224473600125 ER PT S AU Gindonis, M Niemi, T Niinimaki, M AF Gindonis, M Niemi, T Niinimaki, M BE Kiyoki, Y Kawaguchi, E Jaakkola, H Kangassalo, H TI Document similarities in web based collaborative environments SO INFORMATION MODELLING AND KNOWLEDGE BASES XV SE FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS LA English DT Proceedings Paper CT 13th European/Japanese Conference on Information Modelling and Knowledge Bases CY JUN, 2003 CL Kitakyushu, JAPAN HO Kyushu Inst Technol AB Collaborative editing of documents online in organizations helps the collaborators keep up to date with rapidly changing information, especially in technical fields. Because of its speed, editing documents on-line can have disadvantages, too: the repository of documents (document space) can grow large and redundant. To enforce coherence and reduce redundancy, a strong (hierarchical) structure of the document space can be imposed. In this case, each document has a strictly defined slot in the document space, it can be located by navigation and altered or extended. It has been our experience, however, that users do not like hierarchical structures and find the navigation cumbersome. Another possibility, more liked by the users, is to have a loosely structured document space, and support the users with tools for document retrieval and intelligent linking between documents. In this paper, we present a tool with which the user can see which other documents are related to his documents. The intended use for the tool is that the user edits a document to insert a query variable and after editing saves it. Saving a document automatically launches a query, whose results are a set of links to the most similar documents in the work space. In order to measure similarity, we have evaluated several methods, including Myers' O(ND) method (string comparson by edit distance) and a method based on Salton's Vector Model. In our reference document server (that contains about 1000 technical documents entered by the users) it was found that both methods worked quite well. The users of the document server have found our implementation useful, since they can (i) see if someone has already considered the topic that they are working with (ii) find information that is otherwise interesting to them. C1 CERN, Helsinki Inst Phys, CH-1211 Geneva, Switzerland. RP Gindonis, M (reprint author), CERN, Helsinki Inst Phys, CH-1211 Geneva, Switzerland. CR Alavi M., 1999, COMMUNICATION ASS IN, V1, P1 BIELAWSKI L, 1997, ELECT DOCUMENT MANAG COLLINS A, 1975, PSYCHOL REV, V6 CRUZ IF, 1998, LECT NOTES COMPUTER, V1375 FALOUTSOS C, 1995, SURVEY INFORMATION R Jarvelin K., 2000, P 23 ANN INT ACM SIG KEKALAINEN J, 1999, THESIS U TAMPERE KENT A, 1955, AM DOCUMENTATION, V6 Luck M, 1999, SOFTWARE PRACT EXPER, V29, P721, DOI 10.1002/(SICI)1097-024X(19990710)29:8<721::AID-SPE257>3.0.CO;2-0 Myers EW, 1986, ALGORITHMICA, V1, P251, DOI 10.1007/BF01840446 *NETW WORK GROUP, HYP TRANSF PROT HTTP OINASKUKKONEN H, 1998, COMMUNICATIONS ACM, V41 Ong H.-L., 2001, P 10 INT C INF KNOWL, P523 PFEIFER U, 1997, PERL J SUM PUITTINEN M, 2000, THESIS HELSINKI U TE RUGE G, 1995, P 18 ANN INT ACM SIG SALTON G, 1973, J ACM, V20, P258, DOI 10.1145/321752.321757 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THOENY P, 2002, TWIKI WEB BASED COLL *WORLD WID WEB CON, HTML 4 01 SPEC NR 20 TC 0 Z9 0 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 1-58603-396-4 J9 FR ART INT PY 2004 VL 105 BP 178 EP 184 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA BY82A UT WOS:000189470900015 ER PT J AU Ko, Y Park, J Seo, J AF Ko, Y Park, J Seo, J TI Improving text categorization using the importance of sentences SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE text categorization; importance of sentence; text summarization techniques; indexing technique; text classifier ID RETRIEVAL AB Automatic text categorization is a problem of assigning text documents to pre-defined categories. In order to classify text documents, we must extract useful features. In previous researches, a text document is commonly represented by the term frequency and the inverted document frequency of each feature. Since there is a difference between important sentences and unimportant sentences in a document, the features from more important sentences should be considered more than other features. In this paper, we measure the importance of sentences using text summarization techniques. Then we represent a document as a vector of features with different weights according to the importance of each sentence. To verify our new method, we conduct experiments using two language newsgroup data sets: one written by English and the other written by Korean. Four kinds of classifiers are used in our experiments: Naive Bayes, Rocchio, k-NN, and SVM. We observe that our new method makes a significant improvement in all these classifiers and both data sets. (C) 2002 Elsevier Ltd. All rights reserved. C1 Sogang Univ, Dept Comp Sci, NLP Lab, Seoul 121742, South Korea. RP Ko, Y (reprint author), Sogang Univ, Dept Comp Sci, NLP Lab, Sinsu Dong 1, Seoul 121742, South Korea. CR SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Craven M, 2000, ARTIF INTELL, V118, P69, DOI 10.1016/S0004-3702(00)00004-7 Duda R.O., 2001, PATTERN CLASSIFICATI ENDRESNIGGEMEYE.B, 1998, SUMMARIZING INFORMAT, P307 GOLDSTEIN J, 1999, P 23 INT ACM SIGIR C Joachims T, 1998, EUR C MACH LEARN ECM, P137 Ko Y., 2000, P 18 INT C COMP LING, P453 Lewin GR, 1996, ANNU REV NEUROSCI, V19, P289, DOI 10.1146/annurev.ne.19.030196.001445 Li H., 1997, P 35 ANN M ASS COMP Marcu D, 1999, ADVANCES IN AUTOMATIC TEXT SUMMARIZATION, P123 McCallum A., 1998, AAAI 98 WORKSH LEARN, P41 MOCK KJ, 1996, P NAT C ART INT AAAI MURATA M, 2000, J ASS NATURAL LANGUA, V7 RADEV D, 2000, P ANLP NACCL WORKSH SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnic VN, 1995, NATURE STAT LEARNING WESTON V, 1999, P 7 EUR S ART NEUR N ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y., 2002, J INTELLIGENT INFORM, V18 NR 22 TC 36 Z9 44 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2004 VL 40 IS 1 BP 65 EP 79 DI 10.1016/S0306-4573(02)00056-0 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 759LC UT WOS:000187738300005 ER PT B AU Wang, W Ravishankar, CV AF Wang, W Ravishankar, CV BE Bernardino, J Desai, BC TI Adaptive data broadcasting in asymmetric communication environments SO INTERNATIONAL DATABASE ENGINEERING AND APPLICATIONS SYMPOSIUM, PROCEEDINGS LA English DT Proceedings Paper CT International Database Engineering and Applications Symposium (IDEAS 04) CY JUL 07-09, 2004 CL Coimbra, PORTUGAL SP Inst Super Engenharia Coimbra, Univ Coimbra, Ctr Informat & Syst, Oracle Portugal, Byte Press, Camara Municipal Coimbra, Crit Software, S A, IEEE Comp Soc ID SCHEDULING DATA BROADCAST AB We present a new adaptive broadcast dissemination model to support flexible responses to client requests. Several features distinguish our model. First, client queries do not target individual documents, but specify the required information by attributes. Second, clients are satisfied by responses that are sufficiently close to the desired information. Finally, the server in our model solicits randomized feedback from clients to adapt its broadcast program to client needs. Our simulation results show that our model captures the interest patterns of clients more efficiently and more accurately and scales very well with the number of clients, while reducing overall client average waiting times. C1 Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA. RP Wang, W (reprint author), Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA. EM wangw@cs.ucr.edu; ravi@cs.ucr.edu CR ACHARYA R, 1995, P SIGMOD C ACHARYA S, 1996, COMMUNICATION, V2 ACHARYA S, 1997, P SIGMOD C BOWEN TF, 1992, COMMUN ACM, P35 Cetintemel U., 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), DOI 10.1109/ICDE.2000.839477 DEOLASEE P, 2001, P 10 INT WWW C MAY DHILLON I., 2001, DATA MINING SCI ENG DUFF IS, 1989, ACM T MATH SOFTWARE, V15, P1, DOI 10.1145/62038.62043 FRAKES WR, 1992, INFORMATION RETRIEVA FRANKLIN M, 1998, ACM SIGMOD C JUN Hameed S, 1999, WIREL NETW, V5, P183, DOI 10.1023/A:1019194826654 HU CL, P 22 ICDCD 2002 HU JH, 2000, IEEE INT C COMM 2000, V3, P1778 Hu Q., 1998, WORKSH MOB DAT ACC S, P213 KNUTH D, 1981, ART COMPUTER PROGRA, V2 MCDIARMID C, 1989, LOND MATH S, V141, P148 Motwani R., 1995, RANDOMIZED ALGORITHM OH J, 2000, P IEEE C PERF COMP C, P361 PORTER MF, 1980, PROGRAM, V14, P13 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1988, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STATHATOS K, 1997, VLDB Vaidya NH, 1999, WIREL NETW, V5, P171, DOI 10.1023/A:1019142809816 VAIDYA NH, 1996, 96017 DEP COMP SCI T WANG W, 2004, ADAPTIVE DATA BROADC WU KL, 1996, P ICDE NR 27 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-2168-1 PY 2004 BP 27 EP 36 PG 10 WC Computer Science, Information Systems SC Computer Science GA BAO78 UT WOS:000223083100003 ER PT S AU Luo, X Zincir-Heywood, AN AF Luo, X Zincir-Heywood, AN BE Negoita, MG Howlett, RJ Jain, LC TI Analyzing the temporal sequences for text categorization SO KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems CY SEP, 2004 CL Wellington, NEW ZEALAND SP Royal Soc New Zealand, IPENZ, New Zealand Trade & Enterprise, Telecom, Allied Telesyn, Positively Wellington Business HO Wellington Inst Technol AB This paper describes a text categorization approach that is based on a combination of a newly designed text representation with a kNN classifier. The new text document representation explored here is based an unsupervised learning mechanism - a hierarchical structure of Self-Organizing Feature Maps. Through this architecture, a document can be encoded to a sequence of neurons and the corresponding distances to the neurons, while the temporal sequences of words as well as their frequencies are kept. Combining this representation with the power of kNN classifier achieved a good performance (Micro average F1-measure 0.855) on the experimental data set. It shows that this architecture can capture the characteristic temporal sequences of documents/categories which can be used for various text categorization and clustering tasks. C1 Dalhousie Univ, Fac Comp Sci, Halifax, NS B3H 1W5, Canada. RP Luo, X (reprint author), Dalhousie Univ, Fac Comp Sci, 6050 Univ Ave, Halifax, NS B3H 1W5, Canada. EM luo@cs.dal.ca; zincir@cs.dal.ca CR Basili R., 2000, P RIAO 00 6 INT C RE, P331 Brill E., 1992, P 3 C APPL NAT LANG, P152, DOI 10.3115/974499.974526 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Jacobs P. S., 1992, P 3 C APPL NAT LANG, P178, DOI 10.3115/974499.974532 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 10 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-23205-2 J9 LECT NOTES ARTIF INT PY 2004 VL 3215 BP 498 EP 505 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BBB77 UT WOS:000224585500067 ER PT B AU Bouguila, N Ziou, D AF Bouguila, N Ziou, D BE Barros, A Principe, J Larsen, J Adali, T Douglas, S TI Improving content based image retrieval systems using finite multinomial dirichlet mixture SO MACHINE LEARNING FOR SIGNAL PROCESSING XIV LA English DT Proceedings Paper CT 14th IEEE International Workshop on Machine Learning for Signal Processing CY SEP 29-OCT 01, 2004 CL Sao Luis, BRAZIL SP IEEE Signal Proc Soc, TC Machine Learning Signal Proc ID MAXIMUM-LIKELIHOOD; MODEL; EM AB The performance of a statistical signal processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based the Multinomial and the Dirichlet distributions. An unsupervised algorithm for learning this mixture is given, too. The proposed approach for estimating the parameters of the Multinomial Dirichlet mixture is based on the Maximum Likelihood (ML) and Newton-Raphson methods. Experimental results involve improving content based image retrieval systems by integrating semantic features and by image database categorization. C1 Univ Sherbrooke, Fac Sci, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada. RP Bouguila, N (reprint author), Univ Sherbrooke, Fac Sci, Dept Informat, Sherbrooke, PQ J1K 2R1, Canada. CR BANFIELD JD, 1993, BIOMETRICS, V49, P803, DOI 10.2307/2532201 Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214 BOUGUILA N, 2003, P ICANN ICONIP IST S, P150 BOUGUILA N, 2004, PRIS2004, P118 BOUGUILA N, 2004, IN PRESS IEEE T IM P BOUGUILA N, 2004, UNPUB MACHINE LEARNI BOUGUILA N, 2003, LNS2734, P172 Cadez IV, 2002, MACH LEARN, V47, P7, DOI 10.1023/A:1013679611503 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 Kherfi ML, 2003, J VIS COMMUN IMAGE R, V14, P428, DOI 10.1016/S1047-3203(03)00043-9 McLachlan G. J., 1997, EM ALGORITHM EXTENSI McLachlan GJ, 2000, FINITE MIXTURE MODEL Medasani S, 2001, COMPUT VIS IMAGE UND, V83, P216, DOI 10.1006/cviu.2001.0926 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 15 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8608-6 PY 2004 BP 23 EP 32 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BBK54 UT WOS:000225881500003 ER PT S AU Moskovitch, R Hessing, A Shahar, Y AF Moskovitch, R Hessing, A Shahar, Y BE Fieschi, M Coiera, E Li, YCJ TI Vaidurya - A concept-based, context-sensitive search engine for clinical guidelines SO MEDINFO 2004: PROCEEDINGS OF THE 11TH WORLD CONGRESS ON MEDICAL INFORMATICS, PT 1 AND 2 SE STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS LA English DT Proceedings Paper CT 11th World Congress on Medical Informatics CY SEP 07-11, 2004 CL San Francisco, CA SP Int Med Informat Assoc HO Amer Med Informat Assoc DE information retrieval; clinical practice guidelines; context-sensitive search AB A major problem in the effective use of clinical guidelines is fast and accurate access at the point of care. Thus, we are developing a digital electronic guideline library (DeGeL) and a set of tools for incremental conversion of free-text guidelines into increasingly machine-comprehensible representations, which support automated application. Even of guidelines are represented ' in electronic fashion, care providers need to be able to quickly retrieve the guidelines that best fit the clinical situation at hand. We describe Vaidurya, a search and retrieval engine that exploits the hybrid nature of guideline representation in the De,GeL architecture. Vaidurya can use not only free-text keywords, but also multiple semantic indices along which the guidelines are classified, and the mark up of guidelines in DeGeL, using the semantic roles of one or more guideline-representation languages. Preliminary evaluation of Vaidurya in a standard information task and a large guideline repository is encouraging; formal evaluation is under way. C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RP Moskovitch, R (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RI SHAHAR, YUVAL/F-1899-2012 CR Fox J, 1998, ARTIF INTELL MED, V14, P157, DOI 10.1016/S0933-3657(98)00021-9 GRIMSHAW JM, 1993, LANCET, V342, P1317, DOI 10.1016/0140-6736(93)92244-N Musen MA, 1996, J AM MED INFORM ASSN, V3, P367 PELEG M, 2000, P 2000 AMIA ANN S LO Purcell G. P., 1997, International Journal on Digital Libraries, V1, DOI 10.1007/s007990050023 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shahar Y, 1998, ARTIF INTELL MED, V14, P29, DOI 10.1016/S0933-3657(98)00015-3 SHAHAR Y, 2003, P 2003 AMIA ANN FALL Shahar Y., 2003, P 9 C ART INT MED EU TU SW, 1989, COMMUN ACM, V32, P1439, DOI 10.1145/76380.76384 NR 10 TC 0 Z9 0 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0926-9630 BN 1-58603-444-8 J9 ST HEAL T PY 2004 VL 107 BP 140 EP 144 PG 5 WC Computer Science, Information Systems; Medical Informatics SC Computer Science; Medical Informatics GA BBO53 UT WOS:000226723300029 PM 15360791 ER PT S AU Kherfi, ML Brahmi, D Ziou, D AF Kherfi, ML Brahmi, D Ziou, D BE Kittler, J Petrou, M Nixon, M TI Combining visual features with semantics for a more effective image retrieval SO PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 SE INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION LA English DT Proceedings Paper CT 17th International Conference on Pattern Recognition (ICPR) CY AUG 23-26, 2004 CL Cambridge, ENGLAND SP Int Assoc Pattern Recognit, Univ Surrey, UniS, IEEE Comp Soc, HP Res Labs Bristol HO British Machine Vis Assoc AB We present a new framework which tries to improve the effectiveness of CBIR by integrating semantic concepts extracted from text. Our model is inspired from the VSM model developed in information retrieval. We represent each image in our collection with a vector of probabilities linking it to the different keywords. In addition to the semantic content of images, these probabilities capture the user's preference in each step of relevance feedback. The obtained features are then combined with visual ones in retrieval phase. Evaluation carried out on more than 10,000 images shows that this considerably improves retrieval effectiveness. C1 Univ Sherbrooke, Fac Sci, CoRIMedia, Sherbrooke, PQ J1K 2R1, Canada. RP Kherfi, ML (reprint author), Univ Sherbrooke, Fac Sci, CoRIMedia, Sherbrooke, PQ J1K 2R1, Canada. CR Barnard K., 2001, ICCV BENITEZ A, 2002, ICME Fielitz B. D., 1975, DECISION SCI, V6, P1, DOI 10.1111/j.1540-5915.1975.tb00992.x GROSKY WI, 2001, INT C INT MULT CONT HU C, 2000, ACM MULTIMEDIA KHERFI ML, 2004, ACM COMPUTING SURVEY Kherfi ML, 2003, J VIS COMMUN IMAGE R, V14, P428, DOI 10.1016/S1047-3203(03)00043-9 LOWE S, 1999, EUROSPEECH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sjolander K, 1996, COMPUT APPL BIOSCI, V12, P327 Zhou XS, 2002, IEEE MULTIMEDIA, V9, P23 NR 11 TC 1 Z9 1 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 1051-4651 BN 0-7695-2128-2 J9 INT C PATT RECOG PY 2004 BP 961 EP 964 DI 10.1109/ICPR.2004.1334418 PG 4 WC Computer Science, Artificial Intelligence SC Computer Science GA BAW22 UT WOS:000223877400234 ER PT B AU Xu, JC Liu, DY Hu, M AF Xu, JC Liu, DY Hu, M GP ieee TI Feature selection and text classification for Chinese web documents SO PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 LA English DT Proceedings Paper CT International Conference on Machine Learning and Cybernetics CY AUG 26-29, 2004 CL Shanghai, PEOPLES R CHINA DE feature selection; data-mining; information retrieval; web-mining AB A great deal of methods for feature selection and text classification have been widely applied to English web documents, while few studies have been done on Chinese web documents. This paper gives a term weighting method based on inverse document frequency, html tags and length of Chinese phrase, reports our method to select web text feature based on the messy genetic algorithm, provides an algorithm tor web text classification based on improvement on lattice machine approach. Our experiments show that these methods are valuable. C1 Changchun Univ Technol, Sch Comp Sci & Engn, Seoul 130012, South Korea. RP Xu, JC (reprint author), Changchun Univ Technol, Sch Comp Sci & Engn, Seoul 130012, South Korea. CR APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Cohen W, 1996, P 19 ANN INT ACM SIG, P307, DOI 10.1145/243199.243278 HUI W, 1998, P KDD98, P349 HUI W, 1999, P IJCAI99, P254 HUI W, 1998, TEXT CLASSIFICATION Lin S.-H., 1998, P 21 ANN INT ACM SIG, P241, DOI 10.1145/290941.291001 LIU MJ, 2002, FEATURE ACQUIRING AL, V23, P683 MLADENIC D, FEATURE SUBSET SELEC *NAT ARCH OFF CHIN, 1987, METH CLASS CHIN ARCH SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WULFEKUHLER MR, 1997, P 6 INT WORLD WID WE Yang Y., 1997, 14 INT C MACH LEARN, P412 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 Yang Y., 1999, 22 ANN INT ACM SIGIR, P42 NR 14 TC 0 Z9 1 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8403-2 PY 2004 BP 1304 EP 1309 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BBF92 UT WOS:000225293600259 ER PT B AU Luo, X Zincir-Heywood, AN AF Luo, X Zincir-Heywood, AN GP ieee TI Combining word based and word co-occurrence based sequence analysis for text categorization SO PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 LA English DT Proceedings Paper CT International Conference on Machine Learning and Cybernetics CY AUG 26-29, 2004 CL Shanghai, PEOPLES R CHINA DE text categorization; sequence analysis; self organization map AB This paper represents a text categorization system, which is based on the combination of a hierarchical SOMs encoding architecture and the designed kNN classifier. Through the encoding architecture, a document can be encoded to sequences of neurons so that the sequences of word/word co-occurrence as well as their frequencies are kept. A good performance (Micro average F1-measure 0.98) is achieved on the experimental data set by using this system. This sequence analysis system for text categorization could automatically solve the high dimensionality problem for large data set. And it could be utilized for other data categorization where sequences information is significant and important. C1 Dalhousie Univ, Fac Comp Sci, Halifax, NS, Canada. RP Luo, X (reprint author), Dalhousie Univ, Fac Comp Sci, Halifax, NS, Canada. CR Basili R., 2000, P RIAO 00 6 INT C RE, P331 Bekkerman R., 2002, J MACHINE LEARNING R, V1, P1 Brill E., 1992, P 3 C APPL NAT LANG, P152, DOI 10.3115/974499.974526 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Haykin S., 1999, NEURAL NETWORKS COMP Jacobs P. S., 1992, P 3 C APPL NAT LANG, P178, DOI 10.3115/974499.974532 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8403-2 PY 2004 BP 1580 EP 1585 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BBF92 UT WOS:000225293600311 ER PT B AU Wang, ZQ Feng, BQ AF Wang, ZQ Feng, BQ GP ieee TI Information query immune algorithm based on vector space model SO PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 LA English DT Proceedings Paper CT International Conference on Machine Learning and Cybernetics CY AUG 26-29, 2004 CL Shanghai, PEOPLES R CHINA DE immume algorithm; information retrieval; query optimization; vector space model AB To efficiently satisfy the user query requirements in information retrieval, a novel immune query optimization algorithm for information retrieval is proposed. The core of the immune algorithm lies on constructing the immune operator that is realized by vaccination and immune selection. The strategies and the methods of selecting and constructing a vaccine for the problem are given in the paper. Immune algorithm for query optimization introduces the immune operators to genetic algorithms for query optimization. This algorithm properly deals with the degeneration in conventional genetic algorithms, therefore increases the convergence speed. Experimental results show that the novel algorithm has higher precision and faster computation speed. C1 Xian Jiaotong Univ, Dept Comp Sci, Xian 710049, Peoples R China. RP Wang, ZQ (reprint author), Xian Jiaotong Univ, Dept Comp Sci, Xian 710049, Peoples R China. CR AGUILERA AI, 2000, P 9 IEEE INT C FUZZ, P1009 Boughanem M., 1999, Information Retrieval, V1, DOI 10.1023/A:1009931404333 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S HARMAN DK, 1992, P 1 TEXT RETR C TREC, P32 HOLLAND JH, 1975, ADAPTATION NATURAL A Jiao LC, 2000, IEEE T SYST MAN CY A, V30, P552 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ZHANG JS, 1997, SCI CHINA SER E, V27, P154 NR 8 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8403-2 PY 2004 BP 2515 EP 2519 PG 5 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Information Systems SC Computer Science GA BBF92 UT WOS:000225293600498 ER PT B AU Yamada, T Aihara, K Takasu, A Adachi, J AF Yamada, T Aihara, K Takasu, A Adachi, J BE Hamza, MH TI Replica placement for effective document sharing mechanisms in peer-to-peer networks SO PROCEEDINGS OF THE EIGHTH IASTED INTERNATIONAL CONFERENCE ON INTERNET AND MULTIMEDIA SYSTEMS AND APPLICATIONS LA English DT Proceedings Paper CT 8th IASTED International Conference on Internet and Multimedia Systems and Applications CY AUG 16-18, 2004 CL Kauai, HI SP Int Assoc Sci & Technol Dev, Tech Comm Web, Internet & Multimedia DE peer-to-peer; query processing; replication; load-balancing AB Recently, peer-to-peer (P2P) systems have become popular for both data exchange and cooperative working environments. However, an effective document sharing mechanism is required to work cooperatively on P2P systems. In this paper, we propose replication methods to perform effective document sharing on a P2P system where peers frequently join and leave the system. The proposed method uses the relevancy and usefulness of peers to determine how many replications should be made, and where to locate these replications. In addition, we propose an update procedure for replicated documents. This paper shows empirically that the proposed method improves the efficiency of query processing and load-balancing. C1 Grad Univ Adv Studies, Mitaka, Tokyo, Japan. RP Yamada, T (reprint author), Grad Univ Adv Studies, Mitaka, Tokyo, Japan. EM t_yamada@orad.nii.ac.jp; kenro.aihara@nii.ac.jp; takasu@nii.ac.jp; adachi@nii.ac.jp CR AVERER K, 2001, COOPLS 2001, P179 Chen Y., 2002, IPTPS 01 REV PAP 1 I, P306 Crespo A, 2002, INT CON DISTR COMP S, P23 Datta A., 2003, IEEE ICDCS 2003, P76 Kubiatowicz J., 2000, ASPLOS 9, P190 Ratner Steven R., 2001, ACM SIGCOMM, P161 Rowstrons AIT, 2001, IFIP ACM INT C DISTR, P329 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Stoica Ion, 2001, ACM SIGCOMM, P149 SUN Q, 2003, IEEE ICDCS 2003, P58 Yamada Taizo, 2003, PACRIM 03, P139 Yang B., 2001, VLDB J SEP, P561 Yang B, 2002, INT CON DISTR COMP S, P5 Zhao BY, 2000, UCBCSD011141 NR 14 TC 0 Z9 0 PU ACTA PRESS PI CALGARY PA B6, STE 101, 2509 DIEPPE AVE SW, CALGARY, ALBERTA T3E 7J9, CANADA BN 0-88986-420-9 PY 2004 BP 144 EP 149 PG 6 WC Computer Science, Information Systems; Imaging Science & Photographic Technology; Telecommunications SC Computer Science; Imaging Science & Photographic Technology; Telecommunications GA BCB02 UT WOS:000228483400026 ER PT B AU Zhao, YP Zhu, DH AF Zhao, YP Zhu, DH BE Chen, J TI Intelligent scientific information acquisition and dynamic monitoring SO SERVICE SYSTEMS AND SERVICE MANAGEMENT - PROCEEDINGS OF ICSSSM '04, VOLS 1 AND 2 LA English DT Proceedings Paper CT International Conference on Service Systems and Service Management CY JUL 19-21, 2004 CL Beijing, PEOPLES R CHINA SP IEEE Syst, Man & Cybernet Soc, Tsinghua Univ, Sch Econ & Management DE intelligent information collection; web content analysis; web linkage analysis; technology management; crawler AB As Internet developing fast, it draws bigger attention for every government, industrial or military on making use of it to provide quick, efficient and effective material or assessment for decision making and critical technical area selection. This paper presents intelligent and efficient methods for choosing and evaluating document importance. A novel and dynamic selection method for both Chinese and English languages information collection is proposed. Based on these methods, a framework suitable to Internet WWW scientific information collection and dynamic monitoring has been established. Based on this framework, a prototype called BIT, with agent architecture, is realized. In initial running, it works intelligent, efficient and dynamic. The system even works well in paper ranking with restrict amount of relevant information, and suitable to assist professional analysts or analyses systems to provide ranked topic-specific papers in technical direction series, which has potential value and many application prospects. C1 Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China. CR AGGARWAL CC, 2001, ACM T INFORMATION SY, V19 Chakrabarti S., 1999, COMPUTER NETWORKS, V31 CHO J, 1998, EFFICIENT CRAWLING T GURGES CJC, 1998, DATA MINING KNOWLEDG, V2 Haveliwala T.H., 1999, EFFICIENT COMPUTATIO KAGEURA K, 1996, METHODS Kamber M., 2001, DATA MINING CONCEPTS KLEINBERG J, 1998, P ACM SIAM S DISCR A MENCZER F, 2001, SIGIR 01 Ng A. Y., 2001, SIGIR Forum Page L, 1998, PAGERANK CITATION RA PORTER AL, 2000, VANTAGEPOINT Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIZOV S, 2000, BINGO FOCUSED CRAWLE ZHANG HP, MODEL CHINESE WORDS ZHAO YP, 2003, RES SCI SCI, V21, P230 Zhu DH, 2002, TECHNOL FORECAST SOC, V69, P495, DOI 10.1016/S0040-1625(01)00157-3 ZHU DH, 2002, RES HIGH TECHNOLOGY NR 19 TC 0 Z9 0 PU INTERNATIONAL ACADEMIC PUBLISHERS LTD PI HONG KONG PA UNIT 1205, 12 FLOOR, SINO PLAZA, 255 GLOUCESTER ROAD, HONG KONG 00000, CAUSEWAY BAY, PEOPLES R CHINA BN 7-5062-6821-3 PY 2004 BP 513 EP 518 PG 6 WC Computer Science, Artificial Intelligence; Management; Operations Research & Management Science SC Computer Science; Business & Economics; Operations Research & Management Science GA BAV84 UT WOS:000223847800103 ER PT S AU Losada, DE Barreiro, A AF Losada, DE Barreiro, A BE Apostolico, A Melucci, M TI Negations and document length in logical retrieval SO STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 11th International Conference on String Processing and Information Retrieval (SPIRE) CY OCT 05-08, 2004 CL Padova, ITALY SP Natl Res Council, Inst High Performance Comp & Networking, Italian Assoc Informat & Automat Computat, Elsevier HO Univ Padova, Dept Informat Engn ID INFORMATION-RETRIEVAL; MODEL AB Terms which are not explicitly mentioned in the text of a document receive often a minor role in current retrieval systems. In this work we connect the management of such terms with the ability of the retrieval model to handle partial representations. A simple logical indexing process capable of expressing negated terms and omitting some other terms in the representation of a document was designed. Partial representations of documents can be built taking into account document length and global term distribution. A propositional model of information retrieval is used to exemplify the advantages from such expressive modeling. A number of experiments applying these partial representations are reported. The benefits of the expressive framework became apparent in the evaluation. C1 Univ Santiago de Compostela, Dept Elect & Comp Sci, Intelligent Syst Grp, Santiago De Compostela, Spain. Univ A Coruna, AILab, Dept Comp Sci, La Coruna, Spain. RP Losada, DE (reprint author), Univ Santiago de Compostela, Dept Elect & Comp Sci, Intelligent Syst Grp, Santiago De Compostela, Spain. EM dlosada@dec.usc.es; barreiro@udc.es RI Losada, David/A-3202-2009 CR CRESTANI F, 1998, INFORMATION RETIREVA CROFT WB, 2003, LANGUAGE MODELING IN Dalal M, 1988, P AAAI 88, P475 LOSADA D, 2003, LNCS, V2663, P219 Losada D. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312654 Losada DE, 2001, COMPUT J, V44, P410, DOI 10.1093/comjnl/44.5.410 Losada DE, 2003, J AM SOC INF SCI TEC, V54, P285, DOI 10.1002/asi.10209 LOSADA DE, 2000, P SIGIR 2000 WORKSH, P16 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 Zaragoza H., 2003, P 26 ANN INT ACM SIG, P4 NR 16 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-23210-9 J9 LECT NOTES COMPUT SC PY 2004 VL 3246 BP 196 EP 207 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BBA15 UT WOS:000224377200027 ER PT S AU Oikonomakou, N Vazirgiannis, M AF Oikonomakou, N Vazirgiannis, M BE Sirmakessis, S TI A review of Web document clustering approaches SO TEXT MINING AND ITS APPLICATIONS SE STUDIES IN FUZZINESS AND SOFT COMPUTING LA English DT Proceedings Paper CT 1st International Workshop on Text Mining and its Application CY 2003 CL Patras, GREECE HO Univ Patras ID EFFICIENT ALGORITHM; RETRIEVAL; HYPERTEXT AB The impact of the World Wide Web as a main source of information extraction is increasing dramatically. Though, the web search environment is not ideal. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a difficult process for the average user. It is a valid requirement then the development of techniques that can help the users effectively organize and browse the available information, with the ultimate goal of satisfying their information need. Cluster analysis, which deals with the organization of a collection of objects into cohesive groups, can play a very important role towards the achievement of this objective. In this paper, we present an exhaustive survey of web document clustering approaches available on the literature, classified into three main categories: text-based, link-based and hybrid. Furthermore, we present a thorough comparison of the algorithms based on the various facets of their features and functionality. Finally, from the review of the different approaches we conclude that although clustering has been a topic for the scientific community for three decades, there are still many open issues that call for more research. C1 Athens Univ Econ & Business, Dept Informat, Athens 10434, Greece. RP Oikonomakou, N (reprint author), Athens Univ Econ & Business, Dept Informat, Patis 76, Athens 10434, Greece. CR BEZDEK JC, 1984, FCM FUZZY C MEANS AL Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X BOTAFOGO A, 1991, P 3 ACM C HYP, P63 Botafogo R.A., 1993, P 16 ANN INT ACM SIG, P116, DOI 10.1145/160688.160704 Cheeseman P., 1996, ADV KNOWLEDGE DISCOV, P153 CROFT WB, 1993, INFORM PROCESS MANAG, V29, P313, DOI 10.1016/0306-4573(93)90058-L Cutting D.R., 1992, P 15 ANN INT ACM SIG, P318, DOI DOI 10.1145/133160.133214 DEFAYS D, 1977, COMPUT J, V20, P364, DOI 10.1093/comjnl/20.4.364 DHILLON IS, 200105 UT CS TR El-Hamdouchi A, 1986, P ACM C RES DEV INF, P149, DOI 10.1145/253168.253200 ELHAMDOUCHI A, 1989, COMPUTER J, V32 Everitt BS, 1981, FINITE MIXTURE DISTR FREI HP, 1995, INFORM PROCESS MANAG, V31, P1, DOI 10.1016/0306-4573(94)E0005-M HAN EH, 1997, TR97049 U MINN DEP C Jain A.K., 1999, ACM COMPUTING SURVEY, V31 Karypis G., 1999, SIAM J SCI COMPUTING, V20 Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KLEINBERG J, 1997, P 9 ACM SIAM S DISCR Kohonen T., 1995, SELF ORG MAPS Kumar S. R., 1999, P 8 WWW C Larson R. R., 1996, P 1996 AM SOC INF SC LOONEY C, CSUNR1011999 MERKL D, 1998, HDB NATURAL LANGUAGE MODHA D, 2000, P ACM C HYP HYP MURTAGH F, 1983, COMPUT J, V26, P354 Page L, 1998, PAGERANK CITATION RA Pirolli P., 1996, P ACM SIGCHI C HUM F RASMUSSEN E, 1992, INFORMATION RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 Steinbach M., 2000, KDD WORKSH TEXT MIN STENBACH M, 2002, KDD WORKSH TEXT MIN STHEHL A, 2000, P 17 NAT C ART INT W, P30 VANRIJBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X WEISS R, 1996, P 7 ACM C HYP WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 ZHAO Y, 2001, EVALUATION HIERARCHI ZHAO Y, 2001, 0140 U MINN COMP SCI NR 40 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1434-9922 BN 3-540-20238-2 J9 STUD FUZZ SOFT COMP PY 2004 VL 138 BP 65 EP 79 PG 15 WC Computer Science, Artificial Intelligence SC Computer Science GA BY64X UT WOS:000189429800006 ER PT B AU Tang, CQ Dwarkadas, S AF Tang, CQ Dwarkadas, S GP USENIX ASSOCIATION TI Hybrid global-local indexing for efficient peer-to-peer information retrieval SO USENIX ASSOCIATION PROCEEDINGS OF THE FIRST SYMPOSIUM ON NETWORKED SYSTEMS DESIGN AND IMPLEMENTATION (NSDI'04) LA English DT Proceedings Paper CT 1st Symposium on Networked Systems Design and Implementation CY MAR 29-31, 2004 CL San Francisco, CA SP USENIX Assoc, ACM SIGOPS AB Content-based full-text search still remains a particularly challenging problem in peer-to-peer (P2P) systems. Traditionally, there have been two index partitioning structures-partitioning based on the document space or partitioning based on keywords. The former requires search of every node in the system to answer a query whereas the latter transmits a large amount of data when processing multi-term queries. In this paper, we propose eSearch-a P2P keyword search system based on a novel hybrid indexing structure. In eSearch, each node is responsible for certain terms. Given a document, eSearch uses a modem information retrieval algorithm to select a small number of top (important) terms in the document and publishes the complete term list for the document to nodes responsible for those top terms. This selective replication of term lists allows a multi-term query to proceed local to the nodes responsible for query terms. We also propose automatic query expansion to alleviate the degradation of quality of search results due to the selective replication, overlay source multicast to reduce the cost of disseminating term lists, and techniques to balance term list distribution across nodes. eSearch is scalable and efficient, and obtains search results as good as state-of-the-art centralized systems. Despite the use of replication, eSearch actually consumes less bandwidth than systems based on keyword partitioning when publishing metadata for a document. During a retrieval operation, it searches only a small number of nodes and typically transmits a small amount of data (3.3KB) that is independent of the size of the corpus and grows slowly (logarithmically) with the number of nodes in the system. eSearch's efficiency comes at a modest storage cost, 6.8 times that of systems based on keyword partitioning. This cost can be further reduced by adopting index compression or pruning techniques. C1 Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA. RP Tang, CQ (reprint author), Univ Rochester, Dept Comp Sci, Rochester, NY 14627 USA. CR BAWA M, 2003, SIGIR 03 Blake C., 2003, HOTOS 03 BOLOSKY WJ, 2000, SIGMETRICS 00 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Buckley C., 1985, TR85686 CORN U DEP C Calvert Ken, 1997, IEEE COMMUNICATIONS CARMEL D, 2001, SIGIR 01 CHAWATHE Y, 2003, SIGCOMM 03 CHU YH, 2000, SIGMETRICS 00 COHEN E, 2003, INFOCOM 03 Cohen E., 2002, SIGCOMM 02 CRESPO A, 2002, ICDCS 02 CUENCAACUNA FM, 2003, 12 IEEE INT S HIGH P DABEK F, 2001, SOSP 01 Gnawali O. D., 2002, THESIS MIT GRAVANO L, 1999, ACM T DATABASE SYSTE, V24 Gummadi K., 2003, SIGCOMM 03 LI J, 2003, IPTPS 03 LONG X, 2003, VLDB 03 LV Q, 2002, ICS 02 MITRA M, 1998, SIGIR 98 *NAT I STAND TECHN, 1995, 1801 FIPS NAT I STAT NG TSE, 2002, INFOCOM 02 Reynolds P., 2003, MIDDLEWARE 03 RHEA S, 2002, INFOCOM 02 Risvik KM, 2002, COMPUT NETW, V39, P289, DOI 10.1016/S1389-1286(02)00213-X ROBERTSON S, 1994, TREC 3 ROWSTRON A, 2001, SOSP 01 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANKARALINGAM K, 2003, 12 IEEE INT S HIGH P SCHWARTZ M, 1990, CUCS47490 TR U COL Singhal A., 2001, IEEE DATA ENG B, V24, P35 SRIPANIDKULCHAI K, 2001, ACM SIGCOMM COMPUTER, V32 Stoica I, 2001, SIGCOMM 01 SUEL T, 2003, WEBDB 03 TANG C, 2003, SIGCOMM 03 Tomasic A., 1993, VLDB J, V2, P243, DOI 10.1007/BF01228671 Witten I. H., 1999, MANAGING GIGABYTES C NR 38 TC 0 Z9 0 PU USENIX ASSOC PI BERKELEY PA SUITE 215, 2560 NINTH ST, BERKELEY, CA 94710 USA BN 1-931971-19-6 PY 2004 BP 211 EP 224 PG 14 WC Computer Science, Hardware & Architecture; Computer Science, Theory & Methods SC Computer Science GA BAD43 UT WOS:000221664800016 ER PT J AU Whitney, P Cox, D Daly, D Foote, H McQuerry, D Sloughter, JM AF Whitney, P Cox, D Daly, D Foote, H McQuerry, D Sloughter, JM TI Toward the routine analysis of diverse data types SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Article; Proceedings Paper CT Workshop on Statistical Analysis of Massive Data Streams CY DEC 13-14, 2002 CL WASHINGTON, D.C. DE data analysis; multimedia; scaling ID MODEL AB This article describes a variety of data analysis problems. The types of data across these problems included free text, parallel text, an image collection, remote sensing imagery, and network packets. A strategy for approaching the analysis of these diverse types of data is described. A key part of the challenge is mapping the analytic results back into the original domain and data setting. Additionally, a common computational bottleneck encountered in each of these problems is diagnosed as analysis tools and algorithms with unbounded memory characteristics. This experience and the analysis suggest a research and development path that could greatly extend the scale of problems that can be addressed with routine data analysis tools. In particular, there are opportunities associated with developing theory and functioning algorithms with favorable memory-usage characteristics, and there are opportunities associated with developing methods and theory for describing the outcomes of analyses for the various types of data. C1 Pacific NW Natl Lab, Richland, WA 99352 USA. Univ Washington, Dept Stat, Seattle, WA 98195 USA. Rice Univ, Dept Stat, Houston, TX 77005 USA. RP Whitney, P (reprint author), Pacific NW Natl Lab, 920 Battelle Blvd, Richland, WA 99352 USA. EM paul.whitney@pnl.gov; dcox@stat.rice.edu; ds.daly@pnl.gov; harlan.foote@pnl.gov; mcq@pnl.gov; mclean@stat.washington.edu CR Babcock B., 2002, P 21 ACM S PRINC DAT Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Bishop CM, 1998, IEEE T PATTERN ANAL, V20, P281, DOI 10.1109/34.667885 DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 Guha S., 2000, P ANN S FDN COMP SCI Hall Eric A., 2000, INTERNET CORE PROTOC PENNOCK KA, 2002, Patent No. 6484168 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Seber G. F, 1984, MULTIVARIATE OBSERVA Tierney L., 1990, LISP STAT OBJECT ORI *UN, 1994, UN PAR TEXT CORP ENG Wegman E.J., 1995, J COMPUTATIONAL GRAP, V4, P281, DOI 10.2307/1390857 WHITNEY PD, 2002, Patent No. 20020159642 NR 13 TC 3 Z9 3 PU AMER STATISTICAL ASSOC PI ALEXANDRIA PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA SN 1061-8600 J9 J COMPUT GRAPH STAT JI J. Comput. Graph. Stat. PD DEC PY 2003 VL 12 IS 4 BP 915 EP 926 DI 10.1198/1061860032535 PG 12 WC Statistics & Probability SC Mathematics GA 761LX UT WOS:000187911200009 ER PT J AU Leacock, C Chodorow, M AF Leacock, C Chodorow, M TI C-rater: Automated scoring of short-answer questions SO COMPUTERS AND THE HUMANITIES LA English DT Article DE automated scoring; content-based scoring; short answer scoring AB C-rater is an automated scoring engine that has been developed to score responses to content-based short answer questions. It is not simply a string matching program-instead it uses predicate argument structure, pronominal reference, morphological analysis and synonyms to assign full or partial credit to a short answer question. C-rater has been used in two studies: National Assessment for Educational Progress (NAEP) and a statewide assessment in Indiana. In both studies, c-rater agreed with human graders about 84% of the time. C1 Educ Testing Serv, Princeton, NJ 08541 USA. CUNY Hunter Coll, New York, NY 10021 USA. RP Leacock, C (reprint author), Educ Testing Serv, Rosedale Rd 18-E, Princeton, NJ 08541 USA. CR Abney S., 1996, P ESSLLI 96 ROB PARS Burstein J., 2003, AUTOMATED ESSAY SCOR BURSTEIN JC, 1999, DEPTH BREADTH SEMANT Cormen T.H., 2001, INTRO ALGORITHMS Elliott S., 2003, AUTOMATED ESSAY SCOR Fellbaum C, 1998, WORDNET ELECT LEXICA Fleiss JL, 1981, STAT METHODS RATES P, V2nd, P212 KAPLAN RM, 1994, 0408 ETS KUD JM, 1994, P ED TEST SERV C NAT Landauer T., 2003, AUTOMATED ESSAY SCOR Landauer TK, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P412 LIN D, 1998, P 35 ANN M ASS COMP, P898 Morton TS, 2000, P 38 ANN M ASS COMP SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDENE B, IN PRESS MATH ONLINE VIGILANTE R, 1999, J INFORMATION TECHNO, V1, P57 NR 16 TC 33 Z9 34 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0010-4817 J9 COMPUT HUMANITIES JI Comput. Humanit. PD NOV PY 2003 VL 37 IS 4 BP 389 EP 405 DI 10.1023/A:1025779619903 PG 17 WC Computer Science, Interdisciplinary Applications SC Computer Science GA 721DV UT WOS:000185302500002 ER PT J AU Olivas, JA Garces, PJ Romero, FP AF Olivas, JA Garces, PJ Romero, FP TI An application of the FIS-CRM model to the FISS metasearcher: Using fuzzy synonymy and fuzzy generality for representing concepts in documents SO INTERNATIONAL JOURNAL OF APPROXIMATE REASONING LA English DT Article; Proceedings Paper CT 7th International ISKO Conference (ISKO 02) CY FEB 07, 2002 CL GRANADA, SPAIN SP ISKO DE Internet search; fuzzy synonymy; fuzzy generality; concept representation model; metasearcher; soft-clustering algorithm ID INFORMATION-RETRIEVAL; WEB AB The main objective of this work is to improve the quality of the results produced by the Internet search engines. In order to achieve it, the FIS-CRM model (Fuzzy Interrelations and Synonymy based Concept Representation Model) is proposed as a mechanism for representing the concepts (not only terms) contained in any kind of document. This model, based on the vector space model, incorporates a fuzzy readjustment process of the term weights of each document. The readjustment lies on the study of two types of fuzzy interrelations between terms: the fuzzy synonymy interrelation and the fuzzy generality interrelations ("broader than" and "narrower than" interrelations). The model has been implemented in the FISS metasearcher (Fuzzy Interrelations and Synonymy based Searcher) that, using a soft-clustering algorithm (based on the SISC algorithm), dynamically produces a hierarchical structure of groups of ''conceptually related" documents (snippets of web pages, in this case). (C) 2003 Elsevier Inc. All rights reserved. C1 Univ Castilla La Mancha, Dept Comp Sci, E-13071 Ciudad Real, Spain. Univ Castilla La Mancha, Ctr Mixto Invest & Desarollo Soluz, Ciudad Real 13003, Spain. RP Olivas, JA (reprint author), Univ Castilla La Mancha, Dept Comp Sci, Paseo Univ 4, E-13071 Ciudad Real, Spain. EM joseangel.olivas@ucim.es; fpromero@soluziona.com CR BORDOGNA G, 1995, INT J INTELL SYST, V10, P233, DOI 10.1002/int.4550100205 Bordogna G, 2000, KYBERNETIKA, V36, P617 BUELL DA, 1981, INFORM PROCESS MANAG, V17, P127, DOI 10.1016/S0306-4573(81)80004-0 CAO TH, 2001, WORKSH FUZZ LOG INT, P74 CHOI D, 2001, P BISC INT WORKSH FU, P68 CRESTANI F, 2000, SERIES STUDIES FUZZI Fensel D., 2001, IEEE INTELLIGENT SYS, V16 FERNANDEZ S, 2001, THESIS U SANTIAGO CO GONZALO J, 1998, P COLING ACL WORK US Herrera-Viedma E, 2001, INT J UNCERTAIN FUZZ, V9, P77, DOI 10.1142/S0218488501001009 Herrera-Viedma E, 2003, INFORM PROCESS MANAG, V39, P233, DOI 10.1016/S0306-4573(02)00049-3 KINGIP L, 2001, P 7 INT C DAT SYS AD KIRYAKOV A, 1999, P NODALIDA 99 NORD C Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 KRAFT H, 1997, INFORMATION RETRIEVA LAFOURCADE M, 2001, P TALN 2001, P233 Lopez-Pujalte C, 2002, INFORM PROCESS MANAG, V38, P793, DOI 10.1016/S0306-4573(01)00061-9 MARTINBAUTISTA MJ, 2001, P BISC INT WORKSH FU, P19 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MIYAMOTO S, 1990, FUZZY SET SYST, V38, P191, DOI 10.1016/0165-0114(90)90149-Z MIYAMOTO S, 1990, FUZZY SETS INFORMATI NIKRAVESH M, 2002, P 2002 NAFIPS ANN M, P146 OHGAYA R, 2002, P 2002 NAFIPS ANN M, P274 OLIVAS JA, 2000, THESIS U CASTILLA LA Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 Perkowitz M., 1999, P 16 INT JOINT C ART, P264 Perkowitz M, 2000, ARTIF INTELL, V118, P245, DOI 10.1016/S0004-3702(99)00098-3 POINCOT P, 1998, LIBR INFORMATION SER, V153 RICARTE I, 2001, P BISC INT WORKSH FU, P80 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1992, COMPUT J, V35, P268, DOI 10.1093/comjnl/35.3.268 TAKAGI T, 2001, P BISC INT WORKSH FU, P53 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 VORHEES E, 1998, USING WORLD NET TEXT WHALEY J, 1999, PCSTR99352 DARTM COL Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 ZADEH L, 2001, P BISC INT WORKSH FU ZAMIR O, 1999, P WWW 8 NR 38 TC 17 Z9 17 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0888-613X J9 INT J APPROX REASON JI Int. J. Approx. Reasoning PD NOV PY 2003 VL 34 IS 2-3 BP 201 EP 219 DI 10.1016/j.ijar.2003.07.008 PG 19 WC Computer Science, Artificial Intelligence SC Computer Science GA 743LH UT WOS:000186572900008 ER PT J AU Chen, HC Fan, HY Chau, M Zeng, D AF Chen, HC Fan, HY Chau, M Zeng, D TI Testing a Cancer Meta Spider SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES LA English DT Article ID INFORMATION-RETRIEVAL; WEB; INTERNET; ALGORITHM; SEARCH; MAP AB As in many other applications, the rapid proliferation and unrestricted Web-based publishing of health-related content have made finding pertinent and useful healthcare information increasingly difficult. Although the development of healthcare information retrieval systems such as medical search engines and peer-reviewed medical Web directories has helped alleviate this information and cognitive overload problem, the effectiveness of these systems has been limited by low search precision, poor presentation of search results, and the required user search effort. To address these challenges, we have developed a domain-specific meta-search toot called Cancer Spider. By leveraging post-retrieval document clustering techniques, this system aids users in querying multiple medical data sources to gain an overview of the retrieved documents and locating answers of high quality to a wide spectrum of health questions. The system presents the retrieved documents to users in two different views: (1) Web pages organized by a list of key phrases, and (2) Web pages clustered into regions discussing different topics on a two-dimensional map (self-organizing map). In this paper, we present the major components of the Cancer Spider system and a user evaluation study designed to evaluate the effectiveness and efficiency of our approach. Initial results comparing Cancer Spider with NLM Gateway, a premium medical search site, have shown that they achieved comparable performances measured by precision, recall, and F-measure. Cancer Spider required less user searching time, fewer documents that need to be browsed, and less user effort. (C) 2003 Elsevier Ltd. All rights reserved. C1 Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. RP Chen, HC (reprint author), Univ Arizona, Dept Management Informat Syst, 1130 E Helen St,Room 430Z, Tucson, AZ 85721 USA. RI Chau, Michael/A-4427-2010; Zeng, Daniel/A-8072-2012; Zeng, Daniel/B-5888-2013 CR Bin L, 2001, INT J MED INFORM, V62, P155, DOI 10.1016/S1386-5056(01)00159-9 BOWMAN CM, 1994, COMMUN ACM, V37, P98, DOI 10.1145/179606.179704 Brill E, 1995, COMPUT LINGUIST, V21, P543 CHAU M, 2001, P 1 ACM IEEE CS JOIN, P79, DOI 10.1145/379437.379454 Chen HC, 2001, J AM SOC INF SCI TEC, V52, P1134, DOI 10.1002/asi.1180 Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X Chen SS, 1996, J VIS COMMUN IMAGE R, V7, P1, DOI 10.1006/jvci.1996.0001 Gauch S., 1996, J UNIVERS COMPUT SCI, V2, P637 HARMANEC P, 1991, B ASTRON I CZECH, V42, P1 Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 HERSH WR, 1996, INFORMATION RETRIEVA Howe AE, 1997, AI MAG, V18, P19 Hull DA, 1996, J AM SOC INFORM SCI, V47, P70 KEONEMANN J, 1996, P ACM SIGCHI C HUM F, P205 KILEY R, 1999, MED INFORMATION INTE Kohonen T., 1995, SELF ORG MAPS Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, IEEE INTERNET COMPUT, V2, P38, DOI 10.1109/4236.707689 LAWRENCE S, 1998, P 7 INT WORLD WID C Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 LOVINS JB, 1968, MECH TRANSL, V11, P22 ORWIG R, 1997, J AM SOC INFORM SCI, V48, P57 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SELBERG E, 1995, P 4 WORLD WID WEB C SELBERG E, 1997, IEEE EXPERT, V12 Shneiderman B, 1997, INT J HUM-COMPUT ST, V47, P5, DOI 10.1006/ijhc.1997.0127 Sutcliffe AG, 2000, INT J HUM-COMPUT ST, V53, P741, DOI 10.1006/ijhc.2000.0416 Tolle KM, 2000, J AM SOC INFORM SCI, V51, P352, DOI 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.3.CO;2-# VEERASAMY A, 1996, P 19 INT ACM SIGIR C, P85, DOI 10.1145/243199.243218 Voorhees E., 1998, NIST SPECIAL PUBLICA, P1 Westberg EE, 1999, J AM MED INFORM ASSN, V6, P6 ZAMIR O, 1999, P 8 WORLD WID WEB C NR 37 TC 9 Z9 9 PU ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD PI LONDON PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND SN 1071-5819 J9 INT J HUM-COMPUT ST JI Int. J. Hum.-Comput. Stud. PD NOV PY 2003 VL 59 IS 5 BP 755 EP 776 DI 10.1016/S1071-5819(03)00118-6 PG 22 WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary SC Computer Science; Engineering; Psychology GA 740DK UT WOS:000186386100009 ER PT J AU Zitouni, I Kuo, HKJ Lee, CH AF Zitouni, I Kuo, HKJ Lee, CH TI Boosting and combination of classifiers for natural language call routing systems SO SPEECH COMMUNICATION LA English DT Article DE call routing; relevance feedback; boosting; discriminative training; constrained minimization ID RECOGNITION; MODEL AB In this paper, we present different techniques to improve natural language call routing. We first describe methods to improve a single classifier: boosting, discriminative training (DT) and automatic relevance feedback (ARF). An interesting feature of some of these algorithms is the ability to re-weight the training data in order to focus the classifier on documents judged difficult to classify. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy; we discuss specifically the linear interpolation and the constrained minimization techniques. All these approaches are probabilistic and are inspired from the information retrieval domain. They are evaluated using two similarity metrics, a common cosine measure from the vector space model, and a beta measure which had given good results in the similar task of e-mail steering. Compared to the baseline classifiers, we show an interesting improvement in the classification accuracy on call routing for a banking task: up to 20% reported for the ARF method, up to 30% for the boosting technique, and more than 45% for the DT approach. Another relative improvement of 11% is also obtained when we combine the classifiers with the constrained minimization approach using a confusion measure and DT. More importantly, synergistic effects of DT on the boosting algorithm were demonstrated: more iterations were possible because DT reduced the classification error rate of individual classifiers trained on re-weighted data by an average of 72%. (C) 2003 Elsevier B.V. All rights reserved. C1 Bell Labs, Lucent Technol, Murray Hill, NJ 07974 USA. RP Zitouni, I (reprint author), Bell Labs, Lucent Technol, 600 Mt Ave, Murray Hill, NJ 07974 USA. EM zitouni@research.bell-labs.com; hkuo@us.ibm.com; chl@ece.gatech.edu CR Arai K, 1999, SPEECH COMMUN, V27, P43, DOI 10.1016/S0167-6393(98)00065-X Beesley KR, 1988, P 29 ANN C AM TRANSL, P47 BIGI B, 2000, SIGNAL PROCESS J, V6 BIGI B, 2001, STRING PROCESSING IN BIGI B, 2001, RECENT ADV NLP CARPENTER B, 1998, P ICSLP 98 SYDN AUST, P2059 CAVNAR W, 1994, S DOC AN INF RETR LA Chu-Carroll J, 1999, COMPUT LINGUIST, V25, P361 Drucker H., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, DOI 10.1142/S0218001493000352 DURSTON P, 2001, P EUR 01 AALB DENM Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 FREUND Y, 1995, INFORM COMPUT, V121, P256, DOI 10.1006/inco.1995.1136 GORIN A, 2000, P ATR WORKSH MULT SP Gorin AL, 1997, SPEECH COMMUN, V23, P113, DOI 10.1016/S0167-6393(97)00040-X Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop Jelinek F., 1990, READINGS SPEECH RECO, P450 Joachims T, 1998, EUR C MACH LEARN ECM, P137 JURAFSY D, 2000, COMPUTATIONAL LINGUI Katagiri S, 1998, P IEEE, V86, P2345, DOI 10.1109/5.726793 KEARNS M, 1994, J ACM, V41, P67, DOI 10.1145/174644.174647 Kearns M. J., 1994, INTRO COMPUTATIONAL KUHN R, 1990, IEEE T PATTERN ANAL, V12, P570, DOI 10.1109/34.56193 KUO HKJ, 2001, P EUR 01 AALB DENM KUO HKJ, 2002, P INT C SPEECH LANG Kuo HKJ, 2003, IEEE T SPEECH AUDI P, V11, P24, DOI 10.1109/TSA.2002.807352 KUO HKJ, 2000, P INT C SPOK LANG PR, P374 LEE CH, 1998, P INT VOIC TECHN TEL MATSUNAGA S, 1992, P INT C AC SPEECH SI, P165, DOI 10.1109/ICASSP.1992.225946 MCDONOUGH J, 1994, P INT C AC SPEECH SI, P385 Mills P, 2000, EUR PHYS J E, V1, P5, DOI 10.1007/s101890050002 NIYOGI P, 2000, P ICASSP Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCHERY M, 2002, P INT C AC SPEECH SI, P29 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1990, J AM SOC INFORM SCI, V4, P182 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAPIRE R, 1998, INT ACM SIGIR C RES SCHAPIRE R, 2002, MSRI WORKSH LIN ESTI SCHAPIRE RE, 1990, MACH LEARN, V5, P197, DOI 10.1023/A:1022648800760 Schapire RE, 1999, P 16 INT JOINT C ART Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schapire RE, 1998, ANN STAT, V26, P1651 SCHWARTZ R, 1997, P EUR C SPEECH COMM SIBUN P, 1996, S DOC AN INF RETR LA, P183 VALIANT LG, 1984, COMMUN ACM, V27, P1134, DOI 10.1145/1968.1972 WRIGHT JH, 1997, P 5 EUR C SPEECH COM, P1419 ZITOUNI I, 2002, P INT C AC SPEECH SI ZITOUNI I, 2001, P ASRU 2001 MAD CAMP NR 48 TC 9 Z9 9 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0167-6393 EI 1872-7182 J9 SPEECH COMMUN JI Speech Commun. PD NOV PY 2003 VL 41 IS 4 BP 647 EP 661 DI 10.1016/S0167-6393(03)00103-1 PG 15 WC Acoustics; Computer Science, Interdisciplinary Applications SC Acoustics; Computer Science GA 747KC UT WOS:000186802800007 ER PT J AU Shyu, ML Haruechaiyasak, C Chen, SC AF Shyu, ML Haruechaiyasak, C Chen, SC TI Category cluster discovery from distributed WWW directories SO INFORMATION SCIENCES LA English DT Article DE distributed information sources; information integration; cluster analysis; web mining; document classification ID WEB AB Due to the inherently distributed nature of many networks, including the Internet, information and knowledge are generated and organized independently by different groups of people. To discover and exploit all the knowledge from different sources, a method of knowledge integration is usually required. Considering the document category sets as information sources, we define a problem of information integration called category merging. The purpose of category merging is to automatically construct a unified category set which represents and exploits document information from several different sources. This merging process is based on the clustering concept where categories with similar characteristics are merged into the same cluster under certain distributed constraints. To evaluate the quality of the merged category set, we measure the precision and recall values under three classification methods, Naive Bayes, Vector Space Model, and K-Nearest Neighbor. In addition, we propose a performance measure called cluster entropy, which determines how well the categories from different sources are distributed over the resulting clusters. We perform the merging process by using the real data sets collected from three different Web directories. The results show that our merging process improves the classification performance over the non-merged approach and also provides a better representation for all categories from distributed directories. (C) 2003 Elsevier Inc. All rights reserved. C1 Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA. Florida Int Univ, Lab Sch Comp Sci, Distributed Multimedia Informat Syst, Miami, FL 33199 USA. RP Shyu, ML (reprint author), Univ Miami, Dept Elect & Comp Engn, POB 248294, Coral Gables, FL 33124 USA. CR BRAZDIL P, 1990, CURRENT TRENDS ARTIF, P412 Broder A., 1997, P 6 INT WORLD WID WE, P391 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Halkidi M, 2002, SIGMOD RECORD, V31, P40 Haruechaiyasak C., 2002, International Journal of Computational Intelligence and Applications, V2, DOI 10.1142/S1469026802000609 Haruechaiyasak C, 2002, P INT COMP SOFTW APP, P487, DOI 10.1109/CMPSAC.2002.1045052 KAUFMAN L, 1990, FINDING GROU0S DATA Lewis D., 1998, P 10 EUR C MACH LEAR, P4 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 PINTO HS, 1999, P IJCAI WORKSH ONT P Pitkow J., 1999, P 2 USENIX S INT TEC, P139 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P379 SHYU ML, 2001, 7 INT C DISTR MULT S, P494 SHYU ML, 2001, IEEE INT C SYST MAN, P1717 Wang J., 1999, ACM COMPUTER COMM RE, V29, P36, DOI DOI 10.1145/505696.505701 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 Yang Y., 2001, P 24 ANN INT ACM SIG, P137, DOI 10.1145/383952.383975 Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 24 TC 12 Z9 12 PU ELSEVIER SCIENCE INC PI NEW YORK PA 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA SN 0020-0255 J9 INFORM SCIENCES JI Inf. Sci. PD OCT 15 PY 2003 VL 155 IS 3-4 BP 181 EP 197 DI 10.1016/S0020-0255(03)00169-5 PG 17 WC Computer Science, Information Systems SC Computer Science GA 729BR UT WOS:000185751500002 ER PT J AU Kim, S Zhang, BT AF Kim, S Zhang, BT TI Genetic mining of HTML structures for effective Web-document retrieval SO APPLIED INTELLIGENCE LA English DT Article DE genetic algorithms; machine learning; web-documents; information retrieval ID WORLD-WIDE-WEB; ALGORITHMS AB Web-documents have a number of tags indicating the structure of texts. Text segments marked by HTML tags have specific meaning which can be utilized to improve the performance of document retrieval systems. In this paper, we present a machine learning approach to mine the structure of HTML documents for effective Web-document retrieval. A genetic algorithm is described that learns the importance factors of HTML tags which are used to re-rank the documents retrieved by standard weighting schemes. The proposed method has been evaluated on artificial text sets and a large-scale TREC document collection. Experimental evidence supports that the tag weights are well trained by the proposed algorithm in accordance with the importance factors for retrieval, and indicates that the proposed approach significantly improves the performance in retrieval accuracy. In particular, the use of the document-structure mining approach tends to move relevant documents to upper ranks, which is especially important in interactive Web-information retrieval environments. C1 Seoul Natl Univ, Biointelligence Lab, Sch Engn & Comp Sci, Seoul 151742, South Korea. RP Kim, S (reprint author), Seoul Natl Univ, Biointelligence Lab, Sch Engn & Comp Sci, Seoul 151742, South Korea. CR Back T., 1996, EVOLUTIONARY ALGORIT Baker JE, 1985, P 1 INT C GEN ALG TH, P101 BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Blickle T., 1995, P 6 INT C GEN ALG, P9 Boyan J., 1996, P AAAI WORKSH INT BA, P1 Brin S., 1998, P 7 INT WORLD WID WE, P107 BROGLIO J, 1995, P 3 TEXT TRIV C TREC, P29 Callan James, 1992, P 3 INT C DAT EXP SY, P78 Chakarabarti S., 2000, ACM SIGKDD EXPLORATI, V1, P1, DOI 10.1145/846183.846187 CHAKRABARTI S, 1998, ACM SIGIR 98 POST C Cutler M., 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence, DOI 10.1109/TAI.1999.809831 FRIEDER O, 1991, P 14 ANN INT ACM SIG, P230, DOI 10.1145/122860.122884 Goldberg D. E., 1989, GENETIC ALGORITHMS S Goldberg D.E., 1991, FDN GENETIC ALGORITH, V1, P69 Gordon M, 1999, INFORM PROCESS MANAG, V35, P141, DOI 10.1016/S0306-4573(98)00041-7 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 GORDON MD, 1991, J AM SOC INFORM SCI, V42, P311, DOI 10.1002/(SICI)1097-4571(199106)42:5<311::AID-ASI1>3.0.CO;2-J GREFENSTETTE JJ, 1989, PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P20 HOLLAND JH, 1975, ADAPTATION NATURAL A Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X *INT ARCH, BUILD INT LIB Kim S, 2000, P IEEE INT ASIC C&E, P13 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 Koza J. R., 1992, GENETIC PROGRAMMING Mauldin ML, 1997, IEEE EXPERT, V12, P8, DOI 10.1109/64.577466 MICHALEWICZ Z, 1992, GENETIC ALGORITHMS D, P104 Muhlenbein H, 1993, EVOL COMPUT, V1, P25, DOI 10.1162/evco.1993.1.1.25 *NIST, TEXT RETR C HOM PETRY F, 1993, P ASIS ANNU MEET, P122 Picard J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290989 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Robertson S. E., 1995, P 3 TEXT RETR C TREC, P109 SALTON G, 1989, AUTOMATIC TEXT PROCE, P279 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIN DH, 1998, P 7 TEXT RETR C TREC, P501 SPERTUS E, 1997, P WWW6, P1205 SYSWERDA G, 1989, PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P2 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 Voorhees E.M., 1999, P TREC, P1 WHITLEY D, 1989, PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P116 Yang J., 1993, P 2 ANN S DOC AN INF, P271 YANG J, 1993, P 1 TEXT RETR C TREC, P31 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 NR 44 TC 16 Z9 17 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-669X J9 APPL INTELL JI Appl. Intell. PD MAY-JUN PY 2003 VL 18 IS 3 BP 243 EP 256 DI 10.1023/A:1023293820057 PG 14 WC Computer Science, Artificial Intelligence SC Computer Science GA 665AC UT WOS:000182096600002 ER PT J AU Chen, HC Lally, AM Zhu, B Chau, M AF Chen, HC Lally, AM Zhu, B Chau, M TI HelpfulMed: Intelligent searching for medical information over the Internet SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article ID AUTOMATIC CONSTRUCTION; HEALTH INFORMATION; RETRIEVAL; NETWORKS; SYSTEMS; MODEL AB Medical professionals and researchers need information from reputable sources to accomplish their work. Unfortunately, the Web has a large number of documents that are irrelevant to their work, even those documents that purport to be "medically-related." This paper describes an architecture designed to integrate advanced searching and indexing algorithms, an automatic thesaurus, or "concept space," and Kohonen-based Self-Organizing Map (SOM) technologies to provide searchers with fine-grained results. Initial results indicate that these systems provide complementary retrieval functionalities. HelpfulMed not only allows users to search Web pages and other online databases, but also allows them to build searches through the use of an automatic thesaurus and browse a graphical display of medical-related topics. Evaluation results for each of the different components are included. Our spidering algorithm outperformed both breadth-first search and PageRank spiders on a test collection of 100,000 Web pages. The automatically generated thesaurus performed as well as both MeSH and UMLS-systems which require human mediation for currency. Lastly, a variant of the Kohonen SOM was comparable to MeSH terms in perceived cluster precision and significantly better at perceived cluster recall. C1 Univ Arizona, Eller Coll Business, Dept Management Informat Syst, Tucson, AZ 85721 USA. Univ Arizona, Publ Adm, Tucson, AZ 85721 USA. RP Chen, HC (reprint author), Univ Arizona, Eller Coll Business, Dept Management Informat Syst, Tucson, AZ 85721 USA. RI Chau, Michael/A-4427-2010 CR BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 Brin S, 1998, P 7 INT WORLD WID WE Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830 CHEN H, 1995, J AM SOC INFORM SCI, V46, P348, DOI 10.1002/(SICI)1097-4571(199506)46:5<348::AID-ASI6>3.0.CO;2-1 CHENETIER M, 1996, AM BOOK REV, V18, P8 Cho J., 1998, P 7 WORLD WID WEB C CIMINO JJ, 1994, ANN S COMP APPL MED CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Eysenbach G, 2002, JAMA-J AM MED ASSOC, V287, P2691, DOI 10.1001/jama.287.20.2691 Fallis D, 2002, J AM MED INFORM ASSN, V9, P73 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GUNTZER U, 1989, INFORM PROCESS MANAG, V25, P265, DOI 10.1016/0306-4573(89)90044-7 Haveliwala T.H., 1999, EFFICIENT COMPUTATIO HEARST MA, 1996, P 19 ANN INT ACM C R HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554 Houston AL, 2000, DECIS SUPPORT SYST, V30, P171, DOI 10.1016/S0167-9236(00)00097-X JANES JW, 1994, J AM SOC INFORM SCI, V45, P160, DOI 10.1002/(SICI)1097-4571(199404)45:3<160::AID-ASI6>3.0.CO;2-4 KOHONEN T, 1995, SELF ORGANIZED MAPS LYMAN P, 2000, MUCH INFORMATION Mechkour M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291047 Roussinov DG, 1999, DECIS SUPPORT SYST, V27, P67, DOI 10.1016/S0167-9236(99)00037-8 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 Tolle KM, 2000, J AM SOC INFORM SCI, V51, P352, DOI 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.3.CO;2-# VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VELEZ B, 1997, P 20 ACM C RES DEV I Woolf SH, 1999, BRIT MED J, V318, P527 Wu MF, 2001, INFORM PROCESS MANAG, V37, P459, DOI 10.1016/S0306-4573(00)00057-1 ZAMIR O, 1999, P 8 WORLD WID WEB C NR 33 TC 33 Z9 33 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD MAY PY 2003 VL 54 IS 7 BP 683 EP 694 DI 10.1002/asi.10260 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 670TN UT WOS:000182425300010 ER PT J AU Crestani, F Dominich, S Lalmas, M van Rijsbergen, CJ AF Crestani, F Dominich, S Lalmas, M van Rijsbergen, CJ TI Mathematical, logical, and formal methods in information retrieval: An introduction to the special issue SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Editorial Material ID SPACE; MODEL AB Research on the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for a long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval. C1 Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XQ, Lanark, Scotland. Univ Veszprem, Dept Comp Sci, H-8201 Veszprem, Hungary. Univ London Queen Mary Coll, Dept Comp Sci, London E1 4NS, England. Univ Glasgow, Dept Comp Sci, Glasgow G12 8QQ, Lanark, Scotland. RP Crestani, F (reprint author), Univ Strathclyde, Dept Comp & Informat Sci, Glasgow G1 1XQ, Lanark, Scotland. CR BOLLMANNSDORRA P, 1993, J AM SOC INFORM SCI, V44, P579, DOI 10.1002/(SICI)1097-4571(199312)44:10<579::AID-ASI3>3.0.CO;2-B Bollmann-Sdorra P, 1998, J AM SOC INFORM SCI, V49, P1161, DOI 10.1002/(SICI)1097-4571(1998)49:13<1161::AID-ASI3>3.0.CO;2-2 CODD EF, 1970, COMMUN ACM, V13, P377, DOI 10.1145/362384.362685 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 CRESTANI F, 1998, INFORMATION RETRIEVA DEVLIN K, 1991, LOGIC INFORMATION DOMINICH S, 2001, MATH FDN INFORMATION Egghe L, 1998, J AM SOC INFORM SCI, V49, P1144, DOI 10.1002/(SICI)1097-4571(1998)49:13<1144::AID-ASI2>3.0.CO;2-Z FUHR N, 1998, P 6 INT C EXT DAT TE, P24 He DQ, 2002, INFORM PROCESS MANAG, V38, P727, DOI 10.1016/S0306-4573(01)00060-7 LALMAS M, 1992, P BCS 14 INF RETR C, P1 Lalmas M, 1998, INFORM PROCESS MANAG, V34, P19, DOI 10.1016/S0306-4573(97)00041-1 MARON ME, 1960, J ASSOC COMPUT MACH, V7, P219 Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U Pasi G., 2000, SOFT COMPUTING INFOR Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHANNON CE, 1948, AT&T TECH J, V27, P379 van Rijsbergen C. J., 1986, SIGIR Forum, V21 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 20 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI HOBOKEN PA 111 RIVER ST, HOBOKEN, NJ 07030 USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD FEB 15 PY 2003 VL 54 IS 4 BP 281 EP 284 DI 10.1002/asi.10208 PG 4 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 649EW UT WOS:000181194800001 ER PT B AU Cuenca-Acuna, FM Peery, C Martin, RP Nguyen, TD AF Cuenca-Acuna, FM Peery, C Martin, RP Nguyen, TD GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI PlanetP: Using gossiping to build content addressable peer-to-peer information sharing communities SO 12TH IEEE INTERNATIONAL SYMPOSIUM ON HIGH PERFORMANCE DISTRIBUTED COMPUTING, PROCEEDINGS LA English DT Proceedings Paper CT 12th IEEE International Symposium on High-Performance Distributed Computing CY JUN 22-24, 2003 CL SEATTLE, WA SP IEEE Comp Soc Tech Comm Distributed Comp, Univ Arizona, Argonne Natl Lab, Univ Calif San Diego, San Diego Supercomp Ctr AB We introduce PlanetP a content addressable publish/subscribe service for unstructured peer-to-peer (P2P) communities. PlanetP supports content addressing by providing: (1) a gossiping layer used to globally replicate a membership directory and an extremely compact content index, and (2) a completely distributed content search and ranking algorithm that helps users find the most relevant information. PlanetP is a simple, vet powerful system for sharing information. PlanetP is simple because each peer must only perform a periodic, randomized, point-to-point message exchange with other peers. PlanetP is powerful because it maintains a globally content-ranked view of the shared data. Using simulation and a prototype implementation, we show that PlanetP achieves ranking accuracy that is comparable to a centralized solution and scales easily to several thousand peers while remaining resilient to rapid membership changes. C1 Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA. RP Cuenca-Acuna, FM (reprint author), Rutgers State Univ, Dept Comp Sci, 110 Frelinghuysen Rd, Piscataway, NJ 08854 USA. CR BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 BUCKLEY C, 1985, TR85686 CORN U Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Demers A, 1987, P 6 ANN ACM S PRINC, P1, DOI [10.1145/41840.41841, DOI 10.1145/41840.41841] DEMERS A, 1994, P IEEE WORKSH MOB CO Foster I., 2003, P 2 INT WORKSH PEER Gnawali O. D., 2002, THESIS MIT Gravano Luis, 1994, P 1994 ACM SIGMOD IN, P126, DOI 10.1145/191839.191869 Gupta I, 2003, P 2 INT WORKSH PEER HARCHOLBALTER M, 1999, S PRINC DISTR COMP, P229 Harman D., 1993, P 16 ANN INT ACM SIG Keleher PJ, 2000, MOBILE NETW APPL, V5, P299, DOI 10.1023/A:1019181200922 KUBIATOWICZ J, 2000, P ACM AS PLOS LIBENNOWELL D, 2002, P 21 ACM S PRINC DIS MUTHITACHAROEN A, 2002, P 5 USENIX S OP SYST RATNASAMY S, 2001, P ACM SIGCOMM 01 C REYNOLDS P, 2002, EFFICIENT PEER TO PE Rowstron A. I. T., 2001, P IFIP ACM INT C DIS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saroiu S., 2002, P MULT COMP NETW STOICA I, 2001, P ACM SIGCOMM 01 C A van Renesse R., 2003, ACM T COMPUTER SYSTE, V21 WILLIAMS HE, IN PRESS INT J DIGIT Witten I. H., 1999, MANAGING GIGABYTES C ZHAO Y, 2000, UCBCSD011141 NR 25 TC 5 Z9 5 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1965-2 PY 2003 BP 236 EP 246 PG 11 WC Computer Science, Theory & Methods SC Computer Science GA BX12L UT WOS:000184334900022 ER PT S AU Vallim, MS Coello, JMA AF Vallim, MS Coello, JMA GP IEEE IEEE TI An agent for web information dissemination based on a genetic algorithm SO 2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS SE IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics (SMC 03) CY OCT 05-08, 2003 CL WASHINGTON, D.C. SP IEEE, Syst, Man & Cybernet Soc DE web mining; text mining; learning user's interests; genetic algorithms; information retrieval; personal information agents ID RETRIEVAL; SYSTEM AB We present and evaluate the architecture of a personal agent that mines web information sources and retrieves documents according to user's interests. The agent represents and retrieves documents using classical information retrieval techniques and uses user's feedback and genetic algorithms to learn and adapt to changes in user's interests. The agent was customized to help professionals that need to be up to date with the Brazilian Tax Legislation, stored at the Brazilian Federal Revenue site. Experiments discussed on the paper shown that the agent has good precision and recall rates when retrieving documents, and is also able to successfully detect changes in user's interests. CR BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 BINKLEY J, 1996, RAMA ARCHITECTURE IN SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Choi YS, 2001, J INTELL INF SYST, V16, P5, DOI 10.1023/A:1008708725988 FISCHER G, 1991, CHI 91 HUM FACT COMP GOLDBERG DE, 1989, COMMUNICATIONS ACD, V31, P1208 GORDON M, 2000, P 33 HAW INT C SYST HOLLAND JH, 1975, ADAPTATION NATURAL A KILANDER F, 1997, PEFNA PRIVAT FILTERI KILANDER F, 1995, BRIEF COMPARISON NEW Kim S, 2000, P IEEE INT ASIC C&E, P13 Lawrence S., 2000, IEEE DATA ENG B, V23, P25 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 LUHN HP, 1958, IBM J RES DEV, V2, P314 NICK ZZ, 2001, IEEE INT COMPUTI MAR OARD DW, 1996, CSTR3643 U MAR PORTER M, 1980, ACM SIGIR C R D INF, P318 SALTON G, 1997, READINGS INFORMATION, P381 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHETH BD, 1994, LEARNING APPROACH PE SMEATON A, 2000, LESSONS LEARNED DEV TERRY DB, 1993, P ACM C ORG COMP SYS, P21, DOI 10.1145/168555.168558 Yan TW, 1999, ACM T DATABASE SYST, V24, P529, DOI 10.1145/331983.331992 NR 23 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 0-7803-7952-7 J9 IEEE SYS MAN CYBERN PY 2003 BP 3834 EP 3839 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Robotics SC Automation & Control Systems; Computer Science; Robotics GA BX83D UT WOS:000186578600626 ER PT S AU Zhang, JL Qu, WM Du, L Sun, YF AF Zhang, JL Qu, WM Du, L Sun, YF GP IEEE IEEE TI A framework for domain-specific search engine: Design pattern perspective SO 2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS SE IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Systems, Man and Cybernetics (SMC 03) CY OCT 05-08, 2003 CL WASHINGTON, D.C. SP IEEE, Syst, Man & Cybernet Soc DE domain specific search engine; application framework; design pattern AB Domain specific search engine has many advantages over its generic counterparts such as high precision and short update period However it's a time consuming job to construct these applications. We introduce an object-oriented framework for domain specific search engine applications which contain some design patterns. This paper describes the main design patterns that contribute to this object-oriented architecture, revealing the framework's structure and the forces that shaped it. Using this framework we fix a basic architecture and thus increase ability to construct domain specific search engine application. C1 Chinese Acad Sci, Inst Software, Beijing 100080, Peoples R China. RP Zhang, JL (reprint author), Chinese Acad Sci, Inst Software, POB 8717, Beijing 100080, Peoples R China. CR BOLLACKER K, 1998, INGENTS 98 BRIN S, 1998, P WWW7 BRISB AUSTR CHAKRABARTI S, 1999, P WWW8 TOR CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 FALOUTSOS C, 1984, ACM T OFF INF SYST, V2, P267, DOI 10.1145/2275.357411 FAYAD M, 1997, COMMUN ACM, V39, P32 Fuhr N., 1991, P RIAO 91, P606 Gamma E, 1995, DESIGN PATTERNS ELEM KRUGER A, 2000, C INF KNOWL MAN WASH KWOK JTY, 1999, P INT C NEUR INF P I, P347 LAWRENCE S, 1999, IEEE COMPUT, V32, P67 SALTON G, 1989, INFORMATION RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1062-922X BN 0-7803-7952-7 J9 IEEE SYS MAN CYBERN PY 2003 BP 3881 EP 3886 PG 6 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Robotics SC Automation & Control Systems; Computer Science; Robotics GA BX83D UT WOS:000186578600634 ER PT B AU Suzuki, J Hirao, T Sasaki, Y Maeda, E AF Suzuki, J Hirao, T Sasaki, Y Maeda, E GP ACL TI Hierarchical directed acyclic graph kernel: Methods for structured natural language data SO 41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE LA English DT Proceedings Paper CT 41st Annual Meeting of the Association-for-Computational-Linguistics CY JUL 07-12, 2003 CL Sapporo, JAPAN SP Assoc Computat Linguist, Microsoft, JUSTSYSTEM, NTT, OKI, Natl Inst Informat, Fuji Xerox, IBM Japan, CRL, Hokuto Syst, Fujitsu, Toshiba, NEC, Matsushita Elect, Hitachi, JAIST, Hiroshima City Univ, Kyoto Univ, Natl Inst Japanese Language, Tokushima Univ, Tottori Univ, NAIST, ATR AB This paper proposes the "Hierarchical DirectedAcyclic Graph (HDAG) Kernel" for structured natural language data. The HDAG Kernel directly accepts several levels of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs. We applied the proposed method to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a kernel function. The results of the experiments demonstrate that the HDAG Kernel is superior to other kernel functions and baseline methods. C1 NTT Corp, NTT Commun Sci Labs, Seika, Kyoto 6190237, Japan. CR COLLINS M, 2001, UCSCRL0110 UC SANT C Cristianini N., 2000, INTRO SUPPORT VECTOR HAUSSLER D, 1999, UCSCRL9910 UC SANT C HIRAO T, 2003, J NATURAL LANGUAGE P, V10, P81 IKEHARA S, 1997, SEMANTIC ATTRIBUTE S, V1 Isozaki H., 2002, P COLING 2002, P390 Kudo T., 2002, P 6 C NAT LANG LEARN, P63 Lodhi H, 2002, J MACH LEARN RES, V2, P419, DOI 10.1162/153244302760200687 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vapnik V., 1995, NATURE STAT LEARNING Voorhees E., 1999, P 8 TEXT RETR C NR 11 TC 0 Z9 0 PU ASSOCIATION COMPUTATIONAL LINGUISTICS PI SOMERSET PA PO BOX 6090, SOMERSET, NJ 08875 USA BN 1-932432-09-4 PY 2003 BP 32 EP 39 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Statistics & Probability SC Computer Science; Linguistics; Mathematics GA BAP08 UT WOS:000223097500005 ER PT B AU Pado, S Lapata, M AF Pado, S Lapata, M GP ACL TI Constructing semantic space models from parsed corpora SO 41ST ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE LA English DT Proceedings Paper CT 41st Annual Meeting of the Association-for-Computational-Linguistics CY JUL 07-12, 2003 CL Sapporo, JAPAN SP Assoc Computat Linguist, Microsoft, JUSTSYSTEM, NTT, OKI, Natl Inst Informat, Fuji Xerox, IBM Japan, CRL, Hokuto Syst, Fujitsu, Toshiba, NEC, Matsushita Elect, Hitachi, JAIST, Hiroshima City Univ, Kyoto Univ, Natl Inst Japanese Language, Tokushima Univ, Tottori Univ, NAIST, ATR AB Traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. In this paper we present a novel approach for constructing semantic spaces that takes syntactic relations into account. We introduce a formalisation for this class of models and evaluate their adequacy on two modelling tasks: semantic priming and automatic discrimination of lexical relations. C1 Univ Saarland, Dept Computat Linguist, D-66041 Saarbrucken, Germany. CR BALOTA DA, 1986, J EXP PSYCHOL LEARN, V12, P336, DOI 10.1037//0278-7393.12.3.336 BURNARD L, 1995, BRIT NAT CORP CONS O Choi FYY, 2001, PROCEEDINGS OF THE 2001 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P109 Dunning T., 1993, Computational Linguistics, V19 Grefenstette G., 1994, EXPLORATIONS AUTOMAT HODGSON JM, 1991, LANG COGNITIVE PROC, V6, P169, DOI 10.1080/01690969108406942 JONES MP, 1997, P 5 C APPL NAT LANG, P116 KEENAN E, 1977, LINGUIST INQ, P62 Kilgarriff A., 2000, P 2 INT C LANG RES E, P1371 Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211 Lee L., 1999, P 37 ANN M ASS COMP, P25, DOI 10.3115/1034678.1034693 Lin D., 1998, P 17 INT C COMP LING, P768 Livesay K, 1997, PROCEEDINGS OF THE NINETEENTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, P436 Lowe W., 2001, P 23 ANN C COGN SCI, P576 Lowe Will, 2000, P 22 ANN C COGN SCI, P675 Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766 McDonald S, 2000, THESIS U EDINBURGH Patel M., 1998, P 4 NEUR COMP PSYCH, P199 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Tesniere L., 1959, ELEMENTS SYNTAXE STR NR 21 TC 0 Z9 0 PU ASSOCIATION COMPUTATIONAL LINGUISTICS PI SOMERSET PA PO BOX 6090, SOMERSET, NJ 08875 USA BN 1-932432-09-4 PY 2003 BP 128 EP 135 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Statistics & Probability SC Computer Science; Linguistics; Mathematics GA BAP08 UT WOS:000223097500017 ER PT B AU Yang, XD Hayama, T Kunifuji, S AF Yang, XD Hayama, T Kunifuji, S BE Callaos, N Farsi, D EshaghianWilner, M Hanratty, T Rishe, N TI Collaborative Notebook System for acquiring others knowledge in e-learning SO 7TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XVI, PROCEEDINGS: SYSTEMICS AND INFORMATION SYSTEMS, TECHNOLOGIES AND APPLICATION LA English DT Proceedings Paper CT 7th World Multiconference on Systemics, Cybernetics and Informatics CY JUL 27-30, 2003 CL ORLANDO, FL SP Int Inst Informat & System DE Collaborative Notebook System; e-learning; knowledge merger; annotation AB We are designing and developing a Collaborative Notebook System that makes it easy for learners to study in learning processes, such as preparation, lecture and review. In this research, we focus on creating a collaborative notebook. The Collaborative Notebook System supports the creation of the collaborative notebook, which merges the annotations and reading points of a given textbook and contents in the notebook. In this system, the annotations and the reading points of the textbook are accrued using the Extended Skimming Support System, and the contents of the notebook are obtained using the Notebook Support System. The Extended Skimming Support System is a sub-system that adds an annotation function to the existing Skimming Support System, which skims the document on a computer. screen and provides the reading points of the learner. The Notebook Creating Support System is an outline processor that uses the outline created automatically in line with the textbook. The merger approach of the Collaborative Notebook System picks out the learning points of the learners, and adds the annotations elected by the learners and the notebook contents of the learner. The Collaborative Notebook System supports the recall of the learning contents and the acquisition of diverse views from other learners. textbook is an action of writing information on the specified location of the textbook. In addition, the annotation can be considered as a perspective [15] of the user for the content of the specified location in the textbook. The notebook is the collection of the user's unique information, and includes a summary of the contents and a perspective of the user connected to the textbook. Effective remakes and actions of other learners about the learning contents may deepen their knowledge and provide various perspectives. It is possible to support effective learning by creating a collaborative notebook made by blending the annotation of a textbook and contents of a notebook, and add the perspectives of other learners in the learning processes. In section 2 of this paper, we describe our approach for developing an efficient learning system. Section 3 presents the system implementation. In section 4 we compare our system to other systems; and we summarize our study in Section 5. C1 Japan Advance Inst Sci & Technol, Grad Sch Knowledge Sci, Tastunokuchi, Ishikawa 9231292, Japan. RP Yang, XD (reprint author), Japan Advance Inst Sci & Technol, Grad Sch Knowledge Sci, 1-1 Asahidai, Tastunokuchi, Ishikawa 9231292, Japan. CR Abowd GD, 1999, IBM SYST J, V38, P508 ARMITT G, 2001, EUROCSCL BARGERON D, MSRTR2001108 BRUSH AJB, 2002, CSCL 2002 CADIZ JJ, MSRTR2000 EDELSON DC, 1994, NECC 94 HAYAMA T, IN PRESS KES2003 HEILSEN SB, 2002, CSCL 2002 ITO S, 2002, P KES2002 MIYAKE N, 2000, 4 INT C LEARN SCI MA, P41 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SKIDMORE J, 1998, P SC98 7 13 NOV ULICSAK M, 2001, EUROCSCL NR 13 TC 0 Z9 0 PU INT INST INFORMATICS & SYSTEMICS PI ORLANDO PA 14269 LORD BARCLAY DR, ORLANDO, FL 32837 USA BN 980-6560-01-9 PY 2003 BP 193 EP 197 PG 5 WC Computer Science, Information Systems SC Computer Science GA BY51D UT WOS:000189396600036 ER PT B AU Guo, YM Chen, JJ AF Guo, YM Chen, JJ BE Li, JP Liu, JM Zhong, N Yen, J Zhao, J TI The frame of personal active information system based on XML SO ACTIVE MEDIA TECHNOLOGY LA English DT Proceedings Paper CT 2nd International Conference on Active Media Technology CY MAY 29-31, 2003 CL CHONGQING, PEOPLES R CHINA SP Natl Nat Sci Fdn China, Chinese Math Assoc, PLA Gen Logist Minist, Foreign Affairs Bur, Chongqing People Affairs Bur, Chongqing Elect Assoc HO LOGIST ENGN UNIV AB With the rapid development and spread of the Internet technology, there have been significant progresses in the Internet information retrieval and active services. However, the traditional Internet search engines and active services are all based on simple similarity of key word or bag of words, and documents features and users' personal interests aren't well depicted due to the lack of semantics. XML is a newly emerged standard for the www data exchange and description. XML-based information retrieval system has the rapid development because it not only describes the contents of the documents, but also takes the documents' structural information. In this paper, a newer personal active information service system is discussed, under which the heterogeneous, distributed data spreading on the WEB will be integrated to XML data, and users' personal information is described through XML queries. We have proposed the general architecture of this system, and several key technologies for the establishment of such a system are also presented. C1 Taiyuan Univ Technol, Dept Comp Sci, Taiyuan, Shan Xi, Peoples R China. RP Guo, YM (reprint author), Taiyuan Univ Technol, Dept Comp Sci, Taiyuan, Shan Xi, Peoples R China. CR ALTMEL M, 2000, P 26 INT C VER LARG, P53 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 HU CP, 2001, J INFORM SCI, V19, P383 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wang X.P., 1999, J COMPUTER RES DEV, V36, P1039 NR 5 TC 0 Z9 0 PU WORLD SCIENTIFIC PUBL CO PTE LTD PI SINGAPORE PA PO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE BN 981-238-343-3 PY 2003 BP 443 EP 448 DI 10.1142/9789812704313..0061 PG 6 WC Engineering, Electrical & Electronic; Mathematics, Applied SC Engineering; Mathematics GA BW89S UT WOS:000183539300061 ER PT S AU Jones, KS AF Jones, KS BE Sebastiani, F TI Document retrieval: Shallow data, deep theories; Historical reflections, potential directions SO ADVANCES IN INFORMATION RETRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 25th European Conference on Information Retrieval Research (ECIR 2003) CY APR 14-16, 2003 CL PISA, ITALY SP Elsevier, Assoc Italiana Informat Calcolo Automat, CEPIS, LIBERO, Microsoft Res, CRE, fast, Sharp, IBM, Data Port ID INFORMATION-RETRIEVAL; MODEL; RELEVANCE AB This paper reviews the development of statistically-based retrieval. Since the 1950s statistical techniques have clearly demonstrated their practical worth and statistical theories their staying power, for document or text retrieval. In the last decade the TREC programme, and the Web, have offered new retrieval challenges to which these methods have successfully risen. They are now one element in the much wider and very productive spread of statistical methods to all areas of information and language processing, in which innovative approaches to modelling their data and tasks are being applied. C1 Univ Cambridge, Comp Lab, Cambridge CB3 0FD, England. RP Jones, KS (reprint author), Univ Cambridge, Comp Lab, William Gates Bldg,JJ Thomson Ave, Cambridge CB3 0FD, England. CR ATHERTON P, 1965, CLASSIFICATION RES Banko M., 2000, ACL 2000, P318 BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELY N, 1970, PROCEDURES ANAL SEMA Berger A., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312681 BIEBRICHER B, 1988, P 11 ANN INT ACM SIG, P333 Brown P. F., 1992, Computational Linguistics, V18 Classification Research Group, 1969, CLASS INF CONTR PAP CLEVERDO.C, 1967, ASLIB PROC, V19, P173, DOI 10.1108/eb050097 Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864 CROFT WB, IN PRESS LANGUAGE MO CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 Damianos L, 2002, AI MAG, V23, P13 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 Dumais S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 HAWKING D, 2002, P 10 TEXT RETR C TRE Hiemstra D., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1971, J AM SOC INFORM SCI, V22, P66 Jones K. S., 2000, Information Processing & Management, V36 JONES KS, 1980, J INFORM SCI, V1, P325, DOI 10.1177/016555157900100603 Knight K, 2002, ARTIF INTELL, V139, P91, DOI 10.1016/S0004-3702(02)00222-9 KWOK KL, 1995, ACM T INFORM SYST, V13, P324, DOI 10.1145/203052.203067 LUHN HP, 1957, IBM J RES DEV, V1, P309 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 Miller D. R. H., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312680 Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 NEEDHAM RM, 1964, J DOC, V20, P5 Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 ROBERTSON SE, 1981, INFORMATION RETRIEVA ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1988, INFORM PROCESS MANAG, V24, P269, DOI 10.1016/0306-4573(88)90094-5 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 Salton G., 1975, THEORY INDEXING Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1995, M 30 YEARS INF RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y SCHULTZ CK, 1968, HP LUHN PIONEER INFO STEVENS ME, 1965, MISCELLANEOUS PUBLIC, V269 STEVENS ME, 1965, MONOGRAPH NATL BUR S, V91 Taube M., 1952, AM DOC, V3, P213, DOI 10.1002/asi.5090030404 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES EM, IN PRESS TREC EXPT E WILKINSON R, 1998, SPECIAL PUBLICATION, P25 Young SJ, 1998, COMPUT SPEECH LANG, V12, P263, DOI 10.1006/csla.1998.0101 1995, INFORMATION PROCESSI, V31, P269 2000, INFORMATION PROCESSI, V36, P1 NR 53 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-01274-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2633 BP 1 EP 11 PG 11 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BX27M UT WOS:000184816000001 ER PT S AU Garces, PJ Olivas, JA Romero, FP AF Garces, PJ Olivas, JA Romero, FP BE Menasalvas, E Segovia, J Szczepaniak, PS TI Clustered organized conceptual queries in the Internet using Fuzzy Interrelations SO ADVANCES IN WEB INTELLIGENCE SE Lecture Notes in Artificial Intelligence LA English DT Article; Proceedings Paper CT 1st International Atlantic Web Intelligence Conference (AWIC 2003) CY MAY 05-06, 2003 CL MADRID, SPAIN SP Tech Univ Madrid AB This paper introduces a new application of the FIS-CRM model (Fuzzy Interrelations and Synonymy based Concept Representation Model) in order to define a mechanism to achieve the conceptual matching between the concepts contained in a web page and the implicit concepts in the user's query, that is, the proposed system is able to retrieve the web pages that contain the concepts (not the words) specified in the query (called clustered organized conceptual queries in this paper). FIS-CRM may be considered a fuzzy extension of the vector space model that is based on fuzzy interrelations between terms (fuzzy synonymy and fuzzy generality at the moment). The FISS metasearcher was the first system integrating this model and, in that system, the model was used to extract the concepts contained in the snippets retrieved by the search engine (Google) making possible to cluster the results of the query into groups of conceptually related web pages. C1 Univ Castilla La Mancha, Dept Comp Sci, E-13071 Ciudad Real, Spain. Soluz Software Factory, E-13071 Ciudad Real, Spain. RP Garces, PJ (reprint author), Univ Castilla La Mancha, Dept Comp Sci, Paseo Univ 4, E-13071 Ciudad Real, Spain. EM pgarces@upco.es; JoseAngel.Olivas@uclm.es; fromero@uf-isf.es CR Bordogna G, 2000, KYBERNETIKA, V36, P617 CAO TH, 2001, P BISC INT WORKSH FU, P74 CHOI D, 2001, P BISC INT WORKSH FU, P68 CRESATANI F, 2000, SERIES STUDIES FUZZI FERNANDEZ S, 2001, THESIS U SANTIAGO CO GONZALO J, 1998, P COLING ACL WORK US KINGIP L, 2001, P 7 INT C DAT SYS AD Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 MARTINBAUTISTA MJ, 2001, P BISC INT WORKSH FU, P19 NIKRAVESH M, 2002, P 2002 NAFIPS ANN M, P146 OHGAYA R, 2002, P 2002 NAFIPS ANN M, P274 Olivas J., 2002, P 2002 NAFIPS ANN M, P140 Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 RICARTE I, 2001, P BISC INT WORKSH FU, P80 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAKAGI T, 2001, P BISC INT WORKSH FU, P53 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 ZAMIR O, 1999, P WWW8 NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40124-5 J9 LECT NOTES ARTIF INT PY 2003 VL 2663 BP 37 EP 45 PG 9 WC Computer Science, Artificial Intelligence SC Computer Science GA BW88P UT WOS:000183488800005 ER PT S AU Ribeiro, A Fresno, V Garcia-Alegre, MC Guinea, D AF Ribeiro, A Fresno, V Garcia-Alegre, MC Guinea, D BE Menasalvas, E Segovia, J Szczepaniak, PS TI Web page classification: A soft computing approach SO ADVANCES IN WEB INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 1st International Atlantic Web Intelligence Conference (AWIC 2003) CY MAY 05-06, 2003 CL MADRUD, SPAIN SP Tech Univ Madrid AB The Internet makes it possible to share and manipulate a vast quantity of information efficiently and effectively, but the rapid and chaotic growth experienced by the Net has generated a poorly organized environment that hinders the sharing and mining of useful data. The need for meaningful web-page classification techniques is therefore becoming an urgent issue. This paper describes a novel approach to web-page classification based on a fuzzy representation of web pages. A doublet representation that associates a weight with each of the most representative words of the web document so as to characterize its relevance in the document. This weight is derived by taking advantage of the characteristics of HTML language. Then a fuzzy-rule-based classifier is generated from a supervised learning process that uses a genetic algorithm to search for the minimum fuzzy-rule set that best covers the training examples. The proposed system has been demonstrated with two significantly different classes of web pages. C1 Spanish Council Sci Res, Ind Automat Inst, Madrid 28500, Spain. RP Ribeiro, A (reprint author), Spanish Council Sci Res, Ind Automat Inst, Madrid 28500, Spain. RI Ribeiro, Angela/L-3519-2014; Ribeiro, Angela/ OI Ribeiro, Angela/0000-0001-5807-8132 CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 Dasgupta D., 2001, P GEN EV COMP C GECC, P299 Freitas A.A., 2002, NATURAL COMPUTING SE FRESNO V, 2001, P INT C COMP INT MET, P416 Gasos J., 1990, Proceedings of the IASTED International Symposium Artificial Intelligence Application and Neural Networks - AINN '90 Gudivada V.N., 1997, IEEE INTERNET CO SEP, P58 HENZINGER M, 2000, B TECHNICAL COMMITTE, P3 Kosala R., 2000, ACM SIGKDD EXPLORATI, P1 MICHALEWICZ Z, 1996, GENETIC ALGORITHMS P PIERRE JM, 2001, LINKOPING ELECT ARTI, P6 RIBEIRO A, 2003, STUDIES FUZZINESS SO, P19 Sahami M., 1996, P 13 INT C MACH LEAR, P284 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *UN, 2002, UNCTAD E COMM DEV RE YANG Y, 2002, J INTELL INF SYST, P219 NR 16 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40124-5 J9 LECT NOTES ARTIF INT PY 2003 VL 2663 BP 103 EP 112 PG 10 WC Computer Science, Artificial Intelligence SC Computer Science GA BW88P UT WOS:000183488800012 ER PT S AU Last, M Shapira, B Elovici, Y Zaafrany, O Kandel, A AF Last, M Shapira, B Elovici, Y Zaafrany, O Kandel, A BE Menasalvas, E Segovia, J Szczepaniak, PS TI Content-based methodology for anomaly detection on the web SO ADVANCES IN WEB INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 1st International Atlantic Web Intelligence Conference (AWIC 2003) CY MAY 05-06, 2003 CL MADRUD, SPAIN SP Tech Univ Madrid DE information retrieval; unsupervised clustering; user modeling; web security; anomaly detection; activity monitoring ID INTRUSION-DETECTION SYSTEMS; FILTERING SYSTEMS; NEURAL NETWORKS; STEREOTYPES; MODEL AB As became apparent after the tragic events of September 11, 2001, terrorist organizations and other criminal groups are increasingly using the legitimate ways of Internet access to conduct their malicious activities. Such actions cannot be detected by existing intrusion detection systems that are generally aimed at protecting computer systems and networks from some kind of "cyber attacks". Preparation of an attack against the human society itself can only be detected through analysis of the content accessed by the users. The proposed study aims at developing an innovative methodology for abnormal activity detection, which uses web content as the audit information provided to the detection system. The new behavior-based detection method learns the normal behavior by applying an unsupervised clustering algorithm to the contents of publicly available web pages viewed by a group of similar users. In this paper, we represent page content by the well-known vector space model. The content models of normal behavior are used in real-time to reveal deviation from normal behavior at a specific location on the net. The detection algorithm sensitivity is controlled by a threshold parameter. The method is evaluated by the tradeoff between the detection rate (TP) and the false positive rate (FP). C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. Univ S Florida, Dept Comp Sci & Engn, Tampa, FL 33620 USA. RP Last, M (reprint author), Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel. RI ELOVICI, YUVAL/F-1468-2012 CR Balajinath B, 2001, COMPUT COMMUN, V24, P1202, DOI 10.1016/S0140-3664(00)00364-9 Balasubramaniyan J. S., 1998, Proceedings 14th Annual Computer Security Applications Conference (Cat. No.98EX217), DOI 10.1109/CSAC.1998.738563 Bloedorn E., 1998, INTELL DATA ANAL, V2, P3, DOI 10.1016/S1088-467X(98)00003-1 Boger Z, 2001, INFORM PROCESS MANAG, V37, P187, DOI 10.1016/S0306-4573(00)00030-3 Cannady J, 1998, P REC ADV INTR DET 9, P31 CANNADY J, 2000, P IEEE INNS ENNS INT, V5, P405 Debar H, 1999, COMPUT NETW, V31, P805, DOI 10.1016/S1389-1286(98)00017-6 ESKIN E, 2002, DATA MINING SECURITY Fawcett T, 1999, P 5 ACM SIGKDD INT C Fayyad U, 1996, AI MAG, V17, P37 Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674 Hand D., 2001, PRINCIPLES DATA MINI Huang MY, 1999, COMPUT NETW, V31, P2465, DOI 10.1016/S1389-1286(99)00114-0 Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504 Kamber M., 2001, DATA MINING CONCEPTS Karypis G, 2002, CLUTO CLUSTERING TOO Lee W., 1998, P 7 USENIX SEC S SAN LEE W, 2000, ACM T INFORMATION SY, V3 LEE W, 2001, P DISCEX 2 LEMOS R, 2002, WHAT ARE REAL RISKS Lippmann RP, 2000, COMPUT NETW, V34, P597, DOI 10.1016/S1389-1286(00)00140-7 LU X, 1990, INFORM PROCESS MANAG, V26, P209, DOI 10.1016/0306-4573(90)90026-X Marin J, 2001, P DARPA INF SURV C E, V1, P69, DOI 10.1109/DISCEX.2001.932193 Ning P, 2000, COMPUT COMMUN, V23, P1702, DOI 10.1016/S0140-3664(00)00256-5 Paxson V, 1999, COMPUT NETW, V31, P2435, DOI 10.1016/S1389-1286(99)00112-7 Pierre S, 2000, KNOWL-BASED SYST, V13, P307, DOI 10.1016/S0950-7051(00)00090-3 RHODES BC, 2000, 23 NAT INF SYST SEC Richards K, 1999, COMPUT SECUR, V18, P671, DOI 10.1016/S0167-4048(99)80131-X Russell S. J., 1995, ARTIFICIAL INTELLIGE Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHENKER A, 2003, IN PRESS WEB DOCUMEN Sequeira K., 2002, P 8 ACM SIGKDD INT C, P386 Shapira B, 1997, INFORM PROCESS MANAG, V33, P273, DOI 10.1016/S0306-4573(97)00003-4 Shapira B, 1999, DECIS SUPPORT SYST, V27, P5, DOI 10.1016/S0167-9236(99)00034-2 Spafford EH, 2000, COMPUT NETW, V34, P547, DOI 10.1016/S1389-1286(00)00136-5 Whiteside TL, 1996, NAT IMMUN, V15, P5 NR 37 TC 13 Z9 13 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40124-5 J9 LECT NOTES ARTIF INT PY 2003 VL 2663 BP 113 EP 123 PG 11 WC Computer Science, Artificial Intelligence SC Computer Science GA BW88P UT WOS:000183488800013 ER PT J AU Borner, K Chen, CM Boyack, KW AF Borner, K Chen, CM Boyack, KW TI Visualizing knowledge domains SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review ID LATENT SEMANTIC ANALYSIS; CO-WORD ANALYSIS; NONLINEAR DIMENSIONALITY REDUCTION; SCIENTIFIC COLLABORATION NETWORKS; AUTHOR COCITATION ANALYSIS; SELF-ORGANIZING-MAPS; VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; INTELLECTUAL STRUCTURE; RESEARCH PERFORMANCE C1 Indiana Univ, Bloomington, IN 47405 USA. Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA. Sandia Natl Labs, Albuquerque, NM 87185 USA. RP Borner, K (reprint author), Indiana Univ, Bloomington, IN 47405 USA. RI Chen, Chaomei/A-1252-2007; Boyack, Kevin/ OI Chen, Chaomei/0000-0001-8584-1041; Boyack, Kevin/0000-0001-7814-8951 CR Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509 Barabasi AL, 2000, PHYSICA A, V281, P69, DOI 10.1016/S0378-4371(00)00018-2 Barbara D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347145 Basilevsky A, 1994, STAT FACTOR ANAL REL Bassecoulard E, 1999, SCIENTOMETRICS, V44, P323, DOI 10.1007/BF02458483 Battista G. D., 1999, GRAPH DRAWING ALGORI Bederson BB, 1996, J VISUAL LANG COMPUT, V7, P3, DOI 10.1006/jvlc.1996.0002 Bhattacharya S, 1998, SCIENTOMETRICS, V43, P359, DOI 10.1007/BF02457404 Bishop CM, 1998, NEURAL COMPUT, V10, P215, DOI 10.1162/089976698300017953 Borg I., 1996, MODERN MULTIDIMENSIO Borgman C. L., 2000, WEB KNOWLEDGE FESTSC Borgman C. L., 1990, SCHOLARLY COMMUNICAT Borgman CL, 2002, ANNU REV INFORM SCI, V36, P3 BORGMAN CL, 2000, WEB KNOWLEDGE, P143 BORNER K, 2000, P 2000 ACM IEEE JOIN, P234, DOI 10.1145/336597.336672 Borner K., 2001, Proceedings Fifth International Conference on Information Visualisation, DOI 10.1109/IV.2001.942068 BORNER K, 2000, P INF VIS 2000 S DIG, P77 BORNER K, 2000, P INF VIS 2000 S DIG, P192 Boyack KW, 2002, J AM SOC INF SCI TEC, V53, P764, DOI 10.1002/asi.10066 BOYACK KW, 2000, P NEW PAR INF VIS MA Bush V., 1945, ATLANTIC MONTHLY, P101 Cahlik T, 2000, SCIENTOMETRICS, V49, P373, DOI 10.1023/A:1010581421990 CALLON M, 1983, SCIENTOMETRICS, V5, P78 CARD SK, 1999, READINGS INFORMATION Card SK, 1996, IEEE COMPUT GRAPH, V16, P63, DOI 10.1109/38.486683 Chalmers Matthew, 1992, P 15 ANN INT ACM SIG, P330, DOI DOI 10.1145/133160.133215 CHEN C, 1996, J COMPUTER MEDIATED, V2, P125 CHEN C, 1997, AI SOC, V11, P48, DOI 10.1007/BF02812438 Chen C., 1999, P 10 ACM C HYP HYP R, P51, DOI 10.1145/294469.294486 CHEN C, 1999, INFORMATION VISUALIS CHEN CH, 2002, MAPPING SCI FRONTIER Chen CM, 1998, J VISUAL LANG COMPUT, V9, P267, DOI 10.1006/jvlc.1998.0083 Chen CM, 1999, INFORM PROCESS MANAG, V35, P401, DOI 10.1016/S0306-4573(98)00068-5 Chen CM, 2001, J AM SOC INF SCI TEC, V52, P315, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1074>3.3.CO;2-U Chen CM, 1999, IEEE MULTIMEDIA, V6, P54, DOI 10.1109/93.771373 Chen CM, 1998, INTERACT COMPUT, V10, P107, DOI 10.1016/S0953-5438(98)00015-0 Chen CM, 2001, COMPUTER, V34, P65 Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830 Chen SL, 1997, BIODEGRADATION, V8, P97, DOI 10.1023/A:1008233704719 Chen SS, 1996, J VIS COMMUN IMAGE R, V7, P1, DOI 10.1006/jvci.1996.0001 Chung YM, 2001, J AM SOC INF SCI TEC, V52, P283, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1073>3.3.CO;2-X CONKLIN J, 1987, IEEE COMPUT, V20, P9 Couclelis H., 1998, CARTOGRAPHY GEOGRAPH, V25, P209, DOI 10.1559/152304098782383034 Crane D., 1972, INVISIBLE COLL DIFFU Cronin B., 2000, WEB KNOWLEDGE FESTSC Davidson GS, 2001, IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2001, PROCEEDINGS, P23, DOI 10.1109/INFVIS.2001.963275 Davidson GS, 1998, J INTELL INF SYST, V11, P259, DOI 10.1023/A:1008690008856 De Solla Price D. J., 1963, LITTLE SCI BIG SCI DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DeLooze MA, 1997, SCIENTOMETRICS, V39, P267 Ding Y, 2000, SCIENTOMETRICS, V47, P55, DOI 10.1023/A:1005665709109 Ding Y, 1999, J INFORM SCI, V25, P67, DOI 10.1177/016555159902500107 DODGE M, 2001, MAPPA MUNDI MAGAZINE DODGE M., 2000, MAPPING CYBERSPACE Dourish P, 1994, P HCI 94 Eades P., 1994, COMPUTATIONAL GEOMET, V4, P235, DOI 10.1016/0925-7721(94)00014-X Fabrikant S. I., 2000, T GIS, V4, P65, DOI 10.1111/1467-9671.00038 Fabrikant SI, 2001, ANN ASSOC AM GEOGR, V91, P263, DOI 10.1111/0004-5608.00242 FENG Y, 2000, SPIE 2002 C VIS DAT, V4665, P24 FRUCHTERMAN TMJ, 1991, SOFTWARE PRACT EXPER, V21, P1129, DOI 10.1002/spe.4380211102 FRY B, 2000, ORGANIC INFORMATION Furnas G., 1998, P 11 ANN ACM S US IN, P107, DOI 10.1145/288392.288579 Furnas G., 1986, P ACM CHI 86 C HUM F, P16, DOI 10.1145/22627.22342 Garfield E, 2001, BIBLIOGRAPHIC COUPLI GARFIELD E, 1963, AM DOC, V14, P289, DOI 10.1002/asi.5090140405 GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108 Garfield E., 1964, USE CITATION DATA WR Garfield E, 1994, CURRENT CONTENTS SOC, V7, P5 GARFIELD E, 1989, INNOVATION AT THE CROSSROADS BETWEEN SCIENCE AND TECHNOLOGY, P51 GERSHON N, 2001, COMMUNICATIONS ACM, V44 Gershon N., 1998, INTERACTIONS, V5, P9, DOI 10.1145/274430.274432 Glanzel W, 2001, SCIENTOMETRICS, V51, P69, DOI 10.1023/A:1010512628145 Glanzel W, 1997, SCIENTOMETRICS, V40, P605, DOI 10.1007/BF02459304 Golledge R, 1995, COGNITIVE ASPECTS HU, P29 Gorsuch R. L., 1983, FACTOR ANAL GRIFFITH BC, 1966, AM BEHAV SCI, V9, P3, DOI 10.1177/000276426600900601 GRIFFITH BC, 1972, SCIENCE, V177, P959, DOI 10.1126/science.177.4053.959 Halasz F., 1987, P SIGCHI GI C HUM FA, P45, DOI 10.1145/29933.30859 HALASZ FG, 1988, COMMUN ACM, V31, P836, DOI 10.1145/48511.48514 Halliday M. A. K., 1976, COHESION ENGLISH Han J, 2000, DATA MINING CONCEPTS Harman H. H., 1976, MODERN FACTOR ANAL HARTER SP, 1993, J AM SOC INFORM SCI, V44, P543, DOI 10.1002/(SICI)1097-4571(199310)44:9<543::AID-ASI4>3.0.CO;2-F He Q, 1999, LIBR TRENDS, V48, P133 Hearst M., 1999, MODERN INFORM RETRIE, P257 Hendley R. J., 1995, Proceedings. Information Visualization (Cat. No.95TB100000), DOI 10.1109/INFVIS.1995.528691 Herman I, 2000, IEEE T VIS COMPUT GR, V6, P24, DOI 10.1109/2945.841119 Hetzler B., 1998, P IEEE INF VIS 98 HJORLAND B, 1995, J AM SOC INFORM SCI, V46, P400, DOI 10.1002/(SICI)1097-4571(199507)46:6<400::AID-ASI2>3.0.CO;2-Y Hjorland B., 1997, INFORMATION SEEKING HOLLAN JD, 1997, HDB HUMAN COMPUTER I, P33 *I SCI INF, 1981, ISI ATL SCI BIOCH MO ISDALE J, 1998, IEEE INF VIS 98 S VI Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 KESSLER MM, 1963, AM DOC, V14, P10, DOI 10.1002/asi.5090140103 Kim SK, 2001, SCIENCE, V293, P2087, DOI 10.1126/science.1061603 KING J, 1987, J INFORM SCI, V13, P261, DOI 10.1177/016555158701300501 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Kohonen T, 1985, P IEEE, p[73, 1551] Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Kohonen T., 1995, SELF ORG MAPS KOIKE H, 1993, ACM T INFORM SYST, V11, P266, DOI 10.1145/159161.155370 Koike H., 1993, Proceedings 1993 IEEE Symposium on Visual Languages (Cat. No.93TH0562-9), DOI 10.1109/VL.1993.269566 Kostoff RN, 1998, INFORM PROCESS MANAG, V34, P69, DOI 10.1016/S0306-4573(97)00066-6 Kruskal J. B, 1984, MULTIDIMENSIONAL SCA Kruskal J. B., 1977, STAT METHODS DIGITAL KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P115, DOI 10.1007/BF02289694 Lamping J., 1995, P SIGCHI C HUM FACT, P401, DOI DOI 10.1145/223904.223956 Landauer TK, 1998, DISCOURSE PROCESS, V25, P259 LAWRENCE S, 1999, IEEE COMPUT, V32, P67 LEE RCT, 1977, IEEE T COMPUT, V26, P288 LEYDESDORFF L, 1994, SCIENTOMETRICS, V47, P143 Leydesdorff L, 1999, SCIENTOMETRICS, V44, P169, DOI 10.1007/BF02457378 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 Lin Y, 2000, SCIENTOMETRICS, V47, P143, DOI 10.1023/A:1005678011835 MACKINLAY JD, 1995, P C HUM FACT COMP SY Mahlck P, 2000, SCIENTOMETRICS, V49, P81, DOI 10.1023/A:1005661208810 Mandelbrot B., 1988, FRACTAL GEOMETRY NAT McCain KW, 1998, SCIENTOMETRICS, V41, P389, DOI 10.1007/BF02459053 MCCAIN KW, 1995, SCIENTOMETRICS, V32, P153, DOI 10.1007/BF02016892 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q McCormick BH, 1987, COMPUTER GRAPHICS, V21 McQuaid MJ, 1999, DECIS SUPPORT SYST, V27, P163, DOI 10.1016/S0167-9236(99)00042-1 Merton R. K., 1986, LITTLE SCI BIG SCI, pvii Mostafa J, 1998, J AM SOC INFORM SCI, V49, P1304, DOI 10.1002/(SICI)1097-4571(1998)49:14<1304::AID-ASI8>3.0.CO;2-E MUKHERJEA S, 1999, ACM COMPUTING SURVEY, V21 Munzner T., 1997, Proceedings. IEEE Symposium on Information Visualization (Cat. No.97TB100195), DOI 10.1109/INFVIS.1997.636718 Munzner T, 1998, IEEE COMPUT GRAPH, V18, P18, DOI 10.1109/38.689657 NARIN F, 1977, ANNU REV INFORM SCI, V12, P35 NEDERHOF AJ, 1992, J AM SOC INFORM SCI, V43, P249, DOI 10.1002/(SICI)1097-4571(199204)43:3<249::AID-ASI7>3.0.CO;2-I NEDERHOF AJ, 1991, J AM SOC INFORM SCI, V42, P332, DOI 10.1002/(SICI)1097-4571(199106)42:5<332::AID-ASI3>3.0.CO;2-8 Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.016132 Newman MEJ, 2001, PHYS REV E, V64, DOI 10.1103/PhysRevE.64.016131 NISONGER TE, 1992, P ASIS ANNU MEET, V29, P13 Noyons E, 2001, SCIENTOMETRICS, V50, P83, DOI 10.1023/A:1005694202977 Noyons ECM, 1998, J AM SOC INFORM SCI, V49, P68, DOI 10.1002/(SICI)1097-4571(1998)49:1<68::AID-ASI9>3.0.CO;2-Z Noyons ECM, 1999, J AM SOC INFORM SCI, V50, P115, DOI 10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.0.CO;2-J Noyons ECM, 1998, SCIENTOMETRICS, V41, P61, DOI 10.1007/BF02457967 Noyons ECM, 1999, SCIENTOMETRICS, V46, P591, DOI 10.1007/BF02459614 Palmer S. E., 1999, VISION SCI PHOTONS P Persson O, 2001, SCIENTOMETRICS, V50, P339, DOI 10.1023/A:1010534009428 PIROLLI P, 1996, P C HUM FACT COMP SY Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643 Polanco X, 2001, SCIENTOMETRICS, V51, P267, DOI 10.1023/A:1010537316758 Price DJ., 1986, LITTLE SCI BIG SCI Price DJD, 1961, SCI BABYLON PRICE DJD, 1965, SCIENCE, V149, P510 Qin J, 2000, J AM SOC INFORM SCI, V51, P166, DOI 10.1002/(SICI)1097-4571(2000)51:2<166::AID-ASI8>3.3.CO;2-Q RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Raghupathi W., 1999, Intelligence, V10, DOI 10.1145/309697.309703 Rennison E., 1994, P ACM S US INT SOFTW, P3, DOI 10.1145/192426.192429 Rorvig M, 1999, J AM SOC INFORM SCI, V50, P639, DOI 10.1002/(SICI)1097-4571(1999)50:8<639::AID-ASI2>3.0.CO;2-C Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Salvador MR, 2000, RES EVALUAT, V9, P189, DOI 10.3152/147154400781777214 SANDSTROM PE, 1994, LIBR QUART, V64, P414 Sandstrom PE, 2001, SCIENTOMETRICS, V51, P573, DOI 10.1023/A:1019655305286 SARKAR M, 1994, COMMUN ACM, V37, P73, DOI 10.1145/198366.198384 Schvaneveldt R. W., 1990, PATHFINDER ASS NETWO Schwechheimer H, 2001, SCIENTOMETRICS, V51, P311, DOI 10.1023/A:1010541418576 Schwechheimer H, 1999, SCIENTOMETRICS, V44, P547, DOI 10.1007/BF02458495 Shneiderman B., 2000, ACM Transactions on Computer-Human Interaction, V7, DOI 10.1145/344949.345077 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92, DOI 10.1145/102377.115768 Shneiderman B., 1997, DESIGNING USER INTER Shneiderman B., 1996, Proceedings. IEEE Symposium on Visual Languages (Cat. No.96TB100066), DOI 10.1109/VL.1996.545307 Skupin A, 2000, IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2000, P91, DOI 10.1109/INFVIS.2000.885095 SKUPIN A, 2001, P VIS INT DIG LIB IT Skupin A., 1996, P GIS LIS 9L BETH AM, P607 Small H, 1999, LIBR TRENDS, V48, P72 SMALL H, 1995, P ASIS ANNU MEET, V32, P118 Small H, 1997, SCIENTOMETRICS, V38, P275, DOI 10.1007/BF02457414 Small H., 2000, WEB KNOWLEDGE FESTSC, P449 SMALL H, 1994, SCIENTOMETRICS, V30, P229, DOI 10.1007/BF02017225 Small H, 1999, J AM SOC INFORM SCI, V50, P799, DOI 10.1002/(SICI)1097-4571(1999)50:9<799::AID-ASI9>3.3.CO;2-7 SPENCE B, 2001, INFORMATION VISUALIZ Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319 Thurstone LL, 1931, PSYCHOL REV, V38, P406, DOI 10.1037/h0069792 Tryon R.C., 1939, CLUSTER ANAL Tufte E., 1997, VISUAL EXPLANATIONS Tufte E, 1990, ENVISIONING INFORMAT Tufte ER, 1983, VISUAL DISPLAY QUANT van Raan A.F.J., 2000, WEB KNOWLEDGE FESTSC, P301 van Dalen HP, 2001, SCIENTOMETRICS, V50, P455, DOI 10.1023/A:1010510831718 Ware C, 2000, INFORMATION VISUALIZ WHITE HD, 1982, J DOC, V38, P255, DOI 10.1108/eb026731 WHITE HD, 1989, ANNU REV INFORM SCI, V24, P119 White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4 White HD, 1997, ANNU REV INFORM SCI, V32, P99 WHITE HD, 2000, P IEEE INT C INF VIS, P25 Wilson CS, 1999, ANNU REV INFORM SCI, V34, P107 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 WISE JA, 1995, P IEEE S INF VIS 95 Yang C. C., 1999, P 4 INT ACM C DIG LI, P258, DOI 10.1145/313238.313444 Zhu DH, 2002, TECHNOL FORECAST SOC, V69, P495, DOI 10.1016/S0040-1625(01)00157-3 NR 201 TC 267 Z9 275 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 USA SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 2003 VL 37 BP 179 EP 255 DI 10.1002/aris.1440370106 PG 77 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 627DT UT WOS:000179918000006 ER PT S AU Fernandez, J Mones, R Diaz, I Ranilla, J Combarro, EF AF Fernandez, J Mones, R Diaz, I Ranilla, J Combarro, EF BE Peters, C Gonzalo, J Braschler, M Kluck, M TI Experiments with self organizing maps in CLEF 2003 SO COMPARATIVE EVALUATION OF MULTILLINGUAL INFORMATION ACCESS SYSTEMS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 4th Workshop of the Cross-Language Evaluation Forum (CLEF 2003) CY AUG 21-22, 2003 CL Trondheim, NORWAY SP US, Natl Inst Stand & Technol, DELOS, Network Excellence Digit Lib AB In our first participation in the Cross Language Evalution Forum, we tested the performance of a clustering method which uses self organizing maps. C1 Univ Oviedo, Artificial Intelligence Ctr, Oviedo, Spain. RP Combarro, EF (reprint author), Univ Oviedo, Artificial Intelligence Ctr, Oviedo, Spain. EM ir@aic.uniovi.es RI Ranilla, Jose/E-8012-2013; Combarro, Elias/H-2590-2015 OI Ranilla, Jose/0000-0003-2941-3741; Combarro, Elias/0000-0003-3808-4273 CR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Kaski S., 1998, NEURAL COMPUTING SUR, V1, P1 Kohonen T, 1999, KOHONEN MAPS, P171, DOI 10.1016/B978-044450270-4/50013-9 Kohonen T., 1996, LECT NOTES COMPUTER, V1112, P269 Kohonen T., 2001, SPRINGER SERIES INFO, V30 LAGUS K, 1996, P 2 INT C KNOWL DISC, P233 Lagus K, 2002, NEURAL PROCESS LETT, V15, P21, DOI 10.1023/A:1013853012954 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-24017-9 J9 LECT NOTES COMPUT SC PY 2003 VL 3237 BP 358 EP 366 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BBM36 UT WOS:000226159600034 ER PT S AU Wilson, DC Carthy, J Abbey, K Sheppard, J Wang, RC Dunnion, J Drummond, A AF Wilson, DC Carthy, J Abbey, K Sheppard, J Wang, RC Dunnion, J Drummond, A BE Kumar, V Gavrilova, ML Tan, CJK LEcuyer, P TI Textual CBR for incident report retrieval SO COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2003, PT 1, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Conference on Computational Science and Its Applications (ICCSA 2003) CY MAY 18-21, 2003 CL MONTREAL, CANADA SP CERCA, IBM Canada, IBM, United States, Heuchera Technol, Pallas, Queens Univ Belfast, SHARCNET, Soc Ind & Appl Math, Springer Verlag AB Incident Management Systems can play a crucial role in helping to reduce the number of workplace accidents by providing support for incident analysis. In particular, the retrieval of relevant similar incident reports can help safety personnel to identify factors and patterns that have contributed or might potentially contribute to accidents. Incident Report Retrieval is a relatively new research topic in the field of Accident Reporting and Analysis, and we are interested in developing intelligent computational support for retrieving incident information that leverages both the featural and textual components of incident reports. This paper describes InRet-T, an Incident Report Retrieval system that incorporates approaches from textual case-based reasoning to integrate both featural and textual aspects in retrieving civil aviation incident reports. It also provides a preliminary evaluation of InRet-T that offers some insight into the use of textual CBR approaches to incident analysis. C1 Univ Coll Dublin, Dept Comp Sci, Dublin 8, Ireland. Univ Coll Dublin, Ctr Safety & Hlth Work, Dublin 8, Ireland. RP Wilson, DC (reprint author), Univ Coll Dublin, Dept Comp Sci, Dublin 8, Ireland. CR Aamodt A., 1994, CASE BASED REASONING BRUNINGHAUS S, 1997, P 2 INT C CAS BAS RE, P303 BYRNE R, 2001, SAFETY HLTH WELFARE CARTHY J, 2002, EUR C CAS BAS REAS E CASSIDY D, 2002, P STAIRS 2002 LYONS, V1, P165 CUNNINGHAM P, 1998, CBR STRENGTHS WEAKNE DANIELS J, 1997, P 2 INT C CAS BAS RE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 *EUR FACTS, 2002, 19 FACTS EUR EUR AG *HSE, 2001, HLTH SAF COMM HLTH S JOHNSON C, 1999, USING CASE BASED REA KULYUKIN V, 1998, THESIS U CHICAGO Leake D. B., 1996, CASE BASED REASONING LENZ M, 1998, LECT NOTES ARTIFICIA, V1488 PAZZANI M, 1996, P NAT C ART INT Reason J., 1998, MANAGING RISKS ORGAN SALTON G, 1983, SMART SIRE EXPT RETR SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *US BUR LAB STAT, 2002, CENS FAT OCC INJ 200 VANDERSCHAAF TW, 1996, P INT C WORKSH PROC, P242 Watson I., 1997, APPLYING CASE BASED WEBER R, 1998, P AAAI 98 WORKSH TEX, P40 WILSON DC, 1999, P 4 UK CAS BAS REAS, P67 NR 23 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40155-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2667 BP 358 EP 367 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods; Imaging Science & Photographic Technology SC Computer Science; Imaging Science & Photographic Technology GA BX11U UT WOS:000184326400039 ER PT S AU Choi, B Guo, Q AF Choi, B Guo, Q BE Chung, PWH Hinde, C Ali, M TI Applying semantic links for classifying Web pages SO DEVELOPMENTS IN APPLIED ARTIFICIAL INTELLIGENCE SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems CY JUN 23-26, 2003 CL LOUGHBOROUGH, ENGLAND ID RETRIEVAL; MODEL AB Automatic hypertext classification is an essential technique for organizing vast amount of Internet Web pages or HTML documents. One the of problems in classifying Web pages is that Web pages are usually short and contain insufficient text to clearly identify its category. Text classification mechanisms, by analyzing only the contents of the document itself, are relatively ineffective in classifying short Web pages. This paper proposes a new hypertext classification mechanism to address the problem by analyzing not only the Web page itself but also its linked Web pages referred by the URLs contained within the page. The URLs are treated as semantic links. The hypothesis is that the linked Web pages contain related information to help identifying the category of the Web page. Experimental results show that the proposed approach could increase the accuracy by 35% over the approach of analyzing only the Web page itself. C1 Louisiana Tech Univ, Coll Engn & Sci, Ruston, LA 71272 USA. RP Choi, B (reprint author), Louisiana Tech Univ, Coll Engn & Sci, Ruston, LA 71272 USA. CR APTE C, 1994, ACM T INFORMATION SY BOOKSTEIN A, 1976, LIBR QUART, V46, P153 Cohen W. W., 1996, SIGIR Forum GANASCIA JG, 1991, MACH INTELL, V12, P151 KIVINEN J, SIGIR 94, P192 Lewis D. D., 1996, SIGIR Forum SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 THORSTEN J, 1997, P INT C MACH LEARN I VONDASARATHY B, 1991, MCGRAWHILL COMPUTER WIDROW B, 1996, INFORMATION RETRIEVA WIENER E, 1995, P 4 ANN S DOC AN INF YANG Y, 1996, ACM T INFORM SYST, V12, P252 NR 13 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40455-4 J9 LECT NOTES ARTIF INT PY 2003 VL 2718 BP 148 EP 153 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BX29U UT WOS:000184852400015 ER PT B AU Zhou, L Hovy, E AF Zhou, L Hovy, E GP ACL TI A web-trained extraction summarization system SO HLT-NAACL 2003: HUMAN LANGUAGE TECHNOLOGY CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE MAIN CONFERENCE LA English DT Proceedings Paper CT Human Language Technology Conference CY MAY 27-JUN 01, 2003 CL Edmonton, CANADA SP Assoc Computat Linguist, N Amer Chapter AB A serious bottleneck in the development of trainable text summarization systems is the shortage of training data. Constructing such data is a very tedious task, especially because there are in general many different correct ways to summarize a text. Fortunately we can utilize the Internet as a source of suitable training data. In this paper, we present a summarization system that uses the web as the source of training data. The procedure involves structuring the articles downloaded from various websites, building adequate corpora of (summary, text) and (extract, text) pairs, training on positive and negative data, and automatically learning to perform the task of extraction-based summarization at a level comparable to the best DUC systems. C1 USC, Inst Informat Sci, Marina Del Rey, CA 90292 USA. CR CHURCH KW, 1993, P WORKSH VER LARG CO FINN A, 2001, P NSF DELOS WORKSH P FUNG P, 1994, P 15 INT C COMP LING KNIGHT K, 2000, 17 NAT C AM ASS ART Kupiec J., 1995, SIGIR Forum Lin C, 2000, P 18 INT C COMP LING LIN CY, 2001, SUMMARY EVALUATION E MARCU D, 1999, 22 INT ACM SIGIR C R, P137 Melamed ID, 1999, COMPUT LINGUIST, V25, P107 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 ZAJIC D, 2002, P ACL 2002 WORKSH TE NR 11 TC 0 Z9 0 PU ASSOCIATION COMPUTATIONAL LINGUISTICS PI SOMERSET PA PO BOX 6090, SOMERSET, NJ 08875 USA BN 1-932432-04-3 PY 2003 BP 284 EP 290 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics SC Computer Science; Linguistics GA BAP05 UT WOS:000223096100037 ER PT B AU Zakos, J Verma, B Li, X Kulkarni, S AF Zakos, J Verma, B Li, X Kulkarni, S GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Intelligent encoding of concepts in web document retrieval SO ICCIMA 2003: FIFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT 5th International Conference on Computational Intelligence and Multimedia Applications CY SEP 27-30, 2003 CL XIAN, PEOPLES R CHINA SP Natl Nat Sci Fdn, Xidian Univ, Griffith Univ, Computat Intelligence Res Lab, Univ Birmingham, Nat Computat Res Lab, Univ Las Vegas, EE, IEEE Comp Press ID INFORMATION-RETRIEVAL; SYSTEM; MODELS AB The main aim of the proposed approach presented in this paper is to improve web information retrieval effectiveness by overcoming the problems associated with a typical keyword matching retrieval system, through the use of concepts and an intelligent fusion of confidence values. By exploiting the conceptual hierarchy of the WordNet [1] knowledgebase, we show how to effectively encode the conceptual information in a document using the semantic information implied by the words that appear within it. Rather than treating a word as a string made up of a sequence of characters, we consider a word to represent a concept. C1 Griffith Univ, Nathan, Qld 4111, Australia. RP Zakos, J (reprint author), Griffith Univ, Nathan, Qld 4111, Australia. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Clark P., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000) DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 LUCARELLA D, 1991, J INFORM SCI, V17, P81, DOI 10.1177/016555159101700202 Mihalcea R., 2000, P FLOR ART INT RES S, P219 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 RAU LF, 1987, INFORM PROCESS MANAG, V23, P269, DOI 10.1016/0306-4573(87)90018-5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOORHEES E, 1999, P 7 TEXT RETR C WOODS WA, 1997, TR9761 SMLI NR 12 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1957-1 PY 2003 BP 72 EP 77 PG 6 WC Computer Science, Artificial Intelligence SC Computer Science GA BX71U UT WOS:000186203700013 ER PT B AU Hammouda, KM Kamel, MS AF Hammouda, KM Kamel, MS BE Liu, J Liu, C Klusch, M Zhong, N Cercone, N TI Incremental document clustering using cluster similarity histograms SO IEEE/WIC INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, PROCEEDINGS LA English DT Proceedings Paper CT IEEE/WIC International Conference on Web Intelligence (WI 2003) CY OCT 13-17, 2003 CL HALIFAX, CANADA SP IEEE Comp Soc Tech Comm Computat Intelligence, Web Intelligence Consortium AB Clustering of large collections of text documents is a key process in providing a higher level of knowledge about the underlying inherent classification of the documents. Web documents, in particular, are of great interest since managing, accessing, searching, and browsing large repositories of web content requires efficient organization. Incremental clustering algorithms are always preferred to traditional clustering techniques, since they can be applied in a dynamic environment such as the Web. An incremental document clustering algorithm is introduced in this paper, which relies only on pair-wise document similarity information. Clusters are represented using a Cluster Similarity Histogram, a concise statistical representation of the distribution of similarities within each cluster, which provides a measure of cohesiveness. The measure guides the incremental clustering process. Complexity analysis and experimental results are discussed and show that the algorithm requires less computational time than standard methods while achieving a comparable or better clustering quality. C1 Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RP Hammouda, KM (reprint author), Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. CR Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X CHARIKAR M, 1997, 29 ANN ACM S THEOR C, P626 Hammouda KM, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P203, DOI 10.1109/ICDM.2002.1183904 Jain A. K., 1988, ALGORITHMS CLUSTERIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD 2000 WORKSH TEXT WONG W, 2000, 2000 INT C INF SOC I Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 8 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1932-6 PY 2003 BP 597 EP 601 PG 5 WC Computer Science, Artificial Intelligence SC Computer Science GA BX82A UT WOS:000186520200102 ER PT B AU Garces, PJ Olivas, JA Romero, FP AF Garces, PJ Olivas, JA Romero, FP BE Unland, R Ulieru, M Weaver, C TI Searching for concepts in the Internet using FIS-CRM SO INDIN 2003: IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, PROCEEDINGS LA English DT Proceedings Paper CT IEEE International Conference on Industrial Informatics (INDIN 2003) CY AUG 21-24, 2003 CL Banff, CANADA SP IEEE, ICO, I Core, Phoenix Contact, Schneider Elect, Univ Virginia, Allen-Bradley, Rockwell Automat, Univ Calgary, Univ Duisburg-Essen DE internet search; fuzzy logic; synonym; ontology AB This paper presents a search system in the Internet capable to achieve the conceptual matching between the concepts contained in a web page and the implicit concepts in the user's query, that is, the proposed system is able to retrieve the web pages That contain the concepts (not the words) specified in the query (called Clustered Organized Conceptual queries in this paper). This system uses the FIS-CRM model (Fuzzy Interrelations and Synonymy based Concept Representation Model) that may be considered a fuzzy extension of the vector space model that is based on fuzzy inter-relations between terms (fuzzy synonymy and fuzzy generality at the moment). This model has been also implemented in the FISS metasearcher where it was used to extract the concepts contained in the snippets retrieved by the search engine (Google) making possible to cluster the results of the query into groups of conceptually related web pages. C1 Univ Castilla La Mancha, Dept Comp Sci, E-13071 Ciudad Real, Spain. RP Garces, PJ (reprint author), Univ Castilla La Mancha, Dept Comp Sci, Paseo Univ 4, E-13071 Ciudad Real, Spain. EM pgarces@upco.es; JoseAngel.Olivas@uclm.es; fpromero@soluziona.com CR Bordogna G, 2000, KYBERNETIKA, V36, P617 CAO TH, 2001, P BISC INT WORKSH FU, P74 CHOI D, 2001, P BISC INT WORKSH FU, P68 CRESATANI F, 2000, PHYSICAL VERLAG SERI FERNANDEZ S, 2001, THESIS U SANTIAGO DE GONZALO J, 1998, P COLING ACL WORK US KINGIP L, 2001, P 7 INT C DAT SYS AD KIRYAKOV A, 1999, P NODALIDA 99 NORD C Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934 MARTINBAUTISTA MJ, 2001, P BISC INT WORKSH FU, P19 NIKRAVESH M, 2002, P 2002 NAFIPS ANN M, P146 OHGAYA R, 2002, P 2002 NAFIPS ANN M, P274 Olivas J., 2002, P 2002 NAFIPS ANN M, P140 Pasi G, 2008, MATHWARE SOFT COMPUT, V9, P107 RICARTE I, 2001, P BISC INT WORKSH FU, P80 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAKAGI T, 2001, P BISC INT WORKSH FU, P53 Tang YJ, 2001, P SOC PHOTO-OPT INS, V4294, P119, DOI 10.1117/12.420791 Widyantoro D.H., 2001, P BISC INT WORKSH FU, P155 ZAMIR O, 1999, P WWW8 NR 20 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-8200-5 PY 2003 BP 402 EP 407 DI 10.1109/INDIN.2003.1300368 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Engineering, Manufacturing SC Computer Science; Engineering GA BAB98 UT WOS:000221512100061 ER PT S AU Blanken, HM Grabs, T Schek, HJ Schenkel, R Weikum, G AF Blanken, HM Grabs, T Schek, HJ Schenkel, R Weikum, G BE Blanken, H Grabs, T Schek, HJ Schenkel, R Weikum, G TI Intelligent search on XML data - Preface SO INTELLIGENT SEARCH ON XML DATA: APPLICATIONS, LANGUAGES, MODELS IMPLEMENTATIONS AND BENCHMARKS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT Workshop on Intelligent Search in XML Data CY DEC, 2002 CL SCHLOSS DAGSTUHL, GERMANY ID INFORMATION-RETRIEVAL; SEMISTRUCTURED DATA; SEMANTIC SIMILARITY; RELEVANCE FEEDBACK; QUERY LANGUAGE; PROXIMAL NODES; WEB; TEXT; SYSTEMS; MODEL C1 Univ Twente, Fac EEMCS, Dept Comp Sci, NL-7500 AE Enschede, Netherlands. ETH, Database Res Grp, Inst Informat Syst, CH-8092 Zurich, Switzerland. Univ Saarland, Fachrichtung Informat 62, D-66041 Saarbrucken, Germany. RP Blanken, HM (reprint author), Univ Twente, Fac EEMCS, Dept Comp Sci, POB 217, NL-7500 AE Enschede, Netherlands. EM blanken@cs.utwente.nl; grabs@inf.ethz.ch; schek@inf.ethz.ch; schenkel@cs.uni-sb.de; weikum@cs.uni-sb.de; grabs@inf.ethz.ch; schek@inf.ethz.ch; schenkel@cs.uni-sb.de; weikum@cs.uni-sb.de CR Abiteboul S., 1997, International Journal on Digital Libraries, V1 ABITEBOUL S, 1999, P 8 ACM SIGACT SIGMO Abiteboul S, 2000, DATA WEB RELATIONS S Abiteboul S., 1997, P 6 INT C DAT THEOR, P1 ABOULNAGA A, 2001, VLDB, P591 Agirre E., 1996, P 16 INT C COMP LING, P16 AGRAWAL R, 2002, P 18 INT C DAT ENG I AHMEDI L, 2002, P SEM WEB WORKSH SEM AMATO G, 2000, P 4 EUR C ECDL, P328 AMERYAHIA S, 2003, WORLD WID WEB CONS Amsaleg L, 1998, DISTRIB PARALLEL DAT, V6, P217, DOI 10.1023/A:1008646115473 ANDERSON TL, 1990, LECT NOTES COMPUT SC, V416, P317 [Anonymous], INT STAT CLASSIFICAT [Anonymous], MED SUBJECT HEADINGS APERS PMG, 2001, VLDB 2001 P 27 INT C APPELT E, 1999, ITNRO INFORMATION EX ATKINSON MP, 1999, VLDB 99 P 25 INT C V Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BaezaYates R, 2002, J AM SOC INF SCI TEC, V53, P504, DOI 10.1002/asi.10061 BAEZAYATES R, 1996, ACM SIGMOD RECORD, V25, P67, DOI 10.1145/381854.381890 Baker C.F., 1998, P 36 ANN M ASS COMP BARYOSSEF Z, 1999, LECT NOTES COMPUTER, V1649, P2 BATTERYWALA Y, 2000, ACM SIGKDD WORKSH TE Baumgartner R., 2001, VLDB 2001 P 27 INT C, P715 BEERI C, ACM SIGMOD WORKSH WE, P37 BELKIN NJ, 1995, P 4 TEXT RETR C TREC, P181 BELKIN NJ, 1997, P 20 ANN INT ACM SIG BENCHCAPON TJM, 1999, P DEXA 99, P250 BERGER A, 1999, SIGIR 99 P 22 ANN IN Berglund A., 2002, XML PATH LANGUAGE XP BERNERSLEE T, 2001, SCI AM MAY BLOK HE, 2002, THESIS U TWENTE ENSC BLOK HE, 2001, P 2001 ACM CIKM INT Bohannon P, 2002, PROC INT CONF DATA, P64, DOI 10.1109/ICDE.2002.994698 Bohme T, 2001, P GERM DAT C BTW2001, P264 BONCZ P, 2002, THESIS CWI Boncz PA, 1999, VLDB J, V8, P101, DOI 10.1007/s007780050076 Bonifati A, 2000, SIGMOD RECORD, V29, P68 BOURETT R, XML DATABASE PRODUCT Bozsak E, 2002, LECT NOTES COMPUT SC, V2455, P304 Bradley N., 1998, XML COMPANION BRAY T, 2000, EXTENSIBLE MARKUP LA BREMER JM, 2002, P 5 ITN WORKSH WEB D BRESSAN S, 2001, P 10 ACM INT C INF K, P167 BRIGHT MW, 1994, ACM T DATABASE SYST, V19, P212, DOI 10.1145/176567.176569 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BRUDER I, 2000, P 5 INT C APPL NAT L, P227 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUDANITSKY A, 2001, P WORKSH WORDN LEX R Buneman P, 2000, VLDB J, V9, P76, DOI 10.1007/s007780050084 BUNEMAN P, 1994, SIGMOD RECORD Buneman P, 2001, SIGMOD RECORD, V30, P47 Buneman P., 1996, P ACM SIGMOD INT C M, P505, DOI 10.1145/233269.233368 Burges C., 1998, DATA MINING KNOWLEDG, V2 BUXTON S, 2003, WORLD WID WEB CONS F Califf M. E., 1998, THESIS U TEXAS AUSTI CAREY M, 1997, SIGMOD 1997, P135 Carey M. J., 1993, P ACM SIGMOD INT C M, P12, DOI 10.1145/170035.170041 CATTELL RGG, 1992, ACM T DATABASE SYST, V17, P1, DOI 10.1145/128765.128766 CHAKRABARTI S, 1999, 8 WWW C Chakrabarti Soumen, 2002, MINING WEB CHAMBERLIN D, 2002, XML QUERY USE CASES CHAMBERLIN D, XML QUERY LANGUAGE H, P53 CHAUDHURI S, 1995, PROC INT CONF DATA, P190, DOI 10.1109/ICDE.1995.380392 CHEN H, 2000, ACM CHI C HUM FACT C Chen JJ, 2002, PROC INT CONF DATA, P345 CHIARAMELLA Y, 1996, 8134 FERMI ESPRIT BR Chinenyanga T., 2001, P 24 ANN INT C RES D, P163, DOI 10.1145/383952.383982 CHUNG CW, APEX ADAPTIVE PATH I, P121 CIRAVEGNA F, 2000, ECAI WORKSH MACH LEA CLUET S, 1999, ACM SIGMOD WORKSH WE CLUET S, 2001, VLDB, P271 Consens M. P., 1995, Proceedings of the Fourteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1995, DOI 10.1145/212433.212437 Cooper B. F., 2001, VLDB, P341 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 Cormen T.H., 2001, INTRO ALGORITHMS CRESPO A, 1994, SURVEY SEMIAUTOMATIC CROFT W, 2001, P 24 ANN INT C RES D CSUHAJVARHU E, 1994, TOPICS COMPUTER MATH, V5 CUNNINGHAM H, 1999, INFORMATION EXTRACTI CUNNINGHAM H, 2002, P 49 ANN M ASS COMP DECKER S, 1998, IFIP C P, V138, P351 DELBIMBO A, 1999, VISUAL INFORMATION R DELIS A, 1999, SIGMOD 1999 DEUTSCH A, 1998, QL 98 QUER LANG WORK Deutsch A, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P431 DEVRIES A, 1999, INT C DAT SEM DS 8 R, P16 de Vries AP, 2001, J AM SOC INF SCI TEC, V52, P954, DOI 10.1002/asi.1144.abs DEVRIES AP, 1999, THESIS U RWENTE ENSC Dietz P., 1982, P 14 ANN ACM S THEOR, P122, DOI 10.1145/800070.802184 DING Y, 2003, SEM SEM WEB TECHN Duda R., 2000, PATTERN CLASSIFICATI, V2nd Dumais S.T., 2000, SIGIR 00, P256 *DVB, DVB CONS MULT HOM PL EGNOR D, 2000, ACM SIGIR 2000 WORKS *ENV, 1995, 1828 ENV CEN *EUR, EUR RETR ENG Fankhauser P., 1993, Electronic Publishing: Origination, Dissemination and Design, V6 FARQUHAR A, KSL9626 Fellbaum C, WORDNET ELECT LEXICA FERNANDEZ M, 2002, WORLD WID WEB CONS N Florescu D., 1999, IEEE DATA ENG B, V22, P27 Florescu D, 1999, 3680 INRIA FLORESCU D, 2000, P INT WWW C AMST MAY FOX EA, 1988, INFORM PROCESS MANAG, V24, P257, DOI 10.1016/0306-4573(88)90093-3 FRANKLIN MJ, 2002, P 2002 ACM SIGMOD IN FREI HP, 1991, INFORMATIK FACHBERIC, V289, P1 Freitag D., 1998, THESIS CARNEGIE MELL Fuhr N., 2001, P 24 ANN INT ACM SIG, P172, DOI 10.1145/383952.383985 FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 FUHR N, 1997, T INFORMATION SYSTEM, V14, P32 FUHR N, 2002, P 1 INEX WORKSH DAGS Fuhr N., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291005 Fuhr N, 1999, INFORM PROCESS MANAG, V35, P101, DOI 10.1016/S0306-4573(99)00009-6 GEY F, 1999, SIGIR 99 P 22 ANN IN Goldman Roy, 1997, VLDB, P436 GRABS T, 2003, THESIS ETH GRABS T, 2003, P 1 INEX WORKSH DOGS, P35 GRABS T, IN PRESS KNOWLEDGE I GRABS T, 2000, 340 ETH DEP COMP SCI Grabs T., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management Grabs T., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002 GRABS T, 2002, XML INF RETR WORKSH GRAY J, 1993, DATABASES TRANSACTIO Grishman R., 1997, LECT NOTES ARTIF INT, V1299, P10 Grossman DA, 1997, J AM SOC INFORM SCI, V48, P122, DOI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# GRUBER TR, 1993, FORMAL ONTOLOGY CONC, P89 Grust T, 2002, P 2002 ACM SIGMOD IN, P109 Grust T., 2003, P 29 INT C VER LARG GUARNIZO LE, 1998, TRANSNATIONALISM BEL, V6, P3 GUPTA N, 2000, P 16 INT C DAT ENG 2, P84, DOI 10.1109/ICDE.2000.839392 Guttman A, 1984, P ACM SIGMOD INT C M, P47 HALEVY A, 2003, P 1 SEM C INN DAT SY HARMAN D, 1995, SCHRIFTEN INFORMATIO, V20 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 *HAV INC, 2000, HAVI HOM AUD VID INT Health_Level 7, HLTH LEVEL 7 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 HEARST MA, 1999, MODERN INFORMATION R Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 HELMER S, 2001, MA0115 U MANNH HIEMSTRA D, 2003, P 1 INEX WORKSH DAGS Hiemstra D, 1999, LECT NOTES COMPUT SC, V1696, P274 HIEMSTRA D, 1999, NIST SPECIAL PUBLICA, P227 Hirst G., 1998, WORDNET ELECT LEXICA, P305 HITZ O, 1999, 1 INT C DOC AN REC, P374 Horrocks I, 2002, LECT NOTES COMPUT SC, V2287, P2 HUCK G, 1999, P OOPSLA WORKSH JAV Humphreys BL, 1998, J AM MED INFORM ASSN, V5, P1 Ide E., 1971, SMART RETRIEVAL SYST, P337 *ISO IEC, 2000, 1324922000 ISOIEC Ives ZG, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P299 Ives Z. G., 2000, UWCSE20000502 JAAKKOLA J, 1999, C1992 U HELS DEP COM JAMASZ M, 2003, TR200301 U OTT JAMASZ M, 2001, P NAACL WORKSH WORDN Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4 JAOCHIMS T, 2002, THESIS JENSEN CS, 2002, LECT NOTES COMPTUER, V2287 Jiang Jay J., 1997, P INT C RES COMP LIN, P19 JOHNSON B, 1991, TR2657 U MAR COMP SC JONSSON B, 1998, SIGMOD 1999 P ACM SI, P118 Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561 KAZAI G, 2003, 25 EUR C INF RETR RE, P88 Keen M., 1966, FACTORS DETERMINING, V2 KEKALAINEN J, 2002, J AM SOC INFORMATION, V53 Kerschberg L., 2001, P 2 INT C WEB INF SY, P41 KILPELAINEN P, 1993, P 16 ANN INT ACM SIG, P214, DOI 10.1145/160688.160722 KIMOTO H, 1990, P 13 ANN INT ACM SIG, P227, DOI 10.1145/96749.98228 Kleinberg J., 1999, J ACM, V46 KLETTKE M, 2001, WORLD WID WEB DAT 3, P63 Koenemann J., 1996, P ACM SIGCHI C HUM F, P205, DOI 10.1145/238386.238487 Kraaij W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval Kriegel H., 2000, P 26 INT C VER LARG, P407 KRIEWEL S, 2001, THESIS U DORTMOUND Kwon O.-W., 1994, P 3 INT C INF KNOWL, P140, DOI 10.1145/191246.191270 Lalmas M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P110, DOI 10.1145/258525.258546 LEACOCK C, 1998, WORDNET ELECT LEXICL, P264 Lenat D., 1990, BUILDING LARGE KNOWL LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LEWIS WD, 2002, 12 U AR LI Q, 2000, AMIA AN S LOS ANG CA Li Q, 2001, VLDB, P361 Lin Dekang, 1998, P 15 INT C MACH LEAR, P296 LIST JA, 2003, IN EV XML RETR INEX Lundquist C., 1997, Proceedings of the Sixth International Conference on Information and Knowledge Management. CIKM'97, DOI 10.1145/266714.266844 MACLEOD IA, 1991, COMPUT J, V34, P254, DOI 10.1093/comjnl/34.3.254 MAEDCHE A, 2001, CEUR WORKS P, V40 Maedche A., 2000, P 12 INT C SOFTW KNO Manning C. D., 1999, FDN STAT NATURAL LAN Manolescu I., 2001, VLDB, P241 MCHALE ML, 1998, P WORKSH CONT VIS IN McHugh J., 1999, Very Large Data Bases. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528 Miller DRH, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P214 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Milo T, 1999, LECT NOTES COMPUT SC, V1540, P277 Mitchell T., 1996, MACHINE LEARNING Mitra P, 2000, LECT NOTES COMPUT SC, V1777, P86, DOI 10.1007/3-540-46439-5_6 Mizzaro S., 1998, INTERACTING COMPUTER, V10, P305 Myaeng S. H., 1998, P 21 ANN INT ACM SIG, P138, DOI 10.1145/290941.290980 Nanopoulos A, 2002, VLDB J, V11, P138, DOI 10.1007/s00778-002-0068-7 Nascimento MA, 2002, DATA KNOWL ENG, V43, P57, DOI 10.1016/S0169-023X(02)00122-2 Nash B, 2003, BRIT MED J, V326, P674, DOI 10.1136/bmj.326.7391.674 Naughton JF, 2001, IEEE DATA ENG B, V24, P27 Navarro G, 1997, ACM T INFORM SYST, V15, P400, DOI 10.1145/263479.263482 Neumann G, 2002, COMPUT INTELL, V18, P451, DOI 10.1111/0824-7935.00197 Nilsson N. J., 1998, ARTIFICIAL INTELLIGE *NIST, 1998, P 7 MESS UND C MUC Noy NF, 2000, LECT NOTES ARTIF INT, V1937, P17 Ogilvie P., 2003, P 1 INEX WORKSH DAGS, P18 OLEARY DE, 1999, LECT NOTES ARTIF INT, V1621, P1 Papazoglou MP, 1998, LECT NOTES COMPUT SC, V1413, P251 PAQUES H, 2001, P 2001 ACM CIKM INT PIWOWARSKI B, 2003, P 1 INEX WORKSH DAGS, P7 PLASSARD MF, 1998, UBCIM PUBLICATIONS N, V19 QIU U, 1993, P SIGIR 93 16 ACM IN, P160 QIU Y, 1995, 255 SWISS FED I TECH Rabiner L.R., 1990, READINGS SPEECH RECO, P267 RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945 RECTOR AL, 1992, P 7 WORLD C MED INF, P1420 Resnik P, 1999, J ARTIF INTELL RES, V11, P95 Resniks P., 1995, P 14 INT JOINT C ART, P448 RICHARDSON R, 1994, P ACIS C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 RUBENSTE.H, 1965, COMMUN ACM, V8, P627, DOI 10.1145/365628.365657 RUI Y, 1998, P IS T SPIE STOR RET, V6, P25 RUNAPONGSA K, 2002, P EEXTT02 EL VERS Russel S., 1995, ARTIFICIAL INTELLIGE SAHUGUET A, 1999, ACM SIGMOD WORKSH WE SALMINEN A, 1993, ACTA LINGUISTICA HUN, V41, P277 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saracevic T., 1995, P 18 ANN INT ACM SIG, P138, DOI 10.1145/215206.215351 SARAWAGI S, 2002, VLDB 2002 TUR NOT 28, P1 SCHAUBLE P, 1997, MULTIMEDIA INFORMATI SCHLIEDER T, 2000, DELOS WORKSH INF SEE SCHMIDT A, 2002, XMARK QUERIES SCHMIDT A, 2001, LECT NOTES COMPUTER, V1997, P47 Schmidt A, 2001, INSR0103 CWI SCHMIDT A, 2002, VLDB 2002 P 28 INT C SCHMIDT AR, 2000, XML STORE BENCHMARK Schoning H, 2001, PROC INT CONF DATA, P149, DOI 10.1109/ICDE.2001.914823 SCHONING H, 2003, XML DATA MANAGEMENT Schoning H, 2000, LECT NOTES COMPUT SC, V1777, P383 Selinger P.G., 1979, P ACM SIGMOD INT C M, P23 SHANMUGASUNDARA.J, 2000, LECT NOTES COMPUTER, V1997, P17 Shanmugasundaram J., 1999, Very Large Data Bases. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SIZOV S, 2003, P 1 SEM C INN DAT SY SIZOV S, 2002, P 3 INT C WEB INF SY, P323, DOI 10.1109/WISE.2002.1181668 Soderland S., 1999, MACHINE LEARNING SOUTHALL R, 1988, INT C EL PUBL DOC MA, P35 STAAB S, 2000, P 14 EUR C ART INT E Staab S, 2000, COMPUT NETW, V33, P473, DOI 10.1016/S1389-1286(00)00039-6 Staab S., 1999, P 3 WORKSH COOP INF, P113 Suciu D, 2002, ACM T DATABASE SYST, V27, P1, DOI 10.1145/507234.507235 SUCIU D, 2001, LECT NOTES COMPUTER, V1997 Sussna M., 1993, CIKM 93. Proceedings of the Second International Conference on Information and Knowledge Management TAGHVA K, 1995, P IS T SPIE 1995 INT, P317, DOI 10.1117/12.205833 TAN J, 1995, HLTH MANAGEMENT APR THEOBALD A, 2002, LECT NOTES COMPUTER, V2287 THEOBALD A, 2000, LECT NOTES COMPUTER, V1997, P105 THOMPSON A, 2001, XML SCHEMA 1 TIBERIO P, 1995, ENCY MICROCOMPUTERS, V16, P377 TUTTE K, 2002, MERGE LATTIC JOIN XM *UN CONS, UN STAND VERS 3 2 0 Valduriez Patrick, 1999, PRINCIPLES DISTRIBUT VANBEMMEL J, 1997, HD BMED INFORMATICES VANDOORN MGM, 2000, P 5 ACM DIG LIBR C D VANKEULEN M, 2002, TRCTIT0219 U TWENT VANZWOL R, 2002, P 19 BRIT NAT C DAT, P150 VANZWOL R, 2002, MODELLING SEARCHING Vapnik V., 1998, STAT LEARNING THEORY Volz M, 1996, PROC INT CONF DATA, P10, DOI 10.1109/ICDE.1996.492084 VOORHEES EM, 2002, 10 TEXT RETR C TREC *W3C, W3C XML SCH *W3C DOM WORK GROU, 2002, DOC OBJ MOD DOM WACTLAR H, INFOMRATION DIGITAL WEBER R, 1998, VLDB 98 P 24 INT C V Weber R, 2000, LECT NOTES COMPUT SC, V1777, P21 WESTERVELD T, 2003, EURASIP J APPL SIG P, P186 White RW, 2002, LECT NOTES COMPUT SC, V2291, P93 WINDHOUWER MA, 2001, INSR0111 CWI Windhouwer M, 2003, WEB-ENABLED SYSTEMS INTEGRATION: PRACTICES AND CHALLENGES, P200 Windhouwer M.A., 1999, INT WORKSH INF INT W *WORLD WID WEB CON, SEM WEB ACT World Wide Web Consortium, RES DESCR FRAM WU ZB, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P133 XU J, 1994, SURVEY SEMIAUTOMATIC, P105 Xu Y, 2003, ARTIF INTELL MED, V27, P283, DOI 10.1016/S0933-3657(03)00007-1 YANG Y, 1999, J INFORMATION RETRIE, V1 Yang Y., 1997, INT C MACH LEARN ICM YAO BB, 2002, TRCS200239 U WAT Yoshikawa M, 2001, ACM T INTERNET TECHN, V1, P110, DOI 10.1145/383034.383038 ZANIOLO C, 2000, LECT NOTES COMPUTER, V1777 ZEZULA P, 2003, 2003TR04 ISTICNR Zhang C., 2001, P ACM SIGMOD INT C M, V5, P425, DOI [10.1145/375663.375722, DOI 10.1145/375663.375722] MEDPICS CERTIFICATIO GEN ARCHITECTURE LAN NR 305 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40768-5 J9 LECT NOTES COMPUT SC PY 2003 VL 2818 BP V EP + PG 23 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BX78R UT WOS:000186428900001 ER PT S AU Walker, RL AF Walker, RL BE Abraham, A Franke, K Koppen, M TI A framework for high-performance web mining in dynamic environments using honeybee search strategies SO INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS SE ADVANCES IN SOFT COMPUTING LA English DT Proceedings Paper CT 3rd International Conference on Intelligent Systems Design and Applications (ISDA 2003) CY AUG 10-13, 2003 CL TULSA, OK SP IEEE Syst Man & Cybernet Soc, World Federat Soft Comp, European Soc Fuzzy Log & Technol, Springer Velag, Germany, COEITT HO OKLAHOMA STATE UNIV, COLL ARTS & SCI AB The methodology for the knowledge discovery in databases architecture outlines possible approaches taken by search engines to improve their IR systems. The conventional approach provided the requester with query results based on the user's knowledge of respective IR systems. This paper proposes the use of an information sharing model based on the information processing methodology of honeybees and knowledge discovery in databases as opposed to the traditional IR models used by current search engines. The major limitation of IR-based systems is their dependency on human editors which is reflected in static sets of query terms and the use of stemming. Experimental results are presented for data clustering component (Web page indexer) of the Tocorime Apicu search engine which is based on the information sharing model. C1 Tapicu Inc, Los Angeles, CA 90009 USA. RP Walker, RL (reprint author), Tapicu Inc, POB 88492, Los Angeles, CA 90009 USA. CR Abbass HA, 2001, IEEE C EVOL COMPUTAT, P207, DOI 10.1109/CEC.2001.934391 Adriaans P., 1996, DATA MINING [Anonymous], 2003, WALL STREET J Bonabeau E., 1997, BIOCOMPUTATION EMERG, P36 Free J.B., 1987, PHEROMONES SOCIAL BE FREE JB, 1970, STUDIES BIOL, V81 FRITSCH P, 2000, WALL STREET J, V142, P1 KIM S, 2000, P CEC 2000, P1253 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 Maniezzo V, 2001, LECT NOTES COMPUT SC, V2037, P80 Parthasarathy S, 2000, LECT NOTES COMPUT SC, V1800, P399 RAMOS V, 2002, SOFT COMPUTING SYSTE, P500 SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINKA MP, 2002, SOFT COMPUTING SYSTE, P881 WALKER RL, 2002, PDPTA 02 P 2002 INT, P157 WALKER RL, 2002, SOFT COMPUTING SYSTE, P891 WALKER RL, SEAL 02 P 2002 AS PA, P208 WALKER RL, 2003, IN PRESS BIS 2003 P WALKER RL, 2003, THESIS U CALIFONIA L Walker RL, 2001, PARALLEL COMPUT, V27, P71, DOI 10.1016/S0167-8191(00)00089-2 *YAH INC, 1998, YAH WEB PAG NR 22 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 1615-3871 BN 3-540-40426-0 J9 ADV SOFT COMP PY 2003 BP 193 EP 204 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BX56V UT WOS:000185702900019 ER PT B AU Meng, XN Chen, ZX Spink, A AF Meng, XN Chen, ZX Spink, A BE Srimani, PK Bein, W Hashemi, R Lawrence, E Cannataro, M Regentova, E Spink, A TI A multiplicative gradient descent search algorithm for user preference retrieval and its application to web search SO ITCC 2003: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: COMPUTERS AND COMMUNICATIONS, PROCEEDINGS LA English DT Proceedings Paper CT International Conference on Information Technology - Coding and Computing CY APR 28-30, 2003 CL LAS VEGAS, NV SP IEEE Comp Soc Task Force Informat Technol Business Applicat ID VECTOR-SPACE MODEL; INFORMATION-RETRIEVAL; FEEDBACK AB The gradient descent procedure in [19] for user preference retrieval is based on linear additions of documents judged by the user. In contrast we design in this paper a multiplicative gradient descent search algorithm MG that uses a multiplicative query expansion strategy to adaptively improve the query vector. Our work generalizes the work in [19] in the following two aspects: various updating functions may be used in our algorithm; and multiplicative updating for a weight is dependent on the value of the corresponding index term, which is more realistic and applicable to real-valued vector space. The algorithm MG boosts the usefulness of an index term exponentially, while the algorithm in [19] does so linearly. We report a working prototype of the Web search project MAGRADS (Multiplicative Adaptive Gradient Descent Search) which is built upon algorithm MG, and its search performance analysis. C1 Bucknell Univ, Dept Comp Sci, Lewisburg, PA 17837 USA. RP Meng, XN (reprint author), Bucknell Univ, Dept Comp Sci, Lewisburg, PA 17837 USA. CR Spink A, 1996, ANNU REV INFORM SCI, V31, P33 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BOLLMANN P, 1987, P ACM SIGIR C RES DE CHEN Z, 2002, P INT C INT COMP JUL, P643 Chen ZX, 2001, LECT NOTES COMPUT SC, V2108, P540 Chen ZX, 2002, INFORM RETRIEVAL, V5, P61, DOI 10.1023/A:1012730924277 Duda R.O., 1973, PATTERN CLASSIFICATI Fishburn P.C., 1970, UTILITY THEORY DECIS FRAKES WR, 1992, INFORMATION RETRIEVA Ide E., 1971, SMART RETRIEVAL SYST, P337 Kivinen J, 1997, ARTIF INTELL, V97, P325, DOI 10.1016/S0004-3702(97)00039-8 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 SALTON G, 1989, AUTOMATIC TEST PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 18 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1916-4 PY 2003 BP 150 EP 154 PG 5 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BW76A UT WOS:000183074700028 ER PT S AU Khare, A AF Khare, A BE Palade, V Howlett, RJ Jain, L TI Connecting word clusters to represent concepts with application to web searching SO KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES 2003) CY SEP 03-05, 2003 CL OXFORD, ENGLAND HO UNIV OXFORD DE knowledge representation; machine learning AB The need to have a search technique which will help a computer in understanding the user, and his requirements, has long been felt. This paper proposes a new technique, of doing so. It proceeds by first clustering the English language words into clusters of similar meaning, and then connecting those clusters according to their observed relationships and co-occurrences in web pages. These known relationships between word clusters are used to enhance the user's query, and in effect 'understand' it. This process will result in giving results of more value to the user. This procedure does not suffer with the problems faced by many of the presently used techniques. C1 DAVV, Inst Engn & Technol, Dept Informat Technol, Indore, Madhya Pradesh, India. RP Khare, A (reprint author), DAVV, Inst Engn & Technol, Dept Informat Technol, Khandwa Rd, Indore, Madhya Pradesh, India. CR BHARAT K, 2000, P 9 INT WORLD WID WE BHATTACHARYA P, TEXT CLUSTERING USIN BREZILLON P, CONTEXT PROBLEM SOLV Brin S., 1998, P 7 INT WORLD WID WE, P107 DEERWESTER SC, 1990, J AM SOC INFORMATION FINKELSTEIN L, 2001, 10 WORLD WID WEB C M Gonzalo J., 1998, P COLING ACL 98 WORK Harman D., 1992, INFORMATION RETRIEVA, P363 HOWE A, 1997, SAVVYSEARCH METASEAR Lawrence S., 2000, IEEE DATA ENG B, V23, P25 MARTIN S, ALGORITHMS BIGRAM TR MCCARTHY J, 1993, P 13 IJCAI, V1, P555 MILLER GA, 1993, INTRO WORDNET ON LIN RICHARDSON R, P BCS IRSG C CREW SAHAMI M., 1998, THESIS STANFORD U SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, SMART INFORMATION RE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SELBERG E, 1997, IEEE EXPERT, V12, P8 SMEATON, 1996, P 19 INT C RES DEV I Uchida H., 1995, UNL GIFT MILLENNIUM Ushioda A., 1996, P 4 WORKSH VER LARG VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 24 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40803-7 J9 LECT NOTES ARTIF INT PY 2003 VL 2773 BP 816 EP 823 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BX81T UT WOS:000186518000109 ER PT S AU Vinot, R Yvon, F AF Vinot, R Yvon, F BE Lavrac, N Gamberger, D Blockeel, H Todorovski, L TI Improving Rocchio with weakly supervised clustering SO MACHINE LEARNING: ECML 2003 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 14th European Conference on Machine Learning CY SEP 22-26, 2003 CL CAVTAT, CROATIA SP Croatian Minist Sci & Technol, Slovenian Minist Educ, Sci & Sports, Knowledge Discovery Network Excellence AB This paper presents a novel approach for adapting the complexity of a text categorization system to the difficulty of the task. In this study, we adapt a simple text classifier (Rocchio), using weakly supervised clustering techniques. The idea is to identify sub-topics of the original classes which can help improve the categorization process. To this end, we propose several clustering algorithms, and report results of various evaluations on standard benchmark corpora such as the Newsgroups corpus. C1 GET, ENST, F-75634 Paris, France. RP Vinot, R (reprint author), GET, ENST, 46 Rue Barrault, F-75634 Paris, France. CR ANDREW Y, 2001, NEURAL INFORMATION P Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 Cheeseman P., 1988, P 5 INT C MACH LEARN, P54 DEKROON HCM, 1996, INT C MACH LEARN WOR EICHMANN D, 1999, P DARPA BROADC NEWS, P69 Han E., 2000, PRINCIPLES DATA MINI, P424 Joachims T, 1998, EUR C MACH LEARN ECM, P137 Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 MOSCHITTI A, 2003, P 2K EUR C INF RETR OH JH, 2001, P 10 TEXT RETR C TRE, P347 ROBERT E, 2000, MACH LEARN, V39, P135 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283 Singhal A, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P25, DOI 10.1145/258525.258530 VINOT R, 2002, C INT FOUILL TEXTS C, P17 VINOT R, 2001, ASMDA 2001 10 INT S, P992 Wai Lam, 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval ęØäŗ‘å³°, 1999, [č„æ安公č·Æäŗ¤é€šå¤§å­¦å­¦ęŠ„, Journal of Xian Highway University], P67 NR 20 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-20121-1 J9 LECT NOTES ARTIF INT PY 2003 VL 2837 BP 456 EP 467 PG 12 WC Computer Science, Artificial Intelligence SC Computer Science GA BX96X UT WOS:000187061900041 ER PT B AU Maldonado-Naude, MF Baeza-Yates, R Sanchez, JA AF Maldonado-Naude, MF Baeza-Yates, R Sanchez, JA BE Chavez, E Favela, J Mejia, M Oliart, A TI Using Hermes-F: Experiences with a framework for developing information retrieval applications SO PROCEEDINGS OF THE FOURTH MEXICAN INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE (ENC 2003) LA English DT Proceedings Paper CT 4th Mexican International Conference on Computer Science (ENC 2003) CY SEP 08-12, 2003 CL TLAXCALA, MEXICO SP Soc Mexicana Cienca Comp, Consejo Nacl Cienca Tecnol, Natl Sci Fdn, Univ Autonoma Tlaxcala, Univ Texas El Paso, Univ Las Amer, Inst Tecno Autonomo Mexico, Lab Nacl Informat Avanzada AC, Ctr Invest Cient Educ Super Ensenada, Univ Michoacana San Nicolas Hidalgo, Sun Microsyst, Microsoft AB Hermes-F is a framework that enables the development of applications that perform information retrieval(IR) tasks. Its central component is an extensible server that provides access to a variety of IR models, query transformations and digital collections. We have successfully instantiated our proposed framework by implementing a fully operational IR server that is accessed from various digital library applications. We describe the Hermes-F environment and focus on our experiences with this instance of the application framework. We report on various tests, as well as on our results of using and extending the available IR models for diverse collections. C1 Univ Americas Puebla, Libraries & Ctr Res Informat & Automat Technol, Mexico City, DF, Mexico. RP Maldonado-Naude, MF (reprint author), Univ Americas Puebla, Libraries & Ctr Res Informat & Automat Technol, Mexico City, DF, Mexico. EM ferdim@yahoo.com; rbaeza@dcc.uchile.cl CR ARIAS JA, 2003, P 4 ENC CIENC COMP E Baeza-Yates R., 1999, MODERN INFORMATION R, V1st FERNANDEZ L, 2001, P ETD 2003 BERL GERM FURNAS WG, 1988, P 11 ANN INT ACM SIG, P465 HIRANABE K, 1999, JAVATM 3D API 1 2 BE MALDONADO F, 2002, THESIS U AM PUEBLA C MALDONADO F, 2002, HERMES F FRAMEWORK D MEDINA MA, 2002, P 12 CON C AC MEX FE, P74 MYKA A, 1995, DIGITAL LIB RES TECH, P130 RAMIREZ M, 2001, THESIS U AM PUEBLA C SALTON G, 1975, COMMUN ACM, V18, P205 SALTON G, 1987, READINGS INFORMATION, P323 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanchez JA, 1997, J NETW COMPUT APPL, V20, P45 SANCHEZ JA, 2003, P JOINT C DIG LIB JC Sanchez J. A., 2001, Proceedings Seventh International Workshop on Groupware. CRIWG 2001, DOI 10.1109/CRIWG.2001.951810 SANCHEZ JA, 2001, C HUM FACT COMP SYST, P63 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd NR 19 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1915-6 PY 2003 BP 101 EP 108 DI 10.1109/ENC.2003.1232882 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BX62B UT WOS:000185894500020 ER PT S AU Bradshaw, S AF Bradshaw, S BE Koch, T Solvberg, IT TI Reference directed indexing: Redeeming relevance for subject search in citation indexes SO RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 7th European Conference on Research and Advanced Technology for Digital Libraries CY AUG 17-22, 2003 CL TRONDHEIM, NORWAY SP ABM, BIBSYS, ERCIM, NFR, Nordic Council Sci Informat, Norwegian Univ Sci & Technol, Comp & Informat Sci, SINTEF Telecom & Informat, Sun Microsyst ID WEB; RETRIEVAL; TEXT AB Citation indexes are valuable tools for research, in part because they provide a means with which to measure the relative impact of articles in a collection of scientific literature. Recent efforts demonstrate some value in retrieval systems for citation indexes based on measures of impact. However, such approaches use weak measures of relevance, ranking together a few useful documents with many that are frequently cited but irrelevant. We propose an indexing technique that joins measures of relevance and impact in a single retrieval metric. This approach, called Reference Directed Indexing (RDI) is based on a comparison of the terms authors use in reference to documents. Initial retrieval experiments with RDI indicate that it retrieves documents of a quality on par with current ranking metrics, but with significantly improved relevance. C1 Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA. RP Bradshaw, S (reprint author), Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA. EM shannon-bradshaw@uiowa.edu CR BHARAT K, 2001, P 10 WORLD WID WEB C Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X Chakrabarti S, 1998, COMPUT NETWORKS ISDN, V30, P65, DOI 10.1016/S0169-7552(98)00087-7 CRASWELL N, 2001, P ACM SIGIR C RES DE Davison B. D., 2002, P 13 ACM C HYP HYP H, P159 Davison BD, 2000, P 23 ANN INT ACM SIG, P272, DOI 10.1145/345508.345597 FURNKRANZ J, 1999, INTELLIGENT DATA ANA, P487 GARFIELD EG, 1983, CITATION INDEXING Harnad S., 2002, D LIB MAGAZINE, V8 IWAZUME M, 1996, P 9 INT C INT ENG AP, P305 Jones S., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007999900022 KESSLER MM, 1961, P W JOINT COMP C CAM, P247, DOI 10.1145/1460690.1460718 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lawrence S., 1999, P 8 INT C INF KNOWL Menczer F, 2000, MACH LEARN, V39, P203, DOI 10.1023/A:1007653114902 OLIVER A, 1994, P 1 INT WORLD WID WE Rennie J., 1999, P 16 INT C MACH LEAR, P335 RICHARDSON M, 2002, ADV NEURAL INFORMATI, V14 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, SMART SIRE EXPT RETR, P118 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SPARCKJONES K, 1972, J DOC, V28, P11 SPERTUS E, 1998, THESIS MIT Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R WESTBROOK JH, 1960, SCIENCE OCT, P132 NR 25 TC 9 Z9 9 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40726-X J9 LECT NOTES COMPUT SC PY 2003 VL 2769 BP 499 EP 510 PG 12 WC Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BX65E UT WOS:000185995400045 ER PT B AU Lee, J Winslett, M Ma, XS Yu, SK AF Lee, J Winslett, M Ma, XS Yu, SK BE Nittel, S Gunopulos, D TI Declustering large multidimensional data sets for range queries over heterogeneous disks SO SSDBM 2002: 15TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND STATISTICAL DATABASE MANAGEMENT LA English DT Proceedings Paper CT 15th International Conference on Scientific and Statistical Database Management CY JUL 09-11, 2003 CL CAMBRIDGE, MA SP Univ Maine, Natl Ctr Geo Informat & Anal AB Declustering is a technique to distribute data sets over multiple disks so that future retrievals can be well balanced over the disks and be performed in parallel. Although clusters often have heterogeneous disks, most declustering work has focused only on homogeneous environments. In this work, we investigate the declustering problem for a heterogeneous disk environment using virtual servers, and propose novel approaches for deciding the number of virtual servers and the mapping between virtual servers and physical disks. Our experimental results show that by combining our algorithm for choosing the number of virtual servers with a greedy algorithm for mapping virtual servers to disks, users can expect range query retrieval performance within 4% of the optimum achievable in practice on average, in all configurations studied. Compared to an intuitively natural approach to the problem, this represents an improvement of 8-31 % in average fetch ratio, as well a 26-38% reduction in the standard deviation of performance for small queries. C1 Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA. RP Lee, J (reprint author), Univ Illinois, Dept Comp Sci, 1304 W Springfield Ave, Urbana, IL 61801 USA. CR ABDELGHAFFAR K, 1997, P ICDT BHATIA R, 2000, P EDBT BHATIA R, 2000, P ICDE CHEN C, 1992, INFORMATION SYSTEMS, V17 CHEN CM, 2001, P SSDBM CHEN LT, 1995, P VLDB CHEN LT, 1993, P VLDB CHENG C, 2002, P PODS DU HC, 1982, ACM T DATABASE SYSTE, V7 FALOUTSOS C, 1988, IEEE T SOFTWARE ENG, V14 FALOUTSOS C, 1993, P INT C PAR DISTR IN FANG M, 1986, P VLDB FERHATOSMANOGLU H, 1999, P ICDE KIM M, 1988, P SIGMOD KUO S, 1999, P IDEAS KUO S, 1997, P SSDBM LIU D, 1995, P ICDE PRABHAKAR S, 1998, P ICDE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 19 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1964-4 PY 2003 BP 212 EP 221 PG 10 WC Computer Science, Information Systems SC Computer Science GA BX17F UT WOS:000184498600023 ER PT J AU Bauer, T Leake, D AF Bauer, T Leake, D TI Using document access sequences to recommend customized information SO IEEE INTELLIGENT SYSTEMS LA English DT Article C1 Indiana Univ, Dept Comp Sci, Bloomington, IN 47405 USA. RP Bauer, T (reprint author), Indiana Univ, Dept Comp Sci, Lindley Hall,150 S Woodlawn Ave, Bloomington, IN 47405 USA. EM trbauer@acm.org; leake@indiana.edu CR BAUER T, 2001, 10 INT C INF KNOWL M, P568 Bauer T, 2001, LECT NOTES ARTIF INT, V2116, P30 BERRY M, 1995, P SUP SC 95 LOS AL C, P1 Billsus D., 1999, Proceedings of the Third International Conference on Autonomous Agents, DOI 10.1145/301136.301208 Budzik J, 2001, KNOWL-BASED SYST, V14, P37, DOI 10.1016/S0950-7051(00)00105-2 Lieberman H., 1995, P 14 INT JOINT C ART, P924 Pazzani M, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P54 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 8 TC 3 Z9 3 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1541-1672 J9 IEEE INTELL SYST JI IEEE Intell. Syst. PD NOV-DEC PY 2002 VL 17 IS 6 BP 27 EP 33 DI 10.1109/MIS.2002.1134359 PG 7 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 620LH UT WOS:000179534500007 ER PT J AU Chang, E Seide, F Meng, HM Chen, ZR Shi, Y Li, YC AF Chang, E Seide, F Meng, HM Chen, ZR Shi, Y Li, YC TI A system for spoken query information retrievat on mobile devices SO IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING LA English DT Article DE information retrieval; speech recognition; user interface AB With the proliferation of handheld devices, information access on mobile devices is a topic of growing relevance. This paper presents a system that allows the user to search for information on mobile devices using spoken natural-language queries. We explore several issues related to the creation of this system, which combines state-of-the-art speech-recognition and information-retrieval technologies. This is the first work that we, are aware of which evaluates spoken query based information retrieval on a commonly available and well researched text database, the Chinese news corpus used in National Institute of Standards and Technology (NIST)'s TREC-5 and TREC-6 benchmarks. To compare spoken-query retrieval performance for different relevant scenarios and recognition accuracies, the benchmark queries-read verbatim by 20 speakers-were recorded simultaneously through three channels: headset microphone, PDA microphone, and cellular phone. Our results show that for mobile devices with high-quality microphones, spoken-query retrieval based on existing technologies yields retrieval precisions that come close to that for perfect text input (mean average precision 0.459 and 0.489, respectively, on TREC-6). C1 Microsoft Res Asia, Beijing 100080, Peoples R China. Chinese Univ Hong Kong, Shatin, Hong Kong, Peoples R China. RP Chang, E (reprint author), Microsoft Res Asia, Beijing 100080, Peoples R China. RI Meng, Helen/F-6043-2011 CR BAI B, 1998, P INT S CHIN SPOK LA CHANG E, 2000, P INT C SPOK LANG PR CHANG E, 2001, P EUR CHEN B, 2000, P IEEE INT C AC SPEE CHEN H, 2000, P CHI 00 HUM FACT CO CORSTONOLIVER S, 2001, P WORKSH AUT SUMM NA HUANG X, 2001, P IEEE INT C AC SPEE HUANG X, 1998, P 6 TEXT RETR C TREC, P137 Jones K. S., 1996, Information Processing & Management, V32 Kenney Ng, 2000, THESIS MIT Koza J. R., 1992, GENETIC PROGRAMMING KUPIEC J, 1994, P HUM LANG TECHN C A KWOK KL, 1998, P 6 TEXT RETR C TREC LIAO YF, 2000, P INT C SPOK LANG PR MENG H, 2001, P HUM LANG TECHN C MILLER DRH, 1999, P SIGIR 99 ACM Pearce D., 2000, P INT C SPOK LANG PR SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHI Y, 2002, INT C SPOK LANG PROC Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 TONG X, 1997, P 5 TEXT RETR C TREC WILKINSON R, 1998, P 6 TEXT RETR C TREC, P25 YONG S, HTK BOOK UNPUB EVOLVING PROGR NR 26 TC 25 Z9 25 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA SN 1063-6676 J9 IEEE T SPEECH AUDI P JI IEEE Trans. Speech Audio Process. PD NOV PY 2002 VL 10 IS 8 BP 531 EP 541 DI 10.1109/TSA.2002.804301 PG 11 WC Acoustics; Engineering, Electrical & Electronic SC Acoustics; Engineering GA 642TL UT WOS:000180820500002 ER PT J AU Tai, XY Ren, F Kita, K AF Tai, XY Ren, F Kita, K TI An information retrieval model based on vector space method by supervised learning SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; supervised learning; vector space model; relevance feedback; singular value decomposition; linear transformation AB This paper proposes a method to improve retrieval performance of the vector space model (VSM) in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition (SVD) and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection. In order to train the model, multiple partitions are created for each collection. Improvement of average precision of the averages over all partitions, compared with the latent semantic indexing (LSI) model, are 20.57% (Medline) and 22.23% (Cranfield) for the two training data sets, and 0.47% (Medline) and 4.78% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve user-supplied relevance information for the long term in the system in order to use it later. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Univ Tokushima, Fac Engn, Tokushima 7708506, Japan. RP Tai, XY (reprint author), Univ Tokushima, Fac Engn, 2-1 Minamijosanjima, Tokushima 7708506, Japan. CR BARTELL BT, 1994, P S DOC AN INF RETR BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 BERRY MW, 1999, SIAM BOOK SERIES CHISHOLM E, 1998, NEW TERM WEIGHTING F DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Ding C., 1999, P 22 ACM SIGIR C, P59 DUMAIS S, 1988, P CHI 88 C HUM FACT, P285 DUMAIS ST, 1992, ARH017527 DUMAIS ST, 1995, P 3 TEXT RET C TREC Faloutsos C., 1995, CSTR3514 U MAR FRAKES WR, 1992, INFORMATION RETRIEVA GILBET S, 1976, LINEAR ALGEBRA ITS A GUTTMAN L, 1977, STATISTICIAN, V26, P81, DOI 10.2307/2987957 Landauer TK, 1998, ADV NEUR IN, V10, P45 Nie JY, 1999, INFORM PROCESS MANAG, V35, P443, DOI 10.1016/S0306-4573(98)00051-X Pandya A., 1996, PATTERN RECOGNITION Papadimitriou C. H., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275505 PARTRIDGE M, 1998, IDA, V2, P292 Press W., 1988, NUMERICAL RECIPES C Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TOKUNAGA T, 1999, INFORMATION RETRIEVA VOGT CC, 1999, P US MOD 99 NR 24 TC 14 Z9 15 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 2002 VL 38 IS 6 BP 749 EP 764 AR PII S0306-4573(01)00053-X DI 10.1016/S0306-4573(01)00053-X PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 600XR UT WOS:000178417500002 ER PT J AU Sanchez, SN Triantaphyllou, E Kraft, D AF Sanchez, SN Triantaphyllou, E Kraft, D TI A feature mining based approach for the classification of text documents into disjoint classes SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE document classification; document indexing; vector space model; data mining; One Clause At a Time (OCAT) algorithm; machine learning ID LOGICAL CLAUSES; EXAMPLES AB This paper proposes a new approach for classifying text documents into two disjoint classes. The new approach is based on extracting patterns. in the form of two logical expressions, which are defined on various features (indexing terms) or the documents. The pattern extraction is aimed at providing descriptions (in the form of two logical expressions) of the two classes of positive and negative examples. This is achieved by means or a data mining approach, called One Clause At a Time (OCAT), which is based on mathematical logic, The application of a logic-based approach to text document classification is critical when one wishes to be able to justify why a particular document has been assigned to one class versus the other class. This situation occurs, for instance, in declassifying documents that have been previously considered important to national security and thus are currently being kept as secret. Some Computational experiments have investigated the effectiveness of the OCAT-based approach and compared it to the well-known vector space model (VSM). These tests also have investigated finding the best indexing terms that could be used in making these classification decisions. The results of these computational experiments on a sample of 2897 text documents from the TIPSTER collection indicate that the first approach hits many advantages over the VSM approach for solving this type of text document classification problem. Moreover, a guided strategy for the OCAT-based approach is presented for deciding which document one needs to consider next while building the training example sets. (C) 2002 Elsevier Science Ltd. All rights reserved. C1 Louisiana State Univ, Dept Ind & Mfg Syst Engn, Baton Rouge, LA 70803 USA. Louisiana State Univ, Dept Comp Sci, Baton Rouge, LA 70803 USA. RP Sanchez, SN (reprint author), Louisiana State Univ, Dept Ind & Mfg Syst Engn, 3128 CEBA Bldg, Baton Rouge, LA 70803 USA. CR Aldenderfer M., 1984, CLUSTER ANAL Anderberg M. R., 1973, CLUSTER ANAL APPL Barnes J.W., 1994, STAT ANAL ENG SCI CO Buckley C., 1995, P 18 ANN INT ACM SIG, P351, DOI 10.1145/215206.215383 CHEN H, 1996, MACHINE LEARNING APP Cleveland Donald B., 1983, INTRO INDEXING ABSTR Deshpande AS, 1998, MATH COMPUT MODEL, V27, P75, DOI 10.1016/S0895-7177(97)00255-0 *DOE, 1995, GEN COURS CLASS DECL Fox C, 1989, ACM SIGIR FORUM, V24, P19 HARMAN D, 1995, INFORM PROCESS MANAG, V31, P271, DOI 10.1016/0306-4573(94)00047-7 LUHN HP, 1957, IBM J RES DEV, V4, P600 LUHN HP, 1958, IBM J RES DEV, V5, P155 Meadow C.T., 1992, TEXT INFORMATION RET Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANCHEZ SN, 2002, IN PRESS COMPUTERS O SCHOLTES JC, 1993, NEURAL NETWORKS NATU SHAW WM, 1995, INFORMATION PROCESSI, V31, P312 Spath H., 1985, CLUSTER DISSECTION Triantaphyllou E, 1996, MATH COMPUT MODEL, V23, P69, DOI 10.1016/0895-7177(95)00234-0 TRIANTAPHYLLOU E, 1994, J GLOBAL OPTIM, V5, P64 TRIANTAPHYLLOU E, 2001, OCAT APPROACH DATA M Triantaphyllou E., 1995, ORSA Journal on Computing, V7 TRIANTAPHYLLOU E, 1994, COMPUT OPER RES, V21, P185, DOI 10.1016/0305-0548(94)90051-5 Triantaphyllou E, 1996, COMPUT OPER RES, V23, P783, DOI 10.1016/0305-0548(95)00075-5 *US DEP EN, 1996, DECL PROD IN STUD RE VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VOORHEES E, 1998, P 6 TEXT RETR C TREC, P1 ZIPFF HP, 1949, HUMAN BEHAV PRINCIPL NR 30 TC 6 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 2002 VL 38 IS 4 BP 583 EP 604 AR PII S0306-4573(01)00049-8 PG 22 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 549ZX UT WOS:000175479100008 ER PT J AU Kim, HJ Lee, SG AF Kim, HJ Lee, SG TI User feedback-driven document clustering technique for information organization SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS LA English DT Article DE semi-supervised clustering; hierarchical agglomerative clustering; relevance feedback; fuzzy information retrieval AB This paper discusses a new type of semisupervised document clustering that uses partial supervision to partition a large set of documents. Most clustering methods organizes documents into groups based only on similarity measures. In this paper, we attempt to isolate more semantically coherent clusters by employing the domain-specific knowledge provided by a document analyst. By using external human knowledge to guide the clustering mechanism with some flexibility when creating the clusters, clustering efficiency can be considerably enhanced. Experimental results show that the use of only a little external knowledge can considerably enhance the quality of clustering results that satisfy users' constraint. C1 Seoul Natl Univ, Sch Engn & Comp Sci, Seoul, South Korea. RP Kim, HJ (reprint author), Seoul Natl Univ, Sch Engn & Comp Sci, Seoul, South Korea. CR Aggarwal C. C., 1999, P 5 ACM SIGKDD INT C, P352, DOI 10.1145/312129.312279 AGGRAWAL R, 1999, P EDBT99, P365 Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Aslam J., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288659 Bensaid AM, 1998, INT J PATTERN RECOGN, V12, P625, DOI 10.1142/S0218001498000361 DAVIES A, 1997, PALAEOENVIRONMENTAL, P16 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P350 Mechkour M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291047 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Sahami M., 1998, P 3 ACM INT C DIG LI, P200, DOI 10.1145/276675.276697 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schutze H, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P74, DOI 10.1145/258525.258539 TALAVERA L, 1999, P 3 INT C INT DAT AN, P211 NR 13 TC 0 Z9 0 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG MINATO-KU SHIBAKOEN 3 CHOME, TOKYO, 105, JAPAN SN 0916-8532 J9 IEICE T INF SYST JI IEICE Trans. Inf. Syst. PD JUN PY 2002 VL E85D IS 6 BP 1043 EP 1048 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 570GC UT WOS:000176649700013 ER PT J AU Lodhi, H Saunders, C Shawe-Taylor, J Cristianini, N Watkins, C AF Lodhi, H Saunders, C Shawe-Taylor, J Cristianini, N Watkins, C TI Text classification using string kernels SO JOURNAL OF MACHINE LEARNING RESEARCH LA English DT Article DE kernels and support vector machines; string subsequence kernel; approximating kernels; text classification ID SPACE AB We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguously. The subsequences are weighted by an exponentially decaying factor of their full length in the text, hence emphasising those occurrences that are close to contiguous. A direct computation of this feature vector would involve a prohibitive amount of computation even for modest values of k, since the dimension of the feature space grows exponentially with k. The paper describes how despite this fact the inner product can be efficiently evaluated by a dynamic programming technique. C1 Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. RP Lodhi, H (reprint author), Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. EM HUMA@CS.RHUL.AC.UK; CRAIG@CS.RHUL.AC.UK; JOHN@CS.RHUL.AC.UK; NELLO@CS.RUHL.AC.UK; CHRISW@CS.RUHL.AC.UK RI Watkins, Christopher/E-5811-2013 OI Watkins, Christopher/0000-0001-9020-4530 CR Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, DOI 10.1145/130385.130401 Cavnar W.B., 1994, P TREC 3 3 TEXT RETR, P269 CRISTIANINI N, IN PRESS NEURAL INFO Cristianini N., 2000, INTRO SUPPORT VECTOR CRISTIANINI N, 2001, NCTR01087 U LOND Friess TT, 1998, P 15 INT C MACH LEAR Haussler D., 1999, UCSCCRL9910 COMP SCI HUFFMAN S, 1995, P TREC 4 4 TEXT RETR Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims T, 1999, ADVANCES IN KERNEL METHODS, P169 Lodhi H, 2001, ADV NEUR IN, V13, P563 Mercer J, 1909, PHILOS T R SOC LOND, V209, P415, DOI 10.1098/rsta.1909.0016 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Scholkopf B, 1997, SUPPORT VECTOR LEARN Scholkopf B, 1999, IEEE T NEURAL NETWOR, V10, P1000, DOI 10.1109/72.788641 Smola A. J., 2000, P 17 INT C MACH LEAR, P911 Vapnik V., 1995, NATURE STAT LEARNING Watkins C, 2000, ADV NEUR IN, P39 Williams CKI, 2001, ADV NEUR IN, V13, P682 NR 19 TC 357 Z9 376 PU MICROTOME PUBL PI BROOKLINE PA 31 GIBBS ST, BROOKLINE, MA 02446 USA SN 1532-4435 J9 J MACH LEARN RES JI J. Mach. Learn. Res. PD SUM PY 2002 VL 2 IS 3 BP 419 EP 444 DI 10.1162/153244302760200687 PG 26 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA 595GZ UT WOS:000178101500005 ER PT J AU Chowdhury, A Frieder, O Grossman, D McCabe, MC AF Chowdhury, A Frieder, O Grossman, D McCabe, MC TI Collection statistics for fast duplicate document detection SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article AB We present a new algorithm for duplicate document detection that uses collection statistics. We compare our approach with the state-of-the-art approach using multiple collections. These collections include a 30 MB 18,577 web document collection developed by Excite@ Home and three NIST collections. The first NIST collection consists of 100 MB 18,232 LA-Times documents, which is roughly similar in the number of documents to the Excite@ Home collection. The other two collections are both 2 GB and are the 247,491-web document collection and the TREC disks 4 and 5-528,023 document collection. We show that our approach called I-Match, scales in terms of the number of documents and works well for documents of all sizes. We compared our solution to the state of the art and found that in addition to improved accuracy of detection, our approach executed in roughly one-fifth the time. C1 IIT, Informat Retrieval Lab, Chicago, IL 60616 USA. RP Chowdhury, A (reprint author), IIT, Informat Retrieval Lab, 10 W 31st St, Chicago, IL 60616 USA. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BRIN S, 1995, P SPEC INT GROUP MAN, P298 Broder A., 1997, P 6 INT WORLD WID WE, P391 BUCKLEY C, 1999, P TIPSTER PHAS 3 SAN, P107 CHOWDHURY A, 2000, P 9 TEXT RETR C TREC FRIEDER O, 2000, J DIG INF, V1, P5 GROSSMAN D, 1993, P 4 TEXT RETR C TREC Heintze N, 1996, PROCEEDINGS OF THE SECOND USENIX WORKSHOP ON ELECTRONIC COMMERCE, P191 KJELL B, 1994, INFORM PROCESS MANAG, V30, P141, DOI 10.1016/0306-4573(94)90029-9 Kwok Kuilam L., 1996, P 19 ANN INT ACM SIG, P187, DOI 10.1145/243199.243266 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 *NIST, 1995, 1801 NIST US DEP COM PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Robertson S. E., 1999, P 7 TEXT RETR C TREC, P253 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDERSON M, 1997, TR19975 U GLASG DEP Shivakumar N., 1998, P 1 INT WORKSH WORLD, P204 Singh AN, 1996, J PSYCHIATR NEUROSCI, V21, P29 SMEATON A, 1997, P 6 TEXT RETR C TREC NR 21 TC 60 Z9 77 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 USA SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 2002 VL 20 IS 2 BP 171 EP 191 DI 10.1145/506309.506311 PG 21 WC Computer Science, Information Systems SC Computer Science GA 546MB UT WOS:000175276900002 ER PT J AU Sebastiani, F AF Sebastiani, F TI Machine learning in automated text categorization SO ACM COMPUTING SURVEYS LA English DT Review DE algorithms; experimentation; theory; machine learning; text categorization; text classification ID PROBABILISTIC INFORMATION-RETRIEVAL; DOCUMENT CLASSIFICATION; FEATURE-SELECTION; SPECIAL ISSUE; RELEVANCE; MODELS; ASSUMPTIONS AB The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. C1 CNR, Ist Elaboraz Informaz, I-56124 Pisa, Italy. RP Sebastiani, F (reprint author), CNR, Ist Elaboraz Informaz, Via G Moruzzi 1, I-56124 Pisa, Italy. EM fabrizio@iei.pi.cnr.it RI Sebastiani, Fabrizio/C-9501-2015 OI Sebastiani, Fabrizio/0000-0003-4221-6427 CR Amati G, 1999, INFORM PROCESS MANAG, V35, P633, DOI 10.1016/S0306-4573(99)00012-6 Androutsopoulos I., 2000, P 23 ANN INT ACM SIG, P160, DOI 10.1145/345508.345569 APTE C, 1994, ACM T INFORM SYST, V12, P233, DOI 10.1145/183422.183423 Attardi G., 1998, J UNIVERS COMPUT SCI, P719 Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970 BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861 Biebricher P., 1988, P 11 INT C RES DEV I, P333, DOI 10.1145/62437.62470 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cavnar W., 1994, P 3 ANN S DOC AN INF, P161 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S, 1998, VLDB J, V7, P163, DOI 10.1007/s007780050061 Clack C., 1997, Proceedings of the First International Conference on Autonomous Agents, DOI 10.1145/267658.267716 Cleverdon C., 1984, Information Services & Use, V4 Cohen W., 1995, ADV INDUCTIVE LOGIC, P124 Cohen W. W., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 COHEN WW, 1995, P 12 INT C MACH LEAR, P124 COOPER WS, 1995, ACM T INFORM SYST, V13, P100, DOI 10.1145/195705.195735 CREECY RH, 1992, COMMUN ACM, V35, P48, DOI 10.1145/135226.135228 Crestani F, 1998, ACM COMPUT SURV, V30, P528, DOI 10.1145/299917.299920 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DENOYER L, 2001, P ECIR01 23 EUR C IN Domingos P, 1997, MACH LEARN, V29, P103, DOI 10.1023/A:1007413511361 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Escudero G., 2000, P 12 EUR C MACH LEAR, P129 ESTEBAN AD, 1998, P LREC98 1 INT C LAN, P1197 FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 Forsyth R.S., 1999, CAUSAL MODELS INTELL, P151 Frasconi P, 2002, J INTELL INF SYST, V18, P195, DOI 10.1023/A:1013681528748 FUHR N, 1989, INFORM PROCESS MANAG, V25, P55, DOI 10.1016/0306-4573(89)90091-5 Fuhr N., 1984, P 3 BCS ACM S 2 6 JU, P391 FUHR N, 1991, ACM T INFORM SYST, V9, P223, DOI 10.1145/125187.125189 Fuhr N., 1991, P RIAO 91, P606 FUHR N, 1985, P RIAO85 1 INT C REC, P207 FUHR N, 1994, ACM T INFORM SYST, V12, P92, DOI 10.1145/174608.174612 Furnkranz J., 1999, P 3 S INT DAT AN IDA, P487 Galavotti L, 2000, LECT NOTES COMPUT SC, V1923, P59 GALE W, 1993, COMPUT HUMANITIES, V26, P415 Govert N, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P475, DOI 10.1145/319950.320053 GRAY WA, 1971, INFORM STORAGE RET, V7, P167, DOI 10.1016/0020-0271(71)90002-7 Guthrie Louise, 1994, P COLING 94, P1059 Hayes P. J., 1990, P CAIA 90 6 IEEE C A, P320 HEAPS HS, 1973, INFORM CONTROL, V22, P268, DOI 10.1016/S0019-9958(73)90310-0 Hersh William, 1994, P 17 ANN INT ACM SIG, P192 Hull D. A., 1994, P 17 ACM SIGIR C, P282 Hull D. A., 1996, P 19 ANN INT ACM SIG, P279, DOI 10.1145/243199.243275 Ido Dagan, 1997, P 2 C EMP METH NAT L, P55 Ittner David J., 1995, P SDAIR 95 4 ANN S D, P301 Iwayama M., 1995, P 18 ANN INT ACM SIG, P273, DOI 10.1145/215206.215371 Iyer R. D., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354794 Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Joachims T., 1998, P 10 EUR C MACH LEAR, P137 Joachims T, 2002, J INTELL INF SYST, V18, P103, DOI 10.1023/A:1013652626023 Joachims T, 1998, P 10 EUR C MACH LEAR, P143 John G., 1994, P 11 INT C MACH LEAR, P121 JONES KS, 1997, READINGS INFORMATION JUNKER M, 1997, P RANLP 97 2 INT C R, P202 Junker M., 1998, International Journal on Document Analysis and Recognition, V1, DOI 10.1007/s100320050012 Kessler B., 1997, P 35 ANN M ASS COMP, P32 Kim Yu-Hwan, 2000, P 23 ANN INT ACM SIG, P168, DOI 10.1145/345508.345572 Klinkenberg R., 2000, P 17 INT C MACH LEAR, P487 Knight K, 1999, COMMUN ACM, V42, P58, DOI 10.1145/319382.319394 KNORZ G, 1982, P SIGIR 82 5 ACM INT, P174 Koller D., 1997, P 14 INT C MACH LEAR, P170 Korfhage R., 1997, INFORMATION STORAGE Lam S. L. Y., 1999, Proceedings. 6th International Conference on Advanced Systems for Advanced Applications, DOI 10.1109/DASFAA.1999.765752 Lam W, 1999, IEEE T KNOWL DATA EN, V11, P865 Lam W., 1998, P 21 ANN INT ACM SIG, P81, DOI 10.1145/290941.290961 LAM W, 1997, P 15 INT JOINT C ART, P745 Lang K, 1995, P 12 INT C MACH LEAR, P331 Larkey Leah S., 1999, P DL 99 4 ACM C DIG, P179, DOI 10.1145/313238.313304 Larkey L. S., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290965 Larkey Leah S., 1996, P SIGIR 96 19 ACM IN, P289, DOI 10.1145/243199.243276 Lewis D., 1998, P 10 EUR C MACH LEAR, P4 Lewis D., 1994, P 3 ANN S DOC AN INF, P81 Lewis D., 1994, P 17 ANN INT ACM SIG, P3 Lewis D. D., 1995, P 18 ANN INT ACM SIG, P246, DOI 10.1145/215206.215366 Lewis D. D., 1992, P 15 ANN INT ACM SIG, P37, DOI 10.1145/133160.133172 Lewis David D., 1992, THESIS U MASSACHUSET LEWIS DD, 1995, SIGIR FORUM, V29, P2 Lewis D.D., 1994, P 11 INT C MACH LEAR, P148 LEWIS DD, 1994, ACM T INFORM SYST, V12, P231 LEWIS DD, 1995, P TREC 4 4 TEXT RETR, P165 Lewis FD, 1996, J PHOTOCH PHOTOBIO A, V96, P19, DOI 10.1016/1010-6030(95)04285-7 Li H, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P122, DOI 10.1145/319950.319966 Li YH, 1998, COMPUT J, V41, P537, DOI 10.1093/comjnl/41.8.537 LIDDY ED, 1994, ACM T INFORM SYST, V12, P278, DOI 10.1145/183422.183425 Liere R., 1997, P 14 C AM ASS ART IN, P591 LIM JH, 1999, P ACM DIG LIB 99 BER, P139, DOI 10.1145/313238.313290 Manning C. D., 1999, FDN STAT NATURAL LAN MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 MASAND B, 1994, ADV GENETIC PROGRAMM, P459 Masand B., 1992, P 15 ANN INT ACM SIG, P59, DOI 10.1145/133160.133177 McCallum Andrew K., 1998, P 15 INT C MACH LEAR, P350 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Mitchell T., 1996, MACHINE LEARNING Mladenic D., 1998, P ERK 98 7 EL COMP S, P145 Mladenic D., 1998, P 10 EUR C MACH LEAR, P95 Moulinier I., 1996, Proceedings. Fifth Annual Symposium on Document Analysis and Information Retrieval Moulinier I., 1996, Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing Myers K., 2000, P ICML 00 17 INT C M, P655 Ng HT, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P67, DOI 10.1145/258525.258537 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 Oh H J, 2000, P 23 ANN INT ACM SIG, P264, DOI 10.1145/345508.345594 PAZIENZA MT, 1997, LECT NOTES COMPUTER, V1299 RILOFF E, 1994, ACM T INFORM SYST, V12, P296, DOI 10.1145/183422.183428 Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 ROBERTSON SE, 1984, J DOC, V40, P264, DOI 10.1108/eb026768 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roth D., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence Ruiz M. E., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312700 Sable C. L., 2000, International Journal on Digital Libraries, V3, DOI 10.1007/s007990000038 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604 Schapire R. E., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290996 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Schutze H, 1998, COMPUT LINGUIST, V24, P97 Schutze H., 1995, P 18 ANN INT ACM SIG, P229, DOI 10.1145/215206.215365 Scott S., 1999, P 16 INT C MACH LEAR, P379 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 Singhal A, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P25, DOI 10.1145/258525.258530 Singhal A, 1996, INFORM PROCESS MANAG, V32, P619, DOI 10.1016/0306-4573(96)00008-8 Slonim N, 2001, P ECIR 01 23 EUR C I Taira H., 1999, Proceedings Sixteenth National Conference on Artificial Intelligence (AAI-99). Eleventh Innovative Applications of Artificial Intelligence Conference (IAAI-99) Tauritz DR, 2000, INFORM SCIENCES, V122, P121, DOI 10.1016/S0020-0255(99)00123-1 Tumer K., 1996, Connection Science, V8, DOI 10.1080/095400996116839 Tzeras K., 1993, P 16 ANN INT ACM SIG, P22, DOI 10.1145/160688.160691 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Drucker H, 1999, IEEE T NEURAL NETWOR, V10, P1048, DOI 10.1109/72.788645 Weigend A. S., 1999, Information Retrieval, V1, DOI 10.1023/A:1009983522080 Weiss SM, 1999, IEEE INTELL SYST APP, V14, P63, DOI 10.1109/5254.784086 Wiener E, 1995, P 4 ANN S DOC AN INF, P317 WILLETT P, 1988, DOCUMENT RETRIEVAL S Wong J. W. T., 1996, SIGIR Forum, V30 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Yang Y., 1999, P 22 ANN INT ACM SIG, P42, DOI 10.1145/312624.312647 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 Yang Y., 1995, P 18 ANN INT ACM SIG, P256, DOI 10.1145/215206.215367 Yang Y, 1994, P 17 ANN INT ACM SIG, P13 Yang YM, 2002, J INTELL INF SYST, V18, P219, DOI 10.1023/A:1013685612819 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 Yu KL, 1998, P CIKM 98 7 ACM INT, P156, DOI 10.1145/288627.288652 NR 146 TC 1677 Z9 1821 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA SN 0360-0300 J9 ACM COMPUT SURV JI ACM Comput. Surv. PD MAR PY 2002 VL 34 IS 1 BP 1 EP 47 DI 10.1145/505282.505283 PG 47 WC Computer Science, Theory & Methods SC Computer Science GA 546HB UT WOS:000175267600001 ER PT J AU Cristianini, N Shawe-Taylor, J Lodhi, H AF Cristianini, N Shawe-Taylor, J Lodhi, H TI Latent semantic kernels SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article DE kernel methods; latent semantic indexing; latent semantic kernels; Gram-Schmidt kernels; text categorization AB Kernel methods like support vector machines have successfully been used for text categorization. A standard choice of kernel function has been the inner product between the vector-space representation of two documents, in analogy with classical information retrieval (IR) approaches. Latent semantic indexing (LSI) has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between two documents. One of its main drawbacks, in IR, is its computational cost. In this paper we describe how the LSI approach can be implemented in a kernel-defined feature space. We provide experimental results demonstrating that the approach can significantly improve performance, and that it does not impair it. C1 Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. RP Cristianini, N (reprint author), Univ London Royal Holloway & Bedford New Coll, Dept Comp Sci, Egham TW20 0EX, Surrey, England. CR Aizerman M., 1964, AUTOMAT REM CONTR, V25, P821 Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401 Cristianini N., 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Dumais S., 1998, 7 INT C INF KNOWL MA DUMAIS ST, 1997, AAAI SPRING S CROSS, P115 Herbrich R., 2000, ADV LARGE MARGIN CLA JIANG F, 2000, P 17 INT C MACH LEAR Joachims T, 1999, ADV KERNEL METHODS S Joachims T, 1998, P EUR C MACH LEARN E Leopold E, 2002, MACH LEARN, V46, P423, DOI 10.1023/A:1012491419635 Miller G., 1993, 5 PAPERS WORDNET TEC OPPER M, 2000, ADV LARGE MARGIN CLA Press W. H., 1992, NUMERICAL RECIPES C, V2nd SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAUNDERS C, 1998, MACHINE LEARNING Scholkopf B, 1999, ADVANCES IN KERNEL METHODS, P327 Scholkopf B, 2001, NEURAL COMPUT, V13, P1443, DOI 10.1162/089976601750264965 Scholkopf B., 1998, P 8 INT C ART NEUR N, P147 Shawe-Taylor J, 2000, ADV NEUR IN, P349 Shawe-Taylor J, 1998, IEEE T INFORM THEORY, V44, P1926, DOI 10.1109/18.705570 Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Siolas G., 2000, P INT JOINT C NEUR N Smola A., 1999, 9904 U WISC DAT MIN Smola A., 1998, NCTR98030 U LOND ROY, DOI Royal Holloway College, University of London Vapnik V., 1998, STAT LEARNING THEORY WONG SKM, 1985, ACM SIGIR C RES DEV, P18 NR 27 TC 75 Z9 82 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0925-9902 J9 J INTELL INF SYST JI J. Intell. Inf. Syst. PD MAR-MAY PY 2002 VL 18 IS 2-3 BP 127 EP 152 DI 10.1023/A:1013625426931 PG 26 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA 508XA UT WOS:000173114000003 ER PT J AU Visa, A Toivonen, J Vanharanta, H Back, B AF Visa, A Toivonen, J Vanharanta, H Back, B TI Contents matching defined by prototypes: Methodology verification with books of the bible SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS LA English DT Article DE bible; document classification; knowledge discovery; methodology; prototype matching; text mining; verification AB It is common that text documents are characterized and classified by key words, index terms, or headings. We have developed a new methodology based on prototype matching. The prototype is an interesting document or a part of an extracted, interesting text. This prototype is matched with the existing document database or with the monitored document flow. The claim is that the new methodology is capable of extracting the contents of the document. To verify this hypothesis, a test with the Bible was designed. Different translations in English, Latin, Greek, and Finnish were selected to test materials. Verification tests that included the search of the ten nearest books to every book of the Bible were performed with a designed prototype version of the software application. The test results are reported in this paper. C1 Abo Akad Univ, Turku, Finland. RI Visa, Ari/G-4304-2014 CR Back B., 2001, International Journal of Accounting Information Systems, V2, DOI 10.1016/S1467-0895(01)00018-5 Brown Gillian, 1983, DISCOURSE ANAL Cangelosi A, 2001, IEEE T EVOLUT COMPUT, V5, P93, DOI 10.1109/4235.918429 DEWEY M, 1876, US BUREAU ED SPECIAL, P623 Dewey M., 1876, CLASSIFICATION SUBJE Dretske F. I., 1981, KNOWLEDGE FLOW INFOR Harter S. P., 1986, ONLINE INFORMATION R Kirby S, 2001, IEEE T EVOLUT COMPUT, V5, P102, DOI 10.1109/4235.918430 LAHTINEN T, 2000, THESIS U HELSINKI FI Lyons J., 1977, SEMANTICS 1 Manning C. D., 1999, FDN STAT NATURAL LAN OARD D, 1996, CSTR3643 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVAGERUMBAUGH ES, 1978, BRAIN LANG, V6, P265, DOI 10.1016/0093-934X(78)90063-9 Sinclair J., 1987, COLLINS COBUILD ENGL SOUKHANOV AH, 1984, WEBSTERS NEW RIVERSI Sykes J. B., 1976, CONCISE OXFORD DICT VISA A, 2001, P 34 ANN HAW INT C S VISA A, 2000, P SSGRR 2000 INT C A VISA A, 2001, THEORY TOOLS TECHN 3, V4384, P149 NR 21 TC 3 Z9 3 PU M E SHARPE INC PI ARMONK PA 80 BUSINESS PARK DR, ARMONK, NY 10504 USA SN 0742-1222 J9 J MANAGE INFORM SYST JI J. Manage. Inform. Syst. PD SPR PY 2002 VL 18 IS 4 BP 87 EP 100 PG 14 WC Computer Science, Information Systems; Information Science & Library Science; Management SC Computer Science; Information Science & Library Science; Business & Economics GA 532QG UT WOS:000174486100005 ER PT B AU Anagnostopoulos I Kouzas, G Anagnostopoulos, C Vergados, D Papaleonidopoulos I Generalis, A Loumos V Kayafas, E AF Anagnostopoulos, I Kouzas, G Anagnostopoulos, C Vergados, D Papaleonidopoulos, I Generalis, A Loumos, V Kayafas, E GP IEEE IEEE IEEE IEEE TI Automatic Web site classification in a large repository under information filtering and retrieval techniques SO 11TH IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, PROCEEDINGS SE IEEE Mediterranean Electrotechnical Conference-MELECON LA English DT Proceedings Paper CT 11th IEEE Mediterranean Electrotechnical Conference (IEEE MELECON 2002) CY MAY 07-09, 2002 CL CAIRO, EGYPT SP Minist Commun Informat & Technol, EgyNet, Egyptian Cabinet Informat & Decis Support Ctr, TELECOM EGYPT DE information system; web-page classification; knowledge segmentation; e-commerce; Portal ID VECTOR-SPACE MODEL AB In nowadays, there is a huge volume of information on the web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or Portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce sites according their web-content. This system can be implemented for automatic knowledge segmentation in a Portal or in a search engine repository. The system performance reached 96% in the first test sets, after the learning phase. However, the performance significantly increases (up to 98%) as the number of the test sets increases. C1 Natl Tech Univ Athens, Dept Elect & Comp Engn, Athens, Greece. RP Anagnostopoulos I (reprint author), Natl Tech Univ Athens, Dept Elect & Comp Engn, 9 Heroon Polytechneiou Str, Athens, Greece. EM janag@telecom.ece.ntua.gr CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st BORRA S, 2001, ADV CLASSIFICATION D KLOSE M, 1999, P 5 AM C INF SYST AM, P115 MOENS MF, 2000, AUTOMATIC INDEXING A RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 ROCCHIO JJ, 1971, SMART RETRIEVAL SYST, P324 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMID B, ELEMENTS REFERENCE M SELZ S, INT J ELECT MARKETS, V7 2000, MANAGING DIGITAL ENT NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7527-0 J9 IEEE MEDITERR ELECT PY 2002 BP 279 EP 283 DI 10.1109/MELECON.2002.1014574 PG 3 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Automation & Control Systems; Computer Science; Engineering; Telecommunications GA BU89Q UT WOS:000177319600056 ER PT S AU Zitouni, I Kuo, HKJ Lee, CH AF Zitouni, I Kuo, HKJ Lee, CH GP IEEE IEEE TI Combination of boosting and discriminative training for natural language call steering systems SO 2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS SE International Conference on Acoustics Speech and Signal Processing (ICASSP) LA English DT Proceedings Paper CT IEEE International Conference on Acoustics, Speech, and Signal Processing CY MAY 13-17, 2002 CL ORLANDO, FL SP IEEE Signal Proc Soc ID WEAK AB In this paper, we describe the combination of two different techniques to improve natural language call routing: boosting and discriminative training. The goal of boosting is to re-weight the data in order to train a set of classifiers whose errors may be uncorrelated so that when combined, the classification error rate (CER) can be reduced. We propose using discriminative training to improve the individual classifier accuracy at each iteration of the boosting algorithm, Compared to the baseline classifiers, an improvement in the CER of 41-50% was observed on call routing for a banking task. More importantly, synergistic effects of discriminative training on the boosting algorithm were demonstrated: more iterations were possible because discriminative training reduced the CER of individual classifiers trained on re-weighted data by an average of 72%. C1 Bell Labs, Lucent Technol, Murray Hill, NJ 07974 USA. RP Zitouni, I (reprint author), Bell Labs, Lucent Technol, 600 Mt Ave, Murray Hill, NJ 07974 USA. CR BIGI B, 2001, STRING PROCESSING IN Chu-Carroll J, 1999, COMPUT LINGUIST, V25, P361 Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 FREUND Y, 1995, INFORM COMPUT, V121, P256, DOI 10.1006/inco.1995.1136 KEARNS MJ, 1994, J ASSOC COMPUT MACH, V1, P67 KUO HKJ, 2001, P EUR 01 AALB DENM KUO HKJ, 2000, P ICSLP 2000 BEIJ CH LEE CH, 1998, P INT VOIC TECHN TEL SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAPIRE RE, 1990, MACH LEARN, V5, P197, DOI 10.1023/A:1022648800760 Schapire RE, 1999, P 16 INT JOINT C ART Schapire RE, 1998, ANN STAT, V26, P1651 ZITOUNI I, 2001, P ASRU 2001 MAD CAMP NR 14 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1520-6149 BN 0-7803-7402-9 J9 INT CONF ACOUST SPEE PY 2002 BP 25 EP 28 PG 4 WC Acoustics; Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Acoustics; Computer Science; Engineering; Imaging Science & Photographic Technology GA BU96P UT WOS:000177510400007 ER PT B AU Hammouda, KM Kamel, MS AF Hammouda, KM Kamel, MS BE Kumar, V Tsumoto, S Zhong, N Yu, PS Wu, XD TI Phrase-based document similarity based on an Index Graph model SO 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS LA English DT Proceedings Paper CT 2nd IEEE International Conference on Data Mining CY DEC 09-12, 2002 CL MAEBASHI CITY, JAPAN SP IEEE Comp Soc Tech Comm Pattern Anal & Machine Intelligence, IEEE Comp Soc Tech Comm Computat Intelligence, Maebashi Convent Bur, Maebashi City Govt, Gunmma Prefecture Govt, Maebashi Inst Technol, AdIn Res Inc, Japan Res Inst Ltd, Support Ctr Adv Telecommun Technol Res, US AFOSR, AOARD, AROFE, SAS Inst Japan, SPSS Japan Inc, Kankodo, Syst Alpha Inc, GCC Inc, Yamato Inc, Daiei Dream Co Ltd AB Document clustering techniques mostly rely on single term analysis of the document data set, such as the Vector Space Model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the Document Index Graph, which indexes web documents based on phrases, rather than single terms only. The semi-structured web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The Document Index Graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The similarity between documents is based on both single term weights and matching phrases weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, enhances web document clustering quality significantly. C1 Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RP Hammouda, KM (reprint author), Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RI Kamel, Mohamed/ OI Kamel, Mohamed/0000-0001-6173-8082 CR Aas K., 1999, 941 NORW COMP CTR Cios K. J., 1998, DATA MINING METHODS FRAKES WR, 1992, INFORMATION RETRIEVA Hofmann T., 1999, P 16 INT JOINT C ART, P682 Honkela T., 1997, P WSOM 97 WORKSH SEL, P310 ISAACS JD, 1999, PCSTR99357 DARTM COL Jain A. K., 1988, ALGORITHMS CLUSTERIN JUNKER M, 1999, P 1 WORKSH LEARN LAN, P84 LU SY, 1978, IEEE T SYST MAN CYB, V8, P381, DOI 10.1109/TSMC.1978.4309979 Nahm UY, 2000, SEVENTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-2001) / TWELFTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-2000), P627 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Soderland S, 1999, MACH LEARN, V34, P233, DOI 10.1023/A:1007562322031 STEINBACH M, 2000, KDD2000 WORKSH TEXTM Strehl A, 2000, P AAAI WORKSH AI WEB, P58 Yang Y., 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 Zamir O., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining Zamir O., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290956 NR 19 TC 4 Z9 4 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1754-4 PY 2002 BP 203 EP 210 DI 10.1109/ICDM.2002.1183904 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BV87U UT WOS:000180274000026 ER PT B AU Jing, LP Huang, HK Shi, HB AF Jing, LP Huang, HK Shi, HB GP IEEE IEEE TI Improved feature selection approach TFIDF in text mining SO 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS LA English DT Proceedings Paper CT International Conference on Machine Learning and Cybernetics CY NOV 04-05, 2002 CL BEIJING, PEOPLES R CHINA SP IEEE, SMC, Hebei Univ, Machine Learning Ctr DE text mining; TFIDF; evaluation function; VSM; feature selection AB This paper describes one Feature Selection method (TFIDF). With it, we process the data resource and set up the VSM model in order to provide a convenient data structure for text categorization. We calculate the precision of this method with the help of categorization results. According to the empirical results, we analyze its advantages and disadvantages and present a new TFIDF-based feature selection approach to improve its accuracy. C1 No Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China. RP Jing, LP (reprint author), No Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China. CR Joachims T, 1997, P 14 INT C MACH LEAR, P143 JOHN GH, 1998, IRRELEVANTFEATURES S MLADENIC D, 1998, MACHINE LEARNING NON SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yang Y, 1997, CMUCS97127 YANG Y, COMP STUDY FEATURE S NR 6 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7508-4 PY 2002 BP 944 EP 946 PG 3 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics; Engineering, Electrical & Electronic SC Automation & Control Systems; Computer Science; Engineering GA BW27J UT WOS:000181396300206 ER PT S AU Haruechaiyasak, C Shyu, ML Chen, SC Li, XQ AF Haruechaiyasak, C Shyu, ML Chen, SC Li, XQ GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Web document classification based on fuzzy association SO 26TH ANNUAL INTERNATIONAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE, PROCEEDINGS SE PROCEEDINGS - INTERNATIONAL COMPUTER SOFTWARE & APPLICATIONS CONFERENCE LA English DT Proceedings Paper CT 26th Annual International Computer Software and Applications Conference CY AUG 26-29, 2002 CL OXFORD, ENGLAND SP IEEE Comp Soc DE information processing on the Web; data mining; document classification; fuzzy association ID INFORMATION-RETRIEVAL AB In this paper, a method of automatically classifying Web documents into a set of categories using the fuzzy association concept is proposed. Using the same word or vocabulary to describe different entities creates ambiguity, especially in the Web environment where the user population is large. To solve this problem, fuzzy association is used to capture the relationships among different index terms or keywords in the documents, i.e., each pair of words has an associated value to distinguish itself from the others. Therefore, the ambiguity in word usage is avoided. Experiments using data sets collected from two Web portals: Yahoo! (www.yahoo.com) and Open Directory, Project (dmoz.org) are conducted. We compare our approach to the vector space model with the cosine coefficient. The results show that our approach yields higher accuracy compared to the vector space model. C1 Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA. RP Haruechaiyasak, C (reprint author), Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA. CR Broder A., 1997, P 6 INT WORLD WID WE, P391 CHEKURI C, 1997, P 6 INT WORLD WID WE Cooley R, 1997, PROC INT C TOOLS ART, P558, DOI 10.1109/TAI.1997.632303 Dumais S., 2000, P 23 ANN INT ACM SIG, P256, DOI 10.1145/345508.345593 Koller D., 1997, P 14 INT C MACH LEAR, P170 MIYAMOTO S, 1983, IEEE T SYST MAN CYB, V13, P62 MIYAMOTO S, 1986, IEEE T SYST MAN CYB, V16, P278, DOI 10.1109/TSMC.1986.4308950 MIYAMOTO S, 1989, IEEE T SYST MAN CYB, V19, P123, DOI 10.1109/21.24541 OGAWA Y, 1991, FUZZY SET SYST, V39, P163, DOI 10.1016/0165-0114(91)90210-H Pitkow J., 1999, P 2 USENIX S INT TEC, P139 Rasmussen E., 1992, INFORMATION RETRIEVA, P419 Salton G., 1971, PRENTICE HALL SERIES SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHYU ML, 2001, P 7 INT C DISTR MULT SHYU ML, 2001, IEEE INT C SYST MAN, P1717 Tiun S., 2001, P 2 INT C COMP LING, P444 Wang J., 1999, ACM COMPUTER COMM RE, V29, P36, DOI DOI 10.1145/505696.505701 Zadeh L.A., 1993, READINGS FUZZY SETS NR 18 TC 5 Z9 5 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA SN 0730-3157 BN 0-7695-1727-7 J9 P INT COMP SOFTW APP PY 2002 BP 487 EP 492 DI 10.1109/CMPSAC.2002.1045052 PG 4 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BV16P UT WOS:000178041100075 ER PT B AU Chen, ZP Lin, YP Li, JY Peng, Y AF Chen, ZP Lin, YP Li, JY Peng, Y BE Callaos, N He, Y PerezPeraza, JA TI A preprocess algorithm of filtering irrelevant information based on the minimum class difference SO 6TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XVII, PROCEEDINGS: INDUSTRIAL SYSTEMS AND ENGINEERING III LA English DT Proceedings Paper CT 6th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2002)/8th International Conference on Information Systems Analysis and Synthesis (ISAS 2002) CY JUL 14-18, 2002 CL ORLANDO, FL SP Int Inst Informat & System, World Org System & Cybernet, Ctr Syst Studies, Syst Soc Poland, Soc Appl Syst Res, Slovenian Artificial Intelligence Soc, Simon Bolivar Univ, Polish Syst Soc, Italian Soc System, Int Soc Syst Sci, Int Syst Inst, Int Federat Syst Res, Cybernet & Human Knowing, Journal Second Order Cybernet & Cybersemiot, Blaise Pascal Univ, Engineer Sci Inst, CUST, Univ Las Palmas Gran Canaria, Concurrency & Architecture Grp, Telemat Engn Dept, Tunisian Sci Soc, Acad Non Linear Sci, San Luis Natl Univ, Lab Res Computat Intelligence, Dept Informat, Amer Soc Cybernet (Canada), Wolfram Res Inc, IEEE Comp Soc, Venezuela Chapter, Steacie Inst Molec Sci, Natl Res Council Canada DE classification; text categorization; naive Bayesian; k-nearest neighbor classification AB Classification algorithms in text categorization is the high dimensionality nature of document data sets. The words occur in the text become variables or attributes for the classification problem. A relatively moderate size of document sets could easily have number of distinct words in tens of thousands, and result in bad effectiveness of classification schemes. In order to decrease the word dimension, some methods such as feature selection, and stop list are used. Feature selection is too aggressive in reducing the number of words, and may lose critical information for classification. Stop list is used to filter some common words such as a, the and etc in a rough way. Thus the existence of other irrelevant and weak relevant still affects the accuracy of classification. To solve this problem, a new algorithm of preprocessing based on the minimum class difference (PBMCD) is proposed in this paper. This new algorithm filters the irrelevant words or weak relevant words according to their effect imposed on each class, and clears away the rubbish attributes. The experimental results with Naive Bayesian(BN), Expectation Maximum(EM) and K-Nearest Neighbor(KNN) text categorization algorithms also indicate that this preprocess algorithm can classify the WWW pages more accurately. C1 Hunan Univ, Coll Comp & Commun, Changsha 410082, Hunan, Peoples R China. RP Chen, ZP (reprint author), Hunan Univ, Coll Comp & Commun, Changsha 410082, Hunan, Peoples R China. CR Fayyad U. M., 1992, P 10 NAT C ART INT S, P104 Fox C., 1990, SIGIR Forum, V24 HAN EH, 1999, THESIS U MINNESOTA O Koller D., 1997, P 14 INT C MACH LEAR, P170 Langley P., 1993, P 13 INT JOINT C ART, P889 McCallum A., 1998, AAAI 98 WORKSH LEARN NIGAM K, 1998, CMUCS98120R SCH COMP Riloff E. M., 1995, P 18 ANN INT ACM SIG, P130, DOI 10.1145/215206.215349 Sahami M, 1998, BAYESIAN APPROACH FI SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 11 TC 0 Z9 0 PU INT INST INFORMATICS & SYSTEMICS PI ORLANDO PA 14269 LORD BARCLAY DR, ORLANDO, FL 32837 USA PY 2002 BP 309 EP 312 PG 4 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering, Industrial SC Automation & Control Systems; Computer Science; Engineering GA BV43N UT WOS:000178929300059 ER PT S AU Tsikrika, T Lalmas, M AF Tsikrika, T Lalmas, M BE Crestani, F Girolami, M VanRijsbergen, CJ TI Combining Web document representations in a Bayesian inference network model using link and content-based evidence SO ADVANCES IN INFORMATION REFTRIEVAL SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 24th Annual European Colloquium on Information Retrieval Research CY MAR 25-27, 2002 CL GLASGOW, SCOTLAND SP British Comp Soc, Informat Retrieval Specialist Grp, CEPIS IR, Memex Technol, Glasgow City Council ID INFORMATION-RETRIEVAL AB This paper introduces an expressive formal Information Retrieval model developed for the Web. It is based on the Bayesian inference network model and views IR as an evidential reasoning process. It supports the explicit combination of multiple Web document representations under a single framework. Information extracted from the content of Web documents and derived from the analysis of the Web link structure is used as source of evidence in support of the ranking algorithm. This content and link-based evidential information is utilised in the generation of the multiple Web document representations used in the combination. C1 Univ London Queen Mary, Dept Comp Sci, London E1 4NS, England. RP Tsikrika, T (reprint author), Univ London Queen Mary, Dept Comp Sci, Mile End Rd, London E1 4NS, England. CR AGOSTI M, 2001, INFORMATION RETRIEVA, P242 AMITAY E, IEEE INT C MULT EXP ATTARDI G, 1999, EUR S TEL HYP ART IN BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Brin S, 1998, P 7 INT WORLD WID WE Callan James, 1992, P 3 INT C DAT EXP SY, P78 Chakrabarti S., 1998, P 7 INT WORLD WID WE Croft WB, 2000, ADV INFORMATION RETR, P1 CUTLER M, 1999, 11 IEEE INT C TOOLS DAVISON BD, P 2O ANN INT ACM SIG, P272 DUNLOP MD, 1993, INFORMATION PROCESSI, V29 FISCHER H, 1972, NATURE, V238, P109 FOX EA, 1988, P 11 ANN INT ACM SIG, P291, DOI 10.1145/62437.62465 GAUCH S, 1996, J UNIVERSAL COMPUTIN, V2 GERY M, 2001, INT WORKSH WEB DYN JONES KS, 1997, READINGS INFORMATION Katzer J., 1982, Information Technology: Research and Development, V1 KLEINBERG J, 1997, P 9 ACM SIAM S DISCR Lawrence S., 2000, IEEE DATA ENG B, V23, P25 Pearl J., 1997, PROBABILISTIC REASON RAJASHEKAR TB, 1995, J AM SOC INFORM SCI, V46, P272, DOI 10.1002/(SICI)1097-4571(199505)46:4<272::AID-ASI4>3.0.CO;2-T RIBEIRONETO B, 2000, SOFT COMPUTING INFOR, P259 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 RUTHVEN I, 2002, IN PRESS J AM SOC IN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SAVOY J, 1996, P TREC5, P489 SELBERG E, 1997, IEEE EXPERT, V12, P8 SILVA I, 2000, P 23 ANN INT ACM SIG, P96, DOI 10.1145/345508.345554 SMITAY E, 1998, P SIGIR POST C WORKS TSIKRIKA T, 2001, P 10 INT C INF KNOWL TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 Turtle H., 1989, P HYP 89 C PITTSB PE, P213, DOI 10.1145/74224.74242 TURTLE HR, THESIS VANRIJSBERGEN CJ, 1997, READINGS INFORMATION Zhu X., 2000, P 23 ANN INT ACM SIG, P288, DOI 10.1145/345508.345602 NR 37 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-43343-0 J9 LECT NOTES COMPUT SC PY 2002 VL 2291 BP 53 EP 72 PG 20 WC Computer Science, Information Systems; Computer Science, Theory & Methods SC Computer Science GA BW25R UT WOS:000181347800004 ER PT S AU Sadakane, K AF Sadakane, K BE Bose, P Morin, P TI Space-efficient data structures for flexible text retrieval systems SO ALGORITHMS AND COMPUTATION, PROCEEDINGS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 13th International Symposium on Algorithms and Computation CY NOV 21-23, 2002 CL VANCOUVER, CANADA SP Simon Fraser Univ, Pacific Inst Math Sci, Math Informat Technol & Complex Syst, Bajai Inc AB We propose space-efficient data structures for text retrieval systems that have merits of both theoretical data structures like suffix trees and practical ones like inverted files. Traditional text retrieval systems use the inverted files and support ranking queries based on the tf*idf (term frequency times inverse document frequency) scores of documents that contain given keywords, which cannot be solved by using only the suffix trees. A drawback of the systems is that the scores can be computed for only predetermined keywords. We extend the data structure so that the scores can-be computed for any pattern efficiently while keeping the size of the data structures moderate. The size is comparable with the text size, which is an improvement from existing methods using 0 (n log n) bit space for a text collection of length n. C1 Tohoku Univ, Grad Sch Informat Sci, Aoba Ku, Sendai, Miyagi 9808579, Japan. RP Sadakane, K (reprint author), Tohoku Univ, Grad Sch Informat Sci, Aoba Ku, Aramaki Aza Aoba09, Sendai, Miyagi 9808579, Japan. EM sada@dais.is.tohoku.ac.jp CR ANDERSSON A, 1995, ACM S THEORY COMPUTI, P427 Bender MA, 2000, LECT NOTES COMPUT SC, V1776, P88, DOI 10.1007/10719839_9 BLUMER A, 1987, J ACM, V34, P578, DOI 10.1145/28869.28873 Ferragina P, 2000, ANN IEEE SYMP FOUND, P390 GROSSI R, 2002, DIMACS WORKSHOP DATA GROSSI R, 2000, 32 ACM S THEOR COMP, P397 Hui L., 1992, LECT NOTES COMPUTER, V644, P227 Munro J. I., 2001, SIAM J COMPUT, V31, P762, DOI 10.1137/S0097539799364092 Munro JL, 2001, J ALGORITHM, V39, P205, DOI [10.1006/jagm.2000.1151, 10.1006/jagm.2001.1151] Muthukrishnan S, 2002, SIAM PROC S, P657 Raman R, 2002, SIAM PROC S, P233 Sadakane K., 2000, LNCS, V1969, P410 Sadakane K, 2002, SIAM PROC S, P225 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Weiner P., 1973, P 14 IEEE S SWITCH A, P1, DOI DOI 10.1109/SWAT.1973.13 NR 15 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-00142-5 J9 LECT NOTES COMPUT SC PY 2002 VL 2518 BP 14 EP 24 PG 11 WC Computer Science, Theory & Methods SC Computer Science GA BW68S UT WOS:000182826400002 ER PT S AU Mc Dermott, P O'Riordan, C AF Mc Dermott, P O'Riordan, C BE Oneill, M Sutcliffe, RFE Ryan, C Eaton, M Griffith, NJL TI A system for multi-agent information retrieval SO ARFIFICIAL INTELLIGENCE AND COGNITIVE SICENCE, PROCEEDINGS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 13th Irish Conference on Artificial Intelligence and Cognitive Science CY SEP 12-13, 2002 CL LIMERICK, IRELAND SP QAD Ireland, Univ Limerick AB In this paper we discuss a multi-agent information trading system, which we argue provides a suitable and flexible design for distributed information retrieval, and a suitable test-bed for studying agent cooperation, coordination and negotiation. Within the fields of Information Retrieval (IR) and Information Filtering (IF), agents represent a suitable paradigm to conceptualise, design and implement an IR system. C1 NUI, Dept IT, Galway, Ireland. RP Mc Dermott, P (reprint author), NUI, Dept IT, Galway, Ireland. EM Paula.Mc_Dermott@geminga.nuigalway.ie; colmor@geminga.nuigalway.ie CR Axelrod R., 1984, EVOLUTION COOPERATIO Baeza-Yates R., 1999, MODERN INFORMATION R, V1st CALLAN JP, 1995, P 18 INT ACM SIGIR C DORAN JE, 1997, KNOWLEDGE ENG REV, V12 HEWITT C, 1997, VIEWING CONTROL STRU Huhns M. N., 1998, READINGS AGENTS JONES KS, 1998, INFORMATION PROCESSI, V36 JONES KS, 1997, READINGS INFORMATION NWANA H, 1995, J BT LABS NWANA HS, 1996, COORDINATION SOFTWAR ORIORDAN C, 1999, INFORMATION RETRIEVA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEHORY O, 1998, P AG THEOR AG LANG M Tuomela R., 2000, COOPERATION PHILOS S VOORHEES E, 1998, P 4 TEXT RETR C, P121 WEISS G, 1998, LEARNING COORDINATE WOOLDRIDGE M, 1996, P MOD AUT AG MULT AG Wooldridge M., 1995, KNOWLEDGE ENG REV, V10 ZLOTKIN G, 1994, P 12 NAT C ART INT NR 19 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44184-0 J9 LECT NOTES ARTIF INT PY 2002 VL 2464 BP 70 EP 77 PG 8 WC Computer Science, Artificial Intelligence SC Computer Science GA BX94M UT WOS:000186905600009 ER PT S AU Lopresti, D AF Lopresti, D BE Lopresti, EP Hu, J Kashi, R TI Exploiting WWW resources in experimental document analysis research SO DOCUMENT ANALYSIS SYSTEM V, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th International Workshop on Document Analysis Systems CY AUG 19-21, 2002 CL PRINCETON, NEW JERSEY SP Avaya Inc AB Many large collections of document images are now becoming available online as part of digital library initiatives, fueled by the explosive growth of the World Wide Web. In this paper, we examine protocols and system-related issues that arise in attempting to make use of these new resources, both as a target application (building better search engines) and as a way of overcoming the problem of acquiring ground-truth to support experimental document analysis research. We also report on our experiences running two simple tests involving data drawn from one such collection. The potential synergies between document analysis and digital libraries could lead to substantial benefits for both communities. C1 Lucent Technol Inc, Bell Labs, Murray Hill, NJ 07974 USA. RP Lopresti, D (reprint author), Lucent Technol Inc, Bell Labs, 600 Mt Ave, Murray Hill, NJ 07974 USA. CR ANTONACOPOULOS A, 2000, P 4 IAPR WORKSH DOC, P515 Baird H. S., 1992, STRUCTURED DOCUMENT, P546 BAIRD HS, 1992, P IEEE, V80, P1059 Baker N., 2001, DOUBLE FOLD LIB ASSA *CORN U, PROT DIG LIB Downton A. C., 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.2001.953908 Garris MD, 1999, P SOC PHOTO-OPT INS, V3651, P97, DOI 10.1117/12.335807 HITZ O, 2000, P 4 IAPR INT WORKSH, P385 Hu JY, 2000, P SOC PHOTO-OPT INS, V3967, P291, DOI 10.1117/12.381605 *LIB C, DIG LIB IN Lopresti D., 2000, Information Retrieval, V2, DOI 10.1023/A:1009954710479 LOPRESTI D, 1996, P IAPR WORKSH DOC AN, P651 Nagy G, 1984, P 7 INT C PATT REC M, P347 Phillips I. T., 1993, Proceedings of the Second International Conference on Document Analysis and Recognition (Cat. No.93TH0578-5), DOI 10.1109/ICDAR.1993.395691 RICE SV, 1993, TR9308 UNLV INF SCI SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHROEDER H, 1998, INTERACTIVE WEB APPL SHAW EJ, 1997, D LIB MAGAZINE JUL STORK D, OPEN MIND INITIATIVE Wang Y, 2001, P 6 INT C DOC AN REC, P528 WILLARD AR, DEV COLL ARCHITECTUR, P520 2001, INT WORKSH WEB DOC A NR 22 TC 4 Z9 4 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44068-2 J9 LECT NOTES COMPUT SC PY 2002 VL 2423 BP 532 EP 543 PG 12 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BY01S UT WOS:000187252700056 ER PT B AU Gao, X Yang, J Papazoglou, MP AF Gao, X Yang, J Papazoglou, MP GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI The capability matching of web services SO FOURTH INTERNATIONAL SYMPOSIUM ON MULTIMEDIA SOFTWARE ENGINEERING, PROCEEDINGS LA English DT Proceedings Paper CT 4th International Symposium on Multimedia Software Engineering CY DEC 11-13, 2002 CL NEWPORT BEACH, CA SP IEEE Comp Soc AB A large number of Web services are already available on the Web, and the type and number of Web services grow on a daily basis. As the Web service paradigm becomes popular and more and more applications are developed or deployed as Web services, the need for defining different matches of Web services becomes manifest. This has raised the interesting research point for defining Web service matches. In this paper, we concentrate on the vital part of Web service matching - the capability matching and formally analyse and define exact match and plug-in match of Web service capabilities using abstract finite-state machine approaches. Based on the above work, we propose a new lightweight capability description language of Web service (SCDL) and explore the major stages of service capability matching. Then we focus on plug-in match and formally define it using formal logic methods. Moreover, we provide some feasible approaches and solutions for comparing two service capabilities. The work presented in this paper lays down a solid foundation for the framework for tackling the challenges of Web service discovery and composition on which we are working. C1 Tilburg Univ, INFOLAB, NL-5000 LE Tilburg, Netherlands. RP Gao, X (reprint author), Tilburg Univ, INFOLAB, POB 90153, NL-5000 LE Tilburg, Netherlands. CR Fankhauser P., 1991, SIGMOD REC, V20, P59 GERSTING JL, 1992, MATH STRUCTURES COMP Goguen J., 1996, Journal of Systems Integration, V6, DOI 10.1007/BF02262753 JENG JJ, 1995, P ACM SIGSOFT S SOFT Luqi, 1987, Proceedings 1987 Fall Joint Computer Conference - Exploring Technology: Today and Tomorrow (Cat. No.87CH2468-7) MATSUMOTO Y, 1987, TUTORIAL SOFTWARE RE, P155 MCKEE I, 1999, P 7 IEEE WORKSH FUT, P53 MECELLA M, 2001, PR 2 VLD TES WORKSH Muggleton S., 1994, Journal of Logic Programming, V19-20, DOI 10.1016/0743-1066(94)90035-3 NEBEL B, 1990, LECT NOTES ARTIF INT, P422 NISSANKE N, FORMAL SPECIFICATION POTTER B, PRETICEHALL INT SERI Prieto-Diaz R., 1991, COMMUN ACM, V34, P89 REUSSNER RH, 2000, 082000 U KARLSR REUSSNER RH, 2001, P 34 HAW INT C SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STEIGERWALD R, 1991, INFORMATION SOFTWARE, P698 TRASTOUR D, 2001, P SEM WEB WORK S STA VANHEUVEL WJ, 2001, P INT C COOP INF SYS ZAREMSKI AM, 1993, P ACM S FDN SOFTW EN ZAREMSKI AM, 1995, CMUCS95127 NR 21 TC 2 Z9 2 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1857-5 PY 2002 BP 56 EP 63 PG 8 WC Computer Science, Software Engineering; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BV88N UT WOS:000180301700008 ER PT B AU Bakus, J Hussin, MF Kamel, M AF Bakus, J Hussin, MF Kamel, M BE Wang, L Rajapakse, JC Fukushima, K Lee, SY Yao, X TI A SOM-based document clustering using phrases SO ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE LA English DT Proceedings Paper CT 9th International Conference on Neural Information Processing CY NOV 18-22, 2002 CL SINGAPORE, SINGAPORE SP Asia Pacific Neural Network Assembly, Singapore Neurosci Assoc, SEAL & FSKD Conf Steering Comm, Nanyang Technol Univ, Sch Elect & Electr Engn ID TEXT AB Most of the existing techniques for document clustering rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features for the document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as the features in a self-organizing map based document clustering algorithm. We present clustering results using the REUTERS corpus and show an improvement in clustering performance using both entropy and F-measure evaluation measures. C1 Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RP Bakus, J (reprint author), Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada. RI Kamel, Mohamed/D-9323-2011 CR BAKUS J, 2002, 15 C CAN SOC COMP ST, P319 Caropreso MF, 2001, TEXT DATABASES AND DOCUMENT MANAGEMENT: THEORY AND PRACTICE, P78 Cohen WW, 1999, ACM T INFORM SYST, V17, P141, DOI 10.1145/306686.306688 CUTTING DR, 1992, SIGIR 92 : PROCEEDINGS OF THE FIFTEENTH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P318 FURNKRANZ J, 1998, P 1 AAAI WORKSH LEAR, P5 Bote VPG, 2002, INFORM PROCESS MANAG, V38, P79 HAMMOUDA KM, 2002, WEB MINING IDENTIFYI Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 KOHONEN T, 1990, P IEEE, V78, P1464, DOI 10.1109/5.58325 KOHONEN T, 2000, IEEE T NEURAL NETWOR, P170 Koller D., 1997, P 14 INT C MACH LEAR, P170 LEWIS DD, 1994, P 3 ANN S DOC AN RET Manning C. D., 1999, FDN STAT NATURAL LAN Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 MLADENIC D, 1998, P 17 EL COMP SCI C E SALTON G, 1987, 87881 CORN U DEP COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Steinbach M., 2000, KDD WORKSH TEXT MIN VARRIJSBERGENCJ, 1908, INFORMATION RETRIEVA Zamir O., 1998, SIGIR, P46 Zamir O., 1997, KDD 97, P287 NR 21 TC 2 Z9 2 PU NANYANG TECHNOLOGICAL UNIV PI SINGAPORE PA NANYANG AVENUE, SINGAPORE 639815, SINGAPORE BN 981-04-7524-1 PY 2002 BP 2212 EP 2216 PG 5 WC Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Imaging Science & Photographic Technology SC Computer Science; Engineering; Imaging Science & Photographic Technology GA BW69E UT WOS:000182832400453 ER PT J AU Kang, BJ Choi, KS AF Kang, BJ Choi, KS TI Effective foreign word extraction for Korean information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; foreign word recognition; word segmentation AB In Korean text, foreign words, which are mostly transliterations of English words, are frequently used. Foreign words are usually very important index terms in Korean information retrieval since most of them are technical terms or names. So accurate foreign word extraction is crucial for high performance of information retrieval. However, accurate foreign word extraction is not easy because it inevitably accompanies word segmentation and most of the foreign words are unknown. In this paper, we present an effective foreign word recognition and extraction method. In order to accurately extract foreign words, we developed an effective method of word segmentation that involves unknown foreign words. Our word segmentation method effectively utilizes both unknown word information acquired through the automatic dictionary compilation and foreign word recognition information. Our HMM-based foreign word recognition method does not require large labeled examples for the model training unlike the previously proposed method. (C) 2001 Elsevier Science Ltd. All rights reserved. C1 Korea Adv Inst Sci & Technol, Div Comp Sci, Dept Elect Engn & Comp Sci,KORTERM, Adv Informat Technol Res Ctr,Yusong Gu, Taejon 305701, South Korea. RP Kang, BJ (reprint author), Korea Adv Inst Sci & Technol, Div Comp Sci, Dept Elect Engn & Comp Sci,KORTERM, Adv Informat Technol Res Ctr,Yusong Gu, 373-1 Kusong Dong, Taejon 305701, South Korea. RI Choi, Key-Sun/C-1978-2011 CR CHAE YS, 1999, P WORKSH MULT INF PR, P19 Chen K., 1992, P COLING 92, P101, DOI 10.3115/992066.992085 FUNG P, 1994, P 2 ANN WORKSH VER L, P69 Jeong KS, 1999, INFORM PROCESS MANAG, V35, P523, DOI 10.1016/S0306-4573(98)00055-7 JEONG KS, 1997, P 2 INT WORKSH INF R, P114 JIN WY, 1995, P 1995 NAT LANG PROC, P234 Kang Byung-Ju, 2000, P 5 INT WORKSH INF R, P133, DOI 10.1145/355214.355234 KIM J, 1994, P 4 INT C COMP INT M, P378 KWON YH, 1997, P INT C COMP PROC OR, P675 LEE JH, 1995, J KOREA INFORMATION, V12, P225 NAM YS, 1997, LATEST FOREIGN WORLD OH JH, 1999, 11 HANGUL KOREAN INF Park SM, 2000, ARCH PHARM RES, V23, P59, DOI 10.1007/BF02976468 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sproat R, 1996, COMPUT LINGUIST, V22, P377 NR 16 TC 5 Z9 5 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JAN PY 2002 VL 38 IS 1 BP 91 EP 109 DI 10.1016/S0306-4573(00)00065-0 PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 486PX UT WOS:000171827100005 ER PT J AU Chen, ZX Zhu, BH AF Chen, ZX Zhu, BH TI Some formal analysis of Rocchio's similarity-based relevance feedback algorithm SO INFORMATION RETRIEVAL LA English DT Article DE relevance feedback; vector space; supervised learning; similarity; lower bound ID VECTOR-SPACE MODEL; RETRIEVAL AB Rocchio's similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the binary vector space model, if the initial query vector is 0, then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), Rocchio's similarity-based relevance feedback algorithm makes at least n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional binary vector space {0, 1}(n). When an arbitrary initial query vector in {0, 1}(n) is used, it makes at least (n + k - 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification. C1 Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. Montana State Univ, Dept Comp Sci, Bozeman, MT 59717 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, 1201 W Univ Dr, Edinburg, TX 78539 USA. RI Zhu, Binhai/A-2911-2008 CR Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN Z, 2000, IN PRESS J KNOWLEDGE, P192 CHEN Z, 2001, P 7 ANN INT COMP COM, P540 CHEN Z, 2000, P AAAI 2000 WORKSH A, P12 Chen ZX, 2001, J AM SOC INF SCI TEC, V52, P655 FRAKES WR, 1992, INFORMATION RETRIEVA Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Kivinen J, 1997, ARTIF INTELL, V97, P325, DOI 10.1016/S0004-3702(97)00039-8 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 Maass W, 1998, INFORM COMPUT, V141, P66, DOI 10.1006/inco.1997.2686 MAASS W, 1994, COMPUTATIONAL LEARNI, V1, P381 Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sclaroff Stan, 1997, Proceedings 1997 IEEE Workshop on Content-Based Access of Image and Video Libraries, DOI 10.1109/IVL.1997.629714 TAYCHER L, 1997, P 2 INT C VIS INF SY, P85 WONG SKM, 1988, P 11 ANN INT ACM SIG, P219, DOI 10.1145/62437.62452 NR 24 TC 15 Z9 16 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PY 2002 VL 5 IS 1 BP 61 EP 86 DI 10.1023/A:1012730924277 PG 26 WC Computer Science, Information Systems SC Computer Science GA 492KR UT WOS:000172168000003 ER PT B AU Nowell, L Schulman, R Hix, D AF Nowell, L Schulman, R Hix, D BE Wong, PC Andrew, K TI Graphical Encoding for Information Visualization: An empirical study SO INFOVIS 2002: IEEE SYMPOSIUM ON INFORMATION VISUALIZATION 2002 LA English DT Proceedings Paper CT IEEE Symposium on Information Visualization (INFOVIS 2002) CY OCT 28-29, 2002 CL BOSTON, MA SP IEEE Comp Soc ID DISPLAYS; COLOR; INTEGRATION; SPACE; TASK AB Research in several areas provides scientific guidance for use of graphical encoding to convey information in an information visualization display. By graphical encoding we mean the use of visual display elements such as icon color, shape, size, or position to convey information about objects represented by the icons. Literature offers inconclusive and often conflicting viewpoints, including the suggestion that the effectiveness of a graphical encoding depends on the type of data represented. Our empirical study suggests that the nature of the users' perceptual task is more indicative of the effectiveness of a graphical encoding than the type of data represented. C1 Battelle Pacific NW, Richland, WA 99352 USA. RP Nowell, L (reprint author), Battelle Pacific NW, Richland, WA 99352 USA. CR Ahlberg C., 1994, P CHI 94, P313, DOI 10.1145/191666.191775 BATES M, 1984, RQ, V42, P43 CAHILL MC, 1976, HUM FACTORS, V18, P273 CARSWELL CM, 1987, ERGONOMICS, V30, P511, DOI 10.1080/00140138708969741 Chalmers Matthew, 1992, P 15 ANN INT ACM SIG, P330, DOI DOI 10.1145/133160.133215 CHRIST RE, 1984, INFORMATION DESIGN D, P209 CHRIST RE, 1975, HUM FACTORS, V17, P542 CLEVELAND WS, 1984, J AM STAT ASSOC, V79, P531 CLEVELAND WS, 1985, SCIENCE, V229, P828, DOI 10.1126/science.229.4716.828 Fairchild K. M., 1988, COGNITIVE SCI ITS AP, P201 FOX EA, 1993, P 16 ANN INT ACM SIG, P248, DOI 10.1145/160688.160730 FOX EA, 1993, J AM SOC INFORM SCI, V44, P480, DOI 10.1002/(SICI)1097-4571(199309)44:8<480::AID-ASI7>3.0.CO;2-B HEATH LS, 1995, COMMUN ACM, V38, P52, DOI 10.1145/205323.376383 JUBIS RMT, 1990, HUM FACTORS, V32, P287 Kopala C.J., 1979, P HFS, P397 LUDER CB, 1984, HUM FACTORS, V26, P19 MACKINLAY J, 1986, ACM T GRAPHIC, V5, P110, DOI 10.1145/22949.22950 Merwin D.H, 1993, P HFS, V2, P1330 NOWELL L, 1997, THESIS VIRGINIA TECH NOWELL LT, 1993, P HCI INT 93, P56 Nowell L.T., 1996, P SIGIR 96, P67, DOI 10.1145/243199.243214 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schulman R. S., 1992, STAT PLAIN ENGLISH C SMALLMAN HS, 1990, J OPT SOC AM A, V7, P1985, DOI 10.1364/JOSAA.7.001985 SMITH SL, 1964, J APPL PSYCHOL, V48, P137, DOI 10.1037/h0045919 Tufte E, 1990, ENVISIONING INFORMAT UMBERS IG, 1990, APPL ERGON, V21, P187, DOI 10.1016/0003-6870(90)90002-F Walker P.M.B., 1988, CHAMBERS SCI TECHNOL Ware C, 2000, INFORMATION VISUALIZ Wickens C. D., 1992, ENG PSYCHOL HUMAN PE WICKENS CD, 1990, HUM FACTORS, V32, P61 NR 31 TC 3 Z9 3 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1751-X PY 2002 BP 43 EP 50 DI 10.1109/INFVIS.2002.1173146 PG 8 WC Computer Science, Information Systems SC Computer Science GA BV85V UT WOS:000180247100007 ER PT S AU Kerschberg, L Kim, W Scime, A AF Kerschberg, L Kim, W Scime, A BE Truszkowski, W Rouff, C Hinchey, M TI A personalizable agent for semantic taxonomy-based web search SO INNOVATIVE CONCPTS FOR AGENT-BASED SYSTEMS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 1st International Workshop on Radical Agent Concepts CY JAN 16-18, 2002 CL MCLEAN, VIRGINIA SP GSFC, Jet Propuls Lab, Sci Applicat Int Corp ID ENGINE AB This paper addresses the problem of specifying Web searches and retrieving, filtering, and rating Web pages so as to improve the relevance and quality of hits, based on the user's search intent and preferences. We present a methodology and architecture for an agent-based system, called WebSifter II, that captures the semantics of a user's decision-oriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. Consulting a Web taxonomy agent such as WordNet helps refine the terms in the tree. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating. C1 George Mason Univ, E Ctr E Business, Fairfax, VA 22030 USA. SUNY Coll Brockport, Dept Comp Sci, Brockport, NY 14420 USA. RP Kerschberg, L (reprint author), George Mason Univ, E Ctr E Business, 4400 Univ Dr, Fairfax, VA 22030 USA. RI Kim, Wooju/G-7705-2012 CR Ackerman M, 1997, AI MAG, V18, P47 ARIDOR Y, 2000, P 4 INT WORKSH COOP, P15 ARMSTRONG R, 1995, P 1995 AAAI SPRING S Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Bollacker KD, 2000, IEEE INTELL SYST APP, V15, P42, DOI 10.1109/5254.850826 BOOSE JH, 1987, INT J MAN MACH STUD, V26, P3, DOI 10.1016/S0020-7373(87)80032-9 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P545 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 CHEN Z, 2000, P 1 INT C WEB INF SY, V1, P206 Clark D, 1999, IEEE INTELL SYST APP, V14, P75, DOI 10.1109/5254.747911 DEVEL O, 1998, C AUT LEARN DISC CON Fensel D., 1999, P WORLD C WWW INT WE, P25 Guarino N, 1999, IEEE INTELL SYST APP, V14, P70, DOI 10.1109/5254.769887 Howe AE, 1997, AI MAG, V18, P19 Klein D.A., 1994, DECISION ANAL INTELL Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Krulwich B, 1997, AI MAG, V18, P37 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, IEEE INTERNET COMPUT, V2, P38, DOI 10.1109/4236.707689 LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745 Li YH, 1998, IEEE INTERNET COMPUT, V2, P24 Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 Martin P, 2000, IEEE INTELL SYST APP, V15, P18, DOI 10.1109/5254.846281 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Saaty T.L., 1980, ANAL HIERARCHY PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCIME A, 2001, P 9 IFIP 2 6 WORK C SCIME A, 2000, INT C DIG LIB RES PR, P493 Selberg E, 1997, IEEE EXPERT, V12, P11, DOI 10.1109/64.577468 SHAVLIK J, 1998, P C AUT LEARN DISC W STAAB S, 1999, P IWANN 99 INT WORK Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 YOKOI T, 1995, COMMUN ACM, V38, P45 NR 34 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-40725-1 J9 LECT NOTES ARTIF INT PY 2002 VL 2564 BP 3 EP 31 PG 29 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering SC Computer Science GA BX62U UT WOS:000185937000001 ER PT S AU Russell, B Yin, HJ Allinson, NM AF Russell, B Yin, HJ Allinson, NM BE Yin, H Allinson, N Freeman, R Keane, J Hubbard, S TI Document clustering using the 1+1 dimensional Self-Organising Map SO INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2002 SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 3rd International Conference on Intelligent Data Engineering and Automated Learning CY AUG 12-14, 2002 CL MANCHESTER, ENGLAND SP Univ Manchester Inst Sci & Technol AB Automatic clustering of documents is a task that has become increasingly important with the explosion of online information. The Self-Organising Map (SOM) has been used to cluster documents effectively, but efforts to date have used a single or a series of 2-dimensional maps. Ideally, the output of a document-clustering algorithm should be easy for a user to interpret. This paper describes a method of clustering documents using a series of 1-dimensional SOM arranged hierarchically to provide an intuitive tree structure representing document clusters. Wordnet(1) is used to find the base forms of words and only cluster on words that can be nouns. C1 UMIST, Dept Elect Engn & Elect, Manchester M60 1QD, Lancs, England. RP Russell, B (reprint author), UMIST, Dept Elect Engn & Elect, POB 88, Manchester M60 1QD, Lancs, England. CR BLACKMORE J, 1993, P INT C NEUR NETW IC BLAIR DC, 1985, COMMUNICATIONS ACM, V28 CHEN H, 1998, INTERNET BROWSING SE FREEMAN R, 2002, P INT JOINT C NEUR N, V2, P1906 Fritzke B, 1995, NEURAL PROCESS LETT, V2, P1 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 Honkela T., 1995, P ICANN 95 INT C ART, V2, P3 KOHONEN T, 1982, BIOL CYBERN, V43, P69 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 KRISTA L, 1996, WEBSOM STATUS REPORT MERKL D, 1997, EXPLORATION TEXT COL RAUBER A, AUTOMATIC LABELING S RAUBER A, 2000, AUTOMATICALLY DETECT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 15 TC 2 Z9 2 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44025-9 J9 LECT NOTES COMPUT SC PY 2002 VL 2412 BP 154 EP 160 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BY01R UT WOS:000187252500026 ER PT S AU Kurhila, J Lattu, M Pietila, A AF Kurhila, J Lattu, M Pietila, A BE Cerri, SA Gouarderes, G Paraguacu, F TI Using vector-space model in adaptive hypermedia for learning SO INTELLIGENT TUTORING SYSTEMS SE Lecture Notes in Computer Science LA English DT Article; Proceedings Paper CT 6th International Conference on Intelligent Tutoring Systems CY JUN 02-07, 2002 CL SAN SEBASTIAN, SPAIN SP Assoc Comp Machinery, Int Federat Informat Proc, Artificial Intelligence Educ Soc, IEEE CS Learning Technol Task Force, French Direct Gen Armement, Asociac Espanola Inteligencia Artificial AB Although many of the existing adaptive learning environments use other approaches, the vector-space model for information retrieval can be used in providing individualized learning with hypermedia. A system employing a modified version of the vector-space model is described. The approach taken is tested in a real-life setting to evaluate and train basic arithmetics skills of learners in elementary education. Although the impact on learning was not measured, the approach provides a useful evaluational help for the teacher to make observations about the learning processes of the learners. C1 Univ Helsinki, Dept Comp Sci, FIN-00014 Helsinki, Finland. Univ Helsinki, Dept Teacher Educ, FIN-00014 Helsinki, Finland. RP Kurhila, J (reprint author), Univ Helsinki, Dept Comp Sci, POB 26, FIN-00014 Helsinki, Finland. EM jaakko.kurhila@cs.helsinki.fi; mplattu@cc.helsinki.fi; atpietil@cc.helsinki.fi CR Brusilovsky P, 1996, USER MODEL USER-ADAP, V6, P87, DOI 10.1007/BF00143964 Brusilovsky P, 2001, USER MODEL USER-ADAP, V11, P87, DOI 10.1023/A:1011143116306 GRINSTEIN LS, 2001, ENCY MATH ED KURHILA J, 2002, IN PRESS P INT C COM KURHILA J, 2000, P ITS 2000, P658 KURHILA J, 2001, P INT C COMP ED ICCE, P416 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Vilkki J, 1995, Appl Neuropsychol, V2, P93, DOI 10.1207/s15324826an0203&4_1 Vygotsky L. S., 1978, MIND SOC DEV HIGHER NR 9 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-43750-9 J9 LECT NOTES COMPUT SC PY 2002 VL 2363 BP 129 EP 138 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BV79M UT WOS:000180067900017 ER PT B AU Chowdhury, A Beitzel, S Jensen, E AF Chowdhury, A Beitzel, S Jensen, E GP IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY IEEE COMPUTER SOCIETY TI Analysis of combining multiple query representations with varying lengths in a single engine SO INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS LA English DT Proceedings Paper CT 3rd International Conference on Information Technology: Coding and Computing (ITCC 2002) CY APR 08-10, 2002 CL LAS VEGAS, NV SP IEEE Comp Soc ID RETRIEVAL; MODEL AB We examine the issues of combining multiple query representations in a single IP engine. Differing query representations are used to retrieve different documents. Thus, when combining their results, improvements are observed in effectiveness. We use multiple TREC query representations (title, description and narrative) as a basis for experimentation. We examine several combination approaches presented in the literature (vector addition, CombSUM and CombMNZ) and present a new combination approach using query vector length normalization. We examine two query representation combination approaches (title + description and title + narrative) for 150 queries from TREC 6, 7 and 8 topics. Our QLN (Query Length Normalization) technique outperformed vector addition and data fusion approaches by a,3 much as 32% and was on average 24% better. Additionally, QLN always outperformed the single best query representation in terms of effectiveness. C1 Amer Online Inc, Dulles, VA 20166 USA. RP Chowdhury, A (reprint author), Amer Online Inc, Dulles, VA 20166 USA. CR BELKIN NJ, 1995, INFORM PROCESS MANAG, V31, P431, DOI 10.1016/0306-4573(94)00057-A CHOWDHURY A, 2000, P 9 ANN TEXT RETR C Fox E. A., 1994, NIST SPECIAL PUBLICA, P243 LEE, 1997, LEE J, P267 LEE J, 1997, ACM SIGIR, P267 MONTAGUE M, 2001, ACM CIKM, P427 MOUNIER S, 1998, INT C PAR DISTR P TE, V55 ROBERTSON S, 1998, P 7 TEXT RETR C TREC, P7 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1996, P 19 ANN INT ACM SIG TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 NR 11 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1507-X PY 2002 BP 236 EP 241 PG 6 WC Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BU41K UT WOS:000175939100041 ER PT J AU Budzik, J Bradshaw, S Fu, XB Hammond, KJ AF Budzik, J Bradshaw, S Fu, XB Hammond, KJ TI Supporting on-line resource discovery in the context of ongoing tasks with proactive software assistants SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES LA English DT Article AB We present ongoing work on systems aimed at improving a user's awareness of resources available to them on the Internet and in intranets. First, we briefly describe Watson, a system that proactively retrieves documents from on-line repositories that are potentially useful in the context of a task, allowing the user to quickly become aware of document resources available in on-line information repositories. Next, we describe 121, an extension of Watson that builds communities of practice on the fly, based on the work that its users do, so that users with similar goals and interests can discover each other and communicate both synchronously and asynchronously. Both Watson and 121 operate given some knowledge of the user's current task, gleaned automatically from the behavior of users in software tools. As a result, the systems can provide users with useful resources in the context of the work that they are performing. We argue that the systems can foster a greater sense of awareness of the resources available, while minimizing the effort required to discover them. (C) 2002 Elsevier Science Ltd. C1 Northwestern Univ, Dept Comp Sci, Intelligent Informat Lab, Evanston, IL 60201 USA. RP Budzik, J (reprint author), Northwestern Univ, Dept Comp Sci, Intelligent Informat Lab, 1890 Maple Ave, Evanston, IL 60201 USA. RI Hammond, Kristian/B-7165-2009 CR ACKERMAN M, 1995, P 8 ACM S US INT SOF BADUE C, 1998, P 1998 WORLD C WWW I Breslau L., 1999, P IEEE INFOCOM 99 Budzik J, 2001, KNOWL-BASED SYST, V14, P37, DOI 10.1016/S0950-7051(00)00105-2 BUDZIK J, 2000, P CSCW 2000 WORKSH A BUDZIK J, 2000, P 2000 INT C INT US CHEN L, 1998, P 2 INT C AUT AG MIN CHEN LLJ, 1996, P WEBN 96 SAN FRANC CHURCHILL EF, 2000, P CHI 2000 HAUG NETH CUNHA C, 1995, TR95010 BOST U COMP DONATH JS, 1994, P 2 INT WWW C CHIC I DOURISH P, 1999, P UIST 9 ASH NC US DUMAIS ST, 1988, P CHI 88 INT C HUM F FONER L, 1997, P AG 97 MAR REY CA U Gamma E, 1994, DESIGN PATTERNS ELEM GRUDIN J, 1994, COMMUN ACM, V37, P92, DOI 10.1145/175222.175230 Gutwin C., 1999, ACM Transactions on Computer-Human Interaction, V6, DOI 10.1145/329693.329696 Hattori F, 1999, COMMUN ACM, V42, P55, DOI 10.1145/295685.295701 Huberman BA, 1998, SCIENCE, V280, P95, DOI 10.1126/science.280.5360.95 JUNG Y, 2000, P DIS 2000 DES INT S KRAUT R, 1988, P CSCW 88 PORTL OR U KRISTOFFERSEN S, 1999, P CHI 99 PITTSB PA U LIEBERMAN H, 1995, P INT JOINT C ART IN LIEBERMAN H, 1998, P 1998 INT C INT US OLSEN D, 1999, P CHI 99 PITTSB PA PALFREYMAN K, 1996, P CSCW 96 CAMBR MA U PHELPS TA, 2000, P 9 INT WORLD WID WE RHODES B, 1996, P 1 INT C PRACT APPL RHODES B, 2000, P 2000 INT C INT US SALTON G, 1997, READINGS INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SELBERG E, 1997, IEEE EXPERT, V12, P8 Whittaker S., 1997, ACM Transactions on Computer-Human Interaction, V4, DOI 10.1145/254945.254958 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 34 TC 14 Z9 14 PU ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD PI LONDON PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND SN 1071-5819 J9 INT J HUM-COMPUT ST JI Int. J. Hum.-Comput. Stud. PD JAN PY 2002 VL 56 IS 1 BP 47 EP 74 DI 10.1006/ijhc.2001.0515 PG 28 WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary SC Computer Science; Engineering; Psychology GA 525CL UT WOS:000174049700004 ER PT J AU Lagus, K AF Lagus, K TI Text retrieval using self-organized document maps SO NEURAL PROCESSING LETTERS LA English DT Article DE document maps; information retrieval; LSI; SOM; text mining ID WEBSOM AB A map of text documents arranged using the Self-Organizing Map (SOM) algorithm (1) is organized in a meaningful manner so that items with similar content appear at nearby locations of the 2-dimensional map display, and (2) clusters the data, resulting in an approximate model of the data distribution in the high-dimensional document space. This article describes how a document map that is automatically organized for browsing and visualization can be successfully utilized also in speeding up document retrieval. Furthermore, experiments on the well-known CISI collection [3] show significantly improved performance compared to Salton's vector space model, measured by average precision (AP) when retrieving a small, fixed number of best documents. Regarding comparison with Latent Semantic Indexing the results are inconclusive. C1 Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 Espoo, Finland. CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X *CISI COLL, 1981, CISI REF COLL INF RE DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Hearst M., 1999, MODERN INFORM RETRIE, P257 Honkela T., 1996, A32 HELS U TECHN LAB Kaski S., 1998, NEURAL COMPUTING SUR, V1, P1 Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 Kohonen T., 1997, SPRINGER SERIES INFO, V30 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 Koskenniemi K., 1983, THESIS U HELSINKI Lagus K, 1999, ARTIF INTELL REV, V13, P345, DOI 10.1023/A:1006586221250 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1987, 87881 CORN U DEP COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Voorhees E. M., 2000, P 8 TEXT RETR C TREC NR 17 TC 12 Z9 12 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 1370-4621 J9 NEURAL PROCESS LETT JI Neural Process. Lett. PY 2002 VL 15 IS 1 BP 21 EP 29 DI 10.1023/A:1013853012954 PG 9 WC Computer Science, Artificial Intelligence; Neurosciences SC Computer Science; Neurosciences & Neurology GA 512XB UT WOS:000173347900003 ER PT S AU Nurnberger, A Detyniecki, M AF Nurnberger, A Detyniecki, M GP IEEE IEEE TI Visualizing changes in data collections using growing self-organizing maps SO PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 SE IEEE International Joint Conference on Neural Networks (IJCNN) LA English DT Proceedings Paper CT International Joint Conference on Neural Networks (IJCNN 02) CY MAY 12-17, 2002 CL HONOLULU, HI SP IEEE, IEEE Neural Networks Soc, Int Neural Network Soc ID TEXT RETRIEVAL AB In previous work we have proposed a modification of the standard learning algorithm for self-organizing maps that iteratively increases the size of the map during the learning process by adding single neurons. The main advantage of this approach is an automatic control of the size and topology of the map, thus avoiding the problem of misclassification because of an imposed size. In this paper we discuss how this algorithm can be used to visualize changes in data collections. We illustrate our approach with some examples. C1 Univ Calif Berkeley, EECS, Div Comp Sci, Berkeley, CA 94720 USA. RP Nurnberger, A (reprint author), Univ Calif Berkeley, EECS, Div Comp Sci, Berkeley, CA 94720 USA. RI Nurnberger, Andreas/ OI Nurnberger, Andreas/0000-0003-4311-0624 CR Alahakoon D, 2000, IEEE T NEURAL NETWOR, V11, P601, DOI 10.1109/72.846732 ALLINSON N, 2001, P 3 WORKSH SELF ORG BACZAYATES R, 1999, MODERN INFORMATION R DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 FRITZKE B, 1994, NEURAL NETWORKS, V7, P1441, DOI 10.1016/0893-6080(94)90091-4 GREIFF WR, 1998, 21 ANN INT ACM SIGIR Honkela T., 1997, SELF ORG MAPS NATURA ISBELL CL, 1998, P C NEUR INF PROC NI, P480 Kaski S., 1998, P IJCNN 98 INT JOINT, V1, P413 Klose A, 2000, PHYS CHEM EARTH PT A, V25, P649, DOI 10.1016/S1464-1895(00)00100-9 Kohonen T., 1984, SELF ORG ASS MEMORY KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 Lagus K., 1999, P ICANN99 9 INT C AR, V1, P371 Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 NURNBERGER A, 2001, P INT C EUR SOC FUZZ, P377 NURNBERGER A, 2001, P INT WORKSH INF RET, P61 Rauber A., 1999, P INT JOINT C NEUR N, P3527 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1994, COMMUN ACM, V37, P97, DOI 10.1145/175235.175243 Steinbach M., 2000, KDD WORKSH TEXT MIN Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 NR 24 TC 5 Z9 6 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA SN 1098-7576 BN 0-7803-7278-6 J9 IEEE IJCNN PY 2002 BP 1912 EP 1917 DI 10.1109/IJCNN.2002.1007811 PG 2 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic SC Computer Science; Engineering GA BU92F UT WOS:000177402800340 ER PT B AU Diao, LL Hu, KY Lu, YC Shi, CY AF Diao, LL Hu, KY Lu, YC Shi, CY GP TU TU TI Using boosting mechanism to refine the threshold of VSM-based similarity in text classification SO PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4 LA Chinese DT Proceedings Paper CT 4th World Congress on Intelligent Control and Automation CY JUN 10-14, 2002 CL SHANGHAI, PEOPLES R CHINA SP Tongji Univ, Shanghai Jiaotong Univ, E China Univ Sci & Technol, IEEE Robot & Automat Soc, IEEE Control Syst Soc, Beijing Chapter, Chinese Assoc Automat, Shanghai Assoc Automat DE text categorization; boosting learning; similarity AB VSM-based Similarity Classifier is the simplest Text Categorization method. It has high classification speed, but its accuracy is not good. The main reason is that the Similarity Threshold used by Similarity Classifier is decided empirically but not mathematically. This paper introduces a boosting-based mechanism to adaptively compute out relatively accurate Similarity Threshold over specific dataset. This method constructs better similarity-based classification rules via combining the similarity thresholds generated by the constituent classifiers of boosting. It greedily minimizes the error rates on training documents; therefore the Similarity Classifier with thus computed threshold should also have low error rates. C1 Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. RP Diao, LL (reprint author), Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. CR Freund Y, 1997, J COMPUT SYST SCI, V55, P119, DOI 10.1006/jcss.1997.1504 Rocchio J, 1971, RELEVANCE FEEDBACK I, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Vapnik V., 1995, NATURE STAT LEARNING WENOCUR RS, 1981, DISCRETE MATH, V33, P313, DOI 10.1016/0012-365X(81)90274-0 NR 6 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7268-9 PY 2002 BP 2284 EP 2287 PG 4 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA BV45G UT WOS:000179017500488 ER PT B AU Diao, LL Lu, MY Hu, KY Lu, YC Shi, CY AF Diao, LL Lu, MY Hu, KY Lu, YC Shi, CY GP TU TU TI New boosting algorithms for text categorization SO PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4 LA English DT Proceedings Paper CT 4th World Congress on Intelligent Control and Automation CY JUN 10-14, 2002 CL SHANGHAI, PEOPLES R CHINA SP Tongji Univ, Shanghai Jiaotong Univ, E China Univ Sci & Technol, IEEE Robot & Automat Soc, IEEE Control Syst Soc, Beijing Chapter, Chinese Assoc Automat, Shanghai Assoc Automat DE text categorization; boosting; decision tree; VSM AB AdaBoost.SZ is a boosting method specifically designed for solving multi-class, multi-label text categorization problems. Fabrizio Sebastiani et al. provided another idea to improve these base classifiers: combining two or more weak hypothesis as a single base classifier. Its main problem is that the amount of hypothesis selected to combine is determined not by their importance, but by the boosting iteration times already performed. This paper proposes two dynamical ways for combining any number of hypotheses according to their importance. Experimental results show that the new ideas do improve the performance of boosting. C1 Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. RP Diao, LL (reprint author), Tsing Hua Univ, Dept Comp Sci & Technol, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China. CR DIAO L, 2002, IN PRESS J SOFTWARE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 1999, MACH LEARN, V37, P297, DOI 10.1023/A:1007614523901 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Sebastiani F., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354804 NR 5 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7268-9 PY 2002 BP 2326 EP 2329 PG 4 WC Automation & Control Systems; Computer Science, Artificial Intelligence SC Automation & Control Systems; Computer Science GA BV45G UT WOS:000179017500497 ER PT B AU Anagnostopoulos, I Anagnostopoulos, C Papaleonidopoulos, I Loumos, V Kayafas, E AF Anagnostopoulos, I Anagnostopoulos, C Papaleonidopoulos, I Loumos, V Kayafas, E GP ISIF ISIF TI A proposed system for segmentation of information sources in portals and search engines repositories SO PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOL II LA English DT Proceedings Paper CT 5th International Conference on Information Fusion (FUSION 2002) CY JUL 08-11, 2002 CL ANNAPOLIS, MD SP IEEE, Georgia Tech Res Inst, USAF Res Lab, US Dept Defense, Missile Defense Agcy, NAVSEA, Oak Ridge Natl Lab, US DOE, USN, Off Res, AIAA, IEE DE information system; web-site classification; knowledge segmentation; e-commerce; Portal ID VECTOR-SPACE MODEL AB In nowadays, there is a huge volume of information on the web, which is disseminated to the users in a chaotic way. In order to be easily accessed, the information must be clustered and classified in appropriate knowledge areas. Thus, many heavily visited sites or Portals try to unify the access to multiple information sources, providing by this way classification of information. This paper proposes a system, aiming to classify e-commerce sites according their web-content. This system can be implemented for automatic knowledge segmentation in a Portal or in a search engine repository. The system performance reached 96% in the first test sets, after the learning phase. However, the performance significantly increases (up to 98%) as the number of the test sets increases. C1 NTUA, Dept Elect & Comp Engn, Athens, Greece. RP Anagnostopoulos, I (reprint author), NTUA, Dept Elect & Comp Engn, 9 Heroon Polytechneuiou Str, Athens, Greece. CR BORRA S, 2001, ADV CLASSIFICATION D KLOSE M, 1999, P 5 AM C INF SYST AM, P115 MOENS MF, 2000, AUTOMATIC INDEXING A RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RICARDO BY, 1999, MODERN INFORMATION R ROCCHIO JJ, 1971, SMART RETRIEVAL SYST, P324 SALTON G, 1989, AUTOMATIC TEXT PROCE, P231 SALTON G, 1989, AUTOMATIC TEXT PROCE, P301 SALTON G, 1989, AUTOMATIC TEXT PROCE, P313 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHMID B, ELEMENTS REFERENCE M SELZ S, INT J ELECT MARKETS, V7 NR 12 TC 0 Z9 0 PU INT SOC INFORMATION FUSION PI SUNNYVALE PA PO BOX 70188, SUNNYVALE, CA 94086 USA BN 0-9721844-2-2 PY 2002 BP 1450 EP 1456 PG 7 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Remote Sensing SC Computer Science; Remote Sensing GA BV19H UT WOS:000178107500098 ER PT B AU Kerschberg, L Kim, W Scime, A AF Kerschberg, L Kim, W Scime, A BE Ozsu, T Schek, HJ Tanaka, K Zhang, Y Kambayashi, Y TI A semantic taxonomy-based personalizable meta-search agent SO SECOND INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS ENGINEERING, VOL I, PROCEEDINGS LA English DT Proceedings Paper CT 2nd International Conference on Web Information Systems Engineering (WISE 2001) CY DEC 03-06, 2001 CL KYOTO, JAPAN SP Web Informat Syst Engn Soc, Kyoto Univ ID WEB; ENGINE AB This paper addresses the problem of specifying Web searches and retrieving, filtering, and rating Web pages so as to improve the relevance and quality of hits, based on the user's search intent and preferences. We present a methodology and architecture for an agent-based system, called WebSifter 11, that captures the semantics of a user's decision-oriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. Consulting a Web taxonomy agent such as Wordnet helps refine the terms in the tree. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating. C1 George Mason Univ, E Ctr E Business, Fairfax, VA 22030 USA. RP Kerschberg, L (reprint author), George Mason Univ, E Ctr E Business, Fairfax, VA 22030 USA. RI Kim, Wooju/G-7705-2012 CR Ackerman M, 1997, AI MAG, V18, P47 ARIDOR Y, 2000, P 4 INT WORKSH COOP, V4, P15 ARMSTRONG R, 1995, P 1995 AAAI SPR S IN Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124 Bollacker KD, 2000, IEEE INTELL SYST APP, V15, P42, DOI 10.1109/5254.850826 BOOSE JH, 1987, INT J MAN MACH STUD, V26, P3, DOI 10.1016/S0020-7373(87)80032-9 Chakrabarti S, 1998, P ACM SIGMOD INT C M, P307, DOI 10.1145/276304.276332 Chakrabarti S, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL WORLD WIDE WEB CONFERENCE, P545 Chen H., 2000, P ACM SIGCHI C HUM F, P145, DOI 10.1145/332040.332418 CHEN Z, 2000, P 1 INT C WEB INF SY, V1, P206 Clark D, 1999, IEEE INTELL SYST APP, V14, P75, DOI 10.1109/5254.747911 DEVEL O, 1998, WORK NOT LEARN TEXT Fensel D., 1999, P WORLD C WWW INT WE, P25 Guarino N, 1999, IEEE INTELL SYST APP, V14, P70, DOI 10.1109/5254.769887 Howe AE, 1997, AI MAG, V18, P19 KIM W, 2001, P 2001 INT C EL COMM Klein D.A., 1994, DECISION ANAL INTELL Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Krulwich B, 1997, AI MAG, V18, P37 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, IEEE INTERNET COMPUT, V2, P38, DOI 10.1109/4236.707689 LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745 Li YH, 1998, IEEE INTERNET COMPUT, V2, P24 Maes P., 1994, Communications of the ACM, V37, DOI 10.1145/176789.176792 Martin P, 2000, IEEE INTELL SYST APP, V15, P18, DOI 10.1109/5254.846281 MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748 Porter M. F., ALGORITHM SUFFIX STR Saaty T.L., 1980, ANAL HIERARCHY PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCIME A, 2001, P 9 IFIP 2 6 WORK C SCIME A, 2000, INT C DIG LIB RES PR, P493 Selberg E, 1997, IEEE EXPERT, V12, P11, DOI 10.1109/64.577468 SHAVLIK J, 1998, P C AUT LEARN DISC W STAAB S, 1999, P IWANN 99 INT WORK Terveen L, 1997, COMMUN ACM, V40, P59, DOI 10.1145/245108.245122 YOKOI T, 1995, COMMUN ACM, V38, P45 NR 36 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-1393-X PY 2002 BP 41 EP 50 PG 10 WC Computer Science, Information Systems SC Computer Science GA BU40D UT WOS:000175893700005 ER PT S AU Cai, GR AF Cai, GR BE Borner, K Chen, C TI GeoVIBE: A visual interface for geographic digital libraries SO VISUAL INTERFACES TO DIGITAL LIBRARIES SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd International Workshop on Visual Interfaces to Digital Libraries held at the Joint Conference on Digital Libraries (JCDL) CY JUL 18, 2002 CL PORTLAND, OREGON SP ACM, IEEE ID ACCESS AB Users of spatial digital libraries normally select documents of interest based on both their geographical and topical relevancy in relation to specified queries. This paper proposes an integrated and flexible geographic information retrieval and browsing tool, GeoVIBE, which combines a geographical model and a vector space model to form document representations that are tightly linked. At the user interface, GeoVIBE consists of two types of browsing windows, GeoView and VibeView, which work in coordination for visual navigation in the document space. GeoView imposes a geographical order to the document space based on the idea of document "footprints" that may be cartographically visualized to reveal the spatial structure of the document space. VibeView is a visual presentation of document space defined by multiple reference points, similar to that of VIBE interface by Olson and colleagues (1993). By smooth integration of two browsing strategies, GeoVIBE enables users to search information using geographic clues as well as thematic clues. C1 Penn State Univ, Sch Informat Sci & Technol, University Pk, PA 16802 USA. RP Cai, GR (reprint author), Penn State Univ, Sch Informat Sci & Technol, 002K Thomas Bldg, University Pk, PA 16802 USA. EM cai@ist.psu.edu CR ALLEN B, 1998, P 3 ACM C DIG LIB PI ANCONA D, 2002, 2 ACM IEEE JOINT C D BARCLAY T, 2000, P 2000 ACM SIGMOD MA BUTTENFELD BP, 1996, P 3 INT C INT GEOGR CAI G, 2002, LNCS, V2478 Carrico L, 1998, J VISUAL LANG COMPUT, V9, P287, DOI 10.1006/jvlc.1998.0085 CHRISTEL MG, 2001, 1 ACM IEEE JOINT C D FABRIKANT SI, 2001, LECT NOTES COMPUTER, V2205, P156 Fabrikant SI, 2001, ANN ASSOC AM GEOGR, V91, P263, DOI 10.1111/0004-5608.00242 Golledge RG, 2002, ANN ASSOC AM GEOGR, V92, P1, DOI 10.1111/1467-8306.00276 Greene S, 2000, J AM SOC INFORM SCI, V51, P380, DOI 10.1002/(SICI)1097-4571(2000)51:4<380::AID-ASI7>3.3.CO;2-X GROSSMAN D, 1998, INFORMATION RETRIEVA Jackendoff R., 1992, LANGUAGES MIND Jorgensen C, 1999, COMPUT HUMANITIES, V33, P293, DOI 10.1023/A:1002065412222 Korfhage R., 1997, INFORMATION STORAGE LANDAU B, 1993, BEHAV BRAIN SCI, V16, P217 LARSON R.R., 1996, GIS LIB PATRONS MAPS, P81 LAURINI P, 1990, P 4 INT S SPAT DAT H Laurini R, 1992, FUNDAMENTALS SPATIAL MASUI T, 1995, P 8 ANN S US INT SOF MORRIS B, 1988, SPECIAL LIB ASS GEOG, V152, P19 National Research Council, 1999, DISTR GEOL SPAT INF NECHES R, 2001, NEW REV HYPERMEDIA M, V7 North C, 1997, CSTR3854 U MAR COMP OLSON KA, 1993, INFORMATION PROCESSI, V29, P69 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith TR, 1996, COMPUTER, V29, P54, DOI 10.1109/2.493457 SPOERRI A, 1993, P IEEE C VIS CAMBR M Strohecker C, 2000, LECT NOTES ARTIF INT, V1849, P1 Williamson C., 1992, P 15 ANN INT ACM SIG WOODRUFF AG, 1994, J AM SOC INFORM SCI, V45, P645, DOI 10.1002/(SICI)1097-4571(199410)45:9<645::AID-ASI2>3.0.CO;2-8 NR 31 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-00247-2 J9 LECT NOTES COMPUT SC PY 2002 VL 2539 BP 171 EP 187 PG 17 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BW33F UT WOS:000181620100013 ER PT S AU Cuenca-Acuna, FM Nguyen, TD AF Cuenca-Acuna, FM Nguyen, TD BE Gregori, E Cherkasova, L Cugola, G Panzieri, F Picco, GP TI Text-based content search and retrieval in ad hoc P2P communities SO WEB ENGINEERING AND PEER TO PEER COMPUTING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT International Workshop on Web Engineering/International Workshop on Peer-to-Peer Computing held Jointly with Networking 2002 CY MAY 19-24, 2002 CL PISA, ITALY SP Compaq, Microsoft, Softec, CNUCE, iiT, Pisa Cassa Risparmio Spa AB We consider the problem of content search and retrieval in peer-to-peer (P2P) communities. P2P computing is a potentially powerful model for information sharing between ad hoc groups of users because of its low cost of entry and natural model for resource scaling. As P2P communities grow, however, locating information distributed across the large number of peers becomes problematic. We address this problem by adapting a state-of-the-art text-based document ranking algorithm, the vector-space model instantiated with the TFxIDF ranking rule, to the P2P environment. We make three contributions: (a) we show how to approximate TFxIDF using compact summaries of individual peers' inverted indexes rather than the inverted index of the entire communal store; (b) we develop a heuristic for adaptively determining the set of peers that should be contacted for a query; and (c) we show that our algorithm tracks TFADF's performance very closely, giving P2P communities a search and retrieval algorithm as good as that possible assuming a centralized server. C1 Rutgers State Univ, Dept Comp Sci, Piscataway, NJ 08854 USA. RP Cuenca-Acuna, FM (reprint author), Rutgers State Univ, Dept Comp Sci, 110 Frelinghuysen Rd, Piscataway, NJ 08854 USA. CR BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692 Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X BUCKLEY C, 1985, TR85686 CORN U Callan J. P., 1995, P 18 ANN INT ACM SIG, P21, DOI 10.1145/215206.215328 Clarke I., 2000, WORKSH DES ISS AN UN, P46 CUENCAACUNA FM, 2001, DCSTR465 RUTG U Demers A, 1987, P 6 ANN ACM S PRINC, P1, DOI [10.1145/41840.41841, DOI 10.1145/41840.41841] Douglis Fred, 1997, USENIX S INT TECHN S FRENCH JC, 1999, RES DEV INFORMATION, P238 GIFFORD DK, 1991, P 13 ACM S OP SYST P Gravano Luis, 1994, P 1994 ACM SIGMOD IN, P126, DOI 10.1145/191839.191869 Harman D., 1993, P 16 ANN INT ACM SIG ORAM A, 2001, PEER TO PEER HARNESS RATNASAMY S, 2001, P ACM SIGCOMM 01 C ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Roselli D., 2000, P 2000 USENIX ANN TE Rowstron A. I. T., 2001, P IFIP ACM INT C DIS SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Saroiu S., 2002, P MULT COMP NETW STOICA I, 2001, P ACM SIGCOMM 01 C A Weatherspoon Hakim, 2000, P ACM ASPLOS Witten I. H., 1999, MANAGING GIGABYTES C Yang B., 2002, P INT C DISTR COMP S ZHAO Y, 2000, UCBCDS011141 NR 24 TC 5 Z9 5 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-44177-8 J9 LECT NOTES COMPUT SC PY 2002 VL 2376 BP 220 EP 234 PG 15 WC Computer Science, Theory & Methods SC Computer Science GA BY01P UT WOS:000187251900020 ER PT J AU Jones, G Collier, N Sakai, T Sumita, K Hirakawa, H AF Jones, G Collier, N Sakai, T Sumita, K Hirakawa, H TI A framework for cross-language information access: Application to English and Japanese SO COMPUTERS AND THE HUMANITIES LA English DT Article DE cross-language information retrieval; information access; Japanese-English; machine translation; probabilistic retrieval ID RETRIEVAL AB Internet search engines allow access to online information from all over the world. However, there is currently a general assumption that users are fluent in the languages of all documents that they might search for. This has for historical reasons usually been a choice between English and the locally supported language. Given the rapidly growing size of the Internet, it is likely that future users will need to access information in languages in which they are not fluent or have no knowledge of at all. This paper shows how information retrieval and machine translation can be combined in a cross-language information access framework to help overcome the language barrier. We present encouraging preliminary experimental results using English queries to retrieve documents from the standard Japanese language BMIR-J2 retrieval test collection. We outline the scope and purpose of cross-language information access and provide an example application to suggest that technology already exists to provide effective and potentially useful applications. C1 Toshiba Corp, Ctr Res & Dev, Knowledge Media Lab, Saiwai Ku, Kawasaki, Kanagawa 2108582, Japan. RP Jones, G (reprint author), Univ Exeter, Dept Comp Sci, Exeter EX4 4QJ, Devon, England. CR Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958 Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540 Beaulieu M, 1997, P TREC 5, P143 Brown M.G., 1995, P ACM MULT 95 SAN FR, P35, DOI 10.1145/217279.215080 Carbonell J.G., 1997, P 15 INT JOINT C ART, P708 Chien L. F., 1995, P 18 ANN INT ACM SIG, P112, DOI 10.1145/215206.215345 COLLIER N, 1998, P COLINGACL 98 MONTR, P263 Franz M., 1999, P 7 TEXT RETR C TREC, P157 FUJII H, 1993, P 16 ANN INT ACM SIG, P237, DOI 10.1145/160688.160728 GEY FC, 1999, P 7 TEXT RETR C TREC, P527 HARMAN D, 1998, 6 TEXT RETR C TREC 6 HAYASHI Y, 1997, P AAAI 97 SPRING S C, P56 Hearst Marti A., 1995, P ACM SIGCHI C HUM F, P59, DOI 10.1145/223904.223912 HIRAKAWA H, 1991, P MACH TRANSL SUMM, V3, P73 Hull D. A., 1996, P 19 ANN INT ACM SIG, P49, DOI 10.1145/243199.243212 Hutchins W. J., 1992, INTRO MACHINE TRANSL Jones G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312690 Jones GJF, 1996, P 19 ANN INT ACM SIG, P30, DOI 10.1145/243199.243208 Jones G. J. F., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290992 JONES GJF, 1998, INF PROC SOC JAP JOI, P57 JONES KS, 1981, INFORMATION RETRIEVA JONES KS, 1996, LECT NOTES ARTIFICIA, V1083 KAJIURA M, 1997, P 54 INF PROC SOC JA KANDO N, 1999, P 1 NTCIR WORKSH RES Kitani T., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291033 Lee J.H., 1996, P 19 ANN INT ACM SIG, P216, DOI 10.1145/243199.243269 Nie J. Y., 1996, P 19 ANN INT ACM SIG, P225, DOI 10.1145/243199.243270 OARD DW, 1996, UMIACSTR9619 U MARYL Oard DW, 1999, INFORM PROCESS MANAG, V35, P363, DOI 10.1016/S0306-4573(98)00066-1 Ogawa Y, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P226, DOI 10.1145/258525.258576 Ogawa Yasushi, 1995, P 18 ANN INT ACM SIG, P121 ROBERT M, 1994, INT J ANDROL, V17, P232, DOI 10.1111/j.1365-2605.1994.tb01248.x ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1997, 356 U CAMBR COMP LAB ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SEKINE S, 1999, P INF RETR EXTR EX I Sheridan P., 1996, P 19 ANN INT ACM SIG, P58, DOI 10.1145/243199.243213 Turtle H., 1990, P 13 INT C RES DEV I, P1, DOI 10.1145/96749.98006 WALKER S, 1998, P 6 TEXT RETR C TREC, P125 NR 41 TC 0 Z9 0 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 0010-4817 J9 COMPUT HUMANITIES JI Comput. Humanit. PD NOV PY 2001 VL 35 IS 4 BP 371 EP 388 DI 10.1023/A:1011851209975 PG 18 WC Computer Science, Interdisciplinary Applications SC Computer Science GA 472PC UT WOS:000170991800002 ER PT J AU Sadakane, K Imai, H AF Sadakane, K Imai, H TI Fast algorithms for k-word proximity search SO IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES LA English DT Article DE proximity search; text retrieval; plane-sweep; divide-and-conquer AB When we search from a huge amount of documents, we often specify several keywords and use conjunctive queries to narrow the result of the search. Though the searched documents contain all keywords, positions of the keywords are usually not considered. As a result, the search result contains some meaningless documents. It is therefore effective to rank documents according to proximity of keywords in the documents. This ranking is regarded as a kind of text data mining. In this paper, we propose two algorithms for finding documents in which all given keywords appear in neighboring places. One is based on plane-sweep algorithm and the other is based on divide-and-conquer approach. Both algorithms run in O(n log n) time where n is the number of occurrences of given keywords. We run the algorithms on a large collection of html files and verify its effectiveness. C1 Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 9808579, Japan. Univ Tokyo, Dept Informat Sci, Tokyo 1130033, Japan. RP Sadakane, K (reprint author), Tohoku Univ, Grad Sch Informat Sci, Sendai, Miyagi 9808579, Japan. EM sada@dais.is.kohoku.ac.jp CR ANH VN, 1998, P 21 ANN INT ACM SIG, P290 AREF WG, 1995, PROC INT CONF DATA, P147, DOI 10.1109/ICDE.1995.380398 BAEZAYATES R, 1999, P IEEE STRING PROC I, P24 Gonnet G., 1992, INFORMATION RETRIEVA, P66 KASAI T, 1998, P SUMM DB WORKSH, P151 Kleinberg J.M., 1998, P 9 ANN ACM SIAM S D, P668 MANBER U, 1991, INFORM PROCESS LETT, V37, P133, DOI 10.1016/0020-0190(91)90032-D MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058 NIEVERGELT J, 1982, COMMUN ACM, V25, P739, DOI 10.1145/358656.358681 Preparata F. P., 1985, COMPUTATIONAL GEOMET SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TAJIMA K, 1998, P 9 ACM C HYP HYP, P235, DOI 10.1145/276627.276653 NR 12 TC 3 Z9 3 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG, 3-5-8, SHIBA-KOEN, MINATO-KU, TOKYO, 105-0011, JAPAN SN 0916-8508 EI 1745-1337 J9 IEICE T FUND ELECTR JI IEICE Trans. Fundam. Electron. Commun. Comput. Sci. PD SEP PY 2001 VL E84A IS 9 BP 2311 EP 2318 PG 8 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 484QJ UT WOS:000171700800029 ER PT J AU Kim, W Wilbur, WJ AF Kim, W Wilbur, WJ TI Amino acid residue environments and predictions of residue type SO COMPUTERS & CHEMISTRY LA English DT Article DE protein structures; protein threading; amino acid environment; residue type; naive Bayesian learning; k nearest neighbors; contact energy; precision ID PROTEIN FOLD RECOGNITION; KNOWLEDGE-BASED POTENTIALS; SEQUENCE; MODEL; TERM AB The determination of a protein's structure from the knowledge of its linear chain is one of the important problems that remains as a bottleneck in interpreting the rapidly increasing repository of genetic sequence data. One approach to this problem that has shown promise and given a measure of success is threading. In this approach contact energies between different amino acids are first determined by statistical methods applied to known structures. These contact energies are then applied to a sequence whose structure is to be determined by threading it through various known structures and determining the total threading energy for each candidate structure. That structure that yields the lowest total energy is then considered the leading candidate among all the structures tested. Additional information is often needed in order to support the results of threading studies, as it is well known in the field that the contact potentials used are not sufficiently sensitive to allow definitive conclusions. Here, we investigate the hypothesis that the environment of an amino acid residue realized as all those residues not local to it on the chain but sufficiently close spatially can supply information predictive of the type of that residue that is not adequately reflected in the individual contact energies. We present evidence that confirms this hypothesis and suggests a high order cooperativity between the residues that surround a given residue and how they interact with it. We suggest a possible application to threading. (C) 2001 Elsevier Science Ltd. All rights reserved. C1 Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Bethesda, MD 20892 USA. RP Kim, W (reprint author), Natl Lib Med, Natl Ctr Biotechnol Informat, NIH, Rockville Pike, Bethesda, MD 20892 USA. CR Abola E. E., 1987, CRYSTALLOGRAPHIC DAT, P107 ANFINSEN CB, 1973, SCIENCE, V181, P223, DOI 10.1126/science.181.4096.223 AYER M, 1955, ANN MATH STAT, V26, P641, DOI 10.1214/aoms/1177728423 BRYANT SH, 1993, PROTEINS, V16, P92, DOI 10.1002/prot.340160110 Crippen GM, 1996, J MOL BIOL, V260, P467, DOI 10.1006/jmbi.1996.0414 Eisenberg D, 1999, SCIENCE, V285, P1021, DOI 10.1126/science.285.5430.1021 FINKELSTEIN AV, 1995, PROTEINS, V23, P142, DOI 10.1002/prot.340230204 Godzik A, 1996, STRUCTURE, V4, P363, DOI 10.1016/S0969-2126(96)00041-X Hardle W., 1991, SMOOTHING TECHNIQUES JONES DT, 1992, NATURE, V358, P86, DOI 10.1038/358086a0 Jones DT, 1999, J MOL BIOL, V287, P797, DOI 10.1006/jmbi.1999.2583 LANGLEY P, 1994, 10 C UNC ART INT SEA Langley P., 1996, ELEMENTS MACHINE LEA LANGLEY P, 1992, 10 NAT C ART INT SAN Lee P., 1997, BAYESIAN STAT INTRO Matsuo Y, 1999, PROTEINS, V35, P70, DOI 10.1002/(SICI)1097-0134(19990401)35:1<70::AID-PROT7>3.0.CO;2-9 Mirny LA, 1996, J MOL BIOL, V264, P1164, DOI 10.1006/jmbi.1996.0704 Mitchell TM, 1997, MACHINE LEARNING MIYAZAWA S, 1985, MACROMOLECULES, V18, P534, DOI 10.1021/ma00145a039 Miyazawa S, 1996, J MOL BIOL, V256, P623, DOI 10.1006/jmbi.1996.0114 Panchenko A, 1999, Proteins, VSuppl 3, P133 Panchenko AR, 2000, J MOL BIOL, V296, P1319, DOI 10.1006/jmbi.2000.3541 PONTE JM, 1998, SIGIR98 MELB AUSTR ROBERTSON SE, 1994, 17 ANN INT ACM SIGIR Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SELBIG J, 1995, PROTEIN ENG, V8, P339, DOI 10.1093/protein/8.4.339 SIPPL MJ, 1990, J MOL BIOL, V213, P859, DOI 10.1016/S0022-2836(05)80269-4 SIPPL MJ, 1995, CURR OPIN STRUC BIOL, V5, P229, DOI 10.1016/0959-440X(95)80081-6 SIPPL MJ, 1993, J COMPUT AID MOL DES, V7, P473, DOI 10.1007/BF02337562 Thomas PD, 1996, J MOL BIOL, V257, P457, DOI 10.1006/jmbi.1996.0175 WANG YL, 1995, PROTEINS, V21, P127, DOI 10.1002/prot.340210206 Witten Ian H., 1999, MANAGING GIGABYTES, V2nd WONG SKM, 1984, RES DEV INFORMATION YANG Y, 1999, 22 ANN ACM C RES DEV Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290 YU CT, 1983, INFORM TECHNOL R & D, V2, P129 NR 39 TC 2 Z9 2 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0097-8485 J9 COMPUT CHEM JI Comput. Chem. PD JUL PY 2001 VL 25 IS 4 SI SI BP 411 EP 422 DI 10.1016/S0097-8485(01)00075-4 PG 12 WC Chemistry, Multidisciplinary; Computer Science, Interdisciplinary Applications SC Chemistry; Computer Science GA 451QF UT WOS:000169812200009 PM 11459355 ER PT J AU Vert, JP AF Vert, JP TI Adaptive context trees and text clustering SO IEEE TRANSACTIONS ON INFORMATION THEORY LA English DT Article DE adaptive mixture of models; context-tree weighting method; mean Kullback risk; text modeling ID UNIVERSAL AB In the finite-alphabet context we propose four alternatives to fixed-order Markov models to estimate a conditional distribution. They consist in working with a large class of variable-length Markov models represented by context trees, and building an estimator of the conditional distribution with a risk of the same order as the risk of the best estimator for every model simultaneously, in a conditional Kullback-Leibler sense. Such estimators can be used to model complex objects like texts written in natural language and define a notion of similarity between them. This idea is illustrated by experimental results of unsupervised text clustering. C1 Ecole Normale Super, Dept Math & Applicat, F-75230 Paris 05, France. RP Vert, JP (reprint author), Bioinformat Ctr, Inst Chem Res, Uji, Kyoto 6110011, Japan. CR Barron A, 1998, IEEE T INFORM THEORY, V44, P2743, DOI 10.1109/18.720554 BARRON A, 1987, OPEN PROBLEMS COMMUN Bell T.C., 1990, TEXT COMPRESSION Blanchard G, 1999, ANN I H POINCARE-PR, V35, P793, DOI 10.1016/S0246-0203(99)00115-6 CATONI O, 1998, LMENS9821 DEP MATH A CATONI O, IN PRESS ANN STAT Cover T. M., 1991, ELEMENTS INFORMATION DAVISSON LD, 1973, IEEE T INFORM THEORY, V19, P783, DOI 10.1109/TIT.1973.1055092 DAVISSON LD, 1983, IEEE T INFORM THEORY, V29, P211, DOI 10.1109/TIT.1983.1056652 Devroye L. GyorfiL., 1996, PROBABILISTIC THEORY Feder M, 1996, IEEE T INFORM THEORY, V42, P1354, DOI 10.1109/18.532877 Krichevskiy RE, 1998, IEEE T INFORM THEORY, V44, P296, DOI 10.1109/18.651051 Merhav N, 1998, IEEE T INFORM THEORY, V44, P2124, DOI 10.1109/18.720534 RISSANEN J, 1981, IEEE T INFORM THEORY, V27, P12, DOI 10.1109/TIT.1981.1056282 Ryabko B. Ya., 1988, Problems of Information Transmission, V24 Ryabko B. Y., 1984, PROBL INFORM TRANSM, V20, P24 Ryabko B. Ya., 1990, Problems of Information Transmission, V26 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Topsoe F., 1999, Proceedings of the 1999 IEEE Information Theory and Communications Workshop (Cat. No. 99EX253), DOI 10.1109/ITCOM.1999.781430 Vapnik V., 1998, STAT LEARNING THEORY WILLEMS FMJ, 1995, IEEE T INFORM THEORY, V41, P653, DOI 10.1109/18.382012 Willems FMJ, 1996, IEEE T INFORM THEORY, V42, P1514, DOI 10.1109/18.532891 Yang YH, 1999, ANN STAT, V27, P1564 Zampolli A., 1998, SURVEY STATE ART HUM NR 24 TC 9 Z9 11 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA SN 0018-9448 J9 IEEE T INFORM THEORY JI IEEE Trans. Inf. Theory PD JUL PY 2001 VL 47 IS 5 BP 1884 EP 1901 DI 10.1109/18.930925 PG 18 WC Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA 445ZF UT WOS:000169487900015 ER PT J AU Chen, ZX Meng, XN Fowler, RH AF Chen, ZX Meng, XN Fowler, RH TI Features: Real-time adaptive feature and document learning for Web search SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article AB In this article we report our research on building FEATUREs-an intelligent web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Not only does FEATURES learn from the user's document relevance feedback, but it also automatically extracts and suggests indexing keywords relevant to a search query and learns from the user's keyword relevance feedback so that it is able to speed up its search process and to enhance its search performance, We design two efficient and mutual-benefiting learning algorithms that work concurrently, one for feature learning and the other for document learning, FEATURES employs these algorithms together with an internal index database and a real-time meta-searcher to perform adaptive real-time learning to find desired documents with as little relevance feedback from the user as possible, The architecture and performance of FEATURES are also discussed. C1 Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. Montana State Univ, Dept Comp Sci, Bozeman, MT 59717 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, 1201 W Univ Dr, Edinburg, TX 78539 USA. EM chen@cs.panam.edu; meng@cs.panam.edu; fowler@panam.edu CR Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Billsuss D., 1998, P 15 INT C MACH LEAR, P46 Bollacker K. D., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280786 Bollacker K.D., 1999, P 4 ACM C DIG LIB, P105, DOI 10.1145/313238.313270 Brin S., 1998, P 7 WORLD WID WEB C SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H Chakrabarti S., 1998, P 7 INT WORLD WID WE, P65 CHEN Z, 2000, IN PRESS J KNOWLEDGE, P192 CHEN Z, 2000, P AAAI 2000 WORKSH A, P12 CHEN Z, 1999, KNOWLEDGE INFORMATIO, V1, P369 CHEN Z, 2000, LECT NOTES COMPUTER, P108 FREUND Y, 1995, MACHINE LEARNING Gibson D., 1998, P 9 ACM C HYP HYP, P225, DOI 10.1145/276627.276652 Ide E., 1971, SMART RETRIEVAL SYST, P337 IKEJI A, 1999, P ACM CIKM 99 WORKSH, P12, DOI 10.1145/319759.319767 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 Lawrence S, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P139, DOI 10.1145/319950.319970 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 MENG X, 1999, ADV COMPUTER SCI TEC, P985 NAKAMURA A, 1998, MACHINE LEARNING Rennie J., 1999, P 16 INT C MACH LEAR, P335 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1971, Information Processing Letters, V1, DOI 10.1016/0020-0190(71)90001-9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Widyantoro DH, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P405, DOI 10.1145/319950.323230 Yuwono B, 1996, PROC INT CONF DATA, P164, DOI 10.1109/ICDE.1996.492102 NR 29 TC 7 Z9 7 PU WILEY-BLACKWELL PI MALDEN PA COMMERCE PLACE, 350 MAIN ST, MALDEN 02148, MA USA SN 1532-2882 J9 J AM SOC INF SCI TEC JI J. Am. Soc. Inf. Sci. Technol. PD JUN PY 2001 VL 52 IS 8 BP 655 EP 665 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 438AU UT WOS:000169032200006 ER PT J AU Bradshaw, S Hammond, K AF Bradshaw, S Hammond, K TI Using citations to facilitate precise indexing and automatic index creation in collections of research papers SO KNOWLEDGE-BASED SYSTEMS LA English DT Article; Proceedings Paper CT 4th International Conference on Intelligent User Interfaces (IUI2000) CY JAN 09-12, 2000 CL NEW ORLEANS, LOUISIANA SP ACM DE search engines; information retrieval; reference directed indexing; citation indexing ID DIGITAL-LIBRARY; RETRIEVAL AB We describe Rosetta, a digital library system for scientific literature. Rosetta makes it easy for people to find the information for which they are looking even when using short, imprecise queries. Rosetta indexes research articles based on the way they have been described when cited in other documents. The concise descriptions that occur in citations are similar to the short queries people typically form when searching; therefore, citations may make a better basis for indexing than do the words used within a research article itself. Using this indexing technique we are able to provide a user interface that presents users with an automatically generated directory of the information space surrounding a query. Our objective with this interface is to present people with the information for which they have asked as well as the information for which they may have intended to ask. (C) 2001 Elsevier Science B.V. All rights reserved. C1 Northwestern Univ, Intelligent Lab, Evanston, IL 60201 USA. RP Bradshaw, S (reprint author), Northwestern Univ, Intelligent Lab, 1890 Maple Ave, Evanston, IL 60201 USA. RI Hammond, Kristian/B-7165-2009 CR BRADSHAW S, 1998, THESIS U CHICAGO BRADSHAW S, 1999, P 62 ANN M AM SOC IN BRIN S, 1998, P WWW 98 BRISB AUSTR BUDZIK J, 1999, P 62 ANN M AM SOC IN Chen HC, 1996, IEEE T PATTERN ANAL, V18, P771 DAVIS JR, 1995, D LIB MAGAZINE SEP JANSEN BJ, 1998, P WEBNET 98 WORLD C, P472 JONES S, 1998, P ECDL 98 HER CRET G Lawrence S, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P139, DOI 10.1145/319950.319970 Lawrence S, 1999, COMPUTER, V32, P67, DOI 10.1109/2.769447 Marshakova I., 1973, NAUCHNO TEKHNICHES 2, V6, P3 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406 SPINK A, 1998, P WEBNET 98 WORLD C, P828 WARD G, SET LEXICAL RESOURCE Witten IH, 1997, ELECTRON LIBR, V15, P495, DOI 10.1108/eb045602 Yaru D., 1997, J AM SOC INFORM SCI, V48, P946 NR 18 TC 1 Z9 1 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD MAR PY 2001 VL 14 IS 1-2 SI SI BP 29 EP 35 DI 10.1016/S0950-7051(00)00103-9 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA 418JN UT WOS:000167887300004 ER PT J AU Budzik, J Hammond, KJ Birnbaum, L AF Budzik, J Hammond, KJ Birnbaum, L TI Information access in context SO KNOWLEDGE-BASED SYSTEMS LA English DT Article; Proceedings Paper CT 4th International Conference on Intelligent User Interfaces (IUI2000) CY JAN 09-12, 2000 CL NEW ORLEANS, LOUISIANA SP ACM DE information access; agents; context; proactive systems; automated resource discovery; similarity AB Our central claim is that user interactions with productivity applications (e.g, word processors, Web browsers, etc.) provide rich contextual information that can be leveraged to support just-in-time access to task-relevant information. As evidence for our claim, we present Watson, a system which gathers contextual information in the form of the text of the document the user is manipulating, in order to proactively retrieve documents from distributed information repositories related to task at hand, as well as process explicit requests in the context of this task. We close by describing the results of several experiments with Watson, which show it consistently provides useful information to its users. The experiments also suggest that, contrary to the assumptions of many system designers, similar documents are not necessarily useful documents in the context of a particular task. (C) 2001 Elsevier Science B.V. All rights reserved. C1 Northwestern Univ, Intelligent Informat Lab, Evanston, IL 60201 USA. RP Budzik, J (reprint author), Northwestern Univ, Intelligent Informat Lab, 1890 Maple, Evanston, IL 60201 USA. RI Birnbaum, Lawrence/B-7109-2009; Hammond, Kristian/B-7165-2009 CR BADUE C, 1998, 1998 WORLD C WWW INT BILLSUS D, 1998, P 3 INT C AUT AG BRADSHAW S, 1999, P ASIS ANN M WASH DC BRADSHAW S, 2000, P 2000 INT C INT US BUDZIK J, 2000, P AAAI 2000 WORKSH A BUDZIK J, 1998, P 1998 WORLD C WWW I Chen Liren, 1998, P 2 INT C AUT AG CHENG I, 1997, CSD97963 U CAL COMP Dean J., 1999, P 8 INT WORLD WID WE Drori O., 2000, SIGCHI Bulletin, V32 Dumais S., 1998, P ACM CIKM98 DUMAIS ST, 1998, P CHI 88 INT C HUM F FU X, 1996, TR9617 U CHIC DEP CO HEARST M, 1995, P AAAI FALL S AI APP Horvitz E, 1998, P 14 C UNC ART INT JANSEN B, 1998, P 1998 WORLD C WWW I JOHNSON C, 1999, P 1999 INT C INT US JONES KS, 1997, READINGS INFORMATION KULYUKIN V, 1999, P 16 NAT C ART INT LEAKE D, 1999, P AAAI 99 WORKSH INT LIEBERMAN H, 1995, P INT JOINT C ART IN PAZZANI M, 1996, P 14 NAT C ART INT RHODES B, 1996, P 1 INT C PRACT APPL RHODES B, 2000, P 2000 INT C INT US RIESBECK C, 1996, CASE BASED REASONING SACK W, 1995, P AM ASS ART INT FAL SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1997, READINGS INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SELBERG E, 1996, IEEE EXPERT NOV SPERTUS E, 1997, P 6 INT WORLD WID WE SPINK A, 1998, P WORLD C WWW INT IN SUNDHEIM BM, 1993, P HUM LANG TECHN WOR SUZUKI H, 1992, P 14 ANN C COGN SCI TURNEY P, 1999, NRC41622 I INF TECHN Tversky A., 1978, COGNITION CATEGORIZA ZAMIR O, 1999, P 8 INT C WORLD WID NR 37 TC 43 Z9 44 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0950-7051 J9 KNOWL-BASED SYST JI Knowledge-Based Syst. PD MAR PY 2001 VL 14 IS 1-2 SI SI BP 37 EP 53 DI 10.1016/S0950-7051(00)00105-2 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA 418JN UT WOS:000167887300005 ER PT S AU Chen, ZX Zhu, BH AF Chen, ZX Zhu, BH BE Lee, DT Teng, SH TI Some formal analysis of Rocchio's similarity-based relevance feedback algorithm SO ALGORITHM AND COMPUTATION, PROCEEDINGS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 11th Annual International Symposium on Algorithms and Computation (ISAAC 2000) CY DEC, 2000 CL TAIPEI, TAIWAN SP Republic China, Natl Sci Council, Acad Sinica, Inst Informat Sci, ACM Taipei, Taiwan Chapter, Inst Informat & Comp Machinery HO INST INFORMAT SCI ID VECTOR-SPACE MODEL; RETRIEVAL AB Rocchio's similarity-based Relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In spite of its popularity in various applications there is little rigorous analysis of its learning complexity in literature. In this paper we show that in the Boolean vector space model, if the initial query vector is 0, then for any of the four typical similarities (inner product, dice coefficient, cosine coefficient, and Jaccard coefficient), Rocchio's similarity-based relevance feedback algorithm makes at least n mistakes when used to search for a collection of documents represented by a monotone disjunction of at most k relevant features (or terms) over the n-dimensional Boolean vector space {0, 1}(n). When an arbitrary initial query vector in {0, 1}(n) is used, it makes at least (n + k - 3)/2 mistakes to search for the same collection of documents. The linear lower bounds are independent of the choices of the threshold and coefficients that the algorithm may use in updating its query vector and making its classification. C1 Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. Montana State Univ, Dept Comp Sci, Bozeman, MT 59717 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. RI Zhu, Binhai/A-2911-2008 CR Baeza-Yates R., 1999, MODERN INFORMATION R, V1st SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CHEN Z, 2000, P AAAI 2000 WORKSH A, P12 CHEN Z, 2000, CS0023 U TEX PAN AM CHEN Z, 2000, P 2000 2NT C WEB INF, P192 Ide E., 1971, SMART RETRIEVAL SYST, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Kivinen J, 1997, ARTIF INTELL, V97, P325, DOI 10.1016/S0004-3702(97)00039-8 LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCLAROFF S, 1997, P IEEE WORSH CONT BA TAYCHER L, 1997, P 2 INT C VIS INF SY, P85 NR 17 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-41255-7 J9 LECT NOTES COMPUT SC PY 2001 VL 1969 BP 108 EP 119 PG 12 WC Computer Science, Theory & Methods SC Computer Science GA BS90Z UT WOS:000171367600010 ER PT B AU Zitouni, I Kuo, HKJ Lee, CH AF Zitouni, I Kuo, HKJ Lee, CH GP IEEE IEEE TI Natural language call routing: Towards combination and boosting of classifiers SO ASRU 2001: IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING, CONFERENCE PROCEEDINGS LA English DT Proceedings Paper CT IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU 2001) CY DEC 09-13, 2001 CL MADONNA DI CAMPIGLIO, ITALY SP IEEE AB In this paper, we describe different techniques to improve natural language call routing: boosting, relevance feedback, discriminative training, and constrained minimization. Their common goal is to reweight the data in order to let the system focus on documents judged hard to classify by a single classifier. These approaches are evaluated with the common vector-based classifier and also with the beta classifier which had given good results in the similar task of E-mail steering. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy. Compared to the cosine and beta baseline classifiers, we report an improvement of 49% and 10%, respectively. C1 Bell Labs, Lucent Technol, Murray Hill, NJ 07974 USA. RP Zitouni, I (reprint author), Bell Labs, Lucent Technol, 600 Mt Ave, Murray Hill, NJ 07974 USA. CR BIGI B, 2001, STRING PROCESSING IN Chu-Carroll J, 1999, COMPUT LINGUIST, V25, P361 DURSTON P, 2001, P EUR 01 AALB DENM S KEARNS MJ, 1994, J ASSOC COMPUT MACH, V1, P67 KUO HKJ, 2001, P EUR 01 AALB DENM KUO HKJ, 2000, P ICSLP 2000 BEIJ CH NIYOGI P, 2000, P INT C AC SPEECH SI SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1990, J AM SOC INFORM SCI, V4, P182 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 1998, ANN STAT, V26, P1651 NR 11 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-7343-X PY 2001 BP 202 EP 205 PG 4 WC Computer Science, Artificial Intelligence SC Computer Science GA BV37S UT WOS:000178749400048 ER PT B AU Holt, FB Wu, YJJ AF Holt, FB Wu, YJJ BE Berry, MW TI Information retrieval and classification with subspace representations SO COMPUTATIONAL INFORMATION RETRIEVAL LA English DT Proceedings Paper CT 1st Annual Meeting of the Computational Information Retrieval Workshop (CIR 00) CY OCT 22, 2000 CL RALEIGH, NC SP N Carolina State Univ, Jane S McKimmon Ctr C1 Boeing Co, Seattle, WA 98124 USA. RP Holt, FB (reprint author), Boeing Co, POB 3707 M-S 7L-21, Seattle, WA 98124 USA. CR Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Booker A, 1999, COMPUT SCI ENG, V1, P26, DOI 10.1109/5992.774838 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370 FRAKES WR, 1992, INFORMATION RETRIEVA Kolda TG, 1998, ACM T INFORM SYST, V16, P322, DOI 10.1145/291128.291131 PIERCE DJ, 1998, SSGTECH98009 BOEING SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 10 TC 0 Z9 0 PU SIAM PI PHILADELPHIA PA 3600 UNIV CITY SCIENCE CENTER, PHILADELPHIA, PA 19104-2688 USA BN 0-89871-500-8 PY 2001 BP 25 EP 38 PG 4 WC Computer Science, Information Systems; Mathematics, Applied SC Computer Science; Mathematics GA BT60F UT WOS:000173491100002 ER PT S AU Vert, JP AF Vert, JP BE Gelbukh, A TI Text categorization using adaptive context trees SO COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 2nd International Conference on Intelligent Text Processing and Computaional Linguistics (CICLing 2001) CY FEB 18-24, 2001 CL MEXICO CITY, MEXICO SP Nat Language Lab DE text modeling; text categorization; mean kullback risk; context tree model AB A new way of representing texts written in natural language is introduced, as a conditional probability distribution at the letter level learned with a variable length Markov model called adaptive context tree model. Text categorization experiments demonstrates the ability of this representation to catch information about the semantic content of the text. C1 Ecole Normale Super, Dept Math & Applicat, F-75230 Paris 05, France. RP Vert, JP (reprint author), Ecole Normale Super, Dept Math & Applicat, 45 Rue Ulm, F-75230 Paris 05, France. CR BAEZYATES R, 1999, MODERN INFORMATION R Cover T. M., 1991, ELEMENTS INFORMATION Dumais S., 1998, Proceedings of the 1998 ACM CIKM International Conference on Information and Knowledge Management, DOI 10.1145/288627.288651 Joachims T., 1999, P 16 INT C MACH LEAR, V99, P200 Joachims T., 1998, LNCS, V1398, P137 Joachims T, 1997, P 14 INT C MACH LEAR, P143 Lewis D., 1998, LECT NOTES COMPUTER, V1398, P4, DOI DOI 10.1007/BFB0026666 McCallum A., 1998, P AAAI 98 WORKSH LEA, P41 Moulinier I., 1996, Proceedings. Fifth Annual Symposium on Document Analysis and Information Retrieval Nigam K., 1999, P IJCAI 99 WORKSH MA, P61 Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schapire RE, 2000, MACH LEARN, V39, P135, DOI 10.1023/A:1007649029923 Slattery S., 1998, LECT NOTES COMPUTER, V1446, P38 VERT JP, 2000, DMA0005 DEP MATH APP, P1 Yang Y., 1999, J INFORMATION RETRIE, V1, P69 NR 16 TC 1 Z9 1 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-41687-0 J9 LECT NOTES COMPUT SC PY 2001 VL 2004 BP 423 EP 436 PG 14 WC Computer Science, Artificial Intelligence; Computer Science, Theory & Methods SC Computer Science GA BS92Q UT WOS:000171392400041 ER PT S AU Gupta, GK Ghosh, J AF Gupta, GK Ghosh, J BE Dasarathy, BV TI Value balanced agglomerative connectivity clustering SO DATA MINING AND KNOWLEDGE DISCOVERY: THEORY, TOOLS AND TECHNOLOGY III SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) LA English DT Proceedings Paper CT Conference on Data Mining and Knowledge Discovery - Theory, Tools, and Technology III CY APR 16-17, 2001 CL ORLANDO, FL SP SPIE DE data mining; clustering; graph partitioning; metric space; value-based; transactional data; cluster balancing; outlier removal; high dimensional ID IRREGULAR GRAPHS; PERSPECTIVE; SCHEME AB In this paper we propose a new clustering framework for transactional data-sets involving large numbers of customers and products. Such transactional data pose particular issues such as very high dimensionality (> 10,000), and sparse categorical entries, that have been dealt with more effectively using a graph-based approach to clustering such as ROCK.(1) But large transactional data raises certain other issues such as how to compare diverse products (e.g. milk vs. cars) cluster balancing and outlier removal, that need to be addressed. We first propose a new similarity measure that takes the value of the goods purchased into account, and form a value-based graph representation based on this similarity measure. B novel value-based balancing criterion that allows the user to control the balancing of clusters, is then defined. This balancing criterion is integrated with a value-based goodness measure for merging two clusters in an agglomerative clustering routine. Since graph-based clustering algorithms are very sensitive to outliers, we also propose a fast, effective and simple outlier detection and removal method based on under-clustering or over-partitioning. The performance of the proposed clustering framework is compared with leading graph-theoretic approaches such as ROCK and METIS. C1 Univ Texas, Dept Elect & Comp Engn, Austin, TX 78712 USA. RP Gupta, GK (reprint author), Univ Texas, Dept Elect & Comp Engn, Austin, TX 78712 USA. CR AGRAWAL R, 1993, IEEE T KNOWL DATA EN, V5, P914, DOI 10.1109/69.250074 Berry M. J. A., 1997, DATA MINING TECHNIQU Chen MS, 1996, IEEE T KNOWL DATA EN, V8, P866 DEOGUN J, 1998, J ASIS, V49, P397 Everitt B, 1993, CLUSTER ANAL Gupta G.K., 1999, INTELLIGENT ENG SYST, V9, P759 Gupta G.K., 2000, THESIS U TEXAS AUSTI GUPTA GK, 2001, UNPUB SOC IND APPL M Hartigan J, 1975, CLUSTERING ALGORITHM HARVEY AC, 1993, J APPL ECONOM, V8, P231, DOI 10.1002/jae.3950080302 HENDRICKSON B, 1995, SIAM J SCI COMPUT, V16, P452, DOI 10.1137/0916028 Hernandez MA, 1998, DATA MIN KNOWL DISC, V2, P9, DOI 10.1023/A:1009761603038 Karypis G, 1999, COMPUTER, V32, P68, DOI 10.1109/2.781637 KARYPIS G, 1997, P DES AUT C Karypis G, 1998, SIAM J SCI COMPUT, V20, P359, DOI 10.1137/S1064827595287997 Karypis G, 1998, J PARALLEL DISTR COM, V48, P96, DOI 10.1006/jpdc.1997.1404 MASTERS T, 1995, NEURAL NOVEL HYBRID, P9 POTHEN A, 1990, SIAM J MATRIX ANAL A, V11, P430, DOI 10.1137/0611030 Pyle D., 1999, DATA PREPARATION DAT Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shim K., 1999, P 15 INT C DAT ENG Strehl A, 2000, P SOC PHOTO-OPT INS, V4057, P33, DOI 10.1117/12.381756 NR 23 TC 1 Z9 1 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 0-8194-4079-5 J9 P SOC PHOTO-OPT INS PY 2001 VL 4384 BP 6 EP 15 DI 10.1117/12.421079 PG 10 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Optics SC Computer Science; Optics GA BS37H UT WOS:000169645100002 ER PT B AU Ye, HL AF Ye, HL BE Arabnia, HR TI Elaborate document clusters on nested self-organizing maps SO IC-AI'2001: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS I-III LA English DT Proceedings Paper CT International Conference on Artificial Intelligence CY JUN 25-28, 2001 CL LAS VEGAS, NV SP Comp Sci Res, Educ & Appl Press, Int Technol Inst Korea Informat Processing Soc, World Acad Sci Informat Technol, PACT Corp DE self-organising map; document classification and retrieval; software retrieval AB The Self-Organising Map (SOM) is widely used in document classifications, but so far rarely used in direct document retrieval. A document retrieval system needs more accurate clustering than a mere classification system. However, the required accuracy for the clustering is generally difficult to obtain for a high dimensional feature space based on a single SOM. A Nested Self-Organising Map (NSOM) is proposed to solve the problem of inaccurate clustering. The updating of a so-organised document collection is also benefited from the structure of the NSOM. Based on the structure of the NSOM, the updating can be isolated within a limited range without frequent reconstructions of the whole NSOM. The proposed NSOM was tested by a software document collection The experimental result reveals that the NSOM is capable of producing accurate clusters to enhancing retrieval precision without sacrificing recall, and that an NSOM updated by using the proposed updating method achieved a comparable retrieval performance to a non-updated NSOM. C1 Univ Newcastle, Dept Comp Sci & Software Engn, Callaghan, NSW 2480, Australia. RP Ye, HL (reprint author), Univ Newcastle, Dept Comp Sci & Software Engn, Callaghan, NSW 2480, Australia. CR Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X Hiotis A., 1993, AI Expert, V8 HONKELA T, 1996, A32 HELS U TECHN Kohonen T., 1997, SELF ORG MAPS KOHONEN T, 1998, P 8 INT C ART NEUR N, P55 Kohonen T, 1988, SELF ORG ASS MEMORY Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 MAAREK YS, 1989, THESIS ISRAEL I TECH Merkl D., 1997, P 20 ANN INT C RES D, P1 Merkl D, 1998, NEUROCOMPUTING, V21, P61, DOI 10.1016/S0925-2312(98)00032-0 Orwig RE, 1997, J AM SOC INFORM SCI, V48, P157 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 YE H, 1999, SOMIT9901 SO CROSS U YE H, 2000, IN PRESS IEE P SOFTW NR 14 TC 0 Z9 0 PU C S R E A PRESS PI ATHENS PA 115 AVALON DR, ATHENS, GA 30606 USA BN 1-892512-81-5 PY 2001 BP 137 EP 143 PG 7 WC Computer Science, Artificial Intelligence SC Computer Science GA BT75P UT WOS:000173960400022 ER PT J AU Lopresti, DP AF Lopresti, DP TI A comparison of text-based methods for detecting duplication in scanned document databases SO INFORMATION RETRIEVAL LA English DT Article DE duplicate detection; approximate string matching; information retrieval; optical character recognition; document analysis AB This paper presents an experimental evaluation of several text-based methods for detecting duplication in scanned document databases using uncorrected OCR output. This task is made challenging both by the wide range of degradations printed documents can suffer, and by conflicting interpretations of what it means to be a "duplicate." We report results for four sets of experiments exploring various aspects of the problem space. While the techniques studied are generally robust in the face of most types of OCR errors, there are nonetheless important differences which we identify and discuss in detail. C1 Lucent Technol Inc, Bell Labs, Murray Hill, NJ 07974 USA. RP Lopresti, DP (reprint author), Lucent Technol Inc, Bell Labs, 600 Mt Ave,Room 2D-447, Murray Hill, NJ 07974 USA. CR DOERMANN D, 1994, P 4 INT C DOC AN REC, P314 ESAKOV J, 1994, P 3 ANN S DOC AN INF, P401 GILMORE GG, 1997, FORMER ARMY OPERATIO Hull J. J., 1998, International Journal on Document Analysis and Recognition, V1 HULL JJ, 1997, P S DOC IM UND TECHN, P31 Kantor P. B., 2000, Information Retrieval, V2, DOI 10.1023/A:1009902609570 KANTOR PB, 1996, NIST SPECIAL PUBLICA, V500, P65 LEE DS, 1999, P 5 INT C DOC AN REC, P305 LOPRESTI D, 1999, P S DOC IM UND TECHN, P101 Lopresti D. P., 2000, International Journal on Document Analysis and Recognition, V2, DOI 10.1007/s100320050005 Lopresti D. P., 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), DOI 10.1109/ICDAR.1999.791783 LOPRESTI DP, 2000, IS T SPIE ELECT IMAG, V3967, P210 PROKOSKI F, 1999, P S DOC IM UND TECHN, P86 ROGERS R, 1999, P S DOC IM UND TECHN, P119 SALTON G, 1997, P 3 ANN S DOC AN INF, P9 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIVAKUMAR N, 1998, P WORKSH WEB DAT Shivakumar Narayanan, 1995, P 2 INT C THEOR PRAC SMITH TF, 1981, J MOL BIOL, V147, P195, DOI 10.1016/0022-2836(81)90087-5 SPITZ AL, 1997, IS T SPIE ELECT IMAG, P88 TAGHVA K, 1995, ANN REPORT UNLV INFO, P99 WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811 NR 22 TC 5 Z9 5 PU KLUWER ACADEMIC PUBL PI DORDRECHT PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS SN 1386-4564 J9 INFORM RETRIEVAL JI Inf. Retr. PY 2001 VL 4 IS 2 BP 153 EP 173 DI 10.1023/A:1011471129047 PG 21 WC Computer Science, Information Systems SC Computer Science GA 446HN UT WOS:000169508000004 ER PT J AU Hauck, RV Sewell, RR Ng, TD Chen, HS AF Hauck, RV Sewell, RR Ng, TD Chen, HS TI Concept-based searching and browsing: a geoscience experiment SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID VOCABULARY PROBLEM; RETRIEVAL SYSTEMS; MODEL AB In the recent literature, we have seen the expansion of information retrieval techniques to include a variety of different collections of information. Collections can have certain characteristics that can lead to different results for the various classification techniques. In addition, the ways and reasons that users explore each collection can affect the success of the information retrieval technique. The focus of this research was to extend the application of our statistical and neural network techniques to the domain of geological science information retrieval. For this study, a test bed of 22,636 geoscience abstracts was obtained through the NSF/DARPA/NASA funded Alexandria Digital Library Initiative project at the University of California at Santa Barbara. This collection was analyzed using algorithms previously developed by our research group: concept space algorithm for searching and a Kohonen self-organizing map (SOM) algorithm for browsing. Included in this paper are discussions of our techniques, user evaluations and lessons learned. C1 Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. Carnegie Mellon Univ, Pittsburgh, PA 15213 USA. RP Hauck, RV (reprint author), Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. CR BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 Bichteler J., 1989, SPECIAL LIB, V80 Chen HC, 1996, J VIS COMMUN IMAGE R, V7, P88, DOI 10.1006/jvci.1996.0008 Chen HC, 1997, J AM SOC INFORM SCI, V48, P17, DOI 10.1002/(SICI)1097-4571(199701)48:1<17::AID-ASI4>3.0.CO;2-4 Chen HC, 1998, J AM SOC INFORM SCI, V49, P582, DOI 10.1002/(SICI)1097-4571(19980515)49:7<582::AID-ASI2>3.0.CO;2-X Courteau J, 1991, Science, V254, P201, DOI 10.1126/science.1925577 Everitt B., 1980, CLUSTER ANAL FRENKEL KA, 1991, COMMUN ACM, V34, P41 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 FURNAS GW, 1982, P HUM FACT COMP SYST, P251, DOI 10.1145/800049.801789 GOMEZ LM, 1990, J AM SOC INFORM SCI, V41, P547, DOI 10.1002/(SICI)1097-4571(199012)41:8<547::AID-ASI1>3.0.CO;2-P Kohonen T., 1995, SELF ORGANIZING MAPS PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smith TR, 1996, COMPUTER, V29, P54, DOI 10.1109/2.493457 TILLOTSON J, 1995, COLL RES LIBR, V56, P199 NR 18 TC 3 Z9 3 PU BOWKER-SAUR PI E GRINSTEAD PA WINDSOR COURT, EAST GRINSTEAD HOUSE, E GRINSTEAD RH19 1XA, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 2001 VL 27 IS 4 BP 199 EP 210 DI 10.1177/016555101401266638 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 504DR UT WOS:000172842500002 ER PT J AU Lin, FR Lin, SC AF Lin, FR Lin, SC TI A conceptual model for virtual organizational learning SO JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE LA English DT Article; Proceedings Paper CT 4th Pacific Asia Conference on Information Systems CY JUN 01-03, 2000 CL HONG KONG, PEOPLES R CHINA DE virtual organization; organizational learning; organizational memory; transactive memory; knowledge map; social network; cyber community ID JOINT-VENTURES; STRATEGIC ALLIANCES; TRANSACTIVE MEMORY; ABSORPTIVE-CAPACITY; INFORMATION-SYSTEMS; KNOWLEDGE; MANAGEMENT; PERSPECTIVE; PERFORMANCE; INDIVIDUALS AB As a trend toward virtual organizations (VOs) emerges in the era of electronic commerce, an understanding of knowledge processing is essential to sustain performance. Active learning is the key to achieving flexibility in a VO; however, few studies have focused on this dimension. In this article we attempt to assemble insights about learning in a VO by developing a conceptual model for virtual organizational learning (VOL). The VOL model is focused on transactive memory, which is composed of knowledge maps, social networks, and mnemonic functions. The initialization of modeling learning in VOs in this article has been erecting the foundation for transactive memory systems in a cyber community. A case study, Smart Creative Teachers Network, is used for demonstrating the transactive memory system for a teacher community to share and create knowledge and form VOs across school boundaries. C1 Natl Sun Yat Sen Univ, Dept Informat Management, Comp & Network Ctr, Applicat Div, Kaohsiung 804, Taiwan. RP Lin, FR (reprint author), Natl Sun Yat Sen Univ, Dept Informat Management, Comp & Network Ctr, Applicat Div, Kaohsiung 804, Taiwan. CR Abecker A, 1998, IEEE INTELL SYST APP, V13, P40, DOI 10.1109/5254.683209 Argyris C, 1978, ORG LEARNING THEORY BALAKRISHNAN S, 1993, J ECON BEHAV ORGAN, V20, P99, DOI 10.1016/0167-2681(93)90083-2 Barkema HG, 1997, ACAD MANAGE J, V40, P426, DOI 10.2307/256889 BIRU T, 1989, J DOC, V45, P85, DOI 10.1108/eb026840 BOTTOMS D, 1994, IND WEEK, V243, P61 Chesbrough HW, 1996, HARVARD BUS REV, V74, P65 COHEN J, 1981, HUM RELAT, V34, P731, DOI 10.1177/001872678103400901 Cohen M. D., 1991, ORGAN SCI, V2, P1 COHEN WM, 1990, ADMIN SCI QUART, V35, P128, DOI 10.2307/2393553 CONSTANT D, 1994, INFORM SYST RES, V5, P400, DOI 10.1287/isre.5.4.400 CROSSAN MM, 1995, IVEY BUSINESS Q, V60, P68 Cyr DJ, 1996, ORGAN STUD, V17, P207, DOI 10.1177/017084069601700204 Demsetz H., 1991, NATURE FIRM, P159 Dixon N. M., 1994, ORG LEARNING CYCLE W DODGSON M, 1993, ORGAN STUD, V14, P375, DOI 10.1177/017084069301400303 DONOHUE JM, 1993, DECISION SCI, V24, P493, DOI 10.1111/j.1540-5915.1993.tb00485.x Doz YL, 1996, STRATEGIC MANAGE J, V17, P55 Duncan R., 1979, RES ORGAN BEHAV, V1, P75 Easton CJ, 1997, AUST J CHEM, V50, P9, DOI 10.1071/C96168 EDMOND FV, 1999, INFORMATION SYSTEMS, V16, P16 EZEY DM, 1995, IIE T, V27, P265 FIOL CM, 1985, ACAD MANAGE REV, V10, P803, DOI 10.2307/258048 GAIL JE, 1995, ACAD MANAGE J, P469 GALTON G, 1978, ACM T DATABASE SYST, V3, P321 GOLDMAN SL, 1995, AGILE COMPETITORS VI Grant RM, 1996, ORGAN SCI, V7, P375, DOI 10.1287/orsc.7.4.375 Hackbarth G, 1999, INFORM SYST MANAGE, V16, P21, DOI 10.1201/1078/43197.16.3.19990601/31312.4 Halbwachs M., 1950, COLLECTIVE MEMORY HAMEL G, 1991, STRATEGIC MANAGE J, V12, P83, DOI 10.1002/smj.4250120908 HARMON J, 1990, ORGAN BEHAV HUM DEC, V46, P34, DOI 10.1016/0749-5978(90)90021-Z Hattori F, 1999, COMMUN ACM, V42, P55, DOI 10.1145/295685.295701 Heckerman D., 1995, Uncertainty in Artificial Intelligence. Proceedings of the Eleventh Conference (1995) Hildmann T., 1999, Proceedings Fourth ACM Workshop on Role-Based Access Control, DOI 10.1145/319171.319181 HJALAGER A, 1999, HUMAN SYSTEMS MANAGE, V18, P23 Hollingshead AB, 1998, J PERS SOC PSYCHOL, V74, P659, DOI 10.1037//0022-3514.74.3.659 Howard R., 1984, READINGS PRINCIPLES, VII HOWARD RA, 1989, MANAGE SCI, V35, P903, DOI 10.1287/mnsc.35.8.903 Huber GP, 1991, ORGAN SCI, V2, P88, DOI 10.1287/orsc.2.1.88 Inkpen A. C., 1998, ACAD MANAGEMENT EXEC, V12, P69 INKPEN AC, 1995, J MANAGE STUD, V32, P595, DOI 10.1111/j.1467-6486.1995.tb00790.x Jarvenpaa S. L., 1998, Journal of Management Information Systems, V14 JOHNSON J, 1998, J MANAGERIAL ISSUES, V10, P503 KALB DA, 1984, EXPT LEARNING Kaufman L., 1990, FINDING GROUP DATA I KIM DH, 1993, SLOAN MANAGE REV, V35, P37 KLIMOSKI R, 1994, J MANAGE, V20, P403, DOI 10.1177/014920639402000206 KOGUT B, 1988, STRATEGIC MANAGE J, V9, P319, DOI 10.1002/smj.4250090403 KRIPPENDORFF K, 1975, GEN SYST, V20, P15 Lane PJ, 1998, STRATEGIC MANAGE J, V19, P461, DOI 10.1002/(SICI)1097-0266(199805)19:5<461::AID-SMJ953>3.3.CO;2-C Larsson R, 1998, ORGAN SCI, V9, P285, DOI 10.1287/orsc.9.3.285 LAWMAN EO, 1978, ANNU REV SOCIOL, V4, P455 Lei D, 1997, J WORLD BUS, V32, P203, DOI 10.1016/S1090-9516(97)90008-0 LEVINSON NS, 1995, ORGAN DYN, V24, P50, DOI 10.1016/0090-2616(95)90071-3 LEVITT B, 1988, ANNU REV SOCIOL, V14, P516 Lim KH, 1997, INFORM SYST RES, V8, P254, DOI 10.1287/isre.8.3.254 Lin FR, 1998, INT J FLEX MANUF SYS, V10, P197, DOI 10.1023/A:1008069816606 Lyles M. A., 1988, MANAGE INT REV, V28, P85 Makhija MV, 1997, ORGAN SCI, V8, P508, DOI 10.1287/orsc.8.5.508 March N. H., 1971, Crystal Lattice Defects, V2 Martinez M, 1999, TECH COMMUN, V46, P470 Massey AP, 1996, DECIS SUPPORT SYST, V17, P253, DOI 10.1016/0167-9236(96)00004-8 McInerney V, 1997, J EDUC PSYCHOL, V89, P686, DOI 10.1037/0022-0663.89.4.686 Meyerson D., 1996, TRUST ORG FRONTIERS, P166 Miller D, 1996, J MANAGE, V22, P485, DOI 10.1016/S0149-2063(96)90033-1 Moorman C, 1998, ACAD MANAGE REV, V23, P698, DOI 10.2307/259058 Mowery DC, 1996, STRATEGIC MANAGE J, V17, P77 Mumford A, 1992, MANAGE DECIS, V30, P143 Nonaka I., 1998, EUROPEAN MANAGEMENT, V16, P673, DOI 10.1016/S0263-2373(98)00044-9 NONAKA I, 1994, ORGAN SCI, V5, P14, DOI 10.1287/orsc.5.1.14 ONG TH, 1999, P 2 AS DIG LIB C Oslan G. E., 1995, EUR J MARKETING, V29, P52, DOI 10.1108/03090569510079925 PUCIK V, 1988, HUM RESOURCE MANAGE, V27, P77, DOI 10.1002/hrm.3930270105 Punnett B. J., 1994, INT J ORG ANAL, V9, P39, DOI 10.1108/eb028862 RICHTER FJ, 1995, LONG RANGE PLANN, V28, P37 RIEL H, 1998, IVES BUSINESS J, V62, P50 Romme G., 1997, EUROPEAN MANAGEMENT, V15, P68, DOI 10.1016/S0263-2373(96)00075-8 Roth G, 1998, ORGAN DYN, V27, P43, DOI 10.1016/S0090-2616(98)90023-7 Rulke DL, 2000, GROUP ORGAN MANAGE, V25, P373, DOI 10.1177/1059601100254004 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Satzinger J. W., 1999, Journal of Management Information Systems, V15 Schein EH, 1996, SLOAN MANAGE REV, V38, P9 Shenkar O, 1999, ORGAN SCI, V10, P134, DOI 10.1287/orsc.10.2.134 Shrivastava P., 1983, J MANAGE STUD, V20, P1 Simon HA, 1991, ORGAN SCI, V2, P125, DOI 10.1287/orsc.2.1.125 Sinkula JM, 1997, J ACAD MARKET SCI, V25, P305, DOI 10.1177/0092070397254003 Steensma HK, 1996, J ENG TECHNOL MANAGE, V12, P267, DOI 10.1016/0923-4748(95)00013-5 Steil A.V., 1999, ELECT J ORG VIRTUALN, V1, P69 STEIN EW, 1995, INT J INFORM MANAGE, V15, P17, DOI 10.1016/0268-4012(94)00003-C STEIN EW, 1995, INFORM SYST RES, V6, P85, DOI 10.1287/isre.6.2.85 Strasburger VC, 1998, J ADOLESCENT HEALTH, V23, P1 TSAI WH, 1979, IEEE T SYST MAN CYB, V9, P757, DOI 10.1109/TSMC.1979.4310127 Tsang EWK, 1997, HUM RELAT, V50, P73, DOI 10.1177/001872679705000104 Tu HC, 1998, P ANN HICSS, P405 Tulving E., 1983, ELEMENTS EPISODIC ME VOLLRATH DA, 1989, ORGAN BEHAV HUM DEC, V43, P289, DOI 10.1016/0749-5978(89)90040-X WALSH JP, 1991, ACAD MANAGE REV, V16, P57, DOI 10.2307/258607 Wegner D. M., 1986, THEORIES GROUP BEHAV, P185 Wegner DM, 1995, SOC COGNITION, V13, P319, DOI 10.1521/soco.1995.13.3.319 WEGNER DM, 1991, J PERS SOC PSYCHOL, V61, P923, DOI 10.1037/0022-3514.61.6.923 Wijnhoven F., 1998, Journal of Organizational Computing and Electronic Commerce, V8, DOI 10.1207/s15327744joce0801_2 WINTER RP, 1997, INT J ORG ANAL, V5, P9, DOI 10.1108/eb028860 Wong A., 1994, LEADERSHIP ORG DEV J, V15, P31, DOI 10.1108/01437739410059890 YANG CC, 1998, P 1 AS DIG LIB WORKS, P57 NR 104 TC 9 Z9 11 PU LAWRENCE ERLBAUM ASSOC INC PI MAHWAH PA 10 INDUSTRIAL AVE, MAHWAH, NJ 07430-2262 USA SN 1091-9392 J9 J ORG COMP ELECT COM JI J. Organ. Comp. Electron. Commer. PY 2001 VL 11 IS 3 BP 155 EP 178 DI 10.1207/S15327744JOCE1103_02 PG 24 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications SC Computer Science GA 561VU UT WOS:000176164400002 ER PT S AU Visa, A AF Visa, A BE Perner, P TI Technology of text mining SO MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition CY JUL 25-27, 2001 CL LEIPZIG, GERMANY SP Leipzig Inst Comp Vis & Appl Comp Sci AB A large amount of information is stored in databases, in intranets or in Internet. This information is organised in documents or in text documents. The difference depends on the fact if pictures, tables, figures, and formulas are included or not. The common problem is to find the desired piece of information, a trend, or an undiscovered pattern from these sources. The problem is not a new one. Traditionally the problem has been considered under the title of information seeking, this means the science how to find a book in the library. Traditionally the problem has been solved either by classifying and accessing documents by Dewey Decimal Classification system or by giving a number of characteristic keywords. The problem is that nowadays there axe lots of unclassified documents in company databases and in intranet or in Internet. First one defines some terms. Text filtering means an information seeking process in which documents are selected from a dynamic text stream. Text mining is a process of analysing text to extract information from it for particular purposes. Text categorisation means the process of clustering similar documents from a large document set. All these terms have a certain degree of overlapping. Text mining, also know as document information mining, text data mining, or knowledge discovery in textual databases is an merging technology for analysing large collections of unstructured documents for the purposes of extracting interesting and non-trivial patterns or knowledge. Typical subproblems that have been solved axe language identification, feature selection/extraction, clustering, natural language processing, summarisation, categorisation, search, indexing, and visualisation. These subproblems are discussed in detail and the most common approaches axe given. Finally some examples of current uses of text mining are given and some potential application areas are mentioned. C1 Tampere Univ Technol, FIN-33101 Tampere, Finland. RP Visa, A (reprint author), Tampere Univ Technol, POB 553, FIN-33101 Tampere, Finland. RI Visa, Ari/G-4304-2014 CR BACK B, 2001, J EC SOC FINLAND, V54, P39 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Blair D. C., 1990, LANGUAGE REPRESENTAT Bollacker K. D., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280786 BRUGGEMANNKLEIN A, 1999, D LIB MAGAZINE NOV, V5 DEWEY M, 1876, US BUREAU ED SPECIAL, P623 Dewey M., 1876, CLASSIFICATION SUBJE HAHN U, 1990, INFORM PROCESS MANAG, V26, P135, DOI 10.1016/0306-4573(90)90013-R Harter S. P., 1986, ONLINE INFORMATION R HAVRE S, 1999, P IEEE S INF VIS INF Hearst M., 1995, P ACM C HUM FACT COM, P56 Hearst M. A., 1999, P ACL 99 37 ANN M AS Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 Hodge VJ, 2001, NEURAL NETWORKS, V14, P287, DOI 10.1016/S0893-6080(00)00097-6 Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729 LAHTINEN T, 2000, THESIS U HELSINKI FI Lin X., 1991, P 14 ANN INT ACM SIG, P262, DOI 10.1145/122860.122887 Lin X, 1997, J AM SOC INFORM SCI, V48, P40, DOI 10.1002/(SICI)1097-4571(199701)48:1<40::AID-ASI6>3.0.CO;2-1 LUHN HP, 1958, IBM J RES DEV, V2, P159 Manning C. D., 1999, FDN STAT NATURAL LAN NELSON P, 1991, LEARNED INFORMATION, P21 OARD DW, 1996, CSTR3615 U MAR Orwig RE, 1997, J AM SOC INFORM SCI, V48, P157 PAICE CD, 1990, INFORM PROCESS MANAG, V26, P171, DOI 10.1016/0306-4573(90)90014-S Poincot P, 1998, ASTRON ASTROPHYS SUP, V130, P183, DOI 10.1051/aas:1998220 RITTER H, 1989, BIOL CYBERN, V61, P241, DOI 10.1007/BF00203171 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G, 1970, J AM SOC INFORM SCI, V21, P187, DOI 10.1002/asi.4630210305 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schneiderman B., 1996, P IEEE S VIS LANG, P336 SCHOLTES J. C., 1993, THESIS U AMSTERDAM Tufte ER, 1983, VISUAL DISPLAY QUANT VISA A, 2001, P AER 2001 SPIE 15 A, V4384 Wise JA, 1999, J AM SOC INFORM SCI, V50, P1224, DOI 10.1002/(SICI)1097-4571(1999)50:13<1224::AID-ASI8>3.0.CO;2-4 Young SR, 1985, P 2 C ART INT APPL, P402 Zipf G. K., 1949, HUMAN BEHAV PRINCIPL NR 36 TC 3 Z9 3 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-42359-1 J9 LECT NOTES ARTIF INT PY 2001 VL 2123 BP 1 EP 11 PG 11 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BU10B UT WOS:000175011200001 ER PT S AU Toivonen, J Visa, A Vesanen, T Back, B Vanharanta, H AF Toivonen, J Visa, A Vesanen, T Back, B Vanharanta, H BE Perner, P TI Validation of text clustering based on document contents SO MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article; Proceedings Paper CT 2nd International Workshop on Machine Learning and Data Mining in Pattern Recognition CY JUL 25-27, 2001 CL LEIPZIG, GERMANY SP Leipzig Inst Comp Vis & Appl Comp Sci AB In this paper some results of a new text clustering methodology axe presented. A prototype is an interesting document or a part of an extracted, interesting text. The given prototype is matched with the existing document database or the monitored document flow. Our claim is that the new methodology is capable of automatic content-based clustering using the information of the document. To verify this hypothesis an experiment was designed with the Bible. Four different translations, one Greek, one Latin, and two Finnish translations from years 1933/38 and 1992 were selected as test text material. Validation experiments were performed with a designed prototype version of the software application. C1 Tampere Univ Technol, FIN-33101 Tampere, Finland. Abo Akad Univ, FIN-20520 Turku, Finland. Pori Sch Technol & Econ, FIN-28101 Pori, Finland. RP Toivonen, J (reprint author), Tampere Univ Technol, POB 553, FIN-33101 Tampere, Finland. RI Visa, Ari/G-4304-2014 CR DEWEY M, 1876, US BUREAU ED SPECIAL, P623 Dewey M., 1876, CLASSIFICATION SUBJE GEY FC, 2000, TUT HICSS 33 HAW INT LAHTINEN T, 2000, THESIS U HELSINKI FI Manning C. D., 1999, FDN STAT NATURAL LAN OARD DW, 1996, CSTR3643 U MAR Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VISA A, 2001, P 34 ANN HAW INT C S VISA A, 2001, P AER 2001 SPIE 15 A, V4384 VISA A, 2000, P SSGRR 2000 INT C A NR 11 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-42359-1 J9 LECT NOTES ARTIF INT PY 2001 VL 2123 BP 184 EP 195 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems SC Computer Science GA BU10B UT WOS:000175011200015 ER PT B AU Kim, S Zhang, BT AF Kim, S Zhang, BT GP IEEE IEEE TI Evolutionary learning of Web-document structure for information retrieval SO PROCEEDINGS OF THE 2001 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 SE IEEE Congress on Evolutionary Computation LA English DT Proceedings Paper CT Congress on Evolutionary Computation (CEC 2001) CY MAY 27-30, 2001 CL SEOUL, SOUTH KOREA SP IEEE, IEEE Neural Networks Council, Evolutionary Programming Soc, IEE, Inst Control Automat & Syst Engineers ID GENETIC ALGORITHMS AB Web-documents have a number of tags indicating the structure of documents. The tag information can be utilized to improve the performance of document retrieval systems. In this paper, we propose an approach to retrieve Web-documents using HTML tags and then uses a genetic algorithm to adapt the tag weights. This method uses a modified similarity measure based on the tag weights. A genetic learning method is used to select the tags for retrieval and get the optimal tag weights. We evaluate our method via experiments on conference pages and TREC document sets. The experimental results show that the tag weights are well trained by the proposed algorithm in accordance with the importance factors for retrieval. The proposed method has achieved about 10% improvement in retrieval accuracy. C1 Seoul Natl Univ, Sch Comp Sci & Engn, Artificial Intelligence Lab, SCAI, Seoul 151742, South Korea. RP Kim, S (reprint author), Seoul Natl Univ, Sch Comp Sci & Engn, Artificial Intelligence Lab, SCAI, Seoul 151742, South Korea. EM skim@scai.snu.ac.kr; btzhang@scai.snu.ac.kr CR BELKIN NJ, 1987, ANNU REV INFORM SCI, V22, P109 Bharat K., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290972 Boyan J., 1996, P AAAI WORKSH INT BA, P1 Brin S., 1998, 7 INT WORLD WID WEB Chakarabarti S., 2000, ACM SIGKDD EXPLORATI, V1, P1, DOI 10.1145/846183.846187 CHAKRABARTI S, 1998, ACM SIGIR 98 POST C CUTLER M, 1999, 11 IEEE C TOOLS AI Goldberg D. E., 1989, GENETIC ALGORITHMS S Gordon M, 1999, INFORM PROCESS MANAG, V35, P141, DOI 10.1016/S0306-4573(98)00041-7 GORDON M, 1988, COMMUN ACM, V31, P1208, DOI 10.1145/63039.63044 Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X KLEINBERG J, 1998, P 9 ACM SIAM S DISCR, P688 KUSHMERICK N, 1997, P 15 INT JOINT C ART, P729 MICHALEWICZ Z, 1992, GENETIC ALGORITHMS D, P104 Picard J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290989 SALTON G, 1989, AUTOMATIC TEXT PROCE, P279 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHIN DH, 1998, 7 TEXT RETR C SPERTUS E, 1997, 6 INT WORLD WID WEB TSENG LY, 1997, INT C NEUR NETW, V3, P1612 Voorhees E. M., 1999, 8 TEXT RETR C TREC 8 YAN GJ, 1993, 1 TEXT RETR C Yang J., 1998, FEATURE EXTRACTION C, P117 Zobel J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291014 NR 24 TC 0 Z9 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 0-7803-6657-3 J9 IEEE C EVOL COMPUTAT PY 2001 BP 1253 EP 1260 PG 8 WC Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Information Systems; Robotics SC Automation & Control Systems; Computer Science; Robotics GA BT20J UT WOS:000172263900171 ER PT J AU Pratt, W Fagan, L AF Pratt, W Fagan, L TI The usefulness of dynamically categorizing search results SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION LA English DT Article AB Objective: The authors' goal was to determine whether dynamic categorization, a new technique for organizing search results, is more useful than the two existing organizational techniques: relevance ranking and clustering. They define a useful tool as one that helps users learn about the kinds of information that pertain to their query, find answers to their questions efficiently and easily, and feel satisfied with their search experience. Design: Fifteen patients with breast cancer and their family members completed query-related tasks using all three tools. The authors measured the time it took the subjects to accomplish their tasks, the number of answers to the query that the subjects found in four minutes, and the number of new answers that they could recall at the end of the study. Subjects also completed a user-satisfaction questionnaire. Results: The results showed that patients with breast cancer and their family members could find significantly (P < 0.05) more answers in a fixed amount of time and were significantly (P < 0.05) more satisfied with their search experience when they used the dynamic categorization tool than when they used either the cluster tool or the ranking tool. Subjects indicated that the dynamic categorization tool provided an organization of search results that was more clear, easy to use, accurate, precise, and helpful than those of the other tools. Conclusion: The experiments indicate that dynamic categorization is an effective and useful approach for organizing search results. Tools that use this technique will help patients and their families gain quick and easy access to important medical information. C1 Univ Calif Irvine, Irvine, CA 92697 USA. Stanford Univ, Stanford, CA 94305 USA. RP Pratt, W (reprint author), Univ Calif Irvine, Irvine, CA 92697 USA. CR DOLL WJ, 1988, MIS QUART, V12, P259, DOI 10.2307/248851 Efthimiadis E. N., 1993, SIGIR Forum EGAN DE, 1989, ACM T INFORM SYST, V7, P30, DOI 10.1145/64789.64790 HARMAN D, 1992, INFORMATION RETRIEVA Hearst M. A., 1996, SIGIR Forum Hersh W. R., 1995, SIGIR Forum *NA LIB MED, 1999, NLM ONL DAT DAT PIROLLI P, 1996, CHI 96 ACM SIGCHI C PRATT W, 1999, AAAI 99 PRATT W, 1997, AMIA ANN FALL S, P480 PRATT W, 1999, THESIS STANFORD U ME Rasmussen E., 1992, INFORMATION RETRIEVA, P419 SAHAMI M, 1998, DIG LIB 98 3 ACM C D SAHAMI M, 1998, THESIS STANFORD U CO SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TUTTLE MS, 1994, P 17 ANN S COMP APPL, P564 TUTTLE MS, 1996, AMIA ANN FALL S, P150 VANRIJSBERGEN CJ, 1979, INFORMATION RETIREVA WILLETT P, 1988, INFORM PROCESS MANAG, V24, P577, DOI 10.1016/0306-4573(88)90027-1 NR 22 TC 23 Z9 23 PU HANLEY & BELFUS INC PI PHILADELPHIA PA 210 S 13TH ST, PHILADELPHIA, PA 19107 USA SN 1067-5027 J9 J AM MED INFORM ASSN JI J. Am. Med. Inf. Assoc. PD NOV-DEC PY 2000 VL 7 IS 6 BP 605 EP 617 PG 13 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Information Science & Library Science; Medical Informatics SC Computer Science; Information Science & Library Science; Medical Informatics GA 376LT UT WOS:000165459600011 PM 11062234 ER PT J AU Tolle, KM Chen, HC AF Tolle, KM Chen, HC TI Comparing noun phrasing techniques for use with medical digital library tools SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID RETRIEVAL; INTERNET; SYSTEMS; SEARCH; MODEL; TEXT AB In an effort to assist medical researchers and professionals in accessing information necessary for their work, the A1 Lab at the University of Arizona is investigating the use of a natural language processing (NLP) technique called noun phrasing. The goal of this research is to determine whether noun phrasing could be a viable technique to include in medical information retrieval applications. Four noun phrase generation tools were evaluated as to their ability to isolate noun phrases from medical journal abstracts. Tests were conducted using the National Cancer Institute's CANCERLIT database. The NLP tools evaluated were Massachusetts Institute of Technology's (MIT's) Chopper, The University of Arizona's Automatic Indexer, Lingsoft's NPtool, and The University of Arizona's AZ Noun Phraser. In addition, the National Library of Medicine's SPECIALIST Lexicon was incorporated into two versions of the AZ Noun Phraser to be evaluated against the other tools as well as a nonaugmented version of the AZ Noun Phraser. Using the metrics relative subject recall and precision, our results show that, with the exception of Chopper, the phrasing tools were fairly comparable in recall and precision. It was also shown that augmenting the AZ Noun Phraser by including the SPECIALIST Lexicon from the National Library of Medicine resulted in improved recall and precision. C1 Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. RP Tolle, KM (reprint author), Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. CR Adam N, 1996, ACM COMPUT SURV, V28, P818, DOI 10.1145/242223.246871 ANICK PG, 1997, 20 ANN INT ACM SIGIR ARPPE A, 1995, 10 NORD C COMP LING BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BOGURAEV B, 1996, CORPUS PROCESSING LE Brill E, 1995, COMPUT LINGUIST, V21, P543 Brill Eric, 1993, THESIS U PENNSYLVANI CHEN H, 1996, PARALLEL COMPUTING A Chen HC, 1996, J VIS COMMUN IMAGE R, V7, P88, DOI 10.1006/jvci.1996.0008 CHEN H, 1999, UNPUB J AM SOC INFOR CHEN H, 1998, J INFORM SCI, P24 Chen HC, 1998, J AM SOC INFORM SCI, V49, P206, DOI 10.1002/(SICI)1097-4571(1998)49:3<206::AID-ASI3>3.3.CO;2-E CIMINO JJ, 1994, ANN S COMP APPL MED Cooper GF, 1998, J AM MED INFORM ASSN, V5, P62 CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C CULLINGFORD RE, 1986, NATURAL LANGUAGE PRO CUTTING D, 1992, 3 C APPL LANG PROC T DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Detmer WM, 1997, COMMUN ACM, V40, P101, DOI 10.1145/257874.257897 DEVANBU P, 1991, COMMUN ACM, V34, P34, DOI 10.1145/103167.103172 DOSZKOCS TE, 1983, INFORM TECHNOL LIBR, V2, P364 Dumais S. T., 1994, TEXT RETR C TREC 2, P105 EVANS DA, 1994, P RIAO 94 NEW YORK N Fox E., 1998, COMMUN ACM, V41, P29, DOI [10.1145/273035.273043, DOI 10.1145/273035.273043] FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GALLANT SI, 1988, COMMUN ACM, V31, P152, DOI 10.1145/42372.42377 GIRARDI MR, 1993, 3 ANN IRV SOFTW S U HALVERSON P, 1995, SURVEY STATE ART HUM, P255 Harter SP, 1996, J AM SOC INFORM SCI, V47, P37 HARTLEY HO, 1978, BIOMETRICS, V34, P233, DOI 10.2307/2530013 HERSH WR, 1996, INFORMATION RETRIEVA HOUSTON AL, 1999, INT J DECISION SUPPO HOUSTON AL, 1999, AI REV JOHNSON SB, 1994, ANN S COMP APPL MED KARLSSON F, 1995, SUBSENTENIAL PROCESS LEWIS DD, 1990, P 13 INT ACM SIGIR C Lewis DD, 1996, COMMUN ACM, V39, P92, DOI 10.1145/234173.234210 LYNCH C, 1995, INF INFR TECHN APPL MAULDIN M, 1991, P 14 ACM SIGIR C RES Quirk Randolph, 1985, COMPREHENSIVE GRAMMA RAMSEY M, 1999, J AM SOC INFORMATION Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 TOLLE KM, 1997, THESIS U ARIZONA TUC *UMLS, 1998, UMLS KNOWL SOURC VOUTILAINEN A, 1997, SHORT INTRO NPTOOL ZAENEN A, 1995, SURVEY STATE ART HUM, P109 NR 49 TC 56 Z9 56 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAR 1 PY 2000 VL 51 IS 4 BP 352 EP 370 DI 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.3.CO;2-# PG 19 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 282NK UT WOS:000085224800005 ER PT B AU Cardie, C Ng, V Pierce, D Buckley, C AF Cardie, C Ng, V Pierce, D Buckley, C GP acl TI Examining the role of statistical and linguistic knowledge sources in a general-knowledge question-answering system SO 6TH APPLIED NATURAL LANGUAGE PROCESSING CONFERENCE/1ST MEETING OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE AND PROCEEDINGS OF THE ANLP-NAACL 2000 STUDENT RESEARCH WORKSHOP LA English DT Proceedings Paper CT 6th Applied Natural Language Processing Conference/1st Meeting of the North American Chapter of the Association-for-Computational-Linguistics/ANLP-NAACL 2000 Student Research Workshop CY APR 29-MAY 04, 2000 CL Seattle, WA SP Assoc Computat Linguist, N Amer Chapter AB We describe and evaluate an implemented system for general-knowledge question answering. The system combines techniques for standard ad-hoc information retrieval (IR), query-dependent text summarization, and shallow syntactic and semantic sentence analysis. In a series of experiments we examine the role of each statistical and linguistic knowledge source in the question-answering system. In contrast to previous results, we find first that statistical knowledge of word co-occurrences as computed by IR vector space methods can be used to quickly and accurately locate the relevant documents for each question. The use of query-dependent text summarization techniques, however, provides only small increases in performance and severely limits recall levels when inaccurate. Nevertheless, it is the text summarization component that allows subsequent linguistic filters to focus on relevant passages. We find that even very weak linguistic knowledge can offer substantial improvements over purely IR-based techniques for question answering, especially when smoothly integrated with statistical preferences computed by the IR subsystems. C1 Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA. RP Cardie, C (reprint author), Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA. CR BRECK E, 2000, IN PRESS NIST SPECIA BUCKLEY C, 1998, NIST SPECIAL PUBLICA, P285 BUCKLEY C, 1999, IN PRESS P TIPSTER T BUCKLEY C, 1995, THESIS ITHACA BUCKLEY C, 1998, NIST SPECIAL PUBLICA, P107 BURKE R, 1997, TR9705 U CHIC BURKE R, 1995, AAAI FALL S AI APPL, P19 CARDIE C, 1998, P 36 ANN M ACL COLIN, P218 CHALI Y, 1999, P AAAI FALL S QUEST, P52 Fellbaum C, 1998, WORDNET ELECT LEXICA Kupiec J., 1993, P 16 ANN INT ACM SIG, P181, DOI 10.1145/160688.160717 LEHNERT W, 1978, PROCESSES QUESTION A MANI I, 1999, 9 ANN M EACL U BERG Marcus M. P., 1994, COMPUTATIONAL LINGUI, V19, P313 Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 MOLDOVAN D, 2000, IN PRESS NIST SPECIA MORTON TS, 1999, P AAAI FALL S QUEST, P72 OARD DW, 2000, IN PRESS NIST SPECIA PRAGER J, 2000, IN PRESS NIST SPECIA Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schank R. C., 1977, SCRIPTS PLANS GOALS SINGHAL A, 2000, IN PRESS NIST SPECIA Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 SRIHARI R, 2000, IN PRESS NIST SPECIA *TREC 8, 2000, IN PRESS P 8 TEXT RE NR 27 TC 1 Z9 1 PU ASSOCIATION COMPUTATIONAL LINGUISTICS PI SOMERSET PA PO BOX 6090, SOMERSET, NJ 08875 USA BN 1-55860-704-8 PY 2000 BP 180 EP 187 PG 8 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics SC Computer Science; Linguistics GA BAP09 UT WOS:000223099500025 ER PT S AU Kim, HJ Lee, SG AF Kim, HJ Lee, SG BE Mohammadian, M TI Automatic maintenance of evolving topic structure in intelligent information systems SO ADVANCES IN INTELLIGENT SYSTEMS: THEORY AND APPLICATIONS SE FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS LA English DT Proceedings Paper CT International Conference on Advances in Intelligent Systems: Theory and Applications (AISTA 2000) CY FEB 02-04, 2000 CL CANBERRA, AUSTRALIA AB This paper proposes a novel approach for effectively managing hierarchical topical organization in information systems, so that the organization can optimally evolve. To this end, we propose four basic operations for hierarchy reorganization, and apply to these operations a mixture algorithm of the fuzzy c-means clustering algorithm and the semi-supervised point-prototype clustering algorithm so as to incorporate an external human knowledge. C1 Seoul Natl Univ, Dept Comp Sci, Kwanak Ku, Seoul 151742, South Korea. RP Kim, HJ (reprint author), Seoul Natl Univ, Dept Comp Sci, Kwanak Ku, San 56-1,Shillim Dong, Seoul 151742, South Korea. CR AGGARWAL C, 1999, RC21429 IBM AGRAWAL R, 1999, INT C EXT DAT TECHN Bensaid AM, 1996, PATTERN RECOGN, V29, P859, DOI 10.1016/0031-3203(95)00120-4 LABZOUR T, 1998, INT C FUZZ SYST IEEE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 5 TC 0 Z9 0 PU I O S PRESS PI AMSTERDAM PA NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS SN 0922-6389 BN 1-58603-043-4 J9 FR ART INT PY 2000 VL 59 BP 78 EP 83 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Cybernetics SC Computer Science GA BR25D UT WOS:000165948100013 ER PT S AU Chen, H Ho, TK AF Chen, H Ho, TK BE Lopresti, DP Zhou, J TI Evaluation of decision forests on text categorization SO DOCUMENT RECOGNITION AND RETRIEVAL VII SE PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) LA English DT Proceedings Paper CT 7th Annual Document Recognition and Retrieval Conference CY JAN 26-27, 2000 CL SAN JOSE, CA SP SPIE, Soc Imaging Sci & Technol DE text categorization; decision forest; decision tree; C4.5; k-nearest-neighbor; OHSUMED; Reuters; evaluation; information retrieval AB Text categorization is useful for indexing documents for information retrieval, filtering parts for document understanding, and summarizing contents of documents of special interests. We describe a text categorization task and an experiment using documents from the Reuters and OHSUMED collections. We applied the Decision Forest classifier and compared its accuracies to those of C4.5 and kNN classifiers, using both category dependent and category independent term selection schemes. It is found that Decision Forest outperforms both C4.5 and kNN in all cases, and that category dependent term selection yields better accuracies. Performances of all three classifiers degrade from the Reuters collection to the OHSUMED collection, but Decision Forest remains to be superior. C1 Univ Calif Berkeley, Sch Informat Mgmt & Systems, Berkeley, CA 94720 USA. RP Chen, H (reprint author), Univ Calif Berkeley, Sch Informat Mgmt & Systems, Berkeley, CA 94720 USA. CR APTE C, 1994, P 17 ANN ACM SIGIR C DAHLGREN K, 1991, P 3 MESS UND EV C FRAKES WR, 1992, INFORMATION RETRIEVA Fuhr N., 1991, P RIAO 91, P606 HARDT S, 1988, AAAI 88 WORKSH PLAN Ho TK, 1998, IEEE T PATTERN ANAL, V20, P832 LEWIS D, 1994, P 3 ANN S DCOUM AN I Lewis David D., 1996, P 19 ANN INT ACM SIG, P298, DOI 10.1145/243199.243277 Lewis David D., 1992, THESIS U MASSACHUSET Quinlan J. R., 1993, C4 5 PROGRAMS MACHIN SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WIENER E, 1995, P 4 ANN S DOC AN INF YANG Y, 1994, ACM T INFORM SYST, P253 Yang Y, 1997, CMUCS97127 Yang Y., 1997, P 14 INT C MACH LEAR OHSUMED COLLECTION REUTES COLLECTION NR 17 TC 1 Z9 1 PU SPIE-INT SOC OPTICAL ENGINEERING PI BELLINGHAM PA 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA SN 0277-786X BN 0-8194-3585-6 J9 P SOC PHOTO-OPT INS PY 2000 VL 3967 BP 191 EP 199 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Optics SC Computer Science; Optics GA BP40C UT WOS:000085028100022 ER PT J AU Imai, H Inaba, M AF Imai, H Inaba, M TI Divergence-based geometric clustering and its underlying discrete proximity structures SO IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS LA English DT Article DE unsupervised learning; geometric clustering; Voronoi diagram; computational geometry; information geometry AB This paper surveys recent progress in the investigation of the underlying discrete proximity structures of geometric clustering with respect to the divergence in information geometry. Geometric clustering with respect to the divergence provides powerful unsupervised learning algorithms, and can be applied to classifying and obtaining generalizations of complex objects represented in the feature space. The proximity relation, defined by the Voronoi diagram by the divergence, plays an important role in the design and analysis of such algorithms. C1 Univ Tokyo, Dept Informat Sci, Tokyo 1130033, Japan. RP Imai, H (reprint author), Univ Tokyo, Dept Informat Sci, Tokyo 1130033, Japan. CR Amari S., 1985, LECT NOTES STAT, V28 AMARI S, 1993, METHOD INFORMATION G CELEUX G, 1992, COMPUT STAT DATA AN, V14, P315, DOI 10.1016/0167-9473(92)90042-E EDELSBRUNNER H., 1987, ALGORITHMS COMBINATO Faloutsos C., 1994, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V3, DOI 10.1007/BF00962238 HASEGAWA S, 1995, THEOR COMPUT SCI, V137, P145, DOI 10.1016/0304-3975(94)00165-F INABA M, 1998, GEOMETRIC CLUSTERING INABA M, 1999, THESIS U TOKYO Inaba M., 1994, Proceedings of the Tenth Annual Symposium on Computational Geometry, DOI 10.1145/177424.178042 Murray M. K., 1993, DIFFERENTIAL GEOMETR ONISHI K, 1997, JAP KOR JOINT WORKSH, P1 Onishi K, 1996, IEICE T FUND ELECTR, VE79A, P533 ONISHI K, UNPUB RIEMANNIAN COM ONISHI K, 1998, THESIS U TOKYO Sadakane K., 1998, Proceedings of the Fourteenth Annual Symposium on Computational Geometry, DOI 10.1145/276884.276929 SALTON G, 1994, SCIENCE, V264, P1421, DOI 10.1126/science.264.5164.1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 17 TC 1 Z9 1 PU IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG PI TOKYO PA KIKAI-SHINKO-KAIKAN BLDG MINATO-KU SHIBAKOEN 3 CHOME, TOKYO, 105, JAPAN SN 0916-8532 J9 IEICE T INF SYST JI IEICE Trans. Inf. Syst. PD JAN PY 2000 VL E83D IS 1 BP 27 EP 35 PG 9 WC Computer Science, Information Systems; Computer Science, Software Engineering SC Computer Science GA 278EF UT WOS:000084974400005 ER PT J AU Ding, Y Chowdhury, GG Foo, S AF Ding, Y Chowdhury, GG Foo, S TI Incorporating the results of co-word analyses to increase search variety for information retrieval SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID AUTOMATIC CONSTRUCTION; CITATION ANALYSIS; VOCABULARY; RELEVANCE; SCIENCE; MODEL; SPACE; SCIENTOMETRICS; SYSTEMS; ACCESS AB This research aims to incorporate the results of co-word analysis into information retrieval as a means to increase search variety for end users in the domain of information retrieval. Relevant data were first collected fi om Science Citation Index and Social Sciences Citation Index for the period 1987-1997. The results of co-word analysis on the data were compared with similar data obtained from three thesauri: Library and Information Science Abstracts thesaurus, Library of Congress Subject Headings and Thesaurus of Information Technology Terms. The differences detected between them indicate that the search variety may be increased by combining co-word analysis with the use of traditional thesauri. Subsequently, the results of co-word analysis were compared with each other for two different periods (1987-1991 and 1992-1997). The changes among them were identified, implying that co-word analysis may be used directly to identify dynamic changes in its chosen domain area, thereby providing better up-to-date information to aid the information search process. C1 Nanyang Technol Univ, Div Informat Studies, Sch Appl Sci, Singapore, Singapore. RP Chowdhury, GG (reprint author), Nanyang Technol Univ, Div Informat Studies, Sch Appl Sci, Nanyang Ave, Singapore, Singapore. RI Foo, Schubert /H-2070-2013 CR Aitchison J., 1997, THESAURUS CONSTRUCTI BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 Bates MJ, 1998, J AM SOC INFORM SCI, V49, P1185, DOI 10.1002/(SICI)1097-4571(1998110)49:13<1185::AID-ASI6>3.3.CO;2-M Borgman C. L., 1990, SCHOLARLY COMMUNICAT BRAAM RR, 1991, J AM SOC INFORM SCI, V42, P252, DOI 10.1002/(SICI)1097-4571(199105)42:4<252::AID-ASI2>3.0.CO;2-G Byrne CC, 1999, J INFORM SCI, V25, P113, DOI 10.1177/016555159902500203 CAMBROSIO A, 1993, SCIENTOMETRICS, V27, P119, DOI 10.1007/BF02016546 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P175, DOI 10.1002/(SICI)1097-4571(199504)46:3<175::AID-ASI3>3.0.CO;2-U Chen HC, 1997, J AM SOC INFORM SCI, V48, P17, DOI 10.1002/(SICI)1097-4571(199701)48:1<17::AID-ASI4>3.0.CO;2-4 CHEN HC, 1991, INFORM PROCESS MANAG, V27, P405, DOI 10.1016/0306-4573(91)90060-Y CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830 Chen HC, 1998, J AM SOC INFORM SCI, V49, P206, DOI 10.1002/(SICI)1097-4571(1998)49:3<206::AID-ASI3>3.3.CO;2-E CHEN H, 1995, J AM SOC INFORM SCI, V46, P348, DOI 10.1002/(SICI)1097-4571(199506)46:5<348::AID-ASI6>3.0.CO;2-1 CHU H, 1992, SCIENTOMETRICS, V25, P229, DOI 10.1007/BF02028084 Coulter N, 1998, J AM SOC INFORM SCI, V49, P1206, DOI 10.1002/(SICI)1097-4571(1998)49:13<1206::AID-ASI7>3.3.CO;2-6 COURTIAL JP, 1994, SCIENTOMETRICS, V31, P251, DOI 10.1007/BF02016875 COURTIAL JP, 1994, SCIENTOMETRICS, V31, P173, DOI 10.1007/BF02018559 CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C DeLooze MA, 1997, SCIENTOMETRICS, V39, P267 Everitt B., 1980, CLUSTER ANAL GOMEZ LM, 1990, J AM SOC INFORM SCI, V41, P547, DOI 10.1002/(SICI)1097-4571(199012)41:8<547::AID-ASI1>3.0.CO;2-P GREGORY JG, 1983, J INFORM SCI, V7, P73, DOI 10.1177/016555158300700204 HAMERS L, 1989, INFORM PROCESS MANAG, V25, P315, DOI 10.1016/0306-4573(89)90048-4 Harter SP, 1996, J AM SOC INFORM SCI, V47, P311, DOI 10.1002/(SICI)1097-4571(199604)47:4<311::AID-ASI7>3.0.CO;2-# HARTER SP, 1992, J AM SOC INFORM SCI, V43, P602, DOI 10.1002/(SICI)1097-4571(199210)43:9<602::AID-ASI3>3.0.CO;2-Q KING J, 1987, J INFORM SCI, V13, P261, DOI 10.1177/016555158701300501 LAU TY, 1995, SCIENTOMETRICS, V33, P65, DOI 10.1007/BF02020775 LAW J, 1992, SCIENTOMETRICS, V23, P417, DOI 10.1007/BF02029807 MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q Noyons ECM, 1998, J AM SOC INFORM SCI, V49, P68, DOI 10.1002/(SICI)1097-4571(1998)49:1<68::AID-ASI9>3.0.CO;2-Z NOYONS ECM, 1998, COMMUNICATION Noyons ECM, 1999, J AM SOC INFORM SCI, V50, P115, DOI 10.1002/(SICI)1097-4571(1999)50:2<115::AID-ASI3>3.0.CO;2-J PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 PETERS HPF, 1995, J AM SOC INFORM SCI, V46, P9, DOI 10.1002/(SICI)1097-4571(199501)46:1<9::AID-ASI2>3.0.CO;2-3 Quoniam L, 1998, SCIENTOMETRICS, V41, P83, DOI 10.1007/BF02457969 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1988, J AM SOC INFORM SCI, V39, P197, DOI 10.1002/(SICI)1097-4571(198805)39:3<197::AID-ASI4>3.0.CO;2-A SEGLEN PO, 1996, SCIENTOMETRICS, V35, P335 SHALINI R, 1993, INFORM PROCESS MANAG, V29, P463, DOI 10.1016/0306-4573(93)90042-C VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 41 TC 11 Z9 11 PU BOWKER-SAUR PI E GRINSTEAD PA WINDSOR COURT, EAST GRINSTEAD HOUSE, E GRINSTEAD RH19 1XA, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 2000 VL 26 IS 6 BP 429 EP 451 DI 10.1177/016555150002600606 PG 23 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 393KX UT WOS:000166468700005 ER PT B AU Chen, ZX Meng, XN Zhu, B Fowler, RH AF Chen, ZX Meng, XN Zhu, B Fowler, RH BE Li, Q Qzsoyoglu, ZM Wagner, R Kambayashi, Y Zhang, YC TI WebSail: From on-line learning to web search SO PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS ENGINEERING, VOL I LA English DT Proceedings Paper CT 1st International Conference on WEB Information Systems Engineering (WISE 2000) CY JUN 19-21, 2000 CL HONG KONG, PEOPLES R CHINA SP City Univ Hong Kong, Univ So Queensland, ACM, Hong Kong WEB Soc, AIMtech Ctr, Hong Kong Pei Hua Educ Fdn, IEEE, Comp Soc, Hong Kong Sect, Comp Chapter, Kyoto Univ, Kambayashi Lab, K C Wong Educ Fdn, Sun Microsyst AB In this paper we investigate the applicability of on-line learning algorithms to the real-worldproblem of web search. Consider that web documents are indexed using n Boolean features. We first present a practically efficient on-line learning algorithm TW2 to search for web documents represented by a disjunction of at most k relevant features. We then design and implement WebSail, a real-time adaptive web search learner with TW2 as its learning component WebSail learns from the user's relevance feedback in real-time and helps the user to search for the desired web documents. The architecture and performance of WebSail are also discussed. C1 Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. RP Chen, ZX (reprint author), Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA. EM chen@cs.panam.edu; meng@cs.panam.edu; bhz@cs.cityu.edu.hk; fowler@panam.edu CR Angluin D., 1988, Machine Learning, V2, DOI 10.1007/BF00116828 Baeza-Yates R., 1999, MODERN INFORMATION R, V1st Bollacker K. D., 1998, Proceedings of the Second International Conference on Autonomous Agents, DOI 10.1145/280765.280786 Bollacker K.D., 1999, P 4 ACM C DIG LIB, P105, DOI 10.1145/313238.313270 Brin S., 1998, P 7 WORLD WID WEB C Chakrabarti S., 1998, P 7 INT WORLD WID WE, P65 CHEN Z, 2000, P AAAI 2000 WORKSH A CHEN Z, 2000, CS0022 U TEX PAN AM CHEN Z, 1999, KNOWLEDGE INFORMATIO, V1, P369 Gibson D., 1998, P 9 ACM C HYP HYP IDA E, 1971, SMART SYSTEM EXPT AU, P373 Ide E., 1971, SMART RETRIEVAL SYST, P337 Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140 LAWRENCE S, 1999, P 8 ACM INT C INF KN LEWIS D, 1991, P 8 INT WORKSH MACH, P235 Littlestone N., 1988, Machine Learning, V2, DOI 10.1007/BF00116827 Maass W, 1998, INFORM COMPUT, V141, P66, DOI 10.1006/inco.1997.2686 NAKAMURA A, 1998, MACHINE LEARNING RENNIE J, 1999, P 1L INT C MACH LEAR Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Yuwono B, 1996, PROC INT CONF DATA, P164, DOI 10.1109/ICDE.1996.492102 NR 23 TC 0 Z9 0 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA BN 0-7695-0577-5 PY 2000 BP 206 EP 213 PG 4 WC Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications SC Computer Science; Engineering; Telecommunications GA BQ97F UT WOS:000165194800026 ER PT B AU Alford, K Chen, JX AF Alford, K Chen, JX BE Arabnia, HR TI Parallel information retrieval scalability using the relational model on large document collections SO PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED PROCESSING TECHNIQUES AND APPLICATIONS, VOLS I-V LA English DT Proceedings Paper CT International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2000) CY JUN 26-29, 2000 CL LAS VEGAS, NV SP Comp Sci Res, Educ & Applicat Press, Informat Processing Soc Japan, SIGMPS, Int Technol Inst, Java High Performance Comp Res Grp, Korea Informat Processing Soc, World Sci & Engn Soc DE scalability; database; parallel information retrieval ID WEB AB This research investigates the scalability of an information retrieval (IR) system based on the vector space model and implemented using a relational database management system (DBMS). Scalability testing was performed at the U.S. Army Research Laboratory's Major Shared Resource Center in Aberdeen, Maryland on a Sun E10K computer using one to 24 processors and seven document collections ranging in size from two gigabytes to 100 gigabytes of unprocessed web-based text data. Scalability is demonstrated to the limit of physical I/O devices. C1 US Mil Acad, Dept Elect Engn & Comp Sci, W Point, NY 10996 USA. RP Alford, K (reprint author), US Mil Acad, Dept Elect Engn & Comp Sci, W Point, NY 10996 USA. CR GROSSMAN D, 1998, INFORMATION RETRIEVA GROSSMAN D, 1995, INTEGRATING STRUCTUR HAWKINGS D, 1996, P 5 TEXT RETR C TREC JONES KS, 1997, READINGS INFORMATION, P2 Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987 Lawrence S, 1998, SCIENCE, V280, P98, DOI 10.1126/science.280.5360.98 LUNDQUIST C, 1999, J AM SOC INFORMATION, V50 MAMALIS B, 1996, P 5 TEXT RETR C TREC MOOERS C, 1952, P INT C MATH AM MATH, P572 *OR CORP, 1999, OR DOC LIB OR SERV R SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 *SUN MICR, 1997, SUN HIGH PERF COMP NR 12 TC 0 Z9 0 PU C S R E A PRESS PI ATHENS PA 115 AVALON DR, ATHENS, GA 30606 USA BN 1-892512-52-1 PY 2000 BP 1705 EP 1711 PG 7 WC Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA BR82C UT WOS:000167676300234 ER PT J AU Mamalis, B Spirakis, P Tampakas, B AF Mamalis, B Spirakis, P Tampakas, B TI Optimal high-performance parallel text retrieval via fat-trees SO THEORY OF COMPUTING SYSTEMS LA English DT Article ID SEARCH AB We present here a high-performance parallel free-text retrieval method for multiple text queries using the Vector Space Model. Our method employs the fat-tree area universal routing network for connecting the processors of a parallel machine, however (in its general form) it could also be efficiently applied over any other high-bandwidth network of processors or workstations, We provide a theoretical analysis of our technique which shows it is excessively efficient and clearly superior (concerning both the amortized processing times and the average waiting times per query) to parallel text retrieval methods for single queries (e,g,, based on binary trees). Moreover, we prove our method to be optimal with respect to the execution of all the implied communication tasks on ideal fat-trees. We also experimentally demonstrate the high performance and superiority of our technique via suitable embeddings of ideal fat-trees on realistic two-dimensional mesh-oriented parallel machines (e,g,, the GCel Parsytec machine) and via the use of the large-scale TREC document collections. Note that the fat-tree can simulate any other network of the same hardware with only a polylogarithmic loss of efficiency. C1 Comp Technol Inst, Patras 26221, Greece. Univ Patras, Comp Engn & Informat Dept, Patras 26500, Greece. Technol Educ Inst Patras, Patras 26334, Greece. RP Mamalis, B (reprint author), Comp Technol Inst, Kolokotroni 3, Patras 26221, Greece. EM mamalis@cti.gr; spirakis@cti.gr; tampakas@cti.gr CR AALBERSBERG IJ, 1991, P 1 INT C PAR DISTR AMDAL G, AFIPS C P, V30, P483 BILLARDI G, 1997, LECT NOTES COMPUTER, V1300, P196 CRINGEAN J, 1989, BEST MATCH SEARCHING CRINGEAN JK, 1990, P 13 INT C RES DEV I, P429, DOI 10.1145/96749.98249 EFRAIMIDIS P, 1995, P 18 ANN INT ACM SIG, P58, DOI 10.1145/215206.215332 FLYNN M, IEEE T COMPUTERS, V21, P948 FRIEDER O, 1991, P 14 ANN INT ACM SIG, P230, DOI 10.1145/122860.122884 GRANDI F, 1992, P 15 ACM SIGIR INT C, P286, DOI 10.1145/133160.133211 Harman D., 1995, NIST SPECIAL PUBLICA, P1 ILLARDI G, 1994, P 4 EUR S ALG ESA 94 LAFAZANIS M, 1994, P RIAO 94 C NEW YORK, P132 Leighton FT, 1992, INTRO PARALLEL ALGOR Leiserson C. E., 1985, IEEE T COMPUTERS, VC-34-10 Leiserson C.E., 1992, P 4 ANN ACM S PAR AL, P272, DOI 10.1145/140901.141883 MAMALIS B, IN PRESS INT J COMPU MAMALIS B, 1996, NATL I STANDARDS TEC, P251 POGUE CA, 1987, PARALLEL COMPUT, V4, P259, DOI 10.1016/0167-8191(87)90025-1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHARMA R, 1989, INFORMATION PROCESSI, P223 STANFILL C, 1989, SIGIR FORUM, V23, P88, DOI 10.1145/75334.75345 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 Stanfill C, 1990, P 13 ANN INT ACM SIG, P413, DOI [10.1145/96749.98247, DOI 10.1145/96749.98247] STONE H, 1987, COMPUTER OCT, P11 Tampakas B, 1996, PARALLEL AND DISTRIBUTED COMPUTING SYSTEMS - PROCEEDINGS OF THE ISCA 9TH INTERNATIONAL CONFERENCE, VOLS I AND II, P726 TIEDT F, 1992, PARSYTEC GCEL SUPERC VANRIJBERGEN CJ, 1979, INFORMATION RETRIEVA NR 29 TC 0 Z9 0 PU SPRINGER PI NEW YORK PA 233 SPRING ST, NEW YORK, NY 10013 USA SN 1432-4350 J9 THEOR COMPUT SYST JI Theor. Comput. Syst. PD NOV-DEC PY 1999 VL 32 IS 6 BP 591 EP 623 PG 33 WC Computer Science, Theory & Methods; Mathematics SC Computer Science; Mathematics GA 231KZ UT WOS:000082309300001 ER PT J AU Losee, RM Paris, LAH AF Losee, RM Paris, LAH TI Measuring search-engine quality and query difficulty: Ranking with target and freestyle SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; VECTOR-SPACE; RELEVANCE; FOUNDATION; BEHAVIOR; SYSTEMS AB Instead of using traditional performance measures such as precision and recall, information retrieval performance may be measured by considering the probability that the search engine is optimal and the difficulty associated with retrieving documents with a given query or on a given topic. These measures of desirable characteristics are more easily and more directly interpretable than are traditional measures. The performance of the Target and Freestyle search engines is examined, and is very good. Each query in the CF database is assigned a difficulty number, and these numbers: are found to strongly correlate with other measures of retrieval performance such as an E or F value, The query difficulty correlates weakly with query length. C1 Univ N Carolina, Sch Informat & Lib Studies, Chapel Hill, NC 27599 USA. Oklahoma Christian Univ Lib, Oklahoma City, OK 73136 USA. RP Losee, RM (reprint author), Univ N Carolina, Sch Informat & Lib Studies, Manning Hall,CB 3360, Chapel Hill, NC 27599 USA. CR BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 Burgin R, 1999, J AM SOC INFORM SCI, V50, P181, DOI 10.1002/(SICI)1097-4571(1999)50:2<181::AID-ASI8>3.0.CO;2-9 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 Duval B. K., 1994, Library Software Review, V13 HARTER SP, 1997, ANN REV INFORMATION, V32, P1 Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960 KEEN EM, 1994, ONLINE CDROM REV, V18, P285, DOI 10.1108/eb024502 Losee R.M., 1998, TEXT RETRIEVAL FILTE LU XA, 1995, OV 3 TEXT RETR C TRE, P231 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 *MEAD DAT CENTR, 1994, HDB US LEX NEX Paris LAH, 1998, INFORM PROCESS MANAG, V34, P175, DOI 10.1016/S0306-4573(97)00065-4 PARIS LAH, 1998, THESIS U N CAROLINA ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3 SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SHAW WM, 1986, J AM SOC INFORM SCI, V37, P346, DOI 10.1002/asi.4630370511 Shaw WM, 1997, INFORM PROCESS MANAG, V33, P15, DOI 10.1016/S0306-4573(96)00044-1 SHAW WM, 1991, LIBR INFORM SCI RES, V13, P347 SPARCKJONES K, 1972, J DOC, V28, P11 Sperber Dan, 1995, RELEVANCE COMMUNICAT Tang R, 1998, INFORM PROCESS MANAG, V34, P237, DOI 10.1016/S0306-4573(97)00081-2 TENOPIR C, 1994, ONLINE, V18, P31 TURTLE H, 1994, ACM ANN C RES DEV IN, P212 VANRIJSBERGEN CJ, 1974, J DOC, V30, P365 NR 29 TC 13 Z9 13 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD AUG PY 1999 VL 50 IS 10 BP 882 EP 889 DI 10.1002/(SICI)1097-4571(1999)50:10<882::AID-ASI5>3.0.CO;2-6 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 219QE UT WOS:000081617400005 ER PT J AU Booker, A Condliff, M Greaves, M Holt, FB Kao, A Pierce, DJ Poteet, S Wu, YJJ AF Booker, A Condliff, M Greaves, M Holt, FB Kao, A Pierce, DJ Poteet, S Wu, YJJ TI Visualizing text data sets SO COMPUTING IN SCIENCE & ENGINEERING LA English DT Article ID INFORMATION-RETRIEVAL C1 Boeing Co, Boeing Phantom Works, Seattle, WA 98124 USA. Boeing Co, Numer Methods Program, Seattle, WA 98124 USA. CR BERRY MW, 1992, INT J SUPERCOMPUT AP, V6, P13 Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127 Berry MW, 1996, NUMER LINEAR ALGEBR, V3, P301 DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 PIERCE DJ, 1998, SSGTECH98009 BOEING Risch JS, 1996, IEEE COMPUT GRAPH, V16, P33, DOI 10.1109/38.544070 Salton G, 1988, INFORMATION PROCESSI, V24, P512 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 9 TC 12 Z9 12 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA SN 1521-9615 J9 COMPUT SCI ENG JI Comput. Sci. Eng. PD JUL-AUG PY 1999 VL 1 IS 4 BP 26 EP 35 DI 10.1109/5992.774838 PG 10 WC Computer Science, Interdisciplinary Applications SC Computer Science GA 211DZ UT WOS:000081145800008 ER PT J AU Sebastiani, F AF Sebastiani, F TI Towards a logical reconstruction of information retrieval theory SO CYBERNETICS AND SYSTEMS LA English DT Article ID TEXT RETRIEVAL; MODEL AB Here we expand on a previous paper concerning the role of logic in information retrieval (IR) modelling. In that paper, among other things, we had pointed out how different ways of understanding the contribution of logic to IR have sprung from the (always unstated) adherence to either the total or the partial knowledge assumption. Here we make our analysis more precise by relating this dichotomy to the notion of vividness, as used in knowledge representation, and to another dichotomy that has had a profound influence in database theory, namely, the distinction between the proof-theoretic and the model-theoretic views of a database, spelled out by Kelter in his "logical reconstruction of database theory." We show that precisely the same distinction can be applied to logical models of IR developed so far. The strengths and weaknesses of the adoption of either approach in logical models of IR are discussed. C1 CNR, Ist Elaborazione Informaz, I-56126 Pisa, Italy. RP Sebastiani, F (reprint author), CNR, Ist Elaborazione Informaz, Via S Maria 46, I-56126 Pisa, Italy. RI Sebastiani, Fabrizio/C-9501-2015 OI Sebastiani, Fabrizio/0000-0003-4221-6427 CR Baayen R. H., 1997, COMPUT HUMANITIES, V30, P281 COOPER WS, 1981, INFORMATION RETRIEVA, P199 CRESTANI F, 1996, P 2 INT WORKSH LOG U, P48 CRESTANI F, 1995, P 1 INT WORKSH LOG U, P35 CRESTANI F, 1995, J DOC, V51, P3, DOI 10.1108/eb026939 CRESTANI F, 1995, P ACM SIGIR C RES DE, P291, DOI 10.1145/215206.215373 Fuhr N., 1995, P 18 ANN INT ACM SIG, P282, DOI 10.1145/215206.215372 Garey M. R., 1979, COMPUTERS INTRACTABI HALPERN JY, 1991, PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, P325 HALPERN JY, 1990, ARTIF INTELL, V46, P311, DOI 10.1016/0004-3702(90)90019-V HARMAN D, 1995, INFORM PROCESS MANAG, V31, P271, DOI 10.1016/0306-4573(94)00047-7 HARPER WL, 1986, IFS CONDITIONALS BEL JONES KS, 1997, READINGS INFORMATION LEVESQUE HJ, 1988, J PHILOS LOGIC, V17, P355, DOI 10.1007/BF00297511 LEVESQUE HJ, 1986, ARTIF INTELL, V30, P81, DOI 10.1016/0004-3702(86)90068-8 LEWIS D, 1976, PHILOS REV, V85, P297, DOI 10.2307/2184045 LUHN HP, 1957, IBM J RES DEV, V1, P309 Lukaszewicz W., 1990, NONMONOTONIC REASONI MEGHINI C, 1993, P 16 ANN INT ACM SIG, P298, DOI 10.1145/160688.160753 Meghini C., 1996, P 19 ANN INT ACM SIG, P197, DOI 10.1145/243199.243267 MYLOPOULOS J, 1989, READINGS ARTIFICIAL NIE JY, 1989, INFORM PROCESS MANAG, V25, P477, DOI 10.1016/0306-4573(89)90019-8 NIE JY, 1992, P 15 ANN ACM SIGIR C, P140, DOI 10.1145/133160.133188 Ohlbach H. J., 1991, Journal of Logic and Computation, V1, DOI 10.1093/logcom/1.5.691 Reiter R., 1984, CONCEPTUAL MODELLING, P191 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROLLEKE T, 1995, P SIGIR 95, P374 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sebastiani F., 1994, P 17 ANN INT ACM SIG, P122 SEBASTIANI F, 1988, INFORMATION PROCESSI, V34, P1 Sebastiani F, 1998, COMPUT ARTIF INTELL, V17, P35 SMEATON AF, 1997, INFORMATION RETRIEVA, P3 Stalnaker Robert C., 1970, THEORIA-SPAIN, V36, P23 van Benthem J, 1976, THESIS U AMSTERDAM VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 WILLETT P, 1988, DOCUMENT RETRIEVAL S WONG SKM, 1995, ACM T INFORM SYST, V13, P38, DOI 10.1145/195705.195713 NR 41 TC 2 Z9 2 PU TAYLOR & FRANCIS LTD PI LONDON PA ONE GUNPOWDER SQUARE, LONDON EC4A 3DE, ENGLAND SN 0196-9722 J9 CYBERNET SYST JI Cybern. Syst. PD JUL-AUG PY 1999 VL 30 IS 5 BP 411 EP 428 DI 10.1080/019697299125145 PG 18 WC Computer Science, Cybernetics SC Computer Science GA 212TW UT WOS:000081233600003 ER PT J AU Lee, JH Cho, HY Park, HR AF Lee, JH Cho, HY Park, HR TI n-Gram-based indexing for Korean text retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article DE information retrieval; indexing method; Korean text; n-Gram AB Two groups of indexing methods and morpheme-based indexing have been investigated in the literature of Korean text retrieval. The word-based indexing eliminates the suffix of a word, and generates its remaining stem as an index term. The index term is often a compound noun, which results in the serious decrease of retrieval effectiveness. The morpheme-based indexing overcomes the problem of compound nouns by decomposing a compound noun into simple nouns. It, however, requires a large dictionary and complex linguistic knowledge. In this paper we propose a new indexing method based on n-gram-based indexing is considerably faster than the morpheme-based indexing, and also provides better retrieval effectiveness. (C) 1999 Elsevier Science Ltd. All rights reserved. C1 Soongsil Univ, Sch Comp, Dongjak Gu, Seoul 156743, South Korea. Korea Adv Inst Sci & Technol, Korea Res & Dev Informat Ctr, Taejon 305600, South Korea. RP Lee, JH (reprint author), Soongsil Univ, Sch Comp, Dongjak Gu, 1-1 Sangdo Dong, Seoul 156743, South Korea. CR CALLAN JP, 1992, 3RD P INT C DAT EXP, P78 Cavnar W. B., 1994, Second Text REtrieval Conference (TREC-2) (NIST-SP 500-215) CHUNG JS, 1992, THESIS KOREA ADV I S DAMASHEK M, 1995, SCIENCE, V267, P843, DOI 10.1126/science.267.5199.843 HAN SC, 1990, THESIS KOREA ADV I S HARMAN D, 1995, 3 TEXT RETR C TREC 3, P1 KIM SH, 1994, J KOREAN SOC INFORMA, V11, P81 KIM YH, 1982, THESIS KOREA ADV I S LEE HA, 1995, SPRING C KOR INF SCI, P933 LEE JH, 1995, 18 ANN INT ACM SIGIR, P180 LEE JH, 1995, J KOREA INFORMATION, V12, P225 LEE JH, 1996, 18 ANN INT ACM SIGIR, P216 PARK HR, 1996, INT C CHIN COMP BEIJ, P40 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 YAE YH, 1992, INFORMATION MANAGEME, V23, P39 NR 17 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD JUL PY 1999 VL 35 IS 4 BP 427 EP 441 DI 10.1016/S0306-4573(98)00050-8 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA 237AD UT WOS:000082630700002 ER PT B AU Budzik, J Hammond, K AF Budzik, J Hammond, K BE Woods, L TI Watson: Anticipating and contextualizing information needs SO ASIS 99: PROCEEDINGS OF THE 62ND ASIS ANNUAL MEETING, VOL 36, 1999: KNOWLEDGE: CREATION ORGANIZATION AND USE SE PROCEEDINGS OF THE ASIS ANNUAL MEETINGS LA English DT Proceedings Paper CT 62nd Annual Meeting of the American-Society-for-Information-Science on Knowledge - Creation, Organization and Use CY OCT 31-NOV 04, 1999 CL WASHINGTON, D.C. SP Amer Soc Informat Sci AB In this paper, we introduce a class of systems called Information Management Assistants (IMAs). IMAs automatically discover related material on behalf of the user by serving as an intermediary between the user and information retrieval systems. IMAs observe users interact with everyday applications and then anticipate their information needs using a model of the task at hand. IMAs then automatically fulfill these needs using the text of the document the user is manipulating and a knowledge of how to form queries to traditional information retrieval systems (e.g., Internet search engines, abstract databases, etc.). IMAs automatically query information systems on behalf of users as well as provide an interface by which the user can pose queries explicitly. Because IMAs are aware of the user's task, they can augment their explicit query with terms representative of the context of this task. In this way, IMAs provide a framework for bringing implicit task context to bear on servicing explicit information requests, significantly reducing ambiguity. IMAs embody a just-in-time information infrastructure in which information is brought to users as they need it, without requiring explicit requests. In this paper, we present our work on an architecture for this class of system, and our progress implementing Watson, a prototype of such a system. Watson observes users in word processing and Web browsing applications and uses a simple model of the user's tasks, knowledge of term importance, and an understanding of query generation to find relevant documents and service explicit queries. We close by discussing our experimental evaluations of the system. C1 Northwestern Univ, Evanston, IL USA. CR BADUE C, 1998, P WEBNET 98 WORLD C BUDZIK J, 1999, P ASIS 1999 ANN C BUDZIK J, 1998, P WEBNET 98 WORLD C CHEN L, 1998, P AGENTS 98 2 INT C DEAN J, 1999, P WWW 8 8 INT WORLD DUMAIS ST, 1988, P CHI 98 C HUM FACT Francis W. N., 1982, FREQUENCY ANAL ENGLI JACZYNSKI M, 1998, P EWCBR 98 EUR WORKS JANSEN B, 1998, P WEBNET 98 WORLD C JOACHIMS T, 1997, P 14 NAT C ART INT LASHKARI Y, 1994, P 11 NAT C ART INT LIEBERMAN H, 1995, P IJCAI 95 INT JOINT MAES P, 1994, P 11 NAT C ART INT MARCUS RS, 1983, J AM SOC INFORM SCI, V34, P381, DOI 10.1002/asi.4630340605 MARCUS RS, 1988, P ASIS ANNU MEET, V25, P115 MARCUS RS, 1991, P ASIS ANNU MEET, V28, P49 SALTON G, 1990, READINGS INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAMK R, 1977, SCRIPTS PLANS GOALS SPERTUS E, 1997, P 6 INT WORLD WID WE SPINK A, 1998, P WEB NET 98 WORLD C NR 21 TC 1 Z9 1 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055 USA BN 1-57387-091-9 J9 P ASIS ANN PY 1999 VL 36 BP 727 EP 740 PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA BP82C UT WOS:000086299400067 ER PT J AU Voorhees, EM AF Voorhees, EM TI Natural language processing and information retrieval SO INFORMATION EXTRACTION: TOWARDS SCALABLE, ADAPTABLE SYSTEMS SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE LA English DT Article AB Information retrieval addresses the problem of finding those documents whose content matches a user's request from among a large collection of documents. Currently, the most successful general purpose retrieval methods are statistical methods that treat text as little more than a bag of words. However, attempts to improve retrieval performance through more sophisticated linguistic processing have been largely unsuccessful. Indeed, unless done carefully, such processing can degrade retrieval effectiveness. Several factors contribute to the difficulty of improving on a good statistical baseline including: the forgiving nature but broad coverage of the typical retrieval task; the lack of good weighting schemes for compound index terms; and the implicit linguistic processing inherent in the statistical methods. Natural language processing techniques may be more important for related tasks such as question answering or document summarization. C1 NIST, Gaithersburg, MD 20899 USA. RP Voorhees, EM (reprint author), NIST, Gaithersburg, MD 20899 USA. RI Voorhees, Ellen/D-1755-2013 CR BUCKLEY C, 1998, P 6 TEXT RETR C TREC, P107 BURGER JD, 1997, NIST SPECIAL PUBLICA, P433 CORMACK GV, IN PRESS INFORMATION DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fellbaum C, 1998, WORDNET ELECT LEXICA FOX EA, THESIS CORNELL U ITH Garofolo J. S., 1998, P 7 TEXT RETR C TREC HULL DA, 1997, NIST SPECIAL PUBLICA, P167 JONES KS, IN PRESS INFORMATION JONES KS, IN PRESS NATURAL LAN JONES KS, 1997, READINGS INFORMATION KANTOR PB, 1997, NIST SPECIAL PUBLICA, P65 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 LEACOCK C, 1996, CORPUS PROCESSING LE, P98 MAH C. P., 1985, P 8 INT ACM C RES DE, P155, DOI 10.1145/253495.253521 MANI I, 1998, 98W0000138 MITRE MTR Mauldin M.L., 1991, P 14 ANN INT ACM SIG, P347, DOI 10.1145/122860.122896 Paik W., 1996, CORPUS PROCESSING LE, P61 PEREZCARBALLO J, IN PRESS INFORMATION RAU LF, 1997, READINGS INFORMATION, P527 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 STRZALKOWSKI T, 1997, NATL I STANDARDS TEC, P291 STRZALKOWSKI T, 1997, NIST SPECIAL PUBLICA, P97 Taghva K., 1994, P 17 ANN INT ACM SIG, P202 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 VOORHEES EM, 1998, WORDNET ELECT LEXICA, P285 ZHAI C, 1997, NIST SPECIAL PUBLICA, P347 NR 28 TC 9 Z9 9 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 J9 LECT NOTES ARTIF INT PY 1999 VL 1714 BP 32 EP 48 PG 17 WC Computer Science, Artificial Intelligence SC Computer Science GA BQ66F UT WOS:000089103600003 ER PT S AU Shinagawa, N Kitagawa, H AF Shinagawa, N Kitagawa, H BE Hui, LCK Lee, DL TI Dynamic generation and browsing of virtual WWW space based on user profiles SO INTERNET APPLICATIONS SE LECTURE NOTES IN COMPUTER SCIENCE LA English DT Article; Proceedings Paper CT 5th International Computer Science Conference (ICSC 99) CY DEC 13-15, 1999 CL HONG KONG, PEOPLES R CHINA SP IEEE, HK Sect, Comp Chapter AB Due to the recent and rapid advance of the WWW technology, a huge amount of information is available via the Internet. However, it is difficult for users to find and utilize relevant information. Generally, querying and browsing are both indispensable to access to the required information. In this paper, Ne propose a scheme to support the browsing phase of the information search based on specification of the user's interest. In our scheme, each user is given virtual WWW pages reflecting his/her interest. Each virtual WWW page is dynamically generated based on the logical document structure extracted from the original page. Our approach makes it possible to browse the WWW space from his/her viewpoint and to identify required information more easily. We also show how such a scheme can be realized in a non-intrusive way in the current WWW browsing environment. C1 Univ Tsukuba, Doctoral Program Engn, Tsukuba, Ibaraki 3058573, Japan. Univ Tsukuba, Inst Informat Sci & Elect, Tsukuba, Ibaraki 3058573, Japan. RP Shinagawa, N (reprint author), Univ Tsukuba, Doctoral Program Engn, Tennohdai 1-1-1, Tsukuba, Ibaraki 3058573, Japan. RI Kitagawa, Hiroyuki/F-1438-2013 CR ARMSTRONG R, 1995, AAAI SPRING S INF GA Ashish N., 1997, SIGMOD Record, V26 ATZENI P, 1998, LECT NOTES COMPUTER, V1590 Harman D., 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167 KASZKIEL M, 1997, P 20 ANN INT ACM SIG, P21 MIIKE S, 1994, P 17 ANN INT ACM SIG, P152 PARIS CD, INFORMATION PROCESSI, V26, P171 RAGGETT D, 1998, HTML 4 0 REFERENCE S Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Tombros A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290947 Singhal A., 1996, P 19 ANN INT ACM SIG, P21, DOI 10.1145/243199.243206 Wilkinson R, 1994, P 17 ANN INT ACM SIG, P311 ZOBEL J, 1995, INFORM PROCESS MANAG, V31, P361, DOI 10.1016/0306-4573(94)00052-5 NR 15 TC 0 Z9 0 PU SPRINGER-VERLAG BERLIN PI BERLIN PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY SN 0302-9743 BN 3-540-66903-5 J9 LECT NOTES COMPUT SC PY 1999 VL 1749 BP 93 EP 108 PG 16 WC Computer Science, Theory & Methods; Telecommunications SC Computer Science; Telecommunications GA BQ97D UT WOS:000165175400010 ER PT S AU Lagus, K Kaski, S AF Lagus, K Kaski, S GP IEE IEE TI Keyword selection method for characterizing text document maps SO NINTH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (ICANN99), VOLS 1 AND 2 SE IEE CONFERENCE PUBLICATIONS LA English DT Proceedings Paper CT 9th International Conference on Artificial Neural Networks (ICANN99) CY SEP 07-10, 1999 CL EDINBURGH, SCOTLAND SP IEE, Informat Div, Asian Pacific Neural Network Assembly, British Neurosci Assoc, European Neural Network Soc, Inst Math & Applicat, Int Neural Networks Soc, Inst Phys, Neural Comp Applicat Forum HO UNIV EDINBURGH AB Characterization of subsets of data is a recurring problem in data mining. We propose a keyword selection method that can be used for obtaining characterizations of clusters of data whenever textual descriptions can be associated with the data. Several methods that cluster data sets or form projections of data provide an order or distance measure of the clusters. If such an ordering of the clusters exists or can be deduced, the method utilizes the order to improve the characterizations. The proposed method may be applied, for example, to characterizing graphical displays of collections of data ordered e.g. with the SOM algorithm. The method is validated using a collection of 10,000 scientific abstracts from the INSPEC database organized on a WEBSOM document map. C1 Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 HUT, Finland. RP Lagus, K (reprint author), Helsinki Univ Technol, Neural Networks Res Ctr, POB 2200, FIN-02015 HUT, Finland. RI Kaski, Samuel/B-6684-2008 OI Kaski, Samuel/0000-0003-1925-9154 CR Church K., 1995, P 3 WORKSH VER LARG, P121 Honkela T., 1996, A32 HELS U TECHN LAB Kaski S, 1998, NEUROCOMPUTING, V21, P101, DOI 10.1016/S0925-2312(98)00039-3 KOHONEN T, 1982, BIOL CYBERN, V43, P59, DOI 10.1007/BF00337288 KOHONEN T, 1999, IN PRESS KOHONEN MAP, P171 Kohonen T., 1995, SELF ORGANIZING MAPS LAGUS K, 1999, IN PRESS ARTIFICIAL SALTON G, 1987, 87881 CORN U DEP COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 9 TC 1 Z9 1 PU INST ELECTRICAL ENGINEERS INSPEC INC PI EDISON PA 379 THORNALL ST, EDISON, NJ 08837 USA SN 0537-9989 BN 0-85296-721-7 J9 IEE CONF PUBL PY 1999 IS 470 BP 371 EP 376 PG 6 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods SC Computer Science GA BP25T UT WOS:000084507900063 ER PT B AU McKechnie, J AF McKechnie, J BE Kumar, B Topping, BHV TI Towards intelligent information retrieval for the construction industry SO NOVEL DESIGN AND INFORMATION TECHNOLOGY APPLICATIONS FOR CIVIL AND STRUCTURAL ENGINEERING LA English DT Proceedings Paper CT 7th International Conference on Civil and Structural Engineering/5th International Conference on the Applications of Artificial Intelligence to Civil and Structural Engineering CY SEP 13-15, 1999 CL OXFORD, ENGLAND AB In this paper we review current approaches to the improvement of information retrieval systems by means of statistical, artificial intelligence (AI) and information visualisation techniques. We bias our survey in favour of approaches that scale to the needs of practical information retrieval (IR) systems and in particular focus attention on the requirements and potential for IR systems in the construction industry. Most discussions of IR are domain independent, but we intentionally target construction professionals in the belief that by considering a specific class of users, we can provide tools that better fit the needs, habits and working environments of those users. C1 Univ Newcastle Upon Tyne, Construct Informat Res Ctr, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. RP McKechnie, J (reprint author), Univ Newcastle Upon Tyne, Construct Informat Res Ctr, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England. CR CARD SK, 1999, READINGS INFORMATION COOPER WS, 1988, INFORM PROCESS MANAG, V24, P243, DOI 10.1016/0306-4573(88)90091-X Craven M., 1998, P 15 NAT C ART INT FALOUTSOS C, 1995, P 1995 ACM SIGMOD IN FORSYTH R, 1986, MACHINE LEARNING APP, P144 Hayes P. J., 1988, P 2 C APPL NAT LANG, P9, DOI 10.3115/974235.974238 HEARST MA, 1993, P SIGIR 93 16 ANN IN INGRAM RJ, 1995, P IEEE C VIS 1995 IE JONES KS, 1997, READINGS INFORMATION, P2 KORFHAGE RR, 1997, INFORMATION STORAGE, P224 LARKEY L, 1996, P SIGIR 96 19 ANN IN LOCKLEY SR, 1988, P ECPPM 98 2 EUR C P Minsky M. L., 1969, PERCEPTRONS Mitchell M., 1996, INTRO GENETIC ALGORI MOFFAT A, 1993, NIST SPECIAL PUBLICA NOREAULT T, 1981, INFORMATION RETRIEVA QUINLAN JR, 1990, MACH LEARN, V5, P239, DOI 10.1007/BF00117105 Rau L. F., 1988, RIAO 88 Program. Conference with Presentation of Prototypes and Operational Demonstrations: User-Oriented Content-Based Text and Image Handling SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHANK RC, 1981, INFORMATION RETRIEVA STRZALKOWSKI T, 1997, NIST SPECIAL PUBLICA SULLIVAN D, 1999, DIRECT HIT DEBUTS PE WHITE HD, 1997, ANN REV INFORMATION, V32 WISE J, 1999, IN PRESS J AM SOC IN YANG J, 1992, NIST SPECIAL PUBLICA YANG Y, 1999, P SIGIR 99 22 ANN IN NR 27 TC 0 Z9 0 PU CIVIL COMP PRESS PI EDINBURGH PA 10 SAXE-COBURG PLACE, EDINBURGH EH3 5BR, MIDLOTHIAN, SCOTLAND BN 0-948749-63-6 PY 1999 BP 83 EP 91 PG 9 WC Computer Science, Interdisciplinary Applications; Engineering, Civil SC Computer Science; Engineering GA BP26T UT WOS:000084527600012 ER PT J AU Lewis, PH Wilkins, RJ Griffiths, SR Davis, HC Hall, W AF Lewis, PH Wilkins, RJ Griffiths, SR Davis, HC Hall, W TI Content based navigation in an open hypermedia environment SO IMAGE AND VISION COMPUTING LA English DT Article DE hypermedia architecture; microcosm; content based navigation; content based retrieval ID TEXTURE CLASSIFICATION; MOMENTS AB Microcosm is an open hypermedia architecture for developing large scale multimedia systems. Among many other features, Microcosm introduces two link types, generic and dynamic links, which substantially reduce the authoring effort required for hypermedia system development. Generic and dynamic links were initially designed to provide content based navigation and content based retrieval for text documents respectively. Recently the Microcosm architecture has been extended to facilitate generic and dynamic links from non-text media. Generic links from non-text allow the user to author a generic link from an image object in one image and follow the link from a similar image object in another image. Similar facilities are described for authoring links from video and sound files. (C) 1998 Elsevier Science B.V. All rights reserved. C1 Univ Southampton, Dept Elect & Comp Sci, Multimedia Res Grp, Southampton SO9 5NH, Hants, England. RP Lewis, PH (reprint author), Univ Southampton, Dept Elect & Comp Sci, Multimedia Res Grp, Southampton SO9 5NH, Hants, England. EM phl@ecs.soton.ac.uk CR BACH J, 1996, STORAGE RETRIEVAL ST, V4, P76 CASH GL, 1987, COMPUT VISION GRAPH, V39, P291, DOI 10.1016/S0734-189X(87)80183-4 CHEN YQ, 1995, PATTERN RECOGN, V28, P537, DOI 10.1016/0031-3203(94)00116-4 DAVIS H, 1992, 4 ACM C HYP ECHT 92, P181 DURHAM P, 1995, COMPUTER APPL QUANTI, V28, P235 Faloutsos C., 1994, Journal of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, V3, DOI 10.1007/BF00962238 GEVERS T, 1992, 11TH IAPR INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, PROCEEDINGS, VOL II, P697 GUPTA A, 1991, PROC INT CONF VERY L, P69 Hall W., 1996, RETHINKING HYPERMEDI Hill G., 1993, Hypermedia, V5 HIRATA K, 1994, HYPERTEXT 93, P159 HIRATA K, 1996, P ACM HYP 96 C WASH, P11, DOI 10.1145/234828.234830 JAIN R, 1995, STORAGE RETRIEVAL IM, V3, P58 KUAN J, 1997, P 1 INT C INF COMM S, V2, P924 LEWIS P, 1997, STORAGE RETRIEVAL IM, V3022, P238 LEWIS PH, 1996, P 7 ACM C HYP, P215, DOI 10.1145/234828.234849 LI Z, 1992, P 14 BRIT COMP SOC R MAO JC, 1992, PATTERN RECOGN, V25, P173, DOI 10.1016/0031-3203(92)90099-5 MERTZIOS BG, 1993, PATTERN RECOGN LETT, V14, P517, DOI 10.1016/0167-8655(93)90032-9 OAKLEY J, 1994, 5 BRIT MACH VIS C YO, P629 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Smoliar S. W., 1994, IEEE Multimedia, V1, DOI 10.1109/93.311653 Taylor J. M., 1989, Proceedings of the S.I.D., V30 TREAT H, 1995, STORAGE RETRIEVAL IM, V3, P204 1995, IEEE COMPUTER, V28 NR 25 TC 2 Z9 2 PU ELSEVIER SCIENCE BV PI AMSTERDAM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS SN 0262-8856 J9 IMAGE VISION COMPUT JI Image Vis. Comput. PD AUG 24 PY 1998 VL 16 IS 12-13 SI SI BP 921 EP 929 DI 10.1016/S0262-8856(98)00057-2 PG 9 WC Computer Science, Artificial Intelligence; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; Optics SC Computer Science; Engineering; Optics GA 122KQ UT WOS:000076071100008 ER PT J AU Ide, N Veronis, J AF Ide, N Veronis, J TI Introduction to the special issue on word sense disambiguation: The state of the art SO COMPUTATIONAL LINGUISTICS LA English DT Editorial Material ID SPREADING ACTIVATION THEORY; SEMANTIC SIMILARITY; CONTEXT; MODEL; FACILITATION; ASSOCIATION; RETRIEVAL C1 Vassar Coll, Dept Comp Sci, Poughkeepsie, NY 12604 USA. Univ Provence, Lab Parole & Langage, CNRS, ESA 6057, F-13621 Aix En Provence 1, France. RP Ide, N (reprint author), Vassar Coll, Dept Comp Sci, Poughkeepsie, NY 12604 USA. EM ide@cs.vassar.edu; Jean.Veronis@lpl.univ-aix.fr CR AARTS J, 1990, COMPUTERS LIT LINGUI, P13 ADRIAENS G, 1986, LEUVENSCHE BIJDRAGEN, V75, P73 ADRIAENS G, 1988, LEXICAL AMBIGUITY RE, P13 ADRIAENS G, 1989, P INT WORKSH PARS TE, P309 ADRIAENS G, 1987, P 7 EUR C ART INT EC, P222 AHLSWEDE TE, 1993, P 5 MIDW ART INT COG, P21 AHLSWEDE TE, 1992, P 4 MIDW ART INT COG, P112 AHLSWEDE TE, 1993, P 9 ANN C NEW OXF EN, P75 AHLSWEDE TE, 1995, P 6 MIDW ART INT COG, P73 Alshawi H., 1994, Computational Linguistics, V20 Amsler R. A, 1980, THESIS U TEXAS AUSTI AMSLER RA, 1979, DEV COMPUTATIONAL ME ANDERSON JR, 1983, J VERB LEARN VERB BE, V22, P261, DOI 10.1016/S0022-5371(83)90201-3 Anderson J. R., 1976, LANGUAGE MEMORY THOU ANTHONY E, 1954, AM SPEECH, V29, P175, DOI 10.2307/454236 *APLAC, 1966, LANG MACH COMP TRANS Arnold D., 1993, Machine Translation, V8, DOI 10.1007/BF00981238 *ARPA, 1993, P 5 MESS UND C BALT ASPREJAN JD, 1974, LINGUISTICS, V142, P5 Atkins Beryl T, 1987, P 3 ANN C UW CTR NEW, P17 ATKINS BTS, 1988, P 4 ANN C UW CTR NEW Ayto J. R., 1983, LEXICOGRAPHY PRINCIP, p[89, 95] Bahl L. R., 1976, P IEEE INT S INF THE, P88 Bar-Hillel Y., 1960, ADV COMPUTERS Barwise J., 1983, SITUATIONS ATTITUDES BASILI R, 1997, P ACL SIGLEX WORKSH, P66 BELSKAJA IK, 1957, RESEARCH, V10 Berry-Rogghe G.L.M., 1973, COMPUTER LIT STUDIES, P103 BIMBOT F, 1994, P ESCA WORKSH AUT SP, P75 BLACK E, 1988, IBM J RES DEV, V32, P185 Bloomfield Leonard, 1933, LANGUAGE Boas F., 1940, RACE LANGUAGE CULTUR BOGURAEV B, 1979, THESIS U CAMBRIDGE Bookman LA, 1987, P 10 INT JOINT C ART, P611 BRADENHARDER L, 1993, NATURAL LANGUAGE PRO, P247 BRISCOE EJ, 1991, NATURAL LANGUAGE SPE, P39 Brown Gillian, 1983, DISCOURSE ANAL Brown PF, 1991, P 29 ANN M ASS COMP, P264, DOI 10.3115/981344.981378 Brown P. F., 1992, Computational Linguistics, V18 Bruce R., 1994, P 32 ANN M ASS COMP, P139, DOI 10.3115/981732.981752 BRYAN RM, 1973, AUTOMATED LANGUAGE A, P45 BRYAN RM, 1974, AUTOMATED LANGUAGE A, P44 BRYD RJ, 1987, COMPUTATIONAL LINGUI, V13, P219 BUITELAAR P, 1997, P ACL SIGLEX WORKSH, P25 Calzolari N., 1984, P 10 INT C COMP LING, P170 Chen Stanley F., 1996, P 34 ANN M ASS COMP, P310, DOI 10.3115/981863.981904 Chomsky N., 1957, SYNTACTIC STRUCTURES CHOUEKA Y, 1985, COMPUT HUMANITIES, V19, P147, DOI 10.1007/BF02259530 CHOUEKA Y, 1979, P INT C LIT LING COM, P149 CHOUEKA Y, 1976, P INT C COMP LING CO Church K. W., 1993, COMPUTATIONAL LINGUI, V19, P1 Church K. W., 1991, Computer Speech and Language, V5, DOI 10.1016/0885-2308(91)90016-J COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 CONNINE C, 1990, COGNITIVE MODELS SPE COTTRELL GW, 1983, COGNITION BRAIN THEO, V6, P89 COTTRELL GW, 1985, THESIS U ROCHESTER COWIE J, 1992, P 14 INT C COMP LING, V1, P359, DOI 10.3115/992066.992125 Cruse D. A., 1986, LEXICAL SEMANTICS DAGAN I, 1993, P 31 ANN M COL OH JU Dagan I., 1994, P 32 ANN M ASS COMP, P272, DOI 10.3115/981732.981770 Dagan I., 1994, Computational Linguistics, V20 Dahlgren K, 1988, NAIVE SEMANTICS NATU DEGROOT AMB, 1983, J VERB LEARN VERB BE, V22, P417, DOI 10.1016/S0022-5371(83)90273-6 DEBILI F, 1977, THESIS U PARIS 7 Dolan W. B., 1994, P 15 INT C COMP LING, P712 Dostert L., 1955, MACHINE TRANSLATION, P124 EARL LL, 1973, INFORM STORAGE RET, V9, P639, DOI 10.1016/0020-0271(73)90008-9 EATON HS, 1940, SEMANTIC FREQUENCY L Engelson Sean P., 1996, P 34 ANN M ASS COMP, P319, DOI 10.3115/981863.981905 ESTOUP JB, 1902, GAMMES STENOGRAPHIQU FELDMAN JA, 1982, COGNITIVE SCI, V6, P205, DOI 10.1207/s15516709cog0603_1 FELLBAUM C, IN PRESS PREDICATIVE FELLBAUM C, IN PRESS WORDNET ELE FILLMORE CJ, 1991, 29 ANN M ASS COMP LI Firth John Rupert, 1951, PAPERS LINGUISTICS 1, P190 FISCHLER I, 1977, MEM COGNITION, V5, P335, DOI 10.3758/BF03197580 FONTENELLE T, 1990, P 4 INT C LEX EURALE, P89 Fries C., 1952, STRUCTURE ENGLISH FRIES CC, 1940, ENGLISH WORD LISTS S Gale W.A., 1992, P 4 DARPA SPEECH NAT, P233, DOI 10.3115/1075527.1075579 GALE WA, 1992, COMPUT HUMANITIES, V26, P415, DOI 10.1007/BF00136984 Gale William, 1992, P 30 ANN M ASS COMP, P249, DOI 10.3115/981967.981999 Gale William, 1992, P AAAI FALL S PROB A, P54 Gale William, 1992, P 4 INT C THEOR METH, P101 GENTILHOMME Y, 1960, TRANDUCTION AUTOMATI, V1, P9 GOOD IJ, 1953, BIOMETRIKA, V40, P237, DOI 10.2307/2333344 Gougenheim G., 1961, TRADUCTION AUTOMATIQ, V2, P16 Gougenheim Georges, 1956, ELABORATION FRANCAIS GOULD R, 1957, MECH TRANSLATION, V4, P14 Granger R. H., 1977, P 5 INT JOINT C ART, P172 Grishman R., 1993, HUMAN LANGUAGE TECHN, P254 Grishman Ralph, 1994, P 15 INT C COMP LING, P268 Guthrie J, 1991, P 29 ANN M ASS COMP, P146, DOI 10.3115/981344.981363 HAAS W, 1966, MEMORY JR FIRTH, P116 HALLIDAY MAK, 1961, WORD, V17, P241 Halliday Michael A. K., 1966, MEMORY JR FIRTH, P148 HARMAN D, 1995, INFORMATION PROCESSI, V31 HARMAN DK, 1993, SPECIAL PUBLICATION HARPER KE, 1957, MECH TRANSLATION, V4, P68 HARPER KE, 1957, MECH TRANSLATION, V4, P70 Harris Z., 1951, METHODS STRUCTURAL L HARRIS ZS, 1954, LINGUISTICS TODAY, V10, P146 HAYES PJ, 1976, 23 U GEN I ET SEM CO HAYES PJ, 1978, 29 U ROCH DEP COMP S HAYES PJ, 1977, P 5 INT JOINT C ART, P99 HAYES PJ, 1977, THESIS ECOLE POLYTEC Hearst M. A, 1991, P 7 ANN C UW CTR NEW, P1 Hearst M. A., 1994, P 32 ANN M ASS COMP, P9, DOI 10.3115/981732.981734 Hebb DO, 1949, ORG BEHAV NEUROPSYCH Heidorn G., 1985, P 23 ANN M ASS COMP, P299, DOI 10.3115/981210.981247 Hindle D., 1993, Computational Linguistics, V19 Hirschman Lynette, 1996, SURVEY STATE ART HUM Hirst G, 1987, STUDIES NATURAL LANG HJEMSLEV L, 1953, PROLEGOMENA THEORY L HOBBS JR, 1987, P 3 WORKSH THEOR ISS, P20, DOI 10.3115/980304.980308 IDE N, 1993, 3 INT EAMT WORKSH MA IDE N, 1992, COMPUT HUMANITIES, V26, P327, DOI 10.1007/BF00136978 IDE N, 1990, P 9 EUR C ART INT EC, P366 IDE N, 1993, RES HUMANITIES COMPU, V2, P145 IKER HP, 1974, COMPUT HUMANITIES, V8, P313, DOI 10.1007/BF02400292 IKER HP, 1975, COMPUT HUMANITIES, V9, P3, DOI 10.1007/BF02404315 Imbs Paul, 1971, TRESOR LANGUE FRANCA JANSSEN S, 1992, NEW DIRECTIONS ENGLI JELINEK F, 1976, P IEEE, V64, P532, DOI 10.1109/PROC.1976.10159 Jensen K., 1987, COMPUTATIONAL LINGUI, V13, P251 Johansson S., 1980, ALLC Journal, V1 JORGENSEN JC, 1990, J PSYCHOLINGUIST RES, V19, P167, DOI 10.1007/BF01077415 Kaeding F.W., 1897, HAUFIGKEITSWORTERBUC Kaplan A., 1955, MECH TRANSLATION, V2, P39 Kawamoto A., 1988, LEXICAL AMBIGUITY RE, P195 Kelly E. F., 1975, COMPUTER RECOGNITION KILGARRIFF A, 1997, SALT CLUB WORKSH EV Kilgarriff A, 1994, P 6 INT C LEX EURALE, P101 KILGARRIFF A, IN PRESS COMPUTERS H KILGARRIFF A, 1992, THESIS U SUSSEX UK KILGARRIFF A, 1993, COMPUT HUMANITIES, V26, P365 KINTSCH W, 1985, J MEM LANG, V24, P336, DOI 10.1016/0749-596X(85)90032-4 KLAVANS J, 1990, P 6 C UW CTR NEW OED, P110 KOUTSOUDAS AK, 1956, MECH TRANSLATION, V2, P46 KROVETZ R, 1989, SIGIR FORUM, V23, P127, DOI 10.1145/75334.75349 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 Kucera H., 1967, COMPUTATIONAL ANAL P LEACOCK C, 1993, P ARPA HUM LANG TECH LEACOCK C, 1996, CORPUS PROCESSING LE, P97 Leech G., 1991, ENGLISH CORPUS LINGU, P8 LEHMAN JF, 1994, PROCEEDINGS OF THE TWELFTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2, P734 Lenat D., 1990, BUILDING LARGE KNOWL Lesk M., 1986, P 5 ANN INT C SYST D, P24, DOI 10.1145/318723.318728 Liddy E. D., 1993, Probabilistic Approaches to Natural Language. Papers from the 1992 AAAI Fall Symposium LITOWSKI KC, 1997, P ACL SIGLEX WORKSH, P12 LORGE I, 1949, SEMANTIC CONTENT 570 LUK AK, 1995, P 33 ANN M ASS COMP, P181, DOI 10.3115/981658.981683 LUPKER SJ, 1984, J VERB LEARN VERB BE, V23, P709, DOI 10.1016/S0022-5371(84)90434-1 Lyons J., 1966, MEMORY JR FIRTH, P288 Lyons John, 1977, SEMANTICS MACLEOD C, IN PRESS COMPUTERS H MADHU S, 1965, MECH TRANSL, V8, P9 MAHESH K, 1997, P 7 INT C THEOR METH, P1 MAHESH K, 1997, P 7 INT C THEOR METH, P151 MALAKHOVSKI LV, 1987, STUDIES LEXICOGRAPHY, P36 Markowitz J., 1986, P 24 ANN M ASS COMP, P112, DOI 10.3115/981131.981149 Martinet Andre, 1960, ELEMENTS LINGUISTIQU MASTERMAN M, 1962, 1961 INT C MACH TRAN, P437 MASTERMAN M, 1957, MECH TRANSLATION, V4, P1 MCCLELLAND JL, 1981, PSYCHOL REV, V88, P375, DOI 10.1037//0033-295X.88.5.375 McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570 MCINTOSH A, 1966, PAPERS GEN DESCRIPTI, P183 McRoy S. W., 1992, Computational Linguistics, V18 Meillet Antoine, 1926, LINGUISTIQUE HIST LI, V1 Mercer R., 1985, IBM TECHNICAL DISCLO, V28, P2591 Merialdo B., 1994, Computational Linguistics, V20 MEYER DE, 1971, J EXP PSYCHOL, V90, P227, DOI 10.1037/h0031564 MICHEA R, 1964, RECHERCHE TECHNIQUE, P21 MICHIELS A, 1980, P 8 INT C COMP LING, P374, DOI 10.3115/990174.990243 Michiels A, 1982, THESIS U LIEGE LIEGE Miller G. A., 1990, INT J LEXICOGR, V3, P235, DOI DOI 10.1093/IJL/3.4.235 Miller G. A., 1994, P ARPA HUM LANG TECH, P240, DOI DOI 10.3115/1075812.1075866 Miller G. A., 1993, P ARPA WORKSH HUM LA, P303, DOI 10.3115/1075671.1075742 MILLER GA, 1991, LANG COGNITIVE PROC, V6, P1, DOI 10.1080/01690969108406936 Morris J., 1991, Computational Linguistics, V17 MORRIS J, 1988, 219 CSRI U TOR Nagao M., 1988, P 12 INT C COMP LING, P459 NG HT, 1996, P 34 ANN M U CL SANT Niwa Y., 1994, P 15 INT C COMP LING, P304 OETTINGER AG, 1955, MACHINE TRANSLATION, P47 OLNEY JC, 1968, L13579 SYST DEV CORP OSWALD VA, 1953, MODERN LANGUAGE FORU, V38, P1 OSWALD VA, 1957, RES MACHINE TRANSLAT, P63 OSWALD VA, 1952, 1 MIT C MECH TRANSL PALMER H, 1933, 2 INT RES ENGL TEACH PANOV D, 1960, IMPACT, V10, P17 PARKERRHODES AF, 1958, MECH TRANSLATION, V5, P67 PATRICK AB, 1985, THESIS U KANSAS LAWR PENDERGRAFT E, 1967, AUTOMATED LANGUAGE P Pereira F., 1993, P 31 ANN M ASS COMP, P183, DOI DOI 10.3115/981574.981598] PIMSLEUR P, 1957, MECH TRANSLATION, V4, P11 Pustejovsky J., 1995, GENERATIVE LEXICON PUSTEJOVSKY J, 1905, CORE LEXICAL ENGINE Quillian M. R., 1962, MECH TRANSLATION, V7, P17 QUILLIAN MR, 1969, COMMUN ACM, V12, P459, DOI 10.1145/363196.363214 QUILLIAN MR, 1961, SEM PROBL NAT LANG C QUILLIAN MR, 1962, SEMANTIC CODING TECH QUILLIAN MR, 1967, BEHAV SCI, V12, P410, DOI 10.1002/bs.3830120511 Quine Willard van Orman, 1960, WORD OBJECT QUIRK R, 1960, T PHILOL SOC, P40 Rayner Keith, 1991, UNDERSTANDING WORD S, V77, P175 Reifler Erwin, 1955, MACHINE TRANSLATION, P136 RESNIK P, 1997, P ACL SIGLEX WORKSH, P91 Resnik P., 1993, THESIS U PENNSYLVANI Resnik P., 1997, P ACL SIGLEX WORKSH, P79 Resnik P., 1995, P 14 INT JOINT C ART, P448 RESNIK P, 1992, P AAAI WORKSH STAT B, P48 RESNIK P, 9342 U PENNS Resnik P., 1995, P 3 WORKSH VER LARG, P54 Resnik Philip, 1993, P ARPA WORKSH HUM LA, P278, DOI 10.3115/1075671.1075733 RICHARDS IA, 1953, STUDIES CHINESE THOU RICHARDSON R, 1994, CA0595 DUBL CIT U SC RICHENS RH, 1958, COMPUT J, V1, P144, DOI 10.1093/comjnl/1.3.144 Richmond Korin, 1997, P 2 C EMP METH NAT L, P47 Roberts Don D., 1973, EXISTENTIAL GRAPHS C ROBINS RH, 1987, STUDIES LEXICOGRAPHY, P52 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386, DOI 10.1037/h0042519 Ross Quillian M., 1968, SEMANTIC INFORMATION, P227 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SANDERSON M, 1994, P 17 ANN INT ACM SIG, P161 Schank R. C., 1977, SCRIPTS PLANS GOALS Schmidt Klaus M., 1991, MASCHINELLE VERARBEI, VIV, P192 SCHMIDT KM, 1988, MITTELHOCHDEUTSCHES, P25 SCHUTZE H, 1995, P SDAIR 95 LAS VEG N Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895 Schutze H., 1992, Proceedings. Supercomputing '92. (Cat. No.92CH3216-9), DOI 10.1109/SUPERC.1992.236684 Schwall U., 1991, P 29 ANN M ASS COMP, P130, DOI 10.3115/981344.981361 SEDELOW SY, 1992, P 14 INT C COMP LING, P1223 SEDELOW SY, 1988, P AM SOC INF SCI ASI, P209 SEDELOW SY, 1969, ANAL COMMUNICATION C, P487 SEDELOW SY, 1986, P U WAT C LEX WAT CA, P29 SEIDENBERG MS, 1982, COGNITIVE PSYCHOL, V14, P489, DOI 10.1016/0010-0285(82)90017-2 SELZ O, 1922, ZUE PSYCHOL PRODUKTI SELZ O, 1913, GESETZE GEORNETEN DE Seneff S., 1992, Computational Linguistics, V18 SIMPSON GB, 1989, LEXICAL AMBIGUITY RE, P271 Sinclair J., 1987, LOOKING ACCOUNT COBU Sinclair J.M., 1966, MEMORY JR FIRTH, P410 Skorochod'ko EF, 1972, INFORMATION PROCESSI, V71, P1179 SLATOR BM, 1987, P 2 ANN ROCK MOUNT C SLATOR BM, 1992, COMPUT MATH APPL, V23, P391, DOI 10.1016/0898-1221(92)90114-W SMALL S, 1980, THESIS U MARYLAND Small S, 1982, STRATEGIES NATURAL L, P89 SMALL SL, 1983, PARSING NATURAL LANG Small Steven L., 1988, LEXICAL AMBIGUITY RE SPARCKJONES K, IN PRESS NATURAL LAN SPARCKJONES K, 1986, SYNONYMY SEMANTIC CL SPRCKJONES K, 1964, THESIS U CAMBRIDGE C Sproat R., 1992, P INT C SPOK LANG PR STEFFENS P, 1995, LECT NOTES ARTIFICIA, V898 STOCK PF, 1983, P EX LEX C, P131 Stone P. J., 1966, GEN INQUIRER COMPUTE STONE PJ, 1969, ANAL COMMUNICATION C, P199 Sussna M, 1993, P 2 INT C INF KNOWL, P67, DOI 10.1145/170088.170106 SUTCLIFFE RFE, 1995, J QUANTITATIVE LINGU, V2, P43, DOI 10.1080/09296179508590034 SUTCLIFFE RFE, 1996, P WORKSH MULT SOFTW SUTCLIFFE RFE, 1996, AISB 2 TUTORIAL WORK, P14 TABOSSI P, 1989, RESOLVING SEMANTIC A, P25 Tabossi P., 1991, UNDERSTANDING WORD S, P1 TENHACKEN P, 1990, P 12 INT C COMP LING, V2, P162, DOI 10.3115/997939.997967 Thorndike E., 1944, TEACHERS WORD BOOK 3 Thorndike E., 1921, TEACHERS WORD BOOK THORNDIKE EL, 1938, SEMANTIC COUNTS ENGL THORNDIKE EL, 1948, J GEN PSYCHOL, V66, P319 Tishby N., 1992, AAAI FALL S PROB APP, P108 URDANG L, 1984, DATAMATION, V30, P185 VANBUREN P, CAHLEX, V12, P71 VANBUREN P, 1967, CAHLEX, V11, P89 VANDEREIJK P, 1994, 2 ANN WORKSH VER LAR, P143 VERONIS J, 1995, NATURAL LANGUAGE PRO, P251 Veronis J, 1990, P 13 INT C COMP LING, V2, P389, DOI 10.3115/997939.998006 VERONIS J, 1991, P 5 C EUR CHAPT ASS, P227, DOI 10.3115/977180.977220 Veronis Jean, 1990, P 6 ANN C UW CTR NEW, P52 VIEGAS E, IN PRESS TEXT SPEECH Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 VOORHEES EM, 1995, COMPUTATIONAL LEARNI VOSSEN P, IN PRESS COMPUTERS H WAIBEL A, 1990, READINS SPEECH RECOG WALTZ DL, 1985, COGNITIVE SCI, V9, P51, DOI 10.1207/s15516709cog0901_4 Weaver W, 1955, MACHINE TRANSLATION, P15 WEIBE J, 1997, P ACL SIGLEX WORKSH, P8 WEINREICH U, 1980, SEMANTICS WEISS SF, 1973, INFORM STORAGE RET, V9, P33, DOI 10.1016/0020-0271(73)90005-3 WHITTEMORE G, 1990, P 28 ANN M ASS COMP, P23, DOI 10.3115/981823.981827 WILKS Y, 1996, CS9605 U SHEFF SHEFF WILKS Y, 1975, ARTIF INTELL, V6, P53, DOI 10.1016/0004-3702(75)90016-8 Wilks Y., 1973, COMPUTER MODELS THOU, P114 Wilks Y., 1996, ELECT WORDS DICT COM WILKS Y, 1975, COMMUN ACM, V18, P264, DOI 10.1145/360762.360770 WILKS YA, IN PRESS COMPUTERS H WILKS YA, 1990, MCCS90194 NEW MEX ST WILKS YA, 1975, P INT WORKSH THEOR I, P42 WILKS YA, 1968, MECH TRANSLATION, V11, P59 WILKS YA, 1990, SEMANTICS LEXICON WILKS YA, 1969, NEW SOC, V361, P315 Wilks Yorick, 1975, FORMAL SEMANTICS NAT, P329, DOI DOI 10.1017/CBO9780511897696.022 Wittgenstein Ludwig, 1953, PHILOS INVESTIGATION Yarowsky D., 1992, P 14 INT C COMP LING, P454 Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 YAROWSKY D, 1994, P 2 ANN WORKSH VER L, P19 Yarowsky D., 1993, P ARPA HUM LANG TECH, P266, DOI 10.3115/1075671.1075731 Yarowsky D., 1994, P 32 ANN M ASS COMP, P88, DOI 10.3115/981732.981745 Yarowsky D., 1997, PROGR SPEECH SYNTHES, P157 Yngve Victor H., 1955, MACHINE TRANSLATION, P208 ZERNIK U, 1991, P INT TEXT IM HANDL, P567 ZERNIK U, 1990, TEXT BASED INTELLIGE Zipf G. K., 1935, PSYCHOBIOLOGY LANGUA ZIPF G K, 1945, J Gen Psychol, V33, P251 NR 315 TC 244 Z9 252 PU MIT PRESS PI CAMBRIDGE PA 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA SN 0891-2017 J9 COMPUT LINGUIST JI Comput. Linguist. PD MAR PY 1998 VL 24 IS 1 BP 1 EP 40 PG 40 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA ZF043 UT WOS:000072857000001 ER PT J AU Towell, G Voorhees, EM AF Towell, G Voorhees, EM TI Disambiguating highly ambiguous words SO COMPUTATIONAL LINGUISTICS LA English DT Article ID INFORMATION-RETRIEVAL AB A word sense disambiguator that is able to distinguish among the many senses of common words that are found in general-purpose, broad-coverage lexicons would be useful. For example, experiments have shown that, given accurate sense disambiguation. the lexical relations encoded in lexicons such as WordNet can be exploited to improve the effectiveness of information retrieval systems. This paper describes a classifier whose accuracy may be sufficient for such a purpose, The classifier combines the output of a neural network that learns topical context with the output of a network that learns local context to distinguish among the senses of highly ambiguous words. The accuracy of the classifier is tested on three words, the noun line, the verb serve, and the adjective hard; the classifier has an average accuracy of 87%, 90%, and 81%, respectively, when forced to choose a sense for all test cases. When the classifier is not forced to choose a sense and is trained on a subset of the available senses, it rejects test cases containing unknown senses as well as rest cases it would misclassify if forced to select a sense. Finally when there are few labeled training examples available, we describe an extension of our training method that uses information extracted from unlabeled examples to improve classification accuracy. C1 Siemens Corp Res, Princeton, NJ 08540 USA. RP Towell, G (reprint author), Siemens Corp Res, 755 Coll Rd E, Princeton, NJ 08540 USA. CR Atlas L, 1990, ADV NEURAL INFORMATI, V2, P622 BLACK E, 1988, IBM J RES DEV, V32, P185 BRILL E, 1992, P 3 C APPL COMP LING SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H CASTELLI V, 1995, 86 STANF U DEP STAT CHOUEKA Y, 1985, COMPUT HUMANITIES, V19, P147, DOI 10.1007/BF02259530 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 GALE W, 1992, COMPUTERS HUMANITIES, V29 HARMAN DK, 1993, INFORM PROCESS MANAG, V29, P411, DOI 10.1016/0306-4573(93)90037-E Hearst M. A, 1991, P 7 ANN C UW CTR NEW, P1 Kong E., 1995, P 12 INT C MACH LEAR, P313 KROVETZ R, 1992, ACM T INFORM SYST, V10, P115, DOI 10.1145/146802.146810 LEACOCK C, 1993, UNPUB ON LINE LEARNI LEACOCK C, 1996, CORPUS PROCESSING LE, P97 Miller G., 1990, INT J LEXICOGRAPHY, V3 MILLER GA, 1991, LANGUAGE COGNITIVE P, V6 PARAMANTO B, 1996, ADV NEURAL INFORMATI, V8 QUINLAN JR, 1992, C4 5 Rumelhart D.E., 1986, PARALLEL DISTRIBUTED, V1, P318 SALTON G, 1971, SMART RETRIEVAL SYST, P143 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Sanderson M, 1994, P 17 ANN INT ACM SIG, P142 Schtitze H., 1995, P 4 ANN S DOC AN INF, P161 SHAVLIK JW, 1991, MACH LEARN, V6, P111, DOI 10.1007/BF00114160 TOWELL G, 1996, ADV NEURAL INFORMATI, V8 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VERONIS J, 1990, P 13 INT C COMP LING, P389 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 Voorhees E. M, 1994, P 17 ANN INT ACM SIG, P61 VOORHEES EM, 1993, NIST SPECIAL PUBLICA, P343 VOORHEES EM, 1994, NIST SPECIAL PUBLICA, P223 WOLPERT DH, 1992, LAUR903460 LOS AL NA YAROWSKY D, 1993, P ARPA WORKSH HUM LA Yarowsky D., 1995, P 33 ANN M ASS COMP, P189, DOI DOI 10.3115/981658.981684 NR 35 TC 22 Z9 24 PU M I T PRESS PI CAMBRIDGE PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA SN 0891-2017 J9 COMPUT LINGUIST JI Comput. Linguist. PD MAR PY 1998 VL 24 IS 1 BP 125 EP 145 PG 21 WC Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics SC Computer Science; Linguistics GA ZF043 UT WOS:000072857000005 ER PT S AU Paprotte, W AF Paprotte, W BE Weigand, E TI Word sense disambiguation - An experimental study for German SO CONTRASTIVE LEXICAL SEMANTICS SE AMSTERDAM STUDIES IN THE THEORY AND HISTORY OF LINGUISTIC SCIENCE : SERIES IV - CURRENT ISSUES IN LINGUISTIC THEORY LA English DT Proceedings Paper CT International Workshop on Contrastive Lexical Semantics CY MAY, 1997 CL MUNSTER, GERMANY HO UNIV MUNSTER C1 Univ Munster, Fachbereich Philol 11, Engl Seminar, Arbeitsbereich Linguist, D-48149 Munster, Germany. CR BOGURAEV B, 1996, CORPUS PROCESSING LE BRUCE R, 1994, 32ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, P139 BUITELAAR P, 1997, LEXICON UNDERSPECIFI CHOUEKA Y, 1985, COMPUT HUMANITIES, V19, P147, DOI 10.1007/BF02259530 Cottrell G. W., 1989, CONNECTIONIST APPROA Dagan I., 1994, Computational Linguistics, V20 DAGAN I, 1991, 29 ANN M ASS COMP LI, P264 BROWN PF, 1991, 29TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS : PROCEEDINGS OF THE CONFERENCE, P264 Duda R.O., 1973, PATTERN CLASSIFICATI Dunning T., 1993, Computational Linguistics, V19 Fellbaum Christiane, 1997, P WORKSH TAGG TEXT L, P34 GALE WA, 1992, COMPUT HUMANITIES, V26, P415, DOI 10.1007/BF00136984 James M., 1985, CLASSIFICATION ALGOR KAROV Y, 1996, P 4 WORKSH VER LARG Krovetz R, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P72 Lemnitzer L, 1997, AKQUISITION KOMPLEXE Lin DK, 1997, 35TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 8TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE, P64 Ng H.T., 1996, 34 ANN M ACL SOM NEW, P40 Pustejovsky J., 1995, GENERATIVE LEXICON Rivest R. L., 1987, Machine Learning, V2, DOI 10.1007/BF00058680 Rumelhart D.E., 1986, PARALLEL DISTRIBUTED, V1, P318 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUTZE H, 1995, 4 ANN S DOC AN INF R, P161 Schutze H., 1992, Proceedings. Supercomputing '92. (Cat. No.92CH3216-9), DOI 10.1109/SUPERC.1992.236684 Small Steven L., 1988, LEXICAL AMBIGUITY RE WILKS Y, 1997, P SIGLEX WORKSH TAGG Yarowsky D., 1995, 33 ANN M ASS COMP LI, P189 YAROWSKY D, 1994, 32 ANN M ASS COMP LI ZERNIK U, 1991, LEXICAL ACQUISITION NR 30 TC 1 Z9 1 PU JOHN BENJAMINS B V PUBL PI AMSTERDAM ME PA PO BOX 36224, 1020 AMSTERDAM ME, NETHERLANDS SN 0304-0763 BN 1-55619-887-6 J9 AMST STUD THEORY HIS PY 1998 VL 171 BP 243 EP 261 PG 19 WC Language & Linguistics SC Linguistics GA BQ90S UT WOS:000090025200013 ER PT J AU Chen, HC Zhang, Y Houston, AL AF Chen, HC Zhang, Y Houston, AL TI Semantic indexing and searching using a Hopfield net SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID DOCUMENT-RETRIEVAL SYSTEM; AUTOMATIC CONSTRUCTION; VOCABULARY PROBLEM; DIGITAL LIBRARY; QUERY EXPANSION; THESAURI; MODEL; COMPUTATION; DATABASES; NETWORKS AB This paper presents a neural network approach to document semantic indexing. A Hopfield net algorithm was used to simulate human associative memory for concept exploration in the domain of computer science and engineering. INSPEC, a collection of more than 320,000 document abstracts from leading journals, was used as the document testbed. Benchmark tests confirmed that three parameters (maximum number of activated nodes, epsilon-maximum allowable error, and maximum number of iterations) were useful in positively influencing network convergence behavior without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms, Preliminary user tests confirmed our expectation that the Hopfield net algorithm is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end-user vocabularies. C1 Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA. RP Houston, AL (reprint author), Univ Arizona, Dept Management Informat Syst, 430 McClelland Hall, Tucson, AZ 85721 USA. EM ahouston@bpaosf.bpa.arizona.edu CR Ahlswede T., 1988, INT J LEXICOGR, V1, P214, DOI 10.1093/ijl/1.3.214 Anderson J.R., 1983, ARCHITECTURE COGNITI Dalton J., 1991, IEEE Potentials, V10, DOI 10.1109/45.84097 BARTELL BT, 1995, J AM SOC INFORM SCI, V46, P254, DOI 10.1002/(SICI)1097-4571(199505)46:4<254::AID-ASI3>3.0.CO;2-S BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 Bechtel William, 1991, CONNECTIONISM MIND I BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Brachman RJ, 1983, IEEE COMPUT, V16, P30 Buckley C., 1995, Text REtrieval Conference (TREC-3) (NIST SP 500-225) CHAMIS AY, 1991, VOCABULARY CONTROL S CHEN HC, 1994, COMPUTER, V27, P58 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P175, DOI 10.1002/(SICI)1097-4571(199504)46:3<175::AID-ASI3>3.0.CO;2-U CHEN H, 1998, IN PRESS J AM SOC IN CHEN H, 1996, BREAKING SEMANTIC BA CHEN HC, 1993, IEEE EXPERT, V8, P25, DOI 10.1109/64.207426 CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830 Chen HC, 1996, IEEE T PATTERN ANAL, V18, P771 CHEN H, 1995, J AM SOC INFORM SCI, V46, P348, DOI 10.1002/(SICI)1097-4571(199506)46:5<348::AID-ASI6>3.0.CO;2-1 Courteau J, 1991, Science, V254, P201, DOI 10.1126/science.1925577 Croft W. B., 1990, P 13 INT C RES DEV I, P349, DOI 10.1145/96749.98240 CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 Fahlman S.E., 1979, NETL SYSTEM REPRESEN Fox E. A., 1988, Second Conference on Applied Natural Language Processing FRENKEL KA, 1991, COMMUN ACM, V34, P41 FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 GALLANT SI, 1988, COMMUN ACM, V31, P152, DOI 10.1145/42372.42377 Gershon N. D., 1994, SCI VISUALIZATION AD HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554 KNAPP SD, 1984, DATABASE, V7, P70 KNIGHT K, 1990, COMMUN ACM, V33, P59 LUHN HP, 1958, IBM J RES DEV, V2, P159 Miller G., 1990, INT J LEXICOGRAPHY, V3 MINKER J, 1972, INFORM STORAGE RET, V8, P329, DOI 10.1016/0020-0271(72)90021-6 Nadis S, 1996, SCIENCE, V272, P1419, DOI 10.1126/science.272.5267.1419 Orwig RE, 1997, J AM SOC INFORM SCI, V48, P157 PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8 RAU R, 1995, COMMUNICATIONS A APR, P29 SALTON G, 1971, SMART RETRIEVAL SYST, P115 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1972, INFORMATION PROCESSI, V71, P115 Schatz B, 1996, COMPUTER, V29, P28, DOI 10.1109/2.493454 Schatz B. R., 1996, IEEE COMPUT, V29, P22 Smith TR, 1996, COMPUTER, V29, P54, DOI 10.1109/2.493457 SOWA JF, 1991, PRINCIPLES SEMANTIC, P1 TANK DW, 1987, SCI AM, V257, P104 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 Voorhees E., 1993, P 16 ANN INT ACM SIG, P171, DOI 10.1145/160688.160715 NR 51 TC 18 Z9 18 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD,, E GRINSTEAD RH19 1HH, W SUSSEX, ENGLAND SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1998 VL 24 IS 1 BP 3 EP 18 DI 10.1177/016555159802400102 PG 16 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA YV261 UT WOS:000071805500002 ER PT J AU Fujihara, H Simmons, DB Ellis, NC Shannon, RE AF Fujihara, H Simmons, DB Ellis, NC Shannon, RE TI Knowledge conceptualization tool SO IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING LA English DT Article DE knowledge acquisition; information retrieval; intelligent system; knowledge-based system; knowledge engineering; conceptualization ID TEXT RETRIEVAL AB Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process,and few automated support tools exist to help knowledge engineers enhance their performance. This paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process. Furthermore, KCT captured every knowledge component that the knowledge engineer manually captured. KCT demonstrates the effectiveness of the knowledge acquisition process model proposed in this paper. C1 TEXAS A&M UNIV,COGNIT SYST LAB,COLLEGE STN,TX 77843. RP Fujihara, H (reprint author), HEWLETT PACKARD CORP,R&D LAB,BOISE,ID 83714, USA. CR ADMODT A, 1990, CURRENT TRENDS KNOWL, P1 ALLARD F, 1992, P EKAW 92 6 EUR KNOW, P1 BOOSE J, 1988, KNOWL ACQUIS, V1, P3 BREUKER J, 1987, P1098 U AMST BUCHANAN B, 1983, BUILDING EXPERT SYST COOKSON M, 1985, RES DEV EXPERT SYSTE Feigenbaum E., 1988, RISE EXPERT CO Fisher D. H., 1987, Machine Learning, V2, DOI 10.1007/BF00114265 FODOR JA, 1968, J PHILOS, V65, P627, DOI 10.2307/2024316 GAINES B, 1989, P 3 EUR WORKSH KNOWL Haugeland J., 1981, MIND DESIGN, P161 Hayes-Roth F., 1983, BUILDING EXPERT SYST JARDINE N, 1976, INFORMATION STORAGE, V27, P129 JOHANNSEN G, 1991, AUTOMATICA, V27, P97, DOI 10.1016/0005-1098(91)90009-Q KAGAN J, 1972, PSYCHOL INTRO McGraw K. L., 1989, KNOWLEDGE ACQUISITIO Mitchell T. M., 1986, Machine Learning, V1, DOI 10.1007/BF00116250 Musen M. A., 1989, Knowledge Acquisition, V1, DOI 10.1016/S1042-8143(89)80006-8 Musen M.A., 1989, AUTOMATED GENERATION PEARL T, 1988, PROBABILISTIC REASON Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Ryle Gilbert, 1949, CONCEPT MIND Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1970, SCIENCE, P335 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TURTLE HR, 1992, COMPUT J, V35, P279, DOI 10.1093/comjnl/35.3.279 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees Ellen M., 1985, P 8 ANN INT ACM SIGI, P188, DOI 10.1145/253495.253524 WATERMAN DA, 1988, GUIDE EXPERT SYSTEMS NR 35 TC 8 Z9 8 PU IEEE COMPUTER SOC PI LOS ALAMITOS PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 SN 1041-4347 J9 IEEE T KNOWL DATA EN JI IEEE Trans. Knowl. Data Eng. PD MAR-APR PY 1997 VL 9 IS 2 BP 209 EP 220 DI 10.1109/69.591447 PG 12 WC Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic SC Computer Science; Engineering GA WV231 UT WOS:A1997WV23100002 ER PT J AU Grossman, DA Frieder, O Holmes, DO Roberts, DC AF Grossman, DA Frieder, O Holmes, DO Roberts, DC TI Integrating structured data and text: A relational approach SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID SYSTEM AB We integrate structured data and text using the unchanged, standard relational model. We started with the premise that a relational system could be used to implement an information retrieval (in) system. After implementing a prototype to verify that premise, we then began to investigate the performance of a parallel relational database system for this application. We also tested the effect of query reduction on accuracy and found that queries can be reduced prior to their implementation without incurring a significant loss in precision/recall. This reduction also serves to improve run-time performance. After comparing our results to a special purpose in system, we conclude that the relational model offers scalable performance and includes the ability to integrate structured data and text in a portable fashion. C1 OFF INFORMAT TECHNOL,WASHINGTON,DC 20505. AT&T GLOBAL INFORMAT SYST,ROCKVILLE,MD 20850. RP Grossman, DA (reprint author), GEORGE MASON UNIV,DEPT COMP SCI,FAIRFAX,VA 22030, USA. CR BLAIR DC, 1974, UNPUB SQUARE SPECIFY BLAIR DC, 1988, INFORM PROCESS MANAG, V24, P349, DOI 10.1016/0306-4573(88)90101-X GOYAL P, 1987, INFORMATION SYSTEMS, V12, P49 HARMAN D, 1995, P 3 TEXT RETR C Lynch C. A., 1988, Proceedings of the Fourteenth International Conference on Very Large Databases MACLEOD I, 1978, J AM SOC INFORM SCI, V30, P243 MACLEOD I, 1978, P ASIS ANN M, V15, P83 NIEMI T, 1995, INFORM PROCESS MANAG, V31, P215, DOI 10.1016/0306-4573(94)00040-A *RASMUSSEN EM, 1991, INFORMATION PROCESS, V27 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEK HJ, 1982, 8TH P INT C VER LARG, P197 STONEBRAKER M, 1983, ACM T OFF INF SYST, V1, P143, DOI 10.1145/357431.357433 STONEBRAKER M, 1987, ACM T DATABASE SYST, V12, P350, DOI 10.1145/27629.27631 1995, P 4 TEXT RETR EV C 1992, SQL DS SYSTEMS PROGR 1995, INFORMIX ONLINE DYNA NR 18 TC 17 Z9 17 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD FEB PY 1997 VL 48 IS 2 BP 122 EP 132 DI 10.1002/(SICI)1097-4571(199702)48:2<122::AID-ASI3>3.0.CO;2-# PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA WD675 UT WOS:A1997WD67500003 ER PT J AU Aronson, AR Rindflesch, TC AF Aronson, AR Rindflesch, TC TI Query expansion using the UMLS (R) Metathesaurus (R) SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION LA English DT Article AB Recent work has demonstrated the importance of query expansion for improving retrieval effectiveness when applying statistically-based systems to MEDLINE(R) citations. The research has suggested the use of retrieval feedback for enhancing the original text of users' queries. As an alternative method of query expansion, we propose the use of the MetaMap program for associating UMLS Metathesaurus concepts with the original query. Our experiments show that query expansion based on MetaMap compares favorably with retrieval feedback. We conclude that the optimal strategy would be to combine the two techniques. C1 Natl Lib Med, Bethesda, MD 20894 USA. RP Aronson, AR (reprint author), Natl Lib Med, Bethesda, MD 20894 USA. CR Aronson A. R., 1994, P RIAO 94, P197 CALLAN J, 1992, P INT C DAT EXP SYST, P347 Efthimiadis EN, 1996, ANNU REV INFORM SCI, V31, P121 HARMAN D, 1994, P 2 TEXT RETR C TREC, P1 HERSH WR, 1994, J AM MED INFORM ASSN, V1, P51 Ide E., 1971, SMART RETRIEVAL SYST, P337 RAJASHEKAR TB, 1995, J AM SOC INFORM SCI, V46, P272, DOI 10.1002/(SICI)1097-4571(199505)46:4<272::AID-ASI4>3.0.CO;2-T Rindflesch T C, 1994, Proc Annu Symp Comput Appl Med Care, P240 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Srinivasan P, 1996, J AM MED INFORM ASSN, V3, P157 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P503, DOI 10.1016/0306-4573(96)00025-8 Srinivasan P, 1996, Proc AMIA Annu Fall Symp, P353 Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3 NR 13 TC 8 Z9 8 PU HANLEY & BELFUS INC PI PHILADELPHIA PA 210 S 13TH ST, PHILADELPHIA, PA 19107 USA SN 1067-5027 J9 J AM MED INFORM ASSN JI J. Am. Med. Inf. Assoc. PY 1997 SU S BP 485 EP 489 PG 5 WC Computer Science, Information Systems; Computer Science, Interdisciplinary Applications; Information Science & Library Science; Medical Informatics SC Computer Science; Information Science & Library Science; Medical Informatics GA V3182 UT WOS:000171774300098 ER PT J AU Singhal, A Salton, G Mitra, M Buckley, C AF Singhal, A Salton, G Mitra, M Buckley, C TI Document length normalization SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article AB In the TREC collection-a large full-text experimental text collection with widely varying document lengths-we observe that the likelihood of a document being judged relevant by a user increases with the document length. We show that a retrieval strategy, such as the vector-space cosine match, that retrieves documents of different lengths with roughly equal chances, will not optimally retrieve useful documents from such a collection. We present a modified technique-pivoted cosine normalization-that attempts to match the likelihood of retrieving documents of all lengths to the likelihood of their relevance, and show that this technique yields significant improvements in retrieval effectiveness. Copyright (C) 1996 Elsevier Science Ltd RP Singhal, A (reprint author), CORNELL UNIV,DEPT COMP SCI,4130 UPSON HALL,ITHACA,NY 14853, USA. CR BROGLIO J, 1995, NIST SPECIAL PUBLICA, P29 Buckley C., 1995, NIST SPECIAL PUBLICA, P69 BUCKLEY C, 1993, HUMAN LANGUAGE TECHN Callan James P., 1994, P 17 ANN INT ACM SIG, P302 Harman D., 1995, P 3 TEXT RETR C TREC, P1 Hearst MA, 1993, P 16 ANN INT ACM SIG, P59, DOI 10.1145/160688.160695 KWOK KL, 1995, NIST PUBLICATION, P247 Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232 Robertson S.E., 1995, NIST SPECIAL PUBLICA, P109 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1994, 941438 CORN U DEP CO SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1994, RIAO 94 CONFERENCE PROCEEDINGS - INTELLIGENT MULTIMEDIA INFORMATION RETRIEVAL SYSTEMS AND MANAGEMENT, P6 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SINGHAL A, 1995, TR951507 CORN U DEP Turtle H., 1990, INFERENCE NETWORKS D NR 20 TC 49 Z9 49 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD SEP PY 1996 VL 32 IS 5 BP 619 EP 633 DI 10.1016/0306-4573(96)00008-8 PG 15 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA VJ870 UT WOS:A1996VJ87000010 ER PT J AU Mathe, N Chen, JR AF Mathe, N Chen, JR TI User-centered indexing for adaptive information access SO USER MODELING AND USER-ADAPTED INTERACTION LA English DT Article; Proceedings Paper CT 4th International Conference on User Modeling (UM 94) CY 1994 CL HYANNIS, MA DE user-centered indexing; long-term adaptation; adaptive information retrieval; adaptive navigation; user feedback; shared information access ID RETRIEVAL; MODEL AB We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments. RP Mathe, N (reprint author), NASA, AMES RES CTR, MAIL STOP 269-2, MOFFETT FIELD, CA 94035 USA. CR AGOSTI M, 1995, MULTIMEDIA SYST, V3, P15, DOI 10.1007/BF01236576 ARMSTRONG R, 1995, P AAAI SPR S INF GAT BARREAU DK, 1995, J AM SOC INFORM SCI, V46, P327, DOI 10.1002/(SICI)1097-4571(199506)46:5<327::AID-ASI4>3.0.CO;2-C BELEW RK, 1989, 12TH P ANN INT ACM S, P11 BELKIN NJ, 1993, INFORM PROCESS MANAG, V29, P325, DOI 10.1016/0306-4573(93)90059-M BERGSTROM P, 1994, THESIS BOY GA, 1991, P 3 ACM C HYP SAN AN, P51, DOI 10.1145/122974.122980 BRADSHAW JM, 1996, IN PRESS SOFTWARE AG BRADSHAW JM, 1993, P 4 INT C HUM MACH I BRUSILOVSKY P, 1994, 4 INT C US MOD HYANN CALLAN JP, 1992, 3RD P INT C DAT EXP, P78 CHEN HC, 1995, J AM SOC INFORM SCI, V46, P194, DOI 10.1002/(SICI)1097-4571(199504)46:3<194::AID-ASI4>3.0.CO;2-S CHEN J, 1994, 4 INT C US MOD HYANN DUMAIS ST, 1988, MAY CHI 88 C P HUM F, P281 Fischer G., 1991, Human Factors in Computing Systems. Reaching Through Technology. CHI '91. Conference Proceedings FUHR N, 1992, COMPUT J, V35, P243, DOI 10.1093/comjnl/35.3.243 GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867 Haines D., 1993, P 16 ANN INT ACM SIG, P2, DOI 10.1145/160688.160689 JENNINGS A, 1992, IEICE T INF SYST, VE75D, P198 Kaplan C., 1993, User Modeling and User-Adapted Interaction, V3, DOI 10.1007/BF01257889 KATSUMOTO M, 1995, P 1995 PAC WORKSH DI, P76 KIBBY MR, 1989, HYPERTEXT THEORY PRA, P164 KLARK P, 1995, P WORLD C ED MULT HY, P372 LASHKARI L, 1994, P 12 NAT C ART INT S, P444 MAES P, 1993, 11TH P NAT C ART INT, P459 MATHE N, 1994, P 4 INT C US MOD HYA, P107 Monk A., 1989, Interacting with Computers, V1, DOI 10.1016/0953-5438(89)90026-X PARGMAN D, 1994, P WORKSH US MOD INF RABINOWITZ J, 1995, P AAAI FALL S ART IN, P110 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROSE DE, 1991, INT J MAN MACH STUD, V35, P1, DOI 10.1016/S0020-7373(07)80006-X SALTON G, 1989, AUTOMATIC TEST PROCE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHETH B, 1993, NINTH CONFERENCE ON ARTIFICIAL INTELLIGENCE FOR APPLICATIONS : PROCEEDINGS, P345 TORRANCE M, 1995, P AAAI FALL S ART IN, P131 TUDHOPE D, 1995, P C HYP INF RETR MUL, P203 TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VASSILEVA J, 1994, P 4 INT C US MOD HYA, P115 NR 40 TC 10 Z9 10 PU SPRINGER PI DORDRECHT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS SN 0924-1868 J9 USER MODEL USER-ADAP JI User Model. User-Adapt. Interact. PD JUL PY 1996 VL 6 IS 2-3 BP 225 EP 261 DI 10.1007/BF00143968 PG 37 WC Computer Science, Cybernetics SC Computer Science GA VE967 UT WOS:A1996VE96700006 ER PT J AU Wilbur, WJ Yang, YM AF Wilbur, WJ Yang, YM TI An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts SO COMPUTERS IN BIOLOGY AND MEDICINE LA English DT Article DE molecular biology; stop terms; text retrieval; text classification; linear least squares fit; weight; strength; vector model; Bayesian model AB The biological literature presents a difficult challenge to information processing in its complexity, diversity, and in its sheer volume. Much of the diversity resides in its technical terminology, which has also become voluminous. In an effort to deal more effectively with this large vocabulary and improve information processing, a method of focus has been developed which allows one to classify terms based on a measure of their importance in describing the content of the documents in which they occur. The measurement is called the strength of a term and is a measure of how strongly the term's occurrences correlate with the subjects of documents in the database. If term occurrences are random then there will be no correlation and the strength will be zero, but if for any subject, the term is either always present or never present its strength will be one. We give here a new, information theoretical interpretation of term strength, review some of its uses in focusing the processing of documents for information retrieval and describe new results obtained in document categorization. Copyright (C) 1996 Elsevier Science Ltd. C1 MAYO CLIN & MAYO FDN,SECT MED INFORMAT RESOURCES,ROCHESTER,MN 55905. RP Wilbur, WJ (reprint author), NIH,NATL CTR BIOTECHNOL INFORMAT,NATL LIB MED,BETHESDA,MD 20894, USA. CR BARKLA JK, 1969, UNPUB CONSTRUCTION W BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUCKLEY C, 1985, 85686 CORN U DEP COM COOPER WS, 1991, P 54 ANN M AM SOC IN, V28, P366 CROFT WB, 1982, 8221 COINS U MASS FANRIJSBEWRGEN CJ, 1979, INFORMATION RETRIEVA FURNAS GW, 1987, COMMUN ACM, V30, P964, DOI 10.1145/32206.32212 HARMAN D, 1994, 2 TEXT RETR C TREC 2, P1 WILBUR WJ, 1992, J INFORM SCI, V18, P45, DOI 10.1177/016555159201800106 LINDBERG DAB, 1993, JAMA-J AM MED ASSOC, V269, P3124, DOI 10.1001/jama.269.24.3124 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 MILLER WL, 1971, J DOC, V27, P254, DOI 10.1108/eb026520 *NAT CTR BIOT INF, 1992, ENTR SEQ DAT CDROM V *NAT LIB MED, 1995, MEDLINE MED LIT AN R *NAT LIB MED, 1995, MED SUBJ HEAT MESH ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILBUR WJ, 1994, INFORM PROCESS MANAG, V30, P253, DOI 10.1016/0306-4573(94)90068-X WILBUR WJ, 1993, P 55 ANN M AM SOC IN, V29, P216 Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium YANG Y, IN PRESS AM SOC INFO Yang Y, 1994, P 17 ANN INT ACM SIG, P13 YANG Y, 1992, 14TH P INT C COMP LI, P447 YANG Y, 1993, P 16 ANN INT ACM SIG, P281, DOI 10.1145/160688.160738 YANG YM, 1994, ACM T INFORM SYST, V12, P252, DOI 10.1145/183422.183424 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 31 TC 57 Z9 58 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0010-4825 J9 COMPUT BIOL MED JI Comput. Biol. Med. PD MAY PY 1996 VL 26 IS 3 BP 209 EP 222 DI 10.1016/0010-4825(95)00055-0 PG 14 WC Biology; Computer Science, Interdisciplinary Applications; Engineering, Biomedical; Mathematical & Computational Biology SC Life Sciences & Biomedicine - Other Topics; Computer Science; Engineering; Mathematical & Computational Biology GA UN160 UT WOS:A1996UN16000004 PM 8725772 ER PT J AU Cortez, EM Park, SC Kim, S AF Cortez, EM Park, SC Kim, S TI The hybrid application of an inductive learning method and a neural network for intelligent information retrieval SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID DESIGN; MODELS AB Traditional information retrieval systems based on Boolean logic suffer from two inherent problems: (1) inaccurate or incomplete query representation, and (2) inconsistent indexing; While many researchers have demonstrated that neural networks can solve the incomplete query problems for information retrieval, the inconsistent indexing problem still remains unsolved. In this paper, we present a hybrid methodology of integrating an inductive learning technique with a neural network (connectionist model) in order to solve both inconsistent indexing and incomplete query problems. Since an inductive learning technique has the ability to identify the most significant document index terms with various levels of relationship to their semantic significance, it provides a possible solution to the problem of inconsistent indexing. This paper reports the first phase of research that demonstrates how a neural network augmented by an inductive learning technique results in effective information retrieval performance in the areas that demand flexible inferencing and reasoning when incomplete queries and inconsistent indexing problems are present. C1 UNIV WISCONSIN,SCH BUSINESS,MADISON,WI 53706. RP Cortez, EM (reprint author), UNIV WISCONSIN,SCH LIB & INFORMAT STUDIES,MADISON,WI 53706, USA. CR BATES MJ, 1986, J AM SOC INFORM SCI, V37, P357 BELEW RK, 1986, THESIS U MICHIGAN AN BELEW RK, 1987, 1ST P INT C ART INT, P116 BELKIN NJ, 1982, J DOC, V38, P145, DOI 10.1108/eb026726 BLAZEK R, 1988, RQ, V28, P169 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BORGMAN CL, 1986, J AM SOC INFORM SCI, V37, P384 Cherkassky V., 1989, International Journal of Neural Networks - Research & Applications, V1 COYLE K, 1985, INFORM TECHNOL LIBR, V4, P57 DOSZKOCS TE, 1990, ANNU REV INFORM SCI, V25, P209 Katzer J., 1982, Information Technology: Research and Development, V1 KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-# KWOK KL, 1989, SIGIR FORUM, V23, P21, DOI 10.1145/75334.75338 KWOK KL, 1990, INT J C NEURAL NETWO, V2, P623 LARSON RR, 1992, J AM SOC INFORM SCI, V43, P34, DOI 10.1002/(SICI)1097-4571(199201)43:1<34::AID-ASI4>3.0.CO;2-F MICHALSKI RS, 1983, ARTIF INTELL, V20, P111, DOI 10.1016/0004-3702(83)90016-4 Mori H., 1990, International Journal of Human-Computer Interaction, V2 MOZER MC, 1984, INDUCTIVE INFORMATIO PAO ML, 1989, J AM SOC INFORM SCI, V40, P226, DOI 10.1002/(SICI)1097-4571(198907)40:4<226::AID-ASI2>3.0.CO;2-6 Pao Y. H., 1989, ADAPTIVE PATTERN REC PARK SC, 1990, LECT NOTES ARTIF INT, V462, P152 Quinlan J., 1992, C 4 5 PROGRAMS MACHI Quinlan J. R., 1988, MACH INTELL, V11, P305 QUINLAN JR, 1986, MACH LEARN, V1, P82 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 Rendell L., 1986, Machine Learning, V1, DOI 10.1007/BF00114117 Rumelhart D. E., 1986, PARALLEL DISTRIBUTED Rumelhart D. E., 1985, LEARNING INTERNAL RE SALCHENBERGER LM, 1992, DECISION SCI, V23, P899, DOI 10.1111/j.1540-5915.1992.tb00425.x SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shaw M. J., 1990, Computer Science in Economics and Management, V3, DOI 10.1007/BF00436712 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WATTERS CR, 1989, J AM SOC INFORM SCI, V40, P311, DOI 10.1002/(SICI)1097-4571(198909)40:5<311::AID-ASI3>3.0.CO;2-B WILKINSON R, 1991, 14 ANN INT ACM SIGIR, P202 Wilkinson R., 1992, Library Hi Tech, V10, DOI 10.1108/eb047846 Wilson R, 1992, OR MS TODAY AUG, P36 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 NR 40 TC 8 Z9 8 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD NOV PY 1995 VL 31 IS 6 BP 789 EP 813 DI 10.1016/0306-4573(95)00015-9 PG 25 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA TK446 UT WOS:A1995TK44600002 ER PT J AU SALTON, G ALLAN, J AF SALTON, G ALLAN, J TI SELECTIVE TEXT UTILIZATION AND TEXT TRAVERSAL SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES LA English DT Article ID RETRIEVAL AB Many large collections of full-text documents are currently stored in machine-readable form and processed automatically in various ways. These collections may include different types of documents, such as messages, research articles, and books, and the subject matter may vary widely. To process such collections, robust text analysis methods must be used, capable of handling materials in arbitrary subject areas, and flexible access must be provided to texts and text excerpts of varying size. In this study, global text comparison methods are used to identify similarities between text elements, followed by local context-checking operations that resolve ambiguities and distinguish superficially similar texts from texts that actually cover identical topics. A linked text structure, known as a text relationship map, is then created that relates similar texts at various levels of detail. In particular, text links are available for full texts, as well as text sections, paragraphs, and sentence groups. The relationship graphs are usable as conceptualization tools to illustrate various text manipulation operations and may also serve as browsing maps in situations where searches or text traversal operations are conducted under user control. In this study, the relationship maps are used to identify important text passages, to traverse texts selectively both within particular documents and between documents, and to provide flexible text access to large text collections in response to various kinds of user needs. An automated 29-volume encyclopedia is used as an example to illustrate various possible text accessing and traversal operations. Implementation details are not included in this initial study. (C) 1995 Academic Press Limited C1 UNIV MASSACHUSETTS,DEPT COMP SCI,AMHERST,MA 01002. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 Bolter Jay David, 1990, WRITING SPACE COMPUT DELANEY P, 1991, HYPERMEDIA LITERACY EARL LL, 1970, INFORM STORAGE RET, V6, P313, DOI 10.1016/0020-0271(70)90025-2 EDMUNDSON HP, 1964, COMMUN ACM, V7, P259, DOI 10.1145/364005.364088 FUNK, 1979, FUNK WAGNALLS NEW EN HARMAN DK, 1993, NIST500207 SPEC PUBL LUHN HP, 1958, IBM J RES DEV, V2, P159 OCONNOR J, 1980, J AM SOC INFORM SCI, V32, P227 OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7 PAICE CD, 1990, INFORM PROCESS MANAG, V26, P171, DOI 10.1016/0306-4573(90)90014-S REIMER U, 1987, MIP8723 U PASS TECHN RUSH JE, 1964, J ASIS, V22, P260 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1995, SELECTIVE TEXT TRAVE SALTON G, 1993, TR931334 CORN U COMP SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1991, 14TH P INT ACM SIGIR, P21 SALTON G, 1994, SCIENCE, V204, P1421 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Wittgenstein Ludwig, 1953, PHILOS INVESTIGATION NR 24 TC 2 Z9 2 PU ACADEMIC PRESS (LONDON) LTD PI LONDON PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX SN 1071-5819 J9 INT J HUM-COMPUT ST JI Int. J. Hum.-Comput. Stud. PD SEP PY 1995 VL 43 IS 3 BP 483 EP 497 DI 10.1006/ijhc.1995.1055 PG 15 WC Computer Science, Cybernetics; Ergonomics; Psychology, Multidisciplinary SC Computer Science; Engineering; Psychology GA TJ620 UT WOS:A1995TJ62000010 ER PT J AU SALTON, G ALLAN, J BUCKLEY, C SINGHAL, A AF SALTON, G ALLAN, J BUCKLEY, C SINGHAL, A TI AUTOMATIC-ANALYSIS, THEME GENERATION, AND SUMMARY OF MACHINE-READABLE TEXTS SO SCIENCE LA English DT Article ID RETRIEVAL; HYPERTEXT AB Vast amounts of text material are now available in machine-readable form for automatic processing. Here, approaches are outlined for manipulating and accessing texts in arbitrary subject areas in accordance with user needs. In particular, methods are given for determining text themes, traversing texts selectively, and extracting summary statements that reflect text content. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR Al-hawamdeh S., 1989, Electronic Publishing: Origination, Dissemination and Design, V2 Andersen M. H., 1989, Hypermedia, V1 BERNSTEIN M, 1991, P HYPERTEXT 91, P246 Bernstein M., 1990, Hypertext: Concepts, Systems and Applications. Proceedings of the First European Conference on Hypertext Bolter Jay David, 1991, WRITING SPACE COMPUT BOTAFOGO RA, 1992, ACM T INFORM SYST, V10, P142, DOI 10.1145/146802.146826 BUCKLEY C, 1993, NIST SPECIAL PUBLICA, P59 BUCKLEY C, IN PRESS NIST SPECIA Chignell M. H., 1991, Hypermedia, V3 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 DEJONG G, 1982, STRATEGIES NATURAL L, P149 DELANEY P, 1991, HYPERMEDIA LIT STUDI EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 Furuta R., 1989, Hypermedia, V1 GILYAREVSKII RS, 1993, J AM SOC INFORM SCI, V44, P185, DOI 10.1002/(SICI)1097-4571(199305)44:4<185::AID-ASI1>3.0.CO;2-2 GLOOR P, 1991, P ACM HYPERTEXT 91, P107, DOI 10.1145/122974.122985 Guinan C., 1992, Proceeding of the ACM Conference on Hypertext, DOI 10.1145/168466.168506 HEARST MA, 1993, P 31 ANN M ACL COL O, P55 LANDOW GP, 1989, COMPUT HUMANITIES, V23, P173, DOI 10.1007/BF00056142 LUHN HP, 1958, IBM J RES DEV, V2, P159 MURTAGH F, 1982, COMPUT J, V26, P354 OCONNOR J, 1989, J AM SOC INFORM SCI, V32, P227 OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7 PAICE CD, 1990, INFORM PROCESS MANAG, V26, P171, DOI 10.1016/0306-4573(90)90014-S REARICK TC, 1991, HYPERTEXT HYPERMEDIA, P113 RUSH JE, 1964, J ASIS, V22, P260 Salton G, 1989, AUTOMATIC TEXT PROCE Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 Salton G., 1993, P 16 ANN INT ACM SIG, P49, DOI 10.1145/160688.160693 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1991, 14TH P INT ACM SIGIR, P21 Salton G., 1992, Electronic Publishing: Origination, Dissemination and Design, V5 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 NR 35 TC 72 Z9 81 PU AMER ASSOC ADVANCEMENT SCIENCE PI WASHINGTON PA 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 SN 0036-8075 J9 SCIENCE JI Science PD JUN 3 PY 1994 VL 264 IS 5164 BP 1421 EP 1426 DI 10.1126/science.264.5164.1421 PG 6 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA NP221 UT WOS:A1994NP22100032 PM 17838425 ER PT J AU WILBUR, WJ COFFEE, L AF WILBUR, WJ COFFEE, L TI THE EFFECTIVENESS OF DOCUMENT NEIGHBORING IN SEARCH ENHANCEMENT SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID RETRIEVAL PERFORMANCE; RELEVANCE FEEDBACK; INFORMATION AB We consider two kinds of queries that may be applied to a database. The first is a query written by a searcher to express an information need. The second is a request for documents most similar to a document already judged relevant by the searcher. We examine the effectiveness of these two procedures and show that in important cases the latter query type is more effective than the former. This provides a new view of the cluster hypothesis and a justification for document neighboring procedures (precomputation of closely related documents). If all the documents in a database have readily available precomputed nearest neighbors, a new search algorithm, which we call parallel neighborhood searching, is conveniently used. We show that this feedback-based method provides significant improvement in recall over traditional linear searching methods, and even appears superior to traditional feedback methods in overall performance. RP WILBUR, WJ (reprint author), NIH,NATL LIB MED,NATL CTR BIOTECHNOL INFORMAT,BLDG 38A,RM 8S806,8600 ROCKVILLE PIKE,BETHESDA,MD 20894, USA. CR BORODIN A, 1971, SMART RETRIEVAL SYST, P394 BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUCKLEY C, 1985, 85686 CORN U DEP COM Chang Y, 1971, SMART RETRIEVAL SYST, P355 CROFT WB, 1982, 8221 U MASS TECHN RE FOX EA, 1990, VIRGINIA DISC ONE Ide E., 1971, SMART RETRIEVAL SYST, P337 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LESK ME, 1971, SMART RETRIEVAL SYST, P506 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Schuyler P. L., 1989, MEDINFO 89. Proceedings of the Sixth Conference on Medical Informatics JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees E.M., 1985, 8TH P ANN INT C RES, P188 WILBUR WJ, 1992, INFORM SYST, V17, P283, DOI 10.1016/0306-4379(92)90019-J Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium WU H, 1981, J DOC, V37, P194, DOI 10.1108/eb026717 NR 22 TC 34 Z9 35 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PD MAR-APR PY 1994 VL 30 IS 2 BP 253 EP 266 DI 10.1016/0306-4573(94)90068-X PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA MP710 UT WOS:A1994MP71000007 ER PT J AU WILBUR, WJ AF WILBUR, WJ TI RETRIEVAL TESTING WITH HYPERGEOMETRIC DOCUMENT MODELS SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL; SYSTEM; TERMS AB If one could identify the source subject areas of documents and could compute the probability that any given document came from a given source, one could apply Baye's theorem to compute the probability that a query document and any other document came from the same subject area (i.e., were related). Even correct prior probabilities could be assigned under this hypothesis by examining the whole database to obtain the probabilities with which different sources occur. While we do not know how to carry out this scheme in such a way as to account for all the information contained in documents, we show here how it may be realized in a limited way. A method of modeling the sources of documents is described which accounts for the information in global term weights. The methodology is based on the hypergeometric probability distribution. Such a source model may be fit closely to a real database and may be used to convert the real database to an abstract database in which document sources are known and model retrieval is the best retrieval possible based on model document content. We have constructed such an abstract model corresponding to a database of MEDLINE records. Tests of vector retrieval methods on the abstract model indicate they are near optimal but suggest minor improvement with correct parameter choices. Preliminary results based on a test set (human judged) from the real database support these results. RP WILBUR, WJ (reprint author), NIH,NATL LIB MED,NATL CTR BIOTECHNOL INFORMAT,BLDG 38A,ROOM 8S806,8600 ROCKVILLE PIKE,BETHESDA,MD 20894, USA. CR BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Cheeseman P, 1988, 5TH P INT C MACH LEA, P54 FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B FORD N, 1991, J AM SOC INFORM SCI, V42, P72, DOI 10.1002/(SICI)1097-4571(199101)42:1<72::AID-ASI8>3.0.CO;2-9 GORDON MD, 1990, J AM SOC INFORM SCI, V41, P313, DOI 10.1002/(SICI)1097-4571(199007)41:5<313::AID-ASI1>3.0.CO;2-G GRIFFITHS JM, 1978, THESIS U COLLEGE LON Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Larson H, 1982, INTRO PROBABILITY TH Lenat D., 1986, AI MAG, V6, P65 LEWIS DD, 1991, MACHINE LEARNING, P235 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 NELSON MJ, 1985, J AM SOC INFORM SCI, V36, P283, DOI 10.1002/asi.4630360502 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SARACEVIC T, 1991, P ASIS ANNU MEET, V28, P82 SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P92, DOI 10.1002/(SICI)1097-4571(198803)39:2<92::AID-ASI4>3.0.CO;2-P TAGUE J, 1984, CAN J INFORM SCI, V9, P37 TAGUE J, 1981, INFORMATION RETRIEVA, P236 TAGUE JM, 1981, ACM SIGIR FORUM, P66 TAGUE JM, 1982, RES DEV INFORMATION, P194 WILBUR WJ, 1992, INFORM SYST, V17, P283, DOI 10.1016/0306-4379(92)90019-J WILBUR WJ, 1992, J AM SOC INFORM SCI, V43, P358, DOI 10.1002/(SICI)1097-4571(199206)43:5<358::AID-ASI4>3.0.CO;2-I Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium NR 28 TC 2 Z9 2 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JUL PY 1993 VL 44 IS 6 BP 340 EP 351 DI 10.1002/(SICI)1097-4571(199307)44:6<340::AID-ASI4>3.0.CO;2-Z PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA LJ307 UT WOS:A1993LJ30700004 ER PT J AU WILBUR, WJ SIROTKIN, K AF WILBUR, WJ SIROTKIN, K TI THE AUTOMATIC IDENTIFICATION OF STOP WORDS SO JOURNAL OF INFORMATION SCIENCE LA English DT Article ID INFORMATION-RETRIEVAL AB A stop word may be identified as a word that has the same likelihood of occurring in those documents not relevant to a query as in those documents relevant to the query. In this paper we show how the concept of relevance may be replaced by the condition of being highly rated by a similarity measure. Thus it becomes possible to identify the stop words in a collection by automated statistical testing. We describe the nature of the statistical test as it is realized with a vector retrieval methodology based on the cosine coefficient of document-document similarity. As an example, this technique is then applied to a large MEDLINE(R) subset in the area of biotechnology. The initial processing of this database involves a 310 word stop list of common non-content terms. Our technique is then applied and 75% of the remaining terms are identified as stop words. We compare retrieval with and without the removal of these stop words and find that of the top twenty documents retrieved in response to a random query document, seventeen of these are the same on the average for the two methods. We also examine the differences and conclude that where the user prefers one method over the other, the new method with the reduced term set is favored about three times out of four. RP WILBUR, WJ (reprint author), NIH,NATL LIB MED,NATL CTR BIOTECH INFORMAT,BLDG 38A,8600 ROCKVILLE PIKE,BETHESDA,MD 20894, USA. CR BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUCKLEY C, 1985, 85686 CORN U DEP COM CROFT WB, 1982, COINS8221 U MASS TEC Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 REGAZZI JJ, 1988, J AM SOC INFORM SCI, V39, P235, DOI 10.1002/(SICI)1097-4571(198807)39:4<235::AID-ASI3>3.0.CO;2-H Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1989, INTRO MODERN INFORMA SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SWANSON DR, 1988, J AM SOC INFORM SCI, V39, P92, DOI 10.1002/(SICI)1097-4571(198803)39:2<92::AID-ASI4>3.0.CO;2-P VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium NR 15 TC 45 Z9 45 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1992 VL 18 IS 1 BP 45 EP 55 DI 10.1177/016555159201800106 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA HF751 UT WOS:A1992HF75100006 ER PT J AU WILBUR, WJ AF WILBUR, WJ TI A RETRIEVAL-SYSTEM BASED ON AUTOMATIC RELEVANCE WEIGHTING OF SEARCH TERMS SO PROCEEDINGS OF THE ASIS ANNUAL MEETING LA English DT Article; Proceedings Paper CT 55TH ANNUAL MEETING OF THE AMERICAN SOC FOR INFORMATION SCIENCE CY OCT 26-29, 1992 CL PITTSBURGH, PA SP AMER SOC INFORMAT SCI ID DOCUMENT-RETRIEVAL AB We have developed a retrieval methodology based on relevance weighting of search terms with an automatic implementation. It is founded on the familiar Bayesian formulation of the probability of relevance as a function of term occurrence where the contribution from individual terms is assumed to be independent. However our formulation departs from the usual in that it is based on considering pairs of documents, and terms contribute to the score (log odds of relevance) in a symmetric manner. Terms which appear in both documents in a pair contribute positively to the score and terms that occur in only one document contribute negatively. This allows the length of a document to become a negative factor in the scoring much as it is in the cosine formula commonly used in vector retrieval. As a consequence each term has two weights associated with it, one positive which is its contribution when it occurs in both documents of a pair, and one negative which is its contribution when it occurs in only one document of a pair. A method is found to incorporate local term weights based on within document term frequencies, into this retrieval scheme. We term the result the relevance pairs(RP) model. We obtain the necessary statistics to estimate the weights in the model by substituting the set of all highly rated document pairs identified by a successful retrieval method for the set of all relevant pairs of documents. For the successful retrieval method we use the vector cosine (VC) model. In our test environment we find improved retrieval by the RP model when compared with the VC model. RP WILBUR, WJ (reprint author), NATL LIB MED,NCBI,BETHESDA,MD 20209, USA. CR BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUCKLEY C, 1985, 85686 CORN U DEP COM COOPER WS, 1991, 54TH P ANN M AM SOC, V28, P366 CROFT WB, 1982, COINS8221 U MASS TEC FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B LEWIS DD, 1991, MACHINE LEARNING, P235 LUCARELLA D, 1988, J INFORM SCI, V14, P25, DOI 10.1177/016555158801400104 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1991, SCIENCE, V253, P974, DOI 10.1126/science.253.5023.974 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 WILBUR WJ, IN PRESS INFORMATION Wong S. K. M., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 16 TC 0 Z9 0 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0044-7870 J9 P ASIS ANNU MEET JI Proc. ASIS Annu. Meet. PY 1992 VL 29 BP 216 EP 220 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA JV325 UT WOS:A1992JV32500030 ER PT J AU SALTON, G AF SALTON, G TI DEVELOPMENTS IN AUTOMATIC TEXT RETRIEVAL SO SCIENCE LA English DT Article ID INTELLIGENT INFORMATION-RETRIEVAL; VECTOR-SPACE MODEL; DOCUMENT-RETRIEVAL; SYSTEM; RELEVANCE; SEARCH; HYPERTEXT; INTERMEDIARY; PROBABILITY; FEEDBACK AB Recent developments in the storage, retrieval, and manipulation of large text files are described. The text analysis problem is examined, and modern approaches leading to the identification and retrieval of selected text items in response to search requests are discussed. RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR AMSLER RA, 1984, ANNU REV INFORM SCI, V19, P161 BARHILLEL Y, 1964, LANGUAGE INFORMATION, P330 Biebricher P., 1988, 11th International Conference on Research and Development in Information Retrieval BISWAS G, 1987, J AM SOC INFORM SCI, V38, P83, DOI 10.1002/(SICI)1097-4571(198703)38:2<83::AID-ASI2>3.0.CO;2-X BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BOOKSTEIN A, 1983, LECT NOTES COMPUT SC, V146, P118 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Brachman R. J., 1985, READINGS KNOWLEDGE R BRAJNIK G, 1987, INFORM PROCESS MANAG, V23, P305, DOI 10.1016/0306-4573(87)90020-3 Brauen T., 1971, SMART RETRIEVAL SYST, P456 SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CHIARAMELLA Y, 1987, INFORM PROCESS MANAG, V23, P285, DOI 10.1016/0306-4573(87)90019-7 Cleverdon C., 1984, Information Services & Use, V4 COHEN PR, 1987, INFORM PROCESS MANAG, V23, P255, DOI 10.1016/0306-4573(87)90017-3 CONKLIN J, 1987, COMPUTER, V20, P17 COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROFT WB, 1987, INFORM PROCESS MANAG, V23, P249, DOI 10.1016/0306-4573(87)90016-1 CROFT WB, 1989, 1989 P HYP 89 PITTSB, P213 CROUCH DC, 1986, 9TH P ANN INT C RES, P58 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DOCZKOCS TE, 1986, 9TH P ANN INT C RES, P49 Eco Umberto, 1976, THEORY SEMIOTICS FAGAN JL, 1989, J AM SOC INFORM SCI, V40, P115, DOI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B Findler N. V., 1979, ASS NETWORKS REPRESE Fox E. A., 1988, Second Conference on Applied Natural Language Processing FRISSE ME, 1988, COMMUN ACM, V31, P880, DOI 10.1145/48511.48518 Fuhr N, 1990, 13TH P INT C RES DEV, P45 FURUTA R, 1989, COMPUT J, V32, P493 GAY LS, 1990, INFORM PROCESS MANAG, V26, P21, DOI 10.1016/0306-4573(90)90007-O GRIFFITHS A, 1984, J DOC, V40, P175, DOI 10.1108/eb026764 GUIDA G, 1983, AUTOMATICA, V19, P759, DOI 10.1016/0005-1098(83)90044-4 Ide E., 1971, SMART RETRIEVAL SYST, P337 INGWERSEN P, 1986, 9TH P ACM SIGIR C RE, P68 Jacobs P. S., 1988, 11th International Conference on Research and Development in Information Retrieval LANCASTER FW, 1979, INFORMATION RETRIEVA Lenat D., 1986, AI MAG, V6, P65 Lovins J., 1968, MECH TRANSLATION COM, V11, P11 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MURTAGH F, 1982, COMPUT J, V26, P354 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 POLLITT AS, 1984, ASLIB PROC, V36, P229, DOI 10.1108/eb050928 Quillian M. R., 1968, SEMANTIC INFORMATION, P216 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 RAU LF, 1987, INFORM PROCESS MANAG, V23, P269, DOI 10.1016/0306-4573(87)90018-5 RAYMOND DR, 1988, COMMUN ACM, V31, P871, DOI 10.1145/48511.48517 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P294, DOI 10.1108/eb026647 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1990, 901083 CORN U COMP S SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1991, SCIENCE, V253, P1012, DOI 10.1126/science.253.5023.1012 SALTON G, 1990, 901125 CORN U COMP S Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1990, TR901113 CORN U COMP SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1990, INFORM PROCESS MANAG, V26, P73, DOI 10.1016/0306-4573(90)90010-Y SALTON G, 1981, ACM SIGIR FORUM, V16, P22, DOI 10.1145/1095425.1095427 Schank R. C., 1975, CONCEPTUAL INFORMATI SEMBOK TMT, 1990, INFORM PROCESS MANAG, V26, P111, DOI 10.1016/0306-4573(90)90012-Q Smeaton A. F., 1988, 11th International Conference on Research and Development in Information Retrieval SPARCKJONES K, 1971, KEYWORD CLASSIFICATI SPARCKJONES K, 1972, J DOC, V28, P11 SPARCKJONES K, 1983, INTELLIGENT INFORMAT, V7, P136 SPARCK-JONES K, 1984, J DOC, V40, P50 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 Stanfill C., 1986, Communications of the ACM, V29, DOI 10.1145/7902.7906 STANFILL C, 1990, 13TH P INT C RES DEV, P413 TONG RM, 1987, 10TH P ANN INT ACM S, P247 Turtle H. R., 1990, 13TH P INT C RES DEV, P1 VANRIJSBERGEN CJ, 1986, COMPUT J, V29, P481, DOI 10.1093/comjnl/29.6.481 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA Voorhees E.M., 1985, 8TH P ANN INT C RES, P188 VOORHEES EM, 1986, 9TH P ANN INT ACM SI, P164 Walker D., 1986, ANAL LANGUAGE RESTRI Wittgenstein Ludwig, 1953, PHILOS INVESTIGATION WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2 WONG SKM, 1986, 9TH P INT C RES DEV, P175 WONG SKM, 1985, 8TH P ANN INT ACM SI, P18 Wu H., 1981, ACM SIGIR FORUM, V16, P30, DOI 10.1145/1013228.511759 NR 97 TC 247 Z9 272 PU AMER ASSOC ADVANCEMENT SCIENCE PI WASHINGTON PA 1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 SN 0036-8075 J9 SCIENCE JI Science PD AUG 30 PY 1991 VL 253 IS 5023 BP 974 EP 980 DI 10.1126/science.253.5023.974 PG 7 WC Multidisciplinary Sciences SC Science & Technology - Other Topics GA GC982 UT WOS:A1991GC98200033 PM 17775340 ER PT J AU STANFILL, C THAU, R AF STANFILL, C THAU, R TI INFORMATION-RETRIEVAL ON THE CONNECTION MACHINE - 1 TO 8192 GIGABYTES SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article ID TEXT SIGNATURES; PARALLEL; SEARCH AB This paper describes algorithms and data structures for applying a parallel computer to information retrieval. Previous work has described an implementation based on overlap encoded signatures. That system was limited by (a) the necessity of keeping the signatures in primary memory, and (b) the difficulties involved in implementing document-term weighting. Overcoming these limitations required adapting the inverted index techniques used on serial machines. The most obvious adaptation, also previously described, suffers from the fact that data must be sent between processors at query time. Since interprocessor communication is generally slower than local computation, this suggests that an algorithm which does not perform such communication might be faster. This paper presents a data structure, called a partitioned posting file, in which the interprocessor communication takes place at database-construction time, so that no data movement is needed at query-time. Performance characteristics and storage overhead are established by benchmarking against a synthetic database. Based on these figures, it appears that currently available hardware can deliver interactive document ranking on databases containing between 1 and 8192 Gigabytes of text. RP STANFILL, C (reprint author), THINKING MACHINES CORP,245 1ST ST,CAMBRIDGE,MA 02154, USA. CR BATCHER KE, 1980, IEEE T COMPUT, V29, P836 BUCKLEY C, 1985, INT C RES DEV INFORM CROFT WB, 1988, ACM T INFORM SYST, V6, P42, DOI 10.1145/42279.45947 DOSZKOCS TE, 1982, RES DEV INFORM RET, P251 Flanders P. M., 1977, High Speed Computer and Algorithm Organization HARMAN D, IN PRESS J AM ASS IN Hillis D., 1985, CONNECTION MACHINE PATERSON DA, 1988, JUN ACM SIGMOD C MAN POGUE CA, 1987, PARALLEL COMPUT, V4, P259, DOI 10.1016/0167-8191(87)90025-1 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALEM K, 1986, IEEE INT C DATA ENG Salton G., 1971, SMART SYSTEM EXPT AU Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1970, SCIENCE, V168, P335, DOI 10.1126/science.168.3929.335 SALTON G, 1987, 87881 CORN U DEP COM SALTON G, 1988, COMMUN ACM, V31, P202, DOI 10.1145/42372.42380 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 STANFEL LE, 1990, MATH COMPUT MODEL, V13, P33, DOI 10.1016/0895-7177(90)90076-Y STANFILL C, 1988, DR881 THINK MACH COR STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 STANFILL C, 1988, JUN INT C RES DEV IN STANFILL C, 1990, SEP INT C RES DEV IN STONE HS, 1987, COMPUTER, V20, P11 *THINK MACH CORP, 1987, CONN MACH MOD CM2 TE VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 25 TC 12 Z9 12 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1991 VL 27 IS 4 BP 285 EP 310 DI 10.1016/0306-4573(91)90085-Z PG 26 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA FV879 UT WOS:A1991FV87900003 ER PT J AU WATTERS, C SHEPHERD, MA AF WATTERS, C SHEPHERD, MA TI A TRANSIENT HYPERGRAPH-BASED MODEL FOR DATA ACCESS SO ACM TRANSACTIONS ON INFORMATION SYSTEMS LA English DT Article C1 DALHOUSIE UNIV,DEPT MATH STAT & COMP SCI,HALIFAX B3H 3J5,NS,CANADA. RP WATTERS, C (reprint author), UNIV WATERLOO,DEPT COMP SCI,WATERLOO N2L 3G1,ONTARIO,CANADA. CR AUSIELLO G, 1981, LECTURE NOTES COMPUT, P212 BATINI C, 1981, LECTURE NOTES COMPUT, P177 BELKIN NJ, 1986, 1986 P ACM C RES DEV, P11 Berge C, 1973, GRAPHS HYPERGRAPHS BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 Bush V., 1945, ATLANTIC MONTHLY, P101 CATER SC, 1987, 10 ACM SIGIR C RES D, P171 CONKLIN J, 1987, STP35686 MICR COMP T Conklin Jeff, 1987, IEEE COMPUT, V20, P17 CONSENS MP, 1989, 1989 HYP 89 P NEW YO, P269 CRAWFORD RG, 1988, CAN J INFORM SCI, V13, P1 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1989, 1989 P HYP 89 PITTSB, P213 CROUCH DB, 1989, 1989 HYP 89 P NEW YO, P225 DELISLE N, 1986, P ACM SIGMOD 86 WASH, P132, DOI 10.1145/16894.16867 DELISLE NM, 1987, ACM T INFORM SYST, V5, P168, DOI 10.1145/27636.27639 FAGIN R, 1982, ACM T DATABASE SYST, V7, P343, DOI 10.1145/319732.319735 FIDERIO J, 1988, BYTE, V13, P237 FRISSE ME, 1987, P ACM HYP C CHAP HIL, P57, DOI 10.1145/317426.317433 FRISSE ME, 1989, 1989 HYP 89 P, P199 FURUTA R, 1989, 1989 HYP 89 P NEW YO, P27 GARG PK, 1988, COMMUN ACM, V31, P862, DOI 10.1145/48511.48516 GARG PK, 1987, NOV HYP 87 P CHAP HI, P409 HALASZ FG, 1988, COMMUN ACM, V31, P836, DOI 10.1145/48511.48514 KACMAR C, 1988, TAMU88018 TEX A M U LARSON JA, 1986, IEEE COMPUTER MA JUN, V19, P62 LECLUSE C, 1988, 14TH INT C VER LARG, P62 MAIER D, 1983, ACM T DATABASE SYST, V8, P1, DOI 10.1145/319830.319831 MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 NELSON TH, 1988, BYTE, V13, P225 OWRANG MM, 1988, COMPUT J, V31, P155, DOI 10.1093/comjnl/31.2.155 RAYMOND DR, 1988, COMMUN ACM, V31, P836 SALTON G, 1968, AUTOMATIC INFORMATIO, P21 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHNASE JL, 1988, TAMU88017 TEX A M U SHEPHERD MA, 1990, INFORM PROCESS MANAG, V26, P395, DOI 10.1016/0306-4573(90)90099-N SHEPHERD MA, 1989, IN PRESS MAY MID YEA STOTTS PD, 1989, ACM T INFORM SYST, V7, P3, DOI 10.1145/64789.64791 TOMPA FW, 1989, ACM T INFORM SYST, V7, P85, DOI 10.1145/64789.64993 Ullman J. D., 1982, PRINCIPLES DATABASE VANDYKE PH, 1989, 1989 HYP 89 P NEW YO, P43 WATTERS CR, 1989, 17TH ANN C CAN ASS I YANKELOVICH N, 1988, IEEE COMPUT, V21, P81 ZLOOF M, 1975, 1975 P NAT COMP C NR 44 TC 20 Z9 20 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 1046-8188 J9 ACM T INFORM SYST JI ACM Trans. Inf. Syst. PD APR PY 1990 VL 8 IS 2 BP 77 EP 102 DI 10.1145/96105.96107 PG 26 WC Computer Science, Information Systems SC Computer Science GA EH703 UT WOS:A1990EH70300001 ER PT J AU DUNLAP, E SHEPHERD, MA AF DUNLAP, E SHEPHERD, MA TI INTEGRATION OF USER PROFILES INTO THE PARA-NORM RETRIEVAL MODEL SO CANADIAN JOURNAL OF INFORMATION SCIENCE-REVUE CANADIENNE DES SCIENCES DE L INFORMATION LA English DT Article C1 DALHOUSIE UNIV,DEPT MATH STAT & COMP SCI,HALIFAX B3H 3J5,NS,CANADA. RP DUNLAP, E (reprint author), ST MARYS UNIV,DEPT MATH & COMP SCI,HALIFAX B3H 3C3,NS,CANADA. CR DANILOWICZ C, 1984, INT J MAN MACH STUD, V21, P245, DOI 10.1016/S0020-7373(84)80044-9 FOX EA, 1988, INFORM PROCESS MANAG, V24, P257, DOI 10.1016/0306-4573(88)90093-3 FOX EA, 1986, TR861 DEP COMP SCI T KORFHAGE RR, 1985, 8TH P ANN INT ACM SI, P33 KORFHAGE RR, 1982, 6TH P COMPSAC 82 INT, P470 KORFHAGE RR, 1984, 3RD P JOINT BCS ACM, P111 KORFHAGE RR, 1985, 85CSE9 SO METH U DEP KORFHAGE RR, 1985, 85CSE4 SO METH U DEP RADECKI T, 1988, INFORM PROCESS MANAG, V24, P219, DOI 10.1016/0306-4573(88)90089-1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1988, INFORM PROCESS MANAG, V24, P269, DOI 10.1016/0306-4573(88)90094-5 Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1985, 8 P ANN INT ACM SIGI, P54 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SHEPHERD MA, 1985, 8TH P ANN INT ACM SI, P274 SHEPHERD MA, 1986, J AM SOC INFORM SCI, V37, P146 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA NR 18 TC 1 Z9 1 PU CANADIAN ASSOC INFORMATION SCIENCE PI OTTAWA PA PO BOX 6174, STATION J, OTTAWA ON K2A 1T2, CANADA SN 0380-9218 J9 CAN J INFORM SCI PD APR PY 1990 VL 15 IS 1 BP 1 EP 20 PG 20 WC Information Science & Library Science SC Information Science & Library Science GA CZ386 UT WOS:A1990CZ38600001 ER PT J AU CROUCH, CJ AF CROUCH, CJ TI AN APPROACH TO THE AUTOMATIC CONSTRUCTION OF GLOBAL THESAURI SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP CROUCH, CJ (reprint author), UNIV MINNESOTA,DEPT COMP SCI,DULUTH,MN 55812, USA. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 ATTAR R, 1981, INFORM PROCESS MANAG, V17, P115 BERNSTEIN LM, 1984, J AM SOC INFORM SCI, V35, P235, DOI 10.1002/asi.4630350407 BLOCH N, 1988, THESIS TULANE U BUCKLEY C, 85686 CORN U DEP COM CLEVERDON CW, 1966, ASLIB CRANFIELD PROJ, V1 CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1981, ACM SIGIR FORUM, V16, P40, DOI 10.1145/1013228.511760 CROUCH C, 1988, 11TH P INT C RES DEV CROUCH CJ, 1988, INFORM PROCESS MANAG, V24, P5, DOI 10.1016/0306-4573(88)90072-6 CROUCH D, 1989, NOV PYP 89 P PITTSB DOSZKOCS TE, 1986, J AM SOC INFORM SCI, V37, P191, DOI 10.1002/(SICI)1097-4571(198607)37:4<191::AID-ASI3>3.0.CO;2-Y ELHAMDOUCHI A, 1988, INFORM PROCESS MANAG, V24, P17, DOI 10.1016/0306-4573(88)90073-8 FOX E, 8623 DEP COMP SCI TE FOX E, 1988, 2ND P C APPL NAT LAN FOX E, 83561 CORN U DEP COM FOX EA, 1983, THESIS CORNELL U FOX EA, 1980, ACM SIGIR FORUM, V15, P5, DOI 10.1145/1095403.1095404 GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 JARDINE N, 1968, COMPUT J, V11, P177 JONES KS, 1971, AUTOMATIC KEYWORD CL Katzer J., 1982, Information Technology: Research and Development, V1 LANCE GN, 1967, COMPUT J, V9, P373 MINKER J, 1973, J AM SOC INFORM SCI, V24, P246, DOI 10.1002/asi.4630240404 ODDY RN, 1977, J DOC, V33, P1, DOI 10.1108/eb026631 PIETILAINEN P, 1983, INFORMATION PROCESSI, P51 RAGHAVAN V, 1975, J AM SOC INFORM SCI, V37, P33 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1966, SCI REPORTS INFORMAT SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 87881 CORN U DEP COM Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1987, ACM SIGIR FORUM SALTON G, 1968, SCI REPORTS INFORMAT SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1972, INFORMATION PROCESSI, V71, P115 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 SMEATON A, 1983, COMPUT J, V26, P10 WANG YC, 1985, J AM SOC INFORM SCI, V36, P15 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 VOORHEES E, 86765 CORN U DEP COM VOORHEES EM, 1985, THESIS CORNELL U WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 WONG SKM, 1984, 7TH P ANN INT C INF NR 46 TC 60 Z9 62 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1990 VL 26 IS 5 BP 629 EP 640 DI 10.1016/0306-4573(90)90106-C PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA EB227 UT WOS:A1990EB22700004 ER PT J AU FOX, EA WINETT, SG AF FOX, EA WINETT, SG TI USING VECTOR AND EXTENDED BOOLEAN MATCHING IN AN EXPERT SYSTEM FOR SELECTING FOSTER HOMES SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 ROANOKE COLL,DEPT MATH & COMP SCI,SALEM,VA 24153. RP FOX, EA (reprint author), VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061, USA. CR AIKEN JS, 1980, STANCS80814 STANF U BELKIN NJ, 1983, J INFORM SCI, V5, P153 BELKIN NJ, 1987, INFORM PROCESS MANAG, V23, P395, DOI 10.1016/0306-4573(87)90109-9 BISWAS G, 1987, J AM SOC INFORM SCI, V38, P83, DOI 10.1002/(SICI)1097-4571(198703)38:2<83::AID-ASI2>3.0.CO;2-X BISWAS G, 1987, J AM SOC INFORM SCI, V38, P97, DOI 10.1002/(SICI)1097-4571(198703)38:2<97::AID-ASI3>3.0.CO;2-H BOBROW DG, 1985, IEEE T SOFTWARE ENG, V11, P1401, DOI 10.1109/TSE.1985.231888 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 BOOKSTEIN A, 1985, ANNU REV INFORM SCI, V20, P117 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 CAPLAN R, 1983, STRESS RES ISSUES 80 CATER SC, 1987, 10 ACM SIGIR C RES D, P171 CLARK KL, 1982, MACHINE INTELLIGENCE CROFT WB, 1987, J AM SOC INFORM SCI, V38, P389, DOI 10.1002/(SICI)1097-4571(198711)38:6<389::AID-ASI1>3.0.CO;2-4 CROFT WB, 1987, 10 ANN INT ACM SIGIR, P26 DIBENIGNO G, 1986, 1986 P ACM SIGIR C R, P144 FOX EA, 1987, 4TH P ANN USC COMP S, P7 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FOX EA, 1983, SOME CONSIDERATIONS FOX EA, 1983, THESIS CORNELL U FOX EA, 1988, INFORM PROCESS MANAG, V24, P257, DOI 10.1016/0306-4573(88)90093-3 FOX EA, 1986, COMPARISON 2 METHODS FOX EA, 1987, ACM SIGIR FORUM, V21, P18, DOI 10.1145/30075.30078 Fox E. A., 1987, International Journal of Approximate Reasoning, V1, DOI 10.1016/0888-613X(87)90012-0 FRANCE RK, 1986, OCT P IEEE EXP SYST, P135 GAMMACK JG, 1985, RES DEV EXPERT SYSTE, P105 GUIDA G, 1983, AUTOMATICA, V19, P759, DOI 10.1016/0005-1098(83)90044-4 HAYESROTH F, 1986, EXPERT SYSTEMS TECHN, P310 Hayes-Roth F., 1983, BUILDING EXPERT SYST HAYESROTH F, 1985, COMMUN ACM, V28, P921, DOI 10.1145/4284.4286 HELLY JJ, 1984, CSD840038 U CAL REP HELM AR, 1986, OCT P EXP SYST GOVT, P284 HOFFMAN RR, 1987, AI MAG, V8, P53 JEFFERSON S, 1984, AAAI84, P476 Kadushin A, 1980, CHILD WELFARE SERVIC KANTOR PB, 1986, 49TH P ANN M AM SOC, P113 Klahr P., 1986, EXPERT SYSTEMS TECHN LEE NS, 1986, OCT P EXP SYST GOVT, P115 Lee N. S., 1986, IEEE Expert, V1, DOI 10.1109/MEX.1986.4306954 MARCUS RS, 1986, 49TH P ANN M AM SOC, P185 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V2, P939 MICCO HM, 1986, 49TH P ANN M AM SOC, P204 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 Olsen L.I., 1982, SOC WORK RES ABSTR, V18, P9 PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PATELSCHNEIDER PF, 1984, FLAIR654 FAIRCH TECH POLLITT SE, 1983, SIGIR FORUM, V17, P136 PRERAU DS, 1987, AI MAG, V8, P43 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RAPPAPORT J, 1977, COMMUNITY PSCYCHOL Rich E., 1979, COGNITIVE SCI, V3, P329, DOI DOI 10.1207/515516709C0G0304_3 SALTON G, 1980, ENCY LIB INFORM SCI, P1 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1983, INFORMATION PROCESSI, V83, P755 SCHANK RC, 1981, INFORMATION RETRIEVA Schoech D., 1985, Computers in Human Services, V1 SEGAL SP, IN PRESS ISSUES COMM SHYNE AW, 1978, NATIONAL STUDY SOCIA SMITH LC, 1987, ANNU REV INFORM SCI, V22, P41 SMITH LC, 1980, ANNU REV INFORM SCI, V15, P67 Sterling L., 1986, ART PROLOG SUBRAHMANYAM PA, 1985, IEEE T SOFTWARE ENG, V11, P1391, DOI 10.1109/TSE.1985.231887 TISCHLER MA, 1987, COMPUT CHEM, V11, P235, DOI 10.1016/0097-8485(87)85002-7 TONG RM, 1987, 10TH P ANN INT ACM S, P247 *VA DEP SOC SERV, 1984, POL MAN, V7, pCHB WALKER A, 1987, KNOWLEDGE SYSTEMS PR, P219 WATTERS CR, 1987, 10TH P ACM SIGIR C N, P272 Winett S. G., 1985, Second Conference on Artificial Intelligence Applications: The Engineering of Knowledge-Based Systems (Cat. No.85CH2215-2) WINETT SG, 1987, JUL P INT C DAT BAS WINETT SG, 1985, P EXPERT SYSTEMS GOV, P391 WINETT SG, 1987, THESIS BLACKSBURG YAGHMAI NS, 1984, J AM SOC INFORM SCI, V35, P297, DOI 10.1002/asi.4630350508 YIP M, 1979, THESIS CAMBRIDGE ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X ZARRI GP, 1984, INT J MAN MACH STUD, V20, P87, DOI 10.1016/S0020-7373(84)80007-3 NR 78 TC 1 Z9 1 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD JAN PY 1990 VL 41 IS 1 BP 10 EP 26 DI 10.1002/(SICI)1097-4571(199001)41:1<10::AID-ASI2>3.0.CO;2-6 PG 17 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CH630 UT WOS:A1990CH63000002 ER PT J AU WATTERS, CR AF WATTERS, CR TI LOGIC FRAMEWORK FOR INFORMATION-RETRIEVAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP WATTERS, CR (reprint author), DALHOUSIE UNIV,DEPT MATH STAT & COMP SCI,HALIFAX B3H 4H2,NS,CANADA. CR BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BRAUNDET MF, 1987, 10TH P ASS COMP MACH, P33 CLEVERDO.CW, 1972, J DOC, V28, P195, DOI 10.1108/eb026538 COEHLO H, 1982, INFORMATION SYSTEMS, V7, P163 COOPER WS, 1984, 3RD P JOINT BCS ACM, P259 CROFT WB, 1986, 1986 P ACM C RES DEV, P201 CROFT WB, 1987, 10 ANN INT ACM SIGIR, P26 DANILOWICZ C, 1984, INT J MAN MACH STUD, V21, P245, DOI 10.1016/S0020-7373(84)80044-9 Hayes-Roth F., 1983, BUILDING EXPERT SYST KORFHAGE RR, 1984, 3RD P JOINT BCS ACM, P111 LEBOWITZ M, 1983, 6TH P INT ACM SIGIR, P25 Nilsson N. J., 1980, PRINCIPLES ARTIFICIA POLLITT AS, 1986, 866 HUDD POL TECHN R SALTON G, 1983, INTRO MODERN INFORMA, V12, P1022 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHAUBLE P, 1987, THESAURUS BASED CONC SHEPHERD MA, 1986, J AM SOC INFORM SCI, V37, P146 SHOVAL P, 1985, INFORM PROCESS MANAG, V21, P475, DOI 10.1016/0306-4573(85)90002-0 Skuce D., 1985, Data & Knowledge Engineering, V1, DOI 10.1016/0169-023X(85)90017-5 SMITH PJ, 1984, IEEE, P222 SUBRAMANIAN V, 1986, OPT ENG, V25, P445, DOI 10.1117/12.7973842 TESKEY FN, 1987, 10TH P ANN INT ACMSI THOMPSON RH, 1985, OCT EXP SYST GOV S I, P448 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1986, 1986 ACM C RES DEV I, P194 WATTERS CR, IN PRESS INFORMATION WATTERS CR, 1986, 3RD INT C SYST RES I WONG SK, 1985, 7TH P INT C INF STOR, P18 NR 28 TC 6 Z9 6 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD SEP PY 1989 VL 40 IS 5 BP 311 EP 324 DI 10.1002/(SICI)1097-4571(198909)40:5<311::AID-ASI3>3.0.CO;2-B PG 14 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AQ266 UT WOS:A1989AQ26600003 ER PT J AU BIRU, T ELHAMDOUCHI, A REES, RS WILLETT, P AF BIRU, T ELHAMDOUCHI, A REES, RS WILLETT, P TI INCLUSION OF RELEVANCE INFORMATION IN THE TERM DISCRIMINATION MODEL SO JOURNAL OF DOCUMENTATION LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BIRU T, 1987, INCORPORATION RELEVA CAN F, 1987, J AM SOC INFORM SCI, V38, P171, DOI 10.1002/(SICI)1097-4571(198705)38:3<171::AID-ASI6>3.0.CO;2-S CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 CROUCH CJ, 1988, INFORM PROCESS MANAG, V24, P5, DOI 10.1016/0306-4573(88)90072-6 ELHAMDOUCHI A, 1988, INFORM PROCESS MANAG, V24, P17, DOI 10.1016/0306-4573(88)90073-8 ELHAMDOUCHI A, 1987, J INFORM SCI, V13, P361, DOI 10.1177/016555158701300607 PORTER M, 1988, PROGRAM-AUTOM LIBR, V22, P1, DOI 10.1108/eb046983 ROBERTSON SE, 1986, J DOC, V42, P182, DOI 10.1108/eb026792 ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1975, THEORY INDEXING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 NR 22 TC 7 Z9 7 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PD JUN PY 1989 VL 45 IS 2 BP 85 EP 109 DI 10.1108/eb026840 PG 25 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AF775 UT WOS:A1989AF77500001 ER PT J AU FAGAN, JL AF FAGAN, JL TI THE EFFECTIVENESS OF A NONSYNTACTIC APPROACH TO AUTOMATIC PHRASE INDEXING FOR DOCUMENT-RETRIEVAL SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 CORNELL UNIV,DEPT MODERN LANGUAGES & LINGUIST,ITHACA,NY 14853. CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853. CR BAXENDALE PB, 1958, IBM J RES DEV, V2, P354 BAXENDALE PB, 1961, MACHINE INDEXING PRO, P207 BUCKLEY C, 1985, TR85686 DEP COMP SCI CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 DOYLE LB, 1961, J ACM, V8, P553, DOI 10.1145/321088.321095 DOYLE LB, 1962, AM DOC, V13, P378, DOI 10.1002/asi.5090130404 EARL LL, 1972, INFORM STORAGE RET, V8, P277, DOI 10.1016/0020-0271(72)90018-6 EARL LL, 1970, INFORM STORAGE RET, V6, P313, DOI 10.1016/0020-0271(70)90025-2 FAGAN JL, 1985, DEC S PLNLP PROGR LA FOX EA, 1983, THESIS CORNELL U FOX EA, 1983, TR83561 CORN U DEP C GARDIN JC, 1973, J DOC, V29, P137, DOI 10.1108/eb026553 GIULIANO VE, 1963, VISTAS INFORMATION H, V1, P30 HARPER DJ, 1978, J DOC, V34, P189, DOI 10.1108/eb026659 Heidom G., 1975, THEORETICAL ISSUES N, P1 Heidorn G.E., 1972, NPS55HD72101A NAV PO HILLMAN DJ, 1973, INFORM STORAGE RET, V9, P587, DOI 10.1016/0020-0271(73)90040-5 HILLMAN DJ, 1969, AFIPS C P, V34, P447 Jensen K, 1986, PEG 1986 BROAD COVER KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P79, DOI 10.1016/0020-0271(73)90020-X KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P477, DOI 10.1016/0020-0271(73)90034-X LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106 LOVINS JB, 1968, MECH TRANSL, V11, P22 LYONS J, 1968, INTRO THEORETICAL LI, P73 Metzler D. P., 1984, Research and Development in Information Retrieval. Proceedings of the Joint BCS and ACM Symposium PAICE CD, 1985, 1985 RIAO85 C GREN, P293 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1971, SMART RETRIEVAL SYST, P143 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1966, READINGS AUTOMATIC L, P169 SALTON G, 1983, RES DEV INFORMATION, P151 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1986, 1986 P ACM C RES DEV, P103 Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 SPARCKJONES K, 1972, J DOC, V28, P11 STILES HE, 1961, J ACM, V8, P271, DOI 10.1145/321062.321074 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1983, TR83543 CORN U DEP C NR 43 TC 73 Z9 73 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAR PY 1989 VL 40 IS 2 BP 115 EP 132 DI 10.1002/(SICI)1097-4571(198903)40:2<115::AID-ASI6>3.0.CO;2-B PG 18 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA T4595 UT WOS:A1989T459500006 ER PT J AU SALTON, G AF SALTON, G TI MANAGING INFORMATION AND TECHNOLOGY SO PROCEEDINGS OF THE ASIS ANNUAL MEETING LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR DeRose S. J., 1988, Computational Linguistics, V14 DILLON M, 1983, J AM SOC INFORM SCI, V34, P99, DOI 10.1002/asi.4630340202 FAGAN JL, 1987, 87868 CORN U DEP COM Harman D., 1988, 11th International Conference on Research and Development in Information Retrieval HEIDORN GE, 1982, IBM SYST J, V21, P305 Jensen K., 1983, American Journal of Computational Linguistics, V9 Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 KAYE J, 1988, CS490 CORN U DEP COM KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P79, DOI 10.1016/0020-0271(73)90020-X KLINGBIE.PH, 1973, INFORM STORAGE RET, V9, P477, DOI 10.1016/0020-0271(73)90034-X LEECH G, 1983, ICAME J, V7, P13 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 SALTON G, 1988, 88898 CORN U DEP COM SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0 Salton G, 1989, AUTOMATIC TEXT PROCE SALTON G, 1989, 89979 CORN U DEP COM SALTON G, 1988, 88954 CORN U DEP COM SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SMEATON AF, 1983, COMPUT J, V26, P239, DOI 10.1093/comjnl/26.3.239 Tait J. I., 1984, J DOC, V40, P50, DOI 10.1108/eb026757 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7 NR 22 TC 0 Z9 0 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0044-7870 J9 P ASIS ANNU MEET JI Proc. ASIS Annu. Meet. PY 1989 VL 26 BP 3 EP 8 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA AX369 UT WOS:A1989AX36900001 ER PT J AU SALTON, G BUCKLEY, C AF SALTON, G BUCKLEY, C TI PARALLEL TEXT SEARCH METHODS SO COMMUNICATIONS OF THE ACM LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR BERGMARK D, 1980, FPS USERS GROUP M LO BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 Chang Y, 1971, SMART RETRIEVAL SYST, P355 Cleverdon C., 1984, Information Services & Use, V4 COOPER WS, 1983, J AM SOC INFORM SCI, V34, P31, DOI 10.1002/asi.4630340106 COPELAND CP, 1973, 1ST P ANN S COMP ARC, P121 CROFT WB, 1980, INFORM SYST, V5, P189, DOI 10.1016/0306-4379(80)90010-1 FALOUTSOS C, 1984, ACM T OFF INF SYST, V2, P267, DOI 10.1145/2275.357411 FRENKEL KA, 1986, COMMUN ACM, V29, P752, DOI 10.1145/6424.6427 HALL HA, 1967, ISR12 REP HENRY WM, 1980, ON LINE SEARCHING IN HOLLAAR LA, 1977, JUN P AFIPS NAT COMP, V46, P697 Ide E., 1971, SMART RETRIEVAL SYST, P337 IDE E, 1968, ISR14 REP Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526 Jones K. S., 1970, J DOC, V26, P89, DOI 10.1108/eb026488 KNUTH DE, 1973, ART COMPUTER PROGRAM, V1, P224 LANCASTER FW, 1979, INFORMATION RETRIEVA LARSON PA, 1984, DATA BASE, V15, P19 MEADOW CT, 1981, BASICS ON LINE SEARC Mooers C. N., 1951, AM DOC, V2, P20, DOI 10.1002/asi.5090020107 PFALTZ JL, 1980, COMMUN ACM, V23, P522, DOI 10.1145/359007.359013 ROBERTS CS, 1979, P IEEE, V67, P1624, DOI 10.1109/PROC.1979.11543 Rocchio J J, 1971, SMART RETRIEVAL SYST, P313 ROCCHIO JJ, 1965, ISR9 HARV COMP LAB S SADOWSKI PJ, 1978, 4TH P WORKSH COMP AR, P99 SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990 SALTON G, 1971, SMART SYSTEM EXPT AU, P324 Salton G., 1975, REGIONAL C SERIES AP, V18 Salton G., 1971, SMART SYSTEM EXPT AU SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1967, ISR12 CORN U DEP COM SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1981, LECT NOTES COMPUTER, V111, P328 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1965, AM DOC, V16, P209, DOI 10.1002/asi.5090160308 SALTON G, 1979, ENCY COMPUTER SCI TE, V13, P137 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUSTER SA, 1979, IEEE T COMPUT, V28, P446 STANFILL C, 1986, COMMUN ACM, V29, P1229, DOI 10.1145/7902.7907 STELLHORN WH, 1977, IEEE T COMPUT, V26, P1258 STONE HS, 1987, COMPUTER, V20, P11 SU SYW, 1979, COMPUTER, V12, P11 TSICHRITZIS D, 1983, ACM T OFF INF SYST, V1, P88, DOI 10.1145/357423.357429 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VERNIMB C, 1977, INFORM PROCESS MANAG, V13, P339, DOI 10.1016/0306-4573(77)90054-1 WALTZ DL, 1987, COMPUTER, V20, P85 NR 51 TC 35 Z9 37 PU ASSOC COMPUTING MACHINERY PI NEW YORK PA 1515 BROADWAY, NEW YORK, NY 10036 SN 0001-0782 J9 COMMUN ACM JI Commun. ACM PD FEB PY 1988 VL 31 IS 2 BP 202 EP 215 DI 10.1145/42372.42380 PG 14 WC Computer Science, Hardware & Architecture; Computer Science, Software Engineering; Computer Science, Theory & Methods SC Computer Science GA M2090 UT WOS:A1988M209000010 ER PT J AU CROUCH, CJ AF CROUCH, CJ TI AN ANALYSIS OF APPROXIMATE VERSUS EXACT DISCRIMINATION VALUES SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP CROUCH, CJ (reprint author), TULANE UNIV,DEPT COMP SCI,NEW ORLEANS,LA 70118, USA. CR CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 FOX E, 1983, TR83561 CORN U COMP Fox E., 1983, THESIS CORNELL U ITH GLASS GV, 1984, STATISTICAL METHODS HEAPS HS, 1978, INFORMATION RETRIEVA RAGHAVAN VV, 1986, J AM SOC INFORM SCI, V37, P279, DOI 10.1002/asi.4630370502 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUTERS HUMANITIES, V10, P291 VOORHEES E, 1985, TR85705 CORN U COMP WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 WONG SKM, 1984, 7TH P ANN INT C INF, P167 NR 13 TC 7 Z9 7 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 1 BP 5 EP 16 DI 10.1016/0306-4573(88)90072-6 PG 12 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA M2508 UT WOS:A1988M250800001 ER PT J AU ELHAMDOUCHI, A WILLETT, P AF ELHAMDOUCHI, A WILLETT, P TI AN IMPROVED ALGORITHM FOR THE CALCULATION OF EXACT TERM DISCRIMINATION VALUES SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 UNIV SHEFFIELD,DEPT INFORMAT STUDIES,WESTERN BANK,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR GRIFFITHS A, 1986, J AM SOC INFORM SCI, V37, P3, DOI 10.1002/asi.4630370102 Jones K. S., 1973, Information Storage and Retrieval, V9, DOI 10.1016/0020-0271(73)90043-0 LYNCH MF, 1977, J AM SOC INFORM SCI, V28, P19, DOI 10.1002/asi.4630280104 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VOORHEES EM, 1986, INFORM PROCESS MANAG, V22, P465, DOI 10.1016/0306-4573(86)90097-X WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 NR 7 TC 11 Z9 11 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 1 BP 17 EP 22 DI 10.1016/0306-4573(88)90073-8 PG 6 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA M2508 UT WOS:A1988M250800002 ER PT J AU FOX, EA KOLL, MB AF FOX, EA KOLL, MB TI PRACTICAL ENHANCED BOOLEAN RETRIEVAL - EXPERIENCES WITH THE SMART AND SIRE SYSTEMS SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article C1 GEORGE MASON UNIV,DEPT INFORMAT SYST & SYST ENGN,FAIRFAX,VA 22034. RP FOX, EA (reprint author), VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061, USA. CR ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021 BELKIN NJ, 1984, INFORM TECHNOL R & D, V3, P122 BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197 BOOKSTEIN A, 1980, J AM SOC INFORM SCI, V31, P240, DOI 10.1002/asi.4630310403 Borko H., 1978, INDEXING CONCEPTS ME BOVEY JD, 1984, INFORM TECHNOL R & D, V3, P84 Buckley C., 1985, TR85686 CORN U DEP C BUCKLEY C, 1985, 8TH P INT ACM C RES, P97 BUELL DA, 1985, J AM SOC INFORM SCI, V36, P398, DOI 10.1002/asi.4630360609 BUELL DA, 1981, J AM SOC INFORM SCI, V32, P211, DOI 10.1002/asi.4630320307 Cheeseman P., 1985, P 9 INT JOINT C ART, P1002 CHOEKA Y, 1980, COMPUTERS HUMANITIES, V14, P153 CROFT WB, 1986, J AM SOC INFORM SCI, V37, P71, DOI 10.1002/(SICI)1097-4571(198603)37:2<71::AID-ASI3>3.0.CO;2-4 DILLON M, 1980, J DOC, V36, P197, DOI 10.1108/eb026696 DOSZKOCS TE, 1983, 1982 RES DEV INF RET, P38 FOX EA, 1987, INFORM PROCESS MANAG, V23, P341, DOI 10.1016/0306-4573(87)90022-7 FOX EA, 1986, TR861 VIRG TECH DEP FOX EA, 1983, THESIS CORNELL U FOX EA, 1984, FICHE, V5, P253 FOX EA, 1986, CD ROM NEW PAPYRUS, P143 FOX EA, 1983, TR83560 CORN U DEP C Fox E. A., 1986, Microcomputers for Information Management, V3 FOX EA, 1985, 8TH ANN INT ACM SIGI, P42 Fox E. A., 1987, International Journal of Approximate Reasoning, V1, DOI 10.1016/0888-613X(87)90012-0 FOX EA, 1986, 2ND P C COMP INT INT Fox P. J., 1981, SEA, V7, P119 Kandel A, 1986, FUZZY MATH TECHNIQUE Katzer J., 1982, Information Technology: Research and Development, V1 KERRE EE, 1986, J AM SOC INFORM SCI, V37, P341, DOI 10.1002/(SICI)1097-4571(198609)37:5<341::AID-ASI9>3.0.CO;2-A KOLL M, IN PRESS J AM SOC IN KOLL M, 1984, APR P NAT ONL M NEW, P165 Lovins J., 1968, MECH TRANSLATION COM, V11, P11 MCCUNE BP, 1985, IEEE T SOFTWARE ENG, V11, P939, DOI 10.1109/TSE.1985.232827 MCGILL M, 1976, ACM SIGIR FORUM, V10, P37, DOI 10.1145/1095286.1095300 MCGILL M, 1979, EVALUATION FACTORS A MEADOW CT, 1981, BASICS ONLINE SEARCH MORRISSEY J, 1983, 1982 RES DEV INF RET, P38 NEUFELD ML, 1986, J AM SOC INFORM SCI, V37, P183, DOI 10.1002/(SICI)1097-4571(198607)37:4<183::AID-ASI2>3.0.CO;2-W Noreault T., 1982, Information Technology: Research and Development, V1 NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 NOREAULT T, 1981, INFORMATION RETRIEVA PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PORTER MF, 1982, INFORMATION TECHNOLO, V1 RADECKI T, 1983, RES DEV INFORMATION, P133 RADECKI T, 1982, J AM SOC INFORM SCI, V33, P365, DOI 10.1002/asi.4630330603 RADECKI T, 1988, INFORM PROCESS MANAG, V24, P281, DOI 10.1016/0306-4573(88)90095-7 RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RADECKI T, 1982, J DOC, V38, P207, DOI 10.1108/eb026729 RADECKI T, 1983, INT J MAN MACH STUD, V18, P407, DOI 10.1016/S0020-7373(83)80019-4 Rescher N., 1969, MANY VALUED LOGIC ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 Rocchio J., 1971, SMART RETRIEVAL SYST SALTON G, 1980, ENCY LIB INFORM SCI, P1 SALTON G, 1983, INTRO MODERN INFORMA Salton G., 1983, Information Processing 83. Proceedings of the IFIP 9th World Computer Congress SALTON G, 1983, J AM SOC INFORM SCI, V34, P262, DOI 10.1002/asi.4630340406 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1986, COMMUN ACM, V29, P648, DOI 10.1145/6138.6149 SALTON G, 1985, J AM SOC INFORM SCI, V36, P200, DOI 10.1002/asi.4630360311 SALTON G, 1984, INFORM PROCESS MANAG, V20, P637, DOI 10.1016/0306-4573(84)90080-3 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Shafer G., 1976, MATH THEORY EVIDENCE TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 TENOPIR C, 1984, ANNU REV INFORM SCI, V19, P215 TONG RM, 1983, P NATIONAL C ARTIFIC, P411 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X NR 69 TC 15 Z9 15 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1988 VL 24 IS 3 BP 257 EP 267 DI 10.1016/0306-4573(88)90093-3 PG 11 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA N8302 UT WOS:A1988N830200006 ER PT J AU CAN, F OZKARAHAN, EA AF CAN, F OZKARAHAN, EA TI COMPUTATION OF TERM DOCUMENT DISCRIMINATION VALUES BY USE OF THE COVER COEFFICIENT CONCEPT SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article C1 MIDDLE E TECH UNIV,DEPT ELECT & ELECTR ENGN,ANKARA,TURKEY. ARIZONA STATE UNIV,DEPT COMP SCI,TEMPE,AZ 85287. CR BORKO H, 1977, INFORM PROCESS MANAG, V13, P355, DOI 10.1016/0306-4573(77)90055-3 BORKO H, 1982, ACM SIGIR FORUM, V16, P9, DOI 10.1145/1095454.1095456 CAN F, UNPUB VALIDITY ANAL CAN F, 1985, THESIS MIDDLE E TU A CAN F, 1984, J AM SOC INFORM SCI, V35, P268, DOI 10.1002/asi.4630350503 CAN F, 1985, J AM SOC INFORM SCI, V36, P3 CAN F, 1983, JUN P ACM SIGIR C BE, P115 CAN F, 1985, JUN P ACM SIGIR C MO, P204 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 CROFT WB, 1981, J AM SOC INFORMATION, V33, P451 CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683 EVERITT BS, 1979, BIOMETRICS, V35, P169, DOI 10.2307/2529943 LANCASTER FW, 1975, INFORMATION RETRIEVA MARON ME, 1978, J AM SOC INFORMATION, V19, P224 NELSON MJ, 1985, J AM SOC INFORM SCI, V36, P283, DOI 10.1002/asi.4630360502 NIE N, 1975, SPSS STATISTICAL PAC OZKARAHAN E, 1986, DATABASE MACHINES DA Salton G., 1975, REGIONAL C SERIES AP, V18 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1981, J AM SOC INFORM SCI, V32, P175, DOI 10.1002/asi.4630320304 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225, DOI 10.1016/0306-4573(85)90107-4 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 NR 28 TC 9 Z9 9 PU JOHN WILEY & SONS INC PI NEW YORK PA 605 THIRD AVE, NEW YORK, NY 10158-0012 SN 0002-8231 J9 J AM SOC INFORM SCI JI J. Am. Soc. Inf. Sci. PD MAY PY 1987 VL 38 IS 3 BP 171 EP 183 DI 10.1002/(SICI)1097-4571(198705)38:3<171::AID-ASI6>3.0.CO;2-S PG 13 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA H1081 UT WOS:A1987H108100003 ER PT J AU PANYR, J AF PANYR, J TI VECTOR-SPACE MODEL AND CLUSTER-ANALYSIS IN INFORMATION-RETRIEVAL SYSTEMS SO NACHRICHTEN FUR DOKUMENTATION LA German DT Article RP PANYR, J (reprint author), SIEMENS AG,ZENTRALBEREICH FORSCH & TECH,ZENT AUFGABEN INFORMAT TECH,D-8000 MUNICH 83,FED REP GER. CR BOLLMAN P, 1983, LECT NOTES COMPUT SC, V146, P13 BOLLMANN P, 1979, PRAXIS INFORMATION R, V2, P277 BOOKSTEIN A, 1977, J ACM, V24, P410 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BORKO H, 1977, INFORM PROCESS MANAG, V13, P355, DOI 10.1016/0306-4573(77)90055-3 COOPER WS, 1978, J AM SOC INFORM SCI, V29, P107, DOI 10.1002/asi.4630290302 COOPER WS, 1978, J ACM, V25, P67, DOI 10.1145/322047.322053 CROUCH D, 1972, THESIS SO METHODIST DATTOLA RT, 1979, J AM SOC INFORM SCI, V30, P9 DEOGUN JS, 1986, 1986 P ACM C RES DEV, P157 FISCHER D, 1985, P WORKSHOP ARTIFICIA FISCHER H, 1982, INFORMATION RETRIEVA GEBHARDT F, 1981, DOKUMENTATIONSSYSTEM HARTER PS, 1975, J ASIS 2, V26, P280 HARTER PS, 1975, J ASIS 1, V26, P197 Jardine N, 1971, MATH TAXONOMY Jones K. S., 1970, J DOC, V26, P89, DOI 10.1108/eb026488 JONES KS, 1971, AUTOMATIC KEYWORD CL JONES KS, 1976, NATURAL LANGUAGE INF Jones K.S., 1967, Computer Journal, V10 Jones K. S., 1970, Information Storage and Retrieval, V5, DOI 10.1016/0020-0271(70)90046-X KRAFT DH, 1978, J AM SOC INFORM SCI, V29, P31, DOI 10.1002/asi.4630290106 LEFKOWITZ D, 1965, C TECHNICAL PRECONDI, P133 LITOFSKY B, 1969, THESIS U PENNSYLVANI MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MATER E, 1983, TECHNISCHE METHODEN Michalski R. S., 1983, MACHINE LEARNING ART, P331 Michalski R. S., 1980, International Journal of Policy Analysis and Information Systems, V4 MINKER J, 1973, J AM SOC INFORM SCI, V24, P246, DOI 10.1002/asi.4630240404 MOHRENWEIS T, 1984, THESIS TU MUNCHEN MURRAY DM, 1972, ISR20 CORN U SCI REP NEEDHAM RM, 1962, INFORMATION RETRIEVE NEEDHAM RM, 1967, STATISTICIAN, V17, P45, DOI 10.2307/2987201 NEEDHAM RM, 1960, THEORY CLUMPS PANYR J, 1982, INFORMATION RETRIEVA, P353 PANYR J, 1986, NACHR DOK, V37, P163 PANYR J, 1986, NACHR DOK, V37, P2 PANYR J, 1984, STUDIEN KLASSIFIKATI, V15, P240 PANYR J, 1986, AUTOMATISCHE KLASSIF PANYR J, 1982, INT CLASSIF, V9, P11 PANYR J, 1978, NACHR DOKUM, V29, P92 PANYR J, 1986, NACHR DOK, V37, P60 PRYWES NS, 1970, AFIPS C P, V36, P323 PRYWES NS, 1974, INFORM STORAGE RET, V10, P15, DOI 10.1016/0020-0271(74)90039-4 Raghavan V. V., 1979, ACM Transactions on Database Systems, V4, DOI 10.1145/320071.320081 RAGHAVAN VV, 1983, LECT NOTES COMPUT SC, V146, P209 Robertson S. E., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROBERTSON SE, 1979, DREXEL LIBRARY Q, V14, P40 SALTON G, 1974, TR74203 CORN U TECHN SALTON G, 1981, INFORMATION RETRIEVA, P9 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, DYNAMIC INFORMATION Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1980, INFORM PROCESS MANAG, V16, P1, DOI 10.1016/0306-4573(80)90002-3 JONES K S, 1968, Information Storage and Retrieval, V4, P91, DOI 10.1016/0020-0271(68)90013-2 Spath Helmuth, 1977, CLUSTER ANAL ALGORIT STEPP RE, 1986, ARTIF INTELL, V28, P43, DOI 10.1016/0004-3702(86)90030-5 SUSS C, 1981, THESIS TU BERLIN JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSB.CJ, 1974, INFORM STORAGE RET, V10, P1, DOI 10.1016/0020-0271(74)90038-2 WILLIAMSON RE, 1977, P COMSAC 77, P683 WONG A, 1973, ISR22 CORN U SCI REP WONG PCN, 1985, THESIS U REGINA CAN WONG SKM, 1986, 1986 ACM C RES DEV I, P175 YU CT, 1977, J AM SOC INFORM SCI, V28, P345, DOI 10.1002/asi.4630280607 YU CT, 1974, INFORM STORAGE RET, V10, P243, DOI 10.1016/0020-0271(74)90019-9 YU CT, 1974, J AM SOC INFORM SCI, V25, P218, DOI 10.1002/asi.4630250403 YU CT, 1975, J ACM, V22, P17, DOI 10.1145/321864.321867 YU CT, 1973, TR73181 CORN U TECHN YU CT, 1976, J AM SOC INFORM SCI, V27, P248, DOI 10.1002/asi.4630270410 NR 77 TC 4 Z9 4 PU VERLAG HOPPENSTEDT & CO PI DARMSTADT 1 PA POSTFACH 40 06, HAVELSTR 9, D-6100 DARMSTADT 1, GERMANY SN 0027-7436 J9 NACHR DOK JI Nachr. Dok. PD FEB PY 1987 VL 38 IS 1 BP 13 EP 20 PG 8 WC Information Science & Library Science SC Information Science & Library Science GA G9017 UT WOS:A1987G901700003 ER PT J AU PANYR, J AF PANYR, J TI CONCEPTUAL CLUSTERING AND RELEVANCE FEEDBACK SO INTERNATIONAL CLASSIFICATION LA English DT Article RP PANYR, J (reprint author), SIEMENS AG,ZENT BEREICH FORSCH & TECH,ZENT AUFGABEN INFORMAT TECH SYST TECH,D-8000 MUNICH 83,FED REP GER. CR Addis T. R., 1982, Information Technology: Research and Development, V1 Bock H. H., 1974, AUTOMATISCHE KLASSIF CROFT WB, 1986, 1986 P ACM C RES DEV, P201 CROUCH D, 1972, THESIS SO METHODIST DAVIS MC, 1968, IRS14 CORN U SCI REP, pX1 DEJONG G, 1983, SIGIR FORUM, V17, P10 DEOGUN JS, 1986, 1986 P ACM C RES DEV, P157 Diday E., 1980, COMMUNICATION CYBERN, V10, P47 FISHER D, 1984, HIERARCHICAL CONCEPT FISHER D, 1985, P WORKSHOP ARTIFICIA FRIEDMAN SR, 1971, SMART RETRIEVAL SYST, P447 GANTER B, 1986, STUDIEN KLASSIFIKATI, V17, P171 GOWER JC, 1967, BIOMETRICS, V23, P623, DOI 10.2307/2528417 LANCE GN, 1965, COMPUTER J, V8, P245 LANGLEY P, 1984, J AM SOC INFORM SCI, V35, P306, DOI 10.1002/asi.4630350509 LEBOWITZ N, 1986, MACHINE LEARNING ART, V2, P193 LITOFSKY B, 1969, THESIS U PENNSYLVANI Michalski R. S., 1983, MACHINE LEARNING AI, VI, P331 Michalski R.S., 1986, MACHINE LEARNING ART, V2 Michalski R.S., 1983, MACHINE LEARNING ART, V1 Michalski R. S., 1980, International Journal of Policy Analysis and Information Systems, V4 PANYR J, 1986, AUTOMATISCHE KLASSIF, P84 PANYR J, 1987, NACHR DOK, V38, P145 PANYR J, 1987, NACHR DOK, V38, P13 RIEGER B, 1984, DYNAMIK BEDEUTUNGSKO SALTON G, 1986, 1986 P ACM C RES DEV, P1 SALTON G, 1975, DYNAMIC INFORMATION Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 UEBBING M, 1976, THESIS TU BERLIN Wong S.K.M., 1986, INT J MAN MACH STUD, V24, P53 WONG SKM, 1986, 1986 P ACM SIGIR C, P228 NR 32 TC 0 Z9 0 PU INT SOC KNOWLEDGE ORGANIZATION PI FRANKFURT 50 PA 36A WOOGSTR, D-6000 FRANKFURT 50, GERMANY SN 0340-0050 J9 INT CLASSIF PY 1987 VL 14 IS 3 BP 133 EP 137 PG 5 WC Information Science & Library Science SC Information Science & Library Science GA L5619 UT WOS:A1987L561900003 ER PT J AU SUTCLIFFE, JP AF SUTCLIFFE, JP TI DIFFERENTIAL ORDERING OF OBJECTS AND ATTRIBUTES SO PSYCHOMETRIKA LA English DT Article RP SUTCLIFFE, JP (reprint author), UNIV SYDNEY,DEPT PSYCHOL,SYDNEY,NSW 2006,AUSTRALIA. CR ANDERSON AJ, 1971, NATURE, V232, P416, DOI 10.1038/232416a0 ATTNEAVE F, 1953, J EXP PSYCHOL, V46, P81, DOI 10.1037/h0057955 ATTNEAVE F, 1950, AM J PSYCHOL, V63, P516, DOI 10.2307/1418869 Buchanan B. G., 1982, MACHINE INTELLIGENCE, P10 Burr EJ, 1968, AUST COMPUT J, V1, P97 Burt C, 1937, B J PSYCHOL-GEN SECT, V28, P59 CAIN AJ, 1956, SYST ZOOL, V5, P97, DOI 10.2307/2411572 Cattell R. B., 1946, DESCRIPTION MEASUREM CLARK RS, 1950, 1 LIBR ASS PAMPHL Coombs C. H., 1964, THEORY DATA Cox D., 1958, PLANNING EXPT Daniels HE, 1944, BIOMETRIKA, V33, P129, DOI 10.2307/2334112 DELEEUW J, 1973, CANONICAL ANAL CATEG Dewey Godfrey, 1923, RELATIVE FREQUENCY E DOYLE LB, 1975, INFORMATION RETRIEVA Duda R.O., 1973, PATTERN CLASSIFICATI ESTABROOK GEORGE F., 1967, TAXON, V16, P86, DOI 10.2307/1216888 FEIGENBAUM JF, 1963, COMPUTERS THOUGHT Feller W., 1957, INTRO PROBABILITY TH, V1 FERGUSON GA, 1949, PSYCHOMETRIKA, V14, P61 Fisher RA, 1943, J ANIM ECOL, V12, P42, DOI 10.2307/1411 FOULDS LR, 1979, J MOL EVOL, V13, P151, DOI 10.1007/BF01732869 FRECHET M, 1940, ACTUALITES SCI IND FU KS, 1981, PATTERN RECOGN, V13, P3, DOI 10.1016/0031-3203(81)90028-5 FU KS, 1971, IEEE T COMPUT, VC 20, P965 Garner W. R., 1978, COGNITION CATEGORIZA GARNER WR, 1966, AM PSYCHOL, V21, P11, DOI 10.1037/h0023192 Gibson E. J., 1969, PRINCIPLES PERCEPTUA GOWER J. C., 1966, BIOMETRIKA, V53, P315 GOWER JC, 1971, BIOMETRICS, V27, P857, DOI 10.2307/2528823 Gregson R., 1975, PSYCHOMETRICS SIMILA HARMON LD, 1973, SCI AM, V229, P70 HARTIGAN JA, 1967, J AM STAT ASSOC, V62, P1140, DOI 10.2307/2283766 HILL MO, 1974, J ROY STAT SOC C-APP, V23, P340 HU TC, SIAM J APPLIED MATH, V21, P514 HUBERT L, 1974, PSYCHOL BULL, V81, P976, DOI 10.1037/h0037348 HUBERT LJ, 1977, J EDUC STATIST, V2, P79, DOI 10.2307/1164801 HUFFMAN DA, 1952, P IRE, V40, P1098, DOI 10.1109/JRPROC.1952.273898 Hull CL, 1939, PSYCHOL REV, V46, P9, DOI 10.1037/h0054032 Jakobson Roman, 1963, PRELIMINARIES SPEECH, V3rd Jardine N, 1971, MATH TAXONOMY Jevons W. S., 1958, PRINCIPLES SCI JICIN R, 1969, J GEN MICROBIOL, V58, P135 JICIN R, 1972, J THEOR BIOL, V34, P295 JOHNSON SC, 1967, PSYCHOMETRIKA, V32, P241, DOI 10.1007/BF02289588 Kendall M. G., 1970, RANK CORRELATION MET Kolmogorov AN, 1957, ELEMENTS THEORY FUNC, VI KRANTZ DH, 1971, F MEASUREMENT, V1 LOCKHART WR, 1963, J BACTERIOL, V85, P68 Lord F. M., 1968, STATISTICAL THEORIES MARR D, 1976, PHILOS T ROY SOC B, V275, P483, DOI 10.1098/rstb.1976.0090 MCNEILL J, 1972, TAXON, V21, P71, DOI 10.2307/1219225 Minkowski H., 1896, GEOMETRIE ZAHLEN MORSE L E, 1974, Taxon, V23, P29, DOI 10.2307/1218086 MORSE L E, 1971, Taxon, V20, P269, DOI 10.2307/1218880 MORTON J, 1969, PSYCHOL REV, V76, P165, DOI 10.1037/h0027366 MURDOCK BB, 1960, PSYCHOL REV, V67, P16, DOI 10.1037/h0042382 Neisser U., 1966, COGNITIVE PSYCHOL NEISSER U, 1964, SCI AM, V210, P94 Nishisato S., 1980, ANAL CATEGORICAL DAT NOWAKOWSKA M, 1975, PRZEGLAD PSYCHOL, V18, P297 ORLOCI L, 1969, NUMERICAL TAXONOMY Pankhurst R. J., 1975, BIOL IDENTIFICATION Pankhurst R.J., 1978, BIOL IDENTIFICATION PANKHURS.RJ, 1970, COMPUT J, V13, P145, DOI 10.1093/comjnl/13.2.145 PICARD CF, 1965, THEORIE QUESTIONNAIR Pitts Walter, 1947, BULL MATH BIOPHYS, V9, P127, DOI 10.1007/BF02478291 RAMSAY JO, 1980, PSYCHOMETRIKA, V45, P149, DOI 10.1007/BF02294074 Rasch G., 1977, DANISH YB PHILOS, P58 Rescher N., 1955, BRIT J PHILOS SCI, V6, P89 RESCIGNO A, 1960, INFORMATION THEORY RESTLE F, 1959, PSYCHOMETRIKA, V24, P207, DOI 10.1007/BF02289843 RIDER PR, 1951, J AM STAT ASSOC, V46, P375, DOI 10.2307/2280515 Riesz F, 1955, FUNCTIONAL ANAL ROBLIN J, 1955, READING FINGERS LIFE Rosch Eleanor, 1978, COGNITION CATEGORIZA Rosenfeld A, 1969, PICTURE PROCESSING C ROTHKOPF EZ, 1957, J EXP PSYCHOL, V53, P94, DOI 10.1037/h0041867 RUBIN EJ, 1921, STUDIEN PSYCHOL ANAL, V1 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SATTATH S, 1977, PSYCHOMETRIKA, V42, P319, DOI 10.1007/BF02293654 Saussure Ferdinand de, 1959, COURSE GENERAL LINGU Saussure Ferdinand de, 1949, COURS LINGUISTIQUE G Shepard R. N., 1972, MULTIDIMENSIONAL SCA SHEPARD RN, 1962, PSYCHOMETRIKA, V27, P125, DOI 10.1007/BF02289630 Simpson G. G., 1961, PRINCIPLES ANIMAL TA Sneath P.H.A., 1973, NUMERICAL TAXONOMY Sokal Robert, 1963, PRINCIPLES NUMERICAL SOKAL RR, 1961, SYST ZOOL, V10, P70, DOI 10.2307/2411724 SOLOMON RL, 1951, PSYCHOL REV, V58, P256, DOI 10.1037/h0058228 STERNBER.S, 1969, AM SCI, V57, P421 Suppes P., 1963, HDB MATH PSYCHOL, V1 SUTCLIFF.JP, 1966, AUST J PSYCHOL, V18, P26, DOI 10.1080/00049536608255713 SUTCLIFFE JP, 1973, JUN M PSYCH SOC CHIC SUTCLIFFE JP, 1972, PICTORIAL ORG SHAPE SUTCLIFFE JP, 1985, FORTRAN V LISTING US TALKINGT.L, 1967, SYST ZOOL, V16, P149, DOI 10.2307/2411407 Thurstone L. L., 1947, MULTIPLE FACTOR ANAL Tippett LHC, 1925, BIOMETRIKA, V17, P364, DOI 10.2307/2332087 Torgerson W. S., 1958, THEORY METHODS SCALI TORGERSON WS, 1965, PSYCHOMETRIKA, V30, P379, DOI 10.1007/BF02289530 TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327 TVERSKY A, 1970, J MATH PSYCHOL, V7, P572, DOI 10.1016/0022-2496(70)90041-6 UNDERWOO.BJ, 1972, J EXP PSYCHOL, V94, P276, DOI 10.1037/h0032785 UNDERWOOD BJ, 1953, J EXP PSYCHOL, V45, P133, DOI 10.1037/h0057672 NR 105 TC 7 Z9 7 PU PSYCHOMETRIC SOC PI WILLIAMSBURG PA COLLEGE OF WILLIAM AND MARY DEPT PSYCHOLOGY, WILLIAMSBURG, VA 23185 SN 0033-3123 J9 PSYCHOMETRIKA JI Psychometrika PD JUN PY 1986 VL 51 IS 2 BP 209 EP 240 DI 10.1007/BF02293981 PG 32 WC Mathematics, Interdisciplinary Applications; Social Sciences, Mathematical Methods; Psychology, Mathematical SC Mathematics; Mathematical Methods In Social Sciences; Psychology GA C9067 UT WOS:A1986C906700003 ER PT J AU WILLETT, P AF WILLETT, P TI AN ALGORITHM FOR THE CALCULATION OF EXACT TERM DISCRIMINATION VALUES SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article RP WILLETT, P (reprint author), UNIV SHEFFIELD,DEPT INFORMAT STUDIES,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BONWIT K, 1970, ISR21 CORN U DEP COM CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 JONES KS, 1977, RES AUTOMATIC INDEXI NOREAULT T, 1977, J AM SOC INFORM SCI, V28, P333, DOI 10.1002/asi.4630280605 PERRY SA, 1983, J INFORM SCI, V6, P59, DOI 10.1177/016555158300600204 PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814 Porter M. F., 1982, Information Technology: Research and Development, V1 ROBERTSON SE, 1981, ACM SIGIR FORUM, V16, P22, DOI 10.1145/1013228.511758 SALTON G, 1983, INTRO MODERN INFORMA SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 Salton G., 1975, THEORY INDEXING SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 NR 13 TC 22 Z9 22 PU PERGAMON-ELSEVIER SCIENCE LTD PI OXFORD PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB SN 0306-4573 J9 INFORM PROCESS MANAG JI Inf. Process. Manage. PY 1985 VL 21 IS 3 BP 225 EP 232 DI 10.1016/0306-4573(85)90107-4 PG 8 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ANH87 UT WOS:A1985ANH8700005 ER PT J AU FOX, EA AF FOX, EA TI ANALYSIS AND RETRIEVAL OF COMPOSITE DOCUMENTS SO PROCEEDINGS OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE LA English DT Article RP FOX, EA (reprint author), VIRGINIA POLYTECH INST & STATE UNIV,DEPT COMP SCI,BLACKSBURG,VA 24061, USA. CR BIRRELL AD, 1982, COMMUN ACM, V25, P260, DOI 10.1145/358468.358487 COFFIN RW, 1984, THESIS VIRGINIA POLY COMER DE, 1983, CSDTR465 PURD U DEP Correira A., 1980, AM J COMPUTATIONAL L, V6, P135 DAVIDSON L, 1962, COMMUN ACM, V5, P169, DOI 10.1145/366862.366913 Fox E., 1983, THESIS CORNELL U ITH FOX EA, 1985, 14TH P AM SOC INF SC FOX EA, 1983, 83560 CORN U DEP COM FOX EA, 1983, INFRASTRUCTURE INFOR FOX EA, 1984, 13TH AM SOC INF SCI, P253 FOX EA, 1983, 83561 CORN U DEP COM GREENFIELD RH, 1977, METHOD INFORM MED, V16, P230 HALL PAV, 1980, COMPUT SURV, V12, P381 JOSEPH DM, 1979, METH INFORM MED, V18, P238 KIMURA GD, 1984, 840704 U WASH DEP CO KORFHAGE RR, 1982, 6TH P COMPSAC 82 INT, P470 LAMB DA, 1982, RDMAIL MESSAGE MANAG MOOERS CD, 1982, BBN4995 REP PAICE CD, 1984, INFORM TECHNOL R & D, V3, P33 PALME J, 1984, MAR COM PORTACOM C S RIESBECK CK, 1982, STRATEGIES NATURAL L, P37 SALTON G, 1983, COMMUN ACM, V26, P1022, DOI 10.1145/182.358466 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHATZ BR, 1984, TELESOPHY SIMMONS RF, 1984, COMPUTATIONS ENGLISH SIRBU MA, 1984, MAY P IFIP WORK C NO SOLOMON M, 1982, COMPUTER NETWORKS, V6 ZARRI GP, 1983, INFORM TECHNOL R & D, V2, P89 NR 28 TC 0 Z9 0 PU LEARNED INFORMATION LTD PI OXFORD PA WOODSIDE, HINKSEY HILL, OXFORD, ENGLAND OX1 5AU SN 0044-7870 J9 P AM SOC INFORM SCI PY 1985 VL 22 BP 54 EP 58 PG 5 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA ATM61 UT WOS:A1985ATM6100012 ER PT J AU WILLETT, P AF WILLETT, P TI DOCUMENT CLUSTERING USING AN INVERTED FILE APPROACH SO JOURNAL OF INFORMATION SCIENCE LA English DT Article RP WILLETT, P (reprint author), UNIV SHEFFIELD,POSTGRAD SCH LIBRARIANSHIP & INFORMAT SCI,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR ADAMSON GW, 1975, J CHEM INF COMP SCI, V15, P55, DOI 10.1021/ci60001a016 Cleverdon C. W., 1966, FACTORS DETERMINING CODY R, 1973, J AM SOC INFORM SCI, V24, P232, DOI 10.1002/asi.4630240309 Croft W. B., 1978, THESIS U CAMBRIDGE CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 CROUCH DB, 1975, INFORM PROCESS MANAG, V11, P11, DOI 10.1016/0306-4573(75)90031-X DATTOLA RT, 1971, SMART RETRIEVAL SYST DATTOLA RT, 1979, J AM SOC INFORM SCI, V30, P9 DEFAYS D, 1977, COMPUT J, V20, P364, DOI 10.1093/comjnl/20.4.364 EVERITT BS, 1979, BIOMETRICS, V35, P169, DOI 10.2307/2529943 FEINMAN RD, 1973, J AM SOC INFORM SCI, V24, P382, DOI 10.1002/asi.4630240510 FRITSCHE M, 1974, EUR5051E COMM EUR CO HARDING AF, 1980, J AM SOC INFORM SCI, V31, P298, DOI 10.1002/asi.4630310411 HARTIGAN JA, 1974, CLUSTERING ALGORITHM Herdan Gustav, 1966, ADV THEORY LANGUAGE Jardine N, 1971, MATH TAXONOMY LITOFSKY B, 1969, THESIS U PENNSYLVANI MACQUEEN J, 1969, 5TH P BERK S MATH ST MURRAY DM, 1972, THESIS CORNELL U ROHLF FJ, 1977, APR M N AM BRANCH CL, V4, P27 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHIMINO.S, 1971, INFORM STORAGE RET, V6, P417, DOI 10.1016/0020-0271(71)90008-8 SIBSON R, 1973, COMPUT J, V16, P30, DOI 10.1093/comjnl/16.1.30 Sparck Jones K., 1974, J DOC, V30, P393, DOI DOI 10.1108/EB026588 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSBERGEN CJ, 1979, INFORMATION RETRIEVA VANRIJSBERGEN CJ, 1978, DREXEL LIBR QUART, V14, P75 WILLETT P, 1979, J DOC, V35, P296, DOI 10.1108/eb026684 WILLIAMS WT, 1971, COMPUT J, V14, P162, DOI 10.1093/comjnl/14.2.162 WILLIAMSON RE, 1974, THESIS CORNELL U ZHITKOV GN, 1970, AUTOMATIC DOCUMENTAT, V4, P19 NR 33 TC 15 Z9 15 PU BOWKER-SAUR LTD PI E GRINSTEAD PA MAYPOLE HOUSE, MAYPOLE RD, E GRINSTEAD, W SUSSEX, ENGLAND RH19 1HH SN 0165-5515 J9 J INFORM SCI JI J. Inf. Sci. PY 1980 VL 2 IS 5 BP 223 EP 231 DI 10.1177/016555158000200503 PG 9 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA KW808 UT WOS:A1980KW80800003 ER PT J AU NORMAN, DA BOBROW, DG AF NORMAN, DA BOBROW, DG TI DESCRIPTIONS - INTERMEDIATE STAGE IN MEMORY RETRIEVAL SO COGNITIVE PSYCHOLOGY LA English DT Article C1 XEROX CORP,PALO ALTO RES CTR,PALO ALTO,CA 94304. RP NORMAN, DA (reprint author), UNIV CALIF SAN DIEGO,DEPT PSYCHOL,LA JOLLA,CA 92093, USA. RI Norman, Donald/B-7257-2009 CR Anderson J. A., 1977, BASIC PROCESSES READ Bobrow D., 1977, COGNITIVE SCI, V1, P3, DOI 10.1207/s15516709cog0101_2 Bobrow D. G., 1975, REPRESENTATION UNDER BREGMAN AS, 1977, COGNITIVE PSYCHOL, V9, P250, DOI 10.1016/0010-0285(77)90009-3 Carmicheal L, 1932, J EXP PSYCHOL, V15, P73, DOI 10.1037/h0072671 COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407 CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X FISHER RJ, UNPUBLISHED Gibson JJ, 1929, J EXP PSYCHOL, V12, P1, DOI 10.1037/h0072470 KLEIN K, 1976, J EXP PSYCHOL-HUM L, V2, P671 Koffka K., 1935, PRINCIPLES GESTALT P Kohonen T., 1977, ASS MEMORY SYSTEM TH MINSKY M, 1975, PSYCHOLOGY COMPUTER MOORE J, 1974, KNOWLEDGE COGNITION MORRIS CD, 1977, J VERB LEARN VERB BE, V16, P519, DOI 10.1016/S0022-5371(77)80016-9 MOSCOVITCH M, 1976, J VERB LEARN VERB BE, V15, P447, DOI 10.1016/S0022-5371(76)90040-2 NAVON D, 1977, COGNITIVE PSYCHOL, V9, P353, DOI 10.1016/0010-0285(77)90012-3 Norman D. A., 1976, STRUCTURE HUMAN MEMO NORMAN DA, 1975, COGNITIVE PSYCHOL, V7, P44, DOI 10.1016/0010-0285(75)90004-3 OLSON DR, 1970, PSYCHOL REV, V77, P257, DOI 10.1037/h0029436 Osgood C. E., 1971, SEMANTICS INTERDISCI Rock I, 1975, INTRO PERCEPTION SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071 WICKENS DD, 1970, PSYCHOL REV, V77, P1, DOI 10.1037/h0028569 WILLIAMS ML, 1977, THESIS U CALIFORNIA NR 27 TC 177 Z9 177 PU ACADEMIC PRESS INC JNL-COMP SUBSCRIPTIONS PI SAN DIEGO PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 SN 0010-0285 J9 COGNITIVE PSYCHOL JI Cogn. Psychol. PY 1979 VL 11 IS 1 BP 107 EP 123 DI 10.1016/0010-0285(79)90006-9 PG 17 WC Psychology; Psychology, Experimental SC Psychology GA GK613 UT WOS:A1979GK61300005 ER PT J AU SALTON, G AF SALTON, G TI MATHEMATICS AND INFORMATION-RETRIEVAL SO JOURNAL OF DOCUMENTATION LA English DT Article RP SALTON, G (reprint author), CORNELL UNIV,DEPT COMP SCI,ITHACA,NY 14853, USA. CR AUGUSTSO.JG, 1970, J ACM, V17, P571, DOI 10.1145/321607.321608 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BORKO H, 1964, J ACM, V11, P138, DOI 10.1145/321217.321219 BORKO H, 1963, J ACM, V10, P151, DOI 10.1145/321160.321165 Borko H., 1978, INDEXING CONCEPTS ME CLEVERDON C, FACTORS DETERMINING, V2 Cooper W. S., 1968, American Documentation, V19, DOI 10.1002/asi.5090190108 CROFT WB, 1977, J AM SOC INFORM SCI, V28, P341, DOI 10.1002/asi.4630280606 DAMERAU FJ, 1965, AM DOC, V16, P283, DOI 10.1002/asi.5090160403 de Solla Price D., 1976, Journal of the American Society for Information Sciences, V27 DENNIS SF, 1967, INFORMATION RETRIEVA, P67 EDMUNDSON HP, 1961, COMMUN ACM, V4, P226, DOI 10.1145/366532.366545 FAIRTHOR.RA, 1969, J DOC, V25, P319, DOI 10.1108/eb026481 FAIRTHORNE RA, 1961, INFORMATION RETRIEVA GHOSH SP, 1973, INFORM SCIENCES, V6, P1, DOI 10.1016/0020-0255(73)90023-6 GHOSH SP, 1972, COMMUN ACM, V15, P802, DOI 10.1145/361573.361578 GIULIANO VE, 1963, VISTAS INFORMATION H GOFFMAN W, 1964, INFORM STORAGE RET, V2, P73, DOI 10.1016/0020-0271(64)90006-3 Harter SP, 1974, THESIS U CHICAGO HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 Hartigan J, 1975, CLUSTERING ALGORITHM HAYES RM, 1963, NATURAL LANGUAGE COM, P268 HILLMAN DJ, 1962, 1 LEH U CTR INF SCI HODES L, 1976, J CHEM INF COMP SCI, V16, P88, DOI 10.1021/ci60006a012 HOUSTON N, 1964, AM DOC, V15, P105, DOI 10.1002/asi.5090150208 JONKER F, 1959, P INT C SCI INFORMAT Keen M., 1966, FACTORS DETERMINING, V1 KOCHEN M, 1974, PRINCIPLES INFORMATI LANCASTER FW, 1973, INFORMATION RETRIEVA Landry BC, 1970, J AM SOC INFORM SCI, V21, P358, DOI 10.1002/asi.4630210508 LESK ME, 1971, SMART RETRIEVAL SYST, pCH25 LIPSKI W, 1976, INFORMATION STORAGE, V2 Lipski W. Jr., 1976, Theoretical Computer Science, V3, DOI 10.1016/0304-3975(76)90023-2 MAREK W, 1974, CCPAS149 POL AC SCI MARON ME, 1960, J ACM, V7, P216, DOI 10.1145/321033.321035 MARON ME, 1961, J ACM, V8, P404, DOI 10.1145/321075.321084 MOOERS CN, 1959, P INT C SCI INFORMAT MOSTELLER F, 1963, J AM STAT ASSOC, V58, P275, DOI 10.2307/2283270 RADECKI T, 1977, INFORM PROCESS MANAG, V13, P109, DOI 10.1016/0306-4573(77)90044-9 RADECKI T, 1976, INFORM PROCESS MANAG, V12, P313, DOI 10.1016/0306-4573(76)90049-2 RESNIKOFF HL, 1971, ACCESS STUDY INFORMA RESNIKOFF HL, 1971, JAN C BOARD MATH SCI ROBERTSON SE, 1976, J AM SOC INFORMATION, V23, P129 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P85, DOI 10.1002/asi.4630270203 SALTON G, 1978, INFORM PROCESS MANAG, V14, P29, DOI 10.1016/0306-4573(78)90055-9 SALTON G, 1968, AUTOMATIC INFORMATIO Salton G., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320289.320291 SALTON G, 1977, INFORMATION PROCESSI, V77, P441 SALTON G, 1973, J DOC, V29, P351, DOI 10.1108/eb026562 SALTON G, 1971, SMART RETRIEVAL SYST, pCH15 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1975, 18 SOC IND APPL MATH SCHIMINO.S, 1971, INFORM STORAGE RET, V6, P417, DOI 10.1016/0020-0271(71)90008-8 SPARCKJONES K, 1971, AUTOMATIC KEYWORD CL STONE DC, 1968, AM DOC, V19, P411, DOI 10.1002/asi.5090190409 SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245 SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110 TAGUE J, UNPUBLISHED TAHANI V, 1976, INFORM PROCESS MANAG, V12, P177, DOI 10.1016/0306-4573(76)90004-2 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA, P37 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1970, COMPUT J, V13, P324 VICKERY BC, 1959, P INT C SCI INFORMAT WALL E, 1964, PARAMETERS INFORMATI, V1, P457 WONG A, 1978, THESIS CORNELL U Yu C. T., 1978, ACM Transactions on Database Systems, V3, DOI 10.1145/320241.320245 YU CT, 1971, COMMUNICATIONS ASS C, V20, P135 YU CT, 1978, J ACM, V25, P210, DOI 10.1145/322063.322066 YU CT, 1976, J ACM, V23, P76, DOI 10.1145/321921.321930 ZIPF GK, 1949, HUMAN BEHAVIOR PRINC ZUNDE P, 1967, AM DOC, V18, P104, DOI 10.1002/asi.5090180209 NR 73 TC 37 Z9 37 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1979 VL 35 IS 1 BP 1 EP 29 DI 10.1108/eb026671 PG 29 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA GZ743 UT WOS:A1979GZ74300001 ER PT J AU BURNETT, JE COOPER, D LYNCH, MF WILLETT, P WYCHERLEY, M AF BURNETT, JE COOPER, D LYNCH, MF WILLETT, P WYCHERLEY, M TI DOCUMENT-RETRIEVAL EXPERIMENTS USING INDEXING VOCABULARIES OF VARYING SIZE .1. VARIETY GENERATION SYMBOLS ASSIGNED TO THE FRONTS OF INDEX TERMS SO JOURNAL OF DOCUMENTATION LA English DT Article RP BURNETT, JE (reprint author), UNIV SHEFFIELD,POSTGRAD SCH LIBRARIANSHIP & INFORMAT SCI,SHEFFIELD S10 2TN,S YORKSHIRE,ENGLAND. CR BARTON IJ, 1974, COMMUN ACM, V17, P345, DOI 10.1145/355616.364034 BRACK EV, 1978, PROGRAM-NEWS COMPUT, V12, P64, DOI 10.1108/eb046772 BUCHANAN BG, 1978, ARTIF INTELL, V11, P5, DOI 10.1016/0004-3702(78)90010-3 COLOMBO DS, 1969, J CHEM DOC, V9, P47, DOI 10.1021/c160032a011 CRAWFORD RW, 1974, ISR22 CORN U DEP COM DOSZKOCS TE, 1976, J CHEM INF COMP SCI, V16, P131, DOI 10.1021/ci60007a003 FOKKER DW, 1974, J LIBR AUTOMAT, V7, P105 FOKKER DW, 1974, J LIBR AUTOMAT, V7, P201 HICKEY T, 1977, ONLINE REV, V1, P53, DOI 10.1108/eb023935 LEFKOVITZ D, 1975, J CHEM INF COMP SCI, V15, P14, DOI 10.1021/ci60001a005 LYNCH MF, 1973, INFORM STORAGE RET, V9, P331, DOI 10.1016/0020-0271(73)90072-7 Lynch M. F., 1975, Program, V9, DOI 10.1108/eb046715 LYNCH MF, 1977, J AM SOC INFORM SCI, V28, P19, DOI 10.1002/asi.4630280104 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHUEGRAF EJ, 1976, INFORM PROCESS MANAG, V12, P283, DOI 10.1016/0306-4573(76)90066-2 SPARCK-JONES K, 1974, J DOC, V30, P393 JARDINE N, 1971, INFORM STORAGE RET, V7, P217, DOI 10.1016/0020-0271(71)90051-9 VANRIJSB.CJ, 1973, J DOC, V29, P251 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA WILLETT P, J CHEM INFORMATION C WILLIAMS PW, 1977, COMPUT J, V20, P257, DOI 10.1093/comjnl/20.3.257 YU CT, 1977, J AM SOC INFORM SCI, V28, P345, DOI 10.1002/asi.4630280607 NR 23 TC 17 Z9 17 PU ASLIB PI LONDON PA 20-24 OLD ST, LONDON, ENGLAND EC1V 9AP SN 0022-0418 J9 J DOC JI J. Doc. PY 1979 VL 35 IS 3 BP 197 EP 206 DI 10.1108/eb026680 PG 10 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA JF519 UT WOS:A1979JF51900003 ER PT J AU VANRIJSBERGEN, CJ AF VANRIJSBERGEN, CJ TI AUTOMATIC CLASSIFICATION IN INFORMATION-RETRIEVAL SO DREXEL LIBRARY QUARTERLY LA English DT Article RP VANRIJSBERGEN, CJ (reprint author), UNIV CAMBRIDGE,COMP LAB,CAMBRIDGE,ENGLAND. CR CORMACK RM, 1971, J R STAT SOC SER A-G, V134, P321, DOI 10.2307/2344237 CROFT WB, 1978, 1978 P INT C INF STO CROFT WB, ORG SEARCHING LARGE Jardine N, 1971, MATH TAXONOMY JONES KS, 1976, CLASSIFICATION 1970S JONES KS, 1971, AUTOMATIC KEYWORD CL ODDY RN, 1974, THESIS U NEWCASTLE U RABIN M, 1976, ALGORITHMS COMPLEXIT ROBERTSON SE, 1977, J DOC, V33, P126, DOI 10.1108/eb026639 ROHLF FJ, 1978, INFORM PROCESS LETT, V7, P44 SALTON G, 1968, AUTOMATIC INFORMATIO SALTON G, 1975, DYNAMIC INFORMATION Salton G., 1971, SMART RETRIEVAL SYST SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 Slepian D., 1973, KEY PAPERS DEV INFOR Sokal R. R., 1973, NUMERICAL TAXONOMY P VANRIJSBERGEN CJ, 1975, INFORMATION RETRIEVA, pCH3 VANRIJSBERGEN CJ, 1977, J DOC, V33, P106 YU CT, 1977, J ACM, V24, P607, DOI 10.1145/322033.322039 NR 19 TC 10 Z9 10 PU CENTRUM PHILADELPHIA PI PHILADELPHIA PA 3624 MARKET ST UNIV CITY SCI CTR, PHILADELPHIA, PA 19104 SN 0012-6160 J9 DREXEL LIBR QUART PY 1978 VL 14 IS 2 BP 75 EP 89 PG 15 WC Information Science & Library Science SC Information Science & Library Science GA GY548 UT WOS:A1978GY54800006 ER PT J AU LISTON, DM HOWDER, ML AF LISTON, DM HOWDER, ML TI SUBJECT ANALYSIS SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review C1 OPERAT RES INC,DIV INFORMAT SYST,BETHESDA,MD 20014. CR ADAMS MD, 1975, LIBR RESOUR TECH SER, V19, P35 ANTHONY E, 1975, RELC J, V6, P21, DOI 10.1177/003368827500600105 AUSTIN D, 1974, J DOC, V30, P47, DOI 10.1108/eb026570 AUSTIN D, 1976, LIBRI, V26, P181 AUSTIN D, 1976, LIBRARY QUART, V46, P77 AUSTIN DW, 1976, LIBRI, V26, P108 Bakewell K. G. B., 1976, Indexer, V10 BALNAVES J, 1974, LASIE INFORMATION B, V5, P3 BENES J, 1974, CHEM PRUM, V24, P475 BERUL LH, 1974, CHANGING PATTERNS IN, P80 BHATTACH.K, 1974, J DOC, V30, P235, DOI 10.1108/eb026580 BONNER RE, 1964, IBM J RES DEV, V8, P22 BOOKSTEI.A, 1974, J AM SOC INFORM SCI, V25, P312, DOI 10.1002/asi.4630250505 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BOREHAM J, 1976, INFORM PROCESS MANAG, V12, P125, DOI 10.1016/0306-4573(76)90043-1 BORKO H, 1975, ABSTRACTING CONCEPTS, P161 BOYCE B, 1975, B MED LIBR ASSOC, V63, P378 BRAUN S, 1976, INFORM PROCESS MANAG, V12, P147, DOI 10.1016/0306-4573(76)90046-7 BRISNER O, 1975, NSI7504 NORSK SENT I BRUNING R, 1975, 1975 ANN CONV AM ED BUKHALEVA EI, 1974, NAUCHNO TECHNICHES 2, V12, P26 BYFORS I, 1975, PB251937 NTIS BYRNE JR, 1975, J AM SOC INFORM SCI, V26, P223, DOI 10.1002/asi.4630260405 BYRON LS, 1974, THESIS U WISCONSIN CAMPEY LH, 1975, INFORMATION STORAGE, V10, P293 CAMPOS A, 1975, REV BIBLIOTECONOMIA, V3, P23 CARROLL JM, 1975, J AM SOC INFORM SCI, V26, P261, DOI 10.1002/asi.4630260411 CAWKELL AE, 1976, INFORM SCIENTIST, V10, P3 CAWKELL AE, 1974, J AM SOC INFORM SCI, V25, P123, DOI 10.1002/asi.4630250208 CAWKELL AE, 1976, J DOC, V32, P53, DOI 10.1108/eb026615 CLEMENT TY, 1976, J ASSOC COMPUTING MA, V23, P76 CLEMENT TY, 1974, INFORMATION STORAGE, V10, P243 CLEMENT TY, 1975, NATIONAL SCI F NOV CLEMENT TY, 1974, J AM SOC INFORMATION, V25, P218 COHEN SM, 1976, J CHEM INF COMP SCI, V16, P93, DOI 10.1021/ci60006a013 CRAWFORD WC, 1976, J LIBR AUTOMAT, V9, P34 CUSTER BA, 1975, CATALOGUE INDEX ENGL, V39, P1 DAHLBERG I, 1974, INFORMATION UTILITIE, V11, P69 DATTOLA RT, 1968, ISR14 CORN U DEP COM DAVIS CH, 1976, J AM SOC INFORM SCI, V27, P174, DOI 10.1002/asi.4630270307 DAYTON DL, 1976, 172ND NAT M AM CHEM DAYTON DL, 1975, CAS REPORT, V4, P3 DEHEER T, 1974, INFORM STORAGE RET, V10, P133, DOI 10.1016/0020-0271(74)90015-1 DEACON JE, 1976, DSISTM375DIP DEF SCI DEREGT WF, 1974, THESAURI EDUCATIONAL DITTMAR PG, 1976, J CHEM INF COMP SCI, V16, P111, DOI 10.1021/ci60006a016 DOWNING JC, 1976, WILSON LIBRARY B, V50, P799 EBE T, 1976, J CHEM INF COMP SCI, V16, P33, DOI 10.1021/ci60005a011 FANGMEYER H, 1974, AD776642 FIELD BJ, 1975, J DOC, V31, P246, DOI 10.1108/eb026605 FILLIATRE B, 1975, INFORM PROCESS MANAG, V11, P107, DOI 10.1016/0306-4573(75)90012-6 FONTALINA MN, 1975, SOVETSKAIA BIBLIOGRA, V2, P88 FREEMAN FH, 1976, SPEC LIBR, V67, P220 FUGMANN R, 1974, J AM SOC INFORM SCI, V25, P287, DOI 10.1002/asi.4630250503 FUGMANN R, 1975, J CHEM INF COMP SCI, V15, P52, DOI 10.1021/ci60001a015 GAGNE RM, 1976, INSTR SCI, V5, P1, DOI 10.1007/BF00120228 GARDINER GL, 1975, COMPUTER ASSISTED IN GLICKERT P, 1974, J AM SOC INFORM SCI, V25, P308, DOI 10.1002/asi.4630250504 GNANY R, 1975, INT ASSOC AGRICULTUR, V20, P128 GOGUEN JA, 1974, INT J MAN MACH STUD, V6, P513, DOI 10.1016/S0020-7373(74)80017-9 GOLD DL, 1975, INT J TRANSLATION, V21, P69 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HARTSON HR, 1974, J AM SOC INFORM SCI, V25, P162 HARVEY JM, 1976, SPECIALISED INFORMAT, P35 HERMAN E, 1976, FINAL REPORT CATALOG HERSEY DF, 1975, 3RD P INT C CLASS RE HEYM DR, 1976, J CHEM INF COMP SCI, V16, P171, DOI 10.1021/ci60007a013 HICKEY DJ, 1976, LIBR TRENDS, V25, P273 HICKS CE, 1976, ENCYCLOPEDIA COMPUTE HOHNE H, 1976, ZBL BIBLIOTHEKSWESEN, V90, P61 HORSNELL V, 1974, INTERMEDIATE LEXICON HUFFENBERGER MA, 1976, DESIGN DATA ELEMENTS HUNT BL, 1975, J AM SOC INFORM SCI, V26, P230, DOI 10.1002/asi.4630260406 IRWIN J, 1974, FLAGSTAFF COOPERATIV JAHODA G, 1974, J AM SOC INFORM SCI, V25, P139, DOI 10.1002/asi.4630250303 JANOS J, 1975, INFORM PROCESS MANAG, V11, P115, DOI 10.1016/0306-4573(75)90013-8 Janos J., 1975, Ceskoslovenska Informatika, Teorie a Praxe, V17 JONES KP, 1976, J DOC, V32, P118, DOI 10.1108/eb026618 JONES KP, 1974, J AM SOC INFORM SCI, V25, P44, DOI 10.1002/asi.4630250106 KARASEV SA, 1974, NAUCHNO TECHNICHES 2, V12, P14 KAUFFMAN D, 1974, EMPIRICAL DERIVATION KLOVSTAD JW, 1975, IEEE T ACOUST SPEECH, VAS23, P118, DOI 10.1109/TASSP.1975.1162637 KOSA GA, 1975, LASIE INFORMATION B, P6 KUCH TDC, 1974, INFORMATION UTILITIE, V11, P63 KUCH TDC, 1974, COMPUTER SUPPORTED I, P16 KUMAR PSG, 1975, HERALD LIBRARY SCI I, V14, P8 KWOK KL, 1975, INFORM PROCESS MANAG, V11, P201, DOI 10.1016/0306-4573(75)90017-5 KWOK KL, 1974, INFORMATION UTILITIE, V11, P56 LAMBERT G, 1976, PRECIS MULTILINGUA 4 LANCASTER FW, 1974, LIBRARY QUART, V44, P256 LANCASTER FW, 1976, SPECIAL LIBRARIES, V67, P175 LANCASTER FW, 1975, B AM SOC INFORMATION, V2, P23 Leonard LE, 1975, THESIS U ILLINOIS LINEBAUGH RM, 1975, THESAURUS EDU DIVING LUSTIG G, 1974, NACHRICHTEN DOKUMENT, V25, P269 LYON CC, 1975, NSWCDLTR3117 NAV SUR MARSHAKOVA IV, 1974, NAUCHNO TECHNICHES 2, V5, P3 MARTINEZ SJ, 1973, INNOVATIVE DEVELOPME, V10, P137 McClure C., 1976, J ACAD LIBR, V2, P9 MCGILL MJ, 1976, J AM SOC INFORM SCI, V27, P205, DOI 10.1002/asi.4630270403 MEINCKE PPM, 1976, J AM SOC INFORM SCI, V27, P18 MEKHTIEV DM, 1974, NAUCHNO TEKHICHESK 1, V10, P22 MOLL JK, 1975, THESIS RUTGERS U MOSS R, 1975, ASLIB PROC, V27, P273, DOI 10.1108/eb050514 MOWERY RL, 1976, LIBR RESOUR TECH SER, V20, P154 NEELAMEGHAN A, 1976, LIBR SCI SLANT DOC, V13, P24 Neicu S. D., 1975, Probleme de Informare si Documentare, V9 NELSON RD, 1975, J CHEM INF COMP SCI, V15, P85, DOI 10.1021/ci60002a004 NEUFELD ML, 1974, INFORM STORAGE RET, V10, P403, DOI 10.1016/0020-0271(74)90047-3 NIEHOFF RT, 1976, INFORMATION POLITI 2, V13 NIEHOFF RT, 1976, J AM SOC INFORM SCI, V27, P3 NIEHOFF RT, 1976, 39TH ANN M AM SOC IN NIEHOFF RT, 1974, PB2404234ST NTIS FIN NOEL J, 1975, REV LANGUES VIVANTES, V41, P237 NORWOOD JW, 1975, DDCTR751 DEF DOC CTR *OP SYST INC, 1975, COMP EV STRUCT FREE PAINTER AF, 1974, DREXEL LIBRARY QUART, V10 PEPINSKY HB, 1974, J AM SOC INFORM SCI, V25, P59, DOI 10.1002/asi.4630250108 PIETERS DL, 1975, SPEC LIBR, V66, P541 POLLOCK JJ, 1975, J CHEM INF COMP SCI, V15, P226, DOI 10.1021/ci60004a008 RAMDSDEN MJ, 1975, AUSTRALIAN LIBRARY J, V24, P10 REISINGER L, 1974, 1974 P S COMPUT STAT, P119 RIGBY M, 1974, COMPUTERS UDC DECADE ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302 ROBSON A, 1976, J AM SOC INFORM SCI, V27, P213, DOI 10.1002/asi.4630270405 ROCCHIO JJ, 1966, ISR10 CORN U DEP COM ROFES WL, 1976, AM ARCHIVIST, V39, P200 SACHS WM, 1976, J AM SOC INFORM SCI, V27, P85, DOI 10.1002/asi.4630270203 SALTON G, 1976, INFORM PROCESS MANAG, V12, P43, DOI 10.1016/0306-4573(76)90026-1 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1974, INFORMATION STORAGE SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SALTON G, 1976, COMPUT HUMANITIES, V10, P69, DOI 10.1007/BF02426255 SAYERS WB, 1975, SAYERS MANUAL CLASSI SCHENK HR, 1976, J CHEM INF COMP SCI, V16, P153, DOI 10.1021/ci60007a010 SCHULTHEIS J, 1975, BUCH BIBEL, V27, P68 SEDELOW SY, 1974, ADA002463 NTIS FIN R SEETHARAMA S, 1976, LIBR SCI SLANT DOC, V13, P67 SMALLEY NS, 1974, J EXP PSYCHOL, V102, P56, DOI 10.1037/h0035686 SMITH JD, 1976, RELATIONSHIP AUTHOR SMITH LC, 1974, J AM SOC INFORM SCI, V25, P343, DOI 10.1002/asi.4630250602 SPARCK-JONES K, 1974, J DOC, V30, P393 STALCUP WS, 1975, 38TH P ANN M AM SOC, V12 STAMM RP, 1975, SR7501 NTIS NAT TECH STEINACK.I, 1974, J AM SOC INFORM SCI, V25, P237, DOI 10.1002/asi.4630250406 STEINER RJ, 1975, INT J TRANSLATION, V21, P123 STOBERSKI Z, 1975, INT J TRANSLATION, V21, P16 STRONG SM, 1974, J AM SOC INFORM SCI, V25, P10, DOI 10.1002/asi.4630250103 TARR D, 1974, INFORMATION UTILITIE, V11, P50 TATE FA, 1976, RETRIEVAL GEOSCIENCE, P2 TAUBER MF, 1974, DREXEL LIBRARY Q, V10, P56 TROTTER RR, 1975, LIBR RESOUR TECH SER, V19, P41 *US DEP HOUS URB D, 1975, NTISPB205604 REP *US EN RES DEV ADM, 1976, NTISTID7000 *US NAT AFR SPAC A, 1976, SP7050V1 REP *US NAT AFR SPAC A, 1976, SP7050V2 REP *US NAT LIBR MED, 1977, MED7605 REP *US NAT LIBR MED, 1976, PB2559334ST REP *US NAT TECH INF S, 1976, PB2548006ST VANDERSTOUW GG, 1975, J CHEM INF COMP SCI, V15, P232, DOI 10.1021/ci60004a009 VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 WALL E, 1975, J AM SOC INFORM SCI, V26, P71, DOI 10.1002/asi.4630260202 WATERS ST, 1975, J AM SOC INFORM SCI, V26, P126 WEINBERG BH, 1975, 38TH P ANN M AM SOC, V12 WESSEL A, 1975, COMPUTER AIDED INFOR WIEZELL RJ, 1975, HISPANIA, V58, P131, DOI 10.2307/338899 WIGINGTON RL, 1975, CODATA B, V15, P4 WILKINSON J, 1975, COLL RES LIBR, V36, P351 WILKS Y, 1975, COMMUN ACM, V18, P264, DOI 10.1145/360762.360770 WILLETTS M, 1975, J DOC, V31, P158, DOI 10.1108/eb026599 WILLIAMS EB, 1975, INT J T, V21, P60 WILLIAMS EB, 1975, INT J T, V21, P126 ZAMORA A, 1976, J CHEM INF COMP SCI, V16, P36, DOI 10.1021/ci60005a012 ZAMORA A, 1976, J CHEM INF COMP SCI, V16, P40, DOI 10.1021/ci60005a013 1975, ADAO13552 NTIS FIN R 1976, THESAURUS AGRICULTUR 1974, SOERGEL DAGOBERT NR 178 TC 9 Z9 9 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1977 VL 12 BP 81 EP 118 PG 38 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA DV788 UT WOS:A1977DV78800006 ER PT J AU MCGREGOR, PV SHEN, D AF MCGREGOR, PV SHEN, D TI NETWORK DESIGN - ALGORITHM FOR ACCESS FACILITY LOCATION PROBLEM SO IEEE TRANSACTIONS ON COMMUNICATIONS LA English DT Article C1 NETWORK ANAL CORP,VIENNA,VA 22180. GRAPHNET SYST INC,ENGLEWOOD,NJ. CR AUGUSTSO.JG, 1970, J ACM, V17, P571, DOI 10.1145/321607.321608 BAHL LR, 1972, APR P S COMP COMM NE BOONER RE, 1964, IBM J JAN, P22 CAHIT J, 1973, NOV C REC NAT TEL C Chou W., 1973, 3rd Data Communications Symposium CHRISTOFIDES N, 1975, MANAGEMENT SCI, V21 COOPER L, 1967, NAV RES LOGIST Q, V14, P101, DOI 10.1002/nav.3800140110 COOPER L, 1962, OPER RES, P331 DOLL DR, 1971, IEEE T COMMUN TE JUN, P339 DWYER PS, 1966, NAV RES LOGIST Q, V13, P289, DOI 10.1002/nav.3800130306 EFROYMSON MA, 1966, OPER RES MAY ELIZINGA J, 1966, OPER RES MAY Feldman E., 1966, MANAGE SCI, V12, P670, DOI 10.1287/mnsc.12.9.670 FRANK H, 1972, P IEEE, V60, P1385, DOI 10.1109/PROC.1972.8910 GARFINKEL RS, 1968, OPER RES OCT, P848 GOLDMAN AJ, 1971, OPER RES NOV, P407 GOLDMAN AJ, 1970, OPER RES AUG, P212 Gower J.C., 1969, APPL STATIST, V18, P54, DOI 10.2307/2346439 GRAY P, 1967, 520528 STANF RES I M GREENBERG DA, 1973, NOV C REC NAT TEL C JARVIS RA, 1973, IEEE T COMPUT, VC-22, P1025, DOI 10.1109/T-C.1973.223640 JENSEN PA, 1971, OPER RES JUL, P916 KERNIGHA.BW, 1971, J ACM, V18, P34, DOI 10.1145/321623.321627 KERNIGHAN BW, 1970, BELL SYST TECH J, P291 KERSHEMBAUM A, 1972, AUG P ACM ANN C, P518 KUEHN AA, 1963, MANAGE SCI, V9, P643, DOI 10.1287/mnsc.9.4.643 LEVY F, 1966, MANAGEMENT SCI, V12, P245 LING RF, 1973, COMMUN ACM, V16, P355, DOI 10.1145/362248.362263 MANNE AS, 1964, MANAGE SCI, V11, P213, DOI 10.1287/mnsc.11.2.213 PATRICK EA, 1969, IEEE T COMPUT, VC 18, P987, DOI 10.1109/T-C.1969.222567 REVELLE CS, 1970, MANAGEMENT SCI, V16 ROACH C, 1972, RAND CORP AUG, P4878 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHWARTZMANN DH, 1975, IEEE T COMPUT, V24, P1175, DOI 10.1109/T-C.1975.224161 SWAIN RW, 1974, MANAGEMENT SCI, V21 TANG DT, 1973, 5TH P ANN SE S SYST TEITZ MB, 1967, OPERATIONS RES JUL, P955 TRIPPI RR, 1975, MANAGE SCI, V21, P986, DOI 10.1287/mnsc.21.9.986 WOO LS, 1973, NOV NAT TEL C ATL ZAHN CT, 1971, IEEE T COMPUT, VC 20, P68, DOI 10.1109/T-C.1971.223083 1974, 3RD SEM TECH REP NR 41 TC 30 Z9 30 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017-2394 SN 0090-6778 J9 IEEE T COMMUN JI IEEE Trans. Commun. PY 1977 VL 25 IS 1 BP 61 EP 73 DI 10.1109/TCOM.1977.1093710 PG 13 WC Engineering, Electrical & Electronic; Telecommunications SC Engineering; Telecommunications GA CQ799 UT WOS:A1977CQ79900005 ER PT J AU MARRON, B FIFE, D AF MARRON, B FIFE, D TI ONLINE SYSTEMS - TECHNIQUES AND SERVICES SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review C1 NBS,INST COMP SCI & TECHNOL,WASHINGTON,DC 20234. CR AHLGREN AE, 1975, AM SOC INFORMATION S, P123 AHLGREN AE, 1975, AM LIBRARY ASSOC ANN AHLGREN AE, 1975, AM SOC INFORMATION S, V12, P156 ALPER BH, 1975, ANNU REV INFORM SCI, V10, P199 *AM NAT STAND I, 1970, VOC INF PROC ARTANDI S, 1975, AM CHEMICAL SOC DIVI ASH JE, 1974, CHEMICAL INFORMATION ASTRAHAN MM, 1975, COMMUN ACM, V18, P580, DOI 10.1145/361020.361215 BACON GC, 1974, COMPUTER, V7, P64 BECKER J, 1975, INFORMATION SYSTEMS, P3 Bell Daniel, 1973, COMING POST INDUSTRI BENENFELD AR, 1975, AM SOC INFORMATION 1, V12, P151 BENNETT JL, 1972, ANNU REV INFORM SCI, V7, P159 BENTLEY JL, 1975, COMMUN ACM, V18, P509, DOI 10.1145/361002.361007 BLANC RP, 1974, ASSOC COMPUTING MACH, P74 BLANC RP, 1974, COST ANALYSIS COMPUT BOIES SJ, 1974, IBM SYST J, V13, P2 BORGMAN CL, 1975, AM SOC INFORMATION 1, V12, P149 BOYCE RF, 1975, COMMUN ACM, V18, P621, DOI 10.1145/361219.361221 BRANSTAD DK, 1975, I ELECTRICAL ELECTRO BREEN B, 1974, AM SOC INFORMATION S BROWN CP, 1975, AM SOC INFORMATION 1, V12, P132 BUCKLEY JS, 1974, PLANNING EFFECTIVE U BUMP RE, 1975, DATAMATION, V21, P50 BUNTROCK RE, 1974, AM SOC INFORMATION S CANADAY RH, 1974, COMMUN ACM, V17, P575, DOI 10.1145/355620.361172 CANNING RG, 1975, EDP ANAL, V13, P13 CARDENAS AF, 1975, COMMUN ACM, V18, P253, DOI 10.1145/360762.360766 CARMON JL, 1975, MODEL USER INTERFACE CASHMAN M, 1975, DATAMATION, V21, P67 CHOU W, 1975, AFIPS C P, V44, P119 CHRISTIAN RW, 1975, ELECTRONIC LIBRARY B CLARK WO, 1975, DUAL STRUCTURED DATA CODD EF, 1975, RELATIONAL DATA BASE CONVERSE WRM, 1975, CANADIAN ASSOC INFOR, V3 COTTON IW, 1975, I ELECTRICAL ELECTRO CRAWFORD RG, 1975, INFORM PROCESS MANAG, V11, P249, DOI 10.1016/0306-4573(75)90022-9 CROUCH DB, 1975, INFORM PROCESS MANAG, V11, P11, DOI 10.1016/0306-4573(75)90031-X CUADRA CA, 1975, ONLINE INFORMATION S Date C.J., 1975, INTRO DATABASE SYSTE DAY MS, 1974, FED PROC, V33, P1717 DEIROSSI JA, 1975, NBS864 TECHN NOT DESCHERE AR, 1974, AM SOC INFORMATION S DUNN DA, 1975, ANNU REV INFORM SCI, V10, P165 EGELAND J, 1974, AM SOC INFORMATION S, V11, P137 EGELAND J, 1975, B MED LIBR ASSOC, V63, P189 ELMAN SA, 1975, SPEC LIBR, V66, P12 EMBRY JD, 1974, SURVEY ERIC DATA BAS ENGELBART DC, 1975, IEEE CATALOG, P173 EVANS A, 1974, COMMUN ACM, V17, P437, DOI 10.1145/361082.361087 FIFE DW, 1974, NBS819 TECHN NOT FONG E, 1975, NBS887 TECHN NOT FRALICK SC, 1975, AM FEDERATION INFORM, V44, P253 FRIED JB, 1975, B AM SOC INFORMATION, V1, P17 FRIED JB, 1974, AM SOC INFORMATION S FRIEDMAN TD, 1974, COMMUN ACM, V17, P445, DOI 10.1145/361082.361090 FRISCH IT, 1975, AM FEDERATION INFORM, V44, P109 GARDNER J, 1974, ARL MANAGEMENT SUPPL, V2 GERLA M, 1975, AM FEDERATION INFORM, V44, P129 GERRITSEN R, 1975, COMMUN ACM, V18, P551, DOI 10.1145/361020.361022 GIERING RH, 1974, J CHEMICAL INFORMATI, V15, P6 GOTTLIEB D, 1975, AFIPS C P, V44, P453 GRAY JP, 1975, DATAMATION, V21, P51 GREENBERGER M, 1974, NETWORKS RESEARCH ED GREENBLOTT BJ, 1975, AM FEDERATION INFORM, V44, P623 GRIGNETTI MC, 1975, AM FEDERATION INFORM, V44, P775 GRIMSON JB, 1974, INFORM STORAGE RET, V10, P357, DOI 10.1016/0020-0271(74)90042-4 HAHN B, 1974, COMMUN ACM, V17, P434, DOI 10.1145/361082.361086 HALL JL, 1975, ON LINE INFORMATION HAMPEL V, 1975, ENERGY RESEARCH DEVE HARLAN P, 1975, SELECTIVE GUIDE DATA HAWKINS DT, 1975, U ILLINOIS GRADUATE HAY RA, 1974, C DATA ARCH PROGRAM HEER PR, 1974, AM SOC INFORMATION S HIRSCH P, 1975, DATAMATION, V21, P65 HIRSCH P, 1975, DATAMATION, V21, P131 HITCHINGHAM EE, 1975, AM SOC INFORMATION 1, V12, P158 HOHRI W, 1975, DATAMATION, V21, P48 HORN BKP, 1975, DATAMATION, V21, P114 HORN BKP, 1975, DATAMATION, V21, P111 HUMMEL DJ, 1975, J CHEM INF COMP SCI, V15, P24, DOI 10.1021/ci60001a008 *INF INC, 1974, P 11TH ANN S *INT BUS MACH, 1975, INTR IBM 5100 PORT C JOHNSON C, 1975, AFIPS C P, V44, P509 KAHN RE, 1975, AFIPS C P, V44, P177 KAMINECKI RM, 1975, SEARCHING CA CONDENS KATTER RV, 1975, J LIBR AUTOMAT, V8, P87 KELLY J, 1975, COMPUTER, V8, P32, DOI 10.1109/C-M.1975.218864 KEPLINGER MS, 1975, POLICY ORIENTED ANAL KISSMAN HM, 1975, B AM SOC INFORMATION, V1, P36 KISSMAN HM, 1975, B AM SOC INFORM INF, V1, P16 KOHLI JP, 1975, AM FEDERATION INFORM, V44, P47 KUNKEL B, 1975, AM SOC INFORMATION S, V12, P129 KUNKEL B, 1975, AM SOC INFORMATION S, V12, P191 LANCASTER FW, 1973, INFORMATION RETRIEVA LANCASTER FW, 1975, VOCABULARY CONTROL O LANDAU RM, 1975, US NATIONAL BUREAU S, P139 LAWRENCE B, 1975, ON LINE COMMANDS QUI LAWRENCE B, 1974, J AM SOC INFORM SCI, V25, P364, DOI 10.1002/asi.4630250605 LEE ES, 1975, AM SOC INFORMATION S, V12, P134 LEE SY, 1975, I ELECTRICAL ELECTRO, P91 LEVITT G, 1974, PROTOTYPE SYSTEM INT LIAS EJ, 1974, DATAMATION, V20, P72 LIAS EJ, 1974, DATAMATION, V20, P77 LIAS EJ, 1974, DATAMATION, V20, P69 LIAS EJ, 1974, DATAMATION, V20, P79 LLEWELLEN PA, 1975, COMPARISON SYSTEM DE LUM VY, 1975, COMMUN ACM, V18, P318, DOI 10.1145/360825.360843 MACDONALD JE, 1975, IBM J RES DEV, V19, P133 MARCH JF, 1974, IMPACT IMPLEMENTATIO MARCH JF, 1975, DIRECTING FUTURE RES MARCUS RS, 1975, ASIS P, V12, P77 MARIENTHAL LB, 1975, DATAMATION, V21, P62 MARILL T, 1975, AFIPS C P, V44, P389 MARRON B, 1974, MECHANIZED INFORMATI MARRON H, 1975, REPORT ASIS PROJECT MARSHALL DB, 1975, J CHEM INF COMP SCI, V15, P247, DOI 10.1021/ci60004a013 MARSHALL DB, 1975, SPEC LIBR, V66, P501 MARTIN TH, 1975, INFORMATICS INC ANNU, P79 MARTIN TH, 1974, FEATURE ANALYSIS INT MARTIN TH, 1973, ANNU REV INFORM SCI, V8, P203 MARTIN TH, 1975, SIGPLAN NOTICES, V10 Maurer W. D., 1975, Computing Surveys, V7 MCCARN DB, 1974, AM SOC INFORMATION S, V11, P145 MCCARN DB, 1975, GROWTH ECONOMICS ONL MINKER J, 1975, TR369 U MAR COMP SCI MOGHDAM D, 1975, J AM SOC INFORM SCI, V26, P184, DOI 10.1002/asi.4630260307 MORGAN HL, 1974, COMMUN ACM, V17, P139, DOI 10.1145/360860.360867 *NAT BUR STAND, 1975, FIPSPUB41 FED IN REP *NAT BUR STAND, 1976, EX GUID COMP NETW *NAT COMM LIBR INF, 1974, NAT PROGR LIBR INF S *NAT TECHN INF SER, 1975, ONL INF RETR SYST NELSON JC, 1975, AM FEDERATION INFORM, V44, P629 NEUMANN AJ, 1974, NBS843 TECHN NOT NEUMANN AJ, 1975, NBS877 TECHN NOT *NEW ENGL BOARD HI, 1975, YEAR 2 REP Nievergelt J., 1974, Computing Surveys, V6 OXMAN MA, 1975, TOXICOLOGY DATA BANK PAYNE GH, 1975, AM SOC INFORMATION S, P133 PENNIMAN WD, 1974, RHYTHMS DIALOGUE BAS PENNIMAN WD, 1975, AM SOC INFORMATION S, V12, P147 PENSYL ME, 1975, AM SOC INFORMATION 1, V12, P153 PIERCE AR, 1975, COMPARISON DIALOG OR POPEK GJ, 1975, ASSOC COMPUTING MACH, P294 POPEK GJ, 1975, I ELECTRICAL ELECTRO, P40 POUZIN L, 1975, I ELECTRICAL ELECTRO PREWITT BG, 1975, J CHEM INF COMP SCI, V15, P177, DOI 10.1021/ci60003a014 PREWITT BG, 1974, J CHEM DOC, V14, P115, DOI 10.1021/c160054a005 PREWITT BG, 1975, PATRON REACTION ONLI PURDY GB, 1974, COMMUN ACM, V17, P442, DOI 10.1145/361082.361089 REES AM, 1975, EXPT TEACHING MEDLIN REISNER P, 1975, AFIPS C P, V44, P447 ROTHNIE JB, 1974, COMMUN ACM, V17, P63, DOI 10.1145/360827.360831 ROUSE WB, 1975, P IEEE, V63, P847, DOI 10.1109/PROC.1975.9849 RUHL MJ, 1975, INTRO IMPLEMENTING O SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHEFFLER FL, 1974, AM SOC INFORMATION S SCHEFFLER FL, 1975, AM SOC INFORMATION S SCHROEDER JR, 1975, STANFORDS GENERALIZE SEWELL W, 1975, NONMEDIATED USE MEDL SHENG A, 1974, J CHEM DOC, V14, P179, DOI 10.1021/c160055a008 SHORE JE, 1975, COMMUN ACM, V18, P433, DOI 10.1145/360933.360949 SKINNER CW, 1974, COMMUN ACM, V17, P707, DOI 10.1145/361604.361633 SMETANA FO, 1974, STUDY RELATION EFFEC STANDERA O, 1974, USER COSTS 2 ON LINE STANDERA O, 1975, AM SOC INFORMATION 1, V12, P38 STANDERA O, 1974, COMPARATIVE STUDY 2 SUMMIT RK, 1975, U ILLINOIS GRADUATE SUMMIT RK, 1975, J CHEM INF COMP SCI, V15, P40, DOI 10.1021/ci60001a012 SUMMIT RK, 1975, AM SOC INFORMATION S, V12, P155 SUMMIT RK, 1975, ONLINE REFERENCE RET SUMMIT RK, 1975, EUROPEAN ASSOC SCI I SUMMIT RK, 1975, 7TH INF IND ASS ANN TAYLOR BJ, 1975, AM FEDERATION INFORM, V44, P35 TEITELBAUM P, 1975, ASIS P, V12, P130 TENKHOFF PA, 1975, DATAMATION, V2, P44 THOMAS JC, 1975, AFIPS C P, V44, P439 TOMEA AV, 1975, 170TH AM CHEM SOC DI TREU S, 1975, INT J MAN MACH STUD, V7, P135, DOI 10.1016/S0020-7373(75)80004-6 TURN R, 1974, COMPUTERS 1980S VANRIJSBERGEN CJ, 1975, INFORM PROCESS MANAG, V11, P171, DOI 10.1016/0306-4573(75)90006-0 VANRIJSB.CJ, 1974, COMMUN ACM, V17, P648, DOI 10.1145/361179.361205 VANSCOY JD, 1974, AM SOC INFORMATION S VENEZKY R, 1974, ASSOC COMPUTING MACH, V2, P744 WALTHER GH, 1974, AM SOC INFORMATION S, P114 WALTHER GH, 1974, AFIPS NATIONAL COMPU, V43, P379 WANG CP, 1975, IBM J RES DEV, V19, P71 WANGER J, 1974, AM SOC INFORMATION S WANGER J, 1975, AM SOC INFORMATION S, P119 WANGER J, 1975, ONLINE IMPACT STUDY WASSERMAN AI, 1974, ASSOC COMPUTING MACH, V2, P743 WAX DM, 1975, U ILLINOIS GRADUATE WEIL BH, 1975, ANNU REV INFORM SCI, V10, P359 WEISBECK.J, 1974, COMPUTER, V7, P20 WEISSMAN C, 1975, AFIPS C P, V44, P929 WENSLEY JH, 1976, COMPUTER, V9, P44 WHEELER JL, 1974, INT ORGANIZATION STA, P103 WILDE DU, 1975, AM SOC INFORMATION 1, V12, P145 WILKS Y, 1975, COMMUN ACM, V18, P264, DOI 10.1145/360762.360770 WILKS Y, 1974, NATURAL LANGUAGE UND WILLIAMS ME, 1975, J AM SOC INFORM SCI, V26, P305, DOI 10.1002/asi.4630260507 WILLIAMS ME, 1975, J CHEM INF COMP SCI, V15, P3, DOI 10.1021/ci60001a002 WILLIAMS ME, 1975, IMPACT MACHINE READA WISH JR, 1975, AM SOC INFORMATION S, P143 WITHINGT.FG, 1975, DATAMATION, V21, P54 WOOD DC, 1975, I ELECTRICAL ELECTRO, P1 ZAIS HW, 1974, COMPARISON 3 ON LINE ZLOOF M, 1975, AFIPS NCC, V44, P431 1975, CHEMICAL ENGINEERING, V53, P30 NR 210 TC 12 Z9 12 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1976 VL 11 BP 163 EP 210 PG 48 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CN084 UT WOS:A1976CN08400007 ER PT J AU WILDE, DU AF WILDE, DU TI GENERATION AND USE OF MACHINE-READABLE DATA-BASES SO ANNUAL REVIEW OF INFORMATION SCIENCE AND TECHNOLOGY LA English DT Review C1 UNIV CONNECTICUT,NEW ENGLAND RES APPLICATION CTR,STORRS,CT 06268. CR ALBERT T, 1975, B AM SOC INFORMATION, V2, P29 ALSBERG PA, 1975, J CHEM INF COMP SCI, V15, P23, DOI 10.1021/ci60001a007 *AM SOC MET, 1976, THES MET TERMS ANGIONE PV, 1975, J AM SOC INFORM SCI, V26, P112, DOI 10.1002/asi.4630260207 AUSTIN D, 1975, PRECIS MANUAL CONCEP BALENT MZ, 1975, J CHEM INF COMP SCI, V15, P100, DOI 10.1021/ci60002a006 BENENFELD A, 1975, PB241311 BERNIER CL, 1975, J CHEM INF COMP SCI, V15, P189, DOI 10.1021/ci60003a017 BLACKWOOD JE, 1975, J CHEM INF COMP SCI, V15, P67, DOI 10.1021/ci60002a002 BLUM F, 1974, LIBR RESOUR TECH SER, V18, P25 BOOKSTEIN A, 1975, J AM SOC INFORM SCI, V26, P45, DOI 10.1002/asi.4630260107 BORKOWSKI C, 1975, J AM SOC INFORM SCI, V26, P94, DOI 10.1002/asi.4630260204 BRISNER O, 1975, 38TH AM SOC INF SCI, V12, P75 BROOKES BC, 1975, ASLIB PROC, V27, P278 BROOKES BC, 1973, COMPENDEX EVALUATION BROWN JE, 1975, B AM SOC INFORMATION, V1, P29 BUNTROCK RE, 1975, J CHEM INF COMP SCI, V15, P174, DOI 10.1021/ci60003a013 BURCHINAL LG, 1975, B AM SOC INFORM SCI, V1, P7 BURCHINAL LG, 1975, B AM SOC INFORMATION, V1, P39 BURTON HD, 1975, SPEC LIBR, V66, P252 BUTTERLY E, 1975, INFORM PROCESS MANAG, V11, P189, DOI 10.1016/0306-4573(75)90016-3 BUTTERLY E, 1974, PEL241 REP S AFR AT BYRNE JR, 1975, J AM SOC INFORM SCI, V26, P223, DOI 10.1002/asi.4630260405 CAPONIO JF, 1975, J CHEM INF COMP SCI, V15, P158, DOI 10.1021/ci60003a007 CARMON J, 1975, MODELING USER INTERF *CHEM ABSTR SERV, 1975, CASSI19071974 SOURC CHENERY PJ, 1975, INFORMATION REVOLUTI, V12 COTE LG, 1974, DATA BASES LIBRARIES COUNTS RW, 1974, PURDUE U THERMOPHYSI CREPS JE, 1975, COMPUTERIZED PRODUCT CROUCH DB, 1975, INFORM PROCESS MANAG, V11, P11, DOI 10.1016/0306-4573(75)90031-X DAMMERS HF, 1975, J DOC, V31, P38, DOI 10.1108/eb026593 DAVIS CH, 1974, ILLUSTRATIVE COMPUTE DAVIS CH, 1974, INFORMATION RETRIEVA DAY MS, 1975, NFAIS NEWSLETTER, V17, P21 DAYTON DL, 1975, CAS REPORT, V4 DEIROSSI JA, 1975, NBS864 TECHN NOT DEMES SB, 1975, SPECIAL LIBRARIES, V66, pA7 DOYLE LB, 1975, INFORMATION RETRIEVA DRONBERGER GB, 1975, J AM SOC INFORM SCI, V26, P108, DOI 10.1002/asi.4630260206 DROTT MC, 1975, ASLIB PROC, V27, P376, DOI 10.1108/eb050524 DUGGAN M, 1975, B AM SOC INFORMATION, V1, P9 ELHADIDY B, 1975, SPEC LIBR, V66, P260 ELMAN SA, 1975, SPEC LIBR, V66, P12 EMMONS KM, 1975, 38TH AM SOC INF SCI, V12, P57 *ENG IND, 1975, PUBL IND ENG ENGLE RL, 1975, B AM SOC INFORMATION, V2, P24 EVANS L, 1975, METHODS RANKING SDI EVANS L, 1975, SEARCH STRATEGY VARI FELDMAN A, 1975, J CHEM INF COMP SCI, V15, P147, DOI 10.1021/ci60003a004 FISANICK W, 1975, J CHEM INF COMP SCI, V15, P73, DOI 10.1021/ci60002a003 FLETCHER JG, 1975, J CHEM INF COMP SCI, V15, P19, DOI 10.1021/ci60001a006 FORTUNE JS, 1975, 38TH AM SOC INF SCI, V12 FREEMAN J, 1975, LITERATURE LIBRARY S FRIED JB, 1975, J AM SOC INFORMATION, V2, P37 FRYCKI SJ, 1975, J AM SOC INFORM SCI, V26, P174, DOI 10.1002/asi.4630260306 FUGMANN R, 1975, J CHEM INF COMP SCI, V15, P52, DOI 10.1021/ci60001a015 GARDNER JJ, 1974, DELIVERY COMPUTER BA GECHMAN MC, 1972, ANNU REV INFORM SCI, V7, P323 GRIFFITHS JM, 1975, J DOC, V31, P185, DOI 10.1108/eb026600 GRYDER M, 1976, OCR INPUT BIOLOGICAL HANSEN IB, 1974, EVALUATION FOOD SCI HARTER SP, 1975, J AM SOC INFORM SCI, V26, P280, DOI 10.1002/asi.4630260504 HARTER SP, 1975, J AM SOC INFORM SCI, V26, P197, DOI 10.1002/asi.4630260402 HARTLEY D, 1974, OSTI5212 U NOTT UK C HARVEY JF, 1975, SPEC LIBR, V66, P212 HAWKINS DT, 1975, CLINIC LIBRARY APPLI HEAPS HS, 1975, J CHEM INF COMP SCI, V15, P32, DOI 10.1021/ci60001a011 HINES DL, 1975, SPEC LIBR, V66, P197 HORNSELL V, 1975, 38TH AM SOC INF SCI, V12, P79 HOSHOVSKY AG, 1975, B AM SOC INFORMATION, V2, P28 HOUSMAN EM, 1973, ANNU REV INFORM SCI, V8, P221 Housman E. M., 1974, Contemporary problems in technical library and information center management: a state of the art HSU J, 1975, 4TH AM SOC INF SCI M, P159 HUFFENBERGER MA, 1975, J CHEM INF COMP SCI, V15, P43, DOI 10.1021/ci60001a013 HUNT BL, 1975, J AM SOC INFORM SCI, V26, P230, DOI 10.1002/asi.4630260406 *I EL ENG, 1975, INSP THES ISENHOUR TL, 1975, J CHEM INF COMP SCI, V15, P115, DOI 10.1021/ci60002a009 JANOS J, 1975, INFORM PROCESS MANAG, V11, P115, DOI 10.1016/0306-4573(75)90013-8 JONES SG, 1975, 38TH AM SOC INF SCI, V12, P84 KAMINECKI R, 1975, 170TH AM CHEM SOC NA KRUZAS AT, 1974, ENCYCLOPEDIA INFORMA KUNKEL B, 1975, 38TH AM SOC INF SCI, V12, P129 KWOK KL, 1975, INFORM PROCESS MANAG, V11, P201, DOI 10.1016/0306-4573(75)90017-5 Lancaster F. W., 1974, Contemporary problems in technical library and information center management: a state of the art LANDAU HB, 1975, 38TH AM SOC INF SCI, V12, P116 LAWRENCE B, 1974, J AM SOC INFORM SCI, V25, P364, DOI 10.1002/asi.4630250605 LEA G, 1975, GEOARCHIVE GEOSYSTEM LEFKOVITZ D, 1975, J CHEM INF COMP SCI, V15, P14, DOI 10.1021/ci60001a005 LEGGATE P, 1975, J DOC, V31, P93, DOI 10.1108/eb026596 LERNER RG, 1975, B AM SOC INFORMATION, V1, P13 LERNER RG, 1975, B AM SOC INFORMATION, V1, P35 Lewis P. M., 1967, THRESHOLD LOGIC LIBBEY MA, 1975, SPEC LIBR, V66, P363 LINDEN BL, 1975, B AM SOC INFORMATION, V1, P12 LLEWELLEN PA, 1975, STUDY INDEXING INFOR LUDWIG BM, 1975, J AM SOC INFORM SCI, V26, P51, DOI 10.1002/asi.4630260108 LUHN HP, 1961, AM DOC, V12, P131, DOI 10.1002/asi.5090120209 LYNON CC, 1975, COMPARATIVE EVALUATI MAGUIRE C, 1974, INFORMATION SMALL MA MARATECK K, 1975, 38TH P ASIS ANN M, V12, P140 MARCUS RS, 1975, 38TH AM SOC INF SCI, V12, P77 Martin J., 1975, COMPUTER DATA BASE O MAZELLA A, 1975, 38TH AM SOC INF SCI, V12, P127 MCDONALD DD, 1975, J AM SOC INFORM SCI, V26, P132 MCNAUGHTON R, 1957, 4 STANF U APPL MATH MILLER RH, 1975, 38TH AM SOC INF SCI, V12, P93 MOLSTER HC, 1975, J AM SOC INFORMATION, V26, P192 NARIN F, 1975, J AM SOC INFORM SCI, V26, P80, DOI 10.1002/asi.4630260203 *NAT SCI F, 1974, 1974 PROGR REP *NAT TECHN INF SER, 1975, EN MICR HIER LIST EN NELSON RD, 1975, J CHEM INF COMP SCI, V15, P85, DOI 10.1021/ci60002a004 *NEW ENGL BOARD HI, 1975, NASIC SEARCH AIDS *NEW ENGL BOARD HI, 1975, YEAR 2 REP OCONNOR J, 1975, 38TH AM SOC INF SCI, V12, P80 OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7 ODETTE RE, 1975, J CHEM INF COMP SCI, V15, P165, DOI 10.1021/ci60003a010 *OP SYST INC, 1975, COMP EV STRUCT FREE ORNE J, 1975, B AM SOC INFORMATION, V1, P22 PARK MK, 1975, OPERATIONAL EXPERIEN PARKINS PV, 1971, ANNU REV INFORM SCI, V6, P247 PARR T, 1975, B SPECIAL LIBRAR JUN, P26 PAYNE G, 1975, 4TH AM SOC INF SCI M, P134 PETERSON JS, 1975, J CHEM INF COMP SCI, V15, P169, DOI 10.1021/ci60003a011 POLLOCK JJ, 1975, J CHEM INF COMP SCI, V15, P226, DOI 10.1021/ci60004a008 PRAGIER G, 1975, J CHEM INF COMP SCI, V15, P155, DOI 10.1021/ci60003a006 PRATT G, 1975, DATA BASES EUROPE DI PRYOR HE, 1975, SPEC LIBR, V66, P515 ROBERTSON SE, 1975, J AM SOC INFORM SCI, V26, P214, DOI 10.1002/asi.4630260404 ROWLETT RJ, 1975, J CHEM INF COMP SCI, V15, P186, DOI 10.1021/ci60003a016 RULE DF, 1975, J CHEM INF COMP SCI, V15, P31, DOI 10.1021/ci60001a010 SALTON G, 1975, J AM SOC INFORM SCI, V26, P33, DOI 10.1002/asi.4630260106 SALTON G, 1975, DYNAMIC INFORMATION SALTON G, 1976, J ASSOC COMPUT MACH, V23, P76 SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220 SCHIPMA PB, 1975, STUDY INDEXING INFOR SCHIPMA PB, 1975, B AM SOC INFORMATION, V2, P2 SCHIPMA PB, 1975, ANNU REV INFORM SCI, V10, P237 SCHULTZ JL, 1975, J CHEM INF COMP SCI, V15, P94, DOI 10.1021/ci60002a005 Severance D. G., 1975, Information Systems, V1, DOI 10.1016/0306-4379(75)90015-0 SHUMAN JN, 1975, B AM SOC INFORMATION, V1, P11 SHUMAN JN, 1975, B AM SOC INFORMATION, V1, P34 SKOLNIK H, 1975, J CHEM INF COMP SCI, V15, P28, DOI 10.1021/ci60001a009 SLATER M, 1975, USER REQUIREMENTS LI SMITH RS, 1975, B AM SOC INFORMATION, V2, P19 STALCUP WS, 1975, 38TH AM SOC INF SCI, V12, P73 STELLHORN WH, 1974, EXPT INFORMATION RET STEWART AK, 1975, ASIDIC NEWSLETTE FAL STIRLING KH, 1975, 38TH AM SOC INF SCI, V12, P105 SWANSON RW, 1975, J AM SOC INFORM SCI, V26, P140, DOI 10.1002/asi.4630260303 TAUBER SJ, 1975, J CHEM INF COMP SCI, V15, P109, DOI 10.1021/ci60002a008 TEITELBAUM P, 1975, 38TH AM SOC INF SCI, V12, P130 THURONYI GT, 1975, 38TH P ANN M AM SOC, V12, P118 TING RN, 1975, SPEC LIBR, V66, P140 UHLMANN W, 1974, SEARCH SERVICE GOVER VANDERSTOUW GG, 1975, J CHEM INF COMP SCI, V15, P232, DOI 10.1021/ci60004a009 VANITALLIE A, 1975, 38TH AM SOC INF SCI, V12, P120 WALL E, 1975, J AM SOC INFORM SCI, V26, P71, DOI 10.1002/asi.4630260202 WATERS ST, 1975, J AM SOC INFORM SCI, V26, P126 WATSON P, 1974, PB241206 WAX DM, 1976, CLINIC LIBRARY APPLI WEIL BH, 1975, B AM SOC INFORMATION, V1, P26 WEIL BH, 1975, B AM SOC INFORMATION, V1, P23 WILDE DU, 1975, J AM SOC INFORM SCI, V26, P103, DOI 10.1002/asi.4630260205 WILDE DU, 1975, 179TH ADV GROUP AER WILDE DU, 1975, 38TH AM SOC INF SCI WILDE DU, 1975, 38TH AM SOC INF SCI, V12, P145 WILDE DU, 1975, J CHEM INF COMP SCI, V15, P183, DOI 10.1021/ci60003a015 WILLIAMS ME, 1975, J AM SOC INFORM SCI, V26, P305, DOI 10.1002/asi.4630260507 WILLIAMS MC, TO BE PUBLISHED WILLIAMS ME, 1975, SPEC LIBR, V66, P561 WILLIAMS ME, 1974, ANNU REV INFORM SCI, V9, P221 WILLIAMS ME, 1975, B AM SOC INFORMATION, V1, P21 WILLIAMS ME, 1976, DREXEL LIBRARY QUART, V12 WILLIAMS ME, 1975, J CHEM INF COMP SCI, V15, P3, DOI 10.1021/ci60001a002 WILLIAMS ME, 1975, B AM SOC INFORMATION, V2, P26 WILLIAMS ME, 1975, IMPACT MACHINE READA WISWESSER W, 1974, CHEMICAL BIOLOGICAL WOOD JL, 1973, J AM SOC INFORM SCI, V24, P25 WOOD JL, 1972, J AM SOC INFORM SCI, V23, P36, DOI 10.1002/asi.4630230108 ZAIS HW, 1975, THESIS U CALIFORNIA ZAIS HW, 1975, LBL4257 U CAL LAWR B ZERAFFA J, 1975, 38TH AM SOC INF SCI, V12, P160 1975, CHEMICAL ENGINEERING, V53, P30 1974, FED REG 31353, V39 NR 185 TC 4 Z9 4 PU INFORMATION TODAY INC PI MEDFORD PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 SN 0066-4200 J9 ANNU REV INFORM SCI JI Annu. Rev. Inform. Sci. Technol. PY 1976 VL 11 BP 267 EP 298 PG 32 WC Computer Science, Information Systems; Information Science & Library Science SC Computer Science; Information Science & Library Science GA CN084 UT WOS:A1976CN08400011 ER EF