An intelligent information system for organizing online text documents
Identifieur interne : 001A84 ( Istex/Corpus ); précédent : 001A83; suivant : 001A85An intelligent information system for organizing online text documents
Auteurs : Han-Joon Kim ; Sang-Goo LeeSource :
- Knowledge and Information Systems [ 0219-1377 ] ; 2004-03-01.
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Abstract
Abstract: This paper describes an intelligent information system for effectively managing huge amounts of online text documents (such as Web documents) in a hierarchical manner. The organizational capabilities of this system are able to evolve semi-automatically with minimal human input. The system starts with an initial taxonomy in which documents are automatically categorized, and then evolves so as to provide a good indexing service as the document collection grows or its usage changes. To this end, we propose a series of algorithms that utilize text-mining technologies such as document clustering, document categorization, and hierarchy reorganization. In particular, clustering and categorization algorithms have been intensively studied in order to provide evolving facilities for hierarchical structures and categorization criteria. Through experiments using the Reuters-21578 document collection, we evaluate the performance of the proposed clustering and categorization methods by comparing them to those of well-known conventional methods.
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DOI: 10.1007/BF02637152
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The organizational capabilities of this system are able to evolve semi-automatically with minimal human input. The system starts with an initial taxonomy in which documents are automatically categorized, and then evolves so as to provide a good indexing service as the document collection grows or its usage changes. To this end, we propose a series of algorithms that utilize text-mining technologies such as document clustering, document categorization, and hierarchy reorganization. In particular, clustering and categorization algorithms have been intensively studied in order to provide evolving facilities for hierarchical structures and categorization criteria. 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ArticleType="OriginalPaper" ContainsESM="No" Language="En" NumberingStyle="Unnumbered" TocLevels="0"><ArticleID>BF02637152</ArticleID><ArticleDOI>10.1007/BF02637152</ArticleDOI><ArticleSequenceNumber>1</ArticleSequenceNumber><ArticleTitle Language="En">An intelligent information system for organizing online text documents</ArticleTitle><ArticleCategory>Regular Papers</ArticleCategory><ArticleFirstPage>125</ArticleFirstPage><ArticleLastPage>149</ArticleLastPage><ArticleHistory><RegistrationDate><Year>2007</Year><Month>5</Month><Day>23</Day></RegistrationDate></ArticleHistory><ArticleCopyright><CopyrightHolderName>Springer-Verlag London Ltd.</CopyrightHolderName><CopyrightYear>2004</CopyrightYear></ArticleCopyright><ArticleGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant 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ID="Aff2"><OrgDivision>School of Computer Science and Engineering</OrgDivision><OrgName>Seoul National University</OrgName><OrgAddress><City>Seoul</City><Country>Korea</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This paper describes an intelligent information system for effectively managing huge amounts of online text documents (such as Web documents) in a hierarchical manner. The organizational capabilities of this system are able to evolve semi-automatically with minimal human input. The system starts with an initial taxonomy in which documents are automatically categorized, and then evolves so as to provide a good indexing service as the document collection grows or its usage changes. To this end, we propose a series of algorithms that utilize text-mining technologies such as document clustering, document categorization, and hierarchy reorganization. In particular, clustering and categorization algorithms have been intensively studied in order to provide evolving facilities for hierarchical structures and categorization criteria. Through experiments using the Reuters-21578 document collection, we evaluate the performance of the proposed clustering and categorization methods by comparing them to those of well-known conventional methods.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Document categorization</Keyword><Keyword>Document clustering</Keyword><Keyword>Fuzzy relations</Keyword><Keyword>Hierarchical agglomerative clustering</Keyword><Keyword>Information systems</Keyword><Keyword>Naïve Bayes</Keyword><Keyword>Topic hierarchy</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>An intelligent information system for organizing online text documents</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>An intelligent information system for organizing online text documents</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Han-joon</namePart><namePart type="family">Kim</namePart><affiliation>Department of Electrical and Computer Engineering, The University of Seoul, 90 Jeonnong-dong, Dongdaemun-gu, 130-743, Seoul, Korea</affiliation><affiliation>E-mail: khj@uos.ac.kr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sang-goo</namePart><namePart type="family">Lee</namePart><affiliation>School of Computer Science and Engineering, Seoul National University, Seoul, Korea</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer-Verlag</publisher><place><placeTerm type="text">London</placeTerm></place><dateIssued encoding="w3cdtf">2004-03-01</dateIssued><copyrightDate encoding="w3cdtf">2004</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: This paper describes an intelligent information system for effectively managing huge amounts of online text documents (such as Web documents) in a hierarchical manner. The organizational capabilities of this system are able to evolve semi-automatically with minimal human input. The system starts with an initial taxonomy in which documents are automatically categorized, and then evolves so as to provide a good indexing service as the document collection grows or its usage changes. To this end, we propose a series of algorithms that utilize text-mining technologies such as document clustering, document categorization, and hierarchy reorganization. In particular, clustering and categorization algorithms have been intensively studied in order to provide evolving facilities for hierarchical structures and categorization criteria. 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