Exploiting corpus‐related ontologies for conceptualizing document corpora
Identifieur interne : 001188 ( Main/Exploration ); précédent : 001187; suivant : 001189Exploiting corpus‐related ontologies for conceptualizing document corpora
Auteurs : Hai Ao Zheng [République populaire de Chine] ; Charles Borchert [Corée du Sud] ; Hong Ee Kim [Corée du Sud]Source :
- Journal of the American Society for Information Science and Technology [ 1532-2882 ] ; 2009-11.
Abstract
As a greater volume of information becomes increasingly available across all disciplines, many approaches, such as document clustering and information visualization, have been proposed to help users manage information easily. However, most of these methods do not directly extract key concepts and their semantic relationships from document corpora, which could help better illuminate the conceptual structures within given information. To address this issue, we propose an approach called “Clonto” to process a document corpus, identify the key concepts, and automatically generate ontologies based on these concepts for the purpose of conceptualization. For a given document corpus, Clonto applies latent semantic analysis to identify key concepts, allocates documents based on these concepts, and utilizes WordNet to automatically generate a corpus‐related ontology. The documents are linked to the ontology through the key concepts. Based on two test collections, the experimental results show that Clonto is able to identify key concepts, and outperforms four other clustering algorithms. Moreover, the ontologies generated by Clonto show significant informative conceptual structures.
Url:
DOI: 10.1002/asi.21145
Affiliations:
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Am. Soc. Inf. Sci.</title><idno type="ISSN">1532-2882</idno><idno type="eISSN">1532-2890</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2009-11">2009-11</date><biblScope unit="volume">60</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="2287">2287</biblScope><biblScope unit="page" to="2299">2299</biblScope></imprint><idno type="ISSN">1532-2882</idno></series><idno type="istex">A6699445F6190ACD2AFE2314633BF2D355AE47FD</idno><idno type="DOI">10.1002/asi.21145</idno><idno type="ArticleID">ASI21145</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1532-2882</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">As a greater volume of information becomes increasingly available across all disciplines, many approaches, such as document clustering and information visualization, have been proposed to help users manage information easily. However, most of these methods do not directly extract key concepts and their semantic relationships from document corpora, which could help better illuminate the conceptual structures within given information. To address this issue, we propose an approach called “Clonto” to process a document corpus, identify the key concepts, and automatically generate ontologies based on these concepts for the purpose of conceptualization. For a given document corpus, Clonto applies latent semantic analysis to identify key concepts, allocates documents based on these concepts, and utilizes WordNet to automatically generate a corpus‐related ontology. The documents are linked to the ontology through the key concepts. Based on two test collections, the experimental results show that Clonto is able to identify key concepts, and outperforms four other clustering algorithms. Moreover, the ontologies generated by Clonto show significant informative conceptual structures.</div></front></TEI><affiliations><list><country><li>Corée du Sud</li><li>République populaire de Chine</li></country><region><li>Guangdong</li></region><settlement><li>Shenzhen</li><li>Séoul</li></settlement><orgName><li>Université nationale de Séoul</li></orgName></list><tree><country name="République populaire de Chine"><region name="Guangdong"><name sortKey="Zheng, Hai Ao" sort="Zheng, Hai Ao" uniqKey="Zheng H" first="Hai Ao" last="Zheng">Hai Ao Zheng</name></region></country><country name="Corée du Sud"><noRegion><name sortKey="Borchert, Charles" sort="Borchert, Charles" uniqKey="Borchert C" first="Charles" last="Borchert">Charles Borchert</name></noRegion><name sortKey="Kim, Hong Ee" sort="Kim, Hong Ee" uniqKey="Kim H" first="Hong Ee" last="Kim">Hong Ee Kim</name><name sortKey="Kim, Hong Ee" sort="Kim, Hong Ee" uniqKey="Kim H" first="Hong Ee" last="Kim">Hong Ee Kim</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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