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Conference Paper: A comparative study of ontology based term similarity measures on PubMed document clustering

TitleA comparative study of ontology based term similarity measures on PubMed document clustering
Authors
KeywordsDomain ontology
Semantic similarity measure
Document clustering
Issue Date2007
PublisherSpringer.
Citation
12th International Conference on Database Systems for Advanced Applications (DASFAA 2007), Bangkok, Thailand, 9-12 April 2007. In Advances in Databases: Concepts, Systems and Applications: 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, April 9-12, 2007, Proceedings, 2007, p. 115-126 How to Cite?
AbstractRecent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms. © Springer-Verlag Berlin Heidelberg 2007.
Persistent Identifierhttp://hdl.handle.net/10722/276824
ISBN
ISSN
2005 Impact Factor: 0.302
2020 SCImago Journal Rankings: 0.249
Series/Report no.Lecture Notes in Computer Science ; 4443

 

DC FieldValueLanguage
dc.contributor.authorZhang, Xiaodan-
dc.contributor.authorJing, Liping-
dc.contributor.authorHu, Xiaohua-
dc.contributor.authorNg, Michael-
dc.contributor.authorZhou, Xiaohua-
dc.date.accessioned2019-09-18T08:34:46Z-
dc.date.available2019-09-18T08:34:46Z-
dc.date.issued2007-
dc.identifier.citation12th International Conference on Database Systems for Advanced Applications (DASFAA 2007), Bangkok, Thailand, 9-12 April 2007. In Advances in Databases: Concepts, Systems and Applications: 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, April 9-12, 2007, Proceedings, 2007, p. 115-126-
dc.identifier.isbn9783540717027-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276824-
dc.description.abstractRecent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this paper, we conduct a comparative study on how different semantic similarity measures of term including path based similarity measure, information content based similarity measure and feature based similarity measure affect document clustering. We evaluate term re-weighting as an important method to integrate domain ontology to clustering process. Meanwhile, we apply k-means clustering on one real-world text dataset, our own corpus generated from PubMed. Experiment results on 8 different semantic measures have shown that: (1) there is no a certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms. © Springer-Verlag Berlin Heidelberg 2007.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Databases: Concepts, Systems and Applications: 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, April 9-12, 2007, Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 4443-
dc.subjectDomain ontology-
dc.subjectSemantic similarity measure-
dc.subjectDocument clustering-
dc.titleA comparative study of ontology based term similarity measures on PubMed document clustering-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-71703-4_12-
dc.identifier.scopuseid_2-s2.0-38049162356-
dc.identifier.spage115-
dc.identifier.epage126-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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