File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Knowledge-based vector space model for text clustering

TitleKnowledge-based vector space model for text clustering
Authors
KeywordsSemantic relationship
Text clustering
Term similarity
Knowledge-based VSM
Issue Date2010
Citation
Knowledge and Information Systems, 2010, v. 25, n. 1, p. 35-55 How to Cite?
AbstractThis paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM. © 2009 Springer-Verlag London Limited.
Persistent Identifierhttp://hdl.handle.net/10722/276871
ISSN
2020 Impact Factor: 2.822
2020 SCImago Journal Rankings: 0.634
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.contributor.authorHuang, Joshua Z.-
dc.date.accessioned2019-09-18T08:34:54Z-
dc.date.available2019-09-18T08:34:54Z-
dc.date.issued2010-
dc.identifier.citationKnowledge and Information Systems, 2010, v. 25, n. 1, p. 35-55-
dc.identifier.issn0219-1377-
dc.identifier.urihttp://hdl.handle.net/10722/276871-
dc.description.abstractThis paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM. © 2009 Springer-Verlag London Limited.-
dc.languageeng-
dc.relation.ispartofKnowledge and Information Systems-
dc.subjectSemantic relationship-
dc.subjectText clustering-
dc.subjectTerm similarity-
dc.subjectKnowledge-based VSM-
dc.titleKnowledge-based vector space model for text clustering-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10115-009-0256-5-
dc.identifier.scopuseid_2-s2.0-77957556167-
dc.identifier.volume25-
dc.identifier.issue1-
dc.identifier.spage35-
dc.identifier.epage55-
dc.identifier.eissn0219-3116-
dc.identifier.isiWOS:000282514300003-
dc.identifier.issnl0219-3116-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats