http://dx.doi.org/10.1007/s10115-009-0256-5">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Knowledge-based vector space model for text clustering

Language

English

Abstract

This 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.

Keywords

Knowledge-based VSM, Semantic relationship, Term similarity, Text clustering

Publication Date

2010

Source Publication Title

Knowledge and Information Systems

Volume

25

Issue

1

Start Page

35

End Page

55

Publisher

Springer Verlag

ISSN (print)

02191377

ISSN (electronic)

02193116

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