文章基本信息

标题：STUDY OF ONTOLOGY OR THESAURUS BASED DOCUMENT CLUSTERING AND INFORMATION RETRIEVAL
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作者：G.BHARATHI ; D.VENKATESAN
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2012
卷号：40
期号：1
页码：055-061
出版社：Journal of Theoretical and Applied
摘要：Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. Clustering text data faces a number of new challenges. Among others, the volume of text data, dimensionality, sparsity and complex semantics are the most important ones. These characteristics of text data require clustering techniques to be scalable to large and high dimensional data, and able to handle sparsity and semantics. In the traditional vector space model, the unique words occurring in the document set are used as the features. But because of the synonym problem and the polysemous problem, such a bag of original words cannot represent the content of a document precisely. Most of the existing text clustering methods use clustering techniques which depend only on term strength and document frequency where single terms are used as features for representing the documents and they are treated independently which can be easily applied to non-ontological clustering. To overcome the above issues, this paper makes a survey of recent research done on ontology or thesaurus based document clustering.
关键词：Ontology; Thesaurus; Document Clustering; Intelligent Information Retrieval; Semantics; Wikipedia; WordNet.