Web Page Classification System based upon Ontology 


Vol. 11,  No. 6, pp. 723-734, Oct.  2004
10.3745/KIPSTB.2004.11.6.723


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  Abstract

In this paper, we present an automated Web page classification system based upon ontology. As a first step, to identify the representative terms given a set of classes, we compute the product of term frequency and document frequency. Secondly, the information gain of each term prioritizes it based on the possibility of classification. We compile a pair of the terms selected and a web page classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 78 terms out of 240 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 83.52%.

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[IEEE Style]

J. H. Choi, H. S. Seo, S. U. Noh, K. H. Choi, G. H. Jung, "Web Page Classification System based upon Ontology," The KIPS Transactions:PartB , vol. 11, no. 6, pp. 723-734, 2004. DOI: 10.3745/KIPSTB.2004.11.6.723.

[ACM Style]

Jae Hyuk Choi, Hae Sung Seo, Sang Uk Noh, Kyung Hee Choi, and Gi Hyun Jung. 2004. Web Page Classification System based upon Ontology. The KIPS Transactions:PartB , 11, 6, (2004), 723-734. DOI: 10.3745/KIPSTB.2004.11.6.723.

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