Jae Hyuk Choi , Hae Sung Seo , Sang Uk Noh , Kyung Hee Choi , Gi Hyun Jung

The KIPS Transactions:PartB , Vol. 11, No. 6, pp. 723-734, Oct. 2004
10.3745/KIPSTB.2004.11.6.723,   PDF Download:

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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