Classifying Web pages using adaptive ontology
Sanguk Noh, Haesung Seo, Jaehyuk Choi, Kyunghee Choi, Gihyun Jung · 2004
In this paper, we present an automated Web page classifier based on adaptive 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 the selected terms and classification into rules using machine learning algorithms. The compiled rules classify any Web page into categories defined on a domain ontology. In the experiments, 11 terms out of 1,700 terms were identified as representative features given a set of Web pages. The resulting accuracy of the classification was, on the average, 95.2%.