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http://dx.doi.org/10.3745/KIPSTB.2004.11B.6.723

Web Page Classification System based upon Ontology  

Choi Jaehyuk (삼성전자 네트워크 사업부)
Seo Haesung (아주대학교 정보통신전문대학원 정보통신공학과)
Noh Sanguk (가톨릭대학교 컴퓨터정보공학부)
Choi Kyunghee (아주대학교 정보통신전문대학원)
Jung Gihyun (아주대학교 전자공학부)
Publication Information
The KIPS Transactions:PartB / v.11B, no.6, 2004 , pp. 723-734 More about this Journal
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%.
Keywords
Web Page Classification; Ontology; Information Gain; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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