An Intelligent Topic-Specific Crawler Using Degree of Relevance

It is indispensable that the users surfing on the Internet could have web pages classified into a given topic as correct as possible. Toward this ends, this paper presents a topic-specific crawler computing the degree of relevance and refining the preliminary set of related web pages using term frequency/ document frequency, entropy, and compiled rules. In the experiments, we test our topic-specific crawler in terms of the accuracy of its classification, the crawling efficiency, and the crawling consistency. In case of using 51 representative terms, it turned out that the resulting accuracy of the classification was 97.8%.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Sanguk Noh, Youngsoo Choi, Haesung Seo, Kyunghee Choi, Gihyun Jung
• Keywords: Frequency/ Document Frequency, IDEAL 2004, Topic-specific Crawler, Web Pages