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%. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Noh, S., Choi, Y., Seo, H., Choi, K., & Jung, G. (2004). An intelligent topic-specific crawler using degree of relevance. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 491–498. https://doi.org/10.1007/978-3-540-28651-6_72
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