An ontology-based approach to learnable focused crawling

  • Authors:

  • Hai-Tao Zheng;Bo-Yeong Kang;Hong-Gee Kim

  • Affiliations:

  • Biomedical Knowledge Engineering Laboratory, Seoul National University, 28 Yeongeon-dong, Jongro-gu, Seoul, Republic of Korea;Biomedical Knowledge Engineering Laboratory, Seoul National University, 28 Yeongeon-dong, Jongro-gu, Seoul, Republic of Korea;Biomedical Knowledge Engineering Laboratory, Seoul National University, 28 Yeongeon-dong, Jongro-gu, Seoul, Republic of Korea

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

Focused crawling is aimed at selectively seeking out pages that are relevant to a predefined set of topics. Since an ontology is a well-formed knowledge representation, ontology-based focused crawling approaches have come into research. However, since these approaches utilize manually predefined concept weights to calculate the relevance scores of web pages, it is difficult to acquire the optimal concept weights to maintain a stable harvest rate during the crawling process. To address this issue, we proposed a learnable focused crawling framework based on ontology. An ANN (artificial neural network) was constructed using a domain-specific ontology and applied to the classification of web pages. Experimental results show that our approach outperforms the breadth-first search crawling approach, the simple keyword-based crawling approach, the ANN-based focused crawling approach, and the focused crawling approach that uses only a domain-specific ontology.