Learnable Focused Crawling Based on Ontology

摘要：

Focused crawling is proposed to selectively seek 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 apply 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 propose a learnable focused crawling approach based on ontology.An ANN (Artificial Neural Network) is constructed by using a domain-specific ontology and applied to the classification of web pages.Experiments have been performed,and the results show that our approach outperforms the breadth-first search crawling approach,the simple keyword-based crawling approach,and the focused crawling approach using only the domain-specific ontology.

作者: Hai-Tao Zheng Bo-Yeong Kang Hong-Gee Kim

作者单位: Biomedical Knowledge Engineering Laboratory,Dentistry College,Seoul National University,28 Yeongeon-dong,Jongro-gu,Seoul,Korea

会议类型: 国际会议

会议名称: 4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 264-275

在线出版日期: 2008-01-16（万方平台首次上网日期，不代表论文的发表时间）