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PAKDD
2001
ACM

Applying Pattern Mining to Web Information Extraction

9 years 1 months ago
Applying Pattern Mining to Web Information Extraction
Information extraction (IE) from semi-structured Web documents is a critical issue for information integration systems on the Internet. Previous work in wrapper induction aim to solve this problem by applying machine learning to automatically generate extractors. For example, WIEN, Stalker, Softmealy, etc. However, this approach still requires human intervention to provide training examples. In this paper, we propose a novel idea to IE, by repeated pattern mining and multiple pattern alignment. The discovery of repeated patterns are realized through a data structure call PAT tree. In addition, incomplete patterns are further revised by pattern alignment to comprehend all pattern instances. This new track to IE involves no human effort and content-dependent heuristics. Experimental results show that the constructed extraction rules can achieves 97 percent extraction over fourteen popular search engines.
Chia-Hui Chang, Shao-Chen Lui, Yen-Chin Wu
Added 30 Jul 2010
Updated 30 Jul 2010
Type Conference
Year 2001
Where PAKDD
Authors Chia-Hui Chang, Shao-Chen Lui, Yen-Chin Wu
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