Applying pattern mining to web information extraction

Chia Hui Chang; Shao Chen Lui; Yen Chin Wu

doi:10.1007/3-540-45357-1_4

Applying pattern mining to web information extraction

Chia Hui Chang, Shao Chen Lui, Yen Chin Wu

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

18 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
Editors	David Cheung, Graham J. Williams, Qing Li
Publisher	Springer Verlag
Pages	4-15
Number of pages	12
ISBN (Print)	3540419101, 9783540419105
DOIs	https://doi.org/10.1007/3-540-45357-1_4
State	Published - 2001
Event	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong Duration: 16 Apr 2001 → 18 Apr 2001

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2035
ISSN (Print)	0302-9743

Conference

Conference	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
Country/Territory	Hong Kong
City	Kowloon
Period	16/04/01 → 18/04/01

Keywords

Information extraction
Multiple alignment
Pattern discovery
Semi-structured documents
Wrap-per generation

Access to Document

10.1007/3-540-45357-1_4

Cite this

Chang, C. H., Lui, S. C., & Wu, Y. C. (2001). Applying pattern mining to web information extraction. In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (pp. 4-15). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_4

Chang, Chia Hui ; Lui, Shao Chen ; Wu, Yen Chin. / Applying pattern mining to web information extraction. Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. editor / David Cheung ; Graham J. Williams ; Qing Li. Springer Verlag, 2001. pp. 4-15 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

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abstract = "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.",

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Chang, CH, Lui, SC & Wu, YC 2001, Applying pattern mining to web information extraction. in D Cheung, GJ Williams & Q Li (eds), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2035, Springer Verlag, pp. 4-15, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, Kowloon, Hong Kong, 16/04/01. https://doi.org/10.1007/3-540-45357-1_4

Applying pattern mining to web information extraction. / Chang, Chia Hui; Lui, Shao Chen; Wu, Yen Chin.
Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. ed. / David Cheung; Graham J. Williams; Qing Li. Springer Verlag, 2001. p. 4-15 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Chang CH, Lui SC, Wu YC. Applying pattern mining to web information extraction. In Cheung D, Williams GJ, Li Q, editors, Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Springer Verlag. 2001. p. 4-15. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/3-540-45357-1_4

Applying pattern mining to web information extraction

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