Extracting data records from the web using tag path clustering

Extracting data records from the web using tag path clustering (2009)

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by Gengxin Miao , Junichi Tatemura , Wang-pin Hsiung , Arsany Sawires , Louise E. Moser

Venue:	In WWW ’09: Proceedings of the 18th international conference on World wide web
Citations:	6 - 0 self

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Datum	Value	Source
TITLE	Extracting data records from the web using tag path clustering	INFERENCE
AUTHOR NAME	Gengxin Miao	SVM HeaderParse 0.2
AUTHOR AFFIL	2; NEC Laboratories America, 10080 N. Wolfe Rd SW3-350, Cupertino	SVM HeaderParse 0.2
AUTHOR ADDR	CA, 95014	SVM HeaderParse 0.2
AUTHOR NAME	Junichi Tatemura	SVM HeaderParse 0.2
AUTHOR AFFIL	2; NEC Laboratories America, 10080 N. Wolfe Rd SW3-350, Cupertino	SVM HeaderParse 0.2
AUTHOR ADDR	CA, 95014	SVM HeaderParse 0.2
AUTHOR NAME	Wang-pin Hsiung	SVM HeaderParse 0.2
AUTHOR AFFIL	2; NEC Laboratories America, 10080 N. Wolfe Rd SW3-350, Cupertino	SVM HeaderParse 0.2
AUTHOR ADDR	CA, 95014	SVM HeaderParse 0.2
AUTHOR NAME	Arsany Sawires	SVM HeaderParse 0.2
AUTHOR AFFIL	2; NEC Laboratories America, 10080 N. Wolfe Rd SW3-350, Cupertino	SVM HeaderParse 0.2
AUTHOR ADDR	CA, 95014	SVM HeaderParse 0.2
AUTHOR NAME	Louise E. Moser	SVM HeaderParse 0.2
AUTHOR AFFIL	2; NEC Laboratories America, 10080 N. Wolfe Rd SW3-350, Cupertino	SVM HeaderParse 0.2
AUTHOR ADDR	CA, 95014	SVM HeaderParse 0.2
ABSTRACT	Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the first step of this object extraction process, identifies a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation – their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods.	SVM HeaderParse 0.2
YEAR	2009	INFERENCE
VENUE	In WWW ’09: Proceedings of the 18th international conference on World wide web	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	981--990	INFERENCE
CITATIONS	20 found	ParsCit 1.0