NDLI: Combining Similarity and Distribution Features to Match Attributes

Content Provider	IEEE Xplore Digital Library
Author	Wang, Yu Fang, Binxing Guo, Yan
Copyright Year	2009
Abstract	The Web contains much useful semistructued information which can be organized into web objects, and many of them are commercially valuable. The inner structures of these web objects are highly heterogeneous that web objects from different web sites cover different subsets of useful attributes. The complete set of attributes can be mined from web pages through attribute extraction algorithms. However, to construct high quality web object schema, some mined attributes should be merged since they are synonyms for the same concepts. Our empirical study shows that features used by traditional schema matching and deep web integration methods are usually domain specific, so they are not applicable to match attributes extracted from the Web. To overcome this problem, this paper proposes new features to depict attribute distribution characteristics and uses machine learning techniques to combine attribute distribution characteristics with attribute similarity features. We empirically compare the proposed method with existing methods use other features, and the results show the effectiveness of our method.
Starting Page	299
Ending Page	302
File Size	497036
Page Count	4
File Format	PDF
ISBN	9780769538013
DOI	10.1109/WI-IAT.2009.287
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Conferences attribute distribution attribute extraction schema matching deep web integration Spatial databases Data mining Intelligent agent Research and development Web pages Machine learning Skeleton Business
Content Type	Text
Resource Type	Article

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