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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Learning Robust Web Wrappers
verfasst von : B. Fazzinga, S. Flesca, A. Tagarelli
Erschienen in: Database and Expert Systems Applications
Verlag: Springer Berlin Heidelberg
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A main challenge in wrapping web data is to make wrappers robust w.r.t. variations in HTML sources, reducing human effort as much as possible. In this paper we develop a new approach to speed up the specification of robust wrappers, allowing the wrapper designer to not care about detailed definition of extraction rules. The key-idea is to enable a schema-based wrapping system to automatically generalize an original wrapper w.r.t. a set of example HTML documents. To accomplish this objective, we propose to exploit the notions of extraction rule and wrapper subsumption for computing a most general wrapper which still shares the extraction schema with the original wrapper, while maximizes the generalization of extraction rules w.r.t. the set of example documents.