Interactive Tuples Extraction from Semi-Structured Data

Abstract : This paper studies from a machine learning viewpoint the problem of extracting tuples of a target n-ary relation from tree structured data like XML or XHTML documents. Our system can extract, without any post-processing, tuples for all data structures including nested, rotated and cross tables. The wrapper induction algorithm we propose is based on two main ideas. It is incremental: partial tuples are extracted by increasing length. It is based on a representation-enrichment procedure: partial tuples of length i are encoded with the knowledge of extracted tu- ples of length i − 1. The algorithm is then set in a friendly interactive wrapper induction system for Web documents. We evaluate our system on several information extraction tasks over corporate Web sites. It achieves state-of-the-art results on simple data structures and succeeds on complex data structures where previous approaches fail. Experiments also show that our interactive framework significantly reduces the number of user interactions needed to build a wrapper.
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Communication dans un congrès
Web Intelligence, Dec 2006, Hong Kong, China. 2006
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Rémi Gilleron, Patrick Marty, Fabien Torre, Marc Tommasi. Interactive Tuples Extraction from Semi-Structured Data. Web Intelligence, Dec 2006, Hong Kong, China. 2006. 〈inria-00581253〉

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