Exploiting the huge amount of data available on the Web involves the generation of wrappers to extract data from web pages. We argue that existing approaches for web data extraction from data-intensive websites miss the opportunities related to the presence of redundant information on the Web. We propose an innovative approach that aims at pushing further the level of automation of existing wrapper generation systems by leveraging the redundancy of data on the Web. An experimental evaluation of the proposed solution shows a relevant improvement for the precision of the extracted data, without a significant loss in the recall.
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