document: Exploiting content redundancy for web information extraction We propose a novel extraction approach that exploits content redundancy on the web to extract structured data from template-based web sites . We start by populating a seed database with records extracted from a few initial sites . We then identify values within the pages of each new site that match attribute values contained in the seed set of records . To filter out noisy attribute value matches , we exploit the fact that attribute values occur at fixed positions within template-based sites . We develop an efficient Apriori-style algorithm to systematically enumerate attribute position configurations with sufficient matching values across pages . Finally , we conduct an extensive experimental study with real-life web data to demonstrate the effectiveness of our extraction approach .
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