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 match attribute values with diverse representations across sites, we define a new similarity metric that leverages the templatized structure of attribute content. Specifically, our metric discovers the matching pattern between attribute values from two sites, and uses this to ignore extraneous portions of attribute values when computing similarity scores. Further, 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 ...
Pankaj Gulhane, Rajeev Rastogi, Srinivasan H. Seng