Extracting A ribute-Value Pairs from Product Specifications on the Web

Comparison shopping portals integrate product o ers from large numbers of e-shops in order to support consumers in their buying decisions. Product o ers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the speci c vendor. In addition, product o ers might contain structured or semi-structured product speci cations in the form of HTML tables and HTML lists. As product speci cations often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these speci cations is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attributevalue pairs from product speci cations on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product speci cation or not. In order to extract attribute-value pairs from the HTML fragments identi ed by the speci cation detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attributevalue pairs from product speci cations, we introduce several new features for speci cation detection and support the extraction of attribute-value pairs from speci cations having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 di erent e-shops. This experiment con rms the suitability of duplicate-based schema matching for product data integration.