Attribute Similarity and Relevance-Based Product Schema Matching for Targeted Catalog Enrichment

Many eCommerce catalogs rely on structured prod-uct data to provide a good experience for customers. For large scale services, product information is provided by millions of different manufacturer and vendor schemas. Due to inherent heterogeneity of this data, unifying it to a consistent catalog schema remains a challenge. Schema matching is the problem of finding such correspondences between concepts in different distributed, heterogeneous data sources. Most approaches in automated schema matching assume either a small number of source schemas, attributes, and contexts (i.e., matching movie attributes from media knowledge bases). By contrast, schema matching in product catalogs encounter the problem of scaling across millions of noisy, heterogenous schemas spanning thou-sands of categories and attributes. In this paper, we introduce a scalable schema matching framework that utilizes unsupervised domain-specific attribute representations and general attribute similarity metrics. Our method first identifies relevant attributes for a given product based on existing customer signals, and then prioritizes among candidate attributes to consolidate only those relevant product facts from multiple manufacturers and vendors with little to no labeled data. We demonstrate value by experiments that enriched catalog data containing millions of attribute enumer-ations sourced from tens of thousands of schemas across a wide range of product categories. Experimental results show reduced manual annotation efforts by 75% from competing schema matching efforts by automating schema matching on targeted product facts, resulting in high accuracy, precision, and recall for important attributes that contribute to customer interest. We also demonstrate performance improvements of 8% MRR using our approach compared against two well-established approaches to unsupervised schema matching.