Using BMEcat Catalogs as a Lever for Product Master Data on the Semantic Web

Abstract. To date, the automatic exchange of product information be-tween business partners in a value chain is typically done using Business-to-Business (B2B) catalog standards such as EDIFACT, cXML, or BMEcat. At the same time, the Web of Data, in particular the GoodRelations vo-cabulary, offers the necessary means to publish highly-structured product data in a machine-readable format. The advantage of the publication of rich product descriptions can be manifold, including better integration and exchange of information between Web applications, high-quality data along the various stages of the value chain, or the opportunity to support more precise and more effective searches. In this paper, we (1) stress the importance of rich product master data for e-commerce on the Semantic Web, and (2) present a tool to convert BMEcat XML data sources into an RDF-based data model anchored in the GoodRelations vocabulary. The benefits of our proposal are tested using product data collected from a set of 2500+ online retailers of varying sizes and domains.