A Scalable Approach to Learn Semantic Models of Structured Sources

Abstract

Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured data source by exploiting the knowledge of previously modeled sources. Our evaluation shows that the approach generates expressive semantic models with minimal user input, and it is scalable to large ontologies and data sources with many attributes.