Published in

2014 IEEE International Conference on Semantic Computing

DOI: 10.1109/icsc.2014.13

Links

Tools

Export citation

Search in Google Scholar

A Scalable Approach to Learn Semantic Models of Structured Sources

Proceedings article published in 2014 by Mohsen Taheriyan, Craig A. Knoblock, Pedro Szekely, Jose Luis Ambite ORCID
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

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.