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.

Metadata

publication

2014 IEEE International Conference on Semantic Computing, 183-190, 2014

year

2014

publication date

2014/6/16

authors

Mohsen Taheriyan, Craig A Knoblock, Pedro Szekely, José Luis Ambite

link

https://ieeexplore.ieee.org/abstract/document/6882021/

resource_link

https://www.researchgate.net/profile/Pedro-Szekely/publication/268195858_A_Scalable_Approach_to_Learn_Semantic_Models_of_Structured_Sources/links/547a84e60cf293e2da2b5fd7/A-Scalable-Approach-to-Learn-Semantic-Models-of-Structured-Sources.pdf

conference

2014 IEEE International Conference on Semantic Computing

pages

183-190

publisher

IEEE