Semi-automatically mapping structured sources into the semantic web

Authors:
Craig A. Knoblock;Pedro Szekely;José Luis Ambite;Aman Goel;Shubham Gupta;Kristina Lerman;Maria Muslea;Mohsen Taheriyan;Parag Mallick
Affiliations:
Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Information Sciences Institute and Department of Computer Science, University of Southern California;Department of Radiology, Stanford University
Venue:
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Year:
2012

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Abstract

Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.