SPARQL graph pattern rewriting for OWL-DL inference queries

  • Authors:

  • Yixin Jing;Dongwon Jeong;Doo-Kwon Baik

  • Affiliations:

  • Korea University, Department of Computer Science and Engineering, 136-713, Seoul, South Korea;Kunsan National University, Department of Informatics and Statistics, 573-701, Gunsan, South Korea;Korea University, Department of Computer Science and Engineering, 136-713, Seoul, South Korea

  • Venue:
  • Knowledge and Information Systems
  • Year:
  • 2009

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Abstract

This paper focuses on the issue of OWL-DL ontology queries implemented in SPARQL. Currently, ontology repositories construct inference ontology models, and match SPARQL queries to the models, to derive inference results. Because an inference model uses much more storage space than the original model, and cannot be reused as inference requirements vary, this method is not suitable for large-scale deployment. To solve this problem, this paper proposes a novel method that passes rewritten SPARQL queries to the original ontology model, to retrieve inference results. We define OWL-DL inference rules and apply them to rewriting Graph Patterns in queries. The paper classifies the inference rules and discusses how these rules affect query rewriting. To illustrate the advantages of our proposal, we present a prototype system based on Jena, and address query optimization, to eliminate the disadvantages of augmented query sentences. We perform a set of query tests and compare the results with related works. The results show that the proposed method results in significantly improved query efficiency, without compromising completeness or soundness.