SPARQL graph pattern rewriting for OWL-DL inference queries

Yixin Jing, Dongwon Jeong, Doo Kwon Baik

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)243-262
Number of pages20
JournalKnowledge and Information Systems
Issue number2
Publication statusPublished - 2009


  • Graph pattern
  • OWL-DL
  • Ontology inference
  • Query rewriting
  • Semantic web

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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