Advertisement

Reducing the Inferred Type Statements with Individual Grouping Constructs

  • Övünç Öztürk
  • Tuğba Özacar
  • Murat Osman Ünalır
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4273)

Abstract

A common approach for reasoning is to compute the deductive closure of an ontology using the rules specified and to work on the closure at query time. This approach reduces the run time complexity but increases the space requirements. The main reason of this increase is the type and subclass statements in the ontology. Type statements show a significant percentage in most ontologies. Since subclass is a transitive property, derivation of other statements, in particular type statements relying on it, gives rise to cyclic repetition and an excess of inferred type statements. In brief, a major part of closure computation is deriving the type statements relying on subclass statements. In this paper, we propose a syntactic transformation that is based on novel individual grouping constructs. This transformation reduces the number of inferred type statements relying on subclass relations. Thus, the space requirement of reasoning is reduced without affecting the soundness and the completeness.

Keywords

Utilization Rate Type Statement Space Requirement Query Time Partial Extension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Wielemaker, J., Schreiber, G., Wielinga, B.J.: Prolog-based infrastructure for RDF: Scalability and performance. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 644–658. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Haarslev, V., Möller, R.: High performance reasoning with very large knowledge bases: A practical case study. In: IJCAI, pp. 161–168 (2001)Google Scholar
  3. 3.
    Horrocks, I., Li, L., Turi, D., Bechhofer, S.: The instance store: Dl reasoning with large numbers of individuals. In: Description Logics (2004)Google Scholar
  4. 4.
    Lassila, O.: Taking the RDF model theory out for a spin. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 307–317. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Stuckenschmidt, H., Broekstra, J.: Time - space trade-offs in scaling up rdf schema reasoning. In: WISE Workshops, pp. 172–181 (2005)Google Scholar
  6. 6.
    Tartir, S., Arpinar, I.B., Moore, M., Sheth, A.P., Aleman-Meza, B.: Ontoqa: Metric-based ontology quality analysis. In: Proceedings of IEEE ICDM 2005 Workshop on Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources (2005)Google Scholar
  7. 7.
    Lloyd, J.W.: Foundations of Logic Programming, 2nd edn. Springer, Heidelberg (1987)MATHGoogle Scholar
  8. 8.
    Hayes, P.: Rdf semantics (2004)Google Scholar
  9. 9.
    Ünalir, M., Özacar, T., Öztürk, Ö.: Reordering query and rule patterns for query answering in a rete-based inference engine. In: WISE Workshops, pp. 255–265 (2005)Google Scholar
  10. 10.
    Guo, Y., Pan, Z., Heflin, J.: An evaluation of knowledge base systems for large OWL datasets. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 274–288. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Horst, H.J.: Combining RDF and part of OWL with rules: Semantics, decidability, complexity. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 668–684. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Patel-Schneider, P.F., Hayes, P., Horrocks, I.: Rdf semantics (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Övünç Öztürk
    • 1
  • Tuğba Özacar
    • 1
  • Murat Osman Ünalır
    • 1
  1. 1.Department of Computer EngineeringEge UniversityBornovaTurkey

Personalised recommendations