On Logical Consequence for Collections of OWL Documents

  • Yuanbo Guo
  • Jeff Heflin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3729)

Abstract

In this paper, we investigate the (in)dependence among OWL documents with respect to the logical consequence when they are combined, in particular the inference of concept and role assertions about individuals. On the one hand, we present a systematic approach to identifying those documents that affect the inference of a given fact. On the other hand, we consider ways for fast detection of independence. First, we demonstrate several special cases in which two documents are independent of each other. Secondly, we introduce an algorithm for checking the independence in the general case. In addition, we describe two applications in which the above results have allowed us to develop novel approaches to overcome some difficulties in reasoning with large scale OWL data. Both applications demonstrate the usefulness of this work for improving the scalability of a practical Semantic Web system that relies on the reasoning about individuals.

References

  1. 1.
    Amir, E., McIlraith, S.: Partition-Based Logical Reasoning for First-Order and Propositional Theories. Artificial Intelligence journal (2003)Google Scholar
  2. 2.
    Borgida, A., Serafini, L.: Distributed Description Logics - Assimilating Information from Peer Sources. Journal of Data Semantics (1) (2003)Google Scholar
  3. 3.
    Darwiche, A.: A logical notion of conditional independence: properties and applications. Artificial Intelligence 97(1-2), 45–82 (1997)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Dean, M., Schreiber, G. (eds.): OWL Web Ontology Language Reference, W3C Recommendation (February 10, 2004), http://www.w3.org/TR/2004/REC-owl-ref-20040210/
  5. 5.
    Elhaik, Q., Rousset, M.-C.: Making an ABox persistent. In: Proc. of the 1998 Description Logic Workshop, DL 1998 (1998)Google Scholar
  6. 6.
    Greiner, R., Pearl, J., Subramanian, D. (eds.): Artificial Intelligence, vol. 97 (1–2) (1997) (Special Issue on Relevance)Google Scholar
  7. 7.
    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
  8. 8.
    Guo, Y., Heflin, J.: An Initial Investigation into Querying an Untrustworthy and Inconsistent Web. In: ISWC 2004 Workshop on Trust, Security and Reputation on the Semantic Web (2004)Google Scholar
  9. 9.
    Haarslev, V., Möller, R.: A Core Inference Engine for the Semantic Web. In: Workshop on Evaluation on Ontology-based Tools, ISWC 2003 (2003)Google Scholar
  10. 10.
    Haarslev, V., Möller, R.: Optimization Techniques for Retrieving Resources Described in OWL/RDF Documents: First Results. In: Proc. of Ninth International Conference on the Principles of Knowledge Representation and Reasoning, KR 2004 (2004)Google Scholar
  11. 11.
    Haarslev, V., Möller, R., Wessel, M.: Querying the Semantic Web with Racer + nRQL. In: Proc. of the Workshop on Description Logics 2004, ADL 2004 (2004)Google Scholar
  12. 12.
    Horrocks, I.: The faCT system. In: de Swart, H. (ed.) TABLEAUX 1998. LNCS (LNAI), vol. 1397, p. 307. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Horrocks, I., Patel-Schneider, P.F.: Reducing OWL entailment to description logic satisfiability. J. of Web Semantics 1(4), 345–357 (2004)Google Scholar
  14. 14.
    Lang, J., Liberatore, P., Marquis, P.: Conditional independence in propositional logic. Artificial Intelligence Journal 141(1), 79–121 (2002)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Lang, J., Liberatore, P., Marquis, P.: Propositional independence: formula-variable independence and forgetting. Journal of Artificial Intelligence Research 18, 391–443 (2003)MATHMathSciNetGoogle Scholar
  16. 16.
    Levesque, H.: A completeness result for reasoning with incomplete knowledge bases. In: Proc. of KR 1998, Sixth International Conference on Principles of Knowledge Representation and Reasoning (1998)Google Scholar
  17. 17.
    Levy, A.Y., Fikes, R.E., Sagiv, Y.: Speeding up inferences using relevance reasoning: a formalism and algorithms. Artificial Intelligence 97(1-2), 83–136 (1997)MATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    McGuinness, D.L., Borgida, A.: Explaining Subsumption in Description Logics. In: Proc. of the 14th International Joint Conference on Artificial Intelligence (1995)Google Scholar
  19. 19.
    Patel-Schneider, P.F. (ed.): OWL Web Ontology Language Semantics and Abstract Syntax, http://www.w3.org/TR/owl-semantics/
  20. 20.
    Royer, V., Quantz, J.J.: Deriving Inference Rules for Terminological Logics. In: Pearce, D.J., Wagner, G. (eds.) JELIA 1992. LNCS, vol. 633. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  21. 21.
    Royer, V., Quantz, J.J.: Deriving Inference Rules for Description Logics: a Rewriting Approach into Sequent Calculi. KIT REPORT 111 (December 1993)Google Scholar
  22. 22.
    Tsarkov, D., Horrocks, I.: DL reasoner vs. first-order prover. In: Proc. of the, Description Logic Workshop (DL 2003)Google Scholar
  23. 23.
    W3C RDF. Resource Description Framework (RDF), http://www.w3.org/RDF/

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yuanbo Guo
    • 1
  • Jeff Heflin
    • 1
  1. 1.Computer Science & Engineering Dept.Lehigh UniversityBethlehemUSA

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