DAML+OIL: A Reason-able Web Ontology Language

  • Ian Horrocks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2287)

Abstract

Ontologies are set to play a key role in the “Semantic Web”, extending syntactic interoperability to semantic interoperability by providing a source of shared and precisely defined terms. DAML+OIL is an ontology language specifically designed for use on the Web; it exploits existing Web standards (XML and RDF), adding the familiar ontological primitives of object oriented and frame based systems, and the formal rigor of a very expressive description logic. The logical basis of the language means that reasoning services can be provided, both to support ontology design and to make DAML+OIL described Web resources more accessible to automated processes.

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References

  1. 1.
    F. Baader and P. Hanschke. A schema for integrating concrete domains into concept languages. In Proc. of the 12th Int. Joint Conf. on Artificial Intelligence (IJCAI’91), pages 452–457, 1991.Google Scholar
  2. 2.
    F. Baader and R. Küsters. Computing the least common subsumer and the most specific concept in the presence of cyclic ALN-concept descriptions. In Proc. of the 22nd German Annual Conf. on Artificial Intelligence (KI’98), volume 1504 of Lecture Notes in Computer Science, pages 129–140. Springer-Verlag, 1998.Google Scholar
  3. 3.
    F. Baader, R. Küsters, A. Borgida, and D. L. McGuinness. Matching in description logics. J. of Logic and Computation, 9(3):411–447, 1999.MATHCrossRefGoogle Scholar
  4. 4.
    S. Bechhofer, I. Horrocks, C. Goble, and R. Stevens. OilEd: a reason-able ontology editor for the semantic web. In Proc. of the Joint German/Austrian Conf. on Artificial Intelligence (KI 2001), number 2174 in Lecture Notes in Artificial Intelligence, pages 396–408. Springer-Verlag, 2001.Google Scholar
  5. 5.
    T. Berners-Lee. Weaving the Web. Harpur, San Francisco, 1999.Google Scholar
  6. 6.
    A. Borgida and P. F. Patel-Schneider. A semantics and complete algorithm for subsumption in the CLASSIC description logic. J. of Artificial Intelligence Research, 1:277–308, 1994.MATHGoogle Scholar
  7. 7.
    D. Calvanese, G. De Giacomo, and M. Lenzerini. Answering queries using views in description logics. In Proc. of the 1999 Description Logic Workshop (DL’99), pages 9–13. CEUR Electronic Workshop Proceedings, http://ceur-ws.org/Vol-22/, 1999.
  8. 8.
    D. Calvanese, G. De Giacomo, M. Lenzerini, D. Nardi, and R. Rosati. Information integration: Conceptual modeling and reasoning support. In Proc. of the 6th Int. Conf. on Cooperative Information Systems (CoopIS’98), pages 280–291, 1998.Google Scholar
  9. 9.
    S. Decker, F. van Harmelen, J. Broekstra, M. Erdmann, D. Fensel, I. Horrocks, M. Klein, and S. Melnik. The semantic web: The roles of XML and RDF. IEEE Internet Computing, 4(5), 2000.Google Scholar
  10. 10.
    F. M. Donini, M. Lenzerini, D. Nardi, and W. Nutt. The complexity of concept languages. Information and Computation, 134:1–58, 1997.MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    D. Fensel, I. Horrocks, F. van Harmelen, S. Decker, M. Erdmann, and M. Klein. OIL in a nutshell. In R. Dieng, editor, Proc. of the 12th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW’00), number 1937 in Lecture Notes in Artificial Intelligence, pages 1–16. Springer-Verlag, 2000.Google Scholar
  12. 12.
    D. Fensel, F. van Harmelen, I. Horrocks, D. L. McGuinness, and P. F. Patel-Schneider. OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2):38–45, 2001.CrossRefGoogle Scholar
  13. 13.
    R. Fikes and D. L. McGuinness. An axiomatic semantics for rdf, rdf schema, and daml+oil. In Stanford University KSLT echnical Report KSL-01-01. http://www.ksl.stanford.edu/people/dlm/daml-semantics/abstract-axiomaticsemantics.html, 2001.
  14. 14.
    E. Grädel, M. Otto, and E. Rosen. Two-variable logic with counting is decidable. In Proc. of the 12th IEEE Symp. on Logic in Computer Science (LICS’97), pages 306–317. IEEE Computer Society Press, 1997.Google Scholar
  15. 15.
    V. Haarslev and R. Möller. High performance reasoning with very large knowledge bases: A practical case study. In Proc. of the 17th Int. Joint Conf. on Artificial Intelligence (IJCAI 2001), 2001.Google Scholar
  16. 16.
    V. Haarslev and R. Möller. RACER system description. In Proc. of the Int. Joint Conf. on Automated Reasoning (IJCAR 2001), 2001.Google Scholar
  17. 17.
    J. Hendler and D. L. McGuinness. “The darpa agent markup language”. IEEE Intelligent Systems, 15(6):67–73, 2000.CrossRefGoogle Scholar
  18. 18.
    B. Hollunder and F. Baader. Qualifying number restrictions in concept languages. In Proc. of the 2nd Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR’91), pages 335–346, 1991.Google Scholar
  19. 19.
    I. Horrocks. The FaCT system. In H. de Swart, editor, Proc. of the 2nd Int. Conf. on Analytic Tableaux and Related Methods (TABLEAUX’98), volume 1397 of Lecture Notes in Artificial Intelligence, pages 307–312. Springer-Verlag, 1998.Google Scholar
  20. 20.
    I. Horrocks. Using an expressive description logic: FaCT or fiction? In Proc. of the 6th Int. Conf. on Principles of Knowledge Representation and Reasoning (KR’98), pages 636–647, 1998.Google Scholar
  21. 21.
    I. Horrocks and U. Sattler. Ontology reasoning in the SHOQ(D) description logic. In Proc. of the 17th Int. Joint Conf. on Artificial Intelligence (IJCAI 2001). Morgan Kaufmann, Los Altos, 2001.Google Scholar
  22. 22.
    I. Horrocks, U. Sattler, and S. Tobies. Practical reasoning for expressive description logics. In H. Ganzinger, D. McAllester, and A. Voronkov, editors, Proc. of the 6th Int. Conf. on Logic for Programming and Automated Reasoning (LPAR’99), number 1705 in Lecture Notes in Artificial Intelligence, pages 161–180. Springer-Verlag, 1999.Google Scholar
  23. 23.
    I. Horrocks, U. Sattler, and S. Tobies. Reasoning with individuals for the description logic SHIQ. In Proc. of the 17th Int. Conf. on Automated Deduction (CADE 2000), number 1831 in Lecture Notes in Artificial Intelligence, pages 482–496. Springer-Verlag, 2000.Google Scholar
  24. 24.
    I. Horrocks and S. Tessaris. A conjunctive query language for description logic aboxes. In Proc. of the 17th Nat. Conf. on Artificial Intelligence (AAAI 2000), pages 399–404, 2000.Google Scholar
  25. 25.
    D. L. McGuinness. Ontological issues for knowledge-enhanced search. In Proc. of FOIS, Frontiers in Artificial Intelligence and Applications. IOS-press, 1998.Google Scholar
  26. 26.
    D. L. McGuinness. Ontologies for electronic commerce. In Proc. of the AAAI’99 Artificial Intelligence for Electronic Commerce Workshop, 1999.Google Scholar
  27. 27.
    D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. The Chimaera ontology environment. In Proc. of the 17th Nat. Conf. on Artificial Intelligence (AAAI 2000), 2000.Google Scholar
  28. 28.
    S. McIlraith, T. Son, and H. Zeng. Semantic web services. IEEE Intelligent Systems, 16(2):46–53, March/April 2001.CrossRefGoogle Scholar
  29. 29.
    P. F. Patel-Schneider. DLP system description. In Proc. of the 1998 Description Logic Workshop (DL’98), pages 87–89. CEUR Electronic Workshop Proceedings, http://ceur-ws.org/Vol-11/, 1998.
  30. 30.
    M.-C. Rousset. Backward reasoning in ABoxes for query answering. In Proc. of the 1999 Description Logic Workshop (DL’99), pages 18–22. CEUR Electronic Workshop Proceedings, http://ceur-ws.org/Vol-22/, 1999.
  31. 31.
    The Gene Ontology Consortium. Gene ontolgy: tool for the unification of biology. Nature Genetics, 25(1):25–29, 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ian Horrocks
    • 1
  1. 1.Department of Computer ScienceUniversity of ManchesterManchesterUK

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