OWL and Qualitative Reasoning Models

  • Jochem Liem
  • Bert Bredeweg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4314)

Abstract

The desire to share and reuse knowledge has led to the establishment of the Web Ontology Language (OWL) knowledge representation language. The Naturnet-Redime project needs to share qualitative knowledge models of issues relevant to sustainable development and OWL seems the obvious choice for representing such models to allow search and other activities relevant to sharing knowledge models. However, although the design choices made in OWL are properly documented, their implications for Artificial Intelligence (AI) are part of ongoing research. This paper explores the expressiveness of OWL by formalising the vocabulary and models used in Qualitative Reasoning (QR), and the applicability of OWL reasoners to solve QR problems. A parser has been developed to export (and import) the QR representations to (and from) OWL representations. To create the OWL definitions of the QR vocabulary and models, existing OWL patterns were used as much as possible. However, some new patterns, and pattern modifications, had to be developed in order to represent the QR vocabulary and models using OWL.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bechhofer, S., et al.: OWL web ontology language reference. In: Dean, M., Schreiber, G. (eds.) W3C recommendation (Feb. 2004)Google Scholar
  2. 2.
    Bredeweg, B., Salles, P., Neumann, M.: Ecological Applications of Qualitative Reasoning. In: Recknagel, F. (ed.) Ecological Informatics: Scope, Techniques and Applications, 2nd edn., pp. 15–47. Springer, Berlin (2006)Google Scholar
  3. 3.
    Bredeweg, B., Struss, P.: Current topics in qualitative reasoning (editorial introduction). AI Magazine 24(4), 13–16 (2003)Google Scholar
  4. 4.
    de Kleer, J., Brown, J.S.: A qualitative physics based on confluences. Artificial Intelligence 24(1-3), 7–83 (1984)CrossRefGoogle Scholar
  5. 5.
    Forbus, K.D.: Qualitative process theory. Artificial Intelligence 24(1-3), 85–168 (1984)CrossRefGoogle Scholar
  6. 6.
    Noy, N.F., et al.: The protege owl plugin: An open development environment for semantic web applications. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 229–243. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Hayes, P.J.: The Second Naive Physics Manifesto. In: Hobbs, J.R., Moore, R.C. (eds.) Formal Theories of the Commonsense World. Ablex series in Artificial Intelligence, vol. 1, pp. 1–36. Ablex, Norwood (June 1985)Google Scholar
  8. 8.
    Heflin, J.: OWL web ontology language use cases and requirements. W3C recommendation (February 2004)Google Scholar
  9. 9.
    Kuipers, B.: Qualitative reasoning: modeling and simulation with incomplete knowledge. Automatica 25(4), 571–585 (1989)CrossRefGoogle Scholar
  10. 10.
    Noy, N., Rector, A.: Defining n-ary relations on the semantic web. W3C working group note (April 2006), http://www.w3.org/TR/swbp-n-aryRelations/
  11. 11.
    Rector, A.: Representing specified values in OWL: ”value partitions” and ”value sets”. W3C working group note (May 2005), http://www.w3.org/TR/swbp-specified-values/
  12. 12.
    Salles, P., Bredeweg, B.: Qualitative reasoning about population and community ecology. AI Magazine 24(4), 77–90 (2003)MathSciNetGoogle Scholar
  13. 13.
    Schreiber, G., et al.: Knowledge Engineering and Management - The CommonKADS Methodology. MIT Press, Cambridge (2000)CrossRefGoogle Scholar
  14. 14.
    van Heijst, G., et al.: A case study in ontology library contruction. Artificial Intelligence in Medicine 7(3), 227–255 (1995)CrossRefGoogle Scholar
  15. 15.
    Wielemaker, J., Schreiber, G., Wielinga, B.: Using triples for implementation: the Triple20 ontology-manipulation tool. In: Gil, Y., et al. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 773–785. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Wielemaker, J., Schreiber, G., Wielinga, B.J.: Prolog-based infrastructure for RDF: performance and scalability. In: Fensel, D., Sycara, K.P., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 644–658. Springer, Heidelberg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jochem Liem
    • 1
  • Bert Bredeweg
    • 1
  1. 1.Human Computer Studies Laboratory, Informatics Institute, Faculty of Science, Universiteit van AmsterdamThe Netherlands

Personalised recommendations