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Using SWRL and OWL to Capture Domain Knowledge for a Situation Awareness Application Applied to a Supply Logistics Scenario

  • Christopher J. Matheus
  • Kenneth Baclawski
  • Mieczyslaw M. Kokar
  • Jerzy J. Letkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3791)

Abstract

When developing situation awareness applications we begin by constructing an OWL ontology to capture a language of discourse for the domain of interest. Such an ontology, however, is never sufficient for fully representing the complex knowledge needed to identify what is happening in an evolving situation – this usually requires general implication afforded by a rule language such as SWRL. This paper describes the application of SWRL/OWL to the representation of knowledge intended for a supply logistics scenario. The rules are first presented in an abstract syntax based on n-ary predicates. We then describe a process to convert them into a representation that complies with the binary-only properties of SWRL. The application of the SWRL rules is demonstrated using our situation awareness application, SAWA, which can employ either Jess or BaseVISor as its inference engine. We conclude with a summary of the issues encountered in using SWRL along with the steps taken in resolving them.

Keywords

Situation Awareness Inference Engine Supply Level Binary Predicate Rule Language 
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.

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References

  1. 1.
    Endsley, M., Garland, D.: Situation Awareness, Analysis and Measurement. Lawrence Erlbaum Associates, Publishers, Mahwah (2000)Google Scholar
  2. 2.
    Rule Markup Language Initiative, http://www.ruleml.org/
  3. 3.
    Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML (2004), http://www.daml.org/rules/proposal/
  4. 4.
    Matheus, C.: Using Ontology-based Rules for Situation Awareness and Information Fusion. Position Paper presented at the W3C Workshop on Rule Languages for Interoperability (April 2005), http://www.w3.org/2004/12/rules-ws/program2
  5. 5.
    W3C Workshop on Rule Languages for Interoperability, http://www.w3.org/2004/12/rules-ws/
  6. 6.
    Matheus, C., Kokar, M., Baclawski, K., Letkowski, J., Call, C., Hinman, M., Salerno, J., Boulware, D.: SAWA: An Assistant for Higher-Level Fusion and Situation Awareness. In: Proceedings of SPIE Conference on Multisensor, Multisource Information Fusion, Orlando, FL, (March 2005)Google Scholar
  7. 7.
    Matheus, C., Kokar, M., Baclawski, K., Letkowski, J., Call, C., Hinman, M., Salerno, J., Boulware, D.: Lessons Learned From Developing. SAWA: A Situation Awareness Assistant, FUSION 2005, Philadelphia, PA (July 2005)Google Scholar
  8. 8.
    Noy, N., Rector, A.: Defining N-ary Relations on the Semantic Web: Use With Individuals. W3C Working Draft 21 (July 2004)Google Scholar
  9. 9.
    SWRL Editor for Protégé with the OWL plugin, http://protege.stanford.edu/plugins/owl/swrl/
  10. 10.
    ConsVISor Consistency Checking Service, http://www.vistology.com/consvisor/
  11. 11.
    Institut für Informatik, Fachbereich Mathematik und Informatik, Freie Universität An Engine for SWRL rules in RDF graphs, http://www.inf.fu-berlin.de/inst/ag-nbi/research/swrlengine

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christopher J. Matheus
    • 1
  • Kenneth Baclawski
    • 2
  • Mieczyslaw M. Kokar
    • 2
  • Jerzy J. Letkowski
    • 3
  1. 1.Versatile Information Systems, IncFraminghamUSA
  2. 2.Northeastern UniversityBostonUSA
  3. 3.Western New England CollegeSpringfieldUSA

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