Journal of Intelligent Information Systems

, Volume 36, Issue 1, pp 27–48 | Cite as

Flexible rule-based inference exploiting taxonomies

Article

Abstract

Rule-based systems are widely used to implement knowledge-based systems. They are usually intuitive to use, have good performance and can be easily integrated with other software components. However, a critical problem is that the behavior of a rule-based system tends to degrade abruptly whenever the knowledge base is incomplete or not detailed enough or when operating at the borders of its expertise. Various forms of approximate reasoning have been introduced but they solve the problem only in a partial way. In the paper we propose new forms of rule inference that tackle this problem, introducing a form of flexible or common sense reasoning that can support a softer degradation of problem solving ability when knowledge is partial or incomplete. The solution we propose relies on the exploitation of semantic information associated with the concepts involved in the rules. In particular, we show how taxonomical information can be exploited to define flexible forms of match between rule antecedents and the working memory, and flexible forms of conflict resolution. In this way, even when no rule perfectly matches the working memory, the inference engine can select rules that apply to more general or to similar cases and provide some approximate solution. The approach has been motivated by work on context aware (recommender) systems where the problem of incomplete descriptions and brittle degradation of problem solving ability are particularly relevant.

Keywords

Rule-based systems Taxonomy Flexible inference Taxonomies in matching and conflict resolution 

References

  1. Baldauf, M., & Dustdar, S. (2004). A survey on context-aware systems. Technical Report TUV-1841-2004-24, Technical University of Vienna, Vienna, Austria.Google Scholar
  2. Borgida, A., Brachman, R. J., McGuinness, D. L., & Alperin Resnick, L. (1989). Classic: A structural data model for objects. In J. Clifford, B. G. Lindsay, & D. Maier (Eds.), SIGMOD conference (pp. 58–67). New York: ACM.Google Scholar
  3. Borgida, A., Walsh, T., & Hirsh, H. (2005). Towards measuring similarity in description logics. In I. Horrocks, U. Sattler, & F. Wolter (Eds.), Description Logics. CEUR workshop proceedings (Vol. 147). CEUR-WS.org.Google Scholar
  4. Broekstra, J., & Kampman, A. (2004). Serql: An RDF query and transformation language. In Proc. of international semantic web conference.Google Scholar
  5. Buriano, L., Marchetti, M., Carmagnola, F., Cena, F., Gena, C., & Torre, I. (2006). The role of ontologies in context-aware recommender systems. In MDM, (p 80). Washington: IEEE Computer Society.Google Scholar
  6. Cheverst, K., Mitchell, K., & Davies, N. (2002). The role of adaptive hypermedia in a context-aware tourist guide. Communications of the ACM, 45(5), 47–51.CrossRefGoogle Scholar
  7. Dey, A., Salber, D., & Abowd, G. (2001). A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications.Google Scholar
  8. Dourish, P. (2001). Seeking a foundation for context-aware computing.Google Scholar
  9. Dourish, P. (2004). What we talk about when we talk about context. Personal Ubiquitous Computation, 8(1), 19–30.CrossRefGoogle Scholar
  10. Eiter, T., Lukasiewicz, T., Schindlauer, R., & Tompits, H. (2004). Combining answer set programming with description logics for the semantic web. In D. Dubois, C. A. Welty, & M. A. Williams (Eds.), KR (pp. 141–151). Menlo Park: AAAI.Google Scholar
  11. Forgy, C. (1982). Rete: A fast algorithm for the many patterns/many objects match problem. Artificial Intelligence, 19(1), 17–37.CrossRefGoogle Scholar
  12. Levy, A. Y., & Rousset, M. C. (1998). Combining horn rules and description logics in CARIN. Artificial Intelligence, 104(1–2), 165–209.MATHCrossRefMathSciNetGoogle Scholar
  13. Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics, 19, 17–30.CrossRefGoogle Scholar
  14. Resnik, P. (1995). Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the XI international joint conferences on artificial intelligence (IJCAI) (pp. 448–453).Google Scholar
  15. Rosati, R. (2006). Dl+log: Tight integration of description logics and disjunctive datalog. In P. Doherty, J. Mylopoulos, & C. A. Welty (Eds.), KR (pp. 68–78). Menlo Park: AAAI.Google Scholar
  16. Russel, S., & Norvig, P. (2002). Artificial intelligence: A modern approach. Upper Saddle River: Prentice Hall.Google Scholar
  17. Stefik, M. (1995). Introduction to knowledge systems. San Francisco: Morgan Kaufmann.Google Scholar
  18. Torasso, P., & Console, L. (1989). Approximate reasoning and prototypical knowledge. International Journal of Approximate Reasoning, 3(2), 157–178.CrossRefGoogle Scholar
  19. van Setten, M., Pokraev, S., & Koolwaaij, J. (2004). Context-aware recommendations in the mobile tourist application compass. In AH. LNCS (Vol. 3137, pp. 235–244). Berlin: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di TorinoTurinItaly

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