An Hybrid Architecture Integrating Forward Rules with Fuzzy Ontological Reasoning
In recent years there has been a growing interest in the combination of rules and ontologies. Notably, many works have focused on the theoretical aspects of such integration, sometimes leading to concrete solutions. However, solutions proposed so far typically reason upon crisp concepts, while concrete domains require also fuzzy expressiveness.
In this work we combine mature technologies, namely the Drools business rule management system, the Pellet OWL Reasoner and the FuzzyDL system, to provide a unified framework for supporting fuzzy reasoning. After extending the Drools framework (language and engine) to support uncertainty reasoning upon rules, we have integrated it with custom operators that (i) exploit Pellet to perform ontological reasoning, and (ii) exploit FuzzyDL to support fuzzy ontological reasoning.
As a case study, we consider a decision-support system for the tourism domain, where ontologies are used to formally describe package tours, and rules are exploited to evaluate the consistency of such packages.
TopicsFuzzy Reasoning Rule-based Reasoning Rules Integration with Ontologies Decision Support Systems eTourism
Unable to display preview. Download preview PDF.
- 1.Antoniou, G., Damásio, C., Grosof, B., Horrocks, I., Kifer, M., Maluszynski, J., Patel-Schneider, P.: Combining rules and ontologies: A survey. Reasoning on the Web with Rules and Semantics (2005)Google Scholar
- 2.Grosof, B., Horrocks, I., Volz, R.: Description logic programs: Combining logic programs with description logic. In: Proceedings of the 12th international conference on World Wide Web, pp. 48–57. ACM, New York (2003)Google Scholar
- 3.Hustadt, U., Motik, B., Sattler, U.: Reducing SHIQ- description logic to disjunctive datalog programs. In: Proc. KR, pp. 152–162 (2004)Google Scholar
- 4.Motik, B., Vrandečić, D., Hitzler, P., Studer, R.: Dlpconvert–converting OWL DLP statements to logic programs. In: European Semantic Web Conference 2005 Demos and Posters, Citeseer (2005)Google Scholar
- 5.Motik, B.: Reasoning in description logics using resolution and deductive databases. PhD theis, University Karlsruhe, Germany (2006)Google Scholar
- 7.Bobillo, F., Straccia, U.: FuzzyDL: An expressive fuzzy description logic reasoner. In: Proc. 2008 Intl. Conf. on Fuzzy Systems, FUZZ 2008 (2008)Google Scholar
- 8.Kochukuttan, H., Chandrasekaran, A.: Development of a Fuzzy Expert System for Power Quality Applications. In: Proceedings of the Twenty-Ninth Southeastern Symposium on System Theory, pp. 239–243 (1997)Google Scholar
- 10.Forgy, C.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Name: Artif. Intell. (1982)Google Scholar
- 12.Mello, P., Proctor, M., Sottara, D.: A configurable RETE-OO engine for reasoning with different types of imperfect information. IEEE TKDE - Special Issue on Rule Representation, Interchange and Reasoning in Distributed, Heterogeneous Environments (2010) (in Press)Google Scholar
- 14.Novk, V.: Abstract: Mathematical fuzzy logic in narrow and broader sense a unified conceptGoogle Scholar
- 15.Baldwin, J., Martin, T., Pilsworth, B.: Fril-fuzzy and evidential reasoning in artificial intelligence. John Wiley & Sons, Inc., New York (1995)Google Scholar
- 16.de Bruijn, J.: Semantic Web Language Layering with Ontologies, Rules, and Meta-Modeling. PhD thesis, University of Innsbruck (2008)Google Scholar