Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T20:11:07.423Z Has data issue: false hasContentIssue false
  • English
  • Français

A goal-directed implementation of query answering for hybrid MKNF knowledge bases

Published online by Cambridge University Press:  18 January 2013

ANA SOFIA GOMES ,
JOSÉ JÚLIO ALFERES  and
TERRANCE SWIFT
ANA SOFIA GOMES
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: sofia.gomes@campus.fct.unl.pt, jja@fct.unl.pt, tswift@cs.sunysb.edu)
JOSÉ JÚLIO ALFERES
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: sofia.gomes@campus.fct.unl.pt, jja@fct.unl.pt, tswift@cs.sunysb.edu)
TERRANCE SWIFT
Affiliation:
CENTRIA, Departamento de Informática, Faculdade Ciências e Tecnologias Universidade Nova de Lisboa, 2829-516 Caparica, Portugal (emails: sofia.gomes@campus.fct.unl.pt, jja@fct.unl.pt, tswift@cs.sunysb.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Ontologies and rules are usually loosely coupled in knowledge representation formalisms. In fact, ontologies use open-world reasoning, while the leading semantics for rules use non-monotonic, closed-world reasoning. One exception is the tightly coupled framework of Minimal Knowledge and Negation as Failure (MKNF), which allows statements about individuals to be jointly derived via entailment from ontology and inferences from rules. Nonetheless, the practical usefulness of MKNF has not always been clear, although recent work has formalized a general resolution-based method for querying MKNF when rules are taken to have the well-founded semantics, and the ontology is modeled by a general oracle. That work leaves open what algorithms should be used to relate the entailments of the ontology and the inferences of rules. In this paper we provide such algorithms, and describe the implementation of a query-driven system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) rules under the well-founded semantics and a (monotonic) ontology, represented by the Coherent Description Framework Type-1 ( $\mathcal{ALCQ}$ ) theory.

Keywords

knowledge representationwell-founded semanticsdescription logicsimplementation
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 14 , Issue 2 , March 2014 , pp. 239 - 264
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alferes, J. J., Knorr, M. and Swift, T. Queries to hybrid MKNF knowledge bases through oracular tabling. ACM Transactions on Computational Logic. Accessed 6 December 2012. URL: http://tocl.acm.org/accepted/464knorr.pdf.Google Scholar
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D. and Patel-Schneider, P. F., Eds. 2007. The Description Logic Handbook: Theory, Implementation, and Applications, 2nd ed. Cambridge University Press, Cambridge, UK.Google Scholar
Baral, C. 2002. Knowledge Representation, Reasoning, and Declarative Problem Solving. Cambridge University Press, Cambridge, UK.Google Scholar
Calì, A., Gottlob, G. and Lukasiewicz, T. 2012. A general datalog-based framework for tractable query answering over ontologies. Journal of Web Semantics 14 57–83.Google Scholar
Chen, W. and Warren, D. S. 1996. Tabled evaluation with delaying for general logic programs. Journal of the ACM 43 1 (January), 20–74.Google Scholar
Drabent, W. and Małuszynski, J. 2007. Well-founded semantics for hybrid rules. In Proceedings of the International Conference on Web Reasoning and Rule Systems, Marchiori, M., Pan, J. Z. and de Sainte Marie, C., Eds. Springer, New York, 1–15.Google Scholar
Eiter, T., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2004a. Combining answer set programming with description logics for the semantic web. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, Dubois, D., Welty, C. A. and Williams, M.-A., Eds. Springer, Innsbruck, Austria, 141–151.Google Scholar
Eiter, T., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2004b. Well-founded semantics for description logic programs in the semantic web. In Rules and Rule Markup Languages for the Semantic Web, Antoniou, G. and Boley, H., Eds. Springer, Hiroshima, Japan, 81–97.Google Scholar
Gelfond, M. and Lifschitz, V. 1990. Logic programs with classical negation. In International Conference on Logic Programming, Warren, D. H. D. and Szeredi, P., Eds. MIT Press, Jerusalem, Israel, 579–597.Google Scholar
Grimm, S. and Hitzler, P. 2007. Semantic matchmaking of web resources with local closed-world reasoning. International Journal of e-Commerce 12 2, 89–126.Google Scholar
Grosof, B. 2009. SILK: Semantic rules take the next big step in power. Accessed 6 December 2012. URL: http://silk.semwebcentral.org.Google Scholar
Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P. F. and Rudolph, S., Eds. 2009. OWL 2 Web Ontology Language: Primer. W3C Recommendation 27 October 2009. Accessed 6 December 2012. URL: http://www.w3.org/TR/owl2-primer/.Google Scholar
Hitzler, P. and Parsia, B. 2009. Ontologies and rules. In Handbook on Ontologies, 2nd ed., Staab, S. and Studer, R., Eds. Springer, New York.Google Scholar
Horrocks, I., Motik, B., Rosati, R. and Sattler, U. 2006. Can OWL and logic programming live together happily ever after? In Proceedings of the International Semantic Web Conference, Cruz, I. F., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M. and Aroyo, L., Eds. Springer, Athens, GA, USA, 501–514.Google Scholar
Horrocks, I. and Patel-Schneider, P. 1999. Optimizing description logic subsumption. Journal of Logic and Computation 9 3, 267–293.Google Scholar
Knorr, M., Alferes, J. J. and Hitzler, P. 2008. A coherent well-founded model for hybrid MKNF knowledge bases. In Proceedings of European Conference on Artificial Intelligence, Ghallab, M., Spyropoulos, C. D., Fakotakis, N., Avouris, N. M., Eds. IOS Press, Patras, Greece, 99–103.Google Scholar
Knorr, M., Alferes, J. J. and Hitzler, P. 2011. Local closed world reasoning with description logics under the well-founded semantics. Artificial Intelligence 175 9–10, 1528–1554.Google Scholar
Lifschitz, V. 1991. Nonmonotonic databases and epistemic queries. In Proceedings of the International Joint Conference on Artificial Intelligence, Mylopoulos, J. and Reiter, R., Eds. Morgan Kaufmann, Sydney, Australia, 381–386.Google Scholar
Lukacsy, G., Szeredi, P. and Kadàr, B. 2008. Prolog-based description logic reasoning. In Proceedings of the International Conference on Logic Programming, de la Banda, M. G. and Pontelli, E., Eds. Springer, Udine, Italy, 455–469.Google Scholar
Morgenstern, L., Welty, C. and Boley, H., Eds. 2010. RIF Primer. W3C Recommendation, 22 June 2010. Accessed 6 December 2012. URL: http://www.w3.org/2005/rules/wiki/Primer.Google Scholar
Motik, B. 2006. Reasoning in Description Logics Using Resolution and Deductive Databases. PhD thesis, University of Karlsruhe, Karlsruhe, Germany.Google Scholar
Motik, B. and Rosati, R. 2007. A faithful integration of description logics with logic programming. In Proceedings of the International Joint Conference on Artificial Intelligence, Veloso, M. M., Ed. AAAI Press, Hyderabad, India, 477–482.Google Scholar
Patel, C., Cimino, J. J., Dolby, J., Fokoue, A., Kalyanpur, A., Kershenbaum, A., Ma, L., Schonberg, E. and Srinivas, K. 2007. Matching patient records to clinical trials using ontologies. In Proceedings of the International Semantic Web Conference/Asian Semantic Web Conference, Aberer, K., Choi, K.-S., Noy, N. F., Allemang, D., Lee, K.-I., Nixon, L. J. B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G. and Cudré-Mauroux, P., Eds. Springer, Busan, Korea, 816–829.Google Scholar
Schindlauer, R. 2006. Answer-Set Programming for the Semantic Web. PhD thesis, Technischen Universitaet Wien Fakultaet fuer Informatik, Glockengasse 6/19, A-1020 Wien, Austria.Google Scholar
Schmidt-Strauss, M. and Smolka, G. 1990. Attributive concept descriptions with complements. Artificial Intelligence 48 1–26.Google Scholar
Swift, T. 2004. Deduction in ontologies via answer set programming. In Proceedings of the International Conference on Logic Programming and Non-Monotonic Reasoning, Lifschitz, V. and Niemelä, I., Eds. Springer, Fort Lauderdale, FL, USA, 275–289.Google Scholar
Swift, T. and Warren, D. S. 2003. Cold dead fish: A system for managing ontologies. Accessed 6 December 2012. URL: http://xsb.sourceforge.net.Google Scholar
Swift, T. and Warren, D. 2012. XSB: Extending the power of Prolog using tabling. Theory and Practice of Logic Programming 12 1–2, 157–187.Google Scholar
van Gelder, A. 1989. The alternating fixpoint of logic programs with negation. In Proceedings of the ACM Conference on Principles of Database Systems, Silberschatz, A., Ed. ACM Press, Philadelphia, Pennsylvania, USA. 1–10.Google Scholar
van Gelder, A., Ross, K. A. and Schlipf, J. S. 1991. Unfounded sets and well-founded semantics for general logic programs. Journal of the ACM 38 3, 620–650.Google Scholar
Yang, G., Kifer, M. and Zhao, C. 2003. Flora-2: A rule-based knowledge representation and inference infrastructure for the semantic web. In Proceedings of the International Conference on Ontologies, Databases and Applications of Semantics, Meersman, R., Tari, Z. and Schmidt, D. C., Eds. Springer, Catania, Sicily, Italy, 671–688.Google Scholar