Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-09T07:26:55.041Z Has data issue: false hasContentIssue false
  • English
  • Français

Guarded hybrid knowledge bases12

Published online by Cambridge University Press:  01 May 2008

STIJN HEYMANS ,
JOS DE BRUIJN ,
LIVIA PREDOIU ,
CRISTINA FEIER  and
DAVY VAN NIEWENBORGH
STIJN HEYMANS
Affiliation:
Digital Enterprise Research Institute, University of Innsbruck, Technikerstrasse 21a, Innsbruck, Austria (e-mail: [email protected])
JOS DE BRUIJN
Affiliation:
Faculty of Computer Science, Free University of Bozen-Bolzano, I-39100 Bozen-Bolzano, Italy (e-mail: [email protected])
LIVIA PREDOIU
Affiliation:
Institute of Computer Science, University of Mannheim, A5, 6 68159 Mannheim, Germany (e-mail: [email protected])
CRISTINA FEIER
Affiliation:
Digital Enterprise Research Institute, University of Innsbruck, Technikerstrasse 21a, Innsbruck, Austria (e-mail: [email protected])
DAVY VAN NIEWENBORGH
Affiliation:
Department of Computer Science, Vrije Universiteit Brussel, VUB, Pleinlaan 2, B1050 Brussels, Belgium (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Recently, there has been a lot of interest in the integration of Description Logics (DL) and rules on the Semantic Web. We define guarded hybrid knowledge bases (or g-hybrid knowledge bases) as knowledge bases that consist of a Description Logic knowledge base and a guarded logic program, similar to the + log knowledge bases from Rosati (In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning, AAAI Press, Menlo Park, CA, 2006, pp. 68–78.). g-Hybrid knowledge bases enable an integration of Description Logics and Logic Programming where, unlike in other approaches, variables in the rules of a guarded program do not need to appear in positive non-DL atoms of the body, i.e., DL atoms can act as guards as well. Decidability of satisfiability checking of g-hybrid knowledge bases is shown for the particular DL , which is close to OWL DL, by a reduction to guarded programs under the open answer set semantics. Moreover, we show 2-Exptime-completeness for satisfiability checking of such g-hybrid knowledge bases. Finally, we discuss advantages and disadvantages of our approach compared with + log knowledge bases.

Keywords

g-hybrid knowledge basesopen answer set programmingguarded logic programsdescription logics
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 8 , Issue 3 , May 2008 , pp. 411 - 429
Copyright
Copyright © Cambridge University Press 2008

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

Baader, F., Calvanese, D., McGuinness, D., Nardi, D. and Patel-Schneider, P. 2003. The Description Logic Handbook. Cambridge University Press, New York.Google Scholar
Calvanese, D., De Giacomo, G. and Lenzerini, M. 1997. Conjunctive query containment in description logics with n-ary relations. In Proc. of the 1997 Description Logic Workshop (DL'97). 5–9.Google Scholar
de Bruijn, J., Eiter, T., Polleres, A. and Tompits, H. 2006a. On representational issues about combinations of classical theories with nonmonotonic rules. In Proceedings of the First International Conference on Knowledge Science, Engineering and Management (KSEM'06). Lecture Notes in Artificial Intelligence, vol. 4092. Guilin, China. Springer-Verlag, Berlin.Google Scholar
de Bruijn, J., Eiter, T., Polleres, A. and Tompits, H. 2007. Embedding non-ground logic programs into autoepistemic logic for knowledge-base combination. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07). Hyderabad, India. AAAI Press, Menlo Park, CA.Google Scholar
de Bruijn, J., Pearce, D., Polleres, A. and Valverde, A. 2006b. A logic for hybrid rules. In Proceedings of the Ontology and Rule Integration Workshop at the 2nd International Conference on Rules and Rule Markup Languages for the Semantic Web, Athens, GA.Google Scholar
Dean, M. and Schreiber, G. (Eds). 2004. OWL Web Ontology Language Reference. W3C Recommendation 10 February 2004.Google Scholar
Donini, F. M., Lenzerini, M., Nardi, D. and Schaerf, A. 1998. AL-log: Integrating Datalog and Description Logics. Journal of Intelligence and Cooperative Information Systems 10, 227–252.Google Scholar
Eiter, T., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2004. Combining Answer Set Programming with DLs for the Semantic Web. In Proc. of KR 2004. 141–151.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proc. of ICLP'88. MIT Press, Cambridge, MA, 1070–1080.Google Scholar
Grädel, E. and Walukiewicz, I. 1999. Guarded fixed point logic. In Proc. of LICS '99. IEEE Computer Society, Los Alamitos, CA, 45–54.Google Scholar
Heymans, S., Van Nieuwenborgh, D., Fensel, D. and Vermeir, D. 2006a. Reasoning with the Description Logic using Bound Guarded Programs. In Proc. of Reasoning on the Web Workshop (RoW 2006), Edinburgh, UK.Google Scholar
Heymans, S., Van Nieuwenborgh, D. and Vermeir, D. 2005a. Guarded Open Answer Set Programming. In LPNMR 2005. LNAI, vol. 3662. Springer-Verlag, Berlin, 92–104.Google Scholar
Heymans, S., Van Nieuwenborgh, D. and Vermeir, D. 2005b. Nonmonotonic ontological and rule-based reasoning with extended conceptual logic programs. In Proc. of ESWC 2005. LNCS, vol. 3532. Springer-Verlag Berlin, 392–407.Google Scholar
Heymans, S., Van Nieuwenborgh, D. and Vermeir, D. 2006b. Conceptual logic programs. Annals of Mathematics and Artificial Intelligence. Special Issue on Answer Set Programming 47, 1/2, 103–137.Google Scholar
Heymans, S., Van Nieuwenborgh, D. and Vermeir, D. 2006c. Open answer set programming with guarded programs. ACM Transactions on Computational Logic (TOCL). In press.CrossRefGoogle Scholar
Horrocks, I. and Patel-Schneider, P. 2004a. Reducing OWL entailment to description logic satisfiability. Journal of Web Semantics 1, 4, 345–357.Google Scholar
Horrocks, I. and Patel-Schneider, P. F. 2004b. A proposal for an OWL rules language. In Proc. of the Thirteenth International World Wide Web Conference (WWW 2004). ACM, New York, 723–731.Google Scholar
Hustadt, U., Motik, B. and Sattler, U. 2004. Reducing SHIQ− description logic to disjunctive logic programs. In Proceedings of the 9th International Conference on Knowledge Representation and Reasoning KR2004, Whistler, Canada, 152–162.Google Scholar
Inoue, K. and Sakama, C. 1998. Negation as failure in the head. Journal of Logic Programming 35, 1, 39–78.Google Scholar
Levy, A. Y. and Rousset, M. 1996. CARIN: A representation language combining horn rules and description logics. In Proc. of ECAI'96. 323–327.Google Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2001. Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 4, 526–541.CrossRefGoogle Scholar
Motik, B. and Rosati, R. 2007. A faithful integration of description logics with logic programming. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India.Google Scholar
Motik, B., Sattler, U. and Studer, R. 2004. Query answering for OWL-DL with rules. In Proc. of ISWC 2004. LNCS, vol. 3298. Springer-Verlag, Berlin, 549–563.Google Scholar
Rosati, R. 2005a. On the decidability and complexity of integrating ontologies and rules. Web Semantics 3, 1, 41–60.CrossRefGoogle Scholar
Rosati, R. 2005b. Semantic and computational advantages of the safe integration of ontologies and rules. In Proc. of the Third International Workshop on Principles and Practice of Semantic Web Reasoning (PPSWR 2005). LNCS, vol. 3703. Springer-Verlag, Berlin, 50–64.Google Scholar
Rosati, R. 2006. DL+log: Tight integration of description logics and disjunctive datalog. In Proc. of the Tenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2006). AAAI Press, Menlo Park, CA, 68–78.Google Scholar
Swift, T. 2004. Deduction in ontologies via answer set programming. In LPNMR, Lifschitz, V. and Niemelä, I., Eds. LNCS, vol. 2923. Springer-Verlag, Berlin, 275–288.Google Scholar