Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T02:30:26.051Z Has data issue: false hasContentIssue false
  • English
  • Français

Splitting and updating hybrid knowledge bases

Published online by Cambridge University Press:  06 July 2011

MARTIN SLOTA ,
JOÃO LEITE  and
TERRANCE SWIFT
MARTIN SLOTA
Affiliation:
CENTRIA & Departamento de Informática Universidade Nova de Lisboa 2829-516 Caparica, Portugal (e-mail: [email protected], [email protected] and [email protected])
JOÃO LEITE
Affiliation:
CENTRIA & Departamento de Informática Universidade Nova de Lisboa 2829-516 Caparica, Portugal (e-mail: [email protected], [email protected] and [email protected])
TERRANCE SWIFT
Affiliation:
CENTRIA & Departamento de Informática Universidade Nova de Lisboa 2829-516 Caparica, Portugal (e-mail: [email protected], [email protected] and [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Over the years, nonmonotonic rules have proven to be a very expressive and useful knowledge representation paradigm. They have recently been used to complement the expressive power of Description Logics (DLs), leading to the study of integrative formal frameworks, generally referred to as hybrid knowledge bases, where both DL axioms and rules can be used to represent knowledge. The need to use these hybrid knowledge bases in dynamic domains has called for the development of update operators, which, given the substantially different way DLs and rules are usually updated, has turned out to be an extremely difficult task. In Slota and Leite (2010b Towards Closed World Reasoning in Dynamic Open Worlds. Theory and Practice of Logic Programming, 26th Int'l. Conference on Logic Programming (ICLP'10) Special Issue 10(4–6) (July), 547–564.), a first step towards addressing this problem was taken, and an update operator for hybrid knowledge bases was proposed. Despite its significance—not only for being the first update operator for hybrid knowledge bases in the literature, but also because it has some applications—this operator was defined for a restricted class of problems where only the ABox was allowed to change, which considerably diminished its applicability. Many applications that use hybrid knowledge bases in dynamic scenarios require both DL axioms and rules to be updated. In this paper, motivated by real world applications, we introduce an update operator for a large class of hybrid knowledge bases where both the DL component as well as the rule component are allowed to dynamically change. We introduce splitting sequences and splitting theorem for hybrid knowledge bases, use them to define a modular update semantics, investigate its basic properties, and illustrate its use on a realistic example about cargo imports.

Keywords

Hybrid knowledge baseUpdateSplitting theoremOntologyLogic program
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 11 , Special Issue 4-5: 27th International Conference on Logic Programming , July 2011 , pp. 801 - 819
Copyright
Copyright © Cambridge University Press 2011

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., Banti, F., Brogi, A. and Leite, J. A. 2005. The refined extension principle for semantics of dynamic logic programming. Studia Logica 79 (1), 7–32.CrossRefGoogle Scholar
Alferes, J. J., Leite, J. A., Pereira, L. M., Przymusinska, H. andPrzymusinski, T. C. 2000. Dynamic updates of non-monotonic knowledge bases. The Journal of Logic Programming 45 (1–3) (September/October), 43–70.CrossRefGoogle Scholar
Apt, K. R., Blair, H. A. and Walker, A. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming. Morgan Kaufmann, 89–148.CrossRefGoogle Scholar
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D. and Patel-Schneider, P. F., Eds. 2003. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press.Google Scholar
Brewka, G. and Eiter, T. 2007. Equilibria in heterogeneous nonmonotonic multi-context systems. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence. AAAI, Vancouver, British Columbia, Canada, 385–390.Google Scholar
Calvanese, D., Kharlamov, E., Nutt, W. and Zheleznyakov, D. 2010. Evolution of DL-Lite knowledge bases. In International Semantic Web Conference (1), Patel-Schneider, P. F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J. Z., Horrocks, I., and Glimm, B., Eds. Lecture Notes in Computer Science, vol. 6496. Springer, Shanghai, China, 112–128.Google Scholar
Dalal, M. 1988. Investigations into a theory of knowledge base revision. In Proceedings of the 7th National Conference on Artificial Intelligence (AAAI 1988). AAAI/The MIT, St. Paul, MN, USA, 475–479.Google Scholar
Delgrande, J. P., Schaub, T. and Tompits, H. 2007. A preference-based framework for updating logic programs. In Proceedings of the 9th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2007), Baral, C., Brewka, G., and Schlipf, J. S., Eds. Lecture Notes in Computer Science, vol. 4483. Springer, Tempe, AZ, USA, 71–83.CrossRefGoogle Scholar
Delgrande, J. P., Schaub, T., Tompits, H. and Woltran, S. 2008. Belief revision of logic programs under answer set semantics. In Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR 2008), Brewka, G. and Lang, J., Eds. AAAI, Sydney, Australia, 411–421.Google Scholar
Dix, J. 1995. A classification theory of semantics of normal logic programs: II. Weak properties. Fundamenta Informaticae 22 (3), 257–288.CrossRefGoogle Scholar
Eiter, T., Fink, M., Sabbatini, G. and Tompits, H. 2002. On properties of update sequences based on causal rejection. Theory and Practice of Logic Programming (TPLP) 2 (6), 721–777.Google Scholar
Gelder, A. V., Ross, K. A. and Schlipf, J. S. 1991. The well-founded semantics for general logic programs. Journal of the ACM 38 (3), 620–650.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of the 5th International Conference and Symposium on Logic Programming (ICLP/SLP 1988), Kowalski, R. A. and Bowen, K. A., Eds. MIT, Washington, 1070–1080.Google Scholar
Giacomo, G. D., Lenzerini, M., Poggi, A. and Rosati, R. 2006. On the update of description logic ontologies at the instance level. In Proceedings of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference. AAAI, Boston, Massachusetts, USA.Google Scholar
Hitzler, P. and Parsia, B. 2009. Ontologies and rules. In Handbook on Ontologies, second ed., Staab, S. and Studer, R., Eds. International Handbooks on Information Systems. Springer, 111–132.CrossRefGoogle Scholar
Katsuno, H. and Mendelzon, A. O. 1991. On the difference between updating a knowledge base and revising it. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR'91), Allen, J. F., Fikes, R., and Sandewall, E., Eds. Morgan Kaufmann Publishers, Cambridge, MA, USA, 387–394.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.CrossRefGoogle Scholar
Leite, J. A. 2003. Evolving Knowledge Bases. Frontiers of Artificial Intelligence and Applications, xviii + 307 p. Hardcover, vol. 81. IOS.Google Scholar
Leite, J. A. and Pereira, L. M. 1997. Generalizing updates: From models to programs. In Proceedings of the 3rd International Workshop on Logic Programming and Knowledge Representation (LPKR '97), Dix, J., Pereira, L. M., and Przymusinski, T. C., Eds. Lecture Notes in Computer Science, vol. 1471. Springer, Port Jefferson, New York, USA, 224–246.Google Scholar
Lifschitz, V. 1991. Nonmonotonic databases and epistemic queries. In Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI91). 381–386.Google Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2001. Strongly equivalent logic programs. ACM Transactions on Computational Logic (TOCL) 2 (4), 526–541.CrossRefGoogle Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings of the 11th International Conference on Logic Programming (ICLP 1994), Hentenryck, P. V., Ed. MIT, Santa Margherita Ligure, Italy, 23–37.Google Scholar
Liu, H., Lutz, C., Miličić, M. and Wolter, F. 2006. Updating description logic ABoxes. In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR'06), Doherty, P., Mylopoulos, J., and Welty, C. A., Eds. AAAI, Lake District of the United Kingdom, 46–56.Google Scholar
Motik, B. and Rosati, R. 2007. A faithful integration of description logics with logic programming. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Veloso, M. M., Ed. Hyderabad, India, 477–482.Google Scholar
Osorio, M. and Cuevas, V. 2007. Updates in answer set programming: An approach based on basic structural properties. Theory and Practice of Logic Programming 7 (4), 451–479.CrossRefGoogle Scholar
Qi, G. and Du, J. 2009. Model-based revision operators for terminologies in description logics. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena (IJCAI 2009), Boutilier, C., Ed. California, USA, 891–897.Google Scholar
Qi, G., Liu, W. and Bell, D. A. 2006. A revision-based approach to handling inconsistency in description logics. Journal of Artificial Intelligence Review 26 (1–2), 115–128.CrossRefGoogle Scholar
Sakama, C. and Inoue, K. 2003. An abductive framework for computing knowledge base updates. Theory and Practice of Logic Programming (TPLP) 3 (6), 671–713.CrossRefGoogle Scholar
Slota, M. and Leite, J. 2010a. On semantic update operators for answer-set programs. In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010), Coelho, H., Studer, R., and Wooldridge, M., Eds. Frontiers in Artificial Intelligence and Applications, vol. 215. IOS, Lisbon, Portugal, 957–962.Google Scholar
Slota, M. and Leite, J. 2010b. Towards Closed World Reasoning in Dynamic Open Worlds. Theory and Practice of Logic Programming, 26th Int'l. Conference on Logic Programming (ICLP'10) Special Issue 10 (4–6) (July), 547–564.Google Scholar
Turner, H. 2003. Strong equivalence made easy: nested expressions and weight constraints. Theory and Practice of Logic Programming (TPLP) 3 (4–5), 609–622.CrossRefGoogle Scholar
Wang, Z., Wang, K. and Topor, R. W. 2010. A new approach to knowledge base revision in DL-Lite. In Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI 2010), Fox, M. and Poole, D., Eds. AAAI, Atlanta, Georgia, USA.Google Scholar
Winslett, M. 1988. Reasoning about action using a possible models approach. In Proceedings of the 7th National Conference on Artificial Intelligence (AAAI 1988). AAAI/The MIT, Saint Paul, MN, USA, 89–93.Google Scholar
Winslett, M. 1990. Updating Logical Databases. Cambridge University Press, New York, USA.CrossRefGoogle Scholar
Yang, F., Qi, G. and Huang, Z. 2009. A distance-based operator to revising ontologies in DL ). In Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009), Sossai, C. and Chemello, G., Eds. Lecture Notes in Computer Science, vol. 5590. Springer, Verona, Italy, 434–445.CrossRefGoogle Scholar
Zhang, Y. 2006. Logic program-based updates. ACM Transactions on Computational Logic 7 (3), 421–472.CrossRefGoogle Scholar