Abstract
Handling exceptions in a knowledge-based system is an important issue in many application domains, such as medical domain. Recently, there is an increasing interest in nonmonotonic extension of description logics to handle exceptions in ontologies. In this paper, we propose three preferential semantics for plausible subsumption to deal with exceptions in description logic-based knowledge bases. Our preferential semantics are defined in the framework of possibility theory, which is an uncertainty theory devoted to handling incomplete information. We consider the properties of these semantics and their relationships. We also discuss the relationship between two of our preferential semantics and two existing preferential semantics. We extend a description logic-based knowledge base by adding preferential subsumptions. Entailment of plausible subsumptions relative to an extended knowledge base is defined. Properties of the preferential subsumption relations relative to an extended description logic-based knowledge base are discussed. Finally, we show that our semantics for plausible subsumption can be reduced to standard semantics of an expressive description logic. Thus, the problem of plausible subsumption checking under our semantics can be reduced to the problem of subsumption checking under the classical semantics.
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Notes
This term is borrowed from fuzzy set theory, where the kernel of a fuzzy set is the set of the elements with membership degree 1.
This term is borrowed from fuzzy set theory, where the support of a fuzzy set is the set of the elements with non-zero membership degrees.
This kind of statements was originally proposed in (Britz et al. 2008) to constrain an ordered interpretation.
Remind that we consider qualitative possibility theory in our paper.
References
Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., & Patel-Schneider, P. F. (2007). The description logic handbook: Theory, implementation and application. Cambridge: Cambridge University Press.
Baader, F., & Hollunder, B. (1995a). Embedding defaults into terminological knowledge representation formalisms. Journal of Automated Reasoning, 14(1), 149–180.
Baader, F., & Hollunder, B. (1995b). Priorities on defaults with prerequisites, and their application in treating specificity in terminological default logic. Journal of Automated Reasoning, 15(1), 41–68.
Benferhat, S., Dubois, D., & Prade, H. (1997). Nonmonotonic reasoning, conditional objects and possibility theory. Artificial Intelligence, 92(1–2), 259–276.
Benferhat, S., Dubois, D., & Prade, H. (1998). Practical handling of exception-tainted rules and independence information in possibilistic logic. Applied Intelligence, 9(2), 101–127.
Bonatti, P. A., Faella, M., & Sauro, L. (2009). Defeasible inclusions in low-complexity dls: Preliminary notes. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI’09), Pasadena, California (pp. 696–701).
Bonatti, P. A., Faella, M., & Sauro, L. (2010). \(\mathcal{EL}\) with default attributes and overriding. In Proceedings of the 9th International Semantic Web Conference (ISWC’10), volume 6496 of Lecture Notes in Computer Science (pp. 64–79). Springer.
Bonatti, P. A., Lutz, C., & Wolter, F. (2006). Description logics with circumscription. In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning(KR’06), Lake District of the United Kingdom (pp. 400–410). AAAI Press.
Britz, K., Heidema, J., & Meyer, T. (2008). Semantic preferential subsumption. In Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning(KR’08), Sydney, Australia (pp. 476–484). AAAI Press.
Britz, K., Heidema, J., & Meyer, T. (2009). Modelling object typicality in description logics. In Proceedings of the 22nd International Workshop on DescriptionLogics (DL’09), Oxford, UK. CEUR-WS.org.
Casini, G., & Straccia, U. (2010). Rational closure for defeasible description logics. In Proceedings of the 12th European Conference on Logics in Artificial Intelligence (JELIA’10), Helsinki, Finland, volume 6341 of Lecture Notes in Computer Science (pp. 77–90). Springer.
de Saint-Cyr, F. D., & Prade, H. (2006). Possibilistic handling of uncertain default rules with applications to persistence modeling and fuzzy default reasoning. In Proceedings of the 12th International Conference on Principles of Knowledge Representation and Reasoning (KR’10), Lake District of the United Kingdom (pp 440–451). AAAI Press.
Donini, F. M., Nardi, D., & Rosati R. (2002). Description logics of minimal knowledge and negation as failure. ACM Transactions on Computational Logic, 3(2), 177–225.
Dubois, D., Lang, J., & Prade, H. (1994). Possibilistic logic. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Ed.), Handbook of logic in aritificial intelligence and logic programming (Vol. 3, pp. 439–513). Oxford: Oxford University Press.
Dubois, D., & Prade, H. (1986). Possibility theory: An approach to computerized processing of uncertainty. New York and London: Plenum Press.
Dubois, D., & Prade, H. (1998). Possibility theory: Qualitative and quantitative aspects. In D. M. Gabbay & P. Smets (Eds.) Handbook of defeasible reasoning and uncertainty management systems (Vol. 1, pp. 169–226). Dordrecht: Kluwer Academic.
Friedman, N., & Halpern, J. Y. (2001). Plausibility measures and default reasoning. Journal of ACM, 48(4), 648–685.
Giordano, L., Gliozzi, V., Olivetti, N., & Pozzato, G. L. (2007). Preferential description logics. In Proceedings of the 14th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning (LPAR’07), Yerevan, Armenia, volume 4790 of Lecture Notes in Computer Science (pp. 257–272). Springer.
Giordano, L., Gliozzi, V., Olivetti, N., & Pozzato, G. L. (2008). Reasoning about typicality in preferential description logics. In Proceedings of the 11th European Conference on Logics in Artificial Intelligence (JELIA’08), Dresden, Germany, volume 5293 of Lecture Notes in Computer Science (pp. 192–205). Springer.
Giordano, L., Gliozzi, V., Olivetti, N., & Pozzato, G. L. (2010). Preferential vs rational description logics: which one for reasoning about typicality? In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI’10), Lisbon, Portugal, 2010 (pp. 1069–1070).
Giordano, L., Olivetti, N., Gliozzi, V., & Pozzato, G. L. (2009) . ALC+T: A preferential extension of description logics. Fundamenta Informaticae, 96(3), 341–372.
Governatori, G. (2004). Defeasible description logics. In Proceedings of the 3rd International Workshop on Rules and Rule Markup Languages for the Semantic Web (RuleML’04), Hiroshima, Japan, volume 3323 of Lecture Notes in Computer Science ( pp. 98–112). Springer.
Hustadt, U., & Schmidt, R. A. (1998). Issues of decidability for description logics in the framework of resolution. In Selected Papers from Automated Deduction in Classical and Non-Classical Logics, volume 1761 of Lecture Notes in Computer Science, (pp. 191–205). Springer.
Kraus, S., Lehmann, D. J., & Magidor, M. (1990). Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44(1–2), 167–207.
Lehmann, D. J., & Magidor, M. (1990). Preferential logics: The predicate calculus case. In Proceedings of the 3rd Conference on Theoretical Aspects of Reasoning about Knowledge (TARK’90), Pacific Grove, CA (pp. 57–72).
Lehmann, D. J., & Magidor M. (1992). What does a conditional knowledge base entail? Artificial Intelligence, 55(1), 1–60.
Qi, G., Ji, Q., Pan, J. Z., & Du, J. (2011). Extending description logics with uncertainty reasoning in possibilistic logic. International Journal of Intelligent Systems, 26(4), 353–381.
Qi, G., & Zhang, Z. (2010). Preferential semantics for plausible subsumption in possibility theory. In Proceedings of the 12th International Conference on Principles of Knowledge Representation and Reasoning (KR’10), Toronto, Ontario, Canada. AAAI Press.
Rector, Alan L. (2004). Defaults, context, and knowledge: Alternatives for owl-indexed knowledge bases. In Proceedings of the Pacific Symposium on Biocomputing, Hawaii, USA, 6-10 January 2004 (pp. 226–237). World Scientific.
Schmidt-Schauß, M., & Smolka, G. (1991). Attributive concept descriptions with complements. Artificial Intelligence, 48(1), 1–26.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Acknowledgments
We would like to thank three reviewers for their helpful comments, which help to improve the quality of this paper. This work is partially supported by NSFC grants (No. 61003157, No. 61272378 and No. 60803061), Jiangsu Science Foundation (BK2010412 and BK2008293), Excellent Youth Scholars Program of Southeast University, and Doctoral Discipline Foundation for Young Teachers in the Higher Education Institutions of Ministry of Education (No. 20100092120029).
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Qi, G., Zhang, Z. Preferential Semantics for Plausible Subsumption in Possibility Theory. Minds & Machines 23, 47–75 (2013). https://doi.org/10.1007/s11023-012-9300-4
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DOI: https://doi.org/10.1007/s11023-012-9300-4