Abstract
Description Logics (DLs) are gaining more popularity as the foundation of ontology languages for the Semantic Web. On the other hand, uncertainty is a form of deficiency or imperfection commonly found in the real-world information/data. In recent years, there has been an increasing interest in extending the expressive power of DLs to support uncertainty, for which a number of frameworks have been proposed. In this paper, we introduce an extension of DL (ALC) that unifies and/or generalizes a number of existing approaches for DLs with uncertainty. We first provide a classification of the components of existing frameworks for DLs with uncertainty in a generic way. Using this as a basis, we then discuss ways to extend these components with uncertainty, which includes the description language, the knowledge base, and the reasoning services. Detailed explanations and examples are included to describe the proposed completion rules.
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References
Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF, eds (2003) The description logic handbook: theory, implementation, and applications, Cambridge University Press.
Bacchus F (1990) Representing and reasoning with probabilistic knowledge-a logical approach to probabilities, MIT Press.
Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Scientific American 284(5).
Giugno R, Lukasiewicz T (2002) P-SHOQ(D): A probabilistic extension of SHOQ(D) for probabilistic ontologies in the semantic web. In: Proceedings of the European conference on logics in artificial intelligence, Cosenza, Italy, pp 86–97.
Gruber TR (1993) A translation approach to portable ontology specificati ons. Knowledge acquisition 5(2): 199–220.
Haarslev V, Pai HI, Shiri N (2005) A generic framework for description lo gics with uncertainty. In: Proceedings of uncertainty reasoning for the semantic web, Galway, Ireland, pp 77–86.
Hölldobler S, Khang TD, Störr HP (2002) A fuzzy description logic with hedges as concept modifiers. In: Proceedings of the 3rd international conference on intelligent technologies, Science and Technics Publishing House, Hanoi, Vietnam, pp 25–34.
Roller D, Levy AY, Pfeffer A (1997) P-CLASSIC: A tractable probablistic description logic. In: Proceedings of the 14th national conference on artifi cial intelligence, AAAI Press, Providence, Rhode Island, pp 390–397.
Lakshmanan LVS, Sadri F (1994) Probabilistic deductive databases. In: Proceedings of workshop on design and implementation of parallel logic programming systems, MIT Press, Ithaca, NY, pp 254–268.
Lakshmanan LVS, Shiri N (2001a) Logic programming and deductive databases with uncertainty: A survey. Encyclopedia of computer science and technology, vol 45, Marcel Dekker, New York, pp 153–176.
Lakshmanan LVS, Shiri N (2001b) A parametric approach to deductive databases with uncertainty. IEEE transactions on knowledge and data engineering, 13(4):554–570.
Motro A, Smets P, eds. (1997) Uncertainty management in information systems-from needs to solutions, Springer-Verlag.
Parsons S (1996) Current approaches to handling imperfect information in data and knowledge bases. IEEE transactions on knowledge and data engineering, 8(3):353–372.
Ross TJ, Booker JM, Parkinson WJ, eds (2002) Fuzzy logic and probability applications: bridging the gap, SIAM.
Sánchez D, Tettamanzi, AGB (2004) Generalizing quantification in fuzzy description logics. In: Proceedings of the 8th Fuzzy Days, Springer-Verlag, Dortmund, Germany.
Straccia U (1998) A fuzzy description logic. In: Proceedings of the 15th national conference on artificial intelligence, AAAI Press, Menlo Park, CA, USA, pp 594–599.
Straccia U (2001) Reasoning within fuzzy description logics. Journal of artificial intelligence research 14:137–166.
Tresp C, Molitor R (1998) A description logic for vague knowledge. In: Proceedings of the 13th European conference on artificial intelligence, John Wiley and Sons, Brighton, UK, pp 361–365.
Zadeh LA (1965) Fuzzy sets. Information and control, 8:338–353.
Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems l(l):3–28.
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Haarslev, V., Pai, HI., Shiri, N. (2006). Completion Rules for Uncertainty Reasoning with the Description Logic ALC . In: Koné, M.T., Lemire, D. (eds) Canadian Semantic Web. Semantic Web and Beyond, vol 2. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34347-1_14
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DOI: https://doi.org/10.1007/978-0-387-34347-1_14
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