Reasoning within fuzzy description logics

Journal of Artificial Intelligence Research

Volumn 14, Issue , 2001, Pages 147-176

  Straccia, Umberto ^a  

^a CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION RETRIEVAL; MULTIMEDIA SYSTEMS; PROBLEM SOLVING; SEMANTICS;

DESCRIPTION LOGICS (DL);

FUZZY SETS;

EID: 0036375983     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.813     Document Type: Article

References (39)

1. Baader, F., & Hollunder, B. (1991a). KRIS: Knowledge representation and inference system, system description. ACM SIGART Bulletin, 2, 8-14.
2. Baader, F., & Hollunder, B. (1991b). A terminological knowledge representation system with complete inference algorithm. In Proc. of the Workshop on Processing Declarative Knowledge, PDK-91, No. 567 in Lecture Notes in Artificial Intelligence, pp. 67-86. Springer-Verlag.
3. Bacchus, F. (1990). Representing and Reasoning with Probabilistic Knowledge. The MIT Press.
4. Bellman, R., & Giertz, M. (1973). On the analytic formalism of the theory of fuzzy sets. Information Sciences, 5, 149-156.
5. Brachman, R. J. (1992). "reducing" CLASSIC to practice: Knowledge representation meets reality. In Proc. of the 3rd Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR-92), pp. 247-258. Morgan Kaufmann, Los Altos.
6. Buchheit, M., Donini, F. M., & Schaerf, A. (1993a). Decidable reasoning in terminological knowledge representation systems. In Proc. of the 13th Int. Joint Conf. on Artificial Intelligence (IJCAI-93), pp. 704-709 Chambery, France. Morgan Kaufmann, Los Altos.
7. Buchheit, M., Donini, F. M., & Schaerf, A. (1993b). Decidable reasoning in terminological knowledge representation systems. Journal of Artificial Intelligence Research, 1, 109-138.
8. Chen, J., & Kundu, S. (1996). A sound and complete fuzzy logic system using Zadeh's implication operator. In Ras, Z. W., & Maciek, M. (Eds.), Proc. of the 9th Int. Sym. on Methodologies for Intelligent Systems (ISMIS-96), No. 1079 in Lecture Notes in Artificial Intelligence, pp. 233-242. Springer-Verlag.
9. da Silva, R. M., Pereira, A. E. C., & Netto, M. A. (1994). A system of knowledge representation based on formulae of predicate calculus whose variables are annotated by expressions of a fuzzy terminological logic. In Proc. of the 5th Int. Conf. on Information Processing and Managment of Uncertainty in Knowledge-Based Systems, (IPMU-94), No. 945 in Lecture Notes in Computer Science. Springer-Verlag.
10. Donini, F. M., Lenzerini, M., Nardi, D., & Schaerf, A. (1994). Deduction in concept languages: From subsumption to instance checking. Journal of Logic and Computation, 4(4), 423-452.
11. Dubois, D., & Prade, H. (1980). Fuzzy Sets and Systems. Academic Press, New York, NJ.
12. Dubois, D., & Prade, H. (1994). Can we enforce full compositionality in uncertainty calculi?. In Proc. of the 11th Nat. Conf. on Artificial Intelligence (AAAI-94), pp. 149-154 Seattle, Washington.
13. Dubois, D., & Prade, H. (1996). Approximate and commonsense reasoning: From theory to practice. In Ras, Z. W., & Maciek, M. (Eds.), Proc. of the 9th Int. Sym. on Methodologies for Intelligent Systems (ISMIS-96), No. 1079 in Lecture Notes in Artificial Intelligence, pp. 19-33. Springer-Verlag.
14. Halpern, J. Y. (1990). An analysis of first-order logics of probability. Artificial Intelligence Journal, 46, 311-350.
15. Heinsohn, J. (1994). Probabilistic description logics. In de Mantara, R. L., & Pool, D. (Eds.), Proceedings of the 10th Conference on Uncertainty in Artificila Intelligence, pp. 311-318.
16. Hollunder, B. (1994). An alternative proof method for possibilistic logic and its application to terminological logics. In 10th Annual Conference on Uncertainty in Artificial Intelligence Seattle, Washington. R. Lopez de Mantaras and D. Pool.
17. Horrocks, I. (1998). Using an expressive description logic: Fact or fiction?. In Proc. of the 8th Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR-98).
18. Jäger, M. (1994). Probabilistic reasoning in terminological logics. In Proceedings of KR94, 5-th International Conference on Principles of Knowledge Representation and Reasoning, pp. 305-316 Bonn, FRG.
19. Koller, D., Levy, A., & Pfeifer, A. (1997). P-CLASSIC: A tractable probabilistic description logic. In Proc. of the 14th Nat. Conf. on Artificial Intelligence (AAAI-97), pp. 390-397.
20. Kruse, R., Schwecke, E., & Heinsohn, J. (1991). Uncertainty and Vagueness in Knowledge Based Systems. Springer-Verlag, Berlin, Germany.
21. Lee, R. C. T. (1972). Fuzzy logic and the resolution principle. Journal of the ACM, 19(1), 109-119.
22. Meghini, C., Sebastiani, F., & Straccia, U. (1997). Modelling the retrieval of structured documents containing texts and images. In Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries, No. 1324 in Lecture Notes in Computer Science, pp. 325-344 Pisa, Italy.
23. Meghini, C., Sebastiani, F., & Straccia, U. (1998). MIRLOG: A logic for multimedia information retrieval. In Crestani, F., Lalmas, M., & van Rijsbergen, C. (Eds.), Logic and Uncertainty in Information Retrieval: Advanced models for the representation and retrieval of information, Vol. 4 of The Kluwer International Series On Information Retrieval, chap. 7, pp. 151-185. Kluwer Academic Publishers, Boston, USA.
24. Meghini, C., & Straccia, U. (1996). A relevance terminological logic for information retrieval. In Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR-96), pp. 197-205 Zurich, Switzerland.
25. Nebel, B. (1988). Computational complexity of terminological reasoning in BACK. Artificial Intelligence, 34, 371-383.
26. Nebel, B. (1990). Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG.
27. Papadimitriou, C. H., & Steiglitz, K. (1982). Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs, New Jersey.
28. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Los Altos.
29. Peltason, C. (1991). The BACK system - an overview. SIGART Bulletin, 2(3), 114-119.
30. Roth, D. (1996). On the hardness of approximate reasoning. Artificial Intelligence Journal, 82, 273-302.
31. Schmidt-Schauß, M., & Smolka, G. (1991). Attributive concept descriptions with complements. Artificial Intelligence, 48, 1-26.
32. Sebastiani, F. (1994). A probabilistic terminological logic for modelling information retrieval. In Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval, pp. 122-130 Dublin, IRL. Published by Springer Verlag, Heidelberg, FRG.
33. Straccia, U. (1998). A fuzzy description logic. In Proc. of the 15th Nat. Conf. on Artificial Intelligence (AAAI-98), pp. 594-599 Madison, USA.
34. Straccia, U. (2000a). A note on the relationship between fuzzy logic and four-valued logic. Technical report B4-22-10-2000, Istituto di Elaborazione dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy.
35. Straccia, U. (2000b). Reasoning and experimenting within Zadeh's fuzzy propositional logic. Technical report 2000-b4-011, Istituto di Elaborazione dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy.
36. Tresp, C., & Molitor, R. (1998). A description logic for vague knowledge. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98) Brighton (England).
37. Xiachun, C., Yunfei, J., & Xuhua, L. (1995). The rationality and decidability of fuzzy implications. In Proc. of the 14th Int. Joint Conf. on Artificial Intelligence (IJCAI-95), pp. 1910-1911 Montreal, Canada. MK.
38. Yen, J. (1991). Generalizing term subsumption languages to fuzzy logic. In Proc. of the 12th Int. Joint Conf. on Artificial Intelligence (IJCAI-91), pp. 472-477 Sydney, Australia.
39. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.