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Terminologies and rules

  • Hans-Jürgen Bürckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 777)

Abstract

Terminological logics have become a well understood formal basis for taxonomic knowledge representation, both for the semantics (classically by Tarski models) and for the inference services (like concept subsumption, instantiation, classification, and realization) of terminological systems of the KL-ONE family. It has been demonstrated that terminological reasoning can be realized by efficient and logically complete algorithms based on tableaux style calculi.

However, representation of and reasoning with terminological information supports just a rather static form of knowledge representation. Only a fixed description of a domain can be represented: There is the schematic description of concepts in the socalled TBox and the instantiation of concepts by individuals and objects in the ABox of such systems. Terminological inferences can retrieve implicit information, but cannot be used for deriving new data.

In order to overcome this restriction terminological systems often allow for additional rule based formalisms. Those, however, are missing a clear declarative semantics. In this paper we will sketch several declarative forms of rule based extensions of terminological systems that have been developed recently.

Keywords

Default Rule Constraint Language Epistemic Model Constraint Theory Terminological System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    F. Baader, H.-J. Bürckert, B. Hollunder, W. Nutt, J.H. Siekmann. Concept Logics. In: Computational Logic, J.W. Lloyd (ed.). ESPRIT Basic Research Series, Springer, 1990.Google Scholar
  2. 2.
    F. Baader and B. Hollunder. KRIS: Knowledge Representation and Inference System. SIGART Bulletin, 2(3), 1991, pp. 8–14.Google Scholar
  3. 3.
    F. Baader and B. Hollunder. Embedding Defaults into Terminological Knowledge Representation Formalisms. Proc. of 3rd Conference on Principles of Knowledge Representation and Reasoning, 1992, pp. 306–317.Google Scholar
  4. 4.
    R.J. Brachman, R.E. Fikes, H.J. Levesque. KRYPTON: Integrating Terminology and Assertion. Proc. of 3rd National Conference on Artifial Intelligence, 1983, pp. 31–35.Google Scholar
  5. 5.
    M. Buchheit, F.M. Donini, A. Schaerf. ecidable Reasoning in Terminological Knowledge Representation Systems. J. of Artificial Intelligence Research, 1, 1993, 109–138.Google Scholar
  6. 6.
    M. Buchheit, M.A. Jeusfeld, W. Nutt, M. Staudt. Subsumption between Queries to Object-Oriented Databases. DFKI Research Report RR-93-44, Saarbrücken, 1993. To appear also in Information Systems, 1994.Google Scholar
  7. 7.
    H.-J. Bürckert. A Resolution Principle for a Logic with Restricted Quantifiers. Lecture Notes in Artificial Intelligence 568, Springer, 1991.Google Scholar
  8. 8.
    H.-J. Bürckert. A Resolution Principle for Constrained Logics. Artificial Intelligence, to appear.Google Scholar
  9. 9.
    H.-J. Bürckert, B. Hollunder, A. Laux: On Skolemization in Constrained Logics. DFKI-Research-Report RR-93-06, Saarbrücken, 1993Google Scholar
  10. 10.
    H.-J. Bürckert, B. Hollunder, A. Laux: Concept Logics with Function Symbols. DFKI-Research-Report RR-93-07, Saarbrücken, 1993Google Scholar
  11. 11.
    H.-J. Bürckert and W. Nutt. On Abduction and Answer Generation through Constrained Resolution. DFKI Research Report RR-92-51, Saarbrücken, 1992.Google Scholar
  12. 12.
    B.F. Chellas. Modal Logic: An Introduction. Cambridge University Press, 1980.Google Scholar
  13. 13.
    F.M. Donini, B. Hollunder, M. Lenzerini, A. Marchetti Spaccamela, D. Nardi, W. Nutt. The Complexity of Existential Quantification in Concept Languages. Artificial Intelligence, 55, 1992, pp. 309–327.Google Scholar
  14. 14.
    F.M. Donini, M. Lenzerini, D. Nardi, W. Nutt. The Complexity of Concept Languages. Proc. of 2nd International Conference on Pronciples of Knowledge Representation and Reasoning, 1991, pp. 151–162.Google Scholar
  15. 15.
    F.M. Donini, M. Lenzerini, D. Nardi, W. Nutt. Tractable Concept Languages. Proc. of 12th International Joint Conference on Artificial Intelligence, 1991Google Scholar
  16. 16.
    F.M. Donini, M. Lenzerini, D. Nardi, A. Schaerf. A Hybrid System Integrating DATALOG and Concept Languages. Proc. of 2nd Conf. of Italian Association for Artificial Intelligence. Lecture Notes in Artificial Intelligence 549, 1991.Google Scholar
  17. 17.
    F.M. Donini, M. Lenzerini, D. Nardi, A. Schaerf, W. Nutt. Queries and Rules as Epistemic Sentences in Concept Languages. DFKI Research Report RR-93-40, Saarbrücken, 1993.Google Scholar
  18. 18.
    P. Hanschke. A Declarative Integration of Terminological, Constraint-based, Data driven, and Goal-directed Reasoning. Dissertation, Universität Kaiserslautern, 1993.Google Scholar
  19. 19.
    M. Höhfeld and G. Smolka. Definite Relations over Constraint Languages. LILOG-Report 53, IBM Deutschland, 1988.Google Scholar
  20. 20.
    J. Jaffar, J.-L. Lassez. Constrained Logic Programming. Proc. of ACM Symposium on Principles of Programming Languages, 1987, pp. 111–119.Google Scholar
  21. 21.
    H. Levesque. Foundations of a Functional Approach to Knowledge Representation. Artificial Intelligence, 1984, pp. 195–212.Google Scholar
  22. 22.
    H.J. Levesque and R.J. Brachman. Expressivity and Tractability in Knowledge Representation and Reasoning. Computational Intelligence, 3, 1987, pp. 78–93.Google Scholar
  23. 23.
    W. Lifschitz. Minimal Beliefs and Negation as Failure. Proc. of 12th International Joint Conference on Artificial Intelligence, 1991.Google Scholar
  24. 24.
    J.W. Lloyd. Foundations of Logic Programming. Springer, 1987.Google Scholar
  25. 25.
    M. Maher. Logic Semantics for a Class of Committed-choice Programs. Proc. of 4th International Conference on Logic Programming, 1987, pp. 858–876.Google Scholar
  26. 26.
    B. Nebel. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in Computer Science 422, Springer, 1990.Google Scholar
  27. 27.
    P.F. Patel-Schneider and B. Swartout. Description Logic Specification from the KRSS Effort. Working version, 1993.Google Scholar
  28. 28.
    R. Reiter. A Logic for Default Reasoning. Artificial Intelligence, 13, 1980, pp. 81–132.Google Scholar
  29. 29.
    M. Schmidt-Schauss and G. Smolka. Attributive Concept Descriptions with Complements. Artificial Intelligence, 48, 1991, pp.1–26.Google Scholar
  30. 30.
    G. Smolka. Feature Logics with Subsorts. LILOG-Report 33, IBM Deutschland, 1988.Google Scholar
  31. 31.
    G. Smolka. Logic Programming over Polymorphically Order-sorted Types. Dissertation, Kaiserslautern, 1989.Google Scholar
  32. 32.
    G. Smolka and R. Treinen. Records for Logic Programming. J. of Logic Programming, to appear.Google Scholar
  33. 33.
    M.E. Stickel. Automated Deduction by Theory Resolution. J. of Automated Reasoning, 1(4), 1985, pp. 333–357.Google Scholar
  34. 34.
    W.A. Woods and J.G. Schmolze. The KL-ONE Family. In: Semantic Networks in Artificial Intelligence, F.W. Lehmann (ed.), Pergamon Press, 1992, pp. 133–178.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Hans-Jürgen Bürckert
    • 1
  1. 1.Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)Saarbrücken

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