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Locally finite, proper and complete operators for refining Datalog programs

  • Communications Session 5B Learning and Discovery Systems
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Foundations of Intelligent Systems (ISMIS 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1079))

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Abstract

Refinement operators are exploited to change in an automated way incorrect clauses of a logic program. In this paper, we present four refinement operators for Datalog programs and demonstrate that all of them meet the properties of local finiteness,properness, and completeness. Such operators are based on the quasi-ordering induced upon a set of clauses by the generalization model of θ-subsumption under object identity. This model of generalization, as well as the four refinement operators have been implemented in a system for theory revision that proved effective in the area of electronic document classification.

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References

  1. Birkhoff, G., and Mac Lane, S., Algebra, The Macmillan Company, New York, 1965.

    Google Scholar 

  2. De Raedt, L., and Bruynooghe, M., A theory of clausal discovery, Proceed. of the 13th Int'l Conf. on Artificial Intelligence, IJCAI'93, R. Bajcsy (Ed.) Morgan Kaufmann, 1058–1063, 1993.

    Google Scholar 

  3. Esposito, F., Malerba, D., and Semeraro, G., INCR/H: A System for Revising Logical Theories, Proceed. of the MLnet Workshop on Theory Revision and Restructuring in Machine Learning, ECML-94, Arbeitspapiere der GMD N.842, S. Wrobel (Ed.), 13–15, 1994.

    Google Scholar 

  4. Esposito, F., Malerba, D., and Semeraro, G., Multistrategy Learning for Document Recognition, Applied Artificial Intelligence: An International Journal, 8:33–84, 1994.

    Google Scholar 

  5. Esposito, F., Malerba, D., Semeraro, G., Brunk, C., and Pazzani, M., Traps and Pitfalls when Learning Logical Definitions from Relations, in Methodologies for Intelligent Systems — Proceed. of the 8th Int'l Symp., ISMIS '94, LNAI 869, Z. W. Ras and M. Zemankova (Eds.), Springer-Verlag, 376–385, 1994.

    Google Scholar 

  6. Jung, B., On Inverting Generality Relations, Proceed. of the 3rd Int'l Workshop on Inductive Logic Programming, ILP'93, S. Muggleton (Ed.), J. Stefan Institute TR IJS-DP-6707, 87–101, 1993.

    Google Scholar 

  7. Kanellakis,P.C., Elements of Relational Database Theory, in Handbook of Theoretical Computer Science, Volume B, Formal Models and Semantics, J.VanLeeuwen (Ed.), Elsevier Science Publ., 1073–1156, 1990.

    Google Scholar 

  8. Kodratoff, Y., and Ganascia, J. G., Improving the Generalization Step in Learning, in Machine Learning: An Artificial Intelligence Approach, Vol. II, 215–244, Morgan Kaufmann, 1986.

    Google Scholar 

  9. Lloyd, J. W., Foundations of Logic Programming, Second Edition, Springer-Verlag, New York, 1987.

    Google Scholar 

  10. Niblett, T., A note on refinement operators, in Machine Learning: ECML-93. Proceed. of the European Conf. on Machine Learning, LNAI 667, P. B. Brazdil (Ed.), Springer-Verlag, 329–335, 1993.

    Google Scholar 

  11. Plotkin, G. D., A Note on Inductive Generalization, in Machine Intelligence 5, B. Meltzer and D. Michie (Eds.), Edinburgh University Press, 153–163, 1970.

    Google Scholar 

  12. Reynolds, J. C., Transformational Systems and the Algebraic Structure of Atomic Formulas, in Machine Intelligence 5, B. Meltzer and D. Michie (Eds.), Edinburgh University Press, 135–152, 1970.

    Google Scholar 

  13. Semeraro, G., Esposito, F., Malerba, D., Brunk, C., and Pazzani, M., Avoiding Non-Termination when Learning Logic Programs: A Case Study with FOIL and FOCL, in Logic Program Synthesis and Transformation — Meta-Programming in Logic, LNCS 883, L. Fribourg and F. Turini (Eds.), Springer-Verlag, 183–198, 1994.

    Google Scholar 

  14. Semeraro,G., Esposito,F., Fanizzi,N., and Malerba,D., Revision of Logical Theories, in Topics in Artificial Intelligence, LNAI 992, M.Gori and G.Soda (Eds.), Springer-Verlag, 365–376, 1995.

    Google Scholar 

  15. Semeraro, G., Esposito, F., and Malerba, D., Ideal Refinement of Datalog Programs, in Proceed. of LOPSTR '95, LNCS, M. Proietti (Ed.), Springer-Verlag, 1995 (in press).

    Google Scholar 

  16. Shapiro, E. Y., Inductive Inference of Theories from Facts, TR 192, Dept. of Comp. Sci., Yale Univ., New Haven, Connecticut, 1981.

    Google Scholar 

  17. van der Laag, P. R. J., and Nienhuys-Cheng, S.-H., A Note on Ideal Refinement Operators in Inductive LogicProgramming,Proceed. of the 4th Int'l Work shop on Inductive Logic Programming,ILP-94, S.Wrobel (Ed.), GMD-Studien Nr. 237, 247–260, 1994.

    Google Scholar 

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Zbigniew W. Raś Maciek Michalewicz

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© 1996 Springer-Verlag Berlin Heidelberg

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Esposito, F., Laterza, A., Malerba, D., Semeraro, G. (1996). Locally finite, proper and complete operators for refining Datalog programs. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_171

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  • DOI: https://doi.org/10.1007/3-540-61286-6_171

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  • Print ISBN: 978-3-540-61286-5

  • Online ISBN: 978-3-540-68440-4

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