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Inducing integrity constraints from knowledge bases

  • Knowledge Organization and Optimization
  • Conference paper
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KI-95: Advances in Artificial Intelligence (KI 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 981))

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Abstract

Integrity constraints are important logical tools for the general organization of knowledge. Integrity constraints (in short: ICs), which are commonly used in the field of deductive databases, specify general regularities like “a son is not older than his father.” They facilitate the organization of knowledge in expert systems and can speed up the query-response time significantly.

This paper presents an approach for inductively generating compact integrity constraints from knowledge bases, represented in first-order logic. To obtain the most powerful ICs, the huge space of potential ICs, which are principally consistent with a given knowledge base, is restricted by IC-schemes. IC-schemes specify ICs syntactically. The proposed method searches the resulting space of ICs efficiently by pruning away whole subspaces. The approach is also capable of detecting irregularities in “noisy” knowledge bases which might be inconsistent. Empirical results illustrate the appropriateness of this method for finding compact ICs in a reasonable period of time.

This paper reports on work performed while the author was in the ML group at GMD, FIT.KI, Schloss Birlinghoven, 53754 Sankt Augustin.

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References

  1. F. Bergadano and D. Gunetti. An interactive system to learn functional logic programs. In Proceedings of the 13th International Joint Conference on Artifical Intelligence, pages 1044–1049. Morgan Kaufmann, 1993.

    Google Scholar 

  2. F. Bry, R. Manthey, and B. Martens. Integrity Verification in Knowledge Bases. Technical Report D.2.1.a, European Computer-Industry Research Centre GmbH (ECRC), München, West Germany, April 1990.

    Google Scholar 

  3. W.F. Clocksin and C.S. Mellish. Programming in Prolog. Springer-Verlag, 3nd edition, 1987.

    Google Scholar 

  4. Randall Davis. Applications of meta-level knowledge to the construction, maintenance, and use of large knowledge bases. In Randall Davis and Douglas B. Lenat, editors, Knowledge-Based Systems in Artificial Intelligence. McGraw-Hill, New York, 1982.

    Google Scholar 

  5. Lud Dehaspe, Wim Van Laer, and Luc De Raedt. Applications of a logical discovery engine. In Proc. of AAAI Workshop on Knowlege Discovery in Databases, 1994. to appear.

    Google Scholar 

  6. Jörg-Uwe Kietz and Stefan Wrobel. Controlling the Complexity of Learning in Logic through Syntactic and Task-Oriented Models. In Stephen Muggleton, editor, Inductive Logic Programming, chapter 16, pages 335–359. Academic Press, London, 1992.

    Google Scholar 

  7. Nada Lavrač and Sašo DŽeroski. Inductive Logic Programming. Ellis Horwood Limited, 1st edition, 1994.

    Google Scholar 

  8. J.W. Lloyd. Foundations of Logic Programming. Symbolic Computation. Springer-Verlag, 2nd edition, 1987.

    Google Scholar 

  9. Jack Minker. Foundations of Deductive Databases and Logic Programming. Morgan Kaufmann, 1988.

    Google Scholar 

  10. Katharina Morik. Balanced Cooperative Modeling. Machine Learning, 11:217–235, 1993.

    Google Scholar 

  11. Katharina Morik and Anke Rieger. Learning Action-oriented Perceptual Features for Robot Navigation. Technical report, Univ. Dortmund, LS VIII, 1993.

    Google Scholar 

  12. Stephen Muggleton and Luc De Raedt. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming, 1993.

    Google Scholar 

  13. Katharina Morik, Stefan Wrobel, J.-U. Kietz, and Werner Emde. Knowledge Acquisition and Machine Learning: Theory Methods and Applications. Academic Press, London, New York, 1993.

    Google Scholar 

  14. Gordon D. Plotkin. A Note on Inductive Generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence, volume 5, chapter 8, pages 153–163. American Elsevier, 1970.

    Google Scholar 

  15. J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239–266, 1990.

    Google Scholar 

  16. Luc De Raedt and Maurice Bruynooghe. A Theory of Clausal Discovery. In Proc. 22nd International Joint Conference on Artificial Intelligence, 1993.

    Google Scholar 

  17. E.Y. Shapiro. Algorithmic Program Debugging. ACM Distinguished Doctoral Dissertations, Cambridge, Mass., 1983. MIT Press.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Roemerstr. 164, 53117, Bonn, Germany

    Roman Englert

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  1. Roman Englert
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Ipke Wachsmuth Claus-Rainer Rollinger Wilfried Brauer

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© 1995 Springer-Verlag Berlin Heidelberg

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Englert, R. (1995). Inducing integrity constraints from knowledge bases. In: Wachsmuth, I., Rollinger, CR., Brauer, W. (eds) KI-95: Advances in Artificial Intelligence. KI 1995. Lecture Notes in Computer Science, vol 981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60343-3_27

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  • DOI: https://doi.org/10.1007/3-540-60343-3_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60343-6

  • Online ISBN: 978-3-540-44944-7

  • eBook Packages: Springer Book Archive

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