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Towards Learning in CARIN-ALN

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Inductive Logic Programming (ILP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1866))

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Abstract

In this paper we investigate a new language for learning, which combines two well-known representation formalisms, Description Logics and Horn Clause Logics. Our goal is to study the feasability of learning in such a hybrid description - horn clause language, namely CARIN-ALN [LR98b], in the presence of hybrid background knowledge, including a Horn clause and a terminological component. After setting our learning framework, we present algorithms for testing example coverage and subsumption between two hypotheses, based on the existential entailment algorithm studied in[LR98b]. While the hybrid language is more expressive than horn clause logics alone, the complexity of these two steps for CARIN-ALN remains bounded by their respective complexity in horn clause logics.

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References

  1. H. Ait-Kaci and A. Podelski. Towards the meaning of LIFE. In J. Maluszynski and M. Wirsing, editors, Proceedings on 3rd International Symposium on Programming Language Implementation and Logic Programming, pages 255–274, Berlin, 1991.

    Google Scholar 

  2. E. Alphonse and C. Rouveirol. Lazy propositionalisation for relational learning. In W. Horn, editor, Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, 2000. IOS Press. to appear.

    Google Scholar 

  3. P. Brézellec, M. Champesme, and H. Soldano. Tabata, a learning algorithm searching a minimal space using a tabu strategy. In H. Prade, editor, Proceedings of the 13th European Conference on Artificial Intelligence, pages 420–424, Brighton, 1998. Wiley.

    Google Scholar 

  4. H. Blockheel, L. De Raedt, and B. Jacobs, N.and Demoen. Scaling up inductive logic programming by learning from interpretaions. Data Mining and Knowledge Discovery, 3(1):59–93, 1999.

    Article  Google Scholar 

  5. A. Borgida and P. F. Patel-Schneider. Complete algorithm for subsumption in the CLASSIC description logic. Journal of Artificial Intelligence Research, 1:278–308, 1994.

    Google Scholar 

  6. R.J. Brachman. A structural paradigm for representing knowledge. Technical Report 3605, BBN Report, 1978.

    Google Scholar 

  7. W. Buntine. Generalized subsumption and its application to induction and redundancy. Artificial Intelligence, 36:375–399, 1988.

    Article  MathSciNet  Google Scholar 

  8. W. W. Cohen and H. Hirsh. Learnability of the classic knowledge representation language. Technical report, AT Bell Laboratories, New York, 1992.

    Google Scholar 

  9. W. W. Cohen and H. Hirsh. The learnability of the description logics with equality constraints. Machine Learning, 2(4):169–199, 1994.

    Google Scholar 

  10. W. W. Cohen and H. Hirsh. Learning the CLASSIC description logic: Theoretical and experimental results. In International Conference on Knowledge Representation and Reasoning, pages 121–133. 1994.

    Google Scholar 

  11. F.M. Donini, M. Lenzerini, D. Nardi, and W. Nutt. The complexity of concept languages. In J.A. Allen R. Fikes and E Sandewall, editors, Principles of Knowledge Representation and Reasoning: 2nd International Conference, pages 151–162, Cambridge, Mass., 1991.

    Google Scholar 

  12. F.M. Donini, M. Lenzerini, D. Nardi, and A. Schaerf. A hybrid system with datalog and concept languages. In E. Ardizzone, S. Gablio, and F. Sorbello, editors, Trends in Artificial Intelligence, volume 549of Lecture Notes in Artificial Intelligence, pages 88–97. Springer-Verlag, 1991.

    Google Scholar 

  13. L. De Raedt. Logical settings for concept learning. Artificial Intelligence, 95:187–201, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  14. M. Frazier and L. Pitt. Classic learning. Machine Learning, 25:151–193, 1996.

    Article  MATH  Google Scholar 

  15. F. Goasdoué, V. Lattès, and Rousset M.-Ch. The use of carin language and algorithms for information integration: The picsel system. International Journal of Cooperative Systems. submitted.

    Google Scholar 

  16. M-E. Goncalves. Handling quantifiers in ILP. In S. Muggleton, editor, Proc. of the 6th International Workshop on Inductive Logic Programming, pages 337–357. Springer Verlag, 1997.

    Google Scholar 

  17. J-U. Kietz and M. Lübbe. An efficient subsumption algorithm for inductive logic programming. In William W. Cohen and Haym Hirsh, editors, Proc. 11th International Conference on Machine Learning, pages 130–138. Morgan Kaufmann, 1994.

    Google Scholar 

  18. J. U. Kietz and K. Morik. A polynomial approach to the constructive induction of structural knowledge. Machine Learning, 14(2):193–217, 1994.

    Article  MATH  Google Scholar 

  19. A. Levy and M.-Ch. Rousset. Verification of knowledge bases based on containment checking. Artificial Intelligence, 101(1–2), 1998.

    Google Scholar 

  20. A. Y. Levy and M.-Ch. Rousset. Combining horn rules and description logics in carin. Artificial Intelligence, 104:165–209, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  21. S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the 1st Conference on Algorithmic Learning Theory, pages 368–381. Ohmsma, Tokyo, Japan, 1990.

    Google Scholar 

  22. D. Michie, S. Muggleton, D. Page, and A. Srinivasan. To the international computing community: A new East-West challenge. Technical report, Oxford University Computing laboratory, Oxford,UK, 1994. URL: ftp://ftp.comlab.ox.ac.uk/pub/Packages/ILP/trains.tar.Z.

    Google Scholar 

  23. S.-H. Nienhuys-Cheng, W. Van Laer, J. Ramon, and L. De Raedt. Generalizing refinement operators to learn prenex conjunctive normal forms. In S Džeroski and P. Flach, editors, Proc. of the 9th International Conference on Inductive Logic Programming, pages 245–256. Springer Verlag, 1999.

    Google Scholar 

  24. J. Quantz, G. Dunker, F. Bergmann, and I. Kellner. The flex system. Technical report, KIT-Report, Technische Universität, Berlin, Germany, 1996.

    Google Scholar 

  25. L.G. Valiant. A theory of the learnable. Communications of the ACM, 27:1134–1142, 1984.

    Article  MATH  Google Scholar 

  26. F. Van Harmelen and A. Bundy. Explanation based generalisation = partial evaluation. Artificial Intelligence, 36:401–412, 1988.

    Article  MATH  Google Scholar 

  27. J.R. Wright, E.S. Weixelbaum, K. Brown, G.T. Vesonder, S.R. Palmer, J.I. Berman, and H.H. Moore. A knowledge-based configurator that supports sales, engineering and manufacturing at att bell network systems. In Proceedings of the Innovative Applications of Artificial Intelligence Conferences, pages 183–193, Menlo Park, California, 1993.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratoire de Recherche en Informatique, Université Paris-Sud, Bâtiment 490, 91405, Orsay Cedex, France

    Céline Rouveirol & Véronique Ventos

Authors
  1. Céline Rouveirol
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  2. Véronique Ventos
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK

    James Cussens  & Alan Frisch  & 

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© 2000 Springer-Verlag Berlin Heidelberg

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Rouveirol, C., Ventos, V. (2000). Towards Learning in CARIN-ALN . In: Cussens, J., Frisch, A. (eds) Inductive Logic Programming. ILP 2000. Lecture Notes in Computer Science(), vol 1866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44960-4_12

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  • DOI: https://doi.org/10.1007/3-540-44960-4_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67795-6

  • Online ISBN: 978-3-540-44960-7

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