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International Conference on Discovery Science

DS 2001: Discovery Science pp 100–112Cite as

Towards Discovery of Deep and Wide First-Order Structures: A Case Study in the Domain of Mutagenicity

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2226))

Abstract

In recent years, it has been shown that methods from Inductive Logic Programming (ILP) are powerful enough to discover new fist-order knowledge from data, while employing a clausal representation language that is relatively easy for humans to understand. Despite these successes, it is generally acknowledged that there are issues that present fundamental challenges for the current generation of systems. Among these, two problems are particularly prominent: learning deep clauses, i.e., clauses where a long chain of literals is needed to reach certain variables, and learning wide clauses, i.e., clauses with a large number of literals. In this paper we present a case study to show that by building on positive results on acyclic conjunctive query evaluation in relational database theory, it is possible to construct ILP learning algorithms that are capable of discovering clauses of significantly greater depth and width. We give a detailed description of the class of clauses we consider, describe a greedy algorithm to workwith these clauses, and show, on the popular ILP challenge problem of mutagenicity, how indeed our method can go beyond the depth and width barriers of current ILP systems.

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References

  1. S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison-Wesley, Reading, Mass., 1995.

    Google Scholar 

  2. H. Arimura. Learning acyclic first-order Horn sentences from entailment. In M. Li and A. Maruoka, editors, Proceedings of the 8th International Workshop on AlgorithmicLearning Theory, volume 1316of LNAI, pages 432–445, Springer, Berlin, 1997.

    Google Scholar 

  3. C. Beeri, R. Fagin, D. Maier, and M. Yannakakis. On the desirability of acyclic database schemes. Journal of the ACM, 30(3):479–513, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  4. P. A. Bernstein and N. Goodman. The power of natural semijoins. SIAM Journal on Computing, 10(4):751–771, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  5. H. Blockeel and L. D. Raedt. Lookahead and discretization in ILP. In N. Lavraĉ and S. Džeroski, editors, Proceedings of the 7th International Workshop on Inductive Logic Programming, volume 1297 of LNAI, pages 77–84, Springer, Berlin, 1997.

    Google Scholar 

  6. A. K. Chandra and P. M. Merlin. Optimal implementations of conjunctive queries in relational databases. In Proceedings of the 9th ACM Symposium on Theory of Computing, pages 77–90. ACM Press, 1977.

    Google Scholar 

  7. C. Chekuri and A. Rajaraman. Conjunctive query containment revisited. Theoretical Computer Science, 239(2):211–229, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  8. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. MIT Press, Cambridge, Mass., 1990.

    Google Scholar 

  9. R. Fagin. Degrees of acyclicity for hypergraphs and relational database schemes. Journal of the ACM, 30(3):514–550, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  10. A. Giordana and L. Saitta. Phase transitions in relational learning. Machine Learning, 41(2):217–251, 2000.

    Article  MATH  Google Scholar 

  11. G. Gottlob. Subsumption and implication. Information Processing Letters, 24(2):109–111, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  12. G. Gottlob and A. Leitsch. On the efficiency of subsumtion algorithms. Journal of the ACM, 32(2):280–295, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  13. G. Gottlob, N. Leone, and F. Scarcello. The complexity of acyclic conjunctive queries. In Proceedings of the 39th Annual Symposium on Foundations of Computer Science, pages 706–715. IEEE Computer Society Press, 1998.

    Google Scholar 

  14. M. Graham. On the universal relation. Technical report, Univ. of Toronto, Toronto, Canada, 1979.

    Google Scholar 

  15. K. Hirata. On the hardness of learning acyclic conjunctive queries. In Proceedings of the 11th International Conference on Algorithmic Learning Theory, volume 1968 of LNAI, pages 238–251. Springer, Berlin, 2000.

    Chapter  Google Scholar 

  16. T. Horváth and G. Turán. Learning logic programs with structured background knowledge. Artificial Intelligence, 128(1–2):31–97, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  17. J.-U. Kietz. Some lower bounds for the computational complexity of inductive logic programming. In P. Brazdil, editor, Proceedings of the European Conference on Machine Learning, volume 667 of LNAI, pages 115–123. Springer, Berlin, 1993.

    Google Scholar 

  18. J.-U. Kietz and M. Lübbe. An efficient subsumption algorithm for inductive logic programming. In W. Cohen and H. Hirsh, editors, Proc. Eleventh International Conference on Machine Learning (ML-94), pages 130–138, 1994.

    Google Scholar 

  19. Kolaitis and Vardi. Conjunctive-query containment and constraint satisfaction. JCSS: Journal of Computer and System Sciences, 61(2):302–332, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  20. N. Lavraĉ and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.

    Google Scholar 

  21. T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.

    Google Scholar 

  22. S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13(3–4):245–286, 1995.

    Article  Google Scholar 

  23. S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. The Journal of Logic Programming, 19/20:629–680, 1994.

    Article  Google Scholar 

  24. C. Nédellec, C. Rouveirol, H. Adé, F. Bergadano, and B. Tausend. Declarative bias in ILP. In L. De Raedt, editor, Advances in Inductive Logic Programming, pages 82–103. IOS Press, 1996.

    Google Scholar 

  25. S.-H. Nienhuys-Cheng and R. Wolf. Foundations of Inductive Logic Programming, volume 1228 of LNAI. Springer, Berlin, 1997.

    Google Scholar 

  26. T. Scheffer, R. Herbrich, and F. Wysotzki. Efficient ø-subsumption based on graph algorithms. In S. Muggleton, editor, Proceedings of the 6th International Workshopon Inductive Logic Programming, volume 1314 of LNAI, pages 212–228, Springer, Berlin, 1997.

    Google Scholar 

  27. M. Sebag and C. Rouveirol. Resource-bounded relational reasoning: Induction and deduction through stochastic matching. Machine Learning, 38(1/2):41–62, 2000.

    Article  MATH  Google Scholar 

  28. G. Silverstein and M. Pazzani. Relational cliches: Constraining constructive induction during relational learning. In Birnbaum and Collins, editors, Proceedings of the 8th International Workshop on Machine Learning, pages 203–207, Morgan Kaufmann, San Mateo, CA, 1991.

    Google Scholar 

  29. A. Srinivasan, S. Muggleton, M. J. E. Sternberg, and R. D. King. Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence, 85(1/2), 1996.

    Google Scholar 

  30. J. D. Ullman. Database and Knowledge-Base Systems, Volumes I and II. Computer Science Press, 1989.

    Google Scholar 

  31. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134–1142, 1985.

    Article  Google Scholar 

  32. S. Wrobel. Inductive logic programming. In G. Brewka, editor, Advances in Knowledge Representation and Reasoning, pages 153–189. CSLI-Publishers, Stanford, CA, USA, 1996. Studies in Logic, Language and Information.

    Google Scholar 

  33. M. Yannakakis. Algorithms for acyclic database schemes. In Proceedings of the 7th Conference on Very Large Databases, Morgan Kaufman pubs. (Los Altos CA), Zaniolo and Delobel(eds), 1981.

    Google Scholar 

  34. C. T. Yu and Z. M. Ozsoyoglu. On determining tree query membership of a distributed query. INFOR, 22(3), 1984.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Autonomous intelligent Systems, Fraunhofer Gesellschaft, Schloß Birlinghoven, D-53754, Sankt Augustin

    Tamás Horváth

  2. IWS, tto-von-Guericke-Universität Magdeburg, P.O.Box 4120, D-39106, Magdeburg

    Stefan Wrobel

Authors
  1. Tamás Horváth
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  2. Stefan Wrobel
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Editor information

Editors and Affiliations

  1. DFKI GmbH Saarbrücken, 66123, Saarbrücken, Germany

    Klaus P. Jantke

  2. Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Ayumi Shinohara

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© 2001 Springer-Verlag Berlin Heidelberg

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Horváth, T., Wrobel, S. (2001). Towards Discovery of Deep and Wide First-Order Structures: A Case Study in the Domain of Mutagenicity. In: Jantke, K.P., Shinohara, A. (eds) Discovery Science. DS 2001. Lecture Notes in Computer Science(), vol 2226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45650-3_12

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  • DOI: https://doi.org/10.1007/3-540-45650-3_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42956-2

  • Online ISBN: 978-3-540-45650-6

  • eBook Packages: Springer Book Archive

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