An Introduction to Inductive Logic Programming

  • Sašo Džeroski
  • Nada Lavrač
Chapter

Abstract

Inductive logic programming (ILP) is concerned with the development of techniques and tools for relational data mining. Besides the ability to deal with data stored in multiple tables, ILP systems are usually able to take into account generally valid background (domain) knowledge in the form of a logic program. They also use the powerful language of logic programs for describing discovered patterns. This chapter introduces the basics of logic programming and relates logic programming terminology to database terminology. It then defines the task of relational rule induction, the basic data mining task addressed by ILP systems, and presents some basic techniques for solving this task. It concludes with an overview of other relational data mining tasks addressed by ILP systems.

Keywords

Ductive Inference Dition Ather Alse 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 3.1
    F. Bergadano and D. Gunetti. Inductive Logic Programming: From Machine Learning to Software Engineering. MIT Press, Cambridge, MA, 1995.Google Scholar
  2. 3.2
    H. Blockeel and L. De Raedt. Top-down induction of first order logical decision trees. Artificial Intelligence, 101: 285–297, 1998.MathSciNetMATHCrossRefGoogle Scholar
  3. 3.3
    I. Bratko. Prolog Programming for Artificial Intelligence, 3rd edition. Addison-Wesley, Harlow, England, 2001.Google Scholar
  4. 3.4
    L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.MATHGoogle Scholar
  5. 3.5
    W. Buntine. Generalized subsumption and its applications to induction and redundancy. Artificial Intelligence, 36(2): 149–176, 1988.MathSciNetMATHCrossRefGoogle Scholar
  6. 3.6
    P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Proceedings of the Fifth European Working Session on Learning, pages 151–163. Springer, Berlin, 1991.Google Scholar
  7. 3.7
    P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4): 261–283, 1989.Google Scholar
  8. 3.8
    L. De Raedt. Attribute-value learning versus inductive logic programming: the missing links (extended abstract). In Proceedings of the Eighth International Conference on Inductive Logic Programming, pages 1–8. Springer, Berlin, 1998.CrossRefGoogle Scholar
  9. 3.9
    L. De Raedt, editor. Advances in Inductive Logic Programming. IOS Press, Amsterdam, 1996.MATHGoogle Scholar
  10. 3.10
    L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning 26: 99–146, 1997.MATHCrossRefGoogle Scholar
  11. 3.11
    L. De Raedt and S. Dzeroski. First order jfc-clausal theories are PAC-learnable. Artificial Intelligence, 70: 375–392, 1994.MathSciNetMATHCrossRefGoogle Scholar
  12. 3.12
    S. Dzeroski, S. Muggleton, and S. Russell. PAC-learnability of determinate logic programs. In Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pages 128–135. ACM Press, New York, 1992.CrossRefGoogle Scholar
  13. 3.13
    W. Emde and D. Wettschereck. Relational instance-based learning. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 122–130. Morgan Kaufmann, San Mateo, CA, 1996.Google Scholar
  14. 3.14
    P. Flach. Logical approaches to machine learning — an overview. THINK, 1(2): 25–36, 1992.MathSciNetGoogle Scholar
  15. 3.15
    C. Hogger. Essentials of Logic Pogramming. Clarendon Press, Oxford, 1990.Google Scholar
  16. 3.16
    A. Karalic and I. Bratko. First order regression. Machine Learning 26: 147–176, 1997.MATHCrossRefGoogle Scholar
  17. 3.17
    S. Kramer. Structural regression trees. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 812–819. MIT Press, Cambridge, MA, 1996.Google Scholar
  18. 3.18
    R.D. King, A. Karwath, A. Clare, and L. Dehaspe. Genome scale prediction of protein functional class from sequence using data mining. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, pages 384–389. ACM Press, New York, 2000.CrossRefGoogle Scholar
  19. 3.19
    N. Lavrac, S. Džeroski, and M. Grobelnik. Learning nonrecursive definitions of relations with LINUS. In Proceedings of the Fifth European Working Session on Learning, pages 265–281. Springer, Berlin, 1991.Google Scholar
  20. 3.20
    N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994. Freely available at http ://www-ai.ijs.si/SasoDzeroski/ILPBook/.MATHGoogle Scholar
  21. 3.21
    J. Lloyd. Foundations of Logic Programming, 2nd edition. Springer, Berlin, 1987.MATHCrossRefGoogle Scholar
  22. 3.22
    R. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 1041–1045. Morgan Kaufmann, San Mateo, CA, 1986.Google Scholar
  23. 3.23
    Muggleton, S., editor. (1991) Proceedings of the International Workshop on Inductive Logic Programming. University of Porto, Portugal.Google Scholar
  24. 3.24
    S.H. Muggleton, editor. Inductive Logic Programming. Academic Press, London, 1992.MATHGoogle Scholar
  25. 3.25
    S. Muggleton. Inductive logic programming. New Generation Computing, 8(4): 295–318, 1991.MATHCrossRefGoogle Scholar
  26. 3.26
    S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13: 245–286, 1995.CrossRefGoogle Scholar
  27. 3.27
    S. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the Fifth International Conference on Machine Learning, pages 339–352. Morgan Kaufmann, San Mateo, CA, 1988.Google Scholar
  28. 3.28
    S. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pages 368–381. Ohmsha, Tokyo, 1990.Google Scholar
  29. 3.29
    T. Niblett. A study of generalisation in logic programs. In Proceedings of the Third European Working Session on Learning, pages 131–138. Pitman, London, 1988.Google Scholar
  30. 3.30
    S.-H. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer, Berlin, 1997.CrossRefGoogle Scholar
  31. 3.31
    G. Plotkin. A note on inductive generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, pages 153–163. Edinburgh University Press, Edinburgh, 1969.Google Scholar
  32. 3.32
    J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3): 239–266, 1990.Google Scholar
  33. 3.33
    J. R. Quinlan. C4–5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.Google Scholar
  34. 3.34
    J. Robinson. A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1): 23–41, 1965.MATHCrossRefGoogle Scholar
  35. 3.35
    E. Shapiro. Algorithmic Program Debugging. MIT Press, Cambridge, MA, 1983.Google Scholar
  36. 3.36
    A. Srinivasan. The Aleph Manual. Technical Report, Computing Laboratory, Oxford University, 2000. Available at http ://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/ Google Scholar
  37. 3.37
    J. Ullman. Principles of Database and Knowledge Base Systems, volume 1. Computer Science Press, Rockville, MA, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Sašo Džeroski
    • 1
  • Nada Lavrač
    • 1
  1. 1.Jožef Stefan InstituteJamova 39LjubljanaSlovenia

Personalised recommendations