Skip to main content
SpringerLink
Log in

An Introduction to Inductive Logic Programming

  • Chapter
Book cover Relational Data Mining

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Bergadano and D. Gunetti. Inductive Logic Programming: From Machine Learning to Software Engineering. MIT Press, Cambridge, MA, 1995.

    Google Scholar 

  2. H. Blockeel and L. De Raedt. Top-down induction of first order logical decision trees. Artificial Intelligence, 101: 285–297, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  3. I. Bratko. Prolog Programming for Artificial Intelligence, 3rd edition. Addison-Wesley, Harlow, England, 2001.

    Google Scholar 

  4. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.

    MATH  Google Scholar 

  5. W. Buntine. Generalized subsumption and its applications to induction and redundancy. Artificial Intelligence, 36(2): 149–176, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  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. P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4): 261–283, 1989.

    Google Scholar 

  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.

    Chapter  Google Scholar 

  9. L. De Raedt, editor. Advances in Inductive Logic Programming. IOS Press, Amsterdam, 1996.

    MATH  Google Scholar 

  10. L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning 26: 99–146, 1997.

    Article  MATH  Google Scholar 

  11. L. De Raedt and S. Dzeroski. First order jfc-clausal theories are PAC-learnable. Artificial Intelligence, 70: 375–392, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  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.

    Chapter  Google Scholar 

  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. P. Flach. Logical approaches to machine learning — an overview. THINK, 1(2): 25–36, 1992.

    MathSciNet  Google Scholar 

  15. C. Hogger. Essentials of Logic Pogramming. Clarendon Press, Oxford, 1990.

    Google Scholar 

  16. A. Karalic and I. Bratko. First order regression. Machine Learning 26: 147–176, 1997.

    Article  MATH  Google Scholar 

  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. 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.

    Chapter  Google Scholar 

  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. 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/.

    MATH  Google Scholar 

  21. J. Lloyd. Foundations of Logic Programming, 2nd edition. Springer, Berlin, 1987.

    Book  MATH  Google Scholar 

  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. Muggleton, S., editor. (1991) Proceedings of the International Workshop on Inductive Logic Programming. University of Porto, Portugal.

    Google Scholar 

  24. S.H. Muggleton, editor. Inductive Logic Programming. Academic Press, London, 1992.

    MATH  Google Scholar 

  25. S. Muggleton. Inductive logic programming. New Generation Computing, 8(4): 295–318, 1991.

    Article  MATH  Google Scholar 

  26. S. Muggleton. Inverse entailment and Progol. New Generation Computing, 13: 245–286, 1995.

    Article  Google Scholar 

  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. 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. 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. S.-H. Nienhuys-Cheng and R. de Wolf. Foundations of Inductive Logic Programming. Springer, Berlin, 1997.

    Book  Google Scholar 

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

    Google Scholar 

  33. J. R. Quinlan. C4–5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.

    Google Scholar 

  34. J. Robinson. A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1): 23–41, 1965.

    Article  MATH  Google Scholar 

  35. E. Shapiro. Algorithmic Program Debugging. MIT Press, Cambridge, MA, 1983.

    Google Scholar 

  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. J. Ullman. Principles of Database and Knowledge Base Systems, volume 1. Computer Science Press, Rockville, MA, 1988.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Sašo Džeroski & Nada Lavrač

Authors
  1. Sašo Džeroski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nada Lavrač
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Sašo Džeroski  & Nada Lavrač  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Džeroski, S., Lavrač, N. (2001). An Introduction to Inductive Logic Programming. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-04599-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07604-6

  • Online ISBN: 978-3-662-04599-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation