Skip to main content

Four Suggestions and a Rule Concerning the Application of ILP

  • Chapter
Relational Data Mining

Abstract

Since the late 1980s there has been a sustained research effort directed at investigating the application of Inductive Logic Programming (ILP) to problems in biology and chemistry. This essay is a personal view of some interesting issues that have arisen during my involvement in this enterprise. Many of the concerns of the broader field of Knowledge Discovery in Databases manifest themselves during the application of ILP to analyse bio-chemical data. Addressing them in this microcosm has given me some directions on the wider application of ILP, and I present these here in the form of four suggestions and one rule. Readers are invited to consider them in the context of a hypothetical Recommended Codes and Practices for the application of ILP.

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

Access this chapter

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. J. Cussens. Part-of-speech tagging Using Progol. In Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 93–108, Springer, Berlin, 1997.

    Chapter  Google Scholar 

  2. J. Cussens, A. Hunter and A. Srinivasan. Generating explicit orderings for non-monotonic logics. In Proceedings of the Eleventh National Conference on Artificial Intelligence, pages 420–425. MIT Press, Cambridge, MA, 1993.

    Google Scholar 

  3. C. Darwin. Autobiography. Collins, London, 1958.

    Google Scholar 

  4. T. Dietterich, R. Lathrop, and T. Lorano-Perez. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89: 31–71, 1997.

    Article  MATH  Google Scholar 

  5. B. Dolsak and S. Muggleton. The application of inductive logic programming to finite element mesh design. In S. Muggleton, editor, Inductive Logic Programming, pages 453–472. Academic Press, London, 1992.

    Google Scholar 

  6. P. Finn, S. Muggleton, D. Page, and A. Srinivasan. Pharmacophore Discovery using the Inductive Logic Programming system Progol. Machine Learning, 30: 241–270, 1998.

    Article  Google Scholar 

  7. J. D. Hirst, R. D. King, and M. J. E. Sternberg. Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by triazines. Journal of Computer-Aided Molecular Design, 8: 421–432, 1994.

    Article  Google Scholar 

  8. J. D. Hirst, R. D. King, and M. J. E. Sternberg. Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by pyrimidines. Journal of Computer-Aided Molecular Design, 8: 421–432, 1994.

    Article  Google Scholar 

  9. R. D. King, S. Muggleton, A. Srinivasan, C. Feng, R. A. Lewis and M. J. E. Sternberg. Drug design using inductive logic programming. In Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences, IEEE Computer Society Press, Los Alamitos, 1993.

    Google Scholar 

  10. R. D. King, S. Muggleton, A. Srinivasan, and M. J. E. Sternberg. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93: 438–442, 1996.

    Article  Google Scholar 

  11. R. D. King, S. Muggleton, and M. J. E. Sternberg. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 89(23): 11322–11326,1992.

    Article  Google Scholar 

  12. R. D. King and A. Srinivasan. Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environmental Health Perspectives, 104(5): 1031–1040, 1996.

    Article  Google Scholar 

  13. R. D. King, A. Srinivasan, and M. J. E. Sternberg. Relating chemical activity to structure: an examination of ILP successes. New Generation Computing, 13(3,4), 1995.

    Article  Google Scholar 

  14. R. D. King and M. J. E. Sternberg. A machine learning approach for the prediction of protein secondary structure. Journal of Molecular Biology, 216: 441–457, 1990.

    Article  Google Scholar 

  15. P. B. Medawar. Pluto’s Republic.Oxford University Press, Oxford, 1984.

    Google Scholar 

  16. P. B. Medawar. The Strange Case of the Spotted Mice and other classic essays on science. Oxford University Press, Oxford, 1996.

    Google Scholar 

  17. R. S. Michalski. A theory and methodology of inductive learning. In R. S. Michalski, J. Carbonnel, and T. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 83–134. San Mateo, CA, 1983.

    Google Scholar 

  18. D. Michie. The superarticulacy phenomenon in the context of software manufacture. In Proceedings of the Royal Society of London, A 405: 185–212, 1986.

    Google Scholar 

  19. D. Michie. Machine learning in the next five years. In Proceedings of the Third European Working Session on Learning, pages 107–122. Pitman, London, 1988.

    Google Scholar 

  20. D. Michie, D. J. Spiegelhalter, and C. C. Taylor, editors. Machine Learning, Neural and Statistical classification. Ellis Horwood, New York, 1994.

    MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  23. S. Muggleton, M. E. Bain, J. Hayes-Michie, and D. Michie. An experimental comparison of human and machine learning formalisms. In Proceedings of the Sixth International Workshop on Machine Learning. Morgan-Kaufmann, San Mateo, CA, 1989.

    Google Scholar 

  24. S. Muggleton, R. D. King, and M. J. E. Sternberg. Predicting protein secondary structure using inductive logic programming. Protein Engineering, 5: 647–657, 1992.

    Article  Google Scholar 

  25. S. Muggleton, C. D. Page, and A. Srinivasan. An initial experiment into stereochemistry-based drug design using ILP. In Proceedings of the Sixth International Workshop on Inductive Logic Programming. Springer, Berlin, 1996.

    Google Scholar 

  26. S. Muggleton, A. Srinivasan, R. D. King, and M. J. E. Sternberg. Biochemical knowledge discovery using Inductive Logic Programming. In Proceedings of the First Conference on Discovery Science. Springer, Berlin, 1998.

    Google Scholar 

  27. J. R. Quinlan. FOIL: A midterm report. In European Conference on Machine Learning, pages 3–20. Springer, Berlin, 1993.

    Google Scholar 

  28. A. Srinivasan. Five lessons in representation based on the application of ILP. In Proceedings of the Compulog Net Area Meeting on Representation issues in Reasoning and Learning. Czech Technical University, Prague, 1997.

    Google Scholar 

  29. A. Srinivasan. A study of two sampling methods for analysing large datasets with ILP. Data Mining and Knowledge Discovery, 3(1): 95–123, 1999.

    Article  Google Scholar 

  30. A. Srinivasan and R. C. Camacho. Numerical reasoning with an ILP program capable of lazy evaluation and customised search. Journal of Logic Programming, 40(2–3): 185–213, 1999.

    Article  MathSciNet  MATH  Google Scholar 

  31. A. Srinivasan and R. D. King. Feature construction with inductive logic programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Mining and Knowledge Discovery, 3(1): 37–57, 1999.

    Article  Google Scholar 

  32. A. Srinivasan, R. D. King, and S. Muggleton. The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program. Technical report. Oxford university, Oxford, 1999.

    Google Scholar 

  33. A. Srinivasan, S. Muggleton, R. D. King, and M. J. E. Sternberg. Theories for mutagenicity: A study of first-order and feature based induction. Artificial Intelligence, 85: 277–299, 1996.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Srinivasan, A. (2001). Four Suggestions and a Rule Concerning the Application of ILP. In: Džeroski, S., Lavrač, N. (eds) Relational Data Mining. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04599-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-04599-2_15

  • 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