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Scalability Issues in Inductive Logic Programming

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Algorithmic Learning Theory (ALT 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1501))

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Abstract

Inductive Logic Programming is concerned with a difficult problem: learning in first-order representations. If stated in an unrestricted fashion, ILP’s classical learning task, the inductive acquisition of first-order predictive theories from examples, is undecidable; even the more restricted practical tasks are known to be not polynomially PAC-learnable. The idea of using ILP techniques for Knowledge Discovery in Databases (KDD), or Data Mining, where very large datasets need to be analyzed, thus seems impossible at first sight. However, a number of recent advances have allowed ILP to make significant progress on the road to scalability. In this paper, we will give an illustrative overview of the basic aspects of scalability in ILP, and then described recent advances in theory, algorithms and system implementations. We will give examples from implemented algorithms and briefly introduce Midos, a recent first-order subgroup discovery algorithm and its scalability ingredients.

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References

  1. Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, and A. Inkeri Verkamo. Fast discovery of association rules. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 12, pages 307–328. AAAI/MIT Press, Cambridge, USA, 1996.

    Google Scholar 

  2. N. Alon and J. H. Spencer. The Probabilistic Method. John Wiley, New York, 1992.

    MATH  Google Scholar 

  3. Hendrik Blockeel and Luc De Raedt. Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1–2):285–297, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  4. Uta Bohnebeck, Tamas Horvath, and Stefan Wrobel. Term comparisons in first-order similarity measures. In David Page, editor, Proc. 8th Int. Conference on Inductive Logic Programming (ILP98), Madison, WI, USA, July 1998.

    Google Scholar 

  5. Michael Bain and Stephen Muggleton. Non-monotonic learning. In Stephen Muggleton, editor, Inductive Logic Programming. Academic Press, London, New York, 1992.

    Google Scholar 

  6. Bradley P. Carlin and Thomas A. Louis. Bayes and empirical Bayes methods for data analysis. Chapman and Hall, London, 1996.

    MATH  Google Scholar 

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

    Google Scholar 

  8. William Cohen. Learnability of restricted logic programs. In Stephen Muggleton, editor, Proc. Third Int. Workshop on Inductive Logic Programming (ILP-93), Technical Report IJS-DP-6707, pages 41–71, Ljubljana, Slovenia, 1993. Josef Stefan Institute.

    Google Scholar 

  9. S. Dvzeroski, S. Muggleton, and S. Russell. PAC-learnability of determinate logic programs. In Proc. 5th Annual ACM Workshop on Computational Learning Theory (COLT-92), pages 128–135. ACM Press, 1992.

    Google Scholar 

  10. L. De Raedt and S. DŽeroski. First order jk-clausal theories are paclearnable. Artificial Intelligence, 70:375–392, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  11. Luc De Raedt and Luc Dehaspe. Clausal discovery. Machine Learning, 26:99ff., 1997.

    Article  Google Scholar 

  12. Sašo Dvzeroski. Numerical Constraints and Learnability in Inductive Logic Programming. PhD thesis, Faculty of Electrical Engineering and Computer Science, University of Ljubljana, Slovenia, 1995.

    Google Scholar 

  13. Peter Flach. A framework for inductive logic programming. In Stephen Muggleton, editor, Inductive Logic Programming, pages 193–212. Academic Press, London, New York, 1992.

    Google Scholar 

  14. Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From data mining to knowledge discovery: An overview. In Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 1, pages 1–34. AAAI/MIT Press, Cambridge, USA, 1996.

    Google Scholar 

  15. Michael R. Garey and David S. Johnson. Computers and Intractability-A Guide to the Theory of NP-Completeness. Freeman, San Francisco, Cal., 1979.

    MATH  Google Scholar 

  16. G. Gottlob. Subsumption and implication. Information Processing Letters, 24:109–111, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  17. Simon Haykin. Neural networks. Prentice-Hall, Englewood Cliffs, NJ, 2nd edition edition, 1998.

    Google Scholar 

  18. Nicolas Helft. Induction as nonmonotonic inference. In Proceedings of the 1st International Conference on Knowledge Representation and Reasoning, pages 149–156, San Mateo, CA, 1989. Morgan Kaufman.

    Google Scholar 

  19. Jörg-Uwe Kietz and Saso Dzeroski. Inductive logic programming and learnability. SIGART Bulletin, 5(1):22–32, 1994.

    Article  Google Scholar 

  20. Jörg-Uwe Kietz. Some lower bounds for the computational complexity of inductive logic programming. In Proc. Sixth European Conference on Machine Learning (ECML-93), pages 115–123, 1993. Also as Arbeitspapiere der GMD No. 718.

    Google Scholar 

  21. Jörg-Uwe Kietz and Marcus 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 

  22. Willi Klösgen. Explora: A multipattern and multistrategy discovery assistant. In Usama M Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 10, pages 249–271. AAAI/MIT Press, Cambridge, USA, 1996.

    Google Scholar 

  23. Jörg-Uwe Kietz and Stefan Wrobel. Controlling the complexity of learning in logic through syntactic and task-oriented models. In Stephen Muggleton, editor, Inductive Logic Programming, chapter 16, pages 335–359. Academic Press, London, 1992. Presented at the Int. Workshop on Inductive Logic Programming, 1991. Also available as Arbeitspapiere der GMD No. 503.

    Google Scholar 

  24. J.W. Lloyd. Foundations of Logic Programming. Springer Verlag, Berlin, New York, 2nd edition, 1987.

    MATH  Google Scholar 

  25. Stephen Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:629–679, 1994.

    Article  Google Scholar 

  26. S. Muggleton and D. Page. A learnability model for universal representations. In Stefan Wrobel, editor, Proc. Fourth Int. Workshop on Inductive Logic Programming (ILP-94), pages 139–160, Schlo\ Birlinghoven, 53754 Sankt Augustin, Germany, 1994. GMD (German Natl. Research Center for Computer Science). Order from http://teuber@gmd.de.

  27. Stephen Muggleton. Optimal layered learning: a pac approach to incremental sampling. In K. Jantke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Proc. of the 4th Conference onAlgorithmic Learning Theory (ALT93), pages 37–44, Berlin, New York, 1993. Springer Verlag.

    Google Scholar 

  28. Stephen Muggleton. Inverse entailment and Progol. In Koichi Furukawa, Donald Michie, and Stephen Muggleton, editors, Machine Intelligence 14, pages 133–188. Oxford Univ. Press, Oxford, 1995.

    Google Scholar 

  29. Shan-Hwei Nienhuys-Cheng and Ronald de Wolf. Foundations of Inductive Logic Programming. LNAI Tutorial 1228. Springer Verlag, Berlin, New York, 1997.

    Google Scholar 

  30. Frank Olken. Random Sampling From Databases. PhD thesis, Univ. of California at Berkeley, 1993.

    Google Scholar 

  31. Gordon D. Plotkin. A note on inductive generalization. In B. Meltzer and D. Michie, editors, Machine Intelligence 5, chapter 8, pages 153–163. Edinburgh Univ. Press, Edinburgh, 1970.

    Google Scholar 

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

    Google Scholar 

  33. J. Ross Quinlan. C4.5 — programs for machine learning. Morgan Kaufman, San Mateo, CA, 1993. Accompanying software available.

    Google Scholar 

  34. L. De Raedt, N. Lavrač, and S. DŽeroski. Multiple predicate learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1993.

    Google Scholar 

  35. J.A. Robinson. A machine-oriented logic based on the resolution principle. JACM, 12(1):23–41, January 1965.

    Google Scholar 

  36. Céline Rouveirol and Jean FranÇois Puget. Beyond inversion of resolution. In Proc. Sixth Intern. Workshop on Machine Learning, pages 122–130, San Mateo, CA, 1989. Morgan Kaufman.

    Google Scholar 

  37. Edgar Sommer, Katharina Morik, Jean-Michel Andre, and Marc Uszynski. What online machine learning can do for knowledge acquisition — a case study. Knowledge Acquisition, 6:435–460, 1994.

    Article  Google Scholar 

  38. Michéle Sebag and Céline Rouveirol. Tractable induction and classification in first order logic via stochastic matching. In Proc. 15th International Joint Conference on Artificial Intelligence, 1997.

    Google Scholar 

  39. Ashwin Srinivasan. Sampling methods for the analysis of large datasets with ILP. Technical Report PRG-TR-27-97, Oxford University, Oxford, UK, 1997.

    Google Scholar 

  40. Irene Stahl. Properties of inductive logic programming in function-free horn logic. In Machine Learning: ECML-94 (Proc. Seventh European Conference on Machine Learning), pages 423–426, Berlin, New York, 1994. Springer Verlag.

    Google Scholar 

  41. Stefan Wrobel and Saso Dzeroski. The ilp description learning problem: Towards a general model-level definition of data mining in ilp. In K. Morik and J. Herrmann, editors, Proc. Fachgruppentreffen Maschinelles Lernen (FGML-95), 44221 Dortmund, 1995. Univ. Dortmund. Research Report 580.

    Google Scholar 

  42. Ruediger Wirth and Paul O’Rorke. Constraints on predicate invention. In Proc. Eighth Intern. Workshop on Machine Learning, pages 457–461, San Mateo, CA, 1991. Morgan Kaufman.

    Google Scholar 

  43. Stefan Wrobel. Concept formation during interactive theory revision. Machine Learning, 14:169–191, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  44. Stefan Wrobel. Inductive logic programming. In Gerd Brewka, editor, Advances in Knowledge Representation and Reasoning, chapter 5, pages 153–189. CSLI-Publishers, Stanford, CA, USA, 1996. Studies in Logic, Language and Information.

    Google Scholar 

  45. Stefan Wrobel. An algorithm for multi-relational discovery of subgroups. In Jan Komorowski and Jan Zytkow, editors, Proc. First European Symposion on Principles of Data Mining and Knowledge Discovery (PKDD-97), pages 78–87, Berlin, 1997. Springer Verlag.

    Google Scholar 

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Author information

Authors and Affiliations

  1. GMD, SET.KI, Schloß Birlinghoven, 53754, Sankt Augustin, Germany

    Stefan Wrobel

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  1. Stefan Wrobel
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Editor information

Editors and Affiliations

  1. AG Künstliche Intelligenz - Expertensysteme, UniversitÄt Kaiserslautern, Postfach 3049, D-67653, Kaiserslautern, Germany

    Michael M. Richter

  2. Department of Computer Science, University of Maryland, College Park, MD, 20742, USA

    Carl H. Smith

  3. AG Algorithmischesn Lernen, UniversitÄt Kaiserslautern, Postfach 3049, D-67653, Kaiserslautern, Germany

    Rolf Wiehagen

  4. Graduate School of Information Science and Electrical Engineering Department of Informatics, Kyushu University, Kassuga, 816-8580, Japan

    Thomas Zeugmann

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© 1998 Springer-Verlag Berlin Heidelberg

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Wrobel, S. (1998). Scalability Issues in Inductive Logic Programming. In: Richter, M.M., Smith, C.H., Wiehagen, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 1998. Lecture Notes in Computer Science(), vol 1501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49730-7_2

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  • DOI: https://doi.org/10.1007/3-540-49730-7_2

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  • Print ISBN: 978-3-540-65013-3

  • Online ISBN: 978-3-540-49730-1

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