Skip to main content
Springer Nature Link
Log in

Probabilistic Inductive Logic Programming

  • Conference paper
Algorithmic Learning Theory (ALT 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3244))

Included in the following conference series:

Abstract

Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning. A rich variety of different formalisms and learning techniques have been developed. In the present paper, we start from inductive logic programming and sketch how it can be extended with probabilistic methods.

More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be used to learn different types of probabilistic representations.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abney, S.: Stochastic Attribute-Value Grammars. Computational Linguistics 23(4), 597–618 (1997)

    MathSciNet  Google Scholar 

  2. Anderson, C., Domingos, P., Weld, D.: Relational Markov Models and their Application to Adaptive Web Navigation. In: Hand, D., Keim, D., Zaïne, O., Goebel, R. (eds.) Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (KDD 2002), Edmonton, Canada, pp. 143–152. ACM Press, New York (2002)

    Chapter  Google Scholar 

  3. Bergadano, F., Gunetti, D.: Inductive Logic Programming: From Machine Learning to Software Engeneering. MIT Press, Cambridge (1996)

    Google Scholar 

  4. Cussens, J.: Loglinear models for first-order probabilistic reasoning. In: Laskey, K., Prade, H. (eds.) Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1999), Stockholm, Sweden, pp. 126–133. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  5. Cussens, J.: Parameter estimation in stochastic logic programs. Machine Learning 44(3), 245–271 (2001)

    Article  MATH  Google Scholar 

  6. De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95(1), 197–201 (1997)

    Google Scholar 

  7. De Raedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2-3), 99–146 (1997)

    Article  MATH  Google Scholar 

  8. De Raedt, L., Džeroski, S.: First-Order jk-Clausal Theories are PAC-Learnable. Artificial Intelligence 70(1-2), 375–392 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  9. De Raedt, L., Kersting, K.: Probabilistic Logic Learning. ACM-SIGKDD Explorations: Special issue on Multi-Relational Data Mining 5(1), 31–48 (2003)

    Google Scholar 

  10. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc., B 39, 1–39 (1977)

    MATH  MathSciNet  Google Scholar 

  11. Dietterich, T., Getoor, L., Murphy, K. (eds.): Working Notes of the ICML- 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields, SRL-04 (2004)

    Google Scholar 

  12. Eisele, A.: Towards probabilistic extensions of contraint-based grammars. In: Dörne, J. (ed.) Computational Aspects of Constraint-Based Linguistics Decription-II. DYNA-2 deliverable R1.2.B (1994)

    Google Scholar 

  13. Fürnkranz, J.: Separate-and-Conquer Rule Learning. Artificial Intelligence Review 13(1), 3–54 (1999)

    Article  MATH  Google Scholar 

  14. Getoor, L., Jensen, D. (eds.): Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data, SRL-03 (2003)

    Google Scholar 

  15. Haddawy, P.: Generating Bayesian networks from probabilistic logic knowledge bases. In: López de Mántaras, R., Poole, D. (eds.) Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1994), Seattle, Washington, USA, pp. 262–269. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  16. Heckerman, D.: A Tutorial on Learning with Bayesian Networks. Technical Report MSR-TR-95-06, Microsoft Research (March 1995)

    Google Scholar 

  17. Helft, N.: Induction as nonmonotonic inference. In: Brachman, R., Levesque, H., Reiter, R. (eds.) Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (KR 1989), Toronto, Canada, May 15-18, pp. 149–156. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  18. Jaeger, M.: Relational Bayesian networks. In: Geiger, D., Shenoy, P. (eds.) Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1997), Providence, Rhode Island, USA, pp. 266–273. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  19. Jensen, F.: Bayesian networks and decision graphs. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  20. Kersting, K., De Raedt, L.: Adaptive Bayesian Logic Programs. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, p. 104. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  21. Kersting, K., De Raedt, L.: Bayesian logic programs. Technical Report 151, University of Freiburg, Institute for Computer Science (April 2001)

    Google Scholar 

  22. Kersting, K., De Raedt, L.: Towards Combining Inductive Logic Programming and Bayesian Networks. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, p. 118. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  23. Kersting, K., De Raedt, L.: Principles of Learning Bayesian Logic Programs. Technical Report 174, University of Freiburg, Institute for Computer Science (June 2002)

    Google Scholar 

  24. Kersting, K., Raiko, T., Kramer, S., De Raedt, L.: Towards discovering structural signatures of protein folds based on logical hidden markov models. In: Altman, R., Dunker, A., Hunter, L., Jung, T., Klein, T. (eds.) Proceedings of the Pacific Symposium on Biocomputing, Kauai, Hawaii, USA, pp. 192–203. World Scientific, Singapore (2003)

    Google Scholar 

  25. Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Rich, C., Mostow, J. (eds.) Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-1998), Madison, Wisconsin, USA, July 1998, pp. 580–587. AAAI Press, Menlo Park (1998)

    Google Scholar 

  26. Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  27. Marcus, M., Marcinkiewicz, M., Santorini, B.: Building a large annotated corpus of English: The Penn TREEBANK. Computational Linguistics 19(2), 313–330 (1993)

    Google Scholar 

  28. McKachlan, G., Krishnan, T.: The EM Algorithm and Extensions. John Wiley & Sons, Inc., Chichester (1997)

    Google Scholar 

  29. Mitchell, T.M.: Machine Learning. The McGraw-Hill Companies, Inc., New York (1997)

    MATH  Google Scholar 

  30. Muggleton, S.: Inverse Entailment and Progol. New Generation Computing Journal 13(3-4), 245–286 (1995)

    Article  Google Scholar 

  31. Muggleton, S.: Stochastic logic programs. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 254–264. IOS Press, Amsterdam (1996)

    Google Scholar 

  32. Muggleton, S.: Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence 4(041) (2000)

    Google Scholar 

  33. Muggleton, S.: Learning structure and parameters of stochastic logic programs. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 198–206. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  34. Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Muggleton, S. (ed.) Inductive Logic Programming, pp. 281–298. Academic Press, London (1992)

    Google Scholar 

  35. Ngo, L., Haddawy, P.: Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science 171(1–2), 147–177 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  36. Nienhuys-Cheng, S., de Wolf, R.: Foundations of Inductive Logic Programming. Springer, Heidelberg (1997)

    Google Scholar 

  37. Pearl, J.: Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edn. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  38. Pfeffer, A.: Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University (2000)

    Google Scholar 

  39. Plotkin, G.: A note on inductive generalization. In Machine Intelligence, vol. 5, pp. 153–163. Edinburgh University Press (1970)

    Google Scholar 

  40. Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64(1), 81–129 (1993)

    Article  MATH  Google Scholar 

  41. Quinlan, J., Cameron-Jones, R.: Induction of logic programs: FOIL and related systems. New Generation Computing 13(3-4), 287–312 (1995)

    Article  Google Scholar 

  42. Sato, T.: A Statistical Learning Method for Logic Programs with Distribution Semantics. In: Sterling, L. (ed.) Proceedings of the Twelfth International Conference on Logic Programming (ICLP-1995), Tokyo, Japan, pp. 715–729. MIT Press, Cambridge (1995)

    Google Scholar 

  43. Shapiro, E.: Algorithmic Program Debugging. MIT Press, Cambridge (1983)

    Google Scholar 

  44. Srinivasan, A.: The Aleph Manual, Available at http://www.comlab.ox.ac.uk/oucl/~research/areas/machlearn/Aleph/

  45. Srinivasan, A., Muggleton, S., King, R., Sternberg, M.: Theories for mutagenicity: A study of first-order and feature based induction. Artificial Intelligence 85(1–2), 277–299 (1996)

    Article  Google Scholar 

  46. Stolcke, A., Omohundro, S.: Inducing Probabilistic Grammars by Bayesian Model Merging. In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 106–118. Springer, Heidelberg (1994)

    Google Scholar 

  47. Valiant, L.G.: A theory of the learnable. Communications of the ACM 27(11), 1134–1142 (1984)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Computer Science, Machine Learning Lab, Albert-Ludwigs-University, Georges-Köhler-Allee, Gebäude 079, D-79110, Freiburg i. Brg, Germany

    Luc De Raedt & Kristian Kersting

Authors
  1. Luc De Raedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kristian Kersting
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. David R. Cheriton School of Computer Science University of Waterloo,  

    Shoham Ben-David

  2. Department of Computer & Information Sciences, University of Delaware, 103 Smith Hall, DE 19716, Newark

    John Case

  3. Dept. of Information Technology and Electronics, Ishinomaki Senshu University,  

    Akira Maruoka

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

De Raedt, L., Kersting, K. (2004). Probabilistic Inductive Logic Programming. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30215-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23356-5

  • Online ISBN: 978-3-540-30215-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics