Towards Combining Inductive Logic Programming with Bayesian Networks

  • Kristian Kersting
  • Luc De Raedt
Conference paper

DOI: 10.1007/3-540-44797-0_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2157)
Cite this paper as:
Kersting K., De Raedt L. (2001) Towards Combining Inductive Logic Programming with Bayesian Networks. In: Rouveirol C., Sebag M. (eds) Inductive Logic Programming. ILP 2001. Lecture Notes in Computer Science, vol 2157. Springer, Berlin, Heidelberg

Abstract

Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs. More precisely, we show how to combine the ILP setting learning from interpretations with score-based techniques for learning Bayesian networks. Thus, the paper positively answers Koller and Pfeffer’s question, whether techniques from ILP could help to learn the logical component of first order probabilistic models.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Kristian Kersting
    • 1
  • Luc De Raedt
    • 1
  1. 1.Institute for Computer Science, Machine Learning LabAlbert-Ludwigs-UniversityFreiburg i. Brg.Germany

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