Chapter

Inductive Logic Programming

Volume 2157 of the series Lecture Notes in Computer Science pp 118-131

Date:

Towards Combining Inductive Logic Programming with Bayesian Networks

  • Kristian KerstingAffiliated withInstitute for Computer Science, Machine Learning Lab, Albert-Ludwigs-University
  • , Luc De RaedtAffiliated withInstitute for Computer Science, Machine Learning Lab, Albert-Ludwigs-University

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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.