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Towards Combining Inductive Logic Programming with Bayesian Networks

Kristian Kersting; Luc De Raedt
In: Céline Rouveirol; Michèle Sebag (Hrsg.). Inductive Logic Programming, 11th International Conference. International Conference on Inductive Logic Programming (ILP-2001), September 9-11, Strasbourg, France, Pages 118-131, Lecture Notes in Computer Science, Vol. 2157, Springer, 2001.


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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