Towards Learning Non-recursive LPADs by Transforming Them into Bayesian Networks

Authors:
Hendrik Blockeel;Wannes Meert
Affiliations:
Katholieke Universiteit Leuven, Department of Computer Science, Celestijnenlaan 200A, 3001 Leuven, Belgium;Katholieke Universiteit Leuven, Department of Computer Science, Celestijnenlaan 200A, 3001 Leuven, Belgium
Venue:
Inductive Logic Programming
Year:
2007

Citing 2
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Abstract

Logic programs with annotated disjunctions, or LPADs, are an elegant knowledge representation formalism that can be used to combine first order logical and probabilistic inference. While LPADs can be written manually, one can also consider the question of how to learn them from data. Methods for learning restricted classes of LPADs have been proposed before, but the problem of learning any kind of LPADs was still open. In this paper, we describe a reduction of non-recursive LPADs with a finite Herbrand universe to Bayesian networks. This reduction makes it possible to learn such LPADs using standard learning techniques for Bayesian networks. Thus the class of learnable LPADs is extended.