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ICMLA
2010

Heuristic Method for Discriminative Structure Learning of Markov Logic Networks

10 years 16 days ago
Heuristic Method for Discriminative Structure Learning of Markov Logic Networks
Markov Logic Networks (MLNs) combine Markov Networks and first-order logic by attaching weights to firstorder formulas and viewing them as templates for features of Markov Networks. Learning a MLN can be decomposed into structure learning and weights learning. In this paper, we present a heuristic-based algorithm to learn discriminative MLN structures automatically, directly from a training dataset. The algorithm heuristically transforms the relational dataset into boolean tables from which to build candidate clauses for learning the final MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in the three real-world domains show that the proposed algorithm outperforms them in terms of the conditional log likelihood (CLL), and the area under the precision-recall curve (AUC). Keywords-Markov Logic Network, Structure Learning, Discriminative learning, Relational Learning.
Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrai
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICMLA
Authors Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain
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