Heuristic Method for Discriminative Structure Learning of Markov Logic Networks

Abstract : 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 it builds 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).
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Contributor : Christel Vrain <>
Submitted on : Thursday, January 6, 2011 - 12:34:14 PM
Last modification on : Thursday, January 17, 2019 - 3:06:04 PM

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Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain. Heuristic Method for Discriminative Structure Learning of Markov Logic Networks. ICMLA 2010, Dec 2010, Washington DC, United States. pp. 163-168. ⟨hal-00553007⟩

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