Heuristic Method for Discriminative Structure Learning of Markov Logic Networks
Abstract
Markov Logic Networks (MLNs) combine Markov Networks and first-order logic by attaching weights to first order 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).