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Published in:
2010 | OriginalPaper | Chapter
Discriminative Markov Logic Network Structure Learning Based on Propositionalization and χ 2-Test
Authors : Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain
Published in: Advanced Data Mining and Applications
Publisher: Springer Berlin Heidelberg
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In this paper we present a bottom-up discriminative algorithm to learn automatically Markov Logic Network structures. Our approach relies on a new propositionalization method that transforms a learning dataset into an approximative representation in the form of boolean tables, from which to construct a set of candidate clauses according to a
χ
2
-test. To compute and choose clauses, we successively use two different optimization criteria, namely pseudo-log-likelihood (PLL) and conditional log-likelihood (CLL), in order to combine the efficiency of PLL optimization algorithms together with the accuracy of CLL ones. First experiments show that our approach outperforms the existing discriminative MLN structure learning algorithms.