Discriminative Markov logic network structure learning based on propositionalization and X2-test

Authors:
Quang-Thang Dinh;Matthieu Exbrayat;Christel Vrain
Affiliations:
LIFO, Université d'Orléans, ORLEANS Cedex 2, France;LIFO, Université d'Orléans, ORLEANS Cedex 2, France;LIFO, Université d'Orléans, ORLEANS Cedex 2, France
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

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 χ2-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.