Discriminative Markov Logic Network Structure Learning Based on Propositionalization and chi2-Test

In this paper we present a bottom-up discriminative algorithm to automatically learn Markov Logic Network structures. Our approach relies on a new propositionalization method that transforms the learning dataset into an approximative representation in the form of a boolean table. Using this table, the algorithm constructs a set of candidate clauses according to a χ independence test. To compute and choose clauses, we successively use two different optimization criteria, namely log-likelihood (LL) and conditional log-likelihood (CLL), in order to combine the efficiency of LL optimization algorithms together with the accuracy of CLL ones. First experiments show that our approach outperforms existing discriminative MLN structure learning algorithms.