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Discriminative Markov Logic Network Structure Learning Based on Propositionalization and χ2-Test

  • Quang-Thang Dinh
  • Matthieu Exbrayat
  • Christel Vrain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

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 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.

Keywords

Markov Logic Network Structure Learning Relational Learning Propositionalization Inductive Logic Programming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Quang-Thang Dinh
    • 1
  • Matthieu Exbrayat
    • 1
  • Christel Vrain
    • 1
  1. 1.LIFOUniversité d’OrléansOrleans Cedex 2France

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