Learning Compact Markov Logic Networks with Decision Trees

  • Hassan Khosravi
  • Oliver Schulte
  • Jianfeng Hu
  • Tianxiang Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)

Abstract

Markov Logic Networks (MLNs) are a prominent model class that generalizes both first-order logic and undirected graphical models (Markov networks). The qualitative component of an MLN is a set of clauses and the quantitative component is a set of clause weights. Generative MLNs model the joint distribution of relationships and attributes. A state-of-the-art structure learning method is the moralization approach: learn a 1st-order Bayes net, then convert it to conjunctive MLN clauses. The moralization approach takes advantage of the high-quality inference algorithms for MLNs and their ability to handle cyclic dependencies. A weakness of the moralization approach is that it leads to an unnecessarily large number of clauses. In this paper we show that using decision trees to represent conditional probabilities in the Bayes net is an effective remedy that leads to much more compact MLN structures. The accuracy of predictions is competitive with the unpruned model and in many cases superior.

Keywords

Decision Tree Inductive Logic Programming Markov Network Conditional Probability Table Markov Logic Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hassan Khosravi
    • 1
  • Oliver Schulte
    • 1
  • Jianfeng Hu
    • 1
  • Tianxiang Gao
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityVancouver-BurnabyCanada

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