On Lifted Inference for a Relational Probabilistic Conditional Logic with Maximum Entropy Semantics

  • Annika Krämer
  • Christoph Beierle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7153)

Abstract

When extending probabilistic logic to a relational setting, it is desirable to still be able to use efficient inference mechanisms developed for the propositional case. In this paper, we investigate the relational probabilistic conditional logic FO-PCL whose semantics employs the principle of maximum entropy. While in general, this semantics is defined via the ground instances of the rules in an FO-PCL knowledge base \(\mathcal{R}\), the maximum entropy model can be computed on the level of rules rather than on the level of instances of the rules if \(\mathcal{R}\) is parametrically uniform, thus providing lifted inference.We elaborate in detail the reasons precluding \(\mathcal{R}\) from being parametrically uniform. Based on this investigation, we derive a new syntactic criterion for parametric uniformity and develop an algorithm that transforms any FO-PCL knowledge base \(\mathcal{R}\) into an equivalent knowledge base \(\mathcal{R'}\) that is parametrically uniform.

Keywords

Knowledge Base Maximum Entropy Transformation Rule Probabilistic Logic Predicate Symbol 
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

  • Annika Krämer
    • 1
  • Christoph Beierle
    • 1
  1. 1.Fak. für Mathematik und InformatikFernUniversität in HagenHagenGermany

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