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Pairwise Markov Logic

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7842)

Abstract

For many tasks in fields like computer vision, computational biology and information extraction, popular probabilistic inference methods have been devised mainly for propositional models that contain only unary and pairwise clique potentials. In contrast, statistical relational approaches typically do not restrict a model’s representational power and use high-order potentials to capture the rich structure of relational domains. This paper aims to bring both worlds closer together.

We introduce pairwise Markov Logic, a subset of Markov Logic where each formula contains at most two atoms. We show that every non-pairwise Markov Logic Network (MLN) can be transformed or ‘reduced’ to a pairwise MLN. Thus, existing, highly efficient probabilistic inference methods can be employed for pairwise MLNs without the overhead of devising or implementing high-order variants. Experiments on two relational datasets confirm the usefulness of this reduction approach.

Keywords

  • Domain Size
  • Ground Atom
  • Markov Logic Network
  • Unary Formula
  • Pairwise Approach

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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Fierens, D., Kersting, K., Davis, J., Chen, J., Mladenov, M. (2013). Pairwise Markov Logic. In: Riguzzi, F., Železný, F. (eds) Inductive Logic Programming. ILP 2012. Lecture Notes in Computer Science(), vol 7842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38812-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-38812-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38811-8

  • Online ISBN: 978-3-642-38812-5

  • eBook Packages: Computer ScienceComputer Science (R0)