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Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach

  • Navdeep Kaur
  • Gautam KunapuliEmail author
  • Tushar Khot
  • Kristian Kersting
  • William Cohen
  • Sriraam Natarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10759)

Abstract

We consider the problem of learning Boltzmann machine classifiers from relational data. Our goal is to extend the deep belief framework of RBMs to statistical relational models. This allows one to exploit the feature hierarchies and the non-linearity inherent in RBMs over the rich representations used in statistical relational learning (SRL). Specifically, we use lifted random walks to generate features for predicates that are then used to construct the observed features in the RBM in a manner similar to Markov Logic Networks. We show empirically that this method of constructing an RBM is comparable or better than the state-of-the-art probabilistic relational learning algorithms on six relational domains.

Notes

Acknowledgements

Kristian Kersting gratefully acknowledges the support by the DFG Collaborative Research Center SFB 876 projects A6 and B4. Sriraam Natarajan gratefully acknowledges the support of the DARPA DEFT Program under the Air Force Research Laboratory (AFRL) prime contract no. FA8750-13-2-0039. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the DARPA, ARO, AFRL, or the US government.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.The University of Texas at DallasRichardsonUSA
  3. 3.Allen Institute of Artificial IntelligenceSeattleUSA
  4. 4.Technische Universität DarmstadtDarmstadtGermany
  5. 5.Carnegie Mellon UniversityPittsburghUSA

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