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Learning Predictive Categories Using Lifted Relational Neural Networks

  • Gustav ŠourekEmail author
  • Suresh Manandhar
  • Filip Železný
  • Steven Schockaert
  • Ondřej Kuželka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10326)

Abstract

Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced.

Notes

Acknowledgments

GS and FZ acknowledge support by project no. 17-26999S granted by the Czech Science Foundation. OK is supported by a grant from the Leverhulme Trust (RPG-2014-164). SS is supported by ERC Starting Grant 637277. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gustav Šourek
    • 1
    Email author
  • Suresh Manandhar
    • 2
  • Filip Železný
    • 1
  • Steven Schockaert
    • 3
  • Ondřej Kuželka
    • 3
  1. 1.Czech Technical UniversityPragueCzech Republic
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK
  3. 3.School of CS and InformaticsCardiff UniversityCardiffUK

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