Learning Distributed Representations of Relational Data using Linear Relational Embedding

  • Alberto Paccanaro
  • Geoffrey E. Hinton
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


Linear Relational Embedding (LRE) is a new method of learning a distributed representation of concepts from data consisting of binary relations between concepts. The final goal of LRE is to be able to generalize, i.e. to infer new relations among the concepts. The version presented here is capable of handling incomplete information and multiple correct answers. We present results on two simple domains, that show an excellent generalization performance.


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Copyright information

© Springer-Verlag London Limited 2002

Authors and Affiliations

  • Alberto Paccanaro
    • 1
  • Geoffrey E. Hinton
    • 1
  1. 1.Gatsby Computational Neuroscience UnitUniversity College LondonLondonUK

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