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UCL
Learning Distributed Representations of Concepts Using Linear Relational Embedding
Alberto Paccanaro and Geoffrey Hinton
Gatsby Computational Neuroscience Unit
University College London
Alexandra House
17 Queen Square
London WC1N 3ARGCNU TR 2000-002 [March 2000]
Abstract
In this paper we introduce Linear Relational Embedding as a means of learning a distributed representation of concepts from data consisting of binary relations between concepts. The key idea is to represent concepts as vectors, binary relations as matrices, and the operation of applying a relation to a concept as a matrix-vector multiplication that produces an approximation to the related concept. A repesentation for concepts and relations is learned by maximizing an appropriate discriminative goodness function using gradient ascent. On a task involving family relationships, learning is fast and leads to good generalization.