Extracting distributed representations of concepts and relations from positive and negative propositions
Alberto Paccanaro, Geoffrey E. Hinton · 2000
Linear relational embedding (LRE) was introduced previously by the authors (1999) as a means of extracting a distributed representation of concepts from relational data. The original formulation cannot use negative information and cannot properly handle data in which there are multiple correct answers. In this paper we propose an extended formulation of LRE that solves both these problems. We present results in two simple domains, which show that learning leads to good generalization.