Abstract

In this paper we discuss methods for generalizing over relational data. Our approach is to learn distributed representations for the concepts that coincide with their semantic features and then to use these representations to make inferences. We present Linear Relational Embedding (LRE), a method that learns a mapping from the concepts into a feature-space by imposing the constraint that relations in this feature-space are modeled by linear operations. We then show that this linearity constrains the type of relations that LRE can represent. Finally, we introduce Non-Linear Relational Embedding (NLRE), and show that it can represent any relation.

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