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The models developed to date for knowledge base embedding are all based onthe assumption that the relations contained in knowledge bases are binary. Forthe training and testing of these embedding models, multi-fold (or n-ary)relational data are converted to triples (e.g., in FB15K dataset) andinterpreted as instances of binary relations. This paper presents a canonicalrepresentation of knowledge bases containing multi-fold relations. We show thatthe existing embedding models on the popular FB15K datasets correspond to asub-optimal modelling framework, resulting in a loss of structural information.We advocate a novel modelling framework, which models multi-fold relationsdirectly using this canonical representation. Using this framework, theexisting TransH model is generalized to a new model, m-TransH. We demonstrateexperimentally that m-TransH outperforms TransH by a large margin, therebyestablishing a new state of the art.

On the representation and embedding of knowledge bases beyond binary relations