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