On the representation and embedding of knowledge bases beyond binary relations

Understand

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.