Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

On the Representation and Embedding of Knowledge Bases beyond Binary Relations / 1300
Jianfeng Wen, Jianxin Li, Yongyi Mao, Shini Chen, Richong Zhang

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 suboptimal 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.