Knowledge Graph Embedding by Translating on Hyperplanes

Zhen Wang, Jianwen Zhang, Jianlin Feng et al.

2014 · Proceedings of the AAAI Conference on Artificial Intelligence · 3,771 citations

We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good…

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