2019

RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Sun, Zhiqing, Deng, Zhi-Hong, Nie, Jian-Yun et al.

Understand

We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.

The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations.
In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition.
Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space.

Built on

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