RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space

Zhiqing Sun, Zhihong Deng, Jian‐Yun Nie et al.

2019 · arXiv (Cornell University) · 771 citations

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.…

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