RotatE: Knowledge Graph Embedding by Relational Rotation in Complex\n Space

We study the problem of learning representations of entities and relations in\nknowledge graphs for predicting missing links. The success of such a task\nheavily relies on the ability of modeling and inferring the patterns of (or\nbetween) the relations. In this paper, we present a new approach for knowledge\ngraph embedding called RotatE, which is able to model and infer various\nrelation patterns including: symmetry/antisymmetry, inversion, and composition.\nSpecifically, the RotatE model defines each relation as a rotation from the\nsource entity to the target entity in the complex vector space. In addition, we\npropose a novel self-adversarial negative sampling technique for efficiently\nand effectively…