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Group Representation Theory for Knowledge Graph Embedding

Machine Learning 2019-12-02 v2 Artificial Intelligence Representation Theory

Abstract

Knowledge graph embedding has recently become a popular way to model relations and infer missing links. In this paper, we present a group theoretical perspective of knowledge graph embedding, connecting previous methods with different group actions. Furthermore, by utilizing Schur's lemma from group representation theory, we show that the state of the art embedding method RotatE can model relations from any finite Abelian group.

Keywords

Cite

@article{arxiv.1909.05100,
  title  = {Group Representation Theory for Knowledge Graph Embedding},
  author = {Chen Cai},
  journal= {arXiv preprint arXiv:1909.05100},
  year   = {2019}
}

Comments

Paper withdrawn due to company policy

R2 v1 2026-06-23T11:12:23.938Z