# Group Representation Theory for Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1909.05100 · HTML: https://mlsanity.com/paper/1909.05100/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding](https://mlsanity.com/paper/2110.03789/) (2021)
- [Knowledge Graph Embedding by Normalizing Flows](https://mlsanity.com/paper/2409.19977/) (2024)
- [Algebraic Learning: Towards Interpretable Information Modeling](https://mlsanity.com/paper/2203.06690/) (2022)

## Abstract (arXiv)

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.