# TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing

arXiv: https://arxiv.org/abs/2010.11367 · HTML: https://mlsanity.com/paper/2010.11367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion](https://mlsanity.com/paper/2202.00071/) (2022)
- [Learning over Positive and Negative Edges with Contrastive Message Passing](https://mlsanity.com/paper/2605.17854/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predicate-object and higher-order relations. As such, they offer a flexible modeling framework that has been applied to many areas, including biology and pharmacology -- most recently, in the fight against COVID-19. The flexibility of KG modeling is both a blessing and a challenge from the learning point of view. In this paper we propose a novel coupled tensor-matrix framework for KG embedding. We leverage tensor factorization tools to learn concise representations of entities and relations in knowledge bases and employ these representations to perform drug repurposing for COVID-19. Our proposed framework is principled, elegant, and achieves 100% improvement over the best baseline in the COVID-19 drug repurposing task using a recently developed biological KG.