  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.
