论文标题
Tex-Graph：耦合张量 - 矩阵知识图嵌入covid-19药物重新利用
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
论文作者
论文摘要
知识图（kgs）是在知识库中实体之间编写关系行为的强大工具。 KG可以同时建模许多不同类型的主题 - 主体对象和高阶关系。因此，它们提供了一个灵活的建模框架，该框架已应用于许多领域，包括生物学和药理学 - 最近在与Covid-19的斗争中。从学习的角度来看，KG建模的灵活性既是一种祝福，也是挑战。在本文中，我们提出了一个新颖的耦合张量 - 马trix框架，用于kg嵌入。我们利用张量分解工具来学习实体和知识库中关系的简洁表示，并采用这些表示形式对Covid-19进行药物重新利用。我们提出的框架是原则上，优雅的，并且比最近开发的生物学KG在COVID-19药物重新利用任务中的最佳基线相比，取得了100％的改善。
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.
