Knowledge graph (KG) is used to represent data in terms of entities and
structural relations between the entities. This representation can be used to
solve complex problems such as recommendation systems and question answering.
In this study, a set of candidate drugs for COVID-19 are proposed by using Drug
repurposing knowledge graph (DRKG). DRKG is a biological knowledge graph
constructed using a vast amount of open source biomedical knowledge to
understand the mechanism of compounds and the related biological functions.
Node and relation embeddings are learned using knowledge graph embedding models
and neural network and attention related models. Different models are used to
get the node embedding by changing the objective of the model. These embeddings
are later used to predict if a candidate drug is effective to treat a disease
or how likely it is for a drug to bind to a protein associated to a disease
which can be modelled as a link prediction task between two nodes. RESCAL
performed the best on the test dataset in terms of MR, MRR and Hits@3.

使用生物知识图谱进行药物再利用，通过预测候选药物的有效性和药物与与疾病相关的蛋白质结合的可能性，通过嵌入模型和神经网络进行学习并实现了不同的模型来获取节点嵌入。在 MR、MRR 和 Hits@3 方面，RESCAL 在测试数据集上表现最佳。