ABSTRACT
With the rapid proliferation of scientific literature, it has become increasingly impossible for researchers to keep up with all published papers, especially in the biomedical fields with thousands of citations indexed every day. This has created a demand for algorithms to assist in literature search and discovery. A particular case is the literature related to SARS-CoV-2 where a large volume of papers was generated in a short span. As part of the 2021 Smoky Mountains Data Challenge, a COVID-19 knowledge graph constructed using links between concepts and papers from PubMed, Semantic MEDLINE, and CORD-19, was provided for analysis and knowledge mining. In this paper, we analyze this COVID-19 knowledge graph and implement various algorithms to predict as-yet-undiscovered links between concepts, using methods of embedding concepts in Euclidean space followed by link prediction using machine learning algorithms. Three embedding techniques: the Large-scale Information Network Embedding (LINE), the High-Order Proximity-preserved Embedding (HOPE) and the Structural Deep Network Embedding (SDNE) are implemented in conjunction with three machine learning algorithms (logistic regression, random forests, and feed forward neural-networks). We also implement GraphSAGE, another framework for inductive representation on large graphs. Among the methods, we observed that SDNE in conjunction with feed-forward neural network performed the best with an F1 score of 88.0% followed by GraphSAGE with F1 score of 86.3%. The predicted links are ranked using PageRank product to assess the relative importance of predictions. Finally, we visualize the knowledge graphs and predictions to gain insight into the structure of the graph. © 2022, Springer Nature Switzerland AG.