  The coronavirus disease (COVID-19) has claimed the lives of over 350,000
people and infected more than 6 million people worldwide. Several search
engines have surfaced to provide researchers with additional tools to find and
retrieve information from the rapidly growing corpora on COVID-19. These
engines lack extraction and visualization tools necessary to retrieve and
interpret complex relations inherent to scientific literature. Moreover,
because these engines mainly rely upon semantic information, their ability to
capture complex global relationships across documents is limited, which reduces
the quality of similarity-based article recommendations for users. In this
work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for
extracting and visualizing complex relationships between COVID-19 scientific
articles. The CKG combines semantic information with document topological
information for the application of similar document retrieval. The CKG is
constructed using the latent schema of the data, and then enriched with
biomedical entity information extracted from the unstructured text of articles
using scalable AWS technologies to form relations in the graph. Finally, we
propose a document similarity engine that leverages low-dimensional graph
embeddings from the CKG with semantic embeddings for similar article retrieval.
Analysis demonstrates the quality of relationships in the CKG and shows that it
can be used to uncover meaningful information in COVID-19 scientific articles.
The CKG helps power www.cord19.aws and is publicly available.
