Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs

Abstract

The COVID-19 pandemic highlighted two critical barriers hindering rapid response to novel pathogens. These include inefficient use of existing biological knowledge about treatments, compounds, gene interactions, proteins, etc. to fight new diseases, and the lack of assimilation and analysis of the fast-growing knowledge about new diseases to quickly develop new treatments, vaccines, and compounds. Overcoming these critical challenges has the potential to revolutionize global preparedness for future pandemics. Accordingly, this article introduces a novel knowledge graph application that functions as both a repository of life science knowledge and an analytics platform capable of extracting time-sensitive insights to uncover evolving disease dynamics and, importantly, researchers' evolving understanding. Specifically, we demonstrate how to extract time-bounded key concepts, also leveraging existing ontologies, from evolving scholarly articles to create a single temporal connected source of truth specifically related to COVID-19. By doing so, current knowledge can be promptly accessed by both humans and machines, from which further understanding of disease outbreaks can be derived. We present key findings from the temporal analysis, applied to a subset of the resulting knowledge graph known as the temporal keywords knowledge graph, and delve into the detailed capabilities provided by this innovative approach.

Keywords: COVID-19; keywords; keywords analysis; knowledge graphs; knowledge representation; temporal graph analysis; unstructured data.