Big knowledge visualization of the COVID-19 CIDO ontology evolution

Abstract

Abstract Background The extensive international research for medications and vaccines for the devastating COVID-19 pandemic requires a standard reference ontology. Among the current COVID-19 ontologies, the Coronavirus Infectious Disease Ontology (CIDO) is the largest one. Furthermore, it keeps growing very frequently. Researchers using CIDO as a reference ontology, need a quick update about the content added in a recent release to know how relevant the new concepts are to their research needs. Although CIDO is only a medium size ontology, it is still a large knowledge base posing a challenge for a user interested in obtaining the “big picture” of content changes between releases. Both a theoretical framework and a proper visualization are required to provide such a “big picture”. Methods The child-of-based layout of the weighted aggregate partial-area taxonomy summarization network (WAT) provides a “big picture” convenient visualization of the content of an ontology. In this paper we address the “big picture” of content changes between two releases of an ontology. We introduce a new DIFF framework named Diff Weighted Aggregate Taxonomy (DWAT) to display the differences between the WATs of two releases of an ontology. We use a layered approach which consists first of a DWAT of major subjects in CIDO, and then drill down a major subject of interest in the top-level DWAT to obtain a DWAT of secondary subjects and even further refined layers. Results A visualization of the Diff Weighted Aggregate Taxonomy is demonstrated on the CIDO ontology. The evolution of CIDO between 2020 and 2022 is demonstrated in two perspectives. Drilling down for a DWAT of secondary subject networks is also demonstrated. We illustrate how the DWAT of CIDO provides insight into its evolution. Conclusions The new Diff Weighted Aggregate Taxonomy enables a layered approach to view the “big picture” of the changes in the content between two releases of an ontology.