Big knowledge visualization of the COVID-19 CIDO ontology evolution

Ling Zheng, Yehoshua Perl, Yongqun He

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number88
JournalBMC Medical Informatics and Decision Making
Volume23
DOIs
StatePublished - Dec 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Policy
  • Health Informatics
  • Computer Science Applications

Keywords

  • Aggregate partial-area taxonomy
  • Big knowledge visualization
  • Big picture evolution
  • CIDO ontology
  • COVID-19 ontology
  • Coronavirus ontology
  • Evolution of ontologies
  • Summarization network

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