TY - JOUR
T1 - Big knowledge visualization of the COVID-19 CIDO ontology evolution
AU - Zheng, Ling
AU - Perl, Yehoshua
AU - He, Yongqun
N1 - Funding Information:
This study did not receive sponsorship. Publication costs are funded by all authors.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Aggregate partial-area taxonomy
KW - Big knowledge visualization
KW - Big picture evolution
KW - CIDO ontology
KW - Coronavirus ontology
KW - COVID-19 ontology
KW - Evolution of ontologies
KW - Summarization network
UR - http://www.scopus.com/inward/record.url?scp=85158985517&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158985517&partnerID=8YFLogxK
U2 - 10.1186/s12911-023-02184-6
DO - 10.1186/s12911-023-02184-6
M3 - Article
C2 - 37161560
AN - SCOPUS:85158985517
SN - 1472-6947
VL - 23
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
M1 - 88
ER -