Cargando…

Big knowledge visualization of the COVID-19 CIDO ontology evolution

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Ling, Perl, Yehoshua, He, Yongqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169115/
https://www.ncbi.nlm.nih.gov/pubmed/37161560
http://dx.doi.org/10.1186/s12911-023-02184-6
_version_ 1785038984254062592
author Zheng, Ling
Perl, Yehoshua
He, Yongqun
author_facet Zheng, Ling
Perl, Yehoshua
He, Yongqun
author_sort Zheng, Ling
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10169115
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-101691152023-05-11 Big knowledge visualization of the COVID-19 CIDO ontology evolution Zheng, Ling Perl, Yehoshua He, Yongqun BMC Med Inform Decis Mak Research 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. BioMed Central 2023-05-09 /pmc/articles/PMC10169115/ /pubmed/37161560 http://dx.doi.org/10.1186/s12911-023-02184-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zheng, Ling
Perl, Yehoshua
He, Yongqun
Big knowledge visualization of the COVID-19 CIDO ontology evolution
title Big knowledge visualization of the COVID-19 CIDO ontology evolution
title_full Big knowledge visualization of the COVID-19 CIDO ontology evolution
title_fullStr Big knowledge visualization of the COVID-19 CIDO ontology evolution
title_full_unstemmed Big knowledge visualization of the COVID-19 CIDO ontology evolution
title_short Big knowledge visualization of the COVID-19 CIDO ontology evolution
title_sort big knowledge visualization of the covid-19 cido ontology evolution
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169115/
https://www.ncbi.nlm.nih.gov/pubmed/37161560
http://dx.doi.org/10.1186/s12911-023-02184-6
work_keys_str_mv AT zhengling bigknowledgevisualizationofthecovid19cidoontologyevolution
AT perlyehoshua bigknowledgevisualizationofthecovid19cidoontologyevolution
AT heyongqun bigknowledgevisualizationofthecovid19cidoontologyevolution