COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution.

Wu, M; Zhang, Y; Markley, M; Cassidy, C; Newman, N; Porter, A

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution.

Wu, M

Zhang, Y

Markley, M Cassidy, C Newman, N Porter, A

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Scientometrics, 2023, pp. 1-31
Issue Date:: 2023-05-31

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Field	Value	Language
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Markley, M
dc.contributor.author	Cassidy, C
dc.contributor.author	Newman, N
dc.contributor.author	Porter, A
dc.date.accessioned	2024-01-16T04:59:19Z
dc.date.available	2023-05-16
dc.date.available	2024-01-16T04:59:19Z
dc.date.issued	2023-05-31
dc.identifier.citation	Scientometrics, 2023, pp. 1-31
dc.identifier.issn	0138-9130
dc.identifier.issn	1588-2861
dc.identifier.uri	http://hdl.handle.net/10453/174591
dc.description.abstract	COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER
dc.relation	http://purl.org/au-research/grants/arc/DE190100994
dc.relation.ispartof	Scientometrics
dc.relation.isbasedon	10.1007/s11192-023-04747-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0807 Library and Information Studies, 1605 Policy and Administration
dc.subject.classification	Science Studies
dc.subject.classification	4610 Library and information studies
dc.subject.classification	4901 Applied mathematics
dc.title	COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution.
dc.type	Journal Article
utslib.location.activity	Switzerland
utslib.for	0807 Library and Information Studies
utslib.for	1605 Policy and Administration
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T04:59:18Z
pubs.publication-status	Published online

Abstract:

COVID-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood continues surging, researchers have been disadvantaged by not having access to a platform that can quickly synthesize emerging information and link the new knowledge to the latent knowledge foundation. Aiming to fill this gap, we propose a research framework and develop a dashboard that can assist scientists in identifying, retrieving, and understanding COVID-19 knowledge from the ocean of scholarly articles. Incorporating principal component decomposition (PCD), a knowledge mode-based search approach, and hierarchical topic tree (HTT) analysis, the proposed framework profiles the COVID-19 research landscape, retrieves topic-specific latent knowledge foundation, and visualizes knowledge structures. The regularly updated dashboard presents our research results. Addressing 127,971 COVID-19 research papers from PubMed, the PCD topic analysis identifies 35 research hotspots, along with their inner correlations and fluctuating trends. The HTT result segments the global knowledge landscape of COVID-19 into clinical and public health branches and reveals the deeper exploration of those studies. To supplement this analysis, we additionally built a knowledge model from research papers on the topic of vaccination and fetched 92,286 pre-Covid publications as the latent knowledge foundation for reference. The HTT analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/174591