Author
Listed:
- Mengjia Wu
(University of Technology Sydney)
- Yi Zhang
(University of Technology Sydney)
- Mark Markley
(Search Technology, Inc.)
- Caitlin Cassidy
(Search Technology, Inc.)
- Nils Newman
(Search Technology, Inc.)
- Alan Porter
(Search Technology, Inc.
Georgia Institute of Technology)
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.
Suggested Citation
Mengjia Wu & Yi Zhang & Mark Markley & Caitlin Cassidy & Nils Newman & Alan Porter, 2024.
"COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution,"
Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7229-7259, November.
Handle:
RePEc:spr:scient:v:129:y:2024:i:11:d:10.1007_s11192-023-04747-w
DOI: 10.1007/s11192-023-04747-w
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