Covid-19 Knowledge Deconstruction and Retrieval: Solutions of Intelligent Bibliometrics

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

Covid-19 Knowledge Deconstruction and Retrieval: Solutions of Intelligent Bibliometrics

Wu, M

Zhang, Y

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

Permalink

Publication Type:: Conference Proceeding
Citation:: CEUR Workshop Proceedings, 2022, 3210, pp. 92-103
Issue Date:: 2022-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted versionAdobe PDF (688.01 kB)

View statistics

Full metadata record

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	2023-03-17T06:11:06Z
dc.date.available	2023-03-17T06:11:06Z
dc.date.issued	2022-01-01
dc.identifier.citation	CEUR Workshop Proceedings, 2022, 3210, pp. 92-103
dc.identifier.issn	1613-0073
dc.identifier.uri	http://hdl.handle.net/10453/167500
dc.description.abstract	Covid-19 is an unprecedented challenge that disruptively reshapes societies and scientific research communities. Facing the knowledge flood brought by the overwhelming volume of research efforts, there still lacks a platform to link those to previous knowledge foundations and efficiently visualize and understand them. Aiming to fill this gap, we propose a research framework in this paper to assist scientists in identifying, retrieving, and visualizing the emerging Covid-19 knowledge. The proposed framework incorporates principal topic decomposition (PCD), text analytics-based knowledge model (KM), and the hierarchical topic tree (HTT) method to profile the research landscape, retrieve knowledge of specific interest, and visualize the knowledge structures. Initially, our topic analysis of 127, 971 research papers published during 2020-2021 identified 35 research hotspots. Furthermore, we built up a knowledge model on the topic of vaccination and retrieved 92, 286 research papers from the entire PubMed database as the knowledge foundation of this topic. Lastly, the HTT results of the retrieved papers highlighted multiple relevant disciplines, from whose branches we identified four future research directions: Monoclonal antibody treatments, vaccination in diabetic patients, vaccination effectiveness in SARS-CoV-2 antigenic drift, and vaccination-related allergic sensitization.
dc.language	en
dc.relation.ispartof	CEUR Workshop Proceedings
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Covid-19 Knowledge Deconstruction and Retrieval: Solutions of Intelligent Bibliometrics
dc.type	Conference Proceeding
utslib.citation.volume	3210
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-17T06:11:05Z
pubs.publication-status	Published
pubs.volume	3210

Abstract:

Covid-19 is an unprecedented challenge that disruptively reshapes societies and scientific research communities. Facing the knowledge flood brought by the overwhelming volume of research efforts, there still lacks a platform to link those to previous knowledge foundations and efficiently visualize and understand them. Aiming to fill this gap, we propose a research framework in this paper to assist scientists in identifying, retrieving, and visualizing the emerging Covid-19 knowledge. The proposed framework incorporates principal topic decomposition (PCD), text analytics-based knowledge model (KM), and the hierarchical topic tree (HTT) method to profile the research landscape, retrieve knowledge of specific interest, and visualize the knowledge structures. Initially, our topic analysis of 127, 971 research papers published during 2020-2021 identified 35 research hotspots. Furthermore, we built up a knowledge model on the topic of vaccination and retrieved 92, 286 research papers from the entire PubMed database as the knowledge foundation of this topic. Lastly, the HTT results of the retrieved papers highlighted multiple relevant disciplines, from whose branches we identified four future research directions: Monoclonal antibody treatments, vaccination in diabetic patients, vaccination effectiveness in SARS-CoV-2 antigenic drift, and vaccination-related allergic sensitization.

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

http://hdl.handle.net/10453/167500