Analyzing knowledge entities about COVID-19 using entitymetrics

Qi Yu; Qi Wang; Yafei Zhang; Chongyan Chen; Hyeyoung Ryu; Namu Park; Jae Eun Baek; Keyuan Li; Yifei Wu; Daifeng Li; Jian Xu; Meijun Liu; Jeremy J. Yang; Chenwei Zhang; Chao Lu; Peng Zhang; Xin Li; Baitong Chen; Islam Akef Ebeid; Julia Fensel; Chao Min; Yujia Zhai; Min Song; Ying Ding; Yi Bu

doi:10.1007/s11192-021-03933-y

Analyzing knowledge entities about COVID-19 using entitymetrics

Qi Yu, Qi Wang, Yafei Zhang, Chongyan Chen, Hyeyoung Ryu, Namu Park, Jae Eun Baek, Keyuan Li, Yifei Wu, Daifeng Li, Jian Xu, Meijun Liu, Jeremy J. Yang, Chenwei Zhang, Chao Lu, Peng Zhang, Xin Li, Baitong Chen, Islam Akef Ebeid, Julia FenselChao Min, Yujia Zhai, Min Song, Ying Ding, Yi Bu

Department of Library and Information Science

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

Original language	English
Pages (from-to)	4491-4509
Number of pages	19
Journal	Scientometrics
Volume	126
Issue number	5
DOIs	https://doi.org/10.1007/s11192-021-03933-y
Publication status	Published - 2021 May

Bibliographical note

Funding Information:
The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002).

Funding Information:
The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002).

Publisher Copyright:
© 2021, Akadémiai Kiadó, Budapest, Hungary.

All Science Journal Classification (ASJC) codes

Social Sciences(all)
Computer Science Applications
Library and Information Sciences

Access to Document

10.1007/s11192-021-03933-y

Cite this

@article{25fdd88f032b4623a05d2e020e31454a,

title = "Analyzing knowledge entities about COVID-19 using entitymetrics",

abstract = "COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.",

author = "Qi Yu and Qi Wang and Yafei Zhang and Chongyan Chen and Hyeyoung Ryu and Namu Park and Baek, {Jae Eun} and Keyuan Li and Yifei Wu and Daifeng Li and Jian Xu and Meijun Liu and Yang, {Jeremy J.} and Chenwei Zhang and Chao Lu and Peng Zhang and Xin Li and Baitong Chen and Ebeid, {Islam Akef} and Julia Fensel and Chao Min and Yujia Zhai and Min Song and Ying Ding and Yi Bu",

note = "Funding Information: The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002). Funding Information: The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002). Publisher Copyright: {\textcopyright} 2021, Akad{\'e}miai Kiad{\'o}, Budapest, Hungary.",

year = "2021",

month = may,

doi = "10.1007/s11192-021-03933-y",

language = "English",

volume = "126",

pages = "4491--4509",

journal = "Scientometrics",

issn = "0138-9130",

publisher = "Springer Netherlands",

number = "5",

}

Yu, Q, Wang, Q, Zhang, Y, Chen, C, Ryu, H, Park, N, Baek, JE, Li, K, Wu, Y, Li, D, Xu, J, Liu, M, Yang, JJ, Zhang, C, Lu, C, Zhang, P, Li, X, Chen, B, Ebeid, IA, Fensel, J, Min, C, Zhai, Y, Song, M, Ding, Y & Bu, Y 2021, 'Analyzing knowledge entities about COVID-19 using entitymetrics', Scientometrics, vol. 126, no. 5, pp. 4491-4509. https://doi.org/10.1007/s11192-021-03933-y

TY - JOUR

T1 - Analyzing knowledge entities about COVID-19 using entitymetrics

AU - Yu, Qi

AU - Wang, Qi

AU - Zhang, Yafei

AU - Chen, Chongyan

AU - Ryu, Hyeyoung

AU - Park, Namu

AU - Baek, Jae Eun

AU - Li, Keyuan

AU - Wu, Yifei

AU - Li, Daifeng

AU - Xu, Jian

AU - Liu, Meijun

AU - Yang, Jeremy J.

AU - Zhang, Chenwei

AU - Lu, Chao

AU - Zhang, Peng

AU - Li, Xin

AU - Chen, Baitong

AU - Ebeid, Islam Akef

AU - Fensel, Julia

AU - Min, Chao

AU - Zhai, Yujia

AU - Song, Min

AU - Ding, Ying

AU - Bu, Yi

N1 - Funding Information: The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002). Funding Information: The authors are grateful to Allen Institute of AI for publicly sharing the COVID-19 open research dataset and Drs. Zhiyong Lu and Chih-Hsuan Wei for their kind help on PubTator usage. The authors thank the anonymous reviewer to offer many suggestions to improve the quality of this paper. Qi Yu acknowledges financial supports from National Natural Science Foundation of China (Grant Number: 71573162) and the Shanxi Scholarship Council of China (Grant Number: HGKY2019057). This work was supported by Min Song acknowledges financial supports from the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Grant Number: 2020S1A5B1104865). Ying Ding acknowledges financial support from the NSF RAPID (Grant Number: 2028717). Baitong Chen acknowledges financial support from the Ministry of Education of China Project of Humanities and Social Sciences (Grant Number: 18YJC870002). Publisher Copyright: © 2021, Akadémiai Kiadó, Budapest, Hungary.

PY - 2021/5

Y1 - 2021/5

N2 - COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

AB - COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity–entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.

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U2 - 10.1007/s11192-021-03933-y

DO - 10.1007/s11192-021-03933-y

M3 - Article

AN - SCOPUS:85102552744

SN - 0138-9130

VL - 126

SP - 4491

EP - 4509

JO - Scientometrics

JF - Scientometrics

IS - 5

ER -

Analyzing knowledge entities about COVID-19 using entitymetrics

Abstract

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