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

Scientometrics. 2021;126(5):4491-4509. doi: 10.1007/s11192-021-03933-y. Epub 2021 Mar 12.

Authors

Qi Yu^{1

2}, Qi Wang^{1

3}, Yafei Zhang¹, Chongyan Chen⁴, Hyeyoung Ryu⁵, Namu Park⁶, Jae-Eun Baek⁷, Keyuan Li⁸, Yifei Wu⁹, Daifeng Li¹⁰, Jian Xu¹⁰, Meijun Liu^{11

12}, Jeremy J Yang¹³, Chenwei Zhang¹⁴, Chao Lu¹⁵, Peng Zhang⁹, Xin Li¹⁶, Baitong Chen¹⁷, Islam Akef Ebeid⁴, Julia Fensel¹⁸, Chao Min¹⁹, Yujia Zhai^{16

20}, Min Song²¹, Ying Ding^{4

22}, Yi Bu²³

Affiliations

¹ Institute of Medical Data Sciences, Shanxi Medical University, Taiyuan, China.
² School of Management, Shanxi Medical University, Taiyuan, China.
³ Key Laboratory of Cellular Physiology (Shanxi Medical University), Ministry of Education, Taiyuan, China.
⁴ School of Information, University of Texas, Austin, TX USA.
⁵ The Information School, University of Washington, Seattle, WA USA.
⁶ Department of Digital Analytics, Yonsei University, Seoul, South Korea.
⁷ Department of Library and Information Science, Dae-Gu University, Gyeongsan, South Korea.
⁸ Department of Statistics, College of Arts and Sciences, Indiana University, Bloomington, IN USA.
⁹ Department of Computer Science and Technology, Tsinghua University, Beijing, China.
¹⁰ School of Information Management, Sun Yat-Sen University, Guangzhou, Guangdong China.
¹¹ Institute for Global Public Policy, Fudan University, Shanghai, China.
¹² LSE-Fudan Research Centre for Global Public Policy, Fudan University, Shanghai, China.
¹³ Department of Internal Medicine, School of Medicine, University of New Mexico, Albuquerque, NM USA.
¹⁴ Faculty of Education, the University of Hong Kong, Hong Kong, China.
¹⁵ Business School, Hohai University, Nanjing, Jiangsu China.
¹⁶ School of Information Management, Wuhan University, Wuhan, Hubei China.
¹⁷ Department of Library, Information, and Archives, Shanghai University, Shanghai, China.
¹⁸ Westlake High School, Austin, TX USA.
¹⁹ School of Information Management, Nanjing University, Nanjing, Jiangsu China.
²⁰ Department of Information Resource Management, Tianjin Normal University, Tianjin, China.
²¹ Department of Library and Information Science, Yonsei University, Seoul, South Korea.
²² Dell Medical School, University of Texas, Austin, TX USA.
²³ Department of Information Management, Peking University, Beijing, China.

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.

Keywords: Bibliometrics; COVID-19; Entity; Entitymetrics; Knowledge graph; Scientific publications.