Analyzing knowledge entities about COVID-19 using entitymetrics,Scientometrics

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Analyzing knowledge entities about COVID-19 using entitymetrics
Scientometrics ( IF 3.9 ) Pub Date : 2021-03-12 , DOI: 10.1007/s11192-021-03933-y
Qi Yu _{1,

2} , Qi Wang _{1,

3} , Yafei Zhang ₁ , Chongyan Chen ₄ , Hyeyoung Ryu ₅ , Namu Park ₆ , Jae-Eun Baek ₇ , Keyuan Li ₈ , Yifei Wu ₉ , Daifeng Li ₁₀ , Jian Xu ₁₀ , Meijun Liu _{11,

12} , Jeremy J Yang ₁₃ , Chenwei Zhang ₁₄ , Chao Lu ₁₅ , Peng Zhang ₉ , Xin Li ₁₆ , Baitong Chen ₁₇ , Islam Akef Ebeid ₄ , Julia Fensel ₁₈ , Chao Min ₁₉ , Yujia Zhai _{16,

20} , Min Song ₂₁ , Ying Ding _{4,

22} , Yi Bu ₂₃

Affiliation

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

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.

中文翻译：

使用实体度量分析有关 COVID-19 的知识实体

COVID-19 病例已超过 109 + 百万个标记，死亡人数高达 240 万。已经发表了数万篇关于 COVID-19 的论文，以及对 COVID-19 文献进行的无数文献计量分析。尽管如此，没有一项分析关注科学出版物中出现的领域实体。然而，分析这些生物实体及其之间的关系，一种称为实体度量的策略，可以为特定情况下的知识使用和传播提供更多见解。因此，本文对 COVID-19 文献进行了实体度量分析。我们构建了一个实体-实体共现网络，并使用网络指标来分析提取的实体。我们发现 ACE-2 和 C 反应蛋白是两个非常重要的基因，洛匹那韦和利托那韦是两个非常重要的化学物质，

更新日期：2021-03-12

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