Analyzing knowledge entities about Covid-19 using entitymetrics

Datos Bibliográficos

ID21443573
AutoresQi Yu (0000-0002-0426-5407), Yu Qi (0000-0002-4862-5347, Shanxi Medical University), Qi Wang (0000-0003-0464-2118, Shanxi Medical University), Yafei Zhang (0000-0003-0572-3964, Shanxi Medical University), Chongyan Chen (0000-0003-3188-6652, The University of Texas at Austin), Hyeyoung Ryu (0000-0003-2589-834X, University of Washington), Namu Park (Yonsei University), Jae-Eun Baek (Gyeongju University), Keyuan Li (0000-0002-6094-4025, Indiana University Bloomington), Yifei Wu (0000-0002-3202-3368, Tsinghua University), Daifeng Li (0000-0002-6727-2153, Sun Yat-sen University), Jian Xu (0000-0002-1138-1158, Sun Yat-sen University), Meijun Liu (0009-0008-4567-5040, Fudan University), Jeremy J Yang (0000-0002-1476-6192, University of New Mexico), Chenwei Zhang (0000-0002-0488-4603, University of Hong Kong), Chao Lü (0000-0003-4747-4943, Hohai University), Peng Zhang (0000-0001-6953-800X, Tsinghua University), Xin Li (0000-0002-3425-8566, Wuhan University), Baitong Chen (0000-0002-6607-1253, Shanghai University), Islam Akef Ebeid (0000-0001-5345-0775, The University of Texas at Austin), Julia Fensel (IDEA Public Schools), Chao Min (0000-0002-4181-2107, Nanjing University of Information Science and Technology), Yujia Zhai (0000-0001-5687-0038, Tianjin Normal University), Min Song (0000-0003-3255-1600, Yonsei University, autor de correspondencia), Ying Ding (0000-0003-2567-2009, Dell Children's Medical Center of Central Texas), Yi Bu (0000-0003-1737-3252, Peking University, autor de correspondencia)
Año2021
Volumen126
Número5
Páginas4491-4509
Fecha de publicación2021-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScientometrics (JOURNAL)
Identificadores de la revistaISSN: 0138-9130 • E-ISSN: 1588-2861
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-021-03933-y
PMID33746309
OpenAlexW3138756672
IdiomaEN
Citas recibidas7
Referencias citadas35

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

2019-20 coronavirus outbreak · Coronavirus disease 2019 (COVID-19) · Data science · Information retrieval · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) · Biomedical Text Mining and Ontologies · Computer Science · Medicine · Sentiment Analysis and Opinion Mining · Topic Modeling · Virology