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.
Year
DOI
Venue
2021
10.1007/s11192-021-03933-y
SCIENTOMETRICS
Keywords
DocType
Volume
COVID-19, Knowledge graph, Entity, Entitymetrics, Scientific publications, Bibliometrics
Journal
126
Issue
ISSN
Citations 
5
0138-9130
1
PageRank 
References 
Authors
0.63
0
25
Name
Order
Citations
PageRank
Qi Yu110.63
Qi Wang210.63
Yafei Zhang334.69
Chongyan Chen410.63
Hyeyoung Ryu510.96
Namu Park610.63
Jae-Eun Baek710.63
Keyuan Li810.63
Yifei Wu910.96
Daifeng Li1010.63
Jian Xu1110.63
Meijun Liu12114.51
Jeremy J Yang1310.63
Chenwei Zhang1410.96
Chao Lu1510.63
Peng Zhang16324.46
Xin Li1710.96
Baitong Chen1810.63
Islam Akef Ebeid1921.33
Julia Fensel2010.63
Chao Min21224.41
Yujia Zhai2210.63
Min Song2310.96
Ying Ding2410.96
Yi Bu25104.14