Analyzing knowledge entities about COVID-19 using entitymetrics
Yu Qi, 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
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.