401. Analyzing knowledge entities about COVID-19 using entitymetrics.
- Author
-
Yu Q, Wang Q, Zhang Y, Chen C, Ryu H, Park N, Baek JE, Li K, Wu Y, Li D, Xu J, Liu M, Yang JJ, Zhang C, Lu C, Zhang P, Li X, Chen B, Ebeid IA, Fensel J, Min C, Zhai Y, Song M, Ding Y, and Bu Y
- 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., Competing Interests: Conflict of interestThe authors declare no competing interests., (© Akadémiai Kiadó, Budapest, Hungary 2021.)
- Published
- 2021
- Full Text
- View/download PDF