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.

Issues about entitymetrics and paper-entity citation network

“…The journal Scientometrics published a paper presenting an entitymetric analysis on COVID-19 (the COVID-19 paper, Yu et al, 2021 ), and the key part of the paper is an entity–entity co-occurrence network based on the bio-entities extracted from titles and abstracts of COVID-19 publications. According to the original paper proposing entitymetrics (the entitymetrics paper, Ding et al, 2013 ), there are two aspects of entitymetrics: (1) the co-occurrence of entities as mentioned the COVID-19 paper, and (2) citation relationship between entities.…”

“…The first issue about this assumption is that where should the cited entities be extracted. Both the COVID-19 paper (Yu et al, 2021 ) and the entitymetrics paper (Ding et al, 2013 ) extracted the entities from the abstracts and titles of the PubMed Central (PMC) papers. However, instead of the titles and abstracts, we might consider extracting the entities from the full text, specifically the citation context.…”

COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

“…While beneficial, the sheer volume of information published has resulted in an information crisis (Chahrour et al, 2020 ; Xie et al, 2020 ). Apart from the problem of misinformation and rumors, the overwhelming influx of research papers has resulted in severe information overload issues, posing challenges for scientists, healthcare professionals, and the general public in (1) following the rapid accumulation of global novel knowledge in time, (2) accurately and comprehensively acquiring knowledge on specific interested topics; and (3) developing a deep understanding of the new knowledge emerging and tracing the root of such knowledge (Hossain, 2020 ; Wise et al, 2020 ; Yu et al, 2021 ). Although several open-source literature datasets and search tools are accessible online (Trewartha et al, 2020 ; Zhang et al, 2020a ), there is still a need for a comprehensive framework that incorporates effective analytical tools to help scientists overcome these challenges.…”

Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network

“…Previously, citation count prediction of academic papers (CCPAP) has been widely studied with classification or regression models in the field of bibliometrics, and various factors have been demonstrated to be highly related to the citation counts of research papers, such as the writing style, the length of the abstract, and the research topic (Abrishami & Aliakbary, 2019;Huang et al, 2022;Li et al, 2015;Ma et al, 2021;Ruan et al, 2020). However, few studies have explored the clinical citation count prediction of biomedical papers (CCCPBP) since there was no comprehensive and reliable database that tracks citations among biomedical papers (Liang et al, 2021;Xu et al, 2020;Yu et al, 2021). We also don't know whether those features helpful in the CCPAP could be effective predictors for the CCCPBP.…”