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Analyzing knowledge entities about COVID-19 using entitymetrics

Author

Listed:
  • Qi Yu

    (Shanxi Medical University
    Shanxi Medical University)

  • Qi Wang

    (Shanxi Medical University
    Key Laboratory of Cellular Physiology (Shanxi Medical University), Ministry of Education)

  • Yafei Zhang

    (Shanxi Medical University)

  • Chongyan Chen

    (University of Texas)

  • Hyeyoung Ryu

    (University of Washington)

  • Namu Park

    (Yonsei University)

  • Jae-Eun Baek

    (Dae-Gu University)

  • Keyuan Li

    (Indiana University)

  • Yifei Wu

    (Tsinghua University)

  • Daifeng Li

    (Sun Yat-Sen University)

  • Jian Xu

    (Sun Yat-Sen University)

  • Meijun Liu

    (Fudan University
    Fudan University)

  • Jeremy J. Yang

    (University of New Mexico)

  • Chenwei Zhang

    (the University of Hong Kong)

  • Chao Lu

    (Hohai University)

  • Peng Zhang

    (Tsinghua University)

  • Xin Li

    (Wuhan University)

  • Baitong Chen

    (Shanghai University)

  • Islam Akef Ebeid

    (University of Texas)

  • Julia Fensel

    (Westlake High School)

  • Chao Min

    (Nanjing University)

  • Yujia Zhai

    (Wuhan University
    Tianjin Normal University)

  • Min Song

    (Yonsei University)

  • Ying Ding

    (University of Texas
    University of Texas)

  • Yi Bu

    (Peking University)

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.

Suggested Citation

  • Qi Yu & 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 Zha, 2021. "Analyzing knowledge entities about COVID-19 using entitymetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4491-4509, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03933-y
    DOI: 10.1007/s11192-021-03933-y
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    References listed on IDEAS

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    1. Kyle R. Myers & Wei Yang Tham & Yian Yin & Nina Cohodes & Jerry G. Thursby & Marie C. Thursby & Peter Schiffer & Joseph T. Walsh & Karim R. Lakhani & Dashun Wang, 2020. "Unequal effects of the COVID-19 pandemic on scientists," Nature Human Behaviour, Nature, vol. 4(9), pages 880-883, September.
    2. Min Song & Nam-Gi Han & Yong-Hwan Kim & Ying Ding & Tamy Chambers, 2013. "Discovering Implicit Entity Relation with the Gene-Citation-Gene Network," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    3. Noriko Amano-Patiño & Elisa Faraglia & Chryssi Giannitsarou & Zeina Hasna, 2020. "Who is doing new research in the time of COVID-19? Not the female economists," Vox eBook Chapters, in: Sebastian Galliani & Ugo Panizza (ed.), Publishing and Measuring Success in Economics, edition 1, volume 1, chapter 1, pages 137-142, Centre for Economic Policy Research.
    4. Keeheon Lee & SuYeon Kim & Erin Hea-Jin Kim & Min Song, 2017. "Comparative evaluation of bibliometric content networks by tomographic content analysis: An application to Parkinson's disease," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(5), pages 1295-1307, May.
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    Cited by:

    1. Dong, Ke & Wu, Jiang & Wang, Kaili, 2021. "On the inequality of citation counts of all publications of individual authors," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Dongin Nam & Jiwon Kim & Jeeyoung Yoon & Chaemin Song & Seongdeok Kim & Min Song, 2024. "Examining knowledge entities and its relationships based on citation sentences using a multi-anchor bipartite network," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7197-7228, November.
    4. Shiyun Wang & Jin Mao & Yujie Cao & Gang Li, 2022. "Integrated knowledge content in an interdisciplinary field: identification, classification, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6581-6614, November.
    5. Mengjia Wu & Yi Zhang & Mark Markley & Caitlin Cassidy & Nils Newman & Alan Porter, 2024. "COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7229-7259, November.
    6. Li, Xin & Tang, Xuli, 2021. "Characterizing interdisciplinarity in drug research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 15(4).

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