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Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph

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Data Science (ICPCSEE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1628))

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Abstract

The aim is to construct a country-dimension knowledge graph of COVID-19 vaccines from the information of COVID-19 vaccines and to analyze the leading countries of vaccine R&D by combining the advantages of easy operation and intuitive feeling of knowledge graph visualization, to provide a reference for Chinese vaccine R&D departments and international cooperation. In this paper, through data collection, based on entity extraction and relationship construction, a knowledge graph of country dimensions was established by specifying the central vaccine R&D countries and vaccine distribution, and multidimensional microdata such as word frequency and betweenness centrality were combined to analyze the national characteristics of the COVID-19 vaccine. The analysis of the knowledge graph of the country dimension of the COVID-19 vaccine shows that countries with robust technology and economies, such as the US and China, choose to develop vaccine distribution independently, countries with advanced economies, such as Saudi Arabia, decide to purchase vaccine distribution, and less developed countries, such as South Africa and Latin America, need international aid for vaccines or purchase low-cost vaccines. This paper constructs the correlation between nodes and nodes of the COVID-19 vaccine with the help of a knowledge graph, systematically and comprehensively reveals the research mainstay and distribution model of the COVID-19 vaccine from the national level, and provides rationalized suggestions for international cooperation in vaccine R&D in China.

This work is partly supported by the Social science planning foundation of Liaoning province of China (Grant No. L21BGL026), the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (Grant No. 93K172018K01) and the General project of scientific research funds of Liaoning Provincial Department of education (Grant No. LJKZ0085).

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Author information

Authors and Affiliations

  1. College of Information, Liaoning University, Shenyang, 110036, China

    Yong Feng, Ning Zhang, Hongyan Xu & Rongbing Wang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yonggang Zhang

Authors
  1. Yong Feng
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  2. Ning Zhang
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  3. Hongyan Xu
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  4. Rongbing Wang
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  5. Yonggang Zhang
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Corresponding author

Correspondence to Hongyan Xu .

Editor information

Editors and Affiliations

  1. Southwest Petroleum University, Chengdu, China

    Yang Wang

  2. University of Electronic Science and Technology of China, Chengdu, China

    Guobin Zhu

  3. Harbin Engineering University, Harbin, China

    Qilong Han

  4. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  5. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  6. National Academy of Guo Ding Institute of Data Sciences, Beijing, China

    Zeguang Lu

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Feng, Y., Zhang, N., Xu, H., Wang, R., Zhang, Y. (2022). Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_20

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  • DOI: https://doi.org/10.1007/978-981-19-5194-7_20

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5193-0

  • Online ISBN: 978-981-19-5194-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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