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Multi-source data fusion study in scientometrics

Author

Listed:
  • Hai-Yun Xu

    (Chengdu Library of Chinese Academy of Sciences)

  • Zeng-Hui Yue

    (Jining Medical University)

  • Chao Wang

    (Chengdu Library of Chinese Academy of Sciences)

  • Kun Dong

    (Chengdu Library of Chinese Academy of Sciences)

  • Hong-Shen Pang

    (Chinese Academy of Sciences)

  • Zhengbiao Han

    (Nanjing Agricultural University)

Abstract

This paper provides an introduction to multi-source data fusion (MSDF) and comprehensively overviews the ingredients and challenges of MSDF in scientometrics. As compared to the MSDF methods in the sensor and other fields, and considering the features of scientometrics, in this paper an application model and procedure of MSDF in scientometrics are proposed. The model and procedure can be divided into three parts: data type integration, fusion of data relations, and ensemble clustering. Furthermore, the fusion of data relations can be divided into cross-integration of multi-mode data and matrix fusion of multi-relational data. To obtain a clearer and deeper analysis of the MSDF model, this paper further focuses on the application of MSDF in topic identification based on text analysis of scientific literatures. This paper also discusses the application of MSDF for the exploration of scientific literatures. Finally, the most suitable MSDF methods for different situations are discussed.

Suggested Citation

  • Hai-Yun Xu & Zeng-Hui Yue & Chao Wang & Kun Dong & Hong-Shen Pang & Zhengbiao Han, 2017. "Multi-source data fusion study in scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 773-792, May.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:2:d:10.1007_s11192-017-2290-5
    DOI: 10.1007/s11192-017-2290-5
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    References listed on IDEAS

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    Cited by:

    1. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Kun Dong & Haiyun Xu & Rui Luo & Ling Wei & Shu Fang, 2018. "An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 849-868, May.
    3. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    4. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).

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