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An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science

Author

Listed:
  • Kun Dong

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Haiyun Xu

    (Chinese Academy of Sciences)

  • Rui Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ling Wei

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shu Fang

    (Chinese Academy of Sciences)

Abstract

Given that many frontiers and hotspots of science and technology are emerging from interdisciplines, the accurate identification and forecasting of interdisciplinary topics has become increasingly significant. Existing methods of interdisciplinary topic identification have their respective application fields, and each identification result can help researchers acquire partial characteristics of interdisciplinary topics. This paper offers an integrated method for identifying and predicting interdisciplinary topics from scientific literature. It integrates various methods, including co-occurrence networks analysis, high-TI terms analysis and burst detection, and offers an overall perspective into interdisciplinary topic identification. The results of the different methods are mutually confirmed and complemented, further overviewing the characteristics of the interdisciplinary field and highlighting the importance or potential of interdisciplinary topics. In this study, Information Science and Library Science is selected as a case study. The research has clearly shown that more accurate and comprehensive results can be achieved for interdisciplinary topic identification and prediction by employing this integrated method. Further, the integration of different methods has promising potential for application in knowledge discovery and scientific measurement in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:115:y:2018:i:2:d:10.1007_s11192-018-2694-x
    DOI: 10.1007/s11192-018-2694-x
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    References listed on IDEAS

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    1. Haiyun Xu & Ting Guo & Zenghui Yue & Lijie Ru & Shu Fang, 2016. "Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 583-601, February.
    2. 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.
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    Cited by:

    1. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    2. Wolfgang Glänzel & Koenraad Debackere, 2022. "Various aspects of interdisciplinarity in research and how to quantify and measure those," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5551-5569, September.
    3. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.

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