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Tracking Knowledge Evolution Based on the Terminology Dynamics in 4P-Medicine

Author

Listed:
  • Aida Khakimova

    (Research Center for Physical and Technical Informatics, Nizhny Novgorod 603098, Russia)

  • Xuejie Yang

    (School of Management, Hefei University of Technology, Hefei 230009, China)

  • Oleg Zolotarev

    (Russian New University, Moscow 105005, Russia)

  • Maria Berberova

    (Russian New University, Moscow 105005, Russia)

  • Michael Charnine

    (Institute of Informatics Problems of the FRC CSC, the Russian Academy of Sciences, Moscow 119333, Russia)

Abstract

The accelerating evolution of scientific terms connected with 4P-medicine terminology and a need to track this process has led to the development of new methods of analysis and visualization of unstructured information. We built a collection of terms especially extracted from the PubMed database. Statistical analysis showed the temporal dynamics of the formation of derivatives and significant collocations of medical terms. We proposed special linguistic constructs such as megatokens for combining cross-lingual terms into a common semantic field. To build a cyberspace of terms, we used modern visualization technologies. The proposed approaches can help solve the problem of structuring multilingual heterogeneous information. The purpose of the article is to identify trends in the development of terminology in 4P-medicine.

Suggested Citation

  • Aida Khakimova & Xuejie Yang & Oleg Zolotarev & Maria Berberova & Michael Charnine, 2020. "Tracking Knowledge Evolution Based on the Terminology Dynamics in 4P-Medicine," IJERPH, MDPI, vol. 17(20), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7444-:d:427079
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    References listed on IDEAS

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    1. Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.
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